“CAT”-OLOGY

SPECTRALLY RESOLVED NEUROPHOTONICS IN THE MAMMALIAN BRAIN AND PHANTOM STUDIES

BY

KANDICE TANNER

B.S., South Carolina State University, 2002 M.S., University of Illinois at Urbana-Champaign, 2003

DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Physics

in the Graduate College of the University of Illinois at Urbana-Champaign, 2006

Urbana, Illinois

ii

iv

Dedication

To my mother, Allisene,

And of course, Beryl the Cat.

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Acknowledgements

Only by the grace of God have I made it this far, and his presence cannot be disputed.

Secondly, there are several people who have been instrumental in this process. First, I

must thank my advisor, Dr. Enrico Gratton, “Doc” who took me into his lab and accepted

me AS IS, and allowed a graduate student who was 99% wrong most days to relish my

days of being 1% right. The training and experiences cannot be easily expressed in

words. I am eternally grateful to him. I look forward to the day when I could finally beat

him at something.

My other advisor, Dr. Mantulin, “Dr. M”, Thank you for helping me in my research and

providing inspiration when I was in one of my many moods.

Julie Wright was like a mother and could take care of anything in the bat of an eye. I wish

she had moved to California.

To the people of the LFD, when I first joined the lab, how can you describe the

transformation from hell to utopia? I had a lot of fun and countless tea and chocolate

breaks. The LFD was a magical place. It is my sincere hope that Camelot will be realized

again in California.

Dr Monica Fabiani and Dr. Gabriele Gratton and members of the CNL who provided

invaluable help and limitless information about the physiology of the brain to a simple

physics graduate student.

Dr. Joseph Malpeli has been extremely helpful in all aspects to provide additional

training in areas where I had no knowledge. He also allowed me free access to his lab and

would meet with me to discuss my incessant questions. I would also like to thank Rui

Ma, graduate student in Neuroscience and official “cat handler” who tirelessly met with

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me every day for a month, including the entire thanksgiving break to acquire the last set

of data before the lab moved to California. I thank him for taking care of Beryl.

There are other people in the department who I must formally thank:

Dr Phillip Philips who took a chance on a student who knew the Quantum Hall

Coefficient at 3am. Who gives a physics exam at 3am? Dr. Phil.

Dr. Nathan who was extremely supportive throughout my career and for his honesty.

Wendy Wimmer has been like a system administrator, her efficiency and disposition

made it so much easier to deal with life’s little complications like what do I register for

again?

There are others too countless to mention who have helped from my schooling in

Trinidad to my matriculation in the US. Oh, I must acknowledge my funding source, the

NIH

Finally, to the future Dr. E. Red, who has been extremely supportive in this phase of

thesis writing I would have not been able to finish without him.

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Preface (Disclaimer)

My hope is to detail the journey that has brought me to this point of PhDom. In order to

write this thesis, I had to amuse myself in some form or fashion to complete it in a timely

manner. I certainly didn’t enter the field because I was an accomplished author. Hence, I

wrote it the same way I approach life, in that it is always better to laugh than to cry.

Also, I hope that the next Graduate student who enters the lab would find it useful, in the

very least as good bedtime reading who knows it may become a classic- essential reading

to cure insomnia, maybe my committee members would also enjoy this added benefit.

viii

Table of Contents

I. Introduction.................................................................................................................1

I.A Optical characteristics of tissue in the NIR- Absorption and Scattering................3 I.B Light Transport in tissue.........................................................................................7 I.C Theoretical Modeling- Multi-distance Frequency Domain.................................. 11 I.D Chapter Summary .................................................................................................13

II. Theoretical Modeling -Introduction of the Spectral Approach.................................14

II.A Introduction...........................................................................................................14 II.B Spectral Approach.................................................................................................14 II.C Applications of the Spectral Approach .................................................................15 II.D Chapter Summary .................................................................................................16

III. Investigation of Quasi-diffusive regime in Self –Reflectance geometry.................17

III.A Chapter Introduction.............................................................................................17 III.B Description of Self Reflectance Geometry...........................................................17 III.C Experimental Procedure .......................................................................................19 III.D Results ..................................................................................................................20 III.E Discussion ............................................................................................................24 III.F Chapter summary ..................................................................................................25

IV. Investigation of the Cat Visual Cortex- optical BOLD signal ..................................27

IV.A Introduction ..........................................................................................................27 IV.B Experimental Protocol ..........................................................................................27 IV.C Data Acquisition...................................................................................................31 IV.D Results ..................................................................................................................34 IV.E Discussion ............................................................................................................40 IV.F Chapter Summary.................................................................................................43

V. Phantom Studies –Modeling Vasodilation................................................................44

V.A Introduction ..........................................................................................................44 V.B Physiology ............................................................................................................45 V.C Model of Optical Properties of Tissue .................................................................45 V.D Experimental Procedure .......................................................................................46 V.E Data Analysis .......................................................................................................49 V.F Results ..................................................................................................................50 V.G Discussion ............................................................................................................51 V.H Chapter Summary.................................................................................................55

VI. Phantom Studies- Validating technique to recover known optical properties..........57

VI.A Introduction ..........................................................................................................57 VI.B Method..................................................................................................................57 VI.C Data Analysis .......................................................................................................58 VI.D Results ..................................................................................................................59

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VI.E Discussion ............................................................................................................61 VI.F Chapter Summary.................................................................................................62

VII. Investigation of Cat Visual Cortex- Fast Signal .......................................................64

VII.A Introduction ..........................................................................................................64 VII.B Physiology ............................................................................................................64 VII.C Experimental Procedure .......................................................................................65 VII.D Data Acquisition...................................................................................................67 VII.E Data Analysis .......................................................................................................70 VII.F Results ..................................................................................................................70 VII.G Discussion ............................................................................................................89 VII.H Chapter Summary.................................................................................................92

VIII. Investigation of Cat Visual Cortex-Pulse .................................................................93

VIII.A Introduction ..........................................................................................................93 VIII.B Simulations...........................................................................................................93 VIII.C Experimental Procedure .......................................................................................96 VIII.D Data Analysis .......................................................................................................97 VIII.E Results ..................................................................................................................99 VIII.F Discussion ..........................................................................................................105 VIII.G Chapter summary ...............................................................................................106

IX. Summary .................................................................................................................108 X. Future plans.............................................................................................................116

References........................................................................................................................117 Author’s Biography .........................................................................................................127

1

I. Introduction Physicists have made significant contributions to the field of biology and medicine. For

example as recently as 2003, the Nobel Prize for Physiology and Medicine was jointly

awarded to a physicist and a chemist for pioneering work in Magnetic Resonance

Imaging and its applications. This technique was only possible because of the work done

in the field of Nuclear Magnetic Resonance by solid state physicists in the 1940s. This is

just one example where medicine has benefited greatly from applications that were

originally non medical in nature. Another example is the discovery of X-rays by

Roentgen in 1895 is heavily exploited today as a non-invasive method of probing the

human body. The list is extensive. Currently, several groups are heavily involved in the

development of techniques, instrumentation and the basic comprehension of tissue

hemodynamics using visible and near Infrared (NIR) light.1-3

Figure 1.1 Schematic showing the electromagnetic spectrum and the regions in which different techniques are employed.

2

In Figure 1.1, we can see the electromagnetic spectrum and the range of wavelengths in

which different techniques are employed. If we consider this broadband spectrum,

different regions are widely used to probe biological tissue such as X-rays (0.1-10 nm),

Visible light (400-700nm) and Near-Infrared (NIR 650-1100nm). These techniques are

extremely beneficial as they can generate non-invasive, rapid, relatively high resolution,

functional images and real time devices. This in turn can aid the medical practitioners in

diagnosing strokes, hematomas, vascular deficiencies, tumors.4-6 However, these

techniques are not restricted to diagnostics but have great potential for treatment as seen

in recent studies of photodynamic therapy for cancerous tissue. Secondly, the information

that is obtained can aid in the understanding of muscle and brain physiology. The only

way that these optical techniques can be beneficial to clinicians is if one can obtain

quantitative results. However, this poses a severe problem as one must be able to describe

the transport of light in tissue where the light has traveled in the tissue and more

importantly understand processes such as scattering and absorption at both the

macroscopic and microscopic level. Additionally, there are several models that are

touted for light transport depending on the type of tissue, but one must choose one that is

compatible with in vivo measurement of the desired biological system. There is a

common misconception that in the field of biophysics, the concepts involve a hybrid of

interdisciplinary fields as opposed to the traditional probing of basic science. However, it

is quite the opposite as several basic principles of physics must be examined and

understood before we can understand the biological processes. I believe that the new

breed of physicists must also become experts in other disciplines where they may have

not had formal training. This work involved understanding the basic physics as well as

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the application of these theories to describe physiological processes. The importance of

this study can be placed in two categories: one where the emphasis is on the information

that is gained in studying the basic physics of light-matter interactions, specifically

probing tissue in vivo and the other, the information that can be determined about

physiological processes that can be used for diagnostics, treatment or basic

comprehension of the complex system that is the human brain.

I.A Optical characteristics of tissue in the NIR- Absorption and Scattering

First, let us address each of the terms:

• Absorption

When atoms or molecules absorb light, the incoming energy excites a quantized structure

to a higher energy level where the ground state has the lowest energy. It must be noted

that transitions between states are only possible if the energy of the photon is comparable

to the energy differences between states. This process describes Absorption. It is useful to

think of these transitions by examining movement up the rungs of a ladder where it is

possible only to go from rung to rung, but not permissible to place your feet in between

rungs (at least if you have any intention of staying on the ladder).

The absorption spectrum of an atom or molecule depends on its energy level structure.

Absorption spectra are useful for identifying compounds. The energy of any molecule

can be described by the following equation:

Emol = Etrans + E e-spin + Enuc spin + E rot + E vib + Eelec (1)

where the contributions are due to the motion of the center of mass, electronic and

nuclear spin, rotation, vibration and electronic configuration respectively. Table 1 shows

the energy in eV that causes transitions in the respective energy states.

4

Type Frequency Magnitude (eV/molecule)

Translation Continuous Very Small

Spin RF 10-7

Rotational Microwave 10-3

Vibrational Infrared 0.1

Electronic UV/Visible 1-10

Table 1- Table displaying types of transitions and the energy and EM region associated with them

The energy (proportional to the inverse of the wavelength) of the incoming light

determines the information that can be gleaned from specific tissue. It is mainly

determined by the absorption spectrum (absorption of light as a function of wavelength)

of the illuminated tissue that is related to its energy states. Photons that correspond to the

X-Ray are the most energetic, and typically these energies are greater than the energy

spacing in the lighter biological molecules in tissue. Hence, the greatest contrast in tissue

imaging is seen with the bones as they contain heavier atoms like Calcium (greater

difference in energy states) where the energetic X ray photon can knock an electron from

the atom and is comparable to the energy differences. Visible light is more comparable to

the energy difference in soft tissue and causes transitions between the electronic states in

soft tissue. NIR causes transitions in the Vibrational states as shown in Figure 1.2.

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Figure 1.2-Infrared Absorption In tissue, deoxyhemoglobin (HHb) and oxyhemoglobin (O2Hb) are the main absorbers in

the NIR (650- 1150 nm), accounting for 90 % of the photon absorption. 7-8 Melanin also

has a high absorption coefficient in this spectral window but as this chromophore is

restricted to the relatively thin superficial layers of tissue and hemoglobin comprises a

volume fraction ranging from 1-5%, the overall effect of melanin is negligible. Figure 1.3

shows the absorption spectra of tissue chromophores in the NIR.

Abs

orpt

ion

coef

ficie

nt: O

2Hb,

( m

m-1

mM

-1),

H2O

(mm

-1),

Mel

anin

(rel

ativ

e)A

bsor

ptio

n co

effic

ient

: O2H

b, (

mm

-1m

M-1

), H

2O (m

m-1

), M

elan

in (r

elat

ive)

Abs

orpt

ion

coef

ficie

nt: O

2Hb,

( m

m-1

mM

-1),

H2O

(mm

-1),

Mel

anin

(rel

ativ

e)

Figure 1.3-Therapeutic Window and Absorption spectra for the Tissue Chromophores. HHb and

O2Hb- are in units of mm-1mM-1 while water and fat- mm-1. The scattering spectrum in the figure

follows the λ-4 dependence

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• Scattering

Scattering is primarily governed by the wavelength of the incident light, the size and

shape of the particle (radius), geometries, heterogeneities and the mismatch of refractive

indices within the observed medium. The distribution of sizes of particles as well as

random thermal fluctuations results in a non-uniform spatial and temporal distribution of

refractive indices. It results in the re-direction of light due to its interaction with matter

via the principle that the incident electromagnetic (EM) wave causes the oscillation of

electric charges and/or the excitation of the vibrational modes of the excited states. The

scattered or re-directed light is the relaxation of these vibrational modes or the

acceleration of these charges. Scattering may or may not occur with transfer of energy,

i.e., the scattered radiation has a slightly different or the same wavelength. There are

different types of scattering such as Rayleigh or Elastic scattering i.e. no energy loss (or

light in has the same frequency as the light out) and its angular distribution is symmetric.

This scattering also has a dependence on wavelength, specifically (λ-4). (Figure1.3).

Stokes Raman and Anti-Stokes Raman scattering are types of inelastic scattering where

the wavelength of the scattered light is larger and smaller respectively than the incident

wave. Mie scattering is dependent on the size of the scattering centers where the angular

distribution of the scattered light is highly forward. In tissue, the contributors for

scattering are the cells, cellular organelles, proteins resulting in the general heterogeneous

nature of tissue. Figure 1.4 (http://omlc.ogi.edu/classroom/ece532/class3/scatterers.html)

shows the hierarchy of the size distribution of tissue components.9 Rayleigh scattering is

primarily seen due to light interaction with membranes and macromolecular aggregates

on the order of 0.01-0.1µm, while Mie scattering is seen where the interaction with

7

mitochondria and vesicles on the order of a micron. Hence, in tissue there is a mixture of

Mie and Rayleigh scattering. Also most scattering in tissue is forward scattering.

Figure 1.4- Hierarchy of tissue components showing the type of scattering that is comparable.

I.B Light Transport in tissue

The modeling of light transport in tissue is dependent on the region of the EM spectrum

that is used for illumination as the relative magnitudes of the scattering with respect to

the absorption differ for each region. However, in all regions scattering and absorption

occur simultaneously and the main problem is to separate the two processes. This is not a

unique problem and has been explored extensively by Astrophysicists to describe the

process of the propagation of radiation through an atmosphere which is itself emitting

radiation, absorbing radiation and scattering radiation. Chandrasekhar’s Equation of

Radiative Transfer can be applied to stellar systems as well as that of light transport

through tissue; the underlying principles are the same as they simply involve how energy

is attenuated due to losses via absorption and inelastic scattering as well as the redirection

of light due to elastic scattering processes.10 Specifically, there are four independent

macroscopic parameters that are used to characterize light propagation in tissue: the

scattering anisotropy, (g), the absorption coefficient, µa, the scattering coefficient, µs , and

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the index of refraction, (n). The definitions can be found in Table 2 as well as figure 1.5.

The reduced scattering coefficient, µs’ is a quantity that is given by the following

equation:

µs’= µs( 1-g) (2)

This can be used as a length scale for isotropic scattering events where the photon loses

all memory of its initial direction.11 These parameters give us sufficient information to

interpret the biochemical and structural properties of the investigated tissue. Also, the

penetration depth, l, of photons in tissue can be calculated by the following equation:12

l= (µs + µa)-1 (3)

• absorption coefficient, µa(cm-1 )-The inverse of the absorption mean free path.

• scattering coefficient, µs(cm-1 )-The inverse of the single-scattering mean free path.

• reduced scattering coefficient, µs’(cm-1 )-Approximate inverse scale of isotropic scattering.

Figure 1.5 Schematic showing definitions of absorption and scattering mean free paths

Scatter in g C en ters

A bsorber

Source D etector

Scatter in g C en ters

A bsorber

Source D etectorSource D etector

9

Parameter Definition

absorption coefficient, µa

(cm-1 )

The inverse of the absorption mean free

path

scattering coefficient, µs

(cm-1 )

The inverse of the single-scattering mean

free path.

reduced scattering coefficient, µs’

(cm-1 )

Approximate inverse scale of isotropic

scattering.

scattering anisotropy, (g) Average cosine of the scattering angle

index of refraction, (n) Ratio of speed of light in vacuum to the

speed in the medium.

Table 2- Description of terminology associated with tissue optics

Second, let us examine the different light transport modalities. We revisit the concept that

as light passes through tissue, some of it is scattered, absorbed and reflected. When

scattering is the dominant process, the light can be modeled as if it propagates in

spherical waves. This transport regime is known as the diffusive regime. See figure 1.6.

Detector fiber

Laser diodes

ReflectedCollected

Absorbed

Light

Diagram- Diffusive Regime

Figure 1.6- Diagram showing multi-distance technique for light propagation in tissue in diffusive regime.

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However, for smaller distances and/or less-scattering tissue, the photons are not fully

randomized before they are detected, the quasi-diffusive regime. Lastly, when there is no

scattering, as in an optically clear medium, the light is just attenuated by the absorbing

chromophores but there is no change from its initial trajectory. However, the last case is

never observed in the case of tissue. A good analogy is to think of a game of bowling

where the ball represents a photon. In the case where we have no scattering, and if the

person is horrible at the game, a “gutter” ball one where the ball would go through

without hitting any of the bowling pins before exiting. Now, if the ball hits a few pins

before exiting the forward direction of the ball has not been affected by the interaction

with the pins, this is the case of the quasi- diffusive regime where scattering is introduced

but the photon has not been fully randomized. Now, if you can imagine a game where the

pins have an appreciable mass compared to that of the ball that results in the ball being

redirected at each collision in a total randomization before exiting, in fact it may even

come back in the direction in which it came initially, this is the diffusive regime. Figure

1.7 shows this concept in transillumination geometry.

Figure 1.7- Transillumination geometry showing different light transport regimes.

Optically Clear

Intermediate OpticallyTurbid

Non-Diffusive

DiffusiveIntermediate

Optically Clear

Intermediate OpticallyTurbid

Non-Diffusive

DiffusiveIntermediate

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I.C Theoretical Modeling- Multi-distance Frequency Domain

Now, I have provided a qualitative description of light transport in tissue however, if we

wish to do a more quantitative analysis, we must consider the frequency of the incident

light. The following theoretical assumptions hold:

• µa >> µs’ , Beer- Lambert Law, λ < 300nm and λ > 2000nm

• µa << µs’ , Diffusion Approximation, 650 nm < λ < 1150 nm

• µa ~ µs’ , Monte Carlo and Equation of Radiative Transfer, 300nm < λ < 650 nm

and 1150nm < λ < 2000 nm

In the near infrared region, the absorption of the hemoglobin is greatly attenuated with

respect to the value in the visible region, such that scattering is the dominant process. For

example, the reduced scattering coefficient µs’ of the gray matter in the human brain

ranges from 20-30 cm-1 while the absorption coefficient µa is about 0.25cm-1.13 (Figure

3). This region is called the “therapeutic window” and the modeling of light transport for

this region is known as photon migration. The photons are randomized due to the

multiple scattering events in tissue in this spectral window. This allows us to use the

diffusion approximation to the Boltzmann transport equation:

(4)

• ψ-the fluence rate(W/m2)

• D -the reduced scattering coefficient = 1/ (µabs + µs)

• µabs - the absorption coefficient

• S is the light source ( isotropic)

There are physical constraints to this approximation such as the mean free path of the

scattering is much smaller than that due to absorption, the medium is homogeneous and

that the light source must be isotropic. Intuitively, this is an erroneous assumption as

( ) ( ) ( ) ( ) ( ) ( ) ( )kkabskk srtStrrtrrDtrtc

−=+∇⋅∇−∂∂ δψµψψ ,,,1

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tissue by definition is heterogeneous. Also as advertised, scattering in tissue is mainly in

the forward direction, however on the length scale typical of the reduced scattering co-

efficient; the photon density can be assumed to be uniform.14,15,16 Hence, the restrictions

for this equation impose that the source-detector distance must be at least 1-2 cm and that

the photons have traveled at least one mean free path. Therefore, all measurements

cannot be near sources and boundaries. In summary, NIR provides sufficient penetration

in tissue and a light transport modality can be implemented to recover the absolute

concentration of the chromophores that are involved in physiological processes.

However, typically in the field two wavelengths are used to probe the concentrations of

O2Hb and HHb simultaneously. In the Gratton photon migration group, the Multi-

Distance Frequency Domain technique has been developed using amplitude modulated

light at moderately high frequency in the range (100-400MHZ) to study the

hemodynamics of the muscle and brain non-invasively. It has been well documented that

the frequency-domain approach of light propagation in tissues provides high temporal

analysis and a high signal to noise ratio.17,18 Scattering is separated from absorption by

examining the phase delay and attenuation of detected light as shown in figure 1.8.

Figure 1.8-Diagram showing the differences between the input and detected light.

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I.D Chapter Summary

Several groups have been successful in separating the processes of absorption and

scattering in tissue to recover quantitatively the concentrations of O2Hb and HHb. In the

Gratton laboratory, the multi-distance Fd-NIRS method is used where the theoretical

model follows the diffusion approximation to the Boltzmann transport equation.

However, due to the physical limitation of the diffusion approximation and the

instrumental limitations of modulating more that two wavelengths simultaneously, the

need for a technique that enables us to have broadband access as well as be independent

of the light transport modality (diffusive or non-diffusive) is the next logical step in the

field of photon migration. This suggests that one must fully investigate the nature of light

transport in the quasi-diffusive regime, the true effects that the heterogeneous nature of

tissue has on the optical signals, and if the SNR and temporal resolution is comparable to

Fd –NIRS.

2/1

2

2

12

−

Φ

Φ⎟⎟⎠

⎞⎜⎜⎝

⎛+⋅⋅−=

DC

DCa S

SSS

νωµ a

a

DCs

Sµ−

µ=µ

3

2'

From (SDC, SΦ):

[ ] [ ]HbHbO HbHbOaλλλ εεµ += 22 [ ]

12

2

21

2

2

2

11

2

2

λλλλ

λλλλ

εεεεεµεµ

HbHbOHbHbO

HbOaHbOaHb−

−=

THC = [HbO2] + [Hb]

Semi-infinite homogeneous medium:ln(rDC) vs. r ln(rAC) vs. r Φ vs. r

SDC (µa, µs’) SAC (µa, µs’) SΦ (µa, µs’)

SaO2 = [HbO2] / THC

Two-wavelength multi-distance approach:

14

II. Theoretical Modeling -Introduction of the Spectral Approach

II. A Introduction

It was stated earlier that one of the major problems with the diffusion approximation was

that, to be able to recover accurate values, one must be in the diffusive regime typically

where the source-detector distances are greater than 1-2 cm (for the brain). The rule of

thumb says that in this regime, the penetration depth is equal to half the source-detector

regime. So in order to probe tissue depths of less than 5mm or where the photons have

not been fully randomized, we must employ a different technique. In this section, we

propose a new method which separates the absorption and scattering processes by

spectral analysis and is independent of the transport regime.

II. B Spectral Approach

In the spectral approach, we use a large number of wavelength points i.e. a broadband

spectrum; hence we can determine the absorbance of tissue components such as HHb,

O2Hb, fat and water and their spectral shifts with high precision. Assuming that we

know the spectrum of the individual tissue components, we can construct a linear

combination of basis spectra to fit the overall tissue absorbance spectrum (Equation 5).19

I = Scattering (λ) + Water (λ) + Fat (λ) + O2Hb (λ) + HHb (λ) + cytochrome (λ) (5)

The coefficients of each term as well as the scattering power n are determined using a

non-linear least squares method. By knowing the coefficients used to fit the absolute

spectrum, we estimate the fractional contribution of the individual components in the

measured tissue (figure 2.1). All data acquisition and analysis were performed by using

the Elantest software, originated in the LFD photon Migration group.

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Figure 2.1- Comparison of experimental data with theoretical fit using the spectral approach In figure 2.1, one can see the experimental curve that is obtained from a human finger

and the theoretical fit using the spectral approach.

II. C Applications of the Spectral Approach

Experimental Outline

1) A reference spectrum is taken (lamp/baseline, I0).

2) Spectrum is collected (I) using Elantest and all measurements are made with respect

to reference [log (I/ I0)].

3) Weighted Components are determined using theory.

We apply the spectral approach to the differential spectrum obtained by subtracting the

baseline period from the stimulation period. The differential changes are small and we

assume that the changes observed can be described by a linear combination of the basis

components. However, for the application of the spectral approach in the differential

measurements for tissue, the wavelength dependence of the scattering is allowed to vary.

It is not strictly fixed to a λ-4 , which is invalid for tissue. Our system allows us to acquire

spectra at a frequency of 200Hz; hence, the relative tissue component contributions can

be determined with high temporal resolution. Consequently, we can see the optical

16

changes due to water, HHB, O2Hb and scattering as a function of time. These

measurements can be used to potentially detect the blood oxygenation level dependence

response (BOLD) due to the neuronal activation. The spectral method separates

scattering from absorption, as scattering has a characteristic spectral behavior, different

from any other spectral component. The spectral approach is different from the

“frequency domain” method that exploits the time of flight in the diffusive regime to

extract the scattering coefficient. One advantage of the spectral technique is that the

spectral shape (including the scattering contribution) is independent of the light transport

regime, i.e. it is applicable in both the diffusive and non-diffusive regime.

II. D Chapter Summary

This technique must be tested with phantoms and animal models to validate the claim that

this technique can be used to recover the optical signals accurately. Hence, we must apply

the technique to systems where we know the absorbing and scattering properties to

determine the accuracy. First, we must understand the quasi-diffusive regime. Second, we

need to establish if we have enough sensitivity using our instruments. Ultimately, we

would like to apply this technique to recover relative changes as those in physiological

studies as well as to recover absolute concentrations.

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III. Investigation of Quasi-diffusive regime in Self –Reflectance geometry.

III. A Chapter Introduction

We model the different types of tissue based on their optical properties. The physical

structure of the human brain is sufficient to emphasize this point. The outermost portions

of the cerebral hemispheres are continuous folds of cortex comprising grey matter rich in

neuronal cell bodies and dendrites where most of the neuronal activity. This region is

intertwined with white matter rich in myelinated axons which account for its highly

reflective nature. The cerebral hemispheres are not uniform but are comprised of ridges

and grooves (gyri and sulci); hence, the thickness of grey mater with respect to the white

matter is not the same at each point.20 Additionally, for non-invasive measurements, light

must travel through the scalp, skull and Cerebrospinal Fluid (CSF) before encountering

the brain. If we consider the true geometry of the human brain and the optical properties

of its components, the question arises if the reflective white matter plays a critical role in

the observed optical signals of the human brain. Furthermore, is the effect independent of

the light transport regime? Elementary consideration from the basic laws of physics

shows that when light intersects a boundary with a mismatch in refractive indices, the

original trajectory of the light is altered. Hence, what remains is to quantify this effect in

the human brain.

III.B Description of Self-Reflectance Geometry

In our approach, we place our optodes in the side by side configuration known as the self-

reflectance mode where we model the tissue as if it were a semi-infinite medium. (Figure

3.1).

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Figure 3.1- Panel a) Case of no scattering: left shows that the observation volume is small if the reflector surface is close to the surface. Only when the reflector surface is at a fixed depth with respect to the source-detector bundle, a large volume can be detected. Panel b) Case with scattering: left shows that scattering broadens the numerical aperture and the observation volume is larger at a smaller depth with respect to the source-detector bundle.

From figure 3.1, one can see that in this geometry the light that can be detected depends

on three parameters, one being the numerical aperture of the optical fibers, the position of

the reflecting boundary with respect to the optodes and finally the scattering properties of

the medium. The conical distribution of the fibers is given strictly by the formula,

numerical aperture, NA= sinα. Simply, light can only be detected when there is an

intersection of the dispersion volume (conic) of both source and detector. In panel a, one

can see that the closer the reflector is to the optodes (optical fibers for source and

detector), the less light can be detected and as this distance is increased more light can be

detected. Additionally, as S-D increases, the height increases as the conic volumes are

further apart and therefore intersection must occur at a greater height with respect to the

S S

S S

D D

D D

a)

b)

S S

S S

D D

D D

a)

b)Observation

Volume

19

reflector. As scattering is introduced, the conical volumes become distorted and the

intersection occurs at shorter distances.

III. C Experimental Procedure.

To test various aspects of our animal model, we also performed measurements on a

phantom. The phantom model we used is shown in figure 3.2.

Figure 3.2 Left shows the source- detector distances probed to go from non-diffusive to diffusive regimes, while the right shows the experimental setup The light source was a tungsten lamp at a nominal temperature of 3100K (LS-1 Tungsten

Halogen Light Source, Ocean Optics, Dunedin, FL, US). The spectrometer employed

was the model S2000, also from Ocean Optics. Both the light source and the

spectrometer were coupled with a 1000µm core diameter optical fiber. The phantom

consisted of a beaker filled with a milk solution to simulate the optical properties of the

skull and the gray matter. The bottom of the beaker was lined with white tape to simulate

the reflective nature of the white matter. The scattering properties of 2% milk are

comparable to that of the mammalian brain, where the reduced scattering coefficient, µs’ ,

of milk is 5cm-1 and the reduced scattering coefficient, µs’ ,of the brain is 5-10 cm-1 .13,21

The basic experiment consisted of determining the intensity of light as a function of

Source Detectors

4.5 6.0 8.7 9.6 mm Non diffusive--------------Diffusive

S-D

Tungsten Lamp

White Light Source

Data Acquisition

Ocean OpticsS2000 Spectrometer

(Detector)

4.5mm

BATH Height White Tape

20

height above the reflector using the Elantest software (This program is available at

ftp://www.lfd.uiuc.edu/lfd/egratton/elantest/) for each source detector configuration

denoted by “S-D” in figure 3.2, and for each milk solution. Four S-Ds were considered

4.5, 6.0, 8.7 and 9.6mm and for each “D” the height of the source-detector bundle was

controlled using a robotic arm that allowed the entire system to be moved in highly

accurate incremental steps of 0.625mm. The milk solutions investigated varied from

solutions with no scattering (water) to highly scattering (store bought 2% milk). The

notation used refers to the fraction of 2 % milk (store-bought) with respect to the total

solution (milk and water) and were as follows: 0 (pure water), 0.05, 0.1, 0.2, and 1,

where 1 correspond to “undiluted milk”.

III. D Results

As the source-detector distance, S-D, increased, one obvious observation was that the

maximum intensity attained decreased for all of the solutions. In addition, the height at

which the peak of intensity was seen increased as S-D increased. (Figure 3.3) This peak

was asymmetrical in each case. A second striking feature was the plateau that was

observed for the case of the scattering solutions. (Figure 3.3 b, c, d, and e) It was also

seen that as the scattering of the solution increased, the height at which this plateau

occurred decreased irrespective of “S-D”. In the extreme case, most scattering solution

(milk) showed that the plateau is observed after a few mm (Figure3.3e). In the case,

where there was no scattering (water), there was a trend to approach this plateau, but, the

full range of measurements needed to observe this effect was not examined. However, in

this case, it was clear that a significant light intensity is not observed until the height

above the reflector was at least 20mm. (Figure 3.3a) Further investigation of figures

21

3.3b,c and d show that the maximum intensity attained is greatest in the case where the

scattering is greatest, (0.2C > 0.1C > 0.05). From Figure 3.4a, c, e, g, the height at which

the maximum peak is attained is much greater in the case of the optically clear medium

(water) than for the turbid solutions for all Ds. Furthermore, intensities for the turbid

solutions were at their respective plateaus. Additional investigation of each D, for heights

up to 10mm, (figure 3.4b, d, and h) showed that the maximum intensity was always seen

for the 0.2C solution and the minimum for the water. However, the intermediate values

vary for each of the solutions considered.

22

Figure 3.3- Panels a-e show the intensity versus height for different milk solutions.

Intensity vs HeightWater

0

500

1000

1500

2000

2500

0 10 20 30 40 50 60

Height (mm)

Inte

nsity

(cou

nts)

4.5 mm6.0 mm8.7 mm9.6 mm

Intensity vs Height0.05C

0

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0 10 20 30 40 50 60

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(cou

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Intensity vs Height0.1C

0

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0 10 20 30 40 50 60

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(cou

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Intensity vs Height0.2C

0

500

1000

1500

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2500

0 10 20 30 40 50 60

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tens

ity (c

ount

s)

4.5 mm6.0 mm8.7 mm9.6 mm

Intensity vs Height2% fat milk

0

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Intensity vs HeightWater

0

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Intensity vs Height0.1C

0

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0 10 20 30 40 50 60

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(cou

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Intensity vs Height0.2C

0

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1500

2000

2500

0 10 20 30 40 50 60

Height (mm)In

tens

ity (c

ount

s)

4.5 mm6.0 mm8.7 mm9.6 mm

Intensity vs Height2% fat milk

0

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0 10 20 30 40 50 60

Height (mm)

Inte

nsity

(cou

nts)

4.5 mm6.0 mm8.7 mm9.6 mm

b)

c) d)

e)

a)

23

Figure 3.4- Panels a- h show Intensity versus height as a function of source-detector distance and a detailed insert to show the heights corresponding to 0-10mm above the reflector.

Intensity vs HeightD=4.5mm

0

500

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Height (mm)

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(cou

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Intensity vs HeightD=4.5mm Detailed

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a b

c) d

e) f)

hg

24

III. E Discussion

These experiments clearly show the principle that the diffusive regime in the

geometry of the semi-infinite medium is a function of both the scattering properties of

the medium as well as the source-detector distance, S-D. If we consider the

experimental setup, we see clearly that for cases of no scattering, (optically clear), the

detected light is simply a function of the numerical aperture of the optical fibers and

the height with respect to the reflector surface (Figure 3.1a). Simply, light can only be

detected when there is an intersection of the dispersion volume (conic) of both source

and detector. This is illustrated experimentally in the case of the optically clear

solution (water) where we see that the light intensity measured remains negligible

until the height has reached at least 10mm. Additionally, as S-D increases, the height

increases as the conic volumes are further apart and therefore intersection must occur

at a greater height with respect to the reflector. This changes as we introduce

scattering into the solution, as the conic volume of the optical fibers become

distorted. (Figure 3.1b) Hence, in the case of the strongly scattering medium (milk),

after only a few mm, the photons detected increase dramatically. But, the maximum

value attained will be less than in the optically clear solution as some photons will be

lost. However, for the intermediate solutions (0.05, 0.1 and 0.2), the maximum

intensity is not a function of the scattering. This fits with the model as in the quasi-

diffusive regime, the photons are not completely randomized, hence the detected light

can either increase or decrease for a specific S-D. Another striking feature of these

graphs is that despite increasing the height after a certain value, the intensity remains

the same, (plateau). This shows that after these heights the detected intensity is

25

independent of the reflector and we are in the diffusive regime. Hence, these graphs

clearly show the transition from the quasi-diffusive regime to the diffusive regime. It

also shows that in the case where the optical properties were similar to that of the

human brain, (2% milk), we see that the reflector (white matter) restricts the number

of photons detected only for heights of a few mm. Hence, confirming the results as

reported by Okada et al.22However, for practical experiments where the light has not

been fully randomized as in the quasi-diffusive regime, we see that the intensity can

either increase or decrease even if the scattering increases for a fixed source-detector

distance at heights less than 12mm with respect to the white matter. It must be stated

that in this experiment the source –detector geometry remained the same. However, if

the source and detector were interchanged with respect to each other, a different result

would be observed in the quasi-diffusive regime as we cannot say that the light

detected is equivalent to the symmetrical “banana bundle” observed in the diffusive

regime as the photons are not fully randomized before they are detected. This is

another concern that must be accounted for when working in the quasi-diffusive

regime.

III. F Chapter Summary

The results are important for several reasons. The true nature of tissue can have a

significant effect on the observed optical signals; hence we must understand the type and

size of the effect of the different types of tissue under investigation. It is clear that for the

probing of the brain, not only is the source detector separation important to determine

depth of tissue penetration but the position of the white matter with respect to the grey

matter. Additionally, these experiments provided information about the signal to noise

26

ratio when in the quasi-diffusive regime. These results gave a preview into the expected

results of the in vivo measurements by first examining a phantom model. Specifically, we

can now be sure that we have a high SNR to recover the true optical properties in the

animal model.

27

IV. Investigation of the Cat Visual Cortex- optical BOLD signal

IV. A Introduction

In this chapter, we present the experimental results of the animal model studies. First, we

must explain why we chose to perform experiments using the visual cortex of the cat. The

questions that must be addressed are if the technique is independent of the light transport

modality, if it can recover the appropriate optical signals which result from physiological

changes and if our current instrumentation has a comparable SNR to the frequency

domain instrumentation. The cat provided us with an excellent positive control for the

following reasons: extensive knowledge of the cat’s visual cortex was developed by one

of our collaborators (Dr. Joseph Malpeli). Specifically, he determined the exact location

of the neuronal activation based on the type of visual stimulus presented to the cat. The

anatomy of the cat is such that the photons that enter the tissue are not fully randomized

before detection in the reflectance geometry as the thickness of the tissue probed was

3mm where the thickness of the skull was 1mm, and the grey matter was 2mm. Hence,

we are not in the diffusive regime. In addition, the physiological changes are expected to

be similar to that of humans. Hence, it is ideal to test if new technique yields results that

are comparable to Fd-NIRS such as Blood Oxygenation Level Dependence (BOLD)

effect.

IV. B Experimental Procedure

Methods- Cat protocol

1. Preparation of the cat

These experiments were performed on one adult female cat. All procedures were in

accordance with U.S. Public Health Service Policy and protocols approved by the

28

University of Illinois Institutional Animal Care and Use Committee. All surgery was

performed aseptically and under general anesthesia. A gold plated ring was implanted

under the conjunctiva of one eye to allow eye movements to be monitored using the

double magnetic induction method.23 The scalp and muscle overlying the calvarium were

removed, and an aluminum fixture surrounding this area, bonded to the skull to provide

support for a protective cap that was also used to immobilize the cat’s head during the

experiment. A mixture of clear acrylic cement and antibiotics ~ 1 mm thick was then

placed in lieu of the removed tissue to provide a permanent protective barrier.24 Metal

tubes (15 gauge, thin wall hypodermic tubing, inner diameter, 1.52 mm and outer

diameter, 1.83 mm), 7 mm in height, were then embedded in this mixture above the

region corresponding to the visual cortex, area17/18 and the motor cortex, area 4. Figure

4.1 shows schematically the location of the metal tubes.25 The tubes were in direct

contact with the acrylic mixture above the bone. For simplicity, a grid system was

implemented to indicate the position of each tube; this is used to identify the source and

detector locations. The “a” row was located above the frontal lobes and each tube was

numbered in sequence from 1-5. The “b” row was located parasagittally, roughly over

the border between areas 17 and 18 of the visual cortex in the right hemisphere. The “c”

row was located in a coronal plane that cut across the region of area 17 near the surface

of the brain, as well as across the entire coronal extent of areas 18 and 19 of the visual

cortex (figure 4.1). The intersection of the “b” and “c” rows is roughly at the center of the

representation of the area centralis. The distance between adjacent tubes was 2mm, with

the exception of c4 and c5, where the “c” row intersects the “b” row. The tubes served as

holders for the source and detector optical fibers during our measurements.

29

a1………..a5

b1……

.....…b7

c1……………...c7

Figure 4.1- Atlas of the cat’s brains showing the placement of the tubes for optical fibers

2. Visual Stimulation and Behavioral Paradigm

The cat was positioned facing a rear projection screen subtending 60o horizontally and

50o vertically at a distance of 70 cm from the animal’s eyes. The screen was illuminated

uniformly at 0.021cdm-2 with white light from an LCD projector. A computer controlled

laser spot approximately 0.1o in diameter served as a fixation point on the center of the

screen. Visual stimuli consisted of periodic flashes generated by white LED clusters that

were superimposed on the screen raising the luminance to 2.020cdm-2 during the flash.

The cat sat immobile in a bag with its head fixed to a rigid plate, tilted forward 5o with

respect to the Horsley-Clark horizontal plane. During a trial, the cat was trained to focus

on the fixation point regardless of the flashes which had no behavioral significance.

Generally, if the cat maintained this fixation (within +/- 7o) for 10 seconds, it was

rewarded with food. The inter-trial interval was 10 seconds. However, during data

collection sessions, the cat was rewarded at the end of each trial regardless of its

performance. For the purposes of this experiment there were two types of tasks, one in

which the cat was visually stimulated (VS trial), and one where there was no visual

stimulation (NVS).

30

1. VS trials consisted of 10 seconds of a repetitive sequence of 20 flashes where the

flash was on for 250 ms and off for 250 ms, terminated by the reward followed by a 10

second inter-trial interval.

2. NVS trials consisted of 10 seconds of no flashes, with delivery of the reward

followed by a 10 second inter-trial interval.

The trials were done with the NVS trials performed first (in blocks of 100) followed

immediately by the VS trials (with minimum perturbation). This was achieved by

toggling an external switch to activate the flash. Neither the cat nor the optical setup was

disturbed in any way. The cat was monitored at all times with an infrared sensitive

camera and light sources. The detector tubes were largely shielded from these light

sources.

Experimental Procedure

1. Technical Aspects

Spectral measurements were performed using an Ocean Optics (830 Douglas Avenue,

Dunedin, FL 34698, USA) detector system consisting of a S2000 spectrometer, an

ADC2000 PCI card and a tungsten lamp. The tungsten lamp gives a continuum spectrum

following Planck’s blackbody spectrum at a temperature of 3100 Kelvin. The PCI

ADC2000 hardware interface between the spectrometer and the computer performed an

analog to digital conversion at a sampling frequency of 2 MHZ at a 12 bit resolution

which allows spectral acquisition every 5 milliseconds. Additionally, free running

operations and external trigger modes were available for synchronizing external events.

The S2000 Ocean Optics is a miniature spectrometer with large spectral response (350 –

1100 nm) and good spectral resolution (0.3 – 10 nm). The spectral response is optimized

31

for the NIR range. This was achieved by a combination of different accessories: a

diffraction grating with a spectral response in the 550-1100 nm range (#4 in Ocean Optics

catalog) and a long pass filter to transmit wavelengths greater than 550 nm. These

combined with the response of the CCD linear array detector, pre-mounted on the

spectrometer, gave us the required spectral range of analysis in the NIR region, 650- 990

nm. The optical resolution and spectral response depend on the slit entrance on the

spectrometer, groove density of the gratings, fiber optics diameter and number of

elements (pixels) of the detector. Optimization of light detection through highly

scattering tissues was achieved by using an entrance slit of 200 microns and a fiber optic

core of 1000 microns. The grating had a groove density of 600 mm-1 and the CCD array

has 2048 pixels. The combinations of these parameters gave us an optical resolution of

4nm.

IV.C Data Acquisition

The cat was immobilized in a bag facing the screen with its head fixed to ensure minimal

movement. The optical fibers were then positioned in the tubes on the cat’s head. Any

particular pair is indicated according to the labeling system used in figure 4.1. For

example, a configuration of b4b6 refers to the placement of the source fiber in tube b4

and the detector fiber in tube b6. The tip of each fiber was placed in direct contact with

the acrylic which was roughly 1-2 mm above the cat’s skull, and approximately 4mm

above the surface of the cat’s brain. The cat’s head was held rigidly fixed during the

experiment, once the tubes were placed in the tubes; consequently their positions were

stable and could be reproduced from session to session. The spectrometer was armed at

the beginning of each trial by an external pulse coincident with a short auditory signal to

32

indicate the start of the trial. Data acquisition and analysis were performed by the

Elantest software (This program is available at

ftp://www.lfd.uiuc.edu/lfd/egratton/elantest/), which communicated through the PCI

ADC2000 card with the S2000 spectrometer. Synchronization of the data acquisition

with the visual stimulation was achieved by an external Stanford Research (1290-D

Reamwood Avenue, Sunnyvale, CA 94089, USA) pulse generator (model DS345). The

trigger and synchronization system provided the correct time signals to the Elantest

software and the external trigger input port of the spectrometer. Communication with the

software via the parallel port of the PC enabled the system for the start of data

acquisition. The spectrometer acquired spectra every 5 ms. First, a reference spectrum

was taken under the condition of no trial, meaning that the cat observed the screen at its

normal background level as described in the previous section, but otherwise not

performing either task. All differential measurements were calculated with respect to this

initial spectrum. Equal blocks of data consisting of 100 trials were collected under

different conditions. Data acquisition began with the beginning of each trial. The

collection of spectra was synchronized with externally supplied pulses that were

coincident with the onset of each flash in the VS condition (i.e. every 500ms). The same

timing was also provided for the NVS conditions. The spectrometer was armed at the

start of each trial by an initial pulse and acquired a spectrum every 5 ms, giving a total of

95 spectra corresponding to 475 ms. The spectrometer then waited for the next trigger,

which occurred 500 ms after the start of the trial. This cycle was repeated 16 times for a

total of ~16* 95 spectra corresponding to about 8 seconds of the trial. No spectra were

acquired during the final two seconds of the trial, the reward and the inter-trial interval.

33

Each spectrum had 2048 wavelength points. The data matrix was then saved and the

sequence repeated 100 times. The procedure was repeated for different source- detector

configurations. It was identical for NVS trials except that the pulses provided to

synchronize the collection of spectra every 500ms were not accompanied by flashes. The

experiment was performed with minimum perturbation as the only change during the data

acquisition was the activation of the flashes during the VS trials via an external computer.

(See figure 4.2).

Figure 4.2- Schematic showing the synchronization achieved using an external system, and the placement of the source-detector fibers while the cat is observing the screen.

3. Data Analysis

In this chapter, we present the analysis of the hemodynamic signal. The analysis of the

fast neuronal signal will be discussed in a separate chapter. Data were collected for the

first 8 seconds of each trial. However, every 475 ms, one spectrum was deleted due to

the external synchronization of the system, as this spectrum had a different integration

time (30 ms instead of 5 ms). This spectrum was disregarded, but the overall time axis

was maintained correctly, i.e., the matrix had a gap of 30 ms every 500 ms. Since we

were using this sequence only for slow signals, and the time axis was correct, this

Start acquisition pulse

500 ms250 ms

25 ms95 pulses

Visual stimulation signal

Pulse sequence

t

0

0SC

REEN

S D

Start acquisition pulse

500 ms250 ms

25 ms95 pulses

Visual stimulation signal

Pulse sequence

t

0

0SC

REEN

S D

500 ms250 ms

25 ms95 pulses

Visual stimulation signal

Pulse sequence

t

0

0SC

REEN

S D

500 ms250 ms

25 ms95 pulses

Visual stimulation signal

Pulse sequence

t

0

0SC

REEN

S D

34

deletion had no influence on the final result. The remaining spectra were then folded

across the 16 flash cycles and averaged over the desired number of trials. Folding was

also done as a function of the number of trials to see if there were any differences in the

signals observed due to physiological changes occurring across trials. A definitive

spectral pattern and intensity pattern changes were observed in the raw data matrix. We

performed spectral deconvolution and principal component analysis (PCA) on the raw,

but folded data. From the PCA, it was determined that the minimum number of basis

components required to correctly fit the differential spectrum was four: scattering, water,

HHb and O2Hb. The spectrum was then separated into the weighted contributions of

these individual species and their changes observed as a function of time. No

assumptions were made with respect to the baseline hemoglobin concentrations as we

only consider a differential spectrum. In representing the changes of the components as a

function of time, only O2Hb and HHB were plotted using the same scale. The scale of

the changes in water component and that of the scattering component are arbitrary.

IV. D Results

For all of the different source-detector (S-D) configurations, data were processed as

described in the previous section to detect both the changes due to the hemodynamic

signal, as well as to the fast (millisecond) neuronal signal. However, due to the volume

of information that was obtained, only certain aspects of the hemodynamic signal will be

presented in this chapter. First, we present maps of the raw data matrix after folding

followed by the separation of this matrix into the weighted contributions of scattering,

water, O2Hb, and HHb as functions of time. The maps show the average change for all

of the wavelengths as a function of time for the folded data. Finally a comparison of the

35

optical BOLD effect seen as a function of source–detector (S-D) configuration is shown.

Although we show results for only a few experiments, the results are highly reproducible

for any given S-D combinations. For locations in the visual cortex, the optical BOLD

effect is observed during visual stimulation and not seen in the absence of the

stimulation. Similarly for S-D configurations located outside of the visual cortex (frontal

lobes), the Optical BOLD effect was not observed for NVS or VS trials.

1. Raw Data

Maps displaying the raw data matrix of each S-D pair over the visual cortex, areas 17 and

18 show a significant change in the shorter wavelengths for the first 3 seconds of the

visual stimulation. (Figures 4.3a, 4.3c). The data matrix for the control configuration that

was positioned over the frontal lobes, where we didn’t anticipate any changes, remains

relatively constant during the same activation period. (Figure 4.3e). For all of the

configurations during NVS trials, the changes in wavelengths as a function of time were

also minimal (Figures 4.3b, 4.3d and 4.3f).

36

Figure 4.3- Raw Data Matrices showing the relative OD as a function of time and wavelength. 2. Spectral Deconvolution

The raw data matrix was decomposed for each time bin (linked to trial length) using 4

spectral components: O2Hb, HHb, water and scattering. Figures 4.4a-4.4f show three

different S-D locations for VS and NVS trials, one from the para-sagittal row of tubes

over the area 17/18 border (b4b6; figure 4.4a, 4.4b), one roughly in a coronal plane over

areas 17 and 18 (b4c6; figure 4.4c, 4.4d) and one over the frontal lobes (a2a4; figure 4.4e,

4.4f). There is an optically detected BOLD effect due to the activation of the visual

cortex in which the O2Hb starts to increase after 1-2.5 seconds after visual stimulation.

37

This delay is dependent on the area investigated. Differences in the values and time

courses of the signals due to changes in O2Hb and HHb are detected between regions in

the visual cortex which differ by about 2mm. Additionally, significant changes are seen

due to scattering and water. The change due to scattering also has a different time course

with respect to the other components. For source–detector (S-D) configurations that

correspond to areas in the visual cortex, a maximum decrease (trough) in scattering is

seen at roughly the same time as the O2Hb reaches a maximum in the optical BOLD

effect during VS trials. The water component is seen to decrease at the onset of

stimulation and tracks the HHb changes with some time delay (figures 4.4a, 4.4b).

Control experiments (figure 4.4e, 4.4f) show that there are no major changes in the O2Hb

and HHb (BOLD effect) that track with the visual stimuli for the S-D pair located over

the frontal lobes, and the contributions from these components are one order of

magnitude smaller than the signal obtained from the VS trials (Figures 4.4a, 4.4c). The

signals due to the changes in water and scattering are on the same order of magnitude but

they don’t appear to be correlated to visual stimulation. In addition, control experiments

show minimal signal changes in the absence of visual stimulation (figure 4.4b, 4.4d,

4.4f).

38

0 2000 4000 6000

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ativ

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D

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b4b6 VS(a)

0 2000 4000 6000

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Time/ms

b4b6 NVS(b)

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a2a4 VS(e)

0 2000 4000 6000

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Scattering Water Oxy Deoxy

Time/ms

a2a4 NVS(f)

Figure 4.4-Spectral Deconvolution showing changes in scattering, water, O2Hb and HHb as a function of time.

39

3. Optical BOLD effect as a function of S-D locations.

Figures 4.5a and 4.5b show the optically detected BOLD effect for several S-D locations,

two parallel to the 17/18 border (b4b6, b3b5), and one S-D pair located over the frontal

lobes (a2a4), and three that roughly straddled this border orthogonally (b4c6, b4c5,

c5b5), respectively. The signals for the two S-D configurations parallel to the 17/18

border show similar temporal behavior. However, for the configuration b4b6 the

amplitude of the signal is much larger (about a factor of 4) than for any other

configuration. For the orthogonal S-D pairs, it was observed that the amplitude of the

signal due to the optical BOLD effect was approximately the same. However, there were

different delays among the different pairs. In one configuration, b4c6, an initial dip is

seen lasting for ~2 seconds. The initial dip was not observed at all locations, indicating

that its magnitude is not constant everywhere.

0 2000 4000 6000

-0.005

0.000

0.005

0.010

0.015

O2Hb b4b6 HHb b4b6 O2Hb b3b5 HHb b3b5 O2Hb a2a4 HHb a2a4

Abs

orpt

ion

Time/ms

BOLD EffectParasagittal, roughly parallel to the 17/18 border

(a)

0 2000 4000 6000-0.005

0.000

0.005 O2Hb b4c5 HHb b4c5 O2Hb b4c6 HHb b4c6 O2Hb c5b5 HHb c5b5

Abs

orpt

ion

Time/ms

BOLD Effect orthogonal to the 17/18 border

(b)

Figure 4.5-Comparison of O2Hb and HHb as a function of cortical position

40

IV. E Discussion

This work is the first to determine changes of the spectral components in the mammalian

(cat) brain due to external visual stimuli. Previous work has focused on the

determination of the reflectance signal in the near-IR with the purpose of imaging

columnar neuronal organization26, and the optically detected BOLD effect was not

determined in those studies. Furthermore, the fast dynamics were obtained using long-

integration, high sensitivity cameras in which the acquisition was triggered at different

times after visual stimulation rather than utilizing rapid spectral acquisition which was

synchronous with the visual stimulation as in the present approach. The spatial resolution

that is obtained with our instrument is on the order of millimeters, which when compared

to that obtained using multi- distance NIRS in human subjects (centimeters) still gives us

a relatively high spatial resolution. One concern is that the radiation emitted by the

tungsten lamp will be unstable as it is temperature dependent. However, before any

analysis was done, the data was block averaged and folded across trials as is standard in

similar studies. Hence, any random fluctuations that are present would be removed before

the spectral deconvolution is performed. We have observed changes in the O2Hb, and

HHb signals during visual stimulation reminiscent of the fMRI BOLD effect. The origin

of the BOLD effect is attributed to an increase in blood flow following neuronal activity

in one part of the brain. This causes a decrease of the HHb level because HHb is washed

out. In the fMRI signal, only the decrease of the HHb concentration is measured,

whereas with the optical technique, we have access to both O2Hb and HHb. Upon visual

stimulation, we can clearly see the decrease in the HHb and the quasi simultaneous

increase in the O2Hb. Careful examination of the data in figures 4.5a, 4.5b shows that

41

there is a delay between the two signals. A similar delay between the increase of the

O2Hb and decrease of HHB was also observed in optical experiments in humans and was

attributed to oxygen consumption.27, 28 A striking feature of our experiments is that the

optical BOLD effect starts to decrease after a few seconds, (figure 4.5a) although the

stimulation continued throughout the trial.

Another novel outcome of these experiments is that the water signal, as well as the

scattering signal, changes during stimulation. The changes in the water component are

quite surprising since we expect no net change of the tissue water content during the short

time of visual stimulation. For the water content in the tissue to change rapidly (in

seconds), the water should be replaced by something else, which is not plausible. To

explain the decrease of the water content during the optical BOLD effect we must

consider the origin of the optical signal in the tissue. The tissue is highly heterogeneous

both from the physiological and the optical point of view. There are tissue regions that

are optically opaque, such as relatively large blood vessels. If, as we expect, these

vessels change diameter to accommodate an increase of blood flow following neuronal

stimulation, then this opaque optical compartment will increase. The net effect of the

increase in the opaque compartment results in an effective decrease of all the spectral

components, including the O2Hb and HHB. However, these tissue chromophores

undergo additional physiological changes due to the exchange of O2 and CO2 in the

tissue, which results in a net increase of the O2Hb signal (washout effect). We propose

that this optical effect caused by the presence of the opaque blood vessels is at the origin

of the apparent decrease of the water component. Following this reasoning, the water

component could be used to assess the extent of vasodilation due to stimulation. With

42

regard to the scattering signal, it should also decrease due to this optical effect. However,

there are also changes in the size of the microcapillaries, larger blood vessels and

possible changes at the cellular and sub cellular level. Therefore, the scattering signal

will not necessarily track the water signal.

All data presented in figures 4.5a-b refer only to the first 10 trials of a series of 100 trials.

As the trials continued there was a gradual change of the shape of the optical BOLD

effect. This fatigue effect will be discussed separately.

We observed that the optical BOLD effect is strongly dependent on the location in the

visual cortex. Locations 2 mm apart gave significant differences both in the amplitude

and in the time course of the signals.

We have observed several differences between the optical BOLD effect for the cat, when

compared with similar experiments in humans. It is premature to discuss the differences,

when only one animal has been studied. Furthermore, we cannot state unequivocally that

these results are “cat invariant”. However, additional cats are being trained to repeat the

experiments, and to assess if there are differences among individual animals. Certainly

the size of the cat’s brain could generate hemodynamic responses which differ from those

in humans.

One consideration for this discussion is whether or not the spectral approach could be

extended to studies of the human brain. Firstly, the size of the skull will bring the regime

of light propagation into the diffusive regime. However, the spectral approach is

independent of the modality of light propagation. There are two additional factors that

must be considered for the application of this spectral approach in humans. First, with

the present experimental apparatus, the S/N is insufficient to observe the dynamic

43

changes of the tissue chromophores at the distance of centimeters. For the slow

hemodynamic signal, using more sensitive detectors and more efficient monochromators,

we should have enough light for an accurate determination of spectral components.

Secondly, for the fast signal due to neuronal activation, where a very short integration

time is needed, the amount of light needed could be critical. Hence, additional work is

needed for the improvement of the technical aspects of this work.

IV. F Chapter Summary

In summary, we have developed a technique that is independent of the light modality

(diffusive or non-diffusive), where a broad-band spectral approach is used to determine

the individual NIR spectrum of tissue components. For the application in a mammalian

brain, we have examined the behavior of the scattering, O2Hb and HHB (BOLD effect)

simultaneously with other tissue components such as water content. The technique has

proven to have high enough temporal and spatial resolution to adequately determine the

localized hemodynamics. The behavior of water during stimulation has not been

discussed in previous literature. Our proposed model satisfactorily accounts for the

apparent change in the water content which can be used to better qualify the role of water

in vascular dynamics.

44

V. F Phantom Studies - Modeling Vasodilation

V. A Introduction

In the previous chapter, we determined that there was an apparent decrease of water

concentration and scattering concomitant with the optical BOLD effect in an animal

model.19 In regions of the cat visual cortex, we found that as a direct result of visual

stimulation, an optical BOLD effect (increase of O2Hb concentration and decrease of the

HHb concentration) was observed. However, the change in water concentration and

decrease of the scattering contribution were unexpected in the cat model. Previous

measurements of brain activation in humans did not have access to measurements in a

broad spectral range. This paper reported for the first time that there was an apparent

change in water concentration in tissue that occurred during the course of brain

stimulation that lasted for several seconds. Physiologically, this rapid change (in seconds)

of water content cannot be readily explained. It is highly improbable that there would be

such a large change (1-2%) in water concentration on the time scale considered in our

experiments. We proposed that the apparent water change could be due to an optical

artifact. In chapter V, we focus on this surprising result. We reason that vasodilation is

associated with brain stimulation and that vasodilation could be the origin of this optical

artifact. We simulated vasodilation with phantoms, where the water content was kept

constant. Our goal with the phantom studies is to demonstrate that simulated vasodilation

decreases all spectral components of a mixture. Another purpose of this model is to

estimate the amount of spectral changes due to “vasodilation” when compared to the

absorption of the tissue chromophores and scattering.

45

V. B Physiology

Neurovascular coupling or functional hyperemia in the mammalian brain relates the

vascular response to an increase in metabolic rate.29 In simple terms, in response to local

neuronal activity, vasodilation occurs and provides an increased supply of nutrient rich

blood to the activated region. Physiological processes result in exchange of the O2Hb

(oxyhemoglobin) with HHb (deoxy-hemoglobin) such that there is an increase in the

O2Hb and a quasi-simultaneous decrease in HHb during stimulation; the classic BOLD

(Blood Oxygenation Level Dependence) effect as seen using fMRI and optical techniques

by many authors.

V. C Model of Optical Properties of Tissue

The basic idea of the phantom model is that there are at least two optical compartments in

the brain tissue; one opaque, which is comprised of the large (larger than millimeter)

blood vessels and a second compartment, the brain tissue, which includes the small

capillaries (less than 100 microns) (Figure 5.1).

A B

S D DS

A B

S D DS

Figure 5.1- Schematic showing that the size of the opaque vessels change as a function of time will have an effect on the observation volume.

46

We define the opaque compartment as the contribution of those structures that appears

“black” at all wavelengths. Due to the physiological response to external stimuli, the

opaque compartment increases in volume as the result of vasodilation, thereby increasing

the total absorption (at all wavelengths). However, the relative amount of the

chromophores in the tissue decreases. Consequently, vasodilation causes an apparent

decrease in the absorption of all spectral components, including O2Hb, HHb, water and

scattering by the same relative amount (Figure 5.2).

Decrease in relative OD o f all components

Increase in background OD

Figure 5.2-Schematis showing the effect that vasodilation has on Absorption spectra

In the brain tissue, the increased blood flow due to vasodilation causes exchange of HHb

with O2Hb. Therefore, we should observe an increase for the O2Hb spectral component.

V. D Experimental Procedure

The phantom model was set up as shown in Figure 5.3, where the source-detector

distance was 4.5mm. The spoke assembly and the source detector were immersed in a

bath of milk (store bought 2% milk). The scattering properties of 2% milk are

47

comparable to that of the mammalian brain, where the scattering coefficient, µs , of milk

is 5cm-1 and the scattering coefficient, µs ,of the brain is 5-10 cm-1 .13,21 The light source

was a tungsten light source at a nominal temperature of 3100K (LS-1 Tungsten Halogen

Light Source, Ocean Optics, Dunedin, FL, US). The spectrometer employed was the

model S2000, also from Ocean Optics. Both the light source and the spectrometer were

coupled with a 1000µm core diameter optical fiber. The spectrum of the light source was

recorded prior to each measurement using a reflector in place of the milk solution. The

basic phantom design uses a pinwheel comprising of spokes that were rotated by a

stepper motor at a rate of 0.167 rev/s in the path of the “light bundle”. We examined a

specific condition where the size and position of the opaque objects was varied while

keeping the geometry of the optical setup fixed. A reference spectrum was taken of the

“bath”; namely, the milk solution in which the pinwheel rotated. Spectra were taken at a

rate of 50 spectra/sec while the spokes were rotated, which was comparable to the timing

of the spectra acquisition (200 spectra/sec) used in the cat experiments as reported.19

48

Figure 5.3- Schematic showing the Experimental setup for the phantom studies

1. Case I- Black Spokes of varying diameters

The pinwheel was comprised of four solid black spokes of diameters: 0.9mm, 1.2mm,

2.3mm and 3.5mm. These sizes were chosen to be comparable to that of blood vessels.

The spokes mimicked the effect that a change in diameter would impose on the optical

signals observed as the diameters of blood vessels are on the order of a few mm to cm.30

The entire system was placed at a depth of 1mm with respect to the source- detector

configuration. The depth of the pinwheel was then varied using a robotic arm that

allowed the entire system to be moved in highly accurate incremental steps of 0.625mm

and the entire procedure repeated.

Computer: Data acquisition and Analysis

Tungsten Lamp White Light

Source

Ocean Optics S2000

Spectrometer (Detector)

4.5mm

BATH

1mm

49

V. E Data analysis

Data acquisition and analysis were performed by the Elantest software created by E.

Gratton. (This program is available at ftp://www.lfd.uiuc.edu/lfd/egratton/elantest/). First,

a reference spectrum of the “bath” was taken. All measurements were calculated with

respect to this initial spectrum, i.e., only the changes with respect to the spectrum of the

milk bath are reported. The overall spectral changes were calculated by averaging the

changes at all wavelengths. The method of spectral deconvolution and data manipulation

was previously discussed in the animal study in extensive detail. In short, we determined

the changes of the tissue chromophores, including scattering, by forming a linear

combination of the spectrum of each individual tissue chromophore. For case I a

spectrum of the milk solution was also loaded as a basis spectrum to be used in spectral

deconvolution. Relative changes in spectral components are reported in terms of average

contribution to the total (average) absorption. Principle Component Analysis (PCA) was

used to determine the minimum number of spectra required to fit the differential

spectrum. In case I, the milk spectrum was sufficient. The spectrum was then separated

into the weighted contributions of these individual species and their changes observed as

a function of time as the spokes rotated under the source-detector pair. The relative

changes of these components as a function of time were plotted. In addition, the values of

the relative contribution to the absorbance were then plotted to show the relationship

between the relative absorption and spoke diameter (case I)

50

V. F Results

Case I- Black Spokes of varying diameters

As the opaque spoke intercepts the light bundle there is an increase of the total light

absorption (Figure 5.4a). The absorption change is uniform at all wavelengths.

However, when spectral deconvolution is performed for the “milk” component and the

total spectral changes are reported as a function of time, there is a relative decrease of the

amount of “milk” measured when the spoke is in the light path (Figure 5.4b). This effect

increases in a non-linear fashion as the spoke diameter increases (Figure 5.4c). As the

depth of the pinwheel was increased with respect to the source-detector position, one sees

that the effect of the opaque object is still appreciable up to a depth of 6mm (Figure

5.4d).

51

c)

y = 0.035x2 - 0.2645x + 0.158R2 = 0.9989-0.4

-0.35

-0.3

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00.5 1.5 2.5 3.5 4.5

Diameter (mm)

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nges

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0.9 mm1.2 m

m2.3 m

m3.5 m

m

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0

Cha

nges

in A

bsor

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e

Height (mm) Diam

eter

d)

0.9 mm

1.2 mm

2.3 mm

3.5 mm

Figure 5.4- Panel a) Raw data as the black spokes rotate, b) Spectral deconvolution into basis spectrum of the milk, c) The mathematical dependence of the changes in absorbance with spoke diameter, d) Changes in absorbance as a function of diameter of spokes and height below source-detector bundle.

V. G Discussion

The resolution of spectral components in a mixture is a classical problem in spectroscopy.

A common approach is the establishment of the number of independent components

using methods such as principal component analysis.31 When the components of the

mixture are known a priori, then simple spectral deconvolution is sufficient to determine

their relative contributions based on the Beer-Lambert law. In the case of tissue

spectroscopy, there is the additional issue in regard to the validity of the Beer-Lambert

law. In general, in the presence of strong scattering, the Beer-Lambert law must be

52

modified to account for the changes in optical path introduced by multiple scattering. In

our work, we are mainly interested in relative changes of the amount of spectral

components due to physiological activity. We assume that we know the spectral

components and we want to determine their relative changes. What is unique of our work

is that we want to determine fast changes in the brain following a stimulus: from a few

milliseconds corresponding to neuronal activity to several seconds due to metabolic

processes. Furthermore, we need to interpret changes occurring in the brain of a

relatively small animal (the cat). Due to the small size of the cat brain, the total optical

path of the light in our configuration is not sufficient for achieving the conditions of the

diffusion approximation. Also, we believe that superficial effects have a much stronger

influence in our results than for the case of human studies. Spectroscopy of the open

brain in small animals has been studied before using reflected light.32 In our case, we use

fiber optics to illuminate a point on the skull and light is collected after traveling several

millimeters in the brain. In our experiments, light travels some distance in the tissue

before been collected by the detector fiber. In this research we study light propagation in

a phantom system that closely mimics the situation of the cat brain. The source detector

distances as well as the distance from the surface, where we put our scatterer or

absorbers, is similar to the known geometry of the cat brain. Several groups have

addressed the inhomogeneous nature of tissue in both the diffusive and in the cases of

small (a few mm) optoelectrode separation and explored the second order corrections that

must be made to existing models of tissue dynamics.33-42 Our results from the dynamic

phantom made of the various diameters of opaque spokes show that “vasodilation” per se,

as simulated by changing the diameter of the spokes, could cause an apparent decrease in

53

all spectral components, including water and scattering. Therefore in the presence of

vasodilation of the large blood vessels, all spectral components should decrease

proportionally to their contribution to the overall spectrum. We propose that the apparent

decrease in water content (and partially scattering) observed in the cat brain following

visual stimulation is in fact due to vasodilation. In this model, the time course of the

water change reflects the dynamical changes of the diameter of the blood vessels. We

also demonstrated that the quantitative relationship between apparent absorption changes

and vessel (spoke) diameter is non-linear for small diameters (for diameters up to

3.5mm). However, when the diameter of the blood vessel becomes small, the vessel

becomes transparent, in contrast to the spokes in the phantom, which are always opaque

at all diameters. Our phantom studies also show that there is an apparent decrease of the

scattering spectral component when vasodilation occurs. The interpretation of the

changes in scattering in brain studies should consider this effect. However, in the brain,

there is a possibility that there are additional “true” scattering changes due to variation of

the size or number of scattering centers. In the following discussion, we estimate the size

of the changes in spectral components due to vasodilation for comparison with the

changes in chromophore concentration reported in the literature during the optical BOLD

effect.19,43 Typical changes in the concentration of tissue chromophore (O2Hb and HHb)

during stimulation are on the order of 1-2% both for humans and for the cat model.19,43

Similar values are found for the changes in scattering coefficient. It has been estimated

that vasodilation changes the diameter of the blood vessels by about 20%.44 To use our

data to estimate the effect of vasodilation in the human brain, we should estimate the

average diameter of the large blood vessels and also their depth with respect to the

54

surface. From figure 5.4a we can estimate that for a vessel diameter of about 2 mm, a

change of 20% of the diameter will give a change of about 0.04 OD units. However, this

change will depend on the depth of the vessels. At a depth of 6mm or more, the changes

will be much smaller (figure 5.4b). Since the average apparent optical density in tissue is

about 1-2 units, for source-detector distances of a few millimeters, the relative changes

due to vasodilation should be about 4% for superficial blood vessels but less than 0.4%

for vessels at 6 mm or more from the surface. In the case of the cat brain, the changes

due to vasodilation could be more significant in the measurements due to the small size

of the brain with respect to humans.

Note, that this artifact in the estimation of spectral components due to vasodilation would

not have been recognized if we had only used a few wavelengths. In fact, using a broad

band spectral analysis allowed us to distinguish changes that equally affect all spectral

components from specific changes affecting only one spectral component. The effect of

vasodilation on the optical spectrum is shown schematically in figure 5.2. Vasodilation

decreases the total light transmission (offset in figure 5.2) and reduces the spectral

amplitude. Therefore, if we measure only relative spectral changes, we will measure a

reduction of the spectral amplitude at all wavelengths. There is nothing special about

vasodilation in regard to optical tissue spectroscopy. Other mechanisms that broaden the

relatively large blood vessels should produce a similar artifact. For example, the pulse

could have a similar effect. However, following the above discussion, the pulse due to

relatively deep blood vessels has a very minor effect on the overall spectrum offset. Only

pulsating arteries close to the skin surface should produce an appreciable effect in

reducing all spectral components. This observation could explain a common yet seldom

55

reported observation that the pulse is barely visible when measurements are obtained

from deep layers when using, for example, the frequency-domain multi-distance method.

Instead, any method based on steady state continuous illumination (CW measurements)

or time resolved measurements at one point should be affected by the “vasodilation”

artifact which is more prominent at the surface. Our experimental results using phantoms

are independent of the model of light propagation in tissues. They are simply a

consequence of the fact that we cannot “see” inside opaque compartments. Furthermore,

our results can have implications on the way one interprets changes in chromophore

components in the presence of vasodilation, or any other physiological condition that

changes the relative contribution of the “opaque” compartment.

V. Chapter Summary

Our phantom model is clearly a simplification of the real brain structure as we only

consider two compartments, while in the actual brain we presumably have a continuum of

vessel sizes. However, the expected response due to intermediate vessel size will be a

mixture of the two extreme cases (opaque vs. transparent). Even if the real brain

situation is more complex, our conclusion in regard to the decrease of the relative

contribution of the tissue chromophore due to vasodilation is still valid. Note that our

model applies both in the diffusive (human brain) or quasi-diffusive regime (cat brain), as

this effect is seen in the raw data and therefore is independent on the algorithm used for

analysis. When using a few wavelengths and a single measurement point, there is no

simple way of determining the origin of the apparent optical changes (vasodilation or true

spectral changes). As a consequence of our studies, we conclude that determination of

56

chromophore concentration in tissue using only few wavelengths (for O2Hb, HHb and

scattering) is not sufficient to characterize the origin of the changes.

57

VI. Phantom Studies- Validating technique to recover known optical properties

VI. A Introduction

In our second set of phantom studies, we had to prove that we can accurately recover the

changes due to scattering and absorption, separately. This demonstration is vital to

validate the spectral method. In our phantom, we mimic spectral changes in absorption

and scattering by inserting spokes which contain different concentrations of absorbers

and scatterers. Our experimental results using phantoms are independent of the model of

light propagation in tissues.

VI. B Method

Case II- Spokes with varying Scattering Coefficients

Six spokes were attached to the pinwheel and placed at a depth of 1mm with respect to

the source-detector configuration. The pinwheel was made of black rubber in which holes

were made in a radial manner to secure test tubes (wall-thickness 0.4mm) filled with

different scattering solutions. Each test tube was secured using Parafilm to ensure that

there was no leakage of the solutions in the test tubes into the bath. The solutions

consisted of different ratios of milk to water. The notation used refers to the fraction of 2

% milk (store-bought) with respect to the total solution (milk and water) and were as

follows: 0.05 (same as that of the “bath”), 0.1, 0.2, 0.333, 0.5 and 1, where 1 correspond

to “undiluted milk”. For the case II experiments, the bath was made of milk diluted

1/20.

Case III- Spokes with varying absorbing materials (Low Absorbance Range)

For this series of measurements the test tubes were filled with a mixture of the “bath”

solution (milk diluted 1/5) and Blue gel food coloring (sold under the brand name Betty

58

Crocker gel Food colors). The initial dye mixture was diluted by adding the milk

solution (at a dilution equal of that of the bath) in appropriate amounts to get the desired

concentrations. The concentration used is shown as a percent concentration of the blue

dye in the overall mixture as follows: 3.125%, 6.25%, 12.5%, 25%, 50% and 100 %. The

Absorbance of the 100 % mixture was determined using a spectrophotometer (Perkin-

Elmer Lambda 5, Shelton, CT USA) with a standard 1cm cuvette and it gave an

absorbance of 1.44 OD units at 650 nm. The absorption coefficient, µa , could not be

calculated for this dye as the molecular weight was not known. However, the dye was

purely absorbing with no scattering.

Case IV- Spokes with varying absorbing materials (High Absorbance Range)

For these measurements, four spokes were used and the concentrations were 75, 80, 90

and 100 % of a solution of the dye with an Absorbance of 3.3 OD units at 650 nm.

VI. C Data Analysis

Data acquisition and analysis were performed by the Elantest software created by E.

Gratton. (This program is available at ftp://www.lfd.uiuc.edu/lfd/egratton/elantest/). First,

a reference spectrum of the “bath” was taken. All measurements were calculated with

respect to this initial spectrum, i.e., only the changes with respect to the spectrum of the

milk bath are reported. The overall spectral changes were calculated by averaging the

changes at all wavelengths. The method of spectral deconvolution and data manipulation

was previously discussed in the animal study in extensive detail. For all cases a spectrum

of the milk solution was also loaded as a basis spectrum to be used in spectral

deconvolution. For the cases III and IV, in addition to this milk solution spectrum, the

spectrum of the blue dye obtained using a spectrophotometer was also added to the

59

library of basis spectra. Relative changes in spectral components are reported in terms of

average contribution to the total (average) absorption. Principle Component Analysis

(PCA) was used to determine the minimum number of spectra required to fit the

differential spectrum. The milk spectrum was sufficient in all cases, case II required, in

addition to the 0.2 milk solution spectrum, scattering and for the case of the cases III and

IV, the spectrum of the blue dye. The spectrum was then separated into the weighted

contributions of these individual species and their changes observed as a function of time

as the spokes rotated under the source-detector pair. The relative changes of these

components as a function of time were plotted. In addition, the values of the relative

contribution to the absorbance were then plotted to show the relationship between the

relative absorption as a function of concentration for cases II-IV.

VI. D Results

1. Case II- Spokes with varying Scattering Coefficients

As the scattering of each spoke increased, we measured an increase in the scattering

spectral component when spectral deconvolution was performed (Figure 6.1a). In the

case where the spoke contained the same solution as the bath (0.05 milk solution), only a

very small change in the scattering was observed, probably due to thickness of the walls

of the spoke (Figure 6.1a). Furthermore, the changes in the milk spectrum were

extremely small (two orders of magnitude less) in comparison to the changes in the

scattering signal (Figure 6.1a). Hence, we conclude that the changes due to the mismatch

of the refractive indices between the surfaces of the milk solution and the cuvette

produced a negligible effect. The plot of the recovered magnitude of the scattering

60

spectral component as a function of scatterer concentration shows a linear relationship at

low scatterer concentration (0.05-same as bath, 0.1, 0.2, and 0.33) and then displays

saturation as the scatterer concentration increases (0.5, 1-same as pure milk) (Figure 6.1a

and b) .

Figure 6.1 a) Spectral deconvolution into scattering and milk solution, b) Spectral deconvolution into blue dye and milk for low OD, c) Spectral deconvolution into blue dye and milk for high OD.

2. Cases III and IV- Spokes with varying absorbing materials (Low and High

Absorbances)

There is an appreciable change seen in the optical signal due to the addition of the blue

dye (low optical densities) when spectral deconvolution is performed (Figure 6.1b) using

a)

b) c)

a)

b) c)

61

as a spectral component the spectrum of the dye obtained in a clear (non-scattering)

solution. In this regime, a plot of the absorption of the blue dye spectral component as a

function of concentration shows a linear relationship (r2= 0.9976) (Figure 6.1b and figure

6.2c). In the case of the highly absorbing dye solution, a saturation of the recovered

amount of spectral component is seen (Figure 6.1c and figure 6.2d).

-0.4

0

0.4

0.8

1.2

1.6

2

0 0.2 0.4 0.6 0.8 1

Concentration

Cha

nges

in A

bsor

banc

e

R2 = 0.9976

0

0.2

0.4

0.6

0.8

0 20 40 60 80 100

% Concentration

Cha

nges

in A

bsor

banc

e

0.55

0.75

0.95

1.15

75 80 85 90 95 100

% Concentration

Cha

nges

in A

bsor

banc

e

R2 = 0.9758

-0.4

0

0.4

0.8

1.2

1.6

0.05 0.15 0.25 0.35

Concentration

Cha

nges

in A

bsor

banc

e

a) b)

c) d)

-0.4

0

0.4

0.8

1.2

1.6

2

0 0.2 0.4 0.6 0.8 1

Concentration

Cha

nges

in A

bsor

banc

e

R2 = 0.9976

0

0.2

0.4

0.6

0.8

0 20 40 60 80 100

% Concentration

Cha

nges

in A

bsor

banc

e

0.55

0.75

0.95

1.15

75 80 85 90 95 100

% Concentration

Cha

nges

in A

bsor

banc

e

R2 = 0.9758

-0.4

0

0.4

0.8

1.2

1.6

0.05 0.15 0.25 0.35

Concentration

Cha

nges

in A

bsor

banc

e

a) b)

c) d)

Figure 6.2- a) Case of low scattering, plot of linear relationship of changes in absorbance with concentration of scattering, b)Case of highly scattering solution showing plateau, c) Case of low OD showing linear relationship of changes in absorbance with concentration of blue dye, d) Case of high OD showing plateau.

VI. E Discussion

With respect to the true spectral changes (addition of absorbing material), we

demonstrated that the relative absorption changes are linear with the dye concentration

only at low absorbances. At high absorbances, the apparent changes saturate. This is also

62

true for classic Beer-Lambert with no scattering. We show that we can accurately recover

(in two distinct cases) the spectrum (which is measured independently using a

spectrophotometer) of the absorber and the spectral component due to scattering as a

function of concentration (of absorber and scatterer). The range of concentrations was

restricted to the regime of low absorbance. It is seen as advertised that in the diluted

absorber/small scattering changes regime, the relative changes and concentration of

absorbers/scatterer as recovered by the measurement method will follow a linear

relationship. When the concentration of the absorbers and scattering centers increases,

the optical changes are no longer proportional to the concentration of the

absorber/scatterer and the changes start to saturate the measured absorption. Hence, we

demonstrated that saturation of the absorption could occur in real samples and that the

result is to produce artifacts in optical measurements.

VI. F Chapter Summary

In our phantom, we mimic spectral changes in absorption and scattering by inserting

spokes which contain different concentrations of absorbers and scatterers. We show that

we can accurately recover (in two distinct cases) the spectrum (which is measured

independently using a spectrophotometer) of the absorber and the spectral component due

to scattering as a function of concentration (of absorber and scatterer). The range of

concentrations was restricted to the regime of low optical density. It was observed that in

the diluted absorber/small scattering changes regime, the relative changes and

concentration of absorbers/scatterer as recovered by the measurement method followed a

linear relationship. When the concentration of the absorbers and scattering centers

increases, the optical changes were no longer proportional to the concentration of the

63

absorber/scatterer and the changes start to saturate the measured absorption. Our results

can have implications on the way one interprets changes in chromophore components in

the presence of vasodilation, or any other physiological condition that changes the

relative contribution of the “opaque” compartment.

64

VII. Investigation of Cat Visual Cortex- Fast Signal

VII. A Introduction

In this chapter, we return to the animal studies to establish if our technique is sensitive to

the fast optical signal. At the conclusion of the last study, it was determined that the

signal to noise ratio achieved with the old experimental equipment was insufficient to

convincingly detect this fast signal even though the optical BOLD effect was clearly

observed. We acquired new more sensitive equipment as well as changed the

experimental protocol to maximize the SNR for the neuronal response. We observed

some rather startling results where the heartbeat was clearly seen in each trial as well as a

fast global hemodynamic response on the order of 300 ms that has not been described in

the literature. Additionally, we have observed a fast signal with a rise time of less than

100ms that has no wavelength dependence which has been described in the literature as

the fast neuronal signal due to the changes in scattering in areas of the brain that deal

with the somatosensory cortical responses. More importantly, the observed signals are

seen in as little as one trial and observed using a purely spectrally resolved method. In

addition, we observe the highly debated “initial dip”. Here, I discuss the physiology of

this event and focus on this novel observation of the fast optical signal that is due to

stimulation of somatosensory cortex.

VII. B Physiology

In the brain, there are two responses to stimuli as reported in the literature using several

techniques including NMR, magnetic and electrical recordings and optical techniques.45-

51 These signals, fast and slow, are classified by the time they are seen from the onset of

stimulation as well as the physiological origin of the signal. In the context of optical

65

detection, the fast signal, usually on the order of milliseconds after the onset of stimulus,

is said to be seen as a change in the scattering signal due to the contraction of the neurons

during stimulation as they undergo a voltage change as associated with their action

potentials.52-56 This signal has been extensively described in the literature in experiments

in isolated tissue slices and more recently in the exposed cortex of live animals.57,58 The

slow signal, which has also been described previously, is seen seconds after the onset of

stimulation and is the direct result of the coupling of neuronal activation with the local

hemodynamic increase that arises from the vessels dilating to provide oxygenated blood

to the area of activation. There is a great debate about the very initial phase of the

hemodynamic response known as the initial dip.59,60 Physiologically, this initial dip

describes the rapid initial increase in HHb with the quasi-simultaneous decrease of O2Hb

at the onset of stimulus in the activated brain cortex.

VII. C Experimental Procedure

Methods- Cat Protocol

The same cat was used for these experiments hence the preparation of the animal as

described previously is applicable for this study.

Visual Stimulation and Behavioral Paradigm

The cat was positioned facing a rear projection screen subtending 60o horizontally and

50o vertically at a distance of 70 cm from the animal’s eyes. Visual stimuli consisted of

periodic flashes generated by white LED clusters that were superimposed on the screen

raising the luminance to 2.020cdm-2 during the flash. The cat sat immobile in a bag with

its head fixed to a rigid plate, tilted forward 5o with respect to the Horsley-Clark

horizontal plane. During a trial, the cat was trained to focus on the fixation point( a red

66

laser point) regardless of the flashes which had no behavioral significance. Generally, if

the cat maintained this fixation (within +/- 7o) for 10 seconds, it was rewarded with food.

The inter-trial interval was 15 seconds. However, for our experiments, during data

collection sessions, the cat was rewarded at the end of each trial regardless of its

performance. For the purposes of our experiment there were two types of tasks, one in

which the cat was visually stimulated (VS trial), and one where there was no visual

stimulation (NVS).

1. VS trials consisted of 10 seconds of a repetitive sequence of flashes terminated by

the reward followed by a 15 second inter-trial interval.

2. NVS trials consisted of 10 seconds of no flashes, with delivery of the reward

followed by a 15 second inter-trial interval.

For these experiments, we did two sets of trials, where each set consisted of one hundred

trials for the two conditions of VS and NVS to give us a total of two hundred trials. The

trials were then repeated for different stimulation frequencies and for different source-

detector positions. The number of flashes in the VS trials was varied corresponding to a

frequency of 1 Hz, 2 Hz, 2.5Hz, 4 Hz and 5 Hz, in that there were 10 flashes for the 1 Hz

(500ms on, 500ms off), 20 flashes for the 2Hz (250ms 0n, 250 ms off), 25 flashes for the

2.5 Hz (200 ms on, 200 ms off), 40 flashes for the 4 Hz (125 on, 125 off) and 50 flashes

for the 5Hz ( 100ms on, 100ms off).

The trials were done with the VS trials performed first (in blocks of 100) followed

immediately by the NVS trials (with minimum perturbation). This was achieved by

toggling an external switch to activate the flash. Neither the cat nor the optical setup was

disturbed in any way. Subsequently, to better understand the origin of the hemodynamic

67

signal, control experiments were performed where the visual paradigm remained the

same (flashes on and off) but the reward was not given to the cat.

Technical Aspects

For these experiments, the equipment used comprised of two HR (High Resolution) 4000

spectrometers obtained from the Ocean Optics (830 Douglas Avenue, Dunedin, FL

34698, USA) and a tungsten lamp coupled with 1000µm optical fibers to each of the

spectrometers (detector fibers) and the tungsten lamp (source fiber). The spectrometers

were sensitive to the NIR for the range of 680-1100nm, which is different to the

spectrometer in the first study where the spectrometer was sensitive for the range of 650-

990nm. Hence, it was more sensitive to the higher wavelengths which allowed us to

investigate the signal due to changes in the water band with a better SNR than in the

previous study. Secondly, the spectrometers were able to acquire spectra at a rate of

1ms/spectrum but we maintained the previous rate of sampling at 5ms/spectrum. Also

these spectrometers allow rapid data transfer to the host computer via an USB II

interface.

VII. D Data Acquisition

The cat was immobilized as described in a previous section. Two different source-

detector positions were examined simultaneously where we placed the source fiber in

one position for example in b4 and then placed a detector fiber in position b2 and a

second in b6 following the grid system as described in the previous study. The

spectrometer was triggered at the beginning of each trial by an external pulse. Data

acquisition and analysis were performed by the Elantest software (This program is

available at ftp://www.lfd.uiuc.edu/lfd/egratton/elantest/), which communicated via a

68

USB II port with the HR4000 spectrometers where each spectrometer was operated

independently by its own laptop computer. Synchronization of the data acquisition with

the visual stimulation was achieved by an external Stanford Research (1290-D

Reamwood Avenue, Sunnyvale, CA 94089, USA) pulse generator (model DS345). This

was achieved by selecting the trigger mode of the function generator whereby the

external pulse that was provided by the start of the trial from the stimulus routine, was

then used as an input to the function generator where an arbitrary waveform was

generated to provide a continued positive TTL pulse that was split to deliver this

continuous trigger to each spectrometer for the duration of 15 seconds. This was critical

as this spectrometer required that the trigger be maintained for the length of time desired

for data acquisition. The trial lasted for 25 seconds so at the beginning of each trial this

trigger routine was reset. The trigger and synchronization system provided the correct

time signals to the Elantest software and the external trigger input port of the

spectrometer. The spectrometer acquired spectra every 5 ms. First, a reference spectrum

was taken under the condition of no trial, meaning that the cat observed the screen at its

normal background level as described in the previous section, but otherwise not

performing either task. Equal blocks of data consisting of 100 trials were collected under

different conditions. Data acquisition began with the beginning of each trial. The

collection of spectra was synchronized with an externally supplied pulse and lasted for

15seconds for both the VS and NVS trials. At the end of each trial, for each placement of

the source-detector, we have 100 X 20 = 2000 flashes for a 2 Hz stimulation (flashes)

frequency.

69

Figure 7.1- Schematic showing synchronization of visual stimulation and data acquisition

Figure 7.2- Diagram of experimental instrumentation

Source

Tungsten Lamp

DataAcquisition

DetectorHR4000

Spectrometer

DataAcquisition

DetectorHR4000

Spectrometer

Source

Tungsten Lamp

DataAcquisition

DetectorHR4000

Spectrometer

DataAcquisition

DetectorHR4000

Spectrometer

Start acquisition pulse

3500 pulses

0 15 seconds

10

Reward

15 second

Inter-trial

Visual Stimulation

500ms

Start acquisition pulse

3500 pulses

0 15 seconds

10

Reward

15 second

Inter-trial

Visual Stimulation

500ms

3000 Spectra

70

VII. E Data Analysis

In this chapter, we present the analysis of the fast hemodynamic signal. Data were

collected for the first 15seconds of each trial covering the full time of stimulation for VS

trials as well as 5 seconds where the reward (food) is given to the cat corresponding to a

total of 3000 spectra. Folding was done as a function of number of trials where the entire

trial (15 seconds) was block averaged to see if there were any consistent differences in

the spectral signals observed due to physiological changes occurring during the 100 trials.

A definitive spectral pattern and intensity pattern changes were observed in the raw data

matrix. We examined this raw data to interpret if the signals observed had physiological

significance.

VII. F Results

In this section, we focus on the raw data matrices observed by block averaging the entire

15seconds for each 100 trials both under VS and NVS conditions. The data is displayed

as maps where the intensities vary from a light blue (minimum value) to red (maximum

value), in some cases it is deliberately set to a scale where the values lie outside the

chosen scale to enhance the contrast for small signals. We present the data after we

perform the detrending routine to show the heartbeat data more clearly. The histogram

plot of the heartbeats is then presented for each visual stimulation frequency.

1. Raw Data- Block Averaged for entire trial

Two cases were investigated where in one case the cat was rewarded after 10 seconds of

the visual task and another where no reward (food) is given to the cat. The graphs are

atypical for most fields, so to explain the representation of data, we show intensity plots

where the horizontal axis from left to right gives temporal information while the bottom

71

corner of the vertical axis is the shortest wavelength investigated, 650nm increasing

along that axis to the top corner which is the longest wavelength, 1100nm. In the

following figure, (figure 7.3), we explain the data representation. If we examine the plot

choosing wavelengths that correspond to the tissue chromophores of physiological

importance in the NIR such as 690nm for HHb, 830nm for O2Hb, 960nm for water, we

can then determine the changes in these parameters as a function of time. The following

plot shows that for the first two seconds after the onset of the stimulus there is a decrease

in the transmission (increase in absorption) of the HHb (690nm) with a quasi-

simultaneous increase in transmission ( decrease in absorption) of the O2Hb (830nm)

while the water band remains relatively constant. At 2.5 seconds, the optical BOLD effect

is seen where the transmission due to the O2Hb decreases and the signal due to the HHb

increases showing that there is an increase in the absorption of the O2Hb with a decrease

in the HHb. The optical BOLD effect is maintained until the reward is given at 10

seconds, then a sharp transition (~ms) is seen that has no spectral dependence at 11.5

seconds. Then the initial dip (reverse BOLD effect) is seen where the O2Hb signal shows

an increase in transmission while the HHB shows a decrease in transmission.

72

Figure 7.3- Intensity Map describing spectral and temporal changes and the physiological importance.

In figure 7.4, we clearly see that in the panels corresponding to the VS trials, (b4b2 and

b4b6 VS food and no food), for the shorter wavelengths (650-700nm), there is an initial

decrease for the first 4 seconds followed by an increase from the time of 6 seconds after

the onset of stimulation and the reverse is seen for the higher wavelengths. This is the

classic BOLD effect. This is not seen in the NVS trials, (b4b2 and b4b6 NVS food and no

food) where the intensity maps are relatively flat up to the time of 10 seconds. However,

if we consider a trial where the reward is given, (b4b2 and b4b6 food), an additional

feature is seen at roughly 1 second after the reward is given to the cat at time 10 seconds

from the start of the trial in both the VS and NVS trials. There is a large signal which is

seen as an increase in the intensity of the shorter wavelengths and a simultaneous

decrease in the higher wavelengths (opposite to the BOLD effect), (b4b2 and b4b6 food,

VS and NVS). This effect is not seen in the trials where the reward is not given. The

Reward

14,0

00

12,0

00

10,0

00

8,0

00

6,0

00

4,0

00

2,0

00

0

680nm 1100nm820nm 960nm

Optical Bold Effect-decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (red)

Sharp transition in spectral components with decrease in HHb (blue) and increase in O2Hb (red)- Initial Dip

ms

Reward

14,0

00

12,0

00

10,0

00

8,0

00

6,0

00

4,0

00

2,0

00

0

680nm 1100nm820nm 960nm

Optical Bold Effect-decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (red)

Sharp transition in spectral components with decrease in HHb (blue) and increase in O2Hb (red)- Initial Dip

ms

14,0

00

12,0

00

10,0

00

8,0

00

6,0

00

4,0

00

2,0

00

0

680nm 1100nm820nm 960nm

Optical Bold Effect-decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (red)

Sharp transition in spectral components with decrease in HHb (blue) and increase in O2Hb (red)- Initial Dip

14,0

00

12,0

00

10,0

00

8,0

00

6,0

00

4,0

00

2,0

00

0

680nm 1100nm820nm 960nm

14,0

00

12,0

00

10,0

00

8,0

00

6,0

00

4,0

00

2,0

00

014,0

00

12,0

00

10,0

00

8,0

00

6,0

00

4,0

00

2,0

00

0

680nm 1100nm820nm 960nm680nm 1100nm820nm 960nm680nm 1100nm820nm 960nm

Optical Bold Effect-decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (red)

Sharp transition in spectral components with decrease in HHb (blue) and increase in O2Hb (red)- Initial Dip

ms

73

signal is spatially dependent but the trend is the similar at each source-detector pair

examined.

Figure 7.4- Comparison of Intensity Maps for source detector configurations under two conditions of food or no food presented to the cat.

In figure 7.5, we show the raw data obtained for 100 trials block averaged. In the left

panel, for the average change in intensity, a clean sharp transition is seen at 200ms after

the reward is given at 10 seconds, where the rise time of this transition was less than

100ms as shown in the expanded region in the graph below. The intensity map as a

function of wavelengths shown in the right panel shows that the optical BOLD effect (as

described previously) is seen at 2 seconds after the onset of the stimulation. More

importantly, there is a sharp change that is seen at roughly 300 ms after the reward is

B4b2 VS/ no food

B4b2 NVS/ no food

B4b2 VS/food

B4b2 NVS/food

14,00012,00010,0008,0006,0004,0002,0000 14,00012,00010,0008,0006,0004,0002,0000 14,00012,00010,0008,0006,0004,0002,0000 14,00012,00010,0008,0006,0004,0002,0000

Reward

1100nm

680nm

14,00012,00010,0008,0006,0004,0002,0000 14,00012,00010,0008,0006,0004,0002,0000 14,00012,00010,0008,0006,0004,0002,0000

B4b6 VS/ no food

B4b6 NVS/ no food

B4b6 NVS/food

B4b6 VS/food

ms

ms1100nm

680nm

Reward

B4b2 VS/ no food

B4b2 NVS/ no food

B4b2 VS/food

B4b2 NVS/food

14,00012,00010,0008,0006,0004,0002,0000 14,00012,00010,0008,0006,0004,0002,0000 14,00012,00010,0008,0006,0004,0002,0000 14,00012,00010,0008,0006,0004,0002,0000

Reward

1100nm

680nm

14,00012,00010,0008,0006,0004,0002,0000 14,00012,00010,0008,0006,0004,0002,0000 14,00012,00010,0008,0006,0004,0002,0000

B4b6 VS/ no food

B4b6 NVS/ no food

B4b6 NVS/food

B4b6 VS/food

ms

ms1100nm

680nm

Reward

74

administered that has no specific spectral dependence as shown by the insert below the

map that is flat across wavelengths. This sharp transition is seen for the NVS trial, where

the reward is given followed by the initial dip. If the reward is not given, this sharp

transition is not observed.

Figure 7.5- Left panel shows Average change in intensity for b6c5 and the zoomed region to show rise time of sharp transition, right panel shows the corresponding intensity map with an insert to show flat spectral dependence. If we examine the raw data from one trial, we see that this sharp transition is seen

independent of the type of trial (VS and NVS) as shown for b4c5 in the following figure

7.6. The optical BOLD effect is also seen in the VS trials as this fiber configuration lies

in the visual cortex. During the NVS trials we observe a flat spectrum until the sharp

transition at the time of the reward. The rise time for this sharp transition was determined

to be less than 100ms.

Rise Time ~100msRise Time ~100ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

680nm 1100nm

75

Figure 7.6- Raw Data and Intensity Maps for a single trial for b4b6 VS trial (top) and NVS (bottom) to show sharp transition and zoomed to show rise time

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Sharp transition in spectral components

Optical Bold Effect- decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (yellow-red)

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Sharp transition in spectral components

Optical Bold Effect- decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (yellow-red)

Rise Time ~100msRise Time ~100ms

Rise Time ~100msRise Time ~100ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Sharp transition in spectral components

No Optical Bold Effect- Flat Spectrum

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Sharp transition in spectral components

No Optical Bold Effect- Flat Spectrum

76

If we then look at the averaged data for 100 trials, we see that the sharp transition that is

seen in the single trial from both the VS and NVS trials, the sharp transition is maintained

as it is sharp after 100 trials as shown in figure 7.7.

77

Figure 7.7- Raw Data and Intensity Maps for 100 trials for b4b6 VS trial (top) and NVS (bottom) to show sharp transition and zoomed to show rise time

Rise Time ~110msRise Time ~110ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Optical Bold Effect- decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (yellow-blue)

Sharp transition in spectral components

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Optical Bold Effect- decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (yellow-blue)

Sharp transition in spectral components

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

No Optical Bold Effect-

Flat Spectrum

Sharp transition in spectral components

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

No Optical Bold Effect-

Flat Spectrum

Sharp transition in spectral components

Rise Time ~150msRise Time ~150ms

78

In the following plots, figure 7.8-7.10, we show the intensity maps showing the changes

in transmission signals as a function of both spectral and frequency of VS stimulation for

different source-detector positions. Figures 8 and 9 refer to configurations, b4b6 and b4c5

that lie in the visual cortex. One can see that in both cases, the optical BOLD effect is

observed where the onset is at 2 seconds after the beginning of the trial for b4b6 and 2.5

seconds for b4c5. The sharp spectrally independent transition is seen at all frequencies

after the reward is given. The reverse BOLD effect (where the increase in transmission

due to the O2Hb with a decrease in the HHb) is also seen and the magnitude of the effect

varies as a function of frequency of stimulus.

79

Figure 7.8- Intensity Maps as a function of stimulation frequency for b4b6 100 VS trials showing optical BOLD effect and sharp transition after the reward is given.

B4b6 Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Optical Bold Effect- decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (blue-yellow)

Sharp transition in spectral components

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Reward

14,00012,00010,0008,0006,0004,0002,0000

680nm

1100nm

820nm

960nm

ms

Optical Bold Effect- decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (blue-yellow)

Sharp transition in spectral components

1Hz Reward

14,00012,00010,0008,0006,0004,0002,0000

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Optical Bold Effect- decrease in transmission in O2Hb ( blue) while increase in transmission of HHb (blue-yellow)

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Reward

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Sharp transition in spectral components

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Sharp transition in spectral components

5Hz

80

Figure 7.9- Intensity Maps as a function of stimulation frequency for b4c5 100 VS trials showing optical BOLD effect and sharp transition after the reward is given.

Reward

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Sharp transition in spectral components

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4Hz

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5Hz

B4C5

81

In figure 7.10, a3a5, which lies in the motor cortex, shows that there is no observed

optical BOLD effect for the VS trials independent of stimulus frequency but there is a

sharp spectrally independent transition that is seen at roughly 500ms after the onset of the

reward.

82

Figure 7.10- Intensity Maps as a function of stimulation frequency for a3a1 100 VS trials showing no optical BOLD effect and sharp transition after the reward is given.

Reward

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83

In figures 7.11-7.13, we show the average change in intensities and we show that there is

a sharp transition for each configuration. The zoomed regions show that the rise time is

dependent on the spatial location is independent of the frequency of stimulus presented to

the cat.

84

Figure 7.11- Raw Data matrices showing zoomed inserts of rise time of sharp transitions for b4b6 100 VS trials as a function of stimulation frequency.

Rise time ~80msRise time ~80ms

Rise time ~80msRise time ~80ms

Rise time ~80msRise time ~80ms

Rise time ~80msRise time ~80msRise time ~80ms

Rise time ~80msRise time ~80ms

B4b6

85

Figure 7.12 - Raw Data matrices showing zoomed inserts of rise time of sharp transitions for b4c5 100 VS trials as a function of stimulation frequency.

Rise Time ~150msRise Time ~150ms

Rise Time ~110msRise Time ~110msRise Time ~110ms

Rise Time ~50msRise Time ~50msRise Time ~50ms

Rise Time ~110msRise Time ~110ms

Rise Time ~150msRise Time ~150ms

B4c5

86

Figure 7.13- Raw Data matrices showing zoomed inserts of rise time of sharp transitions for a3a1 100 VS trials as a function of stimulation frequency.

Rise time ~100msRise time ~100ms Rise time

~100msRise time ~100ms

Rise time ~100msRise time ~100msRise time ~100ms

Rise time ~100msRise time ~100ms

Rise time ~100msRise time ~100ms

A3a1

87

In the following curves, we show the comparison of the average intensities as a function

of spatial location in the cortex for two different stimulus frequencies 2 and 4Hz trials.

See figure 7.14. We see that the direction of the sharp transition as well as the time at

which it occurs is dependent on the position.

Figure 7.14- Comparison of Intensities vs time as a function of location for 2 and 4 Hz stimulation frequencies. Zoomed areas emphasize differences in latency time of the sharp transition In our setup, we are able to examine two source detector configurations simultaneously.

Graphs of synchronized locations are shown in figures 7.15. The graphs corresponding to

the motor cortex (a3a1 and a3a5) show that, while the general shape is the same, the

Comparison of Intensity as a function of location-2Hz

00.20.40.60.8

1

0 5000 10000 15000

Time(ms)

Nor

mal

ized

In

tens

ity

b4b22HzNorm b4b62HzNorm b4c52HzNormb4c62HzNorm a3a52HzNorm a3a12HzNorm

Intensity vs Time as a function of location-4Hz

00.10.20.30.40.50.60.70.80.9

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0 2000 4000 6000 8000 10000 12000 14000

Time(ms)

Nor

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a3a54HzNor a3a14HzNorm b4c5Norm b4c64HzNormc5b44hzNorm c5c64HzNorm b4b24hzNorm b4b64HzNormb6c54HzNorm b6b44HzNorm

Zoomed Comparison- 2Hz

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Zoomed Graph-4Hz

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Zoomed to show Transition-2 Hz

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88

transition time differed by 300 ms where the a3a1 led the a3a5. However b4b6 and b4b2

have completely different trends where the transitions have different signs. For b4b2, the

signal is seen to increase while the signal for b4b6 decreases. Secondly, b4b2 shows a

significant peak at 10,400 ms that is not seen in b4b46.

Figure 7.15- Comparison of data sets acquired simultaneously from different locations, a3a1/a3a5 (motor cortex) and b4b2/b4b6 (visual cortex) to show differences in temporal behavior and spectral shape.

Comparison of a3a1 and a3a5Simultaneous Measurements

00.2

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0.81

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Zoomed b4b2 and b4b6

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Zoomed to show transtion- a3a1 and a3a5

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89

VII. G Discussion

There is a colossal signal that is seen roughly 0.5 seconds after the time that the reward is

given. The rise time of the signal varies from as little as 50 ms to 150ms dependent on the

source-detector location. This signal is also seen in the visual and frontal cortices and is

independent of the type of trial (VS and NVS) and of the frequency of stimulus presented

as shown in figures 7.12-7.15. This signal is absent when the food is not presented. The

transition is wavelength independent which is typical of a change due mainly to changes

in scattering, reminiscent of the fast signal as described by optical methods.61,62 This

proved that our current instrumental setup as well as broadband spectral technique is able

to detect fast changes and spectrally resolve them. More importantly, this sharp transition

is seen in as little as one trial. This is unheard in the field as for all methods to detect

neuronal activity reliably, employ thousands of trials are required to see this change.63-65

The question is why this signal is seen in all areas related to both motor and visual

cortices after the onset of the reward? Control experiments where the food was not

administered showed that this signal disappeared as shown in figure 7.4. First let us

examine the physiology, to explain why this effect is seen roughly 0.5 seconds after the

reward is given. At this time, the cat is undergoing several processes associated with

activation of the somatosensory cortex and motor cortex especially in the cases where

visual stimulation is presented, there is an additional response when the stimulation has

stopped that would result in an additional stimulus. At the time of the reward, a feeding

tube is presented to the cat touching its mouth initiating the feeding process. This tube is

presented with the same delay after the main task is completed (10 seconds of flashes if

VS trial). This precise synchronization could be at the origin of the sharp transition

90

observed after 100 trials. If we examine the adjacent cortices to the placement of our

tubes in both the visual and motor cortices, we can then determine if this signal is a direct

result of a physiological process. If we look at the schematic showing the placement of

the tubes with respect to the atlas of the cats’ brain, we see that the “a” row lies above the

border of the areas 4 and 6, which are motor cortices, posterior to this is the area 3 which

is the somatosensory cortex.66-67 Hence, any activation in these areas due to the reward

will be detected using our setup. The area of detection can be roughly described by a

circle of radius 1cm when our source-detector separation is 4mm. This explains the signal

that is seen in the “a” row but not in the visual cortices. However, upon further

examination of the areas adjacent to the visual cortex, activation in areas 5 and 7 will be

detected by our optical fibers. Areas 5 and 7 are considered the equivalent of parietal

cortex in the primate. Areas (17-19) are considered similar to occipital, and lateral

suprasylvian cortex similar to visual temporal cortex. They are sometimes referred to as

parietal association areas. They receive sensory input from visual, somatosensory, and

auditory lower-order areas, and project to sensory-motor cortex, particularly to areas 4

(mostly from area 5) and 6 (mostly from area 7).68-70 Hence, the signals that are seen are

indeed due to a physiological process originating in the somatosensory cortex and they

manifest as a fast signal due to scattering. This effect was not seen in the earlier study as

the length of data acquisition was restricted to the time of visual stimulation. Quite

frankly, it seems that the cat is more interested in the food than the visual stimulation, or

said a different way; a larger part of the brain is involved in the reward (just my luck).

Secondly, there are several signals that are superimposed on each other in the raw data;

however they occur at different time scales. There is the slow Optical BOLD effect that

91

was discussed in an earlier chapter, which is on the order of seconds, the pulse which has

a frequency of 3Hz (on the order of 300ms) that will be discussed in the next chapter and

a fast hemodynamic signal that is on the order of 100ms which is referred to as the initial

dip. Let us address each one in turn.

In the first study, we collected data for only 8 seconds of the trial and did not have access

to the reward period. The raw data again shows the optical BOLD effect as discussed

previously for VS trials in the visual cortex. If we look at figure 7.4, which are intensity

graphs of b4b2 VS and NVS, we can see that for the longer wavelengths that correspond

to the O2Hb, there is a decrease in the transmission as shown by the intense blue color,

which indicates an increase in absorption at these wavelengths roughly 2 seconds after

the onset of stimulation, with a quasi-simultaneous increase in transmission ( decrease in

absorption) as shown by the red color in the shorter wavelengths that correspond to the

HHb, the classic BOLD effect. The control experiments again show that the spectral

shape is flat for the NVS trials and the frontal lobes for all trials, VS and NVS. Further

examination of these graphs show that at the end of the visual task, the sharp transition is

seen at the time the reward is administered (at 10 seconds). Immediately following this

response ( on the order of 100ms), we can see that the reverse is true of the signals in that

there is an increase in the absorption due to HHb and a decrease in O2Hb, the initial dip.

This response lasts for approximately 1 second but the onset is less than 500 ms after the

sharp transition is seen. This is a proposed physiological response where there is a

consumption effect that is seen before the vasodilation occurs delivering the much needed

O2Hb. This is a precursor to the BOLD effect that will arise as a result of the

92

somatosensory activation. However, in the trials where the reward was administered and

where the fast signal is strongest, we see that the increase in HHb is larger.

VII. H Chapter Summary

This cat has yielded some novel and exciting results that has not been discussed

previously in the literature. We observed a “fast” signal (on the order of ms) in all areas

due to activity in the somatosensory cortex detected by spectral methods where the signal

is seen in a single trial. We observe that the signal is dependent on the location. The rise

time of the transition was seen to be from 50-150ms and the latency was also location

dependent. Our signal is highly reproducible yet it is seen in as little as one trial. The

optical BOLD effect is seen for the VS trials in the visual cortex and absent in the NVS

trials for the visual cortex and for all trials in the motor cortex confirming the results of

the earlier study. In summary, we can then follow a logical timeline of the physiological

processes: there is the slow optical BOLD effect that is on the order of seconds seen in

the VS trials for configurations in the visual cortex, followed by a sharp transition with a

rise time of less than 100ms seen in all configurations regardless of the trial type and

independent of stimulation frequency followed by the “initial dip” or reverse BOLD

effect which occurs within 500ms of the sharp transition. The control experiments again

show that the spectral shape is flat for the NVS trials and the frontal lobes for all trials,

VS and NVS.

93

VIII. Investigation of Cat Visual Cortex-Pulse

VIII. A Introduction

In the previous chapter, we described the novel finding of the fast signal that was

determined to be due to activation of the somatosensory cortex. Careful examination of

the raw data matrices showed that there were three signals of different physiological

origins and temporal distributions. We also discussed the slower hemodynamic response

that is the optical BOLD effect as well as a faster hemodynamic response that is the

initial dip. The last signal that has not been discussed is the heartbeat which is

hemodynamic but fast in that it occurs about every 300 milliseconds. In this chapter we

determine if the magnitude of this signal is comparable to that of the anticipated signal

due to the scattering changes following visual stimulation. The coupling of the pulse with

the visual stimulation and reward of the cat revealed that additional measures must be

taken to extract the fast neuronal signal due to visual stimulation. In this chapter, we

present simulations that show the SNR limitations of our equipment and of the true

physiology of the brain. We also present the analysis across flashes for both VS and NVS

trials both pre and post- pulse correction.

VIII. B Simulations

In our first study, we concluded that the SNR achieved using the original instrumentation

was insufficient to clearly see the fast neuronal signal. Here, we present simulations to

determine the limits of resolution for our current instrumental setup. If we consider a

sinusoidal signal as our test signal, the following discussion describes the logic. In the

following panels, 8.1, we simulate our fast neuronal signal which has a frequency of 2Hz

which is expected following a visual stimulation frequency of 2 Hz to the animal as

94

shown in the left panel. The right panel shows the result of our folding routine clearly

showing the recovery of the waveform.

Figure 8.1-Simulated Data with a sine wave of frequency 2 Hz as the fast signal due to visual stimulation shown on the left and the recovery of the waveform using our folding technique. If we simulate realistic data with the absolute worse SNR possible where the random

noise is 10% of the DC signal and the simulated fast signal is 0.0001 of the DC signal, we

see that we can still recover the underlying sinusoidal waveform. The Simulated noisy

data is shown in the left panel of figure 8.2 while the right panel shows the results of our

folding manipulation for 10 trials. Hence, it can be inferred that the signal with this SNR

can be clearly recovered after the folding of 100 trials.

Figure 8.2- Simulated Data of a sine wave of frequency 2 Hz where the Random Noise was 10% of DC signal on the left, right panel shows the recovery of the waveform after folding of 10 trials.

95

Now, we consider two signals that have similar frequencies. Physiologically these signals

correspond to the fast signal as described earlier and a hemodynamic signal due to the

pulse. We can separate the two signals with our folding routine provided that they are

equal in magnitude and there is no noise as shown in figure 8.3.

Figure 8.3- Simulated Data of two sine waves of equal magnitude superimposed one with frequency of 2 Hz to simulate fast signal and the second with frequency 3 Hz to simulate the pulse in top left panel. Top right panel shows the recovery of the 2Hz wave using the folding technique. Bottom center shows the recovery of 3 Hz using the folding technique. However, if the signals are not equal where the fast signal is less than 0.05 of the

magnitude of the signal due to the pulse, our folding routine cannot resolve the two

signals even in the absence of noise. This result is seen in the noise- free case and is

independent of the SNR as shown in figure 8.4.

96

Figure 8.4- Simulated Data of two sine waves of unequal magnitude superimposed one with frequency of 2 Hz to simulate fast signal and the second with frequency 3 Hz to simulate the pulse in left panel where the magnitude of the fast signal is 0.05 of the pulse signal. Right panel shows that we cannot recover the 2Hz wave using the folding technique. Hence, from our simulations we conclude that the folding routine and our instrumentation

has the necessary SNR to recover the fast signal due to visual stimulation with a horrible

SNR where the Noise is 10 % of the DC signal even if the magnitude of the fast signal is

0.0001 of the DC signal with the folding of 100 trials. However, with the addition of the

signal due to the pulse, a new complication is revealed in that the relative signal of the

pulse to the fast signal determines the resolution if they have similar frequencies (2Hz for

the fast signal and 3Hz for the pulse of a cat). Our simulations show that if the fast signal

is less than 0.05 of the pulse signal, we cannot resolve the two signals, independently, of

the SNR.

VIII. C Experimental Procedure

Methods- Cat Protocol, Visual Stimulation and Behavioral Paradigm and Technical

Aspects

The same cat was used for these experiments hence the preparation of the animal as

described previously is applicable for this study.

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VIII. D Data Analysis

As stated previously, we block average 100 trials. We also performed a Fast Fourier

Transform (FFT) on this raw data to determine the frequency of a dominant signal. It

yielded a frequency of approximately 3 Hz that we identify with the heartbeat of the cat.

Additionally, after performing the folding manipulation of 100 trials, it was evident that

there was an underlying signal that followed the frequency of the heartbeat of the cat in

conjunction with the familiar optical BOLD effect discussed before. The observation of

the pulse after averaging one hundred independent trials was surprising and indicated that

there was some sort of synchronization of the pulse with the stimulation during the trial.

We reasoned that if the pulse (which is a relatively large signal) becomes synchronized

with the flashes, we will observe a signal that could be erroneously interpreted as due to

the neuronal response. The removal of the pulse is routinely done in experiments in

humans to avoid this possible artifact.71

Description of the Pulse Removal Routine

To better visualize the pulse in the optical signal, in the presence of the slow

hemodynamic response, we first performed an operation where we implemented a high-

pass filter to remove the slower signal (on the order of seconds). This process is referred

to as detrending in our notation. The raw data, after detrending and pulse removal showed

a very large pulse response. See figure 8.5. This routine was not simple because the heart

rate varied in frequency and amplitude and that the heartbeat itself was seen to have a

definite spectral shape. To remove the pulse, for each pulse we must identify the time at

which each pulse occurs during the trial. We achieve this by filtering the data sequence

using a 5th order Bessel band-pass filter in the range 2.4-4.3 Hz. This data is applied in

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both directions to the data set to avoid introducing timing errors via a phase shift. The

time of the pulse was determined using the maxima and minima of the resulting filtered

signal. This normalized data (with respect to time) was then block averaged to obtain the

average shape of the pulse. The average shape of the pulse can be described by a

triangular wave which agrees with our expectations. The average wave was then adapted

to each pulse by interpolating the average wave shape to the actual duration of the pulse.

The adaptation routine performs a best fit of the amplitude of the pulse to the data during

each pulse. The amplitude of the fitted data as well as the timing of each pulse is then

recorded for further data analysis. Once, the heartbeat was extracted, we studied the

statistics of the pulse by constructing a histogram that recorded the time of heartbeats that

were seen during each trial for the full set of trials (100) and their amplitudes. However,

after performing the simulations, it was discovered that the stimulation frequencies

(except for the 5Hz) that were used in the trial were too close to the frequency of the

pulse.

A second approach was implemented to resolve the fast neuronal signal due to visual

stimulation. We employed Principle Component Analysis to determine the principle basis

components to determine if one of the major components followed the frequency of the

stimulation and if another was following the heartbeat. An external program,

Caterpillar.EXE was used for this analysis. We reasoned that this program will be able to

separate the fast signal for the visual stimulation frequencies that differed greatly from

the pulse frequency as the PCA routine shows only the orthogonal components.

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VIII.E Results

Raw Data- Block Averaged – Detrended

After data detrending ( high- pass filtering), in figure 8.5, one can clearly see a periodic

signal shown by the intermittent areas of yellow that has a period of 3 Hz across the

entire trial independently of the presence of Visual stimulation. The signal increases by a

factor of 100 approximately 500 ms after the delivery of the reward (at 10s). This signal

is due to the somatosensory cortex and has been discussed in a previous chapter. This

pattern is seen in all of the source detector configurations both in the visual cortex and the

frontal lobes. It has a definite spectral shape and is still seen after block averaging of 100

trials hence it is synchronized with respect to the time of the trial.

Figure 8.5- Panels show detrended folded data with the pulses highlighted in both VS and NVS trials for different locations in the brain, motor and visual cortices. Scale set to emphasize differences in contrast between pulses and fast signal due to somatosensory after the reward is given at 10 seconds. Removal of the pulse and Analysis

We then applied the pulse removal routine to the first 10 seconds of the raw data. The

results are shown in figure 8.6 to show the effect of the pulse routine removal for b4b6

for a stimulation frequency of 4Hz for 100 VS trials.

14,00012,00010,0008,0006,0004,0002,0000

Scale -50-100

b4b6 1st10files

b4b6 last10files

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Pulses

100

Figure 8.6- Left panel shows raw data for b4b6 VS trials for the 1st ten seconds (before reward) while the right panel shows the data post pulse-correction for a stimulation frequency of 4 Hz.

The following graphs in figure 8.7 show a histogram displaying the timing of the pulses

occurring during the entire trial. We reasoned that if the pulse will occur randomly (with

respect to the beginning of the trial), after plotting the time of the pulse in a histogram

where each time bin ( horizontal axis) is 1/8 of the period of the pulse, the histogram

should be relatively flat. Instead if the pulse occurs roughly at the same time with respect

to the beginning of the trial, then the histogram should show a definite pattern

corresponding to the synchronization of the pulse. Additionally, the graphs show eight

wavelength regions, (the entire spectrum was divided into eight equal regions) to show

the spectral variation as a function of time. The spike at 7.5 seconds is due to an artifact

that derives from the fact that we explore this time interval twice in our algorithm. The

artifact appears at the center of the time interval and it is due to double accounting of the

pulse in this region.

Reward

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ms

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Reward

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b4b6VS

Reward

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Figure 8.7- Left panel top shows the histogram of the timing of the pulses for visual cortex (b4b6) 100 VS trials while the Bottom panel shows the corresponding histogram for the NVS trials. Right panel-Top show the histogram for motor cortex (a3a1) 100 VS trials while the Bottom panel shows the corresponding histogram for the NVS trials. All histograms obtained using the pulse correction routine for a 4 Hz stimulation frequency. In figure 8.7, it is clear that the synchronization of the pulses is highest at the start of the

trial from 0.5 seconds after the onset of stimulation for about 2 seconds for b4b6VS and

NVS trials (visual cortex) and a3a1VS and NVS (motor cortex) trials and at a second

period of roughly 1 second before the reward is given at the 10 second mark.

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Results- Fast Signal due to Visual Stimulation

Removal of the pulse for the analysis of the Fast Signal by Folding

Here, we present folded data pre and post pulse correction. The graphs are of the average

intensity as a function of folded period for all flashes corresponding to a total of 10

seconds. In the following graphs, we show that for a frequency stimulus of 2.5 Hz

corresponding to a period of 400 ms (200ms on, 200 ms off), we can see for the source-

detector configurations that lie in the visual cortex (b4b6, b4c5), we can see that there is a

peak that has a width of approximately 100ms for the VS trials while the profile is flat for

the NVS trials, however for the frontal lobes, a3a5, the profile is flat independent of type

of trial. The graphs show the comparison between each data set. However, the change is

only on the order of a few parts per ten thousand. Figures 8.8 and 8.9

Figure 8.8- Top panels show the fast signal analysis using the folding routine across a period of 400ms corresponding to 2.5 Hz for both VS and NVS trials, right side compares coronal and parasagital regions of visual cortex ( b4c5 and b4b6), while right panel compares motor cortex with visual cortex ( b4c5 and a3a5).

103

If we compare pre and post pulse corrected data, in the following figures shows the

effect of the routine. The following graphs show that the pulse correction period

changes the data by a minimal amount.

Figure 8.9-Graphs comparing the folding results pre and post pulse correction routines, Top panel shows the pre- pulse correction routine for visual and motor cortex for both VS and NVS trials. Bottom panel shows the results of folding after pulse correction routine was applied to the data.

104

Analysis of the fast signal by PCA

Here we present data that was processed using the Principle Component Analysis (PCA)

program. The graphs show the component that showed the visual stimulation for the trials

considered. The following figure 8.10 shows the PCA done for the visual and motor

cortex for a stimulation frequency of 5Hz. It shows that there is a periodic structure only

in the case of the VS trial for the visual cortex while the other components have no

structure comparable to the stimulation frequency for both the VS and NVS trials for the

motor cortex (a3a1) and the NVS trial for the visual cortex (b4b6)

Figure 8.10- Results of the PCA program applied to b4b6 shown in the left panel and a3a1 in the right for both VS (top) and NVS (bottom) trials for a stimulation frequency of 5 Hz. Black line indicates the period corresponding to a folding time of 200ms. Double the period is shown to determine if the periodic structure repeats itself.

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VIII. F Discussion

The heartbeat was clearly seen as we have very high spatial resolution as we probe short

source-detector distances. This signal proved to be significant and is coupled to other

physiological processes. The problem becomes how we can decouple the heartbeat from

the signals as a function of several wavelengths if the heartbeat itself has a spectral shape.

The pulse is seen in all trials and in all cortex areas examined however the magnitude is

cortex dependent. The frequency of this pulse and the synchronization of the pulse

presented possible complications in the detection of the fast neuronal signal during the

VS. Statistical analysis of the pulse as shown in histograms in figures 8.7 show that the

pulse is synchronized at the beginning of the trials for VS trials in the visual cortex

(b4b6) while it is random for the NVS trials. However, in the motor cortex, (a3a1), this

synchronization is seen in both VS and NVS trials. This synchronization is independent

of the frequency of the stimulus presented to the animal. Secondly, the pulse correction

routine appears to have little effect on the data as shown in figure 8.9. At this time, we

believe that this synchronization is an artifact introduced by the pulse correction routine

and that we must fully understand the effect of the pulse correction routine that is

currently used. Clearly modifications must be made. Furthermore, the question arises if

this pulse signal is larger than the proposed fast neuronal signal that is purely a scattering

signal and how do we fully remove this signal to see the signal that has been reported in

the literature to be due to a change in scattering. We implemented a pulse correction

routine on the raw but detrended data as shown in figure 8.7, the data post correction

show that the pulse was indeed removed from the tasks. Simulations showed that the

magnitude of the pulse compared to that of the fast signal is the deciding factor in being

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able to separate the two signals if they are close in frequency for example 2Hz for the fast

signal and 3Hz for the pulse. The SNR of the system has no impact on this conclusion.

We concluded that new data manipulations must be used to extract the scattering signal.

We applied the PCA program to the 5Hz as the PCA is able to separate components if

they are orthogonal with respect to each other. Also, with this program we can determine

if one component follows the timing of the pulse and a second one following the timing

of the fast signal. We reasoned that the 5 Hz stimulation frequency was sufficiently larger

than the frequency of the pulse to ensure orthogonal components. Hence, only for this

stimulation frequency we observed the fast signal following visual stimulation as shown

in figure 8.10. Now, the question becomes why is this signal due to the somatosensory

cortex is seen in as little as one trial and not the signal due to the visual cortex during the

VS trials. However, it is known that the fast signal in the somatosensory cortex is larger

than that due to the visual cortex.72

Future studies must then take this signal to pulse ratio for data analysis techniques.

Secondly, the frequency of stimulation presented to the subject must be at least a factor of

1.3 the frequency of the pulse of the subject in this case would correspond to that of the

cat.

VIII. G Chapter Summary

In conclusion, the simulated data showed that we have the required SNR to recover the

fast signal if the signal is at least 0.0001 of the DC signal. A novel complication was

revealed that the criteria that determined the ability to resolve the fast signal in the

presence of the signal of the pulse was in fact the ratio of magnitudes of these two

signals. It was determined that if the fast signal was below 0.05 of the pulse signal that

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folding was insufficient to resolve these two signals if they were perfect sinusoidal waves

of fixed frequencies. A second parameter of importance was the ratio of the frequencies

of the pulse to the frequency of the fast signal which follows the stimulation frequency

presented to the animal. The coupling of these frequencies was an additional

complication where it was determined that this ratio must be at least 1.3. For this ratio,

we performed PCA on this data as we surmised that the signals should be orthogonal for

the stimulation frequency of 5 Hz. We observed a component that follows the

stimulation frequency only for the visual cortex for VS trials while no component was

observed in the motor cortex VS and NVS trials and the NVS trials for the visual cortex.

Additionally, more work must be done to debug the pulse correction routine and

determine the true magnitude of the fast signal as compared to that of the pulse.

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IX. Summary

In the introduction, I made a claim that the Physicists provide significant contributions to

the field of Biology and Medical Sciences by applying basic physics principles to the

field. Specifically, in this work, we probed the light-matter interactions in the NIR region

to understand physiological processes in the mammalian brain. We sought to improve on

existing principles and propose a new technique by which we can decipher these

processes spectrally. This technique touted to be independent of the light transport regime

allowed us to examine the hemodynamics and neuronal activity.

A new technique was developed to provide complementary information to the existing

Multi- Distance Frequency Domain Photon Migration technique that has been the bread

and butter of the Gratton Laboratory. The group has been the pioneers of the photon

migration field, so it is only natural to continue the tradition of completely

revolutionizing the field. The new technique involves using a broad-band spectrum to

probe the tissue and then recovering the light that has traversed the tissue using an ultra-

fast (5ms/spectra) spectrometer. We examine the raw data by deconvolution into the

individual basis spectra associated with the tissue chromophores, O2Hb, HHb, Water, fat.

We account for scattering by a mathematical description of λ-n , where the n coefficient is

allowed to vary to account for different types of scattering that is present in the probed

volume. Hence, scattering is separated from absorption by its distinctive spectral shape as

opposed to the “time of flight” delay that was previously used.

The aim was then to test this technique and see if it produces results that were

comparable to the well established Fd- NIRS in distinguishing physiological processes.

Secondly, we wanted to prove that this technique was light transport regime independent

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which is not the case for the Fd-NIRS. The cat was chosen as an ideal test subject as its

anatomy is such that photons are not fully diffusive before being detected as the total size

of the grey matter in the cat is roughly 3mm thick. Additionally, we had a priori

information about the activation of the visual cortex as a response to specific stimuli.

First approach was to build a phantom to simulate the anatomy of the smaller brain by

simulating the optical properties accounting for the heterogeneities. The results of that

experiment were important for the following reasons: one to determine if we had

sufficient SNR and to determine the effects that the heterogeneous nature of the brain in

the different light transport regimes (non- diffusive- diffusive). It was determined that

the true nature of tissue can have a significant effect on the observed optical signals;

hence we must understand the type and size of the effect of the different types of tissue

under investigation. It is clear that for the probing of the brain, not only is the source

detector separation important to determine depth of tissue penetration but the position of

the white matter with respect to the grey matter. Additionally, we have sufficient SNR for

the animal study.

Our experiment on the animal was novel in that it was the first study on an awake animal

where a broad-band spectral approach is used to determine the individual NIR spectrum

of tissue components. It was able to detect physiological changes by spectral methods,

reminiscent of the fMRI BOLD signal where we refer to our observation as the optical

BOLD effect. Our technique was seen to be independent of the light modality (diffusive

or non-diffusive), as we were able to recover accurate changes in the brain. For the

application in a mammalian brain, we have examined the behavior of the scattering,

O2Hb and HHB (BOLD effect) simultaneously with other tissue components such as

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water content. Additionally, there was a large change in the water signal that could not be

accounted physiologically. The behavior of water during stimulation has not been

discussed in previous literature. We proposed a model to show that this signal was due to

the heterogeneous nature of the brain and the effects that physiological processes like

vasodilation can have on the observed optical signals. Our model separates the brain into

two compartments optically opaque and transparent. We proposed that the water change

can be an indication on the amount that the vessels dilate upon stimulation as we know

that optically the large blood vessels appear opaque so any changes as seen in

vasodilation will result in a decrease in all chromophores. Furthermore, the technique has

proven to have high enough temporal and spatial resolution to adequately determine the

localized hemodynamics.

As is standard in our field, one must validate any model using phantoms. Our claim that

the water signal is due to the vasodilation raised some concerns in that vasodilation or

any process that involves the change of sizes of the opaque tissue (large blood vessels) to

the transparent tissue (capillaries) can skew the true origin of the signals. The question

arises, is this effect comparable to the real signals and is it possible to distinguish this

effect fro a “true” signal? We built a phantom to simulate vasodilation under the

conditions of different sizes of vessels modeling them as optically opaque. Our results

from the dynamic phantom made of the various diameters of opaque spokes show that

“vasodilation” per se, as simulated by changing the diameter of the spokes, could cause

an apparent decrease in all spectral components, including water and scattering.

Therefore in the presence of vasodilation of the large blood vessels, all spectral

components should decrease proportionally to their contribution to the overall spectrum.

111

We propose that the apparent decrease in water content (and partially scattering)

observed in the cat brain following visual stimulation is in fact due to vasodilation. In

this model, the time course of the water change reflects the dynamical changes of the

diameter of the blood vessels. We also determined that the relative changes due to

vasodilation should be about 4% for superficial blood vessels but less than 0.4% for

vessels at 6 mm or more from the surface. In the case of the cat brain, the changes due to

vasodilation could be more significant in the measurements due to the small size of the

brain with respect to humans. Note, that this artifact in the estimation of spectral

components due to vasodilation would not have been recognized if we had only used a

few wavelengths. In fact, using a broad band spectral analysis allowed us to distinguish

changes that equally affect all spectral components from specific changes affecting only

one spectral component. Vasodilation decreases the total light transmission and reduces

the spectral amplitude. Therefore, if we measure only relative spectral changes, we will

measure a reduction of the spectral amplitude at all wavelengths. As a consequence of

our studies, we conclude that determination of chromophore concentration in tissue using

only few wavelengths (for O2Hb, HHb and scattering) is not sufficient to characterize the

origin of the changes.

We went one step further in our phantom studies to validate our technique to show that

we an accurately recover the changes in scattering and absorption separately. In our

phantom, we mimic spectral changes in absorption and scattering by inserting spokes

which contain different concentrations of absorbers and scatterers. We show that we can

accurately recover (in two distinct cases) the spectrum (which is measured independently

using a spectrophotometer) of the absorber and the spectral component due to scattering

112

as a function of concentration (of absorber and scatterer). The range of concentrations

was restricted to the regime of low optical density. It was observed that in the diluted

absorber/small scattering changes regime, the relative changes and concentration of

absorbers/scatterer as recovered by the measurement method followed a linear

relationship. When the concentration of the absorbers and scattering centers increases,

the optical changes were no longer proportional to the concentration of the

absorber/scatterer and the changes start to saturate the measured absorption. Our results

can have implications on the way one interprets changes in chromophore components in

the presence of vasodilation, or any other physiological condition that changes the

relative contribution of the “opaque” compartment.

In our final quest to validate our technique as a viable one to determine the physiological

processes associated with brain activation, we pursued the detection of the fast neuronal

signal that is believed to be related to a change in scattering. We revisit the cat to

continue our analysis. The first study was limited by the SNR of our instrumentation,

while we were able to detect the slower optical BOLD signal, we could not conclude

definitively that we observed the fast signal. New instrumentation with higher sensitivity

was obtained as well as the protocol changed to significantly improve our detection

sensitivity. We also chose to collect data for a longer period to have additional access to

other physiological processes. We also adopted the collection of data for two different

locations using two separate but synchronized spectrometers. We were able to obtain

results where this cat has yielded some novel and exciting results that has not been

discussed previously in the literature. We observed a “fast” signal (on the order of ms) in

all areas due to activity in the somatosensory cortex detected by spectral methods where

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the signal is seen in a single trial. We observed that the signal is dependent on the

location. The rise time of the transition was seen to be from 50-150ms and the latency

was also location dependent. Our signal is highly reproducible yet it is seen in as little as

one trial. The optical BOLD effect is seen for the VS trials in the visual cortex and absent

in the NVS trials for the visual cortex and for all trials in the motor cortex confirming the

results of the earlier study. Careful examination of the raw data matrices showed that

there were three signals of different physiological origins and temporal distributions. In

summary, we were able to follow a logical timeline of the physiological processes: there

is the slow optical BOLD effect that is on the order of seconds seen in the VS trials for

configurations in the visual cortex, followed by a sharp transition with a rise time of less

than 100ms seen in all configurations regardless of the trial type and independent of

stimulation frequency followed by the “initial dip” or reverse BOLD effect which occurs

within 500ms of the sharp transition. The control experiments again show that the

spectral shape is flat for the NVS trials and the frontal lobes for all trials, VS and NVS.

The last signal that proved to be of importance was that due to the pulse. This pulse

signal was observed to be a large signal and observed in all cortices under all stimulation

frequencies independent of VS or NVS trials.

Consequently, we performed simulations to calculate the SNR required of our new

instrumentation in the determination of the fast signal. We concluded that the folding

routine and our instrumentation has the necessary SNR to recover the fast signal due to

visual stimulation with a horrible SNR where the Noise is 10 % of the DC signal even if

the magnitude of the fast signal is 0.0001 of the DC signal with the folding of 100 trials.

However, with the addition of the signal due to the pulse, a new complication is revealed

114

in that the relative signal of the pulse to the fast signal determines the resolution if they

have similar frequencies (2Hz for the fast signal and 3Hz for the pulse of a cat). Our

simulations show that if the fast signal is less than 0.05 of the pulse signal, we cannot

resolve the two signals, independently, of the SNR. Consequently, we concluded that new

data manipulations must be used to extract the scattering signal. We applied the PCA

method to the 5Hz as the PCA is able to separate components if they are orthogonal with

respect to each other. Also, with this program we can determine if one component

follows the timing of the pulse and a second one following the timing of the fast signal.

We applied a pulse correction routine to our data in an attempt to decouple the pulse from

the fast signal. This routine was complicated as the pulse itself had a distinctive spectral

shape. However, we were able to study the statistics of the extracted pulse and observed a

synchronization of the pulse at the beginning of the trials for VS trials in the visual cortex

while it is random for the NVS trials. However, in the motor cortex, this synchronization

is seen in both VS and NVS trials. This synchronization is independent of the frequency

of the stimulus presented to the animal. We believe that the observed synchronization

could be an artifact of the pulse correction routine. We reasoned that the 5 Hz stimulation

frequency was sufficiently larger than the frequency of the pulse to ensure orthogonal

components. Hence, only for this stimulation frequency we observed the fast signal

following visual stimulation. Now, the question becomes why is this signal due to the

somatosensory cortex is seen in as little as one trial and not the signal due to the visual

cortex during the VS trials. However, it is known that the fast signal in the somatosensory

cortex is larger than that due to the visual cortex.

115

In conclusion, the studies of this dissertation have provided important information on the

different physiological processes as well as provided basic understanding of the spectral

behavior associated with them. The novelties are several and in casual mention include

the detection of the fast signal in as little as one trial (unheard of!), detection of

physiological processes such as the BOLD effect and fast signal shown as changes in

scattering by a spectral method that is independent of the light transport regime, the

observation of the debated “initial dip” as well as the synchronization of the pulse by

spectral methods.

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X. Future Plans

This chapter should really be referred to as if I had a time machine what would I have

done differently. One pressing issue has been the correct removal of the pulse and the

determination of the magnitude of the fast signal due to the visual stimulation as

compared to the pulse. In retrospect, the obvious solution would be to use an external

device to monitor the cat as is done in human studies. Secondly, the exploration of the

PCA as a viable form of data acquisition must be pursued to be more quantitative in our

analysis. This study was done on one cat, at some point it would be nice to examine other

animals to validate the findings within the species as well as apply the technique to other

species. Clearly much more work must be done to fully understand the physiological

processes and the appropriate forms of data analysis

117

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Author’s biography

Kandice Tanner was born on 14th April, 1980 in Port of Spain, Trinidad. She attended

high school in Trinidad where she focused on languages and science for her studies at

the advanced level for the Cambridge Examinations. She attained full distinctions and

secured a presidential scholarship to study at South Carolina State University,

Orangeburg, SC in 1998. She was a presidential scholar for four years. At South

Carolina State, she received the highest academic awards for outstanding engineering

student and top academic honors as a student athlete. She graduated in May, 2002,

summa cum laude with a dual degree in Electrical Engineering Technology and

Physics. She then attended University of Illinois- Urbana-Champaign to pursue her

graduate studies in Physics in August, 2002. She attained her Masters degree in

August, 2003 at the same time she switched from her focus of condensed Matter

physics to Biophysics under the tutelage of Dr. Enrico Gratton. She worked on Brain

Imaging until she attained her doctoral degree in July, 2006. She has accepted a

position as a Post-Doctoral researcher at the University of California, Irvine.