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Biological Intelligence Biological Intellige Biological Intellige Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

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Placenta vs. Brain – 3800 Placenta Array cy3 cy5

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Page 1: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Biological Intelligence

Biological IntelligenceBiological Intelligence

Artificial IntelligenceBiological SensorsCognitive NeuroscienceCognitive ScienceNeuronal Pattern Analysis

Page 2: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

PhysiologicalSaline

MALDI MatrixSolution

Matrix

Sample Plate

Cell

1000 2000 3000 4000 5000m/z

Relative Intensity

ELHAP

AP9-27βγ

δ

εpε

ELH1-14pβ−γ pELH

AP8-27

α1-9

1-8

1-7

ELH15-36

ELH30-36

ELH1-29

AP7-27

Sample Plate

Matrix

Embedded Sample Molecules

Laser Pulses

EnergizedMatrix/SampleCrystals

DesorptionIonization Analysis by

Mass Spectrometer

Single Cell and Subcellular MALDI Mass Spectrometry for the Direct Assay of the Neuropeptides

Neuropeptides and hormones can be directly detected from biological samples ranging in size from femtoliter peptidergic vesicles to large invertebrate neurons. When combined with genetic information, the complete processing of prohormones into biologically active peptides can be measured in a single cell. Current work involves developing mass spectrometric imaging (to determine the precise locations of the peptides) and the ability to measure peptide release from single cells and brain slices.

Page 3: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Placenta vs. Brain – 3800 Placenta Array cy3 cy5

Page 4: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis
Page 5: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Center for Biomedical Computing

• Narendra Ahuja • Bill Greenough • William O'Brien• Mark Band • Steve Boppart • Sariel Har-Peled• Art Kramer

• Harris Lewin • Zhi-Pei Liang • Lei Liu• Greg Miller • Jean Ponce • Jim Zachary

Page 6: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Bruce C. Wheeler, Member of the Neuronal Pattern Analysis and Biosensor Research Groups, Faculty in the Electrical and Computer Engineering Department

Micro Patterned Neuronal Networks in CultureRecent Progress

Robustness:Neurons Stay in Patterns

for One Month

Designability:Neurons Can Be Guided Over

Electrodes on a Microelectrode Array

Patterned fiber track superimposed on electrodes

Single fibers superimposed on electrodes

Page 7: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

0 10 20 30 40 50 60 70 80 90 100-1000

-800

-600

-400

-200

0

200

400

600

800

1000

Bruce C. Wheeler, Member of the Neuronal Pattern Analysis and Biosensor Research Groups, Faculty in the Electrical and Computer Engineering Department

Micro Patterned Neuronal Networks in Culture

Recent Progress

Input/Output:Multiple Channel

Electrical Recordings Can be Obtained Routinely

Function: Are Neurons in Patterns More Active?A. Patterned Networks Have Greater ActivityWithout Patterns: 1% ± 3% active electrodesWith Patterns: 16% 12% active electrodes

% A

ctiv

e El

ectr

odes

10

20

30

40

0

Local Cell Density (per mm2)100 200 500400300

B. Activity Increases with Cell Density

PatternedNeuron Cultures

Page 8: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Detection of weak signals in noisy spike trains

number of spikes in a 10 ms window with mean subtracted

A) Signal due to small prey

B) Signal superimposed on noisy spike train

C) Signal superimposed on regular afferent spike train

Model of electric fish with electroreceptors distributed over its body. (A) Thechange in afferent firing activity due to a small prey. (B) the signal due to theprey superimposed on fluctuations due to spontaneous activity, in the case of a standard (binomial) model for afferent firing activity with the same firing rate asthe afferent and (C) the signal superimposed on the actual afferent baseline activity. In contrast to (B), the afferent spike train exhibits long-term regularity (memory). This limits the fluctuations in baseline firing rate, making weak signals easier to detect.

Page 9: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis
Page 10: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis
Page 11: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Production of transgenic mice using cre-loxP technology

• Create cell-type specific knockout mice

• Two lines of mice required:– cre mice which

express cre in desired cells

– loxP mice with loxP sites flanking the gene of interest

cre mice loxP mice

Page 12: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Creation of NR1 loxP mice

Page 13: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Characterization of NR1 loxP mice

Page 14: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis
Page 15: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Dendritic Development in Barrel Cortex

Page 16: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Dendritic Development in Barrel Cortex

Chang and Greenough, 1988

Page 17: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Dendritic Development in Barrel Cortex

CTL KO

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Page 19: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

v

ey rt

h

i

g

Optical Coherence Tomography

Non-Invasive Imaging of Developing Biology

Fiber-Optic OCT Instrument

Real-Time Endoscopic Imaging

High-Resolution OCT of Cell Mitosis & Migration

Page 20: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Non-invasive optical imaging

• New group of procedures for measuring the optical parameters of the cortex– Scattering and absorption of near-infrared (NIR)

photons traveling through tissue

• These parameters can be inferred by measuring:– The degree of light attenuation (intensity)– The degree of photon (phase) delay

Page 21: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Optical Methods

Assessment of exposed tissue Assessment of deep tissue (UV and visible light) (Near infrared light)

ContrastAgents

IntrinsicContrast

LightScatterBrain cellswellingduring

functionalactivity?

FASTNIRS-Signals

EROS

Fluores-cence

?

Absorp-tion

[Cytochrome-C-Oxidase]

[Oxy-Hb]

[Deoxy-Hb]NIRS

DopplerShiftBloodFlow

IntrinsicContrast

ContrastAgents

Absorp-tion

[Cytochrome-C-Oxidase]

[Oxy-Hb]

[Deoxy-Hb]

‘IntrinsicBrain signals’?

Fluores-cence

[NADH]

[oxy-Flaveo-proteins]

LightScatter

Brain CellSwellingduring

functionalactivity?

‘IntrinsicBrain signals’?

DopplerShiftBloodFlow

BloodVolume

Blood CellVelocity

LDF

Absorp-tion

Blood Flow(e.g.Indicator

Dilutionwith

Cardiogreen)

Fluores-cence

Ion-Conc(Ca, K, Mg)

VoltageSensitive

Dyes

Micro-circulation

Fluores-cence

Principallyfeasible,

dependingon tracer

development?

Absorp-tionBloodFlow

(Indicator dilution

with Cardio-green

oxygen)NIRS

Modified from A. Villringer

Page 22: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Optical effects “Slow” effects

– develop over several seconds after stimulation– correspond to effects observed with fMRI and PET– are presumably due to hemodynamic changes

• “Fast” effects (EROS)– develop within the first 500 ms after stimulation– are most visible on the photon delay parameter– are presumably due to neuronal changes

Page 23: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Hb oxygenation in visual cortex

5 15 25 35 45 550 10 20 30 40 50 60

-0.6

-0.4

-0.2

0.0

0.2

-0.2

0.0

0.2

0.4

0.6

Modified from A. Villringer

[oxy-Hb]

[deoxy-Hb]

Time / s

Con

cent

ratio

n ch

ange

s / m

icro

M

Page 24: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Comparison of PET and NIRS

Modified from A. Villringer

[oxy-Hb]vs.

CBF

[deoxy-Hb]vs.

CBF

[total-Hb]vs.

CBF

C

BF

(PET

)

C

BF

(PET

)

C

BF

(PET

)

12

-14

12

-14

12

-14

oxy-Hb (NIRS) total-Hb (NIRS) deoxy-Hb (NIRS)-20 30 -15 15-30 15

Page 25: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Hb

HbO2

WaterA

bsor

ptio

n C

oeff

icie

nt (c

m-1)

Wavelength (nm)600 700 800 900 1000

0.0

0.1

0.2

0.3

0.4

0.5

NIR Absorption Spectra

Page 26: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

In-vitro scattering effects

Scattering changes duringan action potential

Scattering changes duringtetanic activation of a hippocampal slice

scatteringvoltage

Page 27: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

EROS: Methods

Synthesizer

112 MHz

Phase delay

measuredat 5 kHz

PMT

OpticfiberLEDHead

surface

Cerebral cortex Volume describedby photons reaching fiber

SignalAveraging

Del

ays

(ps)

1 2 3 Time (s)

Stimulus

Del

ays

(ps)

200 400 Time (ms)

Average Evoked Response

Page 28: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Recording helmet

Page 29: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

Neuro-Vascular Relationship

1.001

1.002

1.003

1.004

Relative DC Intensity

0.0 1.0 2.0 Fast Effect x Stimulation Frequency

Slow/Fast Effects Relationship

Baseline

1 Hz

2 Hz10 Hz

5 Hz

• The hemodynamic (NIRS) effect is proportional to the size of the neuronal (EROS) effect integrated over time

• This supports the use of hemodynamic brain imaging methods to quantify neuronal activity

Gratton, Goodman-Wood, & Fabiani,HBM, in press

Page 30: Biological Intelligence Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis

fMRI

RH LH

-0.36 -0.3 -0.24 -0.18 -0.12 -0.06 0 0.06 0.12 0.18 0.24 0.3 0.36

EROS

-0.3 -0.24 -0.18 -0.12 -0.06 0 0.06 0.12 0.18 0.24 0.3

-0.3 -0.24 -0.18 -0.12 -0.06 0 0.06 0.12 0.18 0.24 0.3

100 ms latency

200 mslatency

pre-stimulusbaseline

Upper-left visual stimulation

Gratton et al., NeuroImage, 1997

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Right Visual Field StimulationLeft Hemisphere Response

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