lilly mujica-parodi pp

1

Laboratory for

Computational

Neurodiagnostics

Computational Neuroscience and

Neuroimaging in the 21st Century Advancing Diagnoses and Treatment

Of Psychiatric and Neurological Disorders

LR Mujica-Parodi, Ph.D.

Departments of Biomedical Engineering, Neuroscience, and Psychiatry

Stony Brook University School of Medicine—Stony Brook, NY

2

Question:

“Why, twenty years after the advent of functional

neuroimaging, when the consensus was clear that this

technology would forever alter the way psychiatric and

neurological diagnoses were made...

has the clinical promise been unfulfilled?”

Here, I want to provide:

I. Brief overview of standard fMRI techniques and why they are

inadequate for most computational and clinical applications

II. Some approaches our group has been taking to address

nonlinear dynamics present in systems-based dysregulation.

III. Global Research and Development Study

3

Why does it matter?

Moving from behavior-based definitions of disease to brain-based definitions of

psychiatric and neurological disease provides:

• Insight into etiology, and therefore development of new treatment strategies

• Objective measures of treatment efficacy

• Prodromal assessment of risk

• Necessary precursor to genetic studies

THE VAST MAJORITY OF NEUROIMAGING PROTOCOLS

STILL USE CONTRAST CONDITIONS OPTIMIZED FOR

ANSWERING THE QUESTIONS: WHERE AND HOW MUCH

RATHER THAN CAPTURING TEMPORAL DYNAMICS

5

WHERE?

AVERSIVE ANTICIPATION ACTIVATES

THE LIMBIC SYSTEM

+ + + +

HOW MUCH?

WE OBTAINED STERILE SWEAT FROM

THE SAME INDIVIDUALS UNDER TWO CONDITIONS

EMOTIONAL BUT NOT PHYSICAL STRESS

PHYSICAL BUT NOT EMOTIONAL STRESS

SUBJECTS’ ONLY INSTRUCTIONS WERE TO BREATHE ON CUE. HALF OF

THE SAMPLES WERE STRESS SWEAT, HALF WERE EXERCISE SWEAT—

DESIGNED TO OBTAIN A RIGOROUS BLIND.

Mujica-Parodi et al., PLoS ONE, 2009

FOR AN INDEPENDENT GROUP, AMYGDALA RESPONDS TO

THE STRESS BUT NOT EXERCISE SWEAT. WE REPLICATE.

BLOCK DESIGN IS OPTIMIZED FOR SIGNAL STRENGTH

(SINCE SAMPLING IS HIGHEST OVER THE AMPLITUDE)…

BUT AT THE EXPENSE OF TEMPORAL FEATURES

EVENT-RELATED DESIGN IS OPTIMIZED FOR TEMPORAL

FEATURES LIKE LATENCY AND DURATION, BUT WE’RE STILL

THINKING IN TERMS OF CONTRASTS

MOREOVER, IN BOTH CASES THE TEMPORAL FEATURES

ARE FIT TO A CANONICAL HEMODYNAMIC RESPONSE FUNCTION. THUS,

EVEN WITH EVENT-RELATED DESIGNS,

MOST DYNAMIC FEATURES OF THE TIME-SERIES ARE REMOVED.

WHAT IS AN ACTIVATION MAP?

EACH VOXEL HAS A TIME-COURSE ASSOCIATED WITH IT.

A “voxel” is a 3D pixel

Each voxel has its OWN

BOLD Time-Series

WE THEN PERFORM A PAIRED T-TEST ON THE AMPLITUDE

OF ONE CONDITION VERSUS THE AMPLITUDE OF

ANOTHER…

if p≤0.05, then we consider

the voxel “activated” for that

contrast.

Brightness represents the effect

size (t-value).

For Each Region of Interest, We Can Then

Compute the Mean Maximum BOLD Signal

for All Activated Voxels

A BRIEF HISTORY OF

FUNCTIONAL NEUROIMAGING

From: Marcus E. Raichle’s review “A brief history of human brain mapping.”

Trends in Neuroscience; Volume 32, Issue 2, February 2009, Pages 118–126

20 years!!!

http://www.sciencedirect.com/science/journal/01662236/32/2


















fMRITotal

SEM

Cross-Correla ons

Res ng-State

ICA

DCM

SVM

PPI

GrangerCausality

GraphTheory

ORIGINAL TECHNIQUES (BLOCK/E-R DESIGN) GIVE

ACTIVATION LEVELS.

NEWER TECHNIQUES GIVE ACTIVATION PATTERNS AND

CONNECTIVITY STRENGTH…BUT STILL NO DYNAMICS 1992 Block-Design fMRI

1993 Principal Component Analysis

1996 Event-Related Design fMRI

1999 Structural Equation Modeling

2001 Rest-State (cross-correlations between time-series across voxels)

2001 Independent Components Analysis

2003 Dynamic Causal Modeling

2003 Support Vector Machine (neural decoding, “mind-reading”)

2003 Psychophysiological Interactions Analysis (“correlations w/ lipstick”)

2005 Granger Causality

2007 Graph Theory

LESS than 1% of all fMRI studies published to date

(≈300K) use methods that go beyond the standard fMRI

analytical techniques introduced 20 years ago.

107

1246 1154

352

113 238

61 135 74

PAPERS PUBLISHED TO DATE USING TECHNIQUES DEVELOPED IN THE LAST

TWELVE YEARS (OUT OF 298,303)

CLINICAL APPLICATIONS

ARE ALMOST EXCLUSIVELY CONFINED

TO ADDRESSING FUNCTION-LOCATION

Example:

to functionally localize

Broca’s area before

performing neurosurgery

…

COMPUTATIONAL NEUROSCIENCE, ON THE OTHER HAND,

GENERALLY TAKES ONE OF TWO APPROACHES:

(1)BOTTOM-UP; (2) THIS OUTPUT KIND OF REMINDS ME OF MY

FAVORITE PHYSICS EQUATION…

BOTH APPROACHES ARE ENTIRELY DYNAMICS-BASED

Source: Gerard O’Brien, University of Adelaide.

Left: Model for the general Markov Decision Process (MDP) (Taken from La Camera, PLoS CB 2008)

(A) Policy for the general MDP. In the fragment of MDP shown, the agent is in state i and must decide (1)

whether to leave the state (with probability P(m|i)), and (2) in which state to go in case of a positive

decision (weighting each choice with probability P(i→j|m)). Decision 1 depends on the motivational value of

current state; decision 2 depends on the relative values of the possible arrival states, or choices. Both the

motivational and the choice values are learned with the TD method of the main text. If the agent is not

motivated to perform the trial, it will find itself in the same state one time step later (curved arrow). If the

agent is sufficiently motivated to perform the trial correctly, it proceeds to make a choice. In the figure, this

situation is represented by the curved shaded region from which the arrows to the possible choices reach

out. In the general case, the transition probability Pij is the product of the probabilities P(m|i) and P(i→j|m).

(B) Policy in the reward schedule task. In this case, P(i→j|m) because there is no choice and j can only be

the next schedule state (in this example, i=1/2, j=2/2). Thus, Pij=P(m|i). (C) Policy in the choice task when

considering only correct trials. In this case, P(m|i) is determined to be 1 and thus Pij=(i→j|m).

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UNDERSTANDING REGULATORY PROCESSES IN THE BRAIN

IS LIKELY TO REQUIRE ADDRESSING DYNAMICS

1. Trait Anxiety Study; N=66

2. Clinical Anxiety Study; N=60

3. Skydive Study: N=52

4. Navy EOD: Illustrative Case Study for Extreme Resilience (N=2)

FOUR STUDIES, TOTAL N=180 (DIAGNOSIS AND PREDICTION)

While activation amplitude doesn’t differ between individuals that are trait calm

and excited, the time-course does.

Time-series shows early inhibitory activation in trait calm adults (left), which is

attenuated in trait anxious adults (right). Here, each cluster was comprised of

N=15. Data were acquired using the Affect-Valent Faces task, NEUTRAL-REST

condition.

TIME SERIES ANALYSES REVEAL THAT TEMPORAL

FEATURES CONTAIN IMPORTANT INFORMATION

MISSED BY THE GENERAL LINEAR MODEL (STATISTICS)

CALM ANXIOUS

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CONTROL SYSTEMS REGULATION

From physiology, we know that there are many diseases, in

which a small central dysregulation is responsible for wide

and disparate effects (diabetes, Cushing’s disease, etc.).

Your House Thermostat and Human Homeostatic Regulation

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THE LIMBIC SYSTEM MODELED AS A CONTROL SYSTEM

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LUCKILY, THERE ARE DOMINANT PATHWAYS…

Stein et al., Neuroimage 2007

FOR LOCAL APPROACH, DYNAMIC CAUSAL MODELING CAN

PROVIDE INDIVIDUAL WEIGHTING FACTORS/RATE

CONSTANTS FOR CONTROL CIRCUITS.

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IS FRACTALITY/CHAOS OF THE TIME-SERIES A MEASURE OF

SELF-ORGANIZED CRITICALITY?

(Hurst and Lyaponov exponents, approximate and Shannon

entropy, time-delay embedding, etc.

27

Heart-rate variability analysis quantifies dysregulation of the autonomic nervous system by lack of “suppleness” in springing

back after perturbation. Here, power scale invariance shows significantly decreased ANS regulation in patients with heart

disease.

Peng, et al (1993), Physical Review Letters; Vol 70, No.9

DETRENDED FLUCTUATION ANALYSIS (RELATED TO PSSI) HAS BEEN

SUCCESSFULLY APPLIED TO THE ANS

AS A DIAGNOSTIC FOR HEART-DISEASE…PERHAPS WE CAN EXPLOIT

THE SAME APPROACH IN ADDRESSING THE BRAIN?

INTEGRATING GRAPH THEORY, CONTROL THEORY, AND COMPLEXITY

ANALYSES:

CONNECTION DENSITY AFFECTS COMPLEXITY, WITH FEEDBACK

PRODUCING STRONGER EFFECTS THAN FEED-FORWARD

Modelsimulationsforthesamerangeofemotionalresponses:dependenceofscaleinvariantslopesb onthe

amygdala-prefrontal(directandfeedback)connectivitydensities.Ineachmodule,themodule-meanPSSIslopeb

increaseswithbothconnectivitydensitiesxyM and

yxM .

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ANALYSES: IN A MULTIPLY-CONNECTED SYSTEM OF SYNAPTIC

CONNECTIONS, GRAPH THEORETIC NOTIONS OF CONNECTION

LENGTH…ACTUALLY COME DOWN TO LAG

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INTEGRATING GRAPH THEORY, CONTROL THEORY, AND

COMPLEXITY ANALYSES: EVEN SMALL AMOUNTS OF LAG

PRODUCE MARKEDLY VOLATILE DYNAMICS OVER TIME

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ANALYSES: THOSE “DYSREGULATED”

DYNAMICS CAN BE QUANTIFIED VIA COMPLEXITY MEASURES

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Correlation between the irregularity of

the time series quantified by β and trait

anxiety within the left amygdala (session

1). Size of cluster within the mask is 25

voxels with a p-threshold of 0.05.

For the maximally correlated voxel

MNI=[-24 -3 -18] p=0.000, r =0.49.

The correlation coefficients are color-

coded according to the bar.

Tolkunov D, Rubin D, Mujica-Parodi LR. Neuroimage. 2010.

RESULTS (TRAIT ANXIETY) FIRST WE LOOK AT TRAIT ANXIETY; DYSREGULATION

IS VERY CLEARLY LOCALIZABLE TO THE LEFT AMYGDALA

Table 2

Exploratory Analysis: Correlation Between Scaling Parameter _ and Trait Anxiety

Cluster Size*

Region Hemisphere x y z (Voxels) r value p value

Amygdala L -24 -3 -18 10 0.49 0.000

Parahippocampal Gyrus (BA30, BA27) L -15 -33 -9 6 0.48 0.000

Parahippocampal Gyrus (BA27, BA30) R 12 -33 -3 13 0.44 0.001

Inferior Frontal Gyrus (BA45) R 36 27 9 7 0.54 0.000

Inferior Frontal Gyrus (BA9, BA6) R 57 3 30 66 0.48 0.000

Inferior Frontal Gyrus (BA47) R 30 21 -21 13 0.52 0.000

Superior Frontal Gyrus (BA6, BA8) L -12 33 60 13 0.47 0.001

Superior Temporal Gyrus (BA21, BA22) R 45 -12 -12 15 0.48 0.000

Superior Temporal Gyrus (BA22) R 63 6 -3 13 0.45 0.001

Posterior Insula (BA13) R 36 -21 -3 19 0.51 0.000

Cingulate Gyrus (BA32) L -3 18 42 20 0.49 0.000

Cerebellum R 21 -33 -39 6 0.42 0.002

* Clusters of voxels with p < 0.01; Clusters of size less than 5 voxels discarded.

MNI Coordinates Maximally Correlated Voxel

RESULTS (TRAIT ANXIETY) PSSI DIFFERENCES IN TRAIT ANXIOUS INDIVIDUALS ARE

DISTRIBUTED THROUGHOUT THE LIMBIC CIRCUIT…

GOOD NEWS FOR THE APPLICATION OF NIRS!

*

Tolkunov D, Rubin D, Mujica-Parodi LR. Neuroimage. 2010.

34

Voxel-wise scale invariance of the

power spectral density (PSSI). We

provide here a representative time

series (a) for a healthy control and (b)

for a schizophrenia patient. The log-

log plots of the power spectra were fit

by a straight line over the frequency

range of (0.06-0.2 Hz) resulting in

scaling exponents of (c) =1.39

(S.D.=0.49) for the healthy control and

(d) =0.05 (S.D.=0.50) for the

schizophrenic subject.

Both examples are consistent with the

average standard deviation of

(average S.D.= 0.53) found over all

voxels and subjects, and thus may be

considered a good illustration of the

data as a whole.

RESULTS (PSYCHOSIS) DIFFERENT LIMBIC DYSREGULATORY PATTERNS ARE

ASSOCIATED WITH DIFFERENT MENTAL ILLNESSES

Radulescu A, Rubin D, Strey HH, Mujica-Parodi LR. Human Brain Mapping. 2011.

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Patients and controls showed

distinct PSSI in two clusters:

k1: Z=4.3215, p=0.00002

k2: Z=3.9441, p=0.00008,

localized to the anterior

prefrontal cortex (Brodmann

Area 10), represented by

close to white noise in patients

( 0) and in the pink noise

range in controls ( 1).

Coheres with schizophrenia

symptoms associated with

deficits in working memory,

executive functioning, emotional

regulation.

RESULTS (PSYCHOSIS) IN PSYCHOSIS, THE DYSREGULATION IS SPECIFIC TO BA10

Radulescu A, Rubin D, Strey HH, Mujica-Parodi LR. Human Brain Mapping. 2011.

IN 2009, WE OBTAINED NIRS CAPABILITIES

Advantages for medical diagnostics as compared to fMRI: cheaper, portable, capable of sitting in a

doctor’s office, emergency room, or base. User-friendly (quick to put on and take off with no gel) and

potentially automated once algorithms are developed and integrated with a GUI. More accurate due to higher

temporal resolution.

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Post Stimulus Time Histogram (PSTH)

PST (sec)

0 5 10 15 20 25 30 35

BO

LD

Sig

na

l Ch

an

ge (%

)

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Easy Stimuli

Difficult Stimuli

EEG fMRI

NIRS 10 Hz

(100ms)

512 Hz

(2ms)

.4 Hz

(2500ms)

GREATER TEMPORAL RESOLUTION THAN FMRI

Fekete T, et al., Neuroimage 2011

DEVELOPMENT OF NAP: SOFTWARE FOR THE

PROCESSING, ANALYSIS, & VISUALIZATION OF NIRS DATA

PHYSIOLOGICAL LIMIT ON SPATIAL RESOLUTION IN FMRI IS

THE RELIANCE ON THE COMPENSATORY OXYGENATION

RATHER THAN THE NEURON-DRIVEN DE-OXYGENATION

But…if you want to see the initial

dip, you either need lots of data

(expensive) or a cleaner signal!

Oxygenated Deoxygenated

HAVE WE FOUND A WAY TO ACCESS

THE INITIAL DIP??? PILOT STUDY OF LIMBIC REGULATION IN CHILDREN (2-5 YRS; N=12)

DESIGNED TO PREDICT RISK FOR MENTAL ILLNESS

41

Comparison of design,

length, and modality

(fMRI, EEG, NIRS) on

the power spectrum.

(a) fMRI Block: S.E.=0.24

(b) fMRI ER: S.E.=0.29

(c) EEG Rest: S.E.=0.03

(d) fMRI G-Rest: S.E.=0.11

(e) NIRS Block: S.E.=0.02

(f) NIRS Guided-Rest

(Task Free): S.E.=0.01

DESIGN FOR ACUTE STRESS STUDY

Ambulatory

measurement of

cardiovascular,

respiratory, endocrine,

epigenetic,

immunological,

cognitive, clinical

responses to stress

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PSSI (regulation): Stepwise

(forward and backward) linear

regression identified left BA45

regulation as most strongly

contributing to the variance

(r = .54, P = .003; F=8.95,

P=.006)

*

GLM (reactivity): Anticipatory fear response to jump was predicted

by left amygdala reactivity to anticipatory task.

Combined: r=.72, r2=.52, F=5.47, p=.01; note that no psychological variable was at all predictive!

RESILIENCE TO ACUTE STRESS IS DRIVEN BY AMYGDALA

REACTIVITY…BUT PREFRONTAL REGULATION

*

Dan Riskin, TV Host

Tim White, EOD US Navy

ILLUSTRATION DOES PSSI WORK WHEN WE EXTEND STRESS RESILIENCE

OUT TO THE MORE EXTREME ENDS OF THE SPECTRUM?

TIME

Detrended Time Series Approximate Entropy

Scale Invariance (β) Poincare Map

FILTER SIZE

LOGARITHM OF FREQUENCY

FMRI

SIGNAL

CHAOS

ORDER

X n

In the EOD we see a more extreme example of the BA45 regulatory

differences observed in skydive non-responders…

46

IN SUMMARY, BOTH EXTREME ENDS OF THE STRESS

SPECTRUM ARE CHARACTERIZED

BY “DYSREGULATION”…HOW CAN THIS BE?!

47

IN SUMMARY, BOTH EXTREME ENDS OF THE STRESS

SPECTRUM ARE CHARACTERIZED

BY “DYSREGULATION”…BUT WITH OPPOSITE LOCI

48

DYNAMIC CAUSAL MODELING WITH INTRINSIC CONNECTIVITY AND

MODULATORY EFFECTS SUPPORTS OUR INTERPRETATION OF PSSI

Group analysis shown at left.

As per our hypothesis, individuals

who were less affected by the

skydive (i.e., not as much heart-rate

response) showed weaker

connection from left amygdala to

BA45R

r=.5

(controlling for trait anxiety)

ROI’s extracted from GLM

49

DIAGNOSTIC CLASSIFICATION OF GENERALIZED ANXIETY DISORDER

USING SYSTEM-WIDE REGULATION: BRUTE FORCE APPROACH LEADS

TO 12,000 COMBINATORIAL POSSIBILITIES.

In order to adapt to clinical diagnostics, it’s not enough to see statistically significant

differences…since a diagnosis always involves an N=1. Thus we move to classification and

machine learning techniques.

Graph Theoretic Features

Characteristic path length

Global efficiency

Clustering coefficient

Graph transitivity

Local efficiency

Closeness centrality

Between-ness centrality

Assortativity coefficient

Small-worldness

Modularity

Within-module degree z score

Participation coefficient

Feature Selection

Two-sample t-test

Recursive feature elimination

Concave minimization method

Classification

RBF support vector machine

Adaboost

Random forest classifier

Anatomical Localization

WFU Pick-Atlas (N=100 contiguous regions)

Linear Features

Power spectrum scaling exponent

First auto-regressive coefficient

Spatial correlation scaling factor

Temporal correlation scaling factor

Nonlinear Features (Chaos Theory)

Symbolic dynamic sparseness

Detrended fluctuation analysis

Hurst exponent

Higher-order autocovariance

Time-delayed embedding

Largest Lyapunov exponent

Correlation dimension

Sample entropy

50

PSSI values by

Region Patients Controls p-value

Amygdala L 0.58 0.70 0.018 R 0.59 0.71 0.028

Insula L 0.60 0.68 0.004 R 0.58 0.69 0.008

Anterior

Cingulate L 0.57 0.71 0.001

R 0.55 0.66 0.008

Patient

Control


USING SYSTEM-WIDE REGULATION (PSSI)

Patient

51

Unsupervised Classification of

Entire Dataset: 81% Accuracy



•PSSI computed over entire frequency range (0.0004-0.24Hz)

•PSSI computed over entire

frequency range (0.0004-

0.24Hz)

•Based on excitatory areas

of the limbic circuit

(amygdala, insula, anterior

cingulate,temporal pole)

•Unsupervised classification

(K-Means clustering)

diagnoses 27 out of 30

people correctly:

90% success rate

(1 false positive, 2 false negatives)

52



SYMPTOM-SPECIFIC

Classification

Accuracy

89%

POSITIVE SYMPTOM

Classification

Accuracy

97%

NEGATIVE SYMPTOM

Classification

Accuracy

100%

53


WITH NEAR-INFRARED SPECTROSCOPY:

HURST EXPONENT+RANDOM FOREST CLASSIFIER+CONCAVE

MINIMIZATION IS THE OPTIMAL COMBINATION

Data collected using 52

channel HITACHI ETG-

4000

25 minutes-long task

~Time series were

segmented into 1000 point

segments

Data were preprocessed

and anatomically localized

using NAP (see 2011

publications)

• Classification 93%

accuracy with optimal

configuration…approx. 60%

of combinations were ≥

90%.

54


WITH NIRS IDENTIFIES EXCITATORY AND INHIBITORY NODES

Optode position (above) and

group channels (right) with

highest classification power

(93%). t

x

y

z

Inhibitory

Excitatory

WHAT ABOUT A DYNAMIC THAT IS TOO STIFF?

INTRACTABLE CRYPOGENIC EPILEPSY

INTRACTABLE CRYPOGENIC EPILEPSY

BETA VALUES ARE LARGER BECAUSE OF TOO MUCH CONNECTIVITY

(DRUG THAT TARGETS SYNAPTIC PLASTICITY?)

PATIENT CONTROL

MASK = PSSI β > 1.45

INCREASED CONNECTIVITY IN PATIENTS BETWEEN PSSI MASK, REST OF BRAIN

Correlation

Coefficient

Correlation

Coefficient

SINCE WE ARE USING AS “SIGNAL” WHAT OTHERS HAVE DISCARDED

AS “NOISE,” HOW DO WE KNOW THAT THE TIME-SERIES CONTAINS

GENUINE INFORMATION? WE ARE BUILDING A DYNAMIC PHANTOM

T2-weighted signal depends heavily on concentration of agarose and echo time. [A]

and [B] From top, 1.5%, 2.0%, 2.5%, and 3.0% standard agarose solutions. [A] TE =

47 ms. [B] TE = 140 ms. [C] Signal intensity as a function of echo time with various

agarose concentrations.

CHALLENGES OF INTEGRATION BETWEEN

FUNCTIONAL NEUROIMAGING AND

COMPUTATIONAL NEUROSCIENCE

1. Do we have the experimental techniques needed to validate models at

common scales of time and space?

2. Are emergent properties fully characterized, so that they can function

as concrete goals for the bottom-up approach?

3. What are the most relevant clinical applications?

4. For bottom-up approaches, do we have the computational power to

realistically accomplish our goals, and are there more computationally

efficient ways to code these models?

INTERNATIONAL RESEARCH & DEVELOPMENT STUDY

INTEGRATING COMPUTATIONAL NEUROSCIENCE AND

NEUROIMAGING IN THE 21ST CENTURE: ADVANCING DIAGNOSES AND

TREATMENT OF PSYCHIATRY AND NEUROLOGICAL DISORDERS

1. Do we have the experimental techniques needed to validate models at

common scales of time and space?

NEUROIMAGING

2. Are emergent properties fully characterized, so that they can function

as concrete goals for the bottom-up approach?

COMPUTATIONAL NEUROSCIENCE

3. What are the most relevant clinical applications?

CLINICAL INPUT

4. For bottom-up approaches, do we have the computational power to

realistically accomplish our goals, and are there more computationally

efficient ways to code these models?

OVERCOMING COMPUTATIONAL LIMITATIONS

PROPOSED U.S. DELEGATES

NEUROIMAGING· BruceRosen(MartinosCenter—HarvardUniversity)[foundationalworkinfMRI]

· VinceCalhoun(MindResearchNetwork—UniversityofNewMexico)[fMRIanalysis]

· OlafSporns(DepartmentofNeuroscience—IndianaUniversity)[graphtheory]

· LarryWald(MartinosCenter—HarvardUniversity)[fMRIacquisition]

· AllenSong(BrainImagingandAnalysisCenter—DukeUniversity)[fMRIacquisition]

· MartinLindquist(Dept.ofStatistics—ColumbiaUniversity)[statistics]


COMPUTATIONALNEUROSCIENCE· LarryAbbott(CenterforTheoreticalNeuroscience—ColumbiaUniversity)[neurons

andneuralnetworks]

· GyorgyBuzsaki(CenterforMolecularandBehavioralNeuroscience—RutgersUniversity)[complexityanalyses,oscillations]

· TerrenceJ.Sejnowski(SalkInstitute)[physiology]

· NancyKopell(CenterforBioDynamics—BostonUniversity)[complexityanalyses,

oscillations]


CLINICALAPPLICATIONSOFNEUROIMAGING· AntonioDamasio(DornsifeCenter—UniversityofSouthernCalifornia)

[neuroimagingwithapplicationstoneurology]

· DanielWeinberger(LieberCenter—JohnsHopkinsUniversity)[neuroimagingwithapplicationstopsychiatry]

OVERCOMINGCOMPUTATIONALLIMITATIONS· ThomasCortese(NationalCenterforSupercomputingApplications—Universityof

Illinois)[overcomingcomputationallimitations]

· RichardGranger(DepartmentsofPsychologyandComputerScience—DartmouthCollege)[computervs.neuralcircuits]

PROPOSED

INTERNATIONAL SITES

PROPOSED

INTERNATIONAL SITES

Blue:ComputationalNeuroscienceRed:Neuroimaging

GERMANY· AndreasHerz(BernsteinCenterforComputationalNeuroscience;Ludwig-

MaximiliansUniversitat,Munich—Germany)http://www.bccn-munich.de/people/scientists-2/andreas-herz[interactionofcell-intrinsicrhythmsandlarge-scaleoscillations,collectivepropertiesofneuralnetworks]

· John-DylanHaynes(BernsteinCenterforComputationalNeuroscience;Humboldt

Universitat,Berlin—Germany)http://www.bccn-berlin.de/People/haynes[neuraldecodingfromfMRI]

· NikosLogothetis(MaxPlanckInstituteforBiologicalCybernetics;Tubingen

University,Tubingen—Germany)[multi-scalemodeling/imagingofvisualperception]http://www.kyb.mpg.de/nc/employee/details/nikos.html

PROPOSED

INTERNATIONAL SITES

FRANCE· AlainDestexhe(UnitedeNeurosciencesIntegrativesetComputationnelles;CNRS,

Gif-sur-Yvette/Paris—France)[single-cellandnetworkmodels,dynamicalsystemsv.electrophysiology]

· OlivierFaugeras(NeuroMathComp/Odyssee;Inria,Paris—France)[computational

brainimaging]


PROPOSED

INTERNATIONAL SITES

UK· KarlFriston(WellcomeTrustCentreforNeuroimaging;UniversityCollegeLondon,

London—England)[homeofSPMfMRIsoftware,clinicalapplicationsoffMRI]

· HeidiJohansen-Berg(FMRIBCentre;OxfordUniversity,Oxford—England)[homeofFSLfMRIsoftware,clinicalapplicationsoffMRI]


PROPOSED

INTERNATIONAL SITES

SWITZERLAND· HenryMarkram(BrainMindInstitute;EcolePolytechniqueFederaledeLausanne,

Lausanne—Switzerland)[BlueBrainProject]

· KlaasEnnoStephan(ETHUniversityofZurich,Zurich—Switzerland)[fMRIdynamiccausalmodeling]


PROPOSED

INTERNATIONAL SITES

NETHERLANDSRainerGoebel(BrainInnovation;MaastrichtUniversity,Maastricht—Netherlands)http://www.brainvoyager.com/RainerGoebel.html


69

Most fMRI techniques are grounded in statistics (i.e., linear), which cannot accurately

capture the regulatory processes that most likely underlie mental and neurological

disease.

CONCLUSIONS

70



disease.

Computational neuroscientists describe the brain using nonlinear dynamics, but due to

mismatch in time and length scales, there is very little real interaction between

computational and clinical neuroscientists.

CONCLUSIONS

71



disease.




>99% of all fMRI papers published to date use the original techniques developed 20 years

ago at the technology’s first usage. Widespread adoption seems to be entirely dependent

upon the existence of a “works out of the box” GUI, as most fMRI users are ignorant of the

underlying technical basis (and issues) inherent in each technique.

CONCLUSIONS

72



disease.




>99% of all fMRI papers published to date use the original techniques developed 20 years

ago at the technology’s first usage. Widespread adoption seems to be entirely dependent

upon the existence of a “works out of the box” GUI, as most fMRI users are ignorant of the

underlying technical basis (and issues) inherent in each technique.

Failure to progress has been, in part, been a function of funding mechanisms.

Traditionally, innovative methods development with validation in clinical environments has

been difficult to fund via NIH mechanisms (requiring integrated, rather than parallel, review

by multiple disciplines), while the technology’s cost ($700 per subject) and interdisciplinary

nature (requiring multiple PI’s) makes it challenging to fund via most NSF grants ($80-

$100K/year total costs; $35K-$50K direct costs).

CONCLUSIONS

lilly mujica-parodi pp

Documents

stress sweat

treatment of psychiatric

temporal dynamics

physical stress physical

neurological disease

neurological diagnoses

dynamic features

emotional stress subjects