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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!!!
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).
20
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
22
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
23
THE LIMBIC SYSTEM MODELED AS A CONTROL SYSTEM
24
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.
26
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 .
29
INTEGRATING GRAPH THEORY, CONTROL THEORY, AND COMPLEXITY
ANALYSES: IN A MULTIPLY-CONNECTED SYSTEM OF SYNAPTIC
CONNECTIONS, GRAPH THEORETIC NOTIONS OF CONNECTION
LENGTH…ACTUALLY COME DOWN TO LAG
30
INTEGRATING GRAPH THEORY, CONTROL THEORY, AND
COMPLEXITY ANALYSES: EVEN SMALL AMOUNTS OF LAG
PRODUCE MARKEDLY VOLATILE DYNAMICS OVER TIME
31
INTEGRATING GRAPH THEORY, CONTROL THEORY, AND COMPLEXITY
ANALYSES: THOSE “DYSREGULATED”
DYNAMICS CAN BE QUANTIFIED VIA COMPLEXITY MEASURES
32
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.
35
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.
37
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
43
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
DIAGNOSTIC CLASSIFICATION OF GENERALIZED ANXIETY DISORDER
USING SYSTEM-WIDE REGULATION (PSSI)
Patient
51
Unsupervised Classification of
Entire Dataset: 81% Accuracy
DIAGNOSTIC CLASSIFICATION OF GENERALIZED ANXIETY DISORDER
USING SYSTEM-WIDE REGULATION (PSSI)
•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
DIAGNOSTIC CLASSIFICATION OF GENERALIZED ANXIETY DISORDER
USING SYSTEM-WIDE REGULATION (PSSI)
SYMPTOM-SPECIFIC
Classification
Accuracy
89%
POSITIVE SYMPTOM
Classification
Accuracy
97%
NEGATIVE SYMPTOM
Classification
Accuracy
100%
53
DIAGNOSTIC CLASSIFICATION OF GENERALIZED ANXIETY DISORDER
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
DIAGNOSTIC CLASSIFICATION OF GENERALIZED ANXIETY DISORDER
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]
PROPOSED U.S. DELEGATES
COMPUTATIONALNEUROSCIENCE· LarryAbbott(CenterforTheoreticalNeuroscience—ColumbiaUniversity)[neurons
andneuralnetworks]
· GyorgyBuzsaki(CenterforMolecularandBehavioralNeuroscience—RutgersUniversity)[complexityanalyses,oscillations]
· TerrenceJ.Sejnowski(SalkInstitute)[physiology]
· NancyKopell(CenterforBioDynamics—BostonUniversity)[complexityanalyses,
oscillations]
PROPOSED U.S. DELEGATES
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]
Blue:ComputationalNeuroscienceRed:Neuroimaging
PROPOSED
INTERNATIONAL SITES
UK· KarlFriston(WellcomeTrustCentreforNeuroimaging;UniversityCollegeLondon,
London—England)[homeofSPMfMRIsoftware,clinicalapplicationsoffMRI]
· HeidiJohansen-Berg(FMRIBCentre;OxfordUniversity,Oxford—England)[homeofFSLfMRIsoftware,clinicalapplicationsoffMRI]
Blue:ComputationalNeuroscienceRed:Neuroimaging
PROPOSED
INTERNATIONAL SITES
SWITZERLAND· HenryMarkram(BrainMindInstitute;EcolePolytechniqueFederaledeLausanne,
Lausanne—Switzerland)[BlueBrainProject]
· KlaasEnnoStephan(ETHUniversityofZurich,Zurich—Switzerland)[fMRIdynamiccausalmodeling]
Blue:ComputationalNeuroscienceRed:Neuroimaging
PROPOSED
INTERNATIONAL SITES
NETHERLANDSRainerGoebel(BrainInnovation;MaastrichtUniversity,Maastricht—Netherlands)http://www.brainvoyager.com/RainerGoebel.html
Blue:ComputationalNeuroscienceRed:Neuroimaging
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
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.
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
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.
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.
>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
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.
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.
>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