semi-supervised state space models. a big thanks to prof. jason bohland quantitative neuroscience...
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Semi-Supervised State Space Models
A Big Thanks To
Prof. Jason BohlandQuantitative Neuroscience LaboratoryBoston University
Istavan (Pisti) Morocz, Harvard, MNI
Firdaus Janoos, OSU/Harvard,MIT/Exxon
Sources
http://neufo.org/lecture_eventsNIPS 2011
A Running Example
Difficulty in learning arithmetic that cannot be explained by mental retardation, inappropriate schooling, or poor social environment
Core conceptual deficit dealing with numbers
Very common : 3-6% of school-age children
Heterogeneous
Dyscalculia DyslexiaSelective inability to build a visual representation of a word, used in subsequent language processing, in the absence of general visual impairment or speech disorders
Affects 5-10% of the populationSpelling, phonological processing, word retrievalDisorder of the visual word form systemMultiple varietiesOccipital, temporal, frontal, cerebellum
Experimental protocolsEvent-related designs- single stimuli/“events” at any
time point- Periodic or spread across
frequencies- Require rapidly acquired
data(small TR)- Rapid events (less than ~20s
apart) give rise to temporal summation of BOLD response
- Summation is close to linear, but non-linearities are evident for small ISIs.
Stimulus function (s(t))
Mental Arithmetic Paradigm
Mental ArithmeticInvolves basic manipulation of number and
quantities
Magnitude based system – bilateral IPS
Verbal based system – left AG
Attentional system – ps Parietal Lobule
Other systems – SMA, primary visual cortex, liPFC, insula, etc
Cascadic Recruitment
Classical fMRI Pipeline
State-of-the-Art - ROI
Janoos et al., EuroVis2009
Another Way ?
Multi-voxel pattern analysis
Traditional analyses focus has focused on relationship between task and individual brain voxels (or regions)
MVPA uses patterns of observed activation across sets of voxels to decode represented information– Relies on machine learning / pattern classification
algorithms– Claim: more sensitive detection of cognitive states (Mind
Reading)– Does not employ spatial smoothing– Typically conducted within individual subjects
http://www.mrc-cbu.cam.ac.uk/people/nikolaus.kriegeskorte/infonotacti.html
Inter-voxel differences contain information!
Brain States
Brain States
Inspiration
Haxby, 2001
Mitchell, 2008
Functional Networks
Functional / Effective Connectivity
Standard analysis of fMRI data conforms to a functional segregation approach to brain function
i.e. brain regions are active for a stimulus typeAssumes the inputs have access to all brain regions
Pertinent Question: How do active brain regions interact with one another? [ functional integration ]
Effective Connectivity = the functional strength of a specific anatomical connection during a particular cognitive task; i.e. the influence that one region has on another. ( Inferred )
Functional Connectivity = the temporal correlation between signal from two brain regions during a cognitive task ( Measured )[ But these are exceptionally fuzzy terms ]
A Solution – State Space Models
Functional Distance ?
Zt1 Zt2
Zt3
Is Zt1 < Zt2 ,or Zt2 < Zt3 ,orSort Zt1, Zt2, Zt3
State Space Model
Comprehensive Model
State-Space Model
Janoos et al., MICCAI 2010
Computational Workflow
Feature Space Estimation
Functional Distance
Transportation Distance
Functional Distance
Zt – activation patternsf - transportation
Transportation Distance
Functional Connectivity Estimation
Gaussian smoothing
HAC until f ≈0.25N
Cluster-wise Correlation Estimation and Shrinkage
Voxel-wise Correlation Estimation
Clustering in Functional Space10
0s 4s 8s0s 4s 8s
Bra
in S
tate
Lab
el
5
0
10
5
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CritiqueNo neurophysiologic model
Point estimatesHemodynamic uncertainty Temporal structure
Functional distance - an optimization problemNo metric structureExpensive !
Embeddings
A SolutionDistortion minimizing
Feature Space Φ
Orthogonal Bases Graph Partitioning
Normalized graph Laplacian of F
Working in Feature Space Φ
Feature SelectionY
Φ
Rtimes
Resampling with Replacement
Basis Vector φ(l,m) Computation
Bootstrap Distribution of Correlations ρ (l,m)
Feature SelectionRetain φ(l,m) if Pr[ρ (l,m) ≥ τΦ] ≥ 0.75
Functional Network Estimation
Model Size Selection
Strike balance between model complexity and model fit
Information theoretic or Bayesian criteriaNotion of model complexity
Cross-validationIID Assumption
Estimation
Chosen Method
Model Estimation
State Sequence Estimation
Φ Feature-Space Transformation
y
Until convergence
θ
Until convergence
s
K, λWError Rate
HyperparameterSelection
x
YfMRI Data
Feature-space basis
E-stepCompute q(n)(x,z) from p(y,z,x|θ(n))
M-stepEstimate θ(n+1) : L(q(n), θ(n+1)) > L(q(n), θ(n))
E-stepCompute q(n)(z) from p(z| y,x(n),θ)
M-stepx(n+1) = argmax L(q(n), x)
Stimulus Parameters
Hyperparameters
Premise - EM Algorithm
Generalized EM Algorithm
http://mplab.ucsd.edu/tutorials/EM.pdf
Mean Field Approximation
Experimental Conditions
Comprehensive Model
Comparisons
HRFs
Optimal States
Spatial Maps
Population Studies (sort of)
Interpretation
Janoos et al., NeuroImage, 2011
Control Dyscalculic
Dyslexic
MDS Plots
MDS Plots
Control MaleControl Female
Dyslexic FemaleDyslexic Male
Dyscalculic MaleDyscalculic Female
Stage-wise Error Plots
Phase 1
Phase 2
Phase 1: Product Size
Phase 2: Problem Difficulty
Stage-wise MDS Plots
What Else ?
Maximally Predictive Criteria
Multiple spatio-temporal patterns in fMRINeurophysiological
task related vs. default networksExtraneous
Breathing, pulsatile, scanner driftSelect a model that is maximally
predictive with respect to taskPredictability of optimal state-
sequence from stimulus, s
“Resting State”Rather than evoked responses, rs-fMRI looks at random, low-
frequency fluctuations of BOLD activity (Biswal, 1995) “industry standard” filters data at ~0.01 < f < 0.08 Hz
“Default mode” network (Raichle et al., 2001) Set of regions with correlated BOLD activity Activation decreases when subjects perform an explicit task Ventromedial PFC, precuneus, temporal-parietal junction…
But the default mode is only one network that emerges from the correlation structure of resting state networks
Smith et al (2009) showed various task-active networks emerge from ICA based interrogation of rs-fMRI data
Summary
Process model for fMRI Spatial patterns and the temporal structureIdentification of internal mental processes
Neurophysiologically plausibleTest for the effects of experimental
variablesParameter interpretation
Comparison of mental processesAbstract representation of patterns
Thank You for Putting Up with me for 9 Lectures