Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience...
-
Upload
randell-pope -
Category
Documents
-
view
214 -
download
0
Transcript of Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience...
![Page 1: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/1.jpg)
Semi-Supervised State Space Models
![Page 2: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/2.jpg)
A Big Thanks To
Prof. Jason BohlandQuantitative Neuroscience LaboratoryBoston University
Istavan (Pisti) Morocz, Harvard, MNI
Firdaus Janoos, OSU/Harvard,MIT/Exxon
![Page 3: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/3.jpg)
Sources
http://neufo.org/lecture_eventsNIPS 2011
![Page 4: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/4.jpg)
A Running Example
![Page 5: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/5.jpg)
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
![Page 6: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/6.jpg)
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))
![Page 7: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/7.jpg)
Mental Arithmetic Paradigm
![Page 8: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/8.jpg)
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
![Page 9: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/9.jpg)
Cascadic Recruitment
![Page 10: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/10.jpg)
Classical fMRI Pipeline
![Page 11: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/11.jpg)
State-of-the-Art - ROI
Janoos et al., EuroVis2009
![Page 12: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/12.jpg)
Another Way ?
![Page 13: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/13.jpg)
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!
![Page 14: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/14.jpg)
Brain States
![Page 15: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/15.jpg)
Brain States
![Page 16: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/16.jpg)
Inspiration
![Page 17: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/17.jpg)
Haxby, 2001
![Page 18: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/18.jpg)
Mitchell, 2008
![Page 19: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/19.jpg)
Functional Networks
![Page 20: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/20.jpg)
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 ]
![Page 21: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/21.jpg)
A Solution – State Space Models
![Page 22: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/22.jpg)
Functional Distance ?
Zt1 Zt2
Zt3
Is Zt1 < Zt2 ,or Zt2 < Zt3 ,orSort Zt1, Zt2, Zt3
![Page 23: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/23.jpg)
State Space Model
![Page 24: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/24.jpg)
Comprehensive Model
![Page 25: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/25.jpg)
State-Space Model
Janoos et al., MICCAI 2010
![Page 26: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/26.jpg)
Computational Workflow
![Page 27: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/27.jpg)
Feature Space Estimation
![Page 28: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/28.jpg)
Functional Distance
![Page 29: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/29.jpg)
Transportation Distance
![Page 30: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/30.jpg)
Functional Distance
Zt – activation patternsf - transportation
![Page 31: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/31.jpg)
Transportation Distance
![Page 32: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/32.jpg)
Functional Connectivity Estimation
Gaussian smoothing
HAC until f ≈0.25N
Cluster-wise Correlation Estimation and Shrinkage
Voxel-wise Correlation Estimation
![Page 33: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/33.jpg)
Clustering in Functional Space10
0s 4s 8s0s 4s 8s
Bra
in S
tate
Lab
el
5
0
10
5
0
![Page 34: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/34.jpg)
CritiqueNo neurophysiologic model
Point estimatesHemodynamic uncertainty Temporal structure
Functional distance - an optimization problemNo metric structureExpensive !
![Page 35: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/35.jpg)
Embeddings
![Page 36: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/36.jpg)
A SolutionDistortion minimizing
![Page 37: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/37.jpg)
Feature Space Φ
Orthogonal Bases Graph Partitioning
Normalized graph Laplacian of F
![Page 38: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/38.jpg)
Working in Feature Space Φ
![Page 39: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/39.jpg)
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
![Page 40: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/40.jpg)
Model Size Selection
Strike balance between model complexity and model fit
Information theoretic or Bayesian criteriaNotion of model complexity
Cross-validationIID Assumption
![Page 41: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/41.jpg)
Estimation
![Page 42: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/42.jpg)
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
![Page 43: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/43.jpg)
Premise - EM Algorithm
![Page 44: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/44.jpg)
Generalized EM Algorithm
http://mplab.ucsd.edu/tutorials/EM.pdf
![Page 45: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/45.jpg)
Mean Field Approximation
![Page 46: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/46.jpg)
Experimental Conditions
![Page 47: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/47.jpg)
Comprehensive Model
![Page 48: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/48.jpg)
Comparisons
![Page 49: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/49.jpg)
HRFs
![Page 50: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/50.jpg)
Optimal States
![Page 51: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/51.jpg)
Spatial Maps
![Page 52: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/52.jpg)
Population Studies (sort of)
![Page 53: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/53.jpg)
Interpretation
Janoos et al., NeuroImage, 2011
Control Dyscalculic
Dyslexic
![Page 54: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/54.jpg)
MDS Plots
![Page 55: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/55.jpg)
MDS Plots
Control MaleControl Female
Dyslexic FemaleDyslexic Male
Dyscalculic MaleDyscalculic Female
![Page 56: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/56.jpg)
Stage-wise Error Plots
![Page 57: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/57.jpg)
Phase 1
Phase 2
Phase 1: Product Size
Phase 2: Problem Difficulty
Stage-wise MDS Plots
![Page 58: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/58.jpg)
What Else ?
![Page 59: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/59.jpg)
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
![Page 60: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/60.jpg)
“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
![Page 61: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/61.jpg)
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
![Page 62: Semi-Supervised State Space Models. A Big Thanks To Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University Istavan (Pisti) Morocz,](https://reader030.fdocuments.us/reader030/viewer/2022032801/56649ddf5503460f94ad919c/html5/thumbnails/62.jpg)
Thank You for Putting Up with me for 9 Lectures