Ivy Zhu, Research Scientist, Intel at MLconf SEA - 5/01/15

Model-based machine learning for real-time brain decoding

Ivy Zhu

Intel Labs

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Why bother?

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Functional MRI (fMRI)

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metabolic brain

anatomical brain

• Non-invasive observation• Observation-based inference

Brain Image Analysis/Decoding

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• Huge amount of data• 1 volume per scan period (1~2s)• 100K ~150K voxels per volume• 100’s ~ 1000’s scans per experiment

• Need sophisticated preprocessing to denoise• Thermal and system noise from scanner HW• Head motion, respiration, heart beat, etc., physiological processes• Neuronal activity related to non-task-related brain process

• Prone to overfitting – typically number of observations < number of features

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General Linear Model (GLM)

General linear model

Statistical parametric map (SPM)Design matrix, Sm

Statisticalinference

Realignment Smoothing

Normalisation

Image time-series

Template

Kernel

Y = ( Σ hm conv Sm) + ε

hmi = bi . βm

i

Haemodynamic Response Function (HRF)

And its partial derivatives

Preprocessing to denoise

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Voxels are not independent.

Haxby et al. (2001), Science

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Brain networks are complicated and dynamic.

Turk-Browne, N.B. (2013) Functional interactions as big data in the human brain. Science 342, 580-584.

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Can we have a model that describes local and global spatial dependencies, as well as dynamic

brain networks?

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Topographic Factor Analysis (TFA)

Manning JR, Ranganath R, Norman KA, Blei DM (2014) Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data. PLoS ONE 9(5): e94914. doi:10.1371/journal.pone.0094914

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TFA Matrix Representation

Local Spatial Dependencies

Global DependenciesBrain Networks

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TFA discovers latent factors.

Manning JR, Ranganath R, Norman KA, Blei DM (2014) Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data. PLoS ONE 9(5): e94914. doi:10.1371/journal.pone.0094914

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TFA discovers brain networks.

Manning JR, Ranganath R, Norman KA, Blei DM (2014) Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data. PLoS ONE 9(5): e94914. doi:10.1371/journal.pone.0094914

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How can we discover factors common amongst humans while preserving key individual

differences?

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Hierarchical Topographic Factor Analysis (HTFA)

Manning JR, Stachenfeld K, Ranganath R, Turk-Browne N, Norman KA, Blei DM. A probabilistic approach to full-brain functional connectivity. Submitted to PNAS.

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Graphical Model for HTFA

Manning JR, Stachenfeld K, Ranganath R, Turk-Browne N, Norman KA, Blei DM. A probabilistic approach to full-brain functional connectivity. Submitted to PNAS.

� subject �� trials V voxels y observed voxel activations

� latent factors (µ, ) � weights

Individual difference

Global Factors

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HTFA Inference Algorithm

while global template not converged and nIter < maxOuterIter dofor subject = 1 to � do

while individual factors not converged and mIter < maxInnerIter doEstimate new weight matrix based on existing centers/widthsEstimate new centers/widths based on existing weightsmIter ++

endUpdate global template based on subject’s new centers/widths

endnIter ++

end

for subject = 1 to � doUpdate weight matrix based on converged global template

end

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In essence, TFA/HTFA is a type of factor analysis. How does it compare with other factor

analyses?

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TFA/HTFA vs PCA vs ICA

• Commonality• All decompose observed brain images into a weighted sum of

components

• Difference• PCA & ICA emphasize the orthogonality or independence of

components. They cannot capture dynamic brain networks

• TFA/HTFA relax the orthogonality/independency requirement, and with a closed-form factor function, are able to discover richer information from brain images

• local dependencies• global dependencies• dynamic brain networks

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How can we bring HTFA into reality?

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Intel-Princeton Collaboration

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Bringing HTFA to Reality

Two initiatives:

Reduce the reconstruction error on small number of

factors (K<10) to be lower than 5%

Reduce the overall execution time of a key case study (10

subjects, 10 sources, 200images/subject) to be less than

5mins

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HTFA reconstruction error was …

Need more optimization when the number of factors is small

Results are pretty good when the number of factors is large

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HTFA reconstruction error is smaller.

Global CentersBefore Optimization

Global CentersAfter Optimization

global centers (x) global centers (y) global centers (x) global centers (y)

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HTFA reconstruction error is smaller.

True ConnectivityEstimated ConnectivityBefore Optimization

Estimated ConnectivityAfter Optimization

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Factor

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Factor

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Factor

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Methods for Speeding up HTFA

Used Intel Math Kernel Library (MKL) where appropriate, e.g., single/double precision nonlinear least square solver with/without constraints

Used thread-level parallelism

Optimized matrix operation order to better utilize cache locality

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HTFA Speedup Results

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Raw Data (#factors, #subjects, #img/subject)

HTFA optimization and speedup

Before Optimization

After Optimization

3X to 10X speedup after optimization

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Recap

Real-time brain decoding can save lives!

Bayesian model-based HTFA is promising for decoding real-time fMRI data

Intel is working with Princeton to bring real-time full-brain decoding closer to reality

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