ivy zhu, research scientist, intel at mlconf sea - 5/01/15
TRANSCRIPT
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
Fact
or
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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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1 2 3
No
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Ime
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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