for Machine Learning, CS229 Stanford University

1. • Noisy components can be computationally defined using spatial and temporal features
• Features to distinguish noise are dominated by temporal features
• More work is needed using sub-networks to diagnose disorder
Preprocessing
Tool displays spatial map, time-course,
and frequency distribution to assist user in
defining a standard for a component type
Functional MRI
Developing Fingerprints to Computationally Define Functional Brain Networks and Noise
V.Sochat, Biomedical Informatics, Stanford University School of Medicine, Stanford CA
N = 818 (700) N=46 (1472) N=40 (1478) N=67 (1451) N=48 (1470)
Introduction
Realign /
Reslice
Motion
Correction
Segmentation Smoothing Filtering Normalization
ICA
n x m n x n n x m
Bad Good
Bad 740 121
Good 78 579
N = 1518 Networks
spatial maps
and timecourses
DATA
INDEPENDENT COMPONENT ANALYSIS
SPATIAL AND TEMPORAL FEATURES
Methods
STANDARD DEVELOPMENT
Results
Discussion and Conclusion
FUNCTIONAL NETWORK AND NOISE FINGERPRINTS
EVALUATION OF CLASSIFIERS
Lasso L1 constrained linear regression selects features to distinguish
real from noisy components (N=1518) with cross validation
accuracies of .8689, .9834, .9808, .9675, and .9695 respectively.
ALL NOISE EYEBALLS HEAD MOTION WHITE MATTER PARIETO OCCIPITAL CORTEX
• Independent Component Analysis ICA is a data-driven method to decompose functional
neuroimaging data into independent components.
• The decomposed independent components encompass a mix of true neural signal,
machine artifact, motion, and physiological noise that are typically visually distinguished.
• Neurological disorders are beginning to be understood based on aberrant brain structure
and function on the single network level.
• Methods to computationally define noise and networks are necessary to automatically filter
large publicly available datasets and identify patterns of fMRI that distinguish disorder.
111 Temporal Features
signal metrics, peaks, kurtosis,
skewness, entropy, amplitudes, power
bands, HPSD, auto correlation etc.
135 Spatial Features
Regional activation,
matter types, kurtosis, entropy, skewness,
degree of clustering
• 53 resting BOLD functional magnetic resonance imaging data-sets
• 24 Healthy Control / 29 Schizophrenia
PRELIMINARY WORK WITH UNSUPERVISED CLUSTERING OF SUBNETWORKS
• 8739 subnetworks extracted with higher dimensionality ICA, filtered to 3184
• Unsupervised clustering of filtered networks reveals new type of noise