• 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 121Good 78 579

N = 1518 Networksspatial 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 Featuressignal metrics, peaks, kurtosis, skewness, entropy, amplitudes, power bands, HPSD, auto correlation etc.

135 Spatial FeaturesRegional 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