NAVAL HEALTH RESEARCH CENTER

ANALYSIS OF FMRI DATA BY BLIND SEPARATION

INTO INDEPENDENT SPATIAL COMPONENTS

M. J. McKeown S. Makeig

G. G. Brown T-P Jung

S. S. Kindermann A. J. Bell

T.J. Sejnowski

Report No. 97-42

Approved for public release; distribution unlimited.

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4. TITLE AND SUBTITLE Analysis of fMRI Data by Blind Separation into Independent Spatial Components 6. AUTHORS Martin J McKeown, Scott Makeig, Greg G Brown, Tzyy-Ping Jung, Sandra S. Kindermann, Anthony J Bell & Terrance J Sejnowski

5a. Contract Number: 5b. Grant Number: 5c. Program Element: 5d. Project Number: 5e. Task Number: 5f. Work Unit Number: 6429 5g. IRB Protocol Number:

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Health Research Center P.O. Box 85122 San Diego, CA 92186-5122 8. PERFORMING ORGANIZATION REPORT

NUMBER Report No. 97-42 10. Sponsor/Monitor's Acronyms(s) NMRDC/BUMED

8. SPONSORING/MONITORING AGENCY NAMES(S) AND ADDRESS(ES) Commanding Officer Naval Medical Research & Development Command National Naval Medical Center Bldg 1, Tower 2 Bethesda, MD 20889-5044

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12 DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited. 13. SUPPLEMENTARY NOTES Published in Human Brain Mapping, 1998, 6, 160-188 14. ABSTRACT (maximum 200 words) Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured brain electrical signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing various cognitive tasks. by utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm ad a related fourth-order decomposition technique were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. ICA appears to be a highly promising method for he analysis of fMRI data from normal and clinical populations.

14. SUBJECT TERMS Functional magnetic resonance imaging; independent component analysis, higher-order statistics 16. SECURITY CLASSIFICATION OF: 18a. NAME OF RESPONSIBLE PERSON

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