Analysis of fMRI Data by Blind Separation into Independent Spatial Components

Abstract

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. 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 and 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 the analysis of fMRI data from normal and clinical populations.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1997
Accession Number
ADA460194

Entities

People

  • Anthony J. Bell
  • Greg G. Brown
  • Martin J. Mckeown
  • Sandra S. Kindermann
  • Scott Makeig
  • Terrence J. Sejnowski
  • Tzyy-Ping Jung

Organizations

  • Naval Health Research Center

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Biomedical Research
  • Data Science
  • Data Sets
  • Decomposition
  • Factor Analysis
  • Information Operations
  • Information Science
  • Magnetic Resonance
  • Magnetic Resonance Imaging
  • Neuroimaging
  • Order Statistics
  • Resonance
  • Statistics

Readers

  • Calculus or Mathematical Analysis
  • Mathematical Modeling and Probability Theory.
  • Neural Network Machine Learning.