{'fraction_non_alphanumeric': 0.02771618625277162, 'fraction_numerical': 0.01662971175166297, 'mean_word_length': 5.853164556962025, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analysis of fMRI data by blind separation into independent spatial components

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 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 Stroop color‐naming, the Brown and Peterson word/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three‐dimensional locations (a component “map”), and a unique associated time course of activation. Given data from 144 time points collected during a 6‐min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40‐sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task‐related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task‐related, quasiperiodic, or slowly varying. By utilizing higher‐order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth‐order decomposition technique (Comon [1994]: Signal Processing 36:11–20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task‐related activation. For each subject, the time courses and active regions of the task‐related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task‐related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask‐related signal components, movements, and other artifacts, as well as consistently or transiently task‐related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks. Hum. Brain Mapping 6:160–188, 1998. © 1998 Wiley‐Liss, Inc.