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PLoS One. 2014 Apr 18;9(4):e95493. doi: 10.1371/journal.pone.0095493. eCollection 2014.

A robust classifier to distinguish noise from fMRI independent components.

Author information

1
Stanford Graduate Fellow, Graduate Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, United States of America.
2
Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California, United States of America.
3
The Mind Research Network, Albuquerque, New Mexico, United States of America; Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States of America.
4
The Mind Research Network, Albuquerque, New Mexico, United States of America.
5
The Mind Research Network, Albuquerque, New Mexico, United States of America; Georgia State University, Department of Psychology and Neuroscience Institute, Atlanta, Georgia, United States of America.
6
Department of Radiology, Stanford University School of Medicine, Stanford, California, United States of America.

Abstract

Analyzing Functional Magnetic Resonance Imaging (fMRI) of resting brains to determine the spatial location and activity of intrinsic brain networks--a novel and burgeoning research field--is limited by the lack of ground truth and the tendency of analyses to overfit the data. Independent Component Analysis (ICA) is commonly used to separate the data into signal and Gaussian noise components, and then map these components on to spatial networks. Identifying noise from this data, however, is a tedious process that has proven hard to automate, particularly when data from different institutions, subjects, and scanners is used. Here we present an automated method to delineate noisy independent components in ICA using a data-driven infrastructure that queries a database of 246 spatial and temporal features to discover a computational signature of different types of noise. We evaluated the performance of our method to detect noisy components from healthy control fMRI (sensitivity = 0.91, specificity = 0.82, cross validation accuracy (CVA) = 0.87, area under the curve (AUC) = 0.93), and demonstrate its generalizability by showing equivalent performance on (1) an age- and scanner-matched cohort of schizophrenia patients from the same institution (sensitivity = 0.89, specificity = 0.83, CVA = 0.86), (2) an age-matched cohort on an equivalent scanner from a different institution (sensitivity = 0.88, specificity = 0.88, CVA = 0.88), and (3) an age-matched cohort on a different scanner from a different institution (sensitivity = 0.72, specificity = 0.92, CVA = 0.79). We additionally compare our approach with a recently published method. Our results suggest that our method is robust to noise variations due to population as well as scanner differences, thereby making it well suited to the goal of automatically distinguishing noise from functional networks to enable investigation of human brain function.

PMID:
24748378
PMCID:
PMC3991682
DOI:
10.1371/journal.pone.0095493
[Indexed for MEDLINE]
Free PMC Article

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