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Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data.

Authors
Type
Published Article
Journal
Developmental cognitive neuroscience
Publication Date
Volume
12
Pages
123–133
Identifiers
DOI: 10.1016/j.dcn.2015.01.003
PMID: 25704288
Source
Medline
Keywords
  • Development
  • Functional Brain Networks
  • Functional Connectivity Magnetic Resonance Imaging (Fcmri)
  • Infant
  • Multivariate Pattern Analysis (Mvpa)
  • Support Vector Machine (Svm)

Abstract

Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.

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