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.
Bibliographical noteFunding Information:
We thank Jeffrey Neil, Terrie Inder, and Chris Smyser for contribution of an infant brain atlas and comparison scan sequence. We thank Weili Lin for technical and administrative support. We thank James Harper and Siyang Yang for assistance with selection of the ROIs. We would also like to thank Britt Gott and Teddi Gray for help with manuscript preparation. This work was supported by: R01 MH093510 (Pruett), R01 HD055741 (Piven), P30 NS048056 (Snyder), K12 EY016336 (Pruett, completed), and the McDonnell Center for Systems Neuroscience.
© 2015 The Authors. Published by Elsevier Ltd.
- Functional brain networks
- Functional connectivity magnetic resonance imaging (fcMRI)
- Multivariate pattern analysis (MVPA)
- Support vector machine (SVM)