Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds

Omid Kardan, Sydney Kaplan, Muriah D. Wheelock, Eric Feczko, Trevor K.M. Day, Óscar Miranda-Domínguez, Dominique Meyer, Adam T. Eggebrecht, Lucille A. Moore, Sooyeon Sung, Taylor A. Chamberlain, Eric Earl, Kathy Snider, Alice Graham, Marc G. Berman, Kamil Uğurbil, Essa Yacoub, Jed T. Elison, Christopher D. Smyser, Damien A. FairMonica D. Rosenberg

Research output: Contribution to journalArticlepeer-review

Abstract

Resting-state functional connectivity (rsFC) measured with fMRI has been used to characterize functional brain maturation in typically and atypically developing children and adults. However, its reliability and utility for predicting development in infants and toddlers is less well understood. Here, we use fMRI data from the Baby Connectome Project study to measure the reliability and uniqueness of rsFC in infants and toddlers and predict age in this sample (8-to-26 months old; n = 170). We observed medium reliability for within-session infant rsFC in our sample, and found that individual infant and toddler's connectomes were sufficiently distinct for successful functional connectome fingerprinting. Next, we trained and tested support vector regression models to predict age-at-scan with rsFC. Models successfully predicted novel infants’ age within ± 3.6 months error and a prediction R2 =.51. To characterize the anatomy of predictive networks, we grouped connections into 11 infant-specific resting-state functional networks defined in a data-driven manner. We found that connections between regions of the same network—i.e. within-network connections—predicted age significantly better than between-network connections. Looking ahead, these findings can help characterize changes in functional brain organization in infancy and toddlerhood and inform work predicting developmental outcome measures in this age range.

Original languageEnglish (US)
Article number101123
JournalDevelopmental Cognitive Neuroscience
Volume56
DOIs
StatePublished - Aug 2022
Externally publishedYes

Keywords

  • Age prediction
  • Development
  • FMRI
  • Functional connectivity
  • Machine learning
  • Reliability

ASJC Scopus subject areas

  • Cognitive Neuroscience

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