Evaluating the Prediction of Brain Maturity from Functional Connectivity after Motion Artifact Denoising

Ashley N. Nielsen*, Deanna J. Greene, Caterina Gratton, Nico U.F. Dosenbach, Steven E. Petersen, Bradley L. Schlaggar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

The ability to make individual-level predictions from neuroanatomy has the potential to be particularly useful in child development. Previously, resting-state functional connectivity (RSFC) MRI has been used to successfully predict maturity and diagnosis of typically and atypically developing individuals. Unfortunately, submillimeter head motion in the scanner produces systematic, distancedependent differences in RSFC andmay contaminate, and potentially facilitate, these predictions. Here, we evaluated individual age prediction with RSFC after stringentmotion denoising. Using multivariate machine learning, we found that 57% of the variance in individual RSFC after motion artifact denoising was explained by age, while 4% was explained by residual effects of head motion. When RSFC data were not adequately denoised, 50% of the variance was explained by motion. Reducing motion-related artifact also revealed that prediction did not depend upon characteristics of functional connections previously hypothesized to mediate development (e.g., connection distance). Instead, successful age prediction relied upon sampling functional connections across multiple functional systems with strong, reliable RSFC within an individual. Our results demonstrate that RSFC across the brain is sufficiently robust to make individual-level predictions of maturity in typical development, and hence, may have clinical utility for the diagnosis and prognosis of individuals with atypical developmental trajectories.

Original languageEnglish (US)
Pages (from-to)2455-2469
Number of pages15
JournalCerebral Cortex
Volume29
Issue number6
DOIs
StatePublished - Jun 1 2019

Funding

NIH K01MH104592 (D.J.G.), NARSAD Young Investigator Award (D.J.G.), NIH K23NS088590 (NUFD), NIH NINDS F32NS092290 (C.G.). Original data collection was supported by NIH R01HD057076 (B.L.S.), NIH R01NS046424 (S.E.P.), Simons Foundation Autism Research Initiative (S.E.P.), NIH R21MH091512 (B.L.S.), NIH R21 NS091635 (B.L.S.), Tourette Association of America Neuroimaging Consortium Grant (B.L.S., D.J.G.), NIH NINDS NRSA-F32 NS656492, American Hearing Research Foundation, NIH K23DC006638, P50 MH071616, P60 DK020579-31, and the McDonnell Foundation. Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of Health under Award Number U54 HD087011 to the Intellectual and Developmental Disabilities Research Center at Washington University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Keywords

  • development
  • fMRI
  • functional connectivity
  • machine learning

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience

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