Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic
John R Pruett1, Sridhar Kandala1, Sarah Hoertel1
1Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, St. Louis, MO 63110, United States.
Insights
Brain functional networks change significantly in infants. Resting-state functional connectivity MRI data successfully classified age in 6- and 12-month-old infants, revealing developmental shifts.
Area of Science:
- Neuroscience
- Developmental Neuroscience
- Brain Imaging
Background:
- Human brain networks undergo significant developmental changes.
- Functional brain architecture changes are poorly understood in the second half of infancy.
- Early life brain development is critical for cognitive and social milestones.
Purpose of the Study:
- To investigate age-related changes in functional brain networks during infancy.
- To determine if resting-state functional connectivity MRI data can predict infant age.
- To identify specific functional connectivity patterns associated with infant age.
Main Methods:
- Utilized resting-state functional connectivity magnetic resonance imaging (fcMRI) data from a longitudinal, multi-site infant study.
- Employed multivariate pattern classification, specifically Support Vector Machines (SVMs), to analyze fcMRI data.
- Implemented rigorous motion artifact correction techniques for fcMRI data analysis.
Main Results:
- fcMRI data successfully classified infants aged 6 versus 12 months above chance levels.
- Demonstrated significant alterations in brain functional organization between 6 and 12 months of age.
- Identified distinct functional connectivity patterns correlating with infant age categorization.
Conclusions:
- Infant brain functional organization undergoes substantial changes in the second half of the first year.
- fcMRI is a viable tool for detecting age-related functional brain development in infants.
- These findings align with critical periods of motor, cognitive, and social development in early infancy.
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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