Abstract
The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age.
Highlights
Senescence is an inevitable and complex biological process associated with brain changes
Over the last three decades, functional magnetic resonance imaging, especially resting-state functional connectivity fMRI studies have significantly advanced our knowledge of human brain function and organization (Cole et al, 2014; Dubois, 2016; Dubois and Adolphs, 2016; Bassett and Sporns, 2017)
The principal component analysis was performed on the functional connectivity (FC) matrices across all the subjects
Summary
Senescence is an inevitable and complex biological process associated with brain changes. A prediction of brain age for individuals could serve as a reliable biomarker to detect the risk of neurodegenerative diseases and be used for early diagnosis and therapy (Cole and Franke, 2017). Predicted brain age being older than chronological age for a subject might imply accelerated brain aging arising from brain diseases. Over the last three decades, functional magnetic resonance imaging (fMRI), especially resting-state functional connectivity fMRI (rs-fMRI) studies have significantly advanced our knowledge of human brain function and organization (Cole et al, 2014; Dubois, 2016; Dubois and Adolphs, 2016; Bassett and Sporns, 2017). Increasing variety of studies have employed functional connectivity approaches to explore effects of aging on resting-state functional networks.
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