Enhanced prediction of cognitive function using aging-sensitive networks within the human structural connectome
James W. Madole,
Stuart J. Ritchie,
Simon R Cox,
Colin R Buchanan,
Maria Valdés Hernández,
Susana Muñoz Maniega,
Joanna M. Wardlaw,
Mat A. Harris,
Mark E Bastin,
Ian J. Deary,
Elliot M Tucker-Drob
Posted 13 Dec 2019
bioRxiv DOI: 10.1101/2019.12.13.875559
Posted 13 Dec 2019
Using raw structural and diffusion brain MRI data from the UK Biobank (UKB; N = 3,155, ages 45-75 years) and the Lothian Birth Cohort 1936 (LBC1936; N = 534, all age 73 years), we examine aging of regional grey matter volumes (nodes) and white matter structural connectivity (edges) within networks-of-interest in the human brain connectome. In UKB, the magnitude of age-differences in individual node volumes and edge weights corresponds closely with their loadings on their respective principal components of connectome-wide integrity (| r nodes| = 0.459; | r edges| = 0.595). In LBC1936, connectome-wide and subnetwork-specific composite indices of node integrity were predictive of processing speed, visuospatial ability, and memory, whereas composite indices of edge integrity were associated specifically with processing speed. Childhood IQ was associated with greater node integrity at age 73. However, node and edge integrity remained associated with age 73 cognitive function after controlling for childhood IQ. Adult connectome integrity is therefore both a marker of early-life cognitive function and a substrate of late-life cognitive aging.
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