A single mode of population covariation associates brain networks structure and behavior and predicts individual subjects’ age

• Published in: Communications biology Vol. 4; no. 1; pp. 943 - 16
• Main Authors: McPherson, Brent C., Pestilli, Franco
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.08.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 59/36; 59/57; 631/378/116/2396; 631/378/2612; 631/378/2649/1310; 692/53/2423; Adult; Aged; Aged, 80 and over; Aging; Behavior; Biology; Biomedical and Life Sciences; Brain - physiology; Brain Mapping; Cognitive ability; Functional anatomy; Humans; Life Sciences; Life span; Middle Aged; Nervous system; Neural networks; Neurosciences; Structure-function relationships; Young Adult
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-021-02451-0

Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections ( r  = 0.5808). Individual variability within the single association axis well predicted the age of the subject ( r  = 0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach. Brent McPherson and Franco Pestilli build on a large-scale data set from the Cambridge Centre for Aging Neuroscience to examine multivariate relationships between structural brain networks, behavior, and aging in healthy patients aged 18-88 years. They find that the age of individual subjects is predicted by the association between structural connectivity and behavioral measures. They provide a reproducible data processing pipeline at brainlife.io that can be applied to other datasets.