| Literature DB >> 30004604 |
Paolo Bonifazi1,2, Asier Erramuzpe1, Ibai Diez1, Iñigo Gabilondo1, Matthieu P Boisgontier3, Lisa Pauwels3, Sebastiano Stramaglia4, Stephan P Swinnen3,5, Jesus M Cortes1,2,6.
Abstract
Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age-an age estimator resulting from a multi-scale methodology applied to the structure-function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto-striato-thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural-functional connectivity patterns correlating to other biomarkers than ChA.Entities:
Keywords: brain age; brain connectivity; chronological age; diffusion tensor imaging; physiological aging; resting state
Mesh:
Year: 2018 PMID: 30004604 PMCID: PMC6866396 DOI: 10.1002/hbm.24312
Source DB: PubMed Journal: Hum Brain Mapp ISSN: 1065-9471 Impact factor: 5.038