Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging. Issue 12 (13th July 2018)

• Record Type: Journal Article
• Authors: Bonifazi, Paolo; Erramuzpe, Asier; Diez, Ibai; Gabilondo, Iñigo; Boisgontier, Matthieu P.; Pauwels, Lisa; Stramaglia, Sebastiano; Swinnen, Stephan P.; Cortes, Jesus M.
• Is Part Of: Human brain mapping. Volume 39:Issue 12(2018)
• Page Start: 4663
• Page End: 4677
• Publication Date: 2018-07-13
• Subjects: brain age -- brain connectivity -- chronological age -- diffusion tensor imaging -- physiological aging -- resting state; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24312
• Languages: English
• ISSNs: 1065-9471

Abstract

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.