TY - JOUR
T1 - Structure–function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging
AU - Bonifazi, Paolo
AU - Erramuzpe, Asier
AU - Diez, Ibai
AU - Gabilondo, Iñigo
AU - Boisgontier, Matthieu P.
AU - Pauwels, Lisa
AU - Stramaglia, Sebastiano
AU - Swinnen, Stephan P.
AU - Cortes, Jesus M.
N1 - Publisher Copyright:
© 2018 Wiley Periodicals, Inc.
PY - 2018/12
Y1 - 2018/12
N2 - 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.
AB - 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.
KW - brain age
KW - brain connectivity
KW - chronological age
KW - diffusion tensor imaging
KW - physiological aging
KW - resting state
UR - https://www.scopus.com/pages/publications/85050914244
U2 - 10.1002/hbm.24312
DO - 10.1002/hbm.24312
M3 - Article
C2 - 30004604
AN - SCOPUS:85050914244
SN - 1065-9471
VL - 39
SP - 4663
EP - 4677
JO - Human Brain Mapping
JF - Human Brain Mapping
IS - 12
ER -