Brain Connectivity & Machine Learning

Structure–function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging

Paolo Bonifazi, Asier Erramuzpe, Ibai Diez, Iñigo Gabilondo,
Matthieu P. Boisgontier, Lisa Pauwels, Sebastiano Stramaglia, Stephan P. Swinnen and Jesus M. Cortes. Structure–function multi-scale connectomics reveals a major
role of the fronto-striato-thalamic circuit in brain aging. Human Brain Mapping, 39: 4663–4677, 2018 [pdf]
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.

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