Targeting Age-Related Differences in Brain and Cognition with Multimodal Imaging and Connectome Topography Profiling

Abstract

Aging is characterized by accumulation of structural and metabolic changes in the brain. Recent studies suggest transmodal brain networks are especially sensitive to aging, which, we hypothesize, may be due to their apical position in the cortical hierarchy. Studying an open‐access healthy cohort (n = 102, age range = 30–89 years) with MRI and Aβ PET data, we estimated age‐related cortical thinning, hippocampal atrophy and Aβ deposition. In addition to carrying out surface‐based morphological and metabolic mapping experiments, we stratified effects along neocortical and hippocampal resting‐state functional connectome gradients derived from independent datasets. The cortical gradient depicts an axis of functional differentiation from sensory‐motor regions to transmodal regions, whereas the hippocampal gradient recapitulates its long‐axis. While age‐related thinning and increased Aβ deposition occurred across the entire cortical topography, increased Aβ deposition was especially pronounced toward higher‐order transmodal regions. Age‐related atrophy was greater toward the posterior end of the hippocampal long‐axis. No significant effect of age on Aβ deposition in the hippocampus was observed. Imaging markers correlated with behavioral measures of fluid intelligence and episodic memory in a topography‐specific manner, confirmed using both univariate as well as multivariate analyses. Our results strengthen existing evidence of structural and metabolic change in the aging brain and support the use of connectivity gradients as a compact framework to analyze and conceptualize brain‐based biomarkers of aging.

BrainStat: a toolbox for brain-wide statistics and multimodal feature associations

“…Both approaches can be used to embed findings into the functional architecture of the human brain either by stratifying results in accordance with the functional architecture, or by assessing point-wise relationships to the gradients. Indeed, prior studies have assessed the relationship of the brain's functional architecture to cortical dynamics (Wang et al, 2019), high level cognition (Murphy et al, 2019;Shine et al, 2019;Sormaz et al, 2018), hippocampal subfield connectivity (Vos de Wael et al, 2018), amyloid beta expression and aging (Lowe et al, 2019), microstructural organization (Huntenburg et al, 2017;Paquola et al, 2019), phylogenetic changes (Xu et al, 2020), and alterations in disease states (Caciagli et al, 2021;Hong et al, 2019;Tian et al, 2019). The resting-state decoding module relies on two separate datasets: macroscopic networks as provided by Yeo, Krienen, et al (Yeo et al, 2011), and functional gradients derived from the Human Connectome Project S1200 dataset (Van Essen et al, 2013).…”

“…These gradients highlight gradual transitions between regions and can be used to embed findings into the functional architecture of the human brain by assessing point-wise relationships with other markers. Indeed, prior studies have used functional gradients to assess the relationship of the brain's functional architecture to high level cognition (Murphy et al, 2019;Shine et al, 2019), hippocampal subfield connectivity (Vos de , amyloid beta expression and aging (Lowe et al, 2019), microstructural organization (Huntenburg et al, 2017;Paquola et al, 2019), phylogenetic changes (Xu et al, 2020), and alterations in disease states (Caciagli et al, 2021;Hong et al, 2019;Tian et al, 2019).…”