Linking Inter-Individual Variability in Functional Brain Connectivity to Cognitive Ability in Elderly Individuals

Abstract

Increasing evidence suggests that functional brain connectivity is an important determinant of cognitive aging. However, the fundamental concept of inter-individual variations in functional connectivity in older individuals is not yet completely understood. It is essential to evaluate the extent to which inter-individual variability in connectivity impacts cognitive performance at an older age. In the current study, we aimed to characterize individual variability of functional connectivity in the elderly and to examine its significance to individual cognition. We mapped inter-individual variability of functional connectivity by analyzing whole-brain functional connectivity magnetic resonance imaging data obtained from a large sample of cognitively normal older adults. Our results demonstrated a gradual increase in variability in primary regions of the visual, sensorimotor, and auditory networks to specific subcortical structures, particularly the hippocampal formation, and the prefrontal and parietal cortices, which largely constitute the default mode and fronto-parietal networks, to the cerebellum. Further, the inter-individual variability of the functional connectivity correlated significantly with the degree of cognitive relevance. Regions with greater connectivity variability demonstrated more connections that correlated with cognitive performance. These results also underscored the crucial function of the long-range and inter-network connections in individual cognition. Thus, individual connectivity–cognition variability mapping findings may provide important information for future research on cognitive aging and neurocognitive diseases.

Evaluation of the spatial variability in the major resting‐state networks across human brain functional atlases

“…However, among the associative regions, we found evidence of a medial–lateral dissociation; voxels in the medial prefrontal cortex and precuneus were highly likely to retain their network assignment across the six atlases, while the opposite was the case for voxels in the lateral frontal regions. These results are aligned with reviews (Eriksson, Vogel, Lansner, Bergstrom, & Nyberg, ; Nyberg & Eriksson, ) which have confirmed that the lateral aspects of the cortex show high interindividual variability, since these regions are crucial in implementing cognitive control (Moser et al, 2018; Niendam et al, ) and their variability is thought to underpin interindividual differences in executive functions (Gordon et al, ; Li et al, ; Mueller et al, ).…”

“…The SMN and VIS showed the highest similarity between the six different atlases. This was not surprising as these networks are comprising mostly of sensory and motor regions, which are known to have low interindividual variability in anatomical morphology (White et al, ) and in resting‐state FC (Franco et al, ; Li et al, ; Mueller et al, ) and tend to preferentially participate in single networks (Yeo et al, ).…”

Atlas55+: Brain Functional Atlas of Resting-state Networks for Late Adulthood

“…Across the three late-life cohorts, we found that the most reproducible networks were the SMN and VIS networks. These networks are mostly covering primary cortices, which are known to have high structural-functional coherence (Luo et al, 2020a), low inter-individual variability in anatomical morphology (White et al, 1997) and in resting-state functional connectivity (Franco et al, 2013;Li et al, 2017;Mueller et al, 2013), and tend to preferentially participate in single networks in line with their circumscribed and specific functions (Yeo et al, 2014). In contrast, the SAL network showed the largest spatial differences across the three late-life cohorts.…”

A Guide to Designing a Memory fMRI Paradigm for Pre-surgical Evaluation in Temporal Lobe Epilepsy

“…Understanding normal inter-subject variance can help understand differences in cognitive outcomes between patients and optimize the full potential of neuroimaging applications. Moreover, inter-subject variance may be related to behavioral functions (75,76) and could provide useful clinical information with regards to predicting outcome in a patient population. Whereas, group studies examine mean effects across subjects, inter-subject variance may provide critical information, and should not be ignored.…”