Structural and functional MRI data differentially predict chronological age and behavioral memory performance

Abstract

Human cognitive abilities decline with increasing chronological age, with decreased explicit memory performance being most strongly affected. However, some older adults show "successful aging", that is, relatively preserved cognitive ability in old age. One explanation for this could be higher brain structural integrity in these individuals. Alternatively, the brain might recruit existing resources more efficiently or employ compensatory cognitive strategies. Here, we approached this question by testing multiple candidate variables from structural and functional neuroimaging for their ability to predict chronological age and memory performance, respectively. Prediction was performed using support vector machine (SVM) classification and regression across and within two samples of young (N = 106) and older (N = 153) adults. The candidate variables were (i) behavioral response frequencies in an episodic memory test, (ii) recently described fMRI scores reflecting preservation of functional memory networks, (iii) whole-brain fMRI contrasts for novelty processing and subsequent memory, (iv) resting-state fMRI maps quantifying voxel-wise signal fluctuation and (v) gray matter volume estimated from structural MR images. While age group could be reliably decoded from all variables, chronological age within young and older subjects was best predicted from gray matter volume. In contrast, memory performance was best predicted from task-based fMRI contrasts and particularly single-value fMRI scores, whereas gray matter volume has no predictive power with respect to memory performance in healthy adults. Our results suggest that superior memory performance in healthy older adults is better explained by efficient recruitment of memory networks rather than by preserved brain structure.

The relationship between resting‐state amplitude fluctuations and memory‐related deactivations of the default mode network in young and older adults

“…In prior resting‐state fMRI studies of neurocognitive aging, most studies focused on DMN functional connectivity, but few considered amplitude measures. Those that did found BOLD signal variability to be highly predictive of chronological age (Garrett et al, 2010 ; Grady & Garrett, 2014 ; Soch et al, 2022 ; Xing, 2021 ). Moreover, although there are both regions showing reductions and those that show increases in signal variability, regions showing reduced age‐related signal variability make up the majority (Grady & Garrett, 2014 ).…”

Single-value scores of memory-related brain activity reflect dissociable neuropsychological and anatomical signatures of neurocognitive aging

“…In a large cohort of over 3000 healthy participants, Zonneveld et al (2019) reported an association of global cognition with GMV in the left amygdala, hippocampus, parietal lobule, superior temporal gyrus, insula and posterior temporal lobe. One potential advantage of our fMRI-based scores becomes evident from the recent observation that the scores may be superior to structural MRI data – and also resting-state fMRI – in the prediction of memory performance in older adults (Soch et al, in press). Future investigations should therefore explore the possibility that fMRI-based markers may be suitable as a predictor of cognitive functioning, even when age-related structural changes are not (yet) observable.…”

The relationship between resting-state amplitude fluctuations and memory-related deactivations of the Default Mode Network in young and older adults

“…In prior resting-state fMRI studies of neurocognitive aging, most studies focused on DMN functional connectivity, but few considered amplitude measures. Those that did found BOLD signal variability to be highly predictive of chronological age (Garrett et al, 2010;Grady & Garrett, 2014;Soch et al, 2022;Xing, 2021). Moreover, although there are both regions showing reductions and those that show increases in signal variability, regions showing reduced age-related signal variability make up the majority (Grady & Garrett, 2014).…”