Prediction of cognitive performance differences in older age from multimodal neuroimaging data

Abstract

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55–85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.

Predictability of intelligence and age from structural connectomes

“…prediction of fluid intelligence. Kra ¨mer et al [39] reported a similar outcome to this study for a small trend for a multimodal benefit therefore concluded that developing a biomarker for cognitive aging remained challenging. Their study employed multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC), and generalized results across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS dataset.…”

Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations

“…A possible explanation for these findings is the lack of a common link between brain structure and sleep quality/depressive symptoms, as highlighted previously ( Olfati et al, 2024 ; Weihs et al, 2023 ; Winter et al, 2024 ). Similarly, literature assessing the combined effects of brain and behavioural information found no improvement, or even a decrease, in predictability when compared to using only phenotypic information ( Dadi et al, 2021 ; Krämer et al, 2023 ; Olfati et al, 2024 ; Omidvarnia et al, 2023 ). In our study, this is consistent with the results of the HCP Young sample, where the behavioural rCCA model showed the strongest associations to MP and the addition of GMV did not increase the canonical correlation.…”

The Role of Methionine-Rich Diet in Unhealthy Cerebrovascular and Brain Aging: Mechanisms and Implications for Cognitive Impairment

“…Functional connectivity refers to the temporal correlation of neural activity between different brain regions, which reflects the coordination and integration of neural networks. Disruptions in functional connectivity with aging [376] have been implicated in various cognitive impairments and neurological disorders [377][378][379][380][381][382][383][384][385][386][387][388]. Brain connectivity, essential for efficient cognitive function, can be influenced by dietary factors [389][390][391].…”