Women show an increased lifetime risk of Alzheimer’s disease (AD) compared with men. Characteristic brain connectivity changes, particularly within the default mode network (DMN), have been associated with both symptomatic and preclinical AD, but the impact of sex on DMN function throughout aging is poorly understood. We investigated sex differences in DMN connectivity over the lifespan in 595 cognitively healthy participants from the Human Connectome Project-Aging cohort. We used the intrinsic connectivity distribution (a robust voxel-based metric of functional connectivity) and a seed connectivity approach to determine sex differences within the DMN and between the DMN and whole brain. Compared with men, women demonstrated higher connectivity with age in posterior DMN nodes and lower connectivity in the medial prefrontal cortex. Differences were most prominent in the decades surrounding menopause. Seed-based analysis revealed higher connectivity in women from the posterior cingulate to angular gyrus, which correlated with neuropsychological measures of declarative memory, and hippocampus. Taken together, we show significant sex differences in DMN subnetworks over the lifespan, including patterns in aging women that resemble changes previously seen in preclinical AD. These findings highlight the importance of considering sex in neuroimaging studies of aging and neurodegeneration.

Women show an increased lifetime risk of Alzheimer's disease (AD) compared to men. Characteristic brain connectivity changes, particularly within the default mode network (DMN), have been associated with both symptomatic and preclinical AD, but the impact of sex on DMN function throughout aging is poorly understood. We investigated sex differences in DMN connectivity over the lifespan in 595 cognitively healthy participants from the Human Connectome Project - Aging cohort. We used the intrinsic connectivity distribution (a robust voxel-based metric of functional connectivity) and a seed connectivity approach to determine sex differences within the DMN and between the DMN and whole brain. Compared with men, women demonstrated increased connectivity with age in posterior DMN nodes and decreased connectivity in the medial prefrontal cortex. Differences were most prominent in the decades surrounding menopause. Seed-based analysis revealed increased connectivity in women from the posterior cingulate to angular gyrus and parahippocampal gyrus, which correlated with neuropsychological measures of declarative memory. Taken together, we show significant sex differences in DMN subnetworks over the lifespan, including patterns in aging women that resemble changes previously seen in preclinical AD. These findings highlight the importance of considering sex in neuroimaging studies of aging and neurodegeneration.

Alzheimer’s disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women’s elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n=579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ=0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between-visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women’s elevated risk of AD.

Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n = 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21–0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.

BackgroundAlzheimer’s disease (AD) disproportionately impacts women, yet we have little understanding of the differences in brain circuitry that underlie this vulnerability. We explored aspects of brain connectivity that robustly predict memory performance (measured by RAVLT performance) and neuroticism (measured by NEO‐N) in healthy women vs men by leveraging data‐driven machine learning to model the brain connectome in the Lifespan Human Connectome Project Aging (HCP‐A) dataset.MethodWe used functional MRI scans to create whole‐brain connectivity matrices based on 268 regions‐of‐interest for 725 healthy subjects (319 men, 406 women) aged 36 to 100 enrolled in HCP‐A. Before and after separating the subjects by sex, connectome‐based predictive modeling (CPM) was used with a p‐value threshold of 0.01 and split‐half cross‐validation to identify and implement edges that significantly predicted the neurobehavioral scores to train a predictive model for each group. The models were applied to test sets within each group, outputting the predicted behavioral measures and Pearson correlations between predicted and observed scores.ResultAs anticipated, overall performance in HCP‐A was higher for women than men in RAVLT (p < 1x10‐7) and NEO‐N (p = 0.12). Our model predicted the RAVLT measure (Fig. 1) with accuracies ranging from R = 0.2154 (p < 1x10‐4) to R = 0.4293 (p < 1x10‐4). Our sex‐based models also predicted a high variance in the RAVLT measure between sexes (σ2 = 0.015, Fig. 2). While neuroticism predictors were not as robust for short‐term memory, the models were still able to predict a significant amount of variance (σ2 = 0.023, Fig. 2).ConclusionWe successfully implemented CPM to derive robust brain‐based predictors of memory performance and neuroticism. Models derived separately for each sex differ in their ability to explain variance in these measures. Future analyses will pinpoint specific edges within the connectivity matrices that explain variance in memory performance or neuroticism in men vs women. We anticipate these will yield important insights into why women are at higher risk of developing AD.