Brain Age and Cognitive Functioning: The Moderating Role of Social Connectedness

Cognitive impairment is one of the most disabling symptoms in old age. In light of a rapidly ageing population worldwide, we need to better understand pathways to - and early predictors of – age-related cognitive impairment to identify those most at risk. In addition, better insights into modifiable factors that can alter these pathways in people at risk could improve current pre- and intervention strategies. For example, studies suggest that social connectedness may be a promising target to slow down cognitive decline in mid and old age or delay onset of dementia (Borgeest et al., 2020; Chen et al., 2025; Mahalingam et al., 2023). However, the degree to which this applies to individuals across the adult lifespan and exactly which (biological) risk pathways might be associated is less well understood. Cognitive functions are brain-based processes and have been linked to many aspects of neuroanatomy such as total brain volume (Cox et al., 2019). Some studies suggest that changes in the associated brain features might precede changes in cognitive functioning (Lawrence et al., 2018, Xie et al., 2023), making neuroimaging-derived traits promising biomarkers for later cognitive impairment. However, given the myriads of different features that can be derived from neuroimaging data, one of the key challenges has been how best to combine them into a cumulative score to facilitate and enhance prediction of impairment. In a similar way to how cognitive functioning can worsen with age, aspects of brain structure also show age-related decline (Bethlehem et al., 2022). As this decline is robustly associated with age, it is now possible to estimate a person’s “brain age” based on their neuroimaging scan. Here, brain age refers to a predicted measure of an individual’s biological age derived from neuroimaging features. The deviation between an individual’s brain age and their chronological age – also referred to as brain age gap (BAG) – is thought to indicate the extent to which that individual’s brain health deviates from what is expected based on their chronological age (Cole et al., 2019). // Many different methods have been developed to measure brain age and there is still no consensus on best practice. These methods can be broadly divided into state- and rate-based estimates of brain age. While state-based measures refer to one’s brain age at any single moment in time, rate-based measures describe the pace of brain ageing over time. Although prior studies have found that both state- and rate-based brain ages (and deviations) robustly link to differences in cognitive functioning (e.g., Jawinski et al., 2025; Kolbeinsson et al., 2020; Whitman et al., 2025), three key gaps remain: First, hardly any study has so far considered the role of modifiable moderators such as social connectedness in the risk association between brain age deviations and cognitive functioning. Social connectedness reflects an individual’s engagement with other people (Holt-Lunstad, 2022) and is an important predictive factor for health across the lifespan. Greater social connectedness is associated with lower risk of cardiovascular disease (Valtorta et al., 2016), reduced premature mortality (Elovainio et al., 2017; Holt-Lunstad et al., 2010), and fewer mental health problems, including in older age (Bethell et al., 2021). Importantly, various aspects of social connectedness have also been associated with slower cognitive decline (Samtani et al., 2022) and reduced risk of dementia (Mahalingam et al., 2023). With regard to its potential role as a moderator, Sharifian et al. (2022) found that friend network size attenuates the association between lower cortical thickness and poorer cognitive performance. Similarly, in those with more midlife lifestyle activities (including social activities), cognitive functioning (indexed by fluid intelligence) was less dependent on total grey matter volume, indicative of cognitive reserve (Chan et al., 2018). However, these studies did not directly consider brain age, which is needed to properly place these associations in an age- and lifespan-related context. Second, no study of social connectedness has used rate-based measures of brain age, even though evidence suggest that these predict cognitive functioning better than state-based measures (Whitman et al., 2025). Third, most existing studies are subject to problems of reverse causation and confounding. For example, there is some evidence that lack of social connectedness (i.e. loneliness) is a risk factor for accelerated brain age (Lay-Yee et al., 2023), making it difficult to disentangle the relationship between brain age deviations, social connectedness, and cognitive functioning. The use of genetic methods can address some concerns around reverse causation, as one’s genetic code is set at conception and remains largely unchanged over the lifetime. Applying a genetic approach shows promise. For example, considering allelic variation in APOE (a genetic risk factor for Alzheimer’s disease), Poey et al. (2017) found that social connectedness moderated the association between the genetic risk and cognitive functioning in people aged 70 years or older. It is now possible to proxy BAGs (rather than Alzheimer’s disease) through polygenic scores (PGS). We have recently carried out a large-scale multi-cohort genome-wide association study (GWAS) with an effective sample size of 160,656 individuals (Baltramonaityte et al., 2025) and used this to derive a PGS for brain age deviations, capturing a latent factor across BAGs derived from different brain age models. The PGS could account for up to 6% of variance in phenotypic BAGs in independent samples, and it did so already in childhood, decades before symptom onset (Baltramonaityte et al., 2025). This predictive value is comparable to - or at the upper end of - variance explained by PGS for other complex brain-related traits such as general cognitive function and regional brain volumes (Davies et al., 2018; García-Marín et al., 2024). The current project addresses the above three gaps by investigating the associations between different (genetic-, state- and rate-based) markers of brain age and cognitive functioning and assessing to which degree such relationships are moderated by social connectedness. Findings should shed light on the early pathways to cognitive impairment and how being socially connected might alleviate risk.