Biology subjects

Stahl, L.

Publications and source records attributed to Stahl, L..

2 recordsLinked to original sources

bioRxiv2025

Profiles of Aging Based on Cognition, Affect, and Brain Reserve

The aging paradox describes improvements in emotional wellbeing as a function of aging, despite declines in cognition. Conversely, late life depression has been associated with increased cognitive decline in aging. We sought to understand these seemingly contradictory patterns of cognitive and mental health in older age. Building on cognitive reserve, affective reserve, and brain reserve models of aging, we developed three alternative algorithmic approaches to group N=22,686 participants from the UK Biobank into different profiles of aging. Our results revealed that aging profiles identified using our data-driven brain reserve model, which incorporated measures of cognition, neuroticism, and brain volume, achieved the highest validation results. Importantly, only two of the four aging profiles were characterized by the aging paradox (i.e., improved emotionality and decreased cognition with age). We identified one profile characterized by particularly low levels of neuroticism and relative resilience to cognitive decline. Another profile benefited from relatively preserved brain volumes, potentially driven by younger ages and/or higher socioeconomic status. Conversely, we identified two profiles with poorer health characteristics, including one profile with elevated cardiovascular risk. Taken together, these findings enrich our understanding of the emotion paradox and highlight the value of taking a nuanced and stratified approach when studying aging. In the future, aging profiles could be used to target preventative strategies to address modifiable risk factors and improve lifespan and healthspan.

neuroscience↗

bioRxiv2025

The neuroimaging correlates of depression established across six large-scale datasets

Neuroimaging data offers noninvasive insights into the structural and functional organization of the brain and is therefore commonly used to study the neuroimaging correlates of depression. To date, a substantial body of literature has suggested reduced size of subcortical regions and abnormal functional connectivity in frontal and default mode networks linked to depression. However, recent meta analyses have failed to identify significant converging correlates of depression across the literature such that a conclusive mapping of the neuroimaging correlates of depression remains elusive. Here we leveraged 23,417 participants across six datasets to comprehensively establish the neuroimaging correlates of depression. We found reductions in gray matter volume/ cortical surface area associated with depression in the frontal cortex, anterior cingulate, and insula, confirming prior studies showing the importance of prefrontal and default mode regions in depression. Our findings demonstrate multiple surprising results, including a lack of depression correlates in subcortical brain regions, significant depression correlates in somatomotor and visual regions, and limited functional connectivity findings. Overall, these results shed new light on key brain regions involved in the pathophysiology of depression, updating our understanding of the neuroimaging correlates of depression. We anticipate that these insights will inform further research into the role of sensorimotor and visual regions in depression and into the impact of heterogeneity on functional connectivity correlates of depression.

neuroscience↗