OBJECTIVES
Cortisol dysregulation, reflective of hypothalamic-pituitary-adrenal (HPA) axis dysregulation, has been linked to executive functioning (EF) decline. However, substantial effect-size heterogeneity across studies highlights the potential role of psychosocial moderators. We investigated whether the severity of perceived daily stressors and chronic worry moderated the relationships between baseline cortisol dysregulation and 9-year EF.
METHODS
Community adults (N = 1,617) completed four-day, four-times-daily salivary cortisol sampling and an eight-day interview-driven daily diary at baseline. Three cortisol metrics were computed: area under the curve (AUC), cortisol awakening response (CAR), and diurnal cortisol slope (DCS). Linear mixed models determined whether daily stressor or chronic worry severity moderated associations between baseline cortisol metrics and 9-year EF, adjusting for baseline EF.
RESULTS
Daily stressor severity significantly moderated the relationship between DCS-but not AUC or CAR-and 9-year EF (Cohen's d = 0.113). Flatter DCS was associated with poorer EF across all stressor severity levels, although the observed numerical attenuation at higher severity was not statistically confirmed. Chronic worry severity was significantly associated with lower 9-year EF across all cortisol metrics and significantly moderated the CAR-to-EF relationship (d = -0.176). Exploratory analyses suggested that a flatter DCS was associated with worse EF at higher worry severity, but this interaction was not significant.
DISCUSSION
These outcomes broadly align with moderation, not mediation, suggesting that psychosocial stress and worry can amplify, rather than yield, cortisol-linked relationships with EF. Pending replication, HPA-axis circadian modulation and chronic worry severity can indicate viable targets for prevention research.
N. Zainal, Stephanie Noble· The journals of gerontology....· 0 citations
Machine learning is a ubiquitous part of the modern neuroimaging toolkit, particularly for research aimed towards precision medicine goals of improving individual-level diagnosis and treatment. However, the high dimensionality of neuroimaging data poses significant challenges for constructing interpretable predictive models. Several established methods, such as Connectome-based Predictive Modeling (CPM), Polyneuro Risk Scores (PNRS, inspired by Polygenic Risk Scores), and Polyconnectomic Scoring (PCS), offer an interpretable approach, which we term “Mass Univariate Aggregation” (MUA). MUA approaches evaluate each feature independently and then use a linear combination of weighted features to derive predictions, providing directly interpretable weights for individual features. Despite the existence and widespread usage of various MUA approaches in neuroimaging, tools for their application are still fragmented, and there does not yet exist an open-access unified tool for implementing, evaluating, or comparing these models within a standardized machine learning workflow. Here, we present a unified, flexible, and accessible configurable pipeline that can be used for implementing CPM, PNRS, PCS, and many new MUA configurations facilitated by user-specified parameters. Built in Python as an add-on for scikit-learn, our configurable pipeline enables researchers to leverage the functionality and standards provided by a widely used open-access machine learning tool. We validated the configurable pipeline by replicating the outcomes achieved by existing CPM and PNRS implementations, utilizing resting-state functional connectivity data from the Human Connectome Project to predict fluid intelligence (n = 1067). We further validated the pipeline’s PCS implementation, confirming PCS computation with external connectome summary statistics (CSS) matrices using the same data, and CSS derivation using simulated data. Although designed to fill a gap in neuroimaging, our open-source, configurable pipeline provides a standardized platform for applying the MUA methods to any machine learning setting that features high-dimensional data.
