Vankee-Lin, F. | Search bioRxiv

bioRxiv2026

Amygdala Persistence in Aging: Replication and Extension of Puccetti et al. (2021)

Age-related differences in affect are well established, but their neural mechanisms remain unclear. In a preregistered replication of Puccetti et al. (2021) and extension, we examined age, affect, left-amygdala negative persistence, and amygdala-ventromedial prefrontal cortex (vmPFC) task-based connectivity using MIDUS 3 daily diary and fMRI data. Older age predicted higher positive and lower negative affect in the daily diary sample (N = 1,168; ps < .001). In the primary diary + fMRI sample (N = 81; ages 48-95, 61.7% female, 82.7% White), greater persistence predicted higher raw negative affect ({beta} = 0.278, 95% CI [0.012, 0.544], p = .020); associations with positive and log-transformed negative affect were nonsignificant (ps [≥] .063). Older age predicted lower persistence in the final fMRI sample (N = 127; {beta} = -0.00260, 95% CI [-0.00504, -0.00016], p = .018), but this association was weaker in the primary diary + fMRI sample (p = .200), with no evidence of mediation. Greater anterior vmPFC connectivity predicted lower raw negative affect ({beta} = -0.106, 95% CI [-0.223, 0.012], p = .039) and log-transformed negative affect (p = .014). In MIDUS Refresher 1, affect estimates were directionally similar but nonsignificant (N = 48; ps [≥] .081), whereas the age-persistence estimate reversed direction. These findings provide partial support for amygdala persistence and task-based connectivity as neural correlates of daily affect, while highlighting variability in the magnitude and replicability of fMRI-behavior associations.

neuroscience↗

bioRxiv2026

Foundation models for linking brain functional representations and clinical outcomes across local intervention studies targeting cognitive aging: Validity and generalizability

The benefits of interventions targeting cognitive aging vary substantially across individuals, largely owing to heterogeneity in aging-related comorbidities. It is necessary to robustly identify neural patterns underlying intervention response and test their generalizability across heterogeneous cohorts. Resting-state functional MRI (rsfMRI) offers a potential pathway, but relying on predefined summary features with conventional methods has limited capacity to capture both within-individual longitudinal variation and between-individual differences, particularly in small and heterogeneous studies. Recent rsfMRI foundation models pretrained on large observational cohorts present a promising alternative by learning transferable spatiotemporal representations from time-series signals. Yet their validity and generalizability in local intervention settings remain unclear. Here, we systematically evaluated rsfMRI foundation models using data from two independent randomized controlled trials of older adults with mild cognitive impairment, testing whether these models can robustly extract longitudinal brain representations that predict post-intervention changes in episodic memory across trials. Foundation models outperformed conventional machine learning and deep learning approaches across both trials. Clinically informed adaptation using an external Alzheimers disease cohort further improved performance and robustness to confounders (i.e., head motion, site, and intervention arm), with accuracy up to 82%. Multivariate decomposition of foundation model embeddings identified latent neural patterns associated with episodic memory change with cross-study consistency at baseline that became more spatially distributed at post-intervention. These findings show that rsfMRI foundation models can enable robust and generalizable identification of latent neural patterns linking longitudinal brain dynamics to individual intervention response, laying the foundation for precision-driven neural target discovery in cognitive aging research. Significance StatementInterventions for cognitive aging show highly variable outcomes across individuals, limiting their clinical effectiveness. This study introduces a foundation model-based approach to identify latent neural patterns underlying individual differences in intervention response using resting-state fMRI. By leveraging pretrained models and domain-adaptive fine-tuning, we demonstrate robust and generalizable prediction of cognitive improvement across independent trials. Our findings suggest that latent brain representations, rather than predefined features, provide a scalable pathway toward precision-driven intervention strategies for aging populations.

bioengineering↗