Foundation models for linking brain functional representations and clinical outcomes across local intervention studies targeting cognitive aging: Validity and generalizability
Abstract
The direct cognitive benefits of interventions targeting cognitive aging highly vary across individuals due to individual heterogeneity in aging-related comorbidities. This variation necessitates a better characterization of brain representations reflecting how older individuals uniquely benefit from interventions, thereby revealing the true effect of these interventions. Resting-state functional MRI (rsfMRI) has potential to uncover meaningful brain representation; however, traditional methods analyzing summary features derived from rsfMRI data are limited in capturing both within-individual variations across timepoints and between-individual difference, particularly in small and heterogeneous local intervention studies. Recent rsfMRI foundation models pretrained on large-scale observational cohorts, such ask UK Biobank, offer a potential alternative, yet their validity and generalizability in local intervention studies remain unclear. In this study, we systematically evaluated existing rsfMRI foundation models for classifying intervention responses to changes of episodic memory using data from two independent randomized controlled trials targeting cognitive aging among older adults with mild cognitive impairment. RsfMRI foundation models outperformed conventional machine learning and deep learning approaches, while performance differed across foundation models with different pretraining objectives. Clinically informed fine-tuning using an external Alzheimers disease cohort further improved performance and demonstrated robustness to confounders (e.g., head motion during MRI data acquisition, data collection site, intervention arm). Interpretability analyses confirmed that brain representations learned by the model reflect the brain networks supporting cognitive aging or episodic memory. Together, these findings provide an empirical evaluation of current rsfMRI foundation models and support nuanced interpretation of heterogeneous intervention outcomes by linking individual differences in longitudinal brain dynamics to cognitive change.
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Research summary
The paper claims that resting-state functional MRI foundation models can effectively classify individual responses to cognitive aging interventions. This research is relevant as it seeks to enhance our understanding of brain dynamics in aging and improve intervention outcomes, addressing the complexities of cognitive aging rather than merely treating symptoms.
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