Meishan Ai

All Publications

• BRAIN-DISC: a novel analytical approach integrating data-driven brain pattern discovery with cohort studies for evaluating findings in brain modulation interventions. GeroScience Ai, M., Turnbull, A., Zhao, K., Liu, Y., Zhang, Y., Vankee-Lin, F. 2026

  Abstract

  Brain modulation interventions (BMIs) targeting cognitive and neuropsychiatric symptoms have shown substantial heterogeneity in response, limiting their clinical utility. Resting-state functional connectivity (rsFC) may capture BMI-induced neuroplasticity and support patient stratification. However, examining these biomarkers within small, heterogeneous intervention samples remains challenging. The objective of this study is to present BRAIN-DISC, an analytic framework that links large-scale cohort-derived rsFC patterns with evaluation in targeted BMI trials. Three demonstrations were conducted. In the CogTE trial (n = 74), the Alzheimer's-resilient connectome (ARC), derived from cohort contrasts of Superagers and Alzheimer's disease, was evaluated as a response biomarker for cognitive training in mild cognitive impairment (MCI). In the BEEM trial (n = 26), a brain-derived neuropsychiatric phenotyping (BNP) subtype was used to stratify response to transcranial direct current stimulation combined with training. In a third demonstration, we conducted an end-to-end implementation by discovering rsFC biotypes jointly informed by autonomic nervous system (ANS) and cognitive function (rsFC-AC) in the MIDUS cohort (n = 208), and evaluating it as a predictive biomarker in BREATHE trial (n = 56). In CogTE, greater shifts toward the ARC pattern were associated with improvements in executive function and episodic memory. In BEEM, individuals with the affective dysregulation subtype showed greater improvement in corresponding neuropsychiatric domains. In the third demo, three rsFC-AC biotypes were discovered in MIDUS cohort; the high-ANS subtype showed greater improvement in episodic memory in BREATHE trial. BRAIN-DISC provides a scalable framework for translating cohort-derived rsFC signatures into intervention settings to support both response monitoring and patient stratification.

  View details for DOI 10.1007/s11357-026-02471-w

  View details for PubMedID 42625098

  View details for PubMedCentralID 7355168

• Plasma biomarker-informed brain topology for cognitive resilience: applications in non-pharmacological interventions concerning cognitive aging ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING Ai, M., Zhou, S., Li, D., Yu, F., Heffner, K. L., Zhang, Y., Lin, F. 2026; 18 (2)

  View details for DOI 10.1002/dad2.70330

  View details for Web of Science ID 001741560000001

• Resting-state fMRI foundation models enable robust and generalizable latent neural target discovery in cognitive aging interventions. bioRxiv : the preprint server for biology Zhou, X., Ai, M., Adeli, E., Zhang, Y., Liu, Y. M., Vankee-Lin, F. 2026

  Abstract

  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 Alzheimer's 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.

  View details for DOI 10.64898/2025.12.30.697042

  View details for PubMedID 42039513

  View details for PubMedCentralID PMC13105019