Sara Mostafavi | PubMed Health

PubMed2026

Effects of adjunctive memantine on executive function and global cognition in bipolar disorder (BD): A randomized, double-blind, placebo-controlled clinical trial.

BACKGROUND: Cognitive impairment contributes substantially to disability in bipolar disorder (BD), but effective pharmacologic options remain limited. This trial evaluated whether adjunctive memantine improves global cognition and executive function in BD. METHODS: In this double-blind, placebo-controlled randomized trial, patients with bipolar I disorder (B1D) receiving lithium and olanzapine were assigned to memantine or placebo. Memantine was titrated to 20 mg/day over 6 weeks. Cognitive outcomes were assessed at baseline, week 6, and week 18. Global cognition was measured with the Neurocognitive Assessment Battery (NuCog), and executive function with the Frontal Assessment Battery (FAB). Data were analyzed using generalized estimating equations and Bonferroni-adjusted post-hoc tests. RESULTS: Sixty-three participants were randomized (memantine, n = 31; placebo, n = 32), and all completed follow-up. Groups were comparable at baseline for demographic and clinical variables and for most cognitive measures. Both groups improved over time (P < 0.001), but improvement was greater with memantine for global cognition at week 6 (MD = 9.32; 95% CI, 4.84-13.81; P < 0.001) and week 18 (MD = 12.69; 95% CI, 8.67-16.71; P < 0.001). FAB total scores favored memantine at week 18 (MD = 2.68; 95% CI, 1.76-3.61; P < 0.001), but not at week 6. Domain analyses showed significant benefits for NuCog attention, visuoconstructional ability, memory, and executive function, and for several FAB subscales by week 18. CONCLUSIONS: Adjunctive memantine improved global cognition and, over longer follow-up, executive function in BD. These findings support NMDA receptor modulation as a potential strategy for cognitive dysfunction in BD.

Humans↗

PubMed2025

Refining sequence-to-activity models by increasing model resolution.

Decoding the cis-regulatory syntax that controls gene expression is essential for improving our understanding of cell differentiation and disease. To identify regulatory motifs and their regulatory syntax, deep learning based sequence-to-activity (S2A) models learn transcription factor binding motifs and their combinations from DNA sequence by modeling measured chromatin accessibility. Previously, we developed AI-TAC, a S2A model that predicts chromatin accessibility across various immune cell types in multi-task fashion, effectively decoding the regulatory syntax underlying immune cell differentiation. While ATAC-seq is commonly used to measure regional accessibility, it also provides high-resolution profiles, the distribution of Tn5 insertion sites, that offer additional insights into the precise location and strength of TF binding sites. Here we demonstrate that modeling ATAC-seq profiles alongside accessibility consistently improves predictions of differential chromatin accessibility across cell types. Moreover, we also find that multi-task learning across related immune cell types consistently outperforms single-task models. To understand what additional information bpAITAC learns from ATAC-seq profiles, we systematically compare sequence attributions from models trained with and without ATAC-seq profiles. We identify novel motifs with strong effect sizes that emerge only when profile data is included. Our findings suggest that modeling ATAC-seq at base-pair resolution enables the model to learn a more nuanced and sensitive representation of the cis-regulatory syntax driving immune cell-specific chromatin landscapes.

ATAC-seq↗