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Xiaobai Li

2 papers in the library · 12 citations · publishing 2025-2026

Papers

Advances in the study of NMDA receptors in depression pathogenesis and the antidepressant efficacy of their antagonists.

Asian Journal of Psychiatry June 1, 2025 Mingrui Chen, Jingyan Jin, Hongsheng Bi et al. 12 citations

NMDA receptors, a type of glutamate receptor, are central to neuroplasticity and are implicated in depression, where their dysfunction can cause neuronal damage and disrupt brain adaptability. Ketamine, an NMDA receptor antagonist, rapidly alleviates severe depression and suicidal thoughts within hours by reducing NMDA receptor activity and activating the mTOR pathway, but its use is limited by side effects like hallucinations and dependency. Esketamine, an FDA-approved variant, offers improved efficacy and fewer side effects. Other NMDA-modulating compounds, including memantine and rapastinel, are under investigation. Future research should focus on NMDA receptor molecular mechanisms to develop safer, more effective, and personalized depression treatments with longer-lasting effects.

DOI: 10.1016/j.ajp.2025.104502 (opens in new tab) Full text (opens in new tab) Antidepressants Glutamatergic Nmda receptor antagonists Nmda receptors +4

Machine Learning-Based Classification of Jhana Advanced Concentrative Absorption Meditation (ACAM-J) using 7T fMRI

arXiv.org February 13, 2026 Puneet Kumar, W. F. Yang, Alakhsimar Singh et al.

Functional MRI brain scans can distinguish the advanced meditation state known as Jhana (ACAM-J) from ordinary consciousness with modest accuracy. Using regional homogeneity (ReHo) maps from 20 advanced meditators, machine learning classifiers reached 66.82% accuracy. The prefrontal and anterior cingulate brain regions were most important for the classification, consistent with their roles in attention and metacognition. The results suggest that machine learning can feasibly classify advanced meditation states, supporting future work on neuromodulation and mechanistic models of such states.

DOI: 10.48550/arxiv.2602.13008 (opens in new tab) Computer science Medicine