Psychiatric diagnosis still relies on symptom checklists that were never designed to reflect biology. A peer-reviewed invited review published in Brain Medicine now synthesizes recent advances across four converging domains: conceptual frameworks that move beyond categorical labels, molecular and neurobiological biomarkers, digital phenotyping through smartphones and wearable devices, and machine learning approaches capable of integrating these heterogeneous data streams. The review authors, based at the University of Cambridge, argue that combining objective biological measurement with clinical judgment could yield diagnostic subtypes that predict illness trajectory and guide personalized treatment. They also identify formidable barriers, from data scarcity and algorithmic opacity to regulatory fragmentation and the risk of deepening health inequities.

Epilepsy affects roughly 50 million people worldwide, yet the genetic roots of its most common forms have remained stubbornly obscure. A new mini-review published in Genomic Psychiatry brings together findings from the largest genome-wide association studies and exome sequencing efforts to date, revealing that thousands of common variants, each with tiny individual effects, collectively shape disease risk. The review identifies more than 20 genomic loci associated with genetic generalized epilepsy, highlights convergence between rare and common variant signals in ion channel and synaptic genes, documents extensive genetic overlap with psychiatric disorders, and outlines remaining challenges for polygenic risk prediction in clinical practice.

Researchers at the Icahn School of Medicine at Mount Sinai have created the first comprehensive sex-specific atlas of GLP-1 expression in the mouse brain, mapping the molecule mimicked by blockbuster weight-loss drugs such as semaglutide at single-transcript resolution across 25 brain regions in each sex. Key hindbrain regions showed higher GLP-1 densities in females, while the olfactory bulb showed significantly greater expression in males, and several brain nuclei expressed the peptide in only one sex. These findings provide a neuroanatomical framework that may help explain why GLP-1 appears to exert stronger metabolic effects in females, with implications for the emerging psychiatric applications of GLP-1 analogs.

Exercise is known to improve mood and memory, but the biological pathways connecting physical activity to brain function remain not entirely understood. In a new study published in Brain Medicine, researchers at University College Cork investigated how voluntary wheel running in adult rats reshapes the gut microbiota and its metabolic output, with consequences for the hippocampus. They found that exercise reduced two tryptophan-metabolizing bacterial genera, Alistipes and Clostridium, while enhancing circulating tryptophan metabolites including the serotonin catabolite 5-hydroxytryptophol. Exercise also decreased expression of the aryl hydrocarbon receptor specifically in the dorsal hippocampus, a brain region critical for memory. The findings suggest a coherent microbiota-gut-brain pathway through which exercise may benefit cognitive function.

In their Research review article, “Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation,” Hairong Zheng and Shanshan Wang (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.) et al. provide a comprehensive overview of recent advances in generative modeling for medical imaging.