Guillermo Gonzalez-Burgos, Ruth Benavides-Piccione, Andreas Neef, Wataru Inoue, Lyle Muller, Michelle S Jimenez-Sosa, Jochen F Staiger, Maria Medalla, Jennifer I Luebke
{"title":"Diversity of Layer 3 Pyramidal Neuron Properties Across Areas of the Primate Neocortex.","authors":"Guillermo Gonzalez-Burgos, Ruth Benavides-Piccione, Andreas Neef, Wataru Inoue, Lyle Muller, Michelle S Jimenez-Sosa, Jochen F Staiger, Maria Medalla, Jennifer I Luebke","doi":"10.1016/j.biopsych.2026.08.015","DOIUrl":"https://doi.org/10.1016/j.biopsych.2026.08.015","url":null,"abstract":"<p><p>Impaired activation of cortical circuits might contribute to working memory deficits in schizophrenia. In this disorder, layer 3 pyramidal neurons (L3PNs) of the prefrontal (PFC), primary visual (V1) and posterior parietal (PPC) cortices, three cortical areas essential for working memory, display alterations that may impair network activity. We review evidence suggesting that L3PN morphology and physiology differ significantly across PFC, PPC and V1 in primates. These differences are much less pronounced in rodents, suggesting a primate-enhanced regional variability that may be the substrate for area-specific L3PN vulnerability in schizophrenia. PFC L3PNs exhibit larger dendrites with higher spine density, thus substantially more excitatory synapses than V1 or PPC L3PNs. Furthermore, the PFC contains a unique stripe-like connectivity system mediated by the horizontal axon collaterals of L3PNs that might support robust recurrent excitation, and thus the mnemonic persistent activity thought to contribute to working memory storage. Physiologically, PFC L3PNs display higher spontaneous excitatory post-synaptic current (sEPSC) frequency and amplitude, indicating functionally more potent individual synapses in PFC than in V1 L3PNs. Although sEPSC differences are less pronounced between PPC and PFC L3PNs, the greater spine density in PFC suggests stronger excitatory drive in PFC L3PNs. We conclude by identifying open questions that are relevant for understanding schizophrenia pathophysiology: i) What are the sources of synaptic input on L3PNs in each area?, ii) What is the significance of dendritic spine density differences across areas?, and iii) Do NMDAR-mediated synaptic currents differ in strength between L3PNs in PFC, PPC, and V1?</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":" ","pages":""},"PeriodicalIF":10.3,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148838985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lars A R Ystaas, Pravesh Parekh, Nadine Parker, Ibrahim Akkouh, Viktoria Birkenæs, Ida E Sønderby, Elise Koch, Espen Hagen, Oleksandr Frei, Alexey Shadrin, Ole A Andreassen, Kevin S O'Connell
{"title":"Genetic liability to addiction underlies comorbid bipolar and substance use disorders.","authors":"Lars A R Ystaas, Pravesh Parekh, Nadine Parker, Ibrahim Akkouh, Viktoria Birkenæs, Ida E Sønderby, Elise Koch, Espen Hagen, Oleksandr Frei, Alexey Shadrin, Ole A Andreassen, Kevin S O'Connell","doi":"10.1016/j.biopsych.2026.08.008","DOIUrl":"10.1016/j.biopsych.2026.08.008","url":null,"abstract":"<p><strong>Background: </strong>Bipolar disorder (BIP) frequently co-occurs with heightened substance use (SU) and substance use disorders (SUDs). Although the strong co-occurrence of these heritable traits points to shared genetic susceptibility, the extent to which there are differences in how SU and SUD overlap with BIP genetic architecture remains unclear.</p><p><strong>Methods: </strong>We quantified the polygenic overlap between BIP and SUDs (alcohol, cannabis, opioid, and tobacco), and BIP and SU traits (drinks per week, lifetime cannabis use, prescription opioid use, and smoking initiation) using GWAS summary statistics and trivariate MiXeR. We then isolated the general and unique genetic contributions of SUD and SU using GWAS-by-subtraction via Genomic SEM. Next, we tested associations between polygenic risk scores derived from these latent factors and diagnostic and behavioral outcomes in the Norwegian Mother, Father and Child Cohort Study. Finally, we applied GSA-MiXeR to explore pleiotropic pathway enrichment shared between the latent factors and BIP.</p><p><strong>Results: </strong>We found extensive polygenic overlap between traits, with SUDs being more genetically correlated with BIP than SU traits. The unique SUD factor correlated positively with psychiatric disorders, whereas unique SU correlated negatively. PRS for BIP, shared SUD/SU, and unique SUD were significantly associated with BIP, SUD, and comorbid SUD-BIP; PRS for unique SU was only associated with self-reported lifetime SU. GSA-MiXeR revealed richer gene-set enrichment for SUD/BIP than SU/BIP implicating dopamine signaling and interneuron function.</p><p><strong>Conclusion: </strong>By dissecting the genetic liability to SUD and SU and investigating their relationship with BIP we find a genetic signature correlated with substance dependence but not substance use more broadly.</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":" ","pages":""},"PeriodicalIF":10.3,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148811893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Machine Learning-Based Discrimination of Treatment-Resistant Schizophrenia Using Structural Brain Imaging: A Multi-Site Proof-of-Concept Study.","authors":"Edgardo Torres-Carmona, Mario Graff-Guerrero, Shinichiro Nakajima, Yusuke Iwata, Fumihiko Ueno, Teruki Koizumi, Shiori Honda, Sakiko Tsugawa, Saki Homma, Kamiyu Ogyu, Ryosuke Tarumi, Jianmeng Song, Vincenzo Deluca, Gary Remington, Philip Gerretsen, Ariel Graff-Guerrero","doi":"10.1016/j.biopsych.2026.08.009","DOIUrl":"https://doi.org/10.1016/j.biopsych.2026.08.009","url":null,"abstract":"<p><strong>Background: </strong>Treatment-resistant schizophrenia (TRS) affects 20-30% of individuals with schizophrenia, with persistent symptoms, functional impairment, and reduced quality of life. Clinical identification remains dependent on sequential antipsychotic trials despite reported structural brain differences between TRS and treatment-responsive schizophrenia (TxR). This study evaluated whether structural MRI features could discriminate clinically defined TRS from TxR using machine learning.</p><p><strong>Methods: </strong>A total of 225 participants (122 TRS, 103 TxR) from multi-site studies in Canada and Japan were included. Cortical thickness, brain volume, surface area, and intrinsic curvature were derived from T1-weighted MRI using FreeSurfer, with feature engineering generating volumetric-cortical thickness interaction terms. A voting ensemble was evaluated under a primary leakage-controlled NeuroComBat harmonization and a secondary exploratory full-dataset harmonization. Performance was assessed using ROC-AUC, F1 score, precision, and recall.</p><p><strong>Results: </strong>In the leakage-controlled analysis, the voting ensemble achieved a held-out ROC-AUC of 0.57 and macro F1 of 0.56; training cross-validation yielded ROC-AUC of 0.715. The full-dataset harmonization analysis yielded ROC-AUC of 0.60 and macro F1 of 0.62, interpreted cautiously due to leakage. Temporal-occipital cortical thickness and choroid plexus volume interaction terms contributed most to model performance.</p><p><strong>Conclusion: </strong>Structural MRI features may support cross-sectional discrimination of TRS from TxR. The divergence between harmonization strategies highlights the importance of leakage-aware preprocessing in neuroimaging. Validation in independent cohorts is required to establish whether these features contribute to earlier identification of treatment resistance.</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":" ","pages":""},"PeriodicalIF":10.3,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148787265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cannabis Use and Age-Related Acceleration of Psychosis Onset: A Meta-Analysis","authors":"Carly Stevens, Hannah Pincham, Matthew Large","doi":"10.1016/j.biopsych.2026.06.025","DOIUrl":"https://doi.org/10.1016/j.biopsych.2026.06.025","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"12 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148755133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Junneng Shao, Hongjia Liu, Ting Wang, Yi Wang, Wei Zhang, Zhijian Yao, Qing Lu
{"title":"Neuroimaging of Heterogeneity in Neuropsychiatric Disorders: Toward Disease Progression Modeling.","authors":"Junneng Shao, Hongjia Liu, Ting Wang, Yi Wang, Wei Zhang, Zhijian Yao, Qing Lu","doi":"10.1016/j.biopsych.2026.06.023","DOIUrl":"10.1016/j.biopsych.2026.06.023","url":null,"abstract":"<p><p>Neuropsychiatric disorders are characterized by substantial biological heterogeneity, with patients sharing the same diagnosis often exhibiting distinct symptom profiles, treatment responses, and longitudinal course of the disease. This heterogeneity limits the clinical utility of traditional symptom-based classifications, currently available biomarkers, and traditional group-level neuroimaging analyses, creating a major challenge for precision medicine. In this review, we provide a conceptual overview of recent paradigms for analyzing disease heterogeneity and integrate them into a coherent, comprehensive conceptual framework for the ultimate goal of disease progression modeling. We identify 3 gradual shifts of research paradigms in neuroimaging-based studies: 1) moving from group-level case-control analyses to normative modeling of individual variability; 2) transitioning from traditional subtype-oriented clustering to continuous dimensional generative modeling; and 3) shifting from disease course analyses to virtual transition modeling according to digital twin brain models. These paradigm shifts are not isolated but can be integrated into a layered and interrelated logical framework, aiming to quantify individual deviations, characterize heterogeneous pathological dimensions, and simulate disease progression trajectories over time. We further discuss how these paradigms can facilitate the development of biologically informed biomarkers, the formulation of personalized treatment plans, and the implementation of trajectory-based intervention measures. Finally, we critically examine the key challenges that must be addressed before clinical translation, including mechanistic interpretability, longitudinal validation, multimodal and multisite data integration, model reproducibility, and prospective cohort validation. Our goal is to promote the development of novel approaches for disease progression modeling, thereby accelerating the translation of experimental research into clinical practice.</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":" ","pages":""},"PeriodicalIF":10.3,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148381482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retrospective Susumu Tonegawa (1939-2026): A scientist of three eras.","authors":"George Dragoi","doi":"10.1016/j.biopsych.2026.08.004","DOIUrl":"https://doi.org/10.1016/j.biopsych.2026.08.004","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":" ","pages":""},"PeriodicalIF":10.3,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148763101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jordan C. Foster, Felicia A. Hardi, Taylor J. Keding, Lucinda M. Sisk, Sumedha Mishra, Elizabeth V. Goldfarb, Dylan G. Gee
{"title":"Stress-related psychopathology shapes episodic memory development in youth: Network-level brain predictors and relations with early-life stress","authors":"Jordan C. Foster, Felicia A. Hardi, Taylor J. Keding, Lucinda M. Sisk, Sumedha Mishra, Elizabeth V. Goldfarb, Dylan G. Gee","doi":"10.1016/j.biopsych.2026.08.006","DOIUrl":"https://doi.org/10.1016/j.biopsych.2026.08.006","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"26 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148715525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jason W. Griffin, Brianna Cairney, William E. Carson, Lacey Chetcuti, Adrien E.E. Dubois, Guillaume Dumas, Sebastien Jacquemont, Shafali Jeste, Jacob P. Momsen, Adam J. Naples, James C. McPartland
{"title":"Recent progress of large-scale biomarker consortia and paths forward in biomarker development for autism","authors":"Jason W. Griffin, Brianna Cairney, William E. Carson, Lacey Chetcuti, Adrien E.E. Dubois, Guillaume Dumas, Sebastien Jacquemont, Shafali Jeste, Jacob P. Momsen, Adam J. Naples, James C. McPartland","doi":"10.1016/j.biopsych.2026.08.005","DOIUrl":"https://doi.org/10.1016/j.biopsych.2026.08.005","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"130 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148716938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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