Biological Psychiatry最新文献

{"title":"Identification and external validation of a problem cannabis risk network.","authors":"Sarah D Lichenstein, Brian D Kiluk, Marc N Potenza, Hugh Garavan, Bader Chaarani, Tobias Banaschewski, Arun L W Bokde, Sylvane Desrivières, Herta Flor, Antoine Grigis, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Luise Poustka, Sarah Hohmann, Nathalie Holz, Christian Baeuchl, Michael N Smolka, Nilakshi Vaidya, Henrik Walter, Robert Whelan, Gunter Schumann, Godfrey Pearlson, Sarah W Yip","doi":"10.1016/j.biopsych.2025.01.022","DOIUrl":"10.1016/j.biopsych.2025.01.022","url":null,"abstract":"<p><strong>Background: </strong>Cannabis use is common, particularly during emerging adulthood when brain development is ongoing, and its use is associated with harmful outcomes for a subset of people. An improved understanding of the neural mechanisms underlying risk for problem-level use is critical to facilitate the development of more effective prevention and treatment approaches.</p><p><strong>Methods: </strong>The current study applied a whole-brain, data-driven, machine-learning approach to identify neural features predictive of problem-level cannabis use in a non-clinical sample of college students (n=191, 58% female) based on reward task functional connectivity data. We further examined whether the network identified would generalize to predict cannabis use in an independent sample of European adolescents/emerging adults (n=1320, 53% female), whether it would predict clinical characteristics among adults seeking treatment for cannabis use disorder (n=33, 9% female), and whether it was specific for predicting cannabis versus alcohol use outcomes across datasets.</p><p><strong>Results: </strong>Results demonstrated (i) identification of a problem cannabis risk network, which (ii) generalized to predict cannabis use in an independent sample of adolescents, and (iii) linked to increased addiction severity and poorer treatment outcome in a third sample of treatment-seeking adults; further, (iv) the identified network was specific for predicting cannabis versus alcohol use outcomes across all three datasets.</p><p><strong>Conclusions: </strong>Findings provide insight into neural mechanisms of risk for problem-level cannabis use among adolescents/emerging adults. Future work is needed to assess whether targeting this network can improve prevention and treatment outcomes.</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":" ","pages":""},"PeriodicalIF":9.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254251","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":"Editorial Board Page","authors":"","doi":"10.1016/S0006-3223(24)01764-5","DOIUrl":"10.1016/S0006-3223(24)01764-5","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"97 3","pages":"Page A1"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127901","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":"Fluoxetine and Ketamine Enhance Extinction Memory and Brain Plasticity by Triggering the p75 Neurotrophin Receptor Proteolytic Pathway","authors":"Cassiano Ricardo Alves Faria Diniz ,&nbsp;Ana Paula Crestani ,&nbsp;Plinio Cabrera Casarotto ,&nbsp;Caroline Biojone ,&nbsp;Cecilia Cannarozzo ,&nbsp;Frederike Winkel ,&nbsp;Mikhail A. Prozorov ,&nbsp;Erik F. Kot ,&nbsp;Sergey A. Goncharuk ,&nbsp;Danilo Benette Marques ,&nbsp;Leonardo Rakauskas Zacharias ,&nbsp;Henri Autio ,&nbsp;Madhusmita Priyadarshini Sahu ,&nbsp;Anna Bárbara Borges-Assis ,&nbsp;João Pereira Leite ,&nbsp;Konstantin S. Mineev ,&nbsp;Eero Castrén ,&nbsp;Leonardo Barbosa Moraes Resstel","doi":"10.1016/j.biopsych.2024.06.021","DOIUrl":"10.1016/j.biopsych.2024.06.021","url":null,"abstract":"<div><h3>Background</h3><div>Diverse antidepressants were recently described to bind to TrkB (tyrosine kinase B) and drive a positive allosteric modulation of endogenous BDNF (brain-derived neurotrophic factor). Although neurotrophins such as BDNF can bind to p75NTR (p75 neurotrophin receptor), their precursors are the high-affinity p75NTR ligands. While part of an unrelated receptor family capable of inducing completely opposite physiological changes, TrkB and p75NTR feature a crosslike conformation dimer and carry a cholesterol-recognition amino acid consensus in the transmembrane domain. As such qualities were found to be crucial for antidepressants to bind to TrkB and drive behavioral and neuroplasticity effects, we hypothesized that their effects might also depend on p75NTR.</div></div><div><h3>Methods</h3><div>Enzyme-linked immunosorbent assay–based binding and nuclear magnetic resonance spectroscopy were performed to assess whether antidepressants would bind to p75NTR. HEK293T cells and a variety of in vitro assays were used to investigate whether fluoxetine (FLX) or ketamine (KET) would trigger any α- and γ-secretase–dependent p75NTR proteolysis and lead to p75NTR nuclear localization. Ocular dominance shift was performed with male and female p75NTR knockout mice to study the effects of KET and FLX on brain plasticity, in addition to pharmacological interventions to verify how p75NTR signaling is important for the effects of KET and FLX in enhancing extinction memory in male wild-type mice and rats.</div></div><div><h3>Results</h3><div>Antidepressants were found to bind to p75NTR. FLX and KET triggered the p75NTR proteolytic pathway and induced p75NTR-dependent behavioral/neuroplasticity changes.</div></div><div><h3>Conclusions</h3><div>We hypothesize that antidepressants co-opt both BDNF/TrkB and proBDNF/p75NTR systems to induce a more efficient activity-dependent synaptic competition, thereby boosting the brain’s ability for remodeling.</div></div>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"97 3","pages":"Pages 248-260"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141465994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting Neuroimaging Endophenotypes in the Era of Machine Learning: The Key Role of Clinical Measures in Identifying Risk for Bipolar Disorder","authors":"Nefize Yalin","doi":"10.1016/j.biopsych.2024.11.005","DOIUrl":"10.1016/j.biopsych.2024.11.005","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"97 3","pages":"Pages 215-216"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891767","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":"Measuring Brain Structure-Function Coupling: A Promising Approach to Decode Psychiatric Neuropathology","authors":"Jiajia Zhu ,&nbsp;Yongqiang Yu","doi":"10.1016/j.biopsych.2024.10.009","DOIUrl":"10.1016/j.biopsych.2024.10.009","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"97 3","pages":"Pages 212-214"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891760","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":"Antenatal Depression and Metabolomics: A Step to Understand Transgenerational Mechanisms in Mental Health","authors":"Carmine M. Pariante","doi":"10.1016/j.biopsych.2024.11.004","DOIUrl":"10.1016/j.biopsych.2024.11.004","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"97 3","pages":"Pages 210-211"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891758","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":"Guide for Authors","authors":"","doi":"10.1016/S0006-3223(24)01768-2","DOIUrl":"10.1016/S0006-3223(24)01768-2","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"97 3","pages":"Pages A5-A10"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127897","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":"Enhancing Early Diagnosis of Bipolar Disorder in Adolescents Through Multimodal Neuroimaging","authors":"Jinfeng Wu ,&nbsp;Kangguang Lin ,&nbsp;Weicong Lu ,&nbsp;Wenjin Zou ,&nbsp;Xiaoyue Li ,&nbsp;Yarong Tan ,&nbsp;Jingyu Yang ,&nbsp;Danhao Zheng ,&nbsp;Xiaodong Liu ,&nbsp;Bess Yin-Hung Lam ,&nbsp;Guiyun Xu ,&nbsp;Kun Wang ,&nbsp;Roger S. McIntyre ,&nbsp;Fei Wang ,&nbsp;Kwok-Fai So ,&nbsp;Jie Wang","doi":"10.1016/j.biopsych.2024.07.018","DOIUrl":"10.1016/j.biopsych.2024.07.018","url":null,"abstract":"<div><h3>Background</h3><div>Bipolar disorder (BD), a severe neuropsychiatric condition, often appears during adolescence. Traditional diagnostic methods, which primarily rely on clinical interviews and single-modal magnetic resonance imaging (MRI) techniques, may have limitations in accuracy. This study aimed to improve adolescent BD diagnosis by integrating behavioral assessments with multimodal MRI. We hypothesized that this combination would enhance diagnostic accuracy for at-risk adolescents.</div></div><div><h3>Methods</h3><div>A retrospective cohort of 309 participants, including patients with BD, offspring of patients with BD (with and without subthreshold symptoms), non-BD offspring with subthreshold symptoms, and healthy control participants, was analyzed. Behavioral attributes were integrated with MRI features from T1-weighted, resting-state functional MRI, and diffusion tensor imaging. Three diagnostic models were developed using GLMNET multinomial regression: a clinical diagnosis model based on behavioral attributes, an MRI-based model, and a comprehensive model integrating both datasets.</div></div><div><h3>Results</h3><div>The comprehensive model achieved a prediction accuracy of 0.83 (95% CI, 0.72–0.92), significantly higher than the clinical (0.75) and MRI-based (0.65) models. Validation with an external cohort showed high accuracy (0.89, area under the curve = 0.95). Structural equation modeling revealed that clinical diagnosis (β = 0.487, <em>p</em> &lt; .0001), parental BD history (β = −0.380, <em>p</em> &lt; .0001), and global function (β = 0.578, <em>p</em> &lt; .0001) significantly affected brain health, while psychiatric symptoms showed only a marginal influence (β = −0.112, <em>p</em> = .056).</div></div><div><h3>Conclusions</h3><div>This study highlights the value of integrating multimodal MRI with behavioral assessments for early diagnosis in at-risk adolescents. Combining neuroimaging enables more accurate patient subgroup distinctions, facilitating timely interventions and improving health outcomes. Our findings suggest a paradigm shift in BD diagnostics, advocating for incorporating advanced imaging techniques in routine evaluations.</div></div>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"97 3","pages":"Pages 313-322"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787180","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":"Windows of Opportunity: How Age and Sex Shape the Influence of Prenatal Depression on the Child Brain","authors":"Kathryn Y. Manning ,&nbsp;Aliza Jaffer ,&nbsp;Catherine Lebel","doi":"10.1016/j.biopsych.2024.07.022","DOIUrl":"10.1016/j.biopsych.2024.07.022","url":null,"abstract":"<div><div>Maternal prenatal depression can affect child brain and behavioral development. Specifically, altered limbic network structure and function is a likely mechanism through which prenatal depression impacts the life-long mental health of exposed children. While developmental trajectories are influenced by many factors that exacerbate risk or promote resiliency, the role of child age and sex in the relationship between prenatal depression and the child brain remains unclear. Here, we review studies of associations between prenatal depression and brain structure and function, with a focus on the role of age and sex in these relationships. After exposure to maternal prenatal depression, altered amygdala, hippocampal, and frontal cortical structure, as well as changes in functional and structural connectivity within the limbic network, are evident during the fetal, infant, preschool, childhood, and adolescent stages of development. Sex appears to play a key role in this relationship, with evidence of differential findings particularly in infants, with males showing smaller and females larger hippocampal and amygdala volumes following prenatal depression. Longitudinal studies in this area have only begun to emerge within the last 5 years and will be key to understanding critical windows of opportunity. Future research focused on the role of age and sex in this relationship is essential to further inform screening, policy, and interventions for children exposed to prenatal depression, interrupt the intergenerational transmission of depression, and ultimately support healthy brain development.</div></div>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"97 3","pages":"Pages 227-247"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141905799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal Antenatal Depression Is Associated With Metabolic Alterations That Predict Birth Outcomes and Child Neurodevelopment and Mental Health","authors":"Polina Girchenko ,&nbsp;Marius Lahti-Pulkkinen ,&nbsp;Hannele Laivuori ,&nbsp;Eero Kajantie ,&nbsp;Katri Räikkönen","doi":"10.1016/j.biopsych.2024.07.023","DOIUrl":"10.1016/j.biopsych.2024.07.023","url":null,"abstract":"<div><h3>Background</h3><div>Evidence regarding metabolic alterations associated with maternal antenatal depression (AD) is limited, and their role as potential biomarkers that improve the prediction of AD and adverse childbirth, neurodevelopmental, and mental health outcomes remains unexplored.</div></div><div><h3>Methods</h3><div>In a cohort of 331 mother-child dyads, we studied associations between AD (a history of medical register diagnoses and/or a Center for Epidemiological Studies Depression Scale score during pregnancy ≥ 20) and 95 metabolic measures analyzed 3 times during pregnancy. We tested whether the AD-related metabolic measures increased variance explained in AD over its risk factors and in childbirth, neurodevelopmental, and mental health outcomes over AD. We replicated the findings in a cohort of 416 mother-child dyads.</div></div><div><h3>Results</h3><div>Elastic net regression identified 15 metabolic measures that collectively explained 25% (<em>p</em> &lt; .0001) of the variance in AD, including amino and fatty acids, glucose, inflammation, and lipids. These metabolic measures increased the variance explained in AD over its risk factors (32.3%, <em>p</em> &lt; .0001 vs. 12.6%, <em>p</em> = .004) and in child gestational age (9.0%, <em>p</em> &lt; .0001 vs. 0.7%, <em>p</em> = .34), birth weight (9.0%, <em>p</em> = .03 vs. 0.7%, <em>p</em> = .33), developmental milestones at the age of 2.3 to 5.7 years (21.0%, <em>p</em> = .002 vs. 11.6%, <em>p</em> &lt; .001), and any mental or behavioral disorder by the age of 13.1 to 16.8 years (25.2%, <em>p</em> = .03 vs. 5.0%, <em>p</em> = .11) over AD, child sex, and age. These findings were replicated in the independent cohort.</div></div><div><h3>Conclusions</h3><div>AD was associated with alterations in 15 metabolic measures, which collectively improved the prediction of AD over its risk factors and birth, neurodevelopmental, and mental health outcomes in children over AD. These metabolic measures may become biomarkers that can be used to identify at-risk mothers and children for personalized interventions.</div></div>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":"97 3","pages":"Pages 269-278"},"PeriodicalIF":9.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141911574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}