Biological Psychiatry最新文献

{"title":"Subscribers Page","authors":"","doi":"10.1016/S0006-3223(24)01438-0","DOIUrl":"10.1016/S0006-3223(24)01438-0","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962577","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 Clinical Care for Mental Disorders Through a Deeper Understanding of Psychomotor Functions","authors":"Dusan Hirjak","doi":"10.1016/j.biopsych.2024.06.017","DOIUrl":"10.1016/j.biopsych.2024.06.017","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141900870","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)01441-0","DOIUrl":"10.1016/S0006-3223(24)01441-0","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0006322324014410/pdfft?md5=a7554228195fd3a2a80f0376f049f5eb&pid=1-s2.0-S0006322324014410-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962579","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":"Editorial Board Page","authors":"","doi":"10.1016/S0006-3223(24)01437-9","DOIUrl":"10.1016/S0006-3223(24)01437-9","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0006322324014379/pdfft?md5=ae5f2db7a2bade4f908fed386a60a812&pid=1-s2.0-S0006322324014379-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962576","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":"Brain Network Localization of Gray Matter Atrophy and Neurocognitive and Social Cognitive Dysfunction in Schizophrenia.","authors":"Yan Cheng, Huanhuan Cai, Siyu Liu, Yang Yang, Shan Pan, Yongqi Zhang, Fan Mo, Yongqiang Yu, Jiajia Zhu","doi":"10.1016/j.biopsych.2024.07.021","DOIUrl":"10.1016/j.biopsych.2024.07.021","url":null,"abstract":"<p><strong>Background: </strong>Numerous studies have established the presence of gray matter atrophy and brain activation abnormalities during neurocognitive and social cognitive tasks in schizophrenia. Despite a growing consensus that diseases localize better to distributed brain networks than individual anatomical regions, relatively few studies have examined brain network localization of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia.</p><p><strong>Methods: </strong>To address this gap, we initially identified brain locations of structural and functional abnormalities in schizophrenia from 301 published neuroimaging studies with 8712 individuals with schizophrenia and 9275 healthy control participants. By applying novel functional connectivity network mapping to large-scale resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 brain abnormality networks of schizophrenia.</p><p><strong>Results: </strong>The gray matter atrophy network of schizophrenia comprised a broadly distributed set of brain areas predominantly implicating the ventral attention, somatomotor, and default networks. The neurocognitive dysfunction network was also composed of widespread brain areas primarily involving the frontoparietal and default networks. By contrast, the social cognitive dysfunction network consisted of circumscribed brain regions mainly implicating the default, subcortical, and visual networks.</p><p><strong>Conclusions: </strong>Our findings suggest shared and unique brain network substrates of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia, which may not only refine the understanding of disease neuropathology from a network perspective but may also contribute to more targeted and effective treatments for impairments in different cognitive domains in schizophrenia.</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141892798","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":"Transcranial Magnetic Stimulation-Induced Plasticity Improving Cognitive Control in Obsessive-Compulsive Disorder, Part I: Clinical and Neuroimaging Outcomes From a Randomized Trial.","authors":"Sophie M D D Fitzsimmons, Tjardo S Postma, A Dilene van Campen, Chris Vriend, Neeltje M Batelaan, Patricia van Oppen, Adriaan W Hoogendoorn, Ysbrand D van der Werf, Odile A van den Heuvel","doi":"10.1016/j.biopsych.2024.06.029","DOIUrl":"10.1016/j.biopsych.2024.06.029","url":null,"abstract":"<p><strong>Background: </strong>Repetitive transcranial magnetic stimulation (rTMS) is an emerging treatment for obsessive-compulsive disorder (OCD). The neurobiological mechanisms of rTMS in OCD have been incompletely characterized. We compared clinical outcomes and changes in task-based brain activation following 3 different rTMS protocols, all combined with exposure and response prevention.</p><p><strong>Methods: </strong>In this 3-arm proof-of-concept randomized trial, 61 treatment-refractory adult patients with OCD received 16 sessions of rTMS immediately before exposure and response prevention over 8 weeks, with task-based functional magnetic resonance imaging scans and clinical assessments before and after treatment. Patients received high-frequency rTMS to the left dorsolateral prefrontal cortex (n = 19 [13 women/6 men]), high-frequency rTMS to the left pre-supplementary motor area (preSMA) (n = 23 [13 women/10 men]), or control rTMS to the vertex (n = 19 [13 women/6 men]). Changes in task-based functional magnetic resonance imaging activation before/after treatment were compared using both a Bayesian region of interest and a general linear model whole-brain approach.</p><p><strong>Results: </strong>Mean OCD symptom severity decreased significantly in all treatment groups (Δ = -10.836, p < .001, 95% CI -12.504 to -9.168), with no differences between groups. Response rate in the entire sample was 57.4%. The dorsolateral prefrontal cortex rTMS group showed decreased planning-related activation after treatment that was associated with greater symptom improvement. No group-level activation changes were observed for the preSMA and vertex rTMS groups. Participants in the preSMA group with greater symptom improvement showed decreased error-related activation, and symptom improvement in the vertex group was associated with increased inhibition-related activation.</p><p><strong>Conclusions: </strong>rTMS to preSMA and dorsolateral prefrontal cortex combined with exposure and response prevention led to activation decreases in targeted task networks in individuals showing greater symptom improvement, although we observed no differences in symptom reduction between groups.</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141874103","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":"Dimensions of Early-Life Adversity Are Differentially Associated With Patterns of Delayed and Accelerated Brain Maturation.","authors":"Dani Beck, Lucy Whitmore, Niamh MacSweeney, Alexis Brieant, Valerie Karl, Ann-Marie G de Lange, Lars T Westlye, Kathryn L Mills, Christian K Tamnes","doi":"10.1016/j.biopsych.2024.07.019","DOIUrl":"10.1016/j.biopsych.2024.07.019","url":null,"abstract":"<p><strong>Background: </strong>Different types of early-life adversity (ELA) have been associated with children's brain structure and function. However, understanding the disparate influence of distinct adversity exposures on the developing brain remains a major challenge.</p><p><strong>Methods: </strong>This study investigates the neural correlates of 10 robust dimensions of ELA identified through exploratory factor analysis in a large community sample of youth from the Adolescent Brain Cognitive Development Study. Brain age models were trained, validated, and tested separately on T1-weighted (n = 9524), diffusion tensor (n = 8834), and resting-state functional (n = 8233) magnetic resonance imaging data from two time points (mean age = 10.7 years, SD = 1.2, age range = 8.9-13.8 years).</p><p><strong>Results: </strong>Bayesian multilevel modeling supported distinct associations between different types of ELA exposures and younger- and older-looking brains. Dimensions generally related to emotional neglect, such as lack of primary and secondary caregiver support and lack of caregiver supervision, were associated with lower brain age gaps, i.e., younger-looking brains. In contrast, dimensions generally related to caregiver psychopathology, trauma exposure, family aggression, substance use and separation from biological parent, and socioeconomic disadvantage and neighborhood safety were associated with higher brain age gaps, i.e., older-looking brains.</p><p><strong>Conclusions: </strong>The findings suggest that dimensions of ELA are differentially associated with distinct neurodevelopmental patterns, indicative of dimension-specific delayed and accelerated brain maturation.</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141858870","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":"An astroglial basis of major depressive disorder: Molecular, Cellular, and Circuit Features","authors":"Cheng-lin Lu, Jing Ren, Xiong Cao","doi":"10.1016/j.biopsych.2024.07.017","DOIUrl":"https://doi.org/10.1016/j.biopsych.2024.07.017","url":null,"abstract":"","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796767","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":"Brainwide Anatomical Connectivity and Prediction of Longitudinal Outcomes in Antipsychotic-Naïve First-Episode Psychosis.","authors":"Sidhant Chopra, Priscila T Levi, Alexander Holmes, Edwina R Orchard, Ashlea Segal, Shona M Francey, Brian O'Donoghue, Vanessa L Cropley, Barnaby Nelson, Jessica Graham, Lara Baldwin, Hok Pan Yuen, Kelly Allott, Mario Alvarez-Jimenez, Susy Harrigan, Christos Pantelis, Stephen J Wood, Patrick McGorry, Alex Fornito","doi":"10.1016/j.biopsych.2024.07.016","DOIUrl":"10.1016/j.biopsych.2024.07.016","url":null,"abstract":"<p><strong>Background: </strong>Disruptions of axonal connectivity are thought to be a core pathophysiological feature of psychotic illness, but whether they are present early in the illness, prior to antipsychotic exposure, and whether they can predict clinical outcome remain unknown.</p><p><strong>Methods: </strong>We acquired diffusion-weighted magnetic resonance images to map structural connectivity between each pair of 319 parcellated brain regions in 61 antipsychotic-naïve individuals with first-episode psychosis (15-25 years, 46% female) and a demographically matched sample of 27 control participants. Clinical follow-up data were also acquired in patients 3 and 12 months after the scan. We used connectome-wide analyses to map disruptions of inter-regional pairwise connectivity and connectome-based predictive modeling to predict longitudinal change in symptoms and functioning.</p><p><strong>Results: </strong>Individuals with first-episode psychosis showed disrupted connectivity in a brainwide network linking all brain regions compared with controls (familywise error-corrected p = .03). Baseline structural connectivity significantly predicted change in functioning over 12 months (r = 0.44, familywise error-corrected p = .041), such that lower connectivity within fronto-striato-thalamic systems predicted worse functional outcomes.</p><p><strong>Conclusions: </strong>Brainwide reductions of structural connectivity exist during the early stages of psychotic illness and cannot be attributed to antipsychotic medication. Moreover, baseline measures of structural connectivity can predict change in patient functional outcomes up to 1 year after engagement with treatment services.</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787179","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, Kangguang Lin, Weicong Lu, Wenjin Zou, Xiaoyue Li, Yarong Tan, Jingyu Yang, Danhao Zheng, Xiaodong Liu, Bess Yin-Hung Lam, Guiyun Xu, Kun Wang, Roger S McIntyre, Fei Wang, Kwok-Fai So, Jie Wang","doi":"10.1016/j.biopsych.2024.07.018","DOIUrl":"10.1016/j.biopsych.2024.07.018","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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, p < .0001), parental BD history (β = -0.380, p < .0001), and global function (β = 0.578, p < .0001) significantly affected brain health, while psychiatric symptoms showed only a marginal influence (β = -0.112, p = .056).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-07-26","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}