BMC Psychiatry最新文献

{"title":"A multifeature machine learning and resting-state EEG study reveals differences in beta oscillation in late-life depression with or without mild cognitive impairment.","authors":"Shihan Tian, Gaohong Lin, Yijie Zeng, Qiang Wang, Yicheng Lin, Kexin Yao, Zihan Wang, Zhidai Xiao, Jiayu Chen, Youxuan Zheng, Haoye Tan, Zhangying Wu, Mingfeng Yang, Danyan Xu, Xuanzi Li, Jingyi Lao, Shuang Liang, Yunheng Chen, Qin Liu, Jiafu Li, Yujing Gan, Mingyong Zeng, Ben Chen, Yuping Ning, Xiaomei Zhong","doi":"10.1186/s12888-026-08126-6","DOIUrl":"https://doi.org/10.1186/s12888-026-08126-6","url":null,"abstract":"<p><strong>Background: </strong>Late-life depression (LLD) often co-occurs with mild cognitive impairment (MCI), and patients with LLD and MCI (LLD-MCI) have an increased risk of progression to Alzheimer's disease (AD). However, differences in resting-state neural oscillation and cognitive impairment in LLD patients remain unclear. In this cross-sectional study, electroencephalography (EEG) was used to analyse, local rhythm activity and large-scale network communication to differentiate LLD patients with and without MCI.</p><p><strong>Methods: </strong>We enrolled 113 participants: 74 with LLD (50 with LLD-MCI and 24 with LLD-non-MCI) and 39 healthy older adults (HOAs). All participants underwent comprehensive neuropsychological assessments. Spectral power and source-level functional connectivity (Phase-Locking Value, PLV) were analysed across multiple frequency bands. A machine learning framework using nested stratified cross-validation was implemented to evaluate the potential of EEG features in classifying LLD clinical subtypes.</p><p><strong>Results: </strong>LLD-MCI patients exhibited a distinct dissociation in the beta band: significantly reduced spectral power in the left frontal cortex contrasted with extensive hyperconnectivity primarily centred on the right lateral orbitofrontal cortex (rLOFC). Complementary analyses also revealed widespread hyperconnectivity in the theta band in the LLD-MCI group. The Linear Discriminant Analysis (LDA) model achieved superior performance in distinguishing LLD-MCI patients from LLD-non-MCI patients, with an area under the curve (AUC) of 0.82 and an accuracy of 78.38%. Feature importance analysis revealed rLOFC-mediated beta synchronisation as the most discriminative biomarker.</p><p><strong>Conclusion: </strong>Our findings suggest that beta-band oscillatory disruption-characterised by local power deficits and network hyperconnectivity-may represent a potential neurobiological signature of cognitive vulnerability in LLD patients. Whether this hyperconnectivity reflects a compensatory or pathological process remains a hypothesis for further validation. EEG metrics provide significant diagnostic value for the precise clinical subtyping and early identification of cognitive decline in the LLD population.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147832536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacogenomic-guided prescribing and polypharmacy across age groups in obsessive-compulsive disorder: a retrospective study.","authors":"Sheldon R Garrison, Matthew W Boyer, Anthony W Zoghbi, Rachel A Schwartz, Nicolette Weisensel, Martin E Franklin, Madeline M Hartig, Maharaj Singh, Sreya Vadapalli","doi":"10.1186/s12888-026-08073-2","DOIUrl":"https://doi.org/10.1186/s12888-026-08073-2","url":null,"abstract":"<p><strong>Background: </strong>This study evaluated medication utilization in children, adolescents, and adults with obsessive-compulsive disorder (OCD), a chronic psychiatric condition characterized by intrusive thoughts and repetitive behaviors. Although first-line treatments include selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioral therapy (CBT), the heterogeneous biological underpinnings contribute to suboptimal outcomes, with 40-60% of individuals not responding to SSRIs. This complex phenotype often leads to psychotropic polypharmacy, which may be mitigated by incorporating combinatorial pharmacogenomic (PGx) testing into protocol-based care to identify potential gene-drug interactions.</p><p><strong>Methods: </strong>A retrospective review was conducted of individuals with OCD aged 8 to 65 years who received either PGx testing or treatment as usual (TAU). Co-primary outcomes were polypharmacy rate and quality of life. Secondary outcomes included length of stay, medication utilization, and OCD and depression severity. Individuals prescribed at least one daily psychotropic medication with a gene-drug interaction were classified as \"incongruent\" (PGx-I). Individuals without gene-drug interactions for all prescribed psychotropic medications were categorized as \"congruent\" (PGx-C).</p><p><strong>Results: </strong>A total of 363 individuals with OCD were analyzed. Of these, 241 received TAU and 122 underwent PGx testing. Within the PGx cohort, 67% were prescribed medications with potential gene-drug interactions at discharge. The polypharmacy rate was 71% in the PGx-I cohort, compared with 35% in the PGx-C cohort. Quality-of-life measures revealed similar levels of improvement in the PGx-C and PGx-I cohorts.</p><p><strong>Conclusions: </strong>Psychotropic polypharmacy rates were associated with a higher likelihood that individuals would be prescribed at least one medication with a gene-drug interaction, most notably among adults. Clinical outcomes improved among all cohorts, regardless of PGx testing or medication congruence. These findings suggest that combinatorial PGx testing may be useful as an adjunctive clinical decision support tool when evaluating individuals who admit to higher levels of psychiatric care on multiple psychotropic medications.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ADHD symptoms and financial debt among adults.","authors":"Andrew Stickley, Aya Shirama, Vladislav Ruchkin, Johan Isaksson, Tomiki Sumiyoshi","doi":"10.1186/s12888-026-08087-w","DOIUrl":"https://doi.org/10.1186/s12888-026-08087-w","url":null,"abstract":"<p><strong>Background: </strong>Research has produced conflicting findings on whether ADHD is associated with financial debt. Moreover, as yet, there has been little research on this association in non-Western settings. To address this deficit, this cross-sectional study examined the association between ADHD symptoms and debt in the Japanese general population.</p><p><strong>Methods: </strong>Data were used from an online sample of 3,717 adults aged ≥ 18 years old. A single-item question was used to assess debt. ADHD symptoms were measured with the Adult ADHD Self-Report Scale (ASRS) Screener. Information was also collected on demographic characteristics and mental health.</p><p><strong>Results: </strong>In a fully adjusted logistic regression analysis, ADHD symptoms (as a continuous score) were significantly associated with debt in the total sample (OR: 1.04, 95%CI: 1.01-1.07). In sex-stratified analyses, ADHD symptoms were associated with debt in women (OR: 1.06, 95%CI: 1.01-1.10), while the association was of borderline statistical significance in men (OR: 1.04, 95%CI: 1.00-1.07, p = .053). When the analysis was stratified by age, ADHD symptoms were significantly associated with debt in adults aged 18 to 34 (OR: 1.10, 95%CI: 1.04-1.16) and 60 and above (OR: 1.09, 95%CI: 1.02-1.16) but not in those aged 35 to 59 (OR: 1.01, 95%CI: 0.97-1.05).</p><p><strong>Conclusion: </strong>ADHD symptoms are associated with debt in Japanese adults. More research is needed to determine the causes and consequences of debt in adults with ADHD.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147832850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of urinary metabolite biomarkers of psychiatric disorders using two-sample Mendelian randomization.","authors":"Jihan K Zaki, Jakub Tomasik, Jade A McCune, Oren A Scherman, Sabine Bahn","doi":"10.1186/s12888-026-08133-7","DOIUrl":"https://doi.org/10.1186/s12888-026-08133-7","url":null,"abstract":"<p><strong>Background: </strong>Mental health disorders cause substantial patient suffering, which could be alleviated through early diagnostic biomarkers. While biomarker discovery is costly, genetic methods utilizing data from large-scale studies, such as Mendelian randomization, may provide a cost-effective approach.</p><p><strong>Methods: </strong>A two-sample Mendelian randomization analysis was conducted to identify potential urinary biomarkers of seven psychiatric disorders using summary statistics from GWAS data.</p><p><strong>Results: </strong>The analysis revealed 67 analyte-disorder associations, of which 21 were exclusive to a single disorder. Notable associations were observed between tyrosine and schizophrenia (β = -0.041, SE = 0.013, Q = 0.027), creatine and bipolar disorder (β = -0.077, SE = 0.019, Q = 0.002), pyridoxal (β = 0.10, SE = 0.03, Q = 0.042) and ferulic acid 4-sulfate (β =  0.077, SE = 0.025, Q = 0.037) to anorexia nervosa, and N, N-dimethylglycine to ADHD (β = -0.39, SE = 0.11, Q = 0.008).</p><p><strong>Conclusion: </strong>The results identify candidate urinary biomarkers and demonstrate the utility of genetic instruments for biomarker discovery, warranting experimental validation in independent cohorts.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches to distinguish bipolar disorder from borderline personality disorder: a scoping review.","authors":"Aroldo Dargél, Tanya Tanya, Sara Mahdiabadi, Risa Shorr, Kathleen Pajer","doi":"10.1186/s12888-026-08111-z","DOIUrl":"https://doi.org/10.1186/s12888-026-08111-z","url":null,"abstract":"<p><strong>Background: </strong>Borderline personality disorder (BPD) and bipolar disorder (BD) are debilitating psychiatric illnesses with significant rates of misdiagnosis. This scoping review explores the potential of machine learning (ML) approaches in distinguishing individuals diagnosed with BD from those with BPD, reporting the performance metrics of various predictive models.</p><p><strong>Methods: </strong>We searched Ovid MEDLINE, PubMed, Scopus, and Web of Science from inception to March 2025 for studies involving the terms \"bipolar disorder,\" \"borderline personality disorder,\" \"machine learning\", and \"artificial intelligence.\" Peer-reviewed research was included without restriction on publication date or language. Of 60 studies screened, 5 met the inclusion criteria. The review followed the PCC framework, JBI Reviewer's Manual, and PRISMA guidelines.</p><p><strong>Results: </strong>This study identified five studies that applied predictive models to data from 591 participants to differentiate individuals with BD and BPD. Classification accuracy ranged from 61.7% to 89%. While ML models outperformed DSM-based categorical approaches overall, accuracy differed markedly by diagnosis: correctly 87.8% for BD compared with 57.7% for BPD, illustrating the persistent diagnostic challenges for BPD. Models were more accurate in distinguishing patients with both BD and BPD from those with BD alone (79.6%) than from those with BPD alone (61.7%). ML techniques based on brain imaging features achieved 80% accuracy, while mood ratings collected via smartphone enabled the differentiation of BD, BPD, and controls with 75% accuracy.</p><p><strong>Conclusion: </strong>Currently, few predictive models have been developed to distinguish between BD and BPD. The findings of this review suggest that ML algorithms show moderate to good performance in clinical differentiation of BD and BPD. Further research is warranted to refine and validate predictive tools that aim to improve diagnostic precision in BD and BPD clinical practice.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-population validation of European GWAS loci for metabolic syndrome in Chinese schizophrenia.","authors":"Yangyang You, Ning Qiao, Yao Chen, Zhou Wang, Lirong Zhuang, Xinyu Fang, Xiaowei Tang, Xiangrong Zhang","doi":"10.1186/s12888-026-08144-4","DOIUrl":"https://doi.org/10.1186/s12888-026-08144-4","url":null,"abstract":"<p><strong>Background: </strong>Long-term use of second-generation antipsychotics (SGAs) increases the risk of metabolic syndrome (MS) in patients with schizophrenia (SCZ). Using susceptibility loci identified by European Genome-Wide Association Study (GWAS) as entry points, we conducted a case-control study to verify their association with antipsychotic-induced MS in Chinese Han SCZ patients.</p><p><strong>Methods: </strong>Clinical and metabolic data were collected from 528 chronically SCZ patients who had been treated with SGAs for ≥ 12 months. Patients were divided into MS (n = 232) and non-MS (n = 296) groups. Forty tag single-nucleotide polymorphisms (SNPs) selected from European GWAS data were genotyped; inter-group comparisons and risk analyses for MS-related factors were performed.</p><p><strong>Results: </strong>Compared with the non-MS group, the MS group exhibited significantly elevated waist circumference, systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), fasting plasma glucose (FPG), and body-mass index (BMI), alongside significantly reduced age and high-density lipoprotein cholesterol (HDL-C) levels (all P < 0.05). Allele-based analysis revealed that the G allele of G-protein-coupled receptor 98 (GPR98) rs1967256 was more prevalent in MS patients (χ² = 4.049, P = 0.046), whereas the T allele of Tudor domain-containing protein 15 / Long intergenic non-protein coding RNA 1822 (TDRD15/LINC01822) rs1117324 showed reduced frequency (χ² = 6.639, P = 0.011). Genotype analysis further indicated an over-representation of the rs1117324 T/T genotype in the MS group (χ² = 10.833, P = 0.004). Multivariate logistic regression demonstrated that older age at onset, lower BMI and rs1117324 C/T genotype (compared to T/T) were protective factors, while rs1117324 C/C genotype was risk factor for hyperglycaemia. In addition, male sex, higher BMI and rs1967256 C/C genotype (compared to GG) were identified as risk factors for low HDL-C.</p><p><strong>Conclusions: </strong>Among the 40 MS susceptibility loci previously identified by European GWAS, we validated two loci-GPR98 rs1967256 and TDRD15/LINC01822 rs1117324-as being significantly associated with antipsychotic-induced MS in Chinese Han patients with SCZ.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147832888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lower insulin resistance in Chinese patients with severe major depressive disorder: associations with the inflammatory response.","authors":"Yueyang Luo, Mengqi Niu, Tangcong Chen, Jing Li, Abbas F Almulla, Yingqian Zhang, Michael Maes","doi":"10.1186/s12888-026-08145-3","DOIUrl":"https://doi.org/10.1186/s12888-026-08145-3","url":null,"abstract":"<p><strong>Background: </strong>Major depressive disorder (MDD) is widely acknowledged as stemming from the dysregulation of neuroimmune, metabolic, and oxidative stress (NIMETOX) pathways. The objective of this study was to examine insulin metabolism in Chinese patients with MDD and to examine the relationship between insulin resistance and the acute-phase protein (APP) response, as indicated by lower albumin and transferrin.</p><p><strong>Methods: </strong>This investigation utilized a cross-sectional case-control approach, enrolling 125 inpatients with MDD and 40 healthy controls.</p><p><strong>Results: </strong>Compared with controls, patients with MDD showed lower fasting plasma glucose, insulin, and insulin resistance, together with higher insulin sensitivity. These differences remained significant after adjustment for metabolic syndrome (MetS), body mass index (BMI), and age, but disappeared after adjustment for the negative APP response. Elevated BMI, albumin, transferrin, and age explained 41.4% of the variance in insulin resistance. Insulin resistance was inversely associated with weight loss. In addition, 27.7% of the variance in overall depression severity was explained by adverse childhood experiences (positive association) and insulin resistance (negative association).</p><p><strong>Conclusions: </strong>These findings indicate that, in this Chinese cohort with a relatively low prevalence of comorbid MetS and obesity, severe MDD is associated with lower insulin resistance and higher insulin sensitivity in relation to inflammatory APPs. Therefore, MDD does not appear to be intrinsically associated with increased insulin resistance. Rather, it may be accompanied by a compensatory, hormesis-like response that enhances insulin sensitivity, thereby optimizing glucose utilization to support normal organ function.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a computerized adaptive testing for borderline personality disorder.","authors":"Chongqin Xi, Qisen Lao, Siwei Peng, Shijiang Zuo, Kai Liu","doi":"10.1186/s12888-026-08148-0","DOIUrl":"https://doi.org/10.1186/s12888-026-08148-0","url":null,"abstract":"<p><strong>Background: </strong>Borderline personality disorder (BPD) is characterized by a high prevalence rate, frequent comorbidities, and complex manifestations. The challenges associated with evaluating BPD underscore the need for efficient assessment tools.</p><p><strong>Method: </strong>To facilitate efficient BPD screening in the general population, this study developed a computerized adaptive test for BPD (CAT-BPD) using a Chinese sample (N = 1,097). The CAT-BPD item bank was constructed from several widely used BPD scales and calibrated using item response theory (IRT). A CAT simulation was subsequently conducted using the empirical response data to evaluate the performance of the item bank.</p><p><strong>Results: </strong>The final item bank comprised 71 items. Each retained item assessed at least one BPD symptom criterion and demonstrated local independence, adequate item fit, high discrimination, and an absence of differential item functioning (DIF). Furthermore, the simulation indicated that the CAT-BPD maintained acceptable reliability, criterion-related validity, and predictive utility while substantially reducing the test length.</p><p><strong>Conclusions: </strong>These findings suggest the CAT-BPD is an efficient tool for BPD screening and can facilitate the early identification of BPD in the general population.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147832914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroimaging and neurophysiologic biomarkers for diagnosis and prognosis of depressive disorders, bipolar disorder, anxiety disorders, obsessive compulsive disorder, posttraumatic stress disorder, and substance use disorder: an evidence map.","authors":"Catherine Sowerby, Adrienne Landsteiner, Kristen Ullman, Maylen Anthony, Caleb Kalinowski, Michele R Spoont, Scott Sponheim, Kelvin Lim, Jose V Pardo, Timothy J Wilt, Wei Duan-Porter","doi":"10.1186/s12888-025-07429-4","DOIUrl":"10.1186/s12888-025-07429-4","url":null,"abstract":"<p><strong>Background: </strong>Advancements in precision medicine, particularly the use of neuroimaging and neurophysiologic techniques, may improve diagnosis, prognosis, and treatment of mental health disorders. Recent efforts to develop large neuroimaging datasets have yielded promising results for identifying mental health biomarkers. This scoping review identifies and characterizes studies of neuroimaging and neurophysiologic techniques used to address a variety of mental health disorders.</p><p><strong>Methods: </strong>We searched MEDLINE and Embase (January 2010-September 2023). Eligible studies examined neuroimaging and neurophysiologic techniques (e.g., magnetic resonance imaging [MRI] or electroencephalogram [EEG]) for diagnosis, prognosis, and/or treatment response for eligible mental health disorders. From eligible studies, we abstracted information on populations, clinical settings, imaging techniques, study designs, outcomes, and analytic approaches.</p><p><strong>Results: </strong>From 58,824 unique search results, we identified 441 eligible primary studies and 27 systematic reviews addressing mental health disorders. Most studies focused on depressive disorders (k = 320 primary studies [17 systematic reviews]); fewer examined bipolar disorders (k = 61 [3]), posttraumatic stress disorder (PTSD; k = 39 [2]), obsessive compulsive disorder (OCD; k = 26 [1]), anxiety disorders (k = 22 [3]), or substance use disorders (SUD; k = 25 [0]). Three-quarters of primary studies used MRI-based techniques and 20% employed EEG. Two-thirds of studies focused on diagnosis (nearly all cross-sectional); the remaining studies mostly addressed symptom response to various treatments, including antidepressants and psychotherapy. Most primary studies were small (N < 100; k = 263), and generally included y oung and middle-aged adults; only 5 focused on older adults (sample mean age ≥ 65). Studies were most commonly conducted in China (k = 181), the United States (k = 83), or Canada (k = 22).</p><p><strong>Conclusions: </strong>Although many eligible studies evaluated MRI or EEG for diagnosis and/or treatment response for depressive disorders, most were small and cross-sectional. There was less existing evidence examining other neuroimaging techniques or focusing on other mental health disorders (PTSD, OCD, anxiety disorders, or SUD). Given these evidence gaps, it is likely premature to implement neuroimaging and neurophysiologic tests in clinical settings. To determine clinical utility, future research should use large samples in longitudinal designs and investigate a broader set of disorders.</p><p><strong>Trial registration: </strong>https://doi.org/10.17605/OSF.IO/5PHG2 .</p><p><strong>Clinical trial number: </strong>not applicable.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":"26 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13147643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of the prevalence of fatigue in common mental disorders (mood disorders, anxiety disorders, personality disorders, and trauma and stressor related disorders).","authors":"Ragnar Klein Olsen, Sidse Marie Arnfred, Martin Randau, Christina Madsen, Kåre Donskov Nielsen, Ali Abbas Shaker, Hassan Masri, Oliver Rumle Hovmand","doi":"10.1186/s12888-026-08094-x","DOIUrl":"https://doi.org/10.1186/s12888-026-08094-x","url":null,"abstract":"<p><strong>Background: </strong>Fatigue is a common symptom in the general population, as well as in several somatic conditions. Despite this, no review has yet examined the occurrence of fatigue across common mental disorders (CMD) such as mood disorders, anxiety disorders, personality disorders, and trauma and stressor related disorders. The objective of the review was to examine the prevalence of fatigue in patients with CMD, how fatigue when fatigue occurs in these populations, and it's relationshop with other outcomes.</p><p><strong>Method: </strong>We searched in 2026 Medline, PsychInfo, and Embase for studies reporting on fatigue in CMD and conducted a manual search of the included references. Two reviewers independently selected papers and extracted data. Eligible studies were on patients with CMD without somatic illness, that employed a measure designed to assess fatigue. We conducted and reported the systematic review following the PRISMA statement. The evidence was synthesized according to whether it reports on the prevalence of fatigue, or provides data on the temporal occurrence of fatigue in the trajectory of the disorders.</p><p><strong>Results: </strong>We screened 7861 abstracts, and read full text on 393 papers and finally included 46 papers, of which 40 reported on prevalence. Most research concerned patients with mood disorders. Fatigue was found to be highly prevalent across CMD, with prevalences between 18% and 100%. Research suggests that fatigue can predict the onset of depression and that depression can predict the onset of fatigue.</p><p><strong>Discussion: </strong>Fatigue is highly prevalent across patients with CMD and can therefore be regarded as a transdiagnostic symptom. A host of research suggests that fatigue in depression and anxiety can be explained by biological symptoms such as low-grade inflammation. Limitations includes high heterogeneity in the assessment of fatigue across included studies, and few instruments were validated for this population.</p><p><strong>Conclusions: </strong>The symptom of fatigue was found to be prevalent across samples of patients with CMD, which highlights it as a possible transdiagnostic treatment target.</p>","PeriodicalId":9029,"journal":{"name":"BMC Psychiatry","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147832864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}