Pub Date : 2025-12-23DOI: 10.1016/j.jpsychires.2025.12.046
Luigi Chermont Berni, Leticia Ribeiro Nunes, Rita de Cássia Silva de Oliveira
Objective
Evaluate the treatment of Premenstrual Dysphoric Disorder (PMDD) with antidepressants and combined oral contraceptives (COCs) in order to identify the most appropriate treatments available, with the best tolerability and acceptability.
Results
Sixteen studies were included in the systematic review, of which five were incorporated into the network meta-analysis. Continuous paroxetine showed the greatest effect across all evaluated symptom domains. The 24/4 DROS/EE regimen was the most effective COC, showing particularly positive outcomes for both physical and emotional symptoms.
Conclusions
The network meta-analysis indicates that both SSRIs and COCs are effective in managing PMDD, with continuous paroxetine and DROS/EE 24/4 emerging as the most effective strategies in their respective categories. Treatment choice should consider predominant symptom patterns, individual tolerability, and reproductive planning. Further direct comparative clinical trials between these therapeutic classes are needed to guide clinical decision-making with greater precision.
{"title":"Comparison of premenstrual dysphoric disorder treatment with antidepressants and combined oral contraceptives: a systematic review with network meta-analysis","authors":"Luigi Chermont Berni, Leticia Ribeiro Nunes, Rita de Cássia Silva de Oliveira","doi":"10.1016/j.jpsychires.2025.12.046","DOIUrl":"10.1016/j.jpsychires.2025.12.046","url":null,"abstract":"<div><h3>Objective</h3><div>Evaluate the treatment of Premenstrual Dysphoric Disorder (PMDD) with antidepressants and combined oral contraceptives (COCs) in order to identify the most appropriate treatments available, with the best tolerability and acceptability.</div></div><div><h3>Results</h3><div>Sixteen studies were included in the systematic review, of which five were incorporated into the network meta-analysis. Continuous paroxetine showed the greatest effect across all evaluated symptom domains. The 24/4 DROS/EE regimen was the most effective COC, showing particularly positive outcomes for both physical and emotional symptoms.</div></div><div><h3>Conclusions</h3><div>The network meta-analysis indicates that both SSRIs and COCs are effective in managing PMDD, with continuous paroxetine and DROS/EE 24/4 emerging as the most effective strategies in their respective categories. Treatment choice should consider predominant symptom patterns, individual tolerability, and reproductive planning. Further direct comparative clinical trials between these therapeutic classes are needed to guide clinical decision-making with greater precision.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"194 ","pages":"Pages 99-115"},"PeriodicalIF":3.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145863338","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}
Pub Date : 2025-12-22DOI: 10.1016/j.jpsychires.2025.12.045
Myeongkeun Cho , Heejae Lee , C. Hyung Keun Park
Background
Differentiating bipolar I, bipolar II, and major depressive disorders is essential; therefore, the relationship between affective temperaments and mood disorder diagnoses has garnered considerable attention.
Objectives
This study aimed to explore the representative types of Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Autoquestionnaire (TEMPS-A) profiles and examine their associations with the proportions of bipolar I, bipolar II, and major depressive disorders.
Methods
Psychiatric outpatients diagnosed with bipolar I, bipolar II, or major depressive disorder and aged ≥18 years were analyzed. Latent profile analysis was conducted using TEMPS-A scores, and each patient was classified into a subgroup according to their TEMPS-A scores. After that, multinomial logistic regression was conducted to verify the relationship between TEMPS-A profiles and mood disorder diagnoses.
Results
The results indicated that the seven-profile model was the most appropriate. Furthermore, the cyclothymic profile and anxious, cyclothymic and depressive profile increased the likelihood of bipolar II disorder, while the cyclothymic and hyperthymic profile; cyclothymic, depressive and irritable profile; and anxious, cyclothymic, hyperthymic and irritable profile increased the likelihood of bipolar II and bipolar I disorders.
Conclusion
Cyclothymic temperament can be a risk factor for bipolarity. Moreover, hyperthymic or irritable temperaments might help differentiate between bipolar I and bipolar II disorders.
{"title":"Latent profiles of affective temperaments can support differentiation of bipolar I disorder, bipolar II disorder, and major depressive disorder","authors":"Myeongkeun Cho , Heejae Lee , C. Hyung Keun Park","doi":"10.1016/j.jpsychires.2025.12.045","DOIUrl":"10.1016/j.jpsychires.2025.12.045","url":null,"abstract":"<div><h3>Background</h3><div>Differentiating bipolar I, bipolar II, and major depressive disorders is essential; therefore, the relationship between affective temperaments and mood disorder diagnoses has garnered considerable attention.</div></div><div><h3>Objectives</h3><div>This study aimed to explore the representative types of Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Autoquestionnaire (TEMPS-A) profiles and examine their associations with the proportions of bipolar I, bipolar II, and major depressive disorders.</div></div><div><h3>Methods</h3><div>Psychiatric outpatients diagnosed with bipolar I, bipolar II, or major depressive disorder and aged ≥18 years were analyzed. Latent profile analysis was conducted using TEMPS-A scores, and each patient was classified into a subgroup according to their TEMPS-A scores. After that, multinomial logistic regression was conducted to verify the relationship between TEMPS-A profiles and mood disorder diagnoses.</div></div><div><h3>Results</h3><div>The results indicated that the seven-profile model was the most appropriate. Furthermore, the <em>cyclothymic profile</em> and <em>anxious, cyclothymic and depressive profile</em> increased the likelihood of bipolar II disorder, while the <em>cyclothymic and hyperthymic profile</em>; <em>cyclothymic, depressive and irritable profile</em>; and <em>anxious, cyclothymic, hyperthymic and irritable profile</em> increased the likelihood of bipolar II and bipolar I disorders.</div></div><div><h3>Conclusion</h3><div>Cyclothymic temperament can be a risk factor for bipolarity. Moreover, hyperthymic or irritable temperaments might help differentiate between bipolar I and bipolar II disorders.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"194 ","pages":"Pages 79-84"},"PeriodicalIF":3.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145834244","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}
Both aspirin and selective serotonin reuptake inhibitors (SSRIs) have been linked to an increased risk of gastrointestinal (GI) bleeding, while the interaction between the combined use of these two drugs on GI bleeding remains unclear. We aimed to explore the association between SSRIs use and risk of GI bleeding among aspirin users.
Methods
This is a prospective cohort study of 12,712 new aspirin users with a history of ischemic cardiovascular disease from the UK Biobank. Information on aspirin or SSRIs prescriptions was retrieved from primary care records, and GI bleeding data from hospital inpatient admissions. A competing risk model was used to calculate hazard ratios (HRs) and 95 % confidence intervals (CIs).
Results
The incidence rate was 7.44 and 4.99 per 1000 person-years in the group of SSRIs and non-SSRIs use, respectively. A significant positive association was observed between SSRIs use and GI bleeding risk among aspirin users (Adjusted HR,1.27; 95 % CI, 1.01–1.58). The observed association was more pronounced in younger adults, females, individuals with a history of GI bleeding or those using PPIs. Besides, the association varied across different types of SSRIs, with paroxetine users showing the highest risk of GI bleeding (Adjusted HR, 1.73; 95 % CI, 1.06–2.83).
Conclusion
This study indicated an additional risk of GI bleeding associated with SSRIs use in aspirin users, especially when combining paroxetine with aspirin. This finding underscores the necessity for further investigation into the differential risks of specific SSRIs to guide personalized polypharmacy.
背景和目的：阿司匹林和选择性5 -羟色胺再摄取抑制剂（SSRIs）均与胃肠道出血风险增加有关，而这两种药物联合使用对胃肠道出血的相互作用尚不清楚。我们的目的是探讨阿司匹林使用者使用SSRIs与胃肠道出血风险之间的关系。方法：这是一项前瞻性队列研究，来自英国生物银行的12,712名有缺血性心血管疾病史的新阿司匹林使用者。从初级保健记录和住院患者的胃肠道出血数据中检索阿司匹林或SSRIs处方信息。采用竞争风险模型计算风险比（hr）和95%置信区间（ci）。结果：SSRIs组和非SSRIs组的发病率分别为7.44和4.99 / 1000人年。在阿司匹林使用者中，ssri类药物的使用与胃肠道出血风险之间存在显著的正相关（校正HR，1.27; 95% CI, 1.01-1.58）。观察到的关联在年轻人、女性、有胃肠道出血史的个体或使用PPIs的个体中更为明显。此外，不同类型的SSRIs的相关性也不同，帕罗西汀使用者胃肠道出血的风险最高（调整后风险比1.73;95% CI 1.06-2.83）。结论：本研究表明，阿司匹林使用者使用ssri类药物会增加消化道出血的风险，特别是当帕罗西汀与阿司匹林联合使用时。这一发现强调了进一步研究特定SSRIs的不同风险以指导个性化综合用药的必要性。
{"title":"Selective serotonin re-uptake inhibitor use and risk of gastrointestinal bleeding in aspirin users","authors":"Yihui He , Xin Zhang , Qixuan Luo , Yitian Chen , Ziting Gao , Hongye Wei , Yajing Wei , Ziyi Qiu , Wuqing Huang","doi":"10.1016/j.jpsychires.2025.12.038","DOIUrl":"10.1016/j.jpsychires.2025.12.038","url":null,"abstract":"<div><h3>Background and objective</h3><div>Both aspirin and selective serotonin reuptake inhibitors (SSRIs) have been linked to an increased risk of gastrointestinal (GI) bleeding, while the interaction between the combined use of these two drugs on GI bleeding remains unclear. We aimed to explore the association between SSRIs use and risk of GI bleeding among aspirin users.</div></div><div><h3>Methods</h3><div>This is a prospective cohort study of 12,712 new aspirin users with a history of ischemic cardiovascular disease from the UK Biobank. Information on aspirin or SSRIs prescriptions was retrieved from primary care records, and GI bleeding data from hospital inpatient admissions. A competing risk model was used to calculate hazard ratios (HRs) and 95 % confidence intervals (CIs).</div></div><div><h3>Results</h3><div>The incidence rate was 7.44 and 4.99 per 1000 person-years in the group of SSRIs and non-SSRIs use, respectively. A significant positive association was observed between SSRIs use and GI bleeding risk among aspirin users (Adjusted HR,1.27; 95 % CI, 1.01–1.58). The observed association was more pronounced in younger adults, females, individuals with a history of GI bleeding or those using PPIs. Besides, the association varied across different types of SSRIs, with paroxetine users showing the highest risk of GI bleeding (Adjusted HR, 1.73; 95 % CI, 1.06–2.83).</div></div><div><h3>Conclusion</h3><div>This study indicated an additional risk of GI bleeding associated with SSRIs use in aspirin users, especially when combining paroxetine with aspirin. This finding underscores the necessity for further investigation into the differential risks of specific SSRIs to guide personalized polypharmacy.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"194 ","pages":"Pages 51-57"},"PeriodicalIF":3.2,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145819870","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}
Pub Date : 2025-12-18DOI: 10.1016/j.jpsychires.2025.12.043
Daijie Hu , Kuaile Hu , Tianwei She , Liyong Yu , Yuqi He , Qinhan Yang , Tong Li , Xinyi Wei , Wenting Lin , Lu Yang , Yuxing Wei , Haiyan Zhou , Zhengjie Li , Siyi Yu
Objective
This study aimed to investigate the neural mechanisms underlying impaired response inhibition in patients with perimenopausal insomnia (PMI) using event-related potentials (ERP) and resting-state functional connectivity (rsFC) approaches.
Methods
PMI patients and age-matched healthy controls (HCs) completed clinical assessments, electroencephalography (EEG), and functional magnetic resonance imaging. Response inhibition was evaluated using the stop signal task (SST). EEG data were analyzed for ERP components and event-related spectral perturbations (ERSP), and rsFC analyses were conducted using seed regions in the right inferior frontal cortex (IFC), including the ventral posterior (vpIFC) and anterior (aIFC) subregions. Correlation analyses were conducted among neurophysiological indices and clinical measures.
Results
Compared to HCs, PMI patients exhibited prolonged stop signal reaction time, enhanced Stop-N2 amplitudes, and delayed Stop-P3 responses. ERSP analysis revealed reduced beta-band event-related desynchronization (ERD). RsFC analysis showed decreased connectivity between the vpIFC and inferior temporal gyrus (ITG), and increased connectivity between the aIFC and precuneus and posterior cingulate gyrus in PMI patients. Correlation analysis indicated that ERD power and rsFC alterations were significantly associated with hyperarousal and behavioral activation system traits.
Conclusions
This study confirms impaired response inhibition in PMI patients, primarily manifested through aberrant electrophysiological processes underlying inhibitory control. Altered rsFC between the IFC and both the default mode network and temporal network may represent the neural basis of impaired response inhibition. These findings highlight potential neural markers of inhibitory dysfunction in PMI and suggest that hyperarousal and motivational dysregulation may play a key role in its pathophysiology.
{"title":"Neural mechanisms of response inhibition impairments in patients with perimenopausal insomnia","authors":"Daijie Hu , Kuaile Hu , Tianwei She , Liyong Yu , Yuqi He , Qinhan Yang , Tong Li , Xinyi Wei , Wenting Lin , Lu Yang , Yuxing Wei , Haiyan Zhou , Zhengjie Li , Siyi Yu","doi":"10.1016/j.jpsychires.2025.12.043","DOIUrl":"10.1016/j.jpsychires.2025.12.043","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to investigate the neural mechanisms underlying impaired response inhibition in patients with perimenopausal insomnia (PMI) using event-related potentials (ERP) and resting-state functional connectivity (rsFC) approaches.</div></div><div><h3>Methods</h3><div>PMI patients and age-matched healthy controls (HCs) completed clinical assessments, electroencephalography (EEG), and functional magnetic resonance imaging. Response inhibition was evaluated using the stop signal task (SST). EEG data were analyzed for ERP components and event-related spectral perturbations (ERSP), and rsFC analyses were conducted using seed regions in the right inferior frontal cortex (IFC), including the ventral posterior (vpIFC) and anterior (aIFC) subregions. Correlation analyses were conducted among neurophysiological indices and clinical measures.</div></div><div><h3>Results</h3><div>Compared to HCs, PMI patients exhibited prolonged stop signal reaction time, enhanced Stop-N2 amplitudes, and delayed Stop-P3 responses. ERSP analysis revealed reduced beta-band event-related desynchronization (ERD). RsFC analysis showed decreased connectivity between the vpIFC and inferior temporal gyrus (ITG), and increased connectivity between the aIFC and precuneus and posterior cingulate gyrus in PMI patients. Correlation analysis indicated that ERD power and rsFC alterations were significantly associated with hyperarousal and behavioral activation system traits.</div></div><div><h3>Conclusions</h3><div>This study confirms impaired response inhibition in PMI patients, primarily manifested through aberrant electrophysiological processes underlying inhibitory control. Altered rsFC between the IFC and both the default mode network and temporal network may represent the neural basis of impaired response inhibition. These findings highlight potential neural markers of inhibitory dysfunction in PMI and suggest that hyperarousal and motivational dysregulation may play a key role in its pathophysiology.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"194 ","pages":"Pages 58-68"},"PeriodicalIF":3.2,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145827904","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}
Pub Date : 2025-12-18DOI: 10.1016/j.jpsychires.2025.12.041
Federica Iannotta , Felice Iasevoli , Claudio Caiazza , Michele Fornaro , Maria Nolano , Andrea de Bartolomeis
Background
Autonomic nervous system (ANS) dysfunctions have been increasingly implicated as a feature of psychoses’ pathophysiology. The specificity of these alterations and the role of antipsychotic medication remain controversial.
Methods
We conducted a systematic review and meta-analysis, following a predetermined protocol (PROSPERO/CRD42024510812) to assess heart-rate variability (HRV), electrodermal activity (EDA), and peripheral biomarkers in psychotic disorders. We searched PubMed and EMBASE from inception until July 2024. We conducted random-effects meta-analyses and assessed heterogeneity, publication bias, and risk of bias.
Results
Patients with psychosis showed a significant reduction in HRV compared to healthy controls, especially in indices reflecting parasympathetic activity. This result was evident in both treated and untreated patients and was also observed in the comparison between patients with psychosis and affective disorders. Among all antipsychotics, clozapine was associated with the greatest reduction in HRV. EDA and peripheral markers, i.e., alpha amylase and catecholamines, did not show significant differences between patients with psychosis and healthy controls. However, the skin conductance level (SCL) showed a trend to decrease after the introduction of antipsychotics.
Conclusions
These results suggest that ANS dysregulations may be a core feature of psychosis, only partially dependent on pharmacological treatment, suggesting a potential primary dysregulation within the Central Autonomic Network. Disruptions in neurotransmitter systems, particularly acetylcholine, may contribute. Autonomic profiling could refine psychiatric diagnosis, helping with tailored interventions. Longitudinal studies are needed to explore their potential in predicting treatment response.
{"title":"Autonomic dysfunctions in psychotic disorders, interaction with antipsychotic intervention and treatment resistance: a comprehensive systematic review and meta-analysis","authors":"Federica Iannotta , Felice Iasevoli , Claudio Caiazza , Michele Fornaro , Maria Nolano , Andrea de Bartolomeis","doi":"10.1016/j.jpsychires.2025.12.041","DOIUrl":"10.1016/j.jpsychires.2025.12.041","url":null,"abstract":"<div><h3>Background</h3><div>Autonomic nervous system (ANS) dysfunctions have been increasingly implicated as a feature of psychoses’ pathophysiology. The specificity of these alterations and the role of antipsychotic medication remain controversial.</div></div><div><h3>Methods</h3><div>We conducted a systematic review and meta-analysis, following a predetermined protocol (PROSPERO/CRD42024510812) to assess heart-rate variability (HRV), electrodermal activity (EDA), and peripheral biomarkers in psychotic disorders. We searched PubMed and EMBASE from inception until July 2024. We conducted random-effects meta-analyses and assessed heterogeneity, publication bias, and risk of bias.</div></div><div><h3>Results</h3><div>Patients with psychosis showed a significant reduction in HRV compared to healthy controls, especially in indices reflecting parasympathetic activity. This result was evident in both treated and untreated patients and was also observed in the comparison between patients with psychosis and affective disorders. Among all antipsychotics, clozapine was associated with the greatest reduction in HRV. EDA and peripheral markers, i.e., alpha amylase and catecholamines, did not show significant differences between patients with psychosis and healthy controls. However, the skin conductance level (SCL) showed a trend to decrease after the introduction of antipsychotics.</div></div><div><h3>Conclusions</h3><div>These results suggest that ANS dysregulations may be a core feature of psychosis, only partially dependent on pharmacological treatment, suggesting a potential primary dysregulation within the Central Autonomic Network. Disruptions in neurotransmitter systems, particularly acetylcholine, may contribute. Autonomic profiling could refine psychiatric diagnosis, helping with tailored interventions. Longitudinal studies are needed to explore their potential in predicting treatment response.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"194 ","pages":"Pages 123-135"},"PeriodicalIF":3.2,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878531","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}
Pub Date : 2025-12-18DOI: 10.1016/j.jpsychires.2025.11.044
Muhammed Fatih Tabara , Alper Bibar , Mustafa Koc , Mehmet Gurkan Gurok , Osman Mermi , Sevda Korkmaz , Sema Baykara , Hanefi Yildirim , Murad Atmaca
Introduction
Reduced superior temporal gyrus (STG) volume is associated with major depressive disorder (MDD) and posttraumatic stress disorder (PTSD), but this has not been explored in adjustment disorders. Since adjustment disorders have similar clinical features with MDD and PTSD, it was hypothesized that volume change would also occur in these disorders.
Methods
In the study, nineteen patients with adjustment disorder and eighteen healthy controls were compared using MRI in terms of the STG and its subregions.
Results
The STG volume of the patient group was found to be significantly reduced compared to controls (p < 0.001). In addition, the planum temporale and Heschl's gyrus subregions were significantly smaller in the patient group (in both cases p < 0.001).
Conclusions
Smaller STG volumes were detected in patients with adjustment disorders, such as other psychosocial stress-related disorders, PTSD, and MDD. We should clearly state that our study is pioneering and that studies with large samples are needed to support our findings, including functional changes in the STG region.
{"title":"Superior temporal gyrus volume in patients with adjustment disorder","authors":"Muhammed Fatih Tabara , Alper Bibar , Mustafa Koc , Mehmet Gurkan Gurok , Osman Mermi , Sevda Korkmaz , Sema Baykara , Hanefi Yildirim , Murad Atmaca","doi":"10.1016/j.jpsychires.2025.11.044","DOIUrl":"10.1016/j.jpsychires.2025.11.044","url":null,"abstract":"<div><h3>Introduction</h3><div>Reduced superior temporal gyrus (STG) volume is associated with major depressive disorder (MDD) and posttraumatic stress disorder (PTSD), but this has not been explored in adjustment disorders. Since adjustment disorders have similar clinical features with MDD and PTSD, it was hypothesized that volume change would also occur in these disorders.</div></div><div><h3>Methods</h3><div>In the study, nineteen patients with adjustment disorder and eighteen healthy controls were compared using MRI in terms of the STG and its subregions.</div></div><div><h3>Results</h3><div>The STG volume of the patient group was found to be significantly reduced compared to controls (p < 0.001). In addition, the planum temporale and Heschl's gyrus subregions were significantly smaller in the patient group (in both cases p < 0.001).</div></div><div><h3>Conclusions</h3><div>Smaller STG volumes were detected in patients with adjustment disorders, such as other psychosocial stress-related disorders, PTSD, and MDD. We should clearly state that our study is pioneering and that studies with large samples are needed to support our findings, including functional changes in the STG region.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"194 ","pages":"Pages 25-30"},"PeriodicalIF":3.2,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799445","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}
Pub Date : 2025-12-18DOI: 10.1016/j.jpsychires.2025.12.042
Chunlan Sun, Shuang Ding, Bin Qin, Yun Zhang, Weixuan Qin, Jie Liu, Kaiping Huang, Ruofei Ma, Yingxue Tong, Longlun Wang, Jinhua Cai
Objective
To investigate alterations in both static and dynamic brain functional network connectivity (FNC) in preschool children with autism spectrum disorder (ASD) and their correlation with clinical symptoms, thereby providing neuroimaging evidence for understanding the potential pathogenesis of ASD.
Methods
Clinical and resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 47 preschool children with ASD and 56 matched typically developing children (TDC). Independent component analysis (ICA) and dynamic FNC analysis were employed to compare differences in static FNC and dynamic FNC metrics between the groups. Correlations between altered FNC measures and clinical scale scores were specifically examined within the ASD group.
Results
In the static FNC analysis, the ASD group showed decreased connectivity between the left frontoparietal network (lFPN) and the sensorimotor network (SMN), lateral visual network (lVN), and auditory network (AN) respectively. Connectivity was also reduced between the lVN and the posterior default mode network (pDMN), and between the right frontoparietal network (rFPN) and the posterior visual network (pVN). Conversely, the ASD group showed increased connectivity between the rFPN and both the pDMN and the dorsal attention network (DAN). Scores for repetitive behaviors and restricted interests in the Autism Diagnostic Observation Schedule (ADOS) were positively correlated with the strength of the rFPN-pDMN and rFPN-DAN connections. In the dynamic FNC analysis, the ASD group showed increased functional connectivity variability within the pVN and AN, and decreased variability within the lVN and lFPN. Furthermore, the fraction time spent in state 5 was positively correlated with the communication score in the ADOS.
Conclusion
The brain functional networks of preschool ASD children exhibit a dual characteristic pattern: static dysconnectivity and dynamic rigidity. These alterations may be closely related to the core symptoms of ASD in this age group, including social communication impairments, repetitive behaviors, and restricted interests.
{"title":"Alterations of static and dynamic brain functional network connectivity in preschool children with autism spectrum disorder","authors":"Chunlan Sun, Shuang Ding, Bin Qin, Yun Zhang, Weixuan Qin, Jie Liu, Kaiping Huang, Ruofei Ma, Yingxue Tong, Longlun Wang, Jinhua Cai","doi":"10.1016/j.jpsychires.2025.12.042","DOIUrl":"10.1016/j.jpsychires.2025.12.042","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate alterations in both static and dynamic brain functional network connectivity (FNC) in preschool children with autism spectrum disorder (ASD) and their correlation with clinical symptoms, thereby providing neuroimaging evidence for understanding the potential pathogenesis of ASD.</div></div><div><h3>Methods</h3><div>Clinical and resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 47 preschool children with ASD and 56 matched typically developing children (TDC). Independent component analysis (ICA) and dynamic FNC analysis were employed to compare differences in static FNC and dynamic FNC metrics between the groups. Correlations between altered FNC measures and clinical scale scores were specifically examined within the ASD group.</div></div><div><h3>Results</h3><div>In the static FNC analysis, the ASD group showed decreased connectivity between the left frontoparietal network (lFPN) and the sensorimotor network (SMN), lateral visual network (lVN), and auditory network (AN) respectively. Connectivity was also reduced between the lVN and the posterior default mode network (pDMN), and between the right frontoparietal network (rFPN) and the posterior visual network (pVN). Conversely, the ASD group showed increased connectivity between the rFPN and both the pDMN and the dorsal attention network (DAN). Scores for repetitive behaviors and restricted interests in the Autism Diagnostic Observation Schedule (ADOS) were positively correlated with the strength of the rFPN-pDMN and rFPN-DAN connections. In the dynamic FNC analysis, the ASD group showed increased functional connectivity variability within the pVN and AN, and decreased variability within the lVN and lFPN. Furthermore, the fraction time spent in state 5 was positively correlated with the communication score in the ADOS.</div></div><div><h3>Conclusion</h3><div>The brain functional networks of preschool ASD children exhibit a dual characteristic pattern: static dysconnectivity and dynamic rigidity. These alterations may be closely related to the core symptoms of ASD in this age group, including social communication impairments, repetitive behaviors, and restricted interests.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"194 ","pages":"Pages 1-10"},"PeriodicalIF":3.2,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771901","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}
Pub Date : 2025-12-18DOI: 10.1016/j.jpsychires.2025.12.036
Yuting Zhan , Hongwei Liu , Yu Wang
Background
Despite advances in treatment approaches for Major Depressive Disorder (MDD), significant challenges persist in predicting individual treatment responses. Digital phenotyping—the passive collection of behavioral and physiological data through personal digital devices—offers promising opportunities for identifying objective markers of depression severity and treatment response.
Methods
We conducted a 12-week prospective observational study with 183 participants diagnosed with MDD who were initiating standard treatment. Passive sensing data were collected via smartphone and wearable devices, capturing mobility patterns, social interaction proxies, sleep metrics, physiological parameters, and app usage patterns. Clinical assessments were conducted at baseline, 4, 8, and 12 weeks. Machine learning algorithms were employed to identify digital biomarkers associated with depression severity and treatment response.
Results
Several digital phenotyping markers demonstrated significant associations with depression severity, including reduced geographic mobility (r = −0.42, p < .001), decreased social app usage (r = −0.38, p < .001), and disturbed sleep patterns (r = 0.40, p < .001). A random forest model incorporating 14 digital features predicted treatment response with 76.4 % accuracy (sensitivity 73.1 %, specificity 79.6 %). Four distinct temporal patterns of digital marker trajectories were identified during successful treatment.
Conclusion
This study establishes the utility of passive digital phenotyping in objectively tracking depression severity and predicting treatment response. The identified digital biomarkers hold promise for enhancing precision psychiatry approaches through continuous, ecologically valid monitoring of depression.
{"title":"Digital phenotyping of depression: A multi-modal passive sensing approach to identifying novel behavioral and physiological markers of treatment response","authors":"Yuting Zhan , Hongwei Liu , Yu Wang","doi":"10.1016/j.jpsychires.2025.12.036","DOIUrl":"10.1016/j.jpsychires.2025.12.036","url":null,"abstract":"<div><h3>Background</h3><div>Despite advances in treatment approaches for Major Depressive Disorder (MDD), significant challenges persist in predicting individual treatment responses. Digital phenotyping—the passive collection of behavioral and physiological data through personal digital devices—offers promising opportunities for identifying objective markers of depression severity and treatment response.</div></div><div><h3>Methods</h3><div>We conducted a 12-week prospective observational study with 183 participants diagnosed with MDD who were initiating standard treatment. Passive sensing data were collected via smartphone and wearable devices, capturing mobility patterns, social interaction proxies, sleep metrics, physiological parameters, and app usage patterns. Clinical assessments were conducted at baseline, 4, 8, and 12 weeks. Machine learning algorithms were employed to identify digital biomarkers associated with depression severity and treatment response.</div></div><div><h3>Results</h3><div>Several digital phenotyping markers demonstrated significant associations with depression severity, including reduced geographic mobility (<em>r</em> = −0.42, <em>p</em> < .001), decreased social app usage (<em>r</em> = −0.38, <em>p</em> < .001), and disturbed sleep patterns (<em>r</em> = 0.40, <em>p</em> < .001). A random forest model incorporating 14 digital features predicted treatment response with 76.4 % accuracy (sensitivity 73.1 %, specificity 79.6 %). Four distinct temporal patterns of digital marker trajectories were identified during successful treatment.</div></div><div><h3>Conclusion</h3><div>This study establishes the utility of passive digital phenotyping in objectively tracking depression severity and predicting treatment response. The identified digital biomarkers hold promise for enhancing precision psychiatry approaches through continuous, ecologically valid monitoring of depression.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"194 ","pages":"Pages 40-50"},"PeriodicalIF":3.2,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145819850","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}
Pub Date : 2025-12-17DOI: 10.1016/j.jpsychires.2025.12.037
Lanxia Wu , Qingyang Fu , Yuan Gao , Baoying Jiang , Hao Zheng , Zilong Zhong , Guohui Zhang , Yining Lu , Ziming Zhang , Rui Li , Guohua Lu , Lin Sun
A number of processes and pathways have been reported in the development of Post-Traumatic Stress Disorder (PTSD), however, novel biomarkers need to be identified for a better diagnosis and management. We used the Limma package to identify differential genes, combined with weighted gene co-expression network analysis (WGCNA) and five machine learning algorithms to screen, and finally obtained five key genes. We used eight machine learning algorithms to construct the predictive model, and the results demonstrated that the support vector machine (SVM) algorithm had the best predictive efficiency, with an area under curve (AUC) value of 0.894303363. To clarify the decision-making process of the model, we used the Shapley Additive exPlanations (SHAP) method to rank the importance of the model's display features on all model samples. An immune-cell infiltration analysis revealed significant differences in the relative abundances of immune cells between controls and PTSD patients and a correlation with the key genes. Then we confirmed the expression of these five biomarkers in stress-related brain regions (prefrontal cortex (PFC), hippocampus (HIP), amygdala (AMY)) of Single prolonged stress and electric foot shock (SPS&S) rats through a series of experimental validation, and found that arrestin beta 2 (ARRB2) gene expression was significantly down-regulated in HIP, and verified the expression of ARRB2 in HIP by further Western Blotting as well as immunofluorescence experiments. In conclusion, machine learning and bioinformatics analysis along with experimental techniques identified ARRB2 as potential biomarkers for PTSD.
{"title":"An explainable predictive machine learning model reveals ARRB2 as a key gene in post-traumatic stress disorder: A GEO database study","authors":"Lanxia Wu , Qingyang Fu , Yuan Gao , Baoying Jiang , Hao Zheng , Zilong Zhong , Guohui Zhang , Yining Lu , Ziming Zhang , Rui Li , Guohua Lu , Lin Sun","doi":"10.1016/j.jpsychires.2025.12.037","DOIUrl":"10.1016/j.jpsychires.2025.12.037","url":null,"abstract":"<div><div>A number of processes and pathways have been reported in the development of Post-Traumatic Stress Disorder (PTSD), however, novel biomarkers need to be identified for a better diagnosis and management. We used the Limma package to identify differential genes, combined with weighted gene co-expression network analysis (WGCNA) and five machine learning algorithms to screen, and finally obtained five key genes. We used eight machine learning algorithms to construct the predictive model, and the results demonstrated that the support vector machine (SVM) algorithm had the best predictive efficiency, with an area under curve (AUC) value of 0.894303363. To clarify the decision-making process of the model, we used the Shapley Additive exPlanations (SHAP) method to rank the importance of the model's display features on all model samples. An immune-cell infiltration analysis revealed significant differences in the relative abundances of immune cells between controls and PTSD patients and a correlation with the key genes. Then we confirmed the expression of these five biomarkers in stress-related brain regions (prefrontal cortex (PFC), hippocampus (HIP), amygdala (AMY)) of Single prolonged stress and electric foot shock (SPS&S) rats through a series of experimental validation, and found that arrestin beta 2 (ARRB2) gene expression was significantly down-regulated in HIP, and verified the expression of ARRB2 in HIP by further Western Blotting as well as immunofluorescence experiments. In conclusion, machine learning and bioinformatics analysis along with experimental techniques identified ARRB2 as potential biomarkers for PTSD.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"193 ","pages":"Pages 543-560"},"PeriodicalIF":3.2,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786619","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}
Pub Date : 2025-12-17DOI: 10.1016/j.jpsychires.2025.12.040
Steven L. Lancaster, Xin Wang, Claire E. Lawless, David J. Linkh
Objective
Military-connected clients often experience delays in accessing mental health care. This study examined whether wait times between referral, intake assessment, and first therapy session predicted treatment outcomes in military-serving community clinics.
Material and method
A retrospective analysis was conducted using electronic health record data from 11,229 clients. Predictors included wait times (in days) between referral and intake, and between intake and first therapy session. Outcomes included session attendance (>0, >1, and >3 sessions), clinically significant symptom change, and clinician-rated goal attainment. Logistic regression models assessed associations between wait times and each outcome, controlling for demographic variables (age, gender, race/ethnicity, and client type). Odds ratios (ORs; representing the change in odds per one-day increase in wait time), 95% confidence intervals, and weekly odds ratios (WK ORs; representing the change in odds per one-week increase) were reported.
Results
Longer referral-to-intake delays were associated with lower odds of clinically significant improvement (OR = 0.96 per day, WK OR = 0.95, p < .001) but not with attendance or goal attainment. Intake-to-therapy delays significantly predicted all outcomes, with longer waits associated with reduced odds of session attendance (OR = 0.97–0.98 per day, WK OR = 0.84–0.90), symptom improvement (OR = 0.98 per day, WK OR = 0.91), and goal attainment (OR = 0.99 per day, WK OR = 0.94). Demographic differences existed, but they did not alter the overall results.
Conclusion
Delays following intake assessment were consistently associated with poorer engagement and outcomes. Reducing these delays may enhance treatment effectiveness.
目的与军方有联系的客户在获得心理保健服务方面经常遇到延误。本研究考察了转诊、入院评估和第一次治疗之间的等待时间是否预测了服役军人社区诊所的治疗结果。材料和方法对11,229名患者的电子健康记录数据进行回顾性分析。预测指标包括转诊和入院之间以及入院和首次治疗之间的等待时间（以天为单位）。结果包括治疗出勤（治疗0次、1次和3次）、临床显著症状改变和临床医生评定的目标实现情况。逻辑回归模型评估了等待时间与每个结果之间的关联，控制了人口统计变量（年龄、性别、种族/民族和客户类型）。报告了比值比（ORs；代表等待时间每增加一天的几率变化）、95%置信区间和每周比值比（WK ORs；代表每增加一周的几率变化）。结果：较长的转诊至入院延迟与较低的临床显著改善几率相关（OR = 0.96 /天，WK OR = 0.95, p < .001），但与出勤或目标实现无关。接受治疗延迟显著预测了所有结果，较长的等待与治疗出勤率降低（OR = 0.97-0.98 /天，WK OR = 0.84-0.90）、症状改善（OR = 0.98 /天，WK OR = 0.91）和目标实现（OR = 0.99 /天，WK OR = 0.94）相关。人口统计学上的差异是存在的，但这并没有改变总体结果。结论入学评估后的延迟与较差的敬业度和结果一致相关。减少这些延误可以提高治疗效果。
{"title":"The impact of wait times on treatment engagement and outcomes in military-connected mental health clinics","authors":"Steven L. Lancaster, Xin Wang, Claire E. Lawless, David J. Linkh","doi":"10.1016/j.jpsychires.2025.12.040","DOIUrl":"10.1016/j.jpsychires.2025.12.040","url":null,"abstract":"<div><h3>Objective</h3><div>Military-connected clients often experience delays in accessing mental health care. This study examined whether wait times between referral, intake assessment, and first therapy session predicted treatment outcomes in military-serving community clinics.</div></div><div><h3>Material and method</h3><div>A retrospective analysis was conducted using electronic health record data from 11,229 clients. Predictors included wait times (in days) between referral and intake, and between intake and first therapy session. Outcomes included session attendance (>0, >1, and >3 sessions), clinically significant symptom change, and clinician-rated goal attainment. Logistic regression models assessed associations between wait times and each outcome, controlling for demographic variables (age, gender, race/ethnicity, and client type). Odds ratios (ORs; representing the change in odds per one-day increase in wait time), 95% confidence intervals, and weekly odds ratios (WK ORs; representing the change in odds per one-week increase) were reported.</div></div><div><h3>Results</h3><div>Longer referral-to-intake delays were associated with lower odds of clinically significant improvement (OR = 0.96 per day, WK OR = 0.95, p < .001) but not with attendance or goal attainment. Intake-to-therapy delays significantly predicted all outcomes, with longer waits associated with reduced odds of session attendance (OR = 0.97–0.98 per day, WK OR = 0.84–0.90), symptom improvement (OR = 0.98 per day, WK OR = 0.91), and goal attainment (OR = 0.99 per day, WK OR = 0.94). Demographic differences existed, but they did not alter the overall results.</div></div><div><h3>Conclusion</h3><div>Delays following intake assessment were consistently associated with poorer engagement and outcomes. Reducing these delays may enhance treatment effectiveness.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"194 ","pages":"Pages 21-24"},"PeriodicalIF":3.2,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799444","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}
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