Psychiatry Research[JOURNAL]
Sun
200 papers
Cytokine changes during a 12-month pragmatic randomized trial in schizophrenia spectrum disorders comparing amisulpride, aripiprazole and olanzapine.
Kroken RA, Johnsen E, Alisauskiene R … +20 more , Anda LG, Bartz-Johannesen C, Berle JØ, Bjarke J, Bruserud Ø, Fathian F, Fleischhacker WW, Hugdahl K, Joa I, Kjelby E, Larsen TK, Løberg EM, Ratke I, Rettenbacher M, Sinkeviciute I, Skrede S, Stabell L, Steen VM, Torsvik A, Reitan SK
Psychiatry Res · 2026 Jun · PMID 42322923 · Publisher ↗
BACKGROUND: Schizophrenia is associated with inflammation. Antipsychotic drugs have anti-inflammatory properties but few antipsychotics have been compared head-to head. The aim of this study was to evaluate the immunomod...
BACKGROUND: Schizophrenia is associated with inflammation. Antipsychotic drugs have anti-inflammatory properties but few antipsychotics have been compared head-to head. The aim of this study was to evaluate the immunomodulatory effects of three pharmacologically different atypical antipsychotics, amisulpiride, aripiprazole and olanzapine, in patients with schizophrenia-spectrum disorders. METHODS: The study reports a predefined secondary outcome of the BeSt InTro-study, a double blind randomized head-to-head trial comparing amisulpride, aripiprazole and olanzapine. Patients with a schizophrenia-spectrum disorder (N=144) were randomized to the different treatment arms, and serum levels of nine cytokines (interleukin [IL]-1β, IL-2, IL-4, IL-6, IL-10, IL-12p70, IL-17A, interferon [IFN]-γ, and tumor necrosis factor [TNF]-α) were assessed at baseline and weeks 1, 3, 6, 12, 26, 39 and 52. A combined pro-inflammatory variable was constructed from IL1-β and TNF-α. The primary analyses were done in per protocol (PP) data in the drug-naïve sample (n = 56). Changes in cytokine levels between baseline and the end of the study were analyzed in a mixed effects model with amisulpride as the reference drug. RESULTS: Treatment with amisulpride was associated with a reduction in pro-inflammatory IL-1β levels over 12 months, this change was statistically different compared to the change in aripiprazole and olanzapine. A significant difference between the influence of amisulpride compared to aripiprazole on the anti-inflammatory cytokine IL-10 was detected. Aripiprazole reduced IL-12p70 more than amisulpride. The combined pro-inflammatory variable decreased from baseline to week 52 with a magnitude of 0.52 standard deviation (SD) after treatment with amisulpride.. CONCLUSION: Patients treated with amisulpride showed greater anti-inflammatory changes in cytokine serum levels than those treated with aripiprazole or olanzapine. FUNDING: The Research Council of Norway, the Western Norway Regional Health Trust, and Klinbeforsk supported the study.
Brain functional connectivity patterns associated with adiposity in schizophrenia.
Dodd K, Legget KT, Cornier MA … +5 more , McHugo M, Wylie KP, Novick AM, Berman BD, Tregellas JR
Psychiatry Res Neuroimaging · 2026 Jun · PMID 42322676 · Publisher ↗
Individuals with schizophrenia (SZ) experience significantly greater obesity rates compared to the general population. The underlying biological mechanisms, however, remain poorly understood. Although brain functional co...
Individuals with schizophrenia (SZ) experience significantly greater obesity rates compared to the general population. The underlying biological mechanisms, however, remain poorly understood. Although brain functional connectivity (FC) has been associated with obesity in the general population, its role in obesity in SZ is largely unexplored. As such, this study examined FC contributions to adiposity in participants with SZ (n = 46) and an adiposity-matched comparison group without SZ (n = 46) as they completed resting-state functional magnetic resonance imaging in fasted and fed states. A machine learning approach identified FC patterns associated with adiposity (percent body fat), with model performance evaluated on an independent test set. In both fasted and fed states, brain regions involved in reward and eating behaviors contributed to adiposity prediction in both groups. While fasted, predictive connections in SZ largely involved limbic and sensorimotor networks, whereas comparison group networks were more varied. In the fed state, SZ predictive features included visual, default mode, and executive control networks, while comparison group connections involved limbic, sensorimotor, salience, and executive control networks. Findings may suggest shared, as well as diagnosis- and state-specific, FC patterns associated with adiposity in SZ, which may help inform future development of obesity-related interventions in this high-risk population.
In vivo associations between anticipatory and consummatory reward responsiveness: A cross lagged panel analysis.
Balkind EG, Lord KA, Fields JS … +2 more , Santiago N, Liverant GI
Psychiatry Res · 2026 Jun · PMID 42320290 · Publisher ↗
Reward system functioning involves a complex set of processes, including motivation to pursue rewards, reward salience, and hedonic capacity. Impairments in these processes are implicated in various forms of psychopathol...
Reward system functioning involves a complex set of processes, including motivation to pursue rewards, reward salience, and hedonic capacity. Impairments in these processes are implicated in various forms of psychopathology. Among several temporal elements within reward processing, anticipatory reward responsiveness (ARR) refers to the anticipation of an expected reward (i.e., wanting) and consummatory reward responsiveness (CRR) involves response to an experienced reward (i.e., liking). Conceptually, it may be that one's ability to anticipate future reward would facilitate initial response to reward receipt. Yet, little research has examined temporal associations between these two distinct components of reward processing over time. The current study utilized ecological momentary assessment (EMA) to explore the relationship between ARR and CRR in response to rewarding daily activities. 544 undergraduate students completed baseline questionnaires assessing demographic information and depressive symptoms. Participants then completed EMA questionnaires assessing ARR and CRR three times per day for seven days. Multilevel modeling with random intercepts and slopes using maximum likelihood (ML) estimation was conducted to examine lagged associations between average ARR and CRR across timepoints. ARR was found to predict subsequent CRR, and a bidirectional relationship was found between CRR and future ARR. Only associations between ARR and subsequent CRR held when controlling for anhedonia symptoms. Our findings support temporal associations between two distinct aspects of reward responsiveness and point to ARR as a key treatment target with potential downstream impacts on future hedonic capacity in patients with blunted reward responding.
A network analysis of adolescent nonsuicidal self-injury from the perspective of an invalidating family environment.
Jiang Y, Han X, Wang B … +1 more , Yi Z
Psychiatry Res · 2026 Jun · PMID 42320289 · Publisher ↗
This study used network analysis to examine the conditional association structure of psychological and environmental risk factors related to nonsuicidal self-injury (NSSI) among adolescents in different invalidating fami...
This study used network analysis to examine the conditional association structure of psychological and environmental risk factors related to nonsuicidal self-injury (NSSI) among adolescents in different invalidating family environments. Cross-sectional data were collected from adolescents in Northwest China (initial N = 9697). After data quality control, defensive responding screening, family-environment grouping, and propensity score matching, 3652 adolescents were included in the main analyses, with 1826 in each of the low- and high-invalidating family environment groups. We estimated separate 16-node networks including individual psychological factors, environmental risk factors, and NSSI, and conducted sensitivity analyses to examine whether the community assignment of NSSI affected bridge centrality. Network structure and global strength differed significantly between groups. Psychological resilience, impulsiveness, and cyberbullying showed relatively high absolute expected influence in both networks. NSSI had the highest bridge expected influence in the main analysis; when NSSI was assigned to the individual psychological community, its bridge centrality decreased but its relative ranking remained high. An exploratory 15-node network excluding NSSI was estimated among adolescents with NSSI in the unmatched high-invalidating group. Psychological resilience, impulsiveness, and rumination showed high absolute expected influence in this exploratory network. These findings suggest that adolescent NSSI-related risk structures may involve complex conditional associations among invalidating family environments, individual regulatory resources, impulsiveness, rumination, and bullying exposure.
Patterns of onset and progression of psychoses induced by psychoactive substance use.
Ifteni P, Miron AA, Necula R … +2 more , Dima L, Teodorescu A
Psychiatry Res · 2026 Jun · PMID 42320288 · Publisher ↗
INTRODUCTION: Drug use in Europe has increased in recent years, including a marked rise in new psychoactive substances (NPS). Substance use disorders negatively affect physical and mental health. Psychotic disorders are...
INTRODUCTION: Drug use in Europe has increased in recent years, including a marked rise in new psychoactive substances (NPS). Substance use disorders negatively affect physical and mental health. Psychotic disorders are highly prevalent among drug users. Establishing a link between illicit drug use and schizophrenia would help clarify the progression risk after substance-induced psychosis. AIMS: To investigate the onset and evolution of substance-induced psychosis in patients hospitalized in Brașov Clinical Hospital of Psychiatry and Neurology. MATERIALS AND METHODS: A retrospective observational study, analyzing data from all the patients admitted to the hospital between 2014 and 2024, who were diagnosed with substance-induced psychosis other than alcohol, according to DSM-5 criteria. RESULTS: Of all admissions, 2353 (9.6%) were diagnosed with acute psychotic disorders, of which 129 (5.5%) were related to psychoactive substances. THC (cannabis) was the main substance used, being present in 82.95% of cases. Almost half of the patients (44.96%) reported multiple drug use. 48.84% of patients required subsequent psychiatric readmissions after a first episode of substance-induced psychosis. 11.62% of the patients with substance-induced psychosis were subsequently diagnosed with a major psychiatric disorder, schizophrenia (N = 10, 7.8%) or bipolar disorder (N = 5, 3.9%). The mean duration of conversion to a major psychiatric disorder was 3.37±3.38 years. CONCLUSIONS: Substance-induced psychosis carried a meaningful risk of later transition to schizophrenia-spectrum or bipolar disorders, particularly among patients with higher-risk substance patterns and other vulnerability factors. THC was the most frequently used substance and emerged as an important risk marker in this cohort, supporting the need for timely recognition, sustained follow-up, and integrated intervention for high-risk patients.
Bayesian colocalization of psychiatric disorder GWAS signals with molecular quantitative trait loci highlights histone involvement and neuroimmune dysregulation in pathogenesis.
Ryu J, Lee C
Psychiatry Res · 2026 Jun · PMID 42320287 · Publisher ↗
Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of psychiatric disorders. However, the interpretation of these findings remains challenging, as most risk loci resi...
Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of psychiatric disorders. However, the interpretation of these findings remains challenging, as most risk loci reside in non-coding regions, obscuring the underlying molecular mechanisms. To elucidate the functional consequences of psychiatric risk variants, we implemented a multi-omics Bayesian colocalization framework using transcript, protein and metabolite levels for attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder (BIP), major depressive disorder (MDD), and schizophrenia (SCZ). Sixty-five SCZ, BIP, and MDD GWAS signals colocalized with immune cell-type-specific expression quantitative trait loci (eQTL), targeting 70 genes. Notably, they included 17 histone-encoding genes colocalized from five GWAS signals. These signals subsequently colocalized with protein QTL (pQTL), targeting 310 proteins enriched in various immune-related pathways (P < 5.99 × 10). Further colocalization with metabolite QTL (metabQTL) revealed 23 metabolites, highlighting GABAergic neurotransmission, norepinephrine metabolism, and androgenic neurosteroid pathways. Considering critical histone function as damage-associated molecular patterns, variability in histone expression might have contributed to neuroimmune mechanisms and thus the pathogenesis of the psychiatric disorders. This study underscores the role of common nucleotide variants regulating histone genes in neuroimmune mechanisms contributing to SCZ, BIP, and MDD. These findings enhance our understanding of underlying molecular targets in their pathophysiology, with a potential strategy of precision psychiatry.
Cytomegalovirus-related immune activation and major neuropsychiatric disorders: Mendelian randomization and bulk transcriptomic validation.
Li J, Chen T
Psychiatry Res · 2026 Jun · PMID 42320286 · Publisher ↗
BACKGROUND: Cytomegalovirus (CMV) establishes lifelong latency and induces persistent changes in peripheral immunity. Observational links to neuropsychiatric disorders are difficult to interpret because of confounding an...
BACKGROUND: Cytomegalovirus (CMV) establishes lifelong latency and induces persistent changes in peripheral immunity. Observational links to neuropsychiatric disorders are difficult to interpret because of confounding and reverse causation. METHODS: We performed two-sample, multivariable, and bidirectional Mendelian randomization (MR) on genetically predicted CMV seropositivity and antigen-specific antibody levels for Parkinson's disease (PD), bipolar disorder (BD), schizophrenia (SCZ), major depressive disorder (MDD), and Alzheimer's disease (AD). Instruments were selected at P < 5 × 10 (secondary at P < 5 × 10). Multivariable MR (MVMR) sensitivity analysis accounting for age-related genetic structure, pleiotropy/heterogeneity tests, and reverse MR evaluated robustness. Bulk transcriptomic validation tested whether CMV CD14+ late-response and HCMV reactivation-related host signatures were enriched in public PD and BD whole-blood datasets. RESULTS: Genetically predicted anti-CMV IgG seropositivity was associated with PD (MVMR sensitivity aOR: 1.12, P = 0.020), with directional consistency in a single-instrument secondary analysis (OR: 1.25, P = 0.003). Anti-CMV pp28 antibody levels were associated with BD (aOR: 1.12, P = 0.043). The SCZ signal attenuated after adjustment, and no robust evidence was observed for MDD or AD. Both CMV-related host signatures were significantly enriched among PD-upregulated genes (CMV CD14+ NES = 1.85, FDR = 2.73 × 10; HCMV reactivation NES = 2.03, FDR = 9.11 × 10); BD showed no robust positive enrichment after covariate adjustment. CONCLUSIONS: Integrated genetic and transcriptomic evidence prioritizes CMV-related peripheral immune activation as a plausible contributor to PD risk. The pp28-BD signal is hypothesis-generating and requires validation in datasets with direct CMV serostatus.
Associations between metabolic features and psychotic symptoms in anxious depression across different ages of onset: the mediating role of symptom severity.
Sun QM, Zhang S, Wang C … +1 more , Zhang XY
Psychiatry Res · 2026 Jun · PMID 42320285 · Publisher ↗
BACKGROUND: Anxiety and psychotic symptoms frequently occur in major depressive disorder (MDD) and are associated with metabolic features. Age of onset plays a crucial role in the variability of MDD patients. AIMS: To in...
BACKGROUND: Anxiety and psychotic symptoms frequently occur in major depressive disorder (MDD) and are associated with metabolic features. Age of onset plays a crucial role in the variability of MDD patients. AIMS: To investigate associations between metabolic features and psychotic symptoms in anxious MDD patients categorized by age of onset. METHODS: We conducted a cross-sectional analysis involving 894 anxious MDD patients, utilizing standardized assessments for depression (Hamilton Depression Scale, HAMD), anxiety (Hamilton Anxiety Scale, HAMA), and psychotic symptoms (Positive and Negative Syndrome Scale, PANSS), alongside metabolic features. Patients were categorized into the early adulthood onset (EAO, <45 years old) group and the mid-adulthood onset (MAO, ≥45 years old) group. RESULTS: Our findings revealed that MAO patients exhibited higher prevalence of psychotic symptoms compared to EAO group. In the EAO group, fasting blood glucose (FBG) was independently associated with a higher risk of psychotic symptoms, while in the MAO group, triglyceride (TG) levels and systolic blood pressure (SBP) showed independent associations with increased risk. Exploratory serial mediation analyses indicated that depression/anxiety severity fully statistically mediated the relationship between FBG and psychotic symptoms in EAO patients. Additionally, symptom severity fully statistically mediated the SBP-psychotic symptoms link in MAO patients but only partially statistically mediated the TG-psychotic symptoms relationship. CONCLUSIONS: These results underscore the complex interplay between psychological and physiological factors in anxious MDD, indicating that age of onset group statistically moderated the associations between metabolic features and psychotic symptoms, and depression/anxiety severity showed exploratory indirect associations consistent with statistical mediation between metabolic features and psychotic symptoms.
Machine learning algorithms in jointly developing prediction models for subsequent school bullying victimization in Chinese children and adolescents.
Peng J, Yang H, Lu J … +7 more , Xiang Y, Liu X, Wang X, Gao F, Zheng Y, Yang Z, Xiao Y
Psychiatry Res · 2026 Jun · PMID 42320284 · Publisher ↗
Existing evidence indicates that children and adolescents experiencing bullying victimization (BV) exhibit mental health deterioration, and such effects can persist into adulthood. As current decision-making tools are sc...
Existing evidence indicates that children and adolescents experiencing bullying victimization (BV) exhibit mental health deterioration, and such effects can persist into adulthood. As current decision-making tools are scarce, we aim to develop a tool to predict subsequent BV risk among Chinese youth. Data were retrieved from a three-wave prospective study which incorporated into the Mental Health Survey for Children and Adolescents in Yunnan (MHSCAY). Six common machine learning (ML) algorithms were used. We internally validated the models using 500 times bootstrap approach to assess discrimination, calibration, and utility. A total of 5345 participants aged 10-17 years completed the survey. The internal validation showed the logistic regression (LR) model slightly outperformed other ML algorithms and exhibited more evenly distributed individual-level prediction uncertainty. It was therefore selected as the final model, achieving an AUROC of 0.800 (95% CI: 0.785, 0.815), AUPRC of 0.519 (95% CI: 0.483, 0.553), calibration intercept of -0.001 (95% CI: -0.076, 0.069), calibration slope of 0.990 (95% CI: 0.930, 1.059), and Brier score of 0.122 (95% CI: 0.117, 0.128). Furthermore, the calibration plot indicated excellent precision, and positive net benefits were observed across broad threshold ranges. Fairness analysis revealed no predictive bias in key subpopulations. This novel predictive tool utilizes seven baseline predictors that are readily accessible to generate accurate, individualized predictions of subsequent BV risk in children and adolescents. Upon further validation, the model may facilitate risk stratification, thereby guiding resource allocation and informing targeted interventions for potential BV crises among Chinese children and adolescents.
Retraction notice to Analysis of the application value of multi-slice spiral CT and multimodal MRI in screening patients with bipolar disorder [Psychiatry Research: Neuroimaging 358 (2026) 112178].
Si Y, Yu Y, Fan H … +1 more , Xing Z
Psychiatry Res Neuroimaging · 2026 Jun · PMID 42315454 · Publisher ↗
Abstract loading — click title to view on PubMed.
Multi-omics integration identifies cell-type-specific CISD1 and QDPR as druggable regulators linking hepatocellular carcinoma and major depressive disorder.
Sun L, Bi J, Sun J
Psychiatry Res · 2026 Jun · PMID 42314449 · Publisher ↗
BACKGROUND: Hepatocellular carcinoma (HCC) and major depressive disorder (MDD) often co-occur, but whether this comorbidity reflects shared pharmacologically tractable biology remains unclear. METHODS: We integrated a th...
BACKGROUND: Hepatocellular carcinoma (HCC) and major depressive disorder (MDD) often co-occur, but whether this comorbidity reflects shared pharmacologically tractable biology remains unclear. METHODS: We integrated a three-step mendelian randomization strategy-comprising whole-blood eQTL screening, GTEx eQTL validation, and single-cell immune eQTL refinement-to systematically screen 2,534 druggable genes, to identify targets with consistent causal effects across two diseases. Subsequently, we dissected their potential mediating pathways using 91 circulating inflammatory proteins and 338 cerebrospinal fluid metabolites as mediators, and subsequently validated repurposable compounds via molecular docking. RESULTS: Two genes satisfied all stringent criteria: CISD1and QDPR. Immune-cell-specific expression patterns of CISD1 in CD4 NC, CD8 ET, and NK cells increased shared risk, whereas QDPR transcription in CD4 ET, CD4 NC, CD8 ET, Mono C, and NK R cells reduced risk. Alpha-hydroxyisovalerate mediated 6.98% (HCC) and 8.75% (MDD) of CISD1's causal effect; N-acetyl-β-alanine accounted for 16.15% (HCC) and 8.13% (MDD) of QDPR's protective effect. Dihydroergotamine, an FDA-approved antimigraine agent, exhibited high binding affinity for both proteins (-9.3 and -10.2 kcal mol⁻¹), nominating it as a dual-indication candidate. CONCLUSIONS: CISD1and QDPRconstitute genetically supported, druggable regulators that converge mechanistically on metabolic-inflammatory axes associated with HCC-MDD comorbidity. Repurposing dihydroergotamine warrants immediate preclinical evaluation for simultaneous oncologic and psychiatric benefits.
Machine learning-based prediction of suicide risk: A long-term OCD cohort follow-up.
Tubío-Fungueiriño M, Puialto M, Cernadas E … +6 more , Fernández-Delgado M, Real E, Alonso P, Carracedo A, Segalàs C, Fernández-Prieto M
Psychiatry Res · 2026 Jun · PMID 42314448 · Publisher ↗
BACKGROUND: Obsessive Compulsive Disorder (OCD) is associated with an increased suicide risk in comparison with general population. Among the suicide risk factors we can highlight psychiatric comorbidities, childhood tra...
BACKGROUND: Obsessive Compulsive Disorder (OCD) is associated with an increased suicide risk in comparison with general population. Among the suicide risk factors we can highlight psychiatric comorbidities, childhood trauma, obsessive-compulsive severity and a combination of other risk factors related to social and family environment. This study aims to develop and validate a reliable machine learning algorithm to predict suicide risk in OCD patients, based on a combination of clinical and sociodemographic variables. METHOD: A cohort of 199 OCD patients was followed for an average of 17.8 years (follow-up time range of between 2-28 years) in an OCD-specialized unit. Suicide-related behaviors were documented in the medical records of each participant at the time of occurrence. For the present study, a specialized psychiatrist systematically reviewed all clinical records to extract detailed information on the type of suicide-related behaviors experienced by each participant. Clinical data, including comorbidities, Y-BOCS and CTQ, were collected and used to train supervised machine learning models. RESULTS: The best-performing model included three predictive variables: family history of suicide, affective comorbidities, and substance use. This model achieved a sensitivity of 71.4%, specificity of 74.4%, F1-score=62.7% and area under ROC curve of 0.80, demonstrating moderate predictive capability. OCD severity and childhood trauma did not significantly enhance prediction performance. CONCLUSION: This study highlights the potential of supervised machine learning in identifying suicide risk in OCD patients based on commonly-collected clinical variables. The presence of any of the three predictors that conform the best-performing model (family history of suicide, affective comorbidities, and substance use) is sufficient to detect the presence of suicide-behavior risk. Those predictors are routinely assessed in clinical practice, making this model a feasible instrument for early risk detection. Further research with larger and more diverse cohorts is needed to refine predictive accuracy and integrate additional biomarkers for improved suicide risk stratification.
Anhedonia in patients with major depressive disorder (MDD): State-of-the-art consensus review.
Cutler AJ, Clayton AH, Krystal AD … +3 more , Maletic V, McIntyre RS, Nemeroff CB
Psychiatry Res · 2026 May · PMID 42314447 · Publisher ↗
Anhedonia is a highly prevalent core pathognomonic symptom of a major depressive episode seen in major depressive disorder (MDD) and bipolar disorder. The symptom is associated with functional impairment, increased suici...
Anhedonia is a highly prevalent core pathognomonic symptom of a major depressive episode seen in major depressive disorder (MDD) and bipolar disorder. The symptom is associated with functional impairment, increased suicidality, psychiatric and somatic comorbidity, and poor therapeutic outcomes. It is an understudied and undertreated symptom, without clinically practical scales to measure its different aspects, and there are no therapies specifically designed to target anhedonia. This expert consensus aims to increase the awareness and understanding of anhedonia among healthcare professionals (HCPs) and provide consensus recommendations on the assessment, management, and treatment of anhedonia in patients with MDD. A group (N=6) of psychiatrists formed an expert consensus group that convened in a teleconference meeting in January 2024 to develop 14 consensus statements that provide an overview of the assessment, management, and treatment of anhedonia in patients with MDD. Anhedonia is a highly prevalent symptom of MDD, and it includes anticipatory, consummatory, and cognitive aspects. Several tools exist to assess the specific components of anhedonia, and the neural circuitry mediating anhedonia continues to be elucidated. The expert opinions provided by the authors aim to increase HCP awareness and understanding of anhedonia, and improve the evaluation, management, and treatment of anhedonia in patients with MDD. HCPs should routinely evaluate all aspects of anhedonia using clinical scales and welcome novel treatments that meaningfully target anhedonia. Future studies should assess the effectiveness of treatments for anhedonia and improve measurement-based initial assessment and monitoring of outcomes with comprehensive tools.
Adjunctive intermittent theta-burst stimulation for first-episode schizophrenia: A randomized clinical trial.
Zhang K, Xue H, Wang C … +3 more , Li N, Sun M, Cui J
Psychiatry Res · 2026 May · PMID 42308997 · Publisher ↗
BACKGROUND: The efficacy of intermittent theta-burst stimulation (iTBS) combined with pharmacotherapy and psychotherapy in first-episode schizophrenia remains unclear. This study evaluated adjunctive iTBS with risperidon...
BACKGROUND: The efficacy of intermittent theta-burst stimulation (iTBS) combined with pharmacotherapy and psychotherapy in first-episode schizophrenia remains unclear. This study evaluated adjunctive iTBS with risperidone and cognitive behavioral therapy (CBT) and explored serum biomarkers indicating treatment response. METHODS: In this randomized, assessor-blind trial, 100 first-episode schizophrenia patients received either iTBS plus risperidone and CBT (iTBS group, n = 50) or risperidone and CBT alone (control, n = 50) for 3 months. The primary outcome was change in PANSS total score at 4 weeks and 3 months. Response was defined as a ≥ 50 % PANSS reduction. Secondary outcomes included cognitive function (MCCB subtests) and serum GDNF, cortisol, and dehydroepiandrosterone sulfate (DHEA-S) levels. RESULTS: The iTBS group showed significantly greater reduction in PANSS total scores than controls at both 4 weeks and 3 months (mean difference at 3 months: -13.3, 95 % CI: -16.8 to -9.8; P < 0.001), with a higher responder rate (76 % vs. 48 %). Significant improvements across all cognitive domains were observed in the iTBS group (all P < 0.001). Post-treatment, the iTBS group exhibited higher GDNF and lower cortisol and DHEA-S levels (all P < 0.001). A combined biomarker panel demonstrated superior discriminative performance for treatment efficacy (AUC=0.865 after cross-validation). Adverse events were comparable between groups. CONCLUSIONS: Adding iTBS to risperidone and CBT significantly improves clinical symptoms and cognitive function in first-episode schizophrenia. The combination of GDNF, cortisol, and DHEA-S shows promise as a composite biomarker for treatment response, though sham-controlled validation is warranted.
Pilot randomized trial of intermittent theta-burst stimulation versus H-Coil transcranial magnetic stimulation for treatment-resistant depression.
Desbeaumes Jodoin V, Bousseau E, Trottier-Duclos F … +10 more , Jutras-Aswad D, Lespérance F, Assi ÉB, Nguyen DK, Blumberger DM, Baker TE, Arns M, Daskalakis ZJ, Lespérance P, Miron JP
Psychiatry Res · 2026 Jun · PMID 42302659 · Publisher ↗
BACKGROUND: Intermittent theta burst stimulation (figure-8-coil iTBS) and H7-coil repetitive transcranial magnetic stimulation (rTMS) are FDA-cleared treatments for major depression; yet their comparative effectiveness i...
BACKGROUND: Intermittent theta burst stimulation (figure-8-coil iTBS) and H7-coil repetitive transcranial magnetic stimulation (rTMS) are FDA-cleared treatments for major depression; yet their comparative effectiveness in treatment-resistant depression (TRD) has not been evaluated in randomized trials. This pilot randomized trial was designed to obtain preliminary comparative estimates and to explore whether baseline cognitive functioning relates to early remission. METHODS: Twenty-eight adults with TRD were randomized to six weeks of figure-8-coil iTBS delivered to the dorsolateral prefrontal cortex (DLPFC) (n = 15) or H7-coil rTMS delivered to the dorsomedial prefrontal cortex (DMPFC) (n = 13). The primary outcome was change in 17-item Hamilton Depression Rating Scale (HRSD-17) score from baseline to week 6, analyzed with ANCOVA. Additional outcomes included response, remission, and symptom trajectories through week 18. Exploratory analyses examined the association between baseline cognitive functioning, such as executive functions and memory, and remission. RESULTS: Twenty-five participants completed all 30 sessions. Adjusted week-6 HRSD-17 scores did not differ between groups (mean difference -0.40, 95% CI -5.23 to 4.43; p=.865). Response rates were 40.0% for figure-8-coil iTBS and 50.0% for H7-coil rTMS (p>.60), and remission rates were identical across groups (20.0%). Remitters showed higher baseline executive functioning than non-remitters in exploratory analyses, although these associations were not confirmed in adjusted models. CONCLUSION: In this pilot trial, figure-8-coil iTBS and H7-coil rTMS showed symptom improvement, with no clear between-group differences. Exploratory findings suggest a potential signal involving executive functioning that warrants further investigation. These results inform the feasibility and design of larger comparative trials. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05902312).
Explainable EEG-based prediction of depression therapy outcomes using local Fibonacci pattern analysis.
Akbari H, Liang Z, Najafabadi NN … +5 more , Alsaid M, Rostami R, Kazemi R, Mete M, Sadiq MT
Psychiatry Res Neuroimaging · 2026 Jun · PMID 42302544 · Publisher ↗
Depression treatment outcomes remain difficult to predict for selective serotonin reuptake inhibitor (SSRI) and repetitive transcranial magnetic stimulation (rTMS) therapies, leading to trial-and-error treatment selectio...
Depression treatment outcomes remain difficult to predict for selective serotonin reuptake inhibitor (SSRI) and repetitive transcranial magnetic stimulation (rTMS) therapies, leading to trial-and-error treatment selection and delayed clinical benefit. This study develops an EEG-based prediction model using local Fibonacci pattern (LFP) features to classify treatment responders before therapy initiation. Pre-treatment EEG signals from the Mumtaz SSRI dataset and the Atieh Hospital rTMS datasets were processed using finite impulse response filtering and multi-scale principal component analysis. The LFP method extracts nonlinear temporal features directly from 19-channel EEG recordings through Fibonacci-indexed local differences and histogram encoding. Features were ranked using neighborhood component analysis and classified with a feedforward neural network under segment-level 10-fold cross-validation. The model achieved accuracies of 99.12% for SSRI, 100.00% for the small rTMS dataset, and 94.51% for the big rTMS dataset under this protocol. To address the risk of overfitting and to provide a stricter estimate of subject-level generalization, subject-wise leave-one-subject-out (LOSO) validation was also performed. Under LOSO validation, the corresponding accuracies were 61.83%, 77.93%, and 71.57%, with balanced accuracy/macro-F1 values of 59.07%/59.16%, 65.49%/66.68%, and 75.43%/71.51% for SSRI, small rTMS, and big rTMS datasets, respectively. Key discriminative channels corresponded to frontal, temporal, and parietal regions involved in emotion regulation and cognitive control. The revised findings support LFP as a computationally efficient EEG representation, but the lower LOSO results indicate that larger independent cohorts are required before any clinical-deployment claim can be made.
Dominant cross-frequency analysis-based early diagnosis of Autism Spectrum Disorder in pediatrics using FRHIS and DSTCNN.
Anithalakshmi V, Thiagarajan R
Psychiatry Res Neuroimaging · 2026 Jun · PMID 42302543 · Publisher ↗
Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition that affects children's cognitive, social, and behavioral skills. Thus, early diagnosis of ASD for children below 2 years old is very significant...
Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition that affects children's cognitive, social, and behavioral skills. Thus, early diagnosis of ASD for children below 2 years old is very significant. Nevertheless, the prevailing works overlooked the depth of the dynamic frequency band variation during ASD detection, leading to misclassification. Therefore, a dominant cross-frequency analysis-based early ASD detection in pediatrics using a Fuzzy Root Hesitant Inference System (FRHIS) and Deep Squared Tau Convolutional Neural Network (DSTCNN) is proposed in this paper. Primarily, the Electroencephalogram (EEG) signals of children below 2 years old are gathered. Then, the artifacts are removed using the EWI-BPF. Afterward, by using Variation Frechet Distribution Mode Decomposition (VFDMD), the signals are decomposed. Then, by utilizing the FRHIS, the frequency bands are determined, followed by non-linear variables and feature extraction. In the meantime, the cross-frequency analysis is carried out by estimating the modulation depth using the Phase Amplitude Method (PAM) and determining the cross-frequency strength. Then, to select optimal features, the CRBM is applied. Lastly, the ASD classification is carried out using DSTCNN. When analogized with other traditional works, the proposed work attained superior performance in detecting ASD for pediatrics with a higher accuracy of 99.03%.
Towards a rational ECT treatment approach for severe and treatment resistant schizophrenia using neuroimaging biomarkers: A focused review.
Beckett HT, McClure A, Nasrallah H
Psychiatry Res Neuroimaging · 2026 Jun · PMID 42302542 · Publisher ↗
Treatment resistant schizophrenia (TRS) is a major challenge in psychiatry, and its management remains an unmet need. Given the relatively high prevalence of resistance to first line antipsychotics (35%) and to clozapine...
Treatment resistant schizophrenia (TRS) is a major challenge in psychiatry, and its management remains an unmet need. Given the relatively high prevalence of resistance to first line antipsychotics (35%) and to clozapine (55%) as a last resort agent, electroconvulsive therapy (ECT) represents an important alternative modality for management of psychosis in TRS and is usually used only after non-response to clozapine, despite evidence suggesting earlier intervention with ECT may be more beneficial in TRS. Here, we discuss recent developments in the use of machine learning models and radiomics to employ neuroimaging markers for predicting ECT treatment response in patients with TRS. The orbitofrontal gyrus, temporal lobe, and limbic structures are regions implicated in the neuropathology of schizophrenia and areas of interest that overlap between these machine learning models. The successful development of these experimental models represents a significant step towards the development of clinical tools that may one day guide treatment decisions for patients identified to have TRS, matching them to effective treatments such as ECT. These experimental models may assist in the development of more personalized treatment protocols, which in turn may decrease duration of untreated psychosis and result in better outcomes for patients with TRS.
Suicide attempt-related gray matter volume alterations in major depressive disorder and its associations with childhood trauma.
Jung M, Park J, Kang Y … +5 more , Shin D, Kang JC, Tae WS, Ham BJ, Han KM
Psychiatry Res · 2026 Jun · PMID 42296874 · Publisher ↗
BACKGROUND: Suicide is a major cause of premature mortality in major depressive disorder (MDD), yet its neurobiological substrates remain unclear. Suicide attempters represent a distinct phenotype characterized by impair...
BACKGROUND: Suicide is a major cause of premature mortality in major depressive disorder (MDD), yet its neurobiological substrates remain unclear. Suicide attempters represent a distinct phenotype characterized by impaired affect regulation and fronto-limbic structural alterations. Early-life adversity may further sensitize stress-responsive circuits. This study examined gray matter volume (GMV) alterations associated with suicide attempts in MDD and their relationships with childhood trauma and suicidal ideation. METHODS: T1-weighted magnetic resonance imaging data were obtained from 204 adults (61 suicidal depression [SD], 62 non-suicidal depression [NSD], 81 healthy controls [HC]). Whole-brain voxel-based morphometry (VBM) identified regions differentiating SD from NSD. The cluster showing a significant SD-NSD GMV difference was defined as the region of interest (ROI), and its GMV was correlated with Childhood Trauma Questionnaire (CTQ) and Suicidal Ideation Scale (SSI) scores. Exploratory voxel-wise regression assessed childhood trauma-related GMV patterns across groups. RESULTS: VBM revealed reduced GMV in the septal nucleus, distinguishing SD from NSD. ROI analyses confirmed lower septal GMV in SD than in NSD and HC, and smaller volume correlated with greater suicidal ideation in patients with MDD. Within the SD group, voxel-wise regression showed negative associations between CTQ scores and GMV in the cerebellar vermis. CONCLUSION: Distinct gray matter alterations emerged in MDD, with septal atrophy appearing as a potential suicide-related feature, while cerebellar vermis reductions showed a tentative association with early adversity that warrants cautious interpretation. These findings suggest that interrelated limbic, cerebellar, and septal regions may contribute to stress regulation and suicidality in depression.
Decoding psychotic and other psychiatric disorders through placental organ: From cytoarchitecture to stress-responsive signaling networks.
García-Montero C, Fraile-Martinez O, Álvarez-Mon MÁ … +13 more , Lahera G, Ibañez Á, Presa M, Sáez MA, Bujan J, García-Honduvilla N, Barrena-Blázquez S, De León-Luis JA, Bravo C, Lopez-Gonzalez L, Díaz-Pedrero R, Álvarez-Mon M, Ortega MA
Psychiatry Res · 2026 Jun · PMID 42296873 · Publisher ↗
Psychosis-defined as a profound disconnection between objective external reality and internal mental representations-represents a critical mental health condition, especially when it emerges for the first time during pre...
Psychosis-defined as a profound disconnection between objective external reality and internal mental representations-represents a critical mental health condition, especially when it emerges for the first time during pregnancy. First-episode psychosis (FEP) in pregnant women (FEP-PW), though rare, presents severe risks for both maternal and fetal health. Despite its clinical significance, the biological mechanisms underlying FEP-PW remain poorly understood. This narrative review investigates the human placenta as a valuable and dynamic organ for decoding psychiatric and psychological disorders, particularly FEP-PW, but also encompassing conditions such as psychological stress, metabolic stress, post-traumatic stress disorder (PTSD), affective disorders, anxiety, schizophrenia, and broader psychotic syndromes. We define FEP and its unique presentation during pregnancy, highlighting clinical aspects and current diagnostic challenges. Central to this review is the evolving recognition of the placenta, not as a mere byproduct of parturition, but as a biologically active interface that influences both maternal mental health and fetal neurodevelopment. We argue for the importance of studying the human placenta directly, given the species-specific nature of genomic imprinting and stress-responsive signaling, which limits the translational power of animal models. Accumulating evidence reveals that maternal psychological and metabolic stressors can alter placental structure and function, with implications for both maternal psychiatric vulnerability and the long-term mental health of the offspring. These findings support the Developmental Origins of Health and Disease (DOHaD) framework and suggest that placental molecular dysregulation may contribute to the intergenerational transmission of psychiatric risk. By integrating diverse biological and clinical perspectives, this review positions the placenta as a critical organ for future research in perinatal psychiatry and mental health. Emerging strategies such as the analysis of placenta-derived extracellular vesicles (EVs) offer a promising, non-invasive avenue to monitor placental molecular activity in real time. By integrating EV-based transcriptomic and proteomic profiling from maternal blood, future research may identify biomarkers of stress-related placental dysfunction linked to maternal psychiatric conditions. This approach holds strong potential to bridge current gaps in perinatal mental health research, aligning with the DOHaD framework to inform early detection and intervention strategies.
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