Psychiatry Research[JOURNAL]
Sun
200 papers
Quantum convolution searched binary neural networks based autism spectrum disorder detection using MRI images in cloud computing.
George GM, Kumareshan N
Psychiatry Res Neuroimaging · 2026 Sep · PMID 42102471 · Publisher ↗
Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that causes discrepancies in social interaction and behavioral changes. The developments of neuroimaging techniques, like Magnetic Resonance Imaging (MRI) is...
Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that causes discrepancies in social interaction and behavioral changes. The developments of neuroimaging techniques, like Magnetic Resonance Imaging (MRI) is employed to detect brain abnormalities. Due to the heterogeneity of disease severity and symptoms, the detection of ASD is difficult. To solve such complexity, a novel model named Fractional Painting Training Based Optimization trained Quantum Convolution Searched Binary Neural Network (FPTO_QCSBNN) is proposed for ASD detection in cloud. A cloud-based detection system offers the analysis and storage of large-scale neuroimages. Moreover, it provides faster diagnosis with scalable storage. Initially, the cloud system is simulated, and pre-processing is done using Mid-Point filter and Region of Interest (ROI) extraction. Image enhancement is done by gamma correction method, and pivotal region is extracted using functional connectivity. The optimal grid selection in pivotal region extraction is done using FPTO, and features are extracted from enhanced image. Using features and pivotal region extracted image, QCSBNN detects ASD, and it is trained by FPTO. Furthermore, developed FPTO_QCSBNN attains the accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) of 91.37%, 91.32%, and 91.89%.
Functional dysconnectivity of large-scale functional brain networks in young adults with bipolar disorder with and without low-grade inflammation.
Hsu TW, Li JR, Tsai SJ … +6 more , Bai YM, Su TP, Chen TJ, Hsu JW, Chen MH, Liang CS
Psychiatry Res · 2026 Aug · PMID 42096722 · Publisher ↗
Bipolar disorder (BD) is associated with both functional brain network disruptions and low-grade peripheral inflammation, yet their interplay remains poorly understood. This study aimed to investigate whether low-grade i...
Bipolar disorder (BD) is associated with both functional brain network disruptions and low-grade peripheral inflammation, yet their interplay remains poorly understood. This study aimed to investigate whether low-grade inflammation is linked to altered functional connectivity across major brain networks in young individuals with BD. A total of 160 young adults with BD and 93 age-and sex-matched controls were included. Resting-state functional images were obtained using 3T magnetic resonance imaging (fMRI), and seed-based connectivity (SBC) analyses were conducted to map functional connectivity (FC) patterns with specific regions of interest (ROIs) from well-established resting-state networks, including the Default Mode Network (DMN), Salience Network (SN), Frontoparietal Network (FPN), and reward network. Fasting plasma C-reactive protein (CRP) level were measured, and low-grade inflammation (LGI) was defined based on CRP levels of ≥3 mg/L. There was a total of 27 participants with BD in the LGI group and 133 in the non-LGI group. SBC analyses showed increased FC within the SN in the LGI group compared with HCs, whereas the non-LGI group showed numerically intermediate values (e.g. ACC-precentral gyrus connectivity: LGI > non-LGI > HC, all pairwise comparisons significant). A similar pattern was observed for FC between the ventral tegmental area and the bilateral supramarginal gyrus (LGI > non-LGI > HC) within the reward network. Within the DMN, both BD groups showed decreased functional connectivity between the medial prefrontal cortex and the left putamen compared with the HC group. Low-grade inflammation is linked to distinct brain connectivity changes in young individuals with bipolar disorder, highlighting the role of neuroimmune mechanisms in its pathophysiology.
Diagnosing autism spectrum disorders using ensemble-aided weighted fused features and attention-based residual LSTM with brain MRI images.
V N, K A
Psychiatry Res Neuroimaging · 2026 Sep · PMID 42090939 · Publisher ↗
One of the neuro-developmental conditions is called Autism Spectrum Disorder (ASD), which causes changes in the behavior of the patients, and it delays language and social interactions. Details about the functional activ...
One of the neuro-developmental conditions is called Autism Spectrum Disorder (ASD), which causes changes in the behavior of the patients, and it delays language and social interactions. Details about the functional activity of the brain are provided by Magnetic Resonance Imaging (MRI). Studying each MRI scan of the patients is laborious and time-consuming for doctors and specialists. To tackle these limitations, this paper develops an advanced deep learning diagnosis method. In the beginning, the necessary MRI images are gathered from the available data resource. The input brain images are subjected to an Ensemble Deep Convolutional Neural Network (EDCNN) for feature extraction, which makes the diagnosis easier by reducing the complexities. The ensemble model is created by the integration of the Visual Geometry Group (VGG16), Residual Network (ResNet), and Inception approaches. Further, the resultant features are fused with weights that are optimized using the Improved Random Uniform Number-aided Humboldt Squid Optimization Algorithm (IRUN-HSOA); thus, the weighted fused feature is obtained. The resultant weighted fused feature is fed into Attention-based Residual Long Short-Term Memory (ARLSTM) for the ASD diagnosis. Further, the developed model is compared with different state-of-the-art techniques, and the suitability of the model is discussed for prospects.
Baseline glymphatic efficiency is associated with plasma BDNF changes following rTMS: an exploratory biomarker study in mood disorders.
Hsieh YC, Huang SM, Lu TH … +5 more , Chang WH, Bui VS, Tseng HH, Lin SH, Chen PS
Psychiatry Res Neuroimaging · 2026 Sep · PMID 42090938 · Publisher ↗
Brain-derived neurotrophic factor (BDNF) has been proposed as a potential biological correlate of repetitive transcranial magnetic stimulation (rTMS). However, its relationship with clinical and cognitive outcomes in moo...
Brain-derived neurotrophic factor (BDNF) has been proposed as a potential biological correlate of repetitive transcranial magnetic stimulation (rTMS). However, its relationship with clinical and cognitive outcomes in mood disorders remains unclear. In this prospective exploratory pre-post study, 28 adults with major depressive disorder or bipolar disorder underwent 12 sessions of high-frequency left dorsolateral prefrontal cortex (DLPFC) rTMS while continuing pharmacological treatment. Assessments included Hamilton Rating Scale for Depression (HDRS-24), Wisconsin Card Sorting Test (WCST), plasma BDNF, and baseline glymphatic efficiency (diffusion tensor image analysis along the perivascular space (DTI-ALPS)). Over the study period, HDRS-24 scores and WCST perseverative errors decreased, whereas the number of WCST categories completed did not significantly change at the group level; peripheral plasma BDNF also increased. Greater BDNF change ratios were associated with higher baseline ALPS indices and with individual variability in WCST categories completed, but not with changes in depressive symptoms or perseverative errors. Given the uncontrolled design, these longitudinal changes should be interpreted as associations observed over time rather than treatment effects attributable to rTMS. Baseline glymphatic efficiency may therefore reflect an individual biological characteristic associated with neurotrophic responsiveness to rTMS. These exploratory findings require replication in larger controlled studies using standard clinical protocols.
Alzheimer's disease with progression analysis using a novel dilated convolutional attention based long short term memory model.
Rudraraju A, Lakshmi SV
Psychiatry Res Neuroimaging · 2026 Sep · PMID 42090937 · Publisher ↗
Alzheimer's disease (AD) is an irreversible neurodegenerative syndrome that affects memory, cognitive abilities and behaviour. Detecting AD in the early stage is crucial to improve the quality of life. However, tradition...
Alzheimer's disease (AD) is an irreversible neurodegenerative syndrome that affects memory, cognitive abilities and behaviour. Detecting AD in the early stage is crucial to improve the quality of life. However, traditional diagnostic approaches and manual analysis of neuroimaging data are slow, subjective and lead to human mistakes. Existing machine learning techniques often have difficulty in identifying complex patterns in high-dimensional biomedical data. These drawbacks emphasize the necessity for a more efficient and automated diagnostic system. This study introduced a new deep learning based hybrid framework for classifying and predict progression of AD. The method comprises three main steps: data acquisition, feature extraction and classification. Initially, EEG signals are collected from the CAU-EEG dataset. Then, features such as time domain features, frequency domain features and time frequency domain features are extracted. Finally, classification is performed by dilated convolutions attention based long short term memory (DC-ALSTM). Investigational results show that the proposed model outperforms existing baseline methods. DC-ALSTM achieved a classification 99.26% accuracy, 99.21% precision, recall at 99.23% and 99.22% F1-score, which indicates outstanding diagnostic capability.
Causal ordinal connections based characterization of weighted effective brain network for schizophrenia detection.
Kose MR, Ahirwal MK, Atulkar M
Psychiatry Res Neuroimaging · 2026 Sep · PMID 42090936 · Publisher ↗
The human brain is responsible for a wide range of a person's behavioral and cognitive capabilities. The functionality of the brain is affected by various disorders like schizophrenia, epilepsy, and Alzheimer. This study...
The human brain is responsible for a wide range of a person's behavioral and cognitive capabilities. The functionality of the brain is affected by various disorders like schizophrenia, epilepsy, and Alzheimer. This study presents a novel framework for the automated detection of schizophrenia using EEG-based Weighted Effective Brain Connectivity Networks (WEBCNs). The proposed method introduces a new network descriptor called Weighted Directed Ordinal Connection (WDOC) that integrates causal directionality, connection strength, and ordinal relation between edges to capture complex brain dynamics. EEG signals from schizophrenia patients and healthy controls are preprocessed and transformed into WEBCNs using four causal connectivity estimators: Directed Transfer Function (DTF), Granger Causality (GC), Partial Directed Coherence (PDC), and Transfer Entropy (TE). WDOC-based features are extracted and classified using multiple machine learning algorithms, including KNN, SVM (linear, polynomial, RBF), and Random Forest. Among all models, the SVM with RBF kernel achieved the best performance, yielding 94.44% accuracy, 95% precision, 94% recall, and 89% kappa score for PDC-based networks. Structural and statistical analyses confirm distinct topological alterations in the causal flow between frontal and parietal regions in schizophrenia. The results demonstrate that WDOC-based characterization enhances discriminative power and interpretability in effective brain network analysis.
Brain structural markers of suicidality change in adolescents: A longitudinal neuroimaging study of low family-neighborhood-school environmental risk.
Hong X, Cui X, Chen C … +3 more , Zhang Z, Jin Y, Wang Y
Psychiatry Res Neuroimaging · 2026 Sep · PMID 42090935 · Publisher ↗
Suicide is the leading global cause of death, particularly challenging in adolescent health. The current findings on brain regions associated with suicide are often confounded by environmental factors. Moreover, the emer...
Suicide is the leading global cause of death, particularly challenging in adolescent health. The current findings on brain regions associated with suicide are often confounded by environmental factors. Moreover, the emergence and persistence of suicidality remain largely unknown. Using the Adolescent Brain Cognitive Development study, we analyzed neuroimaging, suicidality, and environmental measures from 11,220 participants at baseline and 1-year-follow-up. Participants with low family-neighborhood-school environmental risk (risk score below mean) were grouped by suicidality changes across two timepoints: risk to risk (R-R), risk to no risk (R-NR), no risk to risk (NR-R), and no risk to no risk (NR-NR). Propensity score matching was performed on demographic variables, comparisons of brain volumes, cortical thickness, and surface area were conducted between R-R and R-NR groups, as well as NR-NR and NR-R groups, with matched sample size. Our results showed reduced gray matter volume in temporal cortex, parahippocampal, pallidum and hippocampus in the comparisons between NR-R and NR-NR (emergence of suicidality). Conversely, comparisons between R-R and R-NR (persistent suicidality) showed reduced gray matter volume in superior temporal, visual cortex and default mode network. These findings suggest that baseline differences in brain regions are distinctly associated with the emergence and persistence of suicidality among adolescents.
Anxiety, depression, and post-traumatic stress disorder among Palestinian refugees in Egypt: Gender-stratified item-level Bayesian network analysis.
Fadl N, Shahtou A, Own HM … +8 more , Alkasaby MA, Abdel-Fattah MA, Tafesh RMA, Alzaanin SH, Zourob HMM, Aljedaili MWA, Shaheen FIA, Abdullah RG
Psychiatry Res · 2026 Aug · PMID 42090876 · Publisher ↗
BACKGROUND: Mental disorders pose a substantial global burden, particularly among conflict-affected populations. This study aimed to examine gender-stratified, item-level networks of anxiety, depression, and posttraumati...
BACKGROUND: Mental disorders pose a substantial global burden, particularly among conflict-affected populations. This study aimed to examine gender-stratified, item-level networks of anxiety, depression, and posttraumatic stress disorder (PTSD) among Palestinian refugees in Egypt following the 2023 war on Gaza. METHODS: A cross-sectional study was conducted among 558 Palestinians aged > 18 years displaced to Egypt. Anxiety, depression, and PTSD were assessed using the Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, and Impact of Event Scale-6, respectively. Bayesian network analyses were applied to identify central symptoms and the strongest within- and cross-diagnostic associations. RESULTS: In the male network, suicidal ideation and loss of energy emerged as the most central symptoms. The strongest cross-diagnostic association was observed between anticipatory fear and depressed mood. Within diagnostic domains, the strongest associations were found between uncontrollable worry and excessive worrying (anxiety), loss of energy and appetite change (depression), and war-related intrusive thoughts and hypervigilance (PTSD). In the female network, psychomotor agitation or retardation and suicidal ideation were the most central symptoms. The strongest cross-diagnostic association was between trouble relaxing and anhedonia. The strongest within-domain associations were observed between feeling anxious and being easily annoyed (anxiety), loss of energy and depressed mood (depression), and war-related intrusive thoughts and reminders of war (PTSD). CONCLUSIONS: Identifying gender-specific core symptoms and both within- and cross-diagnostic associations in this vulnerable population is crucial to inform targeted interventions and reduce comorbidity.
Speech as an objective measure of psychomotor dysfunction in major depressive disorder: validation from non-speech motor measures.
Exton EL, Klemballa D, Walther S … +5 more , Letkiewicz AM, Keshet J, Mittal VA, Shankman SA, Goldrick M
Psychiatry Res · 2026 Aug · PMID 42086001 · Full text
Psychomotor dysfunction, which can manifest as slowing (psychomotor retardation; PmR) and jerkiness or restlessness (psychomotor agitation; PmA) often occurs in individuals with major depressive disorder (MDD). As psycho...
Psychomotor dysfunction, which can manifest as slowing (psychomotor retardation; PmR) and jerkiness or restlessness (psychomotor agitation; PmA) often occurs in individuals with major depressive disorder (MDD). As psychomotor dysfunction predicts a worse treatment response, accurately measuring and tracking PmR and PmA may improve outcomes. Traditionally, psychomotor dysfunction has been assessed using self-report or observer-based methods, which are often insensitive to subtle but potentially relevant movement abnormalities. Instrumental probes often require specialized equipment. Speech presents a promising option as it requires a significant motor component and is easy to collect. This study serves as a preliminary test of the association between speech indicators of PmR and PmA and manual motor tasks (handwriting velocity and force variability). Participants with current MDD (n = 36) and remitted MDD (n = 78) completed a diadochokinetic speech task, quickly repeating syllables in a sequence ("pataka" and "katapa"), as well as a test of handwriting velocity while drawing loops (PmR) and a test of variability in force applied to a transducer (PmA). Velocity of speech production and speech rate variability were automatically measured. For current MDD individuals, speech velocity was positively associated with the manual PmR measure (t(29) = 2.59, p < 0.05), and speech rate variability was positively associated with the manual PmA measure (t(33) = 2.952, p < 0.01). Effects were not significant for remitted MDD individuals, suggesting that this method may detect PmR/PmA only in those with acute depressive symptoms. These results suggest that diadochokinetic speech is a promising, objective measure of psychomotor dysfunction.
Multimodal deep learning neuroimaging approach to enhance CT-based diagnosis of Alzheimer's disease.
Abbas A, Tsai HC, Hsu YL … +4 more , Kenneth MJ, Hussain B, Lai LM, Hsu BM
Psychiatry Res Neuroimaging · 2026 Sep · PMID 42085916 · Publisher ↗
Neuroimaging plays a critical role in the diagnosis of Alzheimer's disease (AD), with Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) providing detailed structural and functional information for d...
Neuroimaging plays a critical role in the diagnosis of Alzheimer's disease (AD), with Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) providing detailed structural and functional information for deep learning (DL) based classification. However, their high cost and limited availability restrict widespread clinical use. Computed Tomography (CT), while affordable and widely accessible, is diagnostically insufficient for detecting subtle neurodegenerative changes associated with early AD. To address this limitation, this study proposes a multimodal DL framework that enhances CT-based AD diagnosis by leveraging complementary feature representations learned from MRI. A custom convolutional neural network (CNN) was trained and evaluated using paired CT and MRI data from the Open Access Series of Imaging Studies (OASIS-3). A total of 772 participants with available MRI and CT scans were selected based on Clinical Dementia Rating (CDR) scores and corresponding clinical diagnoses. Participants were categorized as Normal Control (NC) (CDR = 0, n = 300), mild cognitive impairment (MCI) (CDR = 0.5, n = 250), or AD (CDR ≥ 1, n = 222). The overall sex distribution comprised 352 males and 420 females. The CT-only model achieved an accuracy of 84%, with 92% sensitivity and 83% specificity for AD classification. The proposed multimodal model demonstrated superior performance, achieving 92% accuracy, 95% sensitivity, and 91% specificity. Importantly, during CT-only inference, the multimodal framework retained high diagnostic accuracy in identifying disease status, indicating effective transfer of MRI-derived features to CT. These results demonstrate a scalable solution for improving AD detection using CT imaging in resource-limited healthcare.
Is the Structure of ADHD stable across adulthood? Insights from the CAARS 2.
Clark H, Erhardt D, Sparrow E … +1 more , Solomon J
Psychiatry Res · 2026 Aug · PMID 42070363 · Publisher ↗
It is well established that ADHD persists into adulthood with some changes in the clinical presentation, but structural stability of ADHD across different stages of adulthood has rarely been examined. This study used a m...
It is well established that ADHD persists into adulthood with some changes in the clinical presentation, but structural stability of ADHD across different stages of adulthood has rarely been examined. This study used a multiple-group confirmatory factor analysis (CFA) approach to examine the measurement invariance of the latent 5-factor structure of the Conners Adult ADHD Rating Scales, 2nd Edition (CAARS 2) across stages of emerging (18-24 years), early (25-39 years), and middle adulthood (40-64 years). Data were drawn from the CAARS 2 general population and ADHD subsamples, including self-reported (n = 1684) and observer-generated (n = 1606) ratings. Results provide very strong evidence for configural, threshold, metric, and scalar invariance of the 5-factor correlated traits model across these three adult age groups for both Self-Report and Observer forms of the CAARS 2. This finding suggests a stable latent structure of ADHD across emerging, early, and middle adulthood. Evaluation and treatment of ADHD in adults may be enhanced by expanding beyond the two-factor DSM-5-TR model to reflect this 5-factor model (viz., Inattention/Executive Dysfunction, Hyperactivity, Impulsivity, Emotional Dysregulation, Negative Self-Concept), though further research on this important topic is needed. Additional clinical implications of the stability of the latent structure of ADHD across adulthood are discussed, along with methodological limitations and directions for future research.
Investigating neural correlates in non-prodromal individuals at familial high-risk for psychotic and bipolar disorders: A multimodal MRI approach.
Demir M, Demirlek C, Verim B … +9 more , Çimentepe-Sezer Ç, Eyüboğlu MS, Cesim E, Yalınçetin B, Süt E, Akdede BB, Baykara B, Zorlu N, Bora E
Psychiatry Res Neuroimaging · 2026 Aug · PMID 42070334 · Publisher ↗
Neuroimaging studies in familial high-risk (FHR) individuals are vital for identifying vulnerability markers independent of overt illness. However, research on purely non-prodromal FHR cohorts using comparative multimoda...
Neuroimaging studies in familial high-risk (FHR) individuals are vital for identifying vulnerability markers independent of overt illness. However, research on purely non-prodromal FHR cohorts using comparative multimodal approaches remains limited. This study addresses this gap through multimodal MRI analysis-including cortical morphometry, white matter microstructure, tractography, and functional connectivity-in non-prodromal FHR for psychosis (FHR-P, n = 18), bipolar disorder (FHR-BD, n = 19), and healthy controls (HC, n = 25). FHR-BD showed increased right inferior parietal surface area and right middle temporal volume compared to HC. Conversely, FHR-P exhibited reduced right superior frontal cortical thickness compared to FHR-BD and decreased left pallidum volume compared to HC. White matter analysis revealed significantly lower fractional anisotropy in FHR-P compared to both FHR-BD and HC. FHR-BD showed higher axial diffusivity than HC in the forceps minor, uncinate fasciculus, and right-fronto-occipital fasciculus. No significant differences were found in network-based statistics or graph theoretical measures. These findings reveal shared and distinct neurobiological alterations in non-prodromal FHR-P and FHR-BD, suggesting that grey and white matter disruptions constitute endophenotypes even without clinical symptoms. The lack of network-level findings may reflect the modest sample size, requiring further investigation in larger cohorts.
The manipulation map: How the Dark Triad shapes the brain.
Buravlova A
Psychiatry Res Neuroimaging · 2026 Aug · PMID 42068887 · Publisher ↗
Major advances have been made in understanding the biological mechanisms underlying the Dark Triad - Machiavellianism, narcissism, and psychopathy - yet existing research remains fragmented and rarely examines these trai...
Major advances have been made in understanding the biological mechanisms underlying the Dark Triad - Machiavellianism, narcissism, and psychopathy - yet existing research remains fragmented and rarely examines these traits as an integrated construct. Previous studies have identified structural and functional brain correlates of individual components, but findings are often inconsistent and isolated. This systematic review addresses this gap by synthesizing evidence from 16 empirical studies with a total sample of N = 4246 participants. Integrating results from neuroimaging, lesion, and genetic research, the review provides the first comprehensive overview of the neural architecture of the Dark Triad. The findings indicate a shared core of heightened striatal reward sensitivity, alongside distinct neural profiles: Machiavellianism is associated with enhanced prefrontal and insular activity, psychopathy with deficits in amygdala-orbitofrontal circuits, and narcissism with altered default mode network functioning related to self-referential processing. Overall, the Dark Triad is best understood as emerging from distributed, interacting neural systems rather than isolated brain regions.
Brain iron-sensitive markers (magnetic susceptibility and R2*) predict antidepressant response to ketamine in treatment-resistant depression.
Yonezawa K, Nakajima S, Shibukawa S … +15 more , Kan H, Ohtani Y, Nomoto-Takahashi K, Yatomi T, Tomiyama S, Nagai N, Kusudo K, Honda S, Hondo N, Takahashi K, Moriyama S, Yamada T, Koike S, Uchida H, Tani H
Psychiatry Res Neuroimaging · 2026 Aug · PMID 42068886 · Publisher ↗
BACKGROUND: Ketamine may alleviate treatment-resistant depression (TRD) primarily through glutamatergic modulation, with downstream dopaminergic activation. Iron plays an important role in monoaminergic metabolism, that...
BACKGROUND: Ketamine may alleviate treatment-resistant depression (TRD) primarily through glutamatergic modulation, with downstream dopaminergic activation. Iron plays an important role in monoaminergic metabolism, that is also implicated in the pathophysiology of TRD. Both Quantitative Susceptibility Mapping (QSM) and Effective Transverse Relaxation Rate (R2*) mapping can determine the extent of iron deposition in the brain. Given that abnormal iron accumulation may reflect dopamine dysfunction, we hypothesized that baseline magnetic substances could predict ketamine's antidepressant effects in patients with TRD. METHODS: We used data from a double-blind, randomized placebo-controlled trial followed by an extended single-arm open-label study to assess the efficacy of repeated intravenous ketamine in Japanese patients with TRD (jRCTs031210124). This study analyzed the data from the participants who underwent QSM and R2* mapping before receiving ketamine in either phase. Multivariable regression analyses were conducted to explore the association between baseline magnetic susceptibility and R2* with change in MADRS total and subdomain scores. RESULTS: This study included 17 patients with TRD (7 women; mean ± standard deviation age, 42.9 ± 10.6 years). Baseline magnetic susceptibility in the right nucleus accumbens negatively correlated with the change in MADRS retardation symptom scores (β = -0.73, p = 0.003). Moreover, baseline R2* in the left amygdala was negatively associated with the change in MADRS vegetative symptom scores (β = -0.71, p = 0.004). CONCLUSIONS: Baseline magnetic substances in the right nucleus accumbens and the left amygdala may be biomarkers to predict the effect of repeated ketamine infusions in patients with TRD.
Iron dysregulation across the schizophrenia lifespan: A systematic review from prenatal risk to postmortem findings.
Ramos MA, Hernández C, Arranz B
Psychiatry Res · 2026 Aug · PMID 42068734 · Publisher ↗
Iron plays a central role in neurodevelopment and dopaminergic regulation, yet its relationship with schizophrenia remains conceptually unresolved. Research conducted across the life span, from prenatal exposure to postm...
Iron plays a central role in neurodevelopment and dopaminergic regulation, yet its relationship with schizophrenia remains conceptually unresolved. Research conducted across the life span, from prenatal exposure to postmortem brain tissue, has produced fragmented findings without an integrating physiological framework. We aimed to systematically synthesize this literature and reinterpret prior findings through core principles of iron regulation, with particular attention to the distinction between absolute iron depletion and inflammation-driven functional restriction. We conducted a PROSPERO-registered systematic review (CRD42022382842) following PRISMA guidelines and included 51 studies spanning genetic risk, gestational exposure, peripheral biomarkers, neuroimaging, and postmortem analyses. Genetic studies do not implicate core iron-regulatory pathways in inherited schizophrenia risk. Large population-based cohorts consistently associate maternal iron deficiency during pregnancy with increased schizophrenia risk in offspring. Sixteen studies assessed adult peripheral iron markers; most report lower serum iron, although heterogeneity remains substantial. Few investigations evaluated regulatory markers such as hepcidin, and only one examined the hepcidin-ferroportin pathway directly. Neuroimaging studies in early psychosis report reduced subcortical iron alongside increased dopaminergic activity, whereas postmortem investigations describe cortical iron-ferritin decoupling in chronic stages. Across levels of analysis, apparent inconsistencies converge when iron physiology is considered. Absolute iron depletion and inflammation-driven functional iron restriction represent biologically distinct states that share reduced circulating iron but arise from different mechanisms. Stratifying schizophrenia according to iron phenotype offers a coherent framework to reinterpret prior evidence and guide future mechanistic research.
Characteristics of subjective well-being and communication in individuals with social anxiety disorder assessed through virtual reality tasks.
Cho Y, Kim S, Kim E … +4 more , Kim HE, Kim BH, Kim J, Kim JJ
Psychiatry Res · 2026 Aug · PMID 42068733 · Publisher ↗
Individuals with social anxiety disorder (SAD) have difficulty coping with social situations, resulting in a diminished quality of life. This study aimed to investigate subjective well-being and communication-related cha...
Individuals with social anxiety disorder (SAD) have difficulty coping with social situations, resulting in a diminished quality of life. This study aimed to investigate subjective well-being and communication-related characteristics using virtual reality (VR) tasks. Twenty-eight individuals with SAD and 25 healthy controls performed VR subjective well-being tasks (recognizing experience-based problems, expressing a future self-based success story, and expressing strengths) and VR communication tasks (exploring the communication style, practicing functional communication, and expressing empathy). The SAD group reported lower anti-difficulty, resolvability, and strength utilization scores and showed lower communication score in response to all dysfunctional communication styles (placating, blaming, computing, distracting) than the control group. These results suggest that diminished quality of life in individuals with SAD can be measured in terms of subjective well-being and communication using VR tasks. Multiple training regimes, including repeated execution of these tasks, may be necessary to improve their quality of life.
Transdiagnostic symptom networks of impulsivity, problematic smartphone use, and social media addiction: replication across two samples.
Gong Y, Zhou T, Zhou W … +6 more , Yang L, Li Y, Miao D, Qiu R, Zhu X, Guo Z
Psychiatry Res · 2026 Aug · PMID 42068732 · Publisher ↗
BACKGROUND: Impulsivity is associated with problematic smartphone use (PSU) and social media addiction (SMA). However, it remains unclear which specific impulsivity dimension and which dimension-symptom connections were...
BACKGROUND: Impulsivity is associated with problematic smartphone use (PSU) and social media addiction (SMA). However, it remains unclear which specific impulsivity dimension and which dimension-symptom connections were most important to these associations. In this study, network analysis was applied to examine the connections between impulsivity dimensions and the separate and comorbid symptoms of PSU and SMA. METHODS: Using cross-sectional data from two independent samples of Chinese adults-a main sample (n = 1047, aged 18-26, collected in 2023) and a replication sample (n = 325, aged 18-36, collected in 2022)-three regularized partial-correlation networks were constructed for each sample: an impulsivity-PSU network, an impulsivity-SMA network, and a combined impulsivity-PSU-SMA network. Bridge centrality was calculated to identify key transdiagnostic nodes, and network comparison tests (NCTs) were performed to evaluate the consistency of findings across samples. RESULTS: Across all the networks, motor impulsivity consistently emerged as the most central bridge node, showing robust connections to individual symptoms of both the PSU and the SMA, whether examined separately or comorbidly. Network comparison tests further confirmed that both the bridge centrality of motor impulsivity and its specific symptom-edge weights were comparable between the main and replication samples. CONCLUSIONS: These findings provide novel, symptom-level insight into how impulsivity-particularly motor impulsivity-contributes to the development and comorbidity of PSU and SMA. Motor impulsivity is identified as a key transdiagnostic bridge and a promising target for early intervention. The replication of the core results across independent samples strengthens the reliability of the findings.
Impact of second-generation antipsychotics on white matter microstructure in schizophrenia: A DTI study.
Chen W, Xu C, Deng W … +3 more , Liang J, Zhang C, Xie G
Psychiatry Res · 2026 Aug · PMID 42068731 · Publisher ↗
BACKGROUND: Second-generation antipsychotics are widely prescribed for schizophrenia and other psychiatric disorders; however, their potential effects on cerebral white matter (WM) microstructure and associated cognitive...
BACKGROUND: Second-generation antipsychotics are widely prescribed for schizophrenia and other psychiatric disorders; however, their potential effects on cerebral white matter (WM) microstructure and associated cognitive changes remain poorly characterized. METHODS: Diffusion tensor imaging (DTI) was performed in 43 drug-naïve patients with schizophrenia and 44 healthy controls at baseline, and repeated after 8 weeks during which patients received antipsychotic treatment. Tract-based spatial statistics (TBSS) were applied to assess changes in WM microstructural integrity. Cognitive performance was evaluated using selected measures from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), including story recall, line orientation, picture naming, semantic fluency, digit span, coding, and word recall. RESULTS: At baseline, no significant group differences were observed in fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), or mean diffusivity (MD). Following treatment, compared with healthy controls, patients exhibited decreased FA in the body of the corpus callosum; increased MD across nearly all WM regions; and elevated RD in the genu and body of the corpus callosum, bilateral anterior corona radiata, left superior corona radiata, and left superior longitudinal fasciculus. Spearman correlation analyses revealed that RD values in RD_cluster1 and RD_cluster2 were negatively correlated with digit span scores (r = -0.359, p = 0.018; r = -0.35, p = 0.021). Although clinical symptoms improved during this period, changes in FA, MD, and RD were not associated with symptom improvement or antipsychotic dosage. CONCLUSION: These findings suggest that antipsychotic treatment exerts potential effects on WM microstructure and cognitive function in drug-naïve patients with schizophrenia.
Identifying the neural differences in individuals with traumatic experiences based on neuroimaging data: A narrative review.
Ong ILY, Lian AEZ, Chew BT
Psychiatry Res Neuroimaging · 2026 Aug · PMID 42066522 · Publisher ↗
BACKGROUND: Childhood trauma can lead to lasting psychological and physiological effects, including altered brainwave patterns. This review examines the relationship between childhood trauma and brainwave activity, explo...
BACKGROUND: Childhood trauma can lead to lasting psychological and physiological effects, including altered brainwave patterns. This review examines the relationship between childhood trauma and brainwave activity, exploring the potential use of quantitative electroencephalography (qEEG) as an indicator for trauma-related disorders. METHODS: This narrative review examined six studies that were published from 1997-2025, which investigated the neural differences in the population who have experienced traumatic event(s) during their childhood. RESULTS: Altered brainwave patterns, particularly the delta, theta, alpha, and beta waves, were found in trauma-affected individuals. These changes are linked to emotional regulation, sensory processing, and cognitive control disruptions. CONCLUSIONS: The current review utilizes the polyvagal theory as a potential physiological framework to link childhood trauma to the specific brainwave alterations observed in EEG and qEEG findings. While the theory remains a subject of ongoing debate, it offers a useful perspective for understanding the relationship between trauma and neural changes. Additionally, this study suggests that qEEG could serve as a reliable tool for early trauma detection, although further research is needed to validate these findings across diverse populations.
The effects of physical exercise on simple inflammatory markers, systemic immune inflammation index, and systemic immune response index in patients with schizophrenia.
Koç İ, Koç EA
Psychiatry Res · 2026 Aug · PMID 42066483 · Publisher ↗
OBJECTIVE: This study aimed to investigate the effects of physical exercise on simple inflammation markers, systemic immune inflammation index and systemic immune response index in patients with schizophrenia. METHODS: A...
OBJECTIVE: This study aimed to investigate the effects of physical exercise on simple inflammation markers, systemic immune inflammation index and systemic immune response index in patients with schizophrenia. METHODS: A total of 35 individuals diagnosed with schizophrenia participated in the study (exercise group: n = 17, control group: n = 18). Participants continued their routine psychiatric treatments. The exercise group underwent a 12-week supervised exercise program, while the control group did not receive any additional intervention. Simple inflammatory markers, systemic immune inflammatory index (SII) and systemic immune response index (SIRI) and positive and negative syndrome scale (PANNS) scores were assessed at baseline, after 12 weeks, and after a 12-week follow-up without exercise following the completion of the exercise. RESULTS: In the exercise group, inflammatory markers, SII and SIRI values significantly decreased during the first 12 weeks, whereas the control group showed stability or increases. Similarly, all of the PANSS subgroup scores decreased significantly in the exercise group, but increased in the control group. During the follow-up, these improvements diminished, and group differences were no longer significant. Over the total 24-week period, control group exhibited significant increases in both inflammatory markers and PANSS scores, while the exercise group remained stable. CONCLUSIONS: A 12-week exercise program reduced systemic inflammation and improved clinical symptoms in patients with schizophrenia. However, these benefits diminished after discontinuation. Regular physical activity may serve as a practical adjunctive strategy in schizophrenia management, though long-term and larger studies are needed to confirm sustainability.
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