Brain Imaging and Behavior

Background and Hypothesis Negative symptoms in schizophrenia spectrum disorder (SSD) are associated with poor functional outcomes and reduced quality of life; however, effective treatments remain limited. A better understanding of the underlying brain mechanisms is needed to develop better treatments. Neuroimaging studies implicate multiple brain networks in negative symptoms, though findings remain heterogeneous, potentially reflecting the multidimensionality of both symptoms and brain networks. Larger, more systematic dissections are required to identify the key hub(s) underlying negative symptoms. Study Design Structural MRI data from 394 participants (210 SSD, 184 healthy controls [HC]) were analyzed to identify cortical thickness and subcortical volume demonstrating both SSD-HC differences and associations with negative symptoms measured by the Brief Negative Symptom Scale (BNSS). Regions meeting both criteria were selected as seeds for resting-state functional connectivity analyses in 143 SSD patients to examine association with negative symptoms and their subdomains. Study Results The thalamus was the only region demonstrating significant structural abnormalities both in SSD and an association with BNSS total score (p-FDR<0.05). Functional-network-defined thalamic subregions were subsequently examined. Several thalamic hypoconnectivities were significantly corresponded with negative symptoms, involving thalamic subregions associated with the cortical default mode, somatosensory, medial temporal, and dorsal attention networks (p=0.001, corrected<0.05). Blunted affect showed the most extensive thalamic connectivity alterations, involving the striatum, cerebellum, and temporoparietal junction/superior temporal sulcus (TPJ-STS). Thalamic hypoconnectivity with TPJ-STS was also associated with overall negative symptoms and anhedonia. Conclusions This study identifies the thalamus as a central hub of negative symptom-related dysfunction in SSD and highlights specific thalamic subnetworks as potential targets for circuit-based interventions.

Abstract Background The neuropsychiatric sequelae (cognitive deficits, anxiety and depression) post-COVID-19 exist as part of the spectrum of the multisystemic long COVID. People with these symptoms have been shown to have worse functional outcomes. Hence, this significantly impacts the economy, primarily due to productivity losses. Yet, the precise underlying pathogenesis remains unknown, as do any diagnostic or prognostic biomarkers. Aim This study aims to identify functionally impacted brain regions in those with post-COVID-19 neurological sequelae and to evaluate how alterations in brain function relate to acute serum inflammatory mediators and brain injury biomarkers. Methods The study will utilise data from the COVID-19 Clinical Neuroscience Study (CNS), a large UK multicentre study conducted during the pandemic. This sub-study will be a secondary analysis of the COVID-19 CNS, a prospective observational study that compared COVID-19-positive participants with negative controls. Participants had an MRI, blood tested for acute serum inflammatory mediators and brain injury biomarkers, and neuropsychiatric assessment (cognitron test, PHQ-9 and GAD-7) post-acutely and at follow-up. The DPABI (Data Processing & Analysis for Brain Imaging) toolbox will be used for pre-processing and analysis of the neuroimaging data (resting state functional MRI) to obtain regional homogeneity (ReHo). The association between ReHO and neuropsychiatric symptoms, acute inflammatory mediators and brain injury biomarkers will be explored. Regression models would be used to determine the risk and protective factors for developing these symptoms. Conclusion It is anticipated that the study will provide new insights into the functional basis of post-acute COVID-19 neuropsychiatric manifestations and identify new diagnostic biomarkers. Key words: Regional Homogeneity, COVID-19, functional MRI, neuropsychiatry, biomarkers

Dysfunctions in reward circuits, including projections from the orbitofrontal cortex (OFC) to the nucleus accumbens (NAcc), have been implicated in psychopathology. The OFC is functionally divided into the medial OFC (mOFC), which processes rewards, and the lateral OFC (lOFC), which responds to non-reward or punishment stimuli. We investigated the structural connectivity of the lOFC-NAcc and mOFC-NAcc pathways and their associations with psychopathological traits using tractography-defined ROI analysis. Diffusion-weighted imaging data from 165 participants (aged 22 - 35, 53% male) in the Human Connectome Project (HCP) were analysed using spherical deconvolution tractography. MegaTrack, a novel tool, enabled tract segmentation via a single manual dissection mapped back to native space. Virtual dissection identified four bilateral pathways connecting the lOFC and mOFC to the NAcc, from which fractional anisotropy (FA) was extracted, and an lOFC/mOFC ratio was computed. Psychopathological traits were measured using HCP Achenbach Adult Self-Report scores. The lOFC-NAcc pathway showed positive associations with psychopathology and the mOFC-NAcc pathway negative associations. The lOFC/mOFC ratio emerged as a stronger predictor than individual pathways, with significant associations for depression, somatic problems, anti-social personality traits, and inattention. Effects were lateralized to the left hemisphere. These findings show that the lOFC-NAcc and mOFC-NAcc pathways can be structurally separated with tractography and display opposing correlations with psychopathological traits in a healthy population. Their ratio may index reward-processing dysfunction, and future research should test whether it is sensitive to dysfunction in clinical populations.

Background: Childhood trauma is a well-established risk factor for adverse mental health outcomes, yet large-scale cross-national evidence from the Arab region remains limited. This study examined the prevalence of childhood trauma and its associations with multiple transdiagnostic outcomes across 17 Arab countries. Methods: A cross-sectional online survey was conducted between September and October 2025 among 6,181 adults from 17 Arab countries. Childhood trauma was assessed using the Childhood Trauma Questionnaire-Short Form (CTQ-SF). Transdiagnostic mental health outcomes included depression, anxiety, posttraumatic stress symptoms (PTSS), maladaptive behavior, and social/functional impairment. Descriptive statistics, Pearson correlations, and multiple linear regression analyses were performed via IBM SPSS Statistics. Confirmatory factor analysis and structural equation modeling were conducted via R (Lavaan) to evaluate a second-order latent construct representing global childhood trauma. Results: Childhood trauma was widely reported across the sample, with physical neglect and emotional neglect emerging as the most frequently endorsed domains. All trauma domains were significantly associated with poorer mental health outcomes (p < .001). In regression analyses adjusted for demographic factors, emotional abuse emerged as the strongest predictor of depression ({beta} = .472, p < .001) and PTSS ({beta} =.439, p < .001). Structural equation modeling supported a second-order global trauma construct, which showed strong associations with depression ({beta} = .70), anxiety ({beta} = .64), PTSS ({beta} = .68), maladaptive behavior ({beta} = .68), and social impairment ({beta} = .67). This latent factor showed strong associations with all mental health outcomes, explaining substantial variance in depression (R2 = .52), anxiety (R2 = .43), and PTSS (R2 = .48). The structural model demonstrated borderline-to-acceptable fit (CFI = .899, TLI = .890, RMSEA = .058, SRMR = .085), supporting the hypothesized latent structure while indicating scope for further model refinement. Conclusions: Childhood trauma is highly prevalent across the Arab region and is strongly associated with multiple dimensions of psychological distress, highlighting the urgent need for trauma-informed prevention, screening, and early intervention programs in the Arab region.

Treatment resistance in obsessive-compulsive disorder (OCD) is common, with 40-60% of patients failing to respond to pharmacotherapy. Identifying functional differences between responders and non-responders may guide alternative treatment strategies. We investigated resting-state brain differences between selective serotonin reuptake inhibitor (SSRI) responders and non-responders and compared them with healthy controls (HC). 3T MRI scans were acquired from 80 participants with OCD and 50 HCs. Structural and 5-minute resting-state data were processed using the CONN toolbox. Regions showing group differences in amplitude of low-frequency fluctuation (ALFF) were identified for seed-based connectivity analysis. ANCOVA revealed significant ALFF differences among groups in the anterior middle temporal gyrus (R-aMTG) and temporal pole (R-TP). SSRI responders showed reductions in seed-to-voxel connectivity than non-responders between these regions and the left superior and inferior occipital cortices, left superior parietal lobule, and left temporo-occipital middle temporal gyrus. Responders also showed reductions in connectivity with the left superior parietal lobule compared with HCs. Across the OCD group, Y-BOCS scores positively correlated with ALFF in the R-aMTG (p=0.008) and R-TP (p=0.044). Altered temporal cortical activity and connectivity with posterior regions were associated with SSRI treatment response, supporting a role for temporal-posterior cortical networks in OCD treatment response.

Introduction: Major depressive disorder (MDD) is a highly prevalent and disabling psychiatric disorder. Human neuroimaging studies increasingly frame its neurobiological substrate in terms of alterations of large-scale brain network organization. Resting-state fMRI findings broadly align with this view, yet remaining highly heterogeneous, reflecting both clinical and analytic variability. Predominant approaches largely characterize functional organization in terms of pairwise relationships between regions, which may limit the ability to capture more global features of brain organization associated with depression and its course. Here we use novel methods from Topological Data Analysis to provide new perspectives on these brain-behavior associations Objectives: The main objective of this study was to determine whether whole-brain topological descriptors of functional connectivity capture alterations associated with MDD. In particular, we examined whether these features vary along clinically relevant dimensions of heterogeneity, focusing on illness course (single-episode vs. recurrent MDD) and current antidepressant medication status. To this end, we analyzed the area under the curve (AUC) of the zeroth and first Betti numbers (B and B), which index global network integration and higher-order cycle structure across scales, respectively. Methods: Neuroimaging (resting state fMRI) and phenotypic data were obtained from 1,490 participants (776 individuals with MDD and 714 never-depressed controls) included in the REST-meta-MDD project of the DIRECT consortium. Functional connectivity matrices were computed using Pearson correlations between regions defined by the Power-264 atlas and harmonized across sites using CovBat. Topological Data Analysis was applied to characterize whole-brain network organization across connectivity thresholds using B and B curves and their areas under the curve (AUC). B associated cycle counts and their coarse anatomical configurations were further examined. Group differences and effects of illness course and medication status were assessed using multiple linear and generalized linear regression models. Results: Compared with controls, individuals with MDD showed significantly higher B AUC, indicating altered global integration profiles across connectivity thresholds. B AUC did not differ by illness course, illness duration, or symptom severity. In contrast, B AUC showed a graded increase across illness courses, with the highest values observed in recurrent depression. This effect was driven by an increased number of one-dimensional cycles rather than greater cycle persistence. Medication status further modulated B alterations, with elevated values observed in unmedicated single-episode and medicated recurrent MDD. Anatomical decomposition revealed that higher-order alterations were primarily driven by inter-network configurations spanning multiple large-scale functional systems. Discussion:These findings indicate that MDD is associated with altered multiscale functional organization, combining less compact global integration with an increased prevalence of cross-network hole-defining cycles, with these features varying systematically with illness course and current medication, suggesting that topology-informed measures capture clinically relevant variability in brain organization in depression.

Neuroimaging is becoming an increasingly prominent tool to understand intimate partner violence (IPV) and partner-inflicted brain injury (BI). Yet, by nature of IPV and partner-inflicted BI, those most affected may be the least likely to be eligible, participate, and produce usable data in neuroimaging studies. A critical question therefore remains of who is represented in neuroimaging research on IPV and related BI. Here, we examined representativeness at three major stages of neuroimaging sample construction using screening records from 352 women and a subsequent cohort of 207 enrolled women with lifetime exposure to physical IPV. At the eligibility stage, three in five women eligible for behavioral research were deemed ineligible for magnetic resonance imaging (MRI) at initial screening. At the participation stage, the 61 women who underwent resting-state functional MRI (fMRI) had more education, were more likely to be employed, and reported lower psychiatric and neurobehavioral symptom burden than enrolled women who did not undergo neuroimaging. Finally, at the quality-control stage following fMRI, motion-based exclusion disproportionately removed women who had sustained the most partner-inflicted BIs. Our findings empirically reveal that eligibility, participation, and quality control each bias the demographic, clinical, abuse, and BI burden represented in an fMRI sample, and therefore have key implications for generalizability. Measuring and reporting representation bias can help identify whose experiences are underrepresented in the evidence, and accordingly reduce the risk that IPV neuroimaging research understates the very harms it seeks to characterize.

Objective: Women with premenstrual syndrome (PMS) experience affective lability, irritability, depressed mood, and anxiety during the late luteal phase of the menstrual cycle. Stress reactivity may contribute to the development of these symptoms, yet neural correlates remain unexplored. We examined neural, behavioral and endocrine stress responses across menstrual cycle phases in women with and without PMS. Methods: 26 women with PMS and 24 women without PMS participated in two fMRI sessions, during the mid-follicular (days 5 to 11) and late-luteal phases (days -7 to -1). Participants completed the Montreal Imaging Stress Task (MIST). Subjective stress ratings and salivary cortisol were collected at six time points before and after stress induction. Stress-related brain activation was assessed using whole-brain and region-of-interest analyses. Results: Subjective stress ratings increased as expected from before to after the MIST, with no group or phase differences. Salivary cortisol was significantly higher during the mid-follicular phase, with no group effect or significant pre-post MIST difference. While the stress task elicited activation patterns in stress-related regions, whole-brain analysis revealed no significant effects of group (PMS vs. no PMS) or menstrual cycle phase (follicular vs. luteal) on the neural stress response. Considering premenstrual symptom severity as a predictor revealed significant group-by-symptom severity interactions in the right inferior frontal gyrus and the right superior temporal gyrus. Conclusion: Using a within-subject repeated-measures design, we found neural responses to psychosocial stress to be stable across menstrual cycle phases. Individual differences in symptom severity may nevertheless be associated with differences in stress-related brain activation.

Background: Cervical spinal cord (CSC) compression and myelopathy are frequent, severe complications in mucopolysaccharidosis (MPS) types I and VI. Early detection is critical to prevent irreversible neurological deficits and preserve quality of life. This prospective study aimed to identify quantitative neuroimaging biomarkers of structural CSC damage and myelopathy in MPS I and VI. Methods: Structural and diffusion magnetic resonance imaging (MRI) of the CSC was analyzed in 17 MPS I patients, 3 MPS VI patients (19{+/-}7 years), and 19 case-matched healthy controls (20{+/-}7 years). CSC macrostructure (cross-sectional area, diameters) and microstructure (fractional anisotropy [FA], radial [RD], mean, axial diffusivities) were evaluated within and beyond the maximal compression areas. Results: MPS patients demonstrated reduced CSC cross-sectional area and anterior-posterior diameter at upper cervical levels, alongside widespread microstructural alterations (lower FA, higher RD) throughout the CSC versus controls (pFDR<0.05). Restricted foramen magnum space was associated with microstructural damage at lower cervical levels without overt macrostructural compression. Morphological and microstructural metrics were sensitive to CSC compression and myelopathy severity (pFDR<0.05). Lower CSC integrity correlated with more advanced growth retardation (p<0.001). Among patients receiving hematopoietic stem cell transplantation, earlier treatment correlated with preserved upper CSC morphology (p<0.05). Distinct neuroimaging phenotypes were observed across MPS genotypes (pFDR<0.05). Conclusions: CSC MRI provides candidate neuroimaging biomarkers for early detection and characterization of myelopathy in MPS I and VI. Structural damage extends beyond primary maximal compression areas, highlighting these markers for clinical monitoring and future therapeutic trials.

There is increasing demand for quantitative measurements in neuroimaging research, including absolute quantification of regional myelin density. Fast whole-brain macromolecular proton fraction (MPF) imaging enables acquisition and reconstruction of quantitative maps of MPF, a biomarker of myelin, and has been used in many research applications. To date, no standard protocols have been published for parcellating MPF maps. FreeSurfer is a widely used software package for automated brain parcellation. It is designed for use with high-resolution T1-weighted anatomical images, typically acquired with an MPRAGE sequence. MPF maps inherently provide high contrast between white and gray matter and can potentially be used as input images for parcellation, enabling simultaneous quantitative tissue characterization and volumetric assessment. However, it is unclear how the use of MPF maps as input images with FreeSurfer software affect reliability and repeatability of regional MPF and volume estimates. We evaluated three FreeSurfer parcellation workflows for this purpose. The workflows differed in the amount of neuroimaging data required and computational intensity. MPF and MPRAGE data were acquired in two separate sessions for 11 adults. Reliability was evaluated using mean relative differences in estimates, Bland-Altman analysis, and intra-class correlation coefficients (ICCs). Repeatability was assessed using within-subject coefficients of variation (CVws). All three workflows produced similarly high within-subject repeatability across scans. However, reliability in regional MPF estimates was lower in gray matter than in white matter for workflows that used MPF maps as input. Workflows that use MPF maps as FreeSurfer input may be adequate for estimating mean MPF in GM and WM of cortical parcels and in subcortical regions. However, the MPRAGE-based workflow is recommended when reliable individual-level estimates or volume estimates are required.

Anhedonia emerges in adolescence, has putative substrates in neural reward and dopamine systems, and is a hallmark of poor course in depression. Psychoneuroimmunology models suggest altered immune and reward systems may jointly underlie its development. When dopamine availability (rather than function) is considered, immune activation may be understood as a moderator amplifying how lower dopamine levels translate into motivational deficits. This cross-sectional study analyzed data from 55 youth with current depression (Mage=21.4 years; Female=75%). Plasma was assayed for relative quantification of 92 immune proteins, followed by dimensionality reduction via principal component (PC) analysis, forming five PC scores. Dopamine availability was indexed by basal ganglia tissue iron, quantified by MRI R2', a measure detectable in youth. Anhedonia was assessed using the Snaith-Hamilton Pleasure Scale (SHAPS), thought to capture overall anhedonia, and Positive Valence Systems Scale (PVSS), which provides overall, anticipatory, and consummatory anhedonia scores. PC1 scores--whose loadings indicated broad inflammatory activity, including cytokines, chemokines, and tissue remodeling/angiogenic markers--moderated the relationship between basal ganglia dopamine availability and anhedonia: At higher PC1 levels, lower dopamine availability was linked to greater overall anhedonia (as measured by SHAPS) and anticipatory anhedonia, but not to consummatory anhedonia. Exploratory analyses showed comparable effect patterns for PC4, reflecting T cell-related effector/apoptotic signaling. Analyses of individual basal ganglia regions suggest potential differences in dopaminergic-immune interactions across these regions, with effects most consistently observed in the putamen. Findings highlight nuanced neuroimmune pathways underlying anhedonia components in youth and support inflammation-based models of depression emphasizing dopaminergic-immune interplay.

Background: Behavioral variant frontotemporal dementia (bvFTD) is clinically defined by progressive changes in personality, executive function, and language, yet the structural neural correlates of symptom severity remain unclear. This study aimed to assess the association between clinical findings and neuroimaging biomarkers. Methods: We included 87 patients with bvFTD. Clinical functioning was covering behavior and language. Spearman rank correlations were used to assess univariate associations, and Bonferroni correction was applied for multiple comparisons. Principal component analysis (PCA) was conducted on regional volumes, and multivariable logistic regression was used to model high behavioral impairment (FBI total [&ge;]30), adjusting for age, sex, and education. Results: No individual regional volume was significantly correlated with clinical scores after Bonferroni correction (all adjusted p>0.05), and PCA-derived imaging components failed to predict behavioral severity in multivariable models. Age emerged as the only significant predictor (OR=1.11 per year; p=0.009). Model discrimination was modest (AUC=0.735) and did not improve with the inclusion of imaging features (p=0.453 vs. demographics-only model). Conclusions: In this bvFTD cohort, structural volumes of key frontotemporal and perisylvian regions did not predict behavioral or language impairments after correction for multiple comparisons. These findings show the limitations of regional volumetric analysis in isolation and stress the need for network-based, multimodal, and longitudinal approaches.

Key Points Question: Is striatal hemodynamic latency, a novel indirect correlate of dopamine physiology, altered in individuals with early psychosis? Findings: In this cross-sectional study of 105 individuals with early psychosis and 55 healthy comparison participants, early psychosis was associated with significantly lower striatal hemodynamic latency. Differences were observed in the dorsal striatum (caudate and putamen), whereas ventral striatal (nucleus accumbens) latency did not significantly differ between groups. Meaning: These findings support further evaluation of striatal hemodynamic latency as a noninvasive, indirect marker of dopamine-linked physiology in early psychosis. Abstract Importance: Psychosis is strongly associated with striatal dopamine dysfunction, but noninvasive measures that capture variation in dopamine remain limited. Hemodynamic latency, which quantifies the timing of low-frequency blood oxygen level-dependent (BOLD) fluctuations, may provide a way to indirectly probe dopamine dysfunction in psychosis. Objective: To determine whether striatal hemodynamic latency differs between individuals with early psychosis (EP) and healthy control (HC) participants and whether latency is associated with positive or negative symptom severity. Design: Cross-sectional analysis of resting-state functional magnetic resonance imaging (fMRI) data from the Human Connectome Project (HCP)-EP dataset. Setting: Multisite study at 4 sites using 3T Siemens Prisma MRI scanners. Participants: 105 individuals with EP and 55 HC participants with usable resting-state fMRI and hemodynamic latency data. Exposure: EP status. Main Outcomes and Measures: Voxel-wise hemodynamic latency was estimated using RapidTide and summarized within 6 striatal regions: nucleus accumbens core and shell, anterior and posterior caudate and putamen. Group differences were evaluated using linear mixed-effects models accounting for region and subject-level random effects and controlling for age, sex, and mean framewise displacement. Positive symptoms were measured using the Positive and Negative Syndrome Scale positive subscale; negative symptoms using the Clinical Assessment Interview for Negative Symptoms. Associations with symptoms were assessed using nested linear regressions within the EP group. Results: Latency was significantly lower in the dorsal than ventral striatum in HC and EP, with a greater difference in EP (b=-119.46 ms, SE=52.16, p=.022). Follow-up six-subregion models found significant main effects of group F(1,156.55)=7.79, p=.006, and region F(5,2760)=187.99; p< .001, but no group-by-region interaction F(5,2760)=1.74; p=.121. Region-specific contrasts showed lower latency in EP in the anterior caudate (p=.014), posterior caudate (p<.001), anterior putamen (p=.007), and posterior putamen (p=.029), but not NAc core or shell. Striatal latency was not significantly associated with positive or negative symptom severity among participants with EP. Conclusions and Relevance: EP was associated with broadly reduced striatal hemodynamic latency, with the strongest differences in the caudate and putamen. Altered striatal latency in psychosis warrants further evaluation as a noninvasive indirect marker of striatal pathophysiology linked to dopamine.

The choroid plexus (ChP) is a highly vascularized structure located in the brain ventricles that plays important roles in multiple central nervous system functions. ChP enlargement has been implicated in many neuropsychiatric conditions such as multiple sclerosis, depression, schizophrenia, and Alzheimer's disease. However, limitation in the accuracy of the current segmentation tools highlight the need for more reliable methods to quantify ChP volume in healthy and clinical human populations. The shape and size of the ChP vary drastically from person to person, and currently there are no large ChP datasets which allow comprehensive training of segmentation models. Here we introduce DataPlex, a collection of 700 structural MRI brain images where the lateral ventricle ChP has been manually labeled and checked by expert human raters. DataPlex is balanced and representative across a wide range of scanning parameters, demographics, and clinical profiles, and subject-level information will be available for each labeled image. DataPlex will be open source to support the creation of reliable segmentation tools for the human lateral ventricle ChP.

Identifying regional brain atrophy is critical for understanding neurodegenerative disorders. Traditionally, group comparisons rely on frequentist statistics, utilising $p$-values and point estimates that offer limited characterization of uncertainty. Bayesian methods provide an alternative by estimating effect sizes and credible intervals while incorporating prior knowledge and uncertainty. This study evaluated the feasibility of Bayesian hierarchical modelling to assess regional brain atrophy in Progressive Supranuclear Palsy (PSP) and Corticobasal Syndrome (CBS) compared to healthy controls. Participants meeting diagnostic criteria for CBS or PSP, together with healthy controls, were recruited from the Cambridge Centre for Parkinson-plus. Grey matter volume, cortical thickness, and subcortical volumes were derived using FreeSurfer for 210 cortical and 36 subcortical regions defined by the Human Brainnetome Atlas. Participants with PSP were classified into Richardson syndrome (PSP-RS), corticobasal syndrome variant (PSP-CBS), frontal variant (PSP-F), and other presentations (PSP-Other). Bayesian hierarchical ANOVA models with appropriate priors were used to estimate group differences. Regions of practical equivalence (ROPE) were calculated to assess evidence of meaningful group effects. A total of 428 people were included (113 PSP-RS, 30 PSP-CBS, 23 PSP-Other, 19 PSP-F, 81 CBS, and 162 healthy controls). For grey matter volume, there was evidence of whole brain volume loss compared to controls for all disease groups except PSP-Other, which showed evidence of no difference. For cortical thickness, there was evidence of global cortical thinning in CBS compared to controls, but for no other group. For subcortical volume all groups showed evidence of volume loss compared to controls. Patterns of regional atrophy were identified in keeping with previous literature, with the largest effect observed in the left pre-motor thalamus (30\% reduction in CBS). There was greater loss of subcortical volume in PSP-RS compared to other PSP subgroups. Bayesian hierarchical modelling provides a feasible framework for quantifying regional brain atrophy, with interpretable effect size estimates and uncertainty measures. This approach complements conventional statistical methods, particularly in the assessment of rare neurodegenerative and neuropsychiatric disorders.

Background and Objectives Transient ischemic attack (TIA) diagnosis is challenging because it relies mainly on clinical assessment and exclusion of common differential. Functional MRI (fMRI) may provide a diagnostic biomarker by detecting transient brain dysfunction not visible on conventional imaging. This pilot study aimed to characterize acute motor-task BOLD activation in patients with suspected DWI-negative TIA and compare functional patterns between confirmed TIA and alternative diagnoses. Secondary objectives were to assess longitudinal changes in activation and connectivity at 3 months. Methods In this prospective single-center study, adults presenting within 24 h of transient anterior-circulation neurological symptoms underwent conventional MRI and additional motor-task fMRI. Patients with acute DWI lesions were excluded. Final diagnosis was established after 3-month follow-up. Whole-brain analyses, individualized motor cortex region-of-interest (ROI) analyses, and generalized psychophysiological interaction (gPPI) connectivity analyses were performed during the acute phase and at 3 months. Results Eight patients were enrolled; six had complete longitudinal imaging data and were included in the final analyses. Final diagnoses were TIA (n=2), migraine aura (n=2), functional neurological disorder (n=1), and hypoglycemia (n=1). Whole-brain and ROI analyses showed reduced bilateral motor-related BOLD responses during the acute phase in both TIA patients and in the patient with hypoglycemia. In contrast, patients with migraine and functional neurological disorder exhibited less marked longitudinal changes. In TIA patients, motor cortex activation increased at 3 months, tending to normalize toward values observed in non-TIA participants. Connectivity analyses revealed heterogeneous patterns, but the 2 TIA patients demonstrated increased longitudinal connectivity between the motor cortex contralateral to symptoms and other sensorimotor regions, including supplementary motor, premotor, parietal, and postcentral cortices. Discussion These preliminary findings suggest that TIA may be associated with transient bilateral reductions in motor-network activity. Functional abnormalities appeared to improve over time, supporting the hypothesis of reversible network dysfunction. Although limited by the very small sample size, this study provides the first longitudinal assessment of emergency task-based fMRI in rigorously defined TIA and supports further investigation of acute functional MRI as a potential biomarker of transient cerebral dysfunction.

Objective: Mindfulness is a robust predictor of mental health, yet the psychometric properties of the short-form Five Facet Mindfulness Questionnaire (FFMQ-15) have not been examined in Iranian populations. This study evaluated the psychometric properties, factor structure, and diagnostic utility of the FFMQ-15 among medical sciences students. Participants: A total of 348 students (178 females, 170 males) from Kermanshah University of Medical Sciences participated. Methods: Participants completed the FFMQ-15, Penn State Worry Questionnaire (PSWQ), Difficulties in Emotion Regulation Scale (DERS), and Emotion Regulation Questionnaire (ERQ). Data were analyzed using exploratory and confirmatory factor analyses, composite reliability, and ROC curve analysis. Results: Both EFA and CFA supported the five-factor model, revealing a culturally unique item loading pattern. The FFMQ-15 demonstrated strong convergent and discriminant validity. ROC analysis indicated acceptable diagnostic accuracy for distinguishing high- and low-worry groups (AUC = 0.72, 95% CI: 0.66-0.78). Conclusions: The FFMQ-15 demonstrated acceptable psychometric properties in Iranian medical students. The non-reactivity facet exhibited cultural variability, suggesting that cross-cultural adaptation is essential. The instrument shows promise for screening in high-stress educational settings.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Importance: Anhedonia, social withdrawal and executive deficits represent transdiagnostic symptoms in depression, schizophrenia and addition, yet transdiagnostic brain volumetric alterations and their contribution to these symptom domains have not been systematically characterized. Objective: To determine common and disorder-specific gray matter deficits in patients with schizophrenia (SZ), major depression disorder (MDD) and alcohol use disorder (AUD) compared to heathy controls. Data Sources: A comprehensive literature search using three databases including PubMed, Web of Science and PsycInfo (between January 2000 to March 2024) as well as searches on review articles. Study Selection: Studies had to be published in English, reporting whole-brain results, comparing patients and healthy controls. Data Extraction and Synthesis: This meta-analysis adhered to the PRISMA guidelines and was pre-registered (https://osf.io/s57uq) prior to data analysis. Data was independently extracted and verified by 3 contributors, and statistical brain maps were generated using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) random-effects model. 147 VBM studies encompassing 90 Schizophrenia, 44 MDD and 13 AUD studies were finally included. Main Outcomes and Measures: Statistical maps of common and disorder-specific gray matter deficits in patients versus healthy controls are presented. Statistical inference utilized permutation tests, with threshold at p < .0025 and cluster extent of 10 voxels. Heterogeneity was evaluated using the I2 statistic and publication bias using Eggers regression test. Results: Capitalizing on a coordinate-based meta-analysis involving n=13,041 participants, we found shared GMV alterations in the ventromedial prefrontal cortex, ventral anterior cingulate cortex, insula, superior temporal gyrus. On the network level, we found robust functional interactions of the identified shared regions with the cortical midline structures as well as social and theory of mind-related behavioral functions. Subsequent characterization analyses highlighted the involvement of dopaminergic and serotonergic neurotransmitters as well as associated genetic annotations. Conclusions and Relevance: Overall, the present meta-analysis found common as well as separate GMV abnormalities in schizophrenia, major depressive disorder and alcohol use disorder suggesting a common neurostructural substrate and treatment target for social withdrawal and anhedonia across these disorders. Keywords: alcohol-use disorder, depression, meta-analysis, schizophrenia, voxel-based morphometry

Background. Glioma surgery requires balancing maximal tumor resection against preservation of neurological and cognitive function. Intraoperative direct electrical stimulation guides this balance but is invasive and not always feasible. Preoperative magnetoencephalography (MEG) can identify tumor regions with high functional connectivity (HFC) to the rest of the brain. These HFC areas contain more malignant glioma cells and relate to poorer short-term outcome when resected. We aimed to determine whether HFC voxel resection relates to postoperative neurological and cognitive outcomes. Methods. In this preregistered analysis, 54 adults with diffuse glioma underwent resting-state MEG before resection. Imaginary coherence identified voxels with significantly higher connectivity against their contralateral controls, and these HFC voxels within the resection cavity were counted. Neurological status was assessed at short-term (~1 week) and long-term (~1 year) follow-up, and neuropsychological assessment took place at baseline and long-term follow-up. Logistic regressions related resected HFC voxels to decline at each timepoint, adjusting for Karnofsky performance status, sex and resectability index. Results. Resected HFC voxels were not associated with neurological decline at short-term (48% declined; odds ratio [OR] 0.98, P = .50) or long-term follow-up (27% declined; OR 0.99). For cognition, more resected HFC voxels were associated with less long-term decline (48% declined; OR 0.85, P = .02); a binarized analysis was consistent (OR 0.076, P = .01), indicating that tumor volume did not drive this result. Conclusions. Resecting more HFC voxels associates with preserved long-term cognition, without apparent neurological cost. Non-invasive MEG connectivity mapping may help optimize the onco-functional balance in glioma surgery.