Psychiatry and Clinical Neurosciences

ObjectiveTo determine the clinical relevance of brain temperature (TBr) variation in patients after traumatic brain injury (TBI). DesignCohort study with prospective (healthy participant) and retrospective (TBI patient) arms. SettingSingle neuroimaging site in the UK (prospective arm); intensive care sites contributing to the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) High Resolution ICU (HR ICU) Sub-Study (retrospective arm). Participants40 healthy adults aged 20-40 years recruited for non-invasive brain thermometry and all patients up to May 2020 that had TBr measured directly and were not subjected to Targeted Temperature Management (TTM). Main outcome measuresA diurnal change in TBr (healthy participants); death in intensive care (patients). ResultsIn healthy participants, mean TBr (38.5 SD 0.4{degrees}C) was higher than oral temperature (36.0 SD 0.5{degrees}C), and 0.36{degrees}C higher in luteal females relative to follicular females and males (95% confidence interval 0.17 to 0.55, P=0.0006 and 0.23 to 0.49, P<0.0001, respectively). TBr increased with age, most notably in deep brain regions (0.6{degrees}C over 20 years; 0.11 to 1.07, P=0.0002). The mean maximal spatial TBr range was 2.41 (SD 0.46){degrees}C, with highest temperatures in the thalamus. TBr varied significantly by time of day, especially in deep brain regions (0.86{degrees}C; 0.37 to 1.26, P=0.0001), and was lowest in the late evening. Diurnal TBr in cortical white matter across participants ranged from 37.0 to 40.3{degrees}C. In TBI patients (n=114), mean TBr (38.5 SD 0.8{degrees}C) was significantly higher than body temperature (TBo 37.5 SD 0.5{degrees}C; P<0.0001) and ranged from 32.6 to 42.3{degrees}C. Only 25/110 patients displayed a diurnal temperature rhythm; TBr amplitude was reduced in older patients (P=0.018), and 25/113 patients died in intensive care. Lack of a daily TBr rhythm, or an age increase of 10 years, increased the odds of death 12-fold and 11-fold, respectively (OR for death with rhythm 0.09; 0.01 to 0.84, P=0.035 and for death with ageing by 1 year 1.10; 1.05 to 1.16, P=0.0002). Mean TBr was positively associated with survival (OR for death 0.45 for 1{degrees}C increase; 0.21 to 0.96, P=0.040). ConclusionsHealthy TBr exceeds TBo and varies by sex, age, menstrual cycle, brain region, and time of day. Our 4-dimensional reference resource for healthy TBr can guide interpretation of TBr data in multiple clinical settings. Daily temperature variation is frequently disrupted or absent in TBI patients, in which TBr variation is of greater prognostic use than absolute TBr. Older TBI patients lacking a daily TBr rhythm are at greatest risk of death in intensive care. Appropriately controlled trials are needed to confirm the predictive power of TBr rhythmicity in relation to patient outcome, as well as the clinical utility of TTM protocols in brain-injured patients. RegistrationUK CRN NIHR CPMS 42644; ClinicalTrials.gov number, NCT02210221. SUMMARY BOXO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIBrain temperature (TBr) can be measured directly in brain-injured patients via intracranial probe, but this method cannot be used in healthy individuals. C_LIO_LITBr can be measured non-invasively using magnetic resonance spectroscopy (MRS), but this method is not appropriate for most brain-injured patients. C_LIO_LISince physiological reference ranges for TBr in health have not been established, the clinical relevance of TBr variation in patients is unknown, and the use of TTM in neurocritical care remains controversial. C_LI What this study addsO_LIA reference map for healthy adult TBr at three clinically-relevant time points that can guide interpretation of TBr measured directly, or by MRS, in multiple clinical settings. C_LIO_LIOur results suggest that loss of diurnal TBr rhythmicity after TBI increases the odds of intensive care death 12-fold; some TTM strategies may be clinically inappropriate. C_LI

The current clinical diagnosis of psychiatric disorders relies heavily on subjective assessment of symptoms. While neuroimaging has made an essential contribution to characterizing the brain of psychiatric disorders, it does not currently serve the clinical diagnosis of major psychiatric disorders. Here, we report a neuroimaging-aided diagnostic system for major psychiatric disorders designed for clinical needs. We developed novel deep learning networks with attentional mechanisms and applied them to a large-scale, single-center neuroimaging dataset containing four major psychiatric disorders and healthy groups (n=2490). Both cross-validation and extensive independent validation using multiple open-source datasets (n = 1972) showed that the system could accurately identify any one of the four diagnostic categories and healthy population from brain structural imaging. For the first time, we have constructed an automatic neuroimaging-aid diagnostic system that considers common issues in practice, such as co-morbid diagnoses and the discrimination between specific suspected diagnoses. Furthermore, real-world applications have validated the systems effectiveness. These works contribute to the translation of brain research to objective diagnostic aids for psychiatric disorders.

ObjectiveTo examine the causal relationship between alcohol use and dementia risk across multiple ancestry groups. DesignWe triangulated evidence from observational and univariable and multivariable Mendelian randomization. Setting and participantsCross-ancestry observational analyses were conducted in two large prospective studies: the US Million Veteran Program and UK Biobank. One- and two-sample univariable and multivariable Mendelian randomization used de novo data from a genome-wide association study in Million Veteran Program plus publicly available data. Main outcome measureAll-cause dementia. ResultsAmong 559,559 participants (aged 56-72 years old at baseline) included in observational analyses, 14,540 received developed dementia and 48,034 died during follow-up. Observational associations between alcohol and dementia were U-shaped. Non-, heavy (>40 drinks per week - hazard ratio (HR)1.41; 95% confidence interval [CI], 1.15 to 1.74]) and dependent (1.51[1.42-1.60]) drinkers were at higher dementia risk than light drinkers. In contrast, genetic analyses revealed a monotonically increasing association between alcohol dose and dementia, with no evidence supporting a protective effect of any level of drinking. A two-fold increase in genetically-predicted alcohol use disorder prevalence was associated with a 16% increase in dementia cases (IVW OR=1.16[1.03-1.30]), and a one standard deviation increase in log-transformed drinks per week was associated with a 15% increase (IVW OR=1.15[1.03-1.27]). ConclusionsAlcohol consumption has a causal role for dementia. These findings challenge a purported protective effect of moderate drinking. Reducing alcohol use could be an effective dementia prevention strategy.

This study anonymously examined 2,734 psychiatric patients worldwide for worsening of their pre-existing psychiatric condition during the COVID-19 pandemic. Valid responses mainly from 12 featured countries indicated self-reported worsening of psychiatric conditions in 2/3rd of the patients assessed that was validated through their significantly higher scores on scales for general psychological disturbance, post-traumatic stress disorder, and depression. Female gender, feeling no control of the situation and reporting dissatisfaction with the response of the state during the COVID-19 pandemic, and reduced interaction with family and friends increased the worsening of pre-existing psychiatric conditions, whereas optimism, ability to share concerns with family and friends and using social media like usual were associated with less worsening. An independent clinical investigation from the USA confirmed worsening of psychiatric conditions during the COVID-19 pandemic based on identification of new symptoms that necessiated clinical interventions such as dose adjustment or starting new medications in more than half of the patients.

Delirium is a neurologic syndrome characterized by inattention and cognitive impairment frequently encountered in the medically ill. Peripheral inflammation is a key trigger of delirium, but the patient-specific immune responses associated with delirium development and resolution are unknown. This retrospective cohort study of prospectively collected biospecimens examines RNA sequencing from peripheral blood mononuclear cells of adults hospitalized for COVID-19 to better understand patient-specific factors associated with delirium (n = 64). Longitudinal transcriptomic analyses highlight persistent immune dysregulation in delirium, marked by increasing expression trajectories of genes linked to innate immune pathways, including complement activation, cytokine production, and monocyte/macrophage recruitment. Genes involved adaptive immunity showed a declining trajectory over time in patients with delirium. Although corticosteroid treatment suppressed some aspects of immune hyperactivation, aberrant responses contributing to delirium were exacerbated. Delirium resolution was characterized by normalization of key transcripts such as CCL2 and innate immune markers. Novel associations with delirium were found in genes related to stress granule assembly and DUSP2 and KLF10, which mediate T-cell responses. These findings provide insights into the peripheral immune responses accompanying delirium and their modulation by corticosteroids. Future trials targeting aberrant inflammatory responses may mitigate the severe outcomes associated with delirium due to COVID19.

BackgroundWe investigated delirium prevalence and potential effects of long-term sedation in critically ill COVID-19 patients; to identify opportunities for improving sedation practices and delirium prevention. MethodsThis prospective, single-center, observational cohort study was conducted from April-June 2020. Adult COVID-19 patients were eligible if admitted to an ICU with mechanical ventilation/intravenous sedation; or a general care unit with brain monitoring due to altered mental status. Patients were evaluated daily until discharge using the Richmond Agitation-Sedation Scale, Confusion Assessment Method for the ICU, and CAM-Severity. Cumulative doses of sedation and paralytic medications were recorded. At three months post-enrollment, cognition, mood, and quality of life were measured by the Telephone Interview for Cognitive Status (TICS), Center for Epidemiologic Studies Depression Scale 10-item (CES-10), and EuroQol 5-Dimension-3 Level (EQ-5D-3L), respectively. Results67 patients were enrolled, with a mean (SD) age of 59 (12) years, 30 (45%) Hispanic, 43 (64%) developing acute respiratory distress syndrome, 55 (82%) mechanically ventilated (mean duration of 22.9 days), and 5 comatose for the entire study. Of the 62 patients assessed for delirium, 61 (98%) had delirium at least once, with a mean (SD) of 12.7 (13.0) days. >90% of patients received opioids, benzodiazepines, or propofol at least once; median (IQR) total dose of 37.4 (78.9) mg (fentanyl equivalents), 52.5 (813.3) mg (midazolam equivalents), and 46 (53) g (propofol), respectively. At follow-up, 40 (60%) patients were reached, while 16 (24%) were deceased/comfort measures. Patients showed reductions in cognition, mood, and quality of life with median (IQR) scores for TICS (0-41): 30 (26-33); CES-D-10 (0-30): 6 (4-12); EQ-5D-3L (1-3): 2 (mobility, self-care, usual activities, pain/discomfort). ConclusionCritically and acutely ill patients with COVID-19 early in the pandemic experienced a high rate of delirium and sedation. Large doses of sedatives may contribute to greater delirium burden during hospitalization, and lead to poor clinical outcomes.

Despite a global decrease in suicide rates in recent years, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts in order to combat this growing epidemic. In this study, we use an explainable-artificial intelligence method, iterative Random Forest, to predict suicide attempts using data from the Million Veteran Program. Our predictive model incorporates multiple environmental variables (e.g., elevation, light wavelength absorbance, temperature, humidity, etc) at ZIP code-level geospatial resolution. We additionally consider demographic variables from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people in order to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features. Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.

ObjectiveDetermine whether acute behavioral, electroencephalography (EEG), and functional MRI (fMRI) biomarkers of consciousness are associated with outcome after severe traumatic brain injury (TBI). MethodsPatients with acute severe TBI admitted consecutively to the intensive care unit (ICU) participated in a multimodal battery assessing behavioral level of consciousness (Coma Recovery Scale-Revised [CRS-R]), cognitive motor dissociation (CMD; task-based EEG and fMRI), covert cortical processing (CCP; stimulus-based EEG and fMRI), and default mode network connectivity (DMN; resting-state fMRI). The primary outcome was 6-month Disability Rating Scale (DRS) total scores. ResultsWe enrolled 55 patients with acute severe TBI. Six-month outcome was available in 45 (45.2{+/-}20.7 years old, 70% male), of whom 10 died, all due to withdrawal of life-sustaining treatment (WLST). Behavioral level of consciousness and presence of command-following in the ICU were each associated with lower (i.e., better) DRS scores (p=0.003, p=0.011). EEG and fMRI biomarkers did not strengthen this relationship, but higher DMN connectivity was associated with better recovery on multiple secondary outcome measures. In a subsample of participants without command-following on the CRS-R, CMD (EEG:18%; fMRI:33%) and CCP (EEG:91%; fMRI:79%) were not associated with outcome, an unexpected result that may reflect the high rate of WLST. However, higher DMN connectivity was associated with lower DRS scores ({rho}[95%CI]=-0.41[-0.707, -0.027]; p=0.046) in this group. InterpretationStandardized behavioral assessment in the ICU may improve prediction of recovery from severe TBI. Further research is required to determine whether integrating behavioral, EEG, and fMRI biomarkers of consciousness is more predictive than behavioral assessment alone.

BackgroundIn the last decades, advances in Intensive Care Unit management have led to decreased mortality. However, significant morbidity remains as patients survive after a lesional coma with uncertain quality of awakening and high risk of functional disability. Predicting this level of recovery but also the functional disability of those who will awake constitutes a major challenge for medical, ethical and social perspectives. Among the huge heterogeneity of coma-related injuries, recognising the universality of a common functional pattern which may be focused on a final step of an integrated network would be of great interest for our understanding of disorders of consciousness. The objective of this study is to investigate the neural correlates of arousal and awareness in coma and post-coma to build a prognostic tool based on the detection of a common pattern between patients with a favourable versus an unfavourable outcome. Method/DesignWe will implement this objective in a translational approach which combines PET-MR imaging, neurophysiology, behavioural/clinical assessments and innovative statistical and computational analysis tools in patients with disorders of consciousness in Intensive Care Unit and in Rehabilitation Department.

A primary aim of precision psychiatry is the establishment of predictive models linking individual differences in brain functioning with clinical symptoms. In particular, cognitive impairments are transdiagnostic, treatment resistant, and contribute to poor clinical outcomes. Recent work suggests thousands of participants may be necessary for the accurate and reliable prediction of cognition, calling into question the utility of most patient collection efforts. Here, using a transfer-learning framework, we train a model on functional imaging data from the UK Biobank (n=36,848) to predict cognitive functioning in three transdiagnostic patient samples (n=101-224). The model generalizes across datasets, and brain features driving predictions are consistent between populations, with decreased functional connectivity within transmodal cortex and increased connectivity between unimodal and transmodal regions reflecting a transdiagnostic predictor of cognition. This work establishes that predictive models derived in large population-level datasets can be exploited to boost the prediction of cognitive function across clinical collection efforts.

Traumatic brain injury (TBI) is a leading cause of disability worldwide. Yet, our understanding of the mechanisms of this condition is limited, especially in the acute setting. Here, we investigated the relationship between functional connectivity and common clinical assessments, like the Glasgow Coma Scale (GCS) at admission and modified Rankin scale at 3-months (mRS) to determine if functional connectivity can provide a broader representation of the brains networks than these standard tests. We performed a retrospective analysis of resting state functional MRI and clinical data in 58 patients (41.28 {+/-} 18.63) scanned acutely/subacutely ([&le;] 31 days). Then, for a secondary analysis, we included 50 more patients who presented after either a first or a repeat incident and were scanned either acutely/subacutely or chronically (<2 yrs) (all together 108 patients, 46.4 {+/-} 20.1yrs). Using a 268-node functional atlas, we derived 35,778 unique edges, based on which we calculated the mean functional connectivity of 10 resting state networks and used those to establish a link to TBI severity and functional outcome. Our analysis showed that when dividing sub/acute patients (n=58) based on GCS severity, only the Subcortical network showed a significant discrimination between mild and moderate-severe GCS at admission (P<0.001), with hyperconnectivity noted in mild patients, and hypoconnectivity - in moderate-severe GCS patients. This difference appeared to be mainly driven by the thalami (Right, P=0.002; Left P<0.001). Similar results were observed when investigating GCS subscores at admission (Eyes, Motor, Verbal, all P<0.001). Further, when evaluating mRS outcomes at 3-months against functional connectivity, differences were noted within the Motor, Cerebellum and Medial-Frontal networks, though none survived multiple comparisons. Importantly, we found the DMN and mRS to be correlated but with a limited relationship (r2= 0.18). Lastly, we performed a post-hoc analysis (n=108) to investigate if the hyperconnectivity in the Subcortical network of sub/acute mild GCS patients remained irrespective of acuity of scanning (i.e. acute/ chronic) or frequency of TBI (i.e. first/ repeat). Our analysis showed that GCS severity appeared to be the main driver of functional connectivity within the Subcortical network, whereas acuity of scanning, alongside GCS severity contributed to the results of chronically scanned patients. While GCS and 3-month mRS scores offer some meaningful insights, their limited capture of the neural representation underscores the need to investigate whether other early clinical assessments correlate more robustly with early resting state networks or whether such networks themselves could predict future outcomes.

Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brains in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility and high costs. In this study, we explore the use of normative modelling (NM) for molecular neuroimaging to identify individual patient deviations from a reference cohort of subjects. NM potentially addresses challenges such as small sample sizes and diverse acquisition protocols that are typical of molecular neuroimaging studies. We applied NM to two PET radiotracers targeting the dopaminergic system ([11C]-(+)-PHNO and [18F]FDOPA) to create a normative model to reference groups of controls. The models were subsequently utilized on various independent cohorts of patients experiencing psychosis. These cohorts were characterized by differing disease stages, treatment responses, and the presence or absence of matched controls. Our results showed that patients exhibited a higher degree of extreme deviations ([~]3-fold increase) than controls, although this pattern was heterogeneous, with minimal overlap in extreme deviations topology (max 20%). We also confirmed the value of striatal [18F]FDOPA signal to predict treatment response (striatal AUC ROC: 0.77-0.83). Methodologically, we highlighted the importance of data harmonization before data aggregation. In conclusion, normative modelling can be effectively applied to molecular neuroimaging after proper harmonization, enabling insights into disease mechanisms and advancing precision medicine. The method is valuable in understanding the heterogeneity of patient populations and can contribute to maximising cost efficiency in studies aimed at comparing cases and controls.

ImportanceWhile there is a general consensus that functional connectome pathology is a key mechanism underlying psychosis spectrum disorders, the literature is plagued with inconsistencies and translation into clinical practice is non-existent. This is perhaps because group-level findings may not be accurate reflections of pathology at the individual patient level. ObjectiveTo characterize inter-individual heterogeneity in functional networks and investigate if normative values can be leveraged to identify biologically less heterogeneous subgroups of patients. Design, Setting, and ParticipantsWe used data collected in a case-control study conducted at the University of Alabama at Birmingham (UAB). We recruited antipsychotic medication-naive first-episode psychosis patients from UAB outpatient, inpatient, and emergency room settings. Main Outcome(s) and Measure(s)Individual-level patterns of deviations from a normative reference range in resting-state functional networks using the Yeo-17 atlas for parcellations. ResultsStatistical analyses included 108 medication-naive first-episode psychosis patients. We found that there is a high level of inter-individual heterogeneity in resting-state network connectivity deviations from the normative reference range. Interestingly 48% of patients did not have any functional connectivity deviations, and no more than 11.1% of patients shared functional deviations between the same regions of interest. In a post hoc analysis, we grouped patients based on deviations into four theoretically possible groups. We discovered that all four groups do exist in our experimental data and showed that subgroups based on deviation profiles were significantly less heterogeneous compared to the overall group (positive deviation group: z= -2.88, p = 0.002; negative deviation group: z= -3.36, p<0.001). Conclusions and RelevanceOur findings experimentally demonstrate that there is a high level of inter-individual heterogeneity in resting-state network pathology in first-episode psychosis patients which support the idea that group-level findings are not accurate reflections of pathology at the individual level. We also demonstrated that normative functional connectivity deviations may have utility for identifying biologically less heterogeneous subgroups of patients, even though they are not distinguishable clinically. Our findings constitute a significant step towards making precision psychiatry a reality, where patients are selected for treatments based on their individual biological characteristics. KEY POINTSO_ST_ABSQuestionC_ST_ABSHow heterogeneous is individual-level resting-state functional network pathology in patients suffering from a first psychotic episode? Can normative reference values in functional network connectivity be leveraged to identify biologically more homogenous subgroups of patients? FindingsWe report that functional network pathology is highly heterogeneous, with no more than 11% of patients sharing functional deviations between the same regions of interest. MeaningNormative modeling is a tool that can map individual neurobiological differences and enables the classification of a clinically heterogenous patient group into subgroups that are neurobiologically less heterogenous.

IntroductionAdverse childhood experiences (ACEs) are associated with brain alterations and cognitive decline. In later life, cognitive impairment and mild behavioural impairment (MBI) are associated with greater dementia risk. Objective & Study DesignWe investigated whether more severe ACEs are cross-sectionally associated with worse later-life cognitive and behavioural symptoms. MethodData are from the Canadian Platform for Research Online to Investigate Health, Quality of Life, Cognition, Behaviour, Function, and Caregiving in Aging (CAN-PROTECT). Measures included the Childhood Trauma Screener (CTS-5), neuropsychological testing, Everyday Cognition (ECog)-II scale, and MBI Checklist (MBI-C). Linear regressions modelled associations between ACEs severity and neuropsychological test scores. Multivariable negative binomial regressions (zero-inflated, if appropriate) modelled associations between ACEs severity and ECog-II and MBI-C scores. All models controlled for age, sex, education, and ethnocultural origin. Clinical diagnoses of depression and/or anxiety were explored as covariates or mediators. ResultsIn adjusted analyses, higher ACEs scores were associated with worse performance on Trail-Making B (standardized b=0.10, q=0.003), Switching Stroop (b=-0.08, q=0.027), Paired Associates Learning (b=-0.08, q=0.049), and Digit Span (b=-0.08, q=0.029). Higher ACEs scores were also associated with higher ECog-II (b=1.08, q=0.029) and MBI-C (b=1.20, q<0.001) scores; these associations were neither mediated by affective symptoms (ECog p=0.16; MBI p=0.13) nor moderated by sex (ECog p=0.09; MBI p=0.46). ConclusionOlder adults with a history of more severe ACEs show greater cognitive and behavioural risk markers for dementia that cannot be explained by previous psychiatric history. Further research into ACEs as an early modifiable risk factor for dementia is warranted.

The majority of people worldwide live in cities, yet how urban living affects brain and mental illness is scarcely understood. Urban lives are exposed to a a wide array of environmental factors that may combine and interact to influence mental health. While individual factors of the urban environment have been investigated in isolation, to date no attempt has been made to model how the complex, real life exposure to living in the city relates to brain and mental illness, and how it is moderated by genetic factors. Using data of over 150,000 participants of the UK Biobank, we carried out sparse canonical correlation analyses (sCCA) to investigate the relation of urban living environment with symptoms of mental illness. We found three mental health symptom groups, consisting of affective, anxiety and emotional instability symptoms, respectively. These groups were correlated with distinct profiles of urban environments defined by risk factors related to social deprivation, air pollution and urban density, and protective factors involving green spaces and generous land use. The relations between environment and symptoms of mental illness were mediated by the volume of brain regions involved in reward processing, emotional processing and executive control, and moderated by genes regulating stress response, neurotransmission, neural development and differentiation, as well as epigenetic modifications. Together, these findings indicate distinct biological pathways by which different environmental profiles of urban living may influence mental illness. Our results also provide a quantitative measure of the contribution of each environmental factor to brain volume and symptom group. They will aid in targeting and prioritizing important decisions for planning and public health interventions.

Positive and negative schizotypy reflect distinct patterns of subclinical traits in the general population associated with neurodevelopmental and schizophrenia-spectrum pathologies. Yet, a comprehensive characterization of the unique and shared neuroanatomical signatures of these schizotypy dimensions is lacking. Leveraging 3D brain MRI data from 2,730 unmedicated healthy individuals, we identified neuroanatomical profiles of positive and negative schizotypy and systematically compared them to disorder-specific, micro-architectural, connectome, and neurotransmitter-level measures. Positive and negative schizotypy were associated with thinner frontal and thicker paralimbic cortical areas, respectively, and were differentially linked to cortical patterns of schizophrenia-spectrum and neurodevelopmental conditions. Furthermore, these schizotypal cortical patterns mapped onto local attributes of gene expression, cortical myelination, D1 and histamine receptor distributions. Network models identified cortical hub vulnerability to schizotypy-related thickness reduction and epicenters in sensorimotor-to-association and paralimbic areas. This study yields insights into the complex cortical signatures of schizotypy and their relationship to diverse features of cortical organization.

Schizophrenia spectrum disorder (SSD) is one of the top causes of disease burden; similar to other psychiatric disorders, SSD lacks widely applicable and objective biomarkers. This study aimed to introduce a novel resting-state functional connectivity (rs-FC) magnetic resonance imaging (MRI) biomarker for diagnosing SSD. It was developed using customised machine learning on an anterogradely and retrogradely harmonised dataset from multiple sites, including 617 healthy controls and 116 patients with SSD. Unlike previous rs-FC MRI biomarkers, this new biomarker demonstrated a notable accuracy rate of 77.3% in an independent validation cohort, including 404 healthy controls and 198 patients with SSD from seven different sites, effectively mitigating across-scan variability. Importantly, our biomarker specifically identified SSD, differentiating it from other psychiatric disorders. Our analysis identified 47 important FCs significant in SSD classification, several of which are involved in SSD pathophysiology. Beyond their potential as trait markers, we explored the utility of these FCs as both state and staging markers. First, based on aggregated FCs, we built prediction models for clinical scales of trait and/or state. Thus, we successfully predicted delusional inventory scores (r=0.331, P=0.0177), but not the overall symptom severity (r=0.128, P=0.178). Second, through comprehensive analysis, we uncovered associations between individual FCs and symptom scale scores or disease stages, presenting promising candidate FCs for state or staging markers. This study underscores the potential of rs-FC as a clinically applicable neural phenotype marker for SSD and provides actionable targets to neuromodulation therapies.

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R2 = 0.31; placebo: R2 = 0.22). Remarkably, the sertraline response biomarker is further tested on an independent escitalopram-medicated cohort of MDD patients, validating its generalizability (p = 0.01) and suggesting an overlap of psychopharmacological mechanisms across selective serotonin reuptake inhibitors. Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel and reliable insights into the intricate neuropsychopharmacology of antidepressant treatment, paving the way for advances in precision medicine and development of more targeted antidepressant therapeutics. Trial RegistrationEstablishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094

How genetic risk variants may relate to brain abnormalities is crucial for understanding cross-scale pathophysiological mechanisms underlying schizophrenia. The present study identifies brain structural correlates of variation in gene expression in schizophrenia and its clinical significance. Of 43 patients with schizophrenia, RNA-seq data from blood samples, MRI, and clinical assessments were collected, together with data from 60 healthy controls. Gene expression differentiation between schizophrenia and health controls was assessed and cross-referenced to schizophrenia-related genomic variations (GWAS on 76,755 patients and 243,649 controls and GWAS on 22,778 East Asian patients) and brain gene expressions (samples from 559 patients and 175 individuals). Multivariate correlation analysis was employed to examine associations across gene expression, brain volume, and clinical assessments. Differentially expressed genes in blood samples from patients with schizophrenia were significantly enriched for genes previously reported in genome-wide association studies on schizophrenia (P = 0.002, false discovery rate corrected) and were associated with gene expression differentiation in the brain (P = 0.016, 5,000 permutations). Transcriptional levels of differentially expressed genes were found to significantly correlate with gray matter volume in the frontal and temporal regions of cognitive brain networks in schizophrenia (q < 0.05, false discovery rate corrected). A significant correlation was further observed between gene expression, gray matter volume, and performance in the Wechsler Adult Intelligence Scale test (P = 0.031). Our findings suggest that genomic variations in schizophrenia are associated with differentiation in the blood transcriptome, which further plays a role in individual variations in macroscale brain structure and cognition.

The human brain relies on dynamic interactions among modular networks, where connector and provincial hubs critically enable information integration. However, existing hub characterization remains predominantly qualitative, overlooking the quantitative contributions of potential hub nodes. To address this gap, we introduce the Multi-Indicator Entropy Hub Score (MIEHS), a quantitative framework integrating six graph-theoretical metrics including betweenness/degree centrality, participation coefficient, within-module betweenness/degree centrality, and clustering coefficient, to holistically evaluate hub properties. We validated MIEHS using benchmark networks, random simulated networks, and resting-state fMRI data from the Midnight Scan Club dataset, demonstrating its accuracy and robustness in hub identification. Our findings reveal that high-score connector hubs predominantly localize within the attention network, while high-score provincial hubs are concentrated in the default mode network (DMN). Gradient mapping further indicates that connector hubs bridge unimodal and transmodal regions, facilitating the transition of information from primary sensory areas to higher-order cognitive regions, whereas provincial hubs primarily support intra-network communication. Additionally, random null model analysis highlights the stability of hub nodes within the DMN and limbic networks. Moreover, to explore clinical implications, we applied Partial Least Squares analysis to the UCLA dataset (HC=110, ADHD=37, BD=40, SCHZ=37), examining the relationship between hub properties and psychiatric symptoms. The results reveal significant associations between hub changes in DMN, SMN, limbic, DAN, and control networks with cognition and behavior. Notably, modifications in hub connectivity impact cognitive flexibility, abstract reasoning, and verbal expression. By bridging quantitative hub analysis with clinical phenotypes, MIEHS provides novel insights of brain network organization and demonstrated a robust tool for elucidating brain functional reconfiguration and functional organization.