MRI textural plasticity in limbic gray matter associated with clinical response to electroconvulsive therapy for psychosis.

Electroconvulsive therapy (ECT) is effective against treatment-resistant psychosis, but its mechanisms remain unclear. Conventional volumetry studies have revealed plasticity in limbic structures following ECT but with inconsistent clinical relevance, as they potentially overlook subtle histological alterations. Our study analyzed microstructural changes in limbic structures after ECT using MRI texture analysis and demonstrated a correlation with clinical response. 36 schizophrenia or schizoaffective patients treated with ECT and medication, 27 patients treated with medication only, and 70 healthy controls (HCs) were included in this study. Structural MRI data were acquired before and after ECT for the ECT group and at equivalent intervals for the medication-only group. The gray matter volume and MRI texture, calculated from the gray level size zone matrix (GLSZM), were extracted from limbic structures. After normalizing texture features to HC data, group-time interactions were estimated with repeated-measures mixed models. Repeated-measures correlations between clinical variables and texture were analyzed. Volumetric group-time interactions were observed in seven of fourteen limbic structures. Group-time interactions of the normalized GLSZM large area emphasis of the left hippocampus and the right amygdala reached statistical significance. Changes in these texture features were correlated with changes in psychotic symptoms in the ECT group but not in the medication-only group. These findings provide in vivo evidence that microstructural changes in key limbic structures, hypothetically reflected by MRI texture, are associated with clinical response to ECT for psychosis. These findings support the neuroplasticity hypothesis of ECT and highlight the hippocampus and amygdala as potential targets for neuromodulation in psychosis.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Neurocomputational model of compulsivity: deviating from an uncertain goal-directed system.

Despite a theory that an imbalance in goal-directed versus habitual systems serve as building blocks of compulsions, research has yet to delineate how this occurs during arbitration between the two systems in obsessive-compulsive disorder. Inspired by a brain model in which the inferior frontal cortex selectively gates the putamen to guide goal-directed or habitual actions, this study aimed to examine whether disruptions in the arbitration process via the fronto-striatal circuit would underlie imbalanced decision-making and compulsions in patients. Thirty patients with obsessive-compulsive disorder [mean (standard deviation) age = 26.93 (6.23) years, 12 females (40%)] and 30 healthy controls [mean (standard deviation) age = 24.97 (4.72) years, 17 females (57%)] underwent functional MRI scans while performing the two-step Markov decision task, which was designed to dissociate goal-directed behaviour from habitual behaviour. We employed a neurocomputational model to account for an uncertainty-based arbitration process, in which a prefrontal arbitrator (i.e. inferior frontal gyrus) allocates behavioural control to a more reliable strategy by selectively gating the putamen. We analysed group differences in the neural estimates of uncertainty of each strategy. We also compared the psychophysiological interaction effects of system preference (goal-directed versus habitual) on fronto-striatal coupling between groups. We examined the correlation between compulsivity score and the neural activity and connectivity involved in the arbitration process. The computational model captured the subjects' preferences between the strategies. Compared with healthy controls, patients had a stronger preference for the habitual system (t = -2.88, P = 0.006), which was attributed to a more uncertain goal-directed system (t = 2.72, P = 0.009). Before the allocation of controls, patients exhibited hypoactivity in the inferior frontal gyrus compared with healthy controls when this region tracked the inverse of uncertainty (i.e. reliability) of goal-directed behaviour (P = 0.001, family-wise error rate corrected). When reorienting behaviours to reach specific goals, patients exhibited weaker right ipsilateral ventrolateral prefronto-putamen coupling than healthy controls (P = 0.001, family-wise error rate corrected). This hypoconnectivity was correlated with more severe compulsivity (r = -0.57, P = 0.002). Our findings suggest that the attenuated top-down control of the putamen by the prefrontal arbitrator underlies compulsivity in obsessive-compulsive disorder. Enhancing fronto-striatal connectivity may be a potential neurotherapeutic approach for compulsivity and adaptive decision-making.

Associations between keystroke and stylus metadata and depressive symptoms in adolescents.

BACKGROUND: Adolescents often experience a heightened incidence of depressive symptoms, which can persist without early intervention. However, adolescents often struggle to identify depressive symptoms, and even when they are aware of these symptoms, seeking help is not always their immediate response. This study aimed to explore the relationship between passively collected digital data, specifically keystroke and stylus data collected via mobile devices, and the manifestation of depressive symptoms. METHODS: A total of 927 first-year middle school students from schools in Seoul solved Korean language and math problems. Throughout this study, 77 types of keystroke and stylus data were collected, including parameters such as the number of key presses, tap pressure, stroke speed, and stroke acceleration. Depressive symptoms were measured using the self-rated Patient Health Questionnaire-9 (PHQ-9). RESULTS: Multiple regression analysis highlighted the significance of stroke length, speed, and acceleration, the average y-coordinate, the tap pressure, and the number of incorrect answers in relation to PHQ-9 scores. The keystroke and stylus metadata were able to reflect mood, energy, cognitive abilities, and psychomotor symptoms among adolescents with depressive symptoms. CONCLUSIONS: This study demonstrates the potential of automatically collected data during school exams or classes for the early screening of clinical depressive symptoms in students. This study has the potential to serve as a cornerstone in the development of digital data frameworks for the early detection of depressive symptoms in adolescents.

Magnetic resonance texture analysis reveals stagewise nonlinear alterations of the frontal gray matter in patients with early psychosis.

Although gray matter (GM) abnormalities are present from the early stages of psychosis, subtle/miniscule changes may not be detected by conventional volumetry. Texture analysis (TA), which permits quantification of the complex interrelationship between contrasts at the individual voxel level, may capture subtle GM changes with more sensitivity than does volume or cortical thickness (CTh). We performed three-dimensional TA in nine GM regions of interest (ROIs) using T1 magnetic resonance images from 101 patients with first-episode psychosis (FEP), 85 patients at clinical high risk (CHR) for psychosis, and 147 controls. Via principal component analysis, three features of gray-level cooccurrence matrix - informational measure of correlation 1 (IMC1), autocorrelation (AC), and inverse difference (ID) - were selected to analyze cortical texture in the ROIs that showed a significant change in volume or CTh in the study groups. Significant reductions in GM volume and CTh of various frontotemporal regions were found in the FEP compared with the controls. Increased frontal AC was found in the FEP group compared to the controls after adjusting for volume and CTh changes. While volume and CTh were preserved in the CHR group, a stagewise nonlinear increase in frontal IMC1 was found, which exceeded both the controls and FEP group. Increased frontal IMC1 was also associated with a lesser severity of attenuated positive symptoms in the CHR group, while neither volume nor CTh was. The results of the current study suggest that frontal IMC1 may reflect subtle, dynamic GM changes and the symptomatology of the CHR stage with greater sensitivity, even in the absence of gross GM abnormalities. Some structural mechanisms that may contribute to texture changes (e.g., macrostructural cortical lamina, neuropil/myelination, cortical reorganization) and their possible implications are explored and discussed. Texture may be a useful tool to investigate subtle and dynamic GM abnormalities, especially during the CHR period.

Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium.

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Cortical gyrification differences between early- and late-onset obsessive-compulsive disorder: neurobiological evidence for neurodevelopmentally distinct subtypes.

BACKGROUND: Identifying more homogenous subtypes of patients with obsessive-compulsive disorder (OCD) using biological evidence is critical for understanding complexities of the disorder in this heterogeneous population. Age of onset serves as a useful subtyping scheme for distinguishing OCD into two subgroups that aligns with neurodevelopmental perspectives. The underlying neurobiological markers for these distinct neurodevelopmental differences can be identified by investigating gyrification changes to establish biological evidence-based homogeneous subtypes. METHODS: We compared whole-brain cortical gyrification in 84 patients with early-onset OCD, 84 patients with late-onset OCD, and 152 healthy controls (HCs) to identify potential markers for early neurodevelopmental deficits using the local gyrification index (lGI). Then, the relationships between lGI in clusters showing significant differences and performance in visuospatial memory and verbal fluency, which are considered trait-related neurocognitive impairments in OCD, were further examined in early-onset OCD patients. RESULTS: The early-onset OCD patients exhibited significantly greater gyrification than those with late-onset OCD patients and HCs in frontoparietal and cingulate regions, including the bilateral precentral, postcentral, precuneus, paracentral, posterior cingulate, superior frontal, and caudal anterior cingulate gyri. Moreover, impaired neurocognitive functions in early-onset OCD patients were correlated with increased gyrification. CONCLUSIONS: Our findings provide a neurobiological marker to distinguish the OCD population into more neurodevelopmentally homogeneous subtypes, which may contribute to the understanding of the neurodevelopmental underpinnings of an etiology in early-onset OCD consistent with the accumulated phenotypic evidence of greater neurodevelopmental deficits in early-onset OCD than in late-onset OCD.

Forecasting prognostic trajectories with mismatch negativity in early psychosis.

BACKGROUND: Prognostic heterogeneity in early psychosis patients yields significant difficulties in determining the degree and duration of early intervention; this heterogeneity highlights the need for prognostic biomarkers. Although mismatch negativity (MMN) has been widely studied across early phases of psychotic disorders, its potential as a common prognostic biomarker in early periods, such as clinical high risk (CHR) for psychosis and first-episode psychosis (FEP), has not been fully studied. METHODS: A total of 104 FEP patients, 102 CHR individuals, and 107 healthy controls (HCs) participated in baseline MMN recording. Clinical outcomes were assessed; 17 FEP patients were treatment resistant, 73 FEP patients were nonresistant, 56 CHR individuals were nonremitters (15 transitioned to a psychotic disorder), and 22 CHR subjects were remitters. Baseline MMN amplitudes were compared across clinical outcome groups and tested for utility prognostic biomarkers using binary logistic regression. RESULTS: MMN amplitudes were greatest in HCs, intermediate in CHR subjects, and smallest in FEP patients. In the clinical outcome groups, MMN amplitudes were reduced from the baseline in both FEP and CHR patients with poor prognostic trajectories. Reduced baseline MMN amplitudes were a significant predictor of later treatment resistance in FEP patients [Exp(ß) = 2.100, 95% confidence interval (CI) 1.104-3.993, p = 0.024] and nonremission in CHR individuals [Exp(ß) = 1.898, 95% CI 1.065-3.374, p = 0.030]. CONCLUSIONS: These findings suggest that MMN could be used as a common prognostic biomarker across early psychosis periods, which will aid clinical decisions for early intervention.

Aberrant cortico-striatal white matter connectivity and associated subregional microstructure of the striatum in obsessive-compulsive disorder.

The striatum and its cortical circuits play central roles in the pathophysiology of obsessive-compulsive disorder (OCD). The striatum is subdivided by cortical connections and functions; however, the anatomical aberrations in different cortico-striatal connections and coexisting microstructural anomalies in striatal subregions of OCD patients are poorly understood. Thus, we aimed to elucidate the aberrations in cortico-striatal white matter (WM) connectivity and the associated subregional microstructure of the striatum in patients with OCD. From diffusion tensor/kurtosis imaging of 107 unmedicated OCD patients and 110 matched healthy controls (HCs), we calculated the cortico-striatal WM connectivity and segmented the striatum using probabilistic tractography. For the segmented striatal subregions, we measured average diffusion kurtosis values, which represent microstructural complexity. Connectivity and mean kurtosis values in each cortical target and associated striatal subregions were compared between groups. We identified significantly reduced orbitofrontal WM connectivity with its associated striatal subregion in patients with OCD compared to that in HCs. However, OCD patients exhibited significantly increased caudal-motor and parietal connectivity with the associated striatal subregions. The mean kurtosis values of the striatal subregions connected to the caudal-motor and parietal cortex were significantly decreased in OCD patients. Our results highlighted contrasting patterns of striatal WM connections with the orbitofrontal and caudal-motor/parietal cortices, thus supporting the cortico-striatal circuitry imbalance model of OCD. We suggest that aberrations in WM connections and the microstructure of their downstream regions in the caudal-motor-/parietal-striatal circuits may underlie OCD pathophysiology and further provide potential neuromodulation targets for the treatment of OCD.

Beyond verbal fluency in the verbal fluency task: semantic clustering as a predictor of remission in individuals at clinical high risk for psychosis.

BACKGROUND: There have been conflicting reports on whether conventional verbal fluency measures can predict the prognosis of individuals at clinical high risk (CHR) for psychosis. We aimed to investigate whether verbal fluency task measures that represent semantic processing more directly than conventional measures could be more reliable predictors of later remission in CHR individuals. METHODS: We recruited CHR individuals and healthy controls to participate in a baseline verbal fluency assessment. We identified semantic clusters within the verbal fluency task responses based on cosine similarity between consecutive words, calculated from the word embedding model. Binomial logistic regression was performed to test whether average semantic cluster size and number of words produced could be predictors of remission in CHR individuals. RESULTS: Our study sample included 96 CHR individuals and 178 healthy controls. According to clinical assessment at the last follow-up, 23 CHR individuals were classified as remitters and 73 as nonremitters, including 29 individuals who converted to psychosis. The CHR remitters showed larger average and maximum semantic cluster sizes than CHR nonremitters and healthy controls. Average semantic cluster size, but not the number of words, was a significant predictor of later remission in CHR individuals. LIMITATIONS: Our sample included only native Korean speakers. CONCLUSION: A verbal fluency task measure that more specifically represents semantic processing may be a better neurocognitive predictive marker for remission in CHR individuals than conventional verbal fluency measures. Our results provide an explanation for heterogeneous reports on whether verbal fluency can predict prognosis in CHR individuals and suggest that semantic processing is a putative cognitive predictor of their prognosis.

Unbalanced fronto-pallidal neurocircuit underlying set shifting in obsessive-compulsive disorder.

Maladaptive habitual behaviours of obsessive-compulsive disorder are characterized by cognitive inflexibility, which hypothetically arises from dysfunctions of a certain cortico-basal ganglia-thalamo-cortical circuit including the ventrolateral prefrontal region. Inside this neurocircuit, an imbalance between distinct striatal projections to basal ganglia output nuclei, either directly or indirectly via the external globus pallidus, is suggested to be relevant for impaired arbitration between facilitation and inhibition of cortically initiated activity. However, current evidence of individually altered cortico-striatal or thalamo-cortical connectivities is insufficient to understand how cortical dysconnections are linked to the imbalanced basal ganglia system in patients. In this study, we aimed to identify aberrant ventrolateral prefronto-basal ganglia-thalamic subnetworks representing direct-indirect imbalance and its association with cognitive inflexibility in patients. To increase network detection sensitivity, we constructed a cortico-basal ganglia-thalamo-cortical network model incorporating striatal, pallidal and thalamic subregions defined by unsupervised clustering in 105 medication-free patients with obsessive-compulsive disorder (age = 25.05 ± 6.55 years, male/female = 70/35) and 99 healthy controls (age = 23.93 ± 5.80 years, male/female = 64/35). By using the network-based statistic method, we analysed group differences in subnetworks formed by suprathreshold dysconnectivities. Using linear regression models, we tested subnetwork dysconnectivity effects on symptom severity and set-shifting performance assessed by well-validated clinical and cognitive tests. Compared with the healthy controls, patients were slower to track the Part B sequence of the Trail Making Test when the effects of psychomotor and visuospatial functions were adjusted (t = 3.89, P < 0.001) and made more extradimensional shift errors (t = 4.09, P < 0.001). In addition to reduced fronto-striatal and striato-external pallidal connectivities and hypoconnected striato-thalamic subnetwork [P = 0.001, family-wise error rate (FWER) corrected], patients had hyperconnected fronto-external pallidal (P = 0.012, FWER corrected) and intra-thalamic (P = 0.015, FWER corrected) subnetworks compared with the healthy controls. Among the patients, the fronto-pallidal subnetwork alteration, especially ventrolateral prefronto-external globus pallidal hyperconnectivity, was associated with relatively fewer extradimensional shifting errors (ß = -0.30, P = 0.001). Our findings suggest that the hyperconnected fronto-external pallidal subnetwork may have an opposite effect to the imbalance caused by the reduced indirect pathway (fronto-striato-external pallidal) connectivities in patients. This ventrolateral prefrontal hyperconnectivity may help the external globus pallidus disinhibit basal ganglia output nuclei, which results in behavioural inhibition, so as to compensate for the impaired set shifting. We suggest the ventrolateral prefrontal and external globus pallidus as neuromodulatory targets for inflexible habitual behaviours in obsessive-compulsive disorder.

Data-mining analysis of media frame effects on social perception of schizophrenia renaming in Korea.

BACKGROUND: In 2011, Korean Neuropsychiatric Association renamed schizophrenia from 'mind split disorder' ('Jungshinbunyeolbyung' in Korean) to 'attunement disorder' ('Johyeonbyung' in Korean), in a strategic way to reduce social stigma toward people with schizophrenia. However, there remains an elusive consensus that how the renaming effort has contributed to changes in the social perception of schizophrenia in Korea. METHODS: With this regard, we explored whether media frames alter the social perception, in ways of respecting or disrespecting schizophrenia patients before and after the renaming. This study extensively investigated media keywords related to schizophrenia across the time by applying both language and epidemiologic analyses. RESULTS: In results, the media keywords have been negatively described for schizophrenia patients both before and after the renaming. Further, from an analysis using the regression model, a significant correlation was observed between the frequency of negative keywords and the hospitalization frequency of schizophrenia patients. CONCLUSIONS: These findings suggest that the social perception of schizophrenia has been scarcely changed, but rather remained negatively biased against schizophrenia patients, in spite of the renaming effort. Notably, the biased media frames have been demonstrated to negatively impact on the social perception, and even on the medical use patterns of general schizophrenia patients. In conclusion, we suggest that the unbiased media frames along with the renaming effort may collectively help reduce the negative social perception of schizophrenia. TRIAL REGISTRATION: This study was approved from the Institute of Review Board (IRB) of the Yoing-In Mental Hospital (IRB No. YIMH-IRB-2019-02).

First-trimester exposure to benzodiazepines and risk of congenital malformations in offspring: A population-based cohort study in South Korea.

BACKGROUND: Benzodiazepines are frequently prescribed during pregnancy; however, evidence about possible teratogenicity is equivocal. We aimed to evaluate the association between first-trimester benzodiazepine use and the risk of major congenital malformations. METHODS AND FINDINGS: Using Korea's nationwide healthcare database, we conducted a population-based cohort study of women who gave birth during 2011 to 2018 and their live-born infants. The exposure was defined as one or more benzodiazepine prescriptions during the first trimester. We determined the relative risks (RRs) and confidence intervals (CIs) of overall congenital malformations and 12 types of organ-specific malformations. Infants were followed from birth to death or 31 December 2019, whichever came first (up to 8 years of age). Propensity score fine stratification was employed to control for 45 potential confounders. Among a total of 3,094,227 pregnancies, 40,846 (1.3%) were exposed to benzodiazepines during the first trimester (mean [SD] age, 32.4 [4.1] years). The absolute risk of overall malformations was 65.3 per 1,000 pregnancies exposed to benzodiazepines versus 51.4 per 1,000 unexposed pregnancies. The adjusted RR was 1.09 (95% CI 1.05 to 1.13, p < 0.001) for overall malformations and 1.15 (1.10 to 1.21, p < 0.001) for heart defects. Based on mean daily lorazepam-equivalent doses, the adjusted RRs for overall malformations and heart defects were 1.05 (0.99 to 1.12, p = 0.077) and 1.12 (1.04 to 1.21, p = 0.004) for <1 mg/day and 1.26 (1.17 to 1.36, p < 0.001) and 1.31 (1.19 to 1.45, p < 0.001) for >2.5 mg/day doses, respectively, suggesting a dose-response relationship. A small but significant increase in risk for overall and heart defects was detected with several specific agents (range of adjusted RRs: 1.08 to 2.43). The findings were robust across all sensitivity analyses, and negative control analyses revealed a null association. Study limitations include possible exposure misclassification, residual confounding, and restriction to live births. CONCLUSIONS: In this large nationwide cohort study, we found that first-trimester benzodiazepine exposure was associated with a small increased risk of overall malformations and heart defects, particularly at the higher daily dose. The absolute risks and population attributable fractions were modest. The benefits of benzodiazepines for their major indications must be considered despite the potential risks; if their use is necessary, the lowest effective dosage should be prescribed to minimize the risk. TRIAL REGISTRATION: ClinicalTrials.gov NCT04856436.

An overview of the first 5 years of the ENIGMA obsessive-compulsive disorder working group: The power of worldwide collaboration.

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

Prediction of psychosis: model development and internal validation of a personalized risk calculator.

BACKGROUND: Over the past two decades, early detection and early intervention in psychosis have become essential goals of psychiatry. However, clinical impressions are insufficient for predicting psychosis outcomes in clinical high-risk (CHR) individuals; a more rigorous and objective model is needed. This study aims to develop and internally validate a model for predicting the transition to psychosis within 10 years. METHODS: Two hundred and eight help-seeking individuals who fulfilled the CHR criteria were enrolled from the prospective, naturalistic cohort program for CHR at the Seoul Youth Clinic (SYC). The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was used to develop a predictive model for a psychotic transition. We performed k-means clustering and survival analysis to stratify the risk of psychosis. RESULTS: The predictive model, which includes clinical and cognitive variables, identified the following six baseline variables as important predictors: 1-year percentage decrease in the Global Assessment of Functioning score, IQ, California Verbal Learning Test score, Strange Stories test score, and scores in two domains of the Social Functioning Scale. The predictive model showed a cross-validated Harrell's C-index of 0.78 and identified three subclusters with significantly different risk levels. CONCLUSIONS: Overall, our predictive model showed a predictive ability and could facilitate a personalized therapeutic approach to different risks in high-risk individuals.

The relationship between grey matter volume and striatal dopamine function in psychosis: a multimodal 18F-DOPA PET and voxel-based morphometry study.

A leading hypothesis for schizophrenia and related psychotic disorders proposes that cortical brain disruption leads to subcortical dopaminergic dysfunction, which underlies psychosis in the majority of patients who respond to treatment. Although supported by preclinical findings that prefrontal cortical lesions lead to striatal dopamine dysregulation, the relationship between prefrontal structural volume and striatal dopamine function has not been tested in people with psychosis. We therefore investigated the in vivo relationship between striatal dopamine synthesis capacity and prefrontal grey matter volume in treatment-responsive patients with psychosis, and compared them to treatment non-responsive patients, where dopaminergic mechanisms are not thought to be central. Forty patients with psychosis across two independent cohorts underwent 18F-DOPA PET scans to measure dopamine synthesis capacity (indexed as the influx rate constant Kicer) and structural 3T MRI. The PET, but not MR, data have been reported previously. Structural images were processed using DARTEL-VBM. GLM analyses were performed in SPM12 to test the relationship between prefrontal grey matter volume and striatal Kicer. Treatment responders showed a negative correlation between prefrontal grey matter and striatal dopamine synthesis capacity, but this was not evident in treatment non-responders. Specifically, we found an interaction between treatment response, whole striatal dopamine synthesis capacity and grey matter volume in left (pFWE corr. = 0.017) and right (pFWE corr. = 0.042) prefrontal cortex. We replicated the finding in right prefrontal cortex in the independent sample (pFWE corr. = 0.031). The summary effect size was 0.82. Our findings are consistent with the long-standing hypothesis of dysregulation of the striatal dopaminergic system being related to prefrontal cortex pathology in schizophrenia, but critically also extend the hypothesis to indicate it can be applied to treatment-responsive schizophrenia only. This suggests that different mechanisms underlie the pathophysiology of treatment-responsive and treatment-resistant schizophrenia.

Dopamine dysregulation in psychotic relapse after antipsychotic discontinuation: an [18F]DOPA and [11C]raclopride PET study in first-episode psychosis.

Although antipsychotic drugs are effective for relieving the psychotic symptoms of first-episode psychosis (FEP), psychotic relapse is common during the course of the illness. While some FEPs remain remitted even without medication, antipsychotic discontinuation is regarded as the most common risk factor for the relapse. Considering the actions of antipsychotic drugs on presynaptic and postsynaptic dopamine dysregulation, this study evaluated possible mechanisms underlying relapse after antipsychotic discontinuation. Twenty five FEPs who were clinically stable and 14 matched healthy controls were enrolled. Striatal dopamine activity was assessed as Kicer value using [18F]DOPA PET before and 6 weeks after antipsychotic discontinuation. The D2/3 receptor availability was measured as BPND using [11C]raclopride PET after antipsychotic discontinuation. Healthy controls also underwent PET scans according to the corresponding schedule of the patients. Patients were monitored for psychotic relapse during 12 weeks after antipsychotic discontinuation. 40% of the patients showed psychotic relapse after antipsychotic discontinuation. The change in Kicer value over time significantly differed between relapsed, non-relapsed patients and healthy controls (Week*Group: F = 4.827, df = 2,253.193, p = 0.009). In relapsed patients, a significant correlation was found between baseline striatal Kicer values and time to relapse after antipsychotic discontinuation (R2 = 0.518, p = 0.018). BPND were not significantly different between relapsed, non-relapsed patients and healthy controls (F = 1.402, df = 2,32.000, p = 0.261). These results suggest that dysfunctional dopamine autoregulation might precipitate psychotic relapse after antipsychotic discontinuation in FEP. This finding could be used for developing a strategy for the prevention of psychotic relapse related to antipsychotic discontinuation.

Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry.

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

Association of mental disorders with SARS-CoV-2 infection and severe health outcomes: nationwide cohort study.

BACKGROUND: Epidemiological data on the association between mental disorders and the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) severity are limited. AIMS: To evaluate the association between mental disorders and the risk of SARS-CoV-2 infection and severe outcomes following COVID-19. METHOD: We performed a cohort study using the Korean COVID-19 patient database based on national health insurance data. Each person with a mental or behavioural disorder (diagnosed during the 6 months prior to their first SARS-CoV-2 test) was matched by age, gender and Charlson Comorbidity Index with up to four people without mental disorders. SARS-CoV-2-positivity risk and the risk of death or severe events (intensive care unit admission, use of mechanical ventilation and acute respiratory distress syndrome) post-infection were calculated using conditional logistic regression analysis. RESULTS: Among 230 565 people tested for SARS-CoV-2, 33 653 (14.6%) had mental disorders; 928/33 653 (2.76%) tested SARS-CoV-2 positive and 56/928 (6.03%) died. In multivariable analysis using the matched cohort, there was no association between mental disorders and SARS-CoV-2-positivity risk (odds ratio OR = 0.95; 95% CI 0.87-1.04); however, a higher risk was associated with schizophrenia-related disorders (OR = 1.50; 95% CI 1.14-1.99). Among confirmed COVID-19 patients, the mortality risk was significantly higher in patients with than in those without mental disorders (OR = 1.99, 95% CI 1.15-3.43). CONCLUSIONS: Mental disorders are likely contributing factors to mortality following COVID-19. Although the infection risk was not higher for people with mental disorders overall, those with schizophrenia-related disorders were more vulnerable to infection.