Country-level gender inequality is associated with structural differences in the brains of women and men.

Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women's worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7,876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women's brains and provide initial evidence for neuroscience-informed policies for gender equality.

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

Selective visuoconstructional impairment following mild COVID-19 with inflammatory and neuroimaging correlation findings.

People recovered from COVID-19 may still present complications including respiratory and neurological sequelae. In other viral infections, cognitive impairment occurs due to brain damage or dysfunction caused by vascular lesions and inflammatory processes. Persistent cognitive impairment compromises daily activities and psychosocial adaptation. Some level of neurological and psychiatric consequences were expected and described in severe cases of COVID-19. However, it is debatable whether neuropsychiatric complications are related to COVID-19 or to unfoldings from a severe infection. Nevertheless, the majority of cases recorded worldwide were mild to moderate self-limited illness in non-hospitalized people. Thus, it is important to understand what are the implications of mild COVID-19, which is the largest and understudied pool of COVID-19 cases. We aimed to investigate adults at least four months after recovering from mild COVID-19, which were assessed by neuropsychological, ocular and neurological tests, immune markers assay, and by structural MRI and 18FDG-PET neuroimaging to shed light on putative brain changes and clinical correlations. In approximately one-quarter of mild-COVID-19 individuals, we detected a specific visuoconstructive deficit, which was associated with changes in molecular and structural brain imaging, and correlated with upregulation of peripheral immune markers. Our findings provide evidence of neuroinflammatory burden causing cognitive deficit, in an already large and growing fraction of the world population. While living with a multitude of mild COVID-19 cases, action is required for a more comprehensive assessment and follow-up of the cognitive impairment, allowing to better understand symptom persistence and the necessity of rehabilitation of the affected individuals.

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium.

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls, affecting widespread cortical regions. The possible impacts of these regional alterations in terms of structural network effects have not previously been characterized. Inter-regional morphological covariance analysis can capture network connectivity between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1455 individuals with ASD and 1560 controls, across 43 independent datasets of the ENIGMA consortium's ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered average asymmetry of networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, involving higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve executive functions, language-related and sensorimotor processes. These findings provide a network-level characterization of altered left-right brain asymmetry in ASD, based on a large combined sample. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.

Association between chemosensory impairment with neuropsychiatric morbidity in post-acute COVID-19 syndrome: results from a multidisciplinary cohort study.

Preliminary methodologically limited studies suggested that taste and smell known as chemosensory impairments and neuropsychiatric symptoms are associated in post-COVID-19. The objective of this study is to evaluate whether chemosensory dysfunction and neuropsychiatric impairments in a well-characterized post-COVID-19 sample. This is a cohort study assessing adult patients hospitalized due to moderate or severe forms of COVID-19 between March and August 2020. Baseline information includes several clinical and hospitalization data. Further evaluations were made using several different reliable instruments designed to assess taste and smell functions, parosmia, and neuropsychiatric disorders (using standardized psychiatric and cognitive measures). Out of 1800 eligible individuals, 701 volunteers were assessed on this study. After multivariate analysis, patients reporting parosmia had a worse perception of memory performance (p < 0.001). Moderate/severe hypogeusia was significantly associated with a worse performance on the word list memory task (p = 0.012); Concomitant moderate/severe olfactory and gustatory loss during the acute phase of COVID-19 was also significantly associated with episodic memory impairment (p = 0.006). We found a positive association between reported chemosensory (taste and olfaction) abnormalities and cognition dysfunction in post-COVID-19 patients. These findings may help us identify potential mechanisms linking these two neurobiological functions, and also support the speculation on a possible route through which SARS-CoV-2 may reach the central nervous system.

Dissipating the fog: Cognitive trajectories and risk factors 1 year after COVID-19 hospitalization.

INTRODUCTION: Cognitive impairment is common after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, associations between post-hospital discharge risk factors and cognitive trajectories have not been explored. METHODS: A total of 1105 adults (mean age ± SD 64.9 ± 9.9 years, 44% women, 63% White) with severe coronavirus disease 2019 (COVID-19) were evaluated for cognitive function 1 year after hospital discharge. Scores from cognitive tests were harmonized, and clusters of cognitive impairment were defined using sequential analysis. RESULTS: Three groups of cognitive trajectories were observed during the follow-up: no cognitive impairment, initial short-term cognitive impairment, and long-term cognitive impairment. Predictors of cognitive decline after COVID-19 were older age (ß = -0.013, 95% CI = -0.023;-0.003), female sex (ß = -0.230, 95% CI = -0.413;-0.047), previous dementia diagnosis or substantial memory complaints (ß = -0.606, 95% CI = -0.877;-0.335), frailty before hospitalization (ß = -0.191, 95% CI = -0.264;-0.119), higher platelet count (ß = -0.101, 95% CI = -0.185;-0.018), and delirium (ß = -0.483, 95% CI = -0.724;-0.244). Post-discharge predictors included hospital readmissions and frailty. DISCUSSION: Cognitive impairment was common and the patterns of cognitive trajectories depended on sociodemographic, in-hospital, and post-hospitalization predictors. HIGHLIGHTS: Cognitive impairment after coronavirus disease 2019 (COVID-19) hospital discharge was associated with higher age, less education, delirium during hospitalization, a higher number of hospitalizations post discharge, and frailty before and after hospitalization. Frequent cognitive evaluations for 12-month post-COVID-19 hospitalization showed three possible cognitive trajectories: no cognitive impairment, initial short-term impairment, and long-term impairment. This study highlights the importance of frequent cognitive testing to determine patterns of COVID-19 cognitive impairment, given the high frequency of incident cognitive impairment 1 year after hospitalization.

Biomarkers for dementia in Latin American countries: Gaps and opportunities.

Limited knowledge on dementia biomarkers in Latin American and Caribbean (LAC) countries remains a serious barrier. Here, we reported a survey to explore the ongoing work, needs, interests, potential barriers, and opportunities for future studies related to biomarkers. The results show that neuroimaging is the most used biomarker (73%), followed by genetic studies (40%), peripheral fluids biomarkers (31%), and cerebrospinal fluid biomarkers (29%). Regarding barriers in LAC, lack of funding appears to undermine the implementation of biomarkers in clinical or research settings, followed by insufficient infrastructure and training. The survey revealed that despite the above barriers, the region holds a great potential to advance dementia biomarkers research. Considering the unique contributions that LAC could make to this growing field, we highlight the urgent need to expand biomarker research. These insights allowed us to propose an action plan that addresses the recommendations for a biomarker framework recently proposed by regional experts.

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Greater male than female variability in regional brain structure across the lifespan.

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder: The ENIGMA adventure.

Neuroimaging has been extensively used to study brain structure and function in individuals with attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) over the past decades. Two of the main shortcomings of the neuroimaging literature of these disorders are the small sample sizes employed and the heterogeneity of methods used. In 2013 and 2014, the ENIGMA-ADHD and ENIGMA-ASD working groups were respectively, founded with a common goal to address these limitations. Here, we provide a narrative review of the thus far completed and still ongoing projects of these working groups. Due to an implicitly hierarchical psychiatric diagnostic classification system, the fields of ADHD and ASD have developed largely in isolation, despite the considerable overlap in the occurrence of the disorders. The collaboration between the ENIGMA-ADHD and -ASD working groups seeks to bring the neuroimaging efforts of the two disorders closer together. The outcomes of case-control studies of subcortical and cortical structures showed that subcortical volumes are similarly affected in ASD and ADHD, albeit with small effect sizes. Cortical analyses identified unique differences in each disorder, but also considerable overlap between the two, specifically in cortical thickness. Ongoing work is examining alternative research questions, such as brain laterality, prediction of case-control status, and anatomical heterogeneity. In brief, great strides have been made toward fulfilling the aims of the ENIGMA collaborations, while new ideas and follow-up analyses continue that include more imaging modalities (diffusion MRI and resting-state functional MRI), collaborations with other large databases, and samples with dual diagnoses.

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Innate immune cells and myelin profile in multiple sclerosis: a multi-tracer PET/MR study.

PURPOSE: Neuropathological studies have demonstrated distinct profiles of microglia activation and myelin injury among different multiple sclerosis (MS) phenotypes and disability stages. PET imaging using specific tracers may uncover the in vivo molecular pathology and broaden the understanding of the disease heterogeneity. METHODS: We used the 18-kDa translocator protein (TSPO) tracer (R)-[11C]PK11195 and [11C]PIB PET images acquired in a hybrid PET/MR 3 T system to characterize, respectively, the profile of innate immune cells and myelin content in 47 patients with MS compared to 18 healthy controls (HC). For the volume of interest (VOI)-based analysis of the dynamic data, (R)-[11C]PK11195 distribution volume (VT) was determined for each subject using a metabolite-corrected arterial plasma input function while [11C]PIB distribution volume ratio (DVR) was estimated using a reference region extracted by a supervised clustering algorithm. A voxel-based analysis was also performed using Statistical Parametric Mapping. Functional disability was evaluated by the Expanded Disability Status Scale (EDSS), Multiple Sclerosis Functional Composite (MSFC), and Symbol Digit Modality Test (SDMT). RESULTS: In the VOI-based analysis, [11C]PIB DVR differed between patients and HC in the corpus callosum (P = 0.019) while no differences in (R)-[11C]PK11195 VT were observed in patients relative to HC. Furthermore, no correlations or associations were observed between both tracers within the VOI analyzed. In the voxel-based analysis, high (R)-[11C]PK11195 uptake was observed diffusively in the white matter (WM) when comparing the progressive phenotype and HC, and lower [11C]PIB uptake was observed in certain WM regions when comparing the relapsing-remitting phenotype and HC. None of the tracers were able to differentiate phenotypes at voxel or VOI level in our cohort. Linear regression models adjusted for age, sex, and phenotype demonstrated that higher EDSS was associated with an increased (R)-[11C]PK11195 VT and lower [11C]PIB DVR in corpus callosum (P = 0.001; P = 0.023), caudate (P = 0.015; P = 0.008), and total T2 lesion (P = 0.007; P = 0.012), while better cognitive scores in SDMT were associated with higher [11C]PIB DVR in the corpus callosum (P = 0.001), and lower (R)-[11C]PK11195 VT (P = 0.013). CONCLUSIONS: Widespread innate immune cells profile and marked loss of myelin in T2 lesions and regions close to the ventricles may occur independently and are associated with disability, in both WM and GM structures.

Post-acute sequelae of SARS-CoV-2 infection: relationship of central nervous system manifestations with physical disability and systemic inflammation.

BACKGROUND: Despite the multitude of clinical manifestations of post-acute sequelae of SARS-CoV-2 infection (PASC), studies applying statistical methods to directly investigate patterns of symptom co-occurrence and their biological correlates are scarce. METHODS: We assessed 30 symptoms pertaining to different organ systems in 749 adults (age = 55 ± 14 years; 47% female) during in-person visits conducted at 6-11 months after hospitalization due to coronavirus disease 2019 (COVID-19), including six psychiatric and cognitive manifestations. Symptom co-occurrence was initially investigated using exploratory factor analysis (EFA), and latent variable modeling was then conducted using Item Response Theory (IRT). We investigated associations of latent variable severity with objective indices of persistent physical disability, pulmonary and kidney dysfunction, and C-reactive protein and D-dimer blood levels, measured at the same follow-up assessment. RESULTS: The EFA extracted one factor, explaining 64.8% of variance; loadings were positive for all symptoms, and above 0.35 for 16 of them. The latent trait generated using IRT placed fatigue, psychiatric, and cognitive manifestations as the most discriminative symptoms (coefficients > 1.5, p < 0.001). Latent trait severity was associated with decreased body weight and poorer physical performance (coefficients > 0.240; p ⩽ 0.003), and elevated blood levels of C-reactive protein (coefficient = 0.378; 95% CI 0.215-0.541; p < 0.001) and D-dimer (coefficient = 0.412; 95% CI 0.123-0.702; p = 0.005). Results were similar after excluding subjects with pro-inflammatory comorbidities. CONCLUSIONS: Different symptoms that persist for several months after moderate or severe COVID-19 may unite within one latent trait of PASC. This trait is dominated by fatigue and psychiatric symptoms, and is associated with objective signs of physical disability and persistent systemic inflammation.

Screening for Dementia and Cognitive Decline in Adults With Down Syndrome: A Novel Approach Using the Informant Questionnaire on Cognitive Decline in the Elderly.

OBJECTIVE: The aim was to examine the psychometric properties of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) as a diagnostic tool to screen for dementia in aging individuals with Down syndrome (DS). METHODS: This was a cross-sectional study of 92 individuals with DS 30 y or above of age) evaluated with the IQCODE. Using the informant questionnaire of the Cambridge Examination for Mental Disorders of Older People with Down's Syndrome and Others with Intellectual Disabilities, we divided the subjects into 3 diagnostic groups: stable cognition; prodromal dementia; and dementia. The ability of the IQCODE to discriminate between diagnostic groups was analyzed by calculating the areas under the receiver operator characteristic curves (AUCs). RESULTS: The optimal IQCODE cutoffs were 3.14 for dementia versus stable cognition (AUC=0.993; P<0.001) and 3.11 for prodromal dementia+dementia versus stable cognition (AUC=0.975; P<0.001), with sensitivity/specificity/accuracy of 100%/96.8%/97.3%, and 93.3%/91.9%/92.4%, respectively. The IQCODE showed a weak-to-moderate correlation with cognitive performance (P<0.05). CONCLUSION: The IQCODE is a useful tool to screen for cognitive decline in individuals with DS and is suitable for use in a primary care setting.

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Analysis of structural brain asymmetries in attention-deficit/hyperactivity disorder in 39 datasets.

OBJECTIVE: Some studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium. METHODS: We analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries. RESULTS: There was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t = 2.1, p = .04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t = 2.7, p = .01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen's d from -0.18 to 0.18) and would not survive study-wide correction for multiple testing. CONCLUSION: Prior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait.

Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic cohort, using novel semi-supervised machine learning methods designed to discover patterns associated with disease rather than normal anatomical variation. Structural MRI and clinical measures in established schizophrenia (n = 307) and healthy controls (n = 364) were analysed across three sites of PHENOM (Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging) consortium. Regional volumetric measures of grey matter, white matter, and CSF were used to identify distinct and reproducible neuroanatomical subtypes of schizophrenia. Two distinct neuroanatomical subtypes were found. Subtype 1 showed widespread lower grey matter volumes, most prominent in thalamus, nucleus accumbens, medial temporal, medial prefrontal/frontal and insular cortices. Subtype 2 showed increased volume in the basal ganglia and internal capsule, and otherwise normal brain volumes. Grey matter volume correlated negatively with illness duration in Subtype 1 (r = -0.201, P = 0.016) but not in Subtype 2 (r = -0.045, P = 0.652), potentially indicating different underlying neuropathological processes. The subtypes did not differ in age (t = -1.603, df = 305, P = 0.109), sex (chi-square = 0.013, df = 1, P = 0.910), illness duration (t = -0.167, df = 277, P = 0.868), antipsychotic dose (t = -0.439, df = 210, P = 0.521), age of illness onset (t = -1.355, df = 277, P = 0.177), positive symptoms (t = 0.249, df = 289, P = 0.803), negative symptoms (t = 0.151, df = 289, P = 0.879), or antipsychotic type (chi-square = 6.670, df = 3, P = 0.083). Subtype 1 had lower educational attainment than Subtype 2 (chi-square = 6.389, df = 2, P = 0.041). In conclusion, we discovered two distinct and highly reproducible neuroanatomical subtypes. Subtype 1 displayed widespread volume reduction correlating with illness duration, and worse premorbid functioning. Subtype 2 had normal and stable anatomy, except for larger basal ganglia and internal capsule, not explained by antipsychotic dose. These subtypes challenge the notion that brain volume loss is a general feature of schizophrenia and suggest differential aetiologies. They can facilitate strategies for clinical trial enrichment and stratification, and precision diagnostics.

Genetic polymorphisms of the serotonin transporter are not related with depression in temporal lobe epilepsy caused by hippocampal sclerosis.

BACKGROUND: Mood disorders are the most frequent psychiatric disorders in patients with temporal lobe epilepsy caused by hippocampal sclerosis (TLE-HS). The pathophysiological mechanisms in common between TLE and mood disorders include abnormalities in the serotonergic pathway. We aimed to evaluate the association between serotonin transporter genetic polymorphisms - 5-HTTLPR and 5-HTTVNTR - and the presence of mood disorders in patients with TLE-HS. METHODS: We evaluated 119 patients with TLE-HS, with and without psychiatric disorder; 146 patients diagnosed with major depressive disorder (MDD), and 113 healthy volunteers. Individuals were genotyped for the 5-HTTLPR and 5-HTTVNTR polymorphisms. RESULTS: No difference was observed between the TLE-HS groups, healthy controls, and MDD without epilepsy. There was a correlation between the 12-allele of the 5-HTTVNTR and the family history of patients with epilepsy with TLE-HS (p = 0.013). CONCLUSIONS: In this study conducted in two Brazilian centers, the serotonin transporter polymorphisms evaluated cannot be associated with depressive disorder in patients with TLE-HS. Still, they do have some influence over some clinical characteristics of epilepsy in TLE-HS. These data may not be reproduced in other populations with distinct ethnic characteristics.

Deficits in short-term memory binding are detectable in individuals with brain amyloid deposition in the absence of overt neurodegeneration in the Alzheimer's disease continuum.

The short-term memory binding (STMB) test involves the ability to hold in memory the integration between surface features, such as shapes and colours. The STMB test has been used to detect Alzheimer's disease (AD) at different stages, from preclinical to dementia, showing promising results. The objective of the present study was to verify whether the STMB test could differentiate patients with distinct biomarker profiles in the AD continuum. The sample comprised 18 cognitively unimpaired (CU) participants, 30 mild cognitive impairment (MCI) and 23 AD patients. All participants underwent positron emission tomography (PET) with Pittsburgh compound-B labelled with carbon-11 ([11C]PIB) assessing amyloid beta (Aß) aggregation (A) and 18fluorine-fluorodeoxyglucose ([18F]FDG)-PET assessing neurodegeneration (N) (A-N- [n = 35]); A+N- [n = 11]; A+ N+ [n = 19]). Participants who were negative and positive for amyloid deposition were compared in the absence (A-N- vs. A+N-) of neurodegeneration. When compared with the RAVLT and SKT memory tests, the STMB was the only cognitive task that differentiated these groups, predicting the group outcome in logistic regression analyses. The STMB test showed to be sensitive to the signs of AD pathology and may represent a cognitive marker within the AD continuum.