The results of multiple studies on the association between antipsychotic use and structural brain changes in schizophrenia have been assessed only in qualitative literature reviews to date. We aimed to perform a meta-analysis of voxel-based morphometry (VBM) studies on this association to quantitatively synthesize the findings of these studies.

Nearly one-third of patients with obsessive-compulsive disorder (OCD) fail to respond to adequate therapeutic approaches such as serotonin reuptake inhibitors and/or cognitive-behavioral therapy (CBT). This study investigated structural magnetic resonance imaging (MRI) correlates as potential pre-treatment brain markers to predict treatment response in treatment-naïve OCD patients randomized between trials of fluoxetine or CBT. Treatment-naïve OCD patients underwent structural MRI scans before randomization to a 12-week clinical trial of either fluoxetine or group-based CBT. Voxel-based morphometry was used to identify correlations between pretreatment regional gray matter volume and changes in symptom severity on the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). Brain regional correlations of treatment response differed between treatment groups. Notably, symptom improvement in the fluoxetine treatment group (n=14) was significantly correlated with smaller pretreatment gray matter volume within the right middle lateral orbitofrontal cortex (OFC), whereas symptom improvement in the CBT treatment group (n=15) was significantly correlated with larger pretreatment gray matter volume within the right medial prefrontal cortex (mPFC). No significant a priori regional correlations of treatment response were identified as common between the two treatment groups when considering the entire sample (n=29). These findings suggest that pretreatment gray matter volumes of distinct brain regions within the lateral OFC and mPFC were differentially correlated to treatment response to fluoxetine versus CBT in OCD patients. This study further implicates the mPFC in the fear/anxiety extinction process and stresses the importance of lateral portions of the OFC in mediating fluoxetine's effectiveness in OCD. Clinical registration information: http://clinicaltrials.gov-NCT00680602.

Background: Mnemonic strategy training (MST) has been shown to improve cognitive performance in amnestic mild cognitive impairment (a-MCI), however, several questions remain unresolved. The goal of the present study was to replicate earlier pilot study findings using a randomized controlled design and to evaluate transfer effects and changes in brain activation. Methods: Thirty patients with a-MCI were randomized into MST or education program. At baseline, participants completed clinical and neuropsychological assessments as well as structural and functional magnetic resonance imaging (fMRI). Interventions were administered individually and comprised four sessions, over 2 weeks. MST taught patients to use a three-step process to learn and recall face-name associations. Post-treatment assessment included fMRI, a separate face-name association task, neuropsychological tests, and measures of metamemory. Behavioral (i.e., non-fMRI) measures were repeated after one and 3-months. Results: Participants in the MST condition showed greater improvement on measures of face-name memory, and increased associative strategy use; effects that were accompanied by increased fMRI activation in the left anterior temporal lobe. While all participants reported greater contentment with their everyday memory following intervention, only the MST group reported significant improvements in their memory abilities. There was no clear indication of far-transfer effects to other neuropsychological tests. Conclusion: Results demonstrate that patients with a-MCI not only show stimulus specific benefits of MST, but that they appear capable of transferring training to at least some other cognitive tasks. MST also facilitated the use of brain regions that are involved in face processing, episodic and semantic memory, and social cognition, which are consonant with the cognitive processes engaged by training.

Reduced cognitive reserve (CR) due to very low educational (VLE) levels may influence high dementia rates in low-middle income environments, leading to decreased cognitive resilience (RES) to Alzheimer´s disease (AD) pathology. However, in vivo findings in VLE groups confirming this prediction are lacking.

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.

To compare the volume of the hippocampus and parahippocampal gyrus in elderly individuals with and without depressive disorders, and to determine whether the volumes of these regions correlate with scores on memory tests.

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.

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.

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL).

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.

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.

Mnemonic strategy training (MST) has been shown to improve cognitive performance and increase brain activation in those with mild cognitive impairment (MCI). However, little is known regarding the effects of MST on functional connectivity (FC) at rest. The aim of the present study was to investigate the MST focused on face-name associations effect on resting-state FC in those with MCI.

The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group.

The presence of psychotic features in the course of a depressive disorder is known to increase the risk for bipolarity, but the early identification of such cases remains challenging in clinical practice. In the present study, we evaluated the diagnostic performance of a neuroanatomical pattern classification method in the discrimination between psychotic major depressive disorder (MDD), bipolar I disorder (BD-I), and healthy controls (HC) using a homogenous sample of patients at an early course of their illness. Twenty-three cases of first-episode psychotic mania (BD-I) and 19 individuals with a first episode of psychotic MDD whose diagnosis remained stable during 1 year of followup underwent 1.5 T MRI at baseline. A previously validated multivariate classifier based on support vector machine (SVM) was employed and measures of diagnostic performance were obtained for the discrimination between each diagnostic group and subsamples of age- and gender-matched controls recruited in the same neighborhood of the patients. Based on T1-weighted images only, the SVM-classifier afforded poor discrimination in all 3 pairwise comparisons: BD-I versus HC; MDD versus HC; and BD-I versus MDD. Thus, at the population level and using structural MRI only, we failed to achieve good discrimination between BD-I, psychotic MDD, and HC in this proof of concept study.

The identification of biological markers at early stages of Alzheimer's disease (AD) contributes to diagnostic accuracy and adds prognostic value. However, in spite of recent developments, results of neurostructural imaging studies on predicting conversion to AD are not uniform. We conducted a systematic review of voxel-based morphometry (VBM) studies about the neurostructural predictors of conversion to AD. Ten studies met inclusion criteria and nine reported baseline regional gray matter (GM) atrophy in mild cognitive impairment (MCI) or healthy subjects who progressed to AD. Using the method of Activation Likelihood Estimation, we meta-analyzed the coordinates from the six longitudinal VBM studies that enrolled subjects with amnestic MCI (aMCI) at baseline. These comprised a total of 429 aMCI subjects, of which 142 converted to AD. Meta-analysis yielded one significant cluster of GM volumetric reduction in aMCI patients who converted to AD, located in the left hippocampus and parahippocampal gyrus. In conclusion, left medial temporal lobe atrophy is the most consistent neurostructural biomarker to predict conversion from aMCI to AD.

Attention-Deficit/Hiperactivity Disorder (ADHD) is a prevalent disorder, but its neuroanatomical circuitry is still relatively understudied, especially in the adult population. The few morphometric magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) studies available to date have found heterogeneous results. This may be at least partly attributable to some well-known technical limitations of the conventional voxel-based methods usually employed to analyze such neuroimaging data. Moreover, there is a great paucity of imaging studies of adult ADHD to date that have excluded patients with history of use of stimulant medication.

The hippocampus has been highly implicated in the pathophysiology of bipolar disorder (BD). Nevertheless, no study has longitudinally evaluated hippocampal metabolite levels in bipolar depression under treatment with lithium.

New research on assessing neuropsychiatric manifestations of Alzheimer´s Disease (AD) involves grouping neuropsychiatric symptoms into syndromes. Yet this approach is limited by high inter-subject variability in neuropsychiatric symptoms and a relatively low degree of concordance across studies attempting to cluster neuropsychiatric symptoms into syndromes. An alternative strategy that involves dichotomizing AD subjects into those with few versus multiple neuropsychiatric symptoms is both consonant with real-world clinical practice and can contribute to understanding neurobiological underpinnings of neuropsychiatric symptoms in AD patients. The aim of this study was to address whether the number of neuropsychiatric symptoms (i.e., presence of few [≤2] versus multiple [≥3] symptoms) in AD would be associated with degree of significant gray matter (GM) volume loss. Of particular interest was volume loss in brain regions involved in memory, emotional processing and salience brain networks, including the prefrontal, lateral temporal and parietal cortices, anterior cingulate gyrus, temporo-limbic structures and insula. We recruited 19 AD patients and 13 healthy controls, which underwent an MRI and neuropsychiatric assessment. Regional brain volumes were determined using voxel-based morphometry and other advanced imaging processing methods. Our results indicated the presence of different patterns of GM atrophy in the two AD subgroups relative to healthy controls. AD patients with multiple neuropsychiatric manifestations showed more evident GM atrophy in the left superior temporal gyrus and insula as compared with healthy controls. In contrast, AD subjects with few neuropsychiatric symptoms displayed more GM atrophy in prefrontal regions, as well as in the dorsal anterior cingulate ad post-central gyri, as compared with healthy controls. Our findings suggest that the presence of multiple neuropsychiatric symptoms is more related to the degree of atrophy in specific brain networks rather than dependent on the global severity of widespread neurodegenerative brain changes.

To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT) in Alzheimer's disease (AD).

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.