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The role of both cannabis use and genetic background has been shown in the risk for psychosis. However, the effect of the interplay between cannabis and variability at the endocannabinoid receptor genes on the neurobiological underpinnings of psychosis remains inconclusive. Through a case-only design, including patients with a first-episode of psychosis (n = 40) classified as cannabis users (50%) and non-users (50%), we aimed to evaluate the interaction between cannabis use and common genetic variants at the endocannabinoid receptor genes on brain activity. Genetic variability was assessed by genotyping two Single Nucleotide Polymorphisms (SNP) at the cannabinoid receptor type 1 gene ( CNR1 ; rs1049353) and cannabinoid receptor type 2 gene (CNR2 ; rs2501431). Functional Magnetic Resonance Imaging (fMRI) data were obtained while performing the n-back task. Gene × cannabis interaction models evidenced a combined effect of CNR1 and CNR2 genotypes and cannabis use on brain activity in different brain areas, such as the caudate nucleus, the cingulate cortex and the orbitofrontal cortex. These findings suggest a joint role of cannabis use and cannabinoid receptor genetic background on brain function in first-episode psychosis, possibly through the impact on brain areas relevant to the reward circuit.

Deficits in emotion processing are a core feature of schizophrenia, but their neurobiological bases are poorly understood. Previous research, mainly focused on emotional face processing and emotion recognition deficits, has shown controverted results. Furthermore, the use of faces has been questioned for not entailing an appropriate stimulus to study emotional processing. This highlights the importance of investigating emotional processing abnormalities using evocative stimuli. For the first time, we have studied the brain responses to scenic stimuli in patients with schizophrenia. We selected scenes from the IAPS that elicit fear, disgust, happiness, and sadness. Twenty-six patients with schizophrenia and thirty age-, sex- and premorbid IQ-matched healthy controls were included. Behavioral task results show that patients tended to misclassify disgust and sadness as fear. Brain responses in patients were different from controls in images eliciting disgust and fear. In response to disgust images, patients hyperactivated the right temporal cortex, which was not activated by the controls. With fear images, hyperactivation was observed in brain regions involved in fear processing, including midline regions from the medial frontal cortex to the anterior cingulate cortex, the superior frontal gyrus, inferior and superior temporal cortex, and visual areas. These results suggest that schizophrenia is characterized by hyper-responsivity to stimuli evoking high-arousal, negative emotions, and a bias towards fear in emotion recognition., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: The profiles of cortical abnormalities in schizophrenia and bipolar disorder, and how far they resemble each other, have only been studied to a limited extent. The aim of this study was to identify and compare the changes in cortical morphology associated with these pathologies., Methods: A total of 384 subjects, including 128 patients with schizophrenia, 128 patients with bipolar disorder and 127 sex-age-matched healthy subjects, were examined using cortical surface-based morphology. Four cortical structural measures were studied: cortical volume (CV), cortical thickness (CT), surface area (SA) and gyrification index (GI). Group comparisons for each separate cortical measure were conducted., Results: At a threshold of P = 0.05 corrected, both patient groups showed significant widespread CV and CT reductions in similar areas compared to healthy subjects. However, the changes in schizophrenia were more pronounced. While CV decrease in bipolar disorder was exclusively explained by cortical thinning, in schizophrenia it was driven by changes in CT and partially by SA. Reduced GI was only found in schizophrenia. The direct comparison between both disorders showed significant reductions in all measures in patients with schizophrenia., Conclusions: Cortical volume and cortical thickness deficits are shared between patients with schizophrenia and bipolar disorder, suggesting that both pathologies may be affected by similar environmental and neurodegenerative factors. However, the exclusive alteration in schizophrenia of metrics related to the geometry and curvature of the brain cortical surface (SA, GI) suggests that this group is influenced by additional neurodevelopmental and genetic factors., Competing Interests: Declaration of Competing of Interest All authors declare that they have no conflicts of interest., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Magnetic resonance imaging (MRI) has been proposed as a source of information for automatic prediction of individual diagnosis in schizophrenia. Optimal integration of data from different MRI modalities is an active area of research aimed at increasing diagnostic accuracy. Based on a sample of 96 patients with schizophrenia and a matched sample of 115 healthy controls that had undergone a single multimodal MRI session, we generated individual brain maps of gray matter vbm, 1back, and 2back levels of activation ( n back fMRI), maps of amplitude of low-frequency fluctuations (resting-state fMRI), and maps of weighted global brain connectivity (resting-state fMRI). Four unimodal classifiers (Ridge, Lasso, Random Forests, and Gradient boosting) were applied to these maps to evaluate their classification accuracies. Based on the assignments made by the algorithms on test individuals, we quantified the amount of predictive information shared between maps (what we call redundancy analysis). Finally, we explored the added accuracy provided by a set of multimodal strategies that included post-classification integration based on probabilities, two-step sequential integration, and voxel-level multimodal integration through one-dimensional-convolutional neural networks (1D-CNNs). All four unimodal classifiers showed the highest test accuracies with the 2back maps (80% on average) achieving a maximum of 84% with the Lasso. Redundancy levels between brain maps were generally low (overall mean redundancy score of 0.14 in a 0-1 range), indicating that each brain map contained differential predictive information. The highest multimodal accuracy was delivered by the two-step Ridge classifier (87%) followed by the Ridge maximum and mean probability classifiers (both with 85% accuracy) and by the 1D-CNN, which achieved the same accuracy as the best unimodal classifier (84%). From these results, we conclude that from all MRI modalities evaluated task-based fMRI may be the best unimodal diagnostic option in schizophrenia. Low redundancy values point to ample potential for accuracy improvements through multimodal integration, with the two-step Ridge emerging as a suitable strategy., (Copyright © 2019 Salvador, Canales-Rodríguez, Guerrero-Pedraza, Sarró, Tordesillas-Gutiérrez, Maristany, Crespo-Facorro, McKenna and Pomarol-Clotet.)

Background: Structural imaging studies of borderline personality disorder (BPD) have found regions of reduced cortical volume, but these have varied considerably across studies. Reduced hippocampus and amygdala volume have also been a regular finding in studies using conventional volumetric measurement. How far comorbid major depression, which is common in BPD and can also affect in brain structure, influences the findings is not clear., Methods: Seventy-six women with BPD and 76 matched controls were examined using whole-brain voxel-based morphometry (VBM). The hippocampus and amygdala were also measured, using both conventional volume measurement and VBM within a mask restricted to these two subcortical structures. Lifetime history of major depression was assessed using structured psychiatric interview., Results: At a threshold of p = 0.05 corrected, the BPD patients showed clusters of volume reduction in the dorsolateral prefrontal cortex bilaterally and in the pregenual/subgenual medial frontal cortex. There was no evidence of volume reductions in the hippocampus or amygdala, either on conventional volumetry or using VBM masked to these regions. Instead there was evidence of right-sided enlargement of these structures. No significant structural differences were found between patients with and without lifetime major depression., Conclusions: According to this study, BPD is characterized by a restricted pattern of cortical volume reduction involving the dorsolateral frontal cortex and the medial frontal cortex, both areas of potential relevance for the clinical features of the disorder. Previous findings concerning reduced hippocampus and amygdala volume in the disorder are not supported. Brain structural findings in BPD do not appear to be explainable on the basis of history of associated lifetime major depression.

A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VBM), vertex-based cortical thickness and volume, region of interest volumetric measures and wavelet-based morphometry (WBM) maps. All possible combinations of algorithms and data features were considered in pairwise classifications of matched samples of healthy controls (N = 127), patients with schizophrenia (N = 128) and patients with bipolar disorder (N = 128). Results show that the selection of feature type is important, with grey matter VBM (without data reduction) delivering the best diagnostic prediction rates (averaging over classifiers: schizophrenia vs. healthy 75%, bipolar disorder vs. healthy 63% and schizophrenia vs. bipolar disorder 62%) whereas algorithms usually yielded very similar results. Indeed, those grey matter VBM accuracy rates were not even improved by combining all feature types in a single prediction model. Further multi-class classifications considering the three groups simultaneously made evident a lack of predictive power for the bipolar group, probably due to its intermediate anatomical features, located between those observed in healthy controls and those found in patients with schizophrenia. Finally, we provide MRIPredict (https://www.nitrc.org/projects/mripredict/), a free tool for SPM, FSL and R, to easily carry out voxelwise predictions based on VBM images.

Introduction: Cognitive impairment in the euthymic phase is a well-established finding in bipolar disorder. However, its brain structural and/or functional correlates are uncertain., Methods: Thirty-three euthymic bipolar patients with preserved memory and executive function and 28 euthymic bipolar patients with significant memory and/or executive impairment, as defined using two test batteries, the Rivermead Behavioural Memory Test (RBMT) and the Behavioural Assessment of the Dysexecutive Syndrome (BADS), plus 28 healthy controls underwent structural MRI using voxel-based morphometry (VBM). Twenty-seven of the cognitively preserved patients, 23 of the cognitively impaired patients and 28 controls also underwent fMRI during performance of the n-back working memory task., Results: No clusters of grey or white matter volume difference were found between the two patient groups. During n-back performance, the cognitively impaired patients showed hypoactivation compared to the cognitively preserved patients in a circumscribed region in the right dorsolateral prefrontal cortex. Both patient groups showed failure of de-activation in the medial frontal cortex compared to the healthy controls., Conclusions: Cognitive impairment in euthymic bipolar patients appears from this study to be unrelated to structural brain abnormality, but there was some evidence for an association with altered prefrontal function.

Background: Delusional disorder has been the subject of very little investigation using brain imaging., Aims: To examine potential structural and/or functional brain abnormalities in this disorder., Method: We used structural imaging (voxel-based morphometry, VBM) and functional imaging (during performance of the n-back task and whole-brain resting connectivity analysis) to examine 22 patients meeting DSM-IV criteria for delusional disorder and 44 matched healthy controls., Results: The patients showed grey matter reductions in the medial frontal/anterior cingulate cortex and bilateral insula on unmodulated (but not on modulated) VBM analysis, failure of de-activation in the medial frontal/anterior cingulate cortex during performance of the n-back task, and decreased resting-state connectivity in the bilateral insula., Conclusions: The findings provide evidence of brain abnormality in the medial frontal/anterior cingulate cortex and insula in delusional disorder. A role for the former region in the pathogenesis of delusions is consistent with several other lines of evidence., (© The Royal College of Psychiatrists 2016.)