Schizophrenia is a neurodevelopmental disorder with high heritability. Several lines of evidence indicate that the PRODH gene may be related to the disorder. Therefore, our study investigates the effects of 12 polymorphisms of PRODH on schizophrenia and its phenotypes. To further evaluate the roles of the associated variants in the disorder, we have conducted magnetic resonance imaging (MRI) scans to assess cortical volumes and thicknesses. A total of 192 patients were evaluated using the Structured Clinical Interview for DSM-IV (SCID), Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale, Global Assessment of Functioning (GAF) and Clinical Global Impression (CGI) instruments. The study included 179 controls paired by age and gender. The samples were genotyped using the real-time polymerase chain reaction (PCR), restriction fragment length polymorphism (RFLP)-PCR and Sanger sequencing methods. A sample of 138 patients and 34 healthy controls underwent MRI scans. One polymorphism was associated with schizophrenia (rs2904552), with the G-allele more frequent in patients than in controls. This polymorphism is likely functional, as predicted by PolyPhen and SIFT, but it was not associated with brain morphology in our study. In summary, we report a functional PRODH variant associated with schizophrenia that may have a neurochemical impact, altering brain function, but is not responsible for the cortical reductions found in the disorder.

Meditation is a mental training, which involves attention and the ability to maintain focus on a particular object. In this study we have applied a specific attentional task to simply measure the performance of the participants with different levels of meditation experience, rather than evaluating meditation practice per se or task performance during meditation. Our objective was to evaluate the performance of regular meditators and non-meditators during an fMRI adapted Stroop Word-Colour Task (SWCT), which requires attention and impulse control, using a block design paradigm. We selected 20 right-handed regular meditators and 19 non-meditators matched for age, years of education and gender. Participants had to choose the colour (red, blue or green) of single words presented visually in three conditions: congruent, neutral and incongruent. Non-meditators showed greater activity than meditators in the right medial frontal, middle temporal, precentral and postcentral gyri and the lentiform nucleus during the incongruent conditions. No regions were more activated in meditators relative to non-meditators in the same comparison. Non-meditators showed an increased pattern of brain activation relative to regular meditators under the same behavioural performance level. This suggests that meditation training improves efficiency, possibly via improved sustained attention and impulse control.

Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills.

Multivariate pattern recognition approaches have become a prominent tool in neuroimaging data analysis. These methods enable the classification of groups of participants (e.g. controls and patients) on the basis of subtly different patterns across the whole brain. This study demonstrates that these methods can be used, in combination with automated morphometric analysis of structural MRI, to determine with great accuracy whether a single subject has been engaged in regular mental training or not. The proposed approach allowed us to identify with 94.87% accuracy (p<0.001) if a given participant is a regular meditator (from a sample of 19 regular meditators and 20 non-meditators). Neuroimaging has been a relevant tool for diagnosing neurological and psychiatric impairments. This study may suggest a novel step forward: the emergence of a new field in brain imaging applications, in which participants could be identified based on their mental experience.

The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity) and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages, such as FSL and libSVM. In addition, a user-defined visual neurofeedback module allows users to easily design and run fMRI neurofeedback experiments using ROI-based or multivariate classification approaches. FRIEND is open-source and free for non-commercial use. Processing tutorials and extensive documentation are available.

Humans spend a substantial share of their lives mind-wandering. This spontaneous thinking activity usually comprises autobiographical recall, emotional, and self-referential components. While neuroimaging studies have demonstrated that a specific brain "default mode network" (DMN) is consistently engaged by the "resting state" of the mind, the relative contribution of key cognitive components to DMN activity is still poorly understood. Here we used fMRI to investigate whether activity in neural components of the DMN can be differentially explained by active recall of relevant emotional autobiographical memories as compared with the resting state. Our study design combined emotional autobiographical memory, neutral memory and resting state conditions, separated by a serial subtraction control task. Shared patterns of activation in the DMN were observed in both emotional autobiographical and resting conditions, when compared with serial subtraction. Directly contrasting autobiographical and resting conditions demonstrated a striking dissociation within the DMN in that emotional autobiographical retrieval led to stronger activation of the dorsomedial core regions (medial prefrontal cortex, posterior cingulate cortex), whereas the resting state condition engaged a ventral frontal network (ventral striatum, subgenual and ventral anterior cingulate cortices) in addition to the IPL. Our results reveal an as yet unreported dissociation within the DMN. Whereas the dorsomedial component can be explained by emotional autobiographical memory, the ventral frontal one is predominantly associated with the resting state proper, possibly underlying fundamental motivational mechanisms engaged during spontaneous unconstrained ideation.

Based on previous evidence for individual-specific sets of cortical areas active during simple attention tasks, in this work we intended to perform within individual comparisons of task-induced beta oscillations between visual attention and a reasoning task. Since beta induced oscillations are not time-locked to task events and were first observed by Fourier transforms, in order to analyze the cortical topography of attention induced beta activity, we have previously computed corrected-latency averages based on spontaneous peaks of band-pass filtered epochs. We then used Independent Component Analysis (ICA) only to single out the significant portion of averaged data, above noise levels. In the present work ICA served as the main, exhaustive means for decomposing beta activity in both tasks, using 128-channel EEG data from 24 subjects. Given the previous observed similarity between tasks by visual inspection and by simple descriptive statistics, we now intended another approach: to quantify how much each ICA component obtained in one task could be explained by a linear combination of the topographic patterns from the other task in each individual. Our hypothesis was that the major psychological difference between tasks would not be reflected as important topographic differences within individuals. Results confirmed the high topographic similarity between attention and reasoning beta correlates in that few components in each individual were not satisfactorily explained by the complementary task, and if those could be considered "task-specific", their scalp distribution and estimated cortical sources were not common across subjects. These findings, along with those from fMRI studies preserving individual data and conventional neuropsychological and neurosurgical observations, are discussed in support of a new functional localization hypothesis: individuals use largely different sets of cortical association areas to perform a given task, but those individual sets do not change importantly across tasks that differ in major psychological processes.

In this study we examined differences in fMRI activation and deactivation patterns during episodic verbal memory encoding between individuals with MCI (n = 18) and healthy controls (HCs) (n = 17). Participants were scanned in two different sessions during the application of self-initiated or directed instructions to apply semantic strategies at encoding of word lists. MCI participants showed reduced free recall scores when using self-initiated encoding strategies that were increased to baseline controls' level after directed instructions were provided. During directed strategic encoding, greater recruitment of frontoparietal regions was observed in both MCI and control groups; group differences between sessions were observed in the ventromedial prefrontal cortex and the right superior frontal gyrus. This study provides evidence suggesting that differences of activity in these regions may be related to encoding deficits in MCI, possibly mediating executive functions during task performance.

Understanding how brain activities cluster can help in the diagnosis of neuropsychological disorders. Thus, it is important to be able to identify alterations in the clustering structure of functional brain networks. Here, we provide an R implementation of Analysis of Cluster Variability (ANOCVA), which statistically tests (1) whether a set of brain regions of interest (ROI) are equally clustered between two or more populations and (2) whether the contribution of each ROI to the differences in clustering is significant. To illustrate the usefulness of our method and software, we apply the R package in a large functional magnetic resonance imaging (fMRI) dataset composed of 896 individuals (529 controls and 285 diagnosed with ASD-autism spectrum disorder) collected by the ABIDE (The Autism Brain Imaging Data Exchange) Consortium. Our analysis show that the clustering structure of controls and ASD subjects are different (p < 0.001) and that specific brain regions distributed in the frontotemporal, sensorimotor, visual, cerebellar, and brainstem systems significantly contributed (p < 0.05) to this differential clustering. These findings suggest an atypical organization of domain-specific function brain modules in ASD.

No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.

Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such "brain age prediction" vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47-73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole-brain region-based or voxel-based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross-validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel-level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research.

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer's disease (n = 206) and mild cognitive impairment (n = 354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. To promote open science, we are making all scripts and the trained models available to the wider research community.

Music played in ensembles is a naturalistic model to study joint action and leader-follower relationships. Recently, the investigation of the brain underpinnings of joint musical actions has gained attention; however, the cerebral correlates underlying the roles of leader and follower in music performance remain elusive. The present study addressed this question by simultaneously measuring the hemodynamic correlates of functional neural activity elicited during naturalistic violin duet performance using fNIRS. Findings revealed distinct patterns of functional brain activation when musicians played the Violin 2 (follower) than the Violin 1 part (leader) in duets, both compared to solo performance. More specifically, results indicated that musicians playing the Violin 2 part had greater oxy-Hb activation in temporo-parietal (p = 0.02) and somatomotor (p = 0.04) regions during the duo condition in relation to the solo. On the other hand, there were no significant differences in the activation of these areas between duo/solo conditions during the execution of the Violin 1 part (p's > 0.05). These findings suggest that ensemble cohesion during a musical performance may impose particular demands when musicians play the follower position, especially in brain areas associated with the processing of dynamic social information and motor simulation. This study is the first to use fNIRS hyperscanning technology to simultaneously measure the brain activity of two musicians during naturalistic music ensemble performance, opening new avenues for the investigation of brain correlates underlying joint musical actions with multiple subjects in a naturalistic environment.

No abstract available

Yoga, a mind-body activity that requires attentional engagement, has been associated with positive changes in brain structure and function, especially in areas related to awareness, attention, executive functions and memory. Normal aging, on the other hand, has also been associated with structural and functional brain changes, but these generally involve decreased cognitive functions. The aim of this cross-sectional study was to compare brain cortical thickness (CT) in elderly yoga practitioners and a group of age-matched healthy non-practitioners. We tested 21 older women who had practiced hatha yoga for at least 8 years and 21 women naive to yoga, meditation or any mind-body interventions who were matched to the first group in age, years of formal education and physical activity level. A T1-weighted MPRAGE sequence was acquired for each participant. Yoga practitioners showed significantly greater CT in a left prefrontal lobe cluster, which included portions of the lateral middle frontal gyrus, anterior superior frontal gyrus and dorsal superior frontal gyrus. We found greater CT in the left prefrontal cortex of healthy elderly women who trained yoga for a minimum of 8 years compared with women in the control group.

Advances in neonatal medicine have resulted in a larger proportion of preterm-born individuals reaching adulthood. Their increased liability to psychiatric illness and impairments of cognition and behaviour intimate lasting cerebral consequences; however, the central physiological disturbances remain unclear. Of fundamental importance to efficient brain function is the coordination and contextually-relevant recruitment of neural networks. Large-scale distributed networks emerge perinatally and increase in hierarchical complexity through development. Preterm-born individuals exhibit systematic reductions in correlation strength within these networks during infancy. Here, we investigate resting-state functional connectivity in functional magnetic resonance imaging data from 29 very-preterm (VPT)-born adults and 23 term-born controls. Neurocognitive networks were identified with spatial independent component analysis conducted using the Infomax algorithm and employing Icasso procedures to enhance component robustness. Network spatial focus and spectral power were not generally significantly affected by preterm birth. By contrast, Granger-causality analysis of the time courses of network activity revealed widespread reductions in between-network connectivity in the preterm group, particularly along paths including salience-network features. The potential clinical relevance of these Granger-causal measurements was suggested by linear discriminant analysis of topological representations of connection strength, which classified individuals by group with a maximal accuracy of 86%. Functional connections from the striatal salience network to the posterior default mode network informed this classification most powerfully. In the VPT-born group it was additionally found that perinatal factors significantly moderated the relationship between executive function (which was reduced in the VPT-born as compared with the term-born group) and generalised partial directed coherence. Together these findings show that resting-state functional connectivity of preterm-born individuals remains compromised in adulthood; and present consistent evidence that the striatal salience network is preferentially affected. Therapeutic practices directed at strengthening within-network cohesion and fine-tuning between-network inter-relations may have the potential to mitigate the cognitive, behavioural and psychiatric repercussions of preterm birth.

Recent evidence suggests that immobilization of the upper limb for 2-3 weeks induces changes in cortical thickness as well as motor performance. In constraint induced (CI) therapy, one of the most effective interventions for hemiplegia, the non-paretic arm is constrained to enforce the use of the paretic arm in the home setting. With the present study we aimed to explore whether non-paretic arm immobilization in CI therapy induces structural changes in the non-lesioned hemisphere, and how these changes are related to treatment benefit. 31 patients with chronic hemiparesis participated in CI therapy with (N = 14) and without (N = 17) constraint. Motor ability scores were acquired before and after treatment. Diffusion tensor imaging (DTI) data was obtained prior to treatment. Cortical thickness was measured with the Freesurfer software. In both groups cortical thickness in the contralesional primary somatosensory cortex increased and motor function improved with the intervention. However the cortical thickness change was not associated with the magnitude of motor function improvement. Moreover, the treatment effect and the cortical thickness change were not significantly different between the constraint and the non-constraint groups. There was no correlation between fractional anisotropy changes in the non-lesioned hemisphere and treatment outcome. CI therapy induced cortical thickness changes in contralesional sensorimotor regions, but this effect does not appear to be driven by the immobilization of the non-paretic arm, as indicated by the absence of differences between the constraint and the non-constraint groups. Our data does not suggest that the arm immobilization used in CI therapy is associated with noticeable cortical thinning.

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.

Previously, using fMRI, we demonstrated lower connectivity between right anterior superior temporal (ATL) and anterior subgenual cingulate (SCC) regions while patients with major depressive disorder (MDD) experience guilt. This neural signature was detected despite symptomatic remission which suggested a putative role in vulnerability. This randomised controlled double-blind parallel group clinical trial investigated whether patients with MDD are able to voluntarily modulate this neural signature. To this end, we developed a fMRI neurofeedback software (FRIEND), which measures ATL-SCC coupling and displays its levels in real time. Twenty-eight patients with remitted MDD were randomised to two groups, each receiving one session of fMRI neurofeedback whilst retrieving guilt and indignation/anger-related autobiographical memories. They were instructed to feel the emotion whilst trying to increase the level of a thermometer-like display on a screen. Active intervention group: The thermometer levels increased with increasing levels of ATL-SCC correlations in the guilt condition. Control intervention group: The thermometer levels decreased when correlation levels deviated from the previous baseline level in the guilt condition, thus reinforcing stable correlations. Both groups also received feedback during the indignation condition reinforcing stable correlations. We confirmed our predictions that patients in the active intervention group were indeed able to increase levels of ATL-SCC correlations for guilt vs. indignation and their self-esteem after training compared to before training and that this differed significantly from the control intervention group. These data provide proof-of-concept for a novel treatment target for MDD patients and are in keeping with the hypothesis that ATL-SCC connectivity plays a key role in self-worth. https://clinicaltrials.gov/ct2/show/results/NCT01920490.

Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses.