Structural covariance predictors of clinical improvement at 2-year follow-up in first-episode psychosis

Background: Neural correlates of psychotic disorders encompass multiple brain regions in multiple brain circuits, even at early stages. Previous research has characterized structural brain alterations in ¿rst-episode psychosis (FEP), but few studies have focused on the relationship between brain alterations and disease trajectories. First psychotic episodes typically evolve into a chronic course, affecting quality of life of patients and their families, with huge societal costs. Importantly, up to 80% of the patients relapse in the next five years after a first psychotic episode, with a significant risk of developing treatment resistance. Here, we investigated whether disease course may be predicted from brain structural assessments. Specifically, we measured structural covariance, a well-established approach to identify abnormal patterns of volumetric correlation across distant brain regions, which allows to incorporate network-level information to structural assessments. We performed a whole-brain structural covariance assessment of three bilateral regions form to three different cortical networks - dorsolateral prefrontal cortex (dlPFC) for the executive network, posterior cingulate cortex for the default mode network and insulae for the salience network - and subcortical structures (hippocampi, amygdalae and dorsomedial nucleus of the thalamus) that have shown to play a key role in schizophrenia. Methods: We assessed a sample of 74 subjects from a multicenter, naturalistic, prospective and longitudinal study designed to evaluate clinical, neuropsychological, neuroimaging, biochemical, environmental and pharmacogenetic variables in first episode psychotic patients (PEPs project). Magnetic resonance imaging (MRI) scans were acquired at baseline and at 2-year follow-up, as well as clinical assessments. Psychotic symptoms were assessed using the Positive and Negative Symptom Scale (PANSS) due its widespread use in clinical studies and its reliability in assessing psychopathology across a range of patient populations. The sample was split in two groups as a function of the clinical improvement at 2-year follow-up: responders (i.e. 40% reduction in PANSS global score from baseline; n=29) and non-responders (n=45). Results: Responder patients showed increase structural covariance between the left dlPFC and the left middle frontal gyrus, and between the right dlPFC and the right middle and superior gyrus, the left rectus and inferior frontal gyrus, the right hippocampus, and the vermis of the cerebellum. In addition, they showed increased structural covariance between the left anterior hippocampus and the ipsilateral middle occipital gyrus and the contralateral postcentral gyrus. Likewise, the structural covariance of right anterior hippocampus with right superior occipital gyrus and precentral gyrus was also increased in responder patients. Discussion: This study shows, for the first time in the literature, that increased structural covariance at baseline within the executive network and between the hippocampi and posterior brain regions was associated with a superior treatment response at two-year follow-up. These results indicate that the integrity of structural networks should be taken into account to predict treatment outcome in FEP patients

A common brain network among state, trait, and pathological anxiety from whole-brain functional connectivity

Anxiety is one of the most common mental states of humans. Although it drives us to avoid frightening situations and to achieve our goals, it may also impose significant suffering and burden if it becomes extreme. Because we experience anxiety in a variety of forms, previous studies investigated neural substrates of anxiety in a variety of ways. These studies revealed that individuals with high state, trait, or pathological anxiety showed altered neural substrates. However, no studies have directly investigated whether the different dimensions of anxiety share a common neural substrate, despite its theoretical and practical importance. Here, we investigated a brain network of anxiety shared by different dimensions of anxiety in a unified analytical framework using functional magnetic resonance imaging (fMRI). We analyzed different datasets in a single scale, which was defined by an anxiety-related brain network derived from whole brain. We first conducted the anxiety provocation task with healthy participants who tended to feel anxiety related to obsessive-compulsive disorder (OCD) in their daily life. We found a common state anxiety brain network across participants (1585 trials obtained from 10 participants). Then, using the resting-state fMRI in combination with the participants' behavioral trait anxiety scale scores (879 participants from the Human Connectome Project), we demonstrated that trait anxiety shared the same brain network as state anxiety. Furthermore, the brain network between common to state and trait anxiety could detect patients with OCD, which is characterized by pathological anxiety-driven behaviors (174 participants from multi-site datasets). Our findings provide direct evidence that different dimensions of anxiety have a substantial biological inter-relationship. Our results also provide a biologically defined dimension of anxiety, which may promote further investigation of various human characteristics, including psychiatric disorders, from the perspective of anxiety

The impact of dopaminergic treatment over cognitive networks in Parkinson's disease : Stemming the tide?

Altres ajuts: Fundació la Marató de TV3/20142910Dopamine-replacing therapies are an effective treatment for the motor aspects of Parkinson's disease. However, its precise effect over the cognitive resting-state networks is not clear; whether dopaminergic treatment normalizes their functional connectivity-as in other networks- and the links with cognitive decline are presently unknown. We recruited 35 nondemented PD patients and 16 age-matched controls. Clinical and neuropsychological assessments were performed at baseline, and conversion to dementia was assessed in a 10 year follow-up. Structural and functional brain imaging were acquired in both the ON and practical OFF conditions. We assessed functional connectivity in both medication states compared to healthy controls, connectivity differences within participants related to the ON/OFF condition, and baseline connectivity of PD participants that converted to dementia compared to those who did not convert. PD participants showed and increased frontoparietal connectivity compared to controls: a pattern of higher connectivity between salience (SN) and default-mode (DMN) networks both in the ON and OFF states. Within PD patients, this higher SN-DMN connectivity characterized the participants in the ON state, while within-DMN connectivity prevailed in the OFF state. Interestingly, participants who converted to dementia also showed higher SN-DMN connectivity in their baseline ON scans compared to nonconverters. To conclude, PD patients showed higher frontoparietal connectivity in cognitive networks compared to healthy controls, irrespective of medication status, but dopaminergic treatment specifically promoted SN-DM hyperconnectivity

The impact of dopaminergic treatment over cognitive networks in Parkinson's disease: Stemming the tide?

Dopamine-replacing therapies are an effective treatment for the motor aspects of Parkinson's disease. However, its precise effect over the cognitive resting-state networks is not clear; whether dopaminergic treatment normalizes their functional connectivity-as in other networks- and the links with cognitive decline are presently unknown. We recruited 35 nondemented PD patients and 16 age-matched controls. Clinical and neuropsychological assessments were performed at baseline, and conversion to dementia was assessed in a 10 year follow-up. Structural and functional brain imaging were acquired in both the ON and practical OFF conditions. We assessed functional connectivity in both medication states compared to healthy controls, connectivity differences within participants related to the ON/OFF condition, and baseline connectivity of PD participants that converted to dementia compared to those who did not convert. PD participants showed and increased frontoparietal connectivity compared to controls: a pattern of higher connectivity between salience (SN) and default-mode (DMN) networks both in the ON and OFF states. Within PD patients, this higher SN-DMN connectivity characterized the participants in the ON state, while within-DMN connectivity prevailed in the OFF state. Interestingly, participants who converted to dementia also showed higher SN-DMN connectivity in their baseline ON scans compared to nonconverters. To conclude, PD patients showed higher frontoparietal connectivity in cognitive networks compared to healthy controls, irrespective of medication status, but dopaminergic treatment specifically promoted SN-DM hyperconnectivity

Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

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

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

Abstract 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

Brain structural correlates of sensory phenomena in patients with obsessive-compulsive disorder

Background: sensory phenomena (SP) are uncomfortable feelings, including bodily sensations, sense of inner tension, 'just-right' perceptions, feelings of incompleteness, or 'urge-only' phenomena, which have been described to precede, trigger or accompany repetitive behaviours in individuals with obsessive-compulsive disorder (OCD). Sensory phenomena are also observed in individuals with tic disorders, and previous research suggests that sensorimotor cortex abnormalities underpin the presence of SP in such patients. However, to our knowledge, no studies have assessed the neural correlates of SP in patients with OCD. Methods: we assessed the presence of SP using the University of São Paulo Sensory Phenomena Scale in patients with OCD and healthy controls from specialized units in São Paulo, Brazil, and Barcelona. All participants underwent a structural magnetic resonance examination, and brain images were examined using DARTEL voxel-based morphometry. We evaluated grey matter volume differences between patients with and without SP and healthy controls within the sensorimotor and premotor cortices. Results: we included 106 patients with OCD and 87 controls in our study. Patients with SP (67% of the sample) showed grey matter volume increases in the left sensorimotor cortex in comparison to patients without SP and bilateral sensorimotor cortex grey matter volume increases in comparison to controls. No differences were observed between patients without SP and controls. Limitations: most patients were medicated. Participant recruitment and image acquisition were performed in 2 different centres. Conclusion: we have identified a structural correlate of SP in patients with OCD involving grey matter volume increases within the sensorimotor cortex; this finding is in agreement with those of tic disorder studies showing that abnormal activity and volume increases within this region are associated with the urges preceding tic onset

Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium.

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors