The morphometric co-atrophy networking of schizophrenia, autistic and obsessive spectrum disorders

Cortical thickness reductions associate with brain network architecture in major depressive disorder

BACKGROUND

Cortical thickness reductions in major depressive disorder are distributed across multiple regions. Research has indicated that cortical atrophy is influenced by connectome architecture on a range of neurological and psychiatric diseases. However, whether connectome architecture contributes to changes in cortical thickness in the same manner as it does in depression is unclear. This study aims to explain the distribution of cortical thickness reductions across the cortex in depression by brain connectome architecture.

METHODS

Here, we calculated a differential map of cortical thickness between 110 depression patients and 88 age-, gender-, and education level-matched healthy controls by using T1-weighted images and a structural network reconstructed through the diffusion tensor imaging of control group. We then used a neighborhood deformation model to explore how cortical thickness change in an area is influenced by areas structurally connected to it.

RESULTS

We found that cortical thickness in the frontoparietal and default networks decreased in depression, regional cortical thickness changes were related to reductions in their neighbors and were mainly limited by the frontoparietal and default networks, and the epicenter was in the prefrontal lobe.

CONCLUSION

Current findings suggest that connectome architecture contributes to the irregular topographic distribution of cortical thickness reductions in depression and cortical atrophy is restricted by and dependent on structural foundation.

Alteration of the large-scale white-matter functional networks in autism spectrum disorder

Autism spectrum disorder is a neurodevelopmental disorder whose core deficit is social dysfunction. Previous studies have indicated that structural changes in white matter are associated with autism spectrum disorder. However, few studies have explored the alteration of the large-scale white-matter functional networks in autism spectrum disorder. Here, we identified ten white-matter functional networks on resting-state functional magnetic resonance imaging data using the K-means clustering algorithm. Compared with the white matter and white-matter functional network connectivity of the healthy controls group, we found significantly decreased white matter and white-matter functional network connectivity mainly located within the Occipital network, Middle temporo-frontal network, and Deep network in autism spectrum disorder. Compared with healthy controls, findings from white-matter gray-matter functional network connectivity showed the decreased white-matter gray-matter functional network connectivity mainly distributing in the Occipital network and Deep network. Moreover, we compared the spontaneous activity of white-matter functional networks between the two groups. We found that the spontaneous activity of Middle temporo-frontal and Deep network was significantly decreased in autism spectrum disorder. Finally, the correlation analysis showed that the white matter and white-matter functional network connectivity between the Middle temporo-frontal network and others networks and the spontaneous activity of the Deep network were significantly correlated with the Social Responsiveness Scale scores of autism spectrum disorder. Together, our findings indicate that changes in the white-matter functional networks are associated behavioral deficits in autism spectrum disorder.

Receptor-Independent Therapies for Forensic Detainees with Schizophrenia-Dementia Comorbidity

Forensic institutions throughout the world house patients with severe psychiatric illness and history of criminal violations. Improved medical care, hygiene, psychiatric treatment, and nutrition led to an unmatched longevity in this population, which previously lived, on average, 15 to 20 years shorter than the public at large. On the other hand, longevity has contributed to increased prevalence of age-related diseases, including neurodegenerative disorders, which complicate clinical management, increasing healthcare expenditures. Forensic institutions, originally intended for the treatment of younger individuals, are ill-equipped for the growing number of older offenders. Moreover, as antipsychotic drugs became available in 1950s and 1960s, we are observing the first generation of forensic detainees who have aged on dopamine-blocking agents. Although the consequences of long-term treatment with these agents are unclear, schizophrenia-associated gray matter loss may contribute to the development of early dementia. Taken together, increased lifespan and the subsequent cognitive deficit observed in long-term forensic institutions raise questions and dilemmas unencountered by the previous generations of clinicians. These include: does the presence of neurocognitive dysfunction justify antipsychotic dose reduction or discontinuation despite a lifelong history of schizophrenia and violent behavior? Should neurolipidomic interventions become the standard of care in elderly individuals with lifelong schizophrenia and dementia? Can patients with schizophrenia and dementia meet the Dusky standard to stand trial? Should neurocognitive disorders in the elderly with lifelong schizophrenia be treated differently than age-related neurodegeneration? In this article, we hypothesize that gray matter loss is the core symptom of schizophrenia which leads to dementia. We hypothesize further that strategies to delay or stop gray matter depletion would not only improve the schizophrenia sustained recovery, but also avert the development of major neurocognitive disorders in people living with schizophrenia. Based on this hypothesis, we suggest utilization of both receptor-dependent and independent therapeutics for chronic psychosis.

Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses

Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation (n = 358 regions) from 79 FEAP and 68 controls to comprehensively characterize the networks using a descriptive and perturbational network neuroscience approach. Using graph theoretical methods, we examined network integration, segregation, centrality, community structure, and hub distribution across the small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal "attacks" (removal of nodes and all their edges) to investigate network resilience, calculated DeltaCon similarity scores, and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈ 54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree, were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more nodes of higher centrality in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without impacting efficiency. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge.

Insulin and Disorders of Behavioural Flexibility

Behavioural inflexibility is a symptom of neuropsychiatric and neurodegenerative disorders such as Obsessive-Compulsive Disorder, Autism Spectrum Disorder and Alzheimer's Disease, encompassing the maintenance of a behaviour even when no longer appropriate. Recent evidence suggests that insulin signalling has roles apart from its regulation of peripheral metabolism and mediates behaviourally-relevant central nervous system (CNS) functions including behavioural flexibility. Indeed, insulin resistance is reported to generate anxious, perseverative phenotypes in animal models, with the Type 2 diabetes medication metformin proving to be beneficial for disorders including Alzheimer's Disease. Structural and functional neuroimaging studies of Type 2 diabetes patients have highlighted aberrant connectivity in regions governing salience detection, attention, inhibition and memory. As currently available therapeutic strategies feature high rates of resistance, there is an urgent need to better understand the complex aetiology of behaviour and develop improved therapeutics. In this review, we explore the circuitry underlying behavioural flexibility, changes in Type 2 diabetes, the role of insulin in CNS outcomes and mechanisms of insulin involvement across disorders of behavioural inflexibility.

Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects

Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.

Magnocellular and parvocellular contributions to brain network dysfunction during learning and memory: Implications for schizophrenia

Memory deficits are core features of schizophrenia, and a central aim in biological psychiatry is to identify the etiology of these deficits. Scrutiny is naturally focused on the dorsolateral prefrontal cortex and the hippocampal cortices, given these structures' roles in memory and learning. The fronto-hippocampal framework is valuable but restrictive. Network-based underpinnings of learning and memory are substantially diverse and include interactions between hetero-modal and early sensory networks. Thus, a loss of fidelity in sensory information may impact memorial and cognitive processing in higher-order brain sub-networks, becoming a sensory source for learning and memory deficits. In this overview, we suggest that impairments in magno- and parvo-cellular visual pathways result in degraded inputs to core learning and memory networks. The ascending cascade of aberrant neural events significantly contributes to learning and memory deficits in schizophrenia. We outline the network bases of these effects, and suggest that any network perspectives of dysfunction in schizophrenia must assess the impact of impaired perceptual contributions. Finally, we speculate on how this framework enriches the space of biomarkers and expands intervention strategies to ameliorate this prototypical disconnection syndrome.

Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders

Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

Structural Covariance of the Ipsilesional Primary Motor Cortex in Subcortical Stroke Patients with Motor Deficits

The analysis of structural covariance has emerged as a powerful tool to explore the morphometric correlations among broadly distributed brain regions. However, little is known about the interactions between the damaged primary motor cortex (M1) and other brain regions in stroke patients with motor deficits. This study is aimed at investigating the structural covariance pattern of the ipsilesional M1 in chronic subcortical stroke patients with motor deficits. High-resolution T1-weighted brain images were acquired from 58 chronic subcortical stroke patients with motor deficits (29 with left-sided lesions and 29 with right-sided lesions) and 50 healthy controls. Structural covariance patterns were identified by a seed-based structural covariance method based on gray matter (GM) volume. Group comparisons between stroke patients (left-sided or right-sided groups) and healthy controls were determined by a permutation test. The association between alterations in the regional GM volume and motor recovery after stroke was investigated by a multivariate regression approach. Structural covariance analysis revealed an extensive increase in the structural interactions between the ipsilesional M1 and other brain regions in stroke patients, involving not only motor-related brain regions but also non-motor-related brain regions. We also identified a slightly different pattern of structural covariance between the left-sided stroke group and the right-sided stroke group, thus indicating a lesion-side effect of cortical reorganization after stroke. Moreover, alterations in the GM volume of structural covariance brain regions were significantly correlated to the motor function scores in stroke patients. These findings indicated that the structural covariance patterns of the ipsilesional M1 in chronic subcortical stroke patients were induced by motor-related plasticity. Our findings may help us to better understand the neurobiological mechanisms of motor impairment and recovery in patients with subcortical stroke from different perspectives.

Easy to interpret coordinate based meta-analysis of neuroimaging studies: Analysis of brain coordinates (ABC)

BACKGROUND

Functional MRI and voxel-based morphometry are important in neuroscience. They are technically challenging with no globally optimal analysis method, and the multiple approaches have been shown to produce different results. It is useful to be able to meta-analyse results from such studies that tested a similar hypothesis potentially using different analysis methods. The aim is to identify replicable results and infer hypothesis specific effects. Coordinate based meta-analysis (CBMA) offers this, but the multiple algorithms can produce different results, making interpretation conditional on the algorithm.

NEW METHOD

Here a new model based CBMA algorithm, Analysis of Brain Coordinates (ABC), is presented. ABC aims to be simple to understand by avoiding empirical elements where possible and by using a simple to interpret statistical threshold, which relates to the primary aim of detecting replicable effects.

RESULTS

ABC is compared to both the most used and the most recently developed CBMA algorithms, by reproducing a published meta-analysis of localised grey matter changes in schizophrenia. There are some differences in results and the type of data that can be analysed, which are related to the algorithm specifics.

COMPARISON TO OTHER METHODS

Compared to other algorithms ABC eliminates empirical elements where possible and uses a simple to interpret statistical threshold.

CONCLUSIONS

There may be no optimal way to meta-analyse neuroimaging studies using CBMA. However, by eliminating some empirical elements and relating the statistical threshold directly to the aim of finding replicable effects, ABC makes the impact of the algorithm on any conclusion easier to understand.

Structural covariance networks in schizophrenia: A systematic review Part II

BACKGROUND

Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia.

METHODS

A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria.

RESULTS

A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness.

CONCLUSIONS

Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.

Autism spectrum disorder and personality disorders: Comorbidity and differential diagnosis

BACKGROUND

Differential diagnosis, comorbidities and overlaps with other psychiatric disorders are common among adults with autism spectrum disorder (ASD), but clinical assessments often omit screening for personality disorders (PD), which are especially common in individuals with high-functioning ASD where there is less need for support.

AIM

To summarize the research findings on PD in adults with ASD and without intellectual disability, focusing on comorbidity and differential diagnosis.

METHODS

PubMed searches were performed using the key words "Asperger's Syndrome", "Autism", "Personality", "Personality disorder" and "comorbidity" in order to identify relevant articles published in English. Grey literature was identified through searching Google Scholar. The literature reviews and reference sections of selected papers were also examined for additional potential studies. The search was restricted to studies published up to April 2020. This review is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method.

RESULTS

The search found 22 studies carried out on ASD adults without intellectual disability that met the inclusion criteria: 16 evaluated personality profiles or PD in ASD (comorbidity), five compared ASD and PD (differential diagnosis) and one performed both tasks. There were significant differences in the methodological approaches, including the ASD diagnostic instruments and personality measures. Cluster A and cluster C PD are the most frequent co-occurring PD, but overlapping features should be considered. Data on differential diagnosis were only found with cluster A and cluster B PD.

CONCLUSION

ASD in high-functioning adults is associated with a distinct personality profile even if variability exists. Further studies are needed to explore the complex relationship between ASD and PD.

Altered gray matter structural covariance networks at both acute and chronic stages of mild traumatic brain injury

Cognitive and emotional impairments observed in mild traumatic brain injury (mTBI) patients may reflect variances of brain connectivity within specific networks. Although previous studies found altered functional connectivity (FC) in mTBI patients, the alterations of brain structural properties remain unclear. In the present study, we analyzed structural covariance (SC) for the acute stages of mTBI (amTBI) patients, the chronic stages of mTBI (cmTBI) patients, and healthy controls. We first extracted the mean gray matter volume (GMV) of seed regions that are located in the default-mode network (DMN), executive control network (ECN), salience network (SN), sensorimotor network (SMN), and the visual network (VN). Then we determined and compared the SC for each seed region among the amTBI, the cmTBI and the healthy controls. Compared with healthy controls, the amTBI patients showed lower SC for the ECN, and the cmTBI patients showed higher SC for the both DMN and SN but lower SC for the SMN. The results revealed disrupted ECN in the amTBI patients and disrupted DMN, SN and SMN in the cmTBI patients. These alterations suggest that early disruptions in SC between bilateral insula and the bilateral prefrontal cortices may appear in amTBI and persist into cmTBI, which might be potentially related to the cognitive and emotional impairments.

Interhemispheric co-alteration of brain homotopic regions

Asymmetries in gray matter alterations raise important issues regarding the pathological co-alteration between hemispheres. Since homotopic areas are the most functionally connected sites between hemispheres and gray matter co-alterations depend on connectivity patterns, it is likely that this relationship might be mirrored in homologous interhemispheric co-altered areas. To explore this issue, we analyzed data of patients with Alzheimer’s disease, schizophrenia, bipolar disorder and depressive disorder from the BrainMap voxel-based morphometry database. We calculated a map showing the pathological homotopic anatomical co-alteration between homologous brain areas. This map was compared with the meta-analytic homotopic connectivity map obtained from the BrainMap functional database, so as to have a meta-analytic connectivity modeling map between homologous areas. We applied an empirical Bayesian technique so as to determine a directional pathological co-alteration on the basis of the possible tendencies in the conditional probability of being co-altered of homologous brain areas. Our analysis provides evidence that: the hemispheric homologous areas appear to be anatomically co-altered; this pathological co-alteration is similar to the pattern of connectivity exhibited by the couples of homologues; the probability to find alterations in the areas of the left hemisphere seems to be greater when their right homologues are also altered than vice versa, an intriguing asymmetry that deserves to be further investigated and explained.

BACON: A tool for reverse inference in brain activation and alteration

Over the past decades, powerful MRI‐based methods have been developed, which yield both voxel‐based maps of the brain activity and anatomical variation related to different conditions. With regard to functional or structural MRI data, forward inferences try to determine which areas are involved given a mental function or a brain disorder. A major drawback of forward inference is its lack of specificity, as it suggests the involvement of brain areas that are not specific for the process/condition under investigation. Therefore, a different approach is needed to determine to what extent a given pattern of cerebral activation or alteration is specifically associated with a mental function or brain pathology. In this study, we present a new tool called BACON (Bayes fACtor mOdeliNg) for performing reverse inference both with functional and structural neuroimaging data. BACON implements the Bayes' factor and uses the activation likelihood estimation derived‐maps to obtain posterior probability distributions on the evidence of specificity with regard to a particular mental function or brain pathology.

Cortical morphometric correlational networks associated with cognitive deficits in first episode schizophrenia

Schizophrenia (SCZ) is a chronic cognitive and behavioral disorder associated with abnormal cortical activity during information processing. Several brain structures associated with the seven performance domains evaluated using the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery (MCCB) have shown cortical volume loss in first episode schizophrenia (FES) patients. However, the relationship between morphological organization and MCCB performance remains unclear. Therefore, in the current observational study, high-resolution structural MRI scans were collected from 50 FES patients, and the morphometric correlation network (MCN) using cortical volume was established to characterize the cortical pattern associated with poorer MCCB performance. We also investigated topological properties, such as the modularity, the degree and the betweenness centrality. Our findings show structural volume was directly and strongly associated with the cognitive deficits of FES patients in the precuneus, anterior cingulate, and fusiform gyrus, as well as the prefrontal, parietal, and sensorimotor cortices. The medial orbitofrontal, fusiform, and superior frontal gyri were not only identified as the predominant nodes with high degree and betweenness centrality in the MCN, but they were also found to be critical in performance in several of the MCCB domains. Together, these results suggest a widespread cortical network is altered in FES patients and that performance on the MCCB domains is associated with the core pathophysiology of SCZ.

Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical brain anatomy and connectivity. Graph-theoretical methods have mainly been applied to detect altered patterns of white matter tracts and functional brain activation in individuals with ASD. The network topology of gray matter (GM) abnormalities in ASD remains relatively unexplored.

METHODS

An innovative meta-connectomic analysis on voxel-based morphometry data (45 experiments, 1,786 subjects with ASD) was performed in order to investigate whether GM variations can develop in a distinct pattern of co-alteration across the brain. This pattern was then compared with normative profiles of structural and genetic co-expression maps. Graph measures of centrality and clustering were also applied to identify brain areas with the highest topological hierarchy and core sub-graph components within the co-alteration network observed in ASD.

RESULTS

Individuals with ASD exhibit a distinctive and topologically defined pattern of GM co-alteration that moderately follows the structural connectivity constraints. This was not observed with respect to the pattern of genetic co-expression. Hub regions of the co-alteration network were mainly left-lateralized, encompassing the precuneus, ventral anterior cingulate, and middle occipital gyrus. Regions of the default mode network appear to be central in the topology of co-alterations.

CONCLUSION

These findings shed new light on the pathobiology of ASD, suggesting a network-level dysfunction among spatially distributed GM regions. At the same time, this study supports pathoconnectomics as an insightful approach to better understand neuropsychiatric disorders.

Autism Spectrum Disorder from the Womb to Adulthood: Suggestions for a Paradigm Shift

The wide spectrum of unique needs and strengths of Autism Spectrum Disorders (ASD) is a challenge for the worldwide healthcare system. With the plethora of information from research, a common thread is required to conceptualize an exhaustive pathogenetic paradigm. The epidemiological and clinical findings in ASD cannot be explained by the traditional linear genetic model, hence the need to move towards a more fluid conception, integrating genetics, environment, and epigenetics as a whole. The embryo-fetal period and the first two years of life (the so-called 'First 1000 Days') are the crucial time window for neurodevelopment. In particular, the interplay and the vicious loop between immune activation, gut dysbiosis, and mitochondrial impairment/oxidative stress significantly affects neurodevelopment during pregnancy and undermines the health of ASD people throughout life. Consequently, the most effective intervention in ASD is expected by primary prevention aimed at pregnancy and at early control of the main effector molecular pathways. We will reason here on a comprehensive and exhaustive pathogenetic paradigm in ASD, viewed not just as a theoretical issue, but as a tool to provide suggestions for effective preventive strategies and personalized, dynamic (from womb to adulthood), systemic, and interdisciplinary healthcare approach.

Updating and characterizing neuroanatomical markers in high-risk subjects, recently diagnosed and chronic patients with schizophrenia: A revised coordinate-based meta-analysis

Characterizing neuroanatomical markers of different stages of schizophrenia (SZ) to assess pathophysiological models of how the disorder develops is an important target for the clinical practice. We performed a meta-analysis of voxel-based morphometry studies of genetic and clinical high-risk subjects (g-/c-HR), recently diagnosed (RDSZ) and chronic SZ patients (ChSZ). We quantified gray matter (GM) changes associated with these four conditions and compared them with contrast and conjunctional data. We performed the behavioral analysis and networks decomposition of alterations to obtain their functional characterization. Results reveal a cortical-subcortical, left-to-right homotopic progression of GM loss. The right anterior cingulate is the only altered region found altered among c-HR, RDSZ and ChSZ. Contrast analyses show left-lateralized insular, amygdalar and parahippocampal GM reduction in RDSZ, which appears bilateral in ChSZ. Functional decomposition shows involvement of the salience network, with an enlargement of the sensorimotor network in RDSZ and the thalamus-basal nuclei network in ChSZ. These findings support the current neuroprogressive models of SZ and integrate this deterioration with the clinical evolution of the disease.

Differences in Brain Structural Covariance Network Characteristics in Children and Adults With Autism Spectrum Disorder

Systematically describing the structural topological configuration of human brain during development is an essential task. Autism spectrum disorder (ASD) represents a powerful challenge for psychiatry and neuroscience researchers. In this study, we investigated variations in the structural covariance network properties of 441 patients with ASD ranging in age from 7 to 45 years and in 426 age-matched healthy controls (HCs) using structural magnetic resonance neuroimaging from the ABIDE database. We applied a sliding window approach to study topological variation during development using comprehensive graph theoretical analysis. The main findings are as follows: (1) Cross-sectional trajectories of the network characteristics exhibited inverted U-shapes in both HCs and participants with ASD, with the latter exhibiting a 7-year delay in reaching the maximum value, (2) network resilience to targeted attacks peaked at 18' and 19' in the HCs and at 25' in the participants with ASD, and the weakest resilience occurred at age 7', (3) the HCs and participants with ASD exhibited normalized mean degree differences in the right amygdala, and (4) significant differences in the network characteristics were observed in the 18' age group at most of the densities analyzed. We used cross-sectional analysis to infer distinct neurodevelopmental trajectories in ASD in the brain structural connectome. Our findings are consistent with the notion that adolescence is a sensitive period of brain development with strong potential for brain plasticity, offering opportunities for environmental adaptation and social integration and for increasing vulnerability. ASD may be a product of susceptibility. LAY SUMMARY: We used cross-sectional analysis to preliminarily infer distinct neurodevelopmental trajectories in ASD in the brain structural connectome. The main findings are as follows: (1) Cross-sectional trajectories of the network characteristics exhibited inverted U-shapes in both HCs and participants with ASD, with the latter exhibiting a 7-year delay in reaching the maximum value, (2) Network resilience to targeted attacks peaked at 18' and 19' in the HCs and at 25' in the participants with ASD, and the weakest resilience occurred at age 7', (3) The HCs and participants with ASD exhibited normalized mean degree differences in the right amygdala, and (4) significant differences in the network characteristics were observed in the 18' age group at most of the densities analyzed.

The Gut-Brain-Immune Axis in Autism Spectrum Disorders: A State-of-Art Report

The interest elicited by the large microbial population colonizing the human gut has ancient origins and has gone through a long evolution during history. However, it is only in the last decades that the introduction of high-throughput technologies has allowed to broaden this research field and to disentangle the numerous implications that gut microbiota has in health and disease. This comprehensive ecosystem, constituted mainly by bacteria but also by fungi, parasites, and viruses, is proven to be involved in several physiological and pathological processes that transcend the intestinal homeostasis and are deeply intertwined with apparently unrelated body systems, such as the immune and the nervous ones. In this regard, a novel speculation is the relationship between the intestinal microbial flora and the pathogenesis of some neurological and neurodevelopmental disorders, including the clinical entities defined under the umbrella term of autism spectrum disorders. The bidirectional interplay has led researchers to coin the term gut-brain-immune system axis, subverting the theory of the brain as an immune-privileged site and underscoring the importance of this reciprocal influence already from fetal life and especially during the pre- and post-natal neurodevelopmental process. This revolutionary theory has also unveiled the possibility to modify the gut microbiota as a way to treat and even to prevent different kinds of pathologies. In this sense, some attempts have been made, ranging from probiotic administration to fecal microbiota transplantation, with promising results that need further elaboration. This state-of-art report will describe the main aspects regarding the human gut microbiome and its specific role in the pathogenesis of autism and its related disorders, with a final discussion on the therapeutic and preventive strategies aiming at creating a healthy intestinal microbial environment, as well as their safety and ethical implications.

The pathoconnectivity network analysis of the insular cortex: A morphometric fingerprinting

Brain disorders tend to impact on many different regions in a typical way: alterations do not spread randomly; rather, they seem to follow specific patterns of propagation that show a strong overlap between different pathologies. The insular cortex is one of the brain areas more involved in this phenomenon, as it seems to be altered by a wide range of brain diseases. On these grounds we thoroughly investigated the impact of brain disorders on the insular cortices analyzing the patterns of their structural co-alteration. We therefore investigated, applying a network analysis approach to meta-analytic data, 1) what pattern of gray matter alteration is associated with each of the insular cortex parcels; 2) whether or not this pattern correlates and overlaps with its functional meta-analytic connectivity; and, 3) the behavioral profile related to each insular co-alteration pattern. All the analyses were repeated considering two solutions: one with two clusters and another with three. Our study confirmed that the insular cortex is one of the most altered cerebral regions among the cortical areas, and exhibits a dense network of co-alteration including a prevalence of cortical rather than sub-cortical brain regions. Regions of the frontal lobe are the most involved, while occipital lobe is the less affected. Furthermore, the co-alteration and co-activation patterns greatly overlap each other. These findings provide significant evidence that alterations caused by brain disorders are likely to be distributed according to the logic of network architecture, in which brain hubs lie at the center of networks composed of co-altered areas. For the first time, we shed light on existing differences between insula sub-regions even in the pathoconnectivity domain.

Finding specificity in structural brain alterations through Bayesian reverse inference

In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference-based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference-based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology-specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel-based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.

Hubs of long-distance co-alteration characterize brain pathology

It is becoming clearer that the impact of brain diseases is more convincingly represented in terms of co-alterations rather than in terms of localization of alterations. In this context, areas characterized by a long mean distance of co-alteration may be considered as hubs with a crucial role in the pathology. We calculated meta-analytic transdiagnostic networks of co-alteration for the gray matter decreases and increases, and we evaluated the mean Euclidean, fiber-length, and topological distance of its nodes. We also examined the proportion of co-alterations between canonical networks, and the transdiagnostic variance of the Euclidean distance. Furthermore, disease-specific analyses were conducted on schizophrenia and Alzheimer's disease. The anterodorsal prefrontal cortices appeared to be a transdiagnostic hub of long-distance co-alterations. Also, the disease-specific analyses showed that long-distance co-alterations are more able than classic meta-analyses to identify areas involved in pathology and symptomatology. Moreover, the distance maps were correlated with the normative connectivity. Our findings substantiate the network degeneration hypothesis in brain pathology. At the same time, they suggest that the concept of co-alteration might be a useful tool for clinical neuroscience.

A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders

Numerous studies have investigated grey matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases are independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.

Autism in Adulthood: Clinical and Demographic Characteristics of a Cohort of Five Hundred Persons with Autism Analyzed by a Novel Multistep Network Model

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in communication and relational skills, associated with repetitive verbal and motor behaviors, restricted patterns of interest, need for a predictable and stable environment, and hypo- or hypersensitivity to sensory inputs. Due to the challenging diagnosis and the paucity of specific interventions, persons with autism (PWA) reaching the adult age often display a severe functional regression. In this scenario, the Regional Center for Autism in Adulthood in Turin seeks to develop a personalized rehabilitation and enablement program for PWA who received a diagnosis of autism in childhood/adolescence or for individuals with suspected adulthood ASD. This program is based on a Multistep Network Model involving PWA, family members, social workers, teachers, and clinicians. Our initial analysis of 500 PWA shows that delayed autism diagnosis and a lack of specific interventions at a young age are largely responsible for the creation of a “lost generation” of adults with ASD, now in dire need of effective psychosocial interventions. As PWA often present with psychopathological co-occurrences or challenging behaviors associated with lack of adequate communication and relational skills, interventions for such individuals should be mainly aimed to improve their self-reliance and social attitude. In particular, preparing PWA for employment, whenever possible, should be regarded as an essential part of the intervention program given the social value of work. Overall, our findings indicate that the development of public centers specialized in assisting and treating PWA can improve the accuracy of ASD diagnosis in adulthood and foster specific habilitative interventions aimed to improve the quality of life of both PWA and their families.

Atypical Brain Structures as a Function of Gray Matter Volume (GMV) and Gray Matter Density (GMD) in Young Adults Relating to Autism Spectrum Traits

Individuals with autistic traits are those who present in the normal population with characteristics of social, communication, personality, and cognitive impairments but do not meet the clinical threshold for autism spectrum disorder (ASD). Most studies have focused on the abnormalities in ASD patients rather than on individuals with autistic traits. In this study, we focused on the behaviors of a large sample (N = 401) of Chinese individuals with different levels of autistic traits, measured using the Autism Spectrum Quotient, and applied voxel-based morphometry (VBM) to determine their association to differences in brain structure. The results mainly showed that the correlation between gray matter volume (GMV) and gray matter density of the brain and the Autism Spectrum Quotient was significant in these regions: the right middle frontal gyrus, which are involved in social processing and social reasoning; the left parahippocampal gyrus, which is involved in socioemotional behaviors and unconscious relational memory encoding; and the right superior parietal lobule, which are involved in cognitive control and the ability to show attention to detail. These findings reveal that people with autistic traits in the normal population have atypical development in GMV and gray matter density, which may affect their social functioning and communication ability.

Coordinate based meta-analysis of networks in neuroimaging studies

Meta-analysis of summary results from published neuroimaging studies independently testing a common hypothesis is performed using coordinate based meta-analysis (CBMA), which tests for consistent activation (in the case of functional MRI studies) of the same anatomical regions. Using just the reported coordinates it is also possible to meta-analyse coactivated regions to reveal a network-like structure of coordinate clusters (network nodes) distributed at the coactivated locations and a measure of the coactivation strength (network edges), which is determined by the presence/absence of reported activation. Here a new coordinate-based method to estimate a network of coactivations is detailed, which utilises the Z score accompanying each reported. Coordinate based meta-analysis of networks (CBMAN) assumes that if the activation pattern reported by independent studies is truly consistent, then the relative magnitude of these Z scores might also be consistent. It is hypothesised that this is detectable as Z score covariance between coactivated regions provided the within study variances are small. Advantages of using the Z scores instead of coordinates to measure coactivation strength are that censoring by the significance thresholds can be considered, and that using a continuous measure rather than a dichotomous one can increase statistical power. CBMAN uses maximum likelihood estimation to fit multivariate normal distributions to the standardised Z scores, and the covariances are considered as edges of a network of coactivated clusters (nodes). Here it is validated by numerical simulation and demonstrated on real data used previously to demonstrate CBMA. Software to perform CBMAN is freely available.

Brain structural alterations are distributed following functional, anatomic and genetic connectivity

The pathological brain is characterized by distributed morphological or structural alterations in the grey matter, which tend to follow identifiable network-like patterns. We analysed the patterns formed by these alterations (increased and decreased grey matter values detected with the voxel-based morphometry technique) conducting an extensive transdiagnostic search of voxel-based morphometry studies in a large variety of brain disorders. We devised an innovative method to construct the networks formed by the structurally co-altered brain areas, which can be considered as pathological structural co-alteration patterns, and to compare these patterns with three associated types of connectivity profiles (functional, anatomical, and genetic). Our study provides transdiagnostical evidence that structural co-alterations are influenced by connectivity constraints rather than being randomly distributed. Analyses show that although all the three types of connectivity taken together can account for and predict with good statistical accuracy, the shape and temporal development of the co-alteration patterns, functional connectivity offers the better account of the structural co-alteration, followed by anatomic and genetic connectivity. These results shed new light on the possible mechanisms at the root of neuropathological processes and open exciting prospects in the quest for a better understanding of brain disorders.

Neuroanatomical deficits shared by youth with autism spectrum disorders and psychotic disorders

Autism spectrum disorders (ASD) and early-onset psychosis (EOP) are neurodevelopmental disorders that share genetic, clinical and cognitive facets; it is unclear if these disorders also share spatially overlapping cortical thickness (CT) and surface area (SA) abnormalities. MRI scans of 30 ASD, 29 patients with early-onset first-episode psychosis (EO-FEP) and 26 typically developing controls (TD) (age range 10-18 years) were analyzed by the FreeSurfer suite to calculate vertex-wise estimates of CT, SA, and cortical volume. Two publicly available datasets of ASD and EOP (age range 7-18 years and 5-17 years, respectively) were used for replication analysis. ASD and EO-FEP had spatially overlapping areas of cortical thinning and reduced SA in the bilateral insula (all p's < .00002); 37% of all left insular vertices presenting with significant cortical thinning and 20% (left insula) and 61% (right insula) of insular vertices displaying decreased SA overlapped across both disorders. In both disorders, SA deficits contributed more to cortical volume decreases than reductions in CT did. This finding, as well as the novel finding of an absence of spatial overlap (for ASD) or marginal overlap (for EOP) of deficits in CT and SA, was replicated in the two nonoverlapping independent samples. The insula appears to be a region with transdiagnostic vulnerability for deficits in CT and SA. The finding of nonexistent or small spatial overlap between CT and SA deficits in young people with ASD and psychosis may point to the involvement of common aberrant early neurodevelopmental mechanisms in their pathophysiology.

Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry

A network-level investigation of the volumetric changes of subcortical stroke patients is still lacking. Here, we explored the alterations of structural covariance caused by subcortical stroke with automated brain volumetry. T1-weighed brain MRI scans were obtained from 63 normal controls (NC), 46 stroke patients with infarct in left internal capsule (CI_L), 33 stroke patients with infarct in right internal capsule (CI_R). We performed automatic anatomical segmentation of the T1-weighted brain images with AccuBrain. Volumetric structural covariance analyses were first performed within the basal ganglia structures that were both identified by voxel-based morphometry with AAL atlas and AccuBrain. Subsequently, we additionally included the infratentorial regions that were particularly quantified by AccuBrain for the structural covariance analyses and investigated the alterations of anatomical connections within these subcortical regions in CI_L and CI_R compared with NC. The association between the regional brain volumetry and motor function was also evaluated in stroke groups. There were significant and extensive volumetric differences in stroke patients. These significant regions were generally symmetric for CI_L and CI_R group depending on the side of stroke, involving both regions close to lesions and remote regions. The structural covariance analyses revealed the synergy volume alteration in subcortical regions both in CI_L and CI_R group. In addition, the alterations of volumetric structural covariance were more extensive in CI_L group than CI_R group. Moreover, we found that the subcortical regions with atrophy contributed to the deficits of motor function in CI_R group but not CI_L group, indicating a lesion-side effect of brain volumetric changes after stroke. These findings indicated that the chronic subcortical stroke patients have extensive disordered anatomical connections involving the whole-brain level network, and the connections patterns depend on the lesion-side.

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Chronic subcortical stroke patients show extensive brain volumetric atrophy.

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Subcortical stroke patients show disordered structural covariance network pattern.

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Brain volumetric and connections patterns change depend on the lesion-side.

The alteration landscape of the cerebral cortex

Growing evidence is challenging the assumption that brain disorders are diagnostically clear-cut categories. Transdiagnostic studies show that a set of cerebral areas is frequently altered in a variety of psychiatric as well as neurological syndromes. In order to provide a map of the altered areas in the pathological brain we devised a metric, called alteration entropy (A-entropy), capable of denoting the "structural alteration variety" of an altered region. Using the whole voxel-based morphometry database of BrainMap, we were able to differentiate the brain areas exhibiting a high degree of overlap between different neuropathologies (or high value of A-entropy) from those exhibiting a low degree of overlap (or low value of A-entropy). The former, which are parts of large-scale brain networks with attentional, emotional, salience, and premotor functions, are thought to be more vulnerable to a great range of brain diseases; while the latter, which include the sensorimotor, visual, inferior temporal, and supramarginal regions, are thought to be more informative about the specific impact of brain diseases. Since low A-entropy areas appear to be altered by a smaller number of brain disorders, they are more informative than the areas characterized by high values of A-entropy. It is also noteworthy that even the areas showing low values of A-entropy are substantially altered by a variety of brain disorders. In fact, no cerebral area appears to be only altered by a specific disorder. Our study shows that the overlap of areas with high A-entropy provides support for a transdiagnostic approach to brain disorders but, at the same time, suggests that fruitful differences can be traced among brain diseases, as some areas can exhibit an alteration profile more specific to certain disorders than to others.

Motor dysfunction as research domain across bipolar, obsessive-compulsive and neurodevelopmental disorders

Although genuine motor abnormalities (GMA) are frequently found in schizophrenia, they are also considered as an intrinsic feature of bipolar, obsessive-compulsive, and neurodevelopmental disorders with early onset such as autism, ADHD, and Tourette syndrome. Such transnosological observations strongly suggest a common neural pathophysiology. This systematic review highlights the evidence on GMA and their neuroanatomical substrates in bipolar, obsessive-compulsive, and neurodevelopmental disorders. The data lends support for a common pattern contributing to GMA expression in these diseases that seems to be related to cerebello-thalamo-cortical, fronto-parietal, and cortico-subcortical motor circuit dysfunction. The identified studies provide first evidence for a motor network dysfunction as a correlate of early neurodevelopmental deviance prior to clinical symptom expression. There are also first hints for a developmental risk factor model of these mental disorders. An in-depth analysis of motor networks and related patho-(physiological) mechanisms will not only help promoting Research Domain Criteria (RDoC) Motor System construct, but also facilitate the development of novel psychopharmacological models, as well as the identification of neurobiologically plausible target sites for non-invasive brain stimulation.

The morphometric co-atrophy networking of schizophrenia, autistic and obsessive spectrum disorders

By means of a novel methodology that can statistically derive patterns of co-alterations distribution from voxel-based morphological data, this study analyzes the patterns of brain alterations of three important psychiatric spectra-that is, schizophrenia spectrum disorder (SCZD), autistic spectrum disorder (ASD), and obsessive-compulsive spectrum disorder (OCSD). Our analysis provides five important results. First, in SCZD, ASD, and OCSD brain alterations do not distribute randomly but, rather, follow network-like patterns of co-alteration. Second, the clusters of co-altered areas form a net of alterations that can be defined as morphometric co-alteration network or co-atrophy network (in the case of gray matter decreases). Third, within this network certain cerebral areas can be identified as pathoconnectivity hubs, the alteration of which is supposed to enhance the development of neuronal abnormalities. Fourth, within the morphometric co-atrophy network of SCZD, ASD, and OCSD, a subnetwork composed of eleven highly connected nodes can be distinguished. This subnetwork encompasses the anterior insulae, inferior frontal areas, left superior temporal areas, left parahippocampal regions, left thalamus and right precentral gyri. Fifth, the co-altered areas also exhibit a normal structural covariance pattern which overlaps, for some of these areas (like the insulae), the co-alteration pattern. These findings reveal that, similarly to neurodegenerative diseases, psychiatric disorders are characterized by anatomical alterations that distribute according to connectivity constraints so as to form identifiable morphometric co-atrophy patterns.

The Temporal Propagation of Intrinsic Brain Activity Associate With the Occurrence of PTSD

The abnormal brain activity is a pivotal condition for the occurrence of posttraumatic stress disorder. However, the dynamic time features of intrinsic brain activities still remain unclearly in PTSD patients. Our study aims to perform the resting-state lag analysis (RS-LA) method to explore potential propagated patterns of intrinsic brain activities in PTSD patients. We recruited 27 drug-naive patients with PTSD, 33 trauma-exposed controls (TEC), and 30 demographically matched healthy controls (HC) in the final data statistics. Both RS-LA and conventional voxel-wise functional connectivity strength (FCS) methods were employed on the same dataset. Then, Spearman correlation analysis was conducted on time latency values of those abnormal brain regions with the clinical assessments. Compared with HC group, the time latency patterns of PTSD patients significantly shifted toward later in posterior cingulate cortex/precuneus, middle prefrontal cortex, right angular, and left pre- and post-central cortex. The TEC group tended to have similar time latency in right angular. Additionally, significant time latency in right STG was found in PTSD group relative to TEC group. Spearman correlation analysis revealed that the time latency value of mPFC negatively correlated to the PTSD checklist-civilian version scores (PCL_C) in PTSD group (r = -0.578, P < 0.05). Furthermore, group differences map of FCS exhibited parts of overlapping areas with that of RS-LA, however, less specificity in detecting PTSD patients. In conclusion, apparent alterations of time latency were observed in DMN and primary sensorimotor areas of PTSD patients. These findings provide us with new evidence to explain the neural pathophysiology contributing to PTSD.