Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia

Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI

Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health concern due to the global shortfall in well-qualified professionals. Thanks to the recent advances in neuroimaging techniques, functional magnetic resonance imaging (fMRI) has surfaced as a new solution to characterize neuropathological biomarkers for detecting functional connectivity (FC) anomalies in mental disorders. However, the existing computer-aided diagnosis models for fMRI analysis suffer from unstable performance on large datasets. To address this issue, we propose an efficient multitask learning (MTL) framework for joint diagnosis of multiple mental disorders using resting-state fMRI data. A novel multiobjective evolutionary clustering algorithm is presented to group regions of interests (ROIs) into different clusters for FC pattern analysis. On the optimal clustering solution, the multicluster multigate mixture-of-expert model is used for the final classification by capturing the highly consistent feature patterns among related diagnostic tasks. Extensive simulation experiments demonstrate that the performance of the proposed framework is superior to that of the other state-of-the-art methods. Moreover, the potential for practical application of the framework is also validated in terms of limited computational resources, real-time analysis, and insufficient training data. The proposed model can identify the remarkable interpretative biomarkers associated with specific mental disorders for clinical interpretation analysis.

Network enrichment significance testing in brain-phenotype association studies

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

Common and distinct cortical thickness alterations in youth with autism spectrum disorder and attention-deficit/hyperactivity disorder

BACKGROUND

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental disorders with overlapping behavioral features and genetic etiology. While brain cortical thickness (CTh) alterations have been reported in ASD and ADHD separately, the degree to which ASD and ADHD are associated with common and distinct patterns of CTh changes is unclear.

METHODS

We searched PubMed, Web of Science, Embase, and Science Direct from inception to 8 December 2023 and included studies of cortical thickness comparing youth (age less than 18) with ASD or ADHD with typically developing controls (TDC). We conducted a comparative meta-analysis of vertex-based studies to identify common and distinct CTh alterations in ASD and ADHD.

RESULTS

Twelve ASD datasets involving 458 individuals with ASD and 10 ADHD datasets involving 383 individuals with ADHD were included in the analysis. Compared to TDC, ASD showed increased CTh in bilateral superior frontal gyrus, left middle temporal gyrus, and right superior parietal lobule (SPL) and decreased CTh in right temporoparietal junction (TPJ). ADHD showed decreased CTh in bilateral precentral gyri, right postcentral gyrus, and right TPJ relative to TDC. Conjunction analysis showed both disorders shared reduced TPJ CTh located in default mode network (DMN). Comparative analyses indicated ASD had greater CTh in right SPL and TPJ located in dorsal attention network and thinner CTh in right TPJ located in ventral attention network than ADHD.

CONCLUSIONS

These results suggest shared thinner TPJ located in DMN is an overlapping neurobiological feature of ASD and ADHD. This alteration together with SPL alterations might be related to altered biological motion processing in ASD, while abnormalities in sensorimotor systems may contribute to behavioral control problems in ADHD. The disorder-specific thinner TPJ located in disparate attention networks provides novel insight into distinct symptoms of attentional deficits associated with the two neurodevelopmental disorders.

TRIAL REGISTRATION

PROSPERO CRD42022370620. Registered on November 9, 2022.

Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.

The neuroanatomical substrates of autism and ADHD and their link to putative genomic underpinnings

BACKGROUND

Autism spectrum disorders (ASD) are neurodevelopmental conditions accompanied by differences in brain development. Neuroanatomical differences in autism are variable across individuals and likely underpin distinct clinical phenotypes. To parse heterogeneity, it is essential to establish how the neurobiology of ASD is modulated by differences associated with co-occurring conditions, such as attention-deficit/hyperactivity disorder (ADHD). This study aimed to (1) investigate between-group differences in autistic individuals with and without co-occurring ADHD, and to (2) link these variances to putative genomic underpinnings.

METHODS

We examined differences in cortical thickness (CT) and surface area (SA) and their genomic associations in a sample of 533 individuals from the Longitudinal European Autism Project. Using a general linear model including main effects of autism and ADHD, and an ASD-by-ADHD interaction, we examined to which degree ADHD modulates the autism-related neuroanatomy. Further, leveraging the spatial gene expression data of the Allen Human Brain Atlas, we identified genes whose spatial expression patterns resemble our neuroimaging findings.

RESULTS

In addition to significant main effects for ASD and ADHD in fronto-temporal, limbic, and occipital regions, we observed a significant ASD-by-ADHD interaction in the left precentral gyrus and the right frontal gyrus for measures of CT and SA, respectively. Moreover, individuals with ASD + ADHD differed in CT to those without. Both main effects and the interaction were enriched for ASD-but not for ADHD-related genes.

LIMITATIONS

Although we employed a multicenter design to overcome single-site recruitment limitations, our sample size of N = 25 individuals in the ADHD only group is relatively small compared to the other subgroups, which limits the generalizability of the results. Also, we assigned subjects into ADHD positive groupings according to the DSM-5 rating scale. While this is sufficient for obtaining a research diagnosis of ADHD, our approach did not take into account for how long the symptoms have been present, which is typically considered when assessing ADHD in the clinical setting.

CONCLUSION

Thus, our findings suggest that the neuroanatomy of ASD is significantly modulated by ADHD, and that autistic individuals with co-occurring ADHD may have specific neuroanatomical underpinnings potentially mediated by atypical gene expression.

White and gray matter correlates of theory of mind in autism: a voxel-based morphometry study

Autism spectrum disorder (ASD) is characterized by difficulties in theory of mind (ToM) and social communication. Studying structural and functional correlates of ToM in the brain and how autistic and nonautistic groups differ in terms of these correlates can help with diagnosis and understanding the biological mechanisms of ASD. In this study, we investigated white matter volume (WMV) and gray matter volume (GMV) differences between matching autistic and nonautistic samples, and how these structural features relate to age and ToM skills, indexed by the Reading the Mind in the Eyes (RMIE) measure. The results showed widespread GMV and WMV differences between the two groups in regions crucial for social processes. The autistic group did not express the typically observed negative GMV and positive WMV correlations with age at the same level as the nonautistic group, pointing to abnormalities in developmental structural changes. In addition, we found differences between the two groups in how GMV relates to ToM, particularly in the left frontal regions, and how WMV relates to ToM, mostly in the cingulate and corpus callosum. Finally, GMV in the left insula, a region that is part of the salience network, was found to be crucial in distinguishing ToM performance between the two groups.

A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework

Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.

Age differences and brain maturation provide insight into heterogeneous results in autism spectrum disorder

Studies comparing individuals with autism spectrum disorder (ASD) to typically developing (TD) individuals have yielded inconsistent results. These inconsistencies reflect, in part, atypical trajectories of development in children and young adults with ASD compared to TD peers. These different trajectories alter group differences between children with and without ASD as they age. This paper first summarizes the disparate trajectories evident in our studies and, upon further investigation, laboratories using the same recruiting source. These studies indicated that cognition improves into adulthood typically, and is associated with the maturation of striatal, frontal, and temporal lobes, but these age-related improvements did not emerge in the young adults with ASD. This pattern - of improvement into adulthood in the TD group but not in the group with ASD - occurred in both social and non-social tasks. However, the difference between TD and ASD trajectories was most robust on a social task, face recognition. While tempting to ascribe this uneven deficit to the social differences in ASD, it may also reflect the prolonged typical development of social cognitive tasks such as face recognition into adulthood. This paper then reviews the evidence on age-related and developmental changes from other studies on ASD. The broader literature also suggests that individuals with ASD do not exhibit the typical improvements during adolescence on skills important for navigating the transition to adulthood. These skills include execution function, social cognition and communication, and emotional recognition and self-awareness. Relatedly, neuroimaging studies indicate arrested or atypical brain maturation in striatal, frontal, and temporal regions during adolescence in ASD. This review not only highlights the importance of a developmental framework and explicit consideration of age and/or stage when studying ASD, but also the potential importance of adolescence on outcomes in ASD.

AUTISM SPECTRUM DISORDER AND SCHIZOPHRENIA - SIMILARITIES BETWEEN THE TWO DISORDERS WITH A CASE REPORT OF A PATIENT WITH DUAL DIAGNOSIS

This paper presents the genetic, molecular and neuroanatomical similarities between autism spectrum disorder (ASD) and schizophrenia using the case report of a 34-year-old female patient with a previous diagnosis of schizophrenia as an example. As a result of repeat hospitalization, expanded history, psychological testing and verification of persistent symptoms of psychopathology, a cooccurring diagnosis of autism spectrum disorder was made.

Prenatal exposure to air pollution is associated with altered brain structure, function, and metabolism in childhood

BACKGROUND

Prenatal exposure to air pollution disrupts cognitive, emotional, and behavioral development. The brain disturbances associated with prenatal air pollution are largely unknown.

METHODS

In this prospective cohort study, we estimated prenatal exposures to fine particulate matter (PM_2.5 ) and polycyclic aromatic hydrocarbons (PAH), and then assessed their associations with measures of brain anatomy, tissue microstructure, neurometabolites, and blood flow in 332 youth, 6-14 years old. We then assessed how those brain disturbances were associated with measures of intelligence, ADHD and anxiety symptoms, and socialization.

RESULTS

Both exposures were associated with thinning of dorsal parietal cortices and thickening of postero-inferior and mesial wall cortices. They were associated with smaller white matter volumes, reduced organization in white matter of the internal capsule and frontal lobe, higher metabolite concentrations in frontal cortex, reduced cortical blood flow, and greater microstructural organization in subcortical gray matter nuclei. Associations were stronger for PM_2.5 in boys and PAH in girls. Youth with low exposure accounted for most significant associations of ADHD, anxiety, socialization, and intelligence measures with cortical thickness and white matter volumes, whereas it appears that high exposures generally disrupted these neurotypical brain-behavior associations, likely because strong exposure-related effects increased the variances of these brain measures.

CONCLUSIONS

The commonality of effects across exposures suggests PM_2.5 and PAH disrupt brain development through one or more common molecular pathways, such as inflammation or oxidative stress. Progressively higher exposures were associated with greater disruptions in local volumes, tissue organization, metabolite concentrations, and blood flow throughout cortical and subcortical brain regions and the white matter pathways interconnecting them. Together these affected regions comprise cortico-striato-thalamo-cortical circuits, which support the regulation of thought, emotion, and behavior.

How does brain structure and function on MRI differ in children with autism spectrum disorder, developmental coordination disorder, and/or attention deficit hyperactivity disorder?

AIM

The purpose of this study was to systematically review the neural similarities and differences in brain structure and function, measured by magnetic resonance imaging (MRI), in children with neurodevelopmental disorders that commonly co-occur to understand if and how they have shared neuronal characteristics.

METHOD

Using systematic review methodology, the following databases were comprehensively searched: MEDLINE, EMBASE, CINAHL, CENTRAL, PsycINFO, and ProQuest from the earliest record up to December 2021. Inclusion criteria were: (1) peer-reviewed studies, case reports, or theses; (2) children under 18 years of age with at least one of the following neurodevelopmental disorders: autism spectrum disorder (ASD), attention hyperactivity deficit disorder (ADHD), developmental coordination disorder (DCD), and their co-occurrence; (3) studies based on MRI modalities (i.e., structural MRI, diffusion tensor imaging (DTI), and resting-state fMRI). Thirty-one studies that met the inclusion criteria were included for quality assessment by two independent reviewers using the Appraisal tool for Cross-Sectional Studies (AXIS).

RESULTS

Studies compared brain structure and function of children with DCD and ADHD (n=6), DCD and ASD (n=1), ASD and ADHD (n=17), and various combinations of these co-occurring conditions (n=7). Structural neuroimaging (n=15) was the most commonly reported modality, followed by resting-state (n=8), DTI (n=5), and multi-modalities (n=3).

INTERPRETATION

Evidence indicated that the neural correlates of the co-occurring conditions were more widespread and distinct compared to a single diagnosis. The majority of findings (77%) suggested that each neurodevelopmental disorder had more distinct neural correlates than shared neural features, suggesting that each disorder is distinct despite commonly co-occurring with each other. As the number of papers examining the co-occurrence of ASD, DCD, and/or ADHD was limited and most findings were not corrected for multiple comparisons, these results must be interpreted with caution.

Functional Significance of Human Resting-State Networks Hubs Identified Using MEG During the Transition From Childhood to Adulthood

Cortical hubs identified within resting-state networks (RSNs), areas of the cortex that have a higher-than-average number of connections, are known to be critical to typical cognitive functioning and are often implicated in disorders leading to abnormal cognitive functioning. Functionally defined cortical hubs are also known to change with age in the developing, maturing brain, mostly based on studies carried out using fMRI. We have recently used magnetoencephalography (MEG) to study the maturation trajectories of RSNs and their hubs from age 7 to 29 in 131 healthy participants with high temporal resolution. We found that maturation trajectories diverge as a function of the underlying cortical rhythm. Specifically, we found the beta band (13–30 Hz)-mediated RSNs became more locally efficient with maturation, i.e., more organized into clusters and connected with nearby regions, while gamma (31–80 Hz)-mediated RSNs became more globally efficient with maturation, i.e., prioritizing faster signal transmission between distant cortical regions. We also found that different sets of hubs were associated with each of these networks. To better understand the functional significance of this divergence, we wanted to examine the cortical functions associated with the identified hubs that grew or shrunk with maturation within each of these networks. To that end, we analyzed the results of the prior study using Neurosynth, a platform for large-scale, automated synthesis of fMRI data that links brain coordinates with their probabilistically associated terms. By mapping the Neurosynth terms associated with each of these hubs, we found that maturing hubs identified in the gamma band RSNs were more likely to be associated with bottom-up processes while maturing hubs identified in the beta band RSNs were more likely to be associated with top-down functions. The results were consistent with the idea that beta band-mediated networks preferentially support the maturation of top-down processing, while the gamma band-mediated networks preferentially support the maturation of bottom-up processing.

Parahippocampal deactivation and hyperactivation of central executive, saliency and social cognition networks in autism spectrum disorder

BACKGROUND

The concomitant role of the Central Executive, the Saliency and the Social Cognition networks in autism spectrum disorder (ASD) in demanding ecological tasks remains unanswered. We addressed this question using a novel task-based fMRI virtual-reality task mimicking a challenging daily-life chore that may present some difficulties to individuals with ASD: the EcoSupermarketX.

METHODS

Participants included 29 adolescents: 15 with ASD and 15 with typical neurodevelopment (TD). They performed the EcoSupermarketX (a shopping simulation with three goal-oriented sub-tasks including "no cue", "non-social" or "social" cues), during neuroimaging and eye-tracking.

RESULTS

ASD differed from TD only in total time and distance to complete the "social cue" sub-task with matched eye-tracking measures. Neuroimaging revealed simultaneous hyperactivation across social, executive, and saliency circuits in ASD. In contrast, ASD showed reduced activation in the parahippocampal gyrus, involved in scene recognition.

CONCLUSIONS

When performing a virtual shopping task matching the performance of controls, ASD adolescents hyperactivate three core networks: executive, saliency and social cognition. Parahippocampal hypoactivation is consistent with effortless eidetic scene processing, in line with the notion of peaks and valleys of neural recruitment in individuals with ASD. These hyperactivation/hypoactivation patterns in daily life tasks provide a circuit-level signature of neural diversity in ASD, a possible intervention target.

Visual search in neurodevelopmental disorders: evidence towards a continuum of impairment

Disorders with neurodevelopmental aetiology such as Attention-Deficit/Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD) and Schizophrenia share commonalities at many levels of investigation despite phenotypic differences. Evidence of genetic overlap has led to the concept of a continuum of neurodevelopmental impairment along which these disorders can be positioned in aetiological, pathophysiological and developmental features. This concept requires their simultaneous comparison at different levels, which has not been accomplished so far. Given that cognitive impairments are core to the pathophysiology of these disorders, we provide for the first time differentiated head-to-head comparisons in a complex cognitive function, visual search, decomposing the task with eye movement-based process analyses. N = 103 late-adolescents with schizophrenia, ADHD, ASD and healthy controls took a serial visual search task, while their eye movements were recorded. Patients with schizophrenia presented the greatest level of impairment across different phases of search, followed by patients with ADHD, who shared with patients with schizophrenia elevated intra-subject variability in the pre-search stage. ASD was the least impaired group, but similar to schizophrenia in post-search processes and to schizophrenia and ADHD in pre-search processes and fixation duration while scanning the items. Importantly, the profiles of deviancy from controls were highly correlated between all three clinical groups, in line with the continuum idea. Findings suggest the existence of one common neurodevelopmental continuum of performance for the three disorders, while quantitative differences appear in the level of impairment. Given the relevance of cognitive impairments in these three disorders, we argue in favour of overlapping pathophysiological mechanisms.

Assessment and correlates of autistic symptoms in Schizophrenia Spectrum Disorders measured with the PANSS Autism Severity Score: A systematic review

Schizophrenia Spectrum Disorders (SSD) and Autism Spectrum Disorders (ASD) are considered separate entities, but the two spectra share important similarities, and the study of these areas of overlap represents a field of growing scientific interest. The PANSS Autism Score (PAUSS) was recently developed specifically to assess autistic symptoms in people living with SSD reliably and quickly. The aims of the present systematic review were to provide a comprehensive assessment of the use of the PAUSS scale in available literature and to systematically analyze cognitive, functional and neurobiological correlates of autistic symptoms measured with this instrument in SSD. The systematic literature search included three electronic databases (PubMed, Scopus and PsycINFO) as well as a manual search in Google Scholar and in reference lists of included papers. Screening and extraction were conducted by at least two independent reviewers. Out of 213 identified records, 22 articles referring to 15 original studies were included in the systematic review. Studies were conducted in several different countries by independent groups, showing consistent scientific interest in the use of the scale; most works focused on cognitive and functional correlates of ASD symptoms, but some also considered neurobiological features. Results of included studies showed that autistic symptoms in people with SSD are consistently associated with worse cognitive performance, especially in the social cognition domain, and with worse psychosocial functioning. However, the presence of autistic symptoms appears to also have a protective role, particularly on functioning, in subjects with more severe psychotic symptoms. Further exploring the impact of autistic symptoms could be of significant scientific and clinical interest, allowing the development of tailored interventions to improve treatment for people living with SSDs.

Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder

Schizophrenia (SZ) and autism spectrum disorder (ASD) share considerable clinical features and intertwined historical roots. It is greatly needed to explore their similarities and differences in pathophysiologic mechanisms. We assembled a large sample size of neuroimaging data (about 600 SZ patients, 1000 ASD patients, and 1700 healthy controls) to study the shared and unique brain abnormality of the two illnesses. We analyzed multi-scale brain functional connectivity among functional networks and brain regions, intra-network connectivity, and cerebral gray matter density and volume. Both SZ and ASD showed lower functional integration within default mode and sensorimotor domains, but increased interaction between cognitive control and default mode domains. The shared abnormalties in intra-network connectivity involved default mode, sensorimotor, and cognitive control networks. Reduced gray matter volume and density in the occipital gyrus and cerebellum were observed in both illnesses. Interestingly, ASD had overall weaker changes than SZ in the shared abnormalities. Interaction between visual and cognitive regions showed disorder-unique deficits. In summary, we provide strong neuroimaging evidence of the convergent and divergent changes in SZ and ASD that correlated with clinical features.

Childhood neurodevelopmental disorders and maternal hypertensive disorder of pregnancy

AIM

To examine the association of maternal chronic hypertension and pregnancy-induced hypertension (PIH)/preeclampsia with childhood neurodevelopmental disorders (NDDs) in a large-scale population-based cohort.

METHOD

We conducted a linked Taiwan National Health Insurance Research Database cohort study of children born between 2004 and 2008 (n=877 233). Diagnoses of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), developmental delay, intellectual disability, cerebral palsy (CP), and epilepsy/infantile spasms were identified from birth to the end of 2015. Cox proportional hazards models were fitted with adjustment for potential confounders to estimate the effect of maternal hypertensive disorder of pregnancy on childhood outcomes.

RESULTS

Compared with the offspring of unexposed mothers, offspring of mothers with chronic hypertension or PIH/preeclampsia exhibited increased risk of developing a wide spectrum of NDDs. Chronic hypertension was associated with increased risks of ADHD (hazard ratio 1.22, 95% confidence interval [CI] 1.13-1.​31), developmental delay (1.29, 1.21-1.38), intellectual disability (1.67, 1.43-1.95), CP (1.45, 1.14-1.85), and epilepsy/infantile spasms (1.31, 1.10-1.56) in the offspring, whereas PIH/preeclampsia was associated with increased risks of ASD (1.27, 1.12-1.43), ADHD (1.23, 1.17-1.29), developmental delay (1.29, 1.24-1.35), intellectual disability (1.53, 1.37-1.71), CP (1.44, 1.22-1.70), and epilepsy/infantile spasms (1.36, 1.22-1.52) in the offspring after adjustment for potential confounders. The co-occurrence of maternal diabetes, preterm deliveries, or fetal growth restriction further increased the risk.

INTERPRETATION

Chronic hypertension or PIH/preeclampsia seems to be sufficient to increase the risk of childhood NDDs. What this paper adds Children exposed to maternal hypertensive disorders have a higher cumulative incidence of neurodevelopmental disorders (NDDs) than unexposed children. Chronic hypertension or pregnancy-induced hypertension/preeclampsia seems to be sufficient to increase the risk of childhood NDDs. Co-occurrence of maternal diabetes, preterm deliveries, or fetal growth restriction further increases the risk.

Neuroimaging in Attention-Deficit/Hyperactivity Disorder: Recent Advances

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition, leading to impaired attention and impulsive behaviors diagnosed in, but not limited to, children. ADHD can cause symptoms throughout life. This article summarizes structural (conventional, volumetric, and diffusion tensor imaging MRI) and functional [task-based functional MRI (fMRI), resting state fMRI, PET, and MR spectroscopy] brain findings in patients with ADHD. Consensus is lacking regarding altered anatomic or functional imaging findings of the brain in children with ADHD, likely because of the disorder's heterogeneity. Most anatomic studies report abnormalities in the frontal lobes, basal ganglia, and corpus callosum; decreased surface area in the left ventral frontal and right prefrontal cortex; thinner medial temporal lobes; and smaller caudate nuclei. Using fMRI, researchers have focused on the prefrontal and temporal regions, reflecting perception-action mapping alterations. Artificial intelligence models evaluating brain anatomy have highlighted changes in cortical thickness and shape of the inferior frontal cortex, bilateral sensorimotor cortex, left temporal lobe, and insula. Early intervention and/or normal brain maturation can alter imaging patterns and convert functional imaging studies to a normal pattern. While the imaging findings provide insight into the disease's neuropathophysiology, no definitive structural or functional pattern defines the disorder from a neuroradiologic perspective.

Dissecting autism and schizophrenia through neuroimaging genomics

Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a 'top-down' approach, beginning with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In this review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioural and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions' phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes.

Autistic Traits and Attention-Deficit Hyperactivity Disorder Symptoms Predict the Severity of Internet Gaming Disorder in an Italian Adult Population

Over the last decade, internet gaming has been a fast-growing recreational activity. Gamers risk their leisure activity becoming an addiction. In the present study, we aimed to measure the prevalence of Internet Gaming Disorder (IGD) in an adult population of video game players and to investigate the association between demographic variables, Autism Spectrum Disorder (ASD) traits, Attention-Deficit Hyperactivity Disorder (ADHD) severity, and IGD in adults. Through an online survey, we recruited 4260 individuals aged between 18 and 55 years old, who were members of online communities of video gamers. We collected demographic data and administered three questionnaires: the Internet Gaming Disorder Scale-Short Form (IGD9-SF), the Autism Spectrum Quotient (AQ), and the Adult ADHD Self-Report Scale (ASRS). Of the overall sample, 29.67% scored above the cut-off of 21 points for the IGD9-SF. Multiple linear regression models showed that daily spare time, autistic traits, and ADHD symptoms were positively associated with the severity of IGD in adults, after controlling for demographic variables. Future studies are required in order to explore factors linked to IGD in adults.

Examining the Boundary Sharpness Coefficient as an Index of Cortical Microstructure in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is associated with atypical brain development. However, the phenotype of regionally specific increased cortical thickness observed in ASD may be driven by several independent biological processes that influence the gray/white matter boundary, such as synaptic pruning, myelination, or atypical migration. Here, we propose to use the boundary sharpness coefficient (BSC), a proxy for alterations in microstructure at the cortical gray/white matter boundary, to investigate brain differences in individuals with ASD, including factors that may influence ASD-related heterogeneity (age, sex, and intelligence quotient). Using a vertex-based meta-analysis and a large multicenter structural magnetic resonance imaging (MRI) dataset, with a total of 1136 individuals, 415 with ASD (112 female; 303 male), and 721 controls (283 female; 438 male), we observed that individuals with ASD had significantly greater BSC in the bilateral superior temporal gyrus and left inferior frontal gyrus indicating an abrupt transition (high contrast) between white matter and cortical intensities. Individuals with ASD under 18 had significantly greater BSC in the bilateral superior temporal gyrus and right postcentral gyrus; individuals with ASD over 18 had significantly increased BSC in the bilateral precuneus and superior temporal gyrus. Increases were observed in different brain regions in males and females, with larger effect sizes in females. BSC correlated with ADOS-2 Calibrated Severity Score in individuals with ASD in the right medial temporal pole. Importantly, there was a significant spatial overlap between maps of the effect of diagnosis on BSC when compared with cortical thickness. These results invite studies to use BSC as a possible new measure of cortical development in ASD and to further examine the microstructural underpinnings of BSC-related differences and their impact on measures of cortical morphology.

A Neurodevelopment Approach for a Transitional Model of Early Onset Schizophrenia

In the last decades, the conceptualization of schizophrenia has dramatically changed, moving from a neurodegenerative process occurring in early adult life to a neurodevelopmental disorder starting be-fore birth, showing a variety of premorbid and prodromal symptoms and, in relatively few cases, evolving in the full-blown psychotic syndrome. High rates of co-occurring different neurodevelopmental disorders such as Autism spectrum disorder and ADHD, predating the onset of SCZ, and neurobio-logical underpinning with significant similarities, support the notion of a pan-developmental disturbance consisting of impairments in neuromotor, receptive language, social and cognitive development. Con-sidering that many SCZ risk factors may be similar to symptoms of other neurodevelopmental psychi-atric disorders, transition processes from child & adolescent to adult systems of care should include both high risk people as well as subject with other neurodevelopmental psychiatric disorders with different levels of severity. This descriptive mini-review discuss the need of innovative clinical approaches, re-considering specific diagnostic categories, stimulating a careful analysis of risk factors and promoting the appropriate use of new and safer medications.

The Development of Brain Network in Males with Autism Spectrum Disorders from Childhood to Adolescence: Evidence from fNIRS Study

In the current study, functional near-infrared spectroscopy (fNIRS) was used to collect resting-state signals from 77 males with autism spectrum disorders (ASD, age: 6~16.25) and 40 typically developing (TD) males (age: 6~16.58) in the theory-of-mind (ToM) network. The graph theory analysis was used to obtain the brain network properties in ToM network, and the multiple regression analysis demonstrated that males with ASD showed a comparable global network topology, and a similar age-related decrease in the medial prefrontal cortex area (mPFC) compared to TD individuals. Nevertheless, participants with ASD showed U-shaped trajectories of nodal metrics of right temporo-parietal junction (TPJ), and an age-related decrease in the left middle frontal gyrus (MFG), while trajectories of TD participants were opposite. The nodal metrics of the right TPJ was negatively associated with the social deficits of ASD, while the nodal metrics of the left MFG was negatively associated with the communication deficits of ASD. Current findings suggested a distinct developmental trajectory of the ToM network in males with ASD from childhood to adolescence.

Association between the group III metabotropic glutamate receptor gene polymorphisms and attention-deficit/hyperactivity disorder and functional exploration of risk loci

Existing evidence suggests that the group III metabotropic glutamate receptor (mGluR) gene variations are involved in attention-deficit/hyperactivity disorder (ADHD), but few studies have fully explored this association. We conducted a case-control study with 617 cases and 636 controls to investigate the association between functional single-nucleotide polymorphisms (SNPs) from the group III mGluR gene polymorphisms (GRM4, GRM7, GRM8) and ADHD in the Chinese Han population and initially explored the function of positive SNPs. The GRM4 rs1906953 T genotype showed a significant association with a decreased risk of ADHD (TT:CC, OR = 0.55, 95% CI = 0.40-0.77; recessive model, OR = 0.58, 95% CI = 0.43-0.78). GRM7 rs9826579 C showed a significant association with an increased risk of ADHD (TC:TT, OR = 1.81, 95% CI = 1.39-2.36; dominant model, OR = 1.74, 95% CI = 1.35-2.24; additive model, OR = 1.56, 95% CI = 1.24-1.97). In addition, compared with subjects with the rs1906953 TT genotype, subjects with of the CC genotype showed more obvious attention deficit behaviours and hyperactivity/impulsive behaviours. Dual-luciferase reporter gene assays showed that a promoter reporter with the rs1906953 TT genotype significantly decreased luciferase activity compared with the CC genotype. According to electrophoretic mobility shift assays, the binding capacity of rs1906953 T probe with nucleoprotein was lower than that of the rs1906953 C probe. Our results revealed the association of GRM4 rs1906953 and GRM7 rs9826579 with ADHD. Moreover, we found that rs1906953 disturbs the transcriptional activity of GRM4.

Autism Spectrum Disorder and Childhood Apraxia of Speech: Early Language-Related Hallmarks across Structural MRI Study

Autism Spectrum Disorder (ASD) and Childhood Apraxia of Speech (CAS) are developmental disorders with distinct diagnostic criteria and different epidemiology. However, a common genetic background as well as overlapping clinical features between ASD and CAS have been recently reported. To date, brain structural language-related abnormalities have been detected in both the conditions, but no study directly compared young children with ASD, CAS and typical development (TD). In the current work, we aim: (i) to test the hypothesis that ASD and CAS display neurostructural differences in comparison with TD through morphometric Magnetic Resonance Imaging (MRI)-based measures (ASD vs. TD and CAS vs. TD); (ii) to investigate early possible disease-specific brain structural patterns in the two clinical groups (ASD vs. CAS); (iii) to evaluate predictive power of machine-learning (ML) techniques in differentiating the three samples (ASD, CAS, TD). We retrospectively analyzed the T1-weighted brain MRI scans of 68 children (age range: 34-74 months) grouped into three cohorts: (1) 26 children with ASD (mean age ± standard deviation: 56 ± 11 months); (2) 24 children with CAS (57 ± 10 months); (3) 18 children with TD (55 ± 13 months). Furthermore, a ML analysis based on a linear-kernel Support Vector Machine (SVM) was performed. All but one brain structures displayed significant higher volumes in both ASD and CAS children than TD peers. Specifically, ASD alterations involved fronto-temporal regions together with basal ganglia and cerebellum, while CAS alterations are more focused and shifted to frontal regions, suggesting a possible speech-related anomalies distribution. Caudate, superior temporal and hippocampus volumes directly distinguished the two conditions in terms of greater values in ASD compared to CAS. The ML analysis identified significant differences in brain features between ASD and TD children, whereas only some trends in the ML classification capability were detected in CAS as compared to TD peers. Similarly, the MRI structural underpinnings of two clinical groups were not significantly different when evaluated with linear-kernel SVM. Our results may represent the first step towards understanding shared and specific neural substrate in ASD and CAS conditions, which subsequently may contribute to early differential diagnosis and tailoring specific early intervention.

Potential role of primed microglia during obesity on the mesocorticolimbic circuit in autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disease which involves functional and structural defects in selective central nervous system (CNS) regions that harm function and individual ability to process and respond to external stimuli. Individuals with ASD spend less time engaging in social interaction compared to non-affected subjects. Studies employing structural and functional magnetic resonance imaging reported morphological and functional abnormalities in the connectivity of the mesocorticolimbic reward pathway between the nucleus accumbens and the ventral tegmental area (VTA) in response to social stimuli, as well as diminished medial prefrontal cortex in response to visual cues, whereas stronger reward system responses for the non-social realm (e.g., video games) than social rewards (e.g., approval), associated with caudate nucleus responsiveness in ASD children. Defects in the mesocorticolimbic reward pathway have been modulated in transgenic murine models using D2 dopamine receptor heterozygous (D2+/-) or dopamine transporter knockout mice, which exhibit sociability deficits and repetitive behaviors observed in ASD phenotypes. Notably, the mesocorticolimbic reward pathway is modulated by systemic and central inflammation, such as primed microglia, which occurs during obesity or maternal overnutrition. Therefore, we propose that a positive energy balance during obesity/maternal overnutrition coordinates a systemic and central inflammatory crosstalk that modulates the dopaminergic neurotransmission in selective brain areas of the mesocorticolimbic reward pathway. Here, we will describe how obesity/maternal overnutrition may prime microglia, causing abnormalities in dopamine neurotransmission of the mesocorticolimbic reward pathway, postulating a possible immune role in the development of ASD.

Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis

Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when coupled with machine learning, can assist the diagnostic decision and increase its reliability. Sixty-four schizophrenia, 36 autism spectrum disorder (ASD), and 106 typically developing individuals were analyzed. FreeSurfer was used to obtain the data from the participant's brain scans. Six classifiers were utilized to classify the subjects. Subsequently, 26 ultra-high risk for psychosis (UHR) and 17 first-episode psychosis (FEP) subjects were run through the trained classifiers. Lastly, the classifiers' output of the patient groups was correlated with their clinical severity. All six classifiers performed relatively well to distinguish the subject groups, especially support vector machine (SVM) and Logistic regression (LR). Cortical thickness and subcortical volume feature groups were most useful for the classification. LR and SVM were highly consistent with clinical indices of ASD. When UHR and FEP groups were run with the trained classifiers, majority of the cases were classified as schizophrenia, none as ASD. Overall, SVM and LR were the best performing classifiers. Cortical thickness and subcortical volume were most useful for the classification, compared to surface area. LR, SVM, and DT's output were clinically informative. The trained classifiers were able to help predict the diagnostic category of both UHR and FEP Individuals.

Cortical thickness and formal thought disorder in schizophrenia: An ultra high-field network-based morphometry study

BACKGROUND

Persistent formal thought disorder (FTD) is a core feature of schizophrenia. Recent cognitive and neuroimaging studies indicate a distinct mechanistic pathway underlying the persistent positive FTD (pFTD or disorganized thinking), though its structural determinants are still elusive. Using network-based cortical thickness estimates from ultra-high field 7-Tesla Magnetic Resonance Imaging (7T MRI), we investigated the structural correlates of pFTD.

METHODS

We obtained speech samples and 7T MRI anatomical scans from medicated clinically stable patients with schizophrenia (n = 19) and healthy controls (n = 20). Network-based morphometry was used to estimate the mean cortical thickness of 17 functional networks covering the entire cortical surface from each subject. We also quantified the vertexwise variability of thickness within each network to quantify the spatial coherence of the 17 networks, estimated patients vs. controls differences, and related the thickness of the affected networks to the severity of pFTD.

RESULTS

Patients had reduced thickness of the frontoparietal and default mode networks, and reduced spatial coherence affecting the salience and the frontoparietal control network. A higher burden of positive FTD related to reduced frontoparietal thickness and reduced spatial coherence of the salience network. The presence of positive FTD, but not its severity, related to the reduced thickness of the language network comprising of the superior temporal cortex.

CONCLUSIONS

These results suggest that cortical thickness of both cognitive control and language networks underlie the positive FTD in schizophrenia. The structural integrity of cognitive control networks is a critical determinant of the expressed severity of persistent FTD in schizophrenia.

Machine Learning for Differential Diagnosis Between Clinical Conditions With Social Difficulty: Autism Spectrum Disorder, Early Psychosis, and Social Anxiety Disorder

Differential diagnosis in adult cohorts with social difficulty is confounded by comorbid mental health conditions, common etiologies, and shared phenotypes. Identifying shared and discriminating profiles can facilitate intervention and remediation strategies. The objective of the study was to identify salient features of a composite test battery of cognitive and mood measures using a machine learning paradigm in clinical cohorts with social interaction difficulties. We recruited clinical participants who met standardized diagnostic criteria for autism spectrum disorder (ASD: n = 62), early psychosis (EP: n = 48), or social anxiety disorder (SAD: N = 83) and compared them with a neurotypical comparison group (TYP: N = 43). Using five machine-learning algorithms and repeated cross-validation, we trained and tested classification models using measures of cognitive and executive function, lower- and higher-order social cognition and mood severity. Performance metrics were the area under the curve (AUC) and Brier Scores. Sixteen features successfully differentiated between the groups. The control versus social impairment cohorts (ASD, EP, SAD) were differentiated by social cognition, visuospatial memory and mood measures. Importantly, a distinct profile cluster drawn from social cognition, visual learning, executive function and mood, distinguished the neurodevelopmental cohort (EP and ASD) from the SAD group. The mean AUC range was between 0.891 and 0.916 for social impairment versus control cohorts and, 0.729 to 0.781 for SAD vs neurodevelopmental cohorts. This is the first study that compares an extensive battery of neuropsychological and self-report measures using a machine learning protocol in clinical and neurodevelopmental cohorts characterized by social impairment. Findings are relevant for diagnostic, intervention and remediation strategies for these groups.

Social Cognition in a Research Domain Criteria Perspective: A Bridge Between Schizophrenia and Autism Spectra Disorders

Schizophrenia and autism spectra disorders are currently conceptualized as distinct clinical categories. However, the relationship between these two nosological entities has been revisited in recent years due to the evidence that they share some important clinical and neurobiological features, putting into question the nature and the extent of their commonalities and differences. In this respect, some core symptoms that are present in both disorders, such as social cognitive deficits, could be a primary target of investigation. This review briefly summarizes the commonalities and overlapping features between schizophrenia and autism spectra disorders in social cognitive functions, considering this construct in a Research Domain Criteria perspective. The clinical manifestation of deficits in social cognition are similar in schizophrenia spectrum disorders and autism spectrum disorders, and brain areas that appear to be altered in relation to these impairments are largely shared; however, the results of various studies suggest that, in some cases, the qualitative nature of these alterations may be different in the two spectra. Moreover, relevant differences could be present at the level of brain networks and connections. More research is required in this field, regarding molecular and genetic aspects of both spectra, to better define the neurobiological mechanisms involved in social cognition deficits, with the objective of developing specific and targeted treatments.

Endogenous Retroviruses Activity as a Molecular Signature of Neurodevelopmental Disorders

Human endogenous retroviruses (HERVs) are genetic elements resulting from relics of ancestral infection of germline cells, now recognized as cofactors in the etiology of several complex diseases. Here we present a review of findings supporting the role of the abnormal HERVs activity in neurodevelopmental disorders. The derailment of brain development underlies numerous neuropsychiatric conditions, likely starting during prenatal life and carrying on during subsequent maturation of the brain. Autism spectrum disorders, attention deficit hyperactivity disorders, and schizophrenia are neurodevelopmental disorders that arise clinically during early childhood or adolescence, currently attributed to the interplay among genetic vulnerability, environmental risk factors, and maternal immune activation. The role of HERVs in human embryogenesis, their intrinsic responsiveness to external stimuli, and the interaction with the immune system support the involvement of HERVs in the derailed neurodevelopmental process. Although definitive proofs that HERVs are involved in neurobehavioral alterations are still lacking, both preclinical models and human studies indicate that the abnormal expression of ERVs could represent a neurodevelopmental disorders-associated biological trait in affected individuals and their parents.

Children With Autism Spectrum Disorder and Their Mothers Share Abnormal Expression of Selected Endogenous Retroviruses Families and Cytokines

The Autism Spectrum Disorder (ASD) is a heterogeneous group of neurodevelopmental disorders, only clinically diagnosed since the lack of reliable biomarkers. Autism etiology is probably attributable to the combination of genetic vulnerability and environmental factors, and recently, maternal immune activation has been linked to derailed neurodevelopment, resulting in ASD in the offspring. Human endogenous retroviruses (HERVs) are relics of ancestral infections, stably integrated in the human DNA. Given the HERV persistence in the genome, some of HERVs have been co-opted for physiological functions during evolution, while their reactivation has been associated with several pathological conditions, including cancer, autoimmune, and neurological and psychiatric disorders. Particularly, due to their intrinsic responsiveness to external stimuli, HERVs can modulate the host immune response and in turn HERVs can be activated by the immune effectors. In previous works we demonstrated high expression levels of HERV-H in blood of autistic patients, closely related with the severity of the disease. Moreover, in a preclinical ASD model we proved changes of expression of several ERV families and cytokines from the intrauterine life to the adulthood, and across generations via maternal lineage. Here we analyzed the expression of HEMO and of selected HERVs and cytokines in blood from ASD patients and their parents and corresponding healthy controls, to look for a common molecular trait within family members. ASD patients and their mothers share altered expression of HERV-H and HEMO and of cytokines such as TNF-α, IFN-γ, IL-10. The multivariate regression models showed a mother-child association by HEMO activity and demonstrated in children and mothers an association between HERV-H and HEMO expression and, only in mothers, between HEMO, and TNF-α expression. Furthermore, high diagnostic performance for HERV-H and HEMO was found, suggesting their potential application for the identification of ASD children and their mothers. The present data support the involvement of HERVs in ASD and suggest HERVs and cytokines as ASD-associated traits. Since ASD is a heterogeneous group of neurodevelopmental disorders, a single determinant alone could be not enough to account for the complexity, and HERV/cytokines expression could be considered in a set of biomarkers, easily detectable in blood, and potentially useful for an early diagnosis.

Dynamic functional connectivity in schizophrenia and autism spectrum disorder: Convergence, divergence and classification

BACKGROUND

Over the recent years there has been a growing debate regarding the extent and nature of the overlap in neuropathology between schizophrenia (SZ) and autism spectrum disorder (ASD). Dynamic functional network connectivity (dFNC) is a recent analysis method that explores temporal patterns of functional connectivity (FC). We compared resting-state dFNC in SZ, ASD and healthy controls (HC), characterized the associations between temporal patterns and symptoms, and performed a three-way classification analysis based on dFNC indices.

METHODS

Resting-state fMRI was collected from 100 young adults: 33 SZ, 33 ASD, 34 HC. Independent component analysis (ICA) was performed, followed by dFNC analysis (window = 33 s, step = 1TR, k-means clustering). Temporal patterns were compared between groups, correlated with symptoms, and classified via cross-validated three-way discriminant analysis.

RESULTS

Both clinical groups displayed an increased fraction of time (FT) spent in a state of weak, intra-network connectivity [p < .001] and decreased FT in a highly-connected state [p < .001]. SZ further showed decreased number of transitions between states [p < .001], decreased FT in a widely-connected state [p < .001], increased dwell time (DT) in the weakly-connected state [p < .001], and decreased DT in the highly-connected state [p = .001]. Social behavior scores correlated with DT in the widely-connected state in SZ [r = 0.416, p = .043], but not ASD. Classification correctly identified SZ at high rates (81.8%), while ASD and HC at lower rates.

CONCLUSIONS

Results indicate a severe and pervasive pattern of temporal aberrations in SZ (specifically, being "stuck" in a state of weak connectivity), that distinguishes SZ participants from both ASD and HC, and is associated with clinical symptoms.

Transdiagnostic neuroimaging in psychiatry: A review

Transdiagnostic approach has a long history in neuroimaging, predating its recent ascendance as a paradigm for new psychiatric nosology. Various psychiatric disorders have been compared for commonalities and differences in neuroanatomical features and activation patterns, with different aims and rationales. This review covers both structural and functional neuroimaging publications with direct comparison of different psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, conduct disorder, anorexia nervosa, and bulimia nervosa. Major findings are systematically presented along with specific rationales for each comparison.

Commentary on… the overlapping and distinct resting functional connectivity between autism spectrum disorder and attention-deficit hyperactivity disorder

Altered neural connectivity in neurodevelopmental disorders is likely subtle, meaning that neuroimaging literature studying development has produced heterogeneous findings. A recent study, published in this issue, illustrates the translational potential of functional connectivity magnetic resonance imaging findings as a biomarker for attention-deficit hyperactivity disorder and autism spectrum disorder. Importantly, it highlights the overlap between disorders, emphasising the need for transdiagnostic and dimensional approaches in neurodevelopment.Declaration of interestNone.

Neural cell adhesion molecule Negr1 deficiency in mouse results in structural brain endophenotypes and behavioral deviations related to psychiatric disorders

Neuronal growth regulator 1 (NEGR1) belongs to the immunoglobulin (IgLON) superfamily of cell adhesion molecules involved in cortical layering. Recent functional and genomic studies implicate the role of NEGR1 in a wide spectrum of psychiatric disorders, such as major depression, schizophrenia and autism. Here, we investigated the impact of Negr1 deficiency on brain morphology, neuronal properties and social behavior of mice. In situ hybridization shows Negr1 expression in the brain nuclei which are central modulators of cortical-subcortical connectivity such as the island of Calleja and the reticular nucleus of thalamus. Brain morphological analysis revealed neuroanatomical abnormalities in Negr1^−/− mice, including enlargement of ventricles and decrease in the volume of the whole brain, corpus callosum, globus pallidus and hippocampus. Furthermore, decreased number of parvalbumin-positive inhibitory interneurons was evident in Negr1^−/− hippocampi. Behaviorally, Negr1^−/− mice displayed hyperactivity in social interactions and impairments in social hierarchy. Finally, Negr1 deficiency resulted in disrupted neurite sprouting during neuritogenesis. Our results provide evidence that NEGR1 is required for balancing the ratio of excitatory/inhibitory neurons and proper formation of brain structures, which is prerequisite for adaptive behavioral profiles. Therefore, Negr1^−/− mice have a high potential to provide new insights into the neural mechanisms of neuropsychiatric disorders.

The Protracted Maturation of Associative Layer IIIC Pyramidal Neurons in the Human Prefrontal Cortex During Childhood: A Major Role in Cognitive Development and Selective Alteration in Autism

The human specific cognitive shift starts around the age of 2 years with the onset of self-awareness, and continues with extraordinary increase in cognitive capacities during early childhood. Diffuse changes in functional connectivity in children aged 2-6 years indicate an increase in the capacity of cortical network. Interestingly, structural network complexity does not increase during this time and, thus, it is likely to be induced by selective maturation of a specific neuronal subclass. Here, we provide an overview of a subclass of cortico-cortical neurons, the associative layer IIIC pyramids of the human prefrontal cortex. Their local axonal collaterals are in control of the prefrontal cortico-cortical output, while their long projections modulate inter-areal processing. In this way, layer IIIC pyramids are the major integrative element of cortical processing, and changes in their connectivity patterns will affect global cortical functioning. Layer IIIC neurons have a unique pattern of dendritic maturation. In contrast to other classes of principal neurons, they undergo an additional phase of extensive dendritic growth during early childhood, and show characteristic molecular changes. Taken together, circuits associated with layer IIIC neurons have the most protracted period of developmental plasticity. This unique feature is advanced but also provides a window of opportunity for pathological events to disrupt normal formation of cognitive circuits involving layer IIIC neurons. In this manuscript, we discuss how disrupted dendritic and axonal maturation of layer IIIC neurons may lead into global cortical disconnectivity, affecting development of complex communication and social abilities. We also propose a model that developmentally dictated incorporation of layer IIIC neurons into maturing cortico-cortical circuits between 2 to 6 years will reveal a previous (perinatal) lesion affecting other classes of principal neurons. This "disclosure" of pre-existing functionally silent lesions of other neuronal classes induced by development of layer IIIC associative neurons, or their direct alteration, could be found in different forms of autism spectrum disorders. Understanding the gene-environment interaction in shaping cognitive microcircuitries may be fundamental for developing rehabilitation and prevention strategies in autism spectrum and other cognitive disorders.

ADGRL3 rs6551665 as a Common Vulnerability Factor Underlying Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder

Neurodevelopmental disorders are prevalent, frequently occur in comorbidity and share substantial genetic correlation. Previous evidence has suggested a role for the ADGRL3 gene in Attention-Deficit/Hyperactivity Disorder (ADHD) susceptibility in several samples. Considering ADGRL3 functionality in central nervous system development and its previous association with neurodevelopmental disorders, we aimed to assess ADGRL3 influence in early-onset ADHD (before 7 years of age) and Autism Spectrum Disorder (ASD). The sample comprises 187 men diagnosed with early-onset ADHD, 135 boys diagnosed with ASD and 468 male blood donors. We tested the association of an ADGRL3 variant (rs6551665) with both early-onset ADHD and ASD susceptibility. We observed significant associations between ADGRL3-rs6551665 on ADHD and ASD susceptibilities; we found that G-carriers were at increased risk of ADHD and ASD, in accordance with previous studies. The overall evidence from the literature, corroborated by our results, suggests that ADGRL3 might be involved in brain development, and genetic modifications related to it might be part of a shared vulnerability factor associated with the underlying neurobiology of neurodevelopmental disorders such as ADHD and ASD.

Social difficulties in youth with autism with and without anxiety and ADHD symptoms

Social difficulties inherent to autism spectrum disorder are often linked with co-occurring symptoms of anxiety and attention deficit hyperactivity disorder (ADHD). The present study sought to examine the relation between such co-occurring symptoms and social challenges. Parents of adolescents with autism (N = 113) reported upon social challenges via the social responsiveness scale (SRS) and anxiety and ADHD symptomatology via the Child Behavior Checklist. Results revealed differences in SRS scores across co-occurring symptom subgroups (Anxiety, ADHD, Both, Neither)-namely, adolescents with autism and anxiety as well as those with autism, anxiety, and ADHD showed greater scores on the SRS than the other groups. Implications for research and clinical practice are discussed and recommendations are offered. Autism Research 2018, 11: 1679-1689. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Anxiety and attention deficit hyperactivity disorder (ADHD) symptoms are related to greater social challenges for adolescents with autism spectrum disorder. The present study found that autism with anxiety and autism with anxiety and ADHD, was related to greater social difficulties than autism alone. Findings provide further support for the intertwined nature of anxiety and ADHD symptoms in autism. What this may mean for research and clinical practice is considered and recommendations are suggested.

Zika Virus as a Possible Risk Factor for Autism Spectrum Disorder: Neuroimmunological Aspects

The recent outbreak of the Zika virus (ZIKV) and the discovery that perinatal Zika exposure can lead to the Congenital Zika Syndrome has promoted a call for prevention measures. Due to the increased number of babies born with microcephaly, structural brain abnormalities, and neurological alterations in regions affected by ZIKV, investigations were carried out in order to better understand this process. The maternal immune system directly influences the fetal central nervous system, and complications during pregnancy have been associated with neurodevelopmental disorders. Autism spectrum disorder (ASD), a neurodevelopmental disorder commonly manifested in the first years of life, is a disease with multifactorial etiology and is manifested typically by social and communication impairments, as well as stereotyped behaviors. Brain abnormalities, including both anatomically and functionally, can be observed in this disorder, suggesting delays in neuronal maturation and altered brain connectivity. It is known that some viral congenital infections, such as rubella, and cytomegalovirus can interfere with brain development, being associated with brain calcification, microcephaly, and ASD. Here, we reviewed a range of studies evaluating the aspects concerning brain development, immunological status during pregnancy, and neuroimmunomodulation in congenital viral infections, and we discuss if the fetal brain infection caused by ZIKV could predispose to ASD. Finally, we suggest a mechanism encompassing neurological and immunological pathways that could play a role in the development of ASD in infants after ZIKV infection in pregnancy.