Genetic architecture of the structural connectome.

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.

Gray matter covariations in autism: out-of-sample replication using the ENIGMA autism cohort.

BACKGROUND: Autism spectrum disorder (henceforth autism) is a complex neurodevelopmental condition associated with differences in gray matter (GM) volume covariations, as reported in our previous study of the Longitudinal European Autism Project (LEAP) data. To make progress on the identification of potential neural markers and to validate the robustness of our previous findings, we aimed to replicate our results using data from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) autism working group. METHODS: We studied 781 autistic and 927 non-autistic individuals (6-30 years, IQ ≥ 50), across 37 sites. Voxel-based morphometry was used to quantify GM volume as before. Subsequently, we used spatial maps of the two autism-related independent components (ICs) previously identified in the LEAP sample as templates for regression analyses to separately estimate the ENIGMA-participant loadings to each of these two ICs. Between-group differences in participants' loadings on each component were examined, and we additionally investigated the relation between participant loadings and autistic behaviors within the autism group. RESULTS: The two components of interest, previously identified in the LEAP dataset, showed significant between-group differences upon regressions into the ENIGMA cohort. The associated brain patterns were consistent with those found in the initial identification study. The first IC was primarily associated with increased volumes of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and caudate in the autism group relative to the control group (ß = 0.129, p = 0.013). The second IC was related to increased volumes of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to non-autistic individuals (ß = 0.116, p = 0.024). However, when accounting for the site-by-group interaction effect, no significant main effect of the group can be identified (p > 0.590). We did not find significant univariate association between the brain measures and behavior in autism (p > 0.085). LIMITATIONS: The distributions of age, IQ, and sex between LEAP and ENIGMA are statistically different from each other. Owing to limited access to the behavioral data of the autism group, we were unable to further our understanding of the neural basis of behavioral dimensions of the sample. CONCLUSIONS: The current study is unable to fully replicate the autism-related brain patterns from LEAP in the ENIGMA cohort. The diverse group effects across ENIGMA sites demonstrate the challenges of generalizing the average findings of the GM covariation patterns to a large-scale cohort integrated retrospectively from multiple studies. Further analyses need to be conducted to gain additional insights into the generalizability of these two GM covariation patterns.

Brain metabolite levels in remitted psychotic depression with consideration of effects of antipsychotic medication.

BACKGROUND: The neurobiology of psychotic depression is not well understood and can be confounded by antipsychotics. Magnetic resonance spectroscopy (MRS) is an ideal tool to measure brain metabolites non-invasively. We cross-sectionally assessed brain metabolites in patients with remitted psychotic depression and controls. We also longitudinally assessed the effects of olanzapine versus placebo on brain metabolites. METHODS: Following remission, patients with psychotic depression were randomized to continue sertraline + olanzapine (n = 15) or switched to sertraline + placebo (n = 18), at which point they completed an MRS scan. Patients completed a second scan either 36 weeks later, relapse, or discontinuation. Where water-scaled metabolite levels were obtained and a Point-RESolved Spectroscopy sequence was utilized, choline, myo-inositol, glutamate + glutamine (Glx), N-acetylaspartate, and creatine were measured in the left dorsolateral prefrontal cortex (L-DLPFC) and dorsal anterior cingulate cortex (dACC). An ANCOVA was used to compare metabolites between patients (n = 40) and controls (n = 46). A linear mixed-model was used to compare olanzapine versus placebo groups. RESULTS: Cross-sectionally, patients (compared to controls) had higher myo-inositol (standardized mean difference [SMD] = 0.84; 95%CI = 0.25-1.44; p = 0.005) in the dACC but not different Glx, choline, N-acetylaspartate, and creatine. Longitudinally, patients randomized to placebo (compared to olanzapine) showed a significantly greater change with a reduction of creatine (SMD = 1.51; 95%CI = 0.71-2.31; p = 0.0002) in the dACC but not glutamate + glutamine, choline, myo-inositol, and N-acetylaspartate. CONCLUSIONS: Patients with remitted psychotic depression have higher myo-inositol than controls. Olanzapine may maintain creatine levels. Future studies are needed to further disentangle the mechanisms of action of olanzapine.

Linking functional and structural brain organisation with behaviour in autism: a multimodal EU-AIMS Longitudinal European Autism Project (LEAP) study.

Neuroimaging analyses of brain structure and function in autism have typically been conducted in isolation, missing the sensitivity gains of linking data across modalities. Here we focus on the integration of structural and functional organisational properties of brain regions. We aim to identify novel brain-organisation phenotypes of autism. We utilised multimodal MRI (T1-, diffusion-weighted and resting state functional), behavioural and clinical data from the EU AIMS Longitudinal European Autism Project (LEAP) from autistic (n = 206) and non-autistic (n = 196) participants. Of these, 97 had data from 2 timepoints resulting in a total scan number of 466. Grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps were extracted from respective MRI modalities and were then integrated with Linked Independent Component Analysis. Linear mixed-effects models were used to evaluate the relationship between components and group while accounting for covariates and non-independence of participants with longitudinal data. Additional models were run to investigate associations with dimensional measures of behaviour. We identified one component that differed significantly between groups (coefficient = 0.33, padj = 0.02). This was driven (99%) by variance of the right fusiform gyrus connectopic map 2. While there were multiple nominal (uncorrected p < 0.05) associations with behavioural measures, none were significant following multiple comparison correction. Our analysis considered the relative contributions of both structural and functional brain phenotypes simultaneously, finding that functional phenotypes drive associations with autism. These findings expanded on previous unimodal studies by revealing the topographic organisation of functional connectivity patterns specific to autism and warrant further investigation.

Fine-grained topographic organization within somatosensory cortex during resting-state and emotional face-matching task and its association with ASD traits.

Sensory atypicalities are particularly common in autism spectrum disorders (ASD). Nevertheless, our knowledge about the divergent functioning of the underlying somatosensory region and its association with ASD phenotype features is limited. We applied a data-driven approach to map the fine-grained variations in functional connectivity of the primary somatosensory cortex (S1) to the rest of the brain in 240 autistic and 164 neurotypical individuals from the EU-AIMS LEAP dataset, aged between 7 and 30. We estimated the S1 connection topography ('connectopy') at rest and during the emotional face-matching (Hariri) task, an established measure of emotion reactivity, and accessed its association with a set of clinical and behavioral variables. We first demonstrated that the S1 connectopy is organized along a dorsoventral axis, mapping onto the S1 somatotopic organization. We then found that its spatial characteristics were linked to the individuals' adaptive functioning skills, as measured by the Vineland Adaptive Behavior Scales, across the whole sample. Higher functional differentiation characterized the S1 connectopies of individuals with higher daily life adaptive skills. Notably, we detected significant differences between rest and the Hariri task in the S1 connectopies, as well as their projection maps onto the rest of the brain suggesting a task-modulating effect on S1 due to emotion processing. All in all, variation of adaptive skills appears to be reflected in the brain's mesoscale neural circuitry, as shown by the S1 connectivity profile, which is also differentially modulated during rest and emotional processing.

Systematic comparisons of different quality control approaches applied to three large pediatric neuroimaging datasets.

INTRODUCTION: Poor quality T1-weighted brain scans systematically affect the calculation of brain measures. Removing the influence of such scans requires identifying and excluding scans with noise and artefacts through a quality control (QC) procedure. While QC is critical for brain imaging analyses, it is not yet clear whether different QC approaches lead to the exclusion of the same participants. Further, the removal of poor-quality scans may unintentionally introduce a sampling bias by excluding the subset of participants who are younger and/or feature greater clinical impairment. This study had two aims: (1) examine whether different QC approaches applied to T1-weighted scans would exclude the same participants, and (2) examine how exclusion of poor-quality scans impacts specific demographic, clinical and brain measure characteristics between excluded and included participants in three large pediatric neuroimaging samples. METHODS: We used T1-weighted, resting-state fMRI, demographic and clinical data from the Province of Ontario Neurodevelopmental Disorders network (Aim 1: n = 553, Aim 2: n = 465), the Healthy Brain Network (Aim 1: n = 1051, Aim 2: n = 558), and the Philadelphia Neurodevelopmental Cohort (Aim 1: n = 1087; Aim 2: n = 619). Four different QC approaches were applied to T1-weighted MRI (visual QC, metric QC, automated QC, fMRI-derived QC). We used tetrachoric correlation and inter-rater reliability analyses to examine whether different QC approaches excluded the same participants. We examined differences in age, mental health symptoms, everyday/adaptive functioning, IQ and structural MRI-derived brain indices between participants that were included versus excluded following each QC approach. RESULTS: Dataset-specific findings revealed mixed results with respect to overlap of QC exclusion. However, in POND and HBN, we found a moderate level of overlap between visual and automated QC approaches (rtet=0.52-0.59). Implementation of QC excluded younger participants, and tended to exclude those with lower IQ, and lower everyday/adaptive functioning scores across several approaches in a dataset-specific manner. Across nearly all datasets and QC approaches examined, excluded participants had lower estimates of cortical thickness and subcortical volume, but this effect did not differ by QC approach. CONCLUSION: The results of this study provide insight into the influence of QC decisions on structural pediatric imaging analyses. While different QC approaches exclude different subsets of participants, the variation of influence of different QC approaches on clinical and brain metrics is minimal in large datasets. Overall, implementation of QC tends to exclude participants who are younger, and those who have more cognitive and functional impairment. Given that automated QC is standardized and can reduce between-study differences, the results of this study support the potential to use automated QC for large pediatric neuroimaging datasets.

Excitatory/inhibitory imbalance in autism: the role of glutamate and GABA gene-sets in symptoms and cortical brain structure.

The excitatory/inhibitory (E/I) imbalance hypothesis posits that imbalance between excitatory (glutamatergic) and inhibitory (GABAergic) mechanisms underlies the behavioral characteristics of autism. However, how E/I imbalance arises and how it may differ across autism symptomatology and brain regions is not well understood. We used innovative analysis methods-combining competitive gene-set analysis and gene-expression profiles in relation to cortical thickness (CT) to investigate relationships between genetic variance, brain structure and autism symptomatology of participants from the AIMS-2-TRIALS LEAP cohort (autism = 359, male/female = 258/101; neurotypical control participants = 279, male/female = 178/101) aged 6-30 years. Using competitive gene-set analyses, we investigated whether aggregated genetic variation in glutamate and GABA gene-sets could be associated with behavioral measures of autism symptoms and brain structural variation. Further, using the same gene-sets, we corelated expression profiles throughout the cortex with differences in CT between autistic and neurotypical control participants, as well as in separate sensory subgroups. The glutamate gene-set was associated with all autism symptom severity scores on the Autism Diagnostic Observation Schedule-2 (ADOS-2) and the Autism Diagnostic Interview-Revised (ADI-R) within the autistic group. In adolescents and adults, brain regions with greater gene-expression of glutamate and GABA genes showed greater differences in CT between autistic and neurotypical control participants although in opposing directions. Additionally, the gene expression profiles were associated with CT profiles in separate sensory subgroups. Our results suggest complex relationships between E/I related genetics and autism symptom profiles as well as brain structure alterations, where there may be differential roles for glutamate and GABA.

The Link Between Autism and Sex-Related Neuroanatomy, and Associated Cognition and Gene Expression.

OBJECTIVE: The male preponderance in prevalence of autism is among the most pronounced sex ratios across neurodevelopmental conditions. The authors sought to elucidate the relationship between autism and typical sex-differential neuroanatomy, cognition, and related gene expression. METHODS: Using a novel deep learning framework trained to predict biological sex based on T1-weighted structural brain images, the authors compared sex prediction model performance across neurotypical and autistic males and females. Multiple large-scale data sets comprising T1-weighted MRI data were employed at four stages of the analysis pipeline: 1) pretraining, with the UK Biobank sample (>10,000 individuals); 2) transfer learning and validation, with the ABIDE data sets (1,412 individuals, 5-56 years of age); 3) test and discovery, with the EU-AIMS/AIMS-2-TRIALS LEAP data set (681 individuals, 6-30 years of age); and 4) specificity, with the NeuroIMAGE and ADHD200 data sets (887 individuals, 7-26 years of age). RESULTS: Across both ABIDE and LEAP, features positively predictive of neurotypical males were on average significantly more predictive of autistic males (ABIDE: Cohen's d=0.48; LEAP: Cohen's d=1.34). Features positively predictive of neurotypical females were on average significantly less predictive of autistic females (ABIDE: Cohen's d=1.25; LEAP: Cohen's d=1.29). These differences in sex prediction accuracy in autism were not observed in individuals with ADHD. In autistic females, the male-shifted neurophenotype was further associated with poorer social sensitivity and emotional face processing while also associated with gene expression patterns of midgestational cell types. CONCLUSIONS: The results demonstrate an increased resemblance in both autistic male and female individuals' neuroanatomy with male-characteristic patterns associated with typically sex-differential social cognitive features and related gene expression patterns. The findings hold promise for future research aimed at refining the quest for biological mechanisms underpinning the etiology of autism.

Autism Is Associated With Interindividual Variations of Gray and White Matter Morphology.

BACKGROUND: Although many studies have explored atypicalities in gray matter (GM) and white matter (WM) morphology of autism, most of them relied on unimodal analyses that did not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between individuals with and without autism and explore associations between these brain patterns and clinical measures in the autism group. METHODS: We studied 183 individuals with autism and 157 nonautistic individuals (age range, 6-30 years) in a large, deeply phenotyped autism dataset (EU-AIMS LEAP [European Autism Interventions-A Multicentre Study for Developing New Medications Longitudinal European Autism Project]). Linked independent component analysis was used to link all participants' GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relationships between brain patterns and clinical profiles. RESULTS: One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula and frontal, precentral and postcentral, cingulate, and caudate areas and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM and symptoms of restricted and repetitive behavior in the autism group. CONCLUSIONS: Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.

Cortico-amygdalar connectivity and externalizing/internalizing behavior in children with neurodevelopmental disorders.

BACKGROUND: Externalizing and internalizing behaviors contribute to clinical impairment in children with neurodevelopmental disorders (NDDs). Although associations between externalizing or internalizing behaviors and cortico-amygdalar connectivity have been found in clinical and non-clinical pediatric samples, no previous study has examined whether similar shared associations are present across children with different NDDs. METHODS: Multi-modal neuroimaging and behavioral data from the Province of Ontario Neurodevelopmental Disorders (POND) Network were used. POND participants aged 6-18 years with a primary diagnosis of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or obsessive-compulsive disorder (OCD), as well as typically developing children (TDC) with T1-weighted, resting-state fMRI or diffusion weighted imaging (DWI) and parent-report Child Behavioral Checklist (CBCL) data available, were analyzed (total n = 346). Associations between externalizing or internalizing behavior and cortico-amygdalar structural and functional connectivity indices were examined using linear regressions, controlling for age, gender, and image-modality specific covariates. Behavior-by-diagnosis interaction effects were also examined. RESULTS: No significant linear associations (or diagnosis-by-behavior interaction effects) were found between CBCL-measured externalizing or internalizing behaviors and any of the connectivity indices examined. Post-hoc bootstrapping analyses indicated stability and reliability of these null results. CONCLUSIONS: The current study provides evidence towards an absence of a shared linear relationship between internalizing or externalizing behaviors and cortico-amygdalar connectivity properties across a transdiagnostic sample of children with different primary NDD diagnoses and TDC. Different methodological approaches, including incorporation of multi-dimensional behavioral data (e.g., task-based fMRI) or clustering approaches may be needed to clarify complex brain-behavior relationships relevant to externalizing/internalizing behaviors in heterogeneous clinical NDD populations.

Modulation of Dorsolateral Prefrontal Cortex Glutamate/Glutamine Levels Following Repetitive Transcranial Magnetic Stimulation in Young Adults With Autism.

Altered excitatory and inhibitory neurotransmission has been implicated in autism spectrum disorder (ASD). Interventions using repetitive transcranial magnetic stimulation (rTMS) to enhance or inhibit cortical excitability are under study for various targets, though the mechanistic effects of rTMS have yet to be examined in ASD. Here, we examined whether an excitatory rTMS treatment course modulates glutamatergic (Glx) or γ-aminobutyric acid (GABA) metabolite levels in emerging adults with ASD. Twenty-eight participants with ASD and executive function impairment [23.3 ± 4.69 years; seven-female] underwent two magnetic resonance spectroscopy (MRS) scans of the left dorsolateral prefrontal cortex (DLPFC). MRS scans were acquired before and after participants with ASD were randomized to receive a 20-session course of active or sham rTMS to the DLPFC. Baseline MRS data was available for 19 typically developing controls [23.8 ± 4.47 years; six-female]. Metabolite levels for Glx and GABA+ were compared between ASD and control groups, at baseline, and metabolite level change, pre-to-post-rTMS treatment, was compared in ASD participants that underwent active vs. sham rTMS. Absolute change in Glx was greater in the active vs. sham-rTMS group [F (1, 19) = 6.54, p = 0.02], though the absolute change in GABA+ did not differ between groups. We also examined how baseline metabolite levels related to pre/post-rTMS metabolite level change, in the active vs. sham groups. rTMS group moderated the relation between baseline Glx and pre-to-post-rTMS Glx change, such that baseline Glx predicted Glx change in the active-rTMS group only [b = 1.52, SE = 0.32, t (18) = 4.74, p < 0.001]; Glx levels increased when baseline levels were lower, and decreased when baseline levels were higher. Our results indicate that an interventional course of excitatory rTMS to the DLPFC may modulate local Glx levels in emerging adults with ASD, and align with prior reports of glutamatergic alterations following rTMS. Interventional studies that track glutamatergic markers may provide mechanistic insights into the therapeutic potential of rTMS in ASD. Clinical Trial Registration: Clinicaltrials.gov (ID: NCT02311751), https://clinicaltrials.gov/ct2/show/NCT02311751?term=ameis&rank=1; NCT02311751.

White matter microstructure and structural networks in treatment-resistant schizophrenia patients after commencing clozapine treatment: A longitudinal diffusion imaging study.

This study investigates changes on white matter microstructure and neural networks after 6 months of switching to clozapine in schizophrenia patients compared to controls, and whether any changes are related to clinical variables. T1 and diffusion-weighted MRI images were acquired at baseline before commencing clozapine and after 6 months of treatment for 22 patients with treatment-resistant schizophrenia and 23 controls. The Tract-based spatial statistics approach was used to compare changes over time between groups in fractional anisotropy (FA). Changes in structural network organisation weighted by FA and number of streamlines were assessed using graph theory. Patients displayed a significant reduction of FA over time (p<0.05) compared to controls in the genu and body of the corpus callosum and bilaterally in the anterior and superior corona radiata. There was no correlation between FA change in patients and changes in clinical variables or serum level of clozapine. There was no changes in structural network organisation between groups (F(7,280)=2.80;p = 0.187). This longitudinal study demonstrated progressive focal FA abnormalities in key anterior tracts, but preserved brain structural network organisation in patients. The FA reduction was independent of any clinical measures and may reflect progression of the underlying pathophysiology of this malignant form of schizophrenia illness.

Integration of brain and behavior measures for identification of data-driven groups cutting across children with ASD, ADHD, or OCD.

Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network was used including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Future work is needed to build on this early attempt through replication of the current findings in independent samples and testing longitudinally for prognostic value.

Impaired response inhibition during a stop-signal task in children with Tourette syndrome is related to ADHD symptoms: A functional magnetic resonance imaging study.

OBJECTIVES: Tourette syndrome (TS) is characterised by the presence of sudden, rapid movements and vocalizations (tics). The nature of tics suggests impairments in inhibitory control. However, findings of impaired inhibitory control have so far been inconsistent, possibly due to small sample sizes, wide age ranges, or not taking medication use or attention-deficit/hyperactivity disorder (ADHD) comorbidity into account. METHODS: We investigated group differences in response inhibition using an fMRI-based stop-signal task in 103 8 to 12-year-old children (n = 51 with TS, of whom n = 28 without comorbid ADHD [TS - ADHD] and n = 23 with comorbid ADHD [TS + ADHD]; and n = 52 healthy controls), and related these measures to tic and ADHD severity. RESULTS: We observed an impaired response inhibition performance in children with TS + ADHD, but not in those with TS - ADHD, relative to healthy controls, as evidenced by a slower stop-signal reaction time, slower mean reaction times, and larger variability of reaction times. Dimensional analyses implicated ADHD severity as the driving force in these findings. Neural activation during failed inhibition was stronger in the inferior frontal gyrus and temporal and parietal areas in TS + ADHD compared to healthy controls. CONCLUSIONS: Impaired inhibitory performance and increased neural activity in TS appear to manifest predominantly in relation to ADHD symptomatology.

Gray matter covariations and core symptoms of autism: the EU-AIMS Longitudinal European Autism Project.

BACKGROUND: Voxel-based morphometry (VBM) studies in autism spectrum disorder (autism) have yielded diverging results. This might partly be attributed to structural alterations being associating with the combined influence of several regions rather than with a single region. Further, these structural covariation differences may relate to continuous measures of autism rather than with categorical case-control contrasts. The current study aimed to identify structural covariation alterations in autism, and assessed canonical correlations between brain covariation patterns and core autism symptoms. METHODS: We studied 347 individuals with autism and 252 typically developing individuals, aged between 6 and 30 years, who have been deeply phenotyped in the Longitudinal European Autism Project. All participants' VBM maps were decomposed into spatially independent components using independent component analysis. A generalized linear model (GLM) was used to examine case-control differences. Next, canonical correlation analysis (CCA) was performed to separately explore the integrated effects between all the brain sources of gray matter variation and two sets of core autism symptoms. RESULTS: GLM analyses showed significant case-control differences for two independent components. The first component was primarily associated with decreased density of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and increased density of caudate nucleus in the autism group relative to typically developing individuals. The second component was related to decreased densities of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to typically developing individuals. The CCA results showed significant correlations between components that involved variation of thalamus, putamen, precentral gyrus, frontal, parietal, and occipital lobes, and the cerebellum, and repetitive, rigid and stereotyped behaviors and abnormal sensory behaviors in autism individuals. LIMITATIONS: Only 55.9% of the participants with autism had complete questionnaire data on continuous parent-reported symptom measures. CONCLUSIONS: Covaried areas associated with autism diagnosis and/or symptoms are scattered across the whole brain and include the limbic system, basal ganglia, thalamus, cerebellum, precentral gyrus, and parts of the frontal, parietal, and occipital lobes. Some of these areas potentially subserve social-communicative behavior, whereas others may underpin sensory processing and integration, and motor behavior.

Sex Differences in Variability of Brain Structure Across the Lifespan.

Several brain disorders exhibit sex differences in onset, presentation, and prevalence. Increased understanding of the neurobiology of sex-based differences in variability across the lifespan can provide insight into both disease vulnerability and resilience. In n = 3069 participants, from 8 to 95 years of age, we found widespread greater variability in males compared with females in cortical surface area and global and subcortical volumes for discrete brain regions. In contrast, variance in cortical thickness was similar for males and females. These findings were supported by multivariate analysis accounting for structural covariance, and present and stable across the lifespan. Additionally, we examined variability among brain regions by sex. We found significant age-by-sex interactions across neuroimaging metrics, whereby in very early life males had reduced among-region variability compared with females, while in very late life this was reversed. Overall, our findings of greater regional variability, but less among-region variability in males in early life may aid our understanding of sex-based risk for neurodevelopmental disorders. In contrast, our findings in late life may provide a potential sex-based risk mechanism for dementia.

A graph theory study of resting-state functional connectivity in children with Tourette syndrome.

Little is known about the brain's functional organization during resting-state in children with Tourette syndrome (TS). We aimed to investigate this with a specific focus on the role of comorbid attention-deficit/hyperactivity disorder (ADHD). We applied graph theoretical analysis to resting-state functional magnetic resonance imaging data of 109 8-to-12-year-old children with TS (n = 46), ADHD without tics (n = 23), and healthy controls (n = 40). First, we compared these three groups, and in a second comparison four groups, distinguishing TS with (TS + ADHD, n = 19) and without comorbid ADHD (TS-ADHD, n = 27). Weighted brain graphs were constructed for both comparisons to investigate global efficiency, local efficiency, and clustering coefficient per acquired network. Local efficiency and clustering coefficient were significantly lower in children with TS-ADHD in the default mode network compared with healthy controls, and in the frontoparietal network compared with ADHD; we also found associations with higher tic severity. Our study supports a different functional brain network organization in children with TS-ADHD, compared with healthy controls and children with ADHD.

Executive function in children with Tourette syndrome and attention-deficit/hyperactivity disorder: Cross-disorder or unique impairments?

Findings of executive functioning deficits in Tourette syndrome (TS) have so far been inconsistent, possibly due to methodological challenges of previous studies, such as the use of small sample sizes and not accounting for comorbid attention-deficit/hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), or medication use. We aimed to address these issues by examining several areas of executive functioning (response inhibition, attentional flexibility, cognitive control, and working memory) and psychomotor speed in 174 8-to-12-year-old children with TS [n = 34 without (TS-ADHD) and n = 26 with comorbid ADHD (TS+ADHD)], ADHD without tics (ADHD-TS; n = 54), and healthy controls (n = 60). We compared executive functioning measures and psychomotor speed between these groups and related these to ADHD severity across the whole sample, and tic severity across the TS groups. Children with TS+ADHD, but not TS-ADHD, made more errors on the cognitive control task than healthy children, while TS-ADHD had a slower psychomotor speed compared to healthy controls. The ADHD group showed impairment in cognitive control and working memory versus healthy controls. Moreover, higher ADHD severity was associated with poorer cognitive control and working memory across all groups; there was no relation between any of the executive functioning measures and tic severity. OCD severity or medication use did not influence our results. In conclusion, we found little evidence for executive function impairments inherent to TS. Executive function problems appear to manifest predominantly in relation to ADHD symptomatology, with both cross-disorder and unique features of neuropsychological functioning when cross-comparing TS and ADHD.

Neural reward processing in paediatric Tourette syndrome and/or attention-deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is the most common comorbidity in individuals with Tourette syndrome (TS). Yet, it is unclear to what extent TS and ADHD show overlapping or distinct neural abnormalities. ADHD has been associated with altered reward processing, but there are very few studies on reward processing in TS. This study assessed neural activation of basal ganglia and thalamus during reward anticipation and receipt in children with TS and/or ADHD. We analysed mean activations of a priori specified regions of interest during an fMRI monetary incentive delay task. Data was used from 124 children aged 8-12 years (TS n = 47, of which 29 had comorbid ADHD; ADHD n = 29; healthy controls n = 48). ADHD severity across ADHD and TS groups and healthy controls was marginally related to hypoactivation of the right nucleus accumbens during reward anticipation; this effect was not moderated by TS diagnosis. We detected no associations of neural activation with TS. The association between ADHD severity and hypoactivation of the right nucleus accumbens during reward anticipation, independent of the presence or absence of TS, is in line with the view of nucleus accumbens hypoactivation as a dimensional, neurofunctional marker of ADHD severity, transcending the boundaries of primary diagnosis.

Multi-modal imaging investigation of anterior cingulate cortex cytoarchitecture in neurodevelopment.

Multi-modal imaging may improve our understanding of the relationship between cortical morphology and cytoarchitecture. To this end we integrated the analyses of several magnetic resonance imaging (MRI) and spectroscopy (MRS) metrics within the anterior cingulate cortex (ACC). Considering the ACCs role in neurodevelopmental disorders, we also investigated the association between neuropsychiatric symptoms and the various metrics. T1 and diffusion-weighted MRI and 1H-MRS (ACC voxel) data along with phenotypic information were acquired from children (8-12 years) with various neurodevelopmental disorders (n=95) and healthy controls (n=50). From within the MRS voxel mean diffusivity (MD) of the grey matter fraction, intrinsic curvature (IC) of the surface and concentrations of creatine, choline, glutamate, N-acetylaspartate and myo-inositol were extracted. Linear models were used to investigate if the neurochemicals predicted MD and IC or if MD predicted IC. Finally, measures of various symptom severities were included to determine the influence of symptoms of neurodevelopmental disorders. All five neurochemicals inversely predicted MD (all puncorrected<0.04, ß=0.23-0.36). There was no association between IC and MD or IC and the neurochemicals (all p>0.05). Severity of autism symptoms related positively to MD (puncorrected=0.002, ß=0.39). Our findings support the notion that the neurochemicals relate to cytoarchitecture within the cortex. Additionally, we showed that autism symptoms across participants relate to the ACC cytoarchitecture.