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

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

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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

Objective QC for diffusion MRI data: artefact detection using normative modelling

Diffusion MRI is a neuroimaging modality used to evaluate brain structure at a microscopic level and can be exploited to map white matter fibre bundles and microstructure in the brain. One common issue is the presence of artefacts, such as acquisition artefacts, physiological artefacts, distortions or image processing-related artefacts. These may lead to problems with other downstream processes and can bias subsequent analyses. In this work we use normative modelling to create a semi-automated pipeline for detecting diffusion imaging artefacts and errors by modelling 24 white matter imaging derived phenotypes from the UK Biobank dataset. The considered features comprised 4 microstructural features (from models with different complexity such as fractional anisotropy and mean diffusivity from a diffusion tensor model and parameters from neurite orientation, dispersion and density models), each within six pre-selected white matter tracts of various sizes and geometrical complexity (corpus callosum, bilateral corticospinal tract and uncinate fasciculus and fornix). Our method was compared to two traditional quality control approaches: a visual quality control protocol performed on 500 subjects and quantitative quality control using metrics derived from image pre-processing. The normative modelling framework proves to be comprehensive and efficient in detecting diffusion imaging artefacts arising from various sources (such as susceptibility induced distortions or motion), as well as outliers resulting from inaccurate processing (such as erroneous spatial registrations). This is an important contribution by virtue of this methods’ ability to identify the two problem sources (i) image artefacts and (ii) processing errors, which subsequently allows for a better understanding of our data and informs on inclusion/exclusion criteria of participants

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 (β = 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 (β = 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

Structural neuroimaging correlates of allelic variation of the BDNF val66met polymorphism.

BACKGROUND: The brain-derived neurotrophic factor (BDNF) val66met polymorphism is associated with altered activity dependent secretion of BDNF and a variable influence on brain morphology and cognition. Although a met-dose effect is generally assumed, to date the paucity of met-homozygotes have limited our understanding of the role of the met-allele on brain structure. METHODS: To investigate this phenomenon, we recruited sixty normal healthy subjects, twenty in each genotypic group (val/val, val/met and met/met). Global and local morphology were assessed using voxel based morphometry and surface reconstruction methods. White matter organisation was also investigated using tract-based spatial statistics and constrained spherical deconvolution tractography. RESULTS: Morphological analysis revealed an "inverted-U" shaped profile of cortical changes, with val/met heterozygotes most different relative to the two homozygous groups. These results were evident at a global and local level as well as in tractography analysis of white matter fibre bundles. CONCLUSION: In contrast to our expectations, we found no evidence of a linear met-dose effect on brain structure, rather our results support the view that the heterozygotic BDNF val66met genotype is associated with cortical morphology that is more distinct from the BDNF val66met homozygotes. These results may prove significant in furthering our understanding of the role of the BDNF met-allele in disorders such as Alzheimer's disease and depression

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

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

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, p$_{adj}$ = 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

Achievement of therapeutic antibiotic exposures using Bayesian dosing software in critically unwell children and adults with sepsis

PURPOSE: Early recognition and effective treatment of sepsis improves outcomes in critically ill patients. However, antibiotic exposures are frequently suboptimal in the intensive care unit (ICU) setting. We describe the feasibility of the Bayesian dosing software Individually Designed Optimum Dosing Strategies (ID-ODS™), to reduce time to effective antibiotic exposure in children and adults with sepsis in ICU. METHODS: A multi-centre prospective, non-randomised interventional trial in three adult ICUs and one paediatric ICU. In a pre-intervention Phase 1, we measured the time to target antibiotic exposure in participants. In Phase 2, antibiotic dosing recommendations were made using ID-ODS™, and time to target antibiotic concentrations were compared to patients in Phase 1 (a pre-post-design). RESULTS: 175 antibiotic courses (Phase 1 = 123, Phase 2 = 52) were analysed from 156 participants. Across all patients, there was no difference in the time to achieve target exposures (8.7 h vs 14.3 h in Phase 1 and Phase 2, respectively, p = 0.45). Sixty-one courses in 54 participants failed to achieve target exposures within 24 h of antibiotic commencement (n = 36 in Phase 1, n = 18 in Phase 2). In these participants, ID-ODS™ was associated with a reduction in time to target antibiotic exposure (96 vs 36.4 h in Phase 1 and Phase 2, respectively, p < 0.01). These patients were less likely to exhibit subtherapeutic antibiotic exposures at 96 h (hazard ratio (HR) 0.02, 95% confidence interval (CI) 0.01-0.05, p < 0.01). There was no difference observed in in-hospital mortality. CONCLUSIONS: Dosing software may reduce the time to achieve target antibiotic exposures. It should be evaluated further in trials to establish its impact on clinical outcomes