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Item does not contain fulltext 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 T(1)-weighted structural brain images, the authors compared sex prediction model performance across neurotypical and autistic males and females. Multiple large-scale data sets comprising T(1)-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.

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 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.

The ratio of T1-weighted/T2-weighted magnetic resonance images (T1w/T2w MRI) has been successfully applied at the cortical level since 2011 and is now one of the most used myelin mapping methods. However, no reports have explored the histological validity of T1w/T2w myelin mapping in white matter. Here we compare T1w/T2w with ex vivo postmortem histology and in vivo MRI methods, namely quantitative susceptibility mapping (QSM) and multi-echo T2 myelin water fraction (MWF) mapping techniques. We report a discrepancy between T1w/T2w myelin maps of the human corpus callosum and the histology and analyse the putative causes behind such discrepancy. T1w/T2w does not positively correlate with Luxol Fast Blue (LFB)-Optical Density but shows a weak to moderate, yet significant, negative correlation. On the contrary, MWF is strongly and positively correlated with LFB, whereas T1w/T2w and MWF maps are weakly negatively correlated. The discrepancy between T1w/T2w MRI maps, MWF and histological myelin maps suggests caution in using T1w/T2w as a white matter mapping method at the callosal level. While T1w/T2w imaging may correlate with myelin content at the cortical level, it is not a specific method to map myelin density in white matter.

Contains fulltext : 248865.pdf (Publisher’s version ) (Closed access) OBJECTIVE: Autism spectrum disorder (ASD) is accompanied by highly individualized neuroanatomical deviations that potentially map onto distinct genotypes and clinical phenotypes. This study aimed to link differences in brain anatomy to specific biological pathways to pave the way toward targeted therapeutic interventions. METHODS: The authors examined neurodevelopmental differences in cortical thickness and their genomic underpinnings in a large and clinically diverse sample of 360 individuals with ASD and 279 typically developing control subjects (ages 6-30 years) within the EU-AIMS Longitudinal European Autism Project (LEAP). The authors also examined neurodevelopmental differences and their potential pathophysiological mechanisms between clinical ASD subgroups that differed in the severity and pattern of sensory features. RESULTS: In addition to significant between-group differences in "core" ASD brain regions (i.e., fronto-temporal and cingulate regions), individuals with ASD manifested as neuroanatomical outliers within the neurotypical cortical thickness range in a wider neural system, which was enriched for genes known to be implicated in ASD on the genetic and/or transcriptomic level. Within these regions, the individuals' total (i.e., accumulated) degree of neuroanatomical atypicality was significantly correlated with higher polygenic scores for ASD and other psychiatric conditions, and it scaled with measures of symptom severity. Differences in cortical thickness deviations were also associated with distinct sensory subgroups, especially in brain regions expressing genes involved in excitatory rather than inhibitory neurotransmission. CONCLUSIONS: The study findings corroborate the link between macroscopic differences in brain anatomy and the molecular mechanisms underpinning heterogeneity in ASD, and provide future targets for stratification and subtyping.

The identification of meaningful functional magnetic resonance imaging (fMRI) biomarkers requires measures that reliably capture brain performance across different subjects and over multiple scanning sessions. Recent developments in fMRI acquisition, such as the introduction of multiband (MB) protocols and in-plane acceleration, allow for increased scanning speed and improved temporal resolution. However, they may also lead to reduced temporal signal to noise ratio and increased signal leakage between simultaneously excited slices. These methods have been adopted in several scanning modalities including diffusion weighted imaging and fMRI. To our knowledge, no study has formally compared the reliability of the same resting-state fMRI (rs-fMRI) metrics (amplitude of low-frequency fluctuations; seed-to-voxel and region of interest [ROI]-to-ROI connectivity) across conventional single-band fMRI and different MB acquisitions, with and without in-plane acceleration, across three sessions. In this study, 24 healthy older adults were scanned over three visits, on weeks 0, 1, and 4, and, on each occasion, underwent a conventional single band rs-fMRI scan and three different rs-fMRI scans with MB factors 4 and 6, with and without in-plane acceleration. Across all three rs-fMRI metrics, the reliability scores were highest with MB factor 4 with no in-plane acceleration for cortical areas and with conventional single band for subcortical areas. Recommendations for future research studies are discussed.

Ex vivo diffusion imaging can be used to study healthy and pathological tissue microstructure in the rodent brain with high resolution, providing a link between in vivo MRI and ex vivo microscopy techniques. Major challenges for the successful acquisition of ex vivo diffusion imaging data however are changes in the relaxivity and diffusivity of brain tissue following perfusion fixation. In this study we address this question by examining the combined effects of tissue preparation factors that influence signal-to-noise ratio (SNR) and consequently image quality, including fixative concentration, contrast agent concentration and tissue rehydration time. We present an optimization strategy combining these factors to manipulate the (Formula presented.) and (Formula presented.) of fixed tissue and maximize SNR efficiency. We apply this strategy in the rat brain, for a diffusion-weighted spin echo protocol with TE = 27 ms on a 9.4 T scanner with a 39 mm volume coil and 660 mT/m 114 mm gradient insert. We used a reduced fixative concentration of 2% paraformaldehyde (PFA), rehydration time more than 20 days, 15 mM Gd-DTPA in perfusate and TR 250 ms. This resulted in a doubling of SNR and an increase in SNR per unit time of 135% in cortical grey matter and 88% in white matter compared with 4% PFA and no contrast agent. This improved SNR efficiency enabled the acquisition of excellent-quality high-resolution (78 (Formula presented.) m isotropic voxel size) diffusion data with b = 4000 s/mm (Formula presented.), 30 diffusion directions and a field of view of (Formula presented.) mm3 in less than 4 days. It was also possible to achieve comparable data quality for a standard resolution (150 (Formula presented.) m) diffusion dataset in (Formula presented.) h. In conclusion, the tissue optimization strategy presented here may be used to improve SNR, increase spatial resolution and/or allow faster acquisitions in preclinical ex vivo diffusion MRI experiments.

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.

OBJECTIVE This prospective case-control study was conducted to examine whether spherical deconvolution (SD) can unveil microstructural abnormalities in the corticospinal tract (CST) caused by IDH-mutant gliomas. To determine the significance of abnormal microstructure, the authors investigated the correlation between diffusion parameters and neurophysiological data collected with navigated transcranial magnetic stimulation (nTMS). METHODS Twenty participants (10 patients and 10 healthy controls) were recruited. Diffusion-weighted images were acquired on a 3-T MRI scanner using a cardiac-gated single-shot spin echo echo-planar imaging multiband sequence (TE 80 msec, TR 4000 msec) along 90 diffusion directions with a b-value of 2500 sec/mm2 (FOV 256 × 256 mm). Diffusion tensor imaging tractography and SD tractography were performed with deterministic tracking. The anterior portion of the ipsilateral superior peduncle and the precentral gyrus were used as regions of interest to delineate the CST. Diffusion indices were extracted and analyzed for significant differences between hemispheres in patients and between patient and control groups. A navigated brain stimulation system was used to deliver TMS pulses at hotspots at which motor evoked potentials (MEPs) for the abductor pollicis brevis, first digital interosseous, and abductor digiti minimi muscles are best elicited in patients and healthy controls. Functional measurements such as resting motor threshold (rMT), amplitude of MEPs, and latency of MEPs were noted. Significant differences between hemispheres in patients and between patients and controls were statistically analyzed. The Spearman rank correlation was used to investigate correlations between diffusion indices and functional measurements. RESULTS The hindrance modulated orientational anisotropy (HMOA), measured with SD tractography, is lower in the hemisphere ipsilateral to glioma (p = 0.028). The rMT in the hemisphere ipsilateral to a glioma is significantly greater than that in the contralateral hemisphere (p = 0.038). All measurements contralateral to the glioma, except for the mean amplitude of MEPs (p = 0.001), are similar to those of healthy controls. Mean diffusivity and axial diffusivity from SD tractography are positively correlated with rMT in the hemisphere ipsilateral to glioma (p = 0.02 and 0.006, respectively). The interhemispheric difference in HMOA and rMT is correlated in glioma patients (p = 0.007). CONCLUSIONS SD tractography can demonstrate microstructural abnormality within the CST of patients with IDH1-mutant gliomas that correlates to the functional abnormality measured with nTMS.

Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell’Acqua, 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for mapping pore sizes, in particular large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion times and gradient settings. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time optimises the TDR diffusion sequences in simulation for a range of different tissues and scanner constraints. We extend the original work (which considers substrates containing cylinders) by additionally considering the TDR signal obtained from spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration and short diffusion time with an acquisition with long gradient duration and long diffusion time improves the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for mapping pore sizes and restricted diffusion in general.HighlightsTemporal Diffusion Ratio (TDR) 2-seq approach maps areas with restricted diffusionOptimised gradient waveform pair is: long δ + low G and short δ + high GIf data is noisy calculating TDR using HARDI acquisition subsets increases accuracyWe demonstrate TDR for the first time pre-clinically in rat spinal cordPre-clinical TDR values are strongly correlated with axon diameter

Down syndrome (DS) arises from the triplication of chromosome 21, which leads to an atypical neurodevelopment and the overproduction of the amyloid precursor protein, predisposing to early Alzheimer’s disease (AD). Not surprisingly, trisomy 21 is widely considered a model to study predementia stages of AD. After decades, in which neural loss was the main focus, research in AD is now moving toward understanding the neurodegenerative aspects affecting white matter. Motivated by the development of magnetic resonance imaging (MRI)-based diffusion techniques, this shift in focus has led to several exploratory studies on both young and older individuals with DS. In this review, we synthesise the initial efforts made by researchers in characterising in-vivo structural connectivity in DS, together with the AD footprint on top of such pre-existing connectivity related to atypical brain development. The white matter structures found to be affected in DS are the corpus callosum and all the main long-association fibres, namely the inferior fronto-occipital fasciculus, the inferior and superior longitudinal fasciculus, the uncinate fasciculus and the cingulum bundle. Furthermore, the cingulum bundle and the corpus callosum appear to be particularly sensitive to early AD changes in this population. Findings are discussed in terms of their functional significance, alongside methodological considerations and implications for future research.

Early childhood neglect can impact brain development across the lifespan. Using voxel-based approaches we recently reported that severe and time-limited institutional deprivation in early childhood was linked to substantial reductions in total brain volume in adulthood,20 years later. Here, we extend this analysis to explore deprivation-related regional white matter volume and microstructural organization using diffusion-based techniques. A combination of tensor-based morphometry (TBM) analysis and tractography was conducted on diffusion-weighted imaging (DWI) data from 59 young adults who spent between 3 and 41 months in the severely depriving Romanian institutions of the 1980s before being adopted into United Kingdom families, and 20 nondeprived age-matched United Kingdom controls. Independent of total volume, institutional deprivation was associated with smaller volumes in localized regions across a range of white matter tracts including (1) long-ranging association fibers such as bilateral inferior longitudinal fasciculus (ILF), bilateral inferior fronto-occipital fasciculus (IFOF), left superior longitudinal fasciculi (SLFs), and left arcuate fasciculus; (2) tracts of the limbic circuitry including fornix and cingulum; and (3) projection fibers with the corticospinal tract particularly affected. Tractographic analysis found no evidence of altered microstructural organization of any tract in terms of hindrance modulated orientational anisotropy (HMOA), fractional anisotropy (FA), or mean diffusivity (MD). We provide further evidence for the effects of early neglect on brain development and their persistence in adulthood despite many years of environmental enrichment associated with successful adoption. Localized white matter effects appear limited to volumetric changes with microstructural organization unaffected.

Most neuroimaging modalities use regular grids of voxels to represent the three-dimensional space occupied by the brain. However, a regular 3D voxel grid does not reflect the anatomical and topological complexity represented by the brain’s white matter connections. In contrast, tractography reconstructions based on diffusion MRI provide a closer characterisation of the white matter pathways followed by the neuronal fibres interconnecting different brain regions. In this work, we introduce hypervoxels as a new methodological framework that combines the spatial encoding capabilities of multidimensional voxels with the anatomical and topological information found in tractography data. We provide a detailed description of the framework and evaluate the benefits of using hypervoxels by carrying out comparative voxel and hypervoxel cluster inference analyses on diffusion MRI data from a neuroimaging study on amyotrophic lateral sclerosis (ALS). Compared to the voxel analyses, the use of hypervoxels improved the detection of effects of interest in the data in terms of statistical significance levels and spatial distribution across white matter regions known to be affected in ALS. In these regions, the hypervoxel results also identified specific white matter pathways that resolve the anatomical ambiguity otherwise observed in the results from the voxel analyses. The observed increase in sensitivity and specificity can be explained by the superior ability of hypervoxel-based methods to represent and disentangle the anatomical overlap of white matter connections. Based on this premise, we expect that the use of hypervoxels should improve the analysis of neuroimaging data when the effects of interest under investigation are expected to be aligned along distinct but potentially overlapping white matter pathways.

Autism spectrum disorder (ASD) is a highly complex neurodevelopmental condition that is accompanied by neuroanatomical differences on the macroscopic and microscopic level. Findings from histological, genetic, and more recently in vivo neuroimaging studies converge in suggesting that neuroanatomical abnormalities, specifically around the gray‐white matter (GWM) boundary, represent a crucial feature of ASD. However, no research has yet characterized the GWM boundary in ASD based on measures of diffusion. Here, we registered diffusion tensor imaging data to the structural T1‐weighted images of 92 adults with ASD and 92 matched neurotypical controls in order to examine between‐group differences and group‐by‐sex interactions in fractional anisotropy and mean diffusivity sampled at the GWM boundary, and at different sampling depths within the superficial white and into the gray matter. As hypothesized, we observed atypical diffusion at and around the GWM boundary in ASD, with between‐group differences and group‐by‐sex interactions depending on tissue class and sampling depth. Furthermore, we identified that altered diffusion at the GWM boundary partially (i.e., ~50%) overlapped with atypical gray‐white matter tissue contrast in ASD. Our study thus replicates and extends previous work highlighting the GWM boundary as a crucial target of neuropathology in ASD, and guides future work elucidating etiological mechanisms., The present study observed atypical diffusion at and around the gray‐white matter (GWM) boundary in individuals with autism spectrum disorder (ASD) relative to neurotypical controls, with between‐group differences and group‐by‐sex interactions depending on tissue class and sampling depth. Altered diffusion at the GWM boundary was further identified to partially (i.e., ~50%) and nonrandomly overlap with atypical gray‐white matter tissue contrast in ASD. The study thus replicates and extends previous work highlighting the GWM boundary as a crucial target of neuropathology in ASD, and guides future work elucidating etiological mechanisms.

The frontal lobe is central to distinctive aspects of human cognition and behavior. Some comparative studies link this to a larger frontal cortex and even larger frontal white matter in humans compared with other primates, yet others dispute these findings. The discrepancies between studies could be explained by limitations of the methods used to quantify volume differences across species, especially when applied to white matter connections., The frontal lobe is central to distinctive aspects of human cognition and behavior. Some comparative studies link this to a larger frontal cortex and even larger frontal white matter in humans compared with other primates, yet others dispute these findings. The discrepancies between studies could be explained by limitations of the methods used to quantify volume differences across species, especially when applied to white matter connections. In this study, we used a novel tractography approach to demonstrate that frontal lobe networks, extending within and beyond the frontal lobes, occupy 66% of total brain white matter in humans and 48% in three monkey species: vervets (Chlorocebus aethiops), rhesus macaque (Macaca mulatta) and cynomolgus macaque (Macaca fascicularis), all male. The simian–human differences in proportional frontal tract volume were significant for projection, commissural, and both intralobar and interlobar association tracts. Among the long association tracts, the greatest difference was found for tracts involved in motor planning, auditory memory, top-down control of sensory information, and visuospatial attention, with no significant differences in frontal limbic tracts important for emotional processing and social behaviour. In addition, we found that a nonfrontal tract, the anterior commissure, had a smaller volume fraction in humans, suggesting that the disproportionally large volume of human frontal lobe connections is accompanied by a reduction in the proportion of some nonfrontal connections. These findings support a hypothesis of an overall rearrangement of brain connections during human evolution. SIGNIFICANCE STATEMENT Tractography is a unique tool to map white matter connections in the brains of different species, including humans. This study shows that humans have a greater proportion of frontal lobe connections compared with monkeys, when normalized by total brain white matter volume. In particular, tracts associated with language and higher cognitive functions are disproportionally larger in humans compared with monkeys, whereas other tracts associated with emotional processing are either the same or disproportionally smaller. This supports the hypothesis that the emergence of higher cognitive functions in humans is associated with increased extended frontal connectivity, allowing human brains more efficient cross talk between frontal and other high-order associative areas of the temporal, parietal, and occipital lobes.

Contains fulltext : 288245.pdf (Publisher’s version ) (Open Access) BACKGROUND: The cerebellum contains more than 50% of the brain's neurons and is involved in social cognition. Cerebellar anatomical atypicalities have repeatedly been reported in individuals with autism. However, studies have yielded inconsistent findings, likely because of a lack of statistical power, and did not capture the clinical and neuroanatomical diversity of autism. Our aim was to better understand cerebellar anatomy and its diversity in autism. METHODS: We studied cerebellar gray matter morphology in 274 individuals with autism and 219 control subjects of a multicenter European cohort, EU-AIMS LEAP (European Autism Interventions-A Multicentre Study for Developing New Medications; Longitudinal European Autism Project). To ensure the robustness of our results, we conducted lobular parcellation of the cerebellum with 2 different pipelines in addition to voxel-based morphometry. We performed statistical analyses with linear, multivariate (including normative modeling), and meta-analytic approaches to capture the diversity of cerebellar anatomy in individuals with autism and control subjects. Finally, we performed a dimensional analysis of cerebellar anatomy in an independent cohort of 352 individuals with autism-related symptoms. RESULTS: We did not find any significant difference in the cerebellum when comparing individuals with autism and control subjects using linear models. In addition, there were no significant deviations in our normative models in the cerebellum in individuals with autism. Finally, we found no evidence of cerebellar atypicalities related to age, IQ, sex, or social functioning in individuals with autism. CONCLUSIONS: Despite positive results published in the last decade from relatively small samples, our results suggest that there is no striking difference in cerebellar anatomy of individuals with autism.

Contains fulltext : 282637.pdf (Publisher’s version ) (Closed access) Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication, but also great heterogeneity. To offer individualized medicine approaches, we need to better target interventions by stratifying autistic people into subgroups with different biological profiles and/or prognoses. We sought to validate neural responses to faces as a potential stratification factor in ASD by measuring neural (electroencephalography) responses to faces (critical in social interaction) in N = 436 children and adults with and without ASD. The speed of early-stage face processing (N170 latency) was on average slower in ASD than in age-matched controls. In addition, N170 latency was associated with responses to faces in the fusiform gyrus, measured with functional magnetic resonance imaging, and polygenic scores for ASD. Within the ASD group, N170 latency predicted change in adaptive socialization skills over an 18-month follow-up period; data-driven clustering identified a subgroup with slower brain responses and poor social prognosis. Use of a distributional data-driven cutoff was associated with predicted improvements of power in simulated clinical trials targeting social functioning. Together, the data provide converging evidence for the utility of the N170 as a stratification factor to identify biologically and prognostically defined subgroups in ASD.

BackgroundThe excitatory/inhibitory (E/I) imbalance hypothesis posits that an imbalance between excitatory (glutamatergic) and inhibitory (GABAergic) mechanisms underlies the behavioral characteristics of autism spectrum disorder (autism). However, how E/I imbalance arises and how it may differ across autism symptomatology and brain regions is not well understood.MethodsWe used innovative analysis methods - combining competitive gene-set analysis and gene-expression profiles in relation to cortical thickness (CT)- to investigate the relationship between genetic variance, brain structure and autism symptomatology of participants from the EU-AIMS LEAP cohort (autism=360, male/female=259/101; neurotypical control participants=279, male/female=178/101) aged 6 to 30 years. Competitive gene-set analysis investigated associations between glutamatergic and GABAergic signaling pathway gene-sets and clinical measures, and CT. Additionally, we investigated expression profiles of the genes within those sets throughout the brain and how those profiles relate to differences in CT between autistic and neurotypical control participants in the same regions.ResultsThe 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, while the GABA set was associated with sensory processing measures (using the SSP subscales) across all participants. Brain regions with greater gene expression of both glutamate and GABA genes showed greater differences in CT between autistic and neurotypical control participants.ConclusionsOur results suggest crucial roles for glutamate and GABA genes in autism symptomatology as well as CT, where GABA is more strongly associated with sensory processing and glutamate more with autism symptom severity.