Longitudinal European Autism Project Research Articles

Patterns of Brain Maturation in Autism and Their Molecular Associations

In the neurotypical brain, regions develop in coordinated patterns, providing a fundamental scaffold for brain function and behavior. Whether altered patterns contribute to clinical profiles in neurodevelopmental conditions, including autism, remains unclear. To examine if, in autism, brain regions develop differently in relation to each other and how these differences are associated with molecular/genomic mechanisms and symptomatology. This study was an analysis of one the largest deep-phenotyped, case-control, longitudinal (2 assessments separated by approximately 12-24 months) structural magnetic resonance imaging and cognitive-behavioral autism datasets (EU-AIMS Longitudinal European Autism Project [LEAP]; study dates, February 2014-November 2017) and an out-of-sample validation in the Brain Development Imaging Study (BrainMapASD) independent cohort. Analyses were performed during the 2022 to 2023 period. This multicenter study included autistic and neurotypical children, adolescents, and adults. Autistic participants were included if they had an existing autism diagnosis (DSM-IV/International Statistical Classification of Diseases and Related Health Problems, Tenth Revision or DSM-5 criteria). Autistic participants with co-occurring psychiatric conditions (except psychosis/bipolar disorder) and those taking regular medications were included. Neuroanatomy of neurotypical and autistic participants. Intraindividual changes in surface area and cortical thickness over time, analyzed via surface-based morphometrics. A total of 386 individuals in the LEAP cohort (6-31 years at first visit; 214 autistic individuals, mean [SD] age, 17.3 [5.4] years; 154 male [72.0%] and 172 neurotypical individuals, mean [SD] age, 16.35 [5.7] years; 108 male [62.8%]) and 146 individuals in the BrainMapASD cohort (11-18 years at first visit; 49 autistic individuals, mean [SD] age, 14.31 [2.4] years; 42 male [85.7%] and 97 neurotypical individuals, mean [SD] age, 14.10 [2.5] years; 58 male [59.8%]). Maturational between-group differences in cortical thickness and surface area were established that were mostly driven by sensorimotor regions (eg, across features, absolute loadings for early visual cortex ranged from 0.07 to 0.11, whereas absolute loadings for dorsolateral prefrontal cortex ranged from 0.005 to 0.06). Neurodevelopmental differences were transcriptomically enriched for genes expressed in several cell types and during various neurodevelopmental stages, and autism candidate genes (eg, downregulated genes in autism, including those regulating synaptic transmission; enrichment odds ratio =3.7; P =2.6 × -10). A more neurotypical, less autismlike maturational profile was associated with fewer social difficulties and more typical sensory processing (false discovery rate P <.05; Pearson r ≥0.17). Results were replicated in the independently collected BrainMapASD cohort. Results of this case-control study suggest that the coordinated development of brain regions was altered in autism, involved a complex interplay of temporally sensitive molecular mechanisms, and may be associated with both lower-order (eg, sensory) and higher-order (eg, social) clinical features of autism. Thus, examining maturational patterns may provide an analytic framework to study the neurobiological origins of clinical profiles in neurodevelopmental/mental health conditions.

Sex differences in social brain neural responses in autism: temporal profiles of configural face-processing within data-driven time windows

Face-processing timing differences may underlie visual social attention differences between autistic and non-autistic people, and males and females. This study investigates the timing of the effects of neurotype and sex on face-processing, and their dependence on age. We analysed EEG data during upright and inverted photographs of faces from 492 participants from the Longitudinal European Autism Project (141 neurotypical males, 76 neurotypical females, 202 autistic males, 73 autistic females; age 6–30 years). We detected timings of sex/diagnosis effects on event-related potential amplitudes at the posterior–temporal channel P8 with Bootstrapped Cluster-based Permutation Analysis and conducted Growth Curve Analysis (GCA) to investigate the timecourse and dependence on age of neural signals. The periods of influence of neurotype and sex overlapped but differed in onset (respectively, 260 and 310 ms post-stimulus), with sex effects lasting longer. GCA revealed a smaller and later amplitude peak in autistic female children compared to non-autistic female children; this difference decreased in adolescence and was not significant in adulthood. No age-dependent neurotype difference was significant in males. These findings indicate that sex and neurotype influence longer latency face processing and implicates cognitive rather than perceptual processing. Sex may have more overarching effects than neurotype on configural face processing.

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

BackgroundAutism 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.MethodsWe 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.ResultsThe 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).LimitationsThe 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.ConclusionsThe 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.

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

BackgroundAutism spectrum disorders (ASD) are neurodevelopmental conditions accompanied by differences in brain development. Neuroanatomical differences in autism are variable across individuals and likely underpin distinct clinical phenotypes. To parse heterogeneity, it is essential to establish how the neurobiology of ASD is modulated by differences associated with co-occurring conditions, such as attention-deficit/hyperactivity disorder (ADHD). This study aimed to (1) investigate between-group differences in autistic individuals with and without co-occurring ADHD, and to (2) link these variances to putative genomic underpinnings.MethodsWe examined differences in cortical thickness (CT) and surface area (SA) and their genomic associations in a sample of 533 individuals from the Longitudinal European Autism Project. Using a general linear model including main effects of autism and ADHD, and an ASD-by-ADHD interaction, we examined to which degree ADHD modulates the autism-related neuroanatomy. Further, leveraging the spatial gene expression data of the Allen Human Brain Atlas, we identified genes whose spatial expression patterns resemble our neuroimaging findings.ResultsIn addition to significant main effects for ASD and ADHD in fronto-temporal, limbic, and occipital regions, we observed a significant ASD-by-ADHD interaction in the left precentral gyrus and the right frontal gyrus for measures of CT and SA, respectively. Moreover, individuals with ASD + ADHD differed in CT to those without. Both main effects and the interaction were enriched for ASD—but not for ADHD-related genes.LimitationsAlthough we employed a multicenter design to overcome single-site recruitment limitations, our sample size of N = 25 individuals in the ADHD only group is relatively small compared to the other subgroups, which limits the generalizability of the results. Also, we assigned subjects into ADHD positive groupings according to the DSM-5 rating scale. While this is sufficient for obtaining a research diagnosis of ADHD, our approach did not take into account for how long the symptoms have been present, which is typically considered when assessing ADHD in the clinical setting.ConclusionThus, our findings suggest that the neuroanatomy of ASD is significantly modulated by ADHD, and that autistic individuals with co-occurring ADHD may have specific neuroanatomical underpinnings potentially mediated by atypical gene expression.

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.

Differences in Intrinsic Gray Matter Connectivity and Their Genomic Underpinnings in Autism Spectrum Disorder

BackgroundAutism is a heterogeneous neurodevelopmental condition accompanied by differences in brain connectivity. Structural connectivity in autism has mainly been investigated within the white matter. However, many genetic variants associated with autism highlight genes related to synaptogenesis and axonal guidance, thus also implicating differences in intrinsic (i.e., gray matter) connections in autism. Intrinsic connections may be assessed in vivo via so-called intrinsic global and local wiring costs. MethodsHere, we examined intrinsic global and local wiring costs in the brain of 359 individuals with autism and 279 healthy control participants ages 6 to 30 years from the EU-AIMS LEAP (Longitudinal European Autism Project). FreeSurfer was used to derive surface mesh representations to compute the estimated length of connections required to wire the brain within the gray matter. Vertexwise between-group differences were assessed using a general linear model. A gene expression decoding analysis based on the Allen Human Brain Atlas was performed to link neuroanatomical differences to putative underpinnings. ResultsGroup differences in global and local wiring costs were predominantly observed in medial and lateral prefrontal brain regions, in inferior temporal regions, and at the left temporoparietal junction. The resulting neuroanatomical patterns were enriched for genes that had been previously implicated in the etiology of autism at genetic and transcriptomic levels. ConclusionsBased on intrinsic gray matter connectivity, the current study investigated the complex neuroanatomy of autism and linked between-group differences to putative genomic and/or molecular mechanisms to parse the heterogeneity of autism and provide targets for future subgrouping approaches.

Sensory salience processing moderates attenuated gazes on faces in autism spectrum disorder: a case–control study

BackgroundAttenuated social attention is a key marker of autism spectrum disorder (ASD). Recent neuroimaging findings also emphasize an altered processing of sensory salience in ASD. The locus coeruleus–norepinephrine system (LC-NE) has been established as a modulator of this sensory salience processing (SSP). We tested the hypothesis that altered LC-NE functioning contributes to different SSP and results in diverging social attention in ASD.MethodsWe analyzed the baseline eye-tracking data of the EU-AIMS Longitudinal European Autism Project (LEAP) for subgroups of autistic participants (n = 166, age = 6–30 years, IQ = 61–138, gender [female/male] = 41/125) or neurotypical development (TD; n = 166, age = 6–30 years, IQ = 63–138, gender [female/male] = 49/117) that were matched for demographic variables and data quality. Participants watched brief movie scenes (k = 85) depicting humans in social situations (human) or without humans (non-human). SSP was estimated by gazes on physical and motion salience and a corresponding pupillary response that indexes phasic activity of the LC-NE. Social attention is estimated by gazes on faces via manual areas of interest definition. SSP is compared between groups and related to social attention by linear mixed models that consider temporal dynamics within scenes. Models are controlled for comorbid psychopathology, gaze behavior, and luminance.ResultsWe found no group differences in gazes on salience, whereas pupillary responses were associated with altered gazes on physical and motion salience. In ASD compared to TD, we observed pupillary responses that were higher for non-human scenes and lower for human scenes. In ASD, we observed lower gazes on faces across the duration of the scenes. Crucially, this different social attention was influenced by gazes on physical salience and moderated by pupillary responses.LimitationsThe naturalistic study design precluded experimental manipulations and stimulus control, while effect sizes were small to moderate. Covariate effects of age and IQ indicate that the findings differ between age and developmental subgroups.ConclusionsPupillary responses as a proxy of LC-NE phasic activity during visual attention are suggested to modulate sensory salience processing and contribute to attenuated social attention in ASD.

Patterns of connectome variability in autism across five functional activation tasks: findings from the LEAP project

BackgroundAutism spectrum disorder (autism) is a complex neurodevelopmental condition with pronounced behavioral, cognitive, and neural heterogeneities across individuals. Here, our goal was to characterize heterogeneity in autism by identifying patterns of neural diversity as reflected in BOLD fMRI in the way individuals with autism engage with a varied array of cognitive tasks.MethodsAll analyses were based on the EU-AIMS/AIMS-2-TRIALS multisite Longitudinal European Autism Project (LEAP) with participants with autism (n = 282) and typically developing (TD) controls (n = 221) between 6 and 30 years of age. We employed a novel task potency approach which combines the unique aspects of both resting state fMRI and task-fMRI to quantify task-induced variations in the functional connectome. Normative modelling was used to map atypicality of features on an individual basis with respect to their distribution in neurotypical control participants. We applied robust out-of-sample canonical correlation analysis (CCA) to relate connectome data to behavioral data.ResultsDeviation from the normative ranges of global functional connectivity was greater for individuals with autism compared to TD in each fMRI task paradigm (all tasks p < 0.001). The similarity across individuals of the deviation pattern was significantly increased in autistic relative to TD individuals (p < 0.002). The CCA identified significant and robust brain-behavior covariation between functional connectivity atypicality and autism-related behavioral features.ConclusionsIndividuals with autism engage with tasks in a globally atypical way, but the particular spatial pattern of this atypicality is nevertheless similar across tasks. Atypicalities in the tasks originate mostly from prefrontal cortex and default mode network regions, but also speech and auditory networks. We show how sophisticated modeling methods such as task potency and normative modeling can be used toward unravelling complex heterogeneous conditions like autism.

Connectome-wide Mega-analysis Reveals Robust Patterns of Atypical Functional Connectivity in Autism

BackgroundNeuroimaging studies of functional connectivity (FC) in autism have been hampered by small sample sizes and inconsistent findings with regard to whether connectivity is increased or decreased in individuals with autism, whether these alterations affect focal systems or reflect a brain-wide pattern, and whether these are age and/or sex dependent. MethodsThe study included resting-state functional magnetic resonance imaging and clinical data from the EU-AIMS LEAP (European Autism Interventions Longitudinal European Autism Project) and the ABIDE (Autism Brain Imaging Data Exchange) 1 and 2 initiatives of 1824 (796 with autism) participants with an age range of 5–58 years. Between-group differences in FC were assessed, and associations between FC and clinical symptom ratings were investigated through canonical correlation analysis. ResultsAutism was associated with a brainwide pattern of hypo- and hyperconnectivity. Hypoconnectivity predominantly affected sensory and higher-order attentional networks and correlated with social impairments, restrictive and repetitive behavior, and sensory processing. Hyperconnectivity was observed primarily between the default mode network and the rest of the brain and between cortical and subcortical systems. This pattern was strongly associated with social impairments and sensory processing. Interactions between diagnosis and age or sex were not statistically significant. ConclusionsThe FC alterations observed, which primarily involve hypoconnectivity of primary sensory and attention networks and hyperconnectivity of the default mode network and subcortex with the rest of the brain, do not appear to be age or sex dependent and correlate with clinical dimensions of social difficulties, restrictive and repetitive behaviors, and alterations in sensory processing. These findings suggest that the observed connectivity alterations are stable, trait-like features of autism that are related to the main symptom domains of the condition.

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

BackgroundAlthough 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. MethodsWe 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. ResultsOne 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. ConclusionsOur 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.

Evaluation of data imputation strategies in complex, deeply-phenotyped data sets: the case of the EU-AIMS Longitudinal European Autism Project

An increasing number of large-scale multi-modal research initiatives has been conducted in the typically developing population, e.g. Dev. Cogn. Neur. 32:43-54, 2018; PLoS Med. 12(3):e1001779, 2015; Elam and Van Essen, Enc. Comp. Neur., 2013, as well as in psychiatric cohorts, e.g. Trans. Psych. 10(1):100, 2020; Mol. Psych. 19:659–667, 2014; Mol. Aut. 8:24, 2017; Eur. Child and Adol. Psych. 24(3):265–281, 2015. Missing data is a common problem in such datasets due to the difficulty of assessing multiple measures on a large number of participants. The consequences of missing data accumulate when researchers aim to integrate relationships across multiple measures. Here we aim to evaluate different imputation strategies to fill in missing values in clinical data from a large (total N = 764) and deeply phenotyped (i.e. range of clinical and cognitive instruments administered) sample of N = 453 autistic individuals and N = 311 control individuals recruited as part of the EU-AIMS Longitudinal European Autism Project (LEAP) consortium. In particular, we consider a total of 160 clinical measures divided in 15 overlapping subsets of participants. We use two simple but common univariate strategies—mean and median imputation—as well as a Round Robin regression approach involving four independent multivariate regression models including Bayesian Ridge regression, as well as several non-linear models: Decision Trees (Extra Trees., and Nearest Neighbours regression. We evaluate the models using the traditional mean square error towards removed available data, and also consider the Kullback–Leibler divergence between the observed and the imputed distributions. We show that all of the multivariate approaches tested provide a substantial improvement compared to typical univariate approaches. Further, our analyses reveal that across all 15 data-subsets tested, an Extra Trees regression approach provided the best global results. This not only allows the selection of a unique model to impute missing data for the LEAP project and delivers a fixed set of imputed clinical data to be used by researchers working with the LEAP dataset in the future, but provides more general guidelines for data imputation in large scale epidemiological studies.

Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls.

Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five control EEGs were acquired from two independent studies [Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project]. We extracted frequency and functional network-based features from 10 to 20 s epochs of resting-state EEG, including relative power spectral density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. In addition, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2–5 Hz) (P = 0.0007, hedges’ g = 0.55) and low-alpha (6–9 Hz) (P = 2.9 × 10−8, g = 0.80) frequency bands, peak alpha frequency (P = 0.000007, g = 0.66), functional network mean degree (P = 0.0006, g = 0.48) and brain network ictogenicity (P = 0.00006, g = 0.56) between epilepsy and controls. Since age (P = 0.009) and epoch length (P = 1.7 × 10−8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared with controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in idiopathic generalized epilepsy particularly for their use as biomarkers.

Cerebellar Atypicalities in Autism?

BackgroundThe 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. MethodsWe 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. ResultsWe 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. ConclusionsDespite 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.

Interindividual Differences in Cortical Thickness and Their Genomic Underpinnings in Autism Spectrum Disorder.

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. 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. 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. 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.

Towards robust and replicable sex differences in the intrinsic brain function of autism

BackgroundMarked sex differences in autism prevalence accentuate the need to understand the role of biological sex-related factors in autism. Efforts to unravel sex differences in the brain organization of autism have, however, been challenged by the limited availability of female data.MethodsWe addressed this gap by using a large sample of males and females with autism and neurotypical (NT) control individuals (ABIDE; Autism: 362 males, 82 females; NT: 409 males, 166 females; 7–18 years). Discovery analyses examined main effects of diagnosis, sex and their interaction across five resting-state fMRI (R-fMRI) metrics (voxel-level Z > 3.1, cluster-level P < 0.01, gaussian random field corrected). Secondary analyses assessed the robustness of the results to different pre-processing approaches and their replicability in two independent samples: the EU-AIMS Longitudinal European Autism Project (LEAP) and the Gender Explorations of Neurogenetics and Development to Advance Autism Research.ResultsDiscovery analyses in ABIDE revealed significant main effects of diagnosis and sex across the intrinsic functional connectivity of the posterior cingulate cortex, regional homogeneity and voxel-mirrored homotopic connectivity (VMHC) in several cortical regions, largely converging in the default network midline. Sex-by-diagnosis interactions were confined to the dorsolateral occipital cortex, with reduced VMHC in females with autism. All findings were robust to different pre-processing steps. Replicability in independent samples varied by R-fMRI measures and effects with the targeted sex-by-diagnosis interaction being replicated in the larger of the two replication samples—EU-AIMS LEAP.LimitationsGiven the lack of a priori harmonization among the discovery and replication datasets available to date, sample-related variation remained and may have affected replicability.ConclusionsAtypical cross-hemispheric interactions are neurobiologically relevant to autism. They likely result from the combination of sex-dependent and sex-independent factors with a differential effect across functional cortical networks. Systematic assessments of the factors contributing to replicability are needed and necessitate coordinated large-scale data collection across studies.

Fractionating autism based on neuroanatomical normative modeling

Autism is a complex neurodevelopmental condition with substantial phenotypic, biological, and etiologic heterogeneity. It remains a challenge to identify biomarkers to stratify autism into replicable cognitive or biological subtypes. Here, we aim to introduce a novel methodological framework for parsing neuroanatomical subtypes within a large cohort of individuals with autism. We used cortical thickness (CT) in a large and well-characterized sample of 316 participants with autism (88 female, age mean: 17.2 ± 5.7) and 206 with neurotypical development (79 female, age mean: 17.5 ± 6.1) aged 6–31 years across six sites from the EU-AIMS multi-center Longitudinal European Autism Project. Five biologically based putative subtypes were derived using normative modeling of CT and spectral clustering. Three of these clusters showed relatively widespread decreased CT and two showed relatively increased CT. These subtypes showed morphometric differences from one another, providing a potential explanation for inconsistent case–control findings in autism, and loaded differentially and more strongly onto symptoms and polygenic risk, indicating a dilution of clinical effects across heterogeneous cohorts. Our results provide an important step towards parsing the heterogeneous neurobiology of autism.

P.123 Gray matter covariations and core symptoms of autism spectrum disorder. Results from the EU-AIMS Longitudinal European Autism Project

The corpus callosum modulates interhemispheric communication and cognitive processes. It has been suggested that white matter abnormalities in the corpus callosum are related to the pathophysiology of major depressive disorder (MDD) and bipolar disorder (BD). The aim of this study was to examine microstructural abnormalities in callosal fibers separated by their connection to functional brain regions and determine the relationship of these abnormalities with cognitive function in MDD and BD.The subjects were 18 patients with MDD, 20 patients with BD, and 21 healthy controls. The callosal fibers were divided into 6 segments based on their cortical projection using tract-specific analysis of diffusion tensor imaging. We examined differences in the fractional anisotropy (FA) of callosal fibers in six segments among the three subject groups and examined the correlation between the FA in each segment and cognitive performance in the 3 groups.The FA of anterior callosal fibers were reduced significantly in the MDD and BD groups compared to those in the HC group, and the FA of anterior callosal fibers correlated significantly with the raw scores of the digit sequencing task and symbol coding in the MDD group.The patients were medicated at the time of scanning, and the MDD and BD groups were not matched for symptom severity.Our results suggest that MDD and BD have similar microstructural abnormalities in anterior callosal fibers connecting bilateral frontal cortices, and these abnormalities may be related to impairment of working memory and attention in MDD.

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

BackgroundVoxel-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.MethodsWe 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.ResultsGLM 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.LimitationsOnly 55.9% of the participants with autism had complete questionnaire data on continuous parent-reported symptom measures.ConclusionsCovaried 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.

Cohort studies in child and adolescent psychiatry

HintergrundKohortenstudien mit frühem Beginn und Lebensspannenperspektive sind essenziell, um die Verläufe psychiatrischer Erkrankungen sowie deren Risiko- und Resilienzfaktoren zu beleuchten.Ziel der ArbeitDie Bedeutung von Längsschnittstudien wird dargestellt und exemplarisch die Mannheimer Risikokinderstudie (MARS), die ABCD (Adolescent Brain Cognitive Development), KiGGS (Kinder- und Jugendgesundheitssurvey) und AIMS LEAP (Longitudinal European Autism Project) -Kohortenstudien beschrieben.Material und MethodenEs erfolgte eine Literatursuche in MEDLINE.ErgebnisseDie MARS begleitet Teilnehmer mit psychosozialen und organischen Risiken seit über 30 Jahren von der Geburt an und hat gezeigt, dass Risiken vor und kurz nach Geburt bis in die frühe Kindheit besonders wichtig für die neurobiologische und psychische Entwicklung sind. Die ABCD-Kohortenstudie (Beginn 9–10 Jahre) unterstreicht die Wichtigkeit früher sozioemotionaler, pränataler Risiken sowie Toxinexposition für die Entwicklung. Die KiGGS-Kohortenstudie, die Kinder und Jugendliche von 0 bis 17 Jahren bis zum Alter von 10 bis 28 Jahre verfolgte, hebt die Bedeutung des sozioökonomischen Status sowie auch geschlechtsspezifischer Effekte hinsichtlich sensitiver Perioden für das Auftreten psychischer Auffälligkeiten sowie deren Verlauf hervor. Die AIMS-Kohortenstudie begleitet Menschen mit und ohne Autismusspektrumstörungen im Alter von 6 bis 30 Jahren, wobei erste Befunde auf Gruppenebene kleine Effekte zeigen.SchlussfolgerungenExistierende Kohortenstudien zur frühen psychischen Entwicklung weisen spezifische Schwerpunkte auf. Um allgemeine und spezifische Risiko- und Resilienzfaktoren zu identifizieren und trajektorielle Verläufe zu modellieren, können bestehende multimodale Datensätze integriert werden. Weitere epidemiologische und klinische Kohortenstudien mit Beginn in der Pränatalzeit sowie multidimensionale Untersuchungsstrategien (deep phenotyping) sind erforderlich, um die komplexe Ätiopathogenese psychischer Störungen weiter zu entschlüsseln.

How do core autism traits and associated symptoms relate to quality of life? Findings from the Longitudinal European Autism Project.

Previous studies have reported reduced quality of life in autism. Improving quality of life for autistic people is, therefore, a key priority for clinical research and practice. However, the relative impact of core autism traits (e.g. social-communication difficulties), as compared to associated mental health symptoms (e.g. anxiety, depression) on quality of life remains poorly understood. This is despite at least 20%–50% of autistic individuals experiencing associated anxiety and/or depression symptoms. Hence, we measured subjective quality of life in 573 six to thirty-year-olds (autism spectrum disorder N = 344), using two widely validated questionnaires. Adults self-reported on the World Health Organization Quality of Life–Brief instrument. Parents of children/adolescents completed the Child Health and Illness Profile. We assessed individual variability across both measures and modelled associations between quality of life, core autism traits, anxiety, and depression symptoms. Across both age groups and quality of life measures, autistic individuals scored lower than comparison individuals, on average, particularly for physical health in adults (d = −1.24, 95% confidence interval: [−1.56, −0.93]) and school achievement for children/adolescents (d = −1.06, 95% confidence interval: [−1.29, −0.84]). However, a notable proportion of autistic individuals (36%–71% across quality of life domains) did not have reduced quality of life. Across ages and quality of life measures, severity of associated symptoms was significantly related to reduced quality of life on several domains, after accounting for core autism traits. Most notably, depression symptoms were related to reduced physical/psychological well-being in both adults (β ⩾ −0.34) and children/adolescents (β = −0.29, 95% confidence interval: [−0.36, −0.14]). For children/adolescents, anxiety symptoms (β ⩾ −0.28) and core social-communication difficulties (β ⩾ −0.22) were also related to subjective quality of life outcomes. Overall, findings indicate that not all autistic individuals experience reduced subjective quality of life. Variability in quality of life is significantly influenced by associated symptoms, across developmental stage. This may provide a tractable target for mental health services to improve quality of life for autistic individuals over the lifespan.Lay abstractPrevious studies suggest that some autistic individuals report lower satisfaction, or well-being, with different aspects of everyday life than those without autism. It is unclear whether this might be partly explained by symptoms of anxiety and/or depression, which affect at least 20%–50% of autistic people. In this study, we measured individual differences in well-being in 573 six to thirty-year-olds with and without a diagnosis of autism. We investigated whether individual differences in well-being were explained by autism traits (e.g. social-communication difficulties) and/or anxiety and depression symptoms. We showed that, though well-being was lower for some autistic individuals, compared to those without autism, many autistic individuals reported good well-being. Where well-being was reduced, this was particularly explained by depression symptoms, across all ages. For children/adolescents, anxiety and social-communication difficulties were also related to some aspects of well-being. Our study suggests that support and services for improving mental health, especially depression symptoms, may also improve broader outcomes for autistic people.