Biology subjects

Beckmann, C. F.

Publications and source records attributed to Beckmann, C. F..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Patterns of connectome variability in autism across five functional activation tasks. Fndings from the LEAP project

BackgroundAutism spectrum disorder (autism) is a complex neurodevelopmental condition with pronounced behavioural, cognitive, and neural heterogeneities across individuals. Here, our goal was to characterise 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 and typically developing controls (TD) 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 behavioural 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 brainbehavior 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, moving forward, sophisticated modeling methods such as task-potency and normative modeling will prove key to unravelling complex heterogeneous conditions like autism.

neuroscience↗

bioRxiv2022

Autism is associated with inter-individual variations of gray and white matter morphology

BackgroundAlthough many studies have explored atypicalities in gray and white matter (GM, WM) morphology of autism, most of them rely on unimodal analyses that do not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish multimodal brain patterns that differentiate between autism and typically developing (TD) controls and explore associations between these brain patterns and clinical measures. MethodsWe studied 183 individuals with autism and 157 TD individuals (6-30 years) in a large deeply phenotyped autism dataset (EU-AIMS LEAP). Linked Independent Component Analysis was utilized to link all participants GM and WM images, and group comparisons of modality shared variances were examined. Subsequently, we performed a canonical correlation analysis to explore the aggregated effects between all multimodal GM-WM covariations and clinical profiles. ResultsOne multimodal pattern was significantly related to autism. This pattern was primarily associated with GM in bilateral insula, frontal, pre- and post-central, cingulate, and caudate areas, and co-occurred with altered WM features in the superior longitudinal fasciculus. The canonical analysis showed a significant multivariate correlation primarily between multimodal brain patterns that involved variation of corpus callosum, and symptoms of social affect 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 multimodal brain patterns that are spread across the brain involving both cortical and subcortical areas.

neuroscience↗

bioRxiv2021

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

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.

neuroscience↗

bioRxiv2021

Missing genetic links between general factors of brain resting-state functional magnetic resonance imaging, cognition and psychopathology

General factors capturing the shared genetics in psychiatric (genomic p-factor) and cognitive traits (genomic g-factor), and more recently in resting-state functional magnetic resonance imaging-derived brain networks, have contributed to our increased understanding of the etiologies in their respective domains. Yet it remains unclear whether general factors can capture the three-way genetic overlap of psychopathology, cognition and brain function. Here we tested for the presence of this genetic overlap via genetic correlation analyses using summary statistics of genome-wide association studies of the p-factor (N = 162,151 cases and 276,846 controls), the g-factor (N = 269,867), and the two genomic factors estimated from the amplitude in resting-state functional magnetic resonance imaging-derived brain networks (N = 31,688). Unlike hypothesized, only the genetic correlation between the p-factor and the g-factor was significant. We conclude that specific functional brain network constructs may have more potential than their derived general dimensions to capture relevant genetic variation for cognition and psychopathology.

neuroscience↗

bioRxiv2021

Longitudinal changes of ADHD symptoms in association with white matter microstructure: a tract-specific fixel-based analysis

BackgroundVariation in the longitudinal course of childhood attention deficit/hyperactivity disorder (ADHD) coincides with neurodevelopmental maturation of brain structure and function. Prior work has attempted to determine how alterations in white matter (WM) relate to changes in symptom severity, but much of that work has been done in smaller cross-sectional samples using voxel-based analyses. Using standard diffusion-weighted imaging (DWI) methods, we previously showed WM alterations were associated with ADHD symptom remission over time in a longitudinal sample of probands, siblings, and unaffected individuals. Here, we extend this work by further assessing the nature of these changes in WM microstructure by including an additional follow-up measurement (aged 18 - 34 years), and using the more physiologically informative fixel-based analysis (FBA). MethodsData were obtained from 139 participants over 3 clinical and 2 follow-up DWI waves, and analyzed using FBA in regions-of-interest based on prior findings. We replicated previously reported significant models and extended them by adding another time-point, testing whether changes in combined ADHD and hyperactivity-impulsivity (HI) continuous symptom scores are associated with fixel metrics at follow-up. ResultsClinical improvement in HI symptoms over time was associated with more fiber density at follow-up in the left corticospinal tract (lCST) (tmax=1.092, standardized effect[SE]=0.044, pFWE=0.016). Improvement in combined ADHD symptoms over time was associated with more fiber cross-section at follow-up in the lCST (tmax=3.775, SE=0.051, pFWE=0.019). Conclusions: Aberrant white matter development involves both lCST micro- and macrostructural alterations, and its path may be moderated by preceding symptom trajectory.

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bioRxiv2021

The Normative Modeling Framework for Computational Psychiatry

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus "healthy" control analytic approaches, likely due to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. In this article, we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices, and conclude by demonstrating several examples of down-stream analyses the normative model results may facilitate, such as stratification of high-risk individuals, subtyping, and behavioral predictive modeling. The protocol takes approximately 1-3 hours to complete.

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bioRxiv2021

Normative modeling of neuroimaging data using generalized additive models of location scale and shape

Normative modeling aims to quantify the degree to which an individuals brain deviates from a reference sample with respect to one or more variables, which can be used as a potential biomarker of a healthy brain and as a tool to study heterogeneity of psychiatric disorders. The application of normative models is hindered by methodological challenges and lacks standards for the usage and evaluation of normative models. In this paper, we present generalized additive models for location scale and shape (GAMLSS) for normative modeling of neuroimaging data, a flexible modeling framework that can model heteroskedasticity, non-linear effects of variables, and hierarchical structure of the data. It can model non-Gaussian distributions, and it allows for an automatic model order selection, thus improving the accuracy of normative models while mitigating problems of overfitting. Furthermore, we describe measures and diagnostic tools suitable for evaluating normative models and step-by-step examples of normative modeling, including fitting several candidate models, selecting the best models, and transferring them to new scan sites.

neuroscience↗

bioRxiv2021

Federated Multi-Site Normative Modeling using Hierarchical Bayesian Regression

AO_SCPLOWBSTRACTC_SCPLOWClinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) for multi-site normative modeling. The proposed method completes the life-cycle of normative modeling by providing the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed federated framework closes the technical loop for applying normative modeling across multiple sites in a decentralized manner. This will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.

bioinformatics↗

bioRxiv2021

Warped Bayesian Linear Regression for Normative Modelling of Big Data

Normative modelling is becoming more popular in neuroimaging due to its ability to make predictions of deviation from a normal trajectory at the level of individual participants. It allows the user to model the distribution of several neuroimaging modalities, giving an estimation for the mean and centiles of variation. With the increase in the availability of big data in neuroimaging, there is a need to scale normative modelling to big data sets. However, the scaling of normative models has come with several challenges. So far, most normative modelling approaches used Gaussian process regression, and although suitable for smaller datasets (up to a few thousand participants) it does not scale well to the large cohorts currently available and being acquired. Furthermore, most neuroimaging modelling methods that are available assume the predictive distribution to be Gaussian in shape. However, deviations from Gaussianity can be frequently found, which may lead to incorrect inferences, particularly in the outer centiles of the distribution. In normative modelling, we use the centiles to give an estimation of the deviation of a particular participant from the normal trend. Therefore, especially in normative modelling, the correct estimation of the outer centiles is of utmost importance, which is also where data are sparsest. Here, we present a novel framework based on Bayesian Linear Regression with likelihood warping that allows us to address these problems, that is, to scale normative modelling elegantly to big data cohorts and to correctly model non-Gaussian predictive distributions. In addition, this method provides also likelihood-based statistics, which are useful for model selection. To evaluate this framework, we use a range of neuroimaging-derived measures from the UK Biobank study, including image-derived phenotypes (IDPs) and whole-brain voxel-wise measures derived from diffusion tensor imaging. We show good computational scaling and improved accuracy of the warped BLR for certain IDPs and voxels if there was a deviation from normality of these parameters in their residuals. The present results indicate the advantage of a warped BLR in terms of; computational scalability and the flexibility to incorporate non-linearity and non-Gaussianity of the data, giving a wider range of neuroimaging datasets that can be correctly modelled.

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bioRxiv2021

Shared genetic influences on resting-state functional networks of the brain

The amplitude of activation in brain resting state networks (RSNs), measured with resting-state functional MRI, is heritable and genetically correlated across RSNs, indicating pleiotropy. Recent univariate genome-wide association studies (GWAS) explored the genetic underpinnings of individual variation in RSN activity. Yet univariate genomic analyses do not describe the pleiotropic nature of RSNs. In this study we used a novel multivariate method called genomic SEM to model latent factors that capture the shared genomic influence on RSNs and to identify SNPs and genes driving this pleiotropy. Using summary statistics from GWAS of 21 RSNs reported in UK Biobank (N = 31,688), the genomic latent factor analysis was first conducted in a discovery sample (N = 21,081), and then tested in an independent sample from the same cohort (N = 10,607). In the discovery sample, we show that the genetic organization of RSNs can be best explained by two distinct but correlated genetic factors that divide multimodal association networks and sensory networks. Eleven of the 17 factor loadings were replicated in the independent sample. With the multivariate GWAS, we found and replicated nine independent SNPs associated with the joint architecture of RSNs. Further, by combining the discovery and replication samples, we discovered additional SNP and gene associations with the two factors of RSN amplitude. We conclude that modelling the genetic effects on brain function in a multivariate way is a powerful approach to learn more about the biological mechanisms involved in brain function.

neuroscience↗

bioRxiv2021

Independent Genomic Sources of Brain Structure and Function

IntroductionThe last decade has seen a surge in well powered genome-wide association studies (GWASs) of complex behavioural traits, disorders, and more recently, of brain structural and functional neuroimaging features. However, the extreme polygenicity of these complex traits makes it difficult to translate the GWAS signal into mechanistic biological insights. We postulate that the covariance of SNP-effects across many brain features, as be captured by latent genomic components of SNP effect sizes. These may partly reflect the concerted multi-locus genomic effects through known molecular pathways and protein-protein interactions. Here, we test the feasibility of a new data-driven method to derive such latent components of genome-wide effects on more than thousand neuroimaging derived traits, and investigate their utility in interpreting the complex biological processes that shape the GWAS signal. MethodsWe downloaded the GWAS summary statistics of 3,143 brain imaging-derived phenotypes (IDPs) from the UK Biobank, provided by the Oxford Brain Imaging Genetics (BIG) Server (Elliott et al. 2018). Probabilistic independent component analysis (ICA) was used to extract two hundred independent genomic components from the matrix of SNP-effect sizes. We qualitatively describe the distribution of the latent components loadings in the neuroimaging and the genomic dimensions. Gene-wide statistics were calculated for each genomic component. We tested the genomic components enrichment for molecular pathways using MSigDB, and for single-cell RNA-sequencing of adult and foetal brain cells. Results200 components explained 80% of the variance in SNP-effects sizes. Each MRI modality and data processing method projected the imaging data into a clearly distinct cluster in the genomic component embedded space. Among the 200 genomic components, 157 were clearly driven by a single locus, while 39 were highly polygenic. Together, these 39 components were significantly enriched for 2,274 MSigDB gene sets (fully corrected for multiple testing across gene-sets and components). Several components were sensitive to molecular pathways, single cell expression profiles, and brain traits in patterns consistent with knowledge across these biological levels. To illustrate this, we highlight a component that implicated axonal regeneration pathways, which was specifically enriched for gene expression in oligodendrocyte precursors, microglia and astrocytes, and loaded highly on white matter neuroimaging traits. We highlight a second component that implicated synaptic function and neuron projection organization pathways that was specifically enriched for neuronal cell transcriptomes. ConclusionWe propose genomic ICA as a new method to identify latent genetic factors influencing brain structure and function by multimodal MRI. The derived latent genomic dimensions are highly sensitive to known molecular pathways and cell-specific gene expression profiles. Genomic ICA may help to disentangle the many different biological routes by which the genome defines the inter-individual variation of the brain. Future research is aimed at using this method to profile individual subjects genomic data along the new latent dimensions and evaluating the utility of these dimensions in stratifying heterogeneous patient populations.

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bioRxiv2020

Optimising a Simple Fully Convolutional Network (SFCN) for accurate brain age prediction in the PAC 2019 challenge

Brain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subjects predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subjects age in multicentre datasets. We apply a lightweight deep convolutional neural network architecture, Simple Fully Convolutional Neural Network (SFCN), and combined several techniques including data augmentation, transfer learning, model ensemble, and bias correction for brain age prediction. The model achieved first places in both of the two objectives in the PAC 2019 brain age prediction challenge: Mean absolute error (MAE) = 2.90 years without bias removal, and MAE = 2.95 years with bias removal.

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bioRxiv2020

Anterior cingulate cortex hypofunction causes anti-social aggression in mice

Controlling aggression is a crucial skill in social species like rodents and humans, and has been associated with anterior cingulate cortex (ACC). Here, we demonstrate a causal link between ACC hypofunction and failed aggression control in BALB/cJ mice. We first show that ACC in BALB/cJ mice is structurally degraded: Neuron density is decreased, with pervasive neuron death and neuro-toxic astroglia. Gene-set enrichment analysis suggested that this process is driven by neuronal degeneration, which then causes toxic astrogliosis. cFos expression across ACC indicated functional consequences: During aggressive encounters, ACC was engaged in control mice, but not BALB/cJ mice. Chemogenetically activating ACC during aggressive encounters drastically suppressed anti-social aggression but left adaptive aggression intact. The network effects of our chemogenetic perturbation suggest that this behavioural rescue is mediated by suppression of amygdala and hypothalamus and activation of mediodorsal thalamus. Together, these findings highlight the causal role of ACC in curbing anti-social aggression.

neuroscience↗

bioRxiv2020

Associations between ADHD symptom remission and white matter microstructure: a longitudinal analysis

BackgroundAttention-deficit hyperactivity disorder (ADHD) is associated with white matter (WM) microstructure. Our objective was to investigate how WM microstructure is longitudinally related to symptom remission in adolescents and young adults with ADHD. MethodsWe obtained diffusion-weighted imaging (DWI) data from 99 participants at two time points (mean age baseline: 16.91 years, mean age follow-up: 20.57 years). We used voxel-wise Tract-Based Spatial Statistics (TBSS) with permutation-based inference to investigate associations of inattention (IA) and hyperactivity-impulsivity (HI) symptom change with fractional anisotropy (FA) at baseline, follow-up, and change between time points. ResultsRemission of combined HI and IA symptoms was significantly associated with reduced FA at follow-up in the left superior longitudinal fasciculus and the left corticospinal tract (CST) (PFWE=0.038 and PFWE=0.044, respectively), mainly driven by an association between HI remission and follow-up CST FA (PFWE=0.049). There was no significant association of combined symptom decrease with FA at baseline or with changes in FA between the two assessments. ConclusionsIn this longitudinal DWI study of ADHD using dimensional symptom scores, we show that greater symptom decrease is associated with lower follow-up FA in specific WM tracts. Altered FA thus may appear to follow, rather than precede, changes in symptom remission. Our findings indicate divergent WM developmental trajectories between individuals with persistent and remittent ADHD, and support the role of prefrontal and sensorimotor tracts in the remission of ADHD.

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bioRxiv2020

Multivariate associative patterns between the gut microbiota and large-scale brain network connectivity

Research on the gut-brain axis has accelerated substantially over the course of the last years. Many reviews have outlined the important implications of understanding the relation of the gut microbiota with human brain function and behavior. One substantial drawback in integrating gut microbiome and brain data is the lack of integrative multivariate approaches that enable capturing variance in both modalities simultaneously. To address this issue, we applied a linked independent component analysis (LICA) to microbiota and brain connectivity data. We analyzed data from 58 healthy females (mean age = 21.5 years). Magnetic Resonance Imaging data were acquired using resting state functional imaging data. The assessment of gut microbial composition from feces was based on sequencing of the V4 16S rRNA gene region. We used the LICA model to simultaneously factorize the subjects large-scale brain networks and microbiome relative abundance data into 10 independent components of spatial and abundance variation. LICA decomposition resulted in four components with non-marginal contribution of the microbiota data. The default mode network featured strongly in three components, whereas the two-lateralized fronto-parietal attention networks contributed to one component. The executive-control (with the default mode) network was associated to another component. We found the abundance of Prevotella genus was associated to the strength of expression of all networks, whereas Bifidobacterium was associated with the default mode and frontoparietal-attention networks. We provide the first exploratory evidence for multivariate associative patterns between the gut microbiota and brain network connectivity in healthy humans, taking into account the complexity of both systems.

neuroscience↗

bioRxiv2020

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 (LEAP). 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.

neuroscience↗

bioRxiv2020

Overlapping connectivity gradients in the anterior temporal lobe underlie semantic cognition

Convergent evidence from neuroimaging, computational, and clinical research has shown that the anterior temporal lobe (ATL) is critically involved in two key aspects of semantic cognition: the representation of semantic knowledge, and the executive regulation of this knowledge. Both are necessary for integrating features to understand concepts, and to integrate concepts to understand discourse. Here, we tested the hypothesis that these differential aspects of integration map onto different patterns of ATL connectivity. Specifically, we hypothesized that there are two overlapping modes of functional connectivity of the ATL that each predict distinct aspects of semantic cognition on an individual level. We used a novel analytical approach (connectopic mapping) to identify the first two dominant modes connection topographies (i.e. maps of spatially varying connectivity) in the ATL in 766 participants (Human Connectome Project), and summarized these into 16 parameters that reflect inter-individual differences in their functional organization. If these connection topographies reflect the ATLs functional multiplicity, then we would expect to find a dissociation where one mode (but not the other) correlates with cross-modal matching of verbal and visual information (picture vocabulary naming), and the other (but not the former) correlates with how quickly and accurately relevant semantic information is retrieved (story comprehension). Our analysis revealed a gradient of spatially varying connectivity along the inferior-superior axis, and secondly, an anterior to posterior gradient. Multiple regression analyses revealed a double dissociation such that individual differences in the inferior-superior gradient are predictive of differences in story comprehension, whereas the anterior-posterior gradient maps onto differences in picture vocabulary naming, but not vice versa. These findings indicate that overlapping gradients of functional connectivity in the ATL are related to differential behaviors, which is important for understanding how its functional organization underlies its multiple functions.

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bioRxiv2020

Differential functional neural circuitry behind autism subtypes with marked imbalance between social-communicative and restricted repetitive behavior symptom domains

Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC=RRB, SC>RRB, and RRB>SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n=509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC>RRB and visual association circuitry in SC=RRB. The SC=RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.

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