Connectivity In Autism Spectrum Disorder Research Articles

MTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity

Postmortem studies have revealed increased density of excitatory synapses in the brains of individuals with autism spectrum disorder (ASD), with a putative link to aberrant mTOR-dependent synaptic pruning. ASD is also characterized by atypical macroscale functional connectivity as measured with resting-state fMRI (rsfMRI). These observations raise the question of whether excess of synapses causes aberrant functional connectivity in ASD. Using rsfMRI, electrophysiology and in silico modelling in Tsc2 haploinsufficient mice, we show that mTOR-dependent increased spine density is associated with ASD -like stereotypies and cortico-striatal hyperconnectivity. These deficits are completely rescued by pharmacological inhibition of mTOR. Notably, we further demonstrate that children with idiopathic ASD exhibit analogous cortical-striatal hyperconnectivity, and document that this connectivity fingerprint is enriched for ASD-dysregulated genes interacting with mTOR or Tsc2. Finally, we show that the identified transcriptomic signature is predominantly expressed in a subset of children with autism, thereby defining a segregable autism subtype. Our findings causally link mTOR-related synaptic pathology to large-scale network aberrations, revealing a unifying multi-scale framework that mechanistically reconciles developmental synaptopathy and functional hyperconnectivity in autism.

Overconnectivity of the right Heschl's and inferior temporal gyrus correlates with symptom severity in preschoolers with autism spectrum disorder.

Previous studies have reported varying findings regarding the association of brain connectivity in autism spectrum disorder (ASD) with overconnectivity, underconnectivity, or both. Despite the emerging understanding that ASD is a developmental disconnection syndrome, very little is known about structural brain networks in preschool‐aged children with low‐functioning ASD. We aimed to investigate the structural brain connectivity of low‐functioning ASD using diffusion magnetic resonance imaging and graph theory to examine alterations in different brain network topologies and identify any correlations with the clinical severity of ASD in preschool‐aged children. Fifty‐two preschool‐aged children (28 with ASD and 24 with typical development) were included in the analysis. Graph‐based network analysis was performed to examine the global and local structural brain networks. Nodal network measures exhibited increased nodal strength in the right Heschl's gyrus, which was positively associated with all autistic clinical symptoms (Autism Diagnostic Observation Schedule and Childhood Autism Rating Scale [CARS]). The nodal strength of the right inferior temporal gyrus showed a moderate correlation with the CARS score. Using network‐based statistics, we identified a subnetwork with increased connections encompassing the right Heschl's gyrus and the right inferior temporal gyrus in preschool‐aged children with ASD. The asymmetric value in the inferior temporal gyrus exhibited right dominance of nodal strength in children with ASD compared to that in typically developing children. Our findings support the theory of aberrant brain growth and overconnectivity as the underlying mechanism of ASD and provides new insights into potential regional biomarkers that can detect low‐functioning ASD in preschool‐aged children.Lay SummaryThis study supports the theory of aberrant brain growth and overconnectivity as an explanation for ASD. Measuring the right HG and inferior temporal gyrus provides new insights of potential regional biomarkers underpinning ASD in preschool‐aged children.

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder.

Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks’ time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models’ parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal’s application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain’s right to the left hemisphere is different between ASD and controls.

Atypicalities in the developmental trajectory of cortico-striatal functional connectivity in autism spectrum disorder.

Autism spectrum disorder has long been conceptualized as a disorder of "atypical development of functional brain connectivity (which refers to correlations in activity levels of distant brain regions)." However, most of the research has focused on the connectivity between cortical regions, and much remains unknown about the developmental changes of functional connectivity between subcortical and cortical areas in autism spectrum disorder. We used the technique of resting-state functional magnetic resonance imaging to explore the developmental characteristics of intrinsic functional connectivity (functional brain connectivity when people are asked not to do anything) between subcortical and cortical regions in individuals with and without autism spectrum disorder aged 6-30 years. We focused on one important subcortical structure called striatum, which has roles in motor, cognitive, and affective processes. We found that cortico-striatal intrinsic functional connectivities showed opposite developmental trajectories in autism spectrum disorder and typically developing individuals, with connectivity increasing with age in autism spectrum disorder and decreasing or constant in typically developing individuals. We also found significant negative behavioral correlations between those atypical cortico-striatal intrinsic functional connectivities and autistic symptoms, such as social-communication deficits, and restricted/repetitive behaviors and interests. Taken together, this work highlights that the atypical development of cortico-subcortical functional connectivity might be largely involved in the neuropathological mechanisms of autism spectrum disorder.

Atypical development of emotional face processing networks in autism spectrum disorder from childhood through to adulthood

Impairments in social functioning are hallmarks of autism spectrum disorder (ASD) and atypical functional connectivity may underlie these difficulties. Emotion processing networks typically undergo protracted maturational changes, however, those with ASD show either hyper- or hypo-connectivity with little consensus on the functional connectivity underpinning emotion processing. Magnetoencephalography was used to investigate age-related changes in whole-brain functional connectivity of eight regions of interest during happy and angry face processing in 190 children, adolescents and adults (6–39 years) with and without ASD. Findings revealed age-related changes from child- through to mid-adulthood in functional connectivity in controls and in ASD in theta, as well as age-related between-group differences across emotions, with connectivity decreasing in ASD, but increasing for controls, in gamma. Greater connectivity to angry faces was observed across groups in gamma. Emotion-specific age-related between-group differences in beta were also found, that showed opposite trends with age for happy and angry in ASD. Our results establish altered, frequency-specific developmental trajectories of functional connectivity in ASD, across distributed networks and a broad age range, which may finally help explain the heterogeneity in the literature.

Complement C4 Is Reduced in iPSC-Derived Astrocytes of Autism Spectrum Disorder Subjects.

In recent years, accumulating evidence has shown that the innate immune complement system is involved in several aspects of normal brain development and in neurodevelopmental disorders, including autism spectrum disorder (ASD). Although abnormal expression of complement components was observed in post-mortem brain samples from individuals with ASD, little is known about the expression patterns of complement molecules in distinct cell types in the developing autistic brain. In the present study, we characterized the mRNA and protein expression profiles of a wide range of complement system components, receptors and regulators in induced pluripotent stem cell (iPSC)-derived neural progenitor cells, neurons and astrocytes of individuals with ASD and neurotypical controls, which constitute in vitro cellular models that recapitulate certain features of both human brain development and ASD pathophysiology. We observed that all the analyzed cell lines constitutively express several key complement molecules. Interestingly, using different quantification strategies, we found that complement C4 mRNA and protein are expressed in significantly lower levels by astrocytes derived from ASD individuals compared to control astrocytes. As astrocytes participate in synapse elimination, and diminished C4 levels have been linked to defective synaptic pruning, our findings may contribute to an increased understanding of the atypically enhanced brain connectivity in ASD.

Narrowband Resting-State fNIRS Functional Connectivity in Autism Spectrum Disorder.

Hemispheric asymmetry in the power spectrum of low-frequency spontaneous hemodynamic fluctuations has been previously observed in autism spectrum disorder (ASD). This observation may imply a specific narrow-frequency band in which individuals with ASD could show more significant alteration in resting-state functional connectivity (RSFC). To test this assumption, we evaluated narrowband RSFC at several frequencies for functional near-infrared spectroscopy signals recorded from the bilateral temporal lobes on 25 children with ASD and 22 typically developing (TD) children. In several narrow-frequency bands, we observed altered interhemispheric RSFC in ASD. However, in the band of 0.01–0.02 Hz, more mirrored channel pairs (or cortical sites) showed significantly weaker RSFC in the ASD group. Receiver operating characteristic analysis further demonstrated that RSFC in the narrowband of 0.01–0.02 Hz might have better differentiation ability between the ASD and TD groups. This may indicate that the narrowband RSFC could serve as a characteristic for the prediction of ASD.

Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model.

GoalBrain functional networks (BFNs) constructed using resting-state functional magnetic resonance imaging (fMRI) have proven to be an effective way to understand aberrant functional connectivity in autism spectrum disorder (ASD) patients. It is still challenging to utilize these features as potential biomarkers for discrimination of ASD. The purpose of this work is to classify ASD and normal controls (NCs) using BFNs derived from rs-fMRI.MethodsA deep learning framework was proposed that integrated convolutional neural network (CNN) and channel-wise attention mechanism to model both intra- and inter-BFN associations simultaneously for ASD diagnosis. We investigate the effects of each BFN on performance and performed inter-network connectivity analysis between each pair of BFNs. We compared the performance of our CNN model with some state-of-the-art algorithms using functional connectivity features.ResultsWe collected 79 ASD patients and 105 NCs from the ABIDE-I dataset. The mean accuracy of our classification algorithm was 77.74% for classification of ASD versus NCs.ConclusionThe proposed model is able to integrate information from multiple BFNs to improve detection accuracy of ASD.SignificanceThese findings suggest that large-scale BFNs is promising to serve as reliable biomarkers for diagnosis of ASD.

Corpus callosum size and homotopic connectivity in Autism spectrum disorder

By examining how morphology of the corpus callosum (CC) in autism spectrum disorder (ASD) may affect functional communication across hemispheres, we hope to provide new insights into the structure-function relationship in the brain. We used a sample of 94 participants from the Autism Brain Imaging Data Exchange (ABIDE) database (55 typically-developing (TD) and 39 with ASD). The CC was segmented into five sub-regions (anterior, mid-anterior, central, mid-posterior, posterior) using FreeSurfer software, which were further examined for group differences. The total volume and specific sub-region volumes of the CC, and interhemispheric (homotopic) functional connectivity were calculated, along with the relationship between volume and connectivity. These measures were correlated with social ability assessed by the Social Responsiveness Scale (SRS). The central sub-region of CC was significantly smaller in ASD, although there was no group difference in total CC volume. ASD participants also showed stronger homotopic connectivity in the superior frontal gyrus. SRS scores were negatively correlated with the CC central sub-region volumes in ASD. The findings of this study add to the body of research showing morphological differences in the CC in ASD as well as connectivity differences. The absence of a significant relationship between structure and homotopic functional connectivity aligns with previous findings.

“Unrest while Resting”? Brain entropy in autism spectrum disorder

Biological systems typically exhibit complex behavior with nonlinear dynamic properties. Nonlinear signal processing techniques such as sample entropy is a novel approach to characterize the temporal dynamics of brain connectivity. Estimating entropy is especially important in clinical populations such as autism spectrum disorder (ASD) as differences in entropy may signal functional alterations in the brain. Considering the models of disrupted brain network connectivity in ASD, sample entropy would provide a novel direction to understand brain organization. Resting state fMRI data from 45 high-functioning children with ASD and 45 age-and-IQ-matched typically developing (TD) children were obtained from the Autism Brain Imaging Data Exchange (ABIDE-II) database. Data were preprocessed using the CONN toolbox. Sample entropy was then calculated using the complexity toolbox, in a whole-brain voxelwise manner as well as in regions of interests (ROIs) based methods. ASD participants demonstrated significantly increased entropy in left angular gyrus, superior parietal lobule, and right inferior temporal gyrus; and reduced sample entropy in superior frontal gyrus compared to TD participants. Positive correlations of average entropy in clusters of significant group differences scores across all subjects were found. Finally, ROI analysis revealed a main effect of lobes. Differences in entropy between the ASD and TD groups suggests that entropy may provide another important index of brain dysfunction in clinical populations like ASD. Further, the relationship between increased entropy and ASD symptoms in our study underscores the role of optimal brain synchronization in cognitive and behavioral functions.

Improvement of brain functional connectivity in autism spectrum disorder: an exploratory study on the potential use of virtual reality.

Patients with Autism Spectrum Disorder (ASD) need to be provided with behavioral, psychological, educational, or skill-building interventions as early as possible. Cognitive Behavior Therapy has proven useful to manage such problems. There is also growing evidence on the usefulness of Virtual Reality Therapy (VRT) in treating various functional deficits in ASD. This exploratory study is aimed at assessing the changes in cognitive functions in children with ASD, and the putative subtending neurophysiological mechanisms, following the provision of rehab training using an innovative VRT system. Twenty patients with ASD, aged 6-15years, were provided with 24 sessions of VRT by using the pediatric module of the BTS NIRVANA System. Neuropsychological and EEG evaluations were carried out before and at the end of the training. After VRT, all patients showed a significant improvement in their cognitive-behavioral problems concerning attention processes, visuospatial cognition, and anxiety. These findings were paralleled by an evident reshape of frontoparietal connectivity in the alpha and theta frequency range. Our study suggests that VRT could be a useful and promising tool to improve ASD neurorehabilitation outcomes. This improvement is likely to occur through changes in frontoparietal network connectivity following VRT.

Atypically high influence of subcortical activity on primary sensory regions in autism

BackgroundHypersensitivity, stereotyped behaviors and attentional problems in autism spectrum disorder (ASD) are compatible with inefficient filtering of undesired or irrelevant sensory information at early stages of neural processing. This could stem from the persistent overconnectivity between primary sensory regions and deep brain nuclei in both children and adults with ASD – as reported by several previous studies – which could reflect a decreased or arrested maturation of brain connectivity. However, it has not yet been investigated whether this overconnectivity can be modelled as an excessive directional influence of subcortical brain activity on primary sensory cortical regions in ASD, with respect to age-matched typically developing (TD) individuals. MethodsTo this aim, we used dynamic causal modelling to estimate (1) the directional influence of subcortical activity on cortical processing and (2) the functional segregation of primary sensory cortical regions from subcortical activity in 166 participants with ASD and 193 TD participants from the Autism Brain Imaging Data Exchange (ABIDE). We then specifically tested the hypothesis that the age-related changes of these indicators of brain connectivity would differ between the two groups. ResultsWe found that in TD participants age was significantly associated with decreased influence of subcortical activity on cortical processing, paralleled by an increased functional segregation of cortical sensory processing from subcortical activity. Instead these effects were highly reduced and mostly absent in ASD participants, suggesting a delayed or arrested development of the segregation between subcortical and cortical sensory processing in ASD. ConclusionThis atypical configuration of subcortico-cortical connectivity in ASD can result in an excessive amount of unprocessed sensory input relayed to the cortex, which is likely to impact cognitive functioning in everyday situations where it is beneficial to limit the influence of basic sensory information on cognitive processing, such as activities requiring focused attention or social interactions.

Dynamic time warping outperforms Pearson correlation in detecting atypical functional connectivity in autism spectrum disorders

Resting state fMRI (rsfMRI) is frequently used to study brain function, including in clinical populations. Similarity of blood-oxygen-level-dependent (BOLD) fluctuations during rsfMRI between brain regions is thought to reflect intrinsic functional connectivity (FC), potentially due to history of coactivation. To quantify similarity, studies have almost exclusively relied on Pearson correlation, which assumes linearity and can therefore underestimate FC if the hemodynamic response function differs regionally or there is BOLD signal lag between timeseries. Here we show in three cohorts of children, adolescents and adults, with and without autism spectrum disorders (ASDs), that measuring the similarity of BOLD signal fluctuations using non-linear dynamic time warping (DTW) is more robust to global signal regression (GSR), has higher test-retest reliability and is more sensitive to task-related changes in FC. Additionally, when comparing FC between individuals with ASDs and typical controls, more group differences are detected using DTW. DTW estimates are also more related to ASD symptom severity and executive function, while Pearson correlation estimates of FC are more strongly associated with respiration during rsfMRI. Together these findings suggest that non-linear methods such as DTW improve estimation of resting state FC, particularly when studying clinical populations whose hemodynamics or neurovascular coupling may be altered compared to typical controls.

S1-3 Functional connectivity in autism spectrum disorder evaluated using rs-fMRI and DKI

S1-3 Functional connectivity in autism spectrum disorder evaluated using rs-fMRI and DKI

A preliminary study of atypical cortical change ability of dynamic whole-brain functional connectivity in autism spectrum disorder

Objectives Designing new objectively diagnostic methods of autism spectrum disorder (ASD) are burning questions. Dynamic functional connectivity (DFC) methodology based on fMRI data are an effective lever to investigate changeability evolution of signal synchronization in macroscopic neural activity patterns. Methods Embracing the network dynamics concepts, this paper introduces changeability index (-score)which is focused on time-varying aspects of FCs, and develops a new framework for researching the roots of ASD brains at resting states in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in -scores of between atypical and typical DFCs of 30 healthy controls (HCs) and 48 ASD patients. Results The abnormities of edge C-scores are found across widespread brain cortex in ASD brains. For whole brain regional C-scores of ASD patients, orbitofrontal middle cortex L, inferior triangular frontal gyrus L, middle occipital gyrus L, postcentral gyrus L, supramarginal L, supramarginal R, cerebellum 8 L, and cerebellum 10 Rare endowed with significantly different -scores.At brain subsystems level, C-scores in left hemisphere, right hemisphere, top hemisphere, bottom hemisphere, frontal lobe, parietal lobe, occipital lobe, cerebellum sub systems are abnormal in ASD patients. Conclusions The ASD brains have whole-brain abnormity on widespread regions. Through the strict evidence-based study, it was found that the changeability index (C-score) is a meaningful biological marker to explore cortical activity in ASD.

Poly(I:C) Challenge Alters Brain Expression of Oligodendroglia-Related Genes of Adult Progeny in a Mouse Model of Maternal Immune Activation.

Background: Altered white matter connectivity, as evidenced by pervasive microstructural changes in myelination and axonal integrity in neuroimaging studies, has been implicated in the development of autism spectrum disorder (ASD) and related neurodevelopmental conditions such as schizophrenia. Despite an increasing appreciation that such white matter disconnectivity is linked to social behavior deficits, virtually no etiologically meaningful myelin-related genes have been identified in oligodendrocytes, the key myelinating cells in the CNS, to furnish an account on the causes. The impact of neurodevelopmental perturbations during pregnancy such as maternal immune activation (MIA) on these genes in memory-related neural networks has not been experimentally scrutinized.Methods: In this study, a mouse model of MIA by the viral dsRNA analog poly(I:C) was employed to mimic the effects of inflammation during pregnancy. Transcriptional expression levels of selected myelin- or oligodendroglia-related genes implicated in schizophrenia or ASD development were analyzed by in situ hybridization (ISH) and quantitative real-time PCR (qRT-PCR) with brain samples from MIA and control groups. The analysis focused on SOX-10 (SRY-related HMG-box 10), MAG (myelin-associated glycoprotein), and Tf (transferrin) expression in the hippocampus and the surrounding memory-related cortical regions in either hemisphere.Results: Specifically, ISH reveals that in the brain of prenatal poly(I:C)-exposed mouse offspring in the MIA model (gestation day 9), mRNA expression of the genes SOX10, MAG and Tf were generally reduced in the limbic system including the hippocampus, retrosplenial cortex and parahippocampal gyrus on either side of the hemispheres. qRT-PCR further confirms the reduction of SOX10, MAG, and Tf expression in the medial prefrontal cortex, sensory cortex, amygdala, and hippocampus.Conclusions: Our present results provide direct evidence that prenatal exposure to poly(I:C) elicits profound and long-term changes in transcript level and spatial distribution of myelin-related genes in multiple neocortical and limbic regions, notably the hippocampus and its surrounding memory-related neural networks. Our work demonstrates the potential utility of oligodendroglia-related genes as biomarkers for modeling neurodevelopmental disorders, in agreement with the hypothesis that MIA during pregnancy could lead to compromised white matter connectivity in ASD.

Hypofunctional connectivity between the posterior cingulate cortex and ventromedial prefrontal cortex in autism: Evidence from coordinate-based imaging meta-analysis

BackgroundUnderconnectivity in the posterior cingulate cortex (PCC) may be associated with a weakened ability to interpret social signals in autism spectrum disorder (ASD) and result in cognitive inflexibility – a hallmark feature of ASD. However, previous neuroimaging studies using resting-state functional magnetic resonance imaging in ASD reported inconsistent findings on functional connectivity of the PCC. This study investigated the aberrant resting-state functional connectivity of the PCC in ASD using multilevel kernel density analysis. MethodsOnline databases (MEDLINE/PubMed) were searched for PCC-based functional connectivity in ASD. Ten studies (501 subjects; 161 reported foci) met the inclusion criteria of this meta-analysis. ResultsWe found one consistent and strong abnormal functional connectivity of ASD during the resting state, which was the hypoconnectivity between the PCC and ventromedial prefrontal cortex (VMPFC). Importantly, the Jackknife sensitivity analysis revealed that the VMPFC cluster was stably hypoconnected with the PCC in ASD (maximum spatial overlap rate: 100%). ConclusionsThe reduced PCC–VMPFC functional coupling may provide an early insight into the effects of ASD on multiple dimensions of functioning, including higher-order cognitive and complex social functions.

Behavioural profiling of autism connectivity abnormalities.

Brain regions are functionally diverse, and a given region may engage in a variety of tasks. This functional diversity of brain regions may be one factor that has prevented the finding of consistent biomarkers for brain disorders such as autism spectrum disorder (ASD). Thus, methods to characterise brain regions would help to determine how functional abnormalities contribute to affected behaviours. As the first illustration of the meta-analytic behavioural profiling procedure, we evaluated how the regions with disrupted connectivity in ASD contributed to various behaviours. Connectivity abnormalities were determined from a published degree centrality group comparison based on functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange. Using BrainMap's database of task-based neuroimaging studies, behavioural profiles were created for abnormally connected regions by relating these regions to tasks activating them. Hyperconnectivity in ASD brains was significantly related to memory, attention, reasoning, social, execution and speech behaviours. Hypoconnectivity was related to vision, execution and speech behaviours. The procedure outlines the first clinical neuroimaging application of a behavioural profiling method that estimates the functional diversity of brain regions, allowing for the relation of abnormal functional connectivity to diagnostic criteria. Behavioural profiling and the computational insights it provides can facilitate better understanding of the functional manifestations of various disorders, including ASD.

Altered Structural Connectivity in Autism Spectrum Disorder

Research in Neurological field has been in great trend in recent days, since the need of detection and treatment of various neuropsychological disorders are in increasing order. Automated approaches for the detection are possible by various technological methods. Autism Spectrum Disorder (ASD) is a one such serious disorder which can be diagnosed in early ages of children. The Emerging technology had contributed the neuro imaging techniques to understand the various basic features and characteristics that cause the disorder. This neuro imaging had lead to a better perspective called connectome analysis which deals network structures (connectome) derived from the neuro images and are used in detection and treatment of the disorder. For these analysis functional and structural connectomes / network of brain are utilized. In this work structural connectomes derived from the Diffusion Tensor Imaging of Typically Developing and Autism Spectrum Disordered had been considered . This connectome / network consists of 264 regions (based on PowerNeuron_264 atlas) and thus 69696 connectivity features (connection between regions). Using the structural connectomes, average connectome analysis had been done and 91 connections had been identified as altered in ASD. There are 112 distinct regions involved in these altered connections and are having varied number of altered connections from one to six. 15 regions among them found to have much alteration since more number of (More than 2) altered connectivity are involved with these regions. To prove the finding , Data mining technique, Support Vector Machine was applied over 42 connectivity features (0.06% of original) out of 91 and are involved with the 15 regions filtered and the classification is done (with 82% accuracy) . Classifier rules are utilized in the diagnosis of ASD . The 15 regions extracted through thisprocess are found to be altered in ASD. These altered regions are related to sensory(touch and taste), memory, movements control, Lexical processing, Consciousness and sleep. This proposed system surely have effective use in the process of high dimensional and complex brain data and the identification of typically developed and autism spectrum disordered brain .This methodology can also be used in detection of other diseases, Role of various Regions, influential regions, etc.

Intrinsic Functional Connectivity of Dentate Nuclei in Autism Spectrum Disorder.

Cerebellar abnormalities are commonly reported in autism spectrum disorder (ASD). Dentate nuclei (DNs) are key structures in the anatomical circuits linking the cerebellum to the extracerebellum. Previous resting-state functional connectivity (RsFc) analyses reported DN abnormalities in high-functioning ASD (HF-ASD). This study examined the RsFc of the DN in young adults with HF-ASD compared with healthy controls (HCs) with the aim to expand upon previous findings of DNs in a dataset using advanced, imaging acquisition methods that optimize spatiotemporal resolution and statistical power. Additional seed-to-voxel analyses were carried out using motor and nonmotor DN coordinates reported in previous studies as seeds. We report abnormal dentato-cerebral and dentato-cerebellar functional connectivity in ASD. Our results expand and, in part, replicate previous descriptions of DN RsFc abnormalities in this disorder and reveal correlations between DN-cerebral RsFc and ASD symptom severity.