Basis Of Autism Spectrum Disorder Research Articles

Association of Autism Spectrum Disorder in an Iranian Pedigree with a Novel Hereditary Mutation in SETD5

Autism spectrum disorder has evolved from a rare childhood-onset disorder to a widely acknowledged, extensively researched, and heterogeneous lifelong condition. This study focuses on an Iranian pedigree affected by autism spectrum disorder. By employing whole-exome sequencing, we detected a novel heterozygous (c.3694T>A: p.Tyr1232Asn) in exon 22 (NM_001080517.3) of the SETD5 gene. The presence of this mutation was consistent with observed clinical features, confirming the genetic basis of autism spectrum disorder in the patient and his father. In contrast, the mother, with a normal genotype, did not exhibit the identified mutation. Genetic counseling implications are underscored by the shared heterozygous mutation in both, emphasizing the importance of incorporating genetic insights into psychological counseling. This integration can empower families with informed strategies to navigate the challenges associated with autism spectrum disorder, fostering resilience and tailored support.

Microglia Modulate Neurodevelopment in Autism Spectrum Disorder and Schizophrenia.

Neurodevelopmental disorders (NDDs) include various neurological disorders with high genetic heterogeneity, characterized by delayed or impaired cognition, communication, adaptive behavior, and psychomotor skills. These disorders result in significant morbidity for children, thus burdening families and healthcare/educational systems. However, there is a lack of early diagnosis and effective therapies. Therefore, a more connected approach is required to explore these disorders. Microglia, the primary phagocytic cells within the central nervous system, are crucial in regulating neuronal viability, influencing synaptic dynamics, and determining neurodevelopmental outcomes. Although the neurobiological basis of autism spectrum disorder (ASD) and schizophrenia (SZ) has attracted attention in recent decades, the role of microglia in ASD and SZ remains unclear and requires further discussion. In this review, the important and frequently multifaceted roles that microglia play during neurodevelopment are meticulously emphasized and potential microglial mechanisms that might be involved in conditions such as ASD and SZ are postulated. It is of utmost importance to acquire a comprehensive understanding of the complexities of the interplay between microglia and neurons to design effective, targeted therapeutic strategies to mitigate the effects of NDDs.

Glucose transporter 1 deficiency, AMP-activated protein kinase activation and immune dysregulation in autism spectrum disorder: Novel biomarker sources for clinical diagnosis

The neurophysiological basis of autism spectrum disorder (ASD) is still uncertain. Nevertheless, studies support the hypotheses that oxidative stress, neuroinflammation, immune dysregulation, and metabolic stress are contributors. In this study, the serum levels of 3-nitrotyrosine (3-NT), hypoxia-inducible factor 1 α (HIF-1 α), heat shock protein 70 (HSP-70), interleukin-17A (IL-17A), IL-35, vitamin D3 (VITD), glucose transporter-1 (GUT1), and AMP-activated protein kinase (AMPK) were estimated in Saudi ASD children versus age-matched neurotypical controls, aiming to investigate whether these parameters have potential roles in the pathophysiologic mechanisms of ASD and hoping to find a reliable marker for early ASD diagnosis. This study included 25 ASD children and 25 typically developing children (3–11 years old). The diagnosis of ASD cases was made based on the Autism Diagnostic Observation Schedule (ADOS) and the Statistical Manual of Mental Disorders (DSM-5). ASD subjects were commonly male and revealed an intelligence quotient (IQ) < 70.The results detected that ASD children have remarkable greater serum levels of nitrosative stress (3-NT), hypoxia (HIF-1 α), inflammatory (HSP-70, IL-17A, and AMPK) biomarkers and lower serum levels of anti-inflammatory (IL-35 and VITD) and metabolic stress (GUT-1) biomarkers versus age-matched controls (P ≤ 0.0001). Pearson's correlation study revealed that 3-NT was positively associated with HIF-1 α and HSP-70. HIF-1 α was also positively correlated with HSP-70. AMPK was positively associated with GUT-1, however, IL-17A was negatively correlated with IL-35 and VITD.Limitation:No specific therapeuticdrugs were administered in this study, and further studies are required to confirm the role of the selected biomarkers in ASD managements. ConclusionChanges in concentrations of different biomarkers indicate that they are involved in oxidative stress, metabolic stress, immune dysregulation and ASD pathogenesis. Hence, these parameters can prove to be promising biomarkers as well as therapeutic targets for the timely diagnosis and treatment of ASD patients.

Association of OXTR, AVPR1a, LNPEP, and CD38 Genes' Expression with the Clinical Presentation of Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects social interactions, communication, and behavior. Although the predominant genetic predisposition to ASD seems beyond doubt, its exact nature remains unclear. In the context of social cognition disorders and the basis of ASD, the oxytocinergic and vasopresynergic systems arouse great interest among researchers. The aim of the present study was to analyze gene expression levels for oxytocin and vasopressin receptors, as well as CD38 protein and oxytocinase, in the context of the clinical picture of autism spectrum disorders. The study included 90 people, of whom 63 were diagnosed with ASD based on anamnesis, mental status testing, and the ADOS-2 protocol. The results obtained in the presented study indicate that the balance between the levels of expression of the CD38 gene and the oxytocinase gene plays a key role in the risk and clinical presentation of ASD. In a hypothetical scenario, an imbalance in the expression of CD38 and LNPEP could potentially lead to alterations in the concentrations of oxytocin and vasopressin. At the same time, the most frequently studied genes-AVPR1a and OXTR-seem to be at best of marginal importance for the risk of ASD.

Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder

BackgroundDespite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder. MethodsWe considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns. ResultsAnalysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network–related and cingulo-opercular network–related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD. ConclusionsOur study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.

Neuroactive Amino Acid Profile in Autism Spectrum Disorder: Results from a Clinical Sample

Biological bases of autism spectrum disorder (ASD) include both genetic and epigenetic causes. Patients with ASD show anomalies in the profile of certain plasma amino acids, including neuroactive amino acids. Monitoring plasma amino acids may be relevant for patient care and interventions. We evaluated the plasma amino acid profile in samples extracted from dry blood spots by electrospray ionization-tandem mass spectrometry. Fourteen amino acids and eleven amino acid ratios were examined in patients with ASD and intellectual disability (ID), and neurotypical control subjects (TD). The amino acid profile in the ASD group showed reduced levels of ornithine (p = 0.008), phenylalanine (p = 0.042) and tyrosine (p = 0.013). The statistically significant amino acid ratios were Leu+Val/Phe+Tyr (p = 0.002), Tyr/Leu (p = 0.007) and Val/Phe (p = 0.028), such differences remaining significant only in the comparison between ASD and TD. Finally, a positive correlation emerged between the score of the restricted and repetitive behavior on ADOS-2 and the citrulline levels in the ASD group (p = 0.0047). To conclude, patients with ASD may show a distinguishable metabolic profile useful for studying their metabolic pathways in order to develop screening tests and targeted therapies.

Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders.

Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear. The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state. The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients' clinical characteristics. For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the "triple networks" system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores. Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease.

Using tissue microstructure and multimodal MRI to parse the phenotypic heterogeneity and cellular basis of autism spectrum disorder.

Identifying the brain bases for phenotypic heterogeneity in Autism Spectrum Disorder (ASD) will advance understanding of its pathogenesis and improve its clinical management. We compared Diffusion Tensor Imaging (DTI) indices and connectome measures between 77 ASD and 88 Typically Developing (TD) control participants. We also assessed voxel-wise associations of DTI indices with measures of regional cerebral blood flow (rCBF) and N-acetylaspartate (NAA) to understand how tissue microstructure associates with cellular metabolism and neuronal density, respectively. Autism Spectrum Disorder participants had significantly lower fractional anisotropy (FA) and higher diffusivity values in deep white matter tracts, likely representing ether reduced myelination by oligodendrocytes or a reduced density of myelinated axons. Greater abnormalities in these measures and regions were associated with higher ASD symptom scores. Participant age, sex and IQ significantly moderated these group differences. Path analyses showed that reduced NAA levels accounted significantly for higher diffusivity and higher rCBF values in ASD compared with TD participants. Reduced neuronal density (reduced NAA) likely underlies abnormalities in DTI indices of white matter microstructure in ASD, which in turn are major determinants of elevated blood flow. Together, these findings suggest the presence of reduced axonal density and axonal pathology in ASD white matter. Greater pathology in turn accounts for more severe symptoms, lower intellectual ability, and reduced global efficiency for measures of white matter connectivity in ASD.

Reduced sociability and social agency encoding in adult Shank3-mutant mice are restored through gene re-expression in real time.

Despite a growing understanding of the molecular and developmental basis of autism spectrum disorder (ASD), how the neuronal encoding of social information is precisely disrupted in ASD and whether it contributes to abnormal social behavior remains unclear. Here, we disrupted and then restored expression of the ASD-associated gene Shank3 in adult male mice while tracking the encoding dynamics of neurons in the medial prefrontal cortex (mPFC) over weeks. We find that Shank3 disruption led to reduction of neurons encoding the experience of other mice and increase in neurons encoding the animal’s own. This shift was associated with loss of ability by neurons to distinguish other-from-self and, therefore, the inability to encode social agency. Restoration of Shank3 expression in the mPFC reversed this encoding imbalance and increased sociability over 5-8 weeks. These findings reveal a neuronal encoding process that is necessary for social behavior and that may be disrupted in ASD.

In–Silico Visualization of Gene-Gene Interactions in Autism Spectrum Disorder Genes

ABSTRACT: Autism spectrum disorder is a group of neurodevelopmental disorders with still unknown causative mechanisms. Recent findings implicate a complex etiology with multiple genetic and epigenetic factors. The disruption of vital gene-gene interactions has been proposed to be one of the contributing factors for the disease. NPAS (neuronal PAS domain proteins 1 and 3) have been recognized as critical regulators of neuropsychiatric development and function. In the present study, a genetic network association with NPAS3 and the highest interacting gene CRKL has been mapped for ASD and analyzed to decipher the complex genetic basis of Autism spectrum disorder. CRKL encodes a protein kinase with SH2 and SH3 (SRC homology) domains which activate RAS and Jun signaling pathways. The CRKL gene sequence was retrieved and compared using BLAST homology search. The physiological and chemical features of CRKL protein was studied using Uniprot Database and Protparam followed by homology modelling by constructing a phylogenetic. Physio-chemical properties of CRKL protein was studied by Protparam which gave insight into the role of other interacting proteins involved in this process. Further analysis, a conserved region of intersecting protein of CRKL were identified using multiple sequence alignment. The gene interactions data revealed that CRKL as one of the key genes associated with Autism spectrum disorder. CRKL defects in the deleted region of 22q11. 2 of the human chromosome 22 have a strong correlation with several of the birth defects as it can hamper the development of the brain along with behavior and cognitive function. This may lead to neurodevelopmental disorders like autism spectrum disorder or schizophrenia, depending on which particular region in the gene is affected. CRKL gene could be a potential biomarker for many In-borne diseases. An investigation in current study was done to find out the genetic network and decipher the complex genetic basis of Autism spectrum disorder.

Exploring the Structural and Strategic Bases of Autism Spectrum Disorders With Deep Learning

Deep learning models are applied in clinical research in order to diagnose disease. However, diagnosing autism spectrum disorders (ASD) remains challenging due to its complex psychiatric symptoms as well as a generally insufficient amount of neurobiological evidence. We investigated the structural and strategic bases of ASD using 14 different types of models, including convolutional and recurrent neural networks. Using an open source autism dataset consisting of more than 1000 MRI scan images and a high-resolution structural MRI dataset, we demonstrated how deep neural networks could be used as tools for diagnosing and analyzing psychiatric disorders. We trained 3D convolutional neural networks to visualize combinations of brain regions, thus representing the most referred-to regions used by the model whilst classifying the images. We also implemented recurrent neural networks to classify the sequence of brain regions efficiently. We found emphatic structural and strategic evidence on which the model heavily relies during the classification process. For instance, we observed that the structural and strategic evidence tends to be associated with subcortical structures, including the basal ganglia (BG). Our work identifies the distinct brain structures that characterize a complex psychiatric disorder while streamlining the deductive reasoning that clinicians can use to ensure an economical and time-efficient diagnosis process.

A Protocol for Sedation Free MRI and PET Imaging in Adults with Autism Spectrum Disorder.

Imaging technologies such as positron emission tomography (PET) and magnetic resonance imaging (MRI) present unparalleled opportunities to investigate the neural basis of autism spectrum disorder (ASD). However, challenges such as deficits in social interaction, anxiety around new experiences, impaired language abilities, and hypersensitivity to sensory stimuli make participating in neuroimaging studies challenging for individuals with ASD. In this commentary, we describe the existent training protocols for preparing individuals with ASD for PET/MRI scans and our own experience developing a training protocol to facilitate the inclusion of low-functioning adults with ASD in PET-MRI studies. We hope to raise awareness of the need for more information exchange between research groups about lessons learned in this context in order to include the entire disease spectrum in neuroimaging studies.

Dissecting the Heterogeneous Cortical Anatomy of Autism Spectrum Disorder Using Normative Models

BackgroundThe neuroanatomical basis of autism spectrum disorder (ASD) has remained elusive, mostly owing to high biological and clinical heterogeneity among diagnosed individuals. Despite considerable effort toward understanding ASD using neuroimaging biomarkers, heterogeneity remains a barrier, partly because studies mostly employ case-control approaches, which assume that the clinical group is homogeneous. MethodsHere, we used an innovative normative modeling approach to parse biological heterogeneity in ASD. We aimed to dissect the neuroanatomy of ASD by mapping the deviations from a typical pattern of neuroanatomical development at the level of the individual and to show the necessity to look beyond the case-control paradigm to understand the neurobiology of ASD. We first estimated a vertexwise normative model of cortical thickness development using Gaussian process regression, then mapped the deviation of each participant from the typical pattern. For this, we employed a heterogeneous cross-sectional sample of 206 typically developing individuals (127 males) and 321 individuals with ASD (232 males) (6–31 years of age). ResultsWe found few case-control differences, but the ASD cohort showed highly individualized patterns of deviations in cortical thickness that were widespread across the brain. These deviations correlated with severity of repetitive behaviors and social communicative symptoms, although only repetitive behaviors survived corrections for multiple testing. ConclusionsOur results 1) reinforce the notion that individuals with ASD show distinct, highly individualized trajectories of brain development and 2) show that by focusing on common effects (i.e., the “average ASD participant”), the case-control approach disguises considerable interindividual variation crucial for precision medicine.

Multimodal Functional and Structural Brain Connectivity Analysis in Autism: A Preliminary Integrated Approach With EEG, fMRI, and DTI

This paper proposes a novel approach of integrating different neuroimaging techniques to characterize an autistic brain. Different techniques like EEG, fMRI and DTI have traditionally been used to find biomarkers for autism, but there have been very few attempts for a combined or multimodal approach of EEG, fMRI and DTI to understand the neurobiological basis of autism spectrum disorder (ASD). Here, we explore how the structural brain network correlate with the functional brain network, such that the information encompassed by these two could be uncovered only by using the latter. In this paper, source localization from EEG using independent component analysis (ICA) and dipole fitting has been applied first, followed by selecting those dipoles that are closest to the active regions identified with fMRI. This allows translating the high temporal resolution of EEG to estimate time varying connectivity at the spatial source level. Our analysis shows that the estimated functional connectivity between two active regions can be correlated with the physical properties of the structure obtained from DTI analysis. This constitutes a first step towards opening the possibility of using pervasive EEG to monitor the long-term impact of ASD treatment without the need for frequent expensive fMRI or DTI investigations.

Explorations and perspectives on the neurobiological bases of autism spectrum disorder

Explorations and perspectives on the neurobiological bases of autism spectrum disorder

Cerebellar Volume in Autism: Literature Meta-analysis and Analysis of the Autism Brain Imaging Data Exchange Cohort

BackgroundThe neuroanatomical bases of autism spectrum disorder remain largely unknown. Among the most widely discussed candidate endophenotypes, differences in cerebellar volume have been often reported as statistically significant. MethodsWe aimed at objectifying this possible alteration by performing a systematic meta-analysis of the literature and an analysis of the ABIDE (Autism Brain Imaging Data Exchange) cohort. Our meta-analysis sought to determine a combined effect size of autism spectrum disorder diagnosis on different measures of the cerebellar anatomy as well as the effect of possible factors of variability across studies. We then analyzed the cerebellar volume of 328 patients and 353 control subjects from the ABIDE project. ResultsThe meta-analysis of the literature suggested a weak but significant association between autism spectrum disorder diagnosis and increased cerebellar volume (p = .049, uncorrected), but the analysis of ABIDE did not show any relationship. The studies meta-analyzed were generally underpowered; however, the number of statistically significant findings was larger than expected. ConclusionsAlthough we could not provide a conclusive explanation for this excess of significant findings, our analyses would suggest publication bias as a possible reason. Finally, age, sex, and IQ were important sources of cerebellar volume variability, although independent of autism diagnosis.

The absolute quantitative analysis of cingulate cortex metabolites in preschool children with autism spectrum disorder using proton MR spectroscopy

Objective To investigate the metabolite changes in the preschool children with autism spectrum disorder (ASD) using MR spectroscopy (MRS) and explore the associations between image findings and clinical variables, which may provide a noninvasive brain biochemical method for the early diagnosis and prevention of autism. Methods Twenty one cases of preschool ASD children (3-6 years old) and age-and sex-matched 20 preschool healthy controls underwent single voxel short (SVS) short TE (TE=30 ms) MRS. The absolute metabolite concentrations in the anterior cingulate cortex (ACC), anterior middle anterior cingulate cortex (aMCC) and posterior cingulate (PCC) were quantitatively analyzed using LCModel software. Two independent sample t tests were used for analysis. The relationships between metabolite concentrations and diagnostic and statistical manual of mental disorders (DSM-IV), childhood autism rating scale (CARS) and autism behavior checklist (ABC) were analyzed by Pearson correlation analysis. Results Compared to control subjects, ASD patients had significantly lower N-acetylaspartate (NAA) values (4.35±0.80, 6.34±0.82, 8.04±0.97 mmol/L respectively) in ACC, aMCC and PCC (t=2.640, P=0.012; t=2.182, P=0.035; t=3.343, P=0.002), had significantly lower choline (Cho) 1.32±0.22 mmol/L (t=2.905, P=0.006) and glutamine and glutamate complex (Glx) 10.02±0.88 mmol/L (t=2.090, P=0.043) in PCC. Cho, total creatine (tCr), myo-Inositol (MI) and Glx levels did not differ between groups in other aforementioned regions (P>0.05). Negative correlations between the NAA ualues in the PCC and CARS (r=-0.504, P=0.020) were detected, and no significant correlation among DSM-IV, CARS, ABC and other metabolite values (P>0.05). Conclusions The biochemical changes in the preschool children with ASD in cingulate reflect the neuronal loss, structural or functional damage and cell membrane enzyme metabolic dysfunctions, may reveal the pathological basis of ASD. These results may provide noninvasive and quantitative methods for the diagnosis and prognosis evaluation of ASD child. Key words: Autism spectrum disorder; Magnetic resonance spectroscopy; Cingulate cortex; Child

Genetic Basis of Autism Spectrum Disorder

Genetic Basis of Autism Spectrum Disorder

A Neurodevelopmental Perspective for Autism-Associated Gene Function.

Large-scale genetic sequencing studies have identified a wealth of genes in which mutations are associated with autism spectrum disorder (ASD). Understanding the biological function of these genes sheds light onto the neurodevelopmental basis of ASD. To this end, we defined functional categories representing brain development – (1) Cell Division and Survival, (2) Cell Migration and Differentiation, (3) Neuronal Morphological Elaboration, (4) Development and Regulation of Cellular Excitability, and (5) Synapse Formation and Function – and place 100 high confidence ASD-associated genes yielding at least 50 published PubMed articles into these categories based on keyword searches. We compare the categorization of ASD genes to genes associated with developmental delay (DD) and systematically review the published literature on the function of these genes. We find evidence that ASD-associated genes have important functions that span the neurodevelopmental continuum. Further, examining the temporal expression pattern of these genes using the BrainSpan Atlas of the Developing Human Brain supports their function across development. Thus, our analyses and review of literature on ASD gene function support a model whereby differences in brain development – from very early stages of macroarchitectural patterning to late stages of activity-dependent sculpting of synaptic connectivity – may lead to ASD. It will be important to keep investigating potential points of mechanistic convergence which could explain a common pathophysiological basis of ASD behind this disparate array of genes.

Basal ganglia impairments in autism spectrum disorder are related to abnormal signal gating to prefrontal cortex

Research on the biological basis of autism spectrum disorder has yielded a list of brain abnormalities that are arguably as diverse as the set of behavioral symptoms that characterize the disorder. Among these are patterns of abnormal cortical connectivity and abnormal basal ganglia development. In attempts to integrate the existing literature, the current paper tests the hypothesis that impairments in the basal ganglia's function to flexibly select and route task-relevant neural signals to the prefrontal cortex underpins patterns of abnormal synchronization between the prefrontal cortex and other cortical processing centers observed in individuals with autism spectrum disorder (ASD). We tested this hypothesis using a Dynamic Causal Modeling analysis of neuroimaging data collected from 16 individuals with ASD (mean age=25.3 years; 6 female) and 17 age- and IQ-matched neurotypical controls (mean age=25.6, 6 female), who performed a Go/No-Go test of executive functioning. Consistent with the hypothesis tested, a random-effects Bayesian model selection procedure determined that a model of network connectivity in which basal ganglia activation modulated connectivity between the prefrontal cortex and other key cortical processing centers best fit the data of both neurotypicals and individuals with ASD. Follow-up analyses suggested that the largest group differences were observed for modulation of connectivity between prefrontal cortex and the sensory input region in the occipital lobe [t(31)=2.03, p=0.025]. Specifically, basal ganglia activation was associated with a small decrease in synchronization between the occipital region and prefrontal cortical regions in controls; however, in individuals with ASD, basal ganglia activation resulted in increased synchronization between the occipital region and the prefrontal cortex. We propose that this increased synchronization may reflect a failure in basal ganglia signal gating mechanisms, resulting in a non-selective copying of signals to prefrontal cortex. Such a failure to prioritize and filter signals to the prefrontal cortex could result in the pervasive impairments in cognitive flexibility and executive functioning that characterize autism spectrum disorder, and may offer a mechanistic explanation of some of the observed abnormalities in patterns of cortical synchronization in ASD.