Connectivity In Autism Spectrum Disorder Research Articles

Exploring Altered Neural Connectivity in Autism Spectrum Disorder: A Simulation of Spatial and Topological Proximities

This study presents a computational simulation to explore the distinctive patterns of neural connectivity in Autism Spectrum Disorder (ASD), focusing on the concepts of spatial and topological proximities. ASD is characterized by unique cognitive and behavioral profiles, often attributed to atypical neural development and connectivity. Our simulation employs a two-pronged approach: first, by modeling spatial proximity through the generation of clustered 3D node positions, we mimic the observed over-connectivity in localized brain regions in ASD. Second, we construct a topological network to represent the complex interplay of under-connectivity and over- connectivity in functional brain networks, a hallmark of ASD's neural landscape. The spatial model demonstrates pronounced clustering, reflecting the structural brain differences and atypical growth patterns reported in neuroimaging studies of individuals with ASD. In contrast, the topological model reveals a network with a mix of sparse and dense connections, simulating the diverse and often contradictory findings of functional connectivity in ASD, such as reduced long-range connections and increased local connections. Our simulation provides a visual and conceptual framework for understanding the altered neural connectivity in ASD. It underscores the importance of considering both physical (spatial) and functional (topological) aspects of brain connectivity to grasp the full extent of neurodevelopmental deviations in ASD. This approach not only aids in elucidating the neural underpinnings of ASD's core symptoms but also offers a foundation for developing targeted interventions. Future research, integrating more complex and individual- specific data, could further refine this model, enhancing our understanding of ASD's neural dynamics and contributing to personalized therapeutic strategies.

Altered intra- and inter-network connectivity in autism spectrum disorder.

A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored. We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector machine was used to discriminate ASD from HC. Through different sparsities, ASD exhibited significantly decreased intra-network connectivity within default mode network and dorsal attention network, increased connectivity between limbic network and subcortical network, and decreased connectivity between default mode network and limbic network. Using the classifier trained on altered intra- and inter-network connectivity, multivariate pattern analyses classified the ASD from HC with 71.74% accuracy, 70.21% specificity and 75.56% sensitivity in 10% sparsity of functional connectivity. ASD showed characteristic reorganization of the brain networks and this provided new insight into the underlying process of the functional connectome dysfunction in ASD.

Assessing the impact of repetitive transcranial magnetic stimulation on effective connectivity in autism spectrum disorder: An initial exploration using TMS-EEG analysis

Initial indications propose that repetitive transcranial magnetic stimulation (rTMS) could mitigate clinical manifestations in patients with autism spectrum disorder (ASD). Nevertheless, the precise mechanisms responsible for these therapeutic and behavioral outcomes remain elusive. We examined alterations in effective connectivity induced by rTMS using concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in children with ASD. TMS-EEG data were acquired from 12 children diagnosed with ASD both before and following rTMS treatment. The rTMS intervention regimen included delivering 5-s trains at a frequency of 15 Hz, with 10-min intervals between trains, targeting the left parietal lobe. This was conducted on each consecutive weekday over 3 weeks, totaling 15 sessions. The dynamic EEG network analysis revealed that following the rTMS intervention, long-range feedback connections within the brains of ASD patients were strengthened (e.g., frontal to parietal regions, frontal to occipital regions, and frontal to posterior temporal regions), and short-range connections were weakened (e.g., between the bilateral occipital regions, and between the occipital and posterior temporal regions). In alignment with alterations in network connectivity, there was a corresponding amelioration in fundamental ASD symptoms, as assessed through clinical scales post-treatment. According to our findings, people with ASD may have increased long-range frontal-posterior feedback connection on application of rTMS to the parietal lobe.

Salience network connectivity is altered in 6-week-old infants at heightened likelihood for developing autism

Converging evidence implicates disrupted brain connectivity in autism spectrum disorder (ASD); however, the mechanisms linking altered connectivity early in development to the emergence of ASD symptomatology remain poorly understood. Here we examined whether atypicalities in the Salience Network – an early-emerging neural network involved in orienting attention to the most salient aspects of one’s internal and external environment – may predict the development of ASD symptoms such as reduced social attention and atypical sensory processing. Six-week-old infants at high likelihood of developing ASD based on family history exhibited stronger Salience Network connectivity with sensorimotor regions; infants at typical likelihood of developing ASD demonstrated stronger Salience Network connectivity with prefrontal regions involved in social attention. Infants with higher connectivity with sensorimotor regions had lower connectivity with prefrontal regions, suggesting a direct tradeoff between attention to basic sensory versus socially-relevant information. Early alterations in Salience Network connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with vulnerability to ASD.

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

We evaluated functional connectivity (FC) in patients with adult autism spectrum disorder (ASD) using resting-state functional MRI (rs-fMRI) and diffusion kurtosis imaging (DKI). We acquired rs-fMRI data from 33 individuals with ASD and 33 healthy controls (HC) and DKI data from 18 individuals with ASD and 17 HC. ASD showed attenuated FC between the right frontal pole (FP) and the bilateral temporal fusiform cortex (TFusC) and enhanced FC between the right thalamus and the bilateral inferior division of lateral occipital cortex, and between the cerebellar vermis and the right occipital fusiform gyrus (OFusG) and the right lingual gyrus, compared with HC. ASD demonstrated increased axial kurtosis (AK) and mean kurtosis (MK) in white matter (WM) tracts, including the right anterior corona radiata (ACR), forceps minor (FM), and right superior longitudinal fasciculus (SLF). In ASD, there was also a significant negative correlation between MK and FC between the cerebellar vermis and the right OFusG in the corpus callosum, FM, right SLF and right ACR. Increased DKI metrics might represent neuroinflammation, increased complexity, or disrupted WM tissue integrity that alters long-distance connectivity. Nonetheless, protective or compensating adaptations of inflammation might lead to more abundant glial cells and cytokine activation effectively alleviating the degeneration of neurons, resulting in increased complexity. FC abnormality in ASD observed in rs-fMRI may be attributed to microstructural alterations of the commissural and long-range association tracts in WM as indicated by DKI.

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.

EEG Connectivity Diversity Differences between Children with Autism and Typically Developing Children: A Comparative Study.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication, and repetitive or stereotyped behaviors. Previous studies have reported altered brain connectivity in ASD children compared to typically developing children. In this study, we investigated the diversity of connectivity patterns between children with ASD and typically developing children using phase lag entropy (PLE), a measure of the variability of phase differences between two time series. We also developed a novel wavelet-based PLE method for the calculation of PLE at specific scales. Our findings indicated that the diversity of connectivity in ASD children was higher than that in typically developing children at Delta and Alpha frequency bands, both within brain regions and across hemispheric brain regions. These findings provide insight into the underlying neural mechanisms of ASD and suggest that PLE may be a useful tool for investigating brain connectivity in ASD.

The Relationship between Retained Primitive Reflexes and Hemispheric Connectivity in Autism Spectrum Disorders.

Autism Spectrum Disorder (ASD) can be identified by a general tendency toward a reduction in the expression of low-band, widely dispersed integrative activities, which is made up for by an increase in localized, high-frequency, regionally dispersed activity. The study assessed ASD children and adults all possessing retained primitive reflexes (RPRs) compared with a control group that did not attempt to reduce or remove those RPRs and then examined the effects on qEEG and brain network connectivity. Analysis of qEEG spectral and functional connectivity was performed, to identify associations with the presence or absence of retained primitive reflexes (RPRs), before and after an intervention based on TENS unilateral stimulation. The results point to abnormal lateralization in ASD, including long-range underconnectivity, a greater left-over-right qEEG functional connectivity ratio, and short-range overconnectivity in ASD. Clinical improvement and the absence of RPRs may be linked to variations in qEEG frequency bands and more optimized brain networks, resulting in more developmentally appropriate long-range connectivity links, primarily in the right hemisphere.

Multi-Site MRI Data Harmonization with an Adversarial Learning Approach: Implementation to the Study of Brain Connectivity in Autism Spectrum Disorders

Magnetic resonance imaging (MRI) nowadays plays an important role in the identification of brain underpinnings in a wide range of neuropsychiatric disorders, including Autism Spectrum Disorders (ASD). Characterizing the hallmarks in these pathologies is not a straightforward task and machine learning (ML) is certainly one of the most promising tools for addressing complex and non-linear problems. ML algorithms and, in particular, deep neural networks (DNNs), need large datasets in order to be properly trained and thus ensure generalization capabilities on new data. Large datasets can be obtained by collecting images from different centers, thus bringing unavoidable biases in the analysis due to differences in hardware and scanning protocols between different centers. In this work, we dealt with the issue of multicenter MRI data harmonization by comparing two different approaches: the analytical ComBat-GAM procedure, whose effectiveness is already documented in the literature, and an originally developed site-adversarial deep neural network (ad-DNN). The latter aims to perform a classification task while simultaneously searching for site-relevant patterns in order to make predictions free from site-related biases. As a case study, we implemented DNN and ad-DNN classifiers to distinguish subjects with ASD with respect to typical developing controls based on functional connectivity measures derived from data of the multicenter ABIDE collection. The classification performance of the proposed ad-DNN, measured in terms of the area under the ROC curve (AUC), achieved the value of AUC = 0.70±0.03, which is comparable to that obtained by a DNN on data harmonized according to the analytical procedure (AUC = 0.71±0.01). The relevant functional connectivity alterations identified by both procedures showed an agreement between each other and with the patterns of neuroanatomical alterations previously detected in the same cohort of subjects.

Repetitive transcranial magnetic stimulation modulates long-range functional connectivity in autism spectrum disorder

Repetitive transcranial magnetic stimulation (rTMS) is growingly applied in autism spectrum disorder (ASD) due to its potential therapeutic value, however, its effects on functional network configuration and the mechanism underlying clinical improvement are still unclear. In this study, we examined the alternations of functional connectivity induced by rTMS using resting-state electroencephalogram (EEG) in children with ASD. Resting-state EEG was obtained from 24 children with ASD before and after rTMS intervention and from 24 age- and gender-matched typically developing (TD) children. The rTMS intervention course consisted of five 5-s trains at 15 Hz, with 10-min inter-train intervals, on the left parietal lobe each consecutive weekday for 3 weeks (15 sessions in total). Children with ASD showed significantly hypo-connected networks and sub-optimal network properties at both global and local levels, compared with TD peers. After rTMS intervention, long-range intra- and inter-hemispheric connections were significantly promoted, especially those within the alpha band. Meanwhile, network properties at both local and global levels were greatly promoted in the delta, theta, and alpha bands. Consistent with the changes in the network connectivities and properties, the core symptoms in ASD were also relieved measured by clinical scales after treatment. The findings of this study demonstrate that high-frequency rTMS over the parietal lobe is potentially an effective strategy to improve core symptoms by enhancing long-range connectivity reorganization in ASD.

Atypical local brain connectivity in pediatric autism spectrum disorder? A coordinate-based meta-analysis of regional homogeneity studies

Despite decades of massive neuroimaging research, the comprehensive characterization of short-range functional connectivity in autism spectrum disorder (ASD) remains a major challenge for scientific advances and clinical translation. From the theoretical point of view, it has been suggested a generalized local over-connectivity that would characterize ASD. This stance is known as the general local over-connectivity theory. However, there is little empirical evidence supporting such hypothesis, especially with regard to pediatric individuals with ASD (age ≤\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\le$$\\end{document} 18 years old). To explore this issue, we performed a coordinate-based meta-analysis of regional homogeneity studies to identify significant changes of local connectivity. Our analyses revealed local functional under-connectivity patterns in the bilateral posterior cingulate cortex and superior frontal gyrus (key components of the default mode network) and in the bilateral paracentral lobule (a part of the sensorimotor network). We also performed a functional association analysis of the identified areas, whose dysfunction is clinically consistent with the well-known deficits affecting individuals with ASD. Importantly, we did not find relevant clusters of local hyper-connectivity, which is contrary to the hypothesis that ASD may be characterized by generalized local over-connectivity. If confirmed, our result will provide a valuable insight into the understanding of the complex ASD pathophysiology.

Increased resting-state thalamocortical functional connectivity in children and young adults with autism spectrum disorder.

There is converging evidence that abnormal thalamocortical interactions contribute to attention deficits and sensory sensitivities in autism spectrum disorder (ASD). However, previous functional MRI studies of thalamocortical connectivity in ASD have produced inconsistent findings in terms of both the direction (hyper vs. hypoconnectivity) and location of group differences. This may reflect, in part, the confounding effects of head motion during scans. In the present study, we investigated resting-state thalamocortical functional connectivity in 8-25 year-olds with ASD and their typically developing (TD) peers. We used pre-scan training, on-line motion correction, and rigorous data quality assurance protocols to minimize motion confounds. ASD participants showed increased thalamic connectivity with temporal cortex relative to TD. Both groups showed similar age-related decreases in thalamic connectivity with occipital cortex, consistent with a process of circuit refinement. Findings of thalamocortical hyperconnectivity in ASD are consistent with other evidence that decreased thalamic inhibition leads to increase and less filtered sensory information reaching the cortex where it disrupts attention and contributes to sensory sensitivity. This literature motivates studies of mechanisms, functional consequences, and treatment of thalamocortical circuit dysfunction in ASD.

Atypical structural connectivity of language networks in autism spectrum disorder: A meta-analysis of diffusion tensor imaging studies.

Patients with autism spectrum disorder (ASD) often show pervasive and complex language impairments that are closely associated with aberrant structural connectivity of language networks. However, the characteristics of white matter connectivity in ASD have remained inconclusive in previous diffusion tensor imaging (DTI) studies. The current meta‐analysis aimed to comprehensively elucidate the abnormality in language‐related white matter connectivity in individuals with ASD. We searched PubMed, Web of Science, Scopus, and Medline databases to identify relevant studies. The standardized mean difference was calculated to measure the pooled difference in DTI metrics in each tract between the ASD and typically developing (TD) groups. The moderating effects of age, sex, language ability, and symptom severity were investigated using subgroup and meta‐regression analysis. Thirty‐three DTI studies involving 831 individuals with ASD and 836 TD controls were included in the meta‐analysis. ASD subjects showed significantly lower fractional anisotropy or higher mean diffusivity across language‐associated tracts than TD controls. These abnormalities tended to be more prominent in the left language networks than in the right. In addition, children with ASD exhibit more pronounced and pervasive disturbances in white matter connectivity than adults. These results support the under‐connectivity hypothesis and demonstrate the widespread abnormal microstructure of language‐related tracts in patients with ASD. Otherwise, white matter abnormalities in the autistic brain could vary depending on the developmental stage and hemisphere.Lay SummaryThis meta‐analysis explored abnormalities in white matter connectivity in language networks of individuals with ASD. Significantly reduced white matter integrity was found in all language‐associated tracts in subjects with ASD compared with TD controls. In addition, structural disturbances of language networks in the autistic brain exhibit a leftward tendency, and more prominent abnormalities are observed in younger people with ASD than in adults.

The Antiepileptic Drug and Toxic Teratogen Valproic Acid Alters Microglia in an Environmental Mouse Model of Autism.

Autism spectrum disorder (ASD), a neurodevelopmental condition affecting approximately 1 in 44 children in North America, is thought to be a connectivity disorder. Valproic acid (VPA) is a multi-target drug widely used to treat epilepsy. It is also a toxic teratogen as well as a histone deacetylase inhibitor, and fetal exposure to VPA increases the risk of ASD. While the VPA model has been well-characterized for behavioral and neuronal deficits including hyperconnectivity, microglia, the principal immune cells of CNS that regulate dendrite and synapse formation during early brain development, have not been well-characterized and may provide potential hints regarding the etiology of this disorder. Therefore, in this study, we determined the effect of prenatal exposure to VPA on microglial numbers during early postnatal brain development. We found that prenatal exposure to VPA causes a significant reduction in the number of microglia in the primary motor cortex (PMC) during early postnatal brain development, particularly at postnatal day 6 (P6) and postnatal day 10 (P10) in male mice. The early microglial reduction in the VPA model coincides with active cortical synaptogenesis and is significant because it may potentially play a role in mediating impaired connectivity in ASD.

Identification of Young High-Functioning Autism Individuals Based on Functional Connectome Using Graph Isomorphism Network: A Pilot Study.

Accumulated studies have determined the changes in functional connectivity in autism spectrum disorder (ASD) and spurred the application of machine learning for classifying ASD. Graph Neural Network provides a new method for network analysis in brain disorders to identify the underlying network features associated with functional deficits. Here, we proposed an improved model of Graph Isomorphism Network (GIN) that implements the Weisfeiler-Lehman (WL) graph isomorphism test to learn the graph features while taking into account the importance of each node in the classification to improve the interpretability of the algorithm. We applied the proposed method on multisite datasets of resting-state functional connectome from Autism Brain Imaging Data Exchange (ABIDE) after stringent quality control. The proposed method outperformed other commonly used classification methods on five different evaluation metrics. We also identified salient ROIs in visual and frontoparietal control networks, which could provide potential neuroimaging biomarkers for ASD identification.

Graph Ricci curvatures reveal atypical functional connectivity in autism spectrum disorder

While standard graph-theoretic measures have been widely used to characterize atypical resting-state functional connectivity in autism spectrum disorder (ASD), geometry-inspired network measures have not been applied. In this study, we apply Forman–Ricci and Ollivier–Ricci curvatures to compare networks of ASD and typically developing individuals (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We find brain-wide and region-specific ASD-related differences for both Forman–Ricci and Ollivier–Ricci curvatures, with region-specific differences concentrated in Default Mode, Somatomotor and Ventral Attention networks for Forman–Ricci curvature. We use meta-analysis decoding to demonstrate that brain regions with curvature differences are associated to those cognitive domains known to be impaired in ASD. Further, we show that brain regions with curvature differences overlap with those brain regions whose non-invasive stimulation improves ASD-related symptoms. These results suggest the utility of graph Ricci curvatures in characterizing atypical connectivity of clinically relevant regions in ASD and other neurodevelopmental disorders.

Disrupted functional connectivity of the primary auditory cortex in autism

Autism Spectrum Disorder (ASD) has been found to influence hearing and sensory integration, while brain functional connectivity in ASD has been repeatedly shown to be atypical. However, functional connectivity of the auditory cortex in ASD has not been well studied. In the current study, we used resting-state functional magnetic resonance imaging data, provided by the Autism Brain Imaging Data Exchange (ABIDE), to examine functional connectivity of the primary auditory cortex in ASD. The study subjects included 68 individuals with ASD and 77 individuals without ASD. In the primary dataset, the ASD group showed lesser functional connectivity between the auditory cortex and four regions: the medial occipital cortex, primary motor cortex, insular cortex, and Wernicke's area. In the replication dataset (44 individuals with ASD and 39 individuals without ASD), reduced connectivity to the medial occipital cortex and primary motor cortex was replicated among these four regions, which have previously been shown to be influenced by ASD. Thus, the reduced functional connectivity to these indicated regions may partly explain deficient sensory integration associated with ASD.

Altered transcallosal fiber count and volume in high-functioning adults with autism spectrum disorder

An altered pattern of information processing has been hypothesized in autism spectrum disorder (ASD), characterized by enhanced local network connectivity and reduced long-distance communication. Previous findings of impaired white matter integrity in the genu and the body of the corpus callosum already indicated reduced long-distance connectivity in patients with ASD. However, it remained unclear how this reduced white matter integrity affects the structural connectivity of the corresponding brain areas. To this end, we analyzed magnetic resonance images (MRI) from 30 participants with high-functioning ASD and 30 typically developed individuals using a global tracking approach to estimate the fiber count and volume of the transcallosal fiber tracts of the five corpus callosum subsections. A reduced fiber count and fiber volume in the anterior subsection of the corpus callosum was detected, supporting the hypothesis of reduced long-distance connectivity in ASD.

Neural Connectivity and Episodic Memory in Autism Spectrum Disorder: A Literature Review

Introduction: There is a growing interest in the social and biological context of episodic memory in children with autism spectrum disorder (ASD). Research has previously found that episodic memory deficits are overrepresented in this population. In an attempt to learn why children with ASD are disproportionately impacted by episodic memory impairments, this paper explores literature from 1970-2020 concerning the relationship between functional connectivity (FC), effective connectivity (EC) and structural connectivity (SC) and episodic memory in children with ASD. Methods: The method of this review involved an extensive literature search in scientific databases for experimental studies and magnetic resonance imaging (MRI) data pertaining to episodic memory in children with ASD. The literature review was conducted by searching for literature in electronic databases (Google Scholar, PubMed and MEDLINE) using the following search words: “ASD and memory,” “episodic memory in ASD,” “connectivity in ASD”. Results: In the studies reviewed, children with ASD consistently underperformed on episodic memory tasks relative to typically developing children. Additionally, the MRI scans of the children with ASD showed hyper- and hypoconnectivity of brain regions across the three connectivity metrics. The results indicated that the abnormalities seen in the FC, SC, and EC of children with ASD is an area of research and intervention opportunity for clinicians. Discussion: Research has found that interventions introduced early to children with autism have the potential to reduce symptoms of ASD before adulthood. Therefore, it is important that early interventions related to improving episodic memory are introduced to children early on to increase quality of life later. Additionally, future research must explore if connectivity abnormalities contribute to ASD or if it precedes ASD diagnosis. As a result, clinicians may also consider adding episodic memory deficits to the diagnostic criteria for ASD since it is overrepresented in this population. Conclusion: Clarifying the relationship between ASD, connectivity, and episodic memory will improve the quality of life of children with ASD in the future. This understanding will have broader implications in children and adults with ASD who struggle with episodic memory in terms of improving their experience in education, work and personal life.

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.