Autism Spectrum Disorder Adults Research Articles

Noninvasive brain stimulation in autism: review and outlook for personalized interventions in adult patients.

Noninvasive brain stimulation (NIBS) has been increasingly investigated during the last decade as a treatment option for persons with autism spectrum disorder (ASD). Yet, previous studies did not reach a consensus on a superior treatment protocol or stimulation target. Persons with ASD often suffer from social isolation and high rates of unemployment, arising from difficulties in social interaction. ASD involves multiple neural systems involved in perception, language, and cognition, and the underlying brain networks of these functional domains have been well documented. Aiming to provide an overview of NIBS effects when targeting these neural systems in late adolescent and adult ASD, we conducted a systematic search of the literature starting at 631 non-duplicate publications, leading to six studies corresponding with inclusion and exclusion criteria. We discuss these studies regarding their treatment rationale and the accordingly chosen methodological setup. The results of these studies vary, while methodological advances may allow to explain some of the variability. Based on these insights, we discuss strategies for future clinical trials to personalize the selection of brain stimulation targets taking into account intersubject variability of brain anatomy as well as function.

Attention and sensory integration for gait in young adults with autism spectrum disorder

IntroductionAltered sensorimotor function is a common feature of autism spectrum disorder (ASD). As a result, spatiotemporal walking patterns are typically affected. Attentional processes relevant for locomotion may be altered in people with ASD. This study assessed the extent to which gait alterations observed under sensory challenging conditions are due to reduced attention-related processes in young adults with ASD. MethodsTwenty-one adults with ASD and 21 age- and sex-matched neurotypical participants walked at a self-selected pace on a 10-m walkway under 12 sensory/attention conditions: hard or carpet flooring; well-lit or dim lighting; no attention task, an auditory choice-reaction time information-processing task, or a simple reaction time information-processing task. Gait data were collected with a 12-marker motion capture set and a trunk accelerometer. Spatiotemporal characteristics of gait were derived and compared between the two groups across gait conditions. ResultsFloor/light conditions impacted gait speed, average step length, average stance time, average step width, and step width variability similarly in both groups (p<0.05). The information processing tasks impacted average step length, gait speed, and step length variability (p<0.05). Group differences were found in step length metrics: the ASD group had decreased average step length during the simple reaction time information-processing task and neurotypical participants did not (p=0.039); the ASD group had increased variability on carpet compared to hard floor and the neurotypical group had no change in variability due to floor (p=0.015). SignificanceThese results suggest that attentional set-shifting and somatosensory inputs may play an important role in ASD-related gait alterations. Step length metrics appear to be sensitive to group differences between ASD and neurotypical adults during sensory challenging conditions.

Diagnosis of Autism Spectrum Disorder in Adults by Combining Bayes' Law and Genetic Algorithm

Diagnosis of Autism Spectrum Disorder in Adults by Combining Bayes' Law and Genetic Algorithm

A population pharmacokinetics model of balovaptan to support dose selection in adult and pediatric populations.

Balovaptan is a brain-penetrating vasopressin receptor 1a antagonist previously investigated for the core symptoms of autism spectrum disorder (ASD). A population pharmacokinetic (PK) model of balovaptan was developed, initially to assist clinical dosing for adult and pediatric ASD studies and subsequently for new clinical indications including malignant cerebral edema (MCE) and post-traumatic stress disorder. The final model incorporates one-compartment disposition and describes time- and dose-dependent non-linear PK through empirical drug binding and a gut extraction component with turnover. An age effect on clearance observed in children was modeled by an asymptotic function that predicts adult-equivalent exposures at 40% of the adult dose for children aged 2-4years, 70% for 5-9years, and at the full adult dose for ≥ 10years. The model was adapted for intravenous (IV) balovaptan dosing and combined with in vitro and ex vivo pharmacodynamic data to simulate brain receptor occupancy as a guide for dosing in a phase II trial of MCE prophylaxis after acute ischemic stroke. A sequence of three stepped-dose daily infusions of 50, 25 and 15mg over 30 or 60min was predicted to achieve a target occupancy of ≥ 80% in ≥ 95% of patients over a 3-day period. This model predicts both oral and IV balovaptan exposure across a wide age range and will be a valuable tool to analyze and predict its PK in new indications and target populations, including pediatric patients.

Circulating Levels of 5-HT and BDNF in Adults with Autism Spectrum Conditions: An Investigation in a Sample of Subjects with Autism Spectrum Disorder, their First-degree Relatives and Controls.

Several studies investigated circulating levels of serotonin (5- HT) and brain-derived neurotrophic factor (BDNF) in children with Autism spectrum disorder (ASD). More limited literature focused on ASD adults or on populations with subthreshold autism spectrum manifestations, such as relatives of ASD probands. This study aimed to investigate 5-HT and BDNF levels in adults with autism spectrum conditions. Correlations between levels of biochemical variables and ASD symptoms were also evaluated. a sample of ASD adults, their first-degree relatives (Broad autism phenotype, BAP group), and controls were recruited and assessed with psychometric scales. Blood samples were collected from all participants. 5-HT and BDNF levels were measured by means of ELISA kits. ASD adults showed significantly lower platelet-poor plasma (PPP) 5-HT levels than BAP and control groups. No significant difference was found among groups for PPP BDNF levels and intra-platelet 5-HT levels. 5-HT levels were reported to be specifically correlated with some autism symptoms. This work highlighted the presence in ASD adults of reduced PPP 5-HT levels than in other groups, without significant differences with respect to BDNF levels, supporting the hypothesis that biochemical correlates of ASD in adults may be different from those typically reported in children.

Schema therapy in adults with autism spectrum disorder: A scoping review.

Schema therapy (ST) in adults with autism spectrum disorder (ASD) have gained increasing interest in clinical practice and research. However, to date, there has been no synthesis of the literature on ST as treatment for adults with ASD. Through a scoping review, we aim to summarize the emerging research and literature on ST for adults with ASD. A comprehensive literature search of three electronic databases was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Publications were included that examined ST in adults with ASD and non-clinical adults with autistic traits. Systematic searches identified a total of 11 publications, all published since 2014, with the majority being conducted by Dutch and Japanese researchers. Of the 11 publications, 1 described an ST-informed social interaction training, 1 was an ST protocol, 2 described conceptual ST models, 2 were case examples, 2 examined early maladaptive schemas (EMSs) and 3 investigated the effectiveness of ST interventions. In summary, the theoretical exploration, practical examples of the application of ST and research findings with preliminary evidence of ST, EMSs and schema modes (SMs) in adults with ASD point towards the potential of ST for the treatment of adults with ASD. The current scoping review highlights preliminary research findings and offers valuable suggestions for clinicians treating adults with ASD. This review underscores the need for development of and research in specialized ST protocols and programmes tailored to adults with ASD with chronic mental conditions, such as personality disorders (PDs).

Autism Spectrum Disorder Prediction in Children Using Machine Learning

Life symptoms associated with autism spectrum disorder (ASD) typically manifest during childhood and persist into adolescence and adulthood. ASD, which can be caused by genetic or environmental factors, can be significantly improved through early detection and treatment. Currently, standardized clinical tests are the primary diagnostic method for ASD. However, these tests are time consuming and expensive. Early detection and intervention are pivotal in enhancing the long-term prospects of children diagnosed with ASD. Machine-learning (ML) techniques are being utilized alongside conventional methods to improve the accuracy and efficiency of ASD diagnosis. Therefore, the paper aims to explore the feasibility of employing support vector machines, random forest classifier, naïve Bayes, logistic regression (LR), K-nearest neighbor, and decision tree classification models on our dataset to construct predictive models for predicting and analyzing ASD problems across different age groups: children, adolescents, and adults. The proposed techniques are assessed using publicly available nonclinical ASD datasets of three distinct datasets. The four ASD datasets, namely toddlers, adolescents, children, and adults, were obtained from publicly available repositories, specifically Kaggle and UCI ML. These repositories provide a valuable data source for research and analysis related to ASD. Our main objective is to identify the susceptibility to ASD in children during the early stages, thereby streamlining the diagnosis process. Based on our findings, LR demonstrated the highest accuracy for the selected dataset.

Attention-deficit Hyperactivity Disorder and Autism Spectrum Disorder: Towards Better Diagnosis and Management.

Attention-deficit hyperactivity disorder (ADHA) is one of the most common comorbid disorders of autism spectrum disorder (ASD) that can accompany autism, triggered by it, or be a consequence of it. This review explored the prevalence of the comorbidity of both disorders, neurobiological background, symptoms, latest assessment methods, and therapeutic approaches. Results and Discussion: It concluded that effective assessment, diagnosis and management of ADHD in ASD children and adults is essential for this group of patients to thrive and live a good quality of life. Further research is recommended to explore the most effective intervention for such important members of our society. More studies are needed to understand the mechanisms underlying these comorbidities, and to prevent the misdiagnosis and mismanagement of these disorders. Also, to develop up to date personalized therapeutic plans for such children.

EEG Complexity Analysis of Brain States, Tasks and ASD Risk.

Autism spectrum disorder is an increasingly prevalent and debilitating neurodevelopmental condition and an electroencephalogram (EEG) diagnostic challenge. Despite large amounts of electrophysiological research over many decades, an EEG biomarker for autism spectrum disorder (ASD) has not been found. We hypothesized that reductions in complex dynamical system behaviour in the human central nervous system as part of the macroscale neuronal function during cognitive processes might be detectable in whole EEG for higher-risk ASD adults. In three studies, we compared the medians of correlation dimension, largest Lyapunov exponent, Higuchi's fractal dimension, multiscale entropy, multifractal detrended fluctuation analysis and Kolmogorov complexity during resting, cognitive and social skill tasks in 20 EEG channels of 39 adults over a range of ASD risk. We found heterogeneous complexity distribution with clusters of hierarchical sequences pointing to potential cognitive processing differences, but no clear distinction based on ASD risk. We suggest that there is indication of statistically significant differences between complexity measures of brain states and tasks. Though replication of our studies is needed with a larger sample, we believe that our electrophysiological and analytic approach has potential as a biomarker for earlier ASD diagnosis.

Advancing autism spectrum disorder diagnosis through ensemble learning

Autism Spectrum Disorder (ASD) diagnostics requires specialized clinical expertise, posing accessibility and affordability barriers for many. To widen the availability of precise screening, this paper examines ensemble machine learning models that combine multiple algorithms for improved accuracy and generalizability. Specifically, this paper compares the performance of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes, and a Voting Classifier that integrates Logistic Regression, Decision Trees, and KNN. The comparison was conducted on separate datasets of pediatric and adult ASD questionnaire responses. The ensemble model significantly outperforms individual techniques, achieving higher accuracy for both pediatrics and adults with balanced sensitivity and specificity maintained across groups, indicating the viability of accessible community-available screening to alleviate diagnostic bottlenecks. Before scale-up, further model optimization for interpretability and testing on more diverse multi-site data are warranted. Overall, findings demonstrate the feasibility of mobile distributed pre-screening systems leveraging optimized ensembles to predict ASD with high precision across ages, opening possibilities for explainable AI to lower costs and widen access compared to in-person evaluation.

Corneal structural alterations in autism spectrum disorder: An in vivo confocal microscopy study.

Individuals with autism spectrum disorder (ASD) often exhibit joint hypermobility and connective tissue disorders. However, it remains unclear if ASD individuals also have structural alterations in the connective tissue of the cornea. This study aims to determine whether the Kobayashi structure (K-structure) characteristics differ between adults with ASD and typically developing controls (TDC) and explore the clinical correlates of the K-structure abnormality. We recruited 30 ASD adults and 35 TDC. Corneal structures, particularly the K-structure in the Bowman's layer, of the participants were examined using in vivo confocal microscopy (IVCM), and a K-grading ranging from 1 to 4 was given to each eye based on the level of morphological mosaicism. The ASD participants' eyes received a significantly higher single-eye K-grading than that of the TDC eyes (p < 0.001), and the medians [25th, 75th percentile] of bilateral-eye summed K-grading were 8 [7, 8] and 5 [4, 6] in ASD and TDC, respectively (p < 0.001). A significantly higher K-grading in the ASD participants' eyes was still observed after adjusting for the within-subject inter-eye correlation (p < 0.001). Youden Index showed the optimal cutoffs to differentiate ASD from TDC by bilateral-eye summed K-grading and single-eye K-grading was >6 and >3, respectively. Additionally, a higher K-grading was associated with fewer visual sensation seeking in ASD (Spearman's correlation coefficient ρ = -0.518, p = 0.008) and low visual registration (i.e., higher sensory threshold) in TDC (ρ = 0.446, p = 0.023). This study provided novel evidence of corneal structural alterations in ASD by IVCM. Our findings may not only support the prior hypothesis of the association between ASD and connective tissue abnormalities but also shed light on the relationship between connective tissue disorder and neurodevelopmental disorders.

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.

Relationship between abnormal intrinsic functional connectivity of subcortices and autism symptoms in high-functioning adults with autism spectrum disorder

PurposeThis study explores subcortices and their intrinsic functional connectivity (iFC) in autism spectrum disorder (ASD) adults and investigates their relationship with clinical severity. MethodsResting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 74 ASD patients, and 63 gender and age-matched typically developing (TD) adults. Independent component analysis (ICA) was conducted to evaluate subcortical patterns of basal ganglia (BG) and thalamus. These two brain areas were treated as regions of interest to further calculate whole-brain FC. In addition, we employed multivariate machine learning to identify subcortices-based FC brain patterns and clinical scores to classify ASD adults from those TD subjects. ResultsIn ASD individuals, autism diagnostic observation schedule (ADOS) was negatively correlated with the BG network. Similarly, social responsiveness scale (SRS) was negatively correlated with the thalamus network. The BG-based iFC analysis revealed adults with ASD versus TD had lower FC, and its FC with the right medial temporal lobe (MTL), was positively correlated with SRS and ADOS separately. ASD could be predicted with a balanced accuracy of around 60.0 % using brain patterns and 84.7 % using clinical variables. ConclusionOur results revealed the abnormal subcortical iFC may be related to autism symptoms.

Role of the Autism Risk Gene Shank3 in the Development of Atherosclerosis: Insights from Big Data and Mechanistic Analyses.

Increased medical attention is needed as the prevalence of autism spectrum disorder (ASD) rises. Both cardiovascular disorder (CVD) and hyperlipidemia are closely associated with adult ASD. Shank3 plays a key genetic role in ASD. We hypothesized that Shank3 contributes to CVD development in young adults with ASD. In this study, we investigated whether Shank3 facilitates the development of atherosclerosis. Using Gene Set Enrichment Analysis software (Version No.: GSEA-4.0.3), we analyzed the data obtained from Shank3 knockout mice (Gene Expression Omnibus database), a human population-based study cohort (from Taiwan's National Health Insurance Research Database), and a Shank3 knockdown cellular model. Shank3 knockout upregulated the expression of genes of cholesterol homeostasis and fatty acid metabolism but downregulated the expression of genes associated with inflammatory responses. Individuals with autism had higher risks of hyperlipidemia (adjusted hazard ratio (aHR): 1.39; p < 0.001), major adverse cardiac events (aHR: 2.67; p < 0.001), and stroke (aHR: 3.55; p < 0.001) than age- and sex-matched individuals without autism did. Shank3 downregulation suppressed tumor necrosis factor-α-induced fatty acid synthase expression; vascular cell adhesion molecule 1 expression; and downstream signaling pathways involving p38, Jun N-terminal kinase, and nuclear factor-κB. Thus, Shank3 may influence the development of early-onset atherosclerosis and CVD in ASD. Furthermore, regulating Shank3 expression may reduce inflammation-related disorders, such as atherosclerosis, by inhibiting tumor necrosis factor-alpha-mediated inflammatory cascades.

A - 177 The Self-Concept Scale of Conners Adult Rating Scale in the Diagnosis of Autism Spectrum Disorder in Adults.

Self-report scales are beneficial in identifying symptoms of Autism Spectrum Disorder (ASD) in adults (Hayashi et al., 2021; Hull et al., 2019). Symptoms of ASD and attention-deficit/hyperactivity disorder (ADHD) overlap significantly even without dual-diagnosis (Nakagawa et al., 2021). The Conners Adult ADHD Rating Scale (CAARS) is a reliable and valid screening tool when identifying ADHD in adults, 78% sensitivity and 56% specificity (Abbass et al., 2021). Distinguishing features of ASD include restricted repetitive behaviors (RRB) and social interaction and communication deficits (SIC) (American Psychiatric Association, 2022). In adults, CAARS has shown a moderate correlation of RRB with the inattention (IA) and hyperactivity/impulsivity (HI) of ADHD; SIC moderately correlates with IA and mildly with HI. This study explores group differences when comparing Self-Concept (SC) Scale of CAARS in those diagnosed with ASD from clinical comparisons. Data composed of 93 adult participants, 7 diagnosed with ASD, were extracted from a midwest neuropsychological outpatient clinic and analyzed to determine the sensitivity of CAARS-SC in diagnosing ASD. Independent t-Test identified no significant effect for diagnosis, t(91) = -0.301, p = 0.764, despite those in the ASD group (M = 62.00, SD = 12.432) endorsing more concerns than those without the diagnosis (M = 60.47, SD = 12.996). This study did not identify significant group differences on the CAARS-SC when comparing those diagnosed with ASD to the clinical comparisons. Further implications and limitations will be explored to distinguish ASD from overlapping diagnoses to employ appropriate treatment and future psychometric research directions.

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.

Autism spectrum disorders in adults and the autonomic nervous system: Heart rate variability markers in the diagnostic procedure

The diagnostic assessment of autism spectrum disorders (ASD) in adults is a challenging and time-consuming procedure. In order to address the lack of specialised health-care professionals and improve the waiting time, we aimed to identify specific electrocardiogram (ECG) derived Heart Rate Variability (HRV) parameters that could be used for diagnostic purposes. 152 patients were diagnosed based on a standardised clinical procedure and assigned to one of three groups: ASD (n = 56), any other psychiatric disorder (OD) (n = 72), and patients with no diagnosis (ND) (n = 24). Groups were compared using ANOVA. Discriminative power of biological parameters and the clinical assessment were compared using receiver operating characteristic curves (ROCs). Patients with ASD showed reduced parasympathetic and increased sympathetic activity compared to ND. The accuracy determined by the area under the curve (AUC) of the biological parameters for discrimination between ASD vs. pooled OD/ND was 0.736 (95% CI = 0.652-0.820), compared to .856 (95% CI = 0.795-0.917) for the extensive clinical assessment. Our results confirmed the dysregulation of the autonomic nervous system in ASD with reduced parasympathetic and increased sympathetic activity as compared to ND. The discriminative power of biological markers including HRV was considerable and could supplement less sophisticated clinical assessments.

Towards finding the lost generation of autistic adults: A deep and multi-view learning approach on social media

The detection of mental disorders through social media has received significant attention. With the growing prevalence of Autism Spectrum Disorder (ASD) and the inherent difficulties in diagnosing adults, researchers have attempted to identify undiagnosed adults. Previous studies have primarily concentrated on analyzing ASD characteristics rather than directly detecting ASD. The current study aims to propose a novel framework to assist in identifying the “lost generation” of ASD adults using their social media posts. Combining traditional and deep learning methods makes it possible to model complex aspects of ASD diagnostic characteristics, which have been relatively overlooked in previous studies. To accomplish this, specific formalizations for users’ patterns of interest as a main ASD diagnostic characteristic are proposed first. The latent linguistic and semantic features of ASD users’ postings are then modeled using deep and transformer-based language models. Finally, all these different aspects are considered together to train a detection model by employing the multi-view learning approach. The experiments show that the feature of idiosyncratic interests has more discriminative power than limited and repetitive interests. The results also indicate that the early fusion of interest-related features along with deep linguistic features outperforms the other examined feature combinations. Additionally, the proposed ‘if−iuf’ fusion model demonstrates improved performance in capturing patterns of interests, compared to baselines. These findings suggest the potential application of the proposed framework towards indirectly identifying ASD users on social media, as evidenced by achieving precision and recall rates of 85% and 82% respectively on the used sampled dataset.

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).

Regional Dynamic Neuroimaging Changes of Adults with Autism Spectrum Disorder

Most neuroimaging studies investigating autism spectrum disorder (ASD) have focused on static brain function, but ignored the dynamic features of spontaneous brain activities in the temporal dimension. Research of dynamic brain regional activities might help to fully investigate the mechanisms of ASD patients. This study aimed to examine potential changes in the dynamic characteristics of regional neural activities in adult ASD patients and to detect whether the changes were associated with Autism Diagnostic Observation Schedule (ADOS) scores. Resting-state functional MRI was obtained on 77 adult ASD patients and 76 healthy controls. The dynamic regional homogeneity (dReHo) and dynamic amplitude of low-frequency fluctuations (dALFF) were compared between the two groups. Correlation analyses were also performed between dReHo and dALFF in areas showing group differences and ADOS scores. In ASD group, significant differences in dReHo were observed in the left middle temporal gyrus (MTG.L). Besides, we found increased dALFF in the left middle occipital gyrus (MOG.L), left superior parietal gyrus (SPG.L), left precuneus (PCUN.L), left inferior temporal gyrus (ITG.L), and right inferior frontal gyrus, orbital part (ORBinf.R). Furthermore, a significant positive correlation was found between dALFF in the PCUN.L and the ADOS_TOTAL scores, ADOS_SOCIAL scores; the dALFF in the ITG.L, SPG.L was positively associated with ADOS_SOCIAL scores. In conclusion, adults with ASD have a wide area of dynamic regional brain function abnormalities. These suggested that dynamic regional indexes might be used as a powerful measure to help us obtain a more comprehensive understanding of neural activity in adult ASD patients.