Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity

Individuals with high autistic traits exhibit altered interhemispheric brain functional connectivity patterns

Individuals with high autistic traits (AT) encounter challenges in social interaction, similar to autistic persons. Precise screening and focused interventions positively contribute to improving this situation. Functional connectivity analyses can measure information transmission and integration between brain regions, providing neurophysiological insights into these challenges. This study aimed to investigate the patterns of brain networks in high AT individuals to offer theoretical support for screening and intervention decisions. EEG data were collected during a 4-min resting state session with eyes open and closed from 48 participants. Using the Autism Spectrum Quotient (AQ) scale, participants were categorized into the high AT group (HAT, n = 15) and low AT groups (LAT, n = 15). We computed the interhemispheric and intrahemispheric alpha coherence in two groups. The correlation between physiological indices and AQ scores was also examined. Results revealed that HAT exhibited significantly lower alpha coherence in the homologous hemispheres of the occipital cortex compared to LAT during the eyes-closed resting state. Additionally, significant negative correlations were observed between the degree of AT (AQ scores) and the alpha coherence in the occipital cortex, as well as in the right frontal and left occipital regions. The findings indicated that high AT individuals exhibit decreased connectivity in the occipital region, potentially resulting in diminished ability to process social information from visual inputs. Our discovery contributes to a deeper comprehension of the neural underpinnings of social challenges in high AT individuals, providing neurophysiological signatures for screening and intervention strategies for this population.

Probing the Neurodynamic Mechanisms of Cognitive Flexibility in Depressed Individuals with Autism Spectrum Disorder

Introduction: Autism spectrum disorder (ASD) is characterized by deficits in social behavior and executive function (EF), particularly in cognitive flexibility. Whether transcranial magnetic stimulation (TMS) can improve cognitive outcomes in patients with ASD remains an open question. We examined the acute effects of prefrontal TMS on cortical excitability and fluid cognition in individuals with ASD who underwent TMS for refractory major depression. Methods: We analyzed data from an open-label pilot study involving nine participants with ASD and treatment-resistant depression who received 30 sessions of accelerated theta burst stimulation of the dorsolateral prefrontal cortex, either unilaterally or bilaterally. Electroencephalography data were collected at baseline and 1, 4, and 12-weeks posttreatment and analyzed using a mixed-effects linear model to assess changes in regional cortical excitability using three models of spectral parametrization. Fluid cognition was measured using the National Institutes of Health Toolbox Cognitive Battery. Results: Prefrontal TMS led to a decrease in prefrontal cortical excitability and an increase in right temporoparietal excitability, as measured using spectral exponent analysis. This was associated with a significant improvement in the NIH Toolbox Fluid Cognition Composite score and the Dimensional Change Card Sort subtest from baseline to 12 weeks posttreatment (t = 3.79, p = 0.005, n = 9). Improvement in depressive symptomatology was significant (HDRS-17, F (3, 21) = 28.49, p < 0.001) and there was a significant correlation between cognitive improvement at week 4 and improvement in depression at week 12 (r = 0.71, p = 0.05). Conclusion: These findings link reduced prefrontal excitability in patients with ASD and improvements in cognitive flexibility. The degree to which these mechanisms can be generalized to ASD populations without Major Depressive Disorder remains a compelling question for future research.

Neural correlates of facial recognition deficits in autism spectrum disorder: a comprehensive review

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by significant impairments in social interaction, often manifested in facial recognition deficits. These deficits hinder individuals with ASD from recognizing facial identities and interpreting emotions, further complicating social communication. This review explores the neural mechanisms underlying these deficits, focusing on both functional anomalies and anatomical differences in key brain regions such as the fusiform gyrus (FG), amygdala, superior temporal sulcus (STS), and prefrontal cortex (PFC). It has been found that the reduced activation in the FG and atypical activation of the amygdala and STS contribute to difficulties in processing facial cues, while increased reliance on the PFC for facial recognition tasks imposes a cognitive load. Additionally, disrupted functional and structural connectivity between these regions further exacerbates facial recognition challenges. Future research should emphasize longitudinal, multimodal neuroimaging approaches to better understand developmental trajectories and design personalized interventions, leveraging AI and machine learning to optimize therapeutic outcomes for individuals with ASD.

Effects of Extremely Low-Frequency Electromagnetic Field Treatment on ASD Symptoms in Children: A Pilot Study

BACKGROUND/OBJECTIVES

Autism Spectrum Disorder (ASD) are neurodevelopmental disorders marked by challenges in social interaction, communication, and repetitive behaviors. People with ASD may exhibit repetitive behaviors, unique ways of learning, and different ways of interacting with the world. The term "spectrum" reflects the wide variability in how ASD manifests in individuals, including differences in abilities, symptoms, and support needs, and conditions characterized by difficulties in social interactions, communication, restricted interests, and repetitive behaviors. Inflammation plays a crucial role in the pathophysiology, with increased pro-inflammatory cytokines in cerebrospinal fluid. Previous studies with transcranial magnetic stimulation have shown promising results, suggesting nervous system susceptibility to electromagnetic fields, with evidence indicating that extremely low-frequency electromagnetic field (ELF-EMF) treatment may modulate inflammatory responses through multiple pathways, including the reduction of pro-inflammatory cytokines like IL-6 and TNF-α, and the enhancement of anti-inflammatory mediators.

METHODS

This pilot study included 20 children (ages 2-13) with a confirmed diagnosis of ASD. A 15-week protocol involved ELF-EMF treatments using the SEQEX device, with specific day and night programs. Assessment was conducted through standardized pre- and post-treatment tests: Achenbach Child Behavior Checklist, Peabody Picture Vocabulary Test-4, Expressive One Word Picture Vocabulary Test-4, and Conner's 3GI.

RESULTS

Statistically significant improvements were observed in receptive language (PPVT-4: from 74.07 to 90.40, p = 0.002) and expressive language (EOWPVT-4: from 84.17 to 90.50, p = 0.041). Notable reductions, with statistical significance, were found in externalizing problems across both age groups (1.5-5 years: p = 0.028; 6-18 years: p = 0.027), with particular improvement in attention and behavioral problems. The results were observed over a short period of 15 weeks, therefore excluding the possibility of coincidental age-related gains, that would typically occur during a normal developmental timeframe. Parent evaluations showed significant reduction in ASD symptoms, particularly in the 1.5-5 years group (p = 0.046).

CONCLUSIONS

ELF-EMF treatment demonstrated a high safety profile and efficacy in mitigating ASD-related symptoms. The observed improvements suggest both direct effects on central and autonomic nervous systems and indirect effects through inflammatory response modulation. Further studies are needed to confirm these promising results through broader demographics and randomized control designs.

Decomposing the Brain in Autism: Linking Behavioral Domains to Neuroanatomical Variation and Genomic Underpinnings

BACKGROUND

Autism is accompanied by highly individualized patterns of neurodevelopmental differences in brain anatomy. This variability makes the neuroanatomy of autism inherently difficult to describe at the group level. Here, we examined inter-individual neuroanatomical differences using a dimensional approach that decomposed the domains of social communication and interaction (SCI), restricted and repetitive behaviors (RRB), and atypical sensory processing (ASP) within a neurodiverse study population. Moreover, we aimed to link the resulting neuroanatomical patterns to specific molecular underpinnings.

METHODS

Neurodevelopmental differences in cortical thickness and surface area were correlated with SCI, RRB and ASP domain scores by regression of a General Linear Model in a large neurodiverse sample of N=288 autistic and N=140 non-autistic individuals, aged 6-30, recruited within the EU-AIMS Longitudinal European Autism Project (LEAP). The domain-specific patterns of neuroanatomical variability were subsequently correlated with cortical gene expression profiles via the Allan Human Brain Atlas.

RESULTS

Across groups, behavioral variations in SCI, RRB and ASP were associated with interindividual differences in CT and SA in partially non-overlapping fronto-parietal, temporal, and occipital networks. These domain-specific imaging patterns were enriched for genes (i) differentially expressed in autism, (ii) mediating typical brain development, and that are (iii) associated with specific cortical cell types. Many of these genes were implicated in pathways governing synaptic structure and function.

CONCLUSIONS

Our study corroborates the close relationship between neuroanatomical variation and interindividual differences in autism-related symptoms and traits within the general framework of neurodiversity, and links domain-specific patterns of neuroanatomical differences to putative molecular underpinnings.

High neural noise in autism: A hypothesis currently at the nexus of explanatory power

Autism is a neurodevelopmental difference associated with specific autistic experiences and characteristics. Early models such as Weak Central Coherence and Enhanced Perceptual Functioning have tried to capture complex autistic behaviours in a single framework, however, these models lacked a neurobiological explanation. Conversely, current neurobiological theories of autism at the cellular and network levels suggest excitation/inhibition imbalances lead to high neural noise (or, a 'noisy brain') but lack a thorough explanation of how autistic behaviours occur. Critically, around 15 years ago, it was proposed that high neural noise in autism produced a stochastic resonance (SR) effect, a phenomenon where optimal amounts of noise improve signal quality. High neural noise can thus capture both the enhanced (through SR) and reduced performance observed in autistic individuals during certain tasks. Here, we provide a review and perspective that positions the "high neural noise" hypothesis in autism as best placed to provide research direction and impetus. Emphasis is placed on evidence for SR in autism, as this promising prediction has not yet been reviewed in the literature. Using this updated approach towards autism, we can explain a spectrum of autistic experiences all through a neurobiological lens. This approach can further aid in developing specific support or services for autism.

Medial prefrontal cortex circuitry and social behaviour in autism

Autism spectrum disorder (ASD) has proven to be highly enigmatic due to the diversity of its underlying genetic causes and the huge variability in symptom presentation. Uncovering common phenotypes across people with ASD and pre-clinical models allows us to better understand the influence on brain function of the many different genetic and cellular processes thought to contribute to ASD aetiology. One such feature of ASD is the convergent evidence implicating abnormal functioning of the medial prefrontal cortex (mPFC) across studies. The mPFC is a key part of the 'social brain' and may contribute to many of the changes in social behaviour observed in people with ASD. Here we review recent evidence for mPFC involvement in both ASD and social behaviours. We also highlight how pre-clinical mouse models can be used to uncover important cellular and circuit-level mechanisms that may underly atypical social behaviours in ASD. This article is part of the Special Issue on "PFC circuit function in psychiatric disease and relevant models".

Widespread Associations between Behavioral Metrics and Brain Microstructure in ASD Suggest Age Mediates Subtypes of ASD

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. Our lab has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased in ASD individuals. By associating these differences with performance on cognitive and behavioral tests, this study aims to first associate a broad array of behavioral metrics with neuroimaging markers of ASD, and to explore the prevalence of ASD subtypes using a neuroimaging driven perspective. Analyzing 273 participants (148 with ASD) ages 8 to 17 through an NIH-sponsored Autism Centers of Excellence network (MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics, alongside different directional correlations between different behavioral metrics. Stronger associations with individual subcategories from each test rather than summary scores suggest that different neuronal profiles may be masked by composite test scores. Machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. This suggests that if ASD can be definitively subtyped, these subtypes may show different behavioral trajectories across the developmental period. Clustering identified a pattern of restrictive and repetitive behavior in some participants and a second group that was defined by high sensory sensitivity and language performance.

Increased heterogeneity and task-related reconfiguration of functional connectivity during a lexicosemantic task in autism

Autism spectrum disorder (ASD) is highly heterogeneous in etiology and clinical presentation. Findings on intrinsic functional connectivity (FC) or task-induced FC in ASD have been inconsistent including both over- and underconnectivity and diverse regional patterns. As FC patterns change across different cognitive demands, a novel and more comprehensive approach to network architecture in ASD is to examine the change in FC patterns between rest and task states, referred to as reconfiguration. This approach is suitable for investigating inefficient network connectivity that may underlie impaired behavioral functioning in clinical disorders. We used functional magnetic resonance imaging (fMRI) to examine FC reconfiguration during lexical processing, which is often affected in ASD, with additional focus on interindividual variability. Thirty adolescents with ASD and a matched group of 23 typically developing (TD) participants completed a lexicosemantic decision task during fMRI, using multiecho-multiband pulse sequences with advanced BOLD signal sensitivity and artifact removal. Regions of interest (ROIs) were selected based on task-related activation across both groups, and FC and reconfiguration were compared between groups. The ASD group showed increased interindividual variability and overall greater reconfiguration than the TD group. An ASD subgroup with typical performance accuracy (at the level of TD participants) showed reduced similarity and typicality of FC during the task. In this ASD subgroup, greater FC reconfiguration was associated with increased language skills. Findings suggest that intrinsic functional networks in ASD may be inefficiently organized for lexicosemantic decisions and may require greater reconfiguration during task processing, with high performance levels in some individuals being achieved through idiosyncratic mechanisms.

Investigating the interplay between segregation and integration in developing cortical assemblies

Introduction

The human brain is an intricate structure composed of interconnected modular networks, whose organization is known to balance the principles of segregation and integration, enabling rapid information exchange and the generation of coherent brain states. Segregation involves the specialization of brain regions for specific tasks, while integration facilitates communication among these regions, allowing for efficient information flow. Several factors influence this balance, including maturation, aging, and the insurgence of neurological disorders like epilepsy, stroke, or cancer. To gain insights into information processing and connectivity recovery, we devised a controllable in vitro model to mimic and investigate the effects of different segregation and integration ratios over time.

Methods

We designed a cross-shaped polymeric mask to initially establish four independent sub-populations of cortical neurons and analyzed how the timing of its removal affected network development. We evaluated the morphological and functional features of the networks from 11 to 18 days in vitro (DIVs) with immunofluorescence techniques and micro-electrode arrays (MEAs).

Results

The removal of the mask at different developmental stages of the network lead to strong variations in the degree of intercommunication among the four assemblies (altering the segregation/integration balance), impacting firing and bursting parameters. Early removal (after 5 DIVs) resulted in networks with a level of integration similar to homogeneous controls (without physical constraints). In contrast, late removal (after 15 DIVs) hindered the formation of strong inter-compartment connectivity, leading to more clustered and segregated assemblies.

Discussion

A critical balance between segregation and integration was observed when the mask was removed at DIV 10, allowing for the formation of a strong connectivity among the still-separated compartments, thus demonstrating the existence of a time window in network development in which it is possible to achieve a balance between segregation and integration.

Contracted functional connectivity profiles in autism

OBJECTIVE

Autism spectrum disorder (ASD) is a neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long- relative to short-range functional connections. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes.

METHODS

We studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean ± SD age = 20.8 ± 8.1 years) and 108 neurotypical controls (NT, 19.2 ± 7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types.

RESULTS

Compared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores.

LIMITATIONS

Despite rigorous harmonization across the three different acquisition sites, heterogeneity in autism poses a potential limitation to the generalizability of our results. Additionally, we focussed male participants, warranting future studies in more balanced cohorts.

CONCLUSIONS

Our study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.

Sharing Our World: Impact of Group Motor Skill Learning on Joint Attention in Children with Autism Spectrum Disorder

Impaired joint attention is a common feature of autism spectrum disorder (ASD), affecting social interaction and communication. We explored if group basketball learning could enhance joint attention in autistic children, and how this relates to brain changes, particularly white matter development integrity. Forty-nine autistic children, aged 4-12 years, were recruited from special education centers. The experimental group underwent a 12-week basketball motor skill learning, while the control group received standard care. Eye-tracking and brain scans were conducted. The 12-week basketball motor skill learning improved joint attention in the experimental group, evidenced by better eye tracking metrics and enhanced white matter integrity. Moreover, reduced time to first fixation correlated positively with decreased mean diffusivity of the left superior corona radiata and left superior fronto-occipital fasciculus in the experimental group. Basketball-based motor skill intervention effectively improved joint attention in autistic children. Improved white matter fiber integrity related to sensory perception, spatial and early attention function may underlie this effect. These findings highlight the potential of group motor skill learning within clinical rehabilitation for treating ASD.

Implications of altered pyramidal cell morphology on clinical symptoms of neurodevelopmental disorders

The prevalence of pyramidal cells (PCs) in the mammalian cerebral cortex underscore their value as they play a crucial role in various brain functions, ranging from cognition, sensory processing, to motor output. PC morphology significantly influences brain connectivity and plays a critical role in maintaining normal brain function. Pathological alterations to PC morphology are thought to contribute to the aetiology of neurodevelopmental disorders such as autism spectrum disorder (ASD) and schizophrenia. This review explores the relationship between abnormalities in PC morphology in key cortical areas and the clinical manifestations in schizophrenia and ASD. We focus largely on human postmortem studies and provide evidence that dendritic segment length, complexity and spine density are differentially affected in these disorders. These morphological alterations can lead to disruptions in cortical connectivity, potentially contributing to the cognitive and behavioural deficits observed in these disorders. Furthermore, we highlight the importance of investigating the functional and structural characteristics of PCs in these disorders to illuminate the underlying pathogenesis and stimulate further research in this area.

Altered white matter connectivity of ventral language networks in autism spectrum disorder: An automated fiber quantification analysis with multi-site datasets

Comprehension and pragmatic deficits are prevalent in autism spectrum disorder (ASD) and are potentially linked to altered connectivity in the ventral language networks. However, previous magnetic resonance imaging studies have not sufficiently explored the microstructural abnormalities in the ventral fiber tracts underlying comprehension dysfunction in ASD. Additionally, the precise locations of white matter (WM) changes in the long tracts of patients with ASD remain poorly understood. In the current study, we applied the automated fiber-tract quantification (AFQ) method to investigate the fine-grained WM properties of the ventral language pathway and their relationships with comprehension and symptom manifestation in ASD. The analysis included diffusion/T1 weighted imaging data of 83 individuals with ASD and 83 age-matched typically developing (TD) controls. Case-control comparisons were performed on the diffusion metrics of the ventral tracts at both the global and point-wise levels. We also explored correlations between diffusion metrics, comprehension performance, and ASD traits, and conducted subgroup analyses based on age range to examine developmental moderating effects. Individuals with ASD exhibited remarkable hypoconnectivity in the ventral tracts, particularly in the temporal portions of the left inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF). These WM abnormalities were associated with poor comprehension and more severe ASD symptoms. Furthermore, WM alterations in the ventral tract and their correlation with comprehension dysfunction were more prominent in younger children with ASD than in adolescents. These findings indicate that WM disruptions in the temporal portions of the left ILF/IFOF are most notable in ASD, potentially constituting the core neurological underpinnings of comprehension and communication deficits in autism. Moreover, impaired WM connectivity and comprehension ability in patients with ASD appear to improve with age.

Examining the relationship between functional connectivity and broader autistic traits in non-autistic children

In the current study, we used functional near-infrared spectroscopy (fNIRS) to examine functional connectivity (FC) in relation to measures of cognitive flexibility and autistic features in non-autistic children. Previous research suggests that disruptions in FC between brain regions may underlie the cognitive and behavioral traits of autism. Moreover, research has identified a broader autistic phenotype (BAP), which refers to a set of behavioral traits that fall along a continuum of behaviors typical for autism but which do not cross a clinically relevant threshold. Thus, by examining FC in relation to the BAP in non-autistic children, we can better understand the spectrum of behaviors related to this condition and their neural basis. Results indicated age-related differences in performance across three measures of cognitive flexibility, as expected given the rapid development of this skill within this time period. Additionally, results showed that across the flexibility tasks, measures of autistic traits were associated with weaker FC along the executive control network, though task performance was not associated with FC. These results suggest that behavioral scores may be less sensitive than neural measures to autistic traits. Further, these results corroborate the use of broader autistic traits and the BAP to better understand disruptions to neural function associated with autism.

Cognitive and brain morphological deviations in middle-to-old aged autistic adults: A systematic review and meta-analysis

Cognitive challenges and brain structure variations are common in autism spectrum disorder (ASD) but are rarely explored in middle-to-old aged autistic adults. Cognitive deficits that overlap between young autistic individuals and elderlies with dementia raise an important question: does compromised cognitive ability and brain structure during early development drive autistic adults to be more vulnerable to pathological aging conditions, or does it protect them from further decline? To answer this question, we have synthesized current theoretical models of aging in ASD and conducted a systematic literature review (Jan 1, 1980 - Feb 29, 2024) and meta-analysis to summarize empirical studies on cognitive and brain deviations in middle-to-old aged autistic adults. We explored findings that support different aging theories in ASD and addressed study limitations and future directions. This review sheds light on the poorly understood consequences of aging question raised by the autism community to pave the way for future studies to identify sensitive and reliable measures that best predict the onset, progression, and prognosis of pathological aging in ASD.

Transcranial photobiomodulation for neurodevelopmental disorders: a narrative review

BACKGROUND

Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Down syndrome (DS) significantly impact social, communicative, and behavioral functioning. Transcranial photobiomodulation (t-PBM) with near-infrared light is a promising non-invasive neurostimulation technique for neuropsychiatric disorders, including NDDs. This narrative review aimed to examine the preclinical and clinical evidence of photobiomodulation (PBM) in treating NDDs.

METHODS

A comprehensive search across six databases was conducted, using a combination of MeSH terms and title/abstract keywords: "photobiomodulation", "PBM", "neurodevelopmental disorders", "NDD", and others. Studies applying PBM to diagnosed NDD cases or animal models replicating NDDs were included. Protocols, reviews, studies published in languages other than English, and studies not evaluating clinical or cognitive outcomes were excluded.

RESULTS

Nine studies were identified, including one preclinical and eight clinical studies (five on ASD, two on ADHD, and one on DS). The reviewed studies encompassed various t-PBM parameters (wavelengths: 635-905 nm) and targeted primarily frontal cortex areas. t-PBM showed efficacy in improving disruptive behavior, social communication, cognitive rigidity, sleep quality, and attention in ASD; in enhancing attention in ADHD; and in improving motor skills and verbal fluency in DS. Minimal adverse effects were reported. Proposed mechanisms involve enhanced mitochondrial function, modulated oxidative stress, and reduced neuroinflammation.

CONCLUSIONS

t-PBM emerges as a promising intervention for NDDs, with potential therapeutic effects across ASD, ADHD, and DS. These findings underscore the need for further research, including larger-scale, randomized sham-controlled clinical trials with comprehensive biomarker analyses, to optimize treatment parameters and understand the underlying mechanisms associated with the effects of t-PBM.

Predicting individual autistic symptoms for patients with autism spectrum disorder using interregional morphological connectivity

Intelligent predictive models for autistic symptoms based on neuroimaging datasets were beneficial for the precise intervention of patients with ASD. The goals of this study were twofold: investigating predictive models for autistic symptoms and discovering the brain connectivity patterns for ASD-related behaviors. To achieve these goals, we obtained a cohort of patients with ASD from the ABIDE project. The autistic symptoms were measured using the Autism Diagnostic Observation Schedule (ADOS). The anatomical MRI datasets were preprocessed using the Freesurfer package, resulting in regional morphological features. For each individual, the interregional morphological network was constructed using a novel feature distance-based method. The predictive models for autistic symptoms were built using the support vector regression (SVR) algorithm with feature selection method. The predicted autistic symptoms (i.e., ADOS social score, ADOS behavior) were significantly correlated to the original measures. The most predictive features for ADOS social scores were located in the bilateral fusiform. The most predictive features for ADOS behavior scores were located in the temporal pole and the lingual gyrus. In summary, the autistic symptoms could be predicted using the interregional morphological connectivity and machine learning. The interregional morphological connectivity could be a potential biomarker for autistic symptoms.

Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach

BACKGROUND

Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level.

METHODS

Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5-40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks.

RESULTS

We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms.

CONCLUSIONS

Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.

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.

Identification of Autism Spectrum Disorder Using Topological Data Analysis

Autism spectrum disorder (ASD) is a pervasive brain development disease. Recently, the incidence rate of ASD has increased year by year and posed a great threat to the lives and families of individuals with ASD. Therefore, the study of ASD has become very important. A suitable feature representation that preserves the data intrinsic information and also reduces data complexity is very vital to the performance of established models. Topological data analysis (TDA) is an emerging and powerful mathematical tool for characterizing shapes and describing intrinsic information in complex data. In TDA, persistence barcodes or diagrams are usually regarded as visual representations of topological features of data. In this paper, the Regional Homogeneity (ReHo) data of subjects obtained from Autism Brain Imaging Data Exchange (ABIDE) database were used to extract features by using TDA. The average accuracy of cross validation on ABIDE I database was 95.6% that was higher than any other existing methods (the highest accuracy among existing methods was 93.59%). The average accuracy for sampling with the same resolutions with the ABIDE I on the ABIDE II database was 96.5% that was also higher than any other existing methods (the highest accuracy among existing methods was 75.17%).

A de novo missense mutation in synaptotagmin-1 associated with neurodevelopmental disorder desynchronizes neurotransmitter release

Synaptotagmin-1 (Syt1) is a presynaptic calcium sensor with two calcium binding domains, C2A and C2B, that triggers action potential-induced synchronous neurotransmitter release, while suppressing asynchronous and spontaneous release. We identified a de novo missense mutation (P401L) in the C2B domain in a patient with developmental delay and autistic symptoms. Expressing the orthologous mouse mutant (P400L) in cultured Syt1 null mutant neurons revealed a reduction in dendrite outgrowth with a proportional reduction in synapses. This was not observed in single Syt1PL-rescued neurons that received normal synaptic input when cultured in a control network. Patch-clamp recordings showed that spontaneous miniature release events per synapse were increased more than 500% in Syt1PL-rescued neurons, even beyond the increased rates in Syt1 KO neurons. Furthermore, action potential-induced asynchronous release was increased more than 100%, while synchronous release was unaffected. A similar shift to more asynchronous release was observed during train stimulations. These cellular phenotypes were also observed when Syt1PL was overexpressed in wild type neurons. Our findings show that Syt1PL desynchronizes neurotransmission by increasing the readily releasable pool for asynchronous release and reducing the suppression of spontaneous and asynchronous release. Neurons respond to this by shortening their dendrites, possibly to counteract the increased synaptic input. Syt1PL acts in a dominant-negative manner supporting a causative role for the mutation in the heterozygous patient. We propose that the substitution of a rigid proline to a more flexible leucine at the bottom of the C2B domain impairs clamping of release by interfering with Syt1's primary interface with the SNARE complex. This is a novel cellular phenotype, distinct from what was previously found for other SYT1 disease variants, and points to a role for spontaneous and asynchronous release in SYT1-associated neurodevelopmental disorder.

Cognitive Profile in Autism and ADHD: A Meta-Analysis of Performance on the WAIS-IV and WISC-V

OBJECTIVE

Previous research has suggested that neurodevelopmental conditions may be associated with distinctive cognitive profiles on the Wechsler intelligence tests (of which the most recent editions are the WAIS-IV and WISC-V). However, the extent to which a cognitive profile can be reliably identified for individuals meeting criteria for autism or ADHD remains unclear. The present review investigated this issue.

METHOD

A search was conducted in PsycInfo, Embase, and Medline in October 2022 for papers reporting the performance of children or adults diagnosed with autism or ADHD on the WAIS-IV or the WISC-V. Test scores were aggregated using meta-analysis.

RESULTS

Scores were analyzed from over 1,800 neurodivergent people reported across 18 data sources. Autistic children and adults performed in the typical range for verbal and nonverbal reasoning, but scored ~1 SD below the mean for processing speed and had slightly reduced scores on working memory. This provides evidence for a "spiky" cognitive profile in autism. Performance of children and adults with ADHD was mostly at age-expected levels, with slightly reduced scores for working memory.

CONCLUSION

Although the pattern of performance on the Wechsler tests is not sufficiently sensitive or specific to use for diagnostic purposes, autism appears to be associated with a cognitive profile of relative strengths in verbal and nonverbal reasoning and a weakness in processing speed. Attention deficit hyperactivity disorder appears less associated with a particular cognitive profile. Autistic individuals may especially benefit from a cognitive assessment to identify and support with their strengths and difficulties.

Topography and Ensemble Activity in the Auditory Cortex of a Mouse Model of Fragile X Syndrome

Autism spectrum disorder (ASD) is often associated with social communication impairments and specific sound processing deficits, for example, problems in following speech in noisy environments. To investigate underlying neuronal processing defects located in the auditory cortex (AC), we performed two-photon Ca2+ imaging in FMR1 (fragile X messenger ribonucleoprotein 1) knock-out (KO) mice, a model for fragile X syndrome (FXS), the most common cause of hereditary ASD in humans. For primary AC (A1) and the anterior auditory field (AAF), topographic frequency representation was less ordered compared with control animals. We additionally analyzed ensemble AC activity in response to various sounds and found subfield-specific differences. In A1, ensemble correlations were lower in general, while in secondary AC (A2), correlations were higher in response to complex sounds, but not to pure tones. Furthermore, sound specificity of ensemble activity was decreased in AAF. Repeating these experiments 1 week later revealed no major differences regarding representational drift. Nevertheless, we found subfield- and genotype-specific changes in ensemble correlation values between the two times points, hinting at alterations in network stability in FMR1 KO mice. These detailed insights into AC network activity and topography in FMR1 KO mice add to the understanding of auditory processing defects in FXS.

Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a new approach to calculating axonal conduction velocity termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel formulation for calculating aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.

Intrinsic network abnormalities in children with autism spectrum disorder: an independent component analysis

Aberrant intrinsic brain networks are consistently observed in individuals with autism spectrum disorder. However, studies examining the strength of functional connectivity across brain regions have yielded conflicting results. Therefore, this study aimed to investigate the functional connectivity of the resting brain in children with low-functioning autism, including during the early developmental stages. We explored the functional connectivity of 43 children with autism spectrum disorder and 54 children with typical development aged 2 to 12 years using resting-state functional magnetic resonance imaging data. We used independent component analysis to classify the brain regions into six intrinsic networks and analyzed the functional connectivity within each network. Moreover, we analyzed the relationship between functional connectivity and clinical scores. In children with autism, the under-connectivities were observed within several brain networks, including the cognitive control, default mode, visual, and somatomotor networks. In contrast, we found over-connectivities between the subcortical, visual, and somatomotor networks in children with autism compared with children with typical development. Moderate effect sizes were observed in entire networks (Cohen's d = 0.43-0.77). These network alterations were significantly correlated with clinical scores such as the communication sub-score (r = - 0.442, p = 0.045) and the calibrated severity score (r = - 0.435, p = 0.049) of the Autism Diagnostic Observation Schedule. These opposing results observed based on the brain areas suggest that aberrant neurodevelopment proceeds in various ways depending on the functional brain regions in individuals with autism spectrum disorder.

Granulocyte macrophage colony-stimulating factor-induced macrophages of individuals with autism spectrum disorder adversely affect neuronal dendrites through the secretion of pro-inflammatory cytokines

BACKGROUND

A growing body of evidence suggests that immune dysfunction and inflammation in the peripheral tissues as well as the central nervous system are associated with the neurodevelopmental deficits observed in autism spectrum disorder (ASD). Elevated expression of pro-inflammatory cytokines in the plasma, serum, and peripheral blood mononuclear cells of ASD has been reported. These cytokine expression levels are associated with the severity of behavioral impairments and symptoms in ASD. In a prior study, our group reported that tumor necrosis factor-α (TNF-α) expression in granulocyte-macrophage colony-stimulating factor-induced macrophages (GM-CSF MΦ) and the TNF-α expression ratio in GM-CSF MΦ/M-CSF MΦ (macrophage colony-stimulating factor-induced macrophages) was markedly higher in individuals with ASD than in typically developed (TD) individuals. However, the mechanisms of how the macrophages and the highly expressed cytokines affect neurons remain to be addressed.

METHODS

To elucidate the effect of macrophages on human neurons, we used a co-culture system of control human-induced pluripotent stem cell-derived neurons and differentiated macrophages obtained from the peripheral blood mononuclear cells of five TD individuals and five individuals with ASD. All participants were male and ethnically Japanese.

RESULTS

Our results of co-culture experiments showed that GM-CSF MΦ affect the dendritic outgrowth of neurons through the secretion of pro-inflammatory cytokines, interleukin-1α and TNF-α. Macrophages derived from individuals with ASD exerted more severe effects than those derived from TD individuals.

LIMITATIONS

The main limitations of our study were the small sample size with a gender bias toward males, the use of artificially polarized macrophages, and the inability to directly observe the interaction between neurons and macrophages from the same individuals.

CONCLUSIONS

Our co-culture system revealed the non-cell autonomous adverse effects of GM-CSF MΦ in individuals with ASD on neurons, mediated by interleukin-1α and TNF-α. These results may support the immune dysfunction hypothesis of ASD, providing new insights into its pathology.

Conduction Velocity, G-ratio, and Extracellular Water as Microstructural Characteristics of Autism Spectrum Disorder

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a novel metric termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel neuroimaging metric, aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.

Segregated Dynamical Networks for Biological Motion Perception in the Mu and Beta Range Underlie Social Deficits in Autism

OBJECTIVE

Biological motion perception (BMP) correlating with a mirror neuron system (MNS) is attenuated in underage individuals with autism spectrum disorder (ASD). While BMP in typically-developing controls (TDCs) encompasses interconnected MNS structures, ASD data hint at segregated form and motion processing. This coincides with less fewer long-range connections in ASD than TDC. Using BMP and electroencephalography (EEG) in ASD, we characterized directionality and coherence (mu and beta frequencies). Deficient BMP may stem from desynchronization thereof in MNS and may predict social-communicative deficits in ASD. Clinical considerations thus profit from brain-behavior associations.

METHODS

Point-like walkers elicited BMP using 15 white dots (walker vs. scramble in 21 ASD (mean: 11.3 ± 2.3 years) vs. 23 TDC (mean: 11.9 ± 2.5 years). Dynamic Imaging of Coherent Sources (DICS) characterized the underlying EEG time-frequency causality through time-resolved Partial Directed Coherence (tPDC). Support Vector Machine (SVM) classification validated the group effects (ASD vs. TDC).

RESULTS

TDC showed MNS sources and long-distance paths (both feedback and bidirectional); ASD demonstrated distinct from and motion sources, predominantly local feedforward connectivity, and weaker coherence. Brain-behavior correlations point towards dysfunctional networks. SVM successfully classified ASD regarding EEG and performance.

CONCLUSION

ASD participants showed segregated local networks for BMP potentially underlying thwarted complex social interactions. Alternative explanations include selective attention and global-local processing deficits.

SIGNIFICANCE

This is the first study applying source-based connectivity to reveal segregated BMP networks in ASD regarding structure, cognition, frequencies, and temporal dynamics that may explain socio-communicative aberrancies.

Risky driving behavior among individuals with Autism, ADHD, and typically developing persons

INTRODUCTION

Many individuals with Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) often experience difficulty with driving, including difficulty with obtaining a driver's license as well as driving safely and efficiently. Such difficulties negatively impact their ability to function independently and participate in daily activities that require driving. ASD and ADHD commonly occur co-morbidly and share many overlapping clinical features. Few studies have directly compared the nature of difficulties in driving safety outcomes between ASD and ADHD. The overarching goal of the current study was to characterize and compare self-reported driving behavior among young autistic drivers, ADHD drivers, and typically developing (TD) drivers.

METHOD

Fifty-four participants (14 ASD, 20 ADHD, 20 TD); ages 16-30) completed the Autism Spectrum Quotient and ADHD Adult Rating scale as a method of screening of symptoms. All three groups then completed the Driving Behavior Questionnaire (DBQ), which measured self-reported driving violations, driving errors, and overall risky driving behavior. The three groups of ASD, ADHD, and TD individuals were then compared regarding symptomology and driving behavior differences.

RESULTS

One-way ANOVAs indicated group differences in DBQ total scores and DBQ errors. Drivers with ADHD reported significantly greater overall risky driving behaviors and driving errors compared to ASD and TD drivers. There were no significant differences between ASD and TD drivers in reported risky driving behaviors and errors. Linear regressions indicated that among all drivers, self-reported ADHD symptoms were significantly associated with higher levels of self-reported overall risky driving and driving errors, regardless of diagnostic group.

DISCUSSION

Risky driving and driving errors may be more closely related to symptoms that are characteristic of ADHD. This has implications for individuals with ADHD and autistic individuals who often show or report higher rates of ADHD symptoms. Future studies should compare driving skills of ASD and ADHD drivers using objective measures of driving performance, such as driving simulators or on-road tests.

Effect of Object on Kinesthetic Motor Imagery in Autism Spectrum Disorder: A Pilot Study Based on Eye-Tracking Methodology

Introduction

Social disturbance is a significant autism spectrum disorder (ASD) symptom. Action representation, which is a fundamental component of social interaction, can be investigated through kinesthetic motor imagery (KMI). KMI has been commonly studied with the well-developed laterality judgment paradigm, wherein participants are required to discriminate the laterality of a hand rotated by different angles along one or more axes. Here, we investigated the KMI processing in individuals with ASD by hand laterality judgment paradigm with eye-tracking methodology.

Methods

The current study included 22 participants with ASD and 22 typical developing (TD) peers matched for age, gender, and intelligence. Participants were asked to judge the laterality of hand-with-tooth brush images.

Results

Compared to the TD controls, individuals with ASD performed KMI with lower accuracy and longer response time in both correct and incorrect action conditions. The incorrect action representation had greater effect on KMI for individuals with ASD. Differences in eye-movement patterns were also observed, characterized by individuals with ASD were more focused on the object area while TD peers were more focused on the hand area.

Conclusion

Results suggest that while altered KMI performance was observed, the incorrect action representation elicited more engagement of KMI in both groups. The object-centered eye-movement pattern may contribute to the refine of motor simulation intervention for individuals with ASD.

Divergent electroencephalogram resting-state functional network alterations in subgroups of autism spectrum disorder: a symptom-based clustering analysis

Autism spectrum disorder (ASD) is characterized by etiological and phenotypic heterogeneity. Despite efforts to categorize ASD into subtypes, research on specific functional connectivity changes within ASD subgroups based on clinical presentations is limited. This study proposed a symptom-based clustering approach to identify subgroups of ASD based on multiple clinical rating scales and investigate their distinct Electroencephalogram (EEG) functional connectivity patterns. Eyes-opened resting-state EEG data were collected from 72 children with ASD and 63 typically developing (TD) children. A data-driven clustering approach based on Social Responsiveness Scales-Second Edition and Vinland-3 scores was used to identify subgroups. EEG functional connectivity and topological characteristics in four frequency bands were assessed. Two subgroups were identified: mild ASD (mASD, n = 37) and severe ASD (sASD, n = 35). Compared to TD, mASD showed increased functional connectivity in the beta band, while sASD exhibited decreased connectivity in the alpha band. Significant between-group differences in global and regional topological abnormalities were found in both alpha and beta bands. The proposed symptom-based clustering approach revealed the divergent functional connectivity patterns in the ASD subgroups that was not observed in typical ASD studies. Our study thus provides a new perspective to address the heterogeneity in ASD research.

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

BACKGROUND

The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).

MATERIAL AND METHODS

We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework.

RESULTS

The AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain.

CONCLUSIONS

Our results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic models

Vigorous research has been conducted to accumulate biological and theoretical knowledge about neurodevelopmental disorders, including molecular, neural, computational, and behavioral characteristics; however, these findings remain fragmentary and do not elucidate integrated mechanisms. An obstacle is the heterogeneity of developmental pathways causing clinical phenotypes. Additionally, in symptom formations, the primary causes and consequences of developmental learning processes are often indistinguishable. Herein, we review developmental neurorobotic experiments tackling problems related to the dynamic and complex properties of neurodevelopmental disorders. Specifically, we focus on neurorobotic models under predictive processing lens for the study of developmental disorders. By constructing neurorobotic models with predictive processing mechanisms of learning, perception, and action, we can simulate formations of integrated causal relationships among neurodynamical, computational, and behavioral characteristics in the robot agents while considering developmental learning processes. This framework has the potential to bind neurobiological hypotheses (excitation-inhibition imbalance and functional disconnection), computational accounts (unusual encoding of uncertainty), and clinical symptoms. Developmental neurorobotic approaches may serve as a complementary research framework for integrating fragmented knowledge and overcoming the heterogeneity of neurodevelopmental disorders.

Preventive effects of resveratrol against early-life impairments in the animal model of autism induced by valproic acid

Background

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by social interaction deficits and repetitive/stereotyped behaviors. Its prevalence is increasing, affecting one in 36 children in the United States. The valproic acid (VPA) induced animal model of ASD is a reliable method for investigating cellular, molecular, and behavioral aspects related to the disorder. Trans-Resveratrol (RSV), a polyphenol with anti-inflammatory and antioxidant effects studied in various diseases, has recently demonstrated the ability to prevent cellular, molecular, sensory, and social deficits in the VPA model. In this study, we examined the effects of prenatal exposure to VPA and the potential preventive effects of RSV on the offspring.

Method

We monitored gestational weight from embryonic day 6.5 until 18.5 and assessed the onset of developmental milestones and morphometric parameters in litters. The generalized estimating equations (GEE) were used to analyze longitudinal data.

Results

Exposure to VPA during rat pregnancy resulted in abnormal weight gain fold-changes on embryonic days 13.5 and 18.5, followed by fewer animals per litter. Additionally, we discovered a positive correlation between weight variation during E15.5-E18.5 and the number of rat pups in the VPA group.

Conclusion

VPA exposure led to slight length deficiencies and delays in the onset of developmental milestones. Interestingly, the prenatal RSV treatment not only prevented most of these delays but also led to the early onset of certain milestones and improved morphometric characteristics in the offspring. In summary, our findings suggest that RSV may have potential as a therapeutic intervention to protect against the negative effects of prenatal VPA exposure, highlighting its importance in future studies of prenatal neurodevelopmental disorders.

Slower Processing Speed in Autism Spectrum Disorder: A Meta-analytic Investigation of Time-Based Tasks

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition affecting information processing across domains. The current meta-analysis investigated whether slower processing speed is associated with the ASD neurocognitive profile and whether findings hold across different time-based tasks and stimuli (social vs. nonsocial; linguistic vs. nonlinguistic). Mean RTs of ASD and age-matched neurotypical comparison groups (N = 893 ASD, 1063 neurotypical; mean age ASD group = 17 years) were compared across simple RT, choice RT, and interference control tasks (44 studies, 106 effects) using robust variance estimation meta-analysis. Simple RT tasks required participants to respond to individual stimuli, whereas choice RT tasks required forced-choice responses to two or more stimuli. Interference control tasks required a decision in the context of a distractor or priming stimulus; in an effort to minimize inhibitory demands, we extracted RTs only from baseline and congruent conditions of such tasks. All tasks required nonverbal (motor) responses. The overall effect-size estimate indicated significantly longer mean RTs in ASD groups (g = .35, 95% CI = .16; .54) than comparison groups. Task type moderated effects, with larger estimates drawn from simple RT tasks than interference control tasks. However, across all three task types, ASD groups exhibited significantly longer mean RTs than comparison groups. Stimulus type and age did not moderate effects. Generalized slowing may be a domain-general characteristic of ASD with potential consequences for social, language, and motor development. Assessing processing speed may inform development of interventions to support autistic individuals and their diverse cognitive profiles.

Larger corpus callosum volume is favorable for theory of mind development in healthy children

While previous research has demonstrated a link between the corpus callosum (CC) and theory of mind (ToM) abilities in individuals with corpus callosum agenesis (ACC), the relationship between CC volume and ToM remains unclear in healthy children. The present study examined whether CC volume influences children's performance on ToM tasks that assess their understanding of pretense, emotion recognition, and false beliefs. Forty children aged 6-12 years underwent structural magnetic resonance imaging (MRI) and a cognitive test battery. We found that larger mid-anterior and central subsections of the CC significantly correlated with better ToM abilities. We could also demonstrate age- and sex-related effects, as the CC-ToM relationship differed between younger (6-8 years) and older (9-12 years) children, and between female and male participants. Importantly, the older children drove the association between the CC mid-anterior and central subsection volumes and ToM abilities. This study is the first to demonstrate that CC size is associated with ToM abilities in healthy children, underlining the idea that the CC plays a vital role in their socio-cognitive development. CC subsection volumes may thus not only serve as a measure of heterogeneity in neurodevelopmental populations known to exhibit socio-cognitive deficits, but also in typically developing children.

Alterations of Functional Connectivity in Autism and Attention-Deficit/Hyperactivity Disorder Revealed by Multi-Voxel Pattern Analysis

Background: Autism and attention-deficit/hyperactivity disorder (ADHD) are comorbid neurodevelopmental disorders that share common and distinct neurobiological mechanisms, with disrupted brain connectivity patterns being a hallmark feature of both conditions. It is challenging to gain a mechanistic understanding of the underlying disorder, because brain connectivity changes in autism and ADHD are heterogeneous. Objectives: The present resting state functional MRI (rs-fMRI) study focuses on investigating the shared and distinct resting state-fMRI connectivity (rsFC) patterns in autistic and ADHD adults using multi-voxel pattern analysis (MVPA). By identifying spatial patterns of fMRI activity across a given time course, MVPA is an innovative and powerful method for generating seed regions of interest (ROIs) without a priori hypotheses. Methods: We performed a data-driven, whole-brain, connectome-wide MVPA on rs-fMRI data collected from 15 autistic, 19 ADHD, and 15 neurotypical (NT) young adults. Results: MVPA identified cerebellar vermis 9, precuneus, and the right cerebellum VI for autistic versus NT, right inferior frontal gyrus and vermis 9 for ADHD versus NT, and right dorsolateral prefrontal cortex for autistic versus ADHD as significant clusters. Post hoc seed-to-voxel analyses using these clusters as seed ROIs were performed for further characterization of group differences. The cerebellum VI, vermis, and precuneus in autistic adults, and the vermis and frontal regions in ADHD showed different connectivity patterns in comparison with NT. Conclusions: The study characterizes the rsFC profile of cerebellum with key cortical areas in autism and ADHD, and it emphasizes the importance of studying the role of the functional connectivity of the cerebellum in neurodevelopmental disorders.

Music for autism: a protocol for an international randomized crossover trial on music therapy for children with autism

The notion of a connection between autism and music is as old as the first reported cases of autism, and music has been used as a therapeutic tool for many decades. Music therapy holds promise as an intervention for individuals with autism, harnessing their strengths in music processing to enhance communication and expression. While previous randomized controlled trials have demonstrated positive outcomes in terms of global improvement and quality of life, their reliance on psychological outcomes restricts our understanding of underlying mechanisms. This paper introduces the protocol for the Music for Autism study, a randomized crossover trial designed to investigate the effects of a 12-week music therapy intervention on a range of psychometric, neuroimaging, and biological outcomes in school-aged children with autism. The protocol builds upon previous research and aims to both replicate and expand upon findings that demonstrated improvements in social communication and functional brain connectivity following a music intervention. The primary objective of this trial is to determine whether music therapy leads to improvements in social communication and functional brain connectivity as compared to play-based therapy. In addition, secondary aims include exploring various relevant psychometric, neuroimaging, and biological outcomes. To achieve these objectives, we will enroll 80 participants aged 6-12 years in this international, assessor-blinded, crossover randomized controlled trial. Each participant will be randomly assigned to receive either music therapy or play-based therapy for a period of 12 weeks, followed by a 12-week washout period, after which they will receive the alternate intervention. Assessments will be conducted four times, before and after each intervention period. The protocol of the Music for Autism trial provides a comprehensive framework for studying the effects of music therapy on a range of multidimensional outcomes in children with autism. The findings from this trial have the potential to contribute to the development of evidence-based interventions that leverage strengths in music processing to address the complex challenges faced by individuals with autism. Clinical Trial Registration: Clinicaltrials.gov identifier NCT04936048.

Region-Wise Brain Response Classification of ASD Children Using EEG and BiLSTM RNN

AbstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in sensory modulation. These sensory modulation deficits would ultimately lead them to difficulties in adaptive behavior and intellectual functioning. The purpose of this study was to observe changes in the nervous system with responses to auditory/visual and only audio stimuli in children with autism and typically developing (TD) through electroencephalography (EEG). In this study, 20 children with ASD and 20 children with TD were considered to investigate the difference in the neural dynamics. The neural dynamics could be understood by non-linear analysis of the EEG signal. In this research to reveal the underlying nonlinear EEG dynamics, recurrence quantification analysis (RQA) is applied. RQA measures were analyzed using various parameter changes in RQA computations. In this research, the cosine distance metric was considered due to its capability of information retrieval and the other distance metrics parameters are compared for identifying the best biomarker. Each computational combination of the RQA measure and the responding channel was analyzed and discussed. To classify ASD and TD, the resulting features from RQA were fed to the designed BiLSTM (bi-long short-term memory) network. The classification accuracy was tested channel-wise for each combination. T3 and T5 channels with neighborhood selection as FAN (fixed amount of nearest neighbors) and distance metric as cosine is considered as the best-suited combination to discriminate between ASD and TD with the classification accuracy of 91.86%, respectively.

Examining the Dominant Presence of Brain Grey Matter in Autism During Functional Magnetic Resonance Imaging

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with symptoms appearing from early childhood. Behavioral modifications, special education, and medicines are used to treat ASD; however, the effectiveness of the treatments depends on early diagnosis of the disorder. The primary approach in diagnosing ASD is based on clinical interviews and valid scales. Still, methods based on brain imaging could also be possible diagnostic biomarkers for ASD.

Methods

To identify the amount of information the functional magnetic resonance imaging (fMRI) reveals on ASD, we reviewed 292 task-based fMRI studies on ASD individuals. This study is part of a systematic review with the registration number CRD42017070975.

Results

We observed that face perception, language, attention, and social processing tasks were mainly studied in ASD. In addition, 73 brain regions, nearly 83% of brain grey matter, showed an altered activation between the ASD and normal individuals during these four tasks, either in a lower or a higher activation.

Conclusion

Using imaging methods, such as fMRI, to diagnose and predict ASD is a great objective; research similar to the present study could be the initial step.

Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment

Statistical learning starts at an early age and is intimately linked to brain development and the emergence of individuality. Through such a long period of statistical learning, the brain updates and constructs statistical models, with the model's individuality changing based on the type and degree of stimulation received. However, the detailed mechanisms underlying this process are unknown. This paper argues three main points of statistical learning, including 1) cognitive individuality based on "reliability" of prediction, 2) the construction of information "hierarchy" through chunking, and 3) the acquisition of "1-3Hz rhythm" that is essential for early language and music learning. We developed a Hierarchical Bayesian Statistical Learning (HBSL) model that takes into account both reliability and hierarchy, mimicking the statistical learning processes of the brain. Using this model, we conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning. By modulating the sensitivity to sound stimuli, we simulated three cognitive models with different reliability on bottom-up sensory stimuli relative to top-down prior prediction: hypo-sensitive, normal-sensitive, and hyper-sensitive models. We suggested that statistical learning plays a crucial role in the acquisition of 1-3 Hz rhythm. Moreover, a hyper-sensitive model quickly learned the sensory statistics but became fixated on their internal model, making it difficult to generate new information, whereas a hypo-sensitive model has lower learning efficiency but may be more likely to generate new information. Various individual characteristics may not necessarily confer an overall advantage over others, as there may be a trade-off between learning efficiency and the ease of generating new information. This study has the potential to shed light on the heterogeneous nature of statistical learning, as well as the paradoxical phenomenon in which individuals with certain cognitive traits that impede specific types of perceptual abilities exhibit superior performance in creative contexts.

Diffusion tensor imaging of fetal spinal cord: feasibility and gestational-age-related changes

OBJECTIVES

Diffusion tensor imaging (DTI) of the fetal brain is a relatively new technique that allows evaluation of white matter tracts of the central nervous system throughout pregnancy, as well as in certain pathological conditions. The objectives of this study were to evaluate the feasibility of DTI of the spinal cord in utero and to examine gestational-age (GA)-related changes in DTI parameters during pregnancy.

METHODS

This was a prospective study conducted between December 2021 and June 2022 in the LUMIERE Platform, Necker-Enfants Malades Hospital, Paris, France, as part of the LUMIERE SUR LE FETUS trial. Women with a pregnancy between 18 and 36 weeks of gestation without fetal or maternal abnormality were eligible for inclusion. Sagittal diffusion-weighted scans of the fetal spine were acquired, without sedation, using a 1.5-Tesla magnetic resonance imaging scanner. The imaging parameters were as follows: 15 non-collinear direction diffusion-weighted magnetic-pulsed gradients with a b-value 700 s/mm2 and one B0 image without diffusion-weighting; slice thickness, 3 mm; field of view (FOV), 36 mm; phase FOV, 1.00; voxel size, 4.5 × 2.8 × 3 mm3 ; number of slices, 7-10; repetition time, 2800 ms; echo time, minimum; and total acquisition time, 2.3 min. DTI parameters, including fractional anisotropy (FA) and apparent diffusion coefficient (ADC), were extracted at the cervical, upper thoracic, lower thoracic and lumbar levels of the spinal cord. Cases with motion degradation and those with aberrant reconstruction of the spinal cord on tractography were excluded. Pearson's correlation analysis was performed to evaluate GA-related changes of DTI parameters during pregnancy.

RESULTS

During the study period, 42 pregnant women were included at a median GA of 29.3 (range, 22.0-35.7) weeks. Five (11.9%) patients were not included in the analysis because of fetal movement. Two (4.8%) patients with aberrant tractography reconstruction were also excluded from analysis. Acquisition of DTI parameters was feasible in all remaining cases (35/35). Increasing GA correlated with increasing FA averaged over the entire fetal spinal cord (r, 0.37; P < 0.01), as well as at the individual cervical (r, 0.519; P < 0.01), upper thoracic (r, 0.468; P < 0.01), lower thoracic (r, 0.425; P = 0.02) and lumbar (r, 0.427; P = 0.02) levels. There was no correlation between GA and ADC averaged over the entire spinal cord (r, 0.01; P = 0.99) or at the individual cervical (r, -0.109; P = 0.56), upper thoracic (r, -0.226; P = 0.22), lower thoracic (r, -0.052; P = 0.78) or lumbar (r, -0.11; P = 0.95) levels.

CONCLUSIONS

This study shows that DTI of the spinal cord is feasible in normal fetuses in typical clinical practice and allows extraction of DTI parameters of the spinal cord. There is a significant GA-related change in FA in the fetal spinal cord during pregnancy, which may result from decreasing water content as observed during myelination of fiber tracts occurring in utero. This study may serve as a basis for further investigation of DTI in the fetus, including research into its potential in pathological conditions that impact spinal cord development. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Non-invasive brain stimulation for patient with autism: a systematic review and meta-analysis

Objective

To comprehensively evaluate the efficacy of non-invasive brain stimulation (NIBS) in patients with autism spectrum disorder (ASD) in randomized controlled trials (RCT), providing a reference for future research on the same topic.

Methods

Five databases were searched (Pubmed, Web of Science, Medline, Embase, and Cochrane library) and tracked relevant references, Meta-analysis was performed using RevMan 5.3 software.

Results

Twenty-two references (829 participants) were included. The results of the meta-analysis showed that NIBS had positive effects on repetitive and stereotypical behaviors, cognitive function, and executive function in autistic patients. Most of the included studies had a moderate to high risk of bias, Mainly because of the lack of blinding of subjects and assessors to treatment assignment, as well as the lack of continuous observation of treatment effects.

Conclusion

Available evidence supports an improvement in some aspects of NIBS in patients with ASD. However, due to the quality of the original studies and significant publication bias, this evidence must be treated with caution. Further large multicenter randomized double-blind controlled trials and appropriate follow-up observations are needed to further evaluate the specific efficacy of NIBS in patients with ASD.

Unlocking the Secrets: Exploring the Biochemical Correlates of Suicidal Thoughts and Behaviors in Adults with Autism Spectrum Conditions

Involving 1 million people a year, suicide represents one of the major topics of psychiatric research. Despite the focus in recent years on neurobiological underpinnings, understanding and predicting suicide remains a challenge. Many sociodemographical risk factors and prognostic markers have been proposed but they have poor predictive accuracy. Biomarkers can provide essential information acting as predictive indicators, providing proof of treatment response and proposing potential targets while offering more assurance than psychological measures. In this framework, the aim of this study is to open the way in this field and evaluate the correlation between blood levels of serotonin, brain derived neurotrophic factor, tryptophan and its metabolites, IL-6 and homocysteine levels and suicidality. Blood samples were taken from 24 adults with autism, their first-degree relatives, and 24 controls. Biochemical parameters were measured with enzyme-linked immunosorbent assays. Suicidality was measured through selected items of the MOODS-SR. Here we confirm the link between suicidality and autism and provide more evidence regarding the association of suicidality with increased homocysteine (0.278) and IL-6 (0.487) levels and decreased tryptophan (-0.132) and kynurenic acid (-0.253) ones. Our results suggest a possible transnosographic association between these biochemical parameters and increased suicide risk.

Sensory Processing and Executive Functioning in Autistic Adults

PURPOSE

One of the core features that can be experienced by adults on the autism spectrum is hyper- and hyporeactivity to sensory stimuli. Research suggests that executive functioning (EF) impairments are related to sensory issues. In this study the relationship between sensory processing issues and EF was investigated. We expected sensory processing issues to predict EF impairments.

METHODS

Thirty men and 30 women on the autism spectrum, 20 men and 24 women without autism were included and matched on intelligence and age. Group comparisons were conducted to determine if groups differed regarding self-reported sensory processing issues (GSQ-NL) and self-reports on EF (BRIEF-A). Correlational and regression analyses were carried out to investigate the relationship between self-reports on GSQ-NL and BRIEF-A.

RESULTS

We found significant differences between men and women on the spectrum with regard to sensory processing issues and EF. Hyporeactivity to sensory information explained most of the EF problems.

CONCLUSION

Clinicians should be aware of differences in sensory experiences between adults on the spectrum and non-autistic adults and differences between men and women during assessment and subsequent counselling.

A revisit of the amygdala theory of autism: Twenty years after

The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.

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.

Study subnetwork developing pattern of autism children by non-negative matrix factorization

BACKGROUND

As a developmental disorder, the brain networks of autism children show abnormal patterns compared with that of typically developing. The differences between them are not stable due to the developing progress of children. It has become a choice to study the differences of developing trajectories between autistic and typically developing children by investigating the change of each group respectively. Related researches studied the developing of brain network by analyzing the relationship between network indices of the entire or sub brain networks and the cognitive developing scores.

METHODS

As a matrix decomposition algorithm, non-negative matrix factorization (NMF) was applied to decompose the association matrices of brain networks. By NMF, we can obtain subnetworks in an unsupervised way. The association matrices of autism and control children were estimated by their magnetoencephalography data. NMF was applied to decompose the matrices to obtain common subnetworks of both groups. Then we calculated the expression of each subnetwork in each child's brain network by two indices, energy and entropy. The relationship between the expression and the cognitive and development indices were investigated.

RESULTS

We found a subnetwork with left lateralization pattern in α band showed different expression tendency in two groups. The expression indices of two groups were correlated with cognitive indices in autism and control group in an opposite way. In γ band, a subnetwork with strong connections on right hemisphere of brain showed a negative correlation between the expression indices and development indices in autism group.

CONCLUSION

NMF algorithm can effectively decompose brain network to meaningful subnetworks. The finding of α band subnetworks confirms the results of abnormal lateralization of autistic children mentioned in relevant studies. We assume the results of decrease of expression of the subnetwork may relate to the dysfunction of mirror neuron. The decrease expression of γ subnetwork of autism may be related to the weaken process of high-frequency neurons in the neurotrophic competition.

Increased levels of autistic traits are associated with atypical moral judgments

Despite evidence often showing differences between groups with Autism Spectrum Disorder (ASD) and neurotypical controls in moral judgment, the precise nature of these differences has been difficult to establish. At least two reasons for this are (1) that ASD (and its associated characteristics) is difficult to define and (2) that morality, and the inclinations that undergird it, are hard to measure empirically. These challenges have made conclusive associations between ASD and particular patterns of moral judgment hard to come by. Thus, in the current study, participants levels of a traits associated with ASD were assessed by their responses to a questionnaire (i.e., the Iowa Screener) before they made moral judgments across a set of 20 moral dilemmas that independently assess utilitarian and deontological processing. Interestingly, results indicated that increased levels of autistic traits were associated with fewer moral judgments corresponding to either moral theory; that is, higher levels of autistic traits were associated with atypical patterns of moral judgment. In addition, and consistent with some prior methods (e.g., Gaeth et al., 2016), participant scores on the Iowa Screener, as well as their self-identification, were used to categorize participants between two groups (i.e., ASD and Typical) for exploratory purposes. Taken together, this research better informs the relationship between ASD and its associated traits with moral judgment and can inform certain discrepant findings in the field. Implications and ideas for future research are discussed, such as whether traits associated with ASD might relate to alternative moral inclinations, beyond deontology and utilitarianism.