Specific EEG resting state biomarkers in FXS and ASD.

BACKGROUND: Fragile X syndrome (FXS) and autism spectrum disorder (ASD) are neurodevelopmental conditions that often have a substantial impact on daily functioning and quality of life. FXS is the most common cause of inherited intellectual disability (ID) and the most common monogenetic cause of ASD. Previous literature has shown that electrophysiological activity measured by electroencephalogram (EEG) during resting state is perturbated in FXS and ASD. However, whether electrophysiological profiles of participants with FXS and ASD are similar remains unclear. The aim of this study was to compare EEG alterations found in these two clinical populations presenting varying degrees of cognitive and behavioral impairments. METHODS: Resting state EEG signal complexity, alpha peak frequency (APF) and power spectral density (PSD) were compared between 47 participants with FXS (aged between 5-20), 49 participants with ASD (aged between 6-17), and 52 neurotypical (NT) controls with a similar age distribution using MANCOVAs with age as covariate when appropriate. MANCOVAs controlling for age, when appropriate, and nonverbal intelligence quotient (NVIQ) score were subsequently performed to determine the impact of cognitive functioning on EEG alterations. RESULTS: Our results showed that FXS participants manifested decreased signal complexity and APF compared to ASD participants and NT controls, as well as altered power in the theta, alpha and low gamma frequency bands. ASD participants showed exaggerated beta power compared to FXS participants and NT controls, as well as enhanced low and high gamma power compared to NT controls. However, ASD participants did not manifest altered signal complexity or APF. Furthermore, when controlling for NVIQ, results of decreased complexity in higher scales and lower APF in FXS participants compared to NT controls and ASD participants were not replicated. CONCLUSIONS: These findings suggest that signal complexity and APF might reflect cognitive functioning, while altered power in the low gamma frequency band might be associated with neurodevelopmental conditions, particularly FXS and ASD.

Abnormal multimodal neuroimaging patterns associated with social deficits in male autism spectrum disorder.

Atypical social impairments (i.e., impaired social cognition and social communication) are vital manifestations of autism spectrum disorder (ASD) patients, and the incidence rate of ASD is significantly higher in males than in females. Characterizing the atypical brain patterns underlying social deficits of ASD is significant for understanding the pathogenesis. However, there are no robust imaging biomarkers that are specific to ASD, which may be due to neurobiological complexity and limitations of single-modality research. To describe the multimodal brain patterns related to social deficits in ASD, we highlighted the potential functional role of white matter (WM) and incorporated WM functional activity and gray matter structure into multimodal fusion. Gray matter volume (GMV) and fractional amplitude of low-frequency fluctuations of WM (WM-fALFF) were combined by fusion analysis model adopting the social behavior. Our results revealed multimodal spatial patterns associated with Social Responsiveness Scale multiple scores in ASD. Specifically, GMV exhibited a consistent brain pattern, in which salience network and limbic system were commonly identified associated with all multiple social impairments. More divergent brain patterns in WM-fALFF were explored, suggesting that WM functional activity is more sensitive to ASD's complex social impairments. Moreover, brain regions related to social impairment may be potentially interconnected across modalities. Cross-site validation established the repeatability of our results. Our research findings contribute to understanding the neural mechanisms underlying social disorders in ASD and affirm the feasibility of identifying biomarkers from functional activity in WM.

Physiological and communicative emotional disconcordance in children on the autism spectrum.

BACKGROUND: Individuals on the autism spectrum commonly have differences from non-autistic people in expressing their emotions using communicative behaviors, such as facial expressions. However, it is not yet clear if this reduced expressivity stems from reduced physiological reactivity in emotional contexts or if individuals react internally, but do not show these reactions externally to others. We hypothesized that autism is characterized by a discordance between in-the-moment internal psychophysiological arousal and external communicative expressions of emotion. METHODS: Forty-one children on the autism spectrum and 39 non-autistic, typically developing (TD) children of two age groups (2-4 and 8-12 years) participated in a low-level stress task whilst wearing a wireless electrocardiogram. Children's negative emotional expressions (facial, vocal, bodily) were coded following standardized protocols. Alexithymia traits were assessed using the Children's Alexithymia Measure with school-aged children only. Data analyses involved ANOVAs, correlations, and sensitivity analyses. RESULTS: There were no group differences in physiological arousal (heart rate) or in communicative expressions of stress to the stress task. For TD preschoolers, physiological arousal during the stress task was associated with vocal expressions and for TD school-aged children, they were associated with facial and bodily expressions. By contrast, for children on the autism spectrum, physiological arousal during the stress tasks was not associated with communicative expressions across age groups. CONCLUSIONS: Our findings suggest that children on the autism spectrum might experience emotional disconcordance, in that their physiological arousal does not align with their communicative expressions. Therefore, the internally experienced stress of children on the autism spectrum may be inadvertently missed by teachers and caregivers and, consequently, learning opportunities for teaching emotional communication and regulation may be also missed. Our results support the use of wearable biosensors to facilitate such interventions in children on the autism spectrum.

Pupillary responses for social versus non-social stimuli in autism: A systematic review and meta-analysis.

Pupillometry has gained attention as a valuable tool for assessing autonomic nervous system activity and studying phasic changes in pupil size to comprehend underlying neurocognitive mechanisms. However, knowledge regarding pupillary responses to social processing in autism is limited. We conducted a systematic review and meta-analysis, examining research studies on pupil size changes that compare social and non-social stimuli in autism. Electronic searches were performed for articles up to September 2023 and relevant studies were evaluated following PRISMA guidelines. Out of 284 articles screened, 14 studies were eligible for systematic review. The results indicated that non-autistic individuals showed larger pupil size for social compared to non-social stimuli (g = 0.54; 95 % CI [0.25, 0.82]), whereas autistic individuals seemed to exhibit no differences between the two conditions. However, high heterogeneity was observed between studies in autistic populations, compromising interpretability. Despite such limitations, pupillary responses may constitute an objective physiological marker of social processing in autism. This review emphasizes the need for further investigations into pupillary responses in autism across different life stages.

Task-based functional neural correlates of social cognition across autism and schizophrenia spectrum disorders.

BACKGROUND: Autism and schizophrenia spectrum disorders (SSDs) both feature atypical social cognition. Despite evidence for comparable group-level performance in lower-level emotion processing and higher-level mentalizing, limited research has examined the neural basis of social cognition across these conditions. Our goal was to compare the neural correlates of social cognition in autism, SSDs, and typically developing controls (TDCs). METHODS: Data came from two harmonized studies in individuals diagnosed with autism or SSDs and TDCs (aged 16-35 years), including behavioral social cognitive metrics and two functional magnetic resonance imaging (fMRI) tasks: a social mirroring Imitate/Observe (ImObs) task and the Empathic Accuracy (EA) task. Group-level comparisons, and transdiagnostic analyses incorporating social cognitive performance, were run using FSL's PALM for each task, covarying for age and sex (1000 permutations, thresholded at p < 0.05 FWE-corrected). Exploratory region of interest (ROI)-based analyses were also conducted. RESULTS: ImObs and EA analyses included 164 and 174 participants, respectively (autism N = 56/59, SSD N = 50/56, TDC N = 58/59). EA and both lower- and higher-level social cognition scores differed across groups. While canonical social cognitive networks were activated, no significant whole-brain or ROI-based group-level differences in neural correlates for either task were detected. Transdiagnostically, neural activity during the EA task, but not the ImObs task, was associated with lower- and higher-level social cognitive performance. LIMITATIONS: Despite attempting to match our groups on age, sex, and race, significant group differences remained. Power to detect regional brain differences is also influenced by sample size and multiple comparisons in whole-brain analyses. Our findings may not generalize to autism and SSD individuals with co-occurring intellectual disabilities. CONCLUSIONS: The lack of whole-brain and ROI-based group-level differences identified and the dimensional EA brain-behavior relationship observed across our sample suggest that the EA task may be well-suited to target engagement in novel intervention testing. Our results also emphasize the potential utility of cross-condition approaches to better understand social cognition across autism and SSDs.

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.

Functional alterations of lateral temporal cortex for processing voice prosody in adults with autism spectrum disorder.

The human auditory system includes discrete cortical patches and selective regions for processing voice information, including emotional prosody. Although behavioral evidence indicates individuals with autism spectrum disorder (ASD) have difficulties in recognizing emotional prosody, it remains understudied whether and how localized voice patches (VPs) and other voice-sensitive regions are functionally altered in processing prosody. This fMRI study investigated neural responses to prosodic voices in 25 adult males with ASD and 33 controls using voices of anger, sadness, and happiness with varying degrees of emotion. We used a functional region-of-interest analysis with an independent voice localizer to identify multiple VPs from combined ASD and control data. We observed a general response reduction to prosodic voices in specific VPs of left posterior temporal VP (TVP) and right middle TVP. Reduced cortical responses in right middle TVP were consistently correlated with the severity of autistic symptoms for all examined emotional prosodies. Moreover, representation similarity analysis revealed the reduced effect of emotional intensity in multivoxel activation patterns in left anterior superior temporal cortex only for sad prosody. These results indicate reduced response magnitudes to voice prosodies in specific TVPs and altered emotion intensity-dependent multivoxel activation patterns in adult ASDs, potentially underlying their socio-communicative difficulties.

Sex differences in brain functional specialization and interhemispheric cooperation among children with autism spectrum disorders.

The prevalence of autism spectrum disorders (ASDs) differs substantially between males and females, suggesting that sex-related neurodevelopmental factors are central to ASD pathogenesis. Numerous studies have suggested that abnormal brain specialization patterns and poor regional cooperation contribute to ASD pathogenesis, but relatively little is known about the related sex differences. Therefore, this study examined sex differences in brain functional specialization and cooperation among children with ASD. The autonomy index (AI) and connectivity between functionally homotopic voxels (CFH) derived from resting-state functional magnetic resonance imaging (rs-fMRI) were compared between 58 male and 13 female children with ASD. In addition, correlations were examined between regional CFH values showing significant sex differences and symptom scores on the autism behavior checklist (ABC) and childhood autism rating scale (CARS). Male children with ASD demonstrated significantly greater CFH in the left fusiform gyrus (FG) and right opercular part of the inferior frontal gyrus (IFGoperc) than female children with ASD. In addition, the CFH value of the left FG in male children with ASD was negatively correlated with total ABC score and subscale scores for sensory and social abilities. In contrast, no sex differences were detected in brain specialization. These regional abnormalities in interhemispheric cooperation among male children with ASD may provide clues to the neural mechanisms underlying sex differences in ASD symptomatology and prevalence. Autism spectrum disorders, sex, resting-state functional magnetic resonance imaging, cerebral specialization, interhemispheric cooperation.

Cognitive correlates of math abilities in autism spectrum disorder.

The purpose of the current study was to investigate the contribution of different cognitive processes to specific math abilities in students with autism spectrum disorder (ASD) and typically developing (TD) students. The study involved a group of students with ASD without intellectual disabilities (n = 26) and a group with TD students (n = 52). The two groups aged from six to 20 years old and were matched for age, sex ratio and visuospatial reasoning. To assess math abilities, four math tasks were administered: arithmetic facts, mental calculation, mathematical inferences and math problem solving. Concerning cognitive processes, participants were tested on vocabulary, verbal working memory, visuospatial working memory, response inhibition and interference control. The group with ASD showed lower scores on all specific math measures than the TD group; cognitive processes differently contributed to diverse math abilities, and vocabulary and verbal working memory were stronger associated to specific math abilities in the group with ASD than in the TD group. The current results suggest that students with ASD had lower math abilities that are generalized to different math tasks. Implications for research and clinical assessment and intervention were discussed.

Impairment of affective and cognitive empathy in high functioning autism is mediated by alterations in emotional reactivity.

Empathy impairments are considered a key aspect of autism-spectrum disorders (ASD). Previous research consistently shows reduced cognitive empathy, but findings on affective empathy vary, possibly due to experimental design variations (e.g., stimulus modality, social distance) and individual psychological factors (e.g., perceptual abilities, emotional reactivity). This study aims to clarify deficits in affective and cognitive empathy in ASD by addressing these contributing factors. Empathy was examined in 34 autistic individuals and 33 typically developed controls (TDCs) through the Textual Empathy Test (TET). The TET was developed to assess emotional responses when imagining oneself (emotional reactivity) as compared to a target person (friend, stranger) in emotional situations presented via short verbal descriptions. Participants rated emotional states of the target person (cognitive empathy) as well as their own emotional responses when imagining the target person in that situation (affective empathy). Ratings were interpreted relative to normative mean values through standardized regression coefficients. Results showed that high-functioning autism was associated with lower cognitive and affective empathy irrespective of social distance as well as with decreased emotional reactivity compared to controls. Moreover, emotional reactivity mediated the impact of ASD on both empathic components. In summary, altered emotional reactivity may underlie impaired empathy in autistic individuals.

Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.

Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end-to-end frequency-specific dual-attention-based adversarial network (FDAA-Net) to extend the time series of existing blood oxygen level-dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency-dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial-temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA-Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA-Net effectively overcame linear frequency-specific challenges and outperformed other popular prediction models. Test-retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA-Net using short-term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.

Automated Analysis of Stereotypical Movements in Videos of Children With Autism Spectrum Disorder.

Importance: Stereotypical motor movements (SMMs) are a form of restricted and repetitive behavior, which is a core symptom of autism spectrum disorder (ASD). Current quantification of SMM severity is extremely limited, with studies relying on coarse and subjective caregiver reports or laborious manual annotation of short video recordings. Objective: To assess the utility of a new open-source AI algorithm that can analyze extensive video recordings of children and automatically identify segments with heterogeneous SMMs, thereby enabling their direct and objective quantification. Design, Setting, and Participants: This retrospective cohort study included 241 children (aged 1.4 to 8.0 years) with ASD. Video recordings of 319 behavioral assessments carried out at the Azrieli National Centre for Autism and Neurodevelopment Research in Israel between 2017 and 2021 were extracted. Behavioral assessments included cognitive, language, and autism diagnostic observation schedule, 2nd edition (ADOS-2) assessments. Data were analyzed from October 2020 to May 2024. Exposures: Each assessment was recorded with 2 to 4 cameras, yielding 580 hours of video footage. Within these extensive video recordings, manual annotators identified 7352 video segments containing heterogeneous SMMs performed by different children (21.14 hours of video). Main outcomes and measures: A pose estimation algorithm was used to extract skeletal representations of all individuals in each video frame and was trained an object detection algorithm to identify the child in each video. The skeletal representation of the child was then used to train an SMM recognition algorithm using a 3 dimensional convolutional neural network. Data from 220 children were used for training and data from the remaining 21 children were used for testing. Results: Among 319 behavioral assessment recordings from 241 children (172 [78%] male; mean [SD] age, 3.97 [1.30] years), the algorithm accurately detected 92.53% (95% CI, 81.09%-95.10%) of manually annotated SMMs in our test data with 66.82% (95% CI, 55.28%-72.05%) precision. Overall number and duration of algorithm-identified SMMs per child were highly correlated with manually annotated number and duration of SMMs (r = 0.8; 95% CI, 0.67-0.93; P < .001; and r = 0.88; 95% CI, 0.74-0.96; P < .001, respectively). Conclusions and relevance: This study suggests the ability of an algorithm to identify a highly diverse range of SMMs and quantify them with high accuracy, enabling objective and direct estimation of SMM severity in individual children with ASD.

Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography.

BACKGROUND: Autism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits. METHODS: We used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models. RESULTS: We found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits. LIMITATIONS: Optical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism. CONCLUSIONS: This study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.

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".

The complex interplay between autism spectrum disorder and gut microbiota in children: A comprehensive review.

Autism spectrum disorder (ASD) is characterized by defects in social communication and interaction along with restricted interests and/or repetitive behavior. Children with ASD often also experience gastrointestinal (GI) problems in fact incidence of GI problems in ASD is estimated up to 80 percent. Intestinal microbiota, which is a collection of trillions of microorganisms both beneficial and potentially harmful bacteria living inside the gut, has been considered one of the key elements of gut disorders. The goal of this review is to explore potential link between gut microbiota and ASD in children, based on the recently available data. This review discusses recent advances in this rapidly expanding area of neurodevelopmental disorders, which focuses on what is known about the changes in composition of gut bacteria in children with ASD, exploration of possible mechanisms via which gut microbiota might influence the brain and thus lead to appearance of ASD symptoms, as well as potential treatments that involve modulation of gut flora to improve symptoms in children with ASD, i.e., probiotics, postbiotics or changes in the diet. Of course, it's important to keep in mind inherent difficulties in proving of existence of causal relationships between gut bacteria and ASD. There are significant gaps in understanding of the mechanism of gut-brain axis and the mechanisms that underlie ASD. Standardized approaches for research in this area are needed. This review would provide an overview of this exciting emerging field of research.

Speech and language patterns in autism: Towards natural language processing as a research and clinical tool.

Speech and language differences have long been described as important characteristics of autism spectrum disorder (ASD). Linguistic abnormalities range from prosodic differences in pitch, intensity, and rate of speech, to language idiosyncrasies and difficulties with pragmatics and reciprocal conversation. Heterogeneity of findings and a reliance on qualitative, subjective ratings, however, limit a full understanding of linguistic phenotypes in autism. This review summarizes evidence of both speech and language differences in ASD. We also describe recent advances in linguistic research, aided by automated methods and software like natural language processing (NLP) and speech analytic software. Such approaches allow for objective, quantitative measurement of speech and language patterns that may be more tractable and unbiased. Future research integrating both speech and language features and capturing "natural language" samples may yield a more comprehensive understanding of language differences in autism, offering potential implications for diagnosis, intervention, and research.

The role of nicotinic acetylcholine receptors in the pathophysiology and pharmacotherapy of autism spectrum disorder: Focus on α7 nicotinic receptors.

Postmortem studies have revealed that brains of individuals with autism spectrum disorder (ASD) exhibit abnormalities in various components of the cholinergic system including cholinergic receptors, projections, and nuclei. Deletions in the 15q13.3 region which encompasses CHRNA7, the gene that encodes the α7-nACh receptor, have been linked to various neurodevelopmental disorders, including ASD. In addition, the involvement of α7-nACh receptors in biological phenomena known to play a role in the pathophysiology of ASD such as cognitive functions, learning, memory, neuroinflammation, and oxidative stress, as well as the excitation-inhibition balance in neuronal circuits and maternal immune activation have been reported in previous studies. Furthermore, evolving preclinical and clinical literature supports the potential therapeutic benefits of using selectively acting cholinergic compounds, particularly those targeting the α7-nACh receptor subtype, in the treatment of ASD. This study reviews the previous literature on the involvement of nACh receptors in the pathophysiology of ASD and focuses on the α7-nACh receptor as a potential therapeutic target.

Reduced excitatory activity in the developing mPFC mediates a PVH-to-PVL transition and impaired social cognition in autism spectrum disorders.

Understanding the neuropathogenesis of impaired social cognition in autism spectrum disorders (ASD) is challenging. Altered cortical parvalbumin-positive (PV+) interneurons have been consistently observed in ASD, but their roles and the underlying mechanisms remain poorly understood. In our study, we observed a downward-shifted spectrum of PV expression in the developing medial prefrontal cortex (mPFC) of ASD mouse models due to decreased activity of PV+ neurons. Surprisingly, chemogenetically suppressing PV+ neuron activity during postnatal development failed to induce ASD-like behaviors. In contrast, lowering excitatory activity in the developing mPFC not only dampened the activity state and PV expression of individual PV+ neurons, but also replicated ASD-like social deficits. Furthermore, enhancing excitation, but not PV+ interneuron-mediated inhibition, rescued social deficits in ASD mouse models. Collectively, our findings propose that reduced excitatory activity in the developing mPFC may serve as a shared local circuitry mechanism triggering alterations in PV+ interneurons and mediating impaired social functions in ASD.

Sensory and motor cortices parcellations estimated via distance-weighted sparse representation with application to autism spectrum disorder.

BACKGROUND: Motor impairments and sensory processing abnormalities are prevalent in autism spectrum disorder (ASD), closely related to the core functions of the primary motor cortex (M1) and the primary somatosensory cortex (S1). Currently, there is limited knowledge about potential therapeutic targets in the subregions of M1 and S1 in ASD patients. This study aims to map clinically significant functional subregions of M1 and S1. METHODS: Resting-state functional magnetic resonance imaging data (NTD = 266) from Autism Brain Imaging Data Exchange (ABIDE) were used for subregion modeling. We proposed a distance-weighted sparse representation algorithm to construct brain functional networks. Functional subregions of M1 and S1 were identified through consensus clustering at the group level. Differences in the characteristics of functional subregions were analyzed, along with their correlation with clinical scores. RESULTS: We observed symmetrical and continuous subregion organization from dorsal to ventral aspects in M1 and S1, with M1 subregions conforming to the functional pattern of the motor homunculus. Significant intergroup differences and clinical correlations were found in the dorsal and ventral aspects of M1 (p < 0.05/3, Bonferroni correction) and the ventromedial BA3 of S1 (p < 0.05/5). These functional characteristics were positively correlated with autism severity. All subregions showed significant results in the ROI-to-ROI intergroup differential analysis (p < 0.05/80). LIMITATIONS: The generalizability of the segmentation model requires further evaluation. CONCLUSIONS: This study highlights the significance of M1 and S1 in ASD treatment and may provide new insights into brain parcellation and the identification of therapeutic targets for ASD.