Autism Research最新文献

At least 50% of autistic people experience clinically relevant anxiety symptoms. However, reasons for elevated rates of anxiety in autism remain poorly understood and there is a high unmet need for novel and adapted therapies for anxiety that are accessible to autistic people. This study aimed to establish the feasibility of a novel app-based anxiety management tool (“Molehill Mountain”) that has been developed with, and adapted for, autistic people. A single-centre, single-arm feasibility study design was employed, whereby autistic people (≥ 16 years) with mild-to-severe symptoms of anxiety were recruited to a 13-week intervention period (King's College London, UK; clinicaltrials.gov identifier NCT05302167). Of 123 prospective participants screened, 100 (81%) participants aged 16–74 years (n = 69 female) were enrolled within approximately 15 months. n = 76 (76%) completed an anxiety measure at ~15 weeks (Generalized Anxiety Disorder—7 Item Scale; GAD-7). Most adhered to the full intervention duration: 65% (n = 47), with most using the app weekly (1–6 days per week; 58%). 73% of participants agreed that they found the app easy to use overall and that an app is a good format for offering anxiety support to autistic people. There was a significant reduction in self-reported anxiety symptom severity with mean difference 2.88 (95% CI 1.88, 3.89; p < 0.001; Cohen's d = 0.45). We found that an autism-adapted app-based anxiety management tool is acceptable to the community and associated with reduced anxiety symptom severity in autistic adults, on average. Following optimization to further enhance usability, the efficacy of the Molehill Mountain app for reducing anxiety must now be tested under randomized controlled conditions in a full-scale clinical trial.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication deficits, repetitive behaviors, and sensory abnormalities. Sodium valproate (VPA) exposure during embryonic development is a well-established preclinical model for ASD, leading to increased oxidative stress in the developing brain, including lipid peroxidation, which affects cell proliferation and organization. This study aimed to investigate the potential therapeutic effects of cannabidiol (CBD) and risperidone (RISP) in reversing ASD-like behaviors and associated neurobiological alterations induced by embryonic VPA exposure in a zebrafish model. Zebrafish embryos were exposed to 125 μM VPA for 2 days post-fertilization (dpf). At 3–4 dpf, embryos were treated with 0.06 μM CBD or 1 μM RISP. Behavioral assays were conducted to assess hyperlocomotion and aggressive behavior. At 7 dpf, lipid peroxidation levels were measured, and expression of glial fibrillary acidic protein (GFAP) and calcium/calmodulin (CaM) were analyzed to evaluate neurobiological changes. VPA exposure resulted in increased hyperlocomotion and aggression. CBD treatment effectively reversed these behaviors, while RISP showed limited efficacy. Additionally, CBD reduced lipid peroxidation and restored anandamide levels, whereas RISP did not exhibit these effects. CBD also normalized GFAP and CaM expression, indicating restoration of glial function and excitatory/inhibitory balance. CBD demonstrated a better efficacy and safety profile compared to RISP in reversing ASD-like behaviors and associated neurobiological alterations in the zebrafish model. These findings suggest that CBD may offer a safer and more effective therapeutic alternative for managing ASD-related symptoms.

There is a growing body of evidence suggesting a concurrent association between attentional indices measured via eye tracking and autism symptoms. This meta-analysis examined the utility of eye tracking within longitudinal frameworks for autism interventions, including treatment monitoring and prediction of treatment response. We conducted a multivariate random-effects meta-analysis with a multilevel structure on 25 studies (828 autistic participants; M _age = 3–28 years) to estimate: (a) changes in eye-tracking outcomes from pre- to post-treatment (k = 179); and (b) the correlation between baseline eye-tracking profiles and changes in developmental outcomes following treatment (k = 39). Our analysis revealed a moderate and significant summary effect size for changes in eye-tracking outcomes from pre- to post-treatment (Hedge's g = 0.32, p = 0.010). Additionally, a moderate but non-significant summary effect size was revealed for the correlation between baseline eye-tracking outcomes and changes in developmental outcomes following treatment (Fisher's z = 0.20, p = 0.115), with moderation effects observed based on developmental domain and sex. These findings highlight the potential of eye tracking as a tool for monitoring treatment-induced changes in autistic individuals, while its predictive utility remains less supported. Limitations and implications are discussed.

Axon features that underlie the structural and functional organization of cortical pathways have distinct patterns in the brains of neurotypical controls (CTR) compared to individuals with Autism Spectrum Disorder (ASD). However, detailed axon study demands labor-intensive surveys and time-consuming analysis of microscopic sections from postmortem human brain tissue, making it challenging to systematically examine large regions of the brain. To address these challenges, we developed an approach that uses machine learning to automatically classify microscopic sections from ASD and CTR brains, while also considering different white matter regions: superficial white matter (SWM), which contains a majority of axons that connect nearby cortical areas, and deep white matter (DWM), which is comprised exclusively of axons that participate in long-range pathways. The result was a deep neural network that can successfully classify the white matter below the anterior cingulate cortex (ACC) of ASD and CTR groups with 98% accuracy, while also distinguishing between DWM and SWM pathway composition with high average accuracy, up to 80%. Examination of image regions important for network classification and misclassification, through sensitivity maps, along with multidimensional scaling analysis, helped identify key pathological markers of ASD and highlighted the spectrum of ASD heterogeneity and overlaps with neurotypical characteristics. Large datasets that can be used to expand training, validation, and testing of this network have the potential to automate high-resolution microscopic analysis of postmortem brain tissue, so that it can be used to systematically study white matter across brain regions in health and disease.

Mismatch negativity (MMN) has been frequently used to assess auditory processing and change detection in autism spectrum disorder (ASD), but findings have been fairly inconsistent. To address this issue, we conducted a systematic review and meta-analysis of MMN amplitude (76 effect sizes) and latency (62 effect sizes) in ASD to identify factors contributing to this heterogeneity and to interpret findings within the predictive coding framework. While residual heterogeneity remained, significant effects of the interaction between age group and design type (unifeature vs. multifeature, i.e., one or several types of deviants) and deviant type were found for MMN amplitude. In multifeature designs, autistic children and adolescents exhibited reduced MMN amplitudes compared to neurotypical peers (g = 0.25, p = 0.01), whereas autistic adults showed increased MMN amplitudes (g = −0.26, p = 0.02). In addition, autistic individuals had significantly smaller MMN amplitudes than neurotypical individuals in paradigms using phoneme deviants (g = 0.41, p < 0.001). Across designs, no significant MMN latency differences were observed between neurotypical and autistic individuals. These results are discussed within the predictive coding framework, as MMN responses are thought to reflect prediction errors, aligning with theories suggesting heightened prediction errors in autistic adults. Future studies with larger samples and improved data reporting are needed to further clarify the developmental trajectory and variability of MMN responses in ASD. Additionally, computational modeling approaches can help characterize learning dynamics and disentangle predictive coding accounts among autistic individuals.