Structure, Function, and Genetics of the Cerebellum in Autism

Sydnor, Lindsey M; Aldinger, Kimberly A.

doi:10.20900/jpbs.20220008

Cited by 8 publications

(2 citation statements)

References 125 publications

(127 reference statements)

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“…In addition, it plays a fundamental role in some cognitive functions such as emotional control, attention, language, and social behaviors. Several studies have shown that the cerebellum is structurally and functionally abnormal in autistic individuals (25): Individuals with CNTNAP2 mutations present with cerebellar malformations (26), and CNTNAP2 antibodies have been detected in the cerebrospinal fluid of subjects with mild cerebellar ataxia (27, 28). More recently, strong evidence has been obtained supporting the mechanistic contribution of cerebellar dysfunction to the pathogenesis of ASD-like behaviors in mouse models (29).…”

Section: Discussionmentioning

confidence: 99%

The interplay between oxidative stress and inflammation supports autistic-related behaviors in mice

Pangrazzi,

Cerilli,

Balasco

et al. 2023

Preprint

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Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental condition characterized by social communication deficits and repetitive/restricted behaviors. Several studies showed that inflammation may contribute to ASD. Here we used RT-qPCR, RNA sequencing, immunohistochemistry, and flow cytometry to show that pro-inflammatory molecules were increased in the cerebellum and periphery of mice lacking Cntnap2 (Cntnap2-/-), a robust model of ASD. In parallel, oxidative stress was present in the cerebellum of mutant animals. Systemic treatment with N-acetyl-cysteine (NAC) rescued cerebellar oxidative stress and inflammation as well as motor and social impairments in Cntnap2-/- mice. This was accompanied by improved function of microglia cells in NAC-treated mutant animals. Intriguingly, social deficits, cerebellar inflammation and microglia dysfunction were induced by NAC in Cntnap2+/+ animals. Our findings therefore suggest that the interplay between oxidative stress and inflammation may support ASD-related behaviors in mice.

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Section: Discussionmentioning

confidence: 99%

The interplay between oxidative stress and inflammation supports autistic-related behaviors in mice

Pangrazzi,

Cerilli,

Balasco

et al. 2023

Preprint

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“…Among them, the heschl and superior frontal gyrus areas both play an important role in the classification based on two types of FC. We also find that both have a certain proportion in the cerebellum region [ 48 , 49 ]. Previous classification models or physiological studies also have some similar conclusions that differences exist in these brain areas [ 50 – 53 ].…”

Section: Resultsmentioning

confidence: 97%

A hybrid CNN-SVM model for enhanced autism diagnosis

Qiu,

Zhai

2024

PLoS ONE

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Autism is a representative disorder of pervasive developmental disorder. It exerts influence upon an individual’s behavior and performance, potentially co-occurring with other mental illnesses. Consequently, an effective diagnostic approach proves to be invaluable in both therapeutic interventions and the timely provision of medical support. Currently, most scholars’ research primarily relies on neuroimaging techniques for auxiliary diagnosis and does not take into account the distinctive features of autism’s social impediments. In order to address this deficiency, this paper introduces a novel convolutional neural network-support vector machine model that integrates resting state functional magnetic resonance imaging data with the social responsiveness scale metrics for the diagnostic assessment of autism. We selected 821 subjects containing the social responsiveness scale measure from the publicly available Autism Brain Imaging Data Exchange dataset, including 379 subjects with autism spectrum disorder and 442 typical controls. After preprocessing of fMRI data, we compute the static and dynamic functional connectivity for each subject. Subsequently, convolutional neural networks and attention mechanisms are utilized to extracts their respective features. The extracted features, combined with the social responsiveness scale features, are then employed as novel inputs for the support vector machine to categorize autistic patients and typical controls. The proposed model identifies salient features within the static and dynamic functional connectivity, offering a possible biological foundation for clinical diagnosis. By incorporating the behavioral assessments, the model achieves a remarkable classification accuracy of 94.30%, providing a more reliable support for auxiliary diagnosis.

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