Connectivity-Based Brain Network Supports Restricted and Repetitive Behaviors in Autism Spectrum Disorder Across Development

Zhang, Anyi; Liu, Lin; Chang, Suhua; Shi, Le; Shi, Jie; Lü, Lin; Bao, Yanping; Li, Jiajia

doi:10.3389/fpsyt.2022.874090

Cited by 4 publications

(1 citation statement)

References 49 publications

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“…Most of the significant results were observed in adaptive/cognition behaviors. Previous studies have shown that genetic influences on language, social communication, restricted/repetitive behaviors, and the symptoms can decrease with age ( 46 , 47 ). We speculated that environmental factors play an important role in social communication and restricted/repetitive behaviors, resulting in a reduced effect of genetic risk on these ASD core phenotypes.…”

Section: Discussionmentioning

confidence: 99%

Interactions of genetic risks for autism and the broad autism phenotypes

et al. 2023

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BackgroundCommon polygenic risk and de novo variants (DNVs) capture a small proportion of autism spectrum disorder (ASD) liability, and ASD phenotypic heterogeneity remains difficult to explain. Integrating multiple genetic factors contribute to clarifying the risk and clinical presentation of ASD.MethodsIn our study, we investigated the individual and combined effects of polygenic risk, damaging DNVs (including those in ASD risk genes), and sex among 2,591 ASD simplex families in the Simons Simplex Collection. We also explored the interactions among these factors, along with the broad autism phenotypes of ASD probands and their unaffected siblings. Finally, we combined the effects of polygenic risk, damaging DNVs in ASD risk genes, and sex to explain the total liability of ASD phenotypic spectrum.ResultsOur findings revealed that both polygenic risk and damaging DNVs contribute to an increased risk for ASD, with females exhibiting higher genetic burdens than males. ASD probands that carry damaging DNVs in ASD risk genes showed reduced polygenic risk. The effects of polygenic risk and damaging DNVs on autism broad phenotypes were inconsistent; probands with higher polygenic risk exhibited improvement in some behaviors, such as adaptive/cognitive behaviors, while those with damaging DNVs exhibited more severe phenotypes. Siblings with higher polygenic risk and damaging DNVs tended to have higher scores on broader autism phenotypes. Females exhibited more severe cognitive and behavioral problems compared to males among both ASD probands and siblings. The combination of polygenic risk, damaging DNVs in ASD risk genes, and sex explained 1–4% of the total liability of adaptive/cognitive behavior measurements.ConclusionOur study revealed that the risk for ASD and the autism broad phenotypes likely arises from a combination of common polygenic risk, damaging DNVs (including those in ASD risk genes), and sex.

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Section: Discussionmentioning

confidence: 99%

Interactions of genetic risks for autism and the broad autism phenotypes

et al. 2023

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Autism Spectrum Disorder and Atypical Brain Connectivity: Novel Insights from Brain Connectivity-Associated Genes by Combining Random Forest and Support Vector Machine Algorithm

Gelmez,

Karakoc,

Ulucan

2024

OMICS: A Journal of Integrative Biology

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A multi-frequency approach of the altered functional connectome for autism spectrum disorder identification

Ding,

Zhang,

Cao

et al. 2024

Cerebral Cortex

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Autism spectrum disorder stands as a multifaceted and heterogeneous neurodevelopmental condition. The utilization of functional magnetic resonance imaging to construct functional brain networks proves instrumental in comprehending the intricate interplay between brain activity and autism spectrum disorder, thereby elucidating the underlying pathogenesis at the cerebral level. Traditional functional brain networks, however, typically confine their examination to connectivity effects within a specific frequency band, disregarding potential connections among brain areas that span different frequency bands. To harness the full potential of interregional connections across diverse frequency bands within the brain, our study endeavors to develop a novel multi-frequency analysis method for constructing a comprehensive functional brain networks that incorporates multiple frequencies. Specifically, our approach involves the initial decomposition of functional magnetic resonance imaging into distinct frequency bands through wavelet transform. Subsequently, Pearson correlation is employed to generate corresponding functional brain networks and kernel for each frequency band. Finally, the classification was performed by a multi-kernel support vector machine, to preserve the connectivity effects within each band and the connectivity patterns shared among the different bands. Our proposed multi-frequency functional brain networks method yielded notable results, achieving an accuracy of 89.1%, a sensitivity of 86.67%, and an area under the curve of 0.942 in a publicly available autism spectrum disorder dataset.

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Connectivity-Based Brain Network Supports Restricted and Repetitive Behaviors in Autism Spectrum Disorder Across Development

Cited by 4 publications

References 49 publications

Interactions of genetic risks for autism and the broad autism phenotypes

Interactions of genetic risks for autism and the broad autism phenotypes

Autism Spectrum Disorder and Atypical Brain Connectivity: Novel Insights from Brain Connectivity-Associated Genes by Combining Random Forest and Support Vector Machine Algorithm

A multi-frequency approach of the altered functional connectome for autism spectrum disorder identification

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