Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning.

IF 4.7 2区 心理学 Q1 BEHAVIORAL SCIENCES Behavioral and Brain Functions Pub Date : 2024-01-24 DOI:10.1186/s12993-024-00228-z
Yurim Jang, Hyoungshin Choi, Seulki Yoo, Hyunjin Park, Bo-Yong Park
{"title":"Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning.","authors":"Yurim Jang, Hyoungshin Choi, Seulki Yoo, Hyunjin Park, Bo-Yong Park","doi":"10.1186/s12993-024-00228-z","DOIUrl":null,"url":null,"abstract":"<p><p>Autism spectrum disorder is one of the most common neurodevelopmental conditions associated with sensory and social communication impairments. Previous neuroimaging studies reported that atypical nodal- or network-level functional brain organization in individuals with autism was associated with autistic behaviors. Although dimensionality reduction techniques have the potential to uncover new biomarkers, the analysis of whole-brain structural connectome abnormalities in a low-dimensional latent space is underinvestigated. In this study, we utilized autoencoder-based feature representation learning for diffusion magnetic resonance imaging-based structural connectivity in 80 individuals with autism and 61 neurotypical controls that passed strict quality controls. We generated low-dimensional latent features using the autoencoder model for each group and adopted an integrated gradient approach to assess the contribution of the input data for predicting latent features during the encoding process. Subsequently, we compared the integrated gradient values between individuals with autism and neurotypical controls and observed differences within the transmodal regions and between the sensory and limbic systems. Finally, we identified significant associations between integrated gradient values and communication abilities in individuals with autism. Our findings provide insights into the whole-brain structural connectome in autism and may help identify potential biomarkers for autistic connectopathy.</p>","PeriodicalId":8729,"journal":{"name":"Behavioral and Brain Functions","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10807082/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavioral and Brain Functions","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1186/s12993-024-00228-z","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Autism spectrum disorder is one of the most common neurodevelopmental conditions associated with sensory and social communication impairments. Previous neuroimaging studies reported that atypical nodal- or network-level functional brain organization in individuals with autism was associated with autistic behaviors. Although dimensionality reduction techniques have the potential to uncover new biomarkers, the analysis of whole-brain structural connectome abnormalities in a low-dimensional latent space is underinvestigated. In this study, we utilized autoencoder-based feature representation learning for diffusion magnetic resonance imaging-based structural connectivity in 80 individuals with autism and 61 neurotypical controls that passed strict quality controls. We generated low-dimensional latent features using the autoencoder model for each group and adopted an integrated gradient approach to assess the contribution of the input data for predicting latent features during the encoding process. Subsequently, we compared the integrated gradient values between individuals with autism and neurotypical controls and observed differences within the transmodal regions and between the sensory and limbic systems. Finally, we identified significant associations between integrated gradient values and communication abilities in individuals with autism. Our findings provide insights into the whole-brain structural connectome in autism and may help identify potential biomarkers for autistic connectopathy.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用特征表征学习,研究自闭症患者与神经典型对照组之间的结构连接组改变。
自闭症谱系障碍是最常见的神经发育疾病之一,与感官和社会交流障碍有关。之前的神经影像学研究报告称,自闭症患者的非典型节点或网络级大脑功能组织与自闭症行为有关。虽然降维技术有可能发现新的生物标志物,但在低维潜在空间中分析全脑结构连接组异常的研究还很欠缺。在这项研究中,我们利用基于自动编码器的特征表示学习,对通过严格质量控制的 80 名自闭症患者和 61 名神经典型对照者进行了基于扩散磁共振成像的结构连接性分析。我们使用自动编码器模型为每个组别生成低维潜在特征,并采用综合梯度方法评估输入数据在编码过程中对预测潜在特征的贡献。随后,我们比较了自闭症患者和神经畸形对照组之间的综合梯度值,并观察到跨模态区域内部以及感觉和边缘系统之间的差异。最后,我们确定了自闭症患者的综合梯度值与沟通能力之间的重要关联。我们的研究结果为自闭症患者的全脑结构连接组提供了深入的见解,并可能有助于确定自闭症连接病的潜在生物标记物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Behavioral and Brain Functions
Behavioral and Brain Functions 医学-行为科学
CiteScore
5.90
自引率
0.00%
发文量
11
审稿时长
6-12 weeks
期刊介绍: A well-established journal in the field of behavioral and cognitive neuroscience, Behavioral and Brain Functions welcomes manuscripts which provide insight into the neurobiological mechanisms underlying behavior and brain function, or dysfunction. The journal gives priority to manuscripts that combine both neurobiology and behavior in a non-clinical manner.
期刊最新文献
Investigation in the cannabigerol derivative VCE-003.2 as a disease-modifying agent in a mouse model of experimental synucleinopathy. Cofilin linked to GluN2B subunits of NMDA receptors is required for behavioral sensitization by changing the dendritic spines of neurons in the caudate and putamen after repeated nicotine exposure. Comparative effect of atorvastatin and risperidone on modulation of TLR4/NF-κB/NOX-2 in a rat model of valproic acid-induced autism. Assessing sociability using the Three-Chamber Social Interaction Test and the Reciprocal Interaction Test in a genetic mouse model of ASD. Paternal preconception donepezil exposure enhances learning in offspring.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1