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Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study by Huibin Ma; Yikang Cao; Mengting Li; Linlin Zhan; Zhou Xie; Lina Huang; Yanyan Gao; Xize Jia is a Neuroscience article available to read on EtoBox.
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## Abstract Previous studies have explored resting‐state functional connectivity (rs‐FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency‐specific FC alterations of the amygdala in ASD and whether FC in specific frequency bands can be used to distinguish patients with ASD from typical controls (TCs). Data from 306 patients with ASD and 314 age‐matched and sex‐matched TCs were collected from 28 sites in the Autism Brain Imaging Data Exchange database. The bilateral amygdala, defined as the seed regions, was used to perform seed‐based FC analyses in the conventional, slow‐5, and slow‐4 frequency bands at each site. Image‐based meta‐analyses were used to obtain consistent brain regions across 28 sites in the three frequency bands. By combining generative adversarial networks and deep neural networks, a deep learning approach was applied to distinguish patients with ASD from TCs. The meta‐analysis results showed frequency band specificity of FC in ASD, which was reflected in the slow‐5 frequency band instead of the conventional and slow‐4 frequency bands. The deep learning results showed that, compared with the con
Who reads Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study?
It is typically read by researchers, students, and practitioners in Neuroscience.
- Author
- Huibin Ma; Yikang Cao; Mengting Li; Linlin Zhan; Zhou Xie; Lina Huang; Yanyan Gao; Xize Jia
- Publisher
- Wiley
- Published
- 2022
- Language
- EN
- Field
- Neuroscience (Life Sciences)