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Liu J, Liu QR, Wu ZM, Chen QR, Chen J, Wang Y, Cao XL, Dai MX, Dong C, Liu Q, Zhu J, Zhang LL, Li Y, Wang YF, Liu L, Yang BR. Specific brain imaging alterations underlying autistic traits in children with attention-deficit/hyperactivity disorder. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2023; 19:20. [PMID: 37986005 PMCID: PMC10658985 DOI: 10.1186/s12993-023-00222-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023]
Abstract
BACKGROUND Autistic traits (ATs) are frequently reported in children with Attention-Deficit/Hyperactivity Disorder (ADHD). This study aimed to examine ATs in children with ADHD from both behavioral and neuroimaging perspectives. METHODS We used the Autism Spectrum Screening Questionnaire (ASSQ) to assess and define subjects with and without ATs. For behavioral analyses, 67 children with ADHD and ATs (ADHD + ATs), 105 children with ADHD but without ATs (ADHD - ATs), and 44 typically developing healthy controls without ATs (HC - ATs) were recruited. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data and analyzed the mean amplitude of low-frequency fluctuation (mALFF) values (an approach used to depict different spontaneous brain activities) in a sub-sample. The imaging features that were shared between ATs and ADHD symptoms or that were unique to one or the other set of symptoms were illustrated as a way to explore the "brain-behavior" relationship. RESULTS Compared to ADHD-ATs, the ADHD + ATs group showed more global impairment in all aspects of autistic symptoms and higher hyperactivity/impulsivity (HI). Partial-correlation analysis indicated that HI was significantly positively correlated with all aspects of ATs in ADHD. Imaging analyses indicated that mALFF values in the left middle occipital gyrus (MOG), left parietal lobe (PL)/precuneus, and left middle temporal gyrus (MTG) might be specifically related to ADHD, while those in the right MTG might be more closely associated with ATs. Furthermore, altered mALFF in the right PL/precuneus correlated with both ADHD and ATs, albeit in diverse directions. CONCLUSIONS The co-occurrence of ATs in children with ADHD manifested as different behavioral characteristics and specific brain functional alterations. Assessing ATs in children with ADHD could help us understand the heterogeneity of ADHD, further explore its pathogenesis, and promote clinical interventions.
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Affiliation(s)
- Juan Liu
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Qian-Rong Liu
- Peking University Sixth Hospital/Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Zhao-Min Wu
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Qiao-Ru Chen
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Jing Chen
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Yuan Wang
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Xiao-Lan Cao
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Mei-Xia Dai
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Chao Dong
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Qiao Liu
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Jun Zhu
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Lin-Lin Zhang
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Ying Li
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Yu-Feng Wang
- Peking University Sixth Hospital/Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Lu Liu
- Peking University Sixth Hospital/Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
| | - Bin-Rang Yang
- Children's Healthcare and Mental Health Center, Shenzhen Children's Hospital, Shenzhen, Guangdong, China.
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Ma H, Cao Y, Li M, Zhan L, Xie Z, Huang L, Gao Y, Jia X. Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study. Hum Brain Mapp 2023; 44:1094-1104. [PMID: 36346215 PMCID: PMC9875923 DOI: 10.1002/hbm.26141] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 10/19/2022] [Accepted: 10/25/2022] [Indexed: 11/11/2022] Open
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 conventional and slow-4 frequency bands, the slow-5 frequency band exhibited a higher accuracy of 74.73%, precision of 74.58%, recall of 75.05%, and area under the curve of 0.811 to distinguish patients with ASD from TCs. These findings may help us to understand the pathological mechanisms of ASD and provide preliminary guidance for the clinical diagnosis of ASD.
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Affiliation(s)
- Huibin Ma
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Yikang Cao
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Mengting Li
- College of Teacher Education, Zhejiang Normal University, Jinhua, China.,Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Linlin Zhan
- Faculty of Western Languages, Heilongjiang University, Harbin, China
| | - Zhou Xie
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Lina Huang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
| | - Yanyan Gao
- College of Teacher Education, Zhejiang Normal University, Jinhua, China.,Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Xize Jia
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
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Hao Z, Shi Y, Huang L, Sun J, Li M, Gao Y, Li J, Wang Q, Zhan L, Ding Q, Jia X, Li H. The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study. Front Neurosci 2022; 16:927556. [PMID: 35924226 PMCID: PMC9340667 DOI: 10.3389/fnins.2022.927556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research.
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Affiliation(s)
- Zeqi Hao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Yuyu Shi
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Lina Huang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Mengting Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Yanyan Gao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Jing Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Qianqian Wang
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Linlin Zhan
- School of Western Languages, Heilongjiang University, Harbin, China
| | - Qingguo Ding
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
- Qingguo Ding
| | - Xize Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
- Xize Jia
| | - Huayun Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
- *Correspondence: Huayun Li
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