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Wei X, Shao J, Wang H, Wang X, Xue L, Yan R, Wang X, Yao Z, Lu Q. Individual suicide risk factors with resting-state brain functional connectivity patterns in bipolar disorder patients based on latent Dirichlet allocation model. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111117. [PMID: 39127182 DOI: 10.1016/j.pnpbp.2024.111117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/25/2024] [Accepted: 08/07/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND The widespread problem of suicide and its severe burden in bipolar disorder (BD) necessitate the development of objective risk markers, aiming to enhance individual suicide risk prediction in BD. METHODS This study recruited 123 BD patients (61 patients with prior suicide attempted history (PSAs), 62 without (NSAs)) and 68 healthy controls (HEs). The Latent Dirichlet Allocation (LDA) model was used to decompose the resting state functional connectivity (RSFC) into multiple hyper/hypo-RSFC patterns. Thereafter, according to the quantitative results of individual heterogeneity over latent factor dimensions, the correlations were analyzed to test prediction ability. RESULTS Model constructed without introducing suicide-related labels yielded three latent factors with dissociable hyper/hypo-RSFC patterns. In the subsequent analysis, significant differences in the factor distributions of PSAs and NSAs showed biases on the default-mode network (DMN) hyper-RSFC factor (factor 3) and the salience network (SN) and central executive network (CEN) hyper-RSFC factor (factor 1), indicating predictive value. Correlation analysis of the individuals' expressions with their Nurses' Global Assessment of Suicide Risk (NGASR) revealed factor 3 positively correlated (r = 0.4180, p < 0.0001) and factor 1 negatively correlated (r = - 0.2492, p = 0.0055) with suicide risk. Therefore, it could be speculated that patterns more associated with suicide reflected hyper-connectivity in DMN and hypo-connectivity in SN, CEN. CONCLUSIONS This study provided individual suicide-associated risk factors that could reflect the abnormal RSFC patterns, and explored the suicide related brain mechanisms, which is expected to provide supports for clinical decision-making and timely screening and intervention for individuals at high risks of suicide.
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Affiliation(s)
- Xinruo Wei
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing 210093, China
| | - Xiaoqin Wang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing 210093, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China.
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Yang Y, Tang D, Wang Z, Liu Y, Chen F, Jie B, Ni T, Xu C, Li J, Wang C. Identification of high-functioning autism spectrum disorders based on gray-white matter functional network connectivity. J Psychiatr Res 2024; 178:107-113. [PMID: 39128219 DOI: 10.1016/j.jpsychires.2024.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 08/04/2024] [Accepted: 08/05/2024] [Indexed: 08/13/2024]
Abstract
In the field of autism spectrum disorder (ASD), research on functional connectivity between gray matter and white matter remains under-researched. To address this gap, this study innovatively introduced a nested cross-validation method that integrates gray-white matter functional connectivity with an F-Score algorithm. This method calculates the correlation based on signals extracted from functional magnetic resonance imaging data using gray matter and white matter brain region templates. After applying the method to a New York University Langone Medical Center dataset consisting of 55 individuals with high-functioning ASD and 52 healthy subjects, we achieved a classification accuracy of 72.94%. This study found abnormal functional connectivity, primarily involving the left anterior prefrontal cortex and right superior corona radiata, left retrosplenial cortex and left superior corona radiata, as well as the left ventral anterior cingulate cortex and body of corpus callosum. Besides, we discovered that these abnormal connections are closely related to social impairment and restrictive and repetitive behaviors in ASD. In conclusion, this study provides a gray-white matter functional connectivity perspective for the diagnosis and understanding of ASD.
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Affiliation(s)
- Yang Yang
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Detao Tang
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Zhiwei Wang
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Yifei Liu
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Fulong Chen
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China.
| | - Biao Jie
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Tianjiao Ni
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Chenglong Xu
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Jintao Li
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Chao Wang
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
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Mu C, Dang X, Luo XJ. Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders. Nat Hum Behav 2024; 8:1417-1428. [PMID: 38724650 DOI: 10.1038/s41562-024-01879-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 04/03/2024] [Indexed: 05/19/2024]
Abstract
Dysfunction of brain resting-state functional networks has been widely reported in psychiatric disorders. However, the causal relationships between brain resting-state functional networks and psychiatric disorders remain largely unclear. Here we perform bidirectional two-sample Mendelian randomization (MR) analyses to investigate the causalities between 191 resting-state functional magnetic resonance imaging (rsfMRI) phenotypes (n = 34,691 individuals) and 12 psychiatric disorders (n = 14,307 to 698,672 individuals). Forward MR identified 8 rsfMRI phenotypes causally associated with the risk of psychiatric disorders. For example, the increase in the connectivity of motor, subcortical-cerebellum and limbic network was associated with lower risk of autism spectrum disorder. In adddition, increased connectivity in the default mode and central executive network was associated with lower risk of post-traumatic stress disorder and depression. Reverse MR analysis revealed significant associations between 4 psychiatric disorders and 6 rsfMRI phenotypes. For instance, the risk of attention-deficit/hyperactivity disorder increases the connectivity of the attention, salience, motor and subcortical-cerebellum network. The risk of schizophrenia mainly increases the connectivity of the default mode and central executive network and decreases the connectivity of the attention network. In summary, our findings reveal causal relationships between brain functional networks and psychiatric disorders, providing important interventional and therapeutic targets for psychiatric disorders at the brain functional network level.
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Affiliation(s)
- Changgai Mu
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Advanced Institute for Life and Health, Southeast University, Nanjing, China
| | - Xinglun Dang
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Advanced Institute for Life and Health, Southeast University, Nanjing, China
| | - Xiong-Jian Luo
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Advanced Institute for Life and Health, Southeast University, Nanjing, China.
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Zhou R, Sun C, Sun M, Ruan Y, Li W, Gao X. Altered intra- and inter-network connectivity in autism spectrum disorder. Aging (Albany NY) 2024; 16:10004-10015. [PMID: 38862259 PMCID: PMC11210244 DOI: 10.18632/aging.205913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/03/2024] [Indexed: 06/13/2024]
Abstract
OBJECTIVE A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored. METHODS We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector machine was used to discriminate ASD from HC. RESULTS Through different sparsities, ASD exhibited significantly decreased intra-network connectivity within default mode network and dorsal attention network, increased connectivity between limbic network and subcortical network, and decreased connectivity between default mode network and limbic network. Using the classifier trained on altered intra- and inter-network connectivity, multivariate pattern analyses classified the ASD from HC with 71.74% accuracy, 70.21% specificity and 75.56% sensitivity in 10% sparsity of functional connectivity. CONCLUSIONS ASD showed characteristic reorganization of the brain networks and this provided new insight into the underlying process of the functional connectome dysfunction in ASD.
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Affiliation(s)
- Rui Zhou
- School of Zhang Jian, Nantong University, Nantong, China
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
| | - Chenhao Sun
- Department of Radiology, Rugao Jian’an Hospital, Nantong, China
| | - Mingxiang Sun
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Yudi Ruan
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
| | - Weikai Li
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
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Wang L, Qin Y, Yang S, Jin D, Zhu Y, Li X, Li W, Wang Y, Jin C. Posterior default mode network is associated with the social performance in male children with autism spectrum disorder: A dynamic causal modeling analysis based on triple-network model. Hum Brain Mapp 2024; 45:e26750. [PMID: 38853710 PMCID: PMC11163228 DOI: 10.1002/hbm.26750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/12/2024] [Accepted: 05/22/2024] [Indexed: 06/11/2024] Open
Abstract
The triple-network model has been widely applied in neuropsychiatric disorders including autism spectrum disorder (ASD). However, the mechanism of causal regulations within the triple-network and their relations with symptoms of ASD remains unclear. 81 male ASD and 80 well matched typically developing control (TDC) were included in this study, recruited from Autism Brain Image Data Exchange-I datasets. Spatial reference-based independent component analysis was used to identify the anterior and posterior part of default-mode network (aDMN and pDMN), salience network (SN), and bilateral executive-control network (ECN) from resting-state functional magnetic resonance imaging data. Spectral dynamic causal model and parametric empirical Bayes with Bayesian model reduction/average were adopted to explore the effective connectivity (EC) within triple-network and the relationship between EC and autism diagnostic observation schedule (ADOS) scores. After adjusting for age and site effect, ASD and TDC groups both showed inhibition patterns. Compared with TDC, ASD group showed weaker self-inhibition in aDMN and pDMN, stronger inhibition in pDMN→aDMN, weaker inhibition in aDMN→LECN, pDMN→SN, LECN→SN, and LECN→RECN. Furthermore, negative relationships between ADOS scores and pDMN self-inhibition strength, as well as with the EC of pDMN→aDMN were observed in ASD group. The present study reveals imbalanced effective connections within triple-networks in ASD children. More attentions should be focused at the pDMN, which modulates the core symptoms of ASD and may serve as an important region for ASD diagnosis and the target region for ASD treatments.
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Affiliation(s)
- Lei Wang
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Yue Qin
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Shuhan Yang
- Department of Disease Control and PreventionNinth Hospital of Xi'anXi'anChina
| | - Dayong Jin
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Yinhu Zhu
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Xin Li
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Wei Li
- Department of Radiology, Tangdu HospitalAir Force Military Medical UniversityXi'anChina
| | - Yarong Wang
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Chenwang Jin
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
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Li L, Zheng Q, Xue Y, Bai M, Mu Y. Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02474-y. [PMID: 38814465 DOI: 10.1007/s00787-024-02474-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 05/18/2024] [Indexed: 05/31/2024]
Abstract
Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.
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Affiliation(s)
- Lei Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Qingyu Zheng
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, People's Republic of China
| | - Yang Xue
- Department of Developmental and Behavioral Pediatrics, The First Hospital of Jilin University, Jilin University, Changchun, People's Republic of China
| | - Miaoshui Bai
- Department of Developmental and Behavioral Pediatrics, The First Hospital of Jilin University, Jilin University, Changchun, People's Republic of China
| | - Yueming Mu
- Department of Dermatology, The First Hospital of Jilin University, Jilin University, 71 Xinmin Street, Changchun, 130021, People's Republic of China.
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Xie X, Zhou R, Fang Z, Zhang Y, Wang Q, Liu X. Seeing beyond words: Visualizing autism spectrum disorder biomarker insights. Heliyon 2024; 10:e30420. [PMID: 38694128 PMCID: PMC11061761 DOI: 10.1016/j.heliyon.2024.e30420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/04/2024] Open
Abstract
Objective This study employs bibliometric and visual analysis to elucidate global research trends in Autism Spectrum Disorder (ASD) biomarkers, identify critical research focal points, and discuss the potential integration of diverse biomarker modalities for precise ASD assessment. Methods A comprehensive bibliometric analysis was conducted using data from the Web of Science Core Collection database until December 31, 2022. Visualization tools, including R, VOSviewer, CiteSpace, and gCLUTO, were utilized to examine collaborative networks, co-citation patterns, and keyword associations among countries, institutions, authors, journals, documents, and keywords. Results ASD biomarker research emerged in 2004, accumulating a corpus of 4348 documents by December 31, 2022. The United States, with 1574 publications and an H-index of 213, emerged as the most prolific and influential country. The University of California, Davis, contributed significantly with 346 publications and an H-index of 69, making it the leading institution. Concerning journals, the Journal of Autism and Developmental Disorders, Autism Research, and PLOS ONE were the top three publishers of ASD biomarker-related articles among a total of 1140 academic journals. Co-citation and keyword analyses revealed research hotspots in genetics, imaging, oxidative stress, neuroinflammation, gut microbiota, and eye tracking. Emerging topics included "DNA methylation," "eye tracking," "metabolomics," and "resting-state fMRI." Conclusion The field of ASD biomarker research is dynamically evolving. Future endeavors should prioritize individual stratification, methodological standardization, the harmonious integration of biomarker modalities, and longitudinal studies to advance the precision of ASD diagnosis and treatment.
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Affiliation(s)
- Xinyue Xie
- The First Affiliated Hospital of Henan University of Chinese Medicine, Pediatrics Hospital, Zhengzhou, Henan, 450000, China
- Henan University of Chinese Medicine, School of Pediatrics, Zhengzhou, Henan, 450046, China
| | - Rongyi Zhou
- The First Affiliated Hospital of Henan University of Chinese Medicine, Pediatrics Hospital, Zhengzhou, Henan, 450000, China
- Henan University of Chinese Medicine, School of Pediatrics, Zhengzhou, Henan, 450046, China
| | - Zihan Fang
- Henan University of Chinese Medicine, School of Pediatrics, Zhengzhou, Henan, 450046, China
| | - Yongting Zhang
- The First Affiliated Hospital of Henan University of Chinese Medicine, Pediatrics Hospital, Zhengzhou, Henan, 450000, China
- Henan University of Chinese Medicine, School of Pediatrics, Zhengzhou, Henan, 450046, China
| | - Qirong Wang
- Henan University of Chinese Medicine, School of Pediatrics, Zhengzhou, Henan, 450046, China
| | - Xiaomian Liu
- Henan University of Chinese Medicine, School of Medicine, Zhengzhou, Henan, 450046, China
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Guo X, Zhang X, Liu J, Zhai G, Zhang T, Zhou R, Lu H, Gao L. Resolving heterogeneity in dynamics of synchronization stability within the salience network in autism spectrum disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 131:110956. [PMID: 38296155 DOI: 10.1016/j.pnpbp.2024.110956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 01/16/2024] [Accepted: 01/28/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND Heterogeneity in resting-state functional connectivity (FC) are one of the characteristics of autism spectrum disorder (ASD). Traditional resting-state FC primarily focuses on linear correlations, ignoring the nonlinear properties involved in synchronization between networks or brain regions. METHODS In the present study, the cross-recurrence quantification analysis, a nonlinear method based on dynamical systems, was utilized to quantify the synchronization stability between brain regions within the salience network (SN) of ASD. Using the resting-state functional magnetic resonance imaging data of 207 children (ASD/typically-developing controls (TC): 105/102) in Autism Brain Imaging Data Exchange database, we analyzed the laminarity and trapping time differences of the synchronization stability between the ASD subtype derived by a K-means clustering analysis and the TC group, and examined the relationship between synchronization stability and the severity of clinical symptoms of the ASD subtypes. RESULTS Based on the synchronization stability within the SN of ASD, we identified two subtypes that showed opposite changes in synchronization stability relative to the TC group. In addition, the synchronization stability of ASD subtypes 1 and 2 can predict the social interaction and communication impairments, respectively. CONCLUSIONS These findings reveal that ASD subgroups with different patterns of synchronization stability within the SN appear distinct clinical symptoms, and highlight the importance of exploring the potential neural mechanism of ASD from a nonlinear perspective.
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Affiliation(s)
- Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
| | - Xia Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Junfeng Liu
- Department of Neurology, West China Hospital, Sichuan University, China, Chengdu, 610041, China
| | - Guangjin Zhai
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Tao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Rongjuan Zhou
- Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao 066000, China
| | - Huibin Lu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
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Chiappini E, Massaccesi C, Korb S, Steyrl D, Willeit M, Silani G. Neural Hyperresponsivity During the Anticipation of Tangible Social and Nonsocial Rewards in Autism Spectrum Disorder: A Concurrent Neuroimaging and Facial Electromyography Study. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00108-3. [PMID: 38642898 DOI: 10.1016/j.bpsc.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Atypical anticipation of social reward has been shown to lie at the core of the social challenges faced by individuals with autism spectrum disorder (ASD). However, previous research has yielded inconsistent results and has often overlooked crucial characteristics of stimuli. Here, we investigated ASD reward processing using social and nonsocial tangible stimuli, carefully matched on several key dimensions. METHODS We examined the anticipation and consumption of social (interpersonal touch) and nonsocial (flavored milk) rewards in 25 high-functioning individuals with ASD and 25 neurotypical adult individuals. In addition to subjective ratings of wanting and liking, we measured physical energetic expenditure to obtain the rewards, brain activity with neuroimaging, and facial reactions through electromyography on a trial-by-trial basis. RESULTS Participants with ASD did not exhibit reduced motivation for social or nonsocial rewards; their subjective ratings, motivated efforts, and facial reactions were comparable to those of neurotypical participants. However, anticipation of higher-value rewards increased neural activation in lateral parietal cortices, sensorimotor regions, and the orbitofrontal cortex. Moreover, participants with ASD exhibited hyperconnectivity between frontal medial regions and occipital regions and the thalamus. CONCLUSIONS Individuals with ASD who experienced rewards with tangible characteristics, whether social or nonsocial, displayed typical subjective and objective motivational and hedonic responses. Notably, the observed hyperactivations in sensory and attentional nodes during anticipation suggest atypical sensory overprocessing of forthcoming rewards rather than decreased reward value. While these atypicalities may not have manifested in observable behavior here, they could impact real-life social interactions that require nuanced predictions, potentially leading to the misperception of reduced interest in rewarding social stimuli in ASD.
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Affiliation(s)
- Emilio Chiappini
- Department of Clinical and Health Psychology, University of Vienna, Vienna, Austria; Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany.
| | - Claudia Massaccesi
- Department of Clinical and Health Psychology, University of Vienna, Vienna, Austria; Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria
| | - Sebastian Korb
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria; Centre for Brain Science, Department of Psychology, University of Essex, Colchester, United Kingdom
| | - David Steyrl
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria
| | - Matthäus Willeit
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Vienna, Austria
| | - Giorgia Silani
- Department of Clinical and Health Psychology, University of Vienna, Vienna, Austria.
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10
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Liu M, Yu W, Xu D, Wang M, Peng B, Jiang H, Dai Y. Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics. Front Neurosci 2024; 18:1356241. [PMID: 38694903 PMCID: PMC11061487 DOI: 10.3389/fnins.2024.1356241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/14/2024] [Indexed: 05/04/2024] Open
Abstract
Introduction Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in motor skills, communication, emotional expression, and social interaction. Accurate diagnosis of ASD remains challenging due to the reliance on subjective behavioral observations and assessment scales, lacking objective diagnostic indicators. Methods In this study, we introduced a novel approach for diagnosing ASD, leveraging T1-based gray matter and ASL-based cerebral blood flow network metrics. Thirty preschool-aged patients with ASD and twenty-two typically developing (TD) individuals were enrolled. Brain network features, including gray matter and cerebral blood flow metrics, were extracted from both T1-weighted magnetic resonance imaging (MRI) and ASL images. Feature selection was performed using statistical t-tests and Minimum Redundancy Maximum Relevance (mRMR). A machine learning model based on random vector functional link network was constructed for diagnosis. Results The proposed approach demonstrated a classification accuracy of 84.91% in distinguishing ASD from TD. Key discriminating network features were identified in the inferior frontal gyrus and superior occipital gyrus, regions critical for social and executive functions in ASD patients. Discussion Our study presents an objective and effective approach to the clinical diagnosis of ASD, overcoming the limitations of subjective behavioral observations. The identified brain network features provide insights into the neurobiological mechanisms underlying ASD, potentially leading to more targeted interventions.
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Affiliation(s)
- Mingyang Liu
- School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China
| | - Weibo Yu
- School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China
| | - Dandan Xu
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi, China
| | - Miaoyan Wang
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi, China
| | - Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China
| | - Haoxiang Jiang
- School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, China
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11
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Yang C, Wang XK, Ma SZ, Lee NY, Zhang QR, Dong WQ, Zang YF, Yuan LX. Abnormal functional connectivity of the reward network is associated with social communication impairments in autism spectrum disorder: A large-scale multi-site resting-state fMRI study. J Affect Disord 2024; 347:608-618. [PMID: 38070748 DOI: 10.1016/j.jad.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/28/2023] [Accepted: 12/02/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND The social motivation hypothesis proposes that the social deficits of autism spectrum disorder (ASD) are related to reward system dysfunction. However, functional connectivity (FC) patterns of the reward network in ASD have not been systematically explored yet. METHODS The reward network was defined as eight regions of interest (ROIs) per hemisphere, including the nucleus accumbens (NAc), caudate, putamen, anterior cingulate cortex (ACC), ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), amygdala, and insula. We computed both the ROI-wise resting-state FC and seed-based whole-brain FC in 298 ASD participants and 348 typically developing (TD) controls from the Autism Brain Imaging Data Exchange I dataset. Two-sample t-tests were applied to obtain the aberrant FCs. Then, the association between aberrant FCs and clinical symptoms was assessed with Pearson's correlation or Spearman's correlation. In addition, Neurosynth Image Decoder was used to generate word clouds verifying the cognitive functions of the aberrant pathways. Furthermore, a three-way multivariate analysis of variance (MANOVA) was conducted to examine the effects of gender, subtype and age on the atypical FCs. RESULTS For the within network analysis, the left ACC showed weaker FCs with both the right amygdala and left NAc in ASD compared with TD, which were negatively correlated with the Autism Diagnostic Observation Schedule (ADOS) total scores and Social Responsiveness Scale (SRS) total scores respectively. For the whole-brain analysis, weaker FC (i.e., FC between the left vmPFC and left calcarine gyrus, and between the right vmPFC and left precuneus) accompanied by stronger FC (i.e., FC between the left caudate and right insula) were exhibited in ASD relative to TD, which were positively associated with the SRS motivation scores. Additionally, we detected the main effect of age on FC between the left vmPFC and left calcarine gyrus, of subtype on FC between the right vmPFC and left precuneus, of age and age-by-gender interaction on FC between the left caudate and right insula. CONCLUSIONS Our findings highlight the crucial role of abnormal FC patterns of the reward network in the core social deficits of ASD, which have the potential to reveal new biomarkers for ASD.
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Affiliation(s)
- Chen Yang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Xing-Ke Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Sheng-Zhi Ma
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Nathan Yee Lee
- Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Qiu-Rong Zhang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Wen-Qiang Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China; TMS Center, Hangzhou Normal University Affiliated Deqing Hospital, Huzhou, China
| | - Li-Xia Yuan
- School of Physics, Zhejiang University, Hangzhou, China; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China.
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12
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Hirata R, Yoshimura S, Kobayashi K, Aki M, Shibata M, Ueno T, Miyagi T, Oishi N, Murai T, Fujiwara H. Differences between subclinical attention-deficit/hyperactivity and autistic traits in default mode, salience, and frontoparietal network connectivities in young adult Japanese. Sci Rep 2023; 13:19724. [PMID: 37957246 PMCID: PMC10643712 DOI: 10.1038/s41598-023-47034-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 11/08/2023] [Indexed: 11/15/2023] Open
Abstract
Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with attentional impairments, with both commonalities and differences in the nature of their attention deficits. This study aimed to investigate the neural correlates of ADHD and ASD traits in healthy individuals, focusing on the functional connectivity (FC) of attention-related large-scale brain networks (LSBNs). The participants were 61 healthy individuals (30 men; age, 21.9 ± 1.9 years). The Adult ADHD Self-Report Scale (ASRS) and Autism Spectrum Quotient (AQ) were administered as indicators of ADHD and ASD traits, respectively. Performance in the continuous performance test (CPT) was used as a behavioural measure of sustained attentional function. Functional magnetic resonance imaging scans were performed during the resting state (Rest) and auditory oddball task (Odd). Considering the critical role in attention processing, we focused our analyses on the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Region of interest (ROI)-to-ROI analyses (false discovery rate < 0.05) were performed to determine relationships between psychological measures with within-network FC (DMN, FPN, and SN) as well as with between-network FC (DMN-FPN, DMN-SN, and FPN-SN). ASRS scores, but not AQ scores, were correlated with less frequent commission errors and shorter reaction times in the CPT. During Odd, significant positive correlations with ASRS were demonstrated in multiple FCs within DMN, while significant positive correlations with AQ were demonstrated in multiple FCs within FPN. AQs were negatively correlated with FPN-SN FCs. During Rest, AQs were negatively and positively correlated with one FC within the SN and multiple FCs between the DMN and SN, respectively. These findings of the ROI-to-ROI analysis were only partially replicated in a split-half replication analysis, a replication analysis with open-access data sets, and a replication analysis with a structure-based atlas. The better CPT performance by individuals with subclinical ADHD traits suggests positive effects of these traits on sustained attention. Differential associations between LSBN FCs and ASD/ADHD traits corroborate the notion of differences in sustained and selective attention between clinical ADHD and ASD.
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Affiliation(s)
- Risa Hirata
- Department of Neuropsychiatry, Kyoto University Hospital, 54 Shogoinkawaracho, Sakyo-ku, Kyoto, 6068397, Japan
| | - Sayaka Yoshimura
- Faculty of Human Health Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Organization for Promotion of Neurodevelopmental Disorder Research, Kyoto, Japan
| | - Key Kobayashi
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
| | - Morio Aki
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
| | - Mami Shibata
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
| | - Tsukasa Ueno
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
- Integrated Clinical Education Center, Kyoto University Hospital, Kyoto, Japan
| | - Takashi Miyagi
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
| | - Naoya Oishi
- Medical Innovation Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Toshiya Murai
- Department of Neuropsychiatry, Kyoto University Hospital, 54 Shogoinkawaracho, Sakyo-ku, Kyoto, 6068397, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan
| | - Hironobu Fujiwara
- Department of Neuropsychiatry, Kyoto University Hospital, 54 Shogoinkawaracho, Sakyo-ku, Kyoto, 6068397, Japan.
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Kyoto, Japan.
- Artificial Intelligence Ethics and Society Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
- The General Research Division, Osaka University Research Center on Ethical, Legal and Social Issues, Kyoto, Japan.
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13
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Jones JS, Monaghan A, Leyland-Craggs A, Astle DE. Testing the triple network model of psychopathology in a transdiagnostic neurodevelopmental cohort. Neuroimage Clin 2023; 40:103539. [PMID: 37992501 PMCID: PMC10709083 DOI: 10.1016/j.nicl.2023.103539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/02/2023] [Accepted: 11/07/2023] [Indexed: 11/24/2023]
Abstract
AIM The triple network model of psychopathology posits that altered connectivity between the Salience (SN), Central Executive (CEN), and Default Mode Networks (DMN) may underlie neurodevelopmental conditions. However, this has yet to be tested in a transdiagnostic sample of young people. METHOD We investigated this in 175 children (60 girls) that represent a heterogeneous population who are experiencing neurodevelopmental difficulties in cognition and behavior, and 60 comparison children (33 girls). Hyperactivity/impulsivity and inattention were assessed by parent-report. Resting-state functional Magnetic Resonance Imaging data were acquired and functional connectivity was calculated between independent network components and regions of interest. We then examined whether connectivity between the SN, CEN and DMN was dimensionally related to hyperactivity/impulsivity and inattention, whilst controlling for age, gender, and motion. RESULTS Hyperactivity/impulsivity was associated with increased functional connectivity between the SN, CEN, and DMN in at-risk children, whereas it was associated with decreased functional connectivity between the CEN and DMN in comparison children. These effects replicated in an adult parcellation of brain function and when using increasingly stringent exclusion criteria for in-scanner motion. CONCLUSION Triple network connectivity characterizes transdiagnostic neurodevelopmental difficulties with hyperactivity/impulsivity. We suggest that this may arise from delayed network segregation, difficulties sustaining CEN activity to regulate behavior, and/or a heightened developmental mismatch between neural systems implicated in cognitive control relative to those implicated in reward/affect processing.
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Affiliation(s)
- Jonathan S Jones
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK.
| | - Alicja Monaghan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
| | | | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK
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14
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Pan H, Mao Y, Liu P, Li Y, Wei G, Qiao X, Ren Y, Zhao F. Extracting transition features among brain states based on coarse-grained similarity measurement for autism spectrum disorder analysis. Med Phys 2023; 50:6269-6282. [PMID: 36995984 DOI: 10.1002/mp.16406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/13/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND The abnormal brain functional connectivity (FC) of patients with mental diseases is closely linked to the transition features among brain states. However, the current research on state transition will produce certain division deviations in the measurement method of state division, and also ignore the transition features among multiple states that contain more abundant information for analyzing brain diseases. PURPOSE To investigate the potential of the proposed method based on coarse-grained similarity measurement to solve the problem of state division, and consider the transition features among multiple states to analyze the FC abnormalities of autism spectrum disorder (ASD) patients. METHODS We used resting-state functional magnetic resonance imaging to examine 45 ASD and 47 healthy controls (HC). The FC between brain regions was calculated by the sliding window and correlation algorithm, and a novel coarse-grained similarity measure method was used to cluster the FC networks into five states, and then extract the features both of the state itself and the transition features among multiple states for analysis and diagnosis. RESULTS (1) The state as divided by the coarse-grained measurement method improves the diagnostic performance of individuals with ASD compared with previous methods. (2) The transition features among multiple states can provide complementary information to the features of the state itself in the ASD analysis and diagnosis. (3) ASD individuals have different brain state transitions than HC. Specifically, the abnormalities in intra- and inter-network connectivity of ASD patients mainly occur in the default mode network, the visual network, and the cerebellum. CONCLUSIONS Such results demonstrate that our approach with new measurements and new features is effective and promising in brain state analysis and ASD diagnosis.
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Affiliation(s)
- Hongxin Pan
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yanyan Mao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Peiqiang Liu
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yuan Li
- School of Management Science and Engineering, Shandong Technology and Business University, Yantai, China
| | - Guanglan Wei
- Information Network Center, Shandong Second Provincial General Hospital, Jinan, China
| | - Xiaoyan Qiao
- School of Mathematics and Information Science, Shandong Technology and Business University, Yantai, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
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15
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Ong LT, Fan SWD. Morphological and Functional Changes of Cerebral Cortex in Autism Spectrum Disorder. INNOVATIONS IN CLINICAL NEUROSCIENCE 2023; 20:40-47. [PMID: 38193097 PMCID: PMC10773605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by early-onset impairments in socialization, communication, repetitive behaviors, and restricted interests. ASD exhibits considerable heterogeneity, with clinical presentations varying across individuals and age groups. The pathophysiology of ASD is hypothesized to be due to abnormal brain development influenced by a combination of genetic and environmental factors. One of the most consistent morphological parameters for assessing the abnormal brain structures in patients with ASD is cortical thickness. Studies have shown changes in the cortical thickness within the frontal, temporal, parietal, and occipital lobes of individuals with ASD. These changes in cortical thickness often correspond to specific clinical features observed in individuals with ASD. Furthermore, the aberrant brain anatomical features and cortical thickness alterations may lead to abnormal brain connectivity and synaptic structure. Additionally, ASD is associated with cortical hyperplasia in early childhood, followed by a cortical plateau and subsequent decline in later stages of development. However, research in this area has yielded contradictory findings regarding the cortical thickness across various brain regions in ASD.
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Affiliation(s)
- Leong Tung Ong
- Both authors are with Faculty of Medicine, University of Malaya in Kuala Lumpur, Malaysia
| | - Si Wei David Fan
- Both authors are with Faculty of Medicine, University of Malaya in Kuala Lumpur, Malaysia
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16
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Wang M, Xu D, Zhang L, Jiang H. Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review. Diagnostics (Basel) 2023; 13:3027. [PMID: 37835770 PMCID: PMC10571992 DOI: 10.3390/diagnostics13193027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/13/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.
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Affiliation(s)
- Miaoyan Wang
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China; (M.W.); (D.X.)
| | - Dandan Xu
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China; (M.W.); (D.X.)
| | - Lili Zhang
- Department of Child Health Care, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China
| | - Haoxiang Jiang
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China; (M.W.); (D.X.)
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17
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Openshaw RL, Thomson DM, Bristow GC, Mitchell EJ, Pratt JA, Morris BJ, Dawson N. 16p11.2 deletion mice exhibit compromised fronto-temporal connectivity, GABAergic dysfunction, and enhanced attentional ability. Commun Biol 2023; 6:557. [PMID: 37225770 DOI: 10.1038/s42003-023-04891-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 05/01/2023] [Indexed: 05/26/2023] Open
Abstract
Autism spectrum disorders are more common in males, and have a substantial genetic component. Chromosomal 16p11.2 deletions in particular carry strong genetic risk for autism, yet their neurobiological impact is poorly characterised, particularly at the integrated systems level. Here we show that mice reproducing this deletion (16p11.2 DEL mice) have reduced GABAergic interneuron gene expression (decreased parvalbumin mRNA in orbitofrontal cortex, and male-specific decreases in Gad67 mRNA in parietal and insular cortex and medial septum). Metabolic activity was increased in medial septum, and in its efferent targets: mammillary body and (males only) subiculum. Functional connectivity was altered between orbitofrontal, insular and auditory cortex, and between septum and hippocampus/subiculum. Consistent with this circuit dysfunction, 16p11.2 DEL mice showed reduced prepulse inhibition, but enhanced performance in the continuous performance test of attentional ability. Level 1 autistic individuals show similarly heightened performance in the equivalent human test, also associated with parietal, insular-orbitofrontal and septo-subicular dysfunction. The data implicate cortical and septal GABAergic dysfunction, and resulting connectivity changes, as the cause of pre-attentional and attentional changes in autism.
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Affiliation(s)
- Rebecca L Openshaw
- School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Sir James Black Building, Glasgow, G12 8QQ, UK
| | - David M Thomson
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, G4 0RE, UK
| | - Greg C Bristow
- Department of Biomedical and Life Sciences, Lancaster University, Lancaster, LA1 4YW, UK
- School of Pharmacy and Medical Sciences, University of Bradford, Bradford, BD7 1DP, UK
| | - Emma J Mitchell
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, G4 0RE, UK
| | - Judith A Pratt
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, G4 0RE, UK
| | - Brian J Morris
- School of Psychology and Neuroscience, College of Medical, Veterinary and Life Sciences, University of Glasgow, Sir James Black Building, Glasgow, G12 8QQ, UK.
| | - Neil Dawson
- Department of Biomedical and Life Sciences, Lancaster University, Lancaster, LA1 4YW, UK.
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Strang JF, McClellan LS, Li S, Jack AE, Wallace GL, McQuaid GA, Kenworthy L, Anthony LG, Lai MC, Pelphrey KA, Thalberg AE, Nelson EE, Phan JM, Sadikova E, Fischbach AL, Thomas J, Vaidya CJ. The autism spectrum among transgender youth: default mode functional connectivity. Cereb Cortex 2023; 33:6633-6647. [PMID: 36721890 PMCID: PMC10233301 DOI: 10.1093/cercor/bhac530] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 02/02/2023] Open
Abstract
The common intersection of autism and transgender identities has been described in clinical and community contexts. This study investigates autism-related neurophenotypes among transgender youth. Forty-five transgender youth, evenly balanced across non-autistic, slightly subclinically autistic, and full-criteria autistic subgroupings, completed resting-state functional magnetic resonance imaging to examine functional connectivity. Results confirmed hypothesized default mode network (DMN) hub hyperconnectivity with visual and motor networks in autism, partially replicating previous studies comparing cisgender autistic and non-autistic adolescents. The slightly subclinically autistic group differed from both non-autistic and full-criteria autistic groups in DMN hub connectivity to ventral attention and sensorimotor networks, falling between non-autistic and full-criteria autistic groups. Autism traits showed a similar pattern to autism-related group analytics, and also related to hyperconnectivity between DMN hub and dorsal attention network. Internalizing, gender dysphoria, and gender minority-related stigma did not show connectivity differences. Connectivity differences within DMN followed previously reported patterns by designated sex at birth (i.e. female birth designation showing greater within-DMN connectivity). Overall, findings suggest behavioral diagnostics and autism traits in transgender youth correspond to observable differences in DMN hub connectivity. Further, this study reveals novel neurophenotypic characteristics associated with slightly subthreshold autism, highlighting the importance of research attention to this group.
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Affiliation(s)
- John F Strang
- Gender and Autism Program, Children’s National Hospital, 15245 Shady Grove Road, Suite 350, Rockville, MD 20850, USA
- Departments of Pediatrics, Psychiatry, and Behavioral Sciences, George Washington University School of Medicine, Washington, DC, USA
- Division of Neuropsychology, Children’s National Hospital, Washington, DC, USA
| | - Lucy S McClellan
- Division of Neuropsychology, Children’s National Hospital, Washington, DC, USA
| | - Sufang Li
- Department of Psychology, Georgetown University, Washington, DC, USA
| | - Allison E Jack
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Gregory L Wallace
- Department of Speech, Language, & Hearing Sciences, George Washington University, Washington, DC, USA
| | - Goldie A McQuaid
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Lauren Kenworthy
- Departments of Pediatrics, Psychiatry, and Behavioral Sciences, George Washington University School of Medicine, Washington, DC, USA
- Division of Neuropsychology, Children’s National Hospital, Washington, DC, USA
| | - Laura G Anthony
- Department of Psychiatry and Behavioral Sciences, University of Colorado School of Medicine, Aurora, CO, USA
| | - Meng-Chuan Lai
- Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Kevin A Pelphrey
- Department of Neurology, University of Virginia Medical School, Charlottesville, VA, USA
| | | | - Eric E Nelson
- Center for Biobehavioral Health, The Research Institute at Nationwide Children’s Hospital, Columbus, OH, USA
| | - Jenny M Phan
- Division of Neuropsychology, Children’s National Hospital, Washington, DC, USA
| | - Eleonora Sadikova
- School of Education and Human Development, University of Virginia, Charlottesville, VA, USA
| | - Abigail L Fischbach
- Division of Neuropsychology, Children’s National Hospital, Washington, DC, USA
| | | | - Chandan J Vaidya
- Department of Psychology, Georgetown University, Washington, DC, USA
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19
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Peng L, Chen Z, Gao X. Altered rich-club organization of brain functional network in autism spectrum disorder. Biofactors 2023. [PMID: 36785880 DOI: 10.1002/biof.1933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/20/2022] [Indexed: 02/15/2023]
Abstract
Despite numerous research showing the association between brain network abnormalities and autism spectrum disorder (ASD), contrasting findings have been reported from broad functional underconnectivity to broad overconnectivity. Thus, the significance of rich-hub organizations in the brain functional connectome of individuals with ASD remains largely unknown. High-quality data subset of ASD (n = 45) and healthy controls (HC; n = 47) children (7-15 years old) were retrieved from the ABIDE data set, and rich-club organization and network-based statistic (NBS) were assessed from resting-state functional magnetic resonance imaging (rs-fMRI). The rich-club organization functional network (normalized rich-club coefficients >1) was observed in all subjects under a range of thresholds. Compared with HC, ASD patients had higher degree of feeder connections and lower degree of local connections (degree of feeder connections: ASD = 259.20 ± 32.97, HC = 244.98 ± 30.09, p = 0.041; degree of local connections: ASD = 664.02 ± 39.19, HC = 679.89 ± 34.05, p = 0.033) but had similar in rich-club connections. Further, nonparametric NBS analysis showed the presence of abnormal connectivity in the functional network of ASD individuals. Our findings indicated that local connection might be more vulnerable, and feeder connection may compensate for its disruption in ASD, enhancing our understanding on the mechanism of functional connectome dysfunction in ASD.
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Affiliation(s)
- Liling Peng
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People's Republic of China
| | - Zhuang Chen
- Department of Cardiology, The Fifth People's Hospital of Jinan, Jinan, Shandong, People's Republic of China
| | - Xin Gao
- Department of PET/MR, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People's Republic of China
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20
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Kato S, Maesawa S, Bagarinao E, Nakatsubo D, Tsugawa T, Mizuno S, Kawabata K, Tsuboi T, Suzuki M, Shibata M, Takai S, Ishizaki T, Torii J, Mutoh M, Saito R, Wakabayashi T, Katsuno M, Ozaki N, Watanabe H, Sobue G. Magnetic resonance-guided focused ultrasound thalamotomy restored distinctive resting-state networks in patients with essential tremor. J Neurosurg 2023; 138:306-317. [PMID: 35901706 DOI: 10.3171/2022.5.jns22411] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/19/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy ameliorates symptoms in patients with essential tremor (ET). How this treatment affects canonical brain networks has not been elucidated. The purpose of this study was to clarify changes of brain networks after MRgFUS thalamotomy in ET patients by analyzing resting-state networks (RSNs). METHODS Fifteen patients with ET were included in this study. Left MRgFUS thalamotomy was performed in all cases, and MR images, including resting-state functional MRI (rsfMRI), were taken before and after surgery. MR images of 15 age- and sex-matched healthy controls (HCs) were also used for analysis. Using rsfMRI data, canonical RSNs were extracted by performing dual regression analysis, and the functional connectivity (FC) within respective networks was compared among pre-MRgFUS patients, post-MRgFUS patients, and HCs. The severity of tremor was evaluated using the Clinical Rating Scale for Tremor (CRST) score pre- and postoperatively, and its correlation with RSNs was examined. RESULTS Preoperatively, ET patients showed a significant decrease in FC in the sensorimotor network (SMN), primary visual network (VN), and visuospatial network (VSN) compared with HCs. The decrease in FC in the SMN correlated with the severity of tremor. After MRgFUS thalamotomy, ET patients still exhibited a significant decrease in FC in a small area of the SMN, but they exhibited an increase in the cerebellar network (CN). In comparison between pre- and post-MRgFUS patients, the FC in the SMN and the VSN significantly increased after treatment. Quantitative evaluation of the FCs in these three groups showed that the SMN and VSN increased postoperatively and demonstrated a trend toward those of HCs. CONCLUSIONS The SMN and CN, which are considered to be associated with the cerebello-thalamo-cortical loop, exhibited increased connectivity after MRgFUS thalamotomy. In addition, the FC of the visual network, which declined in ET patients compared with HCs, tended to normalize postoperatively. This could be related to the hypothesis that visual feedback is involved in tremor severity in ET patients. Overall, the analysis of the RSNs by rsfMRI reflected the pathophysiology with the intervention of MRgFUS thalamotomy in ET patients and demonstrated a possibility of a biomarker for successful treatment.
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Affiliation(s)
- Sachiko Kato
- 1Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya.,2Focused Ultrasound Therapy Center, Nagoya Kyoritsu Hospital, Nakagawa, Nagoya
| | - Satoshi Maesawa
- 1Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya.,3Brain and Mind Research Center, Nagoya University, Showa, Nagoya
| | | | - Daisuke Nakatsubo
- 1Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya.,2Focused Ultrasound Therapy Center, Nagoya Kyoritsu Hospital, Nakagawa, Nagoya
| | - Takahiko Tsugawa
- 2Focused Ultrasound Therapy Center, Nagoya Kyoritsu Hospital, Nakagawa, Nagoya
| | - Satomi Mizuno
- 4Department of Rehabilitation, National Hospital Organization, Nagoya Medical Center, Naka, Nagoya
| | - Kazuya Kawabata
- 5Department of Neurology, Nagoya University Graduate School of Medicine, Showa, Nagoya
| | - Takashi Tsuboi
- 5Department of Neurology, Nagoya University Graduate School of Medicine, Showa, Nagoya
| | - Masashi Suzuki
- 5Department of Neurology, Nagoya University Graduate School of Medicine, Showa, Nagoya
| | - Masashi Shibata
- 1Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya
| | - Sou Takai
- 1Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya
| | - Tomotaka Ishizaki
- 1Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya
| | - Jun Torii
- 1Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya
| | - Manabu Mutoh
- 1Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya
| | - Ryuta Saito
- 1Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya
| | | | - Masahisa Katsuno
- 5Department of Neurology, Nagoya University Graduate School of Medicine, Showa, Nagoya
| | - Norio Ozaki
- 3Brain and Mind Research Center, Nagoya University, Showa, Nagoya.,6Department of Psychiatry, Nagoya University Graduate School of Medicine, Showa, Nagoya; and
| | - Hirohisa Watanabe
- 3Brain and Mind Research Center, Nagoya University, Showa, Nagoya.,7Department of Neurology, Fujita Medical University, Kutsukake, Toyoake; and
| | - Gen Sobue
- 3Brain and Mind Research Center, Nagoya University, Showa, Nagoya.,8Aichi Medical University, Karimata, Nagakute, Aichi, Japan
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21
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Guo X, Cao Y, Liu J, Zhang X, Zhai G, Chen H, Gao L. Dysregulated dynamic time-varying triple-network segregation in children with autism spectrum disorder. Cereb Cortex 2022; 33:5717-5726. [PMID: 37128738 DOI: 10.1093/cercor/bhac454] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/19/2022] [Accepted: 10/21/2022] [Indexed: 11/19/2022] Open
Abstract
Abstract
One of the remarkable characteristics of autism spectrum disorder (ASD) is the dysregulation of functional connectivity of the triple-network, which includes the salience network (SN), default mode network (DMN), and central executive network (CEN). However, there is little known about the segregation of the triple-network dynamics in ASD. This study used resting-state functional magnetic resonance imaging data including 105 ASD and 102 demographically-matched typical developing control (TC) children. We compared the dynamic time-varying triple-network segregation and triple-network functional connectivity states between ASD and TC groups, and examined the relationship between dynamic triple-network segregation alterations and clinical symptoms of ASD. The average dynamic network segregation value of the DMN with SN and the DMN with CEN in ASD was lower but the coefficient of variation (CV) of dynamic network segregation of the DMN with CEN was higher in ASD. Furthermore, partially reduced triple-network segregation associated with the DMN was found in connectivity states analysis of ASD. These abnormal average values and CV of dynamic network segregation predicted social communication deficits and restricted and repetitive behaviors in ASD. Our findings indicate abnormal dynamic time-varying triple-network segregation of ASD and highlight the crucial role of the triple-network in the neural mechanisms underlying ASD.
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Affiliation(s)
- Xiaonan Guo
- Department of Electronics and Communication Engineering, School of Information Science and Engineering, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
| | - Yabo Cao
- Department of Electronics and Communication Engineering, School of Information Science and Engineering, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
| | - Junfeng Liu
- Department of Neurology, West China Hospital, Sichuan University , China. No. 37 Guo Xue Xiang, Chengdu, 610041 , China
| | - Xia Zhang
- Department of Electronics and Communication Engineering, School of Information Science and Engineering, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
| | - Guangjin Zhai
- Department of Electronics and Communication Engineering, School of Information Science and Engineering, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
| | - Heng Chen
- Department of Medical Information Engineering, School of Medicine, Guizhou University , Jiaxiu Road, Guiyang, 550025 , China
| | - Le Gao
- Department of Electronics and Communication Engineering, School of Information Science and Engineering, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University , No. 438 West Hebei Avenue, Qinhuangdao, 066004 , China
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22
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Sipes BS, Jakary A, Li Y, Max JE, Yang TT, Tymofiyeva O. Resting state brain subnetwork relates to prosociality and compassion in adolescents. Front Psychol 2022; 13:1012745. [PMID: 36337478 PMCID: PMC9632179 DOI: 10.3389/fpsyg.2022.1012745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/04/2022] [Indexed: 11/30/2022] Open
Abstract
Adolescence is a crucial time for social development, especially for helping (prosocial) and compassionate behaviors; yet brain networks involved in adolescent prosociality and compassion currently remain underexplored. Here, we sought to evaluate a recently proposed domain-general developmental (Do-GooD) network model of prosocial cognition by relating adolescent functional and structural brain networks with prosocial and compassionate disposition. We acquired resting state fMRI and diffusion MRI from 95 adolescents (ages 14–19 years; 46 males; 49 females) along with self-report questionnaires assessing prosociality and compassion. We then applied the Network-Based Statistic (NBS) to inductively investigate whether there is a significant subnetwork related to prosociality and compassion while controlling for age and sex. Based on the Do-GooD model, we expected that this subnetwork would involve connectivity to the ventromedial prefrontal cortex (VMPFC) from three domain-general networks, the default mode network (DMN), the salience network, and the control network, as well as from the DMN to the mirror neuron systems. NBS revealed a significant functional (but not structural) subnetwork related to prosociality and compassion connecting 31 regions (p = 0.02), showing DMN and DLPFC connectivity to the VMPFC; DMN connectivity to mirror neuron systems; and connectivity between the DMN and cerebellum. These findings largely support and extend the Do-GooD model of prosocial cognition in adolescents by further illuminating network-based relationships that have the potential to advance our understanding of brain mechanisms of prosociality.
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Affiliation(s)
- Benjamin S. Sipes
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Angela Jakary
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Yi Li
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Jeffrey E. Max
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
- Rady Children’s Hospital San Diego, San Diego, CA, United States
| | - Tony T. Yang
- Department of Psychiatry and Behavioral Sciences, The Langley Porter Psychiatric Institute, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Olga Tymofiyeva
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
- *Correspondence: Olga Tymofiyeva,
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23
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Simhal AK, Carpenter KLH, Kurtzberg J, Song A, Tannenbaum A, Zhang L, Sapiro G, Dawson G. Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion. Front Psychiatry 2022; 13:1026279. [PMID: 36353577 PMCID: PMC9637553 DOI: 10.3389/fpsyt.2022.1026279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/22/2022] [Indexed: 11/04/2022] Open
Abstract
Diffusion tensor imaging (DTI) has been used as an outcome measure in clinical trials for several psychiatric disorders but has rarely been explored in autism clinical trials. This is despite a large body of research suggesting altered white matter structure in autistic individuals. The current study is a secondary analysis of changes in white matter connectivity from a double-blind placebo-control trial of a single intravenous cord blood infusion in 2-7-year-old autistic children (1). Both clinical assessments and DTI were collected at baseline and 6 months after infusion. This study used two measures of white matter connectivity: change in node-to-node connectivity as measured through DTI streamlines and a novel measure of feedback network connectivity, Ollivier-Ricci curvature (ORC). ORC is a network measure which considers both local and global connectivity to assess the robustness of any given pathway. Using both the streamline and ORC analyses, we found reorganization of white matter pathways in predominantly frontal and temporal brain networks in autistic children who received umbilical cord blood treatment versus those who received a placebo. By looking at changes in network robustness, this study examined not only the direct, physical changes in connectivity, but changes with respect to the whole brain network. Together, these results suggest the use of DTI and ORC should be further explored as a potential biomarker in future autism clinical trials. These results, however, should not be interpreted as evidence for the efficacy of cord blood for improving clinical outcomes in autism. This paper presents a secondary analysis using data from a clinical trial that was prospectively registered with ClinicalTrials.gov(NCT02847182).
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Affiliation(s)
- Anish K. Simhal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Kimberly L. H. Carpenter
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Joanne Kurtzberg
- Marcus Center for Cellular Cures, Duke University Medical Center, Durham, NC, United States
| | - Allen Song
- Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
| | - Allen Tannenbaum
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Lijia Zhang
- Brain Imaging and Analysis Center, Duke University, Durham, NC, United States
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States
- Department of Biomedical Engineering, Computer Science, and Mathematics, Duke University, Durham, NC, United States
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, United States
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24
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Haller S, Montandon ML, Rodriguez C, Giannakopoulos P. Wearing a KN95/FFP2 facemask induces subtle yet significant brain functional connectivity modifications restricted to the salience network. Eur Radiol Exp 2022; 6:50. [PMID: 36210391 PMCID: PMC9548384 DOI: 10.1186/s41747-022-00301-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/03/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The use of facemasks is one of the consequences of the coronavirus disease 2019 (COVID-19) pandemic. We used resting-state functional magnetic resonance imaging (fMRI) to search for subtle changes in brain functional connectivity, expected notably related to the high-level salience network (SN) and default mode network (DMN).
Methods
Prospective crossover design resting 3-T fMRI study with/without wearing a tight FFP2/KN95 facemask, including 23 community-dwelling male healthy controls aged 29.9 ± 6.9 years (mean ± standard deviation). Physiological parameters, respiration frequency, and heart rate were monitored. The data analysis was performed using the CONN toolbox.
Results
Wearing an FFP2/KN95 facemask did not impact respiration or heart rate but resulted in a significant reduction in functional connectivity between the SN as the seed region and the left middle frontal and precentral gyrus. No difference was found when the DMN, sensorimotor, visual, dorsal attention, or language networks were used as seed regions. In the absence of significant changes of physiological parameter respiration and heart rate, and in the absence of changes in lower-level functional networks, we assume that those subtle modifications are cognitive consequence of wearing facemasks.
Conclusions
The effect of wearing a tight FFP2/KN95 facemask in men is limited to high-level functional networks. Using the SN as seed network, we observed subtle yet significant decreases between the SN and the left middle frontal and precentral gyrus. Our observations suggest that wearing a facemask may change the patterns of functional connectivity with the SN known to be involved in communication, social behavior, and self-awareness.
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25
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Jones JS, Astle DE. Segregation and integration of the functional connectome in neurodevelopmentally 'at risk' children. Dev Sci 2022; 25:e13209. [PMID: 34873798 PMCID: PMC7613070 DOI: 10.1111/desc.13209] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 10/08/2021] [Accepted: 11/22/2021] [Indexed: 01/22/2023]
Abstract
Functional connectivity within and between Intrinsic Connectivity Networks (ICNs) transforms over development and is thought to support high order cognitive functions. But how variable is this process, and does it diverge with altered cognitive development? We investigated age-related changes in integration and segregation within and between ICNs in neurodevelopmentally 'at-risk' children, identified by practitioners as experiencing cognitive difficulties in attention, learning, language, or memory. In our analysis we used performance on a battery of 10 cognitive tasks alongside resting-state functional magnetic resonance imaging in 175 at-risk children and 62 comparison children aged 5-16. We observed significant age-by-group interactions in functional connectivity between two network pairs. Integration between the ventral attention and visual networks and segregation of the limbic and fronto-parietal networks increased with age in our comparison sample, relative to at-risk children. Furthermore, functional connectivity between the ventral attention and visual networks in comparison children significantly mediated age-related improvements in executive function, compared to at-risk children. We conclude that integration between ICNs show divergent neurodevelopmental trends in the broad population of children experiencing cognitive difficulties, and that these differences in functional brain organisation may partly explain the pervasive cognitive difficulties within this group over childhood and adolescence.
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Affiliation(s)
- Jonathan S Jones
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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26
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Choi H, Byeon K, Park BY, Lee JE, Valk SL, Bernhardt B, Martino AD, Milham M, Hong SJ, Park H. Diagnosis-informed connectivity subtyping discovers subgroups of autism with reproducible symptom profiles. Neuroimage 2022; 256:119212. [PMID: 35430361 DOI: 10.1016/j.neuroimage.2022.119212] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/28/2022] [Accepted: 04/13/2022] [Indexed: 10/18/2022] Open
Abstract
Clinical heterogeneity has been one of the main barriers to develop effective biomarkers and therapeutic strategies in autism spectrum disorder (ASD). Recognizing this challenge, much effort has been made in recent neuroimaging studies to find biologically more homogeneous subgroups (called 'neurosubtypes') in autism. However, most approaches have rarely evaluated how much the employed features in subtyping represent the core anomalies of ASD, obscuring its utility in actual clinical diagnosis. To address this, we combined two data-driven methods, 'connectome-based gradient' and 'functional random forest', collectively allowing to discover reproducible neurosubtypes based on resting-state functional connectivity profiles that are specific to ASD. Indeed, the former technique provides the features (as input for subtyping) that effectively summarize whole-brain connectome variations in both normal and ASD conditions, while the latter leverages a supervised random forest algorithm to inform diagnostic labels to clustering, which makes neurosubtyping driven by the features of ASD core anomalies. Applying this framework to the open-sharing Autism Brain Imaging Data Exchange repository data (discovery, n = 103/108 for ASD/typically developing [TD]; replication, n = 44/42 for ASD/TD), we found three dominant subtypes of functional gradients in ASD and three subtypes in TD. The subtypes in ASD revealed distinct connectome profiles in multiple brain areas, which are associated with different Neurosynth-derived cognitive functions previously implicated in autism studies. Moreover, these subtypes showed different symptom severity, which degree co-varies with the extent of functional gradient changes observed across the groups. The subtypes in the discovery and replication datasets showed similar symptom profiles in social interaction and communication domains, confirming a largely reproducible brain-behavior relationship. Finally, the connectome gradients in ASD subtypes present both common and distinct patterns compared to those in TD, reflecting their potential overlap and divergence in terms of developmental mechanisms involved in the manifestation of large-scale functional networks. Our study demonstrated a potential of the diagnosis-informed subtyping approach in developing a clinically useful brain-based classification system for future ASD research.
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Affiliation(s)
- Hyoungshin Choi
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| | - Kyoungseob Byeon
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| | - Bo-Yong Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Department of Data Science, Inha University, Incheon, South Korea
| | - Jong-Eun Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| | - Sofie L Valk
- Otto Hahn group, Cognitive neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences; Institute of Neuroscience and Medicine, Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Michael Milham
- Center for the Developing Brain, Child Mind Institute, New York, United States; Nathan S. Kline Institute for Psychiatric Research, New York, United States
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Center for the Developing Brain, Child Mind Institute, New York, United States; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
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27
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Astle DE, Holmes J, Kievit R, Gathercole SE. Annual Research Review: The transdiagnostic revolution in neurodevelopmental disorders. J Child Psychol Psychiatry 2022; 63:397-417. [PMID: 34296774 DOI: 10.1111/jcpp.13481] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/04/2021] [Indexed: 12/11/2022]
Abstract
Practitioners frequently use diagnostic criteria to identify children with neurodevelopmental disorders and to guide intervention decisions. These criteria also provide the organising framework for much of the research focussing on these disorders. Study design, recruitment, analysis and theory are largely built on the assumption that diagnostic criteria reflect an underlying reality. However, there is growing concern that this assumption may not be a valid and that an alternative transdiagnostic approach may better serve our understanding of this large heterogeneous population of young people. This review draws on important developments over the past decade that have set the stage for much-needed breakthroughs in understanding neurodevelopmental disorders. We evaluate contemporary approaches to study design and recruitment, review the use of data-driven methods to characterise cognition, behaviour and neurobiology, and consider what alternative transdiagnostic models could mean for children and families. This review concludes that an overreliance on ill-fitting diagnostic criteria is impeding progress towards identifying the barriers that children encounter, understanding underpinning mechanisms and finding the best route to supporting them.
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Affiliation(s)
- Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Joni Holmes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Rogier Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.,Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Susan E Gathercole
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.,Department of Psychiatry, University of Cambridge, Cambridge, UK
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28
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Zhao L, Sun YK, Xue SW, Luo H, Lu XD, Zhang LH. Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach. Front Neuroinform 2022; 16:761942. [PMID: 35273487 PMCID: PMC8901599 DOI: 10.3389/fninf.2022.761942] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/12/2022] [Indexed: 11/13/2022] Open
Abstract
An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.
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Affiliation(s)
- Lei Zhao
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Yun-Kai Sun
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shao-Wei Xue
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
- *Correspondence: Shao-Wei Xue
| | - Hong Luo
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Xiao-Dong Lu
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Lan-Hua Zhang
- College of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an, China
- Lan-Hua Zhang
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29
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Zhao L, Xue SW, Sun YK, Lan Z, Zhang Z, Xue Y, Wang X, Jin Y. Altered dynamic functional connectivity of insular subregions could predict symptom severity of male patients with autism spectrum disorder. J Affect Disord 2022; 299:504-512. [PMID: 34953921 DOI: 10.1016/j.jad.2021.12.093] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 10/15/2021] [Accepted: 12/19/2021] [Indexed: 12/28/2022]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties with social communication and restricted or repetitive patterns of behavior. This disorder was characterized by widespread abnormalities involving distributed brain networks. As one such key network node, the insular cortex has been regarded as a research focus of ASD neuropathology. The insula is a functionally complex brain structure. However, it is not fully clear if dynamic characteristics of resting-state functional magnetic resonance imaging (R-fMRI) signals in insular heterogeneous could be used to depict abnormalities in ASD. To address this question, we investigated dynamic functional connectivity (dFC) of 12 insular subregions. Data were obtained from 44 individuals with ASD and 65 typically developing age-matched controls (TDC). We assessed dFC by sliding-window method and quantified its temporal variability. Multivariable linear regression models were constructed to determine whether dFC support complementary information about symptom severity of individuals with ASD rather than static functional connectivity (sFC). The results showed that individuals with ASD exhibited dFC and sFC alterations in distinct insular subregions. Some brain regions showed only abnormal dFC but not sFC with insular subregions. These abnormal dFC could significantly predict the symptom severity of individuals with ASD. Our findings might advance our knowledge about the potential of insular heterogeneity and dynamic characteristics in understanding the neuropathology mechanism of ASD and in developing neuroimaging biomarkers for clinical applications.
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Affiliation(s)
- Lei Zhao
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Shao-Wei Xue
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China.
| | - Yun-Kai Sun
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Zhihui Lan
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China; Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Ziqi Zhang
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Yichen Xue
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Xuan Wang
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Jin
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
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30
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Takarae Y, Zanesco A, Keehn B, Chukoskie L, Müller RA, Townsend J. EEG microstates suggest atypical resting-state network activity in high-functioning children and adolescents with Autism Spectrum Development. Dev Sci 2022; 25:e13231. [PMID: 35005839 DOI: 10.1111/desc.13231] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 11/23/2021] [Accepted: 01/06/2022] [Indexed: 11/29/2022]
Abstract
EEG microstates represent transient electrocortical events that reflect synchronized activities of large-scale networks, which allows investigations of brain dynamics with sub-second resolution. We recorded resting EEG from 38 children and adolescents with Autism Spectrum Development (ASD) and 48 age, IQ, sex, and handedness-matched typically developing (TD) participants. The EEG was segmented into a time series of microstates using modified k-means clustering of scalp voltage topographies. The frequency and global explained variance (GEV) of a specific microstate (type C) were significantly lower in the ASD group compared to the TD group while the duration of the same microstate was correlated with the presence of ASD-related behaviors. The duration of this microstate was also positively correlated with participant age in the TD group, but not in the ASD group. Further, the frequency and duration of the microstate were significantly correlated with the overall alpha power only in the TD group. The signal strength and GEV for another microstate (type G) was greater in the ASD group than the TD group, and the associated topographical pattern differed between groups with greater variations in the ASD group. While more work is needed to clarify the underlying neural sources, the existing literature supports associations between the two microstates and the default mode and salience networks. The current study suggests specific alterations of temporal dynamics of the resting cortical network activities as well as their developmental trajectories and relationships to alpha power, which has been proposed to reflect reduced neural inhibition in ASD. This article is protected by copyright. All rights reserved.
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Affiliation(s)
| | | | - Brandon Keehn
- Department of Speech, Language, and Hearing Sciences, Purdue University
| | - Leanne Chukoskie
- Department of Physical Therapy, Movement and Rehabilitation Science, Northeastern University
| | | | - Jeanne Townsend
- Department of Neurosciences, University of California, San Diego
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31
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An age-dependent Connectivity-based computer aided diagnosis system for Autism Spectrum Disorder using Resting-state fMRI. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103108] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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32
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Chen B. A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys. Clin EEG Neurosci 2022; 53:3-11. [PMID: 34152841 DOI: 10.1177/15500594211026282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The abnormal cortices of autism spectrum disorder (ASD) brains are uncertain. However, the pathological alterations of ASD brains are distributed throughout interconnected cortical systems. Functional connections (FCs) methodology identifies cooperation and separation characteristics of information process in macroscopic cortical activity patterns under the context of network neuroscience. Embracing the graph theory concepts, this paper introduces eigenvector centrality index (EC score) ground on the FCs, and further develops a new framework for researching the dysfunctional cortex of ASD in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in EC-scores of 26 ASD boys and 28 matched healthy controls (HCs). For whole brain regional EC scores of ASD boys, orbitofrontal superior medial cortex, insula R, posterior cingulate gyrus L, and cerebellum 9 L are endowed with different EC scores significantly. In the brain subsystems level, EC scores of DMN, prefrontal lobe, and cerebellum are aberrant in the ASD boys. Generally, the EC scores display widespread distribution of diseased regions in ASD brains. Meanwhile, the discovered regions and subsystems, such as MPFC, AMYG, INS, prefrontal lobe, and DMN, are engaged in social processing. Meanwhile, the CBCL externalizing problem scores are associated with EC scores.
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Affiliation(s)
- Bo Chen
- 12626Hangzhou Dianzi University, Hangzhou, Zhejiang, PR China
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33
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Reardon AM, Li K, Hu XP. Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning. Front Comput Neurosci 2021; 15:762781. [PMID: 34924984 PMCID: PMC8674307 DOI: 10.3389/fncom.2021.762781] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 11/04/2021] [Indexed: 11/18/2022] Open
Abstract
Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients. Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls. Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%. Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.
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Affiliation(s)
- Alexandra M Reardon
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Kaiming Li
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Xiaoping P Hu
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States.,Center for Advanced Neuroimaging, University of California, Riverside, Riverside, CA, United States
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34
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Cui J, Wang Y, Liu R, Chen X, Zhang Z, Feng Y, Zhou J, Zhou Y, Wang G. Effects of escitalopram therapy on resting-state functional connectivity of subsystems of the default mode network in unmedicated patients with major depressive disorder. Transl Psychiatry 2021; 11:634. [PMID: 34903712 PMCID: PMC8668990 DOI: 10.1038/s41398-021-01754-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 11/21/2021] [Accepted: 11/30/2021] [Indexed: 11/09/2022] Open
Abstract
Antidepressants are often the first-line medications prescribed for patients with major depressive disorder (MDD). Given the critical role of the default mode network (DMN) in the physiopathology of MDD, the current study aimed to investigate the effects of antidepressants on the resting-state functional connectivity (rsFC) within and between the DMN subsystems. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data from 36 unmedicated MDD patients at baseline and after escitalopram treatment for 12 weeks. The rs-fMRI data were also collected from 61 matched healthy controls at the time point with the same interval. Then, we decomposed the DMN into three subsystems based on a template from previous studies and computed the rsFC within and between the three subsystems. Finally, repeated measures analysis of covariance was conducted to identify the main effect of group and time and their interaction effect. We found that the significantly reduced within-subsystem rsFC in the DMN core subsystem in patients with MDD at baseline was increased after escitalopram treatment and became comparable with that in the healthy controls, whereas the reduced within-subsystem rsFC persisted in the DMN dorsal medial prefrontal cortex (dMPFC) and medial temporal subsystems in patients with MDD following escitalopram treatment. In addition, the reduced between-subsystem rsFC between the core and dMPFC subsystem showed a similar trend of change after treatment in patients with MDD. Moreover, our main results were confirmed using the DMN regions from another brain atlas. In the current study, we found different effects of escitalopram on the rsFC of the DMN subsystems. These findings deepened our understanding of the neuronal basis of antidepressants' effect on brain function in patients with MDD. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.
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Affiliation(s)
- Jian Cui
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Yun Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Rui Liu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Xiongying Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Zhifang Zhang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Yuan Feng
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China
| | - Jingjing Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China
| | - Yuan Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Gang Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China.
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35
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Jones JS, The Calm Team, Astle DE. A transdiagnostic data-driven study of children's behaviour and the functional connectome. Dev Cogn Neurosci 2021; 52:101027. [PMID: 34700195 PMCID: PMC8551598 DOI: 10.1016/j.dcn.2021.101027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 10/06/2021] [Accepted: 10/19/2021] [Indexed: 10/24/2022] Open
Abstract
Behavioural difficulties are seen as hallmarks of many neurodevelopmental conditions. Differences in functional brain organisation have been observed in these conditions, but little is known about how they are related to a child's profile of behavioural difficulties. We investigated whether behavioural difficulties are associated with how the brain is functionally organised in an intentionally heterogeneous and transdiagnostic sample of 957 children aged 5-15. We used consensus community detection to derive data-driven profiles of behavioural difficulties and constructed functional connectomes from a subset of 238 children with resting-state functional Magnetic Resonance Imaging (fMRI) data. We identified three distinct profiles of behaviour that were characterised by principal difficulties with hot executive function, cool executive function, and learning. Global organisation of the functional connectome did not differ between the groups, but multivariate patterns of connectivity at the level of Intrinsic Connectivity Networks (ICNs), nodes, and hubs significantly predicted group membership in held-out data. Fronto-parietal connector hubs were under-connected in all groups relative to a comparison sample and children with hot vs cool executive function difficulties were distinguished by connectivity in ICNs associated with cognitive control, emotion processing, and social cognition. This demonstrates both general and specific neurodevelopmental risk factors in the functional connectome.
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Affiliation(s)
- Jonathan S Jones
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK.
| | - The Calm Team
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK
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36
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Maesawa S, Mizuno S, Bagarinao E, Watanabe H, Kawabata K, Hara K, Ohdake R, Ogura A, Mori D, Nakatsubo D, Isoda H, Hoshiyama M, Katsuno M, Saito R, Ozaki N, Sobue G. Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging. Front Hum Neurosci 2021; 15:753836. [PMID: 34803636 PMCID: PMC8604343 DOI: 10.3389/fnhum.2021.753836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 10/19/2021] [Indexed: 11/17/2022] Open
Abstract
Purpose: Maintenance of cognitive performance is important for healthy aging. This study aims to elucidate the relationship between brain networks and cognitive function in subjects maintaining relatively good cognitive performance. Methods: A total of 120 subjects, with equal number of participants from each age group between 20 and 70 years, were included in this study. Only participants with Addenbrooke’s Cognitive Examination – Revised (ACE-R) total score greater than 83 were included. Anatomical T1-weighted MR images and resting-state functional MR images (rsfMRIs) were taken from all participants using a 3-tesla MRI scanner. After preprocessing, several factors associated with age including the ACE-R total score, scores of five domains, sub-scores of ACE-R, and brain volumes were tested. Morphometric changes associated with age were analyzed using voxel based morphometry (VBM) and changes in resting state networks (RSNs) were examined using dual regression analysis. Results: Significant negative correlations with age were seen in the total gray matter volume (GMV, r = −0.58), and in the memory, attention, and visuospatial domains. Among the different sub-scores, the score of the delayed recall (DR) showed the highest negative correlation with age (r = −0.55, p < 0.001). In VBM analysis, widespread regions demonstrated negative correlation with age, but none with any of the cognitive scores. Quadratic approximations of cognitive scores as functions of age showed relatively delayed decline compared to total GMV loss. In dual regression analysis, some cognitive networks, including the dorsal default mode network, the lateral dorsal attention network, the right / left executive control network, the posterior salience network, and the language network, did not demonstrate negative correlation with age. Some regions in the sensorimotor networks showed positive correlation with the DR, memory, and fluency scores. Conclusion: Some domains of the cognitive test did not correlate with age, and even the highly correlated sub-scores such as the DR score, showed delayed decline compared to the loss of total GMV. Some RSNs, especially involving cognitive control regions, were relatively maintained with age. Furthermore, the scores of memory, fluency, and the DR were correlated with the within-network functional connectivity values of the sensorimotor network, which supported the importance of exercise for maintenance of cognition.
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Affiliation(s)
- Satoshi Maesawa
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan.,Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Satomi Mizuno
- Department of Rehabilitation Medicine, National Hospital Organization, Nagoya Medical Center, Nagoya, Japan
| | | | - Hirohisa Watanabe
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan.,Department of Neurology, Fujita Health University, Toyoake, Japan
| | - Kazuya Kawabata
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan.,Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Reiko Ohdake
- Department of Neurology, Fujita Health University, Toyoake, Japan
| | - Aya Ogura
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Daisuke Mori
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan.,Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Daisuke Nakatsubo
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Haruo Isoda
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Minoru Hoshiyama
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Ryuta Saito
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Norio Ozaki
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan.,Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Gen Sobue
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan.,Department of Neurology, Aichi Medical University, Nagakute, Japan
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37
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Li D, Liu C, Huang Z, Li H, Xu Q, Zhou B, Hu C, Zhang Y, Wang Y, Nie J, Qiao Z, Yin D, Xu X. Common and Distinct Disruptions of Cortical Surface Morphology Between Autism Spectrum Disorder Children With and Without SHANK3 Deficiency. Front Neurosci 2021; 15:751364. [PMID: 34776852 PMCID: PMC8581670 DOI: 10.3389/fnins.2021.751364] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/29/2021] [Indexed: 11/13/2022] Open
Abstract
SH3 and Multiple Ankyrin Repeat Domains 3 (SHANK3)-caused autism spectrum disorder (ASD) may present a unique opportunity to clarify the heterogeneous neuropathological mechanisms of ASD. However, the specificity and commonality of disrupted large-scale brain organization in SHANK3-deficient children remain largely unknown. The present study combined genetic tests, neurobehavioral evaluations, and magnetic resonance imaging, aiming to explore the disruptions of both local and networked cortical structural organization in ASD children with and without SHANK3 deficiency. Multiple surface morphological parameters such as cortical thickness (CT) and sulcus depth were estimated, and the graph theory was adopted to characterize the topological properties of structural covariance networks (SCNs). Finally, a correlation analysis between the alterations in brain morphological features and the neurobehavioral evaluations was performed. Compared with typically developed children, increased CT and reduced nodal degree were found in both ASD children with and without SHANK3 defects mainly in the lateral temporal cortex, prefrontal cortex (PFC), temporo-parietal junction (TPJ), superior temporal gyrus (STG), and limbic/paralimbic regions. Besides commonality, our findings showed some distinct abnormalities in ASD children with SHANK3 defects compared to those without. Locally, more changes in the STG and orbitofrontal cortex were exhibited in ASD children with SHANK3 defects, while more changes in the TPJ and inferior parietal lobe (IPL) in those without SHANK3 defects were observed. For the SCNs, a trend toward regular network topology was observed in ASD children with SHANK3 defects, but not in those without. In addition, ASD children with SHANK3 defects showed more alterations of nodal degrees in the anterior and posterior cingulate cortices and right insular, while there were more disruptions in the sensorimotor areas and the left insular and dorsomedial PFC in ASD without SHANK3 defects. Our findings indicate dissociable disruptions of local and networked brain morphological features in ASD children with and without SHANK3 deficiency. Moreover, this monogenic study may provide a valuable path for parsing the heterogeneity of brain disturbances in ASD.
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Affiliation(s)
- Dongyun Li
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Chunxue Liu
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Ziyi Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, Affiliated Mental Health Center, East China Normal University, Shanghai, China.,School of Psychology, South China Normal University, Guangzhou, China
| | - Huiping Li
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Qiong Xu
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Bingrui Zhou
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Chunchun Hu
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Ying Zhang
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Yi Wang
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
| | - Jingxin Nie
- School of Psychology, South China Normal University, Guangzhou, China
| | - Zhongwei Qiao
- Department of Radiology, Children's Hospital of Fudan University, Shanghai, China
| | - Dazhi Yin
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, Affiliated Mental Health Center, East China Normal University, Shanghai, China
| | - Xiu Xu
- Department of Child Health Care, Children's Hospital of Fudan University, Shanghai, China
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38
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Liang Y, Liu B, Zhang H. A Convolutional Neural Network Combined With Prototype Learning Framework for Brain Functional Network Classification of Autism Spectrum Disorder. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2193-2202. [PMID: 34648452 DOI: 10.1109/tnsre.2021.3120024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The application of deep learning methods in brain disease diagnosis is becoming a new research hotspot. This study constructed brain functional networks based on the functional magnetic resonance imaging (fMRI) data, and proposed a novel convolutional neural network combined with a prototype learning (CNNPL) framework to classify brain functional networks for the diagnosis of autism spectrum disorder (ASD). At the bottom of CNNPL, traditional CNN was employed as the basic feature extractor, while at the top of CNNPL multiple prototypes were automatically learnt on the features to represent different categories. A generalized prototype loss based on distance cross-entropy was proposed to jointly learn the parameters of the CNN feature extractor and the prototypes. The classification was implemented with prototype matching. A transfer learning strategy was introduced to our CNNPL for weight initialization in the subsequent fine-tuning phase to promote model training. We conducted systematic experiments on the aggregate multi-sites ASD dataset. Experimental results revealed that our model outperforms the current state-of-the-art methods in ASD classification and can reliably learn inter-site biomarkers, indicating the robustness of our model on large-scale dataset with inter-site variability. Furthermore, our model demonstrated robust learning capability for high-level organization of brain functionality. Our study also identified important brain regions as biomarkers associated with ASD classification. Together, our proposed model provides a promising solution for learning and classifying brain functional networks, and thus contributes to the biomarker extraction and imaging diagnosis of ASD.
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39
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Djerassi M, Ophir S, Atzil S. What Is Social about Autism? The Role of Allostasis-Driven Learning. Brain Sci 2021; 11:1269. [PMID: 34679334 PMCID: PMC8534207 DOI: 10.3390/brainsci11101269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/16/2021] [Accepted: 09/17/2021] [Indexed: 12/27/2022] Open
Abstract
Scientific research on neuro-cognitive mechanisms of autism often focuses on circuits that support social functioning. However, autism is a heterogeneous developmental variation in multiple domains, including social communication, but also language, cognition, and sensory-motor control. This suggests that the underlying mechanisms of autism share a domain-general foundation that impacts all of these processes. In this Perspective Review, we propose that autism is not a social deficit that results from an atypical "social brain". Instead, typical social development relies on learning. In social animals, infants depend on their caregivers for survival, which makes social information vitally salient. The infant must learn to socially interact in order to survive and develop, and the most prominent learning in early life is crafted by social interactions. Therefore, the most prominent outcome of a learning variation is atypical social development. To support the hypothesis that autism results from a variation in learning, we first review evidence from neuroscience and developmental science, demonstrating that typical social development depends on two domain-general processes that determine learning: (a) motivation, guided by allostatic regulation of the internal milieu; and (b) multi-modal associations, determined by the statistical regularities of the external milieu. These two processes are basic ingredients of typical development because they determine allostasis-driven learning of the social environment. We then review evidence showing that allostasis and learning are affected among individuals with autism, both neurally and behaviorally. We conclude by proposing a novel domain-general framework that emphasizes allostasis-driven learning as a key process underlying autism. Guided by allostasis, humans learn to become social, therefore, the atypical social profile seen in autism can reflect a domain-general variation in allostasis-driven learning. This domain-general view raises novel research questions in both basic and clinical research and points to targets for clinical intervention that can lower the age of diagnosis and improve the well-being of individuals with autism.
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Affiliation(s)
| | | | - Shir Atzil
- Department of Psychology, Hebrew University of Jerusalem, Jerusalem 9190501, Israel; (M.D.); (S.O.)
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Mouga S, Duarte IC, Café C, Sousa D, Duque F, Oliveira G, Castelo-Branco M. Attentional Cueing and Executive Deficits Revealed by a Virtual Supermarket Task Coupled With Eye-Tracking in Autism Spectrum Disorder. Front Psychol 2021; 12:671507. [PMID: 34531782 PMCID: PMC8438237 DOI: 10.3389/fpsyg.2021.671507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 07/20/2021] [Indexed: 11/13/2022] Open
Abstract
Executive functioning (EF) impairments in Autism Spectrum Disorder (ASD) impact on complex functions, such as social cognition. We assessed this link between EF, attentional cueing, and social cognition with a novel ecological task, "EcoSupermarketX." Our task had three blocks of increasing executive load and incorporated social and non-social cues, with different degrees of saliency. Performance of ASD and typical neurodevelopment was compared. The ASD showed a significant performance dependence on the presence of contextual cues. Difficulties increased as a function of cognitive load. Between-group differences were found both for social and non-social salient cues. Eye-tracking measures showed significantly larger fixation time of more salient social cues in ASD. In sum, EcoSupermarketX is sensitive to detect EF and attentional cueing deficits in ASD.
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Affiliation(s)
- Susana Mouga
- CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Coimbra, Portugal.,Institute of Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal.,Center for Neuroscience and Cell Biology - Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Neurodevelopmental and Autism Unit From Child Developmental Centre, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Isabel Catarina Duarte
- CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Coimbra, Portugal.,Institute of Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal
| | - Cátia Café
- Neurodevelopmental and Autism Unit From Child Developmental Centre, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Daniela Sousa
- CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Coimbra, Portugal.,Institute of Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal.,Neurodevelopmental and Autism Unit From Child Developmental Centre, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Frederico Duque
- CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Coimbra, Portugal.,Center for Neuroscience and Cell Biology - Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Neurodevelopmental and Autism Unit From Child Developmental Centre, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.,Centro de Investigação e Formação Clínica, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.,Faculty of Medicine, University Clinic of Pediatrics, University of Coimbra, Coimbra, Portugal
| | - Guiomar Oliveira
- CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Coimbra, Portugal.,Center for Neuroscience and Cell Biology - Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Neurodevelopmental and Autism Unit From Child Developmental Centre, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.,Centro de Investigação e Formação Clínica, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.,Faculty of Medicine, University Clinic of Pediatrics, University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Coimbra, Portugal.,Institute of Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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41
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Moseley RL, Liu CH, Gregory NJ, Smith P, Baron-Cohen S, Sui J. Levels of Self-representation and Their Sociocognitive Correlates in Late-Diagnosed Autistic Adults. J Autism Dev Disord 2021; 52:3246-3259. [PMID: 34460052 PMCID: PMC9213305 DOI: 10.1007/s10803-021-05251-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/21/2021] [Indexed: 12/18/2022]
Abstract
The cognitive representation of oneself is central to other sociocognitive processes, including relations with others. It is reflected in faster, more accurate processing of self-relevant information, a “self-prioritisation effect” (SPE) which is inconsistent across studies in autism. Across two tasks with autistic and non-autistic participants, we explored the SPE and its relationship to autistic traits, mentalizing ability and loneliness. A SPE was intact in both groups, but together the two tasks suggested a reduced tendency of late-diagnosed autistic participants to differentiate between familiar and unfamiliar others and greater ease disengaging from the self-concept. Correlations too revealed a complex picture, which we attempt to explore and disentangle with reference to the inconsistency across self-processing studies in autism, highlighting implications for future research.
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Affiliation(s)
- R L Moseley
- Department of Psychology, Bournemouth University, Bournemouth University, Talbot Campus, Fern Barrow, Poole, Dorset, BH12 5BB, UK.
| | - C H Liu
- Department of Psychology, Bournemouth University, Bournemouth University, Talbot Campus, Fern Barrow, Poole, Dorset, BH12 5BB, UK
| | - N J Gregory
- Department of Psychology, Bournemouth University, Bournemouth University, Talbot Campus, Fern Barrow, Poole, Dorset, BH12 5BB, UK
| | - P Smith
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - S Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - J Sui
- School of Psychology, University of Aberdeen, Aberdeenshire, UK
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42
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Zoltowski AR, Lyu I, Failla M, Mash LE, Dunham K, Feldman JI, Woynaroski TG, Wallace MT, Barquero LA, Nguyen TQ, Cutting LE, Kang H, Landman BA, Cascio CJ. Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing. Cereb Cortex 2021; 31:5188-5205. [PMID: 34195789 DOI: 10.1093/cercor/bhab151] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 04/09/2021] [Accepted: 05/04/2021] [Indexed: 11/14/2022] Open
Abstract
It has been challenging to elucidate the differences in brain structure that underlie behavioral features of autism. Prior studies have begun to identify patterns of changes in autism across multiple structural indices, including cortical thickness, local gyrification, and sulcal depth. However, common approaches to local gyrification indexing used in prior studies have been limited by low spatial resolution relative to functional brain topography. In this study, we analyze the aforementioned structural indices, utilizing a new method of local gyrification indexing that quantifies this index adaptively in relation to specific sulci/gyri, improving interpretation with respect to functional organization. Our sample included n = 115 autistic and n = 254 neurotypical participants aged 5-54, and we investigated structural patterns by group, age, and autism-related behaviors. Differing structural patterns by group emerged in many regions, with age moderating group differences particularly in frontal and limbic regions. There were also several regions, particularly in sensory areas, in which one or more of the structural indices of interest either positively or negatively covaried with autism-related behaviors. Given the advantages of this approach, future studies may benefit from its application in hypothesis-driven examinations of specific brain regions and/or longitudinal studies to assess brain development in autism.
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Affiliation(s)
- Alisa R Zoltowski
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA
| | - Ilwoo Lyu
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, South Korea
| | - Michelle Failla
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,College of Nursing, Ohio State University, Columbus, OH 43210, USA
| | - Lisa E Mash
- San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA 92120, USA
| | - Kacie Dunham
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN 37232, USA
| | - Jacob I Feldman
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA
| | - Tiffany G Woynaroski
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Mark T Wallace
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
| | - Laura A Barquero
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
| | - Tin Q Nguyen
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Special Education, Vanderbilt University, Nashville, TN 37203, USA
| | - Laurie E Cutting
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA.,Department of Special Education, Vanderbilt University, Nashville, TN 37203, USA.,Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hakmook Kang
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Bennett A Landman
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
| | - Carissa J Cascio
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN 37232, USA.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA.,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, TN 37212, USA.,Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
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43
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Bandeira ID, Lins-Silva DH, Barouh JL, Faria-Guimarães D, Dorea-Bandeira I, Souza LS, Alves GS, Brunoni AR, Nitsche M, Fregni F, Lucena R. Neuroplasticity and non-invasive brain stimulation in the developing brain. PROGRESS IN BRAIN RESEARCH 2021; 264:57-89. [PMID: 34167665 DOI: 10.1016/bs.pbr.2021.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The brain is a dynamic organ whose growth and organization varies according to each subject's life experiences. Through adaptations in gene expression and the release of neurotrophins and neurotransmitters, these experiences induce a process of cellular realignment and neural network reorganization, which consolidate what is called neuroplasticity. However, despite the brain's resilience and dynamism, neuroplasticity is maximized during the first years of life, when the developing brain is more sensitive to structural reorganization and the repair of damaged neurons. This review presents an overview of non-invasive brain stimulation (NIBS) techniques that have increasingly been a focus for experimental research and the development of therapeutic methods involving neuroplasticity, especially Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS). Due to its safety risk profile and extensive tolerability, several trials have demonstrated the benefits of NIBS as a feasible experimental alternative for the treatment of brain and mind disorders in children and adolescents. However, little is known about the late impact of neuroplasticity-inducing tools on the developing brain, and there are concerns about aberrant plasticity. There are also ethical considerations when performing interventions in the pediatric population. This article will therefore review these aspects and also obstacles related to the premature application of NIBS, given the limited evidence available concerning the extent to which these methods interfere with the developing brain.
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Affiliation(s)
- Igor D Bandeira
- Laboratory of Neuropsychopharmacology, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, Brazil; Programa de Pós-Graduação em Medicina e Saúde, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil.
| | - Daniel H Lins-Silva
- Laboratory of Neuropsychopharmacology, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, Brazil; Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil
| | - Judah L Barouh
- Laboratory of Neuropsychopharmacology, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, Brazil; Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil
| | - Daniela Faria-Guimarães
- Laboratory of Neuropsychopharmacology, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, Brazil; Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil
| | - Ingrid Dorea-Bandeira
- Laboratory of Neuropsychopharmacology, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, Brazil
| | - Lucca S Souza
- Laboratory of Neuropsychopharmacology, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, Brazil; Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil
| | - Gustavo S Alves
- Laboratory of Neuropsychopharmacology, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, Brazil; Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil
| | - André R Brunoni
- Service of Interdisciplinary Neuromodulation, Instituto de Psiquiatria, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Michael Nitsche
- Department of Psychology and Neurosciences, Leibniz Research Center for Working Environment and Human Factors, Dortmund, Germany; Department of Neurology, University Medical Hospital Bergmannsheil, Bochum, Germany
| | - Felipe Fregni
- Neuromodulation Center and Center for Clinical Research Learning, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, Harvard University, Charlestown, MA, United States
| | - Rita Lucena
- Department of Neuroscience and Mental Health, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil
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44
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Lawrence KE, Hernandez LM, Fuster E, Padgaonkar NT, Patterson G, Jung J, Okada NJ, Lowe JK, Hoekstra JN, Jack A, Aylward E, Gaab N, Van Horn JD, Bernier RA, McPartland JC, Webb SJ, Pelphrey KA, Green SA, Bookheimer SY, Geschwind DH, Dapretto M. Impact of autism genetic risk on brain connectivity: a mechanism for the female protective effect. Brain 2021; 145:378-387. [PMID: 34050743 PMCID: PMC8967090 DOI: 10.1093/brain/awab204] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 04/23/2021] [Accepted: 05/11/2021] [Indexed: 01/27/2023] Open
Abstract
The biological mechanisms underlying the greater prevalence of autism spectrum disorder in males than females remain poorly understood. One hypothesis posits that this female protective effect arises from genetic load for autism spectrum disorder differentially impacting male and female brains. To test this hypothesis, we investigated the impact of cumulative genetic risk for autism spectrum disorder on functional brain connectivity in a balanced sample of boys and girls with autism spectrum disorder and typically developing boys and girls (127 youth, ages 8-17). Brain connectivity analyses focused on the salience network, a core intrinsic functional connectivity network which has previously been implicated in autism spectrum disorder. The effects of polygenic risk on salience network functional connectivity were significantly modulated by participant sex, with genetic load for autism spectrum disorder influencing functional connectivity in boys with and without autism spectrum disorder but not girls. These findings support the hypothesis that autism spectrum disorder risk genes interact with sex differential processes, thereby contributing to the male bias in autism prevalence and proposing an underlying neurobiological mechanism for the female protective effect.
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Affiliation(s)
- Katherine E Lawrence
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA,Correspondence to: Mirella Dapretto Ahmanson-Lovelace Brain Mapping Center 660 Charles E. Young Drive South Los Angeles, CA 90095, USA E-mail:
| | - Leanna M Hernandez
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Emily Fuster
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Namita T Padgaonkar
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Genevieve Patterson
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jiwon Jung
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Nana J Okada
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jennifer K Lowe
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jackson N Hoekstra
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Allison Jack
- Department of Psychology, George Mason University, Fairfax, VA 22030, USA
| | - Elizabeth Aylward
- Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Nadine Gaab
- Harvard Graduate School of Education, Cambridge, MA 02138, USA
| | - John D Van Horn
- Department of Psychology and School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Raphael A Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98195, USA
| | | | - Sara J Webb
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98195, USA,Center on Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Kevin A Pelphrey
- Department of Neurology, University of Virginia, Charlottesville, VA 22904, USA
| | - Shulamite A Green
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Daniel H Geschwind
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA,Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
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45
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Lian F, Northoff G. The Lost Neural Hierarchy of the Autistic Self-Locked-Out of the Mental Self and Its Default-Mode Network. Brain Sci 2021; 11:574. [PMID: 33946964 PMCID: PMC8145974 DOI: 10.3390/brainsci11050574] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/16/2021] [Accepted: 04/23/2021] [Indexed: 12/12/2022] Open
Abstract
Autism spectrum disorder (ASD) is characterized by a fundamental change in self-awareness including seemingly paradoxical features like increased ego-centeredness and weakened self-referentiality. What is the neural basis of this so-called "self-paradox"? Conducting a meta-analytic review of fMRI rest and task studies, we show that ASD exhibits consistent hypofunction in anterior and posterior midline regions of the default-mode network (DMN) in both rest and task with decreased self-non-self differentiation. Relying on a multilayered nested hierarchical model of self, as recently established (Qin et al. 2020), we propose that ASD subjects cannot access the most upper layer of their self, the DMN-based mental self-they are locked-out of their own DMN and its mental self. This, in turn, results in strong weakening of their self-referentiality with decreases in both self-awareness and self-other distinction. Moreover, this blocks the extension of non-DMN cortical and subcortical regions at the lower layers of the physical self to the DMN-based upper layer of the mental self, including self-other distinction. The ASD subjects remain stuck and restricted to their intero- and exteroceptive selves as manifested in a relative increase in ego-centeredness (as compared to self-referentiality). This amounts to what we describe as "Hierarchical Model of Autistic Self" (HAS), which, characterizing the autistic self in hierarchical and spatiotemporal terms, aligns well with and extends current theories of ASD including predictive coding and weak central coherence.
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Affiliation(s)
- Fuxin Lian
- Institute of Psychological Sciences, School of Education, Hangzhou Normal University, Hangzhou 311121, China;
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4, Canada
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4, Canada
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46
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Haghighat H, Mirzarezaee M, Araabi BN, Khadem A. Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity. Brain Topogr 2021; 34:306-322. [PMID: 33905003 DOI: 10.1007/s10548-021-00831-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 03/03/2021] [Indexed: 11/30/2022]
Abstract
Autism spectrum disorder (ASD) is a developmental disorder characterized by defects in social interaction. The past functional connectivity studies using resting-state fMRI have found both patterns of hypo-connectivity and hyper-connectivity in ASD and proposed the age as an important factor on functional connectivity disorders. However, this influence is not clearly characterized yet. Previous studies have often examined the functional connectivity disorders in particular brain regions in an age group or a mixture of age groups. The present study compares whole-brain within-connectivity and between-connectivity between ASD individuals and typically developing (TD) controls in three age groups including children (< 11 years), adolescents (11-18 years), and adults (> 18 years), each comprising 21 ASD individuals and 21 TD controls. The age groups were matched for age, Full IQ, and gender. Independent component analysis and dual regression were used to investigate within-connectivity. The full and partial correlations between ICs were used to investigate between-connectivity. Examination of the within-connectivity showed hyper-connectivity, especially in cerebellum and brainstem in ASD children but both hyper/hypo connectivity in adolescents and ASD adults. In ASD children, difference in the between-connectivity among default mode network (DMN), salience-executive network and fronto-parietal network were observed. There was also a negative correlation between DMN and temporal network. Full correlation comparison between ASD adolescents and TD individuals showed significant differences between cerebellum and DMN. Our results supported just the hyper-connectivity in childhood, but both hypo and hyper-connectivity after childhood and hypothesized that abnormal resting connections in ASD exist in the regions of the brain known to be involved in social cognition.
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Affiliation(s)
- Hossein Haghighat
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mitra Mirzarezaee
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Babak Nadjar Araabi
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | - Ali Khadem
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
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47
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Nair A, Jalal R, Liu J, Tsang T, McDonald NM, Jackson L, Ponting C, Jeste SS, Bookheimer SY, Dapretto M. Altered Thalamocortical Connectivity in 6-Week-Old Infants at High Familial Risk for Autism Spectrum Disorder. Cereb Cortex 2021; 31:4191-4205. [PMID: 33866373 DOI: 10.1093/cercor/bhab078] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 03/11/2021] [Accepted: 03/12/2021] [Indexed: 12/14/2022] Open
Abstract
Converging evidence from neuroimaging studies has revealed altered connectivity in cortical-subcortical networks in youth and adults with autism spectrum disorder (ASD). Comparatively little is known about the development of cortical-subcortical connectivity in infancy, before the emergence of overt ASD symptomatology. Here, we examined early functional and structural connectivity of thalamocortical networks in infants at high familial risk for ASD (HR) and low-risk controls (LR). Resting-state functional connectivity and diffusion tensor imaging data were acquired in 52 6-week-old infants. Functional connectivity was examined between 6 cortical seeds-prefrontal, motor, somatosensory, temporal, parietal, and occipital regions-and bilateral thalamus. We found significant thalamic-prefrontal underconnectivity, as well as thalamic-occipital and thalamic-motor overconnectivity in HR infants, relative to LR infants. Subsequent structural connectivity analyses also revealed atypical white matter integrity in thalamic-occipital tracts in HR infants, compared with LR infants. Notably, aberrant connectivity indices at 6 weeks predicted atypical social development between 9 and 36 months of age, as assessed with eye-tracking and diagnostic measures. These findings indicate that thalamocortical connectivity is disrupted at both the functional and structural level in HR infants as early as 6 weeks of age, providing a possible early marker of risk for ASD.
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Affiliation(s)
- Aarti Nair
- Department of Psychology, School of Behavioral Health, Loma Linda University, Loma Linda, CA 92354, USA
| | - Rhideeta Jalal
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Janelle Liu
- Interdepartmental Neuroscience Program, University of California, Los Angeles, CA 90095, USA
| | - Tawny Tsang
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
| | - Nicole M McDonald
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Lisa Jackson
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Carolyn Ponting
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
| | - Shafali S Jeste
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
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48
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Li X, Zhang K, He X, Zhou J, Jin C, Shen L, Gao Y, Tian M, Zhang H. Structural, Functional, and Molecular Imaging of Autism Spectrum Disorder. Neurosci Bull 2021; 37:1051-1071. [PMID: 33779890 DOI: 10.1007/s12264-021-00673-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 12/20/2020] [Indexed: 12/21/2022] Open
Abstract
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder associated with both genetic and environmental risks. Neuroimaging approaches have been widely employed to parse the neurophysiological mechanisms underlying ASD, and provide critical insights into the anatomical, functional, and neurochemical changes. We reviewed recent advances in neuroimaging studies that focused on ASD by using magnetic resonance imaging (MRI), positron emission tomography (PET), or single-positron emission tomography (SPECT). Longitudinal structural MRI has delineated an abnormal developmental trajectory of ASD that is associated with cascading neurobiological processes, and functional MRI has pointed to disrupted functional neural networks. Meanwhile, PET and SPECT imaging have revealed that metabolic and neurotransmitter abnormalities may contribute to shaping the aberrant neural circuits of ASD. Future large-scale, multi-center, multimodal investigations are essential to elucidate the neurophysiological underpinnings of ASD, and facilitate the development of novel diagnostic biomarkers and better-targeted therapy.
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Affiliation(s)
- Xiaoyi Li
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
| | - Kai Zhang
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Hyogo, 650-0047, Japan
| | - Xiao He
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
| | - Jinyun Zhou
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
| | - Chentao Jin
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
| | - Lesang Shen
- Department of Surgical Oncology, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Yuanxue Gao
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China
| | - Mei Tian
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China.
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China.
| | - Hong Zhang
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China.
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027, China.
- Institute of Nuclear Medicine and Molecular Imaging, Zhejiang University, Hangzhou, 310009, China.
- The College of Biomedical Engineering and Instrument Science of Zhejiang University, Hangzhou, 310027, China.
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Maximo JO, Nelson CM, Kana RK. "Unrest while Resting"? Brain entropy in autism spectrum disorder. Brain Res 2021; 1762:147435. [PMID: 33753068 DOI: 10.1016/j.brainres.2021.147435] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 02/20/2021] [Accepted: 03/15/2021] [Indexed: 11/29/2022]
Abstract
Biological systems typically exhibit complex behavior with nonlinear dynamic properties. Nonlinear signal processing techniques such as sample entropy is a novel approach to characterize the temporal dynamics of brain connectivity. Estimating entropy is especially important in clinical populations such as autism spectrum disorder (ASD) as differences in entropy may signal functional alterations in the brain. Considering the models of disrupted brain network connectivity in ASD, sample entropy would provide a novel direction to understand brain organization. Resting state fMRI data from 45 high-functioning children with ASD and 45 age-and-IQ-matched typically developing (TD) children were obtained from the Autism Brain Imaging Data Exchange (ABIDE-II) database. Data were preprocessed using the CONN toolbox. Sample entropy was then calculated using the complexity toolbox, in a whole-brain voxelwise manner as well as in regions of interests (ROIs) based methods. ASD participants demonstrated significantly increased entropy in left angular gyrus, superior parietal lobule, and right inferior temporal gyrus; and reduced sample entropy in superior frontal gyrus compared to TD participants. Positive correlations of average entropy in clusters of significant group differences scores across all subjects were found. Finally, ROI analysis revealed a main effect of lobes. Differences in entropy between the ASD and TD groups suggests that entropy may provide another important index of brain dysfunction in clinical populations like ASD. Further, the relationship between increased entropy and ASD symptoms in our study underscores the role of optimal brain synchronization in cognitive and behavioral functions.
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Affiliation(s)
- Jose O Maximo
- Department of Psychiatry & Behavioral Neurobiology, University of Alabama at Birmingham, United States
| | - Cailee M Nelson
- Department of Educational Studies in Psychology, Research Methodology, & Counseling, University of Alabama, United States
| | - Rajesh K Kana
- Department of Psychology, University of Alabama, United States; Center for Innovative Research in Autism, University of Alabama, United States.
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50
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Wang Q, Li HY, Li YD, Lv YT, Ma HB, Xiang AF, Jia XZ, Liu DQ. Resting-state abnormalities in functional connectivity of the default mode network in autism spectrum disorder: a meta-analysis. Brain Imaging Behav 2021; 15:2583-2592. [PMID: 33683528 DOI: 10.1007/s11682-021-00460-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 01/12/2021] [Accepted: 01/28/2021] [Indexed: 11/30/2022]
Abstract
Increasing evidence has shown that the resting state brain connectivity of default mode network (DMN) which are important for social cognition are disrupted in autism spectrum disorder (ASD). However, previous neuroimaging studies did not present consistent results. Therefore, we performed a meta-analysis of resting-state functional connectivity (rsFC) studies of DMN in the individuals with ASD and healthy controls (HCs) to provide a new perspective for investigating the pathophysiology of ASD. We carried out a search using the terms: ("ASD" OR "Autism") AND ("resting state" OR "rest") AND ("DMN" OR "default mode network") in PubMed, Web of Science and Embase to identify the researches published before January 2020. Ten resting state datasets including 203 patients and 208 HCs were included. Anisotropic Effect Size version of Signed Differential Mapping (AES-SDM) method was applied to identify group differences. In comparison with the HCs, the patients with ASD showed increased connectivity in cerebellum, right middle temporal gyrus, superior occipital gyrus, right supramarginal gyrus, supplementary motor area and putamen. Decreased connectivity was discovered in some nodes of DMN, such as medial prefrontal cortex, precuneus and angular gyrus. These results may help us to further clarify the neurobiological mechanisms in patients with ASD.
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Affiliation(s)
- Qing Wang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China
- Key Laboratory of Brain and Cognitive Neuroscience, Dalian, Liaoning Province, China
| | - Hua-Yun Li
- College of Teacher Education, Zhejiang Normal University, Jinhua, China
- Laboratory of Intelligent Education Technology and Application, Hangzhou, Zhejiang Province, China
| | - Yun-Da Li
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Ya-Ting Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Hui-Bin Ma
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - An-Feng Xiang
- Tongji University School of Medicine, Shanghai, China
| | - Xi-Ze Jia
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.
| | - Dong-Qiang Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China.
- Key Laboratory of Brain and Cognitive Neuroscience, Dalian, Liaoning Province, China.
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