1
|
Lu H, Dong Q, Gao L, Xue Z, Niu X, Zhou R, Guo X. Sex heterogeneity of dynamic brain activity and functional connectivity in autism spectrum disorder. Autism Res 2024. [PMID: 39243179 DOI: 10.1002/aur.3227] [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: 03/26/2024] [Accepted: 08/22/2024] [Indexed: 09/09/2024]
Abstract
Sex heterogeneity has been frequently reported in autism spectrum disorders (ASD) and has been linked to static differences in brain function. However, given the complexity of ASD and diagnosis-by-sex interactions, dynamic characteristics of brain activity and functional connectivity may provide important information for distinguishing ASD phenotypes between females and males. The aim of this study was to explore sex heterogeneity of functional networks in the ASD brain from a dynamic perspective. Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were analyzed in 128 ASD subjects (64 males/64 females) and 128 typically developing control (TC) subjects (64 males/64 females). A sliding-window approach was adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dynamic functional connectivity (dFC) to characterize time-varying brain activity and functional connectivity respectively. We then examined the sex-related changes in ASD using two-way analysis of variance. Significant diagnosis-by-sex interaction effects were identified in the left anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC) and left precuneus in the dALFF analysis. Furthermore, there were significant diagnosis-by-sex interaction effects of dFC variance between the left ACC/mPFC and right ACC, left postcentral gyrus, left precuneus, right middle temporal gyrus and left inferior frontal gyrus, triangular part. These findings reveal the sex heterogeneity in brain activity and functional connectivity in ASD from a dynamic perspective, and provide new evidence for further exploring sex heterogeneity in ASD.
Collapse
Affiliation(s)
- Huibin Lu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Qi Dong
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Zaifa Xue
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Xiaoxia Niu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Rongjuan Zhou
- Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao, China
| | - Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| |
Collapse
|
2
|
Zhang K, Fang H, Li Z, Ren T, Li BM, Wang C. Sex differences in large-scale brain network connectivity for mental rotation performance. Neuroimage 2024; 298:120807. [PMID: 39179012 DOI: 10.1016/j.neuroimage.2024.120807] [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: 05/24/2024] [Revised: 08/14/2024] [Accepted: 08/21/2024] [Indexed: 08/26/2024] Open
Abstract
Mental rotation has emerged as an important predictor for success in science, technology, engineering, and math fields. Previous studies have shown that males and females perform mental rotation tasks differently. However, how the brain functions to support this difference remains poorly understood. Recent advancements in neuroimaging techniques have enabled the identification of sex differences in large-scale brain network connectivity. Using a classic mental rotation task with functional magnetic resonance imaging, the present study investigated whether there are any sex differences in large-scale brain network connectivity for mental rotation performance. Our results revealed that, relative to females, males exhibited less cross-network interaction (i.e. lower inter-network connectivity and participation coefficient) of the visual network but more intra-network integration (i.e. higher intra-network connectivity and local efficiency) and cross-network interaction (i.e. higher inter-network connectivity and participation coefficient) of the salience network. Across all participants, mental rotation performance was negatively correlated with cross-network interaction (i.e. participation coefficient) of the visual network, was positively correlated with cross-network interaction (i.e. inter-network connectivity) of the salience network, and was positively correlated with intra-network integration (i.e. local efficiency) of the somato-motor network. Interestingly, the cross-network integration indexes of both the visual and salience networks significantly mediated sex difference in mental rotation performance. The present findings suggest that large-scale brain network connectivity may constitute an essential neural basis for sex difference in mental rotation, and highlight the importance of considering sex as a research variable in investigating the complex network underpinnings of spatial cognition.
Collapse
Affiliation(s)
- Kaijie Zhang
- Institute of Brain Science and Department of Psychology, Jing Hengyi School of Education, Hangzhou 311121, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China
| | - Haifeng Fang
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China; School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou 311121, China
| | - Zheng Li
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China; School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou 311121, China
| | - Tian Ren
- Institute of Brain Science and Department of Psychology, Jing Hengyi School of Education, Hangzhou 311121, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China
| | - Bao-Ming Li
- Institute of Brain Science and Department of Psychology, Jing Hengyi School of Education, Hangzhou 311121, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China; School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou 311121, China.
| | - Chunjie Wang
- Institute of Brain Science and Department of Psychology, Jing Hengyi School of Education, Hangzhou 311121, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China.
| |
Collapse
|
3
|
Gao L, Cao Y, Zhang Y, Liu J, Zhang T, Zhou R, Guo X. Sex differences in the flexibility of dynamic network reconfiguration of autism spectrum disorder based on multilayer network. Brain Imaging Behav 2024:10.1007/s11682-024-00907-5. [PMID: 39212890 DOI: 10.1007/s11682-024-00907-5] [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] [Accepted: 08/10/2024] [Indexed: 09/04/2024]
Abstract
Dynamic network reconfiguration alterations in the autism spectrum disorder (ASD) brain have been frequently reported. However, since the prevalence of ASD in males is approximately 3.8 times higher than that in females, and previous studies of dynamic network reconfiguration of ASD have predominantly used male samples, it is unclear whether sex differences exist in dynamic network reconfiguration in ASD. This study used resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database, which included balanced samples of 64 males and 64 females with ASD, along with 64 demographically-matched typically developing control (TC) males and 64 TC females. The multilayer network analysis was used to explore the flexibility of dynamic network reconfiguration. The two-way analysis of variance was further performed to examine the sex-related changes in ASD in flexibility of dynamic network reconfiguration. A diagnosis-by-sex interaction effect was identified in the cingulo-opercular network (CON), central executive network (CEN), salience network (SN), and subcortical network (SUB). Compared with TC females, females with ASD showed lower flexibility in CON, CEN, SN, and SUB. The flexibility of CEN and SUB in males with ASD was higher than that in females with ASD. In addition, the flexibility of CON, CEN, SN, and SUB predicted the severity of social communication impairments and stereotyped behaviors and restricted interests only in females with ASD. These findings highlight significant sex differences in the flexibility of dynamic network reconfiguration in ASD and emphasize the importance of further study of sex differences in future ASD research.
Collapse
Affiliation(s)
- 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
| | - Yabo Cao
- 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
| | - Yigeng Zhang
- Department of Computer Science, University of Houston, Houston, TX, 77204-3010, USA
| | - Junfeng Liu
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, 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, China
| | - 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.
| |
Collapse
|
4
|
Iraji A, Chen J, Lewis N, Faghiri A, Fu Z, Agcaoglu O, Kochunov P, Adhikari BM, Mathalon DH, Pearlson GD, Macciardi F, Preda A, van Erp TGM, Bustillo JR, Díaz-Caneja CM, Andrés-Camazón P, Dhamala M, Adali T, Calhoun VD. Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and Their Links to Genetic Risk. Biol Psychiatry 2024; 96:188-197. [PMID: 38070846 PMCID: PMC11156799 DOI: 10.1016/j.biopsych.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/15/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Schizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations in brain function. Schizophrenia is considered a dysconnectivity syndrome, but the dynamic integration and segregation of brain networks are poorly understood. Recent advances in resting-state functional magnetic resonance imaging allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. Nevertheless, estimating time-resolved networks remains challenging due to low signal-to-noise ratio, limited short-time information, and uncertain network identification. METHODS We adapted a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 193 individuals with schizophrenia and 315 control participants. We focused on time-resolved spatial functional network connectivity, an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data. RESULTS Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spatial functional network connectivity exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and is correlated with genetic risk for schizophrenia. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks. CONCLUSIONS Our method can effectively capture spatially dynamic networks, detect nuanced schizophrenia effects including sex-specific ones, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the clinical potential of dynamic spatial dependence and weak connectivity.
Collapse
Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia.
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Noah Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California; San Francisco Veteran Affairs Medical Center, San Francisco, California
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Juan R Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Pablo Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Mukesh Dhamala
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia.
| |
Collapse
|
5
|
Li C, Wang J, Zhou Y, Li T, Wu B, Yuan X, Li L, Qin R, Liu H, Chen L, Wang X. Sex-related patterns of functional brain networks in children and adolescents with autism spectrum disorder. Autism Res 2024; 17:1344-1355. [PMID: 39051596 DOI: 10.1002/aur.3180] [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: 03/04/2024] [Accepted: 06/13/2024] [Indexed: 07/27/2024]
Abstract
Although numerous studies have emphasized the male predominance in autism spectrum disorder (ASD), how sex differences are related to the topological organization of functional networks remains unclear. This study utilized imaging data from 86 ASD (43 females, aged 7-18 years) and 86 typically developing controls (TCs) (43 females, aged 7-18 years) obtained from Autism Brain Imaging Data Exchange databases, constructed individual whole-brain functional networks, used a graph theory analysis to compute topological metrics, and assessed sex-related differences in topological metrics using a 2 × 2 factorial design. At the global level, females with ASD exhibited significantly higher cluster coefficient and local efficiency than female TCs, while no significant difference was observed between males with ASD and male TCs. Meanwhile, the neurotypical sex differences in cluster coefficient and local efficiency observed in TCs were not present in ASD. At the nodal level, ASD exhibited abnormal nodal centrality in the left middle temporal gyrus.
Collapse
Affiliation(s)
- Cuicui Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Jingxuan Wang
- Department of Painology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yunna Zhou
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Tong Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Baolin Wu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Xianshun Yuan
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Lin Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Rui Qin
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Hongzhu Liu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Linglong Chen
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| |
Collapse
|
6
|
Rippon G. Differently different?: A commentary on the emerging social cognitive neuroscience of female autism. Biol Sex Differ 2024; 15:49. [PMID: 38872228 PMCID: PMC11177439 DOI: 10.1186/s13293-024-00621-3] [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: 02/02/2024] [Accepted: 05/23/2024] [Indexed: 06/15/2024] Open
Abstract
Autism is a neurodevelopmental condition, behaviourally identified, which is generally characterised by social communication differences, and restrictive and repetitive patterns of behaviour and interests. It has long been claimed that it is more common in males. This observed preponderance of males in autistic populations has served as a focussing framework in all spheres of autism-related issues, from recognition and diagnosis through to theoretical models and research agendas. One related issue is the near total absence of females in key research areas. For example, this paper reports a review of over 120 brain-imaging studies of social brain processes in autism that reveals that nearly 70% only included male participants or minimal numbers (just one or two) of females. Authors of such studies very rarely report that their cohorts are virtually female-free and discuss their findings as though applicable to all autistic individuals. The absence of females can be linked to exclusionary consequences of autism diagnostic procedures, which have mainly been developed on male-only cohorts. There is clear evidence that disproportionately large numbers of females do not meet diagnostic criteria and are then excluded from ongoing autism research. Another issue is a long-standing assumption that the female autism phenotype is broadly equivalent to that of the male autism phenotype. Thus, models derived from male-based studies could be applicable to females. However, it is now emerging that certain patterns of social behaviour may be very different in females. This includes a specific type of social behaviour called camouflaging or masking, linked to attempts to disguise autistic characteristics. With respect to research in the field of sex/gender cognitive neuroscience, there is emerging evidence of female differences in patterns of connectivity and/or activation in the social brain that are at odds with those reported in previous, male-only studies. Decades of research have excluded or overlooked females on the autistic spectrum, resulting in the construction of inaccurate and misleading cognitive neuroscience models, and missed opportunities to explore the brain bases of this highly complex condition. A note of warning needs to be sounded about inferences drawn from past research, but if future research addresses this problem of male bias, then a deeper understanding of autism as a whole, as well as in previously overlooked females, will start to emerge.
Collapse
Affiliation(s)
- Gina Rippon
- Emeritus of Cognitive NeuroImaging, Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK.
| |
Collapse
|
7
|
Zhao F, Lv K, Ye S, Chen X, Chen H, Fan S, Mao N, Ren Y. Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis. PeerJ 2024; 12:e17078. [PMID: 38618569 PMCID: PMC11011592 DOI: 10.7717/peerj.17078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/19/2024] [Indexed: 04/16/2024] Open
Abstract
Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.
Collapse
Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Ke Lv
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Shixin Ye
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Hongyu Chen
- School Hospital, Shandong Technology and Business University, Yantai, China
| | - Sizhe Fan
- Canada Qingdao Secondary School (CQSS), Qingdao, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| |
Collapse
|
8
|
Fenske SJ, Liu J, Chen H, Diniz MA, Stephens RL, Cornea E, Gilmore JH, Gao W. Sex differences in brain-behavior relationships in the first two years of life. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578147. [PMID: 38352542 PMCID: PMC10862872 DOI: 10.1101/2024.01.31.578147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Background Evidence for sex differences in cognition in childhood is established, but less is known about the underlying neural mechanisms for these differences. Recent findings suggest the existence of brain-behavior relationship heterogeneities during infancy; however, it remains unclear whether sex underlies these heterogeneities during this critical period when sex-related behavioral differences arise. Methods A sample of 316 infants was included with resting-state functional magnetic resonance imaging scans at neonate (3 weeks), 1, and 2 years of age. We used multiple linear regression to test interactions between sex and resting-state functional connectivity on behavioral scores of working memory, inhibitory self-control, intelligence, and anxiety collected at 4 years of age. Results We found six age-specific, intra-hemispheric connections showing significant and robust sex differences in functional connectivity-behavior relationships. All connections are either with the prefrontal cortex or the temporal pole, which has direct anatomical pathways to the prefrontal cortex. Sex differences in functional connectivity only emerge when associated with behavior, and not in functional connectivity alone. Furthermore, at neonate and 2 years of age, these age-specific connections displayed greater connectivity in males and lower connectivity in females in association with better behavioral scores. Conclusions Taken together, we critically capture robust and conserved brain mechanisms that are distinct to sex and are defined by their relationship to behavioral outcomes. Our results establish brain-behavior mechanisms as an important feature in the search for sex differences during development.
Collapse
Affiliation(s)
- Sonja J Fenske
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048
| | - Janelle Liu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048
| | - Haitao Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- David Geffen School of Medicine, University of California, Los Angeles, CA 90025
| | - Marcio A Diniz
- The Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048
| | - Rebecca L Stephens
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, 27599
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, 27599
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, 27599
| | - Wei Gao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048
- David Geffen School of Medicine, University of California, Los Angeles, CA 90025
| |
Collapse
|
9
|
Lu H, Wang S, Xue Z, Liu J, Niu X, Gao L, Guo X. Decreased functional concordance in male children with autism spectrum disorder. Autism Res 2023; 16:2263-2274. [PMID: 37787080 DOI: 10.1002/aur.3035] [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: 04/03/2023] [Accepted: 09/10/2023] [Indexed: 10/04/2023]
Abstract
Autism spectrum disorder (ASD) is an early-onset neurodevelopmental condition with altered function of the brain. At present, a variety of functional metrics from neuroimaging techniques have been used to explore ASD neurological mechanisms. However, the concordance of these functional metrics in ASD is still unclear. This study used resting-state functional magnetic resonance imaging data, which were obtained from the open-access Autism Brain Imaging Data Exchange database, including 105 children with ASD and 102 demographically matched typically developing (TD) children. Both voxel-wise and volume-wise functional concordance were calculated by combining the dynamic amplitude of low-frequency fluctuations, dynamic regional homogeneity, and dynamic global signal correlation. Furthermore, a two-sample t-test was performed to compare the functional concordance between ASD and TD groups. Finally, the relationship between voxel-wise functional concordance and Autism Diagnostic Observation Schedule subscores was analyzed using the multivariate support vector regression in the ASD group. Compared with the TD group, we found that ASD showed decreased voxel-wise functional concordance in the left superior temporal pole (STGp), right amygdala, and left opercular part of the inferior frontal gyrus (IFGoper). Moreover, decreased functional concordance was associated with restricted and repetitive behaviors in ASD. Our results found altered brain function in the left STGp, right amygdala, and left IFGoper in ASD by functional concordance, indicating that functional concordance may provide new insights into the neurological mechanisms of ASD.
Collapse
Affiliation(s)
- Huibin Lu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Sha Wang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Zaifa Xue
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Jing Liu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Xiaoxia Niu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| |
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
Sidulova M, Park CH. Conditional Variational Autoencoder for Functional Connectivity Analysis of Autism Spectrum Disorder Functional Magnetic Resonance Imaging Data: A Comparative Study. Bioengineering (Basel) 2023; 10:1209. [PMID: 37892939 PMCID: PMC10604768 DOI: 10.3390/bioengineering10101209] [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: 08/18/2023] [Revised: 09/30/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
Generative models, such as Variational Autoencoders (VAEs), are increasingly employed for atypical pattern detection in brain imaging. During training, these models learn to capture the underlying patterns within "normal" brain images and generate new samples from those patterns. Neurodivergent states can be observed by measuring the dissimilarity between the generated/reconstructed images and the input images. This paper leverages VAEs to conduct Functional Connectivity (FC) analysis from functional Magnetic Resonance Imaging (fMRI) scans of individuals with Autism Spectrum Disorder (ASD), aiming to uncover atypical interconnectivity between brain regions. In the first part of our study, we compare multiple VAE architectures-Conditional VAE, Recurrent VAE, and a hybrid of CNN parallel with RNN VAE-aiming to establish the effectiveness of VAEs in application FC analysis. Given the nature of the disorder, ASD exhibits a higher prevalence among males than females. Therefore, in the second part of this paper, we investigate if introducing phenotypic data could improve the performance of VAEs and, consequently, FC analysis. We compare our results with the findings from previous studies in the literature. The results showed that CNN-based VAE architecture is more effective for this application than the other models.
Collapse
Affiliation(s)
- Mariia Sidulova
- Department of Biomedical Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA;
| | - Chung Hyuk Park
- Department of Biomedical Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA;
- Department of Computer Science, School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA
| |
Collapse
|
12
|
O'Reilly C, Huberty S, van Noordt S, Desjardins J, Wright N, Scorah J, Webb SJ, Elsabbagh M. EEG functional connectivity in infants at elevated familial likelihood for autism spectrum disorder. Mol Autism 2023; 14:37. [PMID: 37805500 PMCID: PMC10559476 DOI: 10.1186/s13229-023-00570-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 09/29/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Many studies have reported that autism spectrum disorder (ASD) is associated with atypical structural and functional connectivity. However, we know relatively little about the development of these differences in infancy. METHODS We used a high-density electroencephalogram (EEG) dataset pooled from two independent infant sibling cohorts, to characterize such neurodevelopmental deviations during the first years of life. EEG was recorded at 6 and 12 months of age in infants at typical (N = 92) or elevated likelihood for ASD (N = 90), determined by the presence of an older sibling with ASD. We computed the functional connectivity between cortical sources of EEG during video watching using the corrected imaginary part of phase-locking values. RESULTS Our main analysis found no significant association between functional connectivity and ASD, showing only significant effects for age, sex, age-sex interaction, and site. Given these null results, we performed an exploratory analysis and observed, at 12 months, a negative correlation between functional connectivity and ADOS calibrated severity scores for restrictive and repetitive behaviors (RRB). LIMITATIONS The small sample of ASD participants inherent to sibling studies limits diagnostic group comparisons. Also, results from our secondary exploratory analysis should be considered only as potential relationships to further explore, given their increased vulnerability to false positives. CONCLUSIONS These results are inconclusive concerning an association between EEG functional connectivity and ASD in infancy. Exploratory analyses provided preliminary support for a relationship between RRB and functional connectivity specifically, but these preliminary observations need corroboration on larger samples.
Collapse
Affiliation(s)
- Christian O'Reilly
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA.
- Artificial Intelligence Institute of South Carolina, University of South Carolina, 1112 Greene St, Columbia, SC, 29208, USA.
- Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC, USA.
| | - Scott Huberty
- Azrieli Centre for Autism Research, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - Stefon van Noordt
- Department of Psychology, Mount Saint Vincent University, Halifax, NS, Canada
| | | | - Nicky Wright
- Department of Psychology, Manchester Metropolitan University, Manchester, UK
| | - Julie Scorah
- Azrieli Centre for Autism Research, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | | | - Mayada Elsabbagh
- Azrieli Centre for Autism Research, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| |
Collapse
|
13
|
Li C, Li T, Chen Y, Zhang C, Ning M, Qin R, Li L, Wang X, Chen L. Sex differences of the triple network model in children with autism: A resting-state fMRI investigation of effective connectivity. Autism Res 2023; 16:1693-1706. [PMID: 37565548 DOI: 10.1002/aur.2991] [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: 04/22/2023] [Accepted: 07/06/2023] [Indexed: 08/12/2023]
Abstract
Autism spectrum disorder (ASD) has a pronounced male predominance, but the underlying neurobiological basis of this sex bias remains unclear. Gender incoherence (GI) theory suggests that ASD is more neurally androgynous than same-sex controls. Given its central role, altered structures and functions, and sex-dependent network differences in ASD, the triple network model, including the central executive network (CEN), default mode network (DMN), and salience network (SN), has emerged as a candidate for characterizing this sex difference. Here, we measured the sex-related effective connectivity (EC) differences within and between these three networks in 72 children with ASD (36 females, 8-14 years) and 72 typically developing controls (TCs) (36 females, 8-14 years) from 5 sites of the Autism Brain Imaging Data Exchange repositories using a 2 × 2 analysis of covariance factorial design. We also assessed brain-behavior relationships and the effects of age on EC. We found significant diagnosis-by-sex interactions on EC: females with ASD had significantly higher EC than their male counterparts within the DMN and between the SN and CEN. The interaction pattern supported the GI theory by showing that the higher EC observed in females with ASD reflected a shift towards the higher level of EC displayed in male TCs (neural masculinization), and the lower EC seen in males with ASD reflected a shift towards the lower level of EC displayed in female TCs (neural feminization). We also found significant brain-behavior correlations and significant effects of age on EC.
Collapse
Affiliation(s)
- Cuicui Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Tong Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Ying Chen
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Chunling Zhang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Mingmin Ning
- Department of Neurology, Guangzhou Women and Children's Medical Center, China
| | - Rui Qin
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Lin Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Linglong Chen
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, China
| |
Collapse
|
14
|
Cuppens T, Shatto J, Mangnier L, Kumar AA, Ng ACH, Kaur M, Bui TA, Leclercq M, Droit A, Dunham I, Bolduc FV. Sex difference contributes to phenotypic diversity in individuals with neurodevelopmental disorders. Front Pediatr 2023; 11:1172154. [PMID: 37609366 PMCID: PMC10441218 DOI: 10.3389/fped.2023.1172154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/20/2023] [Indexed: 08/24/2023] Open
Abstract
Objective Gain a better understanding of sex-specific differences in individuals with global developmental delay (GDD), with a focus on phenotypes and genotypes. Methods Using the Deciphering Developmental Disorders (DDD) dataset, we extracted phenotypic information from 6,588 individuals with GDD and then identified statistically significant variations in phenotypes and genotypes based on sex. We compared genes with pathogenic variants between sex and then performed gene network and molecular function enrichment analysis and gene expression profiling between sex. Finally, we contrasted individuals with autism as an associated condition. Results We identified significantly differentially expressed phenotypes in males vs. females individuals with GDD. Autism and macrocephaly were significantly more common in males whereas microcephaly and stereotypies were more common in females. Importantly, 66% of GDD genes with pathogenic variants overlapped between both sexes. In the cohort, males presented with only slightly increased X-linked genes (9% vs. 8%, respectively). Individuals from both sexes harbored a similar number of pathogenic variants overall (3) but females presented with a significantly higher load for GDD genes with high intolerance to loss of function. Sex difference in gene expression correlated with genes identified in a sex specific manner. While we identified sex-specific GDD gene mutations, their pathways overlapped. Interestingly, individuals with GDD but also co-morbid autism phenotypes, we observed distinct mutation load, pathways and phenotypic presentation. Conclusion Our study shows for the first time that males and females with GDD present with significantly different phenotypes. Moreover, while most GDD genes overlapped, some genes were found uniquely in each sex. Surprisingly they shared similar molecular functions. Sorting genes by predicted tolerance to loss of function (pLI) led to identifying an increased mutation load in females with GDD, suggesting potentially a tolerance to GDD genes of higher pLI compared to overall GDD genes. Finally, we show that considering associated conditions (for instance autism) may influence the genomic underpinning found in individuals with GDD and highlight the importance of comprehensive phenotyping.
Collapse
Affiliation(s)
- Tania Cuppens
- Centre de Recherche du CHU de Québec-Université Laval, Département de Médecine Moléculaire de L'Université Laval, Québec, QC, Canada
| | - Julie Shatto
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - Loïc Mangnier
- Centre de Recherche du CHU de Québec-Université Laval, Département de Médecine Moléculaire de L'Université Laval, Québec, QC, Canada
| | - Ajay A. Kumar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI); Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Andy Cheuk-Him Ng
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - Manpreet Kaur
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - Truong An Bui
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - Mickael Leclercq
- Centre de Recherche du CHU de Québec-Université Laval, Département de Médecine Moléculaire de L'Université Laval, Québec, QC, Canada
| | - Arnaud Droit
- Centre de Recherche du CHU de Québec-Université Laval, Département de Médecine Moléculaire de L'Université Laval, Québec, QC, Canada
| | - Ian Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI); Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Francois V. Bolduc
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
- Department of Medical Genetics, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| |
Collapse
|
15
|
Iraji A, Chen J, Lewis N, Faghiri A, Fu Z, Agcaoglu O, Kochunov P, Adhikari BM, Mathalon D, Pearlson G, Macciardi F, Preda A, van Erp T, Bustillo JR, Díaz-Caneja CM, Andrés-Camazón P, Dhamala M, Adali T, Calhoun V. Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.18.548880. [PMID: 37503085 PMCID: PMC10370141 DOI: 10.1101/2023.07.18.548880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects. Methods We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data. Results Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks. Conclusions Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.
Collapse
Affiliation(s)
- A. Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - J. Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - N. Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of CSE, Georgia Institute of Technology, Atlanta, Georgia
| | - A. Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - Z. Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - O. Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - P. Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - B. M. Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - D.H. Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - G.D. Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - F. Macciardi
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - A. Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - T.G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - J. R. Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - C. M. Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - P. Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - M. Dhamala
- Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA
| | - T. Adali
- Department of CSEE, University of Maryland, Baltimore County, Baltimore, Maryland
| | - V.D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of CSE, Georgia Institute of Technology, Atlanta, Georgia
| |
Collapse
|
16
|
Xie J, Zhang W, Shen Y, Wei W, Bai Y, Zhang G, Meng N, Yue X, Wang X, Zhang X, Wang M. Abnormal spontaneous brain activity in females with autism spectrum disorders. Front Neurosci 2023; 17:1189087. [PMID: 37521682 PMCID: PMC10379634 DOI: 10.3389/fnins.2023.1189087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 05/08/2023] [Indexed: 08/01/2023] Open
Abstract
Objectives To date, most studies on autism spectrum disorder (ASD) have focused on sample sets that were primarily or entirely composed of males; brain spontaneous activity changes in females remain unclear. The purpose of this study was to explore changes in the brain spontaneous neural activity in females with ASD. Methods In this study, resting-state functional magnetic resonance images (rs-fMRI) of 41 females with ASD and 41 typically developing (TD) controls were obtained from the ABDIE database. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) of the two groups were calculated to detect the regional brain activity. A two independent sample t-test was used to analyze differences between the ASD and TD groups and a p-value <0.05 was considered statistically significant after false discovery rate (FDR) correction. Pearson correlation analysis was conducted between social responsiveness scale (SRS) scores and the local activity of significantly different brain regions. Results Compared with the typically developing (TD) group, the values of ALFF and ReHo were significantly increased in the left superior temporal gyrus (STG), while the values of ReHo were significantly decreased in the left superior frontal gyrus (SFG), left middle occipital gyrus (MOG), bilateral superior parietal lobule (SPL), and bilateral precuneus in the females with ASD group. Correlation analysis showed that the ReHo of the right precuneus was positively correlated to the total SRS, social communication, and autistic mannerisms. Conclusion Spontaneous activity changes in females with ASD involved multiple brain regions and were related to clinical characteristics. Our results may provide some help for further exploring the neurobiological mechanism of females with ASD.
Collapse
Affiliation(s)
- Jiapei Xie
- Department of Medical Imaging, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Weidong Zhang
- Department of Medical Imaging, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yu Shen
- Department of Medical Imaging, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China
| | - Wei Wei
- Department of Medical Imaging, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yan Bai
- Department of Medical Imaging, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China
| | - Ge Zhang
- Department of Medical Imaging, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China
| | - Nan Meng
- Department of Medical Imaging, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Xipeng Yue
- Department of Medical Imaging, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Xinhui Wang
- Department of Medical Imaging, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China
| | | | - Meiyun Wang
- Department of Medical Imaging, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Jiang S, Zhang H, Fang Y, Yin D, Dong Y, Chao X, Gong X, Wang J, Sun W. Altered Resting-State Brain Activity and Functional Connectivity in Post-Stroke Apathy: An fMRI Study. Brain Sci 2023; 13:brainsci13050730. [PMID: 37239202 DOI: 10.3390/brainsci13050730] [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: 03/08/2023] [Revised: 04/07/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023] Open
Abstract
Apathy is a common neuropsychiatric disease after stroke and is linked to a lower quality of life while undergoing rehabilitation. However, it is still unknown what are the underlying neural mechanisms of apathy. This research aimed to explore differences in the cerebral activity and functional connectivity (FC) of subjects with post-stroke apathy and those without it. A total of 59 individuals with acute ischemic stroke and 29 healthy subjects with similar age, sex, and education were recruited. The Apathy Evaluation Scale (AES) was used to evaluate apathy at 3 months after stroke. Patients were split into two groups-PSA (n = 21) and nPSA (n = 38)-based on their diagnosis. The fractional amplitude of low-frequency fluctuation (fALFF) was used to measure cerebral activity, as well as region-of-interest to region-of-interest analysis to examine functional connectivity among apathy-related regions. Pearson correlation analysis between fALFF values and apathy severity was performed in this research. The values of fALFF in the left middle temporal regions, right anterior and middle cingulate regions, middle frontal region, and cuneus region differed significantly among groups. Pearson correlation analysis showed that the fALFF values in the left middle temporal region (p < 0.001, r = 0.66) and right cuneus (p < 0.001, r = 0.48) were positively correlated with AES scores in stroke patients, while fALFF values in the right anterior cingulate (p < 0.001, r = -0.61), right middle frontal gyrus (p < 0.001, r = -0.49), and middle cingulate gyrus (p = 0.04, r = -0.27) were negatively correlated with AES scores in stroke patients. These regions formed an apathy-related subnetwork, and functional connectivity analysis unveiled that altered connectivity was linked to PSA (p < 0.05). This research found that abnormalities in brain activity and FC in the left middle temporal region, right middle frontal region, right cuneate region, and right anterior and middle cingulate regions in stroke patients were associated with PSA, revealing a possible neural mechanism and providing new clues for the diagnosis and treatment of PSA.
Collapse
Affiliation(s)
- Shiyi Jiang
- Stroke Center & Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Hui Zhang
- Department of Gastroenterology, Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan 528400, China
| | - Yirong Fang
- Stroke Center & Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Dawei Yin
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Yiran Dong
- Stroke Center & Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Xian Chao
- Stroke Center & Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Xiuqun Gong
- Department of Neurology, The First Affiliated Hospital of Anhui University of Science and Technology, Huainan First People's Hospital, Huainan 232000, China
| | - Jinjing Wang
- Department of Neurology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210033, China
| | - Wen Sun
- Stroke Center & Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| |
Collapse
|
19
|
Cong J, Zhuang W, Liu Y, Yin S, Jia H, Yi C, Chen K, Xue K, Li F, Yao D, Xu P, Zhang T. Altered default mode network causal connectivity patterns in autism spectrum disorder revealed by Liang information flow analysis. Hum Brain Mapp 2023; 44:2279-2293. [PMID: 36661190 PMCID: PMC10028659 DOI: 10.1002/hbm.26209] [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: 10/17/2022] [Revised: 12/26/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023] Open
Abstract
Autism spectrum disorder (ASD) is a pervasive developmental disorder with severe cognitive impairment in social communication and interaction. Previous studies have reported that abnormal functional connectivity patterns within the default mode network (DMN) were associated with social dysfunction in ASD. However, how the altered causal connectivity pattern within the DMN affects the social functioning in ASD remains largely unclear. Here, we introduced the Liang information flow method, widely applied to climate science and quantum mechanics, to uncover the brain causal network patterns in ASD. Compared with the healthy controls (HC), we observed that the interactions among the dorsal medial prefrontal cortex (dMPFC), ventral medial prefrontal cortex (vMPFC), hippocampal formation, and temporo-parietal junction showed more inter-regional causal connectivity differences in ASD. For the topological property analysis, we also found the clustering coefficient of DMN and the In-Out degree of anterior medial prefrontal cortex were significantly decreased in ASD. Furthermore, we found that the causal connectivity from dMPFC to vMPFC was correlated with the clinical symptoms of ASD. These altered causal connectivity patterns indicated that the DMN inter-regions information processing was perturbed in ASD. In particular, we found that the dMPFC acts as a causal source in the DMN in HC, whereas it plays a causal target in ASD. Overall, our findings indicated that the Liang information flow method could serve as an important way to explore the DMN causal connectivity patterns, and it also can provide novel insights into the nueromechanisms underlying DMN dysfunction in ASD.
Collapse
Affiliation(s)
- Jing Cong
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Yunhong Liu
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Shunjie Yin
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Hai Jia
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Kai Chen
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Kaiqing Xue
- School of Computer and Software Engineering, Xihua University, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Zhang
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| |
Collapse
|
20
|
Fenske SJ, Liu J, Chen H, Diniz MA, Stephens RL, Cornea E, Gilmore JH, Gao W. Sex differences in resting state functional connectivity across the first two years of life. Dev Cogn Neurosci 2023; 60:101235. [PMID: 36966646 PMCID: PMC10066534 DOI: 10.1016/j.dcn.2023.101235] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/17/2023] [Accepted: 03/19/2023] [Indexed: 03/29/2023] Open
Abstract
Sex differences in behavior have been reported from infancy through adulthood, but little is known about sex effects on functional circuitry in early infancy. Moreover, the relationship between early sex effects on the functional architecture of the brain and later behavioral performance remains to be elucidated. In this study, we used resting-state fMRI and a novel heatmap analysis to examine sex differences in functional connectivity with cross-sectional and longitudinal mixed models in a large cohort of infants (n = 319 neonates, 1-, and 2-year-olds). An adult dataset (n = 92) was also included for comparison. We investigated the relationship between sex differences in functional circuitry and later measures of language (collected in 1- and 2-year-olds) as well as indices of anxiety, executive function, and intelligence (collected in 4-year-olds). Brain areas showing the most significant sex differences were age-specific across infancy, with two temporal regions demonstrating consistent differences. Measures of functional connectivity showing sex differences in infancy were significantly associated with subsequent behavioral scores of language, executive function, and intelligence. Our findings provide insights into the effects of sex on dynamic neurodevelopmental trajectories during infancy and lay an important foundation for understanding the mechanisms underlying sex differences in health and disease.
Collapse
|
21
|
Sex-Related Changes in the Clinical, Genetic, Electrophysiological, Connectivity, and Molecular Presentations of ASD: A Comparison between Human and Animal Models of ASD with Reference to Our Data. Int J Mol Sci 2023; 24:ijms24043287. [PMID: 36834699 PMCID: PMC9965966 DOI: 10.3390/ijms24043287] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/28/2023] [Accepted: 02/03/2023] [Indexed: 02/11/2023] Open
Abstract
The etiology of autism spectrum disorder (ASD) is genetic, environmental, and epigenetic. In addition to sex differences in the prevalence of ASD, which is 3-4 times more common in males, there are also distinct clinical, molecular, electrophysiological, and pathophysiological differences between sexes. In human, males with ASD have more externalizing problems (i.e., attention-deficit hyperactivity disorder), more severe communication and social problems, as well as repetitive movements. Females with ASD generally exhibit fewer severe communication problems, less repetitive and stereotyped behavior, but more internalizing problems, such as depression and anxiety. Females need a higher load of genetic changes related to ASD compared to males. There are also sex differences in brain structure, connectivity, and electrophysiology. Genetic or non-genetic experimental animal models of ASD-like behavior, when studied for sex differences, showed some neurobehavioral and electrophysiological differences between male and female animals depending on the specific model. We previously carried out studies on behavioral and molecular differences between male and female mice treated with valproic acid, either prenatally or early postnatally, that exhibited ASD-like behavior and found distinct differences between the sexes, the female mice performing better on tests measuring social interaction and undergoing changes in the expression of more genes in the brain compared to males. Interestingly, co-administration of S-adenosylmethionine alleviated the ASD-like behavioral symptoms and the gene-expression changes to the same extent in both sexes. The mechanisms underlying the sex differences are not yet fully understood.
Collapse
|
22
|
Lawrence KE, Abaryan Z, Laltoo E, Hernandez LM, Gandal MJ, McCracken JT, Thompson PM. White matter microstructure shows sex differences in late childhood: Evidence from 6797 children. Hum Brain Mapp 2023; 44:535-548. [PMID: 36177528 PMCID: PMC9842921 DOI: 10.1002/hbm.26079] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/29/2022] [Accepted: 08/19/2022] [Indexed: 02/01/2023] Open
Abstract
Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.
Collapse
Affiliation(s)
- Katherine E. Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Zvart Abaryan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Leanna M. Hernandez
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Michael J. Gandal
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
- Department of Human Genetics, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - James T. McCracken
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| |
Collapse
|
23
|
Mali I, Payne M, King C, Maze TR, Davison T, Challans B, Bossmann SH, Plakke B. Adolescent female valproic acid rats have impaired extra-dimensional shifts of attention and enlarged anterior cingulate cortices. Brain Res 2023; 1800:148199. [PMID: 36509128 PMCID: PMC9835202 DOI: 10.1016/j.brainres.2022.148199] [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: 03/15/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022]
Abstract
In order to develop better treatments for autism spectrum disorder (ASD) it is critical to understand the developmental trajectory of the disorder and the accompanying brain changes. This study used the valproic acid (VPA) model to induce ASD-like symptoms in rodents. Prior studies have demonstrated that VPA animals are impaired on executive function tasks, paralleling results in humans with ASD. Here, VPA adolescent female rats were impaired on a set-shifting task and had enlarged frontal cortices compared to control females. The deficits observed in the VPA female rats mirrors results in females with ASD. In addition, adolescent VPA females with enlarged frontal cortices performed the worst across the entire task. These brain changes in adolescence are also found in adolescent humans with ASD. These novel findings highlight the importance of studying the brain at different developmental stages.
Collapse
Affiliation(s)
- Ivina Mali
- Department of Chemistry, Kansas State University, Manhattan, KS, USA
| | - Macy Payne
- Department of Chemistry, Kansas State University, Manhattan, KS, USA
| | - Cole King
- Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA
| | - Tessa R Maze
- Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA
| | - Taylor Davison
- Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA
| | - Brandon Challans
- Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA
| | - Stefan H Bossmann
- Department of Chemistry, Kansas State University, Manhattan, KS, USA
| | - Bethany Plakke
- Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA.
| |
Collapse
|
24
|
Feng Y, Kang X, Wang H, Cong J, Zhuang W, Xue K, Li F, Yao D, Xu P, Zhang T. The relationships between dynamic resting-state networks and social behavior in autism spectrum disorder revealed by fuzzy entropy-based temporal variability analysis of large-scale network. Cereb Cortex 2023; 33:764-776. [PMID: 35297491 DOI: 10.1093/cercor/bhac100] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 02/03/2023] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by a core deficit in social processes. However, it is still unclear whether the core clinical symptoms of the disorder can be reflected by the temporal variability of resting-state network functional connectivity (FC). In this article, we examined the large-scale network FC temporal variability at the local region, within-network, and between-network levels using the fuzzy entropy technique. Then, we correlated the network FC temporal variability to social-related scores. We found that the social behavior correlated with the FC temporal variability of the precuneus, parietal, occipital, temporal, and precentral. Our results also showed that social behavior was significantly negatively correlated with the temporal variability of FC within the default mode network, between the frontoparietal network and cingulo-opercular task control network, and the dorsal attention network. In contrast, social behavior correlated significantly positively with the temporal variability of FC within the subcortical network. Finally, using temporal variability as a feature, we construct a model to predict the social score of ASD. These findings suggest that the network FC temporal variability has a close relationship with social behavioral inflexibility in ASD and may serve as a potential biomarker for predicting ASD symptom severity.
Collapse
Affiliation(s)
- Yu Feng
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Xiaodong Kang
- The Department of Sichuan 81 Rehabilitation Center, Chengdu University of TCM, No.37, Twelfth Bridge Road,Chengdu 610075, China
| | - Hesong Wang
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Institute of Gastroenterology of Guangdong Province, Nanfang Hospital, Southern Medical University, No. 1023-1063, Shatai South Road, Baiyun District, Guangzhou 510515, China
| | - Jing Cong
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Kaiqing Xue
- School of Computer and Software Engineering, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Tao Zhang
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| |
Collapse
|
25
|
Xiong Y, Chen J, Li Y. Microglia and astrocytes underlie neuroinflammation and synaptic susceptibility in autism spectrum disorder. Front Neurosci 2023; 17:1125428. [PMID: 37021129 PMCID: PMC10067592 DOI: 10.3389/fnins.2023.1125428] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/03/2023] [Indexed: 04/07/2023] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder with onset in childhood. The mechanisms underlying ASD are unclear. In recent years, the role of microglia and astrocytes in ASD has received increasing attention. Microglia prune the synapses or respond to injury by sequestrating the injury site and expressing inflammatory cytokines. Astrocytes maintain homeostasis in the brain microenvironment through the uptake of ions and neurotransmitters. However, the molecular link between ASD and microglia and, or astrocytes remains unknown. Previous research has shown the significant role of microglia and astrocytes in ASD, with reports of increased numbers of reactive microglia and astrocytes in postmortem tissues and animal models of ASD. Therefore, an enhanced understanding of the roles of microglia and astrocytes in ASD is essential for developing effective therapies. This review aimed to summarize the functions of microglia and astrocytes and their contributions to ASD.
Collapse
|
26
|
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.
Collapse
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
| |
Collapse
|
27
|
Taddei M, Bulgheroni S, Riva D, Erbetta A. Task‐related functional neuroimaging contribution to sex/gender differences in cognition and emotion during development. J Neurosci Res 2022; 101:575-603. [PMID: 36354127 DOI: 10.1002/jnr.25143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 10/17/2022] [Accepted: 10/25/2022] [Indexed: 11/11/2022]
Abstract
Recent research has shown that sex/gender (s/g) influences on cognitive functions and related brain anatomy, functional responses, and connectivity are less clear than previously assumed, and most studies investigated adult population. In this mini-review, we summarize research progress in the study of s/g differences in the human brain function as investigated by neuroimaging methods adopting a developmental perspective. In particular, we review original studies published from 2000 to 2021 investigating s/g differences in task-related brain functional activation and connectivity in healthy children and adolescents. We summarize results about studies in the domains of language, visuospatial ability, social cognition, and executive functions. Overall, a clear relation between cognition and brain activation or connectivity pattern is far from being established and the few coherent results should be considered exploratory, despite in some cases, brain function seems to present specific patterns in comparison with what reported in adults. Moreover, future studies should address methodological limitations, such as fragmentation of tasks, lack of control for confounding variables, and lack of longitudinal designs to study developmental trajectories.
Collapse
Affiliation(s)
- Matilde Taddei
- Department of Pediatric Neuroscience Fondazione IRCCS Istituto Neurologico Carlo Besta Milan Italy
| | - Sara Bulgheroni
- Department of Pediatric Neuroscience Fondazione IRCCS Istituto Neurologico Carlo Besta Milan Italy
| | - Daria Riva
- Department of Pediatric Neuroscience Fondazione IRCCS Istituto Neurologico Carlo Besta Milan Italy
| | - Alessandra Erbetta
- Department of Neuroradiology Fondazione IRCCS Istituto Neurologico Carlo Besta Milan Italy
| |
Collapse
|
28
|
Shanmugan S, Seidlitz J, Cui Z, Adebimpe A, Bassett DS, Bertolero MA, Davatzikos C, Fair DA, Gur RE, Gur RC, Larsen B, Li H, Pines A, Raznahan A, Roalf DR, Shinohara RT, Vogel J, Wolf DH, Fan Y, Alexander-Bloch A, Satterthwaite TD. Sex differences in the functional topography of association networks in youth. Proc Natl Acad Sci U S A 2022; 119:e2110416119. [PMID: 35939696 PMCID: PMC9388107 DOI: 10.1073/pnas.2110416119] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 07/15/2022] [Indexed: 01/16/2023] Open
Abstract
Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy (P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.
Collapse
Affiliation(s)
- Sheila Shanmugan
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Jakob Seidlitz
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Zaixu Cui
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
- Chinese Institute for Brain Research, Beijing,102206, China
| | - Azeez Adebimpe
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S. Bassett
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
- Santa Fe Institute, Santa Fe, NM 87501
| | - Maxwell A. Bertolero
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Christos Davatzikos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Damien A. Fair
- Department of Behavioral Neuroscience, Department of Psychiatry, Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR 97239
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Hongming Li
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Armin Raznahan
- Section on Developmental Neurogenomics Unit, Intramural Research Program, National Institutes of Mental Health, Bethesda, MD 20892
| | - David R. Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Russell T. Shinohara
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104
| | - Jacob Vogel
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Daniel H. Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| |
Collapse
|
29
|
Zielinski BA, Andrews DS, Lee JK, Solomon M, Rogers SJ, Heath B, Nordahl CW, Amaral DG. Sex-dependent structure of socioemotional salience, executive control, and default mode networks in preschool-aged children with autism. Neuroimage 2022; 257:119252. [PMID: 35500808 PMCID: PMC11107798 DOI: 10.1016/j.neuroimage.2022.119252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 03/12/2022] [Accepted: 04/16/2022] [Indexed: 12/26/2022] Open
Abstract
The structure of large-scale intrinsic connectivity networks is atypical in adolescents diagnosed with autism spectrum disorder (ASD or autism). However, the degree to which alterations occur in younger children, and whether these differences vary by sex, is unknown. We utilized structural magnetic resonance imaging (MRI) data from a sex- and age- matched sample of 122 autistic and 122 typically developing (TD) children (2-4 years old) to investigate differences in underlying network structure in preschool-aged autistic children within three large scale intrinsic connectivity networks implicated in ASD: the Socioemotional Salience, Executive Control, and Default Mode Networks. Utilizing structural covariance MRI (scMRI), we report network-level differences in autistic versus TD children, and further report preliminary findings of sex-dependent differences within network topology.
Collapse
Affiliation(s)
- Brandon A Zielinski
- Departments of Pediatrics and Neurology, University of Utah School of Medicine, University of Utah, Salt Lake City, UT, USA.
| | - Derek S Andrews
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Joshua K Lee
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Marjorie Solomon
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Sally J Rogers
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Brianna Heath
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Christine Wu Nordahl
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - David G Amaral
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| |
Collapse
|
30
|
Cao P, Wen G, Liu X, Yang J, Zaiane OR. Modeling the dynamic brain network representation for autism spectrum disorder diagnosis. Med Biol Eng Comput 2022; 60:1897-1913. [DOI: 10.1007/s11517-022-02558-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/29/2022] [Indexed: 10/18/2022]
|
31
|
Yang W, Wen G, Cao P, Yang J, Zaiane OR. Collaborative learning of graph generation, clustering and classification for brain networks diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106772. [PMID: 35395591 DOI: 10.1016/j.cmpb.2022.106772] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 03/20/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
PURPOSE Accurate diagnosis of autism spectrum disorder (ASD) plays a key role in improving the condition and quality of life for patients. In this study, we mainly focus on ASD diagnosis with functional brain networks (FBNs). The major challenge for brain networks modeling is the high dimensional connectivity in brain networks and limited number of subjects, which hinders the classification capability of graph convolutional networks (GCNs). METHOD To alleviate the influence of the limited data and high dimensional connectivity, we introduce a unified three-stage graph learning framework for brain network classification, involving multi-graph clustering, graph generation and graph classification. The framework combining Graph Generation, Clustering and Classification Networks (GraphCGC-Net) enhances the critical connections by multi-graph clustering (MGC) with a supervision scheme, and generates realistic brain networks by simultaneously preserving the global consistent distribution and local topology properties. RESULTS To demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Autism Brain Imaging Data Exchange (ABIDE) dataset and conduct extensive experiments on the ASD classification problem. Our proposed method achieves an average accuracy of 70.45% and an AUC of 72.76% on ABIDE. Compared with the traditional GCN model, the proposed GraphCGC-Net obtains 9.3%, and 10.64% improvement in terms of accuracy and AUC metrics, respectively. CONCLUSION The comprehensive experiments demonstrate that our GraphCGC-Net is effective for graph classification in brain disorders diagnosis. Moreover, we find that MGC can generate biologically meaningful subnetworks, which is highly consistent with the previous neuroimaging-derived biomarker evidence of ASD. More importantly, the promising results suggest that applying generative adversarial networks (GANs) in brain networks to improve the classification performance is worth further investigation.
Collapse
Affiliation(s)
- Wenju Yang
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Guangqi Wen
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Peng Cao
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Jinzhu Yang
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Osmar R Zaiane
- Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada
| |
Collapse
|
32
|
Long H, Fan M, Yang X, Guan Q, Huang Y, Xu X, Xiao J, Jiang T. Sex-related Difference in Mental Rotation Performance is Mediated by the special Functional Connectivity Between the Default Mode and Salience Networks. Neuroscience 2021; 478:65-74. [PMID: 34655694 DOI: 10.1016/j.neuroscience.2021.10.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 10/05/2021] [Accepted: 10/06/2021] [Indexed: 12/12/2022]
Abstract
The mental rotation task is a particular spatial skill that helps people process visual information and is associated with intelligence and academic performance. Previous studies have found consistent sex difference in mental rotation. However, the neural mechanism of the sex-related difference in mental rotation remains unclear. This study investigates the association between sex, mental rotation and the functional connectivity (FC) of resting-state networks (RSNs) to explore neural correlates of different mental rotation abilities between males and females. Compared with females, males performed better on the mental rotation test. The mental rotation scores were significantly correlated with the special FC between the default mode network (DMN) and salience network (SN). The results of the mediation analysis revealed that the special FC between the DMN and SN mediated the association between sex and mental rotation. Based on these findings, males had higher FC between the DMN and SN, which subsequently promoted their mental rotation performance. These results emphasized the importance of sex in spatial cognition studies of both healthy people and individuals with neuropsychiatric disorders and deepened our understanding of the neural mechanisms underlying sex difference in mental rotation.
Collapse
Affiliation(s)
- Haixia Long
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China
| | - Xuhua Yang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Qiu Guan
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yujiao Huang
- Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China
| | - Xinli Xu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jie Xiao
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 625014, China; The Queensland Brain Institute, University of Queensland, Brisbane, Queensland 4072, Australia.
| |
Collapse
|
33
|
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.
Collapse
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
| |
Collapse
|
34
|
Gao K, Fan Z, Su J, Zeng LL, Shen H, Zhu J, Hu D. Deep Transfer Learning for Cerebral Cortex Using Area-Preserving Geometry Mapping. Cereb Cortex 2021; 32:2972-2984. [PMID: 34791082 DOI: 10.1093/cercor/bhab394] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/01/2021] [Accepted: 10/03/2021] [Indexed: 01/17/2023] Open
Abstract
Limited sample size hinders the application of deep learning in brain image analysis, and transfer learning is a possible solution. However, most pretrained models are 2D based and cannot be applied directly to 3D brain images. In this study, we propose a novel framework to apply 2D pretrained models to 3D brain images by projecting surface-based cortical morphometry into planar images using computational geometry mapping. Firstly, 3D cortical meshes are reconstructed from magnetic resonance imaging (MRI) using FreeSurfer and projected into 2D planar meshes with topological preservation based on area-preserving geometry mapping. Then, 2D deep models pretrained on ImageNet are adopted and fine-tuned for cortical image classification on morphometric shape metrics. We apply the framework to sex classification on the Human Connectome Project dataset and autism spectrum disorder (ASD) classification on the Autism Brain Imaging Data Exchange dataset. Moreover, a 2-stage transfer learning strategy is suggested to boost the ASD classification performance by using the sex classification as an intermediate task. Our framework brings significant improvement in sex classification and ASD classification with transfer learning. In summary, the proposed framework builds a bridge between 3D cortical data and 2D models, making 2D pretrained models available for brain image analysis in cognitive and psychiatric neuroscience.
Collapse
Affiliation(s)
- Kai Gao
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Zhipeng Fan
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Jubo Zhu
- College of Science, National University of Defense Technology, Changsha 410073, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.,Pazhou Lab, Guangzhou 510330, China
| |
Collapse
|
35
|
Special treatment of prediction errors in autism spectrum disorder. Neuropsychologia 2021; 163:108070. [PMID: 34695420 DOI: 10.1016/j.neuropsychologia.2021.108070] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 09/09/2021] [Accepted: 10/21/2021] [Indexed: 12/23/2022]
Abstract
For autistic individuals, sensory stimulation can be experienced as overwhelming. Models of predictive coding postulate that cortical mechanisms disamplify predictable information and amplify prediction errors that surpass a defined precision level. In autism, the neuronal processing is putting an inflexibly high precision on prediction errors according to the HIPPEA theory (High, Inflexible Precision of Prediction Errors in Autism). We used an apparent motion paradigm to test this prediction. In apparent motion paradigms, the illusory motion of an object creates a prediction about where and when an internally generated token would be moving along the apparent motion trace. This illusion facilitates the perception of a flashing stimulus (target) appearing in-time with the apparent motion token and is perceived as a predictable event (predictable target). In contrast, a flashing stimulus appearing out-of-time with the apparent motion illusion is an unpredictable target that is less often detected even though it produces a prediction error signal. If a prediction error does not surpass a given precision threshold the stimulation event is discounted and therefore less often detected than predictable tokens. In autism, the precision threshold is lower and the same prediction errors (unpredictable target) triggers a detection similar to that of a predictable flash stimulus. To test this hypothesis, we recruited 11 autistic males and 9 neurotypical matched controls. The participants were tasked to detect flashing stimuli placed on an apparent motion trace either in-time or out-of-time with the apparent motion illusion. Descriptively, 66% (6/9) of neurotypical and 64% (7/11) of autistic participants were better at detecting predictable targets. The prediction established by illusory motion appears to assist autistic and neurotypical individuals equally in the detection of predictable over unpredictable targets. Importantly, 55% (6/11) of autistic participants had faster responses for unpredictable targets, whereas only 22% (2/9) of neurotypicals had faster responses to unpredictable compared to predictable targets. Hence, these tentative results suggest that for autistic participants, unpredictable targets produce an above threshold prediction error, which leads to faster response. This difference in unpredictable target detection can be encapsulated under the HIPPEA theory, suggesting that precision setting could be aberrant in autistic individuals with respect to prediction errors. These tentative results should be considered in light of the small sample. For this reason, we provide the full set of materials necessary to replicate and extend the results.
Collapse
|
36
|
Mo K, Sadoway T, Bonato S, Ameis SH, Anagnostou E, Lerch JP, Taylor MJ, Lai MC. Sex/gender differences in the human autistic brains: A systematic review of 20 years of neuroimaging research. Neuroimage Clin 2021; 32:102811. [PMID: 34509922 PMCID: PMC8436080 DOI: 10.1016/j.nicl.2021.102811] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 06/25/2021] [Accepted: 08/29/2021] [Indexed: 12/01/2022]
Abstract
Our current understanding of autism is largely based on clinical experiences and research involving male individuals given the male-predominance in prevalence and the under-inclusion of female individuals due to small samples, co-occurring conditions, or simply being missed for diagnosis. There is a significantly biased 'male lens' in this field with autistic females insufficiently understood. We therefore conducted a systematic review to examine how sex and gender modulate brain structure and function in autistic individuals. Findings from the past 20 years are yet to converge on specific brain regions/networks with consistent sex/gender-modulating effects. Despite at least three well-powered studies identifying specific patterns of significant sex/gender-modulation of autism-control differences, many other studies are likely underpowered, suggesting a critical need for future investigation into sex/gender-based heterogeneity with better-powered designs. Future research should also formally investigate the effects of gender, beyond biological sex, which is mostly absent in the current literature. Understanding the roles of sex and gender in the development of autism is an imperative step to extend beyond the 'male lens' in this field.
Collapse
Affiliation(s)
- Kelly Mo
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
| | - Tara Sadoway
- Department of Paediatric Laboratory Medicine, Hospital for Sick Children, Toronto, Canada
| | - Sarah Bonato
- Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
| | - Stephanie H Ameis
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Hospital for Sick Children, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Evdokia Anagnostou
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada; Department of Paediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom; Neurosciences & Mental Health Program, SickKids Research Institute, Toronto, Canada
| | - Margot J Taylor
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Neurosciences & Mental Health Program, SickKids Research Institute, Toronto, Canada; Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Meng-Chuan Lai
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Hospital for Sick Children, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Neurosciences & Mental Health Program, SickKids Research Institute, Toronto, Canada; Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
| |
Collapse
|
37
|
Trojsi F, Di Nardo F, Caiazzo G, Siciliano M, D’Alvano G, Passaniti C, Russo A, Bonavita S, Cirillo M, Esposito F, Tedeschi G. Between-sex variability of resting state functional brain networks in amyotrophic lateral sclerosis (ALS). J Neural Transm (Vienna) 2021; 128:1881-1897. [PMID: 34471976 PMCID: PMC8571222 DOI: 10.1007/s00702-021-02413-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/21/2021] [Indexed: 12/12/2022]
Abstract
The organization of brain functional connectivity (FC) has been shown to differ between sexes. Amyotrophic lateral sclerosis (ALS) is characterized by sexual dimorphism, showing sex-specific trends in site of onset, phenotypes, and prognosis. Here, we explored resting state (RS) FC differences within major large-scale functional networks between women and men in a sample of ALS patients, in comparison to healthy controls (HCs). A group-level independent component analysis (ICA) was performed on RS-fMRI time-series enabling spatial and spectral analyses of large-scale RS FC networks in 45 patients with ALS (20 F; 25 M) and 31 HCs (15 F; 16 M) with a focus on sex-related differences. A whole-brain voxel-based morphometry (VBM) was also performed to highlight atrophy differences. Between-sex comparisons showed: decreased FC in the right middle frontal gyrus and in the precuneus within the default mode network (DMN), in affected men compared to affected women; decreased FC in the right post-central gyrus (sensorimotor network), in the right inferior parietal gyrus (right fronto-parietal network) and increased FC in the anterior cingulate cortex and right insula (salience network), in both affected and non-affected men compared to women. When comparing affected men to affected women, VBM analysis revealed atrophy in men in the right lateral occipital cortex. Our results suggest that in ALS sex-related trends of brain functional and structural changes are more heavily represented in DMN and in the occipital cortex, suggesting that sex is an additional dimension of functional and structural heterogeneity in ALS.
Collapse
Affiliation(s)
- Francesca Trojsi
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Federica Di Nardo
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Giuseppina Caiazzo
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Mattia Siciliano
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Giulia D’Alvano
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Carla Passaniti
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Antonio Russo
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Simona Bonavita
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences, MRI Research Center SUN-FISM, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| |
Collapse
|
38
|
Lainhart JE. Sex Differences in Cerebral Development in Autism. Biol Psychiatry 2021; 90:278-280. [PMID: 34384526 DOI: 10.1016/j.biopsych.2021.06.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 06/22/2021] [Indexed: 10/20/2022]
Affiliation(s)
- Janet Elizabeth Lainhart
- Department of Psychiatry and the Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin.
| |
Collapse
|
39
|
Sex Differences in Functional Connectivity Between Resting State Brain Networks in Autism Spectrum Disorder. J Autism Dev Disord 2021; 52:3088-3101. [PMID: 34272649 PMCID: PMC9213274 DOI: 10.1007/s10803-021-05191-6] [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] [Accepted: 07/06/2021] [Indexed: 11/05/2022]
Abstract
Functional brain connectivity (FBC) has previously been examined in autism spectrum disorder (ASD) between-resting-state networks (RSNs) using a highly sensitive and reproducible hypothesis-free approach. However, results have been inconsistent and sex differences have only recently been taken into consideration using this approach. We estimated main effects of diagnosis and sex and a diagnosis by sex interaction on between-RSNs FBC in 83 ASD (40 females/43 males) and 85 typically developing controls (TC; 43 females/42 males). We found increased connectivity between the default mode (DM) and (a) the executive control networks in ASD (vs. TC); (b) the cerebellum networks in males (vs. females); and (c) female-specific altered connectivity involving visual, language and basal ganglia (BG) networks in ASD—in suggestive compatibility with ASD cognitive and neuroscientific theories.
Collapse
|
40
|
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.
Collapse
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
| | | |
Collapse
|
41
|
Jack A, Sullivan CAW, Aylward E, Bookheimer SY, Dapretto M, Gaab N, Van Horn JD, Eilbott J, Jacokes Z, Torgerson CM, Bernier RA, Geschwind DH, McPartland JC, Nelson CA, Webb SJ, Pelphrey KA, Gupta AR. A neurogenetic analysis of female autism. Brain 2021; 144:1911-1926. [PMID: 33860292 PMCID: PMC8320285 DOI: 10.1093/brain/awab064] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/01/2020] [Accepted: 12/11/2020] [Indexed: 01/08/2023] Open
Abstract
Females versus males are less frequently diagnosed with autism spectrum disorder (ASD), and while understanding sex differences is critical to delineating the systems biology of the condition, female ASD is understudied. We integrated functional MRI and genetic data in a sex-balanced sample of ASD and typically developing youth (8–17 years old) to characterize female-specific pathways of ASD risk. Our primary objectives were to: (i) characterize female ASD (n = 45) brain response to human motion, relative to matched typically developing female youth (n = 45); and (ii) evaluate whether genetic data could provide further insight into the potential relevance of these brain functional differences. For our first objective we found that ASD females showed markedly reduced response versus typically developing females, particularly in sensorimotor, striatal, and frontal regions. This difference between ASD and typically developing females does not resemble differences between ASD (n = 47) and typically developing males (n = 47), even though neural response did not significantly differ between female and male ASD. For our second objective, we found that ASD females (n = 61), versus males (n = 66), showed larger median size of rare copy number variants containing gene(s) expressed in early life (10 postconceptual weeks to 2 years) in regions implicated by the typically developing female > female functional MRI contrast. Post hoc analyses suggested this difference was primarily driven by copy number variants containing gene(s) expressed in striatum. This striatal finding was reproducible among n = 2075 probands (291 female) from an independent cohort. Together, our findings suggest that striatal impacts may contribute to pathways of risk in female ASD and advocate caution in drawing conclusions regarding female ASD based on male-predominant cohorts.
Collapse
Affiliation(s)
- 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
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences and Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles School of Medicine, Los Angeles, CA 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences and Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles School of Medicine, Los Angeles, CA 90095, USA
| | - Nadine Gaab
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA 02115 USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.,Harvard Graduate School of Education, Cambridge, MA 02138, USA
| | - John D Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, USA.,School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Jeffrey Eilbott
- Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA
| | - Zachary Jacokes
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Carinna M Torgerson
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90007, USA
| | - Raphael A Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.,Center for Child Health, Behavior, and Development, Seattle Children's Research Institute, Seattle, WA 98101, USA
| | - Daniel H Geschwind
- Department of Psychiatry and Biobehavioral Sciences and Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles School of Medicine, Los Angeles, CA 90095, USA.,Department of Neurology and Center for Neurobehavioral Genetics, University of California Los Angeles School of Medicine, Los Angeles, CA 90095, USA
| | | | - Charles A Nelson
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA 02115 USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Sara J Webb
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.,Center for Child Health, Behavior, and Development, Seattle Children's Research Institute, Seattle, WA 98101, USA
| | - Kevin A Pelphrey
- Department of Psychology, University of Virginia, Charlottesville, VA, USA.,Department of Neurology, Brain Institute, and School of Education and Human Development, University of Virginia, Charlottesville, VA, USA
| | - Abha R Gupta
- Department of Pediatrics, Yale School of Medicine, New Haven, CT 06510, USA.,Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA.,Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | | |
Collapse
|
42
|
Floris DL, Filho JOA, Lai MC, Giavasis S, Oldehinkel M, Mennes M, Charman T, Tillmann J, Dumas G, Ecker C, Dell'Acqua F, Banaschewski T, Moessnang C, Baron-Cohen S, Durston S, Loth E, Murphy DGM, Buitelaar JK, Beckmann CF, Milham MP, Di Martino A. Towards robust and replicable sex differences in the intrinsic brain function of autism. Mol Autism 2021; 12:19. [PMID: 33648569 PMCID: PMC7923310 DOI: 10.1186/s13229-021-00415-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 01/18/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Marked sex differences in autism prevalence accentuate the need to understand the role of biological sex-related factors in autism. Efforts to unravel sex differences in the brain organization of autism have, however, been challenged by the limited availability of female data. METHODS We addressed this gap by using a large sample of males and females with autism and neurotypical (NT) control individuals (ABIDE; Autism: 362 males, 82 females; NT: 409 males, 166 females; 7-18 years). Discovery analyses examined main effects of diagnosis, sex and their interaction across five resting-state fMRI (R-fMRI) metrics (voxel-level Z > 3.1, cluster-level P < 0.01, gaussian random field corrected). Secondary analyses assessed the robustness of the results to different pre-processing approaches and their replicability in two independent samples: the EU-AIMS Longitudinal European Autism Project (LEAP) and the Gender Explorations of Neurogenetics and Development to Advance Autism Research. RESULTS Discovery analyses in ABIDE revealed significant main effects of diagnosis and sex across the intrinsic functional connectivity of the posterior cingulate cortex, regional homogeneity and voxel-mirrored homotopic connectivity (VMHC) in several cortical regions, largely converging in the default network midline. Sex-by-diagnosis interactions were confined to the dorsolateral occipital cortex, with reduced VMHC in females with autism. All findings were robust to different pre-processing steps. Replicability in independent samples varied by R-fMRI measures and effects with the targeted sex-by-diagnosis interaction being replicated in the larger of the two replication samples-EU-AIMS LEAP. LIMITATIONS Given the lack of a priori harmonization among the discovery and replication datasets available to date, sample-related variation remained and may have affected replicability. CONCLUSIONS Atypical cross-hemispheric interactions are neurobiologically relevant to autism. They likely result from the combination of sex-dependent and sex-independent factors with a differential effect across functional cortical networks. Systematic assessments of the factors contributing to replicability are needed and necessitate coordinated large-scale data collection across studies.
Collapse
Affiliation(s)
- Dorothea L Floris
- Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - José O A Filho
- Autism Center, The Child Mind Institute, 101 E 56 Street, New York City, New York, 10026, USA
| | - Meng-Chuan Lai
- The Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health, Azrieli Adult Neurodevelopmental Centre, and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry and Autism Research Unit, The Hospital for Sick Children, Toronto, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Steve Giavasis
- Autism Center, The Child Mind Institute, 101 E 56 Street, New York City, New York, 10026, USA
| | - Marianne Oldehinkel
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Maarten Mennes
- Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Tony Charman
- Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Julian Tillmann
- Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Applied Psychology: Health, Development, Enhancement, and Intervention, University of Vienna, Vienna, Austria
| | - Guillaume Dumas
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France
- CHU Sainte-Justine Research Center, Department of Psychiatry, Université de Montréal, Montreal, QC, Canada
| | - Christine Ecker
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt am Main, Goethe University, Frankfurt, Germany
| | - Flavio Dell'Acqua
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Carolin Moessnang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sarah Durston
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Eva Loth
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Declan G M Murphy
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Jan K Buitelaar
- Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
- Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, the Netherlands
| | - Christian F Beckmann
- Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
- Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
| | - Michael P Milham
- Autism Center, The Child Mind Institute, 101 E 56 Street, New York City, New York, 10026, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Adriana Di Martino
- Autism Center, The Child Mind Institute, 101 E 56 Street, New York City, New York, 10026, USA.
| |
Collapse
|
43
|
Bathelt J, Geurts HM. Difference in default mode network subsystems in autism across childhood and adolescence. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2021; 25:556-565. [PMID: 33246376 PMCID: PMC7874372 DOI: 10.1177/1362361320969258] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
LAY ABSTRACT Neuroimaging research has identified a network of brain regions that are more active when we daydream compared to when we are engaged in a task. This network has been named the default mode network. Furthermore, differences in the default mode network are the most consistent findings in neuroimaging research in autism. Recent studies suggest that the default mode network is composed of subnetworks that are tied to different functions, namely memory and understanding others' minds. In this study, we investigated if default mode network differences in autism are related to specific subnetworks of the default mode network and if these differences change across childhood and adolescence. Our results suggest that the subnetworks of the default mode network are less differentiated in autism in middle childhood compared to neurotypicals. By late adolescence, the default mode network subnetwork organisation was similar in the autistic and neurotypical groups. These findings provide a foundation for future studies to investigate if this developmental pattern relates to improvements in the integration of memory and social understanding as autistic children grow up.
Collapse
|
44
|
Han YM, Kim MS, Jo J, Shin D, Kwon SH, SEO JB, Kang D, Lee BD, Ryu H, Hwang EM, Kim JM, Patel PD, Lyons DM, Schatzberg AF, Her S. Decoding the temporal nature of brain GR activity in the NFκB signal transition leading to depressive-like behavior. Mol Psychiatry 2021; 26:5087-5096. [PMID: 33483691 PMCID: PMC7821461 DOI: 10.1038/s41380-021-01016-1] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 11/17/2020] [Accepted: 01/05/2021] [Indexed: 01/30/2023]
Abstract
The fine-tuning of neuroinflammation is crucial for brain homeostasis as well as its immune response. The transcription factor, nuclear factor-κ-B (NFκB) is a key inflammatory player that is antagonized via anti-inflammatory actions exerted by the glucocorticoid receptor (GR). However, technical limitations have restricted our understanding of how GR is involved in the dynamics of NFκB in vivo. In this study, we used an improved lentiviral-based reporter to elucidate the time course of NFκB and GR activities during behavioral changes from sickness to depression induced by a systemic lipopolysaccharide challenge. The trajectory of NFκB activity established a behavioral basis for the NFκB signal transition involved in three phases, sickness-early-phase, normal-middle-phase, and depressive-like-late-phase. The temporal shift in brain GR activity was differentially involved in the transition of NFκB signals during the normal and depressive-like phases. The middle-phase GR effectively inhibited NFκB in a glucocorticoid-dependent manner, but the late-phase GR had no inhibitory action. Furthermore, we revealed the cryptic role of basal GR activity in the early NFκB signal transition, as evidenced by the fact that blocking GR activity with RU486 led to early depressive-like episodes through the emergence of the brain NFκB activity. These results highlight the inhibitory action of GR on NFκB by the basal and activated hypothalamic-pituitary-adrenal (HPA)-axis during body-to-brain inflammatory spread, providing clues about molecular mechanisms underlying systemic inflammation caused by such as COVID-19 infection, leading to depression.
Collapse
Affiliation(s)
- Young-Min Han
- grid.410885.00000 0000 9149 5707Seoul Centre, Korea Basic Science Institute, Seoul, South Korea
| | - Min Sun Kim
- grid.410885.00000 0000 9149 5707Seoul Centre, Korea Basic Science Institute, Seoul, South Korea
| | - Juyeong Jo
- grid.410885.00000 0000 9149 5707Seoul Centre, Korea Basic Science Institute, Seoul, South Korea
| | - Daiha Shin
- grid.410885.00000 0000 9149 5707Seoul Centre, Korea Basic Science Institute, Seoul, South Korea
| | - Seung-Hae Kwon
- grid.410885.00000 0000 9149 5707Seoul Centre, Korea Basic Science Institute, Seoul, South Korea
| | - Jong Bok SEO
- grid.410885.00000 0000 9149 5707Seoul Centre, Korea Basic Science Institute, Seoul, South Korea
| | - Dongmin Kang
- grid.255649.90000 0001 2171 7754Department of Life Science, Ewha Womans University, Seoul, South Korea
| | - Byoung Dae Lee
- grid.289247.20000 0001 2171 7818Department of Physiology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Hoon Ryu
- grid.35541.360000000121053345Neuroscience Centre, Korea Institute of Science and Technology, Seoul, South Korea
| | - Eun Mi Hwang
- grid.35541.360000000121053345Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, South Korea
| | - Jae-Min Kim
- grid.14005.300000 0001 0356 9399Department of Psychiatry, Chonnam National University Medical School, Seoul, South Korea
| | - Paresh D. Patel
- grid.412590.b0000 0000 9081 2336Department of Psychiatry, Molecular and Behavioral Neuroscience Institute, University of Michigan Medical Centre, Ann Arbor, MI USA
| | - David M. Lyons
- grid.168010.e0000000419368956Departments of Psychiatry, Stanford University Medical Centre, Stanford, CA USA
| | - Alan F. Schatzberg
- grid.168010.e0000000419368956Departments of Psychiatry, Stanford University Medical Centre, Stanford, CA USA
| | - Song Her
- Seoul Centre, Korea Basic Science Institute, Seoul, South Korea.
| |
Collapse
|
45
|
Kotila A, Järvelä M, Korhonen V, Loukusa S, Hurtig T, Ebeling H, Kiviniemi V, Raatikainen V. Atypical Inter-Network Deactivation Associated With the Posterior Default-Mode Network in Autism Spectrum Disorder. Autism Res 2020; 14:248-264. [PMID: 33206471 DOI: 10.1002/aur.2433] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/27/2020] [Accepted: 10/28/2020] [Indexed: 12/13/2022]
Abstract
Previous studies have suggested that atypical deactivation of functional brain networks contributes to the complex cognitive and behavioral profile associated with autism spectrum disorder (ASD). However, these studies have not considered the temporal dynamics of deactivation mechanisms between the networks. In this study, we examined (a) mutual deactivation and (b) mutual activation-deactivation (i.e., anticorrelated) time-lag patterns between resting-state networks (RSNs) in young adults with ASD (n = 20) and controls (n = 20) by applying the recently defined dynamic lag analysis (DLA) method, which measures time-lag variations peak-by-peak between the networks. In order to achieve temporally accurate lag patterns, the brain imaging data was acquired with a fast functional magnetic resonance imaging (fMRI) sequence (TR = 100 ms). Group-level independent component analysis was used to identify 16 RSNs for the DLA. We found altered mutual deactivation timings in ASD in (a) three of the deactivated and (b) two of the transiently anticorrelated (activated-deactivated) RSN pairs, which survived the strict threshold for significance of surrogate data. Of the significant RSN pairs, 80% included the posterior default-mode network (DMN). We propose that temporally altered deactivation mechanisms, including timings and directionality, between the posterior DMN and RSNs mediating processing of socially relevant information may contribute to the ASD phenotype. LAY SUMMARY: To understand autistic traits on a neural level, we examined temporal fluctuations in information flow between brain regions in young adults with autism spectrum disorder (ASD) and controls. We used a fast neuroimaging procedure to investigate deactivation mechanisms between brain regions. We found that timings and directionality of communication between certain brain regions were temporally altered in ASD, suggesting atypical deactivation mechanisms associated with the posterior default-mode network.
Collapse
Affiliation(s)
- Aija Kotila
- Research Unit of Logopedics, the Faculty of Humanities, University of Oulu, Oulu, Finland
| | - Matti Järvelä
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Vesa Korhonen
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Soile Loukusa
- Research Unit of Logopedics, the Faculty of Humanities, University of Oulu, Oulu, Finland
| | - Tuula Hurtig
- Research Unit of Clinical Neuroscience, Psychiatry, University of Oulu, Oulu, Finland.,Clinic of Child Psychiatry, Oulu University Hospital and PEDEGO Research Unit, University of Oulu, Oulu, Finland
| | - Hanna Ebeling
- Clinic of Child Psychiatry, Oulu University Hospital and PEDEGO Research Unit, University of Oulu, Oulu, Finland
| | - Vesa Kiviniemi
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Ville Raatikainen
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| |
Collapse
|
46
|
Pagni BA, Walsh MJM, Rogers C, Braden BB. Social Cognition in Autism Spectrum Disorder Across the Adult Lifespan: Influence of Age and Sex on Reading the Mind in the Eyes Task in a Cross-sectional Sample. Front Integr Neurosci 2020; 14:571408. [PMID: 33013336 PMCID: PMC7498724 DOI: 10.3389/fnint.2020.571408] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 08/18/2020] [Indexed: 12/31/2022] Open
Abstract
Background: Approximately 50,000 U.S. teens with autism spectrum disorder (ASD) become adults every year, however little is known regarding how age influences social cognition and if men and women with ASD are differentially impacted across the adult lifespan. Social cognition declines non-linearly with age in neurotypical (NT) adults. Moreover, sex differences have been observed on RME tasks in NT adults but not adults with ASD, although aging effects have been largely ignored. Objective: This cross-sectional study examined the influence of age and sex on social cognition in adults with ASD compared to NT adults. Methods: The Reading the Mind in the Eyes (RME) task was administered to evaluate the theory of mind abilities in 95 adults with ASD and 82 NT adults ages 18–71 years. The main effects of diagnosis, age, and sex, as well as two-way and three-way interaction were modeled using linear and quadratic aging terms in a multiple regression analysis. Results: A main effect of diagnosis was observed, indicating poorer performance in adults with ASD relative to NT adults. Age and sex interactions were nonsignificant. Discussion: We replicated previous findings of reduced theory of mind (ToM) abilities in adults with ASD, compared to NT adults. While interactions were nonsignificant, visual inspection of quadratic age curves indicated the possibility of unique ToM trajectories in men and women with and without ASD that should be investigated in larger longitudinal studies.
Collapse
Affiliation(s)
- Broc A Pagni
- Autism Brain Aging Laboratory, Arizona State University, College of Health Solutions, Tempe, AZ, United States
| | - Melissa J M Walsh
- Autism Brain Aging Laboratory, Arizona State University, College of Health Solutions, Tempe, AZ, United States
| | - Carly Rogers
- Autism Brain Aging Laboratory, Arizona State University, College of Health Solutions, Tempe, AZ, United States
| | - B Blair Braden
- Autism Brain Aging Laboratory, Arizona State University, College of Health Solutions, Tempe, AZ, United States
| |
Collapse
|
47
|
Cummings KK, Lawrence KE, Hernandez LM, Wood ET, Bookheimer SY, Dapretto M, Green SA. Sex Differences in Salience Network Connectivity and its Relationship to Sensory Over-Responsivity in Youth with Autism Spectrum Disorder. Autism Res 2020; 13:1489-1500. [PMID: 32860348 PMCID: PMC8351910 DOI: 10.1002/aur.2351] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 05/05/2020] [Accepted: 05/06/2020] [Indexed: 12/22/2022]
Abstract
Individuals with autism spectrum disorder (ASD) are significantly more likely to experience sensory over-responsivity (SOR) compared to neurotypical controls. SOR in autism has been shown to be related to atypical functional connectivity in the salience network (SN), a brain network thought to help direct attention to the most relevant stimuli in one's environment. However, all studies to date which have examined the neurobiological basis of sensory processing in ASD have used primarily male samples so little is known about sex differences in the neural processing of sensory information. This study examined the relationship between SOR and resting-state functional connectivity in the SN for 37 males and 16 females with autism, ages 8-17 years. While there were no sex differences in parent-rated SOR symptoms, there were significant sex differences in how SOR related to SN connectivity. Relative to females with ASD, males with ASD showed a stronger association between SOR and increased connectivity between the salience and primary sensory networks, suggesting increased allocation to sensory information. Conversely, for females with ASD, SOR was more strongly related to increased connectivity between the SN and prefrontal cortex. Results suggest that the underlying mechanisms of SOR in ASD are sex specific, providing insight into the differences seen in the diagnosis rate and symptom profiles of males and females with ASD. LAY SUMMARY: Sensory over-responsivity (SOR) is common in autism. Most research on the neural basis of SOR has focused on males, so little is known about SOR or its neurobiology in females with autism spectrum disorder. Here despite no sex differences in SOR symptoms, we found sex differences in how SOR related to intrinsic connectivity in a salience detection network. Results show sex differences in the neural mechanisms underlying SOR and inform sex differences seen in diagnosis rates and symptom profiles in autism. Autism Res 2020, 13: 1489-1500. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Kaitlin K Cummings
- Jane and Terry Semel Institute of Neuroscience and Human Behavior, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Katherine E Lawrence
- Jane and Terry Semel Institute of Neuroscience and Human Behavior, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Leanna M Hernandez
- Jane and Terry Semel Institute of Neuroscience and Human Behavior, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Emily T Wood
- Jane and Terry Semel Institute of Neuroscience and Human Behavior, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Susan Y Bookheimer
- Jane and Terry Semel Institute of Neuroscience and Human Behavior, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Mirella Dapretto
- Jane and Terry Semel Institute of Neuroscience and Human Behavior, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Shulamite A Green
- Jane and Terry Semel Institute of Neuroscience and Human Behavior, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| |
Collapse
|
48
|
Lawrence KE, Hernandez LM, Eilbott J, Jack A, Aylward E, Gaab N, Van Horn JD, Bernier RA, Geschwind DH, McPartland JC, Nelson CA, Webb SJ, Pelphrey KA, Bookheimer SY, Dapretto M. Neural responsivity to social rewards in autistic female youth. Transl Psychiatry 2020; 10:178. [PMID: 32488083 PMCID: PMC7266816 DOI: 10.1038/s41398-020-0824-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 04/16/2020] [Accepted: 04/22/2020] [Indexed: 01/25/2023] Open
Abstract
Autism is hypothesized to be in part driven by a reduced sensitivity to the inherently rewarding nature of social stimuli. Previous neuroimaging studies have indicated that autistic males do indeed display reduced neural activity to social rewards, but it is unknown whether this finding extends to autistic females, particularly as behavioral evidence suggests that affected females may not exhibit the same reduction in social motivation as their male peers. We therefore used functional magnetic resonance imaging to examine social reward processing during an instrumental implicit learning task in 154 children and adolescents (ages 8-17): 39 autistic girls, 43 autistic boys, 33 typically developing girls, and 39 typically developing boys. We found that autistic girls displayed increased activity to socially rewarding stimuli, including greater activity in the nucleus accumbens relative to autistic boys, as well as greater activity in lateral frontal cortices and the anterior insula compared with typically developing girls. These results demonstrate for the first time that autistic girls do not exhibit the same reduction in activity within social reward systems as autistic boys. Instead, autistic girls display increased neural activation to such stimuli in areas related to reward processing and salience detection. Our findings indicate that a reduced sensitivity to social rewards, as assessed with a rewarded instrumental implicit learning task, does not generalize to affected female youth and highlight the importance of studying potential sex differences in autism to improve our understanding of the condition and its heterogeneity.
Collapse
Affiliation(s)
- Katherine E Lawrence
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA
| | - Leanna M Hernandez
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA
| | - Jeffrey Eilbott
- Autism & Neurodevelopmental Disorders Institute, The George Washington University School of Medicine and Health Sciences, Washington D.C., USA
| | - Allison Jack
- Department of Psychology, George Mason University, Fairfax, USA
| | - Elizabeth Aylward
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, USA
| | - Nadine Gaab
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, USA
- Department of Pediatrics, Harvard Medical School, Boston, USA
- Harvard Graduate School of Education, Cambridge, USA
| | - John D Van Horn
- Department of Psychology, University of Virginia, Charlottesville, USA
| | - Raphael A Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA
| | - Daniel H Geschwind
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA
- Department of Neurology and Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, USA
| | - James C McPartland
- Department of Pediatrics, Yale School of Medicine, New Haven, USA
- Child Study Center, Yale School of Medicine, New Haven, USA
| | - Charles A Nelson
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, USA
- Department of Pediatrics, Harvard Medical School, Boston, USA
| | - Sara J Webb
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA
- Center on Child Health, Behavior, and Development, Seattle Children's Research Institute, Seattle, USA
| | - Kevin A Pelphrey
- Department of Neurology, University of Virginia, Charlottesville, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA.
| |
Collapse
|