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Persichetti AS, Shao J, Gotts SJ, Martin A. A functional parcellation of the whole brain in high-functioning individuals with autism spectrum disorder reveals atypical patterns of network organization. Mol Psychiatry 2024:10.1038/s41380-024-02764-6. [PMID: 39349967 DOI: 10.1038/s41380-024-02764-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 09/19/2024] [Accepted: 09/23/2024] [Indexed: 10/09/2024]
Abstract
Researchers studying autism spectrum disorder (ASD) lack a comprehensive map of the functional network topography in the ASD brain. We used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in a group of seventy high-functioning individuals with ASD and a group of seventy typically developing (TD) individuals. The rs-fMRI data were collected using an imaging sequence optimized to achieve high temporal signal-to-noise ratio (tSNR) across the whole-brain. We identified functional networks using a parcellation routine that intrinsically incorporates internal consistency and repeatability of the networks by keeping only network distinctions that agree across halves of the data over multiple random iterations in each group. The groups were tightly matched on tSNR, in-scanner motion, age, and IQ. We compared the maps from each group and found that functional networks in the ASD group are atypical in three seemingly related ways: (1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, (2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and (3) subcortical structures and the hippocampus are atypically integrated with the neocortex. These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.
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Affiliation(s)
- Andrew S Persichetti
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Jiayu Shao
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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Shao J, Gotts SJ, Li TL, Martin A, Persichetti AS. FunMaps: a method for parcellating functional brain networks using resting-state functional MRI data. Front Hum Neurosci 2024; 18:1461590. [PMID: 39381142 PMCID: PMC11458417 DOI: 10.3389/fnhum.2024.1461590] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Accepted: 09/11/2024] [Indexed: 10/10/2024] Open
Abstract
Parcellations of resting-state functional magnetic resonance imaging (rs-fMRI) data are widely used to create topographical maps of functional networks in the human brain. While such network maps are highly useful for studying brain organization and function, they usually require large sample sizes to make them, thus creating practical limitations for researchers that would like to carry out parcellations on data collected in their labs. Furthermore, it can be difficult to quantitatively evaluate the results of a parcellation since networks are usually identified using a clustering algorithm, like principal components analysis, on the results of a single group-averaged connectivity map. To address these challenges, we developed the FunMaps method: a parcellation routine that intrinsically incorporates stability and replicability of the parcellation by keeping only network distinctions that agree across halves of the data over multiple random iterations. Here, we demonstrate the efficacy and flexibility of FunMaps, while describing step-by-step instructions for running the program. The FunMaps method is publicly available on GitHub (https://github.com/persichetti-lab/FunMaps). It includes source code for running the parcellation and auxiliary code for preparing data, evaluating the parcellation, and displaying the results.
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Affiliation(s)
| | | | | | | | - Andrew S. Persichetti
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
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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.
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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
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4
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Ding N, Fu L, Qian L, Sun B, Li C, Gao H, Lei T, Ke X. The correlation between brain structure characteristics and emotion regulation ability in children at high risk of autism spectrum disorder. Eur Child Adolesc Psychiatry 2024; 33:3247-3262. [PMID: 38402375 DOI: 10.1007/s00787-024-02369-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/08/2024] [Indexed: 02/26/2024]
Abstract
As indicated by longitudinal observation, autism has difficulty controlling emotions to a certain extent in early childhood, and most children's emotional and behavioral problems are further aggravated with the growth of age. This study aimed at exploring the correlation between white matter and white matter fiber bundle connectivity characteristics and their emotional regulation ability in children with autism using machine learning methods, which can lay an empirical basis for early clinical intervention of autism. Fifty-five high risk of autism spectrum disorder (HR-ASD) children and 52 typical development (TD) children were selected to complete the skull 3D-T1 structure and diffusion tensor imaging (DTI). The emotional regulation ability of the two groups was compared using the still-face paradigm (SFP). The classification and regression models of white matter characteristics and white matter fiber bundle connections of emotion regulation ability in the HR-ASD group were built based on the machine learning method. The volume of the right amygdala (R2 = 0.245) and the volume of the right hippocampus (R2 = 0.197) affected constructive emotion regulation strategies. FA (R2 = 0.32) and MD (R2 = 0.34) had the predictive effect on self-stimulating behaviour. White matter fiber bundle connection predicted constructive regulation strategies (positive edging R2 = 0.333, negative edging R2 = 0.334) and mother-seeking behaviors (positive edging R2 = 0.667, negative edging R2 = 0.363). The emotional regulation ability of HR-ASD children is significantly correlated with the connections of multiple white matter fiber bundles, which is a potential neuro-biomarker of emotional regulation ability.
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Affiliation(s)
- Ning Ding
- Child Mental Health Research Center, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
- Qingdao Women and Children' s Hospital, Qingdao University, Qingdao, 266011, China
| | - Linyan Fu
- Child Mental Health Research Center, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Lu Qian
- Child Mental Health Research Center, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Bei Sun
- Child Mental Health Research Center, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Chunyan Li
- Child Mental Health Research Center, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Huiyun Gao
- Child Mental Health Research Center, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Tianyu Lei
- Child Mental Health Research Center, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Xiaoyan Ke
- Child Mental Health Research Center, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.
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Kadlec J, Walsh CR, Sadé U, Amir A, Rissman J, Ramot M. A measure of reliability convergence to select and optimize cognitive tasks for individual differences research. COMMUNICATIONS PSYCHOLOGY 2024; 2:64. [PMID: 39242856 PMCID: PMC11332135 DOI: 10.1038/s44271-024-00114-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 06/18/2024] [Indexed: 09/09/2024]
Abstract
Surging interest in individual differences has faced setbacks in light of recent replication crises in psychology, for example in brain-wide association studies exploring brain-behavior correlations. A crucial component of replicability for individual differences studies, which is often assumed but not directly tested, is the reliability of the measures we use. Here, we evaluate the reliability of different cognitive tasks on a dataset with over 250 participants, who each completed a multi-day task battery. We show how reliability improves as a function of number of trials, and describe the convergence of the reliability curves for the different tasks, allowing us to score tasks according to their suitability for studies of individual differences. We further show the effect on reliability of measuring over multiple time points, with tasks assessing different cognitive domains being differentially affected. Data collected over more than one session may be required to achieve trait-like stability.
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Affiliation(s)
- Jan Kadlec
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Catherine R Walsh
- Department of Psychology, University of California, Los Angeles, CA, USA
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, USA
| | - Uri Sadé
- Faculty of Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Ariel Amir
- Faculty of Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Jesse Rissman
- Department of Psychology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Michal Ramot
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel.
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Zhou D, Hua T, Tang H, Yang R, Huang L, Gong Y, Zhang L, Tang G. Gender and age related brain structural and functional alterations in children with autism spectrum disorder. Cereb Cortex 2024; 34:bhae283. [PMID: 38997211 DOI: 10.1093/cercor/bhae283] [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: 02/04/2024] [Revised: 06/10/2024] [Indexed: 07/14/2024] Open
Abstract
To explore the effects of age and gender on the brain in children with autism spectrum disorder using magnetic resonance imaging. 185 patients with autism spectrum disorder and 110 typically developing children were enrolled. In terms of gender, boys with autism spectrum disorder had increased gray matter volumes in the insula and superior frontal gyrus and decreased gray matter volumes in the inferior frontal gyrus and thalamus. The brain regions with functional alterations are mainly distributed in the cerebellum, anterior cingulate gyrus, postcentral gyrus, and putamen. Girls with autism spectrum disorder only had increased gray matter volumes in the right cuneus and showed higher amplitude of low-frequency fluctuation in the paracentral lobule, higher regional homogeneity and degree centrality in the calcarine fissure, and greater right frontoparietal network-default mode network connectivity. In terms of age, preschool-aged children with autism spectrum disorder exhibited hypo-connectivity between and within auditory network, somatomotor network, and visual network. School-aged children with autism spectrum disorder showed increased gray matter volumes in the rectus gyrus, superior temporal gyrus, insula, and suboccipital gyrus, as well as increased amplitude of low-frequency fluctuation and regional homogeneity in the calcarine fissure and precentral gyrus and decreased in the cerebellum and anterior cingulate gyrus. The hyper-connectivity between somatomotor network and left frontoparietal network and within visual network was found. It is essential to consider the impact of age and gender on the neurophysiological alterations in autism spectrum disorder children when analyzing changes in brain structure and function.
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Affiliation(s)
- Di Zhou
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Ting Hua
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Huan Tang
- Department of Radiology, Huadong Hospital of Fudan University, Shanghai 200040, China
| | - Rong Yang
- Department of Pediatrics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Linsheng Huang
- Department of Pediatrics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Yujiao Gong
- Department of Pediatrics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Lin Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Guangyu Tang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
- Department of Radiology, Shanghai Stomatological Hospital & School of Stomatology, Fudan University, Shanghai 201103, China
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7
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Zhou R, Sun C, Sun M, Ruan Y, Li W, Gao X. Altered intra- and inter-network connectivity in autism spectrum disorder. Aging (Albany NY) 2024; 16:10004-10015. [PMID: 38862259 PMCID: PMC11210244 DOI: 10.18632/aging.205913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/03/2024] [Indexed: 06/13/2024]
Abstract
OBJECTIVE A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored. METHODS We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector machine was used to discriminate ASD from HC. RESULTS Through different sparsities, ASD exhibited significantly decreased intra-network connectivity within default mode network and dorsal attention network, increased connectivity between limbic network and subcortical network, and decreased connectivity between default mode network and limbic network. Using the classifier trained on altered intra- and inter-network connectivity, multivariate pattern analyses classified the ASD from HC with 71.74% accuracy, 70.21% specificity and 75.56% sensitivity in 10% sparsity of functional connectivity. CONCLUSIONS ASD showed characteristic reorganization of the brain networks and this provided new insight into the underlying process of the functional connectome dysfunction in ASD.
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Affiliation(s)
- Rui Zhou
- School of Zhang Jian, Nantong University, Nantong, China
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
| | - Chenhao Sun
- Department of Radiology, Rugao Jian’an Hospital, Nantong, China
| | - Mingxiang Sun
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Yudi Ruan
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
| | - Weikai Li
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
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Wang L, Qin Y, Yang S, Jin D, Zhu Y, Li X, Li W, Wang Y, Jin C. Posterior default mode network is associated with the social performance in male children with autism spectrum disorder: A dynamic causal modeling analysis based on triple-network model. Hum Brain Mapp 2024; 45:e26750. [PMID: 38853710 PMCID: PMC11163228 DOI: 10.1002/hbm.26750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/12/2024] [Accepted: 05/22/2024] [Indexed: 06/11/2024] Open
Abstract
The triple-network model has been widely applied in neuropsychiatric disorders including autism spectrum disorder (ASD). However, the mechanism of causal regulations within the triple-network and their relations with symptoms of ASD remains unclear. 81 male ASD and 80 well matched typically developing control (TDC) were included in this study, recruited from Autism Brain Image Data Exchange-I datasets. Spatial reference-based independent component analysis was used to identify the anterior and posterior part of default-mode network (aDMN and pDMN), salience network (SN), and bilateral executive-control network (ECN) from resting-state functional magnetic resonance imaging data. Spectral dynamic causal model and parametric empirical Bayes with Bayesian model reduction/average were adopted to explore the effective connectivity (EC) within triple-network and the relationship between EC and autism diagnostic observation schedule (ADOS) scores. After adjusting for age and site effect, ASD and TDC groups both showed inhibition patterns. Compared with TDC, ASD group showed weaker self-inhibition in aDMN and pDMN, stronger inhibition in pDMN→aDMN, weaker inhibition in aDMN→LECN, pDMN→SN, LECN→SN, and LECN→RECN. Furthermore, negative relationships between ADOS scores and pDMN self-inhibition strength, as well as with the EC of pDMN→aDMN were observed in ASD group. The present study reveals imbalanced effective connections within triple-networks in ASD children. More attentions should be focused at the pDMN, which modulates the core symptoms of ASD and may serve as an important region for ASD diagnosis and the target region for ASD treatments.
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Affiliation(s)
- Lei Wang
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Yue Qin
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Shuhan Yang
- Department of Disease Control and PreventionNinth Hospital of Xi'anXi'anChina
| | - Dayong Jin
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Yinhu Zhu
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Xin Li
- Department of RadiologyXi'an Daxing HospitalXi'anChina
| | - Wei Li
- Department of Radiology, Tangdu HospitalAir Force Military Medical UniversityXi'anChina
| | - Yarong Wang
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Chenwang Jin
- Department of RadiologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
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Kember J, Patenaude P, Sweatman H, Van Schaik L, Tabuenca Z, Chai XJ. Specialization of anterior and posterior hippocampal functional connectivity differs in autism. Autism Res 2024; 17:1126-1139. [PMID: 38770780 DOI: 10.1002/aur.3170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 05/10/2024] [Indexed: 05/22/2024]
Abstract
Structural and functional differences in the hippocampus have been related to the episodic memory and social impairments observed in autism spectrum disorder (ASD). In neurotypical individuals, hippocampal-cortical functional connectivity systematically varies between anterior and posterior hippocampus, with changes observed during typical development. It remains unknown whether this specialization of anterior-posterior hippocampal connectivity is disrupted in ASD, and whether age-related differences in this specialization exist in ASD. We examined connectivity of the anterior and posterior hippocampus in an ASD (N = 139) and non-autistic comparison group (N = 133) aged 5-21 using resting-state functional magnetic resonance imaging (MRI) data from the Healthy Brain Network (HBN). Consistent with previous results, we observed lower connectivity between the whole hippocampus and medial prefrontal cortex in ASD. Moreover, preferential connectivity of the posterior relative to the anterior hippocampus for memory-sensitive regions in posterior parietal cortex was reduced in ASD, demonstrating a weaker anterior-posterior specialization of hippocampal-cortical connectivity. Finally, connectivity between the posterior hippocampus and precuneus negatively correlated with age in the ASD group but remained stable in the comparison group, suggesting an altered developmental specialization. Together, these differences in hippocampal-cortical connectivity may help us understand the neurobiological basis of the memory and social impairments found in ASD.
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Affiliation(s)
- J Kember
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - P Patenaude
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - H Sweatman
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - L Van Schaik
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Z Tabuenca
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
- Department of Statistics, University of Zaragoza, Zaragoza, Spain
| | - X J Chai
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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10
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Hermiller MS. Effects of continuous versus intermittent theta-burst TMS on fMRI connectivity. Front Hum Neurosci 2024; 18:1380583. [PMID: 38883322 PMCID: PMC11177618 DOI: 10.3389/fnhum.2024.1380583] [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: 02/01/2024] [Accepted: 05/17/2024] [Indexed: 06/18/2024] Open
Abstract
Transcranial magnetic stimulation is a noninvasive technique that can be used to evoke distributed network-level effects. Previous work demonstrated that the Hippocampal-Cortical Network responds preferably (i.e., greater memory improvement and increases in hippocampal-network connectivity) to continuous theta-burst stimulation protocol relative to intermittent theta-burst and to 20-Hz rTMS. Here, these data were further analyzed to characterize effects of continuous versus intermittent theta-burst stimulation on network-level connectivity measures - as well as local connectedness - via resting-state fMRI. In contrast to theories that propose continuous and intermittent theta-burst cause local inhibitory versus excitatory effects, respectively, both protocols caused local decreases in fMRI connectivity around the stimulated parietal site. While iTBS caused decreases in connectivity across the hippocampal-cortical network, cTBS caused increases and decreases in connectivity across the network. cTBS had no effect on the parietal-cortical network, whereas iTBS caused decreases in the right parietal cortex (contralateral hemisphere to the stimulation target). These findings suggest that continuous theta-burst may have entrained the endogenous hippocampal-cortical network, whereas the intermittent train was unable to maintain entrainment that may have yielded the long-lasting effects measured in this study (i.e., within 20-min post-stimulation). Furthermore, these effects were specific to the hippocampal-cortical network, which has a putative endogenous functionally-relevant theta rhythm, and not to the parietal network. These results add to the growing body of evidence that suggests effects of theta-burst stimulation are not fully characterized by excitatory/inhibitory theories. Further work is required to understand local and network-level effects of noninvasive stimulation.
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Affiliation(s)
- Molly S Hermiller
- Department of Psychology, Florida State University, Tallahassee, FL, United States
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11
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Shan X, Uddin LQ, Ma R, Xu P, Xiao J, Li L, Huang X, Feng Y, He C, Chen H, Duan X. Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder. Biol Psychiatry 2024; 95:870-880. [PMID: 37741308 DOI: 10.1016/j.biopsych.2023.09.012] [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: 04/21/2023] [Revised: 08/25/2023] [Accepted: 09/15/2023] [Indexed: 09/25/2023]
Abstract
BACKGROUND Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder. METHODS We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns. RESULTS Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD. CONCLUSIONS Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.
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Affiliation(s)
- Xiaolong Shan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Rui Ma
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Pengfei Xu
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinming Xiao
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinyue Huang
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu Feng
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Changchun He
- College of Blockchain Industry, Chengdu University of Information Technology, Chengdu, China
| | - Huafu Chen
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xujun Duan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
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12
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Huynh N, Deshpande G. A review of the applications of generative adversarial networks to structural and functional MRI based diagnostic classification of brain disorders. Front Neurosci 2024; 18:1333712. [PMID: 38686334 PMCID: PMC11057233 DOI: 10.3389/fnins.2024.1333712] [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: 11/05/2023] [Accepted: 02/19/2024] [Indexed: 05/02/2024] Open
Abstract
Structural and functional MRI (magnetic resonance imaging) based diagnostic classification using machine learning has long held promise, but there are many roadblocks to achieving their potential. While traditional machine learning models suffered from their inability to capture the complex non-linear mapping, deep learning models tend to overfit the model. This is because there is data scarcity and imbalanced classes in neuroimaging; it is expensive to acquire data from human subjects and even more so in clinical populations. Due to their ability to augment data by learning underlying distributions, generative adversarial networks (GAN) provide a potential solution to this problem. Here, we provide a methodological primer on GANs and review the applications of GANs to classification of mental health disorders from neuroimaging data such as functional MRI and showcase the progress made thus far. We also highlight gaps in methodology as well as interpretability that are yet to be addressed. This provides directions about how the field can move forward. We suggest that since there are a range of methodological choices available to users, it is critical for users to interact with method developers so that the latter can tailor their development according to the users' needs. The field can be enriched by such synthesis between method developers and users in neuroimaging.
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Affiliation(s)
- Nguyen Huynh
- Auburn University Neuroimaging Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
| | - Gopikrishna Deshpande
- Auburn University Neuroimaging Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Department of Heritage Science and Technology, Indian Institute of Technology, Hyderabad, India
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13
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Agron AM, Martin A, Gilmore AW. Scene construction and autobiographical memory retrieval in autism spectrum disorder. Autism Res 2024; 17:204-214. [PMID: 38037250 PMCID: PMC10922094 DOI: 10.1002/aur.3066] [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: 05/04/2023] [Accepted: 11/06/2023] [Indexed: 12/02/2023]
Abstract
Individuals with autism spectrum disorder (ASD) frequently exhibit difficulties in retrieving autobiographical memories (AMs) of specific events from their life. Such memory deficits are frequently attributed to underlying disruptions in self-referential or social cognition processes. This makes intuitive sense as these are hallmarks of ASD. However, an emerging literature suggests that parallel deficits also exist in ASD individuals' ability to reconstruct the rich spatial contexts in which events occur. This is a capacity known as scene construction, and in typically developing individuals is considered a core process in retrieving AMs. In this review, we discuss evidence of difficulties with scene construction in ASD, drawing upon experiments that involve AM retrieval, other forms of mental time travel, and spatial navigation. We also highlight aspects of extant data that cannot be accounted for using purely social explanations of memory deficits in ASD. We conclude by identifying key questions raised by our framework and suggest how they might be addressed in future research.
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Affiliation(s)
- Anna M. Agron
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, NIMH/NIH, Bethesda, MD 20892
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, NIMH/NIH, Bethesda, MD 20892
| | - Adrian W. Gilmore
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, NIMH/NIH, Bethesda, MD 20892
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14
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Saponaro S, Lizzi F, Serra G, Mainas F, Oliva P, Giuliano A, Calderoni S, Retico A. Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders. Brain Inform 2024; 11:2. [PMID: 38194126 PMCID: PMC10776521 DOI: 10.1186/s40708-023-00217-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/20/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD). MATERIAL AND METHODS We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework. RESULTS The AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain. CONCLUSIONS Our results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.
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Affiliation(s)
- Sara Saponaro
- Medical Physics School, University of Pisa, Pisa, Italy.
- National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy.
| | - Francesca Lizzi
- National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy
| | - Giacomo Serra
- Department of Physics, University of Cagliari, Cagliari, Italy
- INFN, Cagliari Division, Cagliari, Italy
| | - Francesca Mainas
- INFN, Cagliari Division, Cagliari, Italy
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Piernicola Oliva
- INFN, Cagliari Division, Cagliari, Italy
- Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Sassari, Italy
| | - Alessia Giuliano
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy
| | - Sara Calderoni
- Developmental Psychiatry Unit - IRCCS Stella Maris Foundation, Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Alessandra Retico
- National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy
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15
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Parrella NF, Hill AT, Dipnall LM, Loke YJ, Enticott PG, Ford TC. Inhibitory dysfunction and social processing difficulties in autism: A comprehensive narrative review. J Psychiatr Res 2024; 169:113-125. [PMID: 38016393 DOI: 10.1016/j.jpsychires.2023.11.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/04/2023] [Accepted: 11/15/2023] [Indexed: 11/30/2023]
Abstract
The primary inhibitory neurotransmitter γ-aminobutyric acid (GABA) has a prominent role in regulating neural development and function, with disruption to GABAergic signalling linked to behavioural phenotypes associated with neurodevelopmental disorders, particularly autism. Such neurochemical disruption, likely resulting from diverse genetic and molecular mechanisms, particularly during early development, can subsequently affect the cellular balance of excitation and inhibition in neuronal circuits, which may account for the social processing difficulties observed in autism and related conditions. This comprehensive narrative review integrates diverse streams of research from several disciplines, including molecular neurobiology, genetics, epigenetics, and systems neuroscience. In so doing it aims to elucidate the relevance of inhibitory dysfunction to autism, with specific focus on social processing difficulties that represent a core feature of this disorder. Many of the social processing difficulties experienced in autism have been linked to higher levels of the excitatory neurotransmitter glutamate and/or lower levels of inhibitory GABA. While current therapeutic options for social difficulties in autism are largely limited to behavioural interventions, this review highlights the psychopharmacological studies that explore the utility of GABA modulation in alleviating such difficulties.
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Affiliation(s)
| | - Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia; Department of Psychiatry, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Lillian M Dipnall
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia; Early Life Epigenetics Group, Deakin University, Geelong, Australia
| | - Yuk Jing Loke
- Epigenetics Group, Murdoch Children's Research Institute, Melbourne, Victoria, Australia; Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Peter G Enticott
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia
| | - Talitha C Ford
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia; Centre for Human Psychopharmacology, Faculty of Health, Arts and Design, Swinburne University of Technology, Melbourne, Victoria, Australia
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16
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Persichetti AS, Shao J, Gotts SJ, Martin A. A functional parcellation of the whole brain in individuals with autism spectrum disorder reveals atypical patterns of network organization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571854. [PMID: 38168156 PMCID: PMC10760210 DOI: 10.1101/2023.12.15.571854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
BACKGROUND Researchers studying autism spectrum disorder (ASD) lack a comprehensive map of the functional network topography in the ASD brain. We used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in a group of seventy individuals with ASD and a group of seventy typically developing (TD) individuals. METHODS The rs-fMRI data were collected using an imaging sequence optimized to achieve high temporal signal-to-noise ratio (tSNR) across the whole-brain. We identified functional networks using a parcellation routine that intrinsically incorporates stability and replicability of the networks by keeping only network distinctions that agree across halves of the data over multiple random iterations in each group. The groups were tightly matched on tSNR, in-scanner motion, age, and IQ. RESULTS We compared the maps from each group and found that functional networks in the ASD group are atypical in three seemingly related ways: 1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, 2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and 3) subcortical structures and the hippocampus are atypically integrated with the neocortex. CONCLUSIONS These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.
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Affiliation(s)
- Andrew S Persichetti
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Jiayu Shao
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
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17
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Li X, Qureshi MNI, Laplante DP, Elgbeili G, Jones SL, Long X, Paquin V, Bezgin G, Lussier F, King S, Rosa-Neto P. Atypical brain structure and function in young adults exposed to disaster-related prenatal maternal stress: Project Ice Storm. J Neurosci Res 2023; 101:1849-1863. [PMID: 37732456 DOI: 10.1002/jnr.25246] [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: 11/09/2022] [Revised: 08/29/2023] [Accepted: 09/04/2023] [Indexed: 09/22/2023]
Abstract
Studies have shown that prenatal maternal stress (PNMS) affects brain structure and function in childhood. However, less research has examined whether PNMS effects on brain structure and function extend to young adulthood. We recruited women who were pregnant during or within 3 months following the 1998 Quebec ice storm, assessed their PNMS, and prospectively followed-up their children. T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI were obtained from 19-year-old young adults with (n = 39) and without (n = 65) prenatal exposure to the ice storm. We examined between-group differences in gray matter volume (GMV), surface area (SA), and cortical thickness (CT). We used the brain regions showing between-group GMV differences as seeds to compare between-group functional connectivity. Within the Ice Storm group, we examined (1) associations between PNMS and the atypical GMV, SA, CT, and functional connectivity, and (2) moderation by timing of exposure. Primarily, we found that, compared to Controls, the Ice Storm youth had larger GMV and higher functional connectivity of the anterior cingulate cortex, the precuneus, the left occipital pole, and the right hippocampus; they also had larger CT, but not SA, of the left occipital pole. Within the Ice Storm group, maternal subjective distress during preconception and mid-to-late pregnancy was associated with atypical left occipital pole CT. These results suggest the long-lasting impact of disaster-related PNMS on child brain structure and functional connectivity. Our study also indicates timing-specific effects of the subjective aspect of PNMS on occipital thickness.
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Affiliation(s)
- Xinyuan Li
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Muhammad Naveed Iqbal Qureshi
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
| | - David P Laplante
- Centre for Child Development and Mental Health, Lady Davis Institute-Jewish General Hospital, Montreal, Quebec, Canada
| | | | - Sherri Lee Jones
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Xiangyu Long
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Vincent Paquin
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Gleb Bezgin
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Firoza Lussier
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Suzanne King
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Pedro Rosa-Neto
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Montreal, Quebec, Canada
- Montreal Neurological Institute, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
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18
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Deng J, Liu C, Hu M, Hu C, Lin J, Li Q, Xu X. Dynamic Regulation of brsk2 in the Social and Motor Development of Zebrafish: A Developmental Behavior Analysis. Int J Mol Sci 2023; 24:16506. [PMID: 38003696 PMCID: PMC10671324 DOI: 10.3390/ijms242216506] [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: 08/30/2023] [Revised: 10/26/2023] [Accepted: 11/11/2023] [Indexed: 11/26/2023] Open
Abstract
Both social and motor development play an essential role in an individual's physical, psychological, and social well-being. It is essential to conduct a dynamic analysis at multiple time points during the developmental process as it helps us better understand and evaluate the trajectory and changes in individual development. Recently, some studies found that mutations in the BRSK2 gene may contribute to motor impairments, delays in achieving motor milestones, and deficits in social behavior and communication skills in patients. However, little is known about the dynamic analysis of social and motor development at multiple time points during the development of the brsk2 gene. We generated a novel brsk2-deficient (brsk2ab-/-) zebrafish model through CRISPR/Cas9 editing and conducted comprehensive morphological and neurobehavioral evaluations, including that of locomotor behaviors, social behaviors, and anxiety behaviors from the larval to adult stages of development. Compared to wild-type zebrafish, brsk2ab-/- zebrafish exhibited a catch-up growth pattern of body length and gradually improved locomotor activities during the developmental process. In contrast, multimodal behavior tests showed that the brsk2ab-/- zebrafish displayed escalating social deficiency and anxiety-like behaviors over time. We reported for the first time that the brsk2 gene had dynamic regulatory effects on motor and social development. It helps us understand developmental trends, capture changes, facilitate early interventions, and provide personalized support and development opportunities for individuals.
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Affiliation(s)
- Jingxin Deng
- Division of Child Health Care, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai 201102, China; (J.D.); (M.H.); (C.H.)
| | - Chunxue Liu
- Division of Child Health Care, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai 201102, China; (J.D.); (M.H.); (C.H.)
| | - Meixin Hu
- Division of Child Health Care, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai 201102, China; (J.D.); (M.H.); (C.H.)
| | - Chunchun Hu
- Division of Child Health Care, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai 201102, China; (J.D.); (M.H.); (C.H.)
| | - Jia Lin
- Center for Translational Medicine, Institute of Pediatrics, Shanghai Key Laboratory of Birth Defect, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai 201102, China; (J.L.); (Q.L.)
| | - Qiang Li
- Center for Translational Medicine, Institute of Pediatrics, Shanghai Key Laboratory of Birth Defect, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai 201102, China; (J.L.); (Q.L.)
| | - Xiu Xu
- Division of Child Health Care, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai 201102, China; (J.D.); (M.H.); (C.H.)
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19
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Deserno MK, Bathelt J, Groenman AP, Geurts HM. Probing the overarching continuum theory: data-driven phenotypic clustering of children with ASD or ADHD. Eur Child Adolesc Psychiatry 2023; 32:1909-1923. [PMID: 35687205 PMCID: PMC10533623 DOI: 10.1007/s00787-022-01986-9] [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: 05/21/2021] [Accepted: 04/06/2022] [Indexed: 11/03/2022]
Abstract
The clinical validity of the distinction between ADHD and ASD is a longstanding discussion. Recent advances in the realm of data-driven analytic techniques now enable us to formally investigate theories aiming to explain the frequent co-occurrence of these neurodevelopmental conditions. In this study, we probe different theoretical positions by means of a pre-registered integrative approach of novel classification, subgrouping, and taxometric techniques in a representative sample (N = 434), and replicate the results in an independent sample (N = 219) of children (ADHD, ASD, and typically developing) aged 7-14 years. First, Random Forest Classification could predict diagnostic groups based on questionnaire data with limited accuracy-suggesting some remaining overlap in behavioral symptoms between them. Second, community detection identified four distinct groups, but none of them showed a symptom profile clearly related to either ADHD or ASD in neither the original sample nor the replication sample. Third, taxometric analyses showed evidence for a categorical distinction between ASD and typically developing children, a dimensional characterization of the difference between ADHD and typically developing children, and mixed results for the distinction between the diagnostic groups. We present a novel framework of cutting-edge statistical techniques which represent recent advances in both the models and the data used for research in psychiatric nosology. Our results suggest that ASD and ADHD cannot be unambiguously characterized as either two separate clinical entities or opposite ends of a spectrum, and highlight the need to study ADHD and ASD traits in tandem.
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Affiliation(s)
- M K Deserno
- Dutch Autism and ADHD Research Centre (d'Arc), Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
- Max Planck Institute for Human Development, Berlin, Germany.
| | - J Bathelt
- Dutch Autism and ADHD Research Centre (d'Arc), Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Royal Holloway, University of London, Egham, UK
| | - A P Groenman
- Dutch Autism and ADHD Research Centre (d'Arc), Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - H M Geurts
- Dutch Autism and ADHD Research Centre (d'Arc), Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Leo Kannerhuis, Amsterdam (Youz, Parnassiagroep), Amsterdam, The Netherlands
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20
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Li X, Laplante DP, Elgbeili G, King S. Preconception and prenatal maternal stress are associated with broad autism phenotype in young adults: Project Ice Storm. J Dev Orig Health Dis 2023; 14:481-489. [PMID: 37282623 DOI: 10.1017/s2040174423000156] [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] [Indexed: 06/08/2023]
Abstract
Studies show associations between prenatal maternal stress (PNMS) and child autism, with little attention paid to PNMS and autism in young adulthood. The broad autism phenotype (BAP), encompassing sub-clinical levels of autism, includes aloof personality, pragmatic language impairment and rigid personality. It remains unclear whether different aspects of PNMS explain variance in different BAP domains in young adult offspring. We recruited women who were pregnant during, or within 3 months of, the 1998 Quebec ice storm crisis, and assessed three aspects of their stress (i.e., objective hardship, subjective distress and cognitive appraisal). At age 19, the young adult offspring (n = 33, 22F / 11M) completed a BAP self-report. Linear and logistic regressions were implemented to examine associations between PNMS and BAP traits. Up to 21.4% of the variance in BAP total score and in BAP three domains tended to be explained by at least one aspect of maternal stress, For example, 16.8% of the variance in aloof personality tended to be explained by maternal objective hardship; 15.1% of the variance in pragmatic language impairment tended to be explained by maternal subjective distress; 20.0% of the variance in rigid personality tended to be explained by maternal objective hardship and 14.3% by maternal cognitive appraisal. Given the small sample size, the results should be interpreted with caution. In conclusion, this small prospective study suggests that different aspects of maternal stress could have differential effects on different components of BAP traits in young adults.
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Affiliation(s)
- Xinyuan Li
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, Montreal, QC, Canada
| | - David P Laplante
- Centre for Child Development and Mental Health, Lady Davis Institute-Jewish General Hospital, Montreal, QC, Canada
| | | | - Suzanne King
- Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
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21
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Yang B, Wang M, Zhou W, Wang X, Chen S, Yuan LX, Dong GH. Edge-centric functional network analyses reveal disrupted network configuration in autism spectrum disorder. J Affect Disord 2023; 336:74-80. [PMID: 37201902 DOI: 10.1016/j.jad.2023.05.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Neuroscientific evidence suggests that the pathological symptoms associated with autism spectrum disorders (ASD) are not confined to a single brain region but involve networks of the brain on a larger spatial scale. Analyzing diagrams of edge-edge interactions could provide important perspectives on the organization and function of complex systems. METHODS Resting-state fMRI data from 238 ASD patients and 311 healthy controls (HCs) were included in the current study. We used the thalamus as the mediating node to calculate the edge functional connectivity (eFC) of the brain network and compared the ASD subjects and HCs. RESULTS Compared with the HCs, the ASD subjects exhibited abnormalities in the central node thalamus and four brain regions (amygdala, nucleus accumbens, pallidum and hippocampus), as well as in the eFC formed by the inferior frontal gyrus (IFG) (or middle temporal gyrus (MTG)). In addition, ASD subjects showed variable characteristics of the eFC between nodes in different networks. CONCLUSIONS The changes in these brain regions may be due to the disturbance in the reward system, which leads to coherence in the instantaneous comovement of the functional connections formed by these brain regions in ASD. This notion also reveals a functional network feature between the cortical and subcortical regions in ASD.
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Affiliation(s)
- Bo Yang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Min Wang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, PR China
| | - Weiran Zhou
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Xiuqin Wang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Shuaiyu Chen
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Li-Xia Yuan
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Guang-Heng Dong
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China.
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22
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Wang S, Li X. A revisit of the amygdala theory of autism: Twenty years after. Neuropsychologia 2023; 183:108519. [PMID: 36803966 PMCID: PMC10824605 DOI: 10.1016/j.neuropsychologia.2023.108519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 01/23/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.
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Affiliation(s)
- Shuo Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA; Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
| | - Xin Li
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
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23
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Khandan Khadem-Reza Z, Shahram MA, Zare H. Altered resting-state functional connectivity of the brain in children with autism spectrum disorder. Radiol Phys Technol 2023; 16:284-291. [PMID: 37040021 DOI: 10.1007/s12194-023-00717-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 03/27/2023] [Accepted: 03/27/2023] [Indexed: 04/12/2023]
Abstract
Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders. Brain mapping has shown that functional brain connections are altered in autism. This study investigated the pattern of brain connection changes in autistic people compared to healthy people. This study aimed to analyze functional abnormalities within the brain due to ASD, using resting-state functional magnetic resonance imaging (fMRI). Resting-state functional magnetic resonance images of 26 individuals with ASD and 26 healthy controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The DPARSF (data processing assistant for resting-state fMRI) toolbox was used for resting-state functional image processing, and features related to functional connections were extracted from these images. Then, the extracted features from both groups were compared using an Independent Two-Sample T Test, and the features with significant differences between the two groups were identified. Compared with healthy controls, individuals with ASD showed hyper-connectivity in the frontal lobe, anterior cingulum, parahippocampal, left precuneus, angular, caudate, superior temporal, and left pallidum, as well as hypo-connectivity in the precentral, left superior frontal, left middle orbitofrontal, right amygdala, and left posterior cingulum. Our findings show that abnormal functional connectivity exists in patients with ASD. This study makes an important advancement in our understanding of the abnormal neurocircuits causing autism.
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Affiliation(s)
- Zahra Khandan Khadem-Reza
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Razavi Khorasan, Iran
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Vakil Abad Street, Mashhad, Razavi Khorasan, Iran
| | - Mohammad Amin Shahram
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Razavi Khorasan, Iran
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Vakil Abad Street, Mashhad, Razavi Khorasan, Iran
| | - Hoda Zare
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Razavi Khorasan, Iran.
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Vakil Abad Street, Mashhad, Razavi Khorasan, Iran.
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24
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Jasmin K, Martin A, Gotts SJ. Atypical connectivity aids conversation in autism. Sci Rep 2023; 13:5303. [PMID: 37002277 PMCID: PMC10066277 DOI: 10.1038/s41598-023-32249-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 03/24/2023] [Indexed: 04/04/2023] Open
Abstract
It is well-established that individuals with autism exhibit atypical functional brain connectivity. However, the role this plays in naturalistic social settings has remained unclear. Atypical patterns may reflect core deficits or may instead compensate for deficits and promote adaptive behavior. Distinguishing these possibilities requires measuring the 'typicality' of spontaneous behavior and determining how connectivity relates to it. Thirty-nine male participants (19 autism, 20 typically-developed) engaged in 115 spontaneous conversations with an experimenter during fMRI scanning. A classifier algorithm was trained to distinguish participants by diagnosis based on 81 semantic, affective and linguistic dimensions derived from their use of language. The algorithm's graded likelihood of a participant's group membership (autism vs. typically-developed) was used as a measure of task performance and compared with functional connectivity levels. The algorithm accurately classified participants and its scores correlated with clinician-observed autism signs (ADOS-2). In support of a compensatory role, greater functional connectivity between right inferior frontal cortex and left-hemisphere social communication regions correlated with more typical language behavior, but only for the autism group. We conclude that right inferior frontal functional connectivity increases in autism during communication reflect a neural compensation strategy that can be quantified and tested even without an a priori behavioral standard.
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Affiliation(s)
- Kyle Jasmin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, MD, USA.
- Department of Psychology, Royal Holloway, University of London, Egham, Surrey, UK.
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, MD, USA
| | - Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, MD, USA
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25
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Kim JH, De Asis-Cruz J, Cook KM, Limperopoulos C. Gestational age-related changes in the fetal functional connectome: in utero evidence for the global signal. Cereb Cortex 2023; 33:2302-2314. [PMID: 35641159 PMCID: PMC9977380 DOI: 10.1093/cercor/bhac209] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 05/06/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
The human brain begins to develop in the third gestational week and rapidly grows and matures over the course of pregnancy. Compared to fetal structural neurodevelopment, less is known about emerging functional connectivity in utero. Here, we investigated gestational age (GA)-associated in vivo changes in functional brain connectivity during the second and third trimesters in a large dataset of 110 resting-state functional magnetic resonance imaging scans from a cohort of 95 healthy fetuses. Using representational similarity analysis, a multivariate analytical technique that reveals pair-wise similarity in high-order space, we showed that intersubject similarity of fetal functional connectome patterns was strongly related to between-subject GA differences (r = 0.28, P < 0.01) and that GA sensitivity of functional connectome was lateralized, especially at the frontal area. Our analysis also revealed a subnetwork of connections that were critical for predicting age (mean absolute error = 2.72 weeks); functional connectome patterns of individual fetuses reliably predicted their GA (r = 0.51, P < 0.001). Lastly, we identified the primary principal brain network that tracked fetal brain maturity. The main network showed a global synchronization pattern resembling global signal in the adult brain.
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Affiliation(s)
- Jung-Hoon Kim
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Josepheen De Asis-Cruz
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Kevin M Cook
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Catherine Limperopoulos
- Corresponding author: Developing Brain Institute, Children’s National, 111 Michigan Ave. N.W., Washington D.C. 20010.
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26
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Neurobiological correlates and attenuated positive social intention attribution during laughter perception associated with degree of autistic traits. J Neural Transm (Vienna) 2023; 130:585-596. [PMID: 36808307 PMCID: PMC10049931 DOI: 10.1007/s00702-023-02599-5] [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: 10/20/2022] [Accepted: 02/03/2023] [Indexed: 02/21/2023]
Abstract
Laughter plays an important role in group formation, signaling social belongingness by indicating a positive or negative social intention towards the receiver. In adults without autism, the intention of laughter can be correctly differentiated without further contextual information. In autism spectrum disorder (ASD), however, differences in the perception and interpretation of social cues represent a key characteristic of the disorder. Studies suggest that these differences are associated with hypoactivation and altered connectivity among key nodes of the social perception network. How laughter, as a multimodal nonverbal social cue, is perceived and processed neurobiologically in association with autistic traits has not been assessed previously. We investigated differences in social intention attribution, neurobiological activation, and connectivity during audiovisual laughter perception in association with the degree of autistic traits in adults [N = 31, Mage (SD) = 30.7 (10.0) years, nfemale = 14]. An attenuated tendency to attribute positive social intention to laughter was found with increasing autistic traits. Neurobiologically, autistic trait scores were associated with decreased activation in the right inferior frontal cortex during laughter perception and with attenuated connectivity between the bilateral fusiform face area with bilateral inferior and lateral frontal, superior temporal, mid-cingulate and inferior parietal cortices. Results support hypoactivity and hypoconnectivity during social cue processing with increasing ASD symptoms between socioemotional face processing nodes and higher-order multimodal processing regions related to emotion identification and attribution of social intention. Furthermore, results reflect the importance of specifically including signals of positive social intention in future studies in ASD.
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27
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Bozhilova N, Welham A, Adams D, Bissell S, Bruining H, Crawford H, Eden K, Nelson L, Oliver C, Powis L, Richards C, Waite J, Watson P, Rhys H, Wilde L, Woodcock K, Moss J. Profiles of autism characteristics in thirteen genetic syndromes: a machine learning approach. Mol Autism 2023; 14:3. [PMID: 36639821 PMCID: PMC9837969 DOI: 10.1186/s13229-022-00530-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 12/07/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Phenotypic studies have identified distinct patterns of autistic characteristics in genetic syndromes associated with intellectual disability (ID), leading to diagnostic uncertainty and compromised access to autism-related support. Previous research has tended to include small samples and diverse measures, which limits the generalisability of findings. In this study, we generated detailed profiles of autistic characteristics in a large sample of > 1500 individuals with rare genetic syndromes. METHODS Profiles of autistic characteristics based on the Social Communication Questionnaire (SCQ) scores were generated for thirteen genetic syndrome groups (Angelman n = 154, Cri du Chat n = 75, Cornelia de Lange n = 199, fragile X n = 297, Prader-Willi n = 278, Lowe n = 89, Smith-Magenis n = 54, Down n = 135, Sotos n = 40, Rubinstein-Taybi n = 102, 1p36 deletion n = 41, tuberous sclerosis complex n = 83 and Phelan-McDermid n = 35 syndromes). It was hypothesised that each syndrome group would evidence a degree of specificity in autistic characteristics. To test this hypothesis, a classification algorithm via support vector machine (SVM) learning was applied to scores from over 1500 individuals diagnosed with one of the thirteen genetic syndromes and autistic individuals who did not have a known genetic syndrome (ASD; n = 254). Self-help skills were included as an additional predictor. RESULTS Genetic syndromes were associated with different but overlapping autism-related profiles, indicated by the substantial accuracy of the entire, multiclass SVM model (55% correctly classified individuals). Syndrome groups such as Angelman, fragile X, Prader-Willi, Rubinstein-Taybi and Cornelia de Lange showed greater phenotypic specificity than groups such as Cri du Chat, Lowe, Smith-Magenis, tuberous sclerosis complex, Sotos and Phelan-McDermid. The inclusion of the ASD reference group and self-help skills did not change the model accuracy. LIMITATIONS The key limitations of our study include a cross-sectional design, reliance on a screening tool which focuses primarily on social communication skills and imbalanced sample size across syndrome groups. CONCLUSIONS These findings replicate and extend previous work, demonstrating syndrome-specific profiles of autistic characteristics in people with genetic syndromes compared to autistic individuals without a genetic syndrome. This work calls for greater precision of assessment of autistic characteristics in individuals with genetic syndromes associated with ID.
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Affiliation(s)
| | - Alice Welham
- School of Psychology, University of Leicester, Leicester, UK
| | - Dawn Adams
- Autism Centre of Excellence, Griffith University, Brisbane, Australia
| | - Stacey Bissell
- School of Psychology, University of Birmingham, Edgbaston, UK
| | - Hilgo Bruining
- Department of Child and Adolescent Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Hayley Crawford
- Mental Health and Wellbeing Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | - Kate Eden
- School of Psychology, University of Birmingham, Edgbaston, UK
| | - Lisa Nelson
- School of Psychology, University of Birmingham, Edgbaston, UK
| | | | - Laurie Powis
- School of Psychology, University of Birmingham, Edgbaston, UK
| | | | - Jane Waite
- School of Psychology, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Peter Watson
- MRC Brain and Cognition Unit, University of Cambridge, Cambridge, UK
| | | | - Lucy Wilde
- School of Psychology, Open University, Milton Keynes, UK
| | - Kate Woodcock
- School of Psychology, University of Birmingham, Edgbaston, UK
| | - Joanna Moss
- School of Psychology, University of Surrey, Guilford, UK.
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28
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Wang Z, Xu Y, Peng D, Gao J, Lu F. Brain functional activity-based classification of autism spectrum disorder using an attention-based graph neural network combined with gene expression. Cereb Cortex 2022; 33:6407-6419. [PMID: 36587290 DOI: 10.1093/cercor/bhac513] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/02/2022] [Accepted: 12/03/2022] [Indexed: 01/02/2023] Open
Abstract
Autism spectrum disorder (ASD) is a complex brain neurodevelopmental disorder related to brain activity and genetics. Most of the ASD diagnostic models perform feature selection at the group level without considering individualized information. Evidence has shown the unique topology of the individual brain has a fundamental impact on brain diseases. Thus, a data-constructing method fusing individual topological information and a corresponding classification model is crucial in ASD diagnosis and biomarker discovery. In this work, we trained an attention-based graph neural network (GNN) to perform the ASD diagnosis with the fusion of graph data. The results achieved an accuracy of 79.78%. Moreover, we found the model paid high attention to brain regions mainly involved in the social-brain circuit, default-mode network, and sensory perception network. Furthermore, by analyzing the covariation between functional magnetic resonance imaging data and gene expression, current studies detected several ASD-related genes (i.e. MUTYH, AADAT, and MAP2), and further revealed their links to image biomarkers. Our work demonstrated that the ASD diagnostic framework based on graph data and attention-based GNN could be an effective tool for ASD diagnosis. The identified functional features with high attention values may serve as imaging biomarkers for ASD.
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Affiliation(s)
- Zhengning Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Yuhang Xu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Dawei Peng
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China
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29
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Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder. Mol Autism 2022; 13:52. [PMID: 36572935 PMCID: PMC9793594 DOI: 10.1186/s13229-022-00535-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/20/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks. METHODS Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model. RESULTS Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively. LIMITATIONS Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity. CONCLUSIONS These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.
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30
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Ilioska I, Oldehinkel M, Llera A, Chopra S, Looden T, Chauvin R, Van Rooij D, Floris DL, Tillmann J, Moessnang C, Banaschewski T, Holt RJ, Loth E, Charman T, Murphy DGM, Ecker C, Mennes M, Beckmann CF, Fornito A, Buitelaar JK. Connectome-wide Mega-analysis Reveals Robust Patterns of Atypical Functional Connectivity in Autism. Biol Psychiatry 2022:S0006-3223(22)01852-2. [PMID: 36925414 DOI: 10.1016/j.biopsych.2022.12.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 11/19/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Neuroimaging studies of functional connectivity (FC) in autism have been hampered by small sample sizes and inconsistent findings with regard to whether connectivity is increased or decreased in individuals with autism, whether these alterations affect focal systems or reflect a brain-wide pattern, and whether these are age and/or sex dependent. METHODS The study included resting-state functional magnetic resonance imaging and clinical data from the EU-AIMS LEAP (European Autism Interventions Longitudinal European Autism Project) and the ABIDE (Autism Brain Imaging Data Exchange) 1 and 2 initiatives of 1824 (796 with autism) participants with an age range of 5-58 years. Between-group differences in FC were assessed, and associations between FC and clinical symptom ratings were investigated through canonical correlation analysis. RESULTS Autism was associated with a brainwide pattern of hypo- and hyperconnectivity. Hypoconnectivity predominantly affected sensory and higher-order attentional networks and correlated with social impairments, restrictive and repetitive behavior, and sensory processing. Hyperconnectivity was observed primarily between the default mode network and the rest of the brain and between cortical and subcortical systems. This pattern was strongly associated with social impairments and sensory processing. Interactions between diagnosis and age or sex were not statistically significant. CONCLUSIONS The FC alterations observed, which primarily involve hypoconnectivity of primary sensory and attention networks and hyperconnectivity of the default mode network and subcortex with the rest of the brain, do not appear to be age or sex dependent and correlate with clinical dimensions of social difficulties, restrictive and repetitive behaviors, and alterations in sensory processing. These findings suggest that the observed connectivity alterations are stable, trait-like features of autism that are related to the main symptom domains of the condition.
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Affiliation(s)
- Iva Ilioska
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands; Turner Institute for Brain and Mental Health, School of Psychological Science, and Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.
| | - Marianne Oldehinkel
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands; Turner Institute for Brain and Mental Health, School of Psychological Science, and Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Alberto Llera
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Sidhant Chopra
- Turner Institute for Brain and Mental Health, School of Psychological Science, and Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Tristan Looden
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Roselyne Chauvin
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands; Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Daan Van Rooij
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Dorothea L Floris
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands; Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Julian Tillmann
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Carolin Moessnang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty, University of Heidelberg, Mannheim, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Rosemary J Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Eva Loth
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Tony Charman
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Declan G M Murphy
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Christine Ecker
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Child and Adolescent Psychiatry, University Hospital, Goethe University, Frankfurt am Main, Germany
| | - Maarten Mennes
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Christian F Beckmann
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands; Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Science, and Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands; Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, the Netherlands
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31
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Northoff G. Spatiotemporal Psychopathology - A Novel Approach to Brain and Symptoms. Noro Psikiyatr Ars 2022; 59:S3-S9. [PMID: 36578984 PMCID: PMC9767129 DOI: 10.29399/npa.28146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 03/13/2022] [Indexed: 12/31/2022] Open
Abstract
How can we characterize psychopathological symptoms and connect them to the brain? Current psychopathological symptoms only focus on either the symptoms themselves or predominantly on the brain. This leaves open their intimate connection. A novel approach, Spatiotemporal Psychopathology, proposes that the brain inner spatiotemporal organisation of its neural activity provides the spatiotemporal organization of the psychopathological symptoms. Specifically, the brains' neuronal topography and dynamic is manifest in a more or less analogous spatiotemporal organisation on the mental level, i.e., mental topography and dynamic. This is strongly supported by various examples including major depressive disorder, bipolar disorder, schizophrenia, and autism. We therefore conclude that Spatiotemporal Psychopathology provides a promising approach to intimately connect brain and symptoms.
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Affiliation(s)
- Georg Northoff
- University of Ottawa, Institute of Mental Health Research, Ontario, Canada,Correspondence Address: Georg Northoff, 1145 Carling Avenue, Ottawa, K1L 8K9 Ontario, Canada • E-mail:
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32
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Zhang J, Northoff G. Beyond noise to function: reframing the global brain activity and its dynamic topography. Commun Biol 2022; 5:1350. [PMID: 36481785 PMCID: PMC9732046 DOI: 10.1038/s42003-022-04297-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/24/2022] [Indexed: 12/13/2022] Open
Abstract
How global and local activity interact with each other is a common question in complex systems like climate and economy. Analogously, the brain too displays 'global' activity that interacts with local-regional activity and modulates behavior. The brain's global activity, investigated as global signal in fMRI, so far, has mainly been conceived as non-neuronal noise. We here review the findings from healthy and clinical populations to demonstrate the neural basis and functions of global signal to brain and behavior. We show that global signal (i) is closely coupled with physiological signals and modulates the arousal level; and (ii) organizes an elaborated dynamic topography and coordinates the different forms of cognition. We also postulate a Dual-Layer Model including both background and surface layers. Together, the latest evidence strongly suggests the need to go beyond the view of global signal as noise by embracing a dual-layer model with background and surface layer.
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Affiliation(s)
- Jianfeng Zhang
- grid.263488.30000 0001 0472 9649Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China ,grid.263488.30000 0001 0472 9649School of Psychology, Shenzhen University, Shenzhen, China
| | - Georg Northoff
- grid.13402.340000 0004 1759 700XMental Health Center, Zhejiang University School of Medicine, Hangzhou, China ,grid.28046.380000 0001 2182 2255Institute of Mental Health Research, University of Ottawa, Ottawa, Canada ,grid.410595.c0000 0001 2230 9154Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
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33
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Abnormal Dynamic Functional Network Connectivity in Adults with Autism Spectrum Disorder. Clin Neuroradiol 2022; 32:1087-1096. [PMID: 35543744 DOI: 10.1007/s00062-022-01173-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/12/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE This study sought to explore changes of brain dynamic functional network connectivity (dFNC) in adults with autism spectrum disorder (ASD) and investigate their relationship with clinical manifestations. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 78 adult ASD patients from autism brain imaging data exchange datasets, and 65 age-matched healthy controls subjects from the local community. Independent component analysis was conducted to evaluate dFNC patterns on the basis of 13 independent components (ICs) within 11 resting-state networks (RSN), namely, auditory network (AUDN), basal ganglia network (BGN), language network (LN), sensorimotor network (SMN), precuneus network (PUCN), salience network (SN), visuospatial network (VSN), dorsal default mode network (dDMN), high visual network (hVIS), primary visual network (pVIS), ventral default mode network (vDMN). Fraction time, mean dwell time, number of transitions, and RSN connectivity were calculated for group comparisons. Correlation analyses were performed between abnormal metrics and autism diagnostic observation schedule (ADOS) scores. RESULTS Compared with controls, ASD patients had higher fraction time and mean dwell time in state 2 (P = 0.017, P = 0.014). Reduced dFNC was found in the SMN with PUCN, SMN with hVIS, and increased dFNC was observed in the dDMN with SN in state 2 in the ASD group. Fraction time and mean dwell time was positively correlated with stereotyped behavior scores of ADOS. CONCLUSION The findings demonstrated the importance of evaluating transient alterations in specific neural networks of adult ASD patients. The abnormal metrics and connectivity may be related to symptoms such as stereotyped behavior.
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Persichetti AS, Shao J, Gotts SJ, Martin A. Maladaptive Laterality in Cortical Networks Related to Social Communication in Autism Spectrum Disorder. J Neurosci 2022; 42:9045-9052. [PMID: 36257690 PMCID: PMC9732822 DOI: 10.1523/jneurosci.1229-22.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/29/2022] [Accepted: 09/29/2022] [Indexed: 01/05/2023] Open
Abstract
Neuroimaging studies of individuals with autism spectrum disorders (ASDs) consistently find an aberrant pattern of reduced laterality in brain networks that support functions related to social communication and language. However, it is unclear how the underlying functional organization of these brain networks is altered in ASD individuals. We tested four models of reduced laterality in a social communication network in 70 ASD individuals (14 females) and a control group of the same number of tightly matched typically developing (TD) individuals (19 females) using high-quality resting-state fMRI data and a method of measuring patterns of functional laterality across the brain. We found that a functionally defined social communication network exhibited the typical pattern of left laterality in both groups, whereas there was a significant increase in within- relative to across-hemisphere connectivity of homotopic regions in the right hemisphere in ASD individuals. Furthermore, greater within- relative to across-hemisphere connectivity in the left hemisphere was positively correlated with a measure of verbal ability in both groups, whereas greater within- relative to across-hemisphere connectivity in the right hemisphere in ASD, but not TD, individuals was negatively correlated with the same verbal measure. Crucially, these differences in patterns of laterality were not found in two other functional networks and were specifically correlated to a measure of verbal ability but not metrics of other core components of the ASD phenotype. These results suggest that previous reports of reduced laterality in social communication regions in ASD is because of the two hemispheres functioning more independently than seen in TD individuals, with the atypical right-hemisphere network component being maladaptive.SIGNIFICANCE STATEMENT A consistent neuroimaging finding in individuals with ASD is an aberrant pattern of reduced laterality of the brain networks that support functions related to social communication and language. We tested four models of reduced laterality in a social communication network in ASD individuals and a TD control group using high-quality resting-state fMRI data. Our results suggest that reduced laterality of social communication regions in ASD may be because of the two hemispheres functioning more independently than seen in TD individuals, with atypically greater within- than across-hemisphere connectivity in the right hemisphere being maladaptive.
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Affiliation(s)
- Andrew S Persichetti
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Jiayu Shao
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
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Tubadji A, Boy F, Webber DJ. Narrative Economics, Public Policy and Mental Health. APPLIED RESEARCH IN QUALITY OF LIFE 2022; 18:43-70. [PMID: 36340746 PMCID: PMC9617050 DOI: 10.1007/s11482-022-10109-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/24/2022] [Indexed: 05/31/2023]
Abstract
General public's mental health can be affected by the public policy response to a pandemic threat. Britain, Italy and Sweden have had very distinct approaches to the COVID-19 pandemic: early lock-down, delayed lock-down and no-lock-down. We develop a novel narrative economics of language Culture-Based Development approach, and using Google trend data for seed keywords, death and suicide, we reach two main conclusions: (i) while countries had a pre-existing culturally relative disposition towards death-related anxiety, the sensitivity to the public policy towards COVID-19 was also country specific; (ii) however, significant spillovers from one specific national lockdown public policy to another country's mental health are identified.
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Affiliation(s)
- Annie Tubadji
- Economic Department, Swansea University, Swansea, UK
| | - Frédéric Boy
- Economic Department, Swansea University, Swansea, UK
- University College London, London, UK
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36
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Alamdari SB, Sadeghi Damavandi M, Zarei M, Khosrowabadi R. Cognitive theories of autism based on the interactions between brain functional networks. Front Hum Neurosci 2022; 16:828985. [PMID: 36310850 PMCID: PMC9614840 DOI: 10.3389/fnhum.2022.828985] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 08/15/2022] [Indexed: 12/03/2022] Open
Abstract
Cognitive functions are directly related to interactions between the brain's functional networks. This functional organization changes in the autism spectrum disorder (ASD). However, the heterogeneous nature of autism brings inconsistency in the findings, and specific pattern of changes based on the cognitive theories of ASD still requires to be well-understood. In this study, we hypothesized that the theory of mind (ToM), and the weak central coherence theory must follow an alteration pattern in the network level of functional interactions. The main aim is to understand this pattern by evaluating interactions between all the brain functional networks. Moreover, the association between the significantly altered interactions and cognitive dysfunctions in autism is also investigated. We used resting-state fMRI data of 106 subjects (5-14 years, 46 ASD: five female, 60 HC: 18 female) to define the brain functional networks. Functional networks were calculated by applying four parcellation masks and their interactions were estimated using Pearson's correlation between pairs of them. Subsequently, for each mask, a graph was formed based on the connectome of interactions. Then, the local and global parameters of the graph were calculated. Finally, statistical analysis was performed using a two-sample t-test to highlight the significant differences between autistic and healthy control groups. Our corrected results show significant changes in the interaction of default mode, sensorimotor, visuospatial, visual, and language networks with other functional networks that can support the main cognitive theories of autism. We hope this finding sheds light on a better understanding of the neural underpinning of autism.
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Affiliation(s)
| | | | - Mojtaba Zarei
- University of Southern Denmark, Neurology Unit, Odense, Denmark
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
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Cheng L, Zhan L, Huang L, Zhang H, Sun J, Huang G, Wang Y, Li M, Li H, Gao Y, Jia X. The atypical functional connectivity of Broca's area at multiple frequency bands in autism spectrum disorder. Brain Imaging Behav 2022; 16:2627-2636. [PMID: 36163448 DOI: 10.1007/s11682-022-00718-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/15/2022] [Indexed: 11/30/2022]
Abstract
As a developmental disorder, autism spectrum disorder (ASD) has drawn much attention due to its severe impacts on one's language capacity. Broca's area, an important brain region of the language network, is largely involved in language-related functions. Using the Autism Brain Image Data Exchange (ABIDE) dataset, a mega-analysis was performed involving a total of 1454 participants (including 618 individuals with ASD and 836 healthy controls (HCs). To detect the neural pathophysiological mechanism of ASD from the perspective of language, we conducted a functional connectivity (FC) analysis with Broca's area as the seed in multiple frequency bands (conventional: 0.01-0.08 Hz; slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz). We found that compared with HC, ASD patients demonstrated increased FC in the left thalamus, left precuneus, left anterior cingulate and paracingulate gyri, and left medial orbital of the superior frontal gyrus in the conventional frequency band (0.01-0.08 Hz). The results of the slow-5 frequency band (0.01-0.027 Hz) presented increased FC values of the left precuneus, left medial orbital of the superior frontal gyrus, right medial orbital of the superior frontal gyrus and right thalamus. No significant cluster was detected in the slow-4 frequency band (0.027-0.073 Hz). In conclusion, the abnormal functional connectivity in patients with ASD has frequency-specific properties. Furthermore, the slow-5 frequency band (0.01-0.027 Hz) mainly contributed to the findings of the conventional frequency band (0.01-0.08 Hz). The current study might shed new light on the neural pathophysiological mechanism of language impairments in people with ASD.
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Affiliation(s)
- Lulu Cheng
- School of Foreign Studies, China University of Petroleum (East China), Qingdao, 266580, China.,Shanghai Center for Research in English Language Education, Shanghai International Studies University, Shanghai, China
| | - Linlin Zhan
- Faculty of Western Languages, Heilongjiang University, Harbin, China
| | - Lina Huang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Hongqiang Zhang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Guofeng Huang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Yadan Wang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Mengting Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China.,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China
| | - Huayun Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China.,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China
| | - Yanyan Gao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China. .,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China.
| | - Xize Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China. .,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, 321004, China.
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Duan X, Chen H. Mapping brain functional and structural abnormities in autism spectrum disorder: moving toward precision treatment. PSYCHORADIOLOGY 2022; 2:78-85. [PMID: 38665600 PMCID: PMC10917159 DOI: 10.1093/psyrad/kkac013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/09/2022] [Accepted: 10/12/2022] [Indexed: 04/28/2024]
Abstract
Autism spectrum disorder (ASD) is a formidable challenge for psychiatry and neuroscience because of its high prevalence, lifelong nature, complexity, and substantial heterogeneity. A major goal of neuroimaging studies of ASD is to understand the neurobiological underpinnings of this disorder from multi-dimensional and multi-level perspectives, by investigating how brain anatomy, function, and connectivity are altered in ASD, and how they vary across the population. However, ongoing debate exists within those studies, and neuroimaging findings in ASD are often contradictory. Over the past decade, we have dedicated to delineate a comprehensive and consistent mapping of the abnormal structure and function of the autistic brain, and this review synthesizes the findings across our studies reaching a consensus that the "social brain" are the most affected regions in the autistic brain at different levels and modalities. We suggest that the social brain network can serve as a plausible biomarker and potential target for effective intervention in individuals with ASD.
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Affiliation(s)
- Xujun Duan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Huafu Chen
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, PR China
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Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on Autism Spectrum Disorder Neuroimaging. DISEASE MARKERS 2022; 2022:3372217. [PMID: 35899177 PMCID: PMC9313970 DOI: 10.1155/2022/3372217] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 06/26/2022] [Accepted: 06/30/2022] [Indexed: 11/17/2022]
Abstract
Background Autism spectrum disorder (ASD) is a chronic developmental disability caused by differences in the brain. The gold standard for the diagnosis of this condition is based on behavioral science, but research on the application of neurological detection to diagnose the atypical nervous system of ASD is ongoing. ASD neuroimaging research involves the examination of the brain's structure, functional connections, and neurometabolic. However, limited medical resource and the unique heterogeneity of ASD have resulted in many challenges when neuroimaging is utilized. Objective This bibliometric study is aimed at summarizing themes and trends in research on autism spectrum disorder neuroimaging and at proposing potential directions for future inquiry. Methods Citations were downloaded from the Web of Science Core Collection database on neuroimaging published from January 1, 2012, to December 31, 2021. The retrieved information was analyzed using Bibliometric.com, CiteSpace.5.8. R3, and VOS viewer. Results A total of 1,363 papers were published across 58 regions. The United States was the leading source of publications. The League of European Research Universities published the largest number of articles (171). Burst keywords from 2018 to 2021 include identification and network. The clusters of references that continued into 2020 included graph theory, functional connectivity, and classification, which represent key research topics. Conclusions Imaging data is being used to identify neuro-network models with higher accuracy for ASD discrimination. Functional near-infrared imaging is advantageous compared to other neuroimaging. In the future, research on systematic and accurate computer-aided diagnosis technology should be encouraged. Moreover, the study of neuroimaging of ASD in different psychological and behavioral states can inspire new ideas about the diagnosis and intervention training of ASD and should be explored.
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Csumitta KD, Gotts SJ, Clasen LS, Martin A, Raitano Lee N. Youth with Down syndrome display widespread increased functional connectivity during rest. Sci Rep 2022; 12:9836. [PMID: 35701489 PMCID: PMC9198034 DOI: 10.1038/s41598-022-13437-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 05/16/2022] [Indexed: 12/31/2022] Open
Abstract
Studies of resting-state functional connectivity in young people with Down syndrome (DS) have yielded conflicting results. Some studies have found increased connectivity while others have found a mix of increased and decreased connectivity. No studies have examined whole-brain connectivity at the voxel level in youth with DS during an eyes-open resting-state design. Additionally, no studies have examined the relationship between connectivity and network selectivity in youth with DS. Thus, the current study sought to fill this gap in the literature. Nineteen youth with DS (Mage = 16.5; range 7-23; 13 F) and 33 typically developing (TD) youth (Mage = 17.5; range 6-24; 18 F), matched on age and sex, completed a 5.25-min eyes-open resting-state fMRI scan. Whole-brain functional connectivity (average Pearson correlation of each voxel with every other voxel) was calculated for each individual and compared between groups. Network selectivity was then calculated and correlated with functional connectivity for the DS group. Results revealed that whole-brain functional connectivity was significantly higher in youth with DS compared to TD controls in widespread regions throughout the brain. Additionally, participants with DS had significantly reduced network selectivity compared to TD peers, and selectivity was significantly related to connectivity in all participants. Exploratory behavioral analyses revealed that regions showing increased connectivity in DS predicted Verbal IQ, suggesting differences in connectivity may be related to verbal abilities. These results indicate that network organization is disrupted in youth with DS such that disparate networks are overly connected and less selective, suggesting a potential target for clinical interventions.
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Affiliation(s)
- Kelsey D Csumitta
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, 19103, USA.
| | - Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Liv S Clasen
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Nancy Raitano Lee
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, 19103, USA.
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41
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Peng L, Liu X, Ma D, Chen X, Xu X, Gao X. The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification. Front Neurosci 2022; 16:913377. [PMID: 35600614 PMCID: PMC9120576 DOI: 10.3389/fnins.2022.913377] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/20/2022] [Indexed: 11/25/2022] Open
Abstract
Objective Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance. Methods We propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information. Results The experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network. Conclusion This work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.
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Affiliation(s)
- Liling Peng
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Xiao Liu
- School of Business Administration, José Rizal University, Mandaluyong, Philippines
| | - Di Ma
- College of Information Science and Technology, Nanjing Forestry University, Nanjing, China
| | - Xiaofeng Chen
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
| | - Xiaowen Xu
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Xiaowen Xu,
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
- Xin Gao,
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42
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Krendl AC, Betzel RF. Social cognitive network neuroscience. Soc Cogn Affect Neurosci 2022; 17:510-529. [PMID: 35352125 PMCID: PMC9071476 DOI: 10.1093/scan/nsac020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/27/2022] [Accepted: 03/10/2022] [Indexed: 12/31/2022] Open
Abstract
Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.
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Affiliation(s)
- Anne C Krendl
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
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43
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Rolison M, Lacadie C, Chawarska K, Spann M, Scheinost D. Atypical Intrinsic Hemispheric Interaction Associated with Autism Spectrum Disorder Is Present within the First Year of Life. Cereb Cortex 2022; 32:1212-1222. [PMID: 34424949 PMCID: PMC8924430 DOI: 10.1093/cercor/bhab284] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is characterized by atypical connectivity lateralization of functional networks. However, previous studies have not directly investigated if differences in specialization between ASD and typically developing (TD) peers are present in infancy, leaving the timing of onset of these differences relatively unknown. We studied the hemispheric asymmetries of connectivity in children with ASD and infants later meeting the diagnostic criteria for ASD. Analyses were performed in 733 children with ASD and TD peers and in 71 infants at high risk (HR) or normal risk (NR) for ASD, with data collected at 1 month and 9 months of age. Comparing children with ASD (n = 301) to TDs (n = 432), four regions demonstrated group differences in connectivity: posterior cingulate cortex (PCC), posterior superior temporal gyrus, extrastriate cortex, and anterior prefrontal cortex. At 1 month, none of these regions exhibited group differences between ASD (n = 10), HR-nonASD (n = 15), or NR (n = 18) infants. However, by 9 months, the PCC and extrastriate exhibited atypical connectivity in ASD (n = 11) and HR-nonASD infants (n = 24) compared to NR infants (n = 22). Connectivity did not correlate with symptoms in either sample. Our results demonstrate that differences in network asymmetries associated with ASD risk are observable prior to the age of a reliable clinical diagnosis.
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Affiliation(s)
- Max Rolison
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06519, USA
| | - Cheryl Lacadie
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - Katarzyna Chawarska
- Child Study Center, Yale School of Medicine, New Haven, CT 06519, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06510, USA
- Department of Pediatrics, Yale School of Medicine, New Haven, CT 06511, USA
| | - Marisa Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT 06519, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
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Wang MB, Boring MJ, Ward MJ, Richardson RM, Ghuman AS. Deep brain stimulation for parkinson's disease induces spontaneous cortical hypersynchrony in extended motor and cognitive networks. Cereb Cortex 2022; 32:4480-4491. [PMID: 35136991 PMCID: PMC9574237 DOI: 10.1093/cercor/bhab496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 12/04/2021] [Accepted: 12/05/2021] [Indexed: 11/14/2022] Open
Abstract
The mechanism of action of deep brain stimulation (DBS) to the basal ganglia for Parkinson's disease remains unclear. Studies have shown that DBS decreases pathological beta hypersynchrony between the basal ganglia and motor cortex. However, little is known about DBS's effects on long range corticocortical synchronization. Here, we use machine learning combined with graph theory to compare resting-state cortical connectivity between the off and on-stimulation states and to healthy controls. We found that turning DBS on increased high beta and gamma band synchrony (26 to 50 Hz) in a cortical circuit spanning the motor, occipitoparietal, middle temporal, and prefrontal cortices. The synchrony in this network was greater in DBS on relative to both DBS off and controls, with no significant difference between DBS off and controls. Turning DBS on also increased network efficiency and strength and subnetwork modularity relative to both DBS off and controls in the beta and gamma band. Thus, unlike DBS's subcortical normalization of pathological basal ganglia activity, it introduces greater synchrony relative to healthy controls in cortical circuitry that includes both motor and non-motor systems. This increased high beta/gamma synchronization may reflect compensatory mechanisms related to DBS's clinical benefits, as well as undesirable non-motor side effects.
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Affiliation(s)
- Maxwell B Wang
- Address correspondence to Maxwell B Wang, BS, Medical Scientist Training Program, University of Pittsburgh School of Medicine, Program of Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15213. Tel: 815-200-9533;
| | - Matthew J Boring
- Center for Neuroscience at the University of Pittsburgh, Pittsburgh, PA 15213, USA,Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213, USA,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Michael J Ward
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - R Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA,Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA,Harvard Medical School, Boston, MA 02115, USA
| | - Avniel Singh Ghuman
- Program of Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15213, USA,Center for Neuroscience at the University of Pittsburgh, Pittsburgh, PA 15213, USA,Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213, USA,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
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45
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Ramot M, Martin A. Closed-loop neuromodulation for studying spontaneous activity and causality. Trends Cogn Sci 2022; 26:290-299. [PMID: 35210175 PMCID: PMC9396631 DOI: 10.1016/j.tics.2022.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/30/2022] [Accepted: 01/31/2022] [Indexed: 01/01/2023]
Abstract
Having established that spontaneous brain activity follows meaningful coactivation patterns and correlates with behavior, researchers have turned their attention to understanding its function and behavioral significance. We suggest closed-loop neuromodulation as a neural perturbation tool uniquely well suited for this task. Closed-loop neuromodulation has primarily been viewed as an interventionist tool to teach subjects to directly control their own brain activity. We examine an alternative operant conditioning model of closed-loop neuromodulation which, through implicit feedback, can manipulate spontaneous activity at the network level, without violating the spontaneous or endogenous nature of the signal, thereby providing a direct test of network causality.
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46
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Hermiller MS, Dave S, Wert SL, VanHaerents S, Riley M, Weintraub S, Mesulam MM, Voss JL. Evidence from theta-burst stimulation that age-related de-differentiation of the hippocampal network is functional for episodic memory. Neurobiol Aging 2022; 109:145-157. [PMID: 34740076 PMCID: PMC8671378 DOI: 10.1016/j.neurobiolaging.2021.09.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 05/11/2021] [Accepted: 09/17/2021] [Indexed: 01/03/2023]
Abstract
Episodic memory is supported by hippocampal interactions with a distributed network. Aging is associated with memory decline and network de-differentiation. However, the role of de-differentiation in memory decline has not been directly tested. We reasoned that hippocampal network-targeted stimulation could test these theories, as age-related changes in the network response to stimulation would indicate network reorganization, and corresponding changes in memory would suggest that this reorganization is functional. We compared effects of stimulation on fMRI connectivity and memory in younger versus older adults. Theta-burst network-targeted stimulation of left lateral parietal cortex selectively increased hippocampal network connectivity and modulated memory in younger adults. In contrast, stimulation in older adults increased connectivity throughout the brain, without network selectivity, and did not influence memory. These findings provide evidence that network responses to stimulation are de-differentiated in aging and suggest that age-related de-differentiation is relevant for memory. This manuscript is part of the Special Issue entitled "Cognitive Neuroscience of Healthy and Pathological Aging" edited by Drs. M. N. Rajah, S. Belleville, and R. Cabeza. This article is part of the Virtual Special Issue titled COGNITIVE NEUROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.
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Affiliation(s)
- Molly S. Hermiller
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL,Department of Biomedical Engineering, Columbia University, New York, NY,Department of Psychology, Columbia University, New York, NY,Corresponding author: Molly S. Hermiller, 615 West 131st Street, Studebaker, 4th Floor, New York, NY 10027,
| | - Shruti Dave
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Stephanie L. Wert
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Stephen VanHaerents
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Michaela Riley
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Sandra Weintraub
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL,Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - M.-Marsel Mesulam
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL,Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Joel L. Voss
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL,Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL,Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL
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47
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Matsuo K, Shinoda Y, Abolhassani N, Nakabeppu Y, Fukunaga K. Transcriptome Analysis in Hippocampus of Rats Prenatally Exposed to Valproic Acid and Effects of Intranasal Treatment of Oxytocin. Front Psychiatry 2022; 13:859198. [PMID: 35432011 PMCID: PMC9005872 DOI: 10.3389/fpsyt.2022.859198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/04/2022] [Indexed: 11/19/2022] Open
Abstract
Autism spectrum disorder (ASD) is a heterogeneous disorder characterized by repetitive behaviors and social impairments, often accompanied by learning disabilities. It has been documented that the neuropeptide oxytocin (OXT) ameliorates core symptoms in patients with ASD. We recently reported that chronic administration of intranasal OXT reversed social and learning impairments in prenatally valproic acid (VPA)-exposed rats. However, the underlying molecular mechanisms remain unclear. Here, we explored molecular alterations in the hippocampus of rats and the effects of chronic administration of intranasal OXT (12 μg/kg/d). Microarray analyses revealed that prenatal VPA exposure altered gene expression, a part of which is suggested as a candidate in ASD and is involved in key features including memory, developmental processes, and epilepsy. OXT partly improved the expression of these genes, which were predicted to interact with those involved in social behaviors and hippocampal-dependent memory. Collectively, the present study documented molecular profiling in the hippocampus related to ASD and improvement by chronic treatment with OXT.
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Affiliation(s)
- Kazuya Matsuo
- Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
| | - Yasuharu Shinoda
- Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
| | - Nona Abolhassani
- Division of Neurofunctional Genomics, Department of Immunobiology and Neuroscience, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Yusaku Nakabeppu
- Division of Neurofunctional Genomics, Department of Immunobiology and Neuroscience, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Kohji Fukunaga
- Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
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48
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Attenuated link between the medial prefrontal cortex and the amygdala in children with autism spectrum disorder: Evidence from effective connectivity within the "social brain". Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110147. [PMID: 33096157 DOI: 10.1016/j.pnpbp.2020.110147] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/21/2020] [Accepted: 10/16/2020] [Indexed: 01/27/2023]
Abstract
Although accumulating neuroimaging studies have reported that social behavior deficits in children with autism spectrum disorders (ASD) are commonly attributed to the dysfunction of social brain regions underlying social cognition, the dynamic interaction within the social brain network and its association with social deficits remain unclear. Here, resting-state functional magnetic resonance imaging data obtained from Autism Brain Imaging Data Exchange (I and II) were analyzed in 105 children with ASD and 102 demographically matched typically developing controls (TDCs) (age range: 7-12 years old). Term-based meta-analysis combined the prior reference and anatomical labeling were used to define the regions of interests of the social brain network, and multivariate Granger causality analysis with blind deconvolution was employed to assess the effective connectivity within the social brain network in the ASD and TDC groups. Between-group comparison revealed significantly attenuated effective connectivity from the medial prefrontal cortex (mPFC) to the bilateral amygdala in children with the ASD group compared with TDC group. In addition, raw values of the effective connectivity from the mPFC to the bilateral amygdala were used to predict social deficits in ASD. Our findings indicate the impaired mPFC-amygdala pathway and its association with social deficits in children with ASD and provide a new perspective into the neuropathology of the developing autistic brain.
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Shoham A, Kliger L, Yovel G. Learning Faces as Concepts Improves Face Recognition by Engaging the Social Brain Network. Soc Cogn Affect Neurosci 2021; 17:nsab096. [PMID: 34402904 PMCID: PMC8881637 DOI: 10.1093/scan/nsab096] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/08/2021] [Accepted: 08/16/2021] [Indexed: 11/12/2022] Open
Abstract
Face recognition benefits from associating social information to faces during learning. This has been demonstrated by better recognition for faces that underwent social than perceptual evaluations. Two hypotheses were proposed to account for this effect. According to the feature-elaboration hypothesis, social-evaluations encourage elaborated processing of perceptual information from faces (Winograd, 1981). According to a social-representation hypothesis, social-evaluations convert faces from a perceptual representation to a socially meaningful representation of a person. To decide between these two hypotheses, we ran a functional MRI study in which we functionally localized the posterior face-selective brain areas and social processing brain areas. Participants watched video-clips of young adults and were asked to study them for a recognition test, while making either perceptual evaluations or social evaluations about them. During the fMRI scan, participants performed an old/new recognition test. Behavioural findings replicated better recognition for faces that underwent social then perceptual evaluations. fMRI results showed higher response during the recognition phase for the faces that were learned socially than perceptually, in the social-brain network but not in posterior face-selective network. These results support the social-representation hypothesis and highlight the important role that social processing mechanisms, rather than purely perceptual processes, play in face recognition.
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Affiliation(s)
- Adva Shoham
- The School of Psychological Sciences, Tel Aviv University, Tel-Aviv 6997801, Israel
| | - Libi Kliger
- The School of Psychological Sciences, Tel Aviv University, Tel-Aviv 6997801, Israel
| | - Galit Yovel
- The School of Psychological Sciences, Tel Aviv University, Tel-Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel-Aviv 6997801, Israel
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50
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Whitman ET, Liu S, Torres E, Warling A, Wilson K, Nadig A, McDermott C, Clasen LS, Blumenthal JD, Lalonde FM, Gotts SJ, Martin A, Raznahan A. Resting-State Functional Connectivity and Psychopathology in Klinefelter Syndrome (47, XXY). Cereb Cortex 2021; 31:4180-4190. [PMID: 34009243 PMCID: PMC8485146 DOI: 10.1093/cercor/bhab077] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Klinefelter syndrome (47, XXY; henceforth: XXY syndrome) is a high-impact but poorly understood genetic risk factor for neuropsychiatric impairment. Here, we provide the first study to map alterations of functional brain connectivity in XXY syndrome and relate these changes to brain anatomy and psychopathology. We used resting-state functional magnetic resonance imaging data from 75 individuals with XXY and 84 healthy XY males to 1) implement a brain-wide screen for altered global resting-state functional connectivity (rsFC) in XXY versus XY males and 2) decompose these alterations through seed-based analysis. We then compared these rsFC findings with measures of regional brain anatomy, psychopathology, and cognition. XXY syndrome was characterized by increased global rsFC in the left dorsolateral prefrontal cortex (DLPFC)-reflecting DLPFC overconnectivity with diverse rsFC networks. Functional overconnectivity was partly coupled to co-occurring regional volumetric changes in XXY syndrome, and variation in DLPFC-precuneus rsFC was correlated with the severity of psychopathology. By providing the first view of altered rsFC in XXY syndrome and contextualizing observed changes relative to neuroanatomy and behavior, our study helps to advance biological understanding of XXY syndrome-both as a disorder in its own right and more broadly as a model of genetic risk for psychopathology.
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Affiliation(s)
- Ethan T Whitman
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Siyuan Liu
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Erin Torres
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Allysa Warling
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Kathleen Wilson
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Ajay Nadig
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Cassidy McDermott
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Liv S Clasen
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Jonathan D Blumenthal
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - François M Lalonde
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20814, USA
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