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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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2
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Shen Y, Peng L, Chen H, Xu P, Lv K, Xu Z, Shen H, Ji G, Xiong J, Hu D, Li Y, Lou M, Zeng LL, Qu L. Effects of long-term closed and socially isolating spaceflight analog environment on default mode network connectivity as indicated by fMRI. iScience 2024; 27:109617. [PMID: 38660401 PMCID: PMC11039341 DOI: 10.1016/j.isci.2024.109617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 03/18/2024] [Accepted: 03/26/2024] [Indexed: 04/26/2024] Open
Abstract
Long-term manned spaceflight and extraterrestrial planet settlement become the focus of space powers. However, the potential influence of closed and socially isolating spaceflight on the brain function remains unclear. A 180-day controlled ecological life support system integrated experiment was conducted, establishing a spaceflight analog environment to explore the effect of long-term socially isolating living. Three crewmembers were enrolled and underwent resting-state fMRI scanning before and after the experiment. We performed both seed-based and network-based analyses to investigate the functional connectivity (FC) changes of the default mode network (DMN), considering its key role in multiple higher-order cognitive functions. Compared with normal controls, the leader of crewmembers exhibited significantly reduced within-DMN and between-DMN FC after the experiment, while two others exhibited opposite trends. Moreover, individual differences of FC changes were further supported by evidence from behavioral analyses. The findings may shed new light on the development of psychological protection for space exploration.
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Affiliation(s)
- Yunxia Shen
- Department of Medical Imaging, Longgang Central Hospital of Shenzhen, Shenzhen, Guangdong 518116, China
| | - Limin Peng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Hailong Chen
- State Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing, Beijing 100094, China
| | - Pengfei Xu
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Shenzhen University, Shenzhen, Guangdong 518060, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, Guangdong 518057, China
| | - Ke Lv
- State Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing, Beijing 100094, China
| | - Zi Xu
- Department of Health Technology Research and Development, Space Institute of Southern China, Shenzhen, Guangdong 518117, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Guohua Ji
- State Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing, Beijing 100094, China
| | - Jianghui Xiong
- State Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing, Beijing 100094, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Yinghui Li
- State Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing, Beijing 100094, China
| | - Mingwu Lou
- Department of Medical Imaging, Longgang Central Hospital of Shenzhen, Shenzhen, Guangdong 518116, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Lina Qu
- State Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing, Beijing 100094, China
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3
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Qian S, Yang Q, Cai C, Dong J, Cai S. Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study. Brain Sci 2024; 14:507. [PMID: 38790485 PMCID: PMC11118919 DOI: 10.3390/brainsci14050507] [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: 04/22/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.
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Affiliation(s)
| | | | | | | | - Shuhui Cai
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (S.Q.); (Q.Y.); (C.C.); (J.D.)
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4
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Nagai Y, Kirino E, Tanaka S, Usui C, Inami R, Inoue R, Hattori A, Uchida W, Kamagata K, Aoki S. Functional connectivity in autism spectrum disorder evaluated using rs-fMRI and DKI. Cereb Cortex 2024; 34:129-145. [PMID: 38012112 PMCID: PMC11065111 DOI: 10.1093/cercor/bhad451] [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: 07/22/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 11/29/2023] Open
Abstract
We evaluated functional connectivity (FC) in patients with adult autism spectrum disorder (ASD) using resting-state functional MRI (rs-fMRI) and diffusion kurtosis imaging (DKI). We acquired rs-fMRI data from 33 individuals with ASD and 33 healthy controls (HC) and DKI data from 18 individuals with ASD and 17 HC. ASD showed attenuated FC between the right frontal pole (FP) and the bilateral temporal fusiform cortex (TFusC) and enhanced FC between the right thalamus and the bilateral inferior division of lateral occipital cortex, and between the cerebellar vermis and the right occipital fusiform gyrus (OFusG) and the right lingual gyrus, compared with HC. ASD demonstrated increased axial kurtosis (AK) and mean kurtosis (MK) in white matter (WM) tracts, including the right anterior corona radiata (ACR), forceps minor (FM), and right superior longitudinal fasciculus (SLF). In ASD, there was also a significant negative correlation between MK and FC between the cerebellar vermis and the right OFusG in the corpus callosum, FM, right SLF and right ACR. Increased DKI metrics might represent neuroinflammation, increased complexity, or disrupted WM tissue integrity that alters long-distance connectivity. Nonetheless, protective or compensating adaptations of inflammation might lead to more abundant glial cells and cytokine activation effectively alleviating the degeneration of neurons, resulting in increased complexity. FC abnormality in ASD observed in rs-fMRI may be attributed to microstructural alterations of the commissural and long-range association tracts in WM as indicated by DKI.
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Affiliation(s)
- Yasuhito Nagai
- Department of Psychiatry, Juntendo University School of Medicine, 2-1-1 Hongo Bunkyo-ku Tokyo 113-8421, Japan
| | - Eiji Kirino
- Department of Psychiatry, Juntendo University School of Medicine, 2-1-1 Hongo Bunkyo-ku Tokyo 113-8421, Japan
- Department of Psychiatry, Juntendo University Shizuoka Hospital, 1129 Nagaoka Izunokuni-shi Shizuoka 410-2295, Japan
- Juntendo Institute of Mental Health, 700-1 Fukuroyama Koshigaya-shi Saitama 343-0032, Japan
| | - Shoji Tanaka
- Department of Information and Communication Sciences, Sophia University, 7-1 Kioi-cho Chiyoda-ku Tokyo 102-8554, Japan
| | - Chie Usui
- Department of Psychiatry, Juntendo University School of Medicine, 2-1-1 Hongo Bunkyo-ku Tokyo 113-8421, Japan
| | - Rie Inami
- Department of Psychiatry, Juntendo University School of Medicine, 2-1-1 Hongo Bunkyo-ku Tokyo 113-8421, Japan
| | - Reiichi Inoue
- Juntendo Institute of Mental Health, 700-1 Fukuroyama Koshigaya-shi Saitama 343-0032, Japan
| | - Aki Hattori
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo Bunkyo-ku Tokyo 113-8421, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo Bunkyo-ku Tokyo 113-8421, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo Bunkyo-ku Tokyo 113-8421, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo Bunkyo-ku Tokyo 113-8421, Japan
- Faculty of Health Data Science, Juntendo University, 6-8-1 Hinode Urayasu-shi Chiba 279-0013, Japan
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5
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Tsang T, Green SA, Liu J, Lawrence K, Jeste S, Bookheimer SY, Dapretto M. Salience network connectivity is altered in 6-week-old infants at heightened likelihood for developing autism. Commun Biol 2024; 7:485. [PMID: 38649483 PMCID: PMC11035613 DOI: 10.1038/s42003-024-06016-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/06/2024] [Indexed: 04/25/2024] Open
Abstract
Converging evidence implicates disrupted brain connectivity in autism spectrum disorder (ASD); however, the mechanisms linking altered connectivity early in development to the emergence of ASD symptomatology remain poorly understood. Here we examined whether atypicalities in the Salience Network - an early-emerging neural network involved in orienting attention to the most salient aspects of one's internal and external environment - may predict the development of ASD symptoms such as reduced social attention and atypical sensory processing. Six-week-old infants at high likelihood of developing ASD based on family history exhibited stronger Salience Network connectivity with sensorimotor regions; infants at typical likelihood of developing ASD demonstrated stronger Salience Network connectivity with prefrontal regions involved in social attention. Infants with higher connectivity with sensorimotor regions had lower connectivity with prefrontal regions, suggesting a direct tradeoff between attention to basic sensory versus socially-relevant information. Early alterations in Salience Network connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with vulnerability to ASD.
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Affiliation(s)
| | - Shulamite A Green
- Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Cognitive Neuroscience, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Katherine Lawrence
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Shafali Jeste
- Children's Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, CA, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Cognitive Neuroscience, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mirella Dapretto
- Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
- Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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6
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Yoon N, Kim S, Oh MR, Kim M, Lee JM, Kim BN. Intrinsic network abnormalities in children with autism spectrum disorder: an independent component analysis. Brain Imaging Behav 2024; 18:430-443. [PMID: 38324235 DOI: 10.1007/s11682-024-00858-x] [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] [Accepted: 01/16/2024] [Indexed: 02/08/2024]
Abstract
Aberrant intrinsic brain networks are consistently observed in individuals with autism spectrum disorder. However, studies examining the strength of functional connectivity across brain regions have yielded conflicting results. Therefore, this study aimed to investigate the functional connectivity of the resting brain in children with low-functioning autism, including during the early developmental stages. We explored the functional connectivity of 43 children with autism spectrum disorder and 54 children with typical development aged 2 to 12 years using resting-state functional magnetic resonance imaging data. We used independent component analysis to classify the brain regions into six intrinsic networks and analyzed the functional connectivity within each network. Moreover, we analyzed the relationship between functional connectivity and clinical scores. In children with autism, the under-connectivities were observed within several brain networks, including the cognitive control, default mode, visual, and somatomotor networks. In contrast, we found over-connectivities between the subcortical, visual, and somatomotor networks in children with autism compared with children with typical development. Moderate effect sizes were observed in entire networks (Cohen's d = 0.43-0.77). These network alterations were significantly correlated with clinical scores such as the communication sub-score (r = - 0.442, p = 0.045) and the calibrated severity score (r = - 0.435, p = 0.049) of the Autism Diagnostic Observation Schedule. These opposing results observed based on the brain areas suggest that aberrant neurodevelopment proceeds in various ways depending on the functional brain regions in individuals with autism spectrum disorder.
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Affiliation(s)
- Narae Yoon
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 101 Daehakno, Jongno-gu, Seoul, Korea
| | - Sohui Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Mee Rim Oh
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Minji Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Sanhak-kisulkwan Bldg., #319, 222 Wangsipri-ro, Sungdong-gu, Seoul, 133-791, Republic of Korea.
| | - Bung-Nyun Kim
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 101 Daehakno, Jongno-gu, Seoul, Korea.
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7
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Liloia D, Manuello J, Costa T, Keller R, Nani A, Cauda F. Atypical local brain connectivity in pediatric autism spectrum disorder? A coordinate-based meta-analysis of regional homogeneity studies. Eur Arch Psychiatry Clin Neurosci 2024; 274:3-18. [PMID: 36599959 PMCID: PMC10787009 DOI: 10.1007/s00406-022-01541-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/16/2022] [Indexed: 01/05/2023]
Abstract
Despite decades of massive neuroimaging research, the comprehensive characterization of short-range functional connectivity in autism spectrum disorder (ASD) remains a major challenge for scientific advances and clinical translation. From the theoretical point of view, it has been suggested a generalized local over-connectivity that would characterize ASD. This stance is known as the general local over-connectivity theory. However, there is little empirical evidence supporting such hypothesis, especially with regard to pediatric individuals with ASD (age [Formula: see text] 18 years old). To explore this issue, we performed a coordinate-based meta-analysis of regional homogeneity studies to identify significant changes of local connectivity. Our analyses revealed local functional under-connectivity patterns in the bilateral posterior cingulate cortex and superior frontal gyrus (key components of the default mode network) and in the bilateral paracentral lobule (a part of the sensorimotor network). We also performed a functional association analysis of the identified areas, whose dysfunction is clinically consistent with the well-known deficits affecting individuals with ASD. Importantly, we did not find relevant clusters of local hyper-connectivity, which is contrary to the hypothesis that ASD may be characterized by generalized local over-connectivity. If confirmed, our result will provide a valuable insight into the understanding of the complex ASD pathophysiology.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy.
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
- Neuroscience Institute of Turin (NIT), Turin, Italy.
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Andrea Nani
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
- Neuroscience Institute of Turin (NIT), Turin, Italy
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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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9
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Razzak R, Li J, He S, Sokhadze E. Investigating Sex-Based Neural Differences in Autism and Their Extended Reality Intervention Implications. Brain Sci 2023; 13:1571. [PMID: 38002531 PMCID: PMC10670246 DOI: 10.3390/brainsci13111571] [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: 09/30/2023] [Revised: 10/27/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, and there is growing interest in the use of extended reality (XR) technologies for intervention. Despite the promising potential of XR interventions, there remain gaps in our understanding of the neurobiological mechanisms underlying ASD, particularly in relation to sex-based differences. This scoping review synthesizes the current research on brain activity patterns in ASD, emphasizing the implications for XR interventions and neurofeedback therapy. We examine the brain regions commonly affected by ASD, the potential benefits and drawbacks of XR technologies, and the implications of sex-specific differences for designing effective interventions. Our findings underscore the need for ongoing research into the neurobiological underpinnings of ASD and sex-based differences, as well as the importance of developing tailored interventions that consider the unique needs and experiences of autistic individuals.
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Affiliation(s)
- Rehma Razzak
- Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA; (R.R.); (S.H.)
| | - Joy Li
- Department of Software Engineering and Game Development, Kennesaw State University, Marietta, GA 30060, USA;
| | - Selena He
- Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA; (R.R.); (S.H.)
| | - Estate Sokhadze
- Department of Neurology, Duke University School of Medicine, Durham, NC 27710, USA
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10
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Liloia D, Cauda F, Uddin LQ, Manuello J, Mancuso L, Keller R, Nani A, Costa T. Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1075-1083. [PMID: 35131520 DOI: 10.1016/j.bpsc.2022.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/30/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies. METHODS Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD. RESULTS We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network. CONCLUSIONS The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Jordi Manuello
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Andrea Nani
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
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11
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Shan J, Gu Y, Zhang J, Hu X, Wu H, Yuan T, Zhao D. A scoping review of physiological biomarkers in autism. Front Neurosci 2023; 17:1269880. [PMID: 37746140 PMCID: PMC10512710 DOI: 10.3389/fnins.2023.1269880] [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: 07/31/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by pervasive deficits in social interaction, communication impairments, and the presence of restricted and repetitive behaviors. This complex disorder is a significant public health concern due to its escalating incidence and detrimental impact on quality of life. Currently, extensive investigations are underway to identify prospective susceptibility or predictive biomarkers, employing a physiological biomarker-based framework. However, knowledge regarding physiological biomarkers in relation to Autism is sparse. We performed a scoping review to explore putative changes in physiological activities associated with behaviors in individuals with Autism. We identified studies published between January 2000 and June 2023 from online databases, and searched keywords included electroencephalography (EEG), magnetoencephalography (MEG), electrodermal activity markers (EDA), eye-tracking markers. We specifically detected social-related symptoms such as impaired social communication in ASD patients. Our results indicated that the EEG/ERP N170 signal has undergone the most rigorous testing as a potential biomarker, showing promise in identifying subgroups within ASD and displaying potential as an indicator of treatment response. By gathering current data from various physiological biomarkers, we can obtain a comprehensive understanding of the physiological profiles of individuals with ASD, offering potential for subgrouping and targeted intervention strategies.
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Affiliation(s)
- Jiatong Shan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Arts and Sciences, New York University Shanghai, Shanghai, China
| | - Yunhao Gu
- Graduate School of Education, University of Pennsylvania, Philadelphia, PA, United States
| | - Jie Zhang
- Department of Neurology, Institute of Neurology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoqing Hu
- Department of Psychology, The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- HKU, Shenzhen Institute of Research and Innovation, Shenzhen, China
| | - Haiyan Wu
- Center for Cognitive and Brain Sciences and Department of Psychology, Macau, China
| | - Tifei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Di Zhao
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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12
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Sun H, Lin F, Wu X, Zhang T, Li J. Normalized mutual information of fNIRS signals as a measure for accessing typical and atypical brain activity. JOURNAL OF BIOPHOTONICS 2023; 16:e202200369. [PMID: 36808258 DOI: 10.1002/jbio.202200369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/24/2023] [Accepted: 02/10/2023] [Indexed: 06/07/2023]
Abstract
Normalized mutual information (NMI) can be used to detect statistical correlations between time series. We showed possibility of using NMI to quantify synchronicity of information transmission in different brain regions, thus to characterize functional connections, and ultimately analyze differences in physiological states of brain. Resting-state brain signals were recorded from bilateral temporal lobes by functional near-infrared spectroscopy (fNIRS) in 19 young healthy (YH) adults, 25 children with autism spectrum disorder (ASD), and 22 children with typical development (TD). Using NMI of the fNIRS signals, common information volume was assessed for each of three groups. Results showed that mutual information of children with ASD was significantly smaller than that of TD children, while mutual information of YH adults was slightly larger than that of TD children. This study may suggest that NMI could be a measure for assessing brain activity with different development states.
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Affiliation(s)
- Huiwen Sun
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China
| | - Fang Lin
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China
| | - Xiaoyin Wu
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China
| | - Tingzhen Zhang
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China
| | - Jun Li
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China
- Key Lab for Behavioral Economic Science & Technology, South China Normal University, Guangzhou, China
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13
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Strang JF, McClellan LS, Li S, Jack AE, Wallace GL, McQuaid GA, Kenworthy L, Anthony LG, Lai MC, Pelphrey KA, Thalberg AE, Nelson EE, Phan JM, Sadikova E, Fischbach AL, Thomas J, Vaidya CJ. The autism spectrum among transgender youth: default mode functional connectivity. Cereb Cortex 2023; 33:6633-6647. [PMID: 36721890 PMCID: PMC10233301 DOI: 10.1093/cercor/bhac530] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 02/02/2023] Open
Abstract
The common intersection of autism and transgender identities has been described in clinical and community contexts. This study investigates autism-related neurophenotypes among transgender youth. Forty-five transgender youth, evenly balanced across non-autistic, slightly subclinically autistic, and full-criteria autistic subgroupings, completed resting-state functional magnetic resonance imaging to examine functional connectivity. Results confirmed hypothesized default mode network (DMN) hub hyperconnectivity with visual and motor networks in autism, partially replicating previous studies comparing cisgender autistic and non-autistic adolescents. The slightly subclinically autistic group differed from both non-autistic and full-criteria autistic groups in DMN hub connectivity to ventral attention and sensorimotor networks, falling between non-autistic and full-criteria autistic groups. Autism traits showed a similar pattern to autism-related group analytics, and also related to hyperconnectivity between DMN hub and dorsal attention network. Internalizing, gender dysphoria, and gender minority-related stigma did not show connectivity differences. Connectivity differences within DMN followed previously reported patterns by designated sex at birth (i.e. female birth designation showing greater within-DMN connectivity). Overall, findings suggest behavioral diagnostics and autism traits in transgender youth correspond to observable differences in DMN hub connectivity. Further, this study reveals novel neurophenotypic characteristics associated with slightly subthreshold autism, highlighting the importance of research attention to this group.
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Affiliation(s)
- John F Strang
- Gender and Autism Program, Children’s National Hospital, 15245 Shady Grove Road, Suite 350, Rockville, MD 20850, USA
- Departments of Pediatrics, Psychiatry, and Behavioral Sciences, George Washington University School of Medicine, Washington, DC, USA
- Division of Neuropsychology, Children’s National Hospital, Washington, DC, USA
| | - Lucy S McClellan
- Division of Neuropsychology, Children’s National Hospital, Washington, DC, USA
| | - Sufang Li
- Department of Psychology, Georgetown University, Washington, DC, USA
| | - Allison E Jack
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Gregory L Wallace
- Department of Speech, Language, & Hearing Sciences, George Washington University, Washington, DC, USA
| | - Goldie A McQuaid
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Lauren Kenworthy
- Departments of Pediatrics, Psychiatry, and Behavioral Sciences, George Washington University School of Medicine, Washington, DC, USA
- Division of Neuropsychology, Children’s National Hospital, Washington, DC, USA
| | - Laura G Anthony
- Department of Psychiatry and Behavioral Sciences, University of Colorado School of Medicine, Aurora, CO, USA
| | - Meng-Chuan Lai
- Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Kevin A Pelphrey
- Department of Neurology, University of Virginia Medical School, Charlottesville, VA, USA
| | | | - Eric E Nelson
- Center for Biobehavioral Health, The Research Institute at Nationwide Children’s Hospital, Columbus, OH, USA
| | - Jenny M Phan
- Division of Neuropsychology, Children’s National Hospital, Washington, DC, USA
| | - Eleonora Sadikova
- School of Education and Human Development, University of Virginia, Charlottesville, VA, USA
| | - Abigail L Fischbach
- Division of Neuropsychology, Children’s National Hospital, Washington, DC, USA
| | | | - Chandan J Vaidya
- Department of Psychology, Georgetown University, Washington, DC, USA
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14
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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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15
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Bornstein MH, Mash C, Romero R, Gandjbakhche AH, Nguyen T. Electrophysiological Evidence for Interhemispheric Connectivity and Communication in Young Human Infants. Brain Sci 2023; 13:brainsci13040647. [PMID: 37190612 DOI: 10.3390/brainsci13040647] [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: 03/07/2023] [Revised: 03/30/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Little is known empirically about connectivity and communication between the two hemispheres of the brain in the first year of life, and what theoretical opinion exists appears to be at variance with the meager extant anatomical evidence. To shed initial light on the question of interhemispheric connectivity and communication, this study investigated brain correlates of interhemispheric transmission of information in young human infants. We analyzed EEG data from 12 4-month-olds undergoing a face-related oddball ERP protocol. The activity in the contralateral hemisphere differed between odd-same and odd-difference trials, with the odd-different response being weaker than the response during odd-same trials. The infants' contralateral hemisphere "recognized" the odd familiar stimulus and "discriminated" the odd-different one. These findings demonstrate connectivity and communication between the two hemispheres of the brain in the first year of life and lead to a better understanding of the functional integrity of the developing human infant brain.
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Affiliation(s)
- Marc H Bornstein
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, 8404 Irvington Avenue, Bethesda, MD 20892, USA
- Institute for Fiscal Studies, London WC1E 7AE, UK
- United Nations Children's Fund, New York, NY 10017, USA
| | - Clay Mash
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, 8404 Irvington Avenue, Bethesda, MD 20892, USA
- Environmental Influences on Child Health Outcomes, National Institutes of Health, Bethesda, MD 20852, USA
| | - Roberto Romero
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, 8404 Irvington Avenue, Bethesda, MD 20892, USA
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA
| | - Amir H Gandjbakhche
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, 8404 Irvington Avenue, Bethesda, MD 20892, USA
| | - Thien Nguyen
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, 8404 Irvington Avenue, Bethesda, MD 20892, USA
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16
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Yang B, Wang M, Zhou W, Wang X, Chen S, Potenza MN, Yuan LX, Dong GH. Disrupted network integration and segregation involving the default mode network in autism spectrum disorder. J Affect Disord 2023; 323:309-319. [PMID: 36455716 DOI: 10.1016/j.jad.2022.11.083] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 11/20/2022] [Accepted: 11/24/2022] [Indexed: 11/30/2022]
Abstract
Changes in the brain's default mode network (DMN) in the resting state are closely related to autism spectrum disorder (ASD). Module segmentation can effectively elucidate the neural mechanism of ASD and explore intra- and inter-network connections by means of the participation coefficient (PC). We used that resting-state fMRI data from 269 ASD patients and 340 healthy controls (HCs) in the current study. From the results, ASD subjects showed a significantly higher PC of the DMN than HC subjects. This difference was related to lower intra-module connections within the DMN and higher inter-network connections between the DMN and other networks. When the subjects were split into age groups, the results were verified in the 7-12- and 12-18-year-old age groups but not in the young adult group (18-25 years). When the subjects were divided according to different subtypes of ASD, the results were also observed in the classic autism and pervasive developmental disorder groups, but not in the Asperger disorder group. In conclusions, less developed network segregation in the DMN could be a valid biomarker for ASD. This provides network scientists with new insights into the intermodular connectivity configurations of complex networks from different dimensions in a systematic and comprehensive manner.
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Affiliation(s)
- Bo Yang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Min Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Weiran Zhou
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Xiuqin Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Shuaiyu Chen
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | | | - Li-Xia Yuan
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China.
| | - Guang-Heng Dong
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China.
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17
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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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18
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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: 2] [Impact Index Per Article: 1.0] [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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19
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Pourmotahari F, Doosti H, Borumandnia N, Tabatabaei SM, Alavi Majd H. Group-level comparison of brain connectivity networks. BMC Med Res Methodol 2022; 22:273. [PMID: 36253728 PMCID: PMC9575214 DOI: 10.1186/s12874-022-01712-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Functional connectivity (FC) studies are often performed to discern different patterns of brain connectivity networks between healthy and patient groups. Since many neuropsychiatric disorders are related to the change in these patterns, accurate modelling of FC data can provide useful information about disease pathologies. However, analysing functional connectivity data faces several challenges, including the correlations of the connectivity edges associated with network topological characteristics, the large number of parameters in the covariance matrix, and taking into account the heterogeneity across subjects. METHODS This study provides a new statistical approach to compare the FC networks between subgroups that consider the network topological structure of brain regions and subject heterogeneity. RESULTS The power based on the heterogeneity structure of identity scaled in a sample size of 25 exhibited values greater than 0.90 without influencing the degree of correlation, heterogeneity, and the number of regions. This index had values above 0.80 in the small sample size and high correlation. In most scenarios, the type I error was close to 0.05. Moreover, the application of this model on real data related to autism was also investigated, which indicated no significant difference in FC networks between healthy and patient individuals. CONCLUSIONS The results from simulation data indicated that the proposed model has high power and near-nominal type I error rates in most scenarios.
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Affiliation(s)
- Fatemeh Pourmotahari
- Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hassan Doosti
- Department of Mathematics and Statistics, Macquarie University, Macquarie, Australia
| | - Nasrin Borumandnia
- Urology and Nephrology Research Centre, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyyed Mohammad Tabatabaei
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamid Alavi Majd
- Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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20
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Muta K, Hata J, Kawaguchi N, Haga Y, Yoshimaru D, Hagiya K, Kaneko T, Miyabe-Nishiwaki T, Komaki Y, Seki F, Okano HJ, Okano H. Effect of sedatives or anesthetics on the measurement of resting brain function in common marmosets. Cereb Cortex 2022; 33:5148-5162. [PMID: 36222604 PMCID: PMC10151911 DOI: 10.1093/cercor/bhac406] [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: 05/13/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 11/13/2022] Open
Abstract
Common marmosets are promising laboratory animals for the study of higher brain functions. Although there are many opportunities to use sedatives and anesthetics in resting brain function measurements in marmosets, their effects on the resting-state network remain unclear. In this study, the effects of sedatives or anesthetics such as midazolam, dexmedetomidine, co-administration of isoflurane and dexmedetomidine, propofol, alfaxalone, isoflurane, and sevoflurane on the resting brain function in common marmosets were evaluated using independent component analysis, dual regression analysis, and graph-theoretic analysis; and the sedatives or anesthetics suitable for the evaluation of resting brain function were investigated. The results show that network preservation tendency under light sedative with midazolam and dexmedetomidine is similar regardless of the type of target receptor. Moreover, alfaxalone, isoflurane, and sevoflurane have similar effects on resting state brain function, but only propofol exhibits different tendencies, as resting brain function is more preserved than it is following the administration of the other anesthetics. Co-administration of isoflurane and dexmedetomidine shows middle effect between sedatives and anesthetics.
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Affiliation(s)
- Kanako Muta
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo 116-8551, Japan.,Division of Regenerative Medicine, The Jikei University School of Medicine, Minato, Tokyo 105-8461, Japan
| | - Junichi Hata
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo 116-8551, Japan.,Division of Regenerative Medicine, The Jikei University School of Medicine, Minato, Tokyo 105-8461, Japan.,Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan.,Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan
| | - Naoki Kawaguchi
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo 116-8551, Japan
| | - Yawara Haga
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Arakawa, Tokyo 116-8551, Japan.,Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan.,Live Imaging Center, Central Institute for Experimental Animals, Kawasaki, Kanagawa 210-0821, Japan
| | - Daisuke Yoshimaru
- Division of Regenerative Medicine, The Jikei University School of Medicine, Minato, Tokyo 105-8461, Japan.,Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan.,Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan.,Live Imaging Center, Central Institute for Experimental Animals, Kawasaki, Kanagawa 210-0821, Japan
| | - Kei Hagiya
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan
| | - Takaaki Kaneko
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan.,Systems Neuroscience Section, Primate Research Institute, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Takako Miyabe-Nishiwaki
- Center for Model Human Evolution Research, Primate Research Institute, Kyoto University, Inuyama, Aichi 484-8506, Japan
| | - Yuji Komaki
- Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan.,Live Imaging Center, Central Institute for Experimental Animals, Kawasaki, Kanagawa 210-0821, Japan
| | - Fumiko Seki
- Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan.,Live Imaging Center, Central Institute for Experimental Animals, Kawasaki, Kanagawa 210-0821, Japan
| | - Hirotaka James Okano
- Division of Regenerative Medicine, The Jikei University School of Medicine, Minato, Tokyo 105-8461, Japan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Wako, Saitama 351-0198, Japan.,Department of Physiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan
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21
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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] [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
- *Correspondence: Reza Khosrowabadi
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22
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Young children with autism show atypical prefrontal cortical responses to humanoid robots: An fNIRS study. Int J Psychophysiol 2022; 181:23-32. [PMID: 36037937 DOI: 10.1016/j.ijpsycho.2022.08.008] [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: 01/21/2022] [Revised: 08/21/2022] [Accepted: 08/23/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND Previous behavioral studies have found that children with autism spectrum disorder (ASD) show greater interest in humanoid robots than in humans. However, the neural mechanism underlying this is not clear. This study compared brain activation patterns between children with ASD and neurotypical children while they watched videos with robots and humans. METHOD We recruited 45 children with ASD and 53 neurotypical children aged 4-6 years and recorded their neural activity in the dorsolateral prefrontal cortex (DLPFC) using a functional near-infrared spectroscopy (fNIRS) device when the two groups interacted with a robot or a human in a video. RESULTS First, neural activity in the right DLPFC in children with ASD was significantly lower in the robot condition than in the human condition. Neural activity in the right DLPFC in children with ASD was also significantly lower than that of neurotypical children in the robot condition. Second, the neural activity in the left DLPFC between the human and robot conditions was negatively correlated in children with ASD, while it was positively correlated in neurotypical children. Moreover, neural activity in the left DLPFC in children with ASD was significantly correlated with the ADOS scores in both conditions. CONCLUSIONS While neurotypical children showed comparable neural activity to humanoid robots and human beings, the children with ASD showed significantly different neural activity under those two conditions. Children with ASD may need more selective attention resources for human interaction than for robot interaction. It is also much more difficult for children with ASD to neglect the attraction of robots. Neural activity of the left DLPFC of children with ASD is correlated with their symptoms, which maybe a possible indicator for early diagnosis. Neural activity of the right DLPFC guided their atypical reactions and engagements with robots. Our study contributes to the current understanding of the neural mechanisms responsible for the different behavioral reactions in children with ASD toward robots and humans.
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23
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Prany W, Patrice C, Franck D, Fabrice W, Mahdi M, Pierre D, Christian M, Jean-Marc G, Fabian G, Francis E, Jean-Marc B, Bérengère GG. EEG resting-state functional connectivity: evidence for an imbalance of external/internal information integration in autism. J Neurodev Disord 2022; 14:47. [PMID: 36030210 PMCID: PMC9419397 DOI: 10.1186/s11689-022-09456-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 08/04/2022] [Indexed: 01/12/2023] Open
Abstract
Background Autism spectrum disorder (ASD) is associated with atypical neural activity in resting state. Most of the studies have focused on abnormalities in alpha frequency as a marker of ASD dysfunctions. However, few have explored alpha synchronization within a specific interest in resting-state networks, namely the default mode network (DMN), the sensorimotor network (SMN), and the dorsal attention network (DAN). These functional connectivity analyses provide relevant insight into the neurophysiological correlates of multimodal integration in ASD. Methods Using high temporal resolution EEG, the present study investigates the functional connectivity in the alpha band within and between the DMN, SMN, and the DAN. We examined eyes-closed EEG alpha lagged phase synchronization, using standardized low-resolution brain electromagnetic tomography (sLORETA) in 29 participants with ASD and 38 developing (TD) controls (age, sex, and IQ matched). Results We observed reduced functional connectivity in the ASD group relative to TD controls, within and between the DMN, the SMN, and the DAN. We identified three hubs of dysconnectivity in ASD: the posterior cingulate cortex, the precuneus, and the medial frontal gyrus. These three regions also presented decreased current source density in the alpha band. Conclusion These results shed light on possible multimodal integration impairments affecting the communication between bottom-up and top-down information. The observed hypoconnectivity between the DMN, SMN, and DAN could also be related to difficulties in switching between externally oriented attention and internally oriented thoughts. Supplementary Information The online version contains supplementary material available at 10.1186/s11689-022-09456-8.
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Affiliation(s)
- Wantzen Prany
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.,Université de Paris, LaPsyDÉ, CNRS, F-75005, Paris, France
| | - Clochon Patrice
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Doidy Franck
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Wallois Fabrice
- INSERM UMR-S 1105, GRAMFC, Université de Picardie-Jules Verne, CHU Sud, 80025, Amiens, France
| | - Mahmoudzadeh Mahdi
- INSERM UMR-S 1105, GRAMFC, Université de Picardie-Jules Verne, CHU Sud, 80025, Amiens, France
| | - Desaunay Pierre
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Mille Christian
- Centre Ressources Autisme Picardie, Service de Psychopathologie Enfants et Adolescents, CHU, 4 rue Grenier et Bernard, 80000, Amiens, France
| | - Guilé Jean-Marc
- INSERM UMR-S 1105, GRAMFC, Université de Picardie-Jules Verne, CHU Sud, 80025, Amiens, France.,Centre Ressources Autisme Picardie, Service de Psychopathologie Enfants et Adolescents, CHU, 4 rue Grenier et Bernard, 80000, Amiens, France
| | - Guénolé Fabian
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Eustache Francis
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Baleyte Jean-Marc
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.,Service de Psychiatrie de l'enfant et de l'adolescent, Centre Hospitalier Interuniversitaire de Créteil, 94000, Créteil, France
| | - Guillery-Girard Bérengère
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.
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24
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Kwon H, Kim JI, Son SY, Jang YH, Kim BN, Lee HJ, Lee JM. Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels. Front Neurosci 2022; 16:935431. [PMID: 35873817 PMCID: PMC9301472 DOI: 10.3389/fnins.2022.935431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Machine learning algorithms have been widely applied in diagnostic tools for autism spectrum disorder (ASD), revealing an altered brain connectivity. However, little is known about whether an magnetic resonance imaging (MRI)-based brain network is related to the severity of ASD symptoms in a large-scale cohort. We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. Instead of assigning initial features for each node, we utilized a feature extractor to derive node features and the extracted representations can be fed to a hierarchical graph self-attention framework to effectively represent the entire graph. By incorporating connectivity embeddings in the feature extractor, we propose adjacency embedding networks to characterize the heterogeneous representations of the brain connectivity. Our proposed model variants outperform the benchmarking model with different configurations of adjacency embedding networks and types of functional connectivity matrices. Using this approach with the best configuration (SHEN atlas for node definition, Tikhonov correlation for connectivity estimation, and identity-adjacency embedding), we were able to predict individual ASD severity levels with a meaningful accuracy: the mean absolute error (MAE) and correlation between predicted and observed ASD severity scores resulted in 0.96, and r = 0.61 (P < 0.0001), respectively. To obtain a better understanding on how to generate better representations, we investigate the relationships between the extracted feature embeddings and the graph theory-based nodal measurements using canonical correlation analysis. Finally, we visualized the model to identify the most contributive functional connections for predicting ASD severity scores.
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Affiliation(s)
- Hyeokjin Kwon
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Johanna Inhyang Kim
- Department of Psychiatry, Hanyang University Medical Center, Seoul, South Korea
| | - Seung-Yeon Son
- Department of Artificial Intelligence, Hanyang University, Seoul, South Korea
| | - Yong Hun Jang
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Bung-Nyun Kim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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25
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A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2022.100290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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26
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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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27
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Sobotka D, Ebner M, Schwartz E, Nenning KH, Taymourtash A, Vercauteren T, Ourselin S, Kasprian G, Prayer D, Langs G, Licandro R. Motion correction and volumetric reconstruction for fetal functional magnetic resonance imaging data. Neuroimage 2022; 255:119213. [PMID: 35430359 DOI: 10.1016/j.neuroimage.2022.119213] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/21/2022] [Accepted: 04/13/2022] [Indexed: 10/18/2022] Open
Abstract
Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement and maternal breathing and consequently to suppress erroneous signal correlations. Current motion correction approaches for fetal fMRI choose a single 3D volume from a specific acquisition timepoint with least motion artefacts as reference volume, and perform interpolation for the reconstruction of the motion corrected time series. The results can suffer, if no low-motion frame is available, and if reconstruction does not exploit any assumptions about the continuity of the fMRI signal. Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI. We performed an extensive parameter study to investigate the effectiveness of motion estimation and present in this work benchmark metrics to quantify the effect of motion correction and regularised volumetric reconstruction approaches on functional connectivity computations. We demonstrate the proposed framework's ability to improve functional connectivity estimates, reproducibility and signal interpretability, which is clinically highly desirable for the establishment of prognostic noninvasive imaging biomarkers. The motion correction and volumetric reconstruction framework is made available as an open-source package of NiftyMIC.
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Affiliation(s)
- Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Michael Ebner
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Athena Taymourtash
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Gregor Kasprian
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Daniela Prayer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
| | - Roxane Licandro
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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28
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Intranasal Oxytocin Modulates the Salience Network in Aging. Neuroimage 2022; 253:119045. [PMID: 35259525 PMCID: PMC9450112 DOI: 10.1016/j.neuroimage.2022.119045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/25/2022] [Accepted: 02/28/2022] [Indexed: 11/30/2022] Open
Abstract
Growing evidence supports a role of the neuropeptide oxytocin in promoting social cognition and prosocial behavior, possibly via modulation of the salience of social information. The effect of intranasal oxytocin administration on the salience network, however, is not well understood, including in the aging brain. To address this research gap, 42 young (22.52 ± 3.02 years; 24 in the oxytocin group) and 43 older (71.12 ± 5.25 years; 21 in the oxytocin group) participants were randomized to either self-administer intranasal oxytocin or placebo prior to resting-state functional imaging. The salience network was identified using independent component analysis (ICA). Independent t-tests showed that individuals in the oxytocin compared to the placebo group had lower within-network resting-state functional connectivity, both for left amygdala (MNI coordinates: x = −18, y = 0, z = −15; corrected p < 0.05) within a more ventral salience network and for right insula (MNI coordinates: x = 39, y = 6, z = −6; corrected p < 0.05) within a more dorsal salience network. Age moderation analysis furthermore demonstrated that the oxytocin-reduced functional connectivity between the ventral salience network and the left amygdala was only present in older participants. These findings suggest a modulatory role of exogenous oxytocin on resting-state functional connectivity within the salience network and support age-differential effects of acute intranasal oxytocin administration on this network.
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29
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Classification of Alzheimer’s Disease and Mild-Cognitive Impairment Base on High-Order Dynamic Functional Connectivity at Different Frequency Band. MATHEMATICS 2022. [DOI: 10.3390/math10050805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Functional brain connectivity networks obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been extensively utilized for the diagnosis of Alzheimer’s disease (AD). However, the traditional correlation analysis technique only explores the pairwise relation, which may not be suitable for revealing sufficient and proper functional connectivity links among brain regions. Additionally, previous literature typically focuses on only lower-order dynamics, without considering higher-order dynamic networks properties, and they particularly focus on single frequency range time series of rs-fMRI. To solve these problems, in this article, a new diagnosis scheme is proposed by constructing a high-order dynamic functional network at different frequency level time series (full-band (0.01–0.08 Hz); slow-4 (0.027–0.08 Hz); and slow-5 (0.01–0.027 Hz)) data obtained from rs-fMRI to build the functional brain network for all brain regions. Especially, to tune the precise analysis of the regularized parameters in the Support Vector Machine (SVM), a nested leave-one-out cross-validation (LOOCV) technique is adopted. Finally, the SVM classifier is trained to classify AD from HC based on these higher-order dynamic functional brain networks at different frequency ranges. The experiment results illustrate that for all bands with a LOOCV classification accuracy of 94.10% with a 90.95% of sensitivity, and a 96.75% of specificity outperforms the individual networks. Utilization of the given technique for the identification of AD from HC compete for the most state-of-the-art technology in terms of the diagnosis accuracy. Additionally, results obtained for the all-band shows performance further suggest that our proposed scheme has a high-rate accuracy. These results have validated the effectiveness of the proposed methods for clinical value to the identification of AD.
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30
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Zhao L, Sun YK, Xue SW, Luo H, Lu XD, Zhang LH. Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach. Front Neuroinform 2022; 16:761942. [PMID: 35273487 PMCID: PMC8901599 DOI: 10.3389/fninf.2022.761942] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/12/2022] [Indexed: 11/13/2022] Open
Abstract
An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.
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Affiliation(s)
- Lei Zhao
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Yun-Kai Sun
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shao-Wei Xue
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
- *Correspondence: Shao-Wei Xue
| | - Hong Luo
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Xiao-Dong Lu
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Lan-Hua Zhang
- College of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an, China
- Lan-Hua Zhang
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31
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Zhao F, Han Z, Cheng D, Mao N, Chen X, Li Y, Fan D, Liu P. Hierarchical Synchronization Estimation of Low- and High-Order Functional Connectivity Based on Sub-Network Division for the Diagnosis of Autism Spectrum Disorder. Front Neurosci 2022; 15:810431. [PMID: 35221892 PMCID: PMC8867086 DOI: 10.3389/fnins.2021.810431] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/27/2021] [Indexed: 12/03/2022] Open
Abstract
Functional connectivity network (FCN) calculated by resting-state functional magnetic resonance imaging (rs-fMRI) plays an increasingly important role in the exploration of neurologic and mental diseases. Among the presented researches, the method of constructing FCN based on Matrix Variate Normal Distribution (MVND) theory provides a novel perspective, which can capture both low- and high-order correlations simultaneously with a clear mathematical interpretability. However, when fitting MVND model, the dimension of the parameters (i.e., population mean and population covariance) to be estimated is too high, but the number of samples is relatively quite small, which is insufficient to achieve accurate fitting. To address the issue, we divide the brain network into several sub-networks, and then the MVND based FCN construction algorithm is implemented in each sub-network, thus the spatial dimension of MVND is reduced and more accurate estimates of low- and high-order FCNs is obtained. Furthermore, for making up the functional connectivity which is lost because of the sub-network division, the rs-fMRI mean series of all sub-networks are calculated, and the low- and high-order FCN across sub-networks are estimated with the MVND based FCN construction method. In order to prove the superiority and effectiveness of this method, we design and conduct classification experiments on ASD patients and normal controls. The experimental results show that the classification accuracy of “hierarchical sub-network method” is greatly improved, and the sub-network found most related to ASD in our experiment is consistent with other related medical researches.
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Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Zhongwei Han
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Dapeng Cheng
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yuan Li
- School of Management Science and Engineering, Shandong Technology and Business University, Yantai, China
| | - Deming Fan
- School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, China
| | - Peiqiang Liu
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
- *Correspondence: Peiqiang Liu,
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Lin P, Zang S, Bai Y, Wang H. Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model. Front Hum Neurosci 2022; 16:774921. [PMID: 35211000 PMCID: PMC8861306 DOI: 10.3389/fnhum.2022.774921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/06/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper insight into the underlying mechanism of brain functions for ASD. Therefore, we proposed a framework with Hidden Markov Model (HMM) analysis for resting-state functional MRI (fMRI) from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 subjects with ASD and 298 well-matched health controls across 14 sites from the Autism Brain Imaging Data Exchange (ABIDE). Based on the HMM, we can identify the recurring brain function networks over time across ASD and healthy controls (HCs). Then we assessed the dynamical configuration of the whole-brain networks and further analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states (including fractional occupancies and lifetimes) were significantly altered in ASD compared to HCs. These specific HMM states were characterized by the activation pattern of default mode network (DMN), sensory processing networks [including visual network, auditory network, and sensory and motor network (SMN)]. Meanwhile, we also find that the specific modules of transitions between states were closely related to ASD. Our findings indicate the temporal reconfiguration of the brain network in ASD and provide novel insights into the dynamics of the whole-brain networks for ASD.
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Affiliation(s)
- Pingting Lin
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
| | - Shiyi Zang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
| | - Yi Bai
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
| | - Haixian Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
- *Correspondence: Haixian Wang,
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Marshall NA, Kaplan J, Stoycos SA, Goldenberg D, Khoddam H, Cárdenas SI, Sellery P, Saxbe D. Stronger Mentalizing Network Connectivity in Expectant Fathers Predicts Postpartum Father-Infant Bonding and Parenting Behavior. Soc Neurosci 2022; 17:21-36. [PMID: 35034575 DOI: 10.1080/17470919.2022.2029559] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Fathers play a critical role in parenting and in shaping child outcomes. However, the neurobiological underpinnings of successful adjustment to fatherhood have not been well-specified. Empathy and mentalizing abilities may characterize more effective fathering. These abilities may be supported by the functional connectivity (FC) of brain regions associated with social cognition and executive control. We used a seed-region-based approach to assess resting-state FC (rsFC) of the medial prefrontal cortex (mPFC) in 40 expectant fathers. We tested associations between mPFC whole-brain rsFC and fathers' self-report measures of empathy during pregnancy, as well as their ratings of father-infant bonding and fathering behaviors at six months postpartum. Stronger prenatal rsFC between the mPFC and precuneus, frontal pole, planum polare, and orbitofrontal cortex (OFC) was negatively associated with self-reported empathic concern and perspective-taking, whereas mPFC rsFC with the lateral occipital cortex (LOC) was positively associated with self-reported perspective-taking. Additionally, stronger prenatal connectivity between the mPFC rsFC and the superior parietal lobule and LOC regions predicted father reports of postpartum bonding with infants, and stronger prenatal mPFC rsFC with the LOC predicted more effective postpartum parenting. This study is the first to measure rsFC in expectant fathers as a predictor of subsequent adjustment to fathering.
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Affiliation(s)
| | - Jonas Kaplan
- University of Southern California, Department of Psychology
| | | | | | - Hannah Khoddam
- University of Southern California, Department of Psychology
| | | | - Pia Sellery
- University of Southern California, Department of Psychology
| | - Darby Saxbe
- University of Southern California, Department of Psychology
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34
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Chen B. A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys. Clin EEG Neurosci 2022; 53:3-11. [PMID: 34152841 DOI: 10.1177/15500594211026282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The abnormal cortices of autism spectrum disorder (ASD) brains are uncertain. However, the pathological alterations of ASD brains are distributed throughout interconnected cortical systems. Functional connections (FCs) methodology identifies cooperation and separation characteristics of information process in macroscopic cortical activity patterns under the context of network neuroscience. Embracing the graph theory concepts, this paper introduces eigenvector centrality index (EC score) ground on the FCs, and further develops a new framework for researching the dysfunctional cortex of ASD in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in EC-scores of 26 ASD boys and 28 matched healthy controls (HCs). For whole brain regional EC scores of ASD boys, orbitofrontal superior medial cortex, insula R, posterior cingulate gyrus L, and cerebellum 9 L are endowed with different EC scores significantly. In the brain subsystems level, EC scores of DMN, prefrontal lobe, and cerebellum are aberrant in the ASD boys. Generally, the EC scores display widespread distribution of diseased regions in ASD brains. Meanwhile, the discovered regions and subsystems, such as MPFC, AMYG, INS, prefrontal lobe, and DMN, are engaged in social processing. Meanwhile, the CBCL externalizing problem scores are associated with EC scores.
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Affiliation(s)
- Bo Chen
- 12626Hangzhou Dianzi University, Hangzhou, Zhejiang, PR China
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35
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Reardon AM, Li K, Hu XP. Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning. Front Comput Neurosci 2021; 15:762781. [PMID: 34924984 PMCID: PMC8674307 DOI: 10.3389/fncom.2021.762781] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 11/04/2021] [Indexed: 11/18/2022] Open
Abstract
Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients. Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls. Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%. Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.
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Affiliation(s)
- Alexandra M Reardon
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Kaiming Li
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Xiaoping P Hu
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States.,Center for Advanced Neuroimaging, University of California, Riverside, Riverside, CA, United States
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36
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Comparan-Meza M, Vargas de la Cruz I, Jauregui-Huerta F, Gonzalez-Castañeda RE, Gonzalez-Perez O, Galvez-Contreras AY. Biopsychological correlates of repetitive and restricted behaviors in autism spectrum disorders. Brain Behav 2021; 11:e2341. [PMID: 34472728 PMCID: PMC8553330 DOI: 10.1002/brb3.2341] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 07/31/2021] [Accepted: 08/10/2021] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Autism Spectrum Disorder (ASD) is considered a neurodevelopmental condition that is characterized by alterations in social interaction and communication, as well as patterns of restrictive and repetitive behaviors (RRBs). RRBs are defined as broad behaviors that comprise stereotypies, insistence on sameness, and attachment to objects or routines. RRBs can be divided into lower-level behaviors (motor, sensory, and object-manipulation behaviors) and higher-level behaviors (restrictive interests, insistence on sameness, and repetitive language). According to the DSM-5, the grade of severity in ASD partially depends on the frequency of RRBs and their consequences for disrupting the life of patients, affecting their adaptive skills, and increasing the need for parental support. METHODS We conducted a systematic review to examine the biopsychological correlates of the symptomatic domains of RRBs according to the type of RRBs (lower- or higher-level). We searched for articles from the National Library of Medicine (PubMed) using the terms: autism spectrum disorders, ASD, and autism-related to executive functions, inhibitory control, inflexibility, cognitive flexibility, hyper or hypo connectivity, and behavioral approaches. For describing the pathophysiological mechanism of ASD, we also included animal models and followed PRISMA guidelines. RESULTS One hundred and thirty-one articles were analyzed to explain the etiology, continuance, and clinical evolution of these behaviors observed in ASD patients throughout life. CONCLUSIONS Biopsychological correlates involved in the origin of RRBs include alterations in a) neurotransmission system, b) brain volume, c) inadequate levels of growth factors, d) hypo- or hyper-neural connectivity, e) impairments in behavioral inhibition, cognitive flexibility, and monitoring and f) non-stimulating environments. Understanding these lower- and higher-level of RRBs can help professionals to improve or design novel therapeutic strategies.
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Affiliation(s)
- Miguel Comparan-Meza
- Maestría en Neuropsicología, Departamento de Neurociencias, Centro Universitario de Ciencias de la Salud (CUCS), Universidad de Guadalajara, Guadalajara, JAL, Mexico
| | - Ivette Vargas de la Cruz
- Unidad de Atención en Neurociencias, Departamento de Neurociencias, Centro Universitario de Ciencias de la Salud (CUCS), Universidad de Guadalajara, Guadalajara, JAL, Mexico
| | - Fernando Jauregui-Huerta
- Laboratorio de Microscopia de Alta Resolución, Departamento de Neurociencias, Centro Universitario de Ciencias de la Salud (CUCS), Universidad de Guadalajara, Guadalajara, JAL, Mexico
| | - Rocio E Gonzalez-Castañeda
- Laboratorio de Microscopia de Alta Resolución, Departamento de Neurociencias, Centro Universitario de Ciencias de la Salud (CUCS), Universidad de Guadalajara, Guadalajara, JAL, Mexico
| | - Oscar Gonzalez-Perez
- Laboratorio de Neurociencias, Facultad de Psicología, Universidad de Colima, Colima, COL, Mexico
| | - Alma Y Galvez-Contreras
- Unidad de Atención en Neurociencias, Departamento de Neurociencias, Centro Universitario de Ciencias de la Salud (CUCS), Universidad de Guadalajara, Guadalajara, JAL, Mexico
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37
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Ibrahim K, Soorya LV, Halpern DB, Gorenstein M, Siper PM, Wang AT. Social cognitive skills groups increase medial prefrontal cortex activity in children with autism spectrum disorder. Autism Res 2021; 14:2495-2511. [PMID: 34486810 DOI: 10.1002/aur.2603] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 07/25/2021] [Accepted: 08/09/2021] [Indexed: 12/21/2022]
Abstract
Few studies have examined the neural mechanisms of change following social skills interventions for children with autism spectrum disorder (ASD). This study examined the neural effects of social cognitive skills groups during functional MRI (fMRI) tasks of irony comprehension and eye gaze processing in school-aged children with ASD. Verbally fluent children (ages 8-11) were randomized to social cognitive skills groups or facilitated play comparison groups. Behavioral assessments and fMRI scans were obtained at baseline and endpoint (12 weeks). During fMRI, children completed two separate tasks to engage social cognition circuitry: comprehension of potentially ironic scenarios (n = 34) and viewing emotionally expressive faces with direct or averted gaze (n = 24). Whole-brain analyses were conducted to examine neural changes following treatment. Regression analyses were also conducted to explore the relationship between neural and behavioral changes. When comparing the two groups directly, the social cognitive skills group showed greater increases in activity in the medial prefrontal cortex (mPFC), implicated in theory of mind, relative to the comparison group for both irony comprehension and gaze processing tasks. Increased mPFC activity during the irony task was associated with improvement in social functioning on the Social Responsiveness Scale across both groups. Findings indicate that social cognitive skills interventions may increase activity in regions associated with social cognition and mentalizing abilities. LAY SUMMARY: Social skills groups are a common intervention for school-aged children with ASD. However, few studies have examined the neural response to social skills groups in school-aged children with ASD. Here, we report on a study evaluating neural outcomes from an empirically supported social cognitive skills training curriculum using fMRI. This study seeks to understand the effects of targeting emotion recognition and theory of mind on the brain circuitry involved in social cognition in verbally fluent children ages 8-11. Results indicate increased neural activity in the mPFC, a region considered to be a central hub of the "social brain," in children randomized to social cognitive skills groups relative to a comparison group that received a high-quality, child-directed play approach. In addition, increased activation in the mPFC during an irony comprehension task was associated with gains in social functioning across both groups from pre- to post-treatment. This is the first fMRI study of social skills treatment outcomes following a randomized trial with an active treatment condition in school-aged children with ASD.
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Affiliation(s)
- Karim Ibrahim
- Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Yale Child Study Center, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Latha V Soorya
- Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Rush Medical College, Rush University, Chicago, Illinois, USA
| | - Danielle B Halpern
- Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Michelle Gorenstein
- Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Paige M Siper
- Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - A Ting Wang
- Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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38
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Pruccoli J, Spadoni C, Orsenigo A, Parmeggiani A. Should Echolalia Be Considered a Phonic Stereotypy? A Narrative Review. Brain Sci 2021; 11:brainsci11070862. [PMID: 34209516 PMCID: PMC8301866 DOI: 10.3390/brainsci11070862] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 06/24/2021] [Accepted: 06/28/2021] [Indexed: 11/26/2022] Open
Abstract
The Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) defines echolalia as a pathological, parrotlike, and apparently senseless repetition (echoing) of a word or phrase just uttered by another person and classifies this condition among the “restrictive and repetitive behaviours” of Autism Spectrum Disorder (ASD). The authors reviewed the existing literature on echolalia and its role in the development of children with ASD. Current conceptualizations include echolalia among repetitive behaviors and stereotypies and thus interpret this symptom as lacking any communicative significance, with negative effects on learning and sensory processing. Echoic behaviors, however, have been described in neurotypical infants and children as having a substantial effect on the consequent development of language and communication. Relevant research has documented a functional role of echolalia in ASD children as well since it facilitates the acquisition of verbal competencies and affords a higher degree of semantic generalization. This developmental function could be restricted to specific contexts. Considering echolalia as stereotypy and treating it as a disturbing symptom could impair the development of ASD-specific learning and communication processes. In light of this evidence, the authors propose a different conceptualization of echolalia and suggest that this symptom be considered among atypical communication patterns in children with ASD, with implications for treatment and prognosis.
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Affiliation(s)
- Jacopo Pruccoli
- IRCCS Istituto delle Scienze Neurologiche di Bologna, U.O. Neuropsichiatria dell’Età Pediatrica, 40138 Bologna, Italy; (J.P.); (C.S.); (A.O.)
- Dipartimento di Scienze Mediche e Chirurgiche, University of Bologna, 40138 Bologna, Italy
| | - Chiara Spadoni
- IRCCS Istituto delle Scienze Neurologiche di Bologna, U.O. Neuropsichiatria dell’Età Pediatrica, 40138 Bologna, Italy; (J.P.); (C.S.); (A.O.)
- Dipartimento di Scienze Mediche e Chirurgiche, University of Bologna, 40138 Bologna, Italy
| | - Alex Orsenigo
- IRCCS Istituto delle Scienze Neurologiche di Bologna, U.O. Neuropsichiatria dell’Età Pediatrica, 40138 Bologna, Italy; (J.P.); (C.S.); (A.O.)
- Dipartimento di Scienze Mediche e Chirurgiche, University of Bologna, 40138 Bologna, Italy
| | - Antonia Parmeggiani
- IRCCS Istituto delle Scienze Neurologiche di Bologna, U.O. Neuropsichiatria dell’Età Pediatrica, 40138 Bologna, Italy; (J.P.); (C.S.); (A.O.)
- Dipartimento di Scienze Mediche e Chirurgiche, University of Bologna, 40138 Bologna, Italy
- Correspondence:
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39
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Qian L, Li Y, Wang Y, Wang Y, Cheng X, Li C, Cui X, Jiao G, Ke X. Shared and Distinct Topologically Structural Connectivity Patterns in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder. Front Neurosci 2021; 15:664363. [PMID: 34177449 PMCID: PMC8226092 DOI: 10.3389/fnins.2021.664363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 05/10/2021] [Indexed: 12/04/2022] Open
Abstract
Background Previous neuroimaging studies have described shared and distinct neurobiological mechanisms between autism spectrum disorders (ASDs) and attention-deficit/hyperactivity disorder (ADHD). However, little is known about the similarities and differences in topologically structural connectivity patterns between the two disorders. Methods Diffusion tensor imaging (DTI) and deterministic tractography were used to construct the brain white matter (WM) structural networks of children and adolescents (age range, 6–16 years); 31 had ASD, 34 had ADHD, and 30 were age- and sex-matched typically developing (TD) individuals. Then, graph theoretical analysis was performed to investigate the alterations in the global and node-based properties of the WM structural networks in these groups. Next, measures of ASD traits [Social Responsiveness Scale (SRS)] and ADHD traits (Swanson, Nolan, and Pelham, version IV scale, SNAP-IV) were correlated with the alterations to determine the functional significance of such changes. Results First, there were no significant differences in the global network properties among the three groups; moreover, compared with that of the TD group, nodal degree (Ki) of the right amygdala (AMYG.R) and right parahippocampal gyrus (PHG.R) were found in both the ASD and ADHD groups. Also, the ASD and ADHD groups shared four additional hubs, including the left middle temporal gyrus (MTG.L), left superior temporal gyrus (STG.L), left postcentral gyrus (PoCG.L), and right middle frontal gyrus (MFG.R) compared with the TD group. Moreover, the ASD and ADHD groups exhibited no significant differences regarding regional connectivity characteristics. Second, the ADHD group showed significantly increased nodal betweenness centrality (Bi) of the left hippocampus (HIP.L) compared with the ASD group; also, compared with the ADHD group, the ASD group lacked the left anterior cingulate gyrus (ACG.L) as a hub. Last, decreased nodal efficiency (Enodal) of the AMYG.R, Ki of the AMYG.R, and Ki of the PHG.R were associated with higher SRS scores in the ASD group. Decreased Ki of the PHG.R was associated with higher SRS scores in the full sample, whereas decreased Bi of the PHG.R was associated with lower oppositional defiance subscale scores of the SNAP-IV in the ADHD group, and decreased Bi of the HIP.L was associated with lower inattention subscale scores of the SNAP-IV in the full sample. Conclusion From the perspective of the topological properties of brain WM structural networks, ADHD and ASD have both shared and distinct features. More interestingly, some shared and distinct topological properties of WM structures are related to the core symptoms of these disorders.
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Affiliation(s)
- Lu Qian
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
| | - Yun Li
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Yao Wang
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Yue Wang
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Xin Cheng
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Chunyan Li
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Xiwen Cui
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Gongkai Jiao
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Xiaoyan Ke
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
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40
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Reardon AM, Hu XP, Li K, Langley J. Subtyping Autism Spectrum Disorder via Joint Modeling of Clinical and Connectomic Profiles. Brain Connect 2021; 12:193-205. [PMID: 34102874 DOI: 10.1089/brain.2020.0997] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Autism Spectrum Disorder (ASD) is a highly heterogeneous developmental disorder with diverse clinical manifestations. Neuroimaging studies have explored functional connectivity (FC) of ASD through resting-state functional MRI studies, however the findings have remained inconsistent, thus reflecting the possibility of multiple subtypes. Identification of the relationship between clinical symptoms and FC measures may help clarify the inconsistencies in earlier findings and advance our understanding of ASD subtypes. METHODS Canonical correlation analysis was performed on two-hundred and ten ASD subjects from the Autism Brain Imaging Data Exchange to identify significant linear combinations of resting-state connectomic and clinical profiles of ASD. Then, hierarchical clustering defined ASD subtypes based on distinct brain-behavior relationships. Finally, a support vector machine classifier was used to verify that subtypes were comprised of subjects with distinct clinical and connectivity features. RESULTS Three ASD subtypes were identified. Subtype 1 exhibited increased intra-network FC, increased IQ scores and restricted and repetitive behaviors. Subtype 2 was characterized by decreased whole-brain FC and more severe ADI-R and SRS symptoms. Subtype 3 demonstrated mixed FC, low IQ scores, as well as social motivation and verbal deficits. To verify subtype assignment, a multi-class support vector machine using connectomic and clinical profiles yielded an average accuracy of 71.3% and 65.2% respectively for subtype classification, which is significantly higher than chance (33.3%). CONCLUSION The present study demonstrates that combining connectomic and behavioral measures is a powerful approach for disease subtyping and suggests that there are ASD subtypes with distinct connectomic and clinical profiles.
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Affiliation(s)
- Alexandra M Reardon
- University of California Riverside, 8790, Biomedical Engineering, Riverside, California, United States;
| | - Xiaoping P Hu
- University of California Riverside, 8790, Biomedical Engineering, Riverside, California, United States.,University of California Riverside, 8790, Center for Advanced NeuroImaging, Riverside, California, United States;
| | - Kaiming Li
- University of California Riverside, 8790, Center for Advanced NeuroImaging, Riverside, California, United States;
| | - Jason Langley
- University of California Riverside, 8790, Center for Advanced NeuroImaging, Riverside, California, United States;
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41
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Janouschek H, Chase HW, Sharkey RJ, Peterson ZJ, Camilleri JA, Abel T, Eickhoff SB, Nickl-Jockschat T. The functional neural architecture of dysfunctional reward processing in autism. Neuroimage Clin 2021; 31:102700. [PMID: 34161918 PMCID: PMC8239466 DOI: 10.1016/j.nicl.2021.102700] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/30/2022]
Abstract
Functional imaging studies have found differential neural activation patterns during reward-paradigms in patients with autism spectrum disorder (ASD) compared to neurotypical controls. However, publications report conflicting results on the directionality and location of these aberrant activations. We here quantitatively summarized relevant fMRI papers in the field using the anatomical likelihood estimation (ALE) algorithm. Patients with ASD consistently showed hypoactivations in the striatum across studies, mainly in the right putamen and accumbens. These regions are functionally involved in the processing of rewards and are enrolled in extensive neural networks involving limbic, cortical, thalamic and mesencephalic regions. The striatal hypo-activations found in our ALE meta-analysis, which pooled over contrasts derived from the included studies on reward-processing in ASD, highlight the role of the striatum as a key neural correlate of impaired reward processing in autism. These changes were present for studies using social and non-social stimuli alike. The involvement of these regions in extensive networks associated with the processing of both positive and negative emotion alike might hint at broader impairments of emotion processing in the disorder.
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Affiliation(s)
- Hildegard Janouschek
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Henry W Chase
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Rachel J Sharkey
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Zeru J Peterson
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Julia A Camilleri
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Ted Abel
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Department of Neuroscience and Pharmacology, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Thomas Nickl-Jockschat
- Iowa Neuroscience Institute, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Department of Psychiatry, University of Iowa, Iowa City, IA, USA; Department of Neuroscience and Pharmacology, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
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Leming MJ, Baron-Cohen S, Suckling J. Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI. Mol Autism 2021; 12:34. [PMID: 33971956 PMCID: PMC8112019 DOI: 10.1186/s13229-021-00439-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 04/16/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Autism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied. METHODS We introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs. LIMITATIONS While this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism. RESULTS Our models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity. CONCLUSION This study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism.
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Affiliation(s)
- Matthew J Leming
- Department of Psychiatry, University of Cambridge, Robinson Way, Cambridge, Cambridgeshire, CB2 0SZ, UK.
- Center for Systems Biology, Massachusetts General Hospital, 149 13th Street, Boston, MA, 02129, USA.
| | - Simon Baron-Cohen
- Department of Psychiatry, University of Cambridge, Robinson Way, Cambridge, Cambridgeshire, CB2 0SZ, UK
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Robinson Way, Cambridge, Cambridgeshire, CB2 0SZ, UK
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Harikumar A, Evans DW, Dougherty CC, Carpenter KL, Michael AM. A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder. Brain Connect 2021; 11:253-263. [PMID: 33403915 PMCID: PMC8112713 DOI: 10.1089/brain.2020.0865] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD.
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Affiliation(s)
- Amritha Harikumar
- Department of Psychiatry, Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
- Address correspondence to: Amritha Harikumar, Department of Psychological Sciences, Rice University, 6566 Main St, BRC 780B, Houston, TX 77030, USA
| | - David W. Evans
- Department of Psychology, Bucknell University, Lewisburg, Pennsylvania, USA
| | - Chase C. Dougherty
- Department of Psychiatry, Penn State College of Medicine, Hershey, Pennsylvania, USA
| | - Kimberly L.H. Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA
| | - Andrew M. Michael
- Department of Psychiatry and Behavioral Sciences, Duke Institute for Brain Science, Duke University, Durham, North Carolina, USA
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Lei B, Yu S, Zhao X, Frangi AF, Tan EL, Elazab A, Wang T, Wang S. Diagnosis of early Alzheimer's disease based on dynamic high order networks. Brain Imaging Behav 2021; 15:276-287. [PMID: 32789620 DOI: 10.1007/s11682-019-00255-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Machine learning methods have been widely used for early diagnosis of Alzheimer's disease (AD) via functional connectivity networks (FCNs) analysis from neuroimaging data. The conventional low-order FCNs are obtained by time-series correlation of the whole brain based on resting-state functional magnetic resonance imaging (R-fMRI). However, FCNs overlook inter-region interactions, which limits application to brain disease diagnosis. To overcome this drawback, we develop a novel framework to exploit the high-level dynamic interactions among brain regions for early AD diagnosis. Specifically, a sliding window approach is employed to generate some R-fMRI sub-series. The correlations among these sub-series are then used to construct a series of dynamic FCNs. High-order FCNs based on the topographical similarity between each pair of the dynamic FCNs are then constructed. Afterward, a local weight clustering method is used to extract effective features of the network, and the least absolute shrinkage and selection operation method is chosen for feature selection. A support vector machine is employed for classification, and the dynamic high-order network approach is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our experimental results demonstrate that the proposed approach not only achieves promising results for AD classification, but also successfully recognizes disease-related biomarkers.
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Affiliation(s)
- Baiying Lei
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Shuangzhi Yu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Xin Zhao
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Alejandro F Frangi
- CISTIB Centre for Computational Imaging & Simulation technologies in Biomedicine, School of Computing and the School of Medicine, University of Leeds, Leeds, UK
| | - Ee-Leng Tan
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Ahmed Elazab
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Tianfu Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China.
| | - Shuqiang Wang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, 518000, People's Republic of China.
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45
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Changes in the topological organization of the default mode network in autism spectrum disorder. Brain Imaging Behav 2021; 15:1058-1067. [PMID: 32737824 DOI: 10.1007/s11682-020-00312-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Neuroimaging studies have demonstrated that autism spectrum disorder (ASD) is accompanied by abnormal functional and structural features in specific brain regions of the default mode network (DMN). However, little is known about the alterations of the topological organization and the functional connectivity (FC) of the DMN in ASD patients. Thirty-seven ASD patients and 38 healthy control (HC) participants underwent a resting-state functional magnetic resonance imaging scan. Twenty DMN subregions were specifically selected to construct the DMN architecture. We applied graph theory approaches to the topological configuration and compare the FC patterns of the DMN. We then examined the relationships between the neuroimaging measures of the DMN and clinical characteristics in patients with ASD. The current study revealed that both the ASD and HC participants showed a small-world regimen in the DMN; however there were no significant differences in global network measures. Compared with the HC group, the ASD group exhibited significantly decreased nodal centralities in the bilateral anterior medial prefrontal cortex and increased nodal centralities in the right lateral temporal cortex and the right retrosplenial cortex. Patients with ASD displayed significantly reduced and increased FC within the DMN. Our findings demonstrated that ASD patients showed a pattern of disrupted FC metrics and nodal network metrics in the DMN, which could be a potential biomarker for objective ASD diagnoses and for the level of autism spectrum traits. HIGHLIGHTS: We used graph theoretical approaches and functional connectivity (FC) to investigate the topological configuration and FC patterns of the DMN in ASD. The current study revealed that both ASD and HC participants exhibited small-world regimes in the DMN, however there were no significant differences in global network measures. The ASD group showed abnormal nodal centralities in the bilateral aMPFC, the right LTC and the Rsp of the DMN, and ASD was characterized by altered FC patterns, including decreased and increased FC within the DMN.
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46
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Wang Q, Li HY, Li YD, Lv YT, Ma HB, Xiang AF, Jia XZ, Liu DQ. Resting-state abnormalities in functional connectivity of the default mode network in autism spectrum disorder: a meta-analysis. Brain Imaging Behav 2021; 15:2583-2592. [PMID: 33683528 DOI: 10.1007/s11682-021-00460-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 01/12/2021] [Accepted: 01/28/2021] [Indexed: 11/30/2022]
Abstract
Increasing evidence has shown that the resting state brain connectivity of default mode network (DMN) which are important for social cognition are disrupted in autism spectrum disorder (ASD). However, previous neuroimaging studies did not present consistent results. Therefore, we performed a meta-analysis of resting-state functional connectivity (rsFC) studies of DMN in the individuals with ASD and healthy controls (HCs) to provide a new perspective for investigating the pathophysiology of ASD. We carried out a search using the terms: ("ASD" OR "Autism") AND ("resting state" OR "rest") AND ("DMN" OR "default mode network") in PubMed, Web of Science and Embase to identify the researches published before January 2020. Ten resting state datasets including 203 patients and 208 HCs were included. Anisotropic Effect Size version of Signed Differential Mapping (AES-SDM) method was applied to identify group differences. In comparison with the HCs, the patients with ASD showed increased connectivity in cerebellum, right middle temporal gyrus, superior occipital gyrus, right supramarginal gyrus, supplementary motor area and putamen. Decreased connectivity was discovered in some nodes of DMN, such as medial prefrontal cortex, precuneus and angular gyrus. These results may help us to further clarify the neurobiological mechanisms in patients with ASD.
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Affiliation(s)
- Qing Wang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China
- Key Laboratory of Brain and Cognitive Neuroscience, Dalian, Liaoning Province, China
| | - Hua-Yun Li
- College of Teacher Education, Zhejiang Normal University, Jinhua, China
- Laboratory of Intelligent Education Technology and Application, Hangzhou, Zhejiang Province, China
| | - Yun-Da Li
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Ya-Ting Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Hui-Bin Ma
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - An-Feng Xiang
- Tongji University School of Medicine, Shanghai, China
| | - Xi-Ze Jia
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.
| | - Dong-Qiang Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China.
- Key Laboratory of Brain and Cognitive Neuroscience, Dalian, Liaoning Province, China.
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Yao S, Becker B, Kendrick KM. Reduced Inter-hemispheric Resting State Functional Connectivity and Its Association With Social Deficits in Autism. Front Psychiatry 2021; 12:629870. [PMID: 33746796 PMCID: PMC7969641 DOI: 10.3389/fpsyt.2021.629870] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 02/11/2021] [Indexed: 02/06/2023] Open
Abstract
Autism spectrum disorder (ASD) is an early onset developmental disorder which persists throughout life and is increasing in prevalence over the last few decades. Given its early onset and variable cognitive and emotional functional impairments, it is generally challenging to assess ASD individuals using task-based behavioral and functional MRI paradigms. Consequently, resting state functional MRI (rs-fMRI) has become a key approach for examining ASD-associated neural alterations and revealed functional alterations in large-scale brain networks relative to typically developing (TD) individuals, particularly those involved in social-cognitive and affective processes. Recent progress suggests that alterations in inter-hemispheric resting state functional connectivity (rsFC) between regions in the 2 brain hemispheres, particularly homotopic ones, may be of great importance. Here we have reviewed neuroimaging studies examining inter-hemispheric rsFC abnormities in ASD and its associations with symptom severity. As an index of inter-hemispheric functional connectivity, we have additionally reviewed previous studies on corpus callosum (CC) volumetric and fiber changes in ASD. There are converging findings on reduced inter-hemispheric (including homotopic) rsFC in large-scale brain networks particularly in posterior hubs of the default mode network, reduced volumes in the anterior and posterior CC, and on decreased FA and increased MD or RD across CC subregions. Associations between the strength of inter-hemispheric rsFC and social impairments in ASD together with their classification performance in distinguishing ASD subjects from TD controls across ages suggest that the strength of inter-hemispheric rsFC may be a more promising biomarker for assisting in ASD diagnosis than abnormalities in either brain wide rsFC or brain structure.
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Affiliation(s)
- Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Zink N, Lenartowicz A, Markett S. A new era for executive function research: On the transition from centralized to distributed executive functioning. Neurosci Biobehav Rev 2021; 124:235-244. [PMID: 33582233 DOI: 10.1016/j.neubiorev.2021.02.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023]
Abstract
"Executive functions" (EFs) is an umbrella term for higher cognitive control functions such as working memory, inhibition, and cognitive flexibility. One of the most challenging problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to a breakthrough in understanding how the brain controls and regulates itself. We propose to re-conceptualize EFs as emergent consequences of highly distributed brain processes that communicate with a pool of highly connected hub regions, thus precluding the need for a central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and helps to refine our understanding of the mechanisms underlying EFs by providing new, testable hypotheses and resolves empirical and theoretical inconsistencies in the EF literature.
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Affiliation(s)
- Nicolas Zink
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States.
| | - Agatha Lenartowicz
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States
| | - Sebastian Markett
- Department of Psychology, Humboldt University Berlin, Berlin, Germany
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49
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Helfer B, Boxhoorn S, Songa J, Steel C, Maltezos S, Asherson P. Emotion recognition and mind wandering in adults with attention deficit hyperactivity disorder or autism spectrum disorder. J Psychiatr Res 2021; 134:89-96. [PMID: 33373778 DOI: 10.1016/j.jpsychires.2020.12.059] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 11/19/2020] [Accepted: 12/19/2020] [Indexed: 11/18/2022]
Abstract
Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) in adults are often undiagnosed and overlap in psychopathology. Here we investigated the transdiagnostic traits of emotion recognition and mind wandering in a sample of 103 adults (43 with ADHD and 14 with ASD). The ability to correctly identify a facial expression of anger, fear, disgust or surprise was no different between the adults with ADHD or ASD and neurotypical (NT) adults. However, adults with ADHD or ASD were on average almost 200 ms slower in making a correct decision, suggesting a larger speed-accuracy trade-off in facial emotion recognition compared to NT adults. General processing speed was associated with excessive mind wandering in adults with ADHD, but not with ASD. The deficits in emotional processing were independent from mind wandering in both adults with ADHD or ASD. Emotional dysregulation and functional impairment scales separated adults with ADHD and ASD from the NT adults, but not from each other. When controlling for self-reported ADHD and ASD symptom severity, mind wandering in ADHD was independent from both ADHD and ASD symptom severity. In ASD, mind wandering was related to ASD but not ADHD symptom severity. Our results suggest that ASD and ADHD share a slower ability to recognize emotions, which is exacerbated by excessive mind wandering in ADHD, and by decreased processing speed in ASD.
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Affiliation(s)
- Bartosz Helfer
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, United Kingdom; Institute of Psychology, University of Wroclaw, Poland; Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
| | - Sara Boxhoorn
- Department of Child and Adolescent Psychiatry, Goethe-Universität Frankfurt, Germany
| | - Joanna Songa
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Charlotte Steel
- Division of Psychiatry, Faculty of Brain Sciences, University College London, United Kingdom
| | - Stefanos Maltezos
- Adult Autism and ADHD Service, South London and Maudsley NHS Foundation Trust, London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Philip Asherson
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
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Bathelt J, Geurts HM. Difference in default mode network subsystems in autism across childhood and adolescence. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2021; 25:556-565. [PMID: 33246376 PMCID: PMC7874372 DOI: 10.1177/1362361320969258] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
LAY ABSTRACT Neuroimaging research has identified a network of brain regions that are more active when we daydream compared to when we are engaged in a task. This network has been named the default mode network. Furthermore, differences in the default mode network are the most consistent findings in neuroimaging research in autism. Recent studies suggest that the default mode network is composed of subnetworks that are tied to different functions, namely memory and understanding others' minds. In this study, we investigated if default mode network differences in autism are related to specific subnetworks of the default mode network and if these differences change across childhood and adolescence. Our results suggest that the subnetworks of the default mode network are less differentiated in autism in middle childhood compared to neurotypicals. By late adolescence, the default mode network subnetwork organisation was similar in the autistic and neurotypical groups. These findings provide a foundation for future studies to investigate if this developmental pattern relates to improvements in the integration of memory and social understanding as autistic children grow up.
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