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Cheng S, Zeng F, Zhou J, Dong X, Yang W, Yin T, Huang K, Liang F, Li Z. Altered static and dynamic functional brain network in knee osteoarthritis: A resting-state functional magnetic resonance imaging study: Static and dynamic FNC in KOA. Neuroimage 2024; 292:120599. [PMID: 38608799 DOI: 10.1016/j.neuroimage.2024.120599] [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: 06/24/2023] [Revised: 03/26/2024] [Accepted: 03/30/2024] [Indexed: 04/14/2024] Open
Abstract
This study aimed to investigate altered static and dynamic functional network connectivity (FNC) and its correlation with clinical symptoms in patients with knee osteoarthritis (KOA). One hundred and fifty-nine patients with KOA and 73 age- and gender-matched healthy subjects (HS) underwent resting-state functional magnetic resonance imaging (rs-fMRI) and clinical evaluations. Group independent component analysis (GICA) was applied, and seven resting-state networks were identified. Patients with KOA had decreased static FNC within the default mode network (DM), visual network (VS), and cerebellar network (CB) and increased static FNC between the subcortical network (SC) and VS (p < 0.05, FDR corrected). Four reoccurring FNC states were identified using k-means clustering analysis. Although abnormalities in dynamic FNCs of KOA patients have been found using the common window size (22 TR, 44 s), but the results of the clustering analysis were inconsistent when using different window sizes, suggesting dynamic FNCs might be an unstable method to compare brain function between KOA patients and HS. These recent findings illustrate that patients with KOA have a wide range of abnormalities in the static and dynamic FNCs, which provided a reference for the identification of potential central nervous therapeutic targets for KOA treatment and might shed light on the other musculoskeletal pain neuroimaging studies.
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Affiliation(s)
- Shirui Cheng
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Acupuncture and Brain Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Key Laboratory of Acupuncture for Senile Disease (Chengdu University of TCM), Ministry of Education, Chengdu 611137, China
| | - Fang Zeng
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Acupuncture and Brain Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Key Laboratory of Acupuncture for Senile Disease (Chengdu University of TCM), Ministry of Education, Chengdu 611137, China
| | - Jun Zhou
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Xiaohui Dong
- School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Weihua Yang
- Dali Bai Autonomous Prefecture Chinese Medicine Hospital, Dali 671000, China
| | - Tao Yin
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Acupuncture and Brain Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Key Laboratory of Acupuncture for Senile Disease (Chengdu University of TCM), Ministry of Education, Chengdu 611137, China
| | - Kama Huang
- College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
| | - Fanrong Liang
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Key Laboratory of Acupuncture for Senile Disease (Chengdu University of TCM), Ministry of Education, Chengdu 611137, China.
| | - Zhengjie Li
- Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Acupuncture and Brain Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China; Key Laboratory of Acupuncture for Senile Disease (Chengdu University of TCM), Ministry of Education, Chengdu 611137, China.
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Mecklenbrauck F, Gruber M, Siestrup S, Zahedi A, Grotegerd D, Mauritz M, Trempler I, Dannlowski U, Schubotz RI. The significance of structural rich club hubs for the processing of hierarchical stimuli. Hum Brain Mapp 2024; 45:e26543. [PMID: 38069537 PMCID: PMC10915744 DOI: 10.1002/hbm.26543] [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: 06/09/2023] [Revised: 10/17/2023] [Accepted: 11/09/2023] [Indexed: 03/07/2024] Open
Abstract
The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.
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Affiliation(s)
- Falko Mecklenbrauck
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Marius Gruber
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department for Psychiatry, Psychosomatic Medicine and PsychotherapyUniversity Hospital Frankfurt, Goethe UniversityFrankfurtGermany
| | - Sophie Siestrup
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Anoushiravan Zahedi
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Marco Mauritz
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
| | - Ima Trempler
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Udo Dannlowski
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ricarda I. Schubotz
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
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Zhu G, Li Y, Wan L, Sun C, Liu X, Zhang J, Liang Y, Liu G, Yan H, Li R, Yang G. Divergent electroencephalogram resting-state functional network alterations in subgroups of autism spectrum disorder: a symptom-based clustering analysis. Cereb Cortex 2024; 34:bhad413. [PMID: 37950877 DOI: 10.1093/cercor/bhad413] [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: 09/21/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/13/2023] Open
Abstract
Autism spectrum disorder (ASD) is characterized by etiological and phenotypic heterogeneity. Despite efforts to categorize ASD into subtypes, research on specific functional connectivity changes within ASD subgroups based on clinical presentations is limited. This study proposed a symptom-based clustering approach to identify subgroups of ASD based on multiple clinical rating scales and investigate their distinct Electroencephalogram (EEG) functional connectivity patterns. Eyes-opened resting-state EEG data were collected from 72 children with ASD and 63 typically developing (TD) children. A data-driven clustering approach based on Social Responsiveness Scales-Second Edition and Vinland-3 scores was used to identify subgroups. EEG functional connectivity and topological characteristics in four frequency bands were assessed. Two subgroups were identified: mild ASD (mASD, n = 37) and severe ASD (sASD, n = 35). Compared to TD, mASD showed increased functional connectivity in the beta band, while sASD exhibited decreased connectivity in the alpha band. Significant between-group differences in global and regional topological abnormalities were found in both alpha and beta bands. The proposed symptom-based clustering approach revealed the divergent functional connectivity patterns in the ASD subgroups that was not observed in typical ASD studies. Our study thus provides a new perspective to address the heterogeneity in ASD research.
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Affiliation(s)
- Gang Zhu
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yuhang Li
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau S.A.R., China
- Department of Psychology, Faculty of Social Sciences, University of Macau, Macau S.A.R., China
| | - Lin Wan
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Chunhua Sun
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xinting Liu
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Jing Zhang
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yan Liang
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Guoyin Liu
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Huimin Yan
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Rihui Li
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa, Macau S.A.R., China
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau S.A.R., China
| | - Guang Yang
- Senior Department of Pediatrics, The Seventh Medical Center of PLA General Hospital, Beijing, China
- Department of Pediatrics Medical School of Chinese People's Liberation Army, Beijing, China
- Department of Pediatrics, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
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Májer T, Bódi V, Kelemen V, Szűcs A, Varró P, Világi I. Valproate treatment induces age- and sex-dependent neuronal activity changes according to a patch clamp study. Dev Neurobiol 2024; 84:32-43. [PMID: 38124434 DOI: 10.1002/dneu.22933] [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: 05/29/2023] [Revised: 10/13/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023]
Abstract
Autism spectrum disorder is a heterogeneous neurodevelopmental disorder characterized by impaired social interactions, restricted, and stereotyped behaviors. The valproic acid model is one of the most recognized and broadly used models in rats to induce core symptoms of this disorder. Comorbidity of epilepsy and autism occurs frequently, due to similar background mechanisms that include the imbalance of excitation and inhibition. In this series of experiments, treatment was performed on rat dams with a single 500 mg/kg dose i.p. valproate injection on embryonic day 12.5. Intracellular whole-cell patch clamp recordings were performed on brain slices prepared from adolescent and adult offspring of both sexes on pyramidal neurons of the medial prefrontal cortex and entorhinal cortex. Current clamp stimulation utilizing conventional current step protocols and dynamic clamp stimulation were applied to assess neuronal excitability. Membrane properties and spiking characteristics of layer II-III pyramidal cells were analyzed in both cortical regions. Significant sex-dependent and age-dependent differences were found in several parameters in the control groups. Considering membrane resistance, rheobase, voltage sag slope, and afterdepolarization slope, we observed notable changes mainly in the female groups. Valproate treatment seemed to enhance these differences and increase network excitability. However, it is possible that compensatory mechanisms took place during the maturation of the network while reaching the age-group of 3 months. Based on the results, the expression of the hyperpolarization-activated cyclic nucleotide-gated channels may be appreciably affected by the valproate treatment, which influences fundamental electrophysiological properties of the neurons such as the voltage sag. Remarkable changes appeared in the prefrontal cortex; however, also the entorhinal cortex shows similar tendencies.
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Affiliation(s)
- Tímea Májer
- Department of Physiology and Neurobiology, Institute of Biology, Eötvös Loránd University, Budapest, Hungary
| | - Veronika Bódi
- Department of Physiology and Neurobiology, Institute of Biology, Eötvös Loránd University, Budapest, Hungary
| | - Viktor Kelemen
- Department of Physiology and Neurobiology, Institute of Biology, Eötvös Loránd University, Budapest, Hungary
| | - Attila Szűcs
- Department of Physiology and Neurobiology, Institute of Biology, Eötvös Loránd University, Budapest, Hungary
- Hungarian Center of Excellence for Molecular Medicine, Szeged, Hungary
| | - Petra Varró
- Department of Physiology and Neurobiology, Institute of Biology, Eötvös Loránd University, Budapest, Hungary
| | - Ildikó Világi
- Department of Physiology and Neurobiology, Institute of Biology, Eötvös Loránd University, Budapest, Hungary
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Tian Y, Xu G, Zhang J, Chen K, Liu S. Nodal properties of the resting-state brain functional network in childhood and adolescence. J Neuroimaging 2023; 33:1015-1023. [PMID: 37735776 DOI: 10.1111/jon.13155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND AND PURPOSE Changes in the topological properties of brain functional network nodes during childhood and adolescence can provide more detailed and intuitive information on the rules of brain development. This study aims to explore the characteristics of nodal attributes in child and adolescent brain functional networks and analyze the correlation between nodal attributes in different brain regions and age. METHODS Forty-two healthy volunteers aged 6-18 years who were right-handed primary and middle school students were recruited, and the subgroup analysis included children (6-12 years, n = 19) and adolescents (13-18 years, n = 23). Resting-state functional magnetic resonance imaging data were collected using a 3.0 Tesla MRI scanner. The topological properties of the functional brain network were analyzed using graph theory. RESULTS Compared with the children group, the degree centrality and nodal efficiency of multiple brain regions in the adolescent group were significantly increased, and the nodal shortest path was reduced (q<0.05, false discovery rate corrected). These brain regions were widely distributed in the whole brain and significantly correlated with age. Compared with the children group, reduced degree centralities were observed in the left dorsolateral fusiform gyrus, left rostral cuneus gyrus, and right medial superior occipital gyrus. CONCLUSION The transmission efficiency of the brain's core network gradually increased, and the subnetwork function gradually improved in children and adolescents with age. The functional development of each brain area in the occipital visual cortex was uneven and there was functional differentiation within the occipital visual cortex.
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Affiliation(s)
- Yu Tian
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Department of Radiology, the Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Gaoqiang Xu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Jing Zhang
- Department of Radiology, the Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Kuntao Chen
- Department of Radiology, the Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China
| | - Songjiang Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
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Jing J, Klugah-Brown B, Xia S, Sheng M, Biswal BB. Comparative analysis of group information-guided independent component analysis and independent vector analysis for assessing brain functional network characteristics in autism spectrum disorder. Front Neurosci 2023; 17:1252732. [PMID: 37928736 PMCID: PMC10620743 DOI: 10.3389/fnins.2023.1252732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/02/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction Group information-guided independent component analysis (GIG-ICA) and independent vector analysis (IVA) are two methods that improve estimation of subject-specific independent components in neuroimaging studies. These methods have shown better performance than traditional group independent component analysis (GICA) with respect to intersubject variability (ISV). Methods In this study, we compared the patterns of community structure, spatial variance, and prediction performance of GIG-ICA and IVA-GL, respectively. The dataset was obtained from the publicly available Autism Brain Imaging Data Exchange (ABIDE) database, comprising 75 healthy controls (HC) and 102 Autism Spectrum Disorder (ASD) participants. The greedy rule was used to match components from IVA-GL and GIG-ICA in order to compare the similarities between the two methods. Results Robust correspondence was observed between the two methods the following networks: cerebellum network (CRN; |r| = 0.7813), default mode network (DMN; |r| = 0.7263), self-reference network (SRN; |r| = 0.7818), ventral attention network (VAN; |r| = 0.7574), and visual network (VSN; |r| = 0.7503). Additionally, the Sensorimotor Network demonstrated the highest similarity between IVA-GL and GIG-ICA (SOM: |r| = 0.8125). Our findings revealed a significant difference in the number of modules identified by the two methods (HC: p < 0.001; ASD: p < 0.001). GIG-ICA identified significant differences in FNC between HC and ASD compared to IVA-GL. However, in correlation analysis, IVA-GL identified a statistically negative correlation between FNC of ASD and the social total subscore of the classic Autism Diagnostic Observation Schedule (ADOS: pi = -0.26, p = 0.0489). Moreover, both methods demonstrated similar prediction performances on age within specific networks, as indicated by GIG-ICA-CRN (R2 = 0.91, RMSE = 3.05) and IVA-VAN (R2 = 0.87, RMSE = 3.21). Conclusion In summary, IVA-GL demonstrated lower modularity, suggesting greater sensitivity in estimating networks with higher intersubject variability. The improved age prediction of cerebellar-attention networks underscores their importance in the developmental progression of ASD. Overall, IVA-GL may be appropriate for investigating disorders with greater variability, while GIG-ICA identifies functional networks with distinct modularity patterns.
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Affiliation(s)
- Junlin Jing
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Klugah-Brown
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiyu Xia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat B. Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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Yang HJ, Wu HM, Li XH, Jin R, Zhang L, Dong T, Zhou XQ, Zhang B, Zhang QJ, Mao CP. Functional disruptions of the brain network in low back pain: a graph-theoretical study. Neuroradiology 2023; 65:1483-1495. [PMID: 37608218 DOI: 10.1007/s00234-023-03209-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/03/2023] [Indexed: 08/24/2023]
Abstract
PURPOSE The aim of this study was to investigate alterations in the topological organization of whole-brain functional networks in patients with chronic low back pain (CLBP) and characterize the relationship of these alterations with pain characteristics. METHODS Thirty-three CLBP patients and 34 matched healthy controls (HCs) underwent fMRI scans. A graph-theoretical approach was applied to identify brain network changes in patients suffering from chronic low back pain given its nonspecific etiology and complexity. Graph theory-based analysis was used to construct functional connectivity matrices and extract the features of small-world networks of the brain in both groups. Then, the whole-brain functional connectivity differences were characterized by network-based statistics (NBS) analysis, and the relationship between the altered brain features and clinical measures was explored. RESULTS At the global level, patients with CLBP showed significantly decreased gamma, sigma, global efficiency, and local efficiency and increased lambda and shortest path length compared with HCs. At the regional level, there were deficits in nodal efficiency within the default mode network and salience network. NBS analysis demonstrated that decreased functional connectivity was present in the CLBP patients, mainly in the frontolimbic circuit and temporal regions. Furthermore, aspects of topological dysfunctions in CLBP were correlated with pain severity. CONCLUSION This study highlighted the aberrant topological organization of functional brain networks in CLBP, which may shed light on the pathophysiology of CLBP and support the development of pain management approaches.
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Affiliation(s)
- Hua Juan Yang
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, 157, Xi'wu Road, Xi'an, 710004, Shaanxi, China
| | - Hong Mei Wu
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, 157, Xi'wu Road, Xi'an, 710004, Shaanxi, China
| | - Xiao Hui Li
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, 157, Xi'wu Road, Xi'an, 710004, Shaanxi, China
| | - Rui Jin
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, 157, Xi'wu Road, Xi'an, 710004, Shaanxi, China
| | - Lei Zhang
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, 157, Xi'wu Road, Xi'an, 710004, Shaanxi, China
| | - Ting Dong
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, 157, Xi'wu Road, Xi'an, 710004, Shaanxi, China
| | - Xiao Qian Zhou
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, 157, Xi'wu Road, Xi'an, 710004, Shaanxi, China
| | - Bo Zhang
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, 157, Xi'wu Road, Xi'an, 710004, Shaanxi, China
| | - Qiu Juan Zhang
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, 157, Xi'wu Road, Xi'an, 710004, Shaanxi, China.
| | - Cui Ping Mao
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, 157, Xi'wu Road, Xi'an, 710004, Shaanxi, China.
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Javaheripour N, Wagner G, de la Cruz F, Walter M, Szycik GR, Tietze FA. Altered brain network organization in adults with Asperger's syndrome: decreased connectome transitivity and assortativity with increased global efficiency. Front Psychiatry 2023; 14:1223147. [PMID: 37701094 PMCID: PMC10494541 DOI: 10.3389/fpsyt.2023.1223147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/26/2023] [Indexed: 09/14/2023] Open
Abstract
Introduction Autism spectrum disorder (ASD) is a neurodevelopmental disorder that persists into adulthood with both social and cognitive disturbances. Asperger's syndrome (AS) was a distinguished subcategory of autism in the DSM-IV-TR defined by specific symptoms including difficulties in social interactions, inflexible thinking patterns, and repetitive behaviour without any delay in language or cognitive development. Studying the functional brain organization of individuals with these specific symptoms may help to better understand Autism spectrum symptoms. Methods The aim of this study is therefore to investigate functional connectivity as well as functional network organization characteristics using graph-theory measures of the whole brain in male adults with AS compared to healthy controls (HC) (AS: n = 15, age range 21-55 (mean ± sd: 39.5 ± 11.6), HC: n = 15, age range 22-57 [mean ± sd: 33.5 ± 8.5]). Results No significant differences were found when comparing the region-by-region connectivity at the whole-brain level between the AS group and HC. However, measures of "transitivity," which reflect local information processing and functional segregation, and "assortativity," indicating network resilience, were reduced in the AS group compared to HC. On the other hand, global efficiency, which represents the overall effectiveness and speed of information transfer across the entire brain network, was increased in the AS group. Discussion Our findings suggest that individuals with AS may have alterations in the organization and functioning of brain networks, which could contribute to the distinctive cognitive and behavioural features associated with this condition. We suggest further research to explore the association between these altered functional patterns in brain networks and specific behavioral traits observed in individuals with AS, which could provide valuable insights into the underlying mechanisms of its symptomatology.
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Affiliation(s)
- Nooshin Javaheripour
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Gerd Wagner
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena, Germany
| | - Feliberto de la Cruz
- Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- German Center for Mental Health (DZPG), Jena, Germany
| | - Gregor R. Szycik
- Department of Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Fabian-Alexander Tietze
- Department of Psychiatry and Psychotherapy, Jüdisches Krankenhaus Berlin—Berlin Jewish Hospital, Academic Teaching Hospital of the Charité—Universitätsmedizin Berlin, Berlin, Germany
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Wang C, Yang L, Lin Y, Wang C, Tian P. Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis. Front Integr Neurosci 2023; 16:922577. [PMID: 36743477 PMCID: PMC9892631 DOI: 10.3389/fnint.2022.922577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 12/23/2022] [Indexed: 01/20/2023] Open
Abstract
Background Neurobiological models to explain the vulnerability of autism spectrum disorders (ASDs) are scarce, and previous resting-state functional magnetic resonance imaging (rs-fMRI) studies mostly examined static functional connectivity (FC). Given that FC constantly evolves, it is critical to probe FC dynamic differences in ASD patients. Methods We characterized recurring phase-locking (PL) states during rest in 45 ASD patients and 47 age- and sex-matched healthy controls (HCs) using Leading Eigenvector Dynamics Analysis (LEiDA) and probed the organization of PL states across different fine grain sizes. Results Our results identified five different groups of discrete resting-state functional networks, which can be defined as recurrent PL state overtimes. Specifically, ASD patients showed an increased probability of three PL states, consisting of the visual network (VIS), frontoparietal control network (FPN), default mode network (DMN), and ventral attention network (VAN). Correspondingly, ASD patients also showed a decreased probability of two PL states, consisting of the subcortical network (SUB), somatomotor network (SMN), FPN, and VAN. Conclusion Our findings suggested that the temporal reorganization of brain discrete networks was closely linked to sensory to cognitive systems of the brain. Our study provides new insights into the dynamics of brain networks and contributes to a deeper understanding of the neurological mechanisms of ASD.
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Affiliation(s)
- Chaoyan Wang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lu Yang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanan Lin
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Caihong Wang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peichao Tian
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China,*Correspondence: Peichao Tian,
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10
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Sigar P, Uddin LQ, Roy D. Altered global modular organization of intrinsic functional connectivity in autism arises from atypical node-level processing. Autism Res 2023; 16:66-83. [PMID: 36333956 DOI: 10.1002/aur.2840] [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: 08/12/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by restricted interests and repetitive behaviors as well as social-communication deficits. These traits are associated with atypicality of functional brain networks. Modular organization in the brain plays a crucial role in network stability and adaptability for neurodevelopment. Previous neuroimaging research demonstrates discrepancies in studies of functional brain modular organization in ASD. These discrepancies result from the examination of mixed age groups. Furthermore, recent findings suggest that while much attention has been given to deriving atlases and measuring the connections between nodes, within node information may also be crucial in determining altered modular organization in ASD compared with typical development (TD). However, altered modular organization originating from systematic nodal changes are yet to be explored in younger children with ASD. Here, we used graph-theoretical measures to fill this knowledge gap. To this end, we utilized multicenter resting-state fMRI data collected from 5 to 10-year-old children-34 ASD and 40 TD obtained from the Autism Brain Image Data Exchange (ABIDE) I and II. We demonstrate that alterations in topological roles and modular cohesiveness are the two key properties of brain regions anchored in default mode, sensorimotor, and salience networks, and primarily relate to social and sensory deficits in children with ASD. These results demonstrate that atypical global network organization in children with ASD arises from nodal role changes, and contribute to the growing body of literature suggesting that there is interesting information within nodes providing critical markers of functional brain networks in autistic children.
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Affiliation(s)
- Priyanka Sigar
- Cognitive Brain Dynamics Lab, National Brain Research Center, Manesar, India.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA.,Department of Psychology, University of California Los Angeles, Los Angeles, California, USA
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Center, Manesar, India.,School of AIDE, Centre for Brain Science and Applications, Karwar, India
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11
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Talesh Jafadideh A, Mohammadzadeh Asl B. Structural filtering of functional data offered discriminative features for autism spectrum disorder. PLoS One 2022; 17:e0277989. [PMID: 36472989 PMCID: PMC9725140 DOI: 10.1371/journal.pone.0277989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022] Open
Abstract
This study attempted to answer the question, "Can filtering the functional data through the frequency bands of the structural graph provide data with valuable features which are not valuable in unfiltered data"?. The valuable features discriminate between autism spectrum disorder (ASD) and typically control (TC) groups. The resting-state fMRI data was passed through the structural graph's low, middle, and high-frequency band (LFB, MFB, and HFB) filters to answer the posed question. The structural graph was computed using the diffusion tensor imaging data. Then, the global metrics of functional graphs and metrics of functional triadic interactions were computed for filtered and unfiltered rfMRI data. Compared to TCs, ASDs had significantly higher clustering coefficients in the MFB, higher efficiencies and strengths in the MFB and HFB, and lower small-world propensity in the HFB. These results show over-connectivity, more global integration, and decreased local specialization in ASDs compared to TCs. Triadic analysis showed that the numbers of unbalanced triads were significantly lower for ASDs in the MFB. This finding may indicate the reason for restricted and repetitive behavior in ASDs. Also, in the MFB and HFB, the numbers of balanced triads and the energies of triadic interactions were significantly higher and lower for ASDs, respectively. These findings may reflect the disruption of the optimum balance between functional integration and specialization. There was no significant difference between ASDs and TCs when using the unfiltered data. All of these results demonstrated that significant differences between ASDs and TCs existed in the MFB and HFB of the structural graph when analyzing the global metrics of the functional graph and triadic interaction metrics. Also, these results demonstrated that frequency bands of the structural graph could offer significant findings which were not found in the unfiltered data. In conclusion, the results demonstrated the promising perspective of using structural graph frequency bands for attaining discriminative features and new knowledge, especially in the case of ASD.
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12
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Yoon N, Huh Y, Lee H, Kim JI, Lee J, Yang CM, Jang S, Ahn YD, Oh MR, Lee DS, Kang H, Kim BN. Alterations in Social Brain Network Topology at Rest in Children With Autism Spectrum Disorder. Psychiatry Investig 2022; 19:1055-1068. [PMID: 36588440 PMCID: PMC9806512 DOI: 10.30773/pi.2022.0174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 11/24/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE Underconnectivity in the resting brain is not consistent in autism spectrum disorder (ASD). However, it is known that the functional connectivity of the default mode network is mainly decreased in childhood ASD. This study investigated the brain network topology as the changes in the connection strength and network efficiency in childhood ASD, including the early developmental stages. METHODS In this study, 31 ASD children aged 2-11 years were compared with 31 age and sex-matched children showing typical development. We explored the functional connectivity based on graph filtration by assessing the single linkage distance and global and nodal efficiencies using resting-state functional magnetic resonance imaging. The relationship between functional connectivity and clinical scores was also analyzed. RESULTS Underconnectivities within the posterior default mode network subregions and between the inferior parietal lobule and inferior frontal/superior temporal regions were observed in the ASD group. These areas significantly correlated with the clinical phenotypes. The global, local, and nodal network efficiencies were lower in children with ASD than in those with typical development. In the preschool-age children (2-6 years) with ASD, the anterior-posterior connectivity of the default mode network and cerebellar connectivity were reduced. CONCLUSION The observed topological reorganization, underconnectivity, and disrupted efficiency in the default mode network subregions and social function-related regions could be significant biomarkers of childhood ASD.
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Affiliation(s)
- Narae Yoon
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Youngmin Huh
- Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyekyoung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Johanna Inhyang Kim
- Department of Psychiatry, Hanyang University Medical Center, Seoul, Republic of Korea
| | - Jung Lee
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.,Integrative Care Hub, Seoul National University Children's Hospital, Seoul, Republic of Korea
| | - Chan-Mo Yang
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Soomin Jang
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yebin D Ahn
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Mee Rim Oh
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Molecular Medicine and Biopharmaceutical Science, Seoul National University, Seoul, Republic of Korea
| | - Hyejin Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Bung-Nyun Kim
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
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13
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Thérien VD, Degré-Pelletier J, Barbeau EB, Samson F, Soulières I. Differential neural correlates underlying mental rotation processes in two distinct cognitive profiles in autism. Neuroimage Clin 2022; 36:103221. [PMID: 36228483 PMCID: PMC9668634 DOI: 10.1016/j.nicl.2022.103221] [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: 06/23/2022] [Revised: 09/16/2022] [Accepted: 10/03/2022] [Indexed: 11/11/2022]
Abstract
Enhanced visuospatial abilities characterize the cognitive profile of a subgroup of autistics. However, the neural correlates underlying such cognitive strengths are largely unknown. Using functional magnetic resonance imaging (fMRI), we investigated the neural underpinnings of superior visuospatial functioning in different autistic subgroups. Twenty-seven autistic adults, including 13 with a Wechsler's Block Design peak (AUTp) and 14 without (AUTnp), and 23 typically developed adults (TYP) performed a classic mental rotation task. As expected, AUTp participants were faster at the task compared to TYP. At the neural level, AUTp participants showed enhanced bilateral parietal and occipital activation, stronger occipito-parietal and fronto-occipital connectivity, and diminished fronto-parietal connectivity compared to TYP. On the other hand, AUTnp participants presented greater activation in right and anterior regions compared to AUTp. In addition, reduced connectivity between occipital and parietal regions was observed in AUTnp compared to AUTp and TYP participants. A greater reliance on posterior regions is typically reported in the autism literature. Our results suggest that this commonly reported finding may be specific to a subgroup of autistic individuals with enhanced visuospatial functioning. Moreover, this study demonstrated that increased occipito-frontal synchronization was associated with superior visuospatial abilities in autism. This finding contradicts the long-range under-connectivity hypothesis in autism. Finally, given the relationship between distinct cognitive profiles in autism and our observed differences in brain functioning, future studies should provide an adequate characterization of the autistic subgroups in their research. The main limitations are small sample sizes and the inclusion of male-only participants.
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Affiliation(s)
- Véronique D. Thérien
- Laboratory on Intelligence and Development in Autism, Psychology Department, Université du Québec à Montréal, Montreal, QC, Canada,Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, Montreal, QC, Canada
| | - Janie Degré-Pelletier
- Laboratory on Intelligence and Development in Autism, Psychology Department, Université du Québec à Montréal, Montreal, QC, Canada,Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, Montreal, QC, Canada
| | - Elise B. Barbeau
- Laboratory on Intelligence and Development in Autism, Psychology Department, Université du Québec à Montréal, Montreal, QC, Canada
| | - Fabienne Samson
- Laboratory on Intelligence and Development in Autism, Psychology Department, Université du Québec à Montréal, Montreal, QC, Canada
| | - Isabelle Soulières
- Laboratory on Intelligence and Development in Autism, Psychology Department, Université du Québec à Montréal, Montreal, QC, Canada,Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, Montreal, QC, Canada,Corresponding author at: Psychology Department, Université du Québec à Montréal, C.P. 8888 succursale Centre-ville, Montréal (Québec) H3C 3P8, Canada.
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14
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Ursino M, Serra M, Tarasi L, Ricci G, Magosso E, Romei V. Bottom-up vs. top-down connectivity imbalance in individuals with high-autistic traits: An electroencephalographic study. Front Syst Neurosci 2022; 16:932128. [PMID: 36032324 PMCID: PMC9412751 DOI: 10.3389/fnsys.2022.932128] [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: 05/24/2022] [Accepted: 07/27/2022] [Indexed: 11/25/2022] Open
Abstract
Brain connectivity is often altered in autism spectrum disorder (ASD). However, there is little consensus on the nature of these alterations, with studies pointing to either increased or decreased connectivity strength across the broad autism spectrum. An important confound in the interpretation of these contradictory results is the lack of information about the directionality of the tested connections. Here, we aimed at disambiguating these confounds by measuring differences in directed connectivity using EEG resting-state recordings in individuals with low and high autistic traits. Brain connectivity was estimated using temporal Granger Causality applied to cortical signals reconstructed from EEG. Between-group differences were summarized using centrality indices taken from graph theory (in degree, out degree, authority, and hubness). Results demonstrate that individuals with higher autistic traits exhibited a significant increase in authority and in degree in frontal regions involved in high-level mechanisms (emotional regulation, decision-making, and social cognition), suggesting that anterior areas mostly receive information from more posterior areas. Moreover, the same individuals exhibited a significant increase in the hubness and out degree over occipital regions (especially the left and right pericalcarine regions, where the primary visual cortex is located), suggesting that these areas mostly send information to more anterior regions. Hubness and authority appeared to be more sensitive indices than the in degree and out degree. The observed brain connectivity differences suggest that, in individual with higher autistic traits, bottom-up signaling overcomes top-down channeled flow. This imbalance may contribute to some behavioral alterations observed in ASD.
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Affiliation(s)
- Mauro Ursino
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
- *Correspondence: Mauro Ursino,
| | - Michele Serra
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
| | - Luca Tarasi
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Cesena, Italy
| | - Giulia Ricci
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
| | - Elisa Magosso
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
| | - Vincenzo Romei
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, Rome, Italy
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15
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Elumalai P, Yadav Y, Williams N, Saucan E, Jost J, Samal A. Graph Ricci curvatures reveal atypical functional connectivity in autism spectrum disorder. Sci Rep 2022; 12:8295. [PMID: 35585156 PMCID: PMC9117309 DOI: 10.1038/s41598-022-12171-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/04/2022] [Indexed: 11/20/2022] Open
Abstract
While standard graph-theoretic measures have been widely used to characterize atypical resting-state functional connectivity in autism spectrum disorder (ASD), geometry-inspired network measures have not been applied. In this study, we apply Forman–Ricci and Ollivier–Ricci curvatures to compare networks of ASD and typically developing individuals (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We find brain-wide and region-specific ASD-related differences for both Forman–Ricci and Ollivier–Ricci curvatures, with region-specific differences concentrated in Default Mode, Somatomotor and Ventral Attention networks for Forman–Ricci curvature. We use meta-analysis decoding to demonstrate that brain regions with curvature differences are associated to those cognitive domains known to be impaired in ASD. Further, we show that brain regions with curvature differences overlap with those brain regions whose non-invasive stimulation improves ASD-related symptoms. These results suggest the utility of graph Ricci curvatures in characterizing atypical connectivity of clinically relevant regions in ASD and other neurodevelopmental disorders.
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Affiliation(s)
| | - Yasharth Yadav
- The Institute of Mathematical Sciences (IMSc), Chennai, India.,Indian Institute of Science Education and Research (IISER), Pune, India
| | - Nitin Williams
- Department of Computer Science, Helsinki Institute of Information Technology, Aalto University, Espoo, Finland. .,Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Emil Saucan
- Department of Applied Mathematics, ORT Braude College, Karmiel, Israel
| | - Jürgen Jost
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany.,The Santa Fe Institute, Santa Fe, NM, USA
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, India. .,Homi Bhabha National Institute (HBNI), Mumbai, India.
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16
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Peng L, Liu X, Ma D, Chen X, Xu X, Gao X. The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification. Front Neurosci 2022; 16:913377. [PMID: 35600614 PMCID: PMC9120576 DOI: 10.3389/fnins.2022.913377] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/20/2022] [Indexed: 11/25/2022] Open
Abstract
Objective Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance. Methods We propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information. Results The experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network. Conclusion This work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.
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Affiliation(s)
- Liling Peng
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Xiao Liu
- School of Business Administration, José Rizal University, Mandaluyong, Philippines
| | - Di Ma
- College of Information Science and Technology, Nanjing Forestry University, Nanjing, China
| | - Xiaofeng Chen
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
| | - Xiaowen Xu
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Xiaowen Xu,
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
- Xin Gao,
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17
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Liu G, Zheng W, Liu H, Guo M, Ma L, Hu W, Ke M, Sun Y, Zhang J, Zhang Z. Aberrant dynamic structure-function relationship of rich-club organization in treatment-naïve newly diagnosed juvenile myoclonic epilepsy. Hum Brain Mapp 2022; 43:3633-3645. [PMID: 35417064 PMCID: PMC9294302 DOI: 10.1002/hbm.25873] [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] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/28/2022] [Accepted: 04/03/2022] [Indexed: 11/25/2022] Open
Abstract
Neuroimaging studies have shown that juvenile myoclonic epilepsy (JME) is characterized by impaired brain networks. However, few studies have investigated the potential disruptions in rich‐club organization—a core feature of the brain networks. Moreover, it is unclear how structure–function relationships dynamically change over time in JME. Here, we quantify the anatomical rich‐club organization and dynamic structural and functional connectivity (SC–FC) coupling in 47 treatment‐naïve newly diagnosed patients with JME and 40 matched healthy controls. Dynamic functional network efficiency and its association with SC–FC coupling were also calculated to examine the supporting of structure–function relationship to brain information transfer. The results showed that the anatomical rich‐club organization was disrupted in the patient group, along with decreased connectivity strength among rich‐club hub nodes. Furthermore, reduced SC–FC coupling in rich‐club organization of the patients was found in two functionally independent dynamic states, that is the functional segregation state (State 1) and the strong somatomotor‐cognitive control interaction state (State 5); and the latter was significantly associated with disease severity. In addition, the relationships between SC–FC coupling of hub nodes connections and functional network efficiency in State 1 were found to be absent in patients. The aberrant dynamic SC–FC coupling of rich‐club organization suggests a selective influence of densely interconnected network core in patients with JME at the early phase of the disease, offering new insights and potential biomarkers into the underlying neurodevelopmental basis of behavioral and cognitive impairments observed in JME.
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Affiliation(s)
- Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Hong Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Man Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Laiyang Ma
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Wanjun Hu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Ming Ke
- College of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.,Zhejiang Lab, Hangzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Zhe Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.,School of Physics, Hangzhou Normal University, Hangzhou, China
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18
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Falakshahi H, Rokham H, Fu Z, Iraji A, Mathalon DH, Ford JM, Mueller BA, Preda A, van Erp TGM, Turner JA, Plis S, Calhoun VD. Path Analysis: A Method to Estimate Altered Pathways in Time-varying Graphs of Neuroimaging Data. Netw Neurosci 2022; 6:634-664. [PMID: 36204419 PMCID: PMC9531579 DOI: 10.1162/netn_a_00247] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 03/23/2022] [Indexed: 11/16/2022] Open
Abstract
Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pairwise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.
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Affiliation(s)
- Haleh Falakshahi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Hooman Rokham
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Daniel H. Mathalon
- Department of Psychiatry, University of California, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - Judith M. Ford
- Department of Psychiatry, University of California, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - Bryon A. Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Theo G. M. van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Jessica A. Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Sergey Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
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A Graph Partition-Based Large-Scale Distribution Network Reconfiguration Method. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3169065. [PMID: 35321458 PMCID: PMC8938088 DOI: 10.1155/2022/3169065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/01/2022] [Indexed: 11/25/2022]
Abstract
This article focuses on the analysis of large-scale distribution network reconstruction fused with graph theory and graph partitioning algorithms. Graph theory and graph segmentation algorithms have been rushed by many researchers in the fields of medicine, drone, and neural network. It is a newcomer in the field of computer vision, which can not only realize the division in color but also divide it by image data. The distribution network is also indispensable for new energy, electric machines, but the traditional distribution network has many problems, such as not suitable for distributed power access and excessive network loss. To improve the performance of distribution networks and reduce network losses, this paper A multi-division model for distribution network construction and reconstruction is established, and a graph theory-based division algorithm method is proposed to effectively solve the problem of feeder-to-feeder reconstruction during large-scale distribution in distribution networks. Through its superconductivity phenomenon and the characteristics of clustering algorithm division, this paper uses formulas to show its division principle and gives examples of various distribution network reconstruction algorithms to explore which method of improvement can improve the performance of the distribution network and reduce network losses. The number of iterations is also strictly considered, and the value is taken after multiple iterations to reduce the error. Through the distribution network calculation example, the network loss reduction value is obtained, and the distribution network fault repair model is exemplified. The picture is used to briefly describe the process of distribution network reconstruction and find that the faults of the distribution network can be quickly located and isolated through the FTU, and quickly repaired. Finally, in order to reduce the network loss, reduce the load of power flow calculation, and solve the problem of local optimization, a JA-BE-JA optimization algorithm based on large-scale distribution network reconfiguration is proposed. The mixed sampling method is preferred to test the number of divisions in the four states, and the parameters are selected to test the performance of the improved annealing simulation algorithm, and the conclusion is drawn as follows: the improved graph segmentation algorithm has strong robustness, can avoid local optimization of graph data, and can reduce network loss. Compared with traditional distribution network reconstruction methods, the network loss can be reduced to 454.3 KW, which can be optimized by 10.68% compared with the initial network loss.
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Cong S, Yao X, Xie L, Yan J, Shen L. Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts. Front Genet 2022; 12:782953. [PMID: 35237294 PMCID: PMC8884108 DOI: 10.3389/fgene.2021.782953] [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: 09/25/2021] [Accepted: 11/16/2021] [Indexed: 11/29/2022] Open
Abstract
Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.
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Affiliation(s)
- Shan Cong
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Linhui Xie
- Department of Electrical and Computer Engineering, School of Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Jingwen Yan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Ingalhalikar M, Shinde S, Karmarkar A, Rajan A, Rangaprakash D, Deshpande G. Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset. IEEE Trans Biomed Eng 2021; 68:3628-3637. [PMID: 33989150 PMCID: PMC8696194 DOI: 10.1109/tbme.2021.3080259] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized. METHODS We address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Bayes formulation to remove inter-site differences in data distributions, to improve diagnostic classification accuracy. Specifically, we demonstrate this using ABIDE (Autism Brain Imaging Data Exchange) multi-site data for classifying individuals with Autism from healthy controls using resting state fMRI-based functional connectivity data. RESULTS Our results show that higher classification accuracies across multiple classification models can be obtained (especially for models based on artificial neural networks) from multi-site data post harmonization with the ComBat technique as compared to without harmonization, outperforming earlier results from existing studies using ABIDE. Furthermore, our network ablation analysis facilitated important insights into autism spectrum disorder pathology and the connectivity in networks shown to be important for classification covaried with verbal communication impairments in Autism. CONCLUSION Multi-site data harmonization using ComBat improves neuroimaging-based diagnostic classification of mental disorders. SIGNIFICANCE ComBat has the potential to make AI-based clinical decision-support systems more feasible in psychiatry.
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Papaioannou A, Kalantzi E, Papageorgiou CC, Korombili K, Bokou A, Pehlivanidis A, Papageorgiou CC, Papaioannou G. Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment. Brain Sci 2021; 11:1531. [PMID: 34827530 PMCID: PMC8615740 DOI: 10.3390/brainsci11111531] [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: 07/01/2021] [Revised: 11/09/2021] [Accepted: 11/09/2021] [Indexed: 11/17/2022] Open
Abstract
We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle's valid and invalid syllogisms, and how these differences correlate with brain regions and behavioral data for each subject. We recorded EEGs from 14 scalp electrodes (channels) in 21 adults with ADHD, 21 with ASD, and 21 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), Aristotle's two types of syllogism mentioned above. A set of 39 questions were given to participants related to valid-invalid syllogisms as well as a separate set of questionnaires, in order to collect a number of demographic and behavioral data, with the aim of detecting shared information with values of a feature extracted from EEG, the multiscale entropy (MSE), in the 14 channels ('brain regions'). MSE, a nonlinear information-theoretic measure of complexity, was computed to extract a feature that quantifies the complexity of the EEG. Behavior-Partial Least Squares Correlation, PLSC, is the method to detect the correlation between two sets of data, brain, and behavioral measures. -PLSC, a variant of PLSC, was applied to build a functional connectivity of the brain regions involved in the reasoning tasks. Graph-theoretic measures were used to quantify the complexity of the functional networks. Based on the results of the analysis described in this work, a mixed 14 × 2 × 3 ANOVA showed significant main effects of group factor and brain region* syllogism factor, as well as a significant brain region* group interaction. There are significant differences between the means of MSE (complexity) values at the 14 channels of the members of the 'pathological' groups of participants, i.e., between ASD and ADHD, while the difference in means of MSE between both ASD and ADHD and that of the control group is not significant. In conclusion, the valid-invalid type of syllogism generates significantly different complexity values, MSE, between ASD and ADHD. The complexity of activated brain regions of ASD participants increased significantly when switching from a valid to an invalid syllogism, indicating the need for more resources to 'face' the task escalating difficulty in ASD subjects. This increase is not so evident in both ADHD and control. Statistically significant differences were found also in the behavioral response of ASD and ADHD, compared with those of control subjects, based on the principal brain and behavior saliences extracted by PLSC. Specifically, two behavioral measures, the emotional state and the degree of confidence of participants in answering questions in Aristotle's valid-invalid syllogisms, and one demographic variable, age, statistically and significantly discriminate the three groups' ASD. The seed-PLC generated functional connectivity networks for ASD, ADHD, and control, were 'projected' on the regions of the Default Mode Network (DMN), the 'reference' connectivity, of which the structural changes were found significant in distinguishing the three groups. The contribution of this work lies in the examination of the relationship between brain activity and behavioral responses of healthy and 'pathological' participants in the case of cognitive reasoning of the type of Aristotle's valid and invalid syllogisms, using PLSC, a machine learning approach combined with MSE, a nonlinear method of extracting a feature based on EEGs that captures a broad spectrum of EEGs linear and nonlinear characteristics. The results seem promising in adopting this type of reasoning, in the future, after further enhancements and experimental tests, as a supplementary instrument towards examining the differences in brain activity and behavioral responses of ASD and ADHD patients. The application of the combination of these two methods, after further elaboration and testing as new and complementary to the existing ones, may be considered as a tool of analysis in helping detecting more effectively such types of disorders.
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Affiliation(s)
- Anastasia Papaioannou
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
- Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS” (UMHRI), University Mental Health, Papagou, 15601 Athens, Greece
| | - Eva Kalantzi
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
| | | | - Kalliopi Korombili
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
| | - Anastasia Bokou
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
| | - Artemios Pehlivanidis
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
| | - Charalabos C. Papageorgiou
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
- Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS” (UMHRI), University Mental Health, Papagou, 15601 Athens, Greece
| | - George Papaioannou
- Center for Research of Nonlinear Systems (CRANS), Department of Mathematics, University of Patras, 26500 Patra, Greece;
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Scangos KW, Khambhati AN, Daly PM, Owen LW, Manning JR, Ambrose JB, Austin E, Dawes HE, Krystal AD, Chang EF. Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology. Front Hum Neurosci 2021; 15:746499. [PMID: 34744662 PMCID: PMC8566975 DOI: 10.3389/fnhum.2021.746499] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 09/02/2021] [Indexed: 12/30/2022] Open
Abstract
Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.
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Affiliation(s)
- Katherine Wilson Scangos
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Ankit N. Khambhati
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Patrick M. Daly
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lucy W. Owen
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Jeremy R. Manning
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Josiah B. Ambrose
- Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States
| | - Everett Austin
- Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States
| | - Heather E. Dawes
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Andrew D. Krystal
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Edward F. Chang
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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Moody JF, Adluru N, Alexander AL, Field AS. The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI. Semin Ultrasound CT MR 2021; 42:507-522. [PMID: 34537118 DOI: 10.1053/j.sult.2021.07.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A comprehensive mapping of the structural and functional circuitry of the brain is a major unresolved problem in contemporary neuroimaging research. Diffusion-weighted and functional MRI have provided investigators with the capability to assess structural and functional connectivity in-vivo, driven primarily by methods of white matter tractography and resting-state fMRI, respectively. These techniques have paved the way for the construction of the functional and structural connectomes, which are quantitative representations of brain architecture as neural networks, comprised of nodes and edges. The connectomes, typically depicted as matrices or graphs, possess topological properties that inherently characterize the strength, efficiency, and organization of the connections between distinct brain regions. Graph theory, a general mathematical framework for analyzing networks, can be implemented to derive metrics from the connectomes that are sensitive to changes in brain connectivity associated with age, sex, cognitive function, and disease. These quantities can be assessed at either the global (whole brain) or local levels, allowing for the identification of distinct regional connectivity hubs and associated localized brain networks, which together serve crucial roles in establishing the structural and functional architecture of the brain. As a result, structural and functional connectomes have each been employed to study the brain circuitry underlying early brain development, neuroplasticity, developmental disorders, psychopathology, epilepsy, aging, neurodegenerative disorders, and traumatic brain injury. While these studies have yielded important insights into brain structure, function, and pathology, a precise description of the innate relationship between functional and structural networks across the brain remains unachieved. To date, connectome research has merely scratched the surface of potential clinical applications and related characterizations of brain-wide connectivity. Continued advances in diffusion and functional MRI acquisition, the delineation of functional and structural networks, and the quantification of neural network properties in specific brain regions, will be invaluable to future progress in neuroimaging science.
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Affiliation(s)
- Jason F Moody
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI; Department of Radiology, University of Wisconsin-Madison, Madison, WI
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Aaron S Field
- Department of Radiology, University of Wisconsin-Madison, Madison, WI.
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25
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Fyke W, Velinov M. FMR1 and Autism, an Intriguing Connection Revisited. Genes (Basel) 2021; 12:genes12081218. [PMID: 34440392 PMCID: PMC8394635 DOI: 10.3390/genes12081218] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 12/27/2022] Open
Abstract
Autism Spectrum Disorder (ASD) represents a distinct phenotype of behavioral dysfunction that includes deficiencies in communication and stereotypic behaviors. ASD affects about 2% of the US population. It is a highly heritable spectrum of conditions with substantial genetic heterogeneity. To date, mutations in over 100 genes have been reported in association with ASD phenotypes. Fragile X syndrome (FXS) is the most common single-gene disorder associated with ASD. The gene associated with FXS, FMR1 is located on chromosome X. Accordingly, the condition has more severe manifestations in males. FXS results from the loss of function of FMR1 due to the expansion of an unstable CGG repeat located in the 5'' untranslated region of the gene. About 50% of the FXS males and 20% of the FXS females meet the Diagnostic Statistical Manual 5 (DSM-5) criteria for ASD. Among the individuals with ASD, about 3% test positive for FXS. FMRP, the protein product of FMR1, is a major gene regulator in the central nervous system. Multiple pathways regulated by FMRP are found to be dysfunctional in ASD patients who do not have FXS. Thus, FXS presents the opportunity to study cellular phenomena that may have wider applications in the management of ASD and to develop new strategies for ASD therapy.
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Affiliation(s)
- William Fyke
- SUNY Downstate Medical Center, SUNY Downstate College of Medicine, Brooklyn, NY 11203, USA;
- Graduate Program in Neural and Behavioral Science, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA
| | - Milen Velinov
- Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
- Child Health Institute of New Jersey, New Brunswick, NJ 08901, USA
- Correspondence:
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26
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Fyke W, Premoli M, Echeverry Alzate V, López-Moreno JA, Lemaire-Mayo V, Crusio WE, Marsicano G, Wöhr M, Pietropaolo S. Communication and social interaction in the cannabinoid-type 1 receptor null mouse: Implications for autism spectrum disorder. Autism Res 2021; 14:1854-1872. [PMID: 34173729 DOI: 10.1002/aur.2562] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/04/2021] [Accepted: 05/28/2021] [Indexed: 12/17/2022]
Abstract
Clinical and preclinical findings have suggested a role of the endocannabinoid system (ECS) in the etiopathology of autism spectrum disorder (ASD). Previous mouse studies have investigated the role of ECS in several behavioral domains; however, none of them has performed an extensive assessment of social and communication behaviors, that is, the main core features of ASD. This study employed a mouse line lacking the primary endocannabinoid receptor (CB1r) and characterized ultrasonic communication and social interaction in CB1-/- , CB1+/- , and CB1+/+ males and females. Quantitative and qualitative alterations in ultrasonic vocalizations (USVs) were observed in CB1 null mice both during early development (i.e., between postnatal days 4 and 10), and at adulthood (i.e., at 3 months of age). Adult mutants also showed marked deficits in social interest in the three-chamber test and social investigation in the direct social interaction test. These behavioral alterations were mostly observed in both sexes and appeared more marked in CB1-/- than CB1+/- mutant mice. Importantly, the adult USV alterations could not be attributed to differences in anxiety or sensorimotor abilities, as assessed by the elevated plus maze and auditory startle tests. Our findings demonstrate the role of CB1r in social communication and behavior, supporting the use of the CB1 full knockout mouse in preclinical research on these ASD-relevant core domains. LAY SUMMARY: The endocannabinoid system (ECS) is important for brain development and neural function and is therefore likely to be involved in neurodevelopmental disorders such as Autism Spectrum Disorder (ASD). Here we investigated changes in social behavior and communication, which are core features of ASD, in male and female mice lacking the chief receptor of this system. Our results show that loss of this receptor results in several changes in social behavior and communication both during early development and in adulthood, thus supporting the role of the ECS in these ASD-core behavioral domains.
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Affiliation(s)
- William Fyke
- University of Bordeaux, CNRS, EPHE, INCIA, UMR 5287, Bordeaux, France.,Graduate Program in Neural and Behavioral Science, SUNY Downstate Medical Center, Brooklyn, New York, USA
| | - Marika Premoli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Victor Echeverry Alzate
- Department of Psychobiology and Methodology on Behavioral Sciences, Faculty of Psychology, Madrid Complutense University, Spain.,Unidad Gestión Clínica de Salud Mental, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Regional Universitario de Málaga, Malaga University, Spain
| | - José A López-Moreno
- Department of Psychobiology and Methodology on Behavioral Sciences, Faculty of Psychology, Madrid Complutense University, Spain
| | | | - Wim E Crusio
- University of Bordeaux, CNRS, EPHE, INCIA, UMR 5287, Bordeaux, France
| | - Giovanni Marsicano
- University of Bordeaux, INSERM, U862 NeuroCentre Magendie, Group Endocannabinoids and Neuroadaptation, Bordeaux, France
| | - Markus Wöhr
- KU Leuven, Faculty of Psychology and Educational Sciences, Research Unit Brain and Cognition, Laboratory of Biological Psychology, Social and Affective Neuroscience Research Group, Leuven, Belgium.,KU Leuven, Leuven Brain Institute, Leuven, Belgium.,Faculty of Psychology, Experimental and Biological Psychology, Behavioral Neuroscience, Philipps-University of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, Philipps-University of Marburg, Marburg, Germany
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27
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Roy D, Uddin LQ. Atypical core-periphery brain dynamics in autism. Netw Neurosci 2021; 5:295-321. [PMID: 34189366 PMCID: PMC8233106 DOI: 10.1162/netn_a_00181] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 12/31/2020] [Indexed: 11/06/2022] Open
Abstract
The intrinsic function of the human brain is dynamic, giving rise to numerous behavioral subtypes that fluctuate distinctively at multiple timescales. One of the key dynamical processes that takes place in the brain is the interaction between core-periphery brain regions, which undergoes constant fluctuations associated with developmental time frames. Core-periphery dynamical changes associated with macroscale brain network dynamics span multiple timescales and may lead to atypical behavior and clinical symptoms. For example, recent evidence suggests that brain regions with shorter intrinsic timescales are located at the periphery of brain networks (e.g., sensorimotor hand, face areas) and are implicated in perception and movement. On the contrary, brain regions with longer timescales are core hub regions. These hubs are important for regulating interactions between the brain and the body during self-related cognition and emotion. In this review, we summarize a large body of converging evidence derived from time-resolved fMRI studies in autism to characterize atypical core-periphery brain dynamics and how they relate to core and contextual sensory and cognitive profiles.
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Affiliation(s)
- Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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28
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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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Reiter MA, Jahedi A, Jac Fredo A, Fishman I, Bailey B, Müller RA. Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity. Neural Comput Appl 2021; 33:3299-3310. [PMID: 34149191 PMCID: PMC8210842 DOI: 10.1007/s00521-020-05193-y] [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: 01/12/2023]
Abstract
Autism spectrum disorders (ASDs) are heterogeneous neurodevelopmental conditions. In fMRI studies, including most machine learning studies seeking to distinguish ASD from typical developing (TD) samples, cohorts differing in gender and symptom severity composition are often treated statistically as one "ASD group". Using resting-state functional connectivity (FC) data, we implemented random forest to build diagnostic classifiers in 4 ASD samples including a total of 656 participants (NASD = 306, NTD = 350, ages 6-18). Groups were manipulated to titrate heterogeneity of gender and symptom severity and partially overlapped. Each sample differed on inclusionary criteria: (1) all genders, unrestricted severity range; (2) only male participants, unrestricted severity; (3) all genders, higher severity only; (4) only male participants, higher severity. Each set consisted of 200 participants per group (ASD, TD; matched on age and head motion), 160 for training and 40 for validation. FMRI time series from 237 regions of interest (ROIs) were Pearson correlated in a 237×237 FC matrix and classifiers were built using random forest in training samples. Classification accuracies in validation samples were 62.5%, 65%, 70% and 73.75%, respectively for samples 1-4. Connectivity within cingulo-opercular task control (COTC) network, and between COTC ROIs and default mode and dorsal attention network contributed overall most informative features, but features differed across sets. Findings suggest that diagnostic classifiers vary depending on ASD sample composition. Specifically, greater homogeneity of samples regarding gender and symptom severity enhances classifier performance. However, given the true heterogeneity of ASDs, performance metrics alone may not adequately reflect classifier utility.
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Affiliation(s)
- Maya A. Reiter
- Brain Development Imaging Lab (BDIL), Psychology, San Diego State University (SDSU), 6363 Alvarado Ct. Suite 200, San Diego, CA 92120, USA,Joint Doctoral Program in Clinical Psychology, San Diego State University/UC San Diego, San Diego, CA, USA
| | - Afrooz Jahedi
- Computational Science, San Diego State University/ Claremont Graduate University’s Joint Doctoral Program, San Diego, CA, USA
| | - A.R. Jac Fredo
- Computational Science, San Diego State University/ Claremont Graduate University’s Joint Doctoral Program, San Diego, CA, USA
| | - Inna Fishman
- Brain Development Imaging Lab (BDIL), Psychology, San Diego State University (SDSU), 6363 Alvarado Ct. Suite 200, San Diego, CA 92120, USA
| | - Barbara Bailey
- Department of Mathematics and Statistics, San Diego State University, San Diego, California
| | - Ralph-Axel Müller
- Brain Development Imaging Lab (BDIL), Psychology, San Diego State University (SDSU), 6363 Alvarado Ct. Suite 200, San Diego, CA 92120, USA,Joint Doctoral Program in Clinical Psychology, San Diego State University/UC San Diego, San Diego, CA, USA
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30
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Wang L, Li K, Hu XP. Graph convolutional network for fMRI analysis based on connectivity neighborhood. Netw Neurosci 2021; 5:83-95. [PMID: 33688607 PMCID: PMC7935029 DOI: 10.1162/netn_a_00171] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 09/24/2020] [Indexed: 11/04/2022] Open
Abstract
There have been successful applications of deep learning to functional magnetic resonance imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial features from Euclidean neighbors were usually extracted by the convolutional neural networks (CNNs) in the computer vision field. Recently, CNN has been extended to graph data and demonstrated superior performance. Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis. Such an approach allows us to extract spatial features from connectomic neighborhoods rather than from Euclidean ones, consistent with the functional organization of the brain. To evaluate the performance of cGCN, we applied it to two scenarios with resting-state fMRI data. One is individual identification of healthy participants and the other is classification of autistic patients from normal controls. Our results indicate that cGCN can effectively capture functional connectivity features in fMRI analysis for relevant applications.
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Affiliation(s)
- Lebo Wang
- Department of Electrical and Computer Engineering, University of California, Riverside, Riverside, CA, USA
| | - Kaiming Li
- Department of Bioengineering, University of California, Riverside, Riverside, CA, USA
| | - Xiaoping P. Hu
- Department of Electrical and Computer Engineering, University of California, Riverside, Riverside, CA, USA
- Department of Bioengineering, University of California, Riverside, Riverside, CA, USA
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31
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Paul S, Arora A, Midha R, Vu D, Roy PK, Belmonte MK. Autistic traits and individual brain differences: functional network efficiency reflects attentional and social impairments, structural nodal efficiencies index systemising and theory-of-mind skills. Mol Autism 2021; 12:3. [PMID: 33478557 PMCID: PMC7818759 DOI: 10.1186/s13229-020-00377-8] [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: 12/26/2019] [Accepted: 09/02/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Autism is characterised not only by impaired social cognitive 'empathising' but also by superior rule-based 'systemising'. These cognitive domains intertwine within the categorical diagnosis of autism, yet behavioural genetics suggest largely independent heritability, and separable brain mechanisms. We sought to determine whether quantitative behavioural measures of autistic traits are dimensionally associated with structural and functional brain network integrity, and whether brain bases of autistic traits vary independently across individuals. METHODS Thirty right-handed neurotypical adults (12 females) were administered psychometric (Social Responsiveness Scale, Autism Spectrum Quotient and Systemising Quotient) and behavioural (Attention Network Test and theory-of-mind reaction time) measures of autistic traits, and structurally (diffusion tensor imaging) and functionally (500 s of 2 Hz eyes-closed resting fMRI) derived graph-theoretic measures of efficiency of information integration were computed throughout the brain and within subregions. RESULTS Social impairment was positively associated with functional efficiency (r = .47, p = .006), globally and within temporo-parietal and prefrontal cortices. Delayed orienting of attention likewise was associated with greater functional efficiency (r = - .46, p = .0133). Systemising was positively associated with global structural efficiency (r = .38, p = 0.018), driven specifically by temporal pole; theory-of-mind reaction time was related to structural efficiency (r = - .40, p = 0.0153) within right supramarginal gyrus. LIMITATIONS Interpretation of these relationships is complicated by the many senses of the term 'connectivity', including functional, structural and computational; by the approximation inherent in group functional anatomical parcellations when confronted with individual variation in functional anatomy; and by the validity, sensitivity and specificity of the several survey and experimental behavioural measures applied as correlates of brain structure and function. CONCLUSIONS Functional connectivities highlight distributed networks associated with domain-general properties such as attentional orienting and social cognition broadly, associating more impaired behaviour with more efficient brain networks that may reflect heightened feedforward information flow subserving autistic strengths and deficits alike. Structural connectivity results highlight specific anatomical nodes of convergence, reflecting cognitive and neuroanatomical independence of systemising and theory-of-mind. In addition, this work shows that individual differences in theory-of-mind related to brain structure can be measured behaviourally, and offers neuroanatomical evidence to pin down the slippery construct of 'systemising' as the capacity to construct invariant contextual associations.
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Affiliation(s)
- Subhadip Paul
- MIND Research Network, 1101 Yale Blvd NE, Albuquerque, NM, 87106, USA.,National Brain Research Centre, NH-8, Nainwal Mode, Manesar, 122051, India
| | - Aditi Arora
- National Brain Research Centre, NH-8, Nainwal Mode, Manesar, 122051, India.,Centre for Cognitive Neuroscience, Universität Salzburg, Kapitelgasse 4-6, 5020, Salzburg, Austria
| | - Rashi Midha
- National Brain Research Centre, NH-8, Nainwal Mode, Manesar, 122051, India.,National Institute of Mental Health and Neuro Sciences, Hosur Road, Bangalore, 560029, India
| | - Dinh Vu
- Department of Psychology, University of Oslo, Harald Schjelderups hus, Forskningsveien 3A, 0373, Oslo, Norway.,Department of Psychology, Chaucer Bldg., Nottingham Trent University, Shakespeare Street, Nottingham, NG1 4FQ, UK
| | - Prasun K Roy
- National Brain Research Centre, NH-8, Nainwal Mode, Manesar, 122051, India.,School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Matthew K Belmonte
- National Brain Research Centre, NH-8, Nainwal Mode, Manesar, 122051, India. .,Department of Psychology, Chaucer Bldg., Nottingham Trent University, Shakespeare Street, Nottingham, NG1 4FQ, UK. .,The Com DEALL Trust, 224, 6th 'A' Main Road, near Specialist Hospital, 2nd Block, HRBR Layout, Bangalore, 560043, India.
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32
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Zhou T, Kang J, Li Z, Chen H, Li X. Transcranial direct current stimulation modulates brain functional connectivity in autism. NEUROIMAGE-CLINICAL 2021; 28:102500. [PMID: 33395990 PMCID: PMC7695891 DOI: 10.1016/j.nicl.2020.102500] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 11/05/2020] [Accepted: 11/07/2020] [Indexed: 01/28/2023]
Abstract
Autism spectrum disorder (ASD) is characterized by deficits in social interactions, impairments in language and communication, and highly restricted behavioral interests. Transcranial direct current stimulation (tDCS) is a widely used form of noninvasive stimulation and may have therapeutic potential for ASD. So far, despite the widespread use of this technique in the neuroscience field, its effects on network-level neural activity and the underlying mechanisms of any effects are still unclear. In the present study, we used electroencephalography (EEG) to investigate tDCS induced brain network changes in children with ASD before and after active and sham stimulation. We recorded 5 min of resting state EEG before and after a single session of tDCS (of approximately 20 min) over dorsolateral prefrontal cortex (DLPFC). Two network-based methods were applied to investigate tDCS modulation on brain networks: 1) temporal network dynamics were analyzed by comparing "flexibility" changes before vs after stimulation, and 2) frequency specific network changes were identified using non-negative matrix factorization (NMF). We found 1) an increase in network flexibility following tDCS (rapid network configuration of dynamic network communities), 2) specific increase in interhemispheric connectivity within the alpha frequency band following tDCS. Together, these results demonstrate that tDCS could help modify both local and global brain network dynamics, and highlight stimulation-induced differences in the manifestation of network reconfiguration. Meanwhile, frequency-specific subnetworks, as a way to index local and global information processing, highlight the core modulatory effects of tDCS on the modular architecture of the functional connectivity patterns within higher frequency bands.
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Affiliation(s)
- Tianyi Zhou
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai 519087, China
| | - Jiannan Kang
- College of Electronic & Information Engineering, Hebei University, Baoding, China
| | - Zheng Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai 519087, China
| | - He Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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33
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Barbeau EB, Klein D, Soulières I, Petrides M, Bernhardt B, Mottron L. Age of Speech Onset in Autism Relates to Structural Connectivity in the Language Network. Cereb Cortex Commun 2020; 1:tgaa077. [PMID: 34296136 PMCID: PMC8152885 DOI: 10.1093/texcom/tgaa077] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 10/11/2020] [Accepted: 10/12/2020] [Indexed: 12/13/2022] Open
Abstract
Speech onset delays (SOD) and language atypicalities are central aspects of the autism spectrum (AS), despite not being included in the categorical diagnosis of AS. Previous studies separating participants according to speech onset history have shown distinct patterns of brain organization and activation in perceptual tasks. One major white matter tract, the arcuate fasciculus (AF), connects the posterior temporal and left frontal language regions. Here, we used anatomical brain imaging to investigate the properties of the AF in adolescent and adult autistic individuals with typical levels of intelligence who differed by age of speech onset. The left AF of the AS group showed a significantly smaller volume than that of the nonautistic group. Such a reduction in volume was only present in the younger group. This result was driven by the autistic group without SOD (SOD−), despite their typical age of speech onset. The autistic group with SOD (SOD+) showed a more typical AF as adults relative to matched controls. This suggests that, along with multiple studies in AS-SOD+ individuals, atypical brain reorganization is observable in the 2 major AS subgroups and that such reorganization applies mostly to the language regions in SOD− and perceptual regions in SOD+ individuals.
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Affiliation(s)
- Elise B Barbeau
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Denise Klein
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Isabelle Soulières
- Department of Psychology, Université du Québec à Montreal, Montreal, QC H2X 3P2, Canada
| | - Michael Petrides
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Boris Bernhardt
- Neurology and Neurosurgery Department, McGill University, Montreal, QC H3A 2B4, Canada
| | - Laurent Mottron
- Département de Psychiatrie et d'addictologie, de l'Université de Montréal, Montréal, QC H3T 1J4, Canada
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34
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Comparing different EEG connectivity methods in young males with ASD. Behav Brain Res 2020; 383:112482. [PMID: 31972185 DOI: 10.1016/j.bbr.2020.112482] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 12/24/2019] [Accepted: 01/13/2020] [Indexed: 12/27/2022]
Abstract
Although EEG connectivity data are often used to build models of the association between overt behavioural signs of Autism Spectrum Disorder (ASD) and underlying brain connectivity indices, use of a large number of possible connectivity methods across studies has produced a fairly inconsistent set of results regarding this association. To explore the level of agreement between results from five commonly-used EEG connectivity models (i.e., Coherence, Weighted Phased Lag Index- Debiased, Phase Locking Value, Phase Slope Index, Granger Causality), a sample of 41 young males with ASD provided EEG data under eyes-opened and eyes-closed conditions. There were relatively few statistically significant and/or meaningful correlations between the results obtained from the five connectivity methods, arguing for a re-estimation of the methodology used in such studies so that specific connectivity methods may be matched to particular research questions regarding the links between neural connectivity and overt behaviour within this population.
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35
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Zhou T, Kang J, Cong F, Li DX. Early childhood developmental functional connectivity of autistic brains with non-negative matrix factorization. Neuroimage Clin 2020; 26:102251. [PMID: 32403087 PMCID: PMC7218077 DOI: 10.1016/j.nicl.2020.102251] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 03/17/2020] [Accepted: 03/18/2020] [Indexed: 01/25/2023]
Abstract
Autism spectrum disorder (ASD) is associated with altered patterns of over- and under-connectivity of neural circuits. Age-related changes in neural connectivities remain unclear for autistic children as compared with normal children. In this study, a parts-based network-decomposition technique, known as non-negative matrix factorization (NMF), was applied to identify a set of possible abnormal connectivity patterns in brains affected by ASD, using resting-state electroencephalographic (EEG) data. Age-related changes in connectivities in both inter- and intra-hemispheric areas were studied in a total of 256 children (3-6 years old), both with and without ASD. The findings included the following: (1) the brains of children affected by ASD were characterized by a general trend toward long-range under-connectivity, particularly in interhemispheric connections, combined with short-range over-connectivity; (2) long-range connections were often associated with slower rhythms (δ and θ), whereas synchronization of short-range networks tended to be associated with faster frequencies (α and β); and (3) the α-band specific patterns of interhemispheric connections in ASD could be the most prominent during early childhood neurodevelopment. Therefore, NMF would be useful for further exploring the early childhood developmental functional connectivity of children aged 3-6 with ASD as well as with typical development. Additionally, long-range interhemispheric alterations in connectivity may represent a potential biomarker for the identification of ASD.
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Affiliation(s)
- Tianyi Zhou
- Institute of Electrical Engineering, YanShan University, Qinhuangdao, 066000, China
| | - Jiannan Kang
- College of Electronic & Information Engineering, Hebei University, Baoding, China
| | - Fengyu Cong
- Department of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116000, China
| | - Dr Xiaoli Li
- Institute of Electrical Engineering, YanShan University, Qinhuangdao, 066000, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
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36
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Pijnenburg R, Scholtens LH, Mantini D, Vanduffel W, Barrett LF, van den Heuvel MP. Biological Characteristics of Connection-Wise Resting-State Functional Connectivity Strength. Cereb Cortex 2019; 29:4646-4653. [PMID: 30668705 PMCID: PMC7049309 DOI: 10.1093/cercor/bhy342] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 12/18/2018] [Accepted: 12/19/2018] [Indexed: 01/21/2023] Open
Abstract
Functional connectivity is defined as the statistical dependency of neurophysiological activity between 2 separate brain areas. To investigate the biological characteristics of resting-state functional connectivity (rsFC)-and in particular the significance of connection-wise variation in time-series correlations-rsFC was compared with strychnine-based connectivity measured in the macaque. Strychnine neuronography is a historical technique that induces activity in cortical areas through means of local administration of the substance strychnine. Strychnine causes local disinhibition through GABA suppression and leads to subsequent activation of functional pathways. Multiple resting-state fMRI recordings were acquired in 4 macaques (examining in total 299 imaging runs) from which a group-averaged rsFC matrix was constructed. rsFC was observed to be higher (P < 0.0001) between region-pairs with a strychnine-based connection as compared with region-pairs with no strychnine-based connection present. In particular, higher resting-state connectivity was observed in connections that were relatively stronger (weak < moderate < strong; P < 0.01) and in connections that were bidirectional (P < 0.0001) instead of unidirectional in strychnine-based connectivity. Our results imply that the level of correlation between brain areas as extracted from resting-state fMRI relates to the strength of underlying interregional functional pathways.
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Affiliation(s)
- Rory Pijnenburg
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, De Boelelaan 1081-1087, Amsterdam, The Netherlands
| | - Lianne H Scholtens
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, De Boelelaan 1081-1087, Amsterdam, The Netherlands
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101 - Leuven, Belgium
- Functional Neuroimaging Laboratory, IRCCS San Camillo Hospital Foundation, Via Alberoni, 70, Lido VE, Italy
| | - Wim Vanduffel
- Laboratory for Neuro- and Psychophysiology, O&N II Herestraat 49 - Leuven, Belgium
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Radiology/NMR Ctr - 2nd FL 149 13th Street, Charlestown MA, USA
- Department of Psychiatry and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA, USA
| | - Lisa Feldman Barrett
- Department of Psychiatry and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA, USA
- Department of Psychology, Northeastern University, 125 NI (Nightingale Hall), Boston, MA, USA
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, De Boelelaan 1081-1087, Amsterdam, The Netherlands
- Department of Clinical Genetics, VU University Medical Center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
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37
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Raatikainen V, Korhonen V, Borchardt V, Huotari N, Helakari H, Kananen J, Raitamaa L, Joskitt L, Loukusa S, Hurtig T, Ebeling H, Uddin LQ, Kiviniemi V. Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder. Autism Res 2019; 13:244-258. [PMID: 31637863 PMCID: PMC7027814 DOI: 10.1002/aur.2218] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 08/28/2019] [Accepted: 09/16/2019] [Indexed: 02/06/2023]
Abstract
This study investigated whole‐brain dynamic lag pattern variations between neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) by applying a novel technique called dynamic lag analysis (DLA). The use of 3D magnetic resonance encephalography data with repetition time = 100 msec enables highly accurate analysis of the spread of activity between brain networks. Sixteen resting‐state networks (RSNs) with the highest spatial correlation between NT individuals (n = 20) and individuals with ASD (n = 20) were analyzed. The dynamic lag pattern variation between each RSN pair was investigated using DLA, which measures time lag variation between each RSN pair combination and statistically defines how these lag patterns are altered between ASD and NT groups. DLA analyses indicated that 10.8% of the 120 RSN pairs had statistically significant (P‐value <0.003) dynamic lag pattern differences that survived correction with surrogate data thresholding. Alterations in lag patterns were concentrated in salience, executive, visual, and default‐mode networks, supporting earlier findings of impaired brain connectivity in these regions in ASD. 92.3% and 84.6% of the significant RSN pairs revealed shorter mean and median temporal lags in ASD versus NT, respectively. Taken together, these results suggest that altered lag patterns indicating atypical spread of activity between large‐scale functional brain networks may contribute to the ASD phenotype. Autism Res 2020, 13: 244–258. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc. Lay Summary Autism spectrum disorder (ASD) is characterized by atypical neurodevelopment. Using an ultra‐fast neuroimaging procedure, we investigated communication across brain regions in adults with ASD compared with neurotypical (NT) individuals. We found that ASD individuals had altered information flow patterns across brain regions. Atypical patterns were concentrated in salience, executive, visual, and default‐mode network areas of the brain that have previously been implicated in the pathophysiology of the disorder.
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Affiliation(s)
- Ville Raatikainen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Viola Borchardt
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Niko Huotari
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Heta Helakari
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Janne Kananen
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Lauri Raitamaa
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Leena Joskitt
- Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Soile Loukusa
- Research Unit of Logopedics, Faculty of Humanities, University of Oulu, Oulu, Finland
| | - Tuula Hurtig
- Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Hanna Ebeling
- Clinic of Child Psychiatry, Oulu University Hospital, Oulu, Finland
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida
| | - Vesa Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland.,Research Unit of Medical Imaging, Physics, and Technology, The Faculty of Medicine, University of Oulu, Oulu, Finland
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38
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Guo X, Simas T, Lai MC, Lombardo MV, Chakrabarti B, Ruigrok ANV, Bullmore ET, Baron-Cohen S, Chen H, Suckling J. Enhancement of indirect functional connections with shortest path length in the adult autistic brain. Hum Brain Mapp 2019; 40:5354-5369. [PMID: 31464062 PMCID: PMC6864892 DOI: 10.1002/hbm.24777] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 06/23/2019] [Accepted: 08/18/2019] [Indexed: 12/30/2022] Open
Abstract
Autism is a neurodevelopmental condition characterized by atypical brain functional organization. Here we investigated the intrinsic indirect (semi‐metric) connectivity of the functional connectome associated with autism. Resting‐state functional magnetic resonance imaging scans were acquired from 65 neurotypical adults (33 males/32 females) and 61 autistic adults (30 males/31 females). From functional connectivity networks, semi‐metric percentages (SMPs) were calculated to assess the proportion of indirect shortest functional pathways at global, hemisphere, network, and node levels. Group comparisons were then conducted to ascertain differences between autism and neurotypical control groups. Finally, the strength and length of edges were examined to explore the patterns of semi‐metric connections associated with autism. Compared with neurotypical controls, autistic adults displayed significantly higher SMP at all spatial scales, similar to prior observations in adolescents. Differences were primarily in weaker, longer‐distance edges in the majority between networks. However, no significant diagnosis‐by‐sex interaction effects were observed on global SMP. These findings suggest increased indirect functional connectivity in the autistic brain is persistent from adolescence to adulthood and is indicative of reduced functional network integration.
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Affiliation(s)
- Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Tiago Simas
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Meng-Chuan Lai
- Centre for Addiction and Mental Health and the Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, Canada.,Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Michael V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Italian Institute of Technology, Rovereto, Italy
| | - Bhismadev Chakrabarti
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK
| | - Amber N V Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
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Song Y, Epalle TM, Lu H. Characterizing and Predicting Autism Spectrum Disorder by Performing Resting-State Functional Network Community Pattern Analysis. Front Hum Neurosci 2019; 13:203. [PMID: 31258470 PMCID: PMC6587437 DOI: 10.3389/fnhum.2019.00203] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 05/29/2019] [Indexed: 11/28/2022] Open
Abstract
Growing evidence indicates that autism spectrum disorder (ASD) is a neuropsychological disconnection syndrome that can be analyzed using various complex network metrics used as pathology biomarkers. Recently, community detection and analysis rooted in the complex network and graph theories have been introduced to investigate the changes in resting-state functional network community structure under neurological pathologies. However, the potential of hidden patterns in the modular organization of networks derived from resting-state functional magnetic resonance imaging to predict brain pathology has never been investigated. In this study, we present a novel analysis technique to identify alterations in community patterns in functional networks under ASD. In addition, we design machine learning classifiers to predict the clinical class of patients with ASD and controls by using only community pattern quality metrics as features. Analyses conducted on six publicly available datasets from 235 subjects, including patients with ASD and age-matched controls revealed that the modular structure is significantly disturbed in patients with ASD. Machine learning algorithms showed that the predictive power of our five metrics is relatively high (~85.16% peak accuracy for in-site data and ~75.00% peak accuracy for multisite data). These results lend further credence to the dysconnectivity theory of this pathology.
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Affiliation(s)
- Yuqing Song
- School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China
| | - Thomas Martial Epalle
- School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China
| | - Hu Lu
- School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China
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40
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019; 13:585. [PMID: 31249501 PMCID: PMC6582769 DOI: 10.3389/fnins.2019.00585] [Citation(s) in RCA: 265] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/23/2019] [Indexed: 12/20/2022] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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41
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Tu Y, Fu Z, Zeng F, Maleki N, Lan L, Li Z, Park J, Wilson G, Gao Y, Liu M, Calhoun V, Liang F, Kong J. Abnormal thalamocortical network dynamics in migraine. Neurology 2019; 92:e2706-e2716. [PMID: 31076535 DOI: 10.1212/wnl.0000000000007607] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 02/01/2019] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To investigate the dynamic functional connectivity of thalamocortical networks in interictal migraine patients and whether clinical features are associated with abnormal connectivity. METHODS We investigated dynamic functional network connectivity (dFNC) of the migraine brain in 89 interictal migraine patients and 70 healthy controls. We focused on the temporal properties of thalamocortical connectivity using sliding window cross-correlation, clustering state analysis, and graph-theory methods. Relationships between clinical symptoms and abnormal dFNC were evaluated using a multivariate linear regression model. RESULTS Five dFNC brain states were identified to characterize and compare dynamic functional connectivity patterns. We demonstrated that migraineurs spent more time in a strongly interconnected between-network state, but they spent less time in a sparsely connected state. Interestingly, we found that abnormal posterior thalamus (pulvinar nucleus) dFNC with the visual cortex and the precuneus were significantly correlated with headache frequency of migraine. Further topologic measures revealed that migraineurs had significantly lower efficiency of information transfer in both global and local dFNC. CONCLUSION Our results demonstrated a transient pathologic state with atypical thalamocortical connectivity in migraineurs and extended current findings regarding abnormal thalamocortical networks and dysrhythmia in migraine.
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Affiliation(s)
- Yiheng Tu
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Zening Fu
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Fang Zeng
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Nasim Maleki
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Lei Lan
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Zhengjie Li
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Joel Park
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Georgia Wilson
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Yujie Gao
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Mailan Liu
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Vince Calhoun
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China
| | - Fanrong Liang
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China.
| | - Jian Kong
- From the Department of Psychiatry (Y.T., N.M., J.P., G.W., J.K.), Massachusetts General Hospital and Harvard Medical School, Charlestown; The Mind Research Network (Z.F., V.C.), Albuquerque, NM; Acupuncture and Tuina School/3rd Teaching Hospital (F.Z., L.L., Z.L., F.L.), Chengdu University of Traditional Chinese Medicine, Chengdu; Traditional Chinese Medicine School (Y.G.), Ningxia Medical University, Yinchuan; and The Acupuncture and Tuina School (M.L.), Hunan University of Chinese Medicine, Changsha, China.
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Kilroy E, Aziz-Zadeh L, Cermak S. Ayres Theories of Autism and Sensory Integration Revisited: What Contemporary Neuroscience Has to Say. Brain Sci 2019; 9:brainsci9030068. [PMID: 30901886 PMCID: PMC6468444 DOI: 10.3390/brainsci9030068] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 03/15/2019] [Accepted: 03/17/2019] [Indexed: 11/17/2022] Open
Abstract
Abnormal sensory-based behaviors are a defining feature of autism spectrum disorders (ASD). Dr. A. Jean Ayres was the first occupational therapist to conceptualize Sensory Integration (SI) theories and therapies to address these deficits. Her work was based on neurological knowledge of the 1970’s. Since then, advancements in neuroimaging techniques make it possible to better understand the brain areas that may underlie sensory processing deficits in ASD. In this article, we explore the postulates proposed by Ayres (i.e., registration, modulation, motivation) through current neuroimaging literature. To this end, we review the neural underpinnings of sensory processing and integration in ASD by examining the literature on neurophysiological responses to sensory stimuli in individuals with ASD as well as structural and network organization using a variety of neuroimaging techniques. Many aspects of Ayres’ hypotheses about the nature of the disorder were found to be highly consistent with current literature on sensory processing in children with ASD but there are some discrepancies across various methodological techniques and ASD development. With additional characterization, neurophysiological profiles of sensory processing in ASD may serve as valuable biomarkers for diagnosis and monitoring of therapeutic interventions, such as SI therapy.
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Affiliation(s)
- Emily Kilroy
- Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy, University Southern California, Los Angeles, CA 90089, USA.
- Brain and Creativity Institute, University Southern California, Los Angeles, CA 90089, USA.
| | - Lisa Aziz-Zadeh
- Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy, University Southern California, Los Angeles, CA 90089, USA.
- Brain and Creativity Institute, University Southern California, Los Angeles, CA 90089, USA.
| | - Sharon Cermak
- Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy, University Southern California, Los Angeles, CA 90089, USA.
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43
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Hong SJ, Vos de Wael R, Bethlehem RAI, Lariviere S, Paquola C, Valk SL, Milham MP, Di Martino A, Margulies DS, Smallwood J, Bernhardt BC. Atypical functional connectome hierarchy in autism. Nat Commun 2019; 10:1022. [PMID: 30833582 PMCID: PMC6399265 DOI: 10.1038/s41467-019-08944-1] [Citation(s) in RCA: 207] [Impact Index Per Article: 41.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 02/06/2019] [Indexed: 12/11/2022] Open
Abstract
One paradox of autism is the co-occurrence of deficits in sensory and higher-order socio-cognitive processing. Here, we examined whether these phenotypical patterns may relate to an overarching system-level imbalance-specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Combining connectome gradient and stepwise connectivity analysis based on task-free functional magnetic resonance imaging (fMRI), we demonstrated atypical connectivity transitions between sensory and higher-order default mode regions in a large cohort of individuals with autism relative to typically-developing controls. Further analyses indicated that reduced differentiation related to perturbed stepwise connectivity from sensory towards transmodal areas, as well as atypical long-range rich-club connectivity. Supervised pattern learning revealed that hierarchical features predicted deficits in social cognition and low-level behavioral symptoms, but not communication-related symptoms. Our findings provide new evidence for imbalances in network hierarchy in autism, which offers a parsimonious reference frame to consolidate its diverse features.
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Affiliation(s)
- Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada.
- Center for the Developing Brain, Child Mind Institute, 10022, New York, NY, USA.
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, CB28AH, Cambridge, UK
| | - Sara Lariviere
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - Sofie L Valk
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, 10022, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 10962, Orangeburg, NY, USA
| | | | - Daniel S Margulies
- Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | | | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada.
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Smith R, Sanova A, Alkozei A, Lane RD, Killgore WDS. Higher levels of trait emotional awareness are associated with more efficient global information integration throughout the brain: a graph-theoretic analysis of resting state functional connectivity. Soc Cogn Affect Neurosci 2019; 13:665-675. [PMID: 29931125 PMCID: PMC6121141 DOI: 10.1093/scan/nsy047] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 06/18/2018] [Indexed: 12/25/2022] Open
Abstract
Previous studies have suggested that trait differences in emotional awareness (tEA) are clinically relevant, and associated with differences in neural structure/function. While multiple leading theories suggest that conscious awareness requires widespread information integration across the brain, no study has yet tested the hypothesis that higher tEA corresponds to more efficient brain-wide information exchange. Twenty-six healthy volunteers (13 females) underwent a resting state functional magnetic resonance imaging scan, and completed the Levels of Emotional Awareness Scale (LEAS; a measure of tEA) and the Wechsler Abbreviated Scale of Intelligence (WASI-II; a measure of general intelligence quotient [IQ]). Using a whole-brain (functionally defined) region of interest (ROI) atlas, we computed several graph theory metrics to assess the efficiency of brain-wide information exchange. After statistically controlling for differences in age, gender and IQ, we first observed a significant relationship between higher LEAS scores and greater average degree (i.e. overall whole-brain network density). When controlling for average degree, we found that higher LEAS scores were also associated with shorter average path lengths across the collective network of all included ROIs. These results jointly suggest that individuals with higher tEA display more efficient global information exchange throughout the brain. This is consistent with the idea that conscious awareness requires global accessibility of represented information.
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Affiliation(s)
- Ryan Smith
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA
| | - Anna Sanova
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA
| | - Anna Alkozei
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA
| | - Richard D Lane
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA
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45
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Kazeminejad A, Sotero RC. Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification. Front Neurosci 2019; 12:1018. [PMID: 30686984 PMCID: PMC6335365 DOI: 10.3389/fnins.2018.01018] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 12/18/2018] [Indexed: 01/16/2023] Open
Abstract
Automatic algorithms for disease diagnosis are being thoroughly researched for use in clinical settings. They usually rely on pre-identified biomarkers to highlight the existence of certain problems. However, finding such biomarkers for neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) has challenged researchers for many years. With enough data and computational power, machine learning (ML) algorithms can be used to interpret the data and extract the best biomarkers from thousands of candidates. In this study, we used the fMRI data of 816 individuals enrolled in the Autism Brain Imaging Data Exchange (ABIDE) to introduce a new biomarker extraction pipeline for ASD that relies on the use of graph theoretical metrics of fMRI-based functional connectivity to inform a support vector machine (SVM). Furthermore, we split the dataset into 5 age groups to account for the effect of aging on functional connectivity. Our methodology achieved better results than most state-of-the-art investigations on this dataset with the best model for the >30 years age group achieving an accuracy, sensitivity, and specificity of 95, 97, and 95%, respectively. Our results suggest that measures of centrality provide the highest contribution to the classification power of the models.
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Affiliation(s)
- Amirali Kazeminejad
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada
| | - Roberto C Sotero
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Department of Radiology, University of Calgary, Calgary, AB, Canada
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46
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019. [PMID: 31249501 DOI: 10.3389/fnins.2019.00585/bibtex] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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47
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Gabrielsen TP, Anderson JS, Stephenson KG, Beck J, King JB, Kellems R, Top DN, Russell NCC, Anderberg E, Lundwall RA, Hansen B, South M. Functional MRI connectivity of children with autism and low verbal and cognitive performance. Mol Autism 2018; 9:67. [PMID: 30603063 PMCID: PMC6307191 DOI: 10.1186/s13229-018-0248-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 11/23/2018] [Indexed: 02/19/2023] Open
Abstract
Background Functional neuroimaging research in autism spectrum disorder has reported patterns of decreased long-range, within-network, and interhemispheric connectivity. Research has also reported increased corticostriatal connectivity and between-network connectivity for default and attentional networks. Past studies have excluded individuals with autism and low verbal and cognitive performance (LVCP), so connectivity in individuals more significantly affected with autism has not yet been studied. This represents a critical gap in our understanding of brain function across the autism spectrum. Methods Using behavioral support procedures adapted from Nordahl, et al. (J Neurodev Disord 8:20–20, 2016), we completed non-sedated structural and functional MRI scans of 56 children ages 7–17, including LVCP children (n = 17, mean IQ = 54), children with autism and higher performance (HVCP, n = 20, mean IQ = 106), and neurotypical children (NT, n = 19, mean IQ = 111). Preparation included detailed intake questionnaires, video modeling, behavioral and anxiety reduction techniques, active noise-canceling headphones, and in-scan presentation of the Inscapes movie paradigm from Vanderwal et al. (Neuroimage 122:222–32, 2015). A high temporal resolution multiband echoplanar fMRI protocol analyzed motion-free time series data, extracted from concatenated volumes to mitigate the influence of motion artifact. All participants had > 200 volumes of motion-free fMRI scanning. Analyses were corrected for multiple comparisons. Results LVCP showed decreased within-network connectivity in default, salience, auditory, and frontoparietal networks (LVCP < HVCP) and decreased interhemispheric connectivity (LVCP < HVCP=NT). Between-network connectivity was higher for LVCP than NT between default and dorsal attention and frontoparietal networks. Lower IQ was associated with decreased connectivity within the default network and increased connectivity between default and dorsal attention networks. Conclusions This study demonstrates that with moderate levels of support, including readily available techniques, information about brain similarities and differences in LVCP individuals can be further studied. This initial study suggested decreased network segmentation and integration in LVCP individuals. Further imaging studies of LVCP individuals with larger samples will add to understanding of origins and effects of autism on brain function and behavior. Electronic supplementary material The online version of this article (10.1186/s13229-018-0248-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Terisa P Gabrielsen
- 1Department of Counseling, Psychology and Special Education, Brigham Young University McKay School of Education, Provo, USA
| | - Jeff S Anderson
- 2Department of Radiology and Imaging Sciences, University of Utah School of Medicine, Salt Lake City, USA
| | | | - Jonathan Beck
- 3Department of Psychology, Brigham Young University, Provo, USA
| | - Jace B King
- 4Interdepartmental Program in Neuroscience, University of Utah School of Medicine, Salt Lake City, USA
| | - Ryan Kellems
- 1Department of Counseling, Psychology and Special Education, Brigham Young University McKay School of Education, Provo, USA
| | - David N Top
- 3Department of Psychology, Brigham Young University, Provo, USA
| | | | - Emily Anderberg
- 3Department of Psychology, Brigham Young University, Provo, USA
| | - Rebecca A Lundwall
- 3Department of Psychology, Brigham Young University, Provo, USA.,5Brigham Young University Neuroscience Center and MRI Research Facility, Provo, USA
| | - Blake Hansen
- 1Department of Counseling, Psychology and Special Education, Brigham Young University McKay School of Education, Provo, USA
| | - Mikle South
- 3Department of Psychology, Brigham Young University, Provo, USA.,5Brigham Young University Neuroscience Center and MRI Research Facility, Provo, USA
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Bernas A, Barendse EM, Aldenkamp AP, Backes WH, Hofman PAM, Hendriks MPH, Kessels RPC, Willems FMJ, de With PHN, Zinger S, Jansen JFA. Brain resting-state networks in adolescents with high-functioning autism: Analysis of spatial connectivity and temporal neurodynamics. Brain Behav 2018; 8:e00878. [PMID: 29484255 PMCID: PMC5822569 DOI: 10.1002/brb3.878] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 07/20/2017] [Accepted: 10/10/2017] [Indexed: 12/16/2022] Open
Abstract
Introduction Autism spectrum disorder (ASD) is mainly characterized by functional and communication impairments as well as restrictive and repetitive behavior. The leading hypothesis for the neural basis of autism postulates globally abnormal brain connectivity, which can be assessed using functional magnetic resonance imaging (fMRI). Even in the absence of a task, the brain exhibits a high degree of functional connectivity, known as intrinsic, or resting-state, connectivity. Global default connectivity in individuals with autism versus controls is not well characterized, especially for a high-functioning young population. The aim of this study is to test whether high-functioning adolescents with ASD (HFA) have an abnormal resting-state functional connectivity. Materials and Methods We performed spatial and temporal analyses on resting-state networks (RSNs) in 13 HFA adolescents and 13 IQ- and age-matched controls. For the spatial analysis, we used probabilistic independent component analysis (ICA) and a permutation statistical method to reveal the RSN differences between the groups. For the temporal analysis, we applied Granger causality to find differences in temporal neurodynamics. Results Controls and HFA display very similar patterns and strengths of resting-state connectivity. We do not find any significant differences between HFA adolescents and controls in the spatial resting-state connectivity. However, in the temporal dynamics of this connectivity, we did find differences in the causal effect properties of RSNs originating in temporal and prefrontal cortices. Conclusion The results show a difference between HFA and controls in the temporal neurodynamics from the ventral attention network to the salience-executive network: a pathway involving cognitive, executive, and emotion-related cortices. We hypothesized that this weaker dynamic pathway is due to a subtle trigger challenging the cognitive state prior to the resting state.
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Affiliation(s)
- Antoine Bernas
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | - Evelien M. Barendse
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
- Department of Behavioral SciencesEpilepsy Center KempenhaegheHeezeThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud University NijmegenNijmegenThe Netherlands
| | - Albert P. Aldenkamp
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
- Department of Behavioral SciencesEpilepsy Center KempenhaegheHeezeThe Netherlands
- School for Mental Health and NeuroscienceMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Walter H. Backes
- School for Mental Health and NeuroscienceMaastricht University Medical CenterMaastrichtThe Netherlands
- Department of RadiologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Paul A. M. Hofman
- School for Mental Health and NeuroscienceMaastricht University Medical CenterMaastrichtThe Netherlands
- Department of RadiologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Marc P. H. Hendriks
- Donders Institute for Brain, Cognition and BehaviourRadboud University NijmegenNijmegenThe Netherlands
| | - Roy P. C. Kessels
- Donders Institute for Brain, Cognition and BehaviourRadboud University NijmegenNijmegenThe Netherlands
- Department of Medical PsychologyRadboud University Nijmegen Medical CentreNijmegenThe Netherlands
| | - Frans M. J. Willems
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | - Peter H. N. de With
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | - Svitlana Zinger
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Behavioral SciencesEpilepsy Center KempenhaegheHeezeThe Netherlands
| | - Jacobus F. A. Jansen
- School for Mental Health and NeuroscienceMaastricht University Medical CenterMaastrichtThe Netherlands
- Department of RadiologyMaastricht University Medical CenterMaastrichtThe Netherlands
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49
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Bi XA, Xu Q, Luo X, Sun Q, Wang Z. Weighted Random Support Vector Machine Clusters Analysis of Resting-State fMRI in Mild Cognitive Impairment. Front Psychiatry 2018; 9:340. [PMID: 30090075 PMCID: PMC6068241 DOI: 10.3389/fpsyt.2018.00340] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 07/09/2018] [Indexed: 12/15/2022] Open
Abstract
The identification of abnormal cognitive decline at an early stage becomes an increasingly significant conundrum to physicians and is of major interest in the studies of mild cognitive impairment (MCI). Support vector machine (SVM) as a high-dimensional pattern classification technique is widely employed in neuroimaging research. However, the application of a single SVM classifier may be difficult to achieve the excellent classification performance because of the small-sample size and noise of imaging data. To address this issue, we propose a novel method of the weighted random support vector machine cluster (WRSVMC) in which multiple SVMs were built and different weights were given to corresponding SVMs with different classification performances. We evaluated our algorithm on resting state functional magnetic resonance imaging (RS-fMRI) data of 93 MCI patients and 105 healthy controls (HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The maximum accuracy given by the WRSVMC is 87.67%, demonstrating excellent diagnostic power. Furthermore, the most discriminative brain areas have been found out as follows: gyrus rectus (REC.L), precentral gyrus (PreCG.R), olfactory cortex (OLF.L), and middle occipital gyrus (MOG.R). These findings of the paper provide a new perspective for the clinical diagnosis of MCI.
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Affiliation(s)
- Xia-An Bi
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qian Xu
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xianhao Luo
- College of Mathematics and Statistics, Hunan Normal University, Changsha, China
| | - Qi Sun
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zhigang Wang
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
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50
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Mash LE, Reiter MA, Linke AC, Townsend J, Müller RA. Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective. Dev Neurobiol 2017; 78:456-473. [PMID: 29266810 DOI: 10.1002/dneu.22570] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 12/18/2017] [Accepted: 12/18/2017] [Indexed: 12/22/2022]
Abstract
Atypical functional connectivity has been implicated in autism spectrum disorders (ASDs). However, the literature to date has been largely inconsistent, with mixed and conflicting reports of hypo- and hyper-connectivity. These discrepancies are partly due to differences between various neuroimaging modalities. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) measure distinct indices of functional connectivity (e.g., blood-oxygenation level-dependent [BOLD] signal vs. electrical activity). Furthermore, each method has unique benefits and disadvantages with respect to spatial and temporal resolution, vulnerability to specific artifacts, and practical implementation. Thus far, functional connectivity research on ASDs has remained almost exclusively unimodal; therefore, interpreting findings across modalities remains a challenge. Multimodal integration of fMRI, EEG, and MEG data is critical in resolving discrepancies in the literature, and working toward a unifying framework for interpreting past and future findings. This review aims to provide a theoretical foundation for future multimodal research on ASDs. First, we will discuss the merits and shortcomings of several popular theories in ASD functional connectivity research, using examples from the literature to date. Next, the neurophysiological relationships between imaging modalities, including their relationship with invasive neural recordings, will be reviewed. Finally, methodological approaches to multimodal data integration will be presented, and their future application to ASDs will be discussed. Analyses relating transient patterns of neural activity ("states") are particularly promising. This strategy provides a comparable measure across modalities, captures complex spatiotemporal patterns, and is a natural extension of recent dynamic fMRI research in ASDs. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 456-473, 2018.
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Affiliation(s)
- Lisa E Mash
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California.,Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Maya A Reiter
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California.,Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Annika C Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Jeanne Townsend
- Department of Neurosciences, University of California, San Diego, San Diego, California
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
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