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Doherty JL, Cunningham AC, Chawner SJRA, Moss HM, Dima DC, Linden DEJ, Owen MJ, van den Bree MBM, Singh KD. Atypical cortical networks in children at high-genetic risk of psychiatric and neurodevelopmental disorders. Neuropsychopharmacology 2024; 49:368-376. [PMID: 37402765 PMCID: PMC7615386 DOI: 10.1038/s41386-023-01628-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/04/2023] [Accepted: 06/01/2023] [Indexed: 07/06/2023]
Abstract
Although many genetic risk factors for psychiatric and neurodevelopmental disorders have been identified, the neurobiological route from genetic risk to neuropsychiatric outcome remains unclear. 22q11.2 deletion syndrome (22q11.2DS) is a copy number variant (CNV) syndrome associated with high rates of neurodevelopmental and psychiatric disorders including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia. Alterations in neural integration and cortical connectivity have been linked to the spectrum of neuropsychiatric disorders seen in 22q11.2DS and may be a mechanism by which the CNV acts to increase risk. In this study, magnetoencephalography (MEG) was used to investigate electrophysiological markers of local and global network function in 34 children with 22q11.2DS and 25 controls aged 10-17 years old. Resting-state oscillatory activity and functional connectivity across six frequency bands were compared between groups. Regression analyses were used to explore the relationships between these measures, neurodevelopmental symptoms and IQ. Children with 22q11.2DS had altered network activity and connectivity in high and low frequency bands, reflecting modified local and long-range cortical circuitry. Alpha and theta band connectivity were negatively associated with ASD symptoms while frontal high frequency (gamma band) activity was positively associated with ASD symptoms. Alpha band activity was positively associated with cognitive ability. These findings suggest that haploinsufficiency at the 22q11.2 locus impacts short and long-range cortical circuits, which could be a mechanism underlying neurodevelopmental and psychiatric vulnerability in this high-risk group.
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Affiliation(s)
- Joanne L Doherty
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
- Cardiff University's Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK.
| | - Adam C Cunningham
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Samuel J R A Chawner
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Hayley M Moss
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Diana C Dima
- Cardiff University's Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - David E J Linden
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
- Cardiff University's Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Michael J Owen
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Marianne B M van den Bree
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Krish D Singh
- Cardiff University's Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
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Zhang X, Yang X, Wu B, Pan N, He M, Wang S, Kemp GJ, Gong Q. Large-scale brain functional network abnormalities in social anxiety disorder. Psychol Med 2023; 53:6194-6204. [PMID: 36330833 PMCID: PMC10520603 DOI: 10.1017/s0033291722003439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/06/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value. METHODS Forty-six SAD patients and 52 demographically-matched healthy controls (HC) were recruited to undergo clinical evaluation and resting-state functional MRI scanning. We used group independent component analysis to characterize the functional architecture of brain resting-state networks (RSNs) and investigate between-group differences in intra-/inter-network functional network connectivity (FNC). Furtherly, we explored the associations of FNC abnormalities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses. RESULTS SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC abnormalities among those high-order and primary RSNs. Some aberrant FNC signatures were correlated to disease severity and duration, suggesting pathophysiological relevance. Furthermore, intrinsic FNC anomalies allowed individual classification of SAD v. HC with significant accuracy, indicating potential diagnostic efficacy. CONCLUSIONS SAD patients show distinct patterns of functional synchronization abnormalities both within and across large-scale RSNs, reflecting or causing a network imbalance of bottom-up response and top-down regulation in cognitive, emotional and sensory domains. Therefore, this could offer insights into the neurofunctional substrates of SAD.
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Affiliation(s)
- Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Xun Yang
- School of Public Affairs, Chongqing University, Chongqing 400044, China
| | - Baolin Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Min He
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Graham J. Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3BX, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361000, China
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Wang Q, Yao W, Bai D, Yi W, Yan W, Wang J. Schizophrenia MEG Network Analysis Based on Kernel Granger Causality. Entropy (Basel) 2023; 25:1006. [PMID: 37509953 PMCID: PMC10378589 DOI: 10.3390/e25071006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 06/22/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023]
Abstract
Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizophrenia magnetoencephalography (MEG). We first generate data based on coupled autoregressive processes to test the effectiveness of MKGC in comparison with the bivariate linear Granger causality and bivariate inhomogeneous polynomial kernel Granger causality. The test results suggest that MKGC outperforms the other two methods. Based on these results, we apply MKGC to construct effective connectivity networks of MEG for patients with schizophrenia (SCZs). We measure three network features, i.e., strength, nonequilibrium, and complexity, to characterize schizophrenia MEG. Our results suggest that MEG of the healthy controls (HCs) has a denser effective connectivity network than that of SCZs. The most significant difference in the in-connectivity strength is observed in the right frontal network (p=0.001). The strongest out-connectivity strength for all subjects occurs in the temporal area, with the most significant between-group difference in the left occipital area (p=0.0018). The total connectivity strength of the frontal, temporal, and occipital areas of HCs exhibits higher values compared with SCZs. The nonequilibrium feature over the whole brain of SCZs is significantly higher than that of the HCs (p=0.012); however, the results of Shannon entropy suggest that healthy MEG networks have higher complexity than schizophrenia networks. Overall, MKGC provides a reliable approach to construct MEG brain networks and characterize the network characteristics.
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Affiliation(s)
- Qiong Wang
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
- School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing 210013, China
| | - Wenpo Yao
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Dengxuan Bai
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Wanyi Yi
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Wei Yan
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Jun Wang
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Candelaria-Cook FT, Schendel ME, Flynn L, Cerros C, Hill DE, Stephen JM. Disrupted dynamic functional network connectivity in fetal alcohol spectrum disorders. Alcohol Clin Exp Res (Hoboken) 2023; 47:687-703. [PMID: 36880528 PMCID: PMC10281251 DOI: 10.1111/acer.15046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 02/23/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Prenatal alcohol exposure (PAE) can result in harmful and long-lasting neurodevelopmental changes. Children with PAE or a fetal alcohol spectrum disorder (FASD) have decreased white matter volume and resting-state spectral power compared to typically developing controls (TDC) and impaired resting-state static functional connectivity. The impact of PAE on resting-state dynamic functional network connectivity (dFNC) is unknown. METHODS Using eyes-closed and eyes-open magnetoencephalography (MEG) resting-state data, global dFNC statistics and meta-states were examined in 89 children aged 6-16 years (51 TDC, 38 with FASD). Source analyzed MEG data were used as input to group spatial independent component analysis to derive functional networks from which the dFNC was calculated. RESULTS During eyes-closed, relative to TDC, participants with FASD spent a significantly longer time in state 2, typified by anticorrelation (i.e., decreased connectivity) within and between default mode network (DMN) and visual network (VN), and state 4, typified by stronger internetwork correlation. The FASD group exhibited greater dynamic fluidity and dynamic range (i.e., entered more states, changed from one meta-state to another more often, and traveled greater distances) than TDC. During eyes-open, TDC spent significantly more time in state 1, typified by positive intra- and interdomain connectivity with modest correlation within the frontal network (FN), while participants with FASD spent a larger fraction of time in state 2, typified by anticorrelation within and between DMN and VN and strong correlation within and between FN, attention network, and sensorimotor network. CONCLUSIONS There are important resting-state dFNC differences between children with FASD and TDC. Participants with FASD exhibited greater dynamic fluidity and dynamic range and spent more time in states typified by anticorrelation within and between DMN and VN, and more time in a state typified by high internetwork connectivity. Taken together, these network aberrations indicate that prenatal alcohol exposure has a global effect on resting-state connectivity.
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Affiliation(s)
| | - Megan E. Schendel
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
| | - Lucinda Flynn
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
| | - Cassandra Cerros
- Department of Pediatrics, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Dina E. Hill
- Department of Psychiatry and Behavioral Sciences, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Julia M. Stephen
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
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Sharma B, Obeid J, DeMatteo C, Noseworthy MD, Timmons BW. Exploring the relationship between resting state intra-network connectivity and accelerometer-measured physical activity in pediatric concussion: A cohort study. Appl Physiol Nutr Metab 2022; 47:1014-1022. [PMID: 35858484 DOI: 10.1139/apnm-2022-0085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Our objective was to explore the association between resting state functional connectivity and accelerometer-measured physical activity in pediatric concussion. Fourteen children with concussion (aged 14.54 ± 2.39 years, 8 female) were included in this secondary data analysis of a larger study. Participants had neuroimaging at 15.3 ± 6.7 days post-injury and subsequently a mean of 11.1 ± 5.0 days of accelerometer data. Intra-network connectivity of the default mode network (DMN), sensorimotor network (SMN), salience network (SN), and fronto-parietal network (FPN) was computed using resting state functional MRI. We found that per general linear models, only intra-network connectivity of the DMN was associated with physical activity levels. More specifically, increased intra-network connectivity of the DMN was significantly associated with higher levels of subsequent accelerometer-measured light physical activity (F(2, 11) = 7.053, p = 0.011, Ra2 = 0.562; β = 0.469), moderate physical activity (F(2, 11) = 7.053, p = 0.011, Ra2 = 0.562; β = 0.725), and vigorous physical activity (F(2, 11) = 10.855, p = 0.002, Ra2 = 0.664; β = 0.79). Intra-network connectivity of the DMN did not significantly predict sedentary time. Therefore, these preliminary findings suggest that there is a positive association between the intra-network connectivity of the DMN and device-measured physical activity in children with concussion.
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Affiliation(s)
- Bhanu Sharma
- McMaster University, 3710, Department of Pediatrics, Hamilton, Canada;
| | - Joyce Obeid
- McMaster University, Kinesiology, Hamilton, Ontario, Canada;
| | | | - Michael D Noseworthy
- McMaster University, Electrical and Computer Engineering, Hamilton, Ontario, Canada;
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Tagawa M, Takei Y, Kato Y, Suto T, Hironaga N, Ohki T, Takahashi Y, Fujihara K, Sakurai N, Ujita K, Tsushima Y, Fukuda M. Disrupted local beta band networks in schizophrenia revealed through graph analysis: A magnetoencephalography study. Psychiatry Clin Neurosci 2022; 76:309-320. [PMID: 35397141 DOI: 10.1111/pcn.13362] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 03/14/2022] [Accepted: 03/25/2022] [Indexed: 11/30/2022]
Abstract
AIMS Schizophrenia (SZ) is characterized by psychotic symptoms and cognitive impairment, and is hypothesized to be a 'dysconnection' syndrome due to abnormal neural network formation. Although numerous studies have helped elucidate the pathophysiology of SZ, many aspects of the mechanism underlying psychotic symptoms remain unknown. This study used graph theory analysis to evaluate the characteristics of the resting-state network (RSN) in terms of microscale and macroscale indices, and to identify candidates as potential biomarkers of SZ. Specifically, we discriminated topological characteristics in the frequency domain and investigated them in the context of psychotic symptoms in patients with SZ. METHODS We performed graph theory analysis of electrophysiological RSN data using magnetoencephalography to compare topological characteristics represented by microscale (degree centrality and clustering coefficient) and macroscale (global efficiency, local efficiency, and small-worldness) indices in 29 patients with SZ and 38 healthy controls. In addition, we investigated the aberrant topological characteristics of the RSN in patients with SZ and their relationship with SZ symptoms. RESULTS SZ was associated with a decreased clustering coefficient, local efficiency, and small-worldness, especially in the high beta band. In addition, macroscale changes in the low beta band are closely associated with negative symptoms. CONCLUSIONS The local networks of patients with SZ may disintegrate at both the microscale and macroscale levels, mainly in the beta band. Adopting an electrophysiological perspective of SZ as a failure to form local networks in the beta band will provide deeper insights into the pathophysiology of SZ as a 'dysconnection' syndrome.
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Affiliation(s)
- Minami Tagawa
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.,Gunma Prefectural Psychiatric Medical Center, Gunma, Japan
| | - Yuichi Takei
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Yutaka Kato
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.,Tsutsuji Mental Hospital, Gunma, Japan
| | - Tomohiro Suto
- Gunma Prefectural Psychiatric Medical Center, Gunma, Japan
| | - Naruhito Hironaga
- Brain Center, Faculty of Medicine, Kyushu University, Fukuoka, Japan
| | - Takefumi Ohki
- International Research Center for Neurointelligence (IRCN), The University of Tokyo, Tokyo, Japan
| | - Yumiko Takahashi
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Kazuyuki Fujihara
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.,Department of Genetic and Behavioral Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Noriko Sakurai
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Koichi Ujita
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Yoshito Tsushima
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Masato Fukuda
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
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Liu L, Ren J, Li Z, Yang C. A review of MEG dynamic brain network research. Proc Inst Mech Eng H 2022; 236:763-774. [PMID: 35465768 DOI: 10.1177/09544119221092503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
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Affiliation(s)
- Lu Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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Roberts TPL, Kuschner ES, Edgar JC. Biomarkers for autism spectrum disorder: opportunities for magnetoencephalography (MEG). J Neurodev Disord 2021; 13:34. [PMID: 34525943 PMCID: PMC8442415 DOI: 10.1186/s11689-021-09385-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 05/03/2021] [Indexed: 11/17/2022] Open
Abstract
This paper reviews a candidate biomarker for ASD, the M50 auditory evoked response component, detected by magnetoencephalography (MEG) and presents a position on the roles and opportunities for such a biomarker, as well as converging evidence from allied imaging techniques (magnetic resonance imaging, MRI and spectroscopy, MRS). Data is presented on prolonged M50 latencies in ASD as well as extension to include children with ASD with significant language and cognitive impairments in whom M50 latency delays are exacerbated. Modeling of the M50 latency by consideration of the properties of auditory pathway white matter is shown to be successful in typical development but challenged by heterogeneity in ASD; this, however, is capitalized upon to identify a distinct subpopulation of children with ASD whose M50 latencies lie well outside the range of values predictable from the typically developing model. Interestingly, this subpopulation is characterized by low levels of the inhibitory neurotransmitter GABA. Following from this, we discuss a potential use of the M50 latency in indicating “target engagement” acutely with administration of a GABA-B agonist, potentially distinguishing “responders” from “non-responders” with the implication of optimizing inclusion for clinical trials of such agents. Implications for future application, including potential evaluation of infants with genetic risk factors, are discussed. As such, the broad scope of potential of a representative candidate biological marker, the M50 latency, is introduced along with potential future applications. This paper outlines a strategy for understanding brain dysfunction in individuals with intellectual and developmental disabilities (IDD). It is proposed that a multimodal approach (collection of brain structure, chemistry, and neuronal functional data) will identify IDD subpopulations who share a common disease pathway, and thus identify individuals with IDD who might ultimately benefit from specific treatments. After briefly demonstrating the need and potential for scope, examples from studies examining brain function and structure in children with autism spectrum disorder (ASD) illustrate how measures of brain neuronal function (from magnetoencephalography, MEG), brain structure (from magnetic resonance imaging, MRI, especially diffusion MRI), and brain chemistry (MR spectroscopy) can help us better understand the heterogeneity in ASD and form the basis of multivariate biological markers (biomarkers) useable to define clinical subpopulations. Similar approaches can be applied to understand brain dysfunction in neurodevelopmental disorders (NDD) in general. In large part, this paper represents our endeavors as part of the CHOP/Penn NICHD-funded intellectual and developmental disabilities research center (IDDRC) over the past decade.
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Affiliation(s)
- Timothy P L Roberts
- Dept. of Radiology, Lurie Family Foundations MEG Imaging Center, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA.
| | - Emily S Kuschner
- Dept. of Radiology, Lurie Family Foundations MEG Imaging Center, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA
| | - J Christopher Edgar
- Dept. of Radiology, Lurie Family Foundations MEG Imaging Center, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA
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9
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Asaoka Y, Won M, Morita T, Ishikawa E, Goto Y. Comparable level of aggression between patients with behavioural addiction and healthy subjects. Transl Psychiatry 2021; 11:375. [PMID: 34226502 DOI: 10.1038/s41398-021-01502-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 06/16/2021] [Accepted: 06/24/2021] [Indexed: 11/29/2022] Open
Abstract
Heightened aggression is identified in several psychiatric disorders, including addiction. In this preliminary study with a relatively small number of samples, aggression in subjects diagnosed with behavioural addiction (BA) was implicitly assessed using the point subtraction aggression paradigm (PSAP) test along with measurements of oxy- and deoxyhaemoglobin dynamics in the prefrontal cortex (PFC) during the test using functional near-infrared spectroscopy. Aggression in BA patients was no higher than that of healthy control (CT) subjects in the PSAP test. Although no apparent increase or decrease in haemoglobin concentrations was observed in the PFC of either BA patients or CT subjects, abnormal correlations within the PFC network were present in BA patients. Consistent with comparable aggression between the groups, blood concentrations of the sex hormone testosterone, which has been shown to be associated with aggressiveness, was even lower in BA patients than in CT subjects. In contrast, when a set of questionnaire surveys for the assessment of aggression were administered, BA patients rated themselves as more aggressive than non-BA subjects. Collectively, these results suggest that aggression may not be heightened in BA, but BA patients may overestimate their aggressiveness, raising concerns about the use of questionnaire surveys for assessments of affective traits such as aggression in behavioural addiction.
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10
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Alamian G, Pascarella A, Lajnef T, Knight L, Walters J, Singh KD, Jerbi K. Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia. Neuroimage Clin 2020; 28:102485. [PMID: 33395976 PMCID: PMC7691748 DOI: 10.1016/j.nicl.2020.102485] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/22/2020] [Accepted: 10/24/2020] [Indexed: 12/19/2022]
Abstract
Current theories of schizophrenia emphasize the role of altered information integration as the core dysfunction of this illness. While ample neuroimaging evidence for such accounts comes from investigations of spatial connectivity, understanding temporal disruptions is important to fully capture the essence of dysconnectivity in schizophrenia. Recent electrophysiology studies suggest that long-range temporal correlation (LRTC) in the amplitude dynamics of neural oscillations captures the integrity of transferred information in the healthy brain. Thus, in this study, 25 schizophrenia patients and 25 controls (8 females/group) were recorded during two five-minutes of resting-state magnetoencephalography (once with eyes-open and once with eyes-closed). We used source-level analyses to investigate temporal dysconnectivity in patients by characterizing LRTCs across cortical and sub-cortical brain regions. In addition to standard statistical assessments, we applied a machine learning framework using support vector machine to evaluate the discriminative power of LRTCs in identifying patients from healthy controls. We found that neural oscillations in schizophrenia patients were characterized by reduced signal memory and higher variability across time, as evidenced by cortical and subcortical attenuations of LRTCs in the alpha and beta frequency bands. Support vector machine significantly classified participants using LRTCs in key limbic and paralimbic brain areas, with decoding accuracy reaching 82%. Importantly, these brain regions belong to networks that are highly relevant to the symptomology of schizophrenia. These findings thus posit temporal dysconnectivity as a hallmark of altered information processing in schizophrenia, and help advance our understanding of this pathology.
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Affiliation(s)
- Golnoush Alamian
- CoCo Lab, Department of Psychology, Université de Montréal, Canada.
| | | | - Tarek Lajnef
- CoCo Lab, Department of Psychology, Université de Montréal, Canada
| | - Laura Knight
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, UK
| | - James Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, UK
| | - Krish D Singh
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, UK
| | - Karim Jerbi
- CoCo Lab, Department of Psychology, Université de Montréal, Canada; MEG Center, University of Montreal, Canada; UNIQUE Centre (Unifying AI and Neuroscience - Québec), Quebec, Canada; Mila (Quebec AI Institute), Montreal, QC, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Montreal, QC, Canada
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11
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Stephen JM, Solis I, Janowich J, Stern M, Frenzel MR, Eastman JA, Mills MS, Embury CM, Coolidge NM, Heinrichs-Graham E, Mayer A, Liu J, Wang YP, Wilson TW, Calhoun VD. The Developmental Chronnecto-Genomics (Dev-CoG) study: A multimodal study on the developing brain. Neuroimage 2020; 225:117438. [PMID: 33039623 DOI: 10.1016/j.neuroimage.2020.117438] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/07/2020] [Accepted: 10/05/2020] [Indexed: 01/10/2023] Open
Abstract
Brain development has largely been studied through unimodal analysis of neuroimaging data, providing independent results for structural and functional data. However, structure clearly impacts function and vice versa, pointing to the need for performing multimodal data collection and analysis to improve our understanding of brain development, and to further inform models of typical and atypical brain development across the lifespan. Ultimately, such models should also incorporate genetic and epigenetic mechanisms underlying brain structure and function, although currently this area is poorly specified. To this end, we are reporting here a multi-site, multi-modal dataset that captures cognitive function, brain structure and function, and genetic and epigenetic measures to better quantify the factors that influence brain development in children originally aged 9-14 years. Data collection for the Developmental Chronnecto-Genomics (Dev-CoG) study (http://devcog.mrn.org/) includes cognitive, emotional, and social performance scales, structural and functional MRI, diffusion MRI, magnetoencephalography (MEG), and saliva collection for DNA analysis of single nucleotide polymorphisms (SNPs) and DNA methylation patterns. Across two sites (The Mind Research Network and the University of Nebraska Medical Center), data from over 200 participants were collected and these children were re-tested annually for at least 3 years. The data collection protocol, sample demographics, and data quality measures for the dataset are presented here. The sample will be made freely available through the collaborative informatics and neuroimaging suite (COINS) database at the conclusion of the study.
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Affiliation(s)
- J M Stephen
- The Mind Research Network a division of Lovelace Biomedical Research Institute, Albuquerque, NM, United States.
| | - I Solis
- The Mind Research Network a division of Lovelace Biomedical Research Institute, Albuquerque, NM, United States; Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - J Janowich
- The Mind Research Network a division of Lovelace Biomedical Research Institute, Albuquerque, NM, United States; Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - M Stern
- The Mind Research Network a division of Lovelace Biomedical Research Institute, Albuquerque, NM, United States; Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - M R Frenzel
- University of Nebraska Medical Center, Omaha, NE, United States
| | - J A Eastman
- University of Nebraska Medical Center, Omaha, NE, United States
| | - M S Mills
- University of Nebraska Medical Center, Omaha, NE, United States
| | - C M Embury
- University of Nebraska Medical Center, Omaha, NE, United States
| | - N M Coolidge
- University of Nebraska Medical Center, Omaha, NE, United States
| | | | - A Mayer
- The Mind Research Network a division of Lovelace Biomedical Research Institute, Albuquerque, NM, United States
| | - J Liu
- The Mind Research Network a division of Lovelace Biomedical Research Institute, Albuquerque, NM, United States
| | - Y P Wang
- Tulane University, New Orleans, LA, United States
| | - T W Wilson
- University of Nebraska Medical Center, Omaha, NE, United States
| | - V D Calhoun
- The Mind Research Network a division of Lovelace Biomedical Research Institute, Albuquerque, NM, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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12
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Gawne TJ, Overbeek GJ, Killen JF, Reid MA, Kraguljac NV, Denney TS, Ellis CA, Lahti AC. A multimodal magnetoencephalography 7 T fMRI and 7 T proton MR spectroscopy study in first episode psychosis. NPJ Schizophr 2020; 6:23. [PMID: 32887887 DOI: 10.1038/s41537-020-00113-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 07/23/2020] [Indexed: 11/08/2022]
Abstract
We combined magnetoencephalography (MEG), 7 T proton magnetic resonance spectroscopy (MRS), and 7 T fMRI during performance of a task in a group of 23 first episode psychosis (FEP) patients and 26 matched healthy controls (HC). We recorded both the auditory evoked response to 40 Hz tone clicks and the resting state in MEG. Neurometabolite levels were obtained from the anterior cingulate cortex (ACC). The fMRI BOLD response was obtained during the Stroop inhibitory control task. FEP showed a significant increase in resting state low frequency theta activity (p < 0.05; Cohen d = 0.69), but no significant difference in the 40 Hz auditory evoked response compared to HC. An across-groups whole brain analysis of the fMRI BOLD response identified eight regions that were significantly activated during task performance (p < 0.01, FDR-corrected); the mean signal extracted from those regions was significantly different between the groups (p = 0.0006; d = 1.19). In the combined FEP and HC group, there was a significant correlation between the BOLD signal during task performance and MEG resting state low frequency activity (p < 0.05). In FEP, we report significant alteration in resting state low frequency MEG activity, but no alterations in auditory evoked gamma band response, suggesting that the former is a more robust biomarker of early psychosis. There were no correlations between gamma oscillations and GABA levels in either HC or FEP. Finally, in this study, each of the three imaging modalities differentiated FEP from HC; fMRI with good and MEG and MRS with moderate effect size.
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13
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Castelnovo A, Zago M, Casetta C, Zangani C, Donati F, Canevini M, Riedner BA, Tononi G, Ferrarelli F, Sarasso S, D'Agostino A. Slow wave oscillations in Schizophrenia First-Degree Relatives: A confirmatory analysis and feasibility study on slow wave traveling. Schizophr Res 2020; 221:37-43. [PMID: 32220503 DOI: 10.1016/j.schres.2020.03.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 03/11/2020] [Accepted: 03/13/2020] [Indexed: 12/14/2022]
Abstract
Abnormal sleep oscillations have recently been proposed as endophenotypes of schizophrenia. However, optimization of methodological approaches is still necessary to standardize analyses of their microstructural characteristics. Additionally, some relevant features of these oscillations remain unexplored in pathological conditions. Among others, slow wave traveling is a promising proxy for diurnal processes of brain connectivity and excitability. The study of slow oscillations propagation appears particularly relevant when schizophrenia is conceptualized as a dys-connectivity syndrome. Given the rising knowledge on the neurobiological mechanisms underlying slow wave traveling, this measure might offer substantial advantages over other approaches in investigating brain connectivity. Herein we: 1) confirm the stability of our previous findings on slow waves and sleep spindles in FDRs using different automated algorithms, and 2) report the dynamics of slow wave traveling in FDRs of Schizophrenia patients. A 256-channel, high-density EEG system was employed to record a whole night of sleep of 16 FDRs and 16 age- and gender-matched control subjects. A recently developed, open source toolbox was used for slow wave visualization and detection. Slow waves were confirmed to be significantly smaller in FDRs compared to the control group. Additionally, several traveling parameters were analyzed. Traveled distances were found to be significantly reduced in FDRs, whereas origins showed a different topographical pattern of distribution from control subjects. In contrast, local speed did not differ between groups. Overall, these results suggest that slow wave traveling might be a viable method to study pathological conditions interfering with brain connectivity.
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Affiliation(s)
- Anna Castelnovo
- Department of Health Sciences, Università degli Studi di Milano, Italy; Sleep Center, Neurocenter of Southern Switzerland, Regional Civic Hospital of Lugano, Switzerland; University of Southern Switzerland, Lugano, Switzerland.
| | - Matteo Zago
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy
| | - Cecilia Casetta
- King's College London, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, United Kingdom of Great Britain and Northern Ireland
| | - Caroline Zangani
- Department of Health Sciences, Università degli Studi di Milano, Italy
| | - Francesco Donati
- Department of Health Sciences, Università degli Studi di Milano, Italy
| | | | - Brady A Riedner
- Department of Psychiatry, University of Wisconsin, Madison, United States
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin, Madison, United States
| | - Fabio Ferrarelli
- Department of Psychiatry, University of Pittsburgh, United States
| | - Simone Sarasso
- "L. Sacco" Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Italy
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14
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Abstract
Schizophrenia (Sz) is a chronic mental disorder characterized by disturbances in thought (such as delusions and confused thinking), perception (hearing voices), and behavior (lack of motivation). The lifetime prevalence of Sz is between 0.3% and 0.7%, with late adolescence and early adulthood, the peak period for the onset of psychotic symptoms. Causal factors in Sz include environmental and genetic factors and especially their interaction. About 50% of individuals with a diagnosis of Sz have lifelong impairment.
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Lurie DJ, Kessler D, Bassett DS, Betzel RF, Breakspear M, Kheilholz S, Kucyi A, Liégeois R, Lindquist MA, McIntosh AR, Poldrack RA, Shine JM, Thompson WH, Bielczyk NZ, Douw L, Kraft D, Miller RL, Muthuraman M, Pasquini L, Razi A, Vidaurre D, Xie H, Calhoun VD. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci 2020; 4:30-69. [PMID: 32043043 PMCID: PMC7006871 DOI: 10.1162/netn_a_00116] [Citation(s) in RCA: 247] [Impact Index Per Article: 61.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 11/22/2019] [Indexed: 12/12/2022] Open
Abstract
The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.
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Affiliation(s)
- Daniel J. Lurie
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Daniel Kessler
- Departments of Statistics and Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Richard F. Betzel
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Breakspear
- University of Newcastle, Callaghan, NSW, 2308, Australia
- QIMR Berghofer, Brisbane, Australia
| | - Shella Kheilholz
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford CA, USA
| | - Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Anthony Randal McIntosh
- Rotman Research Institute - Baycrest Centre, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
| | | | - James M. Shine
- Brain and Mind Centre, University of Sydney, NSW, Australia
| | - William Hedley Thompson
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
| | - Dominik Kraft
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | | | - Muthuraman Muthuraman
- Biomedical Statistics and Multimodal Signal Processing Unit, Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adeel Razi
- Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, United Kingdom
| | - Hua Xie
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia, USA
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Naeije G, Wens V, Coquelet N, Sjøgård M, Goldman S, Pandolfo M, De Tiège XP. Age of onset determines intrinsic functional brain architecture in Friedreich ataxia. Ann Clin Transl Neurol 2020; 7:94-104. [PMID: 31854120 PMCID: PMC6952309 DOI: 10.1002/acn3.50966] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 10/30/2019] [Accepted: 11/18/2019] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Friedreich ataxia (FRDA) is the commonest hereditary ataxia in Caucasians. Most patients are homozygous for expanded GAA triplet repeats in the first intron of the frataxin (FXN) gene, involved in mitochondrial iron metabolism. Here, we used magnetoencephalography (MEG) to characterize the main determinants of FRDA-related changes in intrinsic functional brain architecture. METHODS Five minutes of MEG signals were recorded at rest from 18 right-handed FRDA patients (mean age 27 years, 9 females; mean SARA score: 21.4) and matched healthy individuals. The MEG connectome was estimated as resting-state functional connectivity (rsFC) matrices involving 37 nodes from six major resting state networks and the cerebellum. Source-level rsFC maps were computed using leakage-corrected broad-band (3-40 Hz) envelope correlations. Post hoc median-split was used to contrast rsFC in FRDA patients with different clinical characteristics. Nonparametric permutations and Spearman rank correlation test were used for statistics. RESULTS High rank correlation levels were found between rsFC and age of symptoms onset in FRDA mostly between the ventral attention, the default-mode, and the cerebellar networks; patients with higher rsFC developing symptoms at an older age. Increased rsFC was found in FRDA with later age of symptoms onset compared to healthy subjects. No correlations were found between rsFC and other clinical parameters. CONCLUSION Age of symptoms onset is a major determinant of FRDA patients' intrinsic functional brain architecture. Higher rsFC in FRDA patients with later age of symptoms onset supports compensatory mechanisms for FRDA-related neural network dysfunction and position neuromagnetic rsFC as potential marker of FRDA neural reserve.
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Affiliation(s)
- Gilles Naeije
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
- Department of NeurologyCUB Hôpital ErasmeUniversité libre de Bruxelles (ULB)BrusselsBelgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
- Department of Functional NeuroimagingService of Nuclear MedicineCUB Hôpital ErasmeUniversité libre de Bruxelles (ULB)BrusselsBelgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
| | - Martin Sjøgård
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
- Department of Functional NeuroimagingService of Nuclear MedicineCUB Hôpital ErasmeUniversité libre de Bruxelles (ULB)BrusselsBelgium
| | - Massimo Pandolfo
- Department of NeurologyCUB Hôpital ErasmeUniversité libre de Bruxelles (ULB)BrusselsBelgium
| | - Xavier P. De Tiège
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
- Department of Functional NeuroimagingService of Nuclear MedicineCUB Hôpital ErasmeUniversité libre de Bruxelles (ULB)BrusselsBelgium
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Candelaria-Cook FT, Stephen JM. Test-Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity in Schizophrenia. Front Psychiatry 2020; 11:551952. [PMID: 33391043 PMCID: PMC7772354 DOI: 10.3389/fpsyt.2020.551952] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 11/23/2020] [Indexed: 12/17/2022] Open
Abstract
The reliability of magnetoencephalography (MEG) resting-state functional connectivity in schizophrenia (SZ) is unknown as previous research has focused on healthy controls (HC). Here, we examined reliability in 26 participants (13-SZ, 13-HC). Eyes opened and eyes closed resting-state data were collected on 4 separate occasions during 2 visits, 1 week apart. For source modeling, we used minimum norm software to apply dynamic statistical parametric mapping. Source analyses compared the following functional connectivity metrics from each data run: coherence (coh), imaginary coherence (imcoh), pairwise phase consistency (ppc), phase-locking value (plv), phase lag index (pli), weighted phase lag index (wpli), and weighted phase lag index debiased (wpli2). Intraclass correlation coefficients (ICCs) were calculated for whole brain, network, and network pair averages. For reliability, ICCs above 0.75 = excellent, above 0.60 = good, above 0.40 = fair, and below 0.40 = poor reliability. We found the reliability of these metrics varied greatly depending on frequency band, network, network pair, and participant group examined. Broadband (1-58 Hz) whole brain averages in both HC and SZ showed excellent reliability for wpli2, and good to fair reliability for ppc, plv, and coh. Broadband network averages showed excellent to good reliability across 1 hour and 1 week for coh, imcoh, ppc, plv, wpli within default mode, cognitive control, and visual networks in HC, while the same metrics had excellent to fair reliability in SZ. Regional network pair averages showed good to fair reliability for coh, ppc, plv within default mode, cognitive control and visual network pairs in HC and SZ. In general, HC had higher reliability compared to SZ, and the default mode, cognitive control, and visual networks had higher reliability compared to somatosensory and auditory networks. Similar reliability levels occurred for both eyes opened and eyes closed resting-states for most metrics. The functional connectivity metrics of coh, ppc, and plv performed best across 1 hour and 1 week in HC and SZ. We also found that SZ had reduced coh, plv, and ppc in the dmn average and pair values indicating dysconnectivity in SZ. These findings encourage collecting both eyes opened and eyes closed resting-state MEG, while demonstrating that clinical populations may differ in reliability.
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18
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Candelaria-Cook FT, Schendel ME, Ojeda CJ, Bustillo JR, Stephen JM. Reduced parietal alpha power and psychotic symptoms: Test-retest reliability of resting-state magnetoencephalography in schizophrenia and healthy controls. Schizophr Res 2020; 215:229-240. [PMID: 31706785 PMCID: PMC7036030 DOI: 10.1016/j.schres.2019.10.023] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 10/07/2019] [Accepted: 10/09/2019] [Indexed: 11/29/2022]
Abstract
BACKGROUND Despite increased reporting of resting-state magnetoencephalography (MEG), reliability of those measures remains scarce and predominately reported in healthy controls (HC). As such, there is limited knowledge on MEG resting-state reliability in schizophrenia (SZ). METHODS To address test-retest reliability in psychosis, a reproducibility study of 26 participants (13-SZ, 13-HC) was performed. We collected eyes open and eyes closed resting-state data during 4 separate instances (2 Visits, 2 runs per visit) to estimate spectral power reliability (power, normalized power, alpha reactivity) across one hour and one week. Intraclass correlation coefficients (ICCs) were calculated. For source modeling, we applied an anatomically constrained linear estimation inverse model known as dynamic statistical parametric mapping (MNE dSPM) and source-based connectivity using the weighted phase lag index. RESULTS Across one week there was excellent test-retest reliability in global spectral measures in theta-gamma bands (HC ICCAvg = 0.87, SZ ICCAvg = 0.87), regional spectral measures in all bands (HC ICCAvg = 0.86, SZ ICCAvg = 0.80), and parietal alpha measures (HC ICCAvg = 0.90, SZ ICCAvg = 0.84). Conversely, functional connectivity had poor reliability, as did source spectral power across one hour for SZ. Relative to HC, SZ also had reduced parietal alpha normalized power during eyes closed only, reduced alpha reactivity, and an association between higher PANSS positive scores and lower parietal alpha power. CONCLUSIONS There was excellent to good test-retest reliability in most MEG spectral measures with a few exceptions in the schizophrenia patient group. Overall, these findings encourage the use of resting-state MEG while emphasizing the importance of determining reliability in clinical populations.
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Affiliation(s)
| | | | - Cesar J. Ojeda
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Research, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Juan R. Bustillo
- Department of Psychiatry and Behavioral Sciences, Center for Psychiatric Research, University of New Mexico School of Medicine, Albuquerque, New Mexico
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Gjini K, Bowyer SM, Wang F, Boutros NN. Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence-Preliminary Data. Clin EEG Neurosci 2020; 51:34-44. [PMID: 31379210 DOI: 10.1177/1550059419867561] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This study investigated the magneto- and electroencephalography (MEG and EEG, respectively) resting state to identify the deviations closely associated with the deficit syndrome (DS) in schizophrenia patients. Ten subjects in each group (control, DS, and nondeficit schizophrenia [NDS]) were included. Subjects underwent MEG-EEG recordings during a resting state condition. MEG coherence source imaging (CSI) in source space and spectral analysis in sensor space were performed. Significant differences were found between the 2 patient groups: (1) MEG and EEG spectral analysis showed significantly higher power at low frequencies (delta band) at sensor space in DS compared with NDS patients; (2) source analysis revealed larger power in the DS compared with NDS group at low frequencies in the frontal region; (3) NDS patients showed significantly higher MEG signal relative power in beta bands in sensor space compared with DS patients; (4) both DS and NDS patients showed higher EEG absolute power at higher beta band compared to controls; and (5) patients with DS were found to have a significantly higher MEG CSI than controls in the beta frequency band. These data support the observation of increased power in the low-frequency EEG/MEG rhythms associated with the DS. Increased power in the beta rhythms was more associated with the NDS.
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Affiliation(s)
- Klevest Gjini
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Susan M Bowyer
- Department of Neurology, Henry Ford Hospital, Detroit, MI, USA.,Wayne State University, Detroit, MI, USA
| | - Frank Wang
- University of California, Berkeley, Berkeley, CA, USA
| | - Nash N Boutros
- Department of Psychiatry, Wayne State University, Detroit, MI, USA
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20
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Sanfratello L, Houck JM, Calhoun VD. Dynamic Functional Network Connectivity in Schizophrenia with Magnetoencephalography and Functional Magnetic Resonance Imaging: Do Different Timescales Tell a Different Story? Brain Connect 2019; 9:251-262. [PMID: 30632385 DOI: 10.1089/brain.2018.0608] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The importance of how brain networks function together to create brain states has become increasingly recognized. Therefore, an investigation of eyes-open resting-state dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) via both functional magnetic resonance imaging (fMRI) and a novel magnetoencephalography (MEG) pipeline was completed. The fMRI analysis used a spatial independent component analysis (ICA) to determine the networks on which the dFNC was based. The MEG analysis utilized a source space activity estimate (minimum norm estimate [MNE]/dynamic statistical parametric mapping [dSPM]) whose result was the input to a spatial ICA, on which the networks of the MEG dFNC were based. We found that dFNC measures reveal significant differences between HC and SP, which depended on the imaging modality. Consistent with previous findings, a dFNC analysis predicated on fMRI data revealed HC and SP remain in different overall brain states (defined by a k-means clustering of network correlations) for significantly different periods of time, with SP spending less time in a highly connected state. The MEG dFNC, in contrast, revealed group differences in more global statistics: SP changed between meta-states (k-means cluster states that are allowed to overlap in time) significantly more often and to states that were more different, relative to HC. MEG dFNC also revealed a highly connected state where a significant difference was observed in interindividual variability, with greater variability among SP. Overall, our results show that fMRI and MEG reveal between-group functional connectivity differences in distinct ways, highlighting the utility of using each of the modalities individually, or potentially a combination of modalities, to better inform our understanding of disorders such as schizophrenia.
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Affiliation(s)
| | - Jon M Houck
- 1 The Mind Research Network, Albuquerque, New Mexico.,2 Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico
| | - Vince D Calhoun
- 1 The Mind Research Network, Albuquerque, New Mexico.,2 Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico.,3 Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
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Lottman KK, Gawne TJ, Kraguljac NV, Killen JF, Reid MA, Lahti AC. Examining resting-state functional connectivity in first-episode schizophrenia with 7T fMRI and MEG. Neuroimage Clin 2019; 24:101959. [PMID: 31377556 DOI: 10.1016/j.nicl.2019.101959] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 07/12/2019] [Accepted: 07/21/2019] [Indexed: 01/08/2023]
Abstract
Schizophrenia is often characterized by dysconnections in the brain, which can be estimated via functional connectivity analyses. Commonly measured using resting-state functional magnetic resonance imaging (fMRI) in order to characterize the intrinsic or baseline function of the brain, fMRI functional connectivity has significantly contributed to the understanding of schizophrenia. However, these measures may not capture the full extent of functional connectivity abnormalities in schizophrenia as fMRI is temporally limited by the hemodynamic response. In order to extend fMRI functional connectivity findings, the complementary modality of magnetoencephalography (MEG) can be utilized to capture electrophysiological functional connectivity abnormalities in schizophrenia that are not obtainable with fMRI. Therefore, we implemented a multimodal functional connectivity analysis using resting-state 7 Tesla fMRI and MEG data in a sample of first-episode patients with schizophrenia (n = 19) and healthy controls (n = 24). fMRI and MEG data were decomposed into components reflecting resting state networks using a group spatial independent component analysis. Functional connectivity between resting-state networks was computed and group differences were observed. In fMRI, patients demonstrated hyperconnectivity between subcortical and auditory networks, as well as hypoconnectivity between interhemispheric homotopic sensorimotor network components. In MEG, patients demonstrated hypoconnectivity between sensorimotor and task positive networks in the delta frequency band. Results not only support the dysconnectivity hypothesis of schizophrenia, but also suggest the importance of jointly examining multimodal neuroimaging data as critical disorder-related information may not be detectable in a single modality alone.
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22
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Han L, Na Z, Chunli L, Yuchen C, Pengfei Z, Hao W, Xu C, Peng Z, Zheng W, Zhenghan Y, Shusheng G, Zhenchang W. Baseline Functional Connectivity Features of Neural Network Nodes Can Predict Improvement After Sound Therapy Through Adjusted Narrow Band Noise in Tinnitus Patients. Front Neurosci 2019; 13:614. [PMID: 31333394 PMCID: PMC6620714 DOI: 10.3389/fnins.2019.00614] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 05/29/2019] [Indexed: 12/17/2022] Open
Abstract
Previous resting-state functional magnetic resonance imaging (fMRI) studies have shown neural connectivity alterations after the treatment of tinnitus. We aim to study the value of the baseline functional connectivity features of neural network nodes to predict outcomes of sound therapy through adjusted narrow band noise. The fMRI data of 27 untreated tinnitus patients and 27 matched healthy controls were analyzed. We calculated the graph-theoretical metric degree centrality (DC) to characterize the functional connectivity of the neural network nodes. Therapeutic outcomes are determined by the changes in the Tinnitus Handicap Inventory (THI) score after a 12-week intervention. The connectivity of 10 brain nodes in tinnitus patients was significantly increased at baseline. The functional connectivity of right insula, inferior parietal lobule (IPL), bilateral thalami, and left middle temporal gyrus was significantly modified with the sound therapy, and such changes correlated with THI changes in tinnitus patients. Receiver operating characteristic curve analyses revealed that the measurements from the five brain regions were effective at classifying improvement after therapy. After age, gender, and education correction, the adjusted area under the curve (AUC) values for the bilateral thalami were the highest (left, 0.745; right, 0.708). Our study further supported the involvement of the fronto-parietal-cingulate network in tinnitus and found that the connectivity of the thalamus at baseline is an object neuroimaging-based indicator to predict clinical outcome of sound therapy through adjusted narrow band noise.
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Affiliation(s)
- Lv Han
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zeng Na
- National Clinical Research Center for Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Liu Chunli
- Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China.,Department of Otolaryngology, Affiliated Hospital of Chengde Medical College, Chengde, China
| | - Chen Yuchen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhao Pengfei
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wang Hao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Cheng Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhang Peng
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wang Zheng
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yang Zhenghan
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Gong Shusheng
- Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wang Zhenchang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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Sanfratello L, Houck J, Calhoun V. Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia. Schizophr Res 2019; 209:129-134. [PMID: 31130399 PMCID: PMC6661190 DOI: 10.1016/j.schres.2019.05.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 02/22/2019] [Accepted: 05/05/2019] [Indexed: 01/14/2023]
Abstract
An investigation of differences in dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) was completed, using eyes-open resting state MEG data. The MEG analysis utilized a source-space activity estimate (MNE/dSPM) whose result was the input to a group spatial independent component analysis (ICA), on which the networks of our MEG dFNC analysis were based. We have previously reported that our MEG dFNC revealed that SP change between brain meta-states (repeating patterns of network correlations which are allowed to overlap in time) significantly more often and to states which are more different, relative to HC. Here, we extend our previous work to investigate the relationship between symptomology in SP and four meta-state metrics. We found a significant correlation between positive symptoms and the two meta-state metrics which showed significant differences between HC and SP. These two statistics quantified 1) how often individuals change state and 2) the total distance traveled within the state-space. We additionally found that a clustering of the meta-state metrics divides SP into groups which vary in symptomology. These results indicate specific relationships between symptomology and brain function for SP.
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Affiliation(s)
| | - J.M. Houck
- The Mind Research Network,The University of New Mexico
| | - V.D. Calhoun
- The Mind Research Network,The University of New Mexico
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24
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Xia M, Womer FY, Chang M, Zhu Y, Zhou Q, Edmiston EK, Jiang X, Wei S, Duan J, Xu K, Tang Y, He Y, Wang F. Shared and Distinct Functional Architectures of Brain Networks Across Psychiatric Disorders. Schizophr Bull 2019; 45:450-463. [PMID: 29897593 PMCID: PMC6403059 DOI: 10.1093/schbul/sby046] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Brain network alterations have increasingly been implicated in schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). However, little is known about the similarities and differences in functional brain networks among patients with SCZ, BD, and MDD. A total of 512 participants (121 with SCZ, 100 with BD, 108 with MDD, and 183 healthy controls, matched for age and sex) completed resting-state functional magnetic resonance imaging at a single site. Four global measures (the clustering coefficient, the characteristic shortest path length, the normalized clustering coefficient, and the normalized characteristic path length) were computed at a voxel level to quantify segregated and integrated configurations. Inter-regional functional associations were examined based on the Euclidean distance between regions. Distance strength maps were used to localize regions with altered distances based on functional connectivity. Patient groups exhibited shifts in their network architectures toward randomized configurations, with SCZ>BD>MDD in the degree of randomization. Patient groups displayed significantly decreased short-range connectivity and increased medium-/long-range connectivity. Decreases in short-range connectivity were similar across the SZ, BD, and MDD groups and were primarily distributed in the primary sensory and association cortices and the thalamus. Increases in medium-/long-range connectivity were differentially localized within the prefrontal cortices among the patient groups. We highlight shared and distinct connectivity features in functional brain networks among patients with SCZ, BD, and MDD, which expands our understanding of the common and distinct pathophysiological mechanisms and provides crucial insights into neuroimaging-based methods for the early diagnosis of and interventions for psychiatric disorders.
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Affiliation(s)
- Mingrui Xia
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, PR China,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, PR China,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, PR China
| | - Fay Y Womer
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Miao Chang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Yue Zhu
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Qian Zhou
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Elliot Kale Edmiston
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Xiaowei Jiang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Shengnan Wei
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Jia Duan
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Ke Xu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Yanqing Tang
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, PR China,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, PR China,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, PR China
| | - Fei Wang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, PR China,To whom correspondence should be addressed; Department of Psychiatry and Radiology, The First Affiliated Hospital, China Medical University, 155 Nanjing North Street, Shenyang 110001, Liaoning, PR China; tel/fax: 8624-83283405, e-mail:
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Aine CJ, Bockholt HJ, Bustillo JR, Cañive JM, Caprihan A, Gasparovic C, Hanlon FM, Houck JM, Jung RE, Lauriello J, Liu J, Mayer AR, Perrone-Bizzozero NI, Posse S, Stephen JM, Turner JA, Clark VP, Calhoun VD. Multimodal Neuroimaging in Schizophrenia: Description and Dissemination. Neuroinformatics 2017; 15:343-64. [PMID: 28812221 DOI: 10.1007/s12021-017-9338-9] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network's collaborative informatics and neuroimaging suite (COINS).
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26
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Proudfoot M, Colclough GL, Quinn A, Wuu J, Talbot K, Benatar M, Nobre AC, Woolrich MW, Turner MR. Increased cerebral functional connectivity in ALS: A resting-state magnetoencephalography study. Neurology 2018; 90:e1418-e1424. [PMID: 29661904 PMCID: PMC5902786 DOI: 10.1212/wnl.0000000000005333] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 01/11/2018] [Indexed: 12/11/2022] Open
Abstract
Objective We sought to assess cortical function in amyotrophic lateral sclerosis (ALS) using noninvasive neural signal recording. Methods Resting-state magnetoencephalography was used to measure power fluctuations in neuronal oscillations from distributed cortical parcels in 24 patients with ALS and 24 healthy controls. A further 9 patients with primary lateral sclerosis and a group of 15 asymptomatic carriers of genetic mutations associated with ALS were also studied. Results Increased functional connectivity, particularly from the posterior cingulate cortex, was demonstrated in both patient groups compared to healthy controls. Directionally similar patterns were also evident in the asymptomatic genetic mutation carrier group. Conclusion Increased cortical functional connectivity elevation is a quantitative marker that reflects ALS pathology across its clinical spectrum, and may develop during the presymptomatic period. The amelioration of pathologic magnetoencephalography signals might be a marker sensitive enough to provide proof-of-principle in the development of future neuroprotective therapeutics.
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Affiliation(s)
- Malcolm Proudfoot
- From the Nuffield Department of Clinical Neurosciences (M.P., K.T., M.R.T.), and Oxford Centre for Human Brain Activity (M.P., G.L.C., A.Q., A.C.N., M.W.W., M.R.T.), University of Oxford, UK; and Miller School of Medicine (J.W., M.B.), University of Miami, FL
| | - Giles L Colclough
- From the Nuffield Department of Clinical Neurosciences (M.P., K.T., M.R.T.), and Oxford Centre for Human Brain Activity (M.P., G.L.C., A.Q., A.C.N., M.W.W., M.R.T.), University of Oxford, UK; and Miller School of Medicine (J.W., M.B.), University of Miami, FL
| | - Andrew Quinn
- From the Nuffield Department of Clinical Neurosciences (M.P., K.T., M.R.T.), and Oxford Centre for Human Brain Activity (M.P., G.L.C., A.Q., A.C.N., M.W.W., M.R.T.), University of Oxford, UK; and Miller School of Medicine (J.W., M.B.), University of Miami, FL
| | - Joanne Wuu
- From the Nuffield Department of Clinical Neurosciences (M.P., K.T., M.R.T.), and Oxford Centre for Human Brain Activity (M.P., G.L.C., A.Q., A.C.N., M.W.W., M.R.T.), University of Oxford, UK; and Miller School of Medicine (J.W., M.B.), University of Miami, FL
| | - Kevin Talbot
- From the Nuffield Department of Clinical Neurosciences (M.P., K.T., M.R.T.), and Oxford Centre for Human Brain Activity (M.P., G.L.C., A.Q., A.C.N., M.W.W., M.R.T.), University of Oxford, UK; and Miller School of Medicine (J.W., M.B.), University of Miami, FL
| | - Michael Benatar
- From the Nuffield Department of Clinical Neurosciences (M.P., K.T., M.R.T.), and Oxford Centre for Human Brain Activity (M.P., G.L.C., A.Q., A.C.N., M.W.W., M.R.T.), University of Oxford, UK; and Miller School of Medicine (J.W., M.B.), University of Miami, FL
| | - Anna C Nobre
- From the Nuffield Department of Clinical Neurosciences (M.P., K.T., M.R.T.), and Oxford Centre for Human Brain Activity (M.P., G.L.C., A.Q., A.C.N., M.W.W., M.R.T.), University of Oxford, UK; and Miller School of Medicine (J.W., M.B.), University of Miami, FL
| | - Mark W Woolrich
- From the Nuffield Department of Clinical Neurosciences (M.P., K.T., M.R.T.), and Oxford Centre for Human Brain Activity (M.P., G.L.C., A.Q., A.C.N., M.W.W., M.R.T.), University of Oxford, UK; and Miller School of Medicine (J.W., M.B.), University of Miami, FL.
| | - Martin R Turner
- From the Nuffield Department of Clinical Neurosciences (M.P., K.T., M.R.T.), and Oxford Centre for Human Brain Activity (M.P., G.L.C., A.Q., A.C.N., M.W.W., M.R.T.), University of Oxford, UK; and Miller School of Medicine (J.W., M.B.), University of Miami, FL.
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Alamian G, Hincapié AS, Pascarella A, Thiery T, Combrisson E, Saive AL, Martel V, Althukov D, Haesebaert F, Jerbi K. Measuring alterations in oscillatory brain networks in schizophrenia with resting-state MEG: State-of-the-art and methodological challenges. Clin Neurophysiol 2017; 128:1719-1736. [DOI: 10.1016/j.clinph.2017.06.246] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 05/08/2017] [Accepted: 06/19/2017] [Indexed: 02/06/2023]
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28
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Hsiao FJ, Wang SJ, Lin YY, Fuh JL, Ko YC, Wang PN, Chen WT. Altered insula-default mode network connectivity in fibromyalgia: a resting-state magnetoencephalographic study. J Headache Pain 2017; 18:89. [PMID: 28831711 PMCID: PMC5567574 DOI: 10.1186/s10194-017-0799-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 08/15/2017] [Indexed: 11/11/2022] Open
Abstract
Background Fibromyalgia (FM) is a disabling chronic pain syndrome with unknown pathophysiology. Functional magnetic resonance imaging studies on FM have suggested altered brain connectivity between the insula and the default mode network (DMN). However, this connectivity change has not been characterized through direct neural signals for exploring the embedded spectrotemporal features and the pertinent clinical relevance. Methods We recorded the resting-state magnetoencephalographic activities of 28 patients with FM and 28 age- and sex-matched controls, and analyzed the source-based functional connectivity between the insula and the DMN at 1–40 Hz by using the minimum norm estimates and imaginary coherence methods. We also measured the connectivity between the DMN and the primary visual (V1) and somatosensory (S1) cortices as intrapatient negative controls. Connectivity measurement was further correlated with the clinical parameters of FM. Results Compared with the controls, patients with FM reported more tender points (15.2±2.0 vs. 5.9±3.7) and higher total tenderness score (TTS; 29.1±7.0 vs. 7.7±5.5; both p < 0.001); they also had decreased insula–DMN connectivity at the theta band (4–8 Hz; left, p = 0.007; right, p = 0.035), but displayed unchanged V1–DMN and S1–DMN connectivity (p > 0.05). When patients with FM and the controls were combined together, the insula-DMN theta connectivity was negatively correlated with the number of tender points (left insula, r = −0.428, p = 0.001; right insula, r = −0.4, p = 0.002) and TTS score (left insula, r = −0.429, p = 0.001; right insula, r = −0.389, p = 0.003). Furthermore, in patients with FM, the right insula–DMN connectivity at the beta band (13–25 Hz) was negatively correlated with the number of tender points (r = −0.532, p = 0.004) and TTS (r = −0.428, p = 0.023), and the bilateral insula–DMN connectivity at the delta band (1–4 Hz) was negatively correlated with FM Symptom Severity (left: r = −0.423, p = 0.025; right: r = −0.437, p = 0.020) and functional disability (Fibromyalgia Impact Questionnaire; left: r = −0.415, p = 0.028; right: r = −0.374, p = 0.050). Conclusions We confirmed the frequency-specific reorganization of the insula–DMN connectivity in FM. The clinical relevance of this connectivity change may warrant future studies to elucidate its causal relationship and potential as a neurological signature for FM. Electronic supplementary material The online version of this article (doi:10.1186/s10194-017-0799-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Fu-Jung Hsiao
- Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Shuu-Jiun Wang
- Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Neurological Institute, Taipei Veterans General Hospital, No. 201, Sec. 2 Shih-Pai Rd, Taipei, Taiwan
| | - Yung-Yang Lin
- Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Neurological Institute, Taipei Veterans General Hospital, No. 201, Sec. 2 Shih-Pai Rd, Taipei, Taiwan
| | - Jong-Ling Fuh
- Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Neurological Institute, Taipei Veterans General Hospital, No. 201, Sec. 2 Shih-Pai Rd, Taipei, Taiwan
| | - Yu-Chieh Ko
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Pei-Ning Wang
- Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Neurological Institute, Taipei Veterans General Hospital, No. 201, Sec. 2 Shih-Pai Rd, Taipei, Taiwan
| | - Wei-Ta Chen
- Brain Research Center, National Yang-Ming University, Taipei, Taiwan. .,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan. .,School of Medicine, National Yang-Ming University, Taipei, Taiwan. .,Neurological Institute, Taipei Veterans General Hospital, No. 201, Sec. 2 Shih-Pai Rd, Taipei, Taiwan.
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Cetin MS, Houck JM, Rashid B, Agacoglu O, Stephen JM, Sui J, Canive J, Mayer A, Aine C, Bustillo JR, Calhoun VD. Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures. Front Neurosci 2016; 10:466. [PMID: 27807403 PMCID: PMC5070283 DOI: 10.3389/fnins.2016.00466] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2016] [Accepted: 09/28/2016] [Indexed: 11/13/2022] Open
Abstract
Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of "synchronicity" of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone.
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Affiliation(s)
- Mustafa S. Cetin
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Jon M. Houck
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
- Psychology Department, University of New MexicoAlbuquerque, NM, USA
| | - Barnaly Rashid
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA
| | - Oktay Agacoglu
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA
| | - Julia M. Stephen
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Jing Sui
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
| | - Jose Canive
- Psychiatry Department, University of New Mexico School of MedicineAlbuquerque, NM, USA
- Psychiatry Research Program, New Mexico VA Health Care SystemAlbuquerque, NM, USA
- Department of Neurosciences, University of New Mexico School of MedicineAlbuquerque, NM, USA
| | - Andy Mayer
- Psychology Department, University of New MexicoAlbuquerque, NM, USA
- Psychiatry Department, University of New Mexico School of MedicineAlbuquerque, NM, USA
- Neurology Department, University of New Mexico School of MedicineAlbuquerque, NM, USA
| | - Cheryl Aine
- Department of Radiology, University of New Mexico School of MedicineAlbuquerque, NM, USA
| | - Juan R. Bustillo
- Psychiatry Department, University of New Mexico School of MedicineAlbuquerque, NM, USA
- Department of Neurosciences, University of New Mexico School of MedicineAlbuquerque, NM, USA
| | - Vince D. Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA
- Psychiatry Department, University of New Mexico School of MedicineAlbuquerque, NM, USA
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Cetin MS, Houck JM, Vergara VM, Miller RL, Calhoun V. Multimodal based classification of schizophrenia patients. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2015:2629-32. [PMID: 26736831 PMCID: PMC4880008 DOI: 10.1109/embc.2015.7318931] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Schizophrenia is currently diagnosed by physicians through clinical assessment and their evaluation of patient's self-reported experiences over the longitudinal course of the illness. There is great interest in identifying biologically based markers at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity shows promise in providing individual subject predictive power. The majority of previous studies considered the analysis of functional connectivity during resting-state using only fMRI. However, exclusive reliance on fMRI to generate such networks, may limit inference on dysfunctional connectivity, which is hypothesized to underlie patient symptoms. In this work, we propose a framework for classification of schizophrenia patients and healthy control subjects based on using both fMRI and band limited envelope correlation metrics in MEG to interrogate functional network components in the resting state. Our results show that the combination of these two methods provide valuable information that captures fundamental characteristics of brain network connectivity in schizophrenia. Such information is useful for prediction of schizophrenia patients. Classification accuracy performance was improved significantly (up to ≈ 7%) relative to only the fMRI method and (up to ≈ 21%) relative to only the MEG method.
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