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Lim S, Dong SY, Tran BX, Boyer L, Fond G, Rahmati M, Duy NC, Latkin C, Do H, Ho R. Network-based alterations in task-induced functional connectivity in bipolar disorder: An functional near-infrared spectroscopy study. J Affect Disord 2025; 389:119637. [PMID: 40499819 DOI: 10.1016/j.jad.2025.119637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2025] [Revised: 05/21/2025] [Accepted: 06/07/2025] [Indexed: 06/19/2025]
Abstract
BACKGROUND Functional near-infrared spectroscopy (fNIRS) is increasingly used to examine cognitive abilities and support the diagnosis of bipolar disorder (BD). Despite its growing application, research on functional connectivity in BD remains limited, and task-induced functional connectivity remains under-investigated. This study aims to differentiate patients with BD from healthy controls (HCs) based on fNIRS-derived functional connectivity. METHODS We analyzed fNIRS data from 50 patients with BD and 50 HCs during resting state and two cognitive tasks, including the verbal fluency test (VFT) and Stroop color-word test (Stroop). Functional connectivity was quantified using coherence and correlation evaluations, and four network characteristics, such as local efficiency, global efficiency, global clustering coefficient, and average closeness centrality, were extracted. A thresholding method was applied by filtering the top 10 % to 50 % to highlight significant network connections. Group differences were evaluated using t-tests with false discovery rate. FINDINGS Patients with BD showed significantly altered coherence values, especially between channels 4 and 10 (p < 0.001, t = -5.534, η2 = 0.562) and lower global clustering coefficients (p < 0.001, t = -5.666, η2 = 0.578) compared to HCs. The k-nearest neighbor (KNN) classifier using coherence-based network metrics during the VFT achieved the highest classification performance with an accuracy of 0.818, precision of 0.819, recall of 0.824, and an F1-score of 0.817. CONCLUSION Our findings suggest that task-induced functional connectivity, particularly coherence-based metrics derived during the VFT, reflects distinct functional connectivity patterns that differentiate patients with BD from HCs. These findings support the utility of network-based approaches under cognitive task conditions in characterizing functional connectivity alterations associated with BD.
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Affiliation(s)
- Seongyeon Lim
- Department of Information Technology Engineering, Sookmyung Women's University, Seoul 04310, Republic of Korea
| | - Suh-Yeon Dong
- Division of Artificial Intelligence Engineering, Sookmyung Women's University, Seoul 04310, Republic of Korea.
| | - Bach Xuan Tran
- Faculty of Public Health, VNU University of Medicine and Pharmacy, Vietnam National University, Hanoi 100000, Viet Nam; INSTAR International Institute, VNU University of Medicine and Pharmacy, Vietnam National University, Hanoi 100000, Viet Nam
| | - Laurent Boyer
- CEReSS - Health Service Research and Quality of Life Center, Aix-Marseille University, 13385 Marseille Cedex 05, France
| | - Guillaume Fond
- CEReSS - Health Service Research and Quality of Life Center, Aix-Marseille University, 13385 Marseille Cedex 05, France
| | - Masoud Rahmati
- CEReSS - Health Service Research and Quality of Life Center, Aix-Marseille University, 13385 Marseille Cedex 05, France
| | - Nguyen Cao Duy
- Institute for Global Health Innovation, Duy Tan University, Da Nang 550000, Viet Nam
| | - Carl Latkin
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Huyen Do
- Dong Nai Technology University, Viet Nam
| | - Roger Ho
- Division of Life Science (LIFS), Hong Kong University of Science and Technology (HKUST), Clear Water Bay, Hong Kong; Division of Functional Near Infrared Spectroscopy, Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 119077, Singapore
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2
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Alyan E, Arnau S, Getzmann S, Reiser JE, Karthaus M, Wascher E. Age-related differences in eye blink-related neural activity and functional connectivity during driving. Heliyon 2025; 11:e41164. [PMID: 39758399 PMCID: PMC11699334 DOI: 10.1016/j.heliyon.2024.e41164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 12/11/2024] [Accepted: 12/11/2024] [Indexed: 01/07/2025] Open
Abstract
Driving is a complex task that requires effective neural processing and coordination, which degrade with aging. Previous studies suggest that age-related changes in cognitive and motor functions can influence driving performance. Herein, we investigated age-related differences and differences between reactive and proactive driving in blink behavior-related potentials, and source-level functional connectivity. Seventy-six subjects participated in two experiments with reactive (19 young, 28 older) and proactive (16 young, 13 older) driving scenarios, consisting of a lane-keeping task with either varying levels of crosswind or curve road, respectively. While blink rate analysis revealed no significant age or driving condition effects, blink duration was notably longer in younger participants. Also, significant age effects were observed in blink-related potentials, mainly in the frontal N2 and occipital P0 and P2 components, with higher amplitudes in younger participants, signifying more efficient neural processing. The parietal N2 component showed significant age and interaction effects, with older individuals showing higher amplitudes in reactive conditions, potentially due to increased cognitive effort and attentional demands. Furthermore, functional connectivity analysis revealed that aging most significantly affects the visual network in the beta band. More specifically, younger participants showed an increase in the clustering coefficient and degrees of the networks, reflecting more robust neural network integration. This pattern of higher connectivity measures in younger participants was also observed in the default mode, control, and limbic networks. Conversely, the dorsal attention network in the theta band showed an increased degree and clustering coefficient in older adults, which could indicate a compensatory mechanism for maintaining cognitive demands. This study highlights the impact of aging on neural activity and connectivity characteristics during driving and emphasizes the requirement of age-tailored interventions, aimed to improve driving safety.
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Affiliation(s)
- Emad Alyan
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, 44139, Dortmund, Germany
| | - Stefan Arnau
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, 44139, Dortmund, Germany
| | - Stephan Getzmann
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, 44139, Dortmund, Germany
| | - Julian Elias Reiser
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, 44139, Dortmund, Germany
| | - Melanie Karthaus
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, 44139, Dortmund, Germany
| | - Edmund Wascher
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, 44139, Dortmund, Germany
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3
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Siddiqui A, Abu Hasan R, Saad Azhar Ali S, Elamvazuthi I, Lu CK, Tang TB. Detection of Low Resilience Using Data-Driven Effective Connectivity Measures. IEEE Trans Neural Syst Rehabil Eng 2024; 32:3657-3668. [PMID: 39302782 DOI: 10.1109/tnsre.2024.3465269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
Conventional thresholding techniques for graph theory analysis, such as absolute, proportional and mean degree, have often been used in characterizing human brain networks under different mental disorders, such as mental stress. However, these approaches may not always be reliable as conventional thresholding approaches are subjected to human biases. Using a mental resilience study, we investigate if data-driven thresholding techniques such as Global Cost Efficiency (GCE-abs) and Orthogonal Minimum Spanning Trees (OMSTs) could provide equivalent results, whilst eliminating human biases. We implemented Phase Slope Index (PSI) to compute effective brain connectivity, and applied data-driven thresholding approaches to filter the brain networks in order to identify key features of low resilience within a cohort of healthy individuals. Our dataset encompassed resting-state EEG recordings gathered from a total of 36 participants (31 females and 5 males). Relevant features were extracted to train and validate a classifier model (Support Vector Machine, SVM). The detection of low stress resilience among healthy individuals using the SVM model scores an accuracy of 80.6% with GCE-abs, and 75% with OMSTs, respectively.
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4
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Kebaya LMN, Tang L, Altamimi T, Kowalczyk A, Musabi M, Roychaudhuri S, Vahidi H, Meyerink P, de Ribaupierre S, Bhattacharya S, de Moraes LTAR, Lawrence KS, Duerden EG. Altered functional connectivity in preterm neonates with intraventricular hemorrhage assessed using functional near-infrared spectroscopy. Sci Rep 2024; 14:22300. [PMID: 39333278 PMCID: PMC11437059 DOI: 10.1038/s41598-024-72515-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Accepted: 09/09/2024] [Indexed: 09/29/2024] Open
Abstract
Intraventricular hemorrhage (IVH) is a common neurological injury following very preterm birth. Resting-state functional connectivity (RSFC) using functional magnetic resonance imaging (fMRI) is associated with injury severity; yet, fMRI is impractical for use in intensive care settings. Functional near-infrared spectroscopy (fNIRS) measures RSFC through cerebral hemodynamics and has greater bedside accessibility than fMRI. We evaluated RSFC in preterm neonates with IVH using fNIRS and fMRI at term-equivalent age, and compared fNIRS connectivity between healthy newborns and those with IVH. Sixteen very preterm born neonates were scanned with fMRI and fNIRS. Additionally, fifteen healthy newborns were scanned with fNIRS. In preterms with IVH, fNIRS and fMRI connectivity maps were compared using Euclidean and Jaccard distances. The severity of IVH in relation to fNIRS-RSFC strength was examined using generalized linear models. fNIRS and fMRI RSFC maps showed good correspondence. Connectivity strength was significantly lower in healthy newborns (p-value = 0.023) and preterm infants with mild IVH (p-value = 0.026) compared to infants with moderate/severe IVH. fNIRS has potential to be a new bedside tool for assessing brain injury and monitoring cerebral hemodynamics, as well as a promising biomarker for IVH severity in very preterm born infants.
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Affiliation(s)
- Lilian M N Kebaya
- Neonatal-Perinatal Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Paediatrics, Division of Neonatal-Perinatal Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Lingkai Tang
- Biomedical Engineering, Faculty of Engineering, Western University, London, ON, Canada
| | - Talal Altamimi
- Neonatal-Perinatal Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Alexandra Kowalczyk
- Neonatal-Perinatal Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Melab Musabi
- Neonatal-Perinatal Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Sriya Roychaudhuri
- Neonatal-Perinatal Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Homa Vahidi
- Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Paige Meyerink
- Neonatal-Perinatal Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Sandrine de Ribaupierre
- Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Biomedical Engineering, Faculty of Engineering, Western University, London, ON, Canada
- Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Soume Bhattacharya
- Neonatal-Perinatal Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | | | - Keith St Lawrence
- Biomedical Engineering, Faculty of Engineering, Western University, London, ON, Canada
- Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Emma G Duerden
- Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
- Biomedical Engineering, Faculty of Engineering, Western University, London, ON, Canada.
- Applied Psychology, Faculty of Education, Western University, 1137 Western Road, London, ON, N6G 1G7, Canada.
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5
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Duong-Tran D, Nguyen N, Mu S, Chen J, Bao J, Xu F, Garai S, Cadena-Pico J, Kaplan AD, Chen T, Zhao Y, Shen L, Goñi J. A principled framework to assess the information-theoretic fitness of brain functional sub-circuits. ARXIV 2024:arXiv:2406.18531v2. [PMID: 38979488 PMCID: PMC11230349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations.
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Affiliation(s)
- Duy Duong-Tran
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Nghi Nguyen
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Shizhuo Mu
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Frederick Xu
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sumita Garai
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jose Cadena-Pico
- Machine Learning Group, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Alan David Kaplan
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Tianlong Chen
- Department of Computer Science, The University of North Carolina at Chapel Hill
| | - Yize Zhao
- School of Public Health, Yale University, New Heaven, CT, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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6
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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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7
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Guichet C, Banjac S, Achard S, Mermillod M, Baciu M. Modeling the neurocognitive dynamics of language across the lifespan. Hum Brain Mapp 2024; 45:e26650. [PMID: 38553863 PMCID: PMC10980845 DOI: 10.1002/hbm.26650] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that default mode network (DMN) suppression coupled with fronto-parietal network (FPN) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel synergistic, economical, nonlinear, emergent, cognitive aging model, integrating connectomic and cognitive dimensions within a complex system perspective.
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Affiliation(s)
| | - Sonja Banjac
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
| | - Sophie Achard
- LJK, UMR CNRS 5224, Université Grenoble AlpesGrenobleFrance
| | | | - Monica Baciu
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
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8
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Metzen D, Stammen C, Fraenz C, Schlüter C, Johnson W, Güntürkün O, DeYoung CG, Genç E. Investigating robust associations between functional connectivity based on graph theory and general intelligence. Sci Rep 2024; 14:1368. [PMID: 38228689 DOI: 10.1038/s41598-024-51333-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/29/2023] [Indexed: 01/18/2024] Open
Abstract
Previous research investigating relations between general intelligence and graph-theoretical properties of the brain's intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.
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Affiliation(s)
- Dorothea Metzen
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801, Bochum, Germany.
- Institute of Psychology, Department of Educational Sciences and Psychology, TU Dortmund University, 44227, Dortmund, Germany.
| | - Christina Stammen
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139, Dortmund, Germany
| | - Christoph Fraenz
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139, Dortmund, Germany
| | - Caroline Schlüter
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801, Bochum, Germany
| | - Wendy Johnson
- Department of Psychology, University of Edinburgh, EH8 9JZ, Edinburgh, UK
| | - Onur Güntürkün
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801, Bochum, Germany
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota, 55455, Minneapolis, MN, USA
| | - Erhan Genç
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139, Dortmund, Germany
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9
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Dimitriadis SI, Routley B, Linden DEJ, Singh KD. Multiplexity of human brain oscillations as a personal brain signature. Hum Brain Mapp 2023; 44:5624-5640. [PMID: 37668332 PMCID: PMC10619372 DOI: 10.1002/hbm.26466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/11/2023] [Accepted: 08/08/2023] [Indexed: 09/06/2023] Open
Abstract
Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.
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Affiliation(s)
- Stavros I. Dimitriadis
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of MedicineCardiff UniversityCardiffWalesUK
- Department of Clinical Psychology and PsychobiologyUniversity of BarcelonaBarcelonaSpain
| | - B. Routley
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
| | - David E. J. Linden
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of MedicineCardiff UniversityCardiffWalesUK
- School for Mental Health and Neuroscience, Faculty of Health Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Krish D. Singh
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffWalesUK
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10
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Williamson BJ, Greiner HM, Kadis DS. Virtual lesions in MEG reveal increasing vulnerability of the language network from early childhood through adolescence. Nat Commun 2023; 14:7313. [PMID: 37951971 PMCID: PMC10640569 DOI: 10.1038/s41467-023-43165-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 11/02/2023] [Indexed: 11/14/2023] Open
Abstract
In childhood, language outcomes following brain injury are inversely related to age. Neuroimaging findings suggest that extensive representation and/or topological redundancy may confer the pediatric advantage. Here, we assess whole brain and language network resilience using in silico attacks, for 85 children participating in a magnetoencephalography (MEG) study. Nodes are targeted based on eigenvector centrality, betweenness centrality, or at random. The size of each connected component is assessed after iterated node removal; the percolation point, or moment of dis-integration, is defined as the first instance where the second largest component peaks in size. To overcome known effects of fixed thresholding on subsequent graph and resilience analyses, we study percolation across all possible network densities, within a Functional Data Analysis (FDA) framework. We observe age-related increases in vulnerability for random and betweenness centrality-based attacks for whole-brain and stories networks (adjusted-p < 0.05). Here we show that changes in topology underlie increasing language network vulnerability in development.
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Affiliation(s)
| | - Hansel M Greiner
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Darren S Kadis
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
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Wang G, Zeng M, Li J, Liu Y, Wei D, Long Z, Chen H, Zang X, Yang J. Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality. Neuroscience 2023; 530:66-78. [PMID: 37619767 DOI: 10.1016/j.neuroscience.2023.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 08/01/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023]
Abstract
INTRODUCTION Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear. METHODS We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset. RESULTS SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control. CONCLUSION Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.
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Affiliation(s)
- Guangtong Wang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Mei Zeng
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Jiwen Li
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Yadong Liu
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Dongtao Wei
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Zhiliang Long
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Haopeng Chen
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Xinlei Zang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Juan Yang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China.
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Li Y, Li C, Jiang L. Well-being is associated with cortical thickness network topology of human brain. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2023; 19:16. [PMID: 37749598 PMCID: PMC10521404 DOI: 10.1186/s12993-023-00219-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 09/18/2023] [Indexed: 09/27/2023]
Abstract
BACKGROUND Living a happy and meaningful life is an eternal topic in positive psychology, which is crucial for individuals' physical and mental health as well as social functioning. Well-being can be subdivided into pleasure attainment related hedonic well-being or emotional well-being, and self-actualization related eudaimonic well-being or psychological well-being plus social well-being. Previous studies have mostly focused on human brain morphological and functional mechanisms underlying different dimensions of well-being, but no study explored brain network mechanisms of well-being, especially in terms of topological properties of human brain morphological similarity network. METHODS Therefore, in the study, we collected 65 datasets including magnetic resonance imaging (MRI) and well-being data, and constructed human brain morphological network based on morphological distribution similarity of cortical thickness to explore the correlations between topological properties including network efficiency and centrality and different dimensions of well-being. RESULTS We found emotional well-being was negatively correlated with betweenness centrality in the visual network but positively correlated with eigenvector centrality in the precentral sulcus, while the total score of well-being was positively correlated with local efficiency in the posterior cingulate cortex of cortical thickness network. CONCLUSIONS Our findings demonstrated that different dimensions of well-being corresponded to different cortical hierarchies: hedonic well-being was involved in more preliminary cognitive processing stages including perceptual and attentional information processing, while hedonic and eudaimonic well-being might share common morphological similarity network mechanisms in the subsequent advanced cognitive processing stages.
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Affiliation(s)
- Yubin Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, No. 16 Lincui Road, Chaoyang District, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Shijingshan, Beijing, China
| | - Chunlin Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, No. 16 Lincui Road, Chaoyang District, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Shijingshan, Beijing, China
| | - Lili Jiang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, No. 16 Lincui Road, Chaoyang District, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Shijingshan, Beijing, China.
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Sassenberg TA, Burton PC, Mwilambwe-Tshilobo L, Jung RE, Rustichini A, Spreng RN, DeYoung CG. Conscientiousness associated with efficiency of the salience/ventral attention network: Replication in three samples using individualized parcellation. Neuroimage 2023; 272:120081. [PMID: 37011715 PMCID: PMC10132286 DOI: 10.1016/j.neuroimage.2023.120081] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 03/21/2023] [Accepted: 03/31/2023] [Indexed: 04/03/2023] Open
Abstract
Conscientiousness, and related constructs impulsivity and self-control, have been related to structural and functional properties of regions in the prefrontal cortex (PFC) and anterior insula. Network-based conceptions of brain function suggest that these regions belong to a single large-scale network, labeled the salience/ventral attention network (SVAN). The current study tested associations between conscientiousness and resting-state functional connectivity in this network using two community samples (N's = 244 and 239) and data from the Human Connectome Project (N = 1000). Individualized parcellation was used to improve functional localization accuracy and facilitate replication. Functional connectivity was measured using an index of network efficiency, a graph theoretical measure quantifying the capacity for parallel information transfer within a network. Efficiency of a set of parcels in the SVAN was significantly associated with conscientiousness in all samples. Findings are consistent with a theory of conscientiousness as a function of variation in neural networks underlying effective prioritization of goals.
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Affiliation(s)
- Tyler A Sassenberg
- Department of Psychology, University of Minnesota, N616 Elliott Hall, 75 East River Parkway, Minneapolis, MN 55455, USA.
| | - Philip C Burton
- Department of Psychology, University of Minnesota, N616 Elliott Hall, 75 East River Parkway, Minneapolis, MN 55455, USA
| | - Laetitia Mwilambwe-Tshilobo
- Department of Psychology, Princeton University, USA; University of Pennsylvania, Annenberg School for Communication, USA
| | - Rex E Jung
- Department of Neurosurgery, University of New Mexico, USA
| | | | - R Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Canada
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota, N616 Elliott Hall, 75 East River Parkway, Minneapolis, MN 55455, USA
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14
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Martin S, Williams KA, Saur D, Hartwigsen G. Age-related reorganization of functional network architecture in semantic cognition. Cereb Cortex 2023; 33:4886-4903. [PMID: 36190445 PMCID: PMC10110455 DOI: 10.1093/cercor/bhac387] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/02/2022] [Accepted: 09/03/2022] [Indexed: 11/15/2022] Open
Abstract
Cognitive aging is associated with widespread neural reorganization processes in the human brain. However, the behavioral impact of such reorganization is not well understood. The current neuroimaging study investigated age differences in the functional network architecture during semantic word retrieval in young and older adults. Combining task-based functional connectivity, graph theory and cognitive measures of fluid and crystallized intelligence, our findings show age-accompanied large-scale network reorganization even when older adults have intact word retrieval abilities. In particular, functional networks of older adults were characterized by reduced decoupling between systems, reduced segregation and efficiency, and a larger number of hub regions relative to young adults. Exploring the predictive utility of these age-related changes in network topology revealed high, albeit less efficient, performance for older adults whose brain graphs showed stronger dedifferentiation and reduced distinctiveness. Our results extend theoretical accounts on neurocognitive aging by revealing the compensational potential of the commonly reported pattern of network dedifferentiation when older adults can rely on their prior knowledge for successful task processing. However, we also demonstrate the limitations of such compensatory reorganization and show that a youth-like network architecture in terms of balanced integration and segregation is associated with more economical processing.
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Affiliation(s)
- Sandra Martin
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
- Language & Aphasia Laboratory, Department of Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Kathleen A Williams
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Dorothee Saur
- Language & Aphasia Laboratory, Department of Neurology, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
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15
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Dimitriadis SI, Perry G, Lancaster TM, Tansey KE, Singh KD, Holmans P, Pocklington A, Davey Smith G, Zammit S, Hall J, O’Donovan MC, Owen MJ, Jones DK, Linden DE. Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk. Cereb Cortex 2023; 33:2997-3011. [PMID: 35830871 PMCID: PMC10016061 DOI: 10.1093/cercor/bhac256] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 02/02/2023] Open
Abstract
Research studies based on tractography have revealed a prominent reduction of asymmetry in some key white-matter tracts in schizophrenia (SCZ). However, we know little about the influence of common genetic risk factors for SCZ on the efficiency of routing on structural brain networks (SBNs). Here, we use a novel recall-by-genotype approach, where we sample young adults from a population-based cohort (ALSPAC:N genotyped = 8,365) based on their burden of common SCZ risk alleles as defined by polygenic risk score (PRS). We compared 181 individuals at extremes of low (N = 91) or high (N = 90) SCZ-PRS under a robust diffusion MRI-based graph theoretical SBN framework. We applied a semi-metric analysis revealing higher SMR values for the high SCZ-PRS group compared with the low SCZ-PRS group in the left hemisphere. Furthermore, a hemispheric asymmetry index showed a higher leftward preponderance of indirect connections for the high SCZ-PRS group compared with the low SCZ-PRS group (PFDR < 0.05). These findings might indicate less efficient structural connectivity in the higher genetic risk group. This is the first study in a population-based sample that reveals differences in the efficiency of SBNs associated with common genetic risk variants for SCZ.
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Affiliation(s)
- S I Dimitriadis
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Neuroinformatics Group, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - G Perry
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - T M Lancaster
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Department of Psychology, Bath University, Claverton Down BA2 7AY, Bath, Wales, UK
| | - K E Tansey
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Queens Road BS8 1QU, Bristol, Wales, UK
| | - K D Singh
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - P Holmans
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - A Pocklington
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - G Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Queens Road BS8 1QU, Bristol, Wales, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road BS8 1NU, Bristol, Wales, UK
| | - S Zammit
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road BS8 1NU, Bristol, Wales, UK
| | - J Hall
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - M C O’Donovan
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - M J Owen
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - D K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - D E Linden
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road BS8 1NU, Bristol, Wales, UK
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Universiteitssingel 40 UNS40 6229 ER, Maastricht, The Netherlands
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16
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Exarchos TP, Whelan R, Tarnanas I. Dynamic Reconfiguration of Dominant Intrinsic Coupling Modes in Elderly at Prodromal Alzheimer's Disease Risk. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1424:1-22. [PMID: 37486474 DOI: 10.1007/978-3-031-31982-2_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Large-scale human brain networks interact across both spatial and temporal scales. Especially for electro- and magnetoencephalography (EEG/MEG), there are many evidences that there is a synergy of different subnetworks that oscillate on a dominant frequency within a quasi-stable brain temporal frame. Intrinsic cortical-level integration reflects the reorganization of functional brain networks that support a compensation mechanism for cognitive decline. Here, a computerized intervention integrating different functions of the medial temporal lobes, namely, object-level and scene-level representations, was conducted. One hundred fifty-eight patients with mild cognitive impairment underwent 90 min of training per day over 10 weeks. An active control (AC) group of 50 subjects was exposed to documentaries, and a passive control group of 55 subjects did not engage in any activity. Following a dynamic functional source connectivity analysis, the dynamic reconfiguration of intra- and cross-frequency coupling mechanisms before and after the intervention was revealed. After the neuropsychological and resting state electroencephalography evaluation, the ratio of inter versus intra-frequency coupling modes and also the contribution of β1 frequency was higher for the target group compared to its pre-intervention period. These frequency-dependent contributions were linked to neuropsychological estimates that were improved due to intervention. Additionally, the time-delays of the cortical interactions were improved in {δ, θ, α2, β1} compared to the pre-intervention period. Finally, dynamic networks of the target group further improved their efficiency over the total cost of the network. This is the first study that revealed a dynamic reconfiguration of intrinsic coupling modes and an improvement of time-delays due to a target intervention protocol.
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Affiliation(s)
| | - Robert Whelan
- Trinity College Institute of Neurosciences, Trinity College, Dublin, Ireland
| | - Ioannis Tarnanas
- Altoida Inc, Houston, TX, USA
- Global Brain Health Institute, Trinity College, Dublin, Ireland
- University of California, San Francisco, CA, USA
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17
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Dimitriadis SI. Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths. Neuroinformatics 2023; 21:71-88. [PMID: 36372844 DOI: 10.1007/s12021-022-09610-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2022] [Indexed: 11/15/2022]
Abstract
There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.
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Affiliation(s)
- Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Institut de Neurociències, University of Barcelona, Campus Mundet, Edifici de PonentPasseig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Integrative Neuroimaging Lab, 55133, Thessaloniki, Greece.
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Wales, CF24 4HQ, Cardiff, UK.
- Neuroinformatics Group, School of Psychology, College of Biomedical and Life Sciences, Cardiff University Brain Research Imaging Centre (CUBRIC), CF24 4HQ, Cardiff, Wales, UK.
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, CF24 4HQ, Cardiff, Wales, UK.
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
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18
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Simos NJ, Manolitsi K, Luppi AI, Kagialis A, Antonakakis M, Zervakis M, Antypa D, Kavroulakis E, Maris TG, Vakis A, Stamatakis EA, Papadaki E. Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion. Neuroinformatics 2022; 21:427-442. [PMID: 36456762 PMCID: PMC10085953 DOI: 10.1007/s12021-022-09615-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/25/2022] [Accepted: 11/13/2022] [Indexed: 12/04/2022]
Abstract
AbstractTraumatic Brain Injury (TBI) is a frequently occurring condition and approximately 90% of TBI cases are classified as mild (mTBI). However, conventional MRI has limited diagnostic and prognostic value, thus warranting the utilization of additional imaging modalities and analysis procedures. The functional connectomic approach using resting-state functional MRI (rs-fMRI) has shown great potential and promising diagnostic capabilities across multiple clinical scenarios, including mTBI. Additionally, there is increasing recognition of a fundamental role of brain dynamics in healthy and pathological cognition. Here, we undertake an in-depth investigation of mTBI-related connectomic disturbances and their emotional and cognitive correlates. We leveraged machine learning and graph theory to combine static and dynamic functional connectivity (FC) with regional entropy values, achieving classification accuracy up to 75% (77, 74 and 76% precision, sensitivity and specificity, respectively). As compared to healthy controls, the mTBI group displayed hypoconnectivity in the temporal poles, which correlated positively with semantic (r = 0.43, p < 0.008) and phonemic verbal fluency (r = 0.46, p < 0.004), while hypoconnectivity in the right dorsal posterior cingulate correlated positively with depression symptom severity (r = 0.54, p < 0.0006). These results highlight the importance of residual FC in these regions for preserved cognitive and emotional function in mTBI. Conversely, hyperconnectivity was observed in the right precentral and supramarginal gyri, which correlated negatively with semantic verbal fluency (r=-0.47, p < 0.003), indicating a potential ineffective compensatory mechanism. These novel results are promising toward understanding the pathophysiology of mTBI and explaining some of its most lingering emotional and cognitive symptoms.
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Affiliation(s)
- Nicholas J. Simos
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece
| | - Katina Manolitsi
- Department of Neurosurgery, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
- Department of Psychiatry, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Andrea I. Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Rd, CB2 0SP Cambridge, UK
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Rd, CB2 0SP Cambridge, UK
| | - Antonios Kagialis
- Department of Psychiatry, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Marios Antonakakis
- Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
| | - Michalis Zervakis
- Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
| | - Despina Antypa
- Department of Psychiatry, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Eleftherios Kavroulakis
- Department of Radiology, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Thomas G. Maris
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece
- Department of Radiology, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Antonios Vakis
- Department of Neurosurgery, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
| | - Emmanuel A. Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Rd, CB2 0SP Cambridge, UK
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Rd, CB2 0SP Cambridge, UK
| | - Efrosini Papadaki
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece
- Department of Radiology, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
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19
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Kozak S, Dezachyo O, Stanford W, Bar-Haim Y, Censor N, Dayan E. Elevated integration within the reward network underlies vulnerability to distress. Cereb Cortex 2022; 33:5797-5807. [PMID: 36453462 DOI: 10.1093/cercor/bhac460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/26/2022] [Accepted: 10/31/2022] [Indexed: 12/05/2022] Open
Abstract
Abstract
Distress tolerance (DT), the capability to persist under negative circumstances, underlies a range of psychopathologies. It has been proposed that DT may originate from the activity and connectivity in diverse neural networks integrated by the reward system. To test this hypothesis, we examined the link between DT and integration and segregation in the reward network as derived from resting-state functional connectivity data. DT was measured in 147 participants from a large community sample using the Behavioral Indicator of Resiliency to Distress task. Prior to DT evaluation, participants underwent a resting-state functional magnetic resonance imaging scan. For each participant, we constructed a whole-brain functional connectivity network and calculated the degree of reward network integration and segregation based on the extent to which reward network nodes showed functional connections within and outside their network. We found that distress-intolerant participants demonstrated heightened reward network integration relative to the distress-tolerant participants. In addition, these differences in integration were higher relative to the rest of the brain and, more specifically, the somatomotor network, which has been implicated in impulsive behavior. These findings support the notion that increased integration in large-scale brain networks may constitute a risk for distress intolerance and its psychopathological correlates.
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Affiliation(s)
- Stas Kozak
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - Or Dezachyo
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
- Sagol School of Neuroscience, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - William Stanford
- Biological & Biomedical Sciences Program, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599 , United States
| | - Yair Bar-Haim
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
- Sagol School of Neuroscience, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - Nitzan Censor
- School of Psychological Sciences, Tel Aviv University , Tel Aviv 6997801 , Israel
- Sagol School of Neuroscience, Tel Aviv University , Tel Aviv 6997801 , Israel
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599 , United States
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20
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Adamovich T, Zakharov I, Tabueva A, Malykh S. The thresholding problem and variability in the EEG graph network parameters. Sci Rep 2022; 12:18659. [PMID: 36333413 PMCID: PMC9636266 DOI: 10.1038/s41598-022-22079-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary, and the effect of the threshold choice is not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graph's global connectivity measures as a function of the chosen threshold which may influence the outcome of the study. The choice of the threshold could lead to different study conclusions; thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider the adoption of different analytic approaches. We also proposed some ways of improving the procedure of thresholding in functional connectivity research.
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Affiliation(s)
- Timofey Adamovich
- Psychological Institute of Russian Academy of Education, Moscow, Russia.
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia.
| | - Ilya Zakharov
- Psychological Institute of Russian Academy of Education, Moscow, Russia
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Anna Tabueva
- Psychological Institute of Russian Academy of Education, Moscow, Russia
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Sergey Malykh
- Psychological Institute of Russian Academy of Education, Moscow, Russia
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
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21
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Stanford WC, Mucha PJ, Dayan E. A robust core architecture of functional brain networks supports topological resilience and cognitive performance in middle- and old-aged adults. Proc Natl Acad Sci U S A 2022; 119:e2203682119. [PMID: 36282912 PMCID: PMC9636938 DOI: 10.1073/pnas.2203682119] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 09/21/2022] [Indexed: 11/18/2022] Open
Abstract
Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against age-related cognitive decline. The topological characteristics of brain networks that promote protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness and resilience of brain networks, queried via the delineation of the brain's core network structure, relate to age and cognitive performance in a cross-sectional dataset of healthy middle- and old-aged adults (n = 478, ages 40 to 90 y). First, we decomposed each subject's functional brain network using k-shell decomposition and found that age was negatively associated with robust core network structures. Next, we perturbed these networks, via attack simulations, and found that resilience of core brain network nodes also declined in relationship to age. We then partitioned our dataset into middle- (ages 40 to 65 y, n = 300) and old- (ages 65 to 90 y, n = 178) aged subjects and observed that older individuals had less robust core connectivity and resilience. Following these analyses, we found that episodic memory was positively related to robust connectivity and core resilience, particularly within the default node, limbic, and frontoparietal control networks. Importantly, we found that age-related differences in episodic memory were positively related to core resilience, which indicates a potential role for core resilience in protection against cognitive decline. Together, these findings suggest that robust core connectivity and resilience of brain networks could facilitate high cognitive performance in aging.
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Affiliation(s)
- William C. Stanford
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH 03755
| | - Eran Dayan
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
- Department of Radiology, Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
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22
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Odor Pleasantness Modulates Functional Connectivity in the Olfactory Hedonic Processing Network. Brain Sci 2022; 12:brainsci12101408. [PMID: 36291341 PMCID: PMC9599424 DOI: 10.3390/brainsci12101408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/10/2022] [Accepted: 10/14/2022] [Indexed: 11/18/2022] Open
Abstract
Olfactory hedonic evaluation is the primary dimension of olfactory perception and thus central to our sense of smell. It involves complex interactions between brain regions associated with sensory, affective and reward processing. Despite a recent increase in interest, several aspects of olfactory hedonic evaluation remain ambiguous: uncertainty surrounds the communication between, and interaction among, brain areas during hedonic evaluation of olfactory stimuli with different levels of pleasantness, as well as the corresponding supporting oscillatory mechanisms. In our study we investigated changes in functional interactions among brain areas in response to odor stimuli using electroencephalography (EEG). To this goal, functional connectivity networks were estimated based on phase synchronization between EEG signals using the weighted phase lag index (wPLI). Graph theoretic metrics were subsequently used to quantify the resulting changes in functional connectivity of relevant brain regions involved in olfactory hedonic evaluation. Our results indicate that odor stimuli of different hedonic values evoke significantly different interaction patterns among brain regions within the olfactory cortex, as well as in the anterior cingulate and orbitofrontal cortices. Furthermore, significant hemispheric laterality effects have been observed in the prefrontal and anterior cingulate cortices, specifically in the beta ((13–30) Hz) and gamma ((30–40) Hz) frequency bands.
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23
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Troisi Lopez E, Colonnello V, Liparoti M, Castaldi M, Alivernini F, Russo PM, Sorrentino G, Lucidi F, Mandolesi L, Sorrentino P. Brain network topology and personality traits: A source level magnetoencephalographic study. Scand J Psychol 2022; 63:495-503. [PMID: 35674278 PMCID: PMC9796445 DOI: 10.1111/sjop.12835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 04/08/2022] [Accepted: 05/04/2022] [Indexed: 01/01/2023]
Abstract
Personality neuroscience is focusing on the correlation between individual differences and the efficiency of large-scale networks from the perspective of the brain as an interconnected network. A suitable technique to explore this relationship is the magnetoencephalography (MEG), but not many MEG studies are aimed at investigating topological properties correlated to personality traits. By using MEG, the present study aims to evaluate how individual differences described in Cloninger's psychobiological model are correlated with specific cerebral structures. Fifty healthy individuals (20 males, 30 females, mean age: 27.4 ± 4.8 years) underwent Temperament and Character Inventory examination and MEG recording during a resting state condition. High harm avoidance scores were associated with a reduced centrality of the left caudate nucleus and this negative correlation was maintained in females when we analyzed gender differences. Our data suggest that the caudate nucleus plays a key role in adaptive behavior and could be a critical node in insular salience network. The clear difference between males and females allows us to suggest that topological organization correlated to personality is highly dependent on gender. Our findings provide new insights to evaluate the mutual influences of topological and functional connectivity in neural communication efficiency and disruption as biomarkers of psychopathological traits.
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Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Valentina Colonnello
- Department of Experimental, Diagnostic and Specialty Medicine, Alma Mater StudiorumUniversity of Bologna, Policlinico S. Orsola‐MalpighiBolognaItaly
| | - Marianna Liparoti
- Department of Social and Developmental Psychology, Faculty of Medicine and PsychologyUniversity of Roma “Sapienza”RomeItaly
| | - Mauro Castaldi
- Institute for Diagnosis and Cure Hermitage CapodimonteNaplesItaly
| | - Fabio Alivernini
- Department of Social and Developmental Psychology, Faculty of Medicine and PsychologyUniversity of Roma “Sapienza”RomeItaly
| | - Paolo Maria Russo
- Department of Experimental, Diagnostic and Specialty Medicine, Alma Mater StudiorumUniversity of Bologna, Policlinico S. Orsola‐MalpighiBolognaItaly
| | - Giuseppe Sorrentino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly,Institute for Diagnosis and Cure Hermitage CapodimonteNaplesItaly
| | - Fabio Lucidi
- Department of Social and Developmental Psychology, Faculty of Medicine and PsychologyUniversity of Roma “Sapienza”RomeItaly
| | - Laura Mandolesi
- Department of HumanitiesUniversity of Naples “Federico II”NaplesItaly
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24
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Baboo GK, Prasad R, Mahajan P, Baths V. Tracking the Progression and Influence of Beta-Amyloid Plaques Using Percolation Centrality and Collective Influence Algorithm: A Study Using PET Images. Ann Neurosci 2022; 29:209-224. [PMID: 37064283 PMCID: PMC10101156 DOI: 10.1177/09727531221117633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 03/24/2022] [Indexed: 04/18/2023] Open
Abstract
Background The study of brain networks, particularly the spread of disease, is made easier thanks to the network theory. The aberrant accumulation of beta-amyloid plaques and tau protein tangles in Alzheimer's disease causes disruption in brain networks. The evaluation scores, such as the mini-mental state examination (MMSE) and neuropsychiatric inventory questionnaire, which provide a clinical diagnosis, are affected by this build-up. Purpose The percolation of beta-amyloid/tau tangles and their impact on cognitive tests are still unspecified. Methods Percolation centrality could be used to investigate beta-amyloid migration as a characteristic of positron emission tomography (PET)-image-based networks. The PET-image-based network was built utilizing a public database containing 551 scans published by the Alzheimer's Disease Neuroimaging Initiative. Each image in the Julich atlas has 121 zones of interest, which are network nodes. Furthermore, the influential nodes for each scan are computed using the collective influence algorithm. Results For five nodal metrics, analysis of variance (ANOVA; P < .05) reveals the region of interest (ROI) in gray matter (GM) Broca's area for Pittsburgh compound B (PiB) tracer type. The GM hippocampus area is significant for three nodal metrics in the case of florbetapir (AV45). Pairwise variance analysis of the clinical groups reveals five to twelve statistically significant ROIs for AV45 and PiB, respectively, that can distinguish between pairs of clinical situations. Based on multivariate linear regression, the MMSE is a trustworthy evaluation tool. Conclusion Percolation values suggest that around 50 of the memory, visual-spatial skills, and language ROIs are critical to the percolation of beta-amyloids within the brain network when compared to the other extensively used nodal metrics. The anatomical areas rank higher with the advancement of the disease, according to the collective influence algorithm.
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Affiliation(s)
- Gautam Kumar Baboo
- Department of Biological Sciences,
Birla Institute of Technology and Science (BITS), Pilani-K.K. Birla Goa Campus,
Zuarinagar, Sancoale, Goa, India
| | - Raghav Prasad
- Department of Computer Science and
Information Systems, Birla Institute of Technology and Science (BITS), Pilani-K.K.
Birla Goa Campus, Zuarinagar, Sancoale, Goa, India
| | - Pranav Mahajan
- Department of Biological Sciences,
Birla Institute of Technology and Science (BITS), Pilani-K.K. Birla Goa Campus,
Zuarinagar, Sancoale, Goa, India
| | - Veeky Baths
- Department of Biological Sciences,
Birla Institute of Technology and Science (BITS), Pilani-K.K. Birla Goa Campus,
Zuarinagar, Sancoale, Goa, India
- Veeky Baths, Department of Biological
Sciences, Cognitive Neuroscience Lab, Birla Institute of Technology and Science
(BITS), Pilani-K.K. Birla Goa Campus, NH-17B, Zuarinagar, Sancoale, Goa 403726,
India. E-mail:
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25
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Klepl D, He F, Wu M, Blackburn DJ, Sarrigiannis P. EEG-Based Graph Neural Network Classification of Alzheimer's Disease: An Empirical Evaluation of Functional Connectivity Methods. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2651-2660. [PMID: 36067099 DOI: 10.1109/tnsre.2022.3204913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Alzheimer's disease (AD) is the leading form of dementia worldwide. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder. Many studies demonstrate the power of functional connectivity (FC) graph-based biomarkers for automated diagnosis of AD using electroencephalography (EEG). However, various FC measures are commonly utilised, as each aims to quantify a unique aspect of brain coupling. Graph neural networks (GNN) provide a powerful framework for learning on graphs. While a growing number of studies use GNN to classify EEG brain graphs, it is unclear which method should be utilised to estimate the brain graph. We use eight FC measures to estimate FC brain graphs from sensor-level EEG signals. GNN models are trained in order to compare the performance of the selected FC measures. Additionally, three baseline models based on literature are trained for comparison. We show that GNN models perform significantly better than the other baseline models. Moreover, using FC measures to estimate brain graphs improves the performance of GNN compared to models trained using a fixed graph based on the spatial distance between the EEG sensors. However, no FC measure performs consistently better than the other measures. The best GNN reaches 0.984 area under sensitivity-specificity curve (AUC) and 92% accuracy, whereas the best baseline model, a convolutional neural network, has 0.924 AUC and 84.7% accuracy.
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26
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Jiang M, Dimitriadis S, Seet MS, Hamano J, Saba M, Thakor NV, Dragomir A. Multilayer Network Framework Reveals Cross-Frequency Coupling Hubs in Cortical Olfactory Perception. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3338-3341. [PMID: 36085838 DOI: 10.1109/embc48229.2022.9871445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Olfactory perception is shaped by dynamic in-teractions among networks of widely distributed brain regions involved in several neurocognitive processes. However, the neural mechanisms that enable effective coordination and integrative processing across these brain regions, which have different functions and operating characteristics, are not yet fully understood. In this study we use electroencephalography (EEG) signals and a multilayer network formalism to model cross-frequency coupling across the brain and identify brain regions that operate as connecting hubs, thus facilitating inte-grative function. To this goal, we investigate α-γ coupling and θ-γ coupling during exposure to olfactory stimuli of different pleasantness levels. We found that a wider distributed network of hubs emerges in the higher pleasantness condition and that significant differences in the hub connectivity are located in the middle frontal and central regions. Our results indicate the consistent functional role that γ band activity plays in information integration in olfactory perception.
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27
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Li Y, Jiang L. State and Trait Anxiety Share Common Network Topological Mechanisms of Human Brain. Front Neuroinform 2022; 16:859309. [PMID: 35811997 PMCID: PMC9260038 DOI: 10.3389/fninf.2022.859309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/04/2022] [Indexed: 12/01/2022] Open
Abstract
Anxiety is a future-oriented unpleasant and negative mental state induced by distant and potential threats. It could be subdivided into momentary state anxiety and stable trait anxiety, which play a complex and combined role in our mental and physical health. However, no studies have systematically investigated whether these two different dimensions of anxiety share a common or distinct topological mechanism of human brain network. In this study, we used macroscale human brain morphological similarity network and functional connectivity network as well as their spatial and temporal variations to explore the topological properties of state and trait anxiety. Our results showed that state and trait anxiety were both negatively correlated with the coefficient of variation of nodal efficiency in the left frontal eyes field of volume network; state and trait anxiety were both positively correlated with the median and mode of pagerank centrality distribution in the right insula for both static and dynamic functional networks. In summary, our study confirmed that state and trait anxiety shared common human brain network topological mechanisms in the insula and the frontal eyes field, which were involved in preliminary cognitive processing stage of anxiety. Our study also demonstrated that the common brain network topological mechanisms had high spatiotemporal robustness and would enhance our understanding of human brain temporal and spatial organization.
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Affiliation(s)
- Yubin Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Lili Jiang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Lili Jiang
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28
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Gonuguntla V, Yang E, Guan Y, Koo B, Kim J. Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD. Hum Brain Mapp 2022; 43:2845-2860. [PMID: 35289025 PMCID: PMC9120560 DOI: 10.1002/hbm.25820] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 01/23/2022] [Accepted: 02/08/2022] [Indexed: 12/05/2022] Open
Abstract
Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging data-sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer's disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network-based applications.
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Affiliation(s)
| | - Ehwa Yang
- Medical Science Research InstituteSamsung Medical CenterSeoulSouth Korea
| | - Yi Guan
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Bang‐Bon Koo
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Jae‐Hun Kim
- Department of Radiology, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulSouth Korea
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29
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Al-Shargie F, Katmah R, Tariq U, Babiloni F, Al-Mughairbi F, Al-Nashash H. Stress management using fNIRS and binaural beats stimulation. BIOMEDICAL OPTICS EXPRESS 2022; 13:3552-3575. [PMID: 35781942 PMCID: PMC9208616 DOI: 10.1364/boe.455097] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/21/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
In this study, we investigate the effectiveness of binaural beats stimulation (BBs) in enhancing cognitive vigilance and mitigating mental stress level at the workplace. We developed an experimental protocol under four cognitive conditions: high vigilance (HV), vigilance enhancement (VE), mental stress (MS) and stress mitigation (SM). The VE and SM conditions were achieved by listening to 16 Hz of BBs. We assessed the four cognitive conditions using salivary alpha-amylase, behavioral responses, and Functional Near-Infrared Spectroscopy (fNIRS). We quantified the vigilance and stress levels using the reaction time (RT) to stimuli, accuracy of detection, and the functional connectivity metrics of the fNIRS estimated by Phase Locking Values (PLV). We propose using the orthogonal minimum spanning tree (OMST) to determine the true connectivity network patterns of the PLV. Our results show that listening to 16-Hz BBs has significantly reduced the level of alpha amylase by 44%, reduced the RT to stimuli by 20% and increased the accuracy of target detection by 25%, (p < 0.001). The analysis of the connectivity network across the four different cognitive conditions revealed several statistically significant trends. Specifically, a significant increase in connectivity between the right and left dorsolateral prefrontal cortex (DLPFC) areas and left orbitofrontal cortex was found during the vigilance enhancement condition compared to the high vigilance. Likewise, similar patterns were found between the right and left DLPFC, orbitofrontal cortex, right ventrolateral prefrontal cortex (VLPFC) and right frontopolar PFC (prefrontal cortex) area during stress mitigation compared to mental stress. Furthermore, the connectivity network under stress condition alone showed significant connectivity increase between the VLPFC and DLPFC compared to other areas. The laterality index demonstrated left frontal laterality under high vigilance and VE conditions, and right DLPFC and left frontopolar PFC while under mental stress. Overall, our results showed that BBs can be used for vigilance enhancement and stress mitigation.
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Affiliation(s)
- Fares Al-Shargie
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Rateb Katmah
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Usman Tariq
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
| | - Fabio Babiloni
- Department Molecular Medicine, University of Sapienza Rome, 00185 Rome, Italy
| | - Fadwa Al-Mughairbi
- Department of Clinical Psychology, College of Medicines and Health Sciences, UAE University, Al Ain, United Arab Emirates
| | - Hasan Al-Nashash
- Department of Electrical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
- Biomedical Engineering Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates
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30
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Hybrid High-order Brain Functional Networks for Schizophrenia-Aided Diagnosis. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10014-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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31
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Ren B, Yang K, Zhu L, Hu L, Qiu T, Kong W, Zhang J. Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke. Front Comput Neurosci 2022; 16:785397. [PMID: 35431850 PMCID: PMC9008254 DOI: 10.3389/fncom.2022.785397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/16/2022] [Indexed: 11/13/2022] Open
Abstract
Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha (p = 0.0186 in the left-hand mental rotation task, p = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500–800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke.
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Affiliation(s)
- Bin Ren
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Kun Yang
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Li Zhu
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Lang Hu
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Tao Qiu
- Department of Neurology, Zhejiang Provincial Hospital of Chinese Medicine, Hangzhou, China
| | - Wanzeng Kong
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Jianhai Zhang
- College of Computer Science, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
- *Correspondence: Jianhai Zhang
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32
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Shang Y, Yang Y, Zheng G, Zhao Z, Wang Y, Yang L, Han L, Yao Z, Hu B. Aberrant functional network topology and effective connectivity in burnout syndrome. Clin Neurophysiol 2022; 138:163-172. [DOI: 10.1016/j.clinph.2022.03.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/16/2022] [Accepted: 03/18/2022] [Indexed: 12/11/2022]
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33
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Zhang J, Liu T, Shi Z, Tan S, Suo D, Dai C, Wang L, Wu J, Funahashi S, Liu M. Impaired Self-Referential Cognitive Processing in Bipolar Disorder: A Functional Connectivity Analysis. Front Aging Neurosci 2022; 14:754600. [PMID: 35197839 PMCID: PMC8859154 DOI: 10.3389/fnagi.2022.754600] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 01/10/2022] [Indexed: 11/21/2022] Open
Abstract
Patients with bipolar disorder have deficits in self-referenced information. The brain functional connectivity during social cognitive processing in bipolar disorder is unclear. Electroencephalogram (EEG) was recorded in 23 patients with bipolar disorder and 19 healthy comparison subjects. We analyzed the time-frequency distribution of EEG power for each electrode associated with self, other, and font reflection conditions and used the phase lag index to characterize the functional connectivity between electrode pairs for 4 frequency bands. Then, the network properties were assessed by graph theoretic analysis. The results showed that bipolar disorder induced a weaker response power and phase lag index values over the whole brain in both self and other reflection conditions. Moreover, the characteristic path length was increased in patients during self-reflection processing, whereas the global efficiency and the node degree were decreased. In addition, when discriminating patients from normal controls, we found that the classification accuracy was high. These results suggest that patients have impeded integration of attention, memory, and other resources of the whole brain, resulting in a deficit of efficiency and ability in self-referential processing.
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Affiliation(s)
- Jian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Tiantian Liu
- School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Zhongyan Shi
- School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Shuping Tan
- Center for Psychiatric Research, Beijing Huilongguan Hospital, Beijing, China
| | - Dingjie Suo
- School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Chunyang Dai
- School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Li Wang
- School of Life Sciences, Beijing Institute of Technology, Beijing, China
- *Correspondence: Li Wang,
| | - Jinglong Wu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
- Cognitive Neuroscience Laboratory, Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing, China
| | - Miaomiao Liu
- School of Psychology, Shenzhen University, Shenzhen, China
- Miaomiaos Liu,
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34
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Pourmotabbed H, de Jongh Curry AL, Clarke DF, Tyler-Kabara EC, Babajani-Feremi A. Reproducibility of graph measures derived from resting-state MEG functional connectivity metrics in sensor and source spaces. Hum Brain Mapp 2022; 43:1342-1357. [PMID: 35019189 PMCID: PMC8837594 DOI: 10.1002/hbm.25726] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 10/29/2021] [Accepted: 11/11/2021] [Indexed: 11/30/2022] Open
Abstract
Prior studies have used graph analysis of resting‐state magnetoencephalography (MEG) to characterize abnormal brain networks in neurological disorders. However, a present challenge for researchers is the lack of guidance on which network construction strategies to employ. The reproducibility of graph measures is important for their use as clinical biomarkers. Furthermore, global graph measures should ideally not depend on whether the analysis was performed in the sensor or source space. Therefore, MEG data of the 89 healthy subjects of the Human Connectome Project were used to investigate test–retest reliability and sensor versus source association of global graph measures. Atlas‐based beamforming was used for source reconstruction, and functional connectivity (FC) was estimated for both sensor and source signals in six frequency bands using the debiased weighted phase lag index (dwPLI), amplitude envelope correlation (AEC), and leakage‐corrected AEC. Reliability was examined over multiple network density levels achieved with proportional weight and orthogonal minimum spanning tree thresholding. At a 100% density, graph measures for most FC metrics and frequency bands had fair to excellent reliability and significant sensor versus source association. The greatest reliability and sensor versus source association was obtained when using amplitude metrics. Reliability was similar between sensor and source spaces when using amplitude metrics but greater for the source than the sensor space in higher frequency bands when using the dwPLI. These results suggest that graph measures are useful biomarkers, particularly for investigating functional networks based on amplitude synchrony.
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Affiliation(s)
- Haatef Pourmotabbed
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.,Magnetoencephalography Laboratory, Dell Children's Medical Center, Austin, Texas, USA.,Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, USA
| | - Amy L de Jongh Curry
- Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, USA
| | - Dave F Clarke
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA
| | - Elizabeth C Tyler-Kabara
- Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, Texas, USA
| | - Abbas Babajani-Feremi
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.,Magnetoencephalography Laboratory, Dell Children's Medical Center, Austin, Texas, USA.,Department of Neurosurgery, Dell Medical School, University of Texas at Austin, Austin, Texas, USA
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35
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Low J, Seet M, Hamano J, Saba M, Thakor NV, Dragomir A. Community Analysis of Brain Functional Networks Reveals Systems-Level Integration in Olfactory Hedonic Perception. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5995-5998. [PMID: 34892484 DOI: 10.1109/embc46164.2021.9629919] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Olfactory hedonic perception involves complex interplay among an ensemble of neurocognitive systems implicated in sensory, affective and reward processing. However, the mechanisms of these inter-system interactions have yet to be well-characterized. Here, we employ directed functional connectivity networks estimated from source-localized EEG to uncover how brain regions across the olfactory, emotion and reward systems integrate organically into cross-system communities. Using the integration coefficient, a graph theoretic measure, we quantified the effect of exposure to fragrance stimuli of different hedonic values (high vs low pleasantness levels) on inter-systems interactions. Our analysis focused on beta band activity (13-30 Hz), which is known to facilitate integration of cortical areas involved in sensory perception. Higher-pleasantness stimuli induced elevated integration for the reward system, but not for the emotion and olfactory systems. Furthermore, the nodes of reward system showed more outward connections to the emotion and olfactory systems than inward connections from the respective systems. These results suggest the centrality of the reward system-supported by beta oscillations-in actively coordinating multi-system interactivity to give rise to hedonic experiences during olfactory perception.
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36
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Tenney JR, Williamson BJ, Kadis DS. Cross-Frequency Coupling in Childhood Absence Epilepsy. Brain Connect 2021; 12:489-496. [PMID: 34405685 DOI: 10.1089/brain.2021.0119] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Objective: Absence seizures are the prototypic primarily generalized seizures, but there is incomplete understanding regarding their generation and maintenance. A core network for absence seizures has been defined, including focal cortical and thalamic regions that have frequency-dependent interactions. The purpose of this study was to investigate within-frequency coupling and cross-frequency coupling (CFC) during human absence seizures, to identify key regions (hubs) within the absence network that contribute to propagation and maintenance. Methods: Thirteen children with new-onset and untreated childhood absence epilepsy had over 60 typical absence seizures during both electroencephalography-functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) recordings. The spatial map of the ictal network was defined using fMRI and used as prior information for MEG connectivity. A multilayer network approach was used to investigate within-frequency coupling and CFC for canonical frequency bands. A rigorous null-modeling approach was used to determine connections outside the noise floor. Results: Strong coupling between beta and gamma frequencies, within the left frontal cortex, and between the left frontal and right parietal regions was observed. There was also strong connectivity between left frontal and right parietal nodes within the gamma band. Multilayer versatility analysis identified a cluster of network hubs in the left frontal region. Interpretation: Cortical regions commonly identified as being critical for absence seizure generation (frontal cortex, precuneus) have strong CFC and within-frequency coupling between beta and gamma bands. As nonpharmacologic treatments, such as neuromodulation, become available for generalized epilepsies, detailed mechanistic understanding of how "diffuse" seizures are generated and maintained will be necessary to provide optimal outcomes.
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Affiliation(s)
- Jeffrey R Tenney
- Division of Neurology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Brady J Williamson
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Darren S Kadis
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Physiology, University of Toronto, Toronto, Ontario, Canada
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37
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Dimitriadis SI, Lyssoudis C, Tsolaki AC, Lazarou E, Kozori M, Tsolaki M. Greek High Phenolic Early Harvest Extra Virgin Olive Oil Reduces the Over-Excitation of Information-Flow Based on Dominant Coupling Mode (DoCM) Model in Patients with Mild Cognitive Impairment: An EEG Resting-State Validation Approach. J Alzheimers Dis 2021; 83:191-207. [PMID: 34308906 DOI: 10.3233/jad-210454] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Extra virgin olive oil (EVOO) constitutes a natural compound with high protection over cognitive function that could positively alter brain dynamics and the mixture of within and between-frequency connectivity. OBJECTIVE The balance of cross-frequency coupling over within-frequency coupling can build a nonlinearity index (NI) that encapsulates the over-excitation of information flow between brain areas and across experimental time. The present study investigated for the very first time how the Greek High Phenolic Early Harvest Extra Virgin Olive Oil (HP-EH-EVOO) versus Moderate Phenolic (MP-EVOO) and Mediterranean Diet (MeDi) intervention in people with mild cognitive impairment (MCI) could affect their spontaneous EEG dynamic connectivity. METHODS Forty-three subjects (14 in MeDi, 16 in MP-EVOO, and 13 in HP-EH-EVOO) followed an EEG resting-state recording session (eyes-open and closed) before and after the treatment. Following our dominant coupling mode model, we built a dynamic integrated dynamic functional connectivity graph that tabulates the functional strength and the dominant coupling mode model of every pair of brain areas. RESULTS Signal spectrum within 1-13 Hz and theta/beta ratio have decreased in the HP-EH-EVOO group in the eyes-open condition. The intervention improved the FIDoCM across groups and conditions but was more prominent in the HP-EH-EVOO group (p < 0.001). Finally, we revealed a significant higher post-intervention reduction of NI (ΔNITotal and α) for the HP-EH-EVOO compared to the MP-EVOO and MeDi groups (p < 0.0001). CONCLUSION Long-term intervention with HP-EH-EVOO reduced the over-excitation of information flow in spontaneous brain activity and altered the signal spectrum of EEG rhythms.
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Affiliation(s)
- Stavros I Dimitriadis
- 1st Department of Neurology, G.H. "AHEPA, " School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece.,Integrative Neuroimaging Lab, Thessaloniki, Greece.,Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, United Kingdom.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Christos Lyssoudis
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| | - Anthoula C Tsolaki
- 1st Department of Neurology, G.H. "AHEPA, " School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Eftychia Lazarou
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| | - Mahi Kozori
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| | - Magda Tsolaki
- 1st Department of Neurology, G.H. "AHEPA, " School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
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38
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Marimpis AD, Dimitriadis SI, Goebel R. Dyconnmap: Dynamic connectome mapping-A neuroimaging python module. Hum Brain Mapp 2021; 42:4909-4939. [PMID: 34250674 PMCID: PMC8449119 DOI: 10.1002/hbm.25589] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/10/2021] [Accepted: 06/25/2021] [Indexed: 11/16/2022] Open
Abstract
Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time‐averaged approaches and sophisticated algorithms that can capture the time‐varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word ‘chronnectome’ (integration of the Greek word ‘Chronos’, which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well‐known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi‐faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans.
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Affiliation(s)
- Avraam D Marimpis
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Brain Innovation B.V, Maastricht, The Netherlands
| | - Stavros I Dimitriadis
- Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Rainer Goebel
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Brain Innovation B.V, Maastricht, The Netherlands
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39
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Dimitriadis SI, Messaritaki E, K Jones D. The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks. Hum Brain Mapp 2021; 42:4261-4280. [PMID: 34170066 PMCID: PMC8356981 DOI: 10.1002/hbm.25545] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 05/14/2021] [Indexed: 12/20/2022] Open
Abstract
A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organisation of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test–retest data from the Human Connectome Project), the repeatability of thirty‐three community detection algorithms, each paired with seven different graph construction schemes were assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can belong to more than one community). The highest repeatability was observed for the fast multi‐scale community detection algorithm paired with a graph construction scheme that combines nine white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on the optimal pairing of community detection algorithm and graph construction scheme.
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Affiliation(s)
- Stavros I Dimitriadis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK.,Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK
| | - Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK.,BRAIN Biomedical Research Unit, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK
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40
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Dimitriadis SI. Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders. Prog Neuropsychopharmacol Biol Psychiatry 2021; 108:110073. [PMID: 32805332 DOI: 10.1016/j.pnpbp.2020.110073] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 08/10/2020] [Accepted: 08/10/2020] [Indexed: 12/16/2022]
Abstract
Electroencephalography (EEG) based biomarkers have been shown to correlate with the presence of psychotic disorders. Increased delta and decreased alpha power in psychosis indicate an abnormal arousal state. We investigated brain activity across the basic EEG frequencies and also dynamic functional connectivity of both intra and cross-frequency coupling that could reveal a neurophysiological biomarker linked to an aberrant modulating role of alpha frequency in adolescents with schizophrenia spectrum disorders (SSDs). A dynamic functional connectivity graph (DFCG) has been estimated using the imaginary part of phase lag value (iPLV) and correlation of the envelope (corrEnv). We analyzed DFCG profiles of electroencephalographic resting state (eyes closed) recordings of healthy controls (HC) (n = 39) and SSDs subjects (n = 45) in basic frequency bands {δ,θ,α1,α2,β1,β2,γ}. In our analysis, we incorporated both intra and cross-frequency coupling modes. Adopting our recent Dominant Coupling Mode (DοCM) model leads to the construction of an integrated DFCG (iDFCG) that encapsulates the functional strength and the DοCM of every pair of brain areas. We revealed significantly higher ratios of delta/alpha1,2 power spectrum in SSDs subjects versus HC. The probability distribution (PD) of amplitude driven DoCM mediated by alpha frequency differentiated SSDs from HC with absolute accuracy (100%). The network Flexibility Index (FI) was significantly lower for subjects with SSDs compared to the HC group. Our analysis supports the central role of alpha frequency alterations in the neurophysiological mechanisms of SSDs. Currents findings open up new diagnostic pathways to clinical detection of SSDs and support the design of rational neurofeedback training.
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Affiliation(s)
- Stavros I Dimitriadis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom; Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom; School of Psychology, College of Biomedical and Life Sciences,Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences,Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.
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41
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Li C, Qiao K, Mu Y, Jiang L. Large-Scale Morphological Network Efficiency of Human Brain: Cognitive Intelligence and Emotional Intelligence. Front Aging Neurosci 2021; 13:605158. [PMID: 33732136 PMCID: PMC7959829 DOI: 10.3389/fnagi.2021.605158] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 02/01/2021] [Indexed: 12/13/2022] Open
Abstract
Network efficiency characterizes how information flows within a network, and it has been used to study the neural basis of cognitive intelligence in adolescence, young adults, and elderly adults, in terms of the white matter in the human brain and functional connectivity networks. However, there were few studies investigating whether the human brain at different ages exhibited different underpins of cognitive and emotional intelligence (EI) from young adults to the middle-aged group, especially in terms of the morphological similarity networks in the human brain. In this study, we used 65 datasets (aging 18–64), including sMRI and behavioral measurements, to study the associations of network efficiency with cognitive intelligence and EI in young adults and the middle-aged group. We proposed a new method of defining the human brain morphological networks using the morphological distribution similarity (including cortical volume, surface area, and thickness). Our results showed inverted age × network efficiency interactions in the relationship of surface-area network efficiency with cognitive intelligence and EI: a negative age × global efficiency (nodal efficiency) interaction in cognitive intelligence, while a positive age × global efficiency (nodal efficiency) interaction in EI. In summary, this study not only proposed a new method of morphological similarity network but also emphasized the developmental effects on the brain mechanisms of intelligence from young adult to middle-aged groups and may promote mental health study on the middle-aged group in the future.
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Affiliation(s)
- Chunlin Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Kaini Qiao
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yan Mu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Lili Jiang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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42
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Gallos IK, Gkiatis K, Matsopoulos GK, Siettos C. ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia. AIMS Neurosci 2021; 8:295-321. [PMID: 33709030 PMCID: PMC7940114 DOI: 10.3934/neuroscience.2021016] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/18/2021] [Indexed: 11/18/2022] Open
Abstract
We construct Functional Connectivity Networks (FCN) from resting state fMRI (rsfMRI) recordings towards the classification of brain activity between healthy and schizophrenic subjects using a publicly available dataset (the COBRE dataset) of 145 subjects (74 healthy controls and 71 schizophrenic subjects). First, we match the anatomy of the brain of each individual to the Desikan-Killiany brain atlas. Then, we use the conventional approach of correlating the parcellated time series to construct FCN and ISOMAP, a nonlinear manifold learning algorithm to produce low-dimensional embeddings of the correlation matrices. For the classification analysis, we computed five key local graph-theoretic measures of the FCN and used the LASSO and Random Forest (RF) algorithms for feature selection. For the classification we used standard linear Support Vector Machines. The classification performance is tested by a double cross-validation scheme (consisting of an outer and an inner loop of "Leave one out" cross-validation (LOOCV)). The standard cross-correlation methodology produced a classification rate of 73.1%, while ISOMAP resulted in 79.3%, thus providing a simpler model with a smaller number of features as chosen from LASSO and RF, namely the participation coefficient of the right thalamus and the strength of the right lingual gyrus.
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Affiliation(s)
- Ioannis K Gallos
- School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Greece
| | - Kostakis Gkiatis
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - George K Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Constantinos Siettos
- Dipartimento di Matematica e Applicazioni “Renato Caccioppoli”, Università degli Studi di Napoli Federico II, Italy
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43
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Luppi AI, Stamatakis EA. Combining network topology and information theory to construct representative brain networks. Netw Neurosci 2021; 5:96-124. [PMID: 33688608 PMCID: PMC7935031 DOI: 10.1162/netn_a_00170] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 09/23/2020] [Indexed: 01/21/2023] Open
Abstract
Network neuroscience employs graph theory to investigate the human brain as a complex network, and derive generalizable insights about the brain's network properties. However, graph-theoretical results obtained from network construction pipelines that produce idiosyncratic networks may not generalize when alternative pipelines are employed. This issue is especially pressing because a wide variety of network construction pipelines have been employed in the human network neuroscience literature, making comparisons between studies problematic. Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data. We do so by minimizing an information-theoretic measure of divergence between network topologies, known as the portrait divergence. Based on functional and diffusion MRI data from the Human Connectome Project, we consider anatomical, functional, and multimodal parcellations at three different scales, and 48 distinct ways of defining network edges. We show that the highest representativeness can be obtained by using parcellations in the order of 200 regions and filtering functional networks based on efficiency-cost optimization-though suitable alternatives are also highlighted. Overall, we identify specific node definition and thresholding procedures that neuroscientists can follow in order to derive representative networks from their human neuroimaging data.
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Affiliation(s)
- Andrea I Luppi
- Division of Anesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Emmanuel A Stamatakis
- Division of Anesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
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44
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Williamson BJ, De Domenico M, Kadis DS. Multilayer Connector Hub Mapping Reveals Key Brain Regions Supporting Expressive Language. Brain Connect 2021; 11:45-55. [PMID: 33317399 PMCID: PMC7891212 DOI: 10.1089/brain.2020.0776] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction: How components of the distributed brain networks that support cognition participate in typical functioning remains a largely unanswered question. An important subgroup of regions in the larger network are connector hubs, which are areas that are highly connected to several other functionally specialized sets of regions, and are likely important for sensorimotor integration. The present study attempts to characterize connector hubs involved in typical expressive language functioning using a data-driven, multimodal, full multilayer magnetoencephalography (MEG) connectivity-based pipeline. Methods: Twelve adolescents, 16-18 years of age (five males), participated in this study. Participants underwent MEG scanning during a verb generation task. MEG and structural connectivity were calculated at the whole-brain level. Amplitude/amplitude coupling (AAC) was used to compute functional connections both within and between discrete frequency bins. AAC values were then multiplied by a binary structural connectivity matrix, and then entered into full multilayer network analysis. Initially, hubs were defined based on multilayer versatility and subsequently reranked by a novel measure called delta centrality on interconnectedness (DCI). DCI is defined as the percent change in interfrequency interconnectedness after removal of a hub. Results: We resolved regions that are important for between-frequency communication among other areas during expressive language, with several potential theoretical and clinical applications that can be generalized to other cognitive domains. Conclusion: Our multilayer, data-driven framework captures nonlinear connections that span across scales that are often missed in conventional analyses. The present study suggests that crucial hubs may be conduits for interfrequency communication between action and perception systems that are crucial for typical functioning.
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Affiliation(s)
- Brady J Williamson
- Department of Radiology, University of Cincinnati, Cincinnati, Ohio, USA
| | - Manlio De Domenico
- Fondazione Bruno Kessler, Center for Information and Communication Technology, Trento, Italy
| | - Darren S Kadis
- Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Physiology, University of Toronto, Toronto, Ontario, Canada
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Simos NJ, Dimitriadis SI, Kavroulakis E, Manikis GC, Bertsias G, Simos P, Maris TG, Papadaki E. Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach. Brain Sci 2020; 10:brainsci10110777. [PMID: 33113768 PMCID: PMC7692139 DOI: 10.3390/brainsci10110777] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 10/21/2020] [Accepted: 10/23/2020] [Indexed: 01/15/2023] Open
Abstract
Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.
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Affiliation(s)
- Nicholas John Simos
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece; (N.J.S.); (G.C.M.); (T.G.M.); (E.P.)
- Department of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece
| | - Stavros I. Dimitriadis
- Integrative Neuroimaging Lab, 55133 Thessaloniki, Greece;
- 1st Department of Neurology, G.H. “AHEPA”, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Research Institute School of Medicine, & MRC Centre for Neuropsychiatric Genetics and Genomics, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF14 4EP, UK
| | - Eleftherios Kavroulakis
- Department of Radiology, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece;
| | - Georgios C. Manikis
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece; (N.J.S.); (G.C.M.); (T.G.M.); (E.P.)
| | - George Bertsias
- Department of Rheumatology, Clinical Immunology and Allergy, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece;
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece
| | - Panagiotis Simos
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece; (N.J.S.); (G.C.M.); (T.G.M.); (E.P.)
- Department of Psychiatry, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece
- Correspondence: or
| | - Thomas G. Maris
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece; (N.J.S.); (G.C.M.); (T.G.M.); (E.P.)
- Department of Radiology, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece;
| | - Efrosini Papadaki
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology–Hellas, 70013 Heraklion, Greece; (N.J.S.); (G.C.M.); (T.G.M.); (E.P.)
- Department of Radiology, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece;
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Chan YL, Ung WC, Lim LG, Lu CK, Kiguchi M, Tang TB. Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation With a Case Study on Alzheimer's Disease. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1691-1701. [PMID: 32746314 DOI: 10.1109/tnsre.2020.3007589] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain connectivity. The proposed method was applied to an Alzheimer's disease (AD) dataset and was compared with a number of well-known thresholding techniques. The results demonstrated that the proposed method outperformed the benchmarks in filtering cost-effective networks and in differentiation between patients with mild AD and healthy controls. The results also supported the proposed method as a feasible technique to analyze fNIRS-FC, especially with cost-efficiency, assortativity and laterality as a set of effective features for the diagnosis of AD.
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Ran Q, Jamoulle T, Schaeverbeke J, Meersmans K, Vandenberghe R, Dupont P. Reproducibility of graph measures at the subject level using resting-state fMRI. Brain Behav 2020; 10:2336-2351. [PMID: 32614515 PMCID: PMC7428495 DOI: 10.1002/brb3.1705] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 05/07/2020] [Accepted: 05/17/2020] [Indexed: 01/08/2023] Open
Abstract
INTRODUCTION Graph metrics have been proposed as potential biomarkers for diagnosis in clinical work. However, before it can be applied in a clinical setting, their reproducibility should be evaluated. METHODS This study systematically investigated the effect of two denoising pipelines and different whole-brain network constructions on reproducibility of subject-specific graph measures. We used the multi-session fMRI dataset from the Brain Genomics Superstruct Project consisting of 69 healthy young adults. RESULTS In binary networks, the test-retest variability for global measures was large at low density irrespective of the denoising strategy or the type of correlation. Weighted networks showed very low test-retest values (and thus a good reproducibility) for global graph measures irrespective of the strategy used. Comparing the test-retest values for different strategies, there were significant main effects of the type of correlation (Pearson correlation vs. partial correlation), the (partial) correlation value (absolute vs. positive vs. negative), and weight calculation (based on the raw (partial) correlation values vs. based on transformed Z-values). There was also a significant interaction effect between type of correlation and weight calculation. Similarly as for the binary networks, there was no main effect of the denoising pipeline. CONCLUSION Our results demonstrated that normalized global graph measures based on a weighted network using the absolute (partial) correlation as weight were reproducible. The denoising pipeline and the granularity of the whole-brain parcellation used to define the nodes were not critical for the reproducibility of normalized graph measures.
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Affiliation(s)
- Qian Ran
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
- Department of RadiologyXinqiao HospitalChongqingChina
| | - Tarik Jamoulle
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
- Alzheimer Research Centre KU LeuvenLeuven Brain Instititute, KU LeuvenLeuvenBelgium
| | - Karen Meersmans
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
| | - Rik Vandenberghe
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
- Alzheimer Research Centre KU LeuvenLeuven Brain Instititute, KU LeuvenLeuvenBelgium
- Neurology DepartmentUniversity Hospitals Leuven (UZ Leuven)LeuvenBelgium
| | - Patrick Dupont
- Laboratory for Cognitive NeurologyDepartment of Neurosciences, KU LeuvenLeuvenBelgium
- Alzheimer Research Centre KU LeuvenLeuven Brain Instititute, KU LeuvenLeuvenBelgium
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Gonuguntla V, Kim JH. EEG-Based Functional Connectivity Representation using Phase Locking Value for Brain Network Based Applications. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2853-2856. [PMID: 33018601 DOI: 10.1109/embc44109.2020.9175397] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
In more recent times, the network perspective study of the human brain has expanded enormously due to the advancements in the field of network neuroscience. Existing methods to form the functional connectivity from the multichannel EEG leads to a fully connected network. Representation of a fully connected functional network with a significant functional network (SFN) can help to characterize and quantify the complex brain networks. Further, it can also provide novel insight into the brain cognition analysis and is crucial in several brain network-based applications. This paper presents a framework to find the SFN corresponding to any event from its fully connected network. Using the phase-locking value (PLV) in EEG we first identify the difference PLV of an event to the rest. Based on the difference PLV, we then identify the reactive band and event-associated most reactive pairs (MRPs). The SFNs corresponding to their events is then formed with the identified MRPs. The proposed method is employed on 'database for emotion analysis using physiological signals (DEAP)' data set to find the SFNs associated with emotions. Comparable state-of-the-art multiple emotion classification accuracies are obtained with the identified SFNs. Results show that the proposed methods can be used as a general thresholding technique to identify the event-related SFNs which are crucial in brain network-based applications.
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Chan YL, Boon Tang T. Characterizing Functional Near Infrared Spectroscopy (fNIRS)-based Connectivity as Cost-effective Small World Network using Orthogonal Minimal Spanning Trees (OMSTs). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2901-2904. [PMID: 33018613 DOI: 10.1109/embc44109.2020.9175241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This paper reported data-driven functional connectivity (FC) analytical method to investigate functional near infrared spectroscopy (fNIRS)-based connectivity. We evaluated the synchronization of oxygenated hemoglobin using Pearson's correlation and employed orthogonal minimal spanning trees (OMSTs) in characterizing brain connectivity. Then we compared the resultant global cost efficiency and robustness with those generated by non-human i.e. lattice and random networks. We also further benchmarked our method using proportional threshold. Results from 59 healthy subjects demonstrated global cost efficiency and assortativity varied in lattice and random network significantly (p < 0.05), highlighting the potential of OMSTs in extracting true neuronal network. Moreover, the inadequate of proportional threshold in extracting small world network from the same dataset supported that the OMSTs might be the better alternative in FC analysis especially in evaluating cost-efficiency and robustness of network.
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Li W, Wen W, Chen X, Ni B, Lin X, Fan W, The Alzheimer's Disease Neuroimaging Initiative. Functional Evolving Patterns of Cortical Networks in Progression of Alzheimer's Disease: A Graph-Based Resting-State fMRI Study. Neural Plast 2020; 2020:7839536. [PMID: 32684923 PMCID: PMC7341396 DOI: 10.1155/2020/7839536] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 04/22/2020] [Indexed: 11/18/2022] Open
Abstract
AD is a common chronic progressive neurodegenerative disorder. However, the understanding of the dynamic longitudinal change of the brain in the progression of AD is still rough and sometimes conflicting. This paper analyzed the brain networks of healthy people and patients at different stages (EMCI, LMCI, and AD). The results showed that in global network properties, most differences only existed between healthy people and patients, and few were discovered between patients at different stages. However, nearly all subnetwork properties showed significant differences between patients at different stages. Moreover, the most interesting result was that we found two different functional evolving patterns of cortical networks in progression of AD, named 'temperature inversion' and "monotonous decline," but not the same monotonous decline trend as the external functional assessment observed in the course of disease progression. We suppose that those subnetworks, showing the same functional evolving pattern in AD progression, may have something the same in work mechanism in nature. And the subnetworks with 'temperature inversion' evolving pattern may play a special role in the development of AD.
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Affiliation(s)
- Wei Li
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wen Wen
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xi Chen
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
| | - BingJie Ni
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xuefeng Lin
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
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