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Hirsch F, Bumanglag Â, Zhang Y, Wohlschlaeger A. Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis. Neuroimage Clin 2024; 43:103657. [PMID: 39208481 DOI: 10.1016/j.nicl.2024.103657] [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: 05/31/2024] [Revised: 08/05/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state functional magnetic resonance imaging (rs-fMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains. METHODS rs-fMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron emission tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients. RESULTS Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta-power. Exploratory analyses revealed a close statistical relationship between LEN and positive symptom severity in patients. CONCLUSION Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.
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Affiliation(s)
- Fabian Hirsch
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany.
| | - Ângelo Bumanglag
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
| | - Yifei Zhang
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
| | - Afra Wohlschlaeger
- Department of Diagnostic and Interventional Neuroradiology, Klinikum R.d.Isar, Technical University Munich, Ismaninger Str. 22, Munich 81675, Germany
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2
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Rossi A, Deslauriers-Gauthier S, Natale E. On null models for temporal small-worldness in brain dynamics. Netw Neurosci 2024; 8:377-394. [PMID: 38952813 PMCID: PMC11142454 DOI: 10.1162/netn_a_00357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 01/03/2024] [Indexed: 07/03/2024] Open
Abstract
Brain dynamics can be modeled as a temporal brain network starting from the activity of different brain regions in functional magnetic resonance imaging (fMRI) signals. When validating hypotheses about temporal networks, it is important to use an appropriate statistical null model that shares some features with the treated empirical data. The purpose of this work is to contribute to the theory of temporal null models for brain networks by introducing the random temporal hyperbolic (RTH) graph model, an extension of the random hyperbolic (RH) graph, known in the study of complex networks for its ability to reproduce crucial properties of real-world networks. We focus on temporal small-worldness which, in the static case, has been extensively studied in real-world complex networks and has been linked to the ability of brain networks to efficiently exchange information. We compare the RTH graph model with standard null models for temporal networks and show it is the null model that best reproduces the small-worldness of resting brain activity. This ability to reproduce fundamental features of real brain networks, while adding only a single parameter compared with classical models, suggests that the RTH graph model is a promising tool for validating hypotheses about temporal brain networks.
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Affiliation(s)
- Aurora Rossi
- Université Côte d’Azur, COATI, INRIA, CNRS, I3S, France
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3
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Park J, Fagerquist CK. Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses. PLoS One 2024; 19:e0299287. [PMID: 38701058 PMCID: PMC11068200 DOI: 10.1371/journal.pone.0299287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 02/07/2024] [Indexed: 05/05/2024] Open
Abstract
Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.
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Affiliation(s)
- Jihyun Park
- Western Regional Research Center, Agricultural Research Service, USDA, Albany, CA, United States of America
- U.S. Department of Energy, Research Participation Program Administered by the Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States of America
| | - Clifton K. Fagerquist
- Western Regional Research Center, Agricultural Research Service, USDA, Albany, CA, United States of America
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He X, Ghasemian A, Lee E, Clauset A, Mucha PJ. Sequential stacking link prediction algorithms for temporal networks. Nat Commun 2024; 15:1364. [PMID: 38355612 PMCID: PMC10866871 DOI: 10.1038/s41467-024-45598-0] [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: 01/31/2023] [Accepted: 01/29/2024] [Indexed: 02/16/2024] Open
Abstract
Link prediction algorithms are indispensable tools in many scientific applications by speeding up network data collection and imputing missing connections. However, in many systems, links change over time and it remains unclear how to optimally exploit such temporal information for link predictions in such networks. Here, we show that many temporal topological features, in addition to having high computational cost, are less accurate in temporal link prediction than sequentially stacked static network features. This sequential stacking link prediction method uses 41 static network features that avoid detailed feature engineering choices and is capable of learning a highly accurate predictive distribution of future connections from historical data. We demonstrate that this algorithm works well for both partially observed and completely unobserved target layers, and on two temporal stochastic block models achieves near-oracle-level performance when combined with other single predictor methods as an ensemble learning method. Finally, we empirically illustrate that stacking multiple predictive methods together further improves performance on 19 real-world temporal networks from different domains.
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Affiliation(s)
- Xie He
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
| | - Amir Ghasemian
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
| | - Eun Lee
- Department of Scientific Computing, Pukyong National University, Busan, South Korea
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado, Boulder, Boulder, CO, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, USA.
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5
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Chen C, Chen Z, Hu M, Zhou S, Xu S, Zhou G, Zhou J, Li Y, Chen B, Yao D, Li F, Liu Y, Su S, Xu P, Ma X. EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit. Brain Res Bull 2024; 207:110881. [PMID: 38232779 DOI: 10.1016/j.brainresbull.2024.110881] [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: 05/08/2023] [Revised: 12/13/2023] [Accepted: 01/13/2024] [Indexed: 01/19/2024]
Abstract
Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.
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Affiliation(s)
- Chunli Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Zhaojin Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Meiling Hu
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Sha Zhou
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Shiyun Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Guan Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Jixuan Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Baodan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yizhou Liu
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Simeng Su
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
| | - Xuntai Ma
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China.
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Li Y, Gao J, Yang Y, Zhuang Y, Kang Q, Li X, Tian M, Lv H, He J. Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness. CNS Neurosci Ther 2024; 30:e14641. [PMID: 38385681 PMCID: PMC10883110 DOI: 10.1111/cns.14641] [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: 10/10/2023] [Revised: 01/17/2024] [Accepted: 02/01/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Accurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%. METHODS A method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting-state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI-based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS-based MI functional connectivity network (MIFCN) was constructed in the cortex. RESULTS The group-average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS-based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%. CONCLUSION Constructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.
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Affiliation(s)
- Yaqian Li
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Junfeng Gao
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Ying Yang
- College of Foreign LanguagesWuhan University of TechnologyWuhanChina
| | - Yvtong Zhuang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Qianruo Kang
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Xiang Li
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Min Tian
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Haoan Lv
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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Cinaglia P, Milano M, Cannataro M. Multilayer network alignment based on topological assessment via embeddings. BMC Bioinformatics 2023; 24:416. [PMID: 37932663 PMCID: PMC10629033 DOI: 10.1186/s12859-023-05508-5] [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: 08/10/2023] [Accepted: 10/02/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a user-friendly command line interface, also having a built-in guided mode (step-by-step) for defining input parameters. RESULTS We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively. CONCLUSIONS DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer networks, of which node mappings can be validated statistically. Our experimentation reported a high degree of reliability and effectiveness for the proposed solution.
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Affiliation(s)
- Pietro Cinaglia
- Department of Health Sciences, Magna Graecia University, 88100, Catanzaro, Italy.
| | - Marianna Milano
- Department of Experimental and Clinical Medicine, Magna Graecia University, 88100, Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, 88100, Catanzaro, Italy
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Sundiang M, Hatsopoulos NG, MacLean JN. Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information. Netw Neurosci 2023; 7:661-678. [PMID: 37397877 PMCID: PMC10312288 DOI: 10.1162/netn_a_00298] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/02/2022] [Indexed: 01/28/2024] Open
Abstract
Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of coactivity between neurons. Using pairwise spike time statistics, coactivity can be summarized as a functional network (FN). Here, we show that the structure of FNs constructed from an instructed-delay reach task in nonhuman primates is behaviorally specific: Low-dimensional embedding and graph alignment scores show that FNs constructed from closer target reach directions are also closer in network space. Using short intervals across a trial, we constructed temporal FNs and found that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly decodable shortly after the Instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the Instruction cue, consistent with the hypothesis that information external to the recorded population temporarily alters the structure of the network at this moment.
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Affiliation(s)
- Marina Sundiang
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Nicholas G. Hatsopoulos
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
- University of Chicago Neuroscience Institute, Chicago, IL, USA
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Jason N. MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
- University of Chicago Neuroscience Institute, Chicago, IL, USA
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
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10
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Cinaglia P, Cannataro M. A Method Based on Temporal Embedding for the Pairwise Alignment of Dynamic Networks. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25040665. [PMID: 37190452 PMCID: PMC10138164 DOI: 10.3390/e25040665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/07/2023] [Accepted: 04/13/2023] [Indexed: 05/17/2023]
Abstract
In network analysis, real-world systems may be represented via graph models, where nodes and edges represent the set of biological objects (e.g., genes, proteins, molecules) and their interactions, respectively. This representative knowledge-graph model may also consider the dynamics involved in the evolution of the network (i.e., dynamic networks), in addition to a classic static representation (i.e., static networks). Bioinformatics solutions for network analysis allow knowledge extraction from the features related to a single network of interest or by comparing networks of different species. For instance, we may align a network related to a well known species to a more complex one in order to find a match able to support new hypotheses or studies. Therefore, the network alignment is crucial for transferring the knowledge between species, usually from simplest (e.g., rat) to more complex (e.g., human). Methods: In this paper, we present Dynamic Network Alignment based on Temporal Embedding (DANTE), a novel method for pairwise alignment of dynamic networks that applies the temporal embedding to investigate the topological similarities between the two input dynamic networks. The main idea of DANTE is to consider the evolution of interactions and the changes in network topology. Briefly, the proposed solution builds a similarity matrix by integrating the tensors computed via the embedding process and, subsequently, it aligns the pairs of nodes by performing its own iterative maximization function. Results: The performed experiments have reported promising results in terms of precision and accuracy, as well as good robustness as the number of nodes and time points increases. The proposed solution showed an optimal trade-off between sensitivity and specificity on the alignments produced on several noisy versions of the dynamic yeast network, by improving by ∼18.8% (with a maximum of 20.6%) the Area Under the Receiver Operating Characteristic (ROC) Curve (i.e., AUC or AUROC), compared to two well known methods: DYNAMAGNA++ and DYNAWAVE. From the point of view of quality, DANTE outperformed these by ∼91% as nodes increase and by ∼75% as the number of time points increases. Furthermore, a ∼23.73% improvement in terms of node correctness was reported with our solution on real dynamic networks.
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Affiliation(s)
- Pietro Cinaglia
- Department of Health Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, Data Analytics Research Center, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
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11
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Caligiuri A, Eguíluz VM, Di Gaetano L, Galla T, Lacasa L. Lyapunov exponents for temporal networks. Phys Rev E 2023; 107:044305. [PMID: 37198801 DOI: 10.1103/physreve.107.044305] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/01/2023] [Indexed: 05/19/2023]
Abstract
By interpreting a temporal network as a trajectory of a latent graph dynamical system, we introduce the concept of dynamical instability of a temporal network and construct a measure to estimate the network maximum Lyapunov exponent (nMLE) of a temporal network trajectory. Extending conventional algorithmic methods from nonlinear time-series analysis to networks, we show how to quantify sensitive dependence on initial conditions and estimate the nMLE directly from a single network trajectory. We validate our method for a range of synthetic generative network models displaying low- and high-dimensional chaos and finally discuss potential applications.
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Affiliation(s)
- Annalisa Caligiuri
- Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, Palma de Mallorca, Spain
| | - Victor M Eguíluz
- Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, Palma de Mallorca, Spain
- Basque Centre for Climate Change (BC3), Scientific Campus of the University of the Basque Country, 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain
| | - Leonardo Di Gaetano
- Department of Network and Data Science, Central European University, 1100 Vienna, Austria
| | - Tobias Galla
- Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, Palma de Mallorca, Spain
| | - Lucas Lacasa
- Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB, Palma de Mallorca, Spain
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12
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Investigation of phase synchronization in functional brain networks of children with ADHD using nonlinear recurrence measure. J Theor Biol 2023; 560:111381. [PMID: 36528091 DOI: 10.1016/j.jtbi.2022.111381] [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: 04/26/2022] [Revised: 08/24/2022] [Accepted: 12/11/2022] [Indexed: 12/16/2022]
Abstract
Measuring the phase synchronization between different brain regions in functional brain networks is a common approach to investigate many psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD). The emotional processing deficit in ADHD children is one of the main obstacles in their social interactions. In this study, the nonlinear Correlation between Probability of Recurrences (CPR) method is used for the first time to construct functional brain networks of 22 boys with ADHD and 22 healthy ones during watching four visual-emotional stimuli types. Topological features of brain networks, including shortest path length, clustering coefficient, and nodes strengths, are investigated in groups of ADHD and healthy. The results indicate a significantly (P-Values < 0.01) greater average clustering coefficient and lower shortest path length in the brain networks of ADHD individuals than the healthy ones. Accordingly, in the ADHD brain networks, the information exchange in both local and global scales is abnormally more than the healthy ones, leading to a hyper-synchronization in this group. The topological alterations of ADHD brain networks are mainly observed in the brain's frontal and occipital lobes, indicating impaired brain function of this group in emotional and visual processing. This survey demonstrates that the CPR method can be a good candidate for distinguishing the phase interactions of ADHD and healthy brain networks. Therefore, this study can contribute to further insights into the nonlinear dynamics analysis of brain networks in ADHD individuals.
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13
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Hosseinzadeh MM, Cannataro M, Guzzi PH, Dondi R. Temporal networks in biology and medicine: a survey on models, algorithms, and tools. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2022; 12:10. [PMID: 36618274 PMCID: PMC9803903 DOI: 10.1007/s13721-022-00406-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 01/01/2023]
Abstract
The use of static graphs for modelling and analysis of biological and biomedical data plays a key role in biomedical research. However, many real-world scenarios present dynamic behaviours resulting in both node and edges modification as well as feature evolution. Consequently, ad-hoc models for capturing these evolutions along the time have been introduced, also referred to as dynamic, temporal, time-varying graphs. Here, we focus on temporal graphs, i.e., graphs whose evolution is represented by a sequence of time-ordered snapshots. Each snapshot represents a graph active in a particular timestamp. We survey temporal graph models and related algorithms, presenting fundamentals aspects and the recent advances. We formally define temporal graphs, focusing on the problem setting and we present their main applications in biology and medicine. We also present temporal graph embedding and the application to recent problems such as epidemic modelling. Finally, we further state some promising research directions in the area. Main results of this study include a systematic review of fundamental temporal network problems and their algorithmic solutions considered in the literature, in particular those having application in computational biology and medicine. We also include the main software developed in this context.
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Affiliation(s)
| | - Mario Cannataro
- Department of Surgical and Medical Sciences and Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences and Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Riccardo Dondi
- Department of Literature, Philosophy, Communication Studies, University of Bergamo, Bergamo, Italy
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14
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Blevins AS, Bassett DS, Scott EK, Vanwalleghem GC. From calcium imaging to graph topology. Netw Neurosci 2022; 6:1125-1147. [PMID: 38800465 PMCID: PMC11117109 DOI: 10.1162/netn_a_00262] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/13/2022] [Indexed: 05/29/2024] Open
Abstract
Systems neuroscience is facing an ever-growing mountain of data. Recent advances in protein engineering and microscopy have together led to a paradigm shift in neuroscience; using fluorescence, we can now image the activity of every neuron through the whole brain of behaving animals. Even in larger organisms, the number of neurons that we can record simultaneously is increasing exponentially with time. This increase in the dimensionality of the data is being met with an explosion of computational and mathematical methods, each using disparate terminology, distinct approaches, and diverse mathematical concepts. Here we collect, organize, and explain multiple data analysis techniques that have been, or could be, applied to whole-brain imaging, using larval zebrafish as an example model. We begin with methods such as linear regression that are designed to detect relations between two variables. Next, we progress through network science and applied topological methods, which focus on the patterns of relations among many variables. Finally, we highlight the potential of generative models that could provide testable hypotheses on wiring rules and network progression through time, or disease progression. While we use examples of imaging from larval zebrafish, these approaches are suitable for any population-scale neural network modeling, and indeed, to applications beyond systems neuroscience. Computational approaches from network science and applied topology are not limited to larval zebrafish, or even to systems neuroscience, and we therefore conclude with a discussion of how such methods can be applied to diverse problems across the biological sciences.
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Affiliation(s)
- Ann S. Blevins
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Ethan K. Scott
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, University of Melbourne, Parkville, Australia
| | - Gilles C. Vanwalleghem
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
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15
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Fanton S, Altawil R, Ellerbrock I, Lampa J, Kosek E, Fransson P, Thompson WH. Multiple spatial scale mapping of time-resolved brain network reconfiguration during evoked pain in patients with rheumatoid arthritis. Front Neurosci 2022; 16:942136. [PMID: 36017179 PMCID: PMC9397124 DOI: 10.3389/fnins.2022.942136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
Functional brain networks and the perception of pain can fluctuate over time. However, how the time-dependent reconfiguration of functional brain networks contributes to chronic pain remains largely unexplained. Here, we explored time-varying changes in brain network integration and segregation during pain over a disease-affected area (joint) compared to a neutral site (thumbnail) in 28 patients with rheumatoid arthritis (RA) in comparison with 22 healthy controls (HC). During functional magnetic resonance imaging, all subjects received individually calibrated pain pressures corresponding to visual analog scale 50 mm at joint and thumbnail. We implemented a novel approach to track changes of task-based network connectivity over time. Within this framework, we quantified measures of integration (participation coefficient, PC) and segregation (within-module degree z-score). Using these network measures at multiple spatial scales, both at the level of single nodes (brain regions) and communities (clusters of nodes), we found that PC at the community level was generally higher in RA patients compared to HC during and after painful pressure over the inflamed joint and corresponding site in HC. This shows that all brain communities integrate more in RA patients than in HC for time points following painful stimulation to a disease-relevant body site. However, the elevated community-related integration seen in patients appeared to not pertain uniquely to painful stimulation at the inflamed joint, but also at the neutral thumbnail, as integration and segregation at the community level did not differ across body sites in patients. Moreover, there was no specific nodal contribution to brain network integration or segregation. Altogether, our findings indicate widespread and persistent changes in network interaction in RA patients compared to HC in response to painful stimulation.
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Affiliation(s)
- Silvia Fanton
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Reem Altawil
- Rheumatology Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Isabel Ellerbrock
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Jon Lampa
- Rheumatology Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Eva Kosek
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - William H. Thompson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Division of Cognition and Communication, Department of Applied IT, University of Gothenburg, Gothenburg, Sweden
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16
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Effects of Rest-Break on mental fatigue recovery based on EEG dynamic functional connectivity. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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17
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Geras A, Siudem G, Gagolewski M. Time to vote: Temporal clustering of user activity on Stack Overflow. J Assoc Inf Sci Technol 2022. [DOI: 10.1002/asi.24658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Agnieszka Geras
- Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland
| | - Grzegorz Siudem
- Faculty of Physics Warsaw University of Technology Warsaw Poland
| | - Marek Gagolewski
- Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland
- School of IT Deakin University Geelong Victoria Australia
- Polish Academy of Sciences Systems Research Institute Warsaw Poland
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18
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Obando C, Rosso C, Siegel J, Corbetta M, De Vico Fallani F. Temporal exponential random graph models of longitudinal brain networks after stroke. J R Soc Interface 2022; 19:20210850. [PMID: 35232279 PMCID: PMC8889176 DOI: 10.1098/rsif.2021.0850] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Plasticity after stroke is a complex phenomenon. Functional reorganization occurs not only in the perilesional tissue but throughout the brain. However, the local connection mechanisms generating such global network changes remain largely unknown. To address this question, time must be considered as a formal variable of the problem rather than a simple repeated observation. Here, we hypothesized that the presence of temporal connection motifs, such as the formation of temporal triangles (T) and edges (E) over time, would explain large-scale brain reorganization after stroke. To test our hypothesis, we adopted a statistical framework based on temporal exponential random graph models (tERGMs), where the aforementioned temporal motifs were implemented as parameters and adapted to capture global network changes after stroke. We first validated the performance on synthetic time-varying networks as compared to standard static approaches. Then, using real functional brain networks, we showed that estimates of tERGM parameters were sufficient to reproduce brain network changes from 2 weeks to 1 year after stroke. These temporal connection signatures, reflecting within-hemisphere segregation (T) and between hemisphere integration (E), were associated with patients' future behaviour. In particular, interhemispheric temporal edges significantly correlated with the chronic language and visual outcome in subcortical and cortical stroke, respectively. Our results indicate the importance of time-varying connection properties when modelling dynamic complex systems and provide fresh insights into modelling of brain network mechanisms after stroke.
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Affiliation(s)
- Catalina Obando
- Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
| | - Charlotte Rosso
- Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013 Paris, France,AP-HP, Urgences Cerebro-Vasculaires, Hopital Pitie-Salpetriere, Paris, France,ICM Infrastructure Stroke Network, STAR team, Hopital Pitie-Salpetriere, Paris, France
| | - Joshua Siegel
- Department of Psychiatry, Washington University, St Louis, MO, USA
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center, University of Padova, Padova, Italy,Venetian Institute of Molecular Medicine (VIMM), Padova, Italy
| | - Fabrizio De Vico Fallani
- Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013 Paris, France
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19
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Gao L, Yu J, Zhu L, Wang S, Yuan J, Li G, Cai J, Qi X, Sun Y, Sun Y. Dynamic Reorganization of Functional Connectivity During Post-break Task Reengagement. IEEE Trans Neural Syst Rehabil Eng 2022; 30:157-166. [PMID: 35025746 DOI: 10.1109/tnsre.2022.3142855] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Because of the undesired fatigue-related consequences, accumulating efforts have been made to find an effective intervention to alleviate the suboptimal cognitive function caused by mental fatigue. Nonetheless, limitations of intervention and evaluation methods may hinder the revealing of underlying neural mechanisms of fatigue recovery. Through the newly-developed dynamic functional connectivity (FC) analysis framework, this study aims to investigate the effects of two types of mid-task interventions (i.e., rest-break and moderate-intensity exercise-break) on the dynamic reorganization of FC during the execution of psychomotor vigilance test (PVT). Using a sliding window approach, temporal brain networks within each frequency band (i.e., δ, θ, α, & β) were estimated before and immediate after the intervention, and towards the end of the task to investigate the immediate and delayed effects respectively during post-break task reengagement. Behaviourally, similar beneficial effects of exercise- and rest-break on performance were observed, manifested by the immediate improvements after both interventions and a long-lasting influence towards the end of tasks. Moreover, temporal brain networks assessment showed significant immediate decreases of fluctuability, which followed by an increase of fluctuability towards the end of intervention tasks. Furthermore, the temporal nodal measure revealed the channels with significant differences across tasks were mainly resided in the fronto-parietal areas that exhibited interesting frequency-dependent distribution. The observations of immediate and delayed dynamic FC reorganizations extend previous fatigue-related intervention and static FC studies, and provide new insight into the dynamic characteristics of FC during post-break task reengagement.
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20
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Zhao Z, Zhang Y, Chen N, Li Y, Guo H, Guo M, Yao Z, Hu B. Altered temporal reachability highlights the role of sensory perception systems in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2022; 112:110426. [PMID: 34389436 DOI: 10.1016/j.pnpbp.2021.110426] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/11/2021] [Accepted: 08/05/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND The latest studies have considered the time-dependent structures in dynamic brain networks. However, the effect of periphery structures on the temporal flow of information remains unexplored in patients with major depressive disorder (MDD). In this work, we aimed to explore the pattern of interactions between brain regions in MDD across space and time. METHODS We concentrated on the temporal reachability of nodes in temporal brain networks derived from the resting-state functional magnetic resonance imaging (rs-fMRI) of 55 MDD patients and 62 sex-, age-matched healthy controls. Specifically, temporal connectedness and temporal efficiency (TEF) were estimated based on the length of temporal paths between node pairs. Subsequently, the temporal clustering coefficient (TCC) and temporal distance were jointly employed to explore the patterns in which a node's periphery structure affects its reachability. RESULTS Significantly higher TEF and lower TCC were found in temporal brain networks in MDD. Besides, significant between-group differences of nodal TCC were detected in regions of sensory perception systems. Considering the temporal paths that begin or end at these regions, MDD patients showed several altered temporal distances. CONCLUSION Our results showed that the temporal reachability of specific brain regions in MDD could be affected as their periphery structures evolve, which may explain the dysfunction of sensory perception systems in the spatiotemporal domain.
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Affiliation(s)
- Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yinghui Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; Guangyuan Mental Health Center, Guangyuan, China
| | - Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Hanning Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Man Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Ministry of Education, Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Lanzhou, China.
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21
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Kastrati G, Thompson WH, Schiffler B, Fransson P, Jensen KB. Brain Network Segregation and Integration during Painful Thermal Stimulation. Cereb Cortex 2022; 32:4039-4049. [PMID: 34997959 PMCID: PMC9476629 DOI: 10.1093/cercor/bhab464] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/15/2021] [Accepted: 11/16/2021] [Indexed: 11/13/2022] Open
Abstract
The present study aimed to determine changes in brain network integration/segregation during thermal pain using methods optimized for network connectivity events with high temporal resolution. Participants (n = 33) actively judged whether thermal stimuli applied to the volar forearm were painful or not and then rated the warmth/pain intensity after each trial. We show that the temporal evolution of integration/segregation within trials correlates with the subjective ratings of pain. Specifically, the brain shifts from a segregated state to an integrated state when processing painful stimuli. The association with subjective pain ratings occurred at different time points for all networks. However, the degree of association between ratings and integration/segregation vanished for several brain networks when time-varying functional connectivity was measured at lower temporal resolution. Moreover, the increased integration associated with pain is explained to some degree by relative increases in between-network connectivity. Our results highlight the importance of investigating the relationship between pain and brain network connectivity at a single time point scale, since commonly used temporal aggregations of connectivity data may result in that fine-scale changes in network connectivity may go unnoticed. The interplay between integration/segregation reflects shifting demands of information processing between brain networks and this adaptation occurs both for cognitive tasks and nociceptive processing.
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Affiliation(s)
- Gránit Kastrati
- Department of Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden
| | - William H Thompson
- Department of Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden
| | - Björn Schiffler
- Department of Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden
| | - Karin B Jensen
- Department of Clinical Neuroscience, Karolinska Institutet, 17176 Stockholm, Sweden
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22
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Abé C, Petrovic P, Ossler W, Thompson WH, Liberg B, Song J, Bergen SE, Sellgren CM, Fransson P, Ingvar M, Landén M. Genetic risk for bipolar disorder and schizophrenia predicts structure and function of the ventromedial prefrontal cortex. J Psychiatry Neurosci 2021; 46:E441-E450. [PMID: 34291628 PMCID: PMC8519489 DOI: 10.1503/jpn.200165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Bipolar disorder is highly heritable and polygenic. The polygenic risk for bipolar disorder overlaps with that of schizophrenia, and polygenic scores are normally distributed in the population. Bipolar disorder has been associated with structural brain abnormalities, but it is unknown how these are linked to genetic risk factors for psychotic disorders. METHODS We tested whether polygenic risk scores for bipolar disorder and schizophrenia predict structural brain alterations in 98 patients with bipolar disorder and 81 healthy controls. We derived brain cortical thickness, surface area and volume from structural MRI scans. In post-hoc analyses, we correlated polygenic risk with functional hub strength, derived from resting-state functional MRI and brain connectomics. RESULTS Higher polygenic risk scores for both bipolar disorder and schizophrenia were associated with a thinner ventromedial prefrontal cortex (vmPFC). We found these associations in the combined group, and separately in patients and drug-naive controls. Polygenic risk for bipolar disorder was correlated with the functional hub strength of the vmPFC within the default mode network. LIMITATIONS Polygenic risk is a cumulative measure of genomic burden. Detailed genetic mechanisms underlying brain alterations and their cognitive consequences still need to be determined. CONCLUSION Our multimodal neuroimaging study linked genomic burden and brain endophenotype by demonstrating an association between polygenic risk scores for bipolar disorder and schizophrenia and the structure and function of the vmPFC. Our findings suggest that genetic factors might confer risk for psychotic disorders by influencing the integrity of the vmPFC, a brain region involved in self-referential processes and emotional regulation. Our study may also provide an imaging-genetics vulnerability marker that can be used to help identify individuals at risk for developing bipolar disorder.
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Affiliation(s)
- Christoph Abé
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
| | - Predrag Petrovic
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
| | - William Ossler
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
| | - William H Thompson
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
| | - Benny Liberg
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
| | - Jie Song
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
| | - Sarah E Bergen
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
| | - Carl M Sellgren
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
| | - Peter Fransson
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
| | - Martin Ingvar
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
| | - Mikael Landén
- From the Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Abé, Petrovic, Ossler, Thompson, Liberg, Fransson, Ingvar, Landén); the Department of Psychology, Stanford University, Stanford, California, USA (Thompson); the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (Song, Bergen); the Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (Sellgren); Karolinska University Hospital, Department of Neuroradiology, Stockholm, Sweden (Ingvar); and the Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the Gothenburg University, Sweden (Landén)
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Li F, Jiang L, Liao Y, Si Y, Yi C, Zhang Y, Zhu X, Yang Z, Yao D, Cao Z, Xu P. Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study. J Neural Eng 2021; 18. [PMID: 34153948 DOI: 10.1088/1741-2552/ac0d41] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/21/2021] [Indexed: 11/12/2022]
Abstract
Objective.Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.Approach.In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300).Main results.The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.Significance.This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.
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Affiliation(s)
- Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Lin Jiang
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yuanyuan Liao
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yajing Si
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Psychology, Xinxiang Medical University, Xinxiang 453003, People's Republic of China
| | - Chanli Yi
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yangsong Zhang
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, People's Republic of China
| | - Xianjun Zhu
- The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,Research Unit for Blindness Prevention of Chinese Academy of Medical Sciences (2019RU026), Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, People's Republic of China
| | - Zhenglin Yang
- The Sichuan Provincial Key Laboratory for Human Disease Gene Study, Prenatal Diagnosis Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,Research Unit for Blindness Prevention of Chinese Academy of Medical Sciences (2019RU026), Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Zehong Cao
- Discipline of Information and Communication Technology, University of Tasmania, TAS, Australia
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
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24
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Pierrelée M, Reynders A, Lopez F, Moqrich A, Tichit L, Habermann BH. Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs). Sci Rep 2021; 11:13691. [PMID: 34211067 PMCID: PMC8249521 DOI: 10.1038/s41598-021-93128-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 06/18/2021] [Indexed: 12/13/2022] Open
Abstract
Integrating -omics data with biological networks such as protein-protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus .
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Affiliation(s)
- Michaël Pierrelée
- Aix-Marseille University, CNRS, IBDM UMR 7288, Computational Biology Team, Turing Centre for Living Systems (CENTURI), Marseille, France
| | - Ana Reynders
- Aix-Marseille University, CNRS, IBDM UMR 7288, Team Chronic Pain: Molecular and Cellular Mechanisms, Turing Centre for Living systems (CENTURI), Marseille, France
| | - Fabrice Lopez
- Aix-Marseille University, INSERM, TAGC U 1090, Marseille, France
| | - Aziz Moqrich
- Aix-Marseille University, CNRS, IBDM UMR 7288, Team Chronic Pain: Molecular and Cellular Mechanisms, Turing Centre for Living systems (CENTURI), Marseille, France
| | - Laurent Tichit
- Aix-Marseille University, CNRS, I2M UMR 7373, Turing Centre for Living Systems (CENTURI), Marseille, France
| | - Bianca H Habermann
- Aix-Marseille University, CNRS, IBDM UMR 7288, Computational Biology Team, Turing Centre for Living Systems (CENTURI), Marseille, France. .,Aix-Marseille University, CNRS, IBDM UMR 7288, Turing Center for Living Systems (CENTURI), Parc Scientifique de Luminy, Case 907, 163, Avenue de Luminy, 13009, Marseille, France.
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25
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Yin D, Kaiser M. Understanding neural flexibility from a multifaceted definition. Neuroimage 2021; 235:118027. [PMID: 33836274 DOI: 10.1016/j.neuroimage.2021.118027] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 01/19/2021] [Accepted: 03/27/2021] [Indexed: 11/19/2022] Open
Abstract
Flexibility is a hallmark of human intelligence. Emerging studies have proposed several flexibility measurements at the level of individual regions, to produce a brain map of neural flexibility. However, flexibility is usually inferred from separate components of brain activity (i.e., intrinsic/task-evoked), and different definitions are used. Moreover, recent studies have argued that neural processing may be more than a task-driven and intrinsic dichotomy. Therefore, the understanding to neural flexibility is still incomplete. To address this issue, we propose a multifaceted definition of neural flexibility according to three key features: broad cognitive engagement, distributed connectivity, and adaptive connectome dynamics. For these three features, we first review the advances in computational approaches, their functional relevance, and their potential pitfalls. We then suggest a set of metrics that can help us assign a flexibility rating to each region. Subsequently, we present an emergent probabilistic view for further understanding the functional operation of individual regions in the unified framework of intrinsic and task-driven states. Finally, we highlight several areas related to the multifaceted definition of neural flexibility for future research. This review not only strengthens our understanding of flexible human brain, but also suggests that the measure of neural flexibility could bridge the gap between understanding intrinsic and task-driven brain function dynamics.
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Affiliation(s)
- Dazhi Yin
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China.
| | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK; School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK; Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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26
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Bayram U, Roy R, Assalil A, BenHiba L. The unknown knowns: a graph-based approach for temporal COVID-19 literature mining. ONLINE INFORMATION REVIEW 2021. [DOI: 10.1108/oir-12-2020-0562] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research.Design/methodology/approachThe authors construct the semantic graph using state-of-the-art natural language processing (NLP) techniques on COVID-19 publication texts (>100,000 texts). Next, the authors conduct an evolutionary analysis to capture the changes in COVID-19 research across time. Finally, the authors apply a link prediction study to detect novel COVID-19 research directions that are so far undiscovered.FindingsFindings reveal the success of the semantic graph in capturing scientific knowledge and its evolution. Meanwhile, the prediction experiments provide 79% accuracy on returning intelligible links, showing the reliability of the methods for predicting novel connections that could help scientists discover potential new directions.Originality/valueTo the authors’ knowledge, this is the first study to propose a holistic framework that includes encoding the scientific knowledge in a semantic graph, demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques.
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27
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Pedreschi N, Bernard C, Clawson W, Quilichini P, Barrat A, Battaglia D. Dynamic core-periphery structure of information sharing networks in entorhinal cortex and hippocampus. Netw Neurosci 2021; 4:946-975. [PMID: 33615098 PMCID: PMC7888487 DOI: 10.1162/netn_a_00142] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 04/16/2020] [Indexed: 02/01/2023] Open
Abstract
Neural computation is associated with the emergence, reconfiguration, and dissolution of cell assemblies in the context of varying oscillatory states. Here, we describe the complex spatiotemporal dynamics of cell assemblies through temporal network formalism. We use a sliding window approach to extract sequences of networks of information sharing among single units in hippocampus and entorhinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve through time and as a function of changing global brain state (theta vs. slow-wave oscillations). First, we find that information sharing networks display, at any time, a core-periphery structure in which an integrated core of more tightly functionally interconnected units links to more loosely connected network leaves. However the units participating to the core or to the periphery substantially change across time windows, with units entering and leaving the core in a smooth way. Second, we find that discrete network states can be defined on top of this continuously ongoing liquid core-periphery reorganization. Switching between network states results in a more abrupt modification of the units belonging to the core and is only loosely linked to transitions between global oscillatory states. Third, we characterize different styles of temporal connectivity that cells can exhibit within each state of the sharing network. While inhibitory cells tend to be central, we show that, otherwise, anatomical localization only poorly influences the patterns of temporal connectivity of the different cells. Furthermore, cells can change temporal connectivity style when the network changes state. Altogether, these findings reveal that the sharing of information mediated by the intrinsic dynamics of hippocampal and entorhinal cortex cell assemblies have a rich spatiotemporal structure, which could not have been identified by more conventional time- or state-averaged analyses of functional connectivity. It is generally thought that computations performed by local brain circuits rely on complex neural processes, associated with the flexible waxing and waning of cell assemblies, that is, an ensemble of cells firing in tight synchrony. Although cell assembly formation is inherently and unavoidably dynamical, it is still common to find studies in which essentially “static” approaches are used to characterize this process. In the present study, we adopt instead a temporal network approach. Avoiding usual time-averaging procedures, we reveal that hub neurons are not hardwired but that cells vary smoothly their degree of integration within the assembly core. Furthermore, our temporal network framework enables the definition of alternative possible styles of “hubness.” Some cells may share information with a multitude of other units but only in an intermittent manner, as “activists” in a flash mob. In contrast, some other cells may share information in a steadier manner, as resolute “lobbyists.” Finally, by avoiding averages over preimposed states, we show that within each global oscillatory state rich switching dynamics can take place between a repertoire of many available network states. We thus show that the temporal network framework provides a natural and effective language to rigorously describe the rich spatiotemporal patterns of information sharing instantiated by cell assembly evolution.
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Affiliation(s)
- Nicola Pedreschi
- Aix-Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| | - Christophe Bernard
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Wesley Clawson
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Pascale Quilichini
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Alain Barrat
- Aix-Marseille University, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| | - Demian Battaglia
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
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28
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Omidvarnia A, Zalesky A, Mansour L S, Van De Ville D, Jackson GD, Pedersen M. Temporal complexity of fMRI is reproducible and correlates with higher order cognition. Neuroimage 2021; 230:117760. [PMID: 33486124 DOI: 10.1016/j.neuroimage.2021.117760] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/18/2020] [Accepted: 01/05/2021] [Indexed: 02/08/2023] Open
Abstract
It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time TR) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed between temporal complexity of RSNs and fluid intelligence suggesting that complex dynamics of the human brain is an important attribute of high-level brain function.
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Affiliation(s)
- Amir Omidvarnia
- Institute of Bioengineering, Center for Neuroprosthetics, Center for Biomedical Imaging, EPFL, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Center for Biomedical Imaging, EPFL, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - Graeme D Jackson
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia; Department of Neurology, Austin Health, Melbourne, Australia.
| | - Mangor Pedersen
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia; Department of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand.
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Breastfeeding improves dynamic reorganization of functional connectivity in preterm infants: a temporal brain network study. Med Biol Eng Comput 2020; 58:2805-2819. [PMID: 32945999 DOI: 10.1007/s11517-020-02244-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 07/31/2020] [Indexed: 10/23/2022]
Abstract
Substantial evidences have shown the benefits of breastfeeding to infants in terms of better nutrition and neurodevelopmental outcome. However, the relationship between brain development and feeding in preterm infants, who are physiologically and developmentally immature at birth, is only beginning to be quantitatively assessed, coinciding with the recent advent of neuroimaging techniques. In the current work, we studied a sample of 50 preterm infants-born between 29 and 33 weeks (32.20 ± 0.89 weeks) of gestational age, where 30 of them were breastfed and the remaining 20 were formula-fed. Resting-state functional magnetic resonance imaging (fMRI) was recorded around term-equivalent age (40.00 ± 1.31 weeks, range 39-44 weeks) using a 1.5-T scanner under sedation condition. Temporal brain networks were estimated by the correlation of sliding time-window time courses among regions of a predefined atlas. Through our newly introduced temporal efficiency approach, we examined, for the first time, the 3D spatiotemporal architecture of the temporal brain network. We found prominent temporal small-world properties in both groups, suggesting the arrangement of dynamic functional connectivity permits effective coordination of various brain regions for efficient information transfer over time at both local and global levels. More importantly, we showed that breastfed preterm infants exhibited greater temporal global efficiency in comparison with formula-fed preterm infants. Specifically, we found localized elevation of temporal nodal properties in the right temporal gyrus and bilateral caudate. Taken together, these findings provide new evidence to support the notion that breast milk promotes early brain development and cognitive function, which may have neurobiological and public health implications for parents and pediatricians.Breastfeeding has long been recognized to have beneficial effect on early neurodevelopment in infants. However, the influence of breastfeeding on reorganization of functional connectivity in preterm infants are largely unknown. To this end, we utilized our recently developed temporal brain network analysis framework to investigate the dynamic reorganization of brain functional connectivity in preterm infants fed with breast milk. We found that beyond an optimal temporal small-world topology, breastfed preterm infants exhibited improved network efficiency at both global and regional levels in comparisons with those of formula-fed infants. Graphical abstract: Breastfeeding has long been recognized to have beneficial effect on early neurodevelopment in infants. However, the influence of breastfeeding on reorganization of brain functional connectivity in preterm infants are largely unknown. To this end, we utilized our recently developed temporal brain network analysis framework to investigate the dynamic reorganization of functional connectivity in preterm infants fed with breast milk. We found that beyond an optimal temporal small-world topology, breastfed preterm infants exhibited improved network efficiency at both global and regional levels in comparisons with those of formula-fed infants.
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30
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Fransson P, Thompson WH. Temporal flow of hubs and connectivity in the human brain. Neuroimage 2020; 223:117348. [PMID: 32898675 DOI: 10.1016/j.neuroimage.2020.117348] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 08/11/2020] [Accepted: 09/01/2020] [Indexed: 02/07/2023] Open
Abstract
Hubs in brain network connectivity have previously been observed using neuroimaging techniques and are generally believed to be of pivotal importance to establish and maintain a functional platform on which cognitively meaningful and energy-efficient neuronal communication can occur. However, little is known if hubs are static (i.e. a brain region is always a hub) or if these properties change over time (i.e. brain regions fluctuate in their 'hubness'). To address this question, we introduce two new methodological concepts, the flow of brain connectivity and node penalized shortest paths which are then applied to time-varying functional connectivity fMRI BOLD data. We show that the constellations of active hubs change over time in a non-trivial way and that activity of hubs is dependent on the temporal scale of investigation. Slower fluctuations in the number of active hubs that exceeded the degree expected by chance alone were detected primarily in subcortical structures. Moreover, we observed faster fluctuations in hub activity residing predominately in the default mode network that suggests dynamic events in brain connectivity. Our results suggest that the temporal behavior of connectivity hubs is a multilayered and complex issue where method-specific properties of temporal sensitivity to time-varying connectivity must be taken into account. We discuss our results in relation to the on-going discussion of the existence of discrete and stable states in the resting-brain and the role of network hubs in providing a scaffold for neuronal communication across time.
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Affiliation(s)
- Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, Nobels väg 9, SE-171 77, Stockholm, Sweden.
| | - William H Thompson
- Department of Clinical Neuroscience, Karolinska Institutet, Nobels väg 9, SE-171 77, Stockholm, Sweden; Department of Psychology, Stanford University, USA
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31
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Ghahari S, Salehi F, Farahani N, Coben R, Motie Nasrabadi A. Representing Temporal Network based on dDTF of EEG signals in Children with Autism and Healthy Children. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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32
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Wang H, Liu X, Hu H, Wan F, Li T, Gao L, Bezerianos A, Sun Y, Jung TP. Dynamic Reorganization of Functional Connectivity Unmasks Fatigue Related Performance Declines in Simulated Driving. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1790-1799. [DOI: 10.1109/tnsre.2020.2999599] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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33
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Thompson WH, Kastrati G, Finc K, Wright J, Shine JM, Poldrack RA. Time-varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation. Hum Brain Mapp 2020; 41:2347-2356. [PMID: 32058633 PMCID: PMC7268033 DOI: 10.1002/hbm.24950] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 01/10/2020] [Accepted: 02/03/2020] [Indexed: 11/07/2022] Open
Abstract
In network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behavior. Here, we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient [PC]) in time-varying contexts can potentially lead to misleading results. Specifically, with regards to the participation coefficient, increases in integration can be inferred when the opposite is occurring. Further, we present a temporal extension to the PC measure (temporal PC) that circumnavigates this problem by jointly considering all community partitions assigned to a node through time. The proposed method allows us to track a node's integration through time while adjusting for the possible changes in the community structure of the overall network.
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Affiliation(s)
- William Hedley Thompson
- Department of PsychologyStanford UniversityStanfordCalifornia
- Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
| | - Granit Kastrati
- Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
| | - Karolina Finc
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus University in ToruńToruńPoland
| | - Jessey Wright
- Department of PsychologyStanford UniversityStanfordCalifornia
- Department of PhilosophyStanford UniversityStanfordCalifornia
| | - James M. Shine
- Brain and Mind CentreThe University of SydneySydneyNew South WalesAustralia
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Ding K, Dragomir A, Bose R, Osborn LE, Seet MS, Bezerianos A, Thakor NV. Towards machine to brain interfaces: sensory stimulation enhances sensorimotor dynamic functional connectivity in upper limb amputees. J Neural Eng 2020; 17:035002. [DOI: 10.1088/1741-2552/ab882d] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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35
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Espinoza JL, Shah N, Singh S, Nelson KE, Dupont CL. Applications of weighted association networks applied to compositional data in biology. Environ Microbiol 2020; 22:3020-3038. [PMID: 32436334 DOI: 10.1111/1462-2920.15091] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 12/14/2022]
Abstract
Next-generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini-review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static-, temporal-, sample-specific- and differential-networks. The intention of this mini-review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)-unsupervised data-driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.
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Affiliation(s)
- Josh L Espinoza
- J. Craig Venter Institute, La Jolla, USA.,Applied Sciences, Durban University of Technology, Durban, South Africa
| | | | - Suren Singh
- Applied Sciences, Durban University of Technology, Durban, South Africa
| | - Karen E Nelson
- J. Craig Venter Institute, La Jolla, USA.,Applied Sciences, Durban University of Technology, Durban, South Africa.,J. Craig Venter Institute, Rockville, USA
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36
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Gilson M, Zamora-López G, Pallarés V, Adhikari MH, Senden M, Campo AT, Mantini D, Corbetta M, Deco G, Insabato A. Model-based whole-brain effective connectivity to study distributed cognition in health and disease. Netw Neurosci 2020; 4:338-373. [PMID: 32537531 PMCID: PMC7286310 DOI: 10.1162/netn_a_00117] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 12/02/2019] [Indexed: 12/12/2022] Open
Abstract
Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.
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Affiliation(s)
- Matthieu Gilson
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gorka Zamora-López
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Vicente Pallarés
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mohit H. Adhikari
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mario Senden
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands
| | | | - Dante Mantini
- Neuroplasticity and Motor Control Research Group, KU Leuven, Leuven, Belgium
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Maurizio Corbetta
- Department of Neuroscience, Venetian Institute of Molecular Medicine (VIMM) and Padova Neuroscience Center (PNC), University of Padua, Italy
- Department of Neurology, Radiology, and Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Gustavo Deco
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Andrea Insabato
- Institut de Neurosciences de la Timone, CNRS, Marseille, France
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Ghahari S, Farahani N, Fatemizadeh E, Motie Nasrabadi A. Investigating time-varying functional connectivity derived from the Jackknife Correlation method for distinguishing between emotions in fMRI data. Cogn Neurodyn 2020; 14:457-471. [PMID: 32655710 DOI: 10.1007/s11571-020-09579-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 01/27/2020] [Accepted: 03/06/2020] [Indexed: 02/06/2023] Open
Abstract
Investigating human brain activity during expressing emotional states provides deep insight into complex cognitive functions and neurological correlations inside the brain. To be able to resemble the brain function in the best manner, a complex and natural stimulus should be applied as well, the method used for data analysis should have fewer assumptions, simplifications, and parameter adjustment. In this study, we examined a functional magnetic resonance imaging dataset obtained during an emotional audio-movie stimulus associated with human life. We used Jackknife Correlation (JC) method to derive a representation of time-varying functional connectivity. We applied different binary measures and thoroughly investigated two weighted measures to study different properties of binary and weighted temporal networks. Using this approach, we indicated different aspects of human brain function during expressing different emotions. The findings of global and nodal measures could demonstrate a significant difference between emotions and significant regions in each emotion, respectively. Also, the temporal centrality properties of nodes were different in emotional states. Ultimately, we showed that the resulting measures of temporal snapshots created by JC method can distinguish between different emotions.
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Affiliation(s)
- Shabnam Ghahari
- Department of Biomedical Engineering-Bioelectric, Faculty of Medical Sciences and Technologies, Islamic Azad University Science and Research Branch, Tehran, Iran
| | - Naemeh Farahani
- Department of Biomedical Engineering-Bioelectric, Faculty of Medical Sciences and Technologies, Islamic Azad University Science and Research Branch, Tehran, Iran
| | - Emad Fatemizadeh
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Ali Motie Nasrabadi
- Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran
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38
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Lurie DJ, Kessler D, Bassett DS, Betzel RF, Breakspear M, Kheilholz S, Kucyi A, Liégeois R, Lindquist MA, McIntosh AR, Poldrack RA, Shine JM, Thompson WH, Bielczyk NZ, Douw L, Kraft D, Miller RL, Muthuraman M, Pasquini L, Razi A, Vidaurre D, Xie H, Calhoun VD. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci 2020; 4:30-69. [PMID: 32043043 PMCID: PMC7006871 DOI: 10.1162/netn_a_00116] [Citation(s) in RCA: 279] [Impact Index Per Article: 69.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 11/22/2019] [Indexed: 12/12/2022] Open
Abstract
The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.
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Affiliation(s)
- Daniel J. Lurie
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Daniel Kessler
- Departments of Statistics and Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Richard F. Betzel
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Breakspear
- University of Newcastle, Callaghan, NSW, 2308, Australia
- QIMR Berghofer, Brisbane, Australia
| | - Shella Kheilholz
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford CA, USA
| | - Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Anthony Randal McIntosh
- Rotman Research Institute - Baycrest Centre, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
| | | | - James M. Shine
- Brain and Mind Centre, University of Sydney, NSW, Australia
| | - William Hedley Thompson
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
| | - Dominik Kraft
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | | | - Muthuraman Muthuraman
- Biomedical Statistics and Multimodal Signal Processing Unit, Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adeel Razi
- Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, United Kingdom
| | - Hua Xie
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia, USA
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39
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Gao X, Zheng Q, Vega-Oliveros DA, Anghinoni L, Zhao L. Temporal Network Pattern Identification by Community Modelling. Sci Rep 2020; 10:240. [PMID: 31937862 PMCID: PMC6959265 DOI: 10.1038/s41598-019-57123-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 12/20/2019] [Indexed: 11/30/2022] Open
Abstract
Temporal network mining tasks are usually hard problems. This is because we need to face not only a large amount of data but also its non-stationary nature. In this paper, we propose a method for temporal network pattern representation and pattern change detection following the reductionist approach. The main idea is to model each stable (durable) state of a given temporal network as a community in a sampled static network and the temporal state change is represented by the transition from one community to another. For this purpose, a reduced static single-layer network, called a target network, is constructed by sampling and rearranging the original temporal network. Our approach provides a general way not only for temporal networks but also for data stream mining in topological space. Simulation results on artificial and real temporal networks show that the proposed method can group different temporal states into different communities with a very reduced amount of sampled nodes.
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Affiliation(s)
- Xubo Gao
- Henan Key Laboratory on Public Opinion Intelligent Analysis, School of Computer Science, Zhongyuan University of Technology, ZhengZhou, China
| | - Qiusheng Zheng
- Henan Key Laboratory on Public Opinion Intelligent Analysis, School of Computer Science, Zhongyuan University of Technology, ZhengZhou, China
| | - Didier A Vega-Oliveros
- Faculty of Philosophy, Sciences and Letters at Ribeirão Preto (FFCLRP),University of São Paulo (USP), Ribeirão Preto, SP, Brazil
- Indiana University, School of Informatics, Computing and Engineering, Bloomington, IN, USA
| | - Leandro Anghinoni
- Institute of Mathematical and Computer Sciences (ICMC-USP), University of São Paulo (USP), São Carlos, SP, Brazil.
| | - Liang Zhao
- Faculty of Philosophy, Sciences and Letters at Ribeirão Preto (FFCLRP),University of São Paulo (USP), Ribeirão Preto, SP, Brazil
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40
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Long Y, Cao H, Yan C, Chen X, Li L, Castellanos FX, Bai T, Bo Q, Chen G, Chen N, Chen W, Cheng C, Cheng Y, Cui X, Duan J, Fang Y, Gong Q, Guo W, Hou Z, Hu L, Kuang L, Li F, Li K, Li T, Liu Y, Luo Q, Meng H, Peng D, Qiu H, Qiu J, Shen Y, Shi Y, Si T, Wang C, Wang F, Wang K, Wang L, Wang X, Wang Y, Wu X, Wu X, Xie C, Xie G, Xie H, Xie P, Xu X, Yang H, Yang J, Yao J, Yao S, Yin Y, Yuan Y, Zhang A, Zhang H, Zhang K, Zhang L, Zhang Z, Zhou R, Zhou Y, Zhu J, Zou C, Zang Y, Zhao J, Kin-Yuen Chan C, Pu W, Liu Z. Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium. NEUROIMAGE-CLINICAL 2020; 26:102163. [PMID: 31953148 PMCID: PMC7229351 DOI: 10.1016/j.nicl.2020.102163] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 11/17/2019] [Accepted: 01/02/2020] [Indexed: 02/05/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. METHODS Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. RESULTS The group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10-3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients. CONCLUSIONS Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China; Mental Health Institute, Central South University, Changsha, Hunan 410011, China; Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Hengyi Cao
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06511, USA.
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; Magnetic Resonance Imaging Research Center and Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Child and Adolescent Psychiatry, NYU School of Medicine, New York, NY 10016, USA
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Le Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU School of Medicine, New York, NY 10016, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
| | - Tongjian Bai
- The first affiliated hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Qijing Bo
- Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
| | - Guanmao Chen
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510630, China
| | - Ningxuan Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Chen
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China
| | - Chang Cheng
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
| | - Xilong Cui
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Jia Duan
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, Liaoning 110001, China
| | - Yiru Fang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Qiyong Gong
- Huaxi MR Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Wenbin Guo
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Zhenghua Hou
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, Jiangsu 210096, China
| | - Lan Hu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Li Kuang
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Feng Li
- Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
| | - Kaiming Li
- Huaxi MR Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Tao Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yansong Liu
- Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu 215137, China
| | - Qinghua Luo
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Huaqing Meng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Daihui Peng
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Haitang Qiu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jiang Qiu
- Faculty of Psychology, Southwest University, Chongqing 400716, China
| | - Yuedi Shen
- Department of Diagnostics, Affiliated Hospital, Medical School, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Yushu Shi
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Tianmei Si
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
| | - Chuanyue Wang
- Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
| | - Fei Wang
- Department of Psychiatry, First Affiliated Hospital, China Medical University, Shenyang, Liaoning 110001, China
| | - Kai Wang
- The first affiliated hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Li Wang
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
| | - Xiang Wang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Ying Wang
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510630, China
| | - Xiaoping Wu
- Xi'an Central Hospital, Xi'an, Shaanxi 710003, China
| | - Xinran Wu
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Chunming Xie
- Department of Neurology, Affiliated Zhongda Hospital of Southeast University, Nanjing, Jiangsu 210009, China
| | - Guangrong Xie
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Haiyan Xie
- Department of Psychiatry, The Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Peng Xie
- Institute of Neuroscience, Chongqing Medical University, Chongqing 400016, China; Chongqing Key Laboratory of Neurobiology, Chongqing 400016, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
| | - Hong Yang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Jian Yang
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Jiashu Yao
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China
| | - Shuqiao Yao
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Yingying Yin
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, Jiangsu 210096, China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, Jiangsu 210096, China
| | - Aixia Zhang
- First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Hong Zhang
- Xi'an Central Hospital, Xi'an, Shaanxi 710003, China
| | - Kerang Zhang
- First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Lei Zhang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhijun Zhang
- Xi'an Central Hospital, Xi'an, Shaanxi 710003, China
| | - Rubai Zhou
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yiting Zhou
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Junjuan Zhu
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Chaojie Zou
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
| | - Yufeng Zang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang 311121, China
| | - Jingping Zhao
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | | | - Weidan Pu
- Medical Psychological Institute, Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China; Mental Health Institute, Central South University, Changsha, Hunan 410011, China.
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Colliri T, Zhao L. Analyzing the Bills-Voting Dynamics and Predicting Corruption-Convictions Among Brazilian Congressmen Through Temporal Networks. Sci Rep 2019; 9:16754. [PMID: 31728035 PMCID: PMC6856083 DOI: 10.1038/s41598-019-53252-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 10/24/2019] [Indexed: 11/11/2022] Open
Abstract
In this paper, we propose a network-based technique to analyze bills-voting data comprising the votes of Brazilian congressmen for a period of 28 years. The voting sessions are initially mapped into static networks, where each node represents a congressman and each edge stands for the similarity of votes between a pair of congressmen. Afterwards, the constructed static networks are converted to temporal networks. Our analyses on the temporal networks capture some of the main political changes happened in Brazil during the period of time under consideration. Moreover, we find out that the bills-voting networks can be used to identify convicted politicians, who commit corruption or other financial crimes. Therefore, we propose two conviction prediction methods, one is based on the highest weighted convicted neighbor and the other is based on link prediction techniques. It is a surprise to us that the high accuracy (up to 90% by the link prediction method) on predicting convictions is achieved only through bills-voting data, without taking into account any financial information beforehand. Such a feature makes possible to monitor congressmen just by considering their legal public activities. In this way, our work contributes to the large scale public data study using complex networks.
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Affiliation(s)
- Tiago Colliri
- Dept. of Computer Science, ICMC-USP, Sao Carlos, Brazil.
| | - Liang Zhao
- Dept. of Computing and Mathematics, FFCLRP-USP, Ribeirao Preto, Brazil
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Surano FV, Bongiorno C, Zino L, Porfiri M, Rizzo A. Backbone reconstruction in temporal networks from epidemic data. Phys Rev E 2019; 100:042306. [PMID: 31770979 PMCID: PMC7217498 DOI: 10.1103/physreve.100.042306] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Indexed: 01/22/2023]
Abstract
Many complex systems are characterized by time-varying patterns of interactions. These interactions comprise strong ties, driven by dyadic relationships, and weak ties, based on node-specific attributes. The interplay between strong and weak ties plays an important role on dynamical processes that could unfold on complex systems. However, seldom do we have access to precise information about the time-varying topology of interaction patterns. A particularly elusive question is to distinguish strong from weak ties, on the basis of the sole node dynamics. Building upon analytical results, we propose a statistically-principled algorithm to reconstruct the backbone of strong ties from data of a spreading process, consisting of the time series of individuals' states. Our method is numerically validated over a range of synthetic datasets, encapsulating salient features of real-world systems. Motivated by compelling evidence, we propose the integration of our algorithm in a targeted immunization strategy that prioritizes influential nodes in the inferred backbone. Through Monte Carlo simulations on synthetic networks and a real-world case study, we demonstrate the viability of our approach.
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Affiliation(s)
- Francesco Vincenzo Surano
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
- Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, New York 11201, USA
| | - Christian Bongiorno
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
- Laboratoire de Mathématiques et Informatique pour les Systèmes Complexes, CentraleSupélec, Université Paris Saclay, 91190 Gif-sur-Yvette, France
| | - Lorenzo Zino
- Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, New York 11201, USA
| | - Maurizio Porfiri
- Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, New York 11201, USA
| | - Alessandro Rizzo
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
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Kaboodvand N, van den Heuvel MP, Fransson P. Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates. Netw Neurosci 2019; 3:1094-1120. [PMID: 31637340 PMCID: PMC6779267 DOI: 10.1162/netn_a_00104] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Accepted: 07/24/2019] [Indexed: 11/25/2022] Open
Abstract
Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal coactivation patterns that are highly similar to empirical functional connectivity patterns during resting state. Importantly, previous studies have shown that spontaneous fluctuations in coactivation patterns of distributed brain regions have an inherent dynamic nature with regard to the frequency spectrum of intrinsic brain oscillations. In this modeling study, we introduced frequency dynamics into a system of coupled oscillators, where each oscillator represents the local mean-field model of a brain region. We first showed that the collective behavior of interacting oscillators reproduces previously shown features of brain dynamics. Second, we examined the effect of simulated lesions in gray matter by applying an in silico perturbation protocol to the brain model. We present a new approach to map the effects of vulnerability in brain networks and introduce a measure of regional hazardousness based on mapping of the degree of divergence in a feature space.
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Affiliation(s)
- Neda Kaboodvand
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Martijn P. van den Heuvel
- Dutch Connectome Lab, Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, VU Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, 1081 HV, The Netherlands
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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Aspembitova A, Feng L, Melnikov V, Chew LY. Fitness preferential attachment as a driving mechanism in bitcoin transaction network. PLoS One 2019; 14:e0219346. [PMID: 31442228 PMCID: PMC6707628 DOI: 10.1371/journal.pone.0219346] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Accepted: 06/18/2019] [Indexed: 11/19/2022] Open
Abstract
Bitcoin is the earliest cryptocurrency and among the most successful ones to date. Recently, its dynamical evolution has attracted the attention of the research community due to its completeness and richness in historical records. In this paper, we focus on the detailed evolution of bitcoin trading with the aim of elucidating the mechanism that drives the formation of the bitcoin transaction network. Our empirical investigation reveals that although the temporal properties of the transaction network possesses scale-free degree distribution like many other networks, its formation mechanism is different from the commonly assumed models of degree preferential attachment or wealth preferential attachment. By defining the fitness value of each node as the ability of the node to attract new connections, we have instead uncovered that the observed scale-free degree distribution results from the intrinsic fitness of each node following a power-law distribution. Our finding thus suggests that the "good-get-richer" rather than the "rich-get-richer" paradigm operates within the bitcoin ecosystem. Based on these findings, we propose a model that captures the temporal generative process by means of a fitness preferential attachment and data-driven birth/death mechanism. Our proposed model is able to produce structural properties in good agreement with those obtained from the empirical bitcoin network.
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Affiliation(s)
- Ayana Aspembitova
- Division of Physics and Applied Physics, Nanyang Technological University, 21 Nanyang Link, Singapore, Singapore
- Institute of High Performance Computing, Agency for Science, Technology and Research, 1 Fusionopolis Way, Singapore, Singapore
| | - Ling Feng
- Institute of High Performance Computing, Agency for Science, Technology and Research, 1 Fusionopolis Way, Singapore, Singapore
- Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore, Singapore
| | - Valentin Melnikov
- Complexity Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore, Singapore
| | - Lock Yue Chew
- Division of Physics and Applied Physics, Nanyang Technological University, 21 Nanyang Link, Singapore, Singapore
- Complexity Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore, Singapore
- Data Science and Artificial Intelligence Research Centre, Block N4 #02a-32, Nanyang Avenue, Nanyang Technological University, Singapore, Singapore
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45
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Gilson M, Kouvaris NE, Deco G, Mangin JF, Poupon C, Lefranc S, Rivière D, Zamora-López G. Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability. Neuroimage 2019; 201:116007. [PMID: 31306771 DOI: 10.1016/j.neuroimage.2019.116007] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 06/04/2019] [Accepted: 07/09/2019] [Indexed: 11/26/2022] Open
Abstract
Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often "static" despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time, moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.
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Affiliation(s)
- Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer de Ramon Trias Fargas 25-27, Barcelona, 08005, Spain.
| | - Nikos E Kouvaris
- Namur Institute for Complex Systems (naXys), Department of Mathematics, University of Namur, Rempart de la Vierge 8, B 5000, Namur, Belgium; DRIBIA Data Research S.L., Barcelona, Spain
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer de Ramon Trias Fargas 25-27, Barcelona, 08005, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona, 08010, Spain
| | | | - Cyril Poupon
- Neurospin, CEA, Paris Saclay University, Gif-sur-Yvette, 91191, France
| | - Sandrine Lefranc
- Neurospin, CEA, Paris Saclay University, Gif-sur-Yvette, 91191, France
| | - Denis Rivière
- Neurospin, CEA, Paris Saclay University, Gif-sur-Yvette, 91191, France
| | - Gorka Zamora-López
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer de Ramon Trias Fargas 25-27, Barcelona, 08005, Spain
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Sun Y, Collinson SL, Suckling J, Sim K. Dynamic Reorganization of Functional Connectivity Reveals Abnormal Temporal Efficiency in Schizophrenia. Schizophr Bull 2019; 45:659-669. [PMID: 29878254 PMCID: PMC6483577 DOI: 10.1093/schbul/sby077] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Emerging evidence suggests that schizophrenia is associated with brain dysconnectivity. Nonetheless, the implicit assumption of stationary functional connectivity (FC) adopted in most previous resting-state functional magnetic resonance imaging (fMRI) studies raises an open question of schizophrenia-related aberrations in dynamic properties of resting-state FC. This study introduces an empirical method to examine the dynamic functional dysconnectivity in patients with schizophrenia. Temporal brain networks were estimated from resting-state fMRI of 2 independent datasets (patients/controls = 18/19 and 53/57 for self-recorded dataset and a publicly available replication dataset, respectively) by the correlation of sliding time-windowed time courses among regions of a predefined atlas. Through the newly introduced temporal efficiency approach and temporal random network models, we examined, for the first time, the 3D spatiotemporal architecture of the temporal brain network. We found that although prominent temporal small-world properties were revealed in both groups, temporal brain networks of patients with schizophrenia in both datasets showed a significantly higher temporal global efficiency, which cannot be simply attributable to head motion and sampling error. Specifically, we found localized changes of temporal nodal properties in the left frontal, right medial parietal, and subcortical areas that were associated with clinical features of schizophrenia. Our findings demonstrate that altered dynamic FC may underlie abnormal brain function and clinical symptoms observed in schizophrenia. Moreover, we provide new evidence to extend the dysconnectivity hypothesis in schizophrenia from static to dynamic brain network and highlight the potential of aberrant brain dynamic FC in unraveling the pathophysiologic mechanisms of the disease.
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Affiliation(s)
- Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, China,Centre for Life Sciences, National University of Singapore, Singapore,To whom correspondence should be addressed; Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, 310000, Zhejiang, China; tel: +86-18321575735, fax: +86-57187951676, e-mail:
| | - Simon L Collinson
- Department of Psychology, Faculty of Arts & Social Sciences, National University of Singapore, Singapore
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, School of Clinical Medicine, Herchel Smith Building for Brain and Mind Sciences, University of Cambridge, Cambridge, United Kingdom
| | - Kang Sim
- Department of General Psychiatry, Institute of Mental Health (IMH), Singapore,Department of Research, Institute of Mental Health (IMH), Singapore
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Zhang H, Shen D, Lin W. Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts. Neuroimage 2019; 185:664-684. [PMID: 29990581 PMCID: PMC6289773 DOI: 10.1016/j.neuroimage.2018.07.004] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 05/19/2018] [Accepted: 07/02/2018] [Indexed: 12/16/2022] Open
Abstract
Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects. Recently, infant rs-fMRI studies have become an area of active research. After a decade of gap filling studies, many facets of the brain functional development from early infancy to toddler has been uncovered. However, infant rs-fMRI is still in its infancy. The image analysis tools for neonates and young infants can be quite different from those for adults. From data analysis to result interpretation, more questions and issues have been raised, and new hypotheses have been formed. With the anticipated availability of unprecedented high-resolution rs-fMRI and dedicated analysis pipelines from the Baby Connectome Project (BCP), it is important now to revisit previous findings and hypotheses, discuss and comment existing issues and problems, and make a "to-do-list" for the future studies. This review article aims to comprehensively review a decade of the findings, unveiling hidden jewels of the fields of developmental neuroscience and neuroimage computing. Emphases will be given to early infancy, particularly the first few years of life. In this review, an end-to-end summary, from infant rs-fMRI experimental design to data processing, and from the development of individual functional systems to large-scale brain functional networks, is provided. A comprehensive summary of the rs-fMRI findings in developmental patterns is highlighted. Furthermore, an extensive summary of the neurodevelopmental disorders and the effects of other hazardous factors is provided. Finally, future research trends focusing on emerging dynamic functional connectivity and state-of-the-art functional connectome analysis are summarized. In next decade, early infant rs-fMRI and developmental connectome study could be one of the shining research topics.
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Affiliation(s)
- Han Zhang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA.
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48
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Chen B. Abnormal cortical region and subsystem complexity in dynamical functional connectivity of chronic schizophrenia: A new graph index for fMRI analysis. J Neurosci Methods 2018; 311:28-37. [PMID: 30316890 DOI: 10.1016/j.jneumeth.2018.10.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 10/10/2018] [Accepted: 10/10/2018] [Indexed: 11/28/2022]
Abstract
OBJECTIVES Schizophrenia is a predominant product of pathological alterations distributed throughout interconnected neural systems. Designing new objectively diagnostic methods are burning questions. Dynamical functional connectivity (DFCs) methodology based on fMRI data is an effective lever to investigate changeability evolution in macroscopic neural activity patterns underlying critical aspects of cognition and behavior. However, region properties of brain architecture have been less investigated by special indexes of dynamical graph in general mental disorders. METHODS Embracing the network dynamics concept, we introduce topology entropy index (TE-scores) which is focused on time-varying aspects of FCs, hence develop a new framework for researching the dysfunctional roots of schizophrenia in holism significance. In this work, the important process is to uncover noticeable regions endowed with antagonistic stance in TE-scores of between morbid and normal DFCs of 63 healthy controls (HCs) and 57 chronic schizophrenia patients (SZs). RESULTS For the whole brain region levels, right olfactory, right hippocampus, left parahippocampal gyrus, right parahippocampal gyrus, left amygdala, and left cuneus in SZs are endowed with significantly different TE-scores. At brain subsystems level, TE-scores in DMN are abnormal in the SZs. Comparison with existing method(s): Topology entropy in DFCs is introduced to explore the dynamical information organization of diverse regions and their abnormal changes in mental illness. Several classical graph indexes (such as degree strength, betweenness, centrality) in the static brain network measure the region importance of FCs under senses of information integration and separation process. Although highly related to degree strength by comparing the corresponding values, topology entropy further explores the regions' aberrant adaptability of functional contact and function switching. CONCLUSION TE-scores of abnormal regions in SZs are associated to the passive apathetic social withdrawal, unusual thought content, disturbance of volition, preoccupation, active social avoidance and hallucinatory symptoms. Thought the strict contrastive study, it is worth stressing that topology entropy is a meaningful biological marker to excavating schizophrenic psychopathology.
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Affiliation(s)
- Bo Chen
- School of Science, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, PR China.
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49
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Latapy M, Viard T, Magnien C. Stream graphs and link streams for the modeling of interactions over time. SOCIAL NETWORK ANALYSIS AND MINING 2018. [DOI: 10.1007/s13278-018-0537-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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50
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Fransson P, Schiffler BC, Thompson WH. Brain network segregation and integration during an epoch-related working memory fMRI experiment. Neuroimage 2018; 178:147-161. [PMID: 29777824 DOI: 10.1016/j.neuroimage.2018.05.040] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 05/15/2018] [Accepted: 05/15/2018] [Indexed: 10/16/2022] Open
Abstract
The characterization of brain subnetwork segregation and integration has previously focused on changes that are detectable at the level of entire sessions or epochs of imaging data. In this study, we applied time-varying functional connectivity analysis together with temporal network theory to calculate point-by-point estimates in subnetwork segregation and integration during an epoch-based (2-back, 0-back, baseline) working memory fMRI experiment as well as during resting-state. This approach allowed us to follow task-related changes in subnetwork segregation and integration at a high temporal resolution. At a global level, the cognitively more taxing 2-back epochs elicited an overall stronger response of integration between subnetworks compared to the 0-back epochs. Moreover, the visual, sensorimotor and fronto-parietal subnetworks displayed characteristic and distinct temporal profiles of segregation and integration during the 0- and 2-back epochs. During the interspersed epochs of baseline, several subnetworks, including the visual, fronto-parietal, cingulo-opercular and dorsal attention subnetworks showed pronounced increases in segregation. Using a drift diffusion model we show that the response time for the 2-back trials are correlated with integration for the fronto-parietal subnetwork and correlated with segregation for the visual subnetwork. Our results elucidate the fast-evolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.
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Affiliation(s)
- Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institute, Sweden.
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