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Wang Y, Han Z, Wang C, Liu J, Guo J, Miao P, Wei Y, Wu L, Wang X, Wang P, Zhang Y, Cheng J, Fan S. Withdrawn: The altered dynamic community structure for adaptive adjustment in stroke patients with multidomain cognitive impairments: A multilayer network analysis. Comput Biol Med 2024:108712. [PMID: 38906761 DOI: 10.1016/j.compbiomed.2024.108712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/10/2024] [Accepted: 06/03/2024] [Indexed: 06/23/2024]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconveniencethis may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.
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Affiliation(s)
- Yingying Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zongli Han
- Department of Neurosurgery, Peking University Shenzhen Hospital, Futian District Shenzhen Guangdong, P.R. China
| | - Caihong Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingchun Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jun Guo
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Peifang Miao
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ying Wei
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Luobing Wu
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peipei Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Siyuan Fan
- Cardiovascular Center, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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2
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Kenett YN, Chrysikou EG, Bassett DS, Thompson-Schill SL. Neural Dynamics During the Generation and Evaluation of Creative and Non-Creative Ideas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.15.589621. [PMID: 38659810 PMCID: PMC11042297 DOI: 10.1101/2024.04.15.589621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
What are the neural dynamics that drive creative thinking? Recent studies have provided much insight into the neural mechanisms of creative thought. Specifically, the interaction between the executive control, default mode, and salience brain networks has been shown to be an important marker of individual differences in creative ability. However, how these different brain systems might be recruited dynamically during the two key components of the creative process-generation and evaluation of ideas-remains far from understood. In the current study we applied state-of-the-art network neuroscience methodologies to examine the neural dynamics related to the generation and evaluation of creative and non-creative ideas using a novel within-subjects design. Participants completed two functional magnetic resonance imaging sessions, taking place a week apart. In the first imaging session, participants generated either creative (alternative uses) or non-creative (common characteristics) responses to common objects. In the second imaging session, participants evaluated their own creative and non-creative responses to the same objects. Network neuroscience methods were applied to examine and directly compare reconfiguration, integration, and recruitment of brain networks during these four conditions. We found that generating creative ideas led to significantly higher network reconfiguration than generating non-creative ideas, whereas evaluating creative and non-creative ideas led to similar levels of network integration. Furthermore, we found that these differences were attributable to different dynamic patterns of neural activity across the executive control, default mode, and salience networks. This study is the first to show within-subject differences in neural dynamics related to generating and evaluating creative and non-creative ideas.
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Affiliation(s)
- Yoed N Kenett
- Faculty of Data and Decision Sciences, Technion, Israel Institute of Technology, Haifa, Israel, 3200003
| | - Evangelia G Chrysikou
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
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3
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Lima Dias Pinto I, Garcia JO, Bansal K. Optimizing parameter search for community detection in time-evolving networks of complex systems. CHAOS (WOODBURY, N.Y.) 2024; 34:023133. [PMID: 38386910 DOI: 10.1063/5.0168783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 01/20/2024] [Indexed: 02/24/2024]
Abstract
Network representations have been effectively employed to analyze complex systems across various areas and applications, leading to the development of network science as a core tool to study systems with multiple components and complex interactions. There is a growing interest in understanding the temporal dynamics of complex networks to decode the underlying dynamic processes through the temporal changes in network structures. Community detection algorithms, which are specialized clustering algorithms, have been instrumental in studying these temporal changes. They work by grouping nodes into communities based on the structure and intensity of network connections over time, aiming to maximize the modularity of the network partition. However, the performance of these algorithms is highly influenced by the selection of resolution parameters of the modularity function used, which dictate the scale of the represented network, in both size of communities and the temporal resolution of the dynamic structure. The selection of these parameters has often been subjective and reliant on the characteristics of the data used to create the network. Here, we introduce a method to objectively determine the values of the resolution parameters based on the elements of self-organization and scale-invariance. We propose two key approaches: (1) minimization of biases in spatial scale network characterization and (2) maximization of scale-freeness in temporal network reconfigurations. We demonstrate the effectiveness of these approaches using benchmark network structures as well as real-world datasets. To implement our method, we also provide an automated parameter selection software package that can be applied to a wide range of complex systems.
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Affiliation(s)
| | - Javier Omar Garcia
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, USA
| | - Kanika Bansal
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, USA
- Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Maryland 21250, USA
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4
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Yi C, Fan Y, Wu Y. Cross-module switching diversity of brain network nodes in resting and cognitive states. Cogn Neurodyn 2023; 17:1485-1499. [PMID: 37974588 PMCID: PMC10640499 DOI: 10.1007/s11571-022-09894-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 11/03/2022] Open
Abstract
Large-scale brain network dynamics reflect state change in brain activities and have potential effects on cognition. Such dynamics can be described by node temporal switching between modules; however, there are only a few studies on the influence of brain network node switching on brain cognition. Based on the functional magnetic resonance imaging (fMRI) data of resting and task states, we constructed dynamic functional networks using overlap sliding-time windows and applied multilayer network analysis to study the behaviours of nodes across brain modules. We found that (i) nodes with a high level switching rate in the resting-state mainly come from the default network, while nodes with a low level of switching rate mainly come from the visual network, (ii) nodes with a high switching rate have lower clustering coefficients and shorter characteristic path lengths, which are mainly affected by the somatomotor network and dorsal attention network; and (iii) in task states, there is still a negative correlation between switching rate, clustering coefficient and characteristic path length. However, the main subsystems that affect brain functions are regulated by the tasks. Our findings not only reveal the relevant characteristics of network node switching behaviours but also provide new insights for further understanding the complex functions of the brain. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09894-z.
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Affiliation(s)
- Chao Yi
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an, 710049 China
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5
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Jiahui G, Feilong M, Nastase SA, Haxby JV, Gobbini MI. Cross-movie prediction of individualized functional topography. eLife 2023; 12:e86037. [PMID: 37994909 PMCID: PMC10666932 DOI: 10.7554/elife.86037] [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: 01/09/2023] [Accepted: 11/09/2023] [Indexed: 11/24/2023] Open
Abstract
Participant-specific, functionally defined brain areas are usually mapped with functional localizers and estimated by making contrasts between responses to single categories of input. Naturalistic stimuli engage multiple brain systems in parallel, provide more ecologically plausible estimates of real-world statistics, and are friendly to special populations. The current study shows that cortical functional topographies in individual participants can be estimated with high fidelity from naturalistic stimuli. Importantly, we demonstrate that robust, individualized estimates can be obtained even when participants watched different movies, were scanned with different parameters/scanners, and were sampled from different institutes across the world. Our results create a foundation for future studies that allow researchers to estimate a broad range of functional topographies based on naturalistic movies and a normative database, making it possible to integrate high-level cognitive functions across datasets from laboratories worldwide.
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Affiliation(s)
- Guo Jiahui
- Center for Cognitive Neuroscience, Dartmouth CollegeHanoverUnited States
| | - Ma Feilong
- Center for Cognitive Neuroscience, Dartmouth CollegeHanoverUnited States
| | - Samuel A Nastase
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - James V Haxby
- Center for Cognitive Neuroscience, Dartmouth CollegeHanoverUnited States
| | - M Ida Gobbini
- Department of Medical and Surgical Sciences (DIMEC), University of BolognaBolognaItaly
- IRCCS, Istituto delle Scienze Neurologiche di BolognaBolognaItaly
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6
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von Schwanenflug N, Koch SP, Krohn S, Broeders TAA, Lydon-Staley DM, Bassett DS, Schoonheim MM, Paul F, Finke C. Increased flexibility of brain dynamics in patients with multiple sclerosis. Brain Commun 2023; 5:fcad143. [PMID: 37188221 PMCID: PMC10176242 DOI: 10.1093/braincomms/fcad143] [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: 09/16/2022] [Revised: 03/08/2023] [Accepted: 04/28/2023] [Indexed: 05/17/2023] Open
Abstract
Patients with multiple sclerosis consistently show widespread changes in functional connectivity. Yet, alterations are heterogeneous across studies, underscoring the complexity of functional reorganization in multiple sclerosis. Here, we aim to provide new insights by applying a time-resolved graph-analytical framework to identify a clinically relevant pattern of dynamic functional connectivity reconfigurations in multiple sclerosis. Resting-state data from 75 patients with multiple sclerosis (N = 75, female:male ratio of 3:2, median age: 42.0 ± 11.0 years, median disease duration: 6 ± 11.4 years) and 75 age- and sex-matched controls (N = 75, female:male ratio of 3:2, median age: 40.2 ± 11.8 years) were analysed using multilayer community detection. Local, resting-state functional system and global levels of dynamic functional connectivity reconfiguration were characterized using graph-theoretical measures including flexibility, promiscuity, cohesion, disjointedness and entropy. Moreover, we quantified hypo- and hyper-flexibility of brain regions and derived the flexibility reorganization index as a summary measure of whole-brain reorganization. Lastly, we explored the relationship between clinical disability and altered functional dynamics. Significant increases in global flexibility (t = 2.38, PFDR = 0.024), promiscuity (t = 1.94, PFDR = 0.038), entropy (t = 2.17, PFDR = 0.027) and cohesion (t = 2.45, PFDR = 0.024) were observed in patients and were driven by pericentral, limbic and subcortical regions. Importantly, these graph metrics were correlated with clinical disability such that greater reconfiguration dynamics tracked greater disability. Moreover, patients demonstrate a systematic shift in flexibility from sensorimotor areas to transmodal areas, with the most pronounced increases located in regions with generally low dynamics in controls. Together, these findings reveal a hyperflexible reorganization of brain activity in multiple sclerosis that clusters in pericentral, subcortical and limbic areas. This functional reorganization was linked to clinical disability, providing new evidence that alterations of multilayer temporal dynamics play a role in the manifestation of multiple sclerosis.
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Affiliation(s)
- Nina von Schwanenflug
- Department of Neurology and Experimental Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10098, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin 10117, Germany
| | - Stefan P Koch
- Department of Experimental Neurology, Center for Stroke Research Berlin, Berlin 10117, Germany
- NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Stephan Krohn
- Department of Neurology and Experimental Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10098, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin 10117, Germany
| | - Tommy A A Broeders
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007 MB, The Netherlands
| | - David M Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, PA, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia 19104, PA, USA
| | - Dani S Bassett
- Department of Biological Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, PA, USA
- Santa Fe Institute, Santa Fe 87501, NM, USA
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007 MB, The Netherlands
| | - Friedemann Paul
- Department of Neurology and Experimental Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10098, Germany
- Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité—Universitätsmedizin Berlin, Berlin 10117, Germany
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10017, Germany
| | - Carsten Finke
- Correspondence to: Carsten Finke Charité - Universitätsklinikum Berlin Department of Neurology and Experimental Neurology Campus Mitte, Bonhoeffer Weg 3, 10098 Berlin, Germany E-mail:
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7
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Wu Z, Xu J, Nürnberger A, Sabel BA. Global brain network modularity dynamics after local optic nerve damage following noninvasive brain stimulation: an EEG-tracking study. Cereb Cortex 2022; 33:4729-4739. [PMID: 36197322 DOI: 10.1093/cercor/bhac375] [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: 04/13/2022] [Revised: 08/25/2022] [Accepted: 08/26/2022] [Indexed: 11/13/2022] Open
Abstract
Tightly connected clusters of nodes, called communities, interact in a time-dependent manner in brain functional connectivity networks (FCN) to support complex cognitive functions. However, little is known if and how different nodes synchronize their neural interactions to form functional communities ("modules") during visual processing and if and how this modularity changes postlesion (progression or recovery) following neuromodulation. Using the damaged optic nerve as a paradigm, we now studied brain FCN modularity dynamics to better understand module interactions and dynamic reconfigurations before and after neuromodulation with noninvasive repetitive transorbital alternating current stimulation (rtACS). We found that in both patients and controls, local intermodule interactions correlated with visual performance. However, patients' recovery of vision after treatment with rtACS was associated with improved interaction strength of pathways linked to the attention module, and it improved global modularity and increased the stability of FCN. Our results show that temporal coordination of multiple cortical modules and intermodule interaction are functionally relevant for visual processing. This modularity can be neuromodulated with tACS, which induces a more optimal balanced and stable multilayer modular structure for visual processing by enhancing the interaction of neural pathways with the attention network module.
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Affiliation(s)
- Zheng Wu
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Haus 65, Leipziger Strasse 44, Magdeburg 39120, Germany.,Data and Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke University of Magdeburg, Gebaeude 29, Universitaetsplatz 2, Magdeburg 39106, Germany
| | - Jiahua Xu
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Haus 65, Leipziger Strasse 44, Magdeburg 39120, Germany.,Hertie Institute for Clinical Brain Research, Department Neurology and Stroke, Hoppe-Seyler-Strasse 3, Tübingen 72076, Germany
| | - Andreas Nürnberger
- Data and Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke University of Magdeburg, Gebaeude 29, Universitaetsplatz 2, Magdeburg 39106, Germany
| | - Bernhard A Sabel
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Haus 65, Leipziger Strasse 44, Magdeburg 39120, Germany
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8
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Wang B, Pan T, Guo M, Li Z, Yu X, Li D, Niu Y, Cui X, Xiang J. Abnormal dynamic reconfiguration of the large-scale functional network in schizophrenia during the episodic memory task. Cereb Cortex 2022; 33:4135-4144. [PMID: 36030383 DOI: 10.1093/cercor/bhac331] [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: 05/11/2022] [Revised: 07/27/2022] [Accepted: 07/28/2022] [Indexed: 11/13/2022] Open
Abstract
Episodic memory deficits are the core feature in schizophrenia (SCZ). Numerous studies have revealed abnormal brain activity associated with this disorder during episodic memory, however previous work has only relied on static analysis methods that treat the brain as a static monolithic structure, ignoring the dynamic features at different time scales. Here, we applied dynamic functional connectivity analysis to functional magnetic resonance imaging data during episodic memory and quantify integration and recruitment metrics to reveal abnormal dynamic reconfiguration of brain networks in SCZ. In the specific frequency band of 0.06-0.125 Hz, SCZ showed significantly higher integration during encoding and retrieval, and the abnormalities were mainly in the default mode, frontoparietal, and cingulo-opercular modules. Recruitment of SCZ was significantly higher during retrieval, mainly in the visual module. Interestingly, interactions between groups and task status in recruitment were found in the dorsal attention, visual modules. Finally, we observed that integration was significantly associated with memory performance in frontoparietal regions. Our findings revealed the time-varying evolution of brain networks in SCZ, while improving our understanding of cognitive decline and other pathophysiologies in brain diseases.
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Affiliation(s)
- Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Tingting Pan
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Min Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Zhifeng Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xuexue Yu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Dandan Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
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9
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Ogihara T, Tanioka K, Hiroyasu T, Hiwa S. Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study. FRONTIERS IN NEUROERGONOMICS 2022; 3:864938. [PMID: 38235448 PMCID: PMC10790849 DOI: 10.3389/fnrgo.2022.864938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 06/21/2022] [Indexed: 01/19/2024]
Abstract
Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 102 ms for the BRT of the 12 participants. Furthermore, the regression model with the highest prediction accuracy for each participant was analyzed to gain a better understanding of the neural basis of distracted driving. The 11 of 12 models that showed significant accuracy were classified into five clusters by hierarchical clustering based on their functional connectivity edges used in each cluster. The results showed that the combinations of the dorsal attention network (DAN)-sensory-motor network (SMN) and DAN-ventral attention network (VAN) connections were common in all clusters and that these networks were essential to predict the degree of distraction in complex multitask driving. They also confirmed the existence of multiple types of prediction models with different within- and between-network connectivity patterns. These results indicate that it is possible to predict the degree of distracted driving based on the driver's brain activity during actual driving. These results are expected to contribute to the development of safe driving systems and elucidate the neural basis of distracted driving.
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Affiliation(s)
- Takahiko Ogihara
- Graduate School of Life and Medical Sciences, Doshisha University, Kyoto, Japan
| | - Kensuke Tanioka
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
| | - Tomoyuki Hiroyasu
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
| | - Satoru Hiwa
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
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10
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Lima Dias Pinto I, Rungratsameetaweemana N, Flaherty K, Periyannan A, Meghdadi A, Richard C, Berka C, Bansal K, Garcia JO. Intermittent brain network reconfigurations and the resistance to social media influence. Netw Neurosci 2022; 6:870-896. [PMID: 36605415 PMCID: PMC9810364 DOI: 10.1162/netn_a_00255] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 05/10/2022] [Indexed: 01/09/2023] Open
Abstract
Since its development, social media has grown as a source of information and has a significant impact on opinion formation. Individuals interact with others and content via social media platforms in a variety of ways, but it remains unclear how decision-making and associated neural processes are impacted by the online sharing of informational content, from factual to fabricated. Here, we use EEG to estimate dynamic reconfigurations of brain networks and probe the neural changes underlying opinion change (or formation) within individuals interacting with a simulated social media platform. Our findings indicate that the individuals who changed their opinions are characterized by less frequent network reconfigurations while those who did not change their opinions tend to have more flexible brain networks with frequent reconfigurations. The nature of these frequent network configurations suggests a fundamentally different thought process between intervals in which individuals are easily influenced by social media and those in which they are not. We also show that these reconfigurations are distinct to the brain dynamics during an in-person discussion with strangers on the same content. Together, these findings suggest that brain network reconfigurations may not only be diagnostic to the informational context but also the underlying opinion formation.
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Affiliation(s)
| | | | - Kristen Flaherty
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Cornell Tech, New York, NY, USA
| | - Aditi Periyannan
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Tufts University, Medford, MA, USA
| | | | | | - Chris Berka
- Advanced Brain Monitoring, Carlsbad, CA, USA
| | - Kanika Bansal
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,Department of Biomedical Engineering, Columbia University, New York, NY, USA,* Corresponding Authors: ;
| | - Javier Omar Garcia
- US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA,* Corresponding Authors: ;
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11
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Li R, Deng C, Wang X, Zou T, Biswal B, Guo D, Xiao B, Zhang X, Cheng JL, Liu D, Yang M, Chen H, Wu Q, Feng L. Interictal dynamic network transitions in mesial temporal lobe epilepsy. Epilepsia 2022; 63:2242-2255. [PMID: 35699346 DOI: 10.1111/epi.17325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To reveal the possible routine of brain network dynamic alterations in patients with mesial temporal lobe epilepsy (mTLE) and to establish a predicted model of seizure recurrence during interictal periods. METHODS Seventy-nine unilateral mTLE patients with hippocampal sclerosis and 97 healthy controls from two centers were retrospectively enrolled. Dynamic brain configuration analyses were performed with resting-state functional magnetic resonance imaging (MRI) data to quantify the functional stability over time and the dynamic interactions between brain regions. Relationships between seizure frequency and ipsilateral hippocampal module allegiance were evaluated using a machine learning predictive model. RESULTS Compared to the healthy controls, patients with mTLE displayed an overall higher dynamic network, switching mainly in the epileptogenic regions (false discovery rate [FDR] corrected p-FDR < .05). Moreover, the dynamic network configuration in mTLE was characterized by decreased recruitment (intra-network communication), and increased integration (inter-network communication) among hippocampal systems and large-scale higher-order brain networks (p-FDR < .05). We further found that the dynamic interactions between the hippocampal system and the default-mode network (DMN) or control networks exhibited an opposite distribution pattern (p-FDR < .05). Strikingly, we showed that there was a robust association between predicted seizure frequency based on the ipsilateral hippocampal-DMN dynamics model and actual seizure frequency (p-perm < .001). SIGNIFICANCE These findings suggest that the interictal brain of mTLE is characterized by dynamical shifts toward unstable state. Our study provides novel insights into the brain dynamic network alterations and supports the potential use of DMN dynamic parameters as candidate neuroimaging markers in monitoring the seizure frequency clinically during interictal periods.
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Affiliation(s)
- Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chijun Deng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xuyang Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ting Zou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Danni Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaonan Zhang
- Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jing Liang Cheng
- Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ding Liu
- Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Mi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Wu
- Department of Neurology, First Affiliated Hospital, Kunming Medical University, Kunming, China
| | - Li Feng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
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12
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Askarisichani O, Bullo F, Friedkin NE, Singh AK. Predictive models for human-AI nexus in group decision making. Ann N Y Acad Sci 2022; 1514:70-81. [PMID: 35581156 DOI: 10.1111/nyas.14783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Machine learning (ML) and artificial intelligence (AI) have had a profound impact on our lives. Domains like health and learning are naturally helped by human-AI interactions and decision making. In these areas, as ML algorithms prove their value in making important decisions, humans add their distinctive expertise and judgment on social and interpersonal issues that need to be considered in tandem with algorithmic inputs of information. Some questions naturally arise. What rules and regulations should be invoked on the employment of AI, and what protocols should be in place to evaluate available AI resources? What are the forms of effective communication and coordination with AI that best promote effective human-AI teamwork? In this review, we highlight factors that we believe are especially important in assembling and managing human-AI decision making in a group setting.
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Affiliation(s)
- Omid Askarisichani
- Department of Computer Science, University of California, Santa Barbara, California, USA
| | - Francesco Bullo
- Department of Mechanical Engineering, University of California, Santa Barbara, California, USA.,Center for Control, Dynamical Systems and Computation, University of California, Santa Barbara, California, USA
| | - Noah E Friedkin
- Center for Control, Dynamical Systems and Computation, University of California, Santa Barbara, California, USA.,Department of Sociology, University of California, Santa Barbara, California, USA
| | - Ambuj K Singh
- Department of Computer Science, University of California, Santa Barbara, California, USA
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13
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Di Plinio S, Ebisch SJH. Probabilistically Weighted Multilayer Networks disclose the link between default mode network instability and psychosis-like experiences in healthy adults. Neuroimage 2022; 257:119291. [PMID: 35577023 DOI: 10.1016/j.neuroimage.2022.119291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 11/30/2022] Open
Abstract
The brain is a complex system in which the functional interactions among its subunits vary over time. The trajectories of this dynamic variation contribute to inter-individual behavioral differences and psychopathologic phenotypes. Despite many methodological advancements, the study of dynamic brain networks still relies on biased assumptions in the temporal domain. The current paper has two goals. First, we present a novel method to study multilayer networks: by modelling intra-nodal connections in a probabilistic, biologically driven way, we introduce a temporal resolution of the multilayer network based on signal similarity across time series. This new method is tested on synthetic networks by varying the number of modules and the sources of noise in the simulation. Secondly, we implement these probabilistically weighted (PW) multilayer networks to study the association between network dynamics and subclinical, psychosis-relevant personality traits in healthy adults. We show that the PW method for multilayer networks outperforms the standard procedure in modular detection and is less affected by increasing noise levels. Additionally, the PW method highlighted associations between the temporal instability of default mode network connections and psychosis-like experiences in healthy adults. PW multilayer networks allow an unbiased study of dynamic brain functioning and its behavioral correlates.
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Affiliation(s)
- Simone Di Plinio
- Department of Neuroscience, Imaging, and Clinical Sciences, G D'Annunzio University of Chieti-Pescara, Chieti, Italy.
| | - Sjoerd J H Ebisch
- Department of Neuroscience, Imaging, and Clinical Sciences, G D'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies (ITAB), G D'Annunzio University of Chieti-Pescara, Chieti, Italy
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14
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Broeders TA, Douw L, Eijlers AJ, Dekker I, Uitdehaag BM, Barkhof F, Hulst HE, Vinkers CH, Geurts JJ, Schoonheim MM. A more unstable resting-state functional network in cognitively declining multiple sclerosis. Brain Commun 2022; 4:fcac095. [PMID: 35620116 PMCID: PMC9128379 DOI: 10.1093/braincomms/fcac095] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/14/2022] [Accepted: 04/11/2022] [Indexed: 11/24/2022] Open
Abstract
Cognitive impairment is common in people with multiple sclerosis and strongly
affects their daily functioning. Reports have linked disturbed cognitive
functioning in multiple sclerosis to changes in the organization of the
functional network. In a healthy brain, communication between brain regions and
which network a region belongs to is continuously and dynamically adapted to
enable adequate cognitive function. However, this dynamic network adaptation has
not been investigated in multiple sclerosis, and longitudinal network data
remain particularly rare. Therefore, the aim of this study was to longitudinally
identify patterns of dynamic network reconfigurations that are related to the
worsening of cognitive decline in multiple sclerosis. Resting-state functional
MRI and cognitive scores (expanded Brief Repeatable Battery of
Neuropsychological tests) were acquired in 230 patients with multiple sclerosis
and 59 matched healthy controls, at baseline (mean disease duration: 15 years)
and at 5-year follow-up. A sliding-window approach was used for functional MRI
analyses, where brain regions were dynamically assigned to one of seven
literature-based subnetworks. Dynamic reconfigurations of subnetworks were
characterized using measures of promiscuity (number of subnetworks switched to),
flexibility (number of switches), cohesion (mutual switches) and disjointedness
(independent switches). Cross-sectional differences between cognitive groups and
longitudinal changes were assessed, as well as relations with structural damage
and performance on specific cognitive domains. At baseline, 23% of
patients were cognitively impaired (≥2/7 domains
Z < −2) and 18% were mildly
impaired (≥2/7 domains
Z < −1.5). Longitudinally,
28% of patients declined over time (0.25 yearly change on ≥2/7
domains based on reliable change index). Cognitively impaired patients displayed
more dynamic network reconfigurations across the whole brain compared with
cognitively preserved patients and controls, i.e. showing higher promiscuity
(P = 0.047), flexibility
(P = 0.008) and cohesion
(P = 0.008). Over time, cognitively
declining patients showed a further increase in cohesion
(P = 0.004), which was not seen in stable
patients (P = 0.544). More cohesion was
related to more severe structural damage (average
r = 0.166,
P = 0.015) and worse verbal memory
(r = −0.156,
P = 0.022), information processing speed
(r = −0.202,
P = 0.003) and working memory
(r = −0.163,
P = 0.017). Cognitively impaired multiple
sclerosis patients exhibited a more unstable network reconfiguration compared to
preserved patients, i.e. brain regions switched between subnetworks more often,
which was related to structural damage. This shift to more unstable network
reconfigurations was also demonstrated longitudinally in patients that showed
cognitive decline only. These results indicate the potential relevance of a
progressive destabilization of network topology for understanding cognitive
decline in multiple sclerosis.
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Affiliation(s)
- Tommy A.A. Broeders
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anand J.C. Eijlers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Iris Dekker
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bernard M.J. Uitdehaag
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, UK
| | - Hanneke E. Hulst
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Christiaan H. Vinkers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Departments of Psychiatry, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J.G. Geurts
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M. Schoonheim
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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15
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Dynamic reconfiguration of human brain networks across altered states of consciousness. Behav Brain Res 2022; 419:113685. [PMID: 34838931 DOI: 10.1016/j.bbr.2021.113685] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 10/29/2021] [Accepted: 11/20/2021] [Indexed: 01/01/2023]
Abstract
Consciousness is supported by rich neuronal dynamics to orchestrate behaviors and conscious processing can be disrupted by general anesthetics. Previous studies suggested that dynamic reconfiguration of large-scale functional network is critical for learning and higher-order cognitive function. During altered states of consciousness, how brain functional networks are dynamically changed and reconfigured at the whole-brain level is still unclear. To fill this gap, using multilayer network approach and functional magnetic resonance imaging (fMRI) data of 21 healthy subjects, we investigated the dynamic network reconfiguration in three different states of consciousness: wakefulness, dexmedetomidine-induced sedation, and recovery. Applying time-varying community detection algorithm, we constructed multilayer modularity networks to track and quantify dynamic interactions among brain areas that span time and space. We compared four high-level network features (i.e., switching, promiscuity, integration, and recruitment) derived from multilayer modularity across the three conditions. We found that sedation state is primarily characterized by increased switching rates as well as decreased integration, representing a whole-brain pattern with higher modular dynamics and more fragmented communication; such alteration can be mostly reversed after the recovery of consciousness. Thus, our work can provide additional insights to understand the modular network reconfiguration across different states of consciousness and may provide some clinical implications for disorders of consciousness.
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16
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Gu S, Fotiadis P, Parkes L, Xia CH, Gur RC, Gur RE, Roalf DR, Satterthwaite TD, Bassett DS. Network controllability mediates the relationship between rigid structure and flexible dynamics. Netw Neurosci 2022; 6:275-297. [PMID: 36605890 PMCID: PMC9810281 DOI: 10.1162/netn_a_00225] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 12/15/2021] [Indexed: 01/07/2023] Open
Abstract
Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid interareal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain's structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region's functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes' boundary controllability, suggesting that a region's strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.
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Affiliation(s)
- Shi Gu
- Brain and Intelligence Group, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Cedric H. Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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17
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Zamani Esfahlani F, Jo Y, Puxeddu MG, Merritt H, Tanner JC, Greenwell S, Patel R, Faskowitz J, Betzel RF. Modularity maximization as a flexible and generic framework for brain network exploratory analysis. Neuroimage 2021; 244:118607. [PMID: 34607022 DOI: 10.1016/j.neuroimage.2021.118607] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 09/03/2021] [Accepted: 09/20/2021] [Indexed: 11/28/2022] Open
Abstract
The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and development. There exist many data-driven methods for detecting and comparing modular structure, the most popular of which is modularity maximization. Although modularity maximization is a general framework that can be modified and reparamaterized to address domain-specific research questions, its application to neuroscientific datasets has, thus far, been narrow. Here, we highlight several strategies in which the "out-of-the-box" version of modularity maximization can be extended to address questions specific to neuroscience. First, we present approaches for detecting "space-independent" modules and for applying modularity maximization to signed matrices. Next, we show that the modularity maximization frame is well-suited for detecting task- and condition-specific modules. Finally, we highlight the role of multi-layer models in detecting and tracking modules across time, tasks, subjects, and modalities. In summary, modularity maximization is a flexible and general framework that can be adapted to detect modular structure resulting from a wide range of hypotheses. This article highlights multiple frontiers for future research and applications.
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Affiliation(s)
- Farnaz Zamani Esfahlani
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Youngheun Jo
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Rome 00185, Italy; IRCCS Fondazione Santa Lucia, Rome 00179, Italy
| | - Haily Merritt
- Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
| | - Jacob C Tanner
- Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States
| | - Sarah Greenwell
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Riya Patel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States; Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; Network Science Institute, Indiana University, Bloomington, IN 47405, United States.
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18
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Duda M, Koutra D, Sripada C. Validating dynamicity in resting state fMRI with activation-informed temporal segmentation. Hum Brain Mapp 2021; 42:5718-5735. [PMID: 34510647 PMCID: PMC8559473 DOI: 10.1002/hbm.25649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/11/2021] [Accepted: 08/24/2021] [Indexed: 12/18/2022] Open
Abstract
Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test-retest reliability. We hypothesize that time-varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here, we introduce a data-driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole-brain functional activation, rather than a fixed-length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block-design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject-specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole-brain changes in activation can be used as a marker for changes in connectivity states and provides new evidence for the existence of time-varying FC in rest.
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Affiliation(s)
- Marlena Duda
- Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMichiganUSA
| | - Danai Koutra
- Department of Computer Science and EngineeringUniversity of MichiganAnn ArborMichiganUSA
| | - Chandra Sripada
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
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19
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Büchel D, Lehmann T, Sandbakk Ø, Baumeister J. EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks. Sci Rep 2021; 11:20803. [PMID: 34675312 PMCID: PMC8531386 DOI: 10.1038/s41598-021-00371-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022] Open
Abstract
The interaction of acute exercise and the central nervous system evokes increasing interest in interdisciplinary research fields of neuroscience. Novel approaches allow to monitor large-scale brain networks from mobile electroencephalography (EEG) applying graph theory, but it is yet uncertain whether brain graphs extracted after exercise are reliable. We therefore aimed to investigate brain graph reliability extracted from resting state EEG data before and after submaximal exercise twice within one week in male participants. To obtain graph measures, we extracted global small-world-index (SWI), clustering coefficient (CC) and characteristic path length (PL) based on weighted phase leg index (wPLI) and spectral coherence (Coh) calculation. For reliability analysis, Intraclass-Correlation-Coefficient (ICC) and Coefficient of Variation (CoV) were computed for graph measures before (REST) and after POST) exercise. Overall results revealed poor to excellent measures at PRE and good to excellent ICCs at POST in the theta, alpha-1 and alpha-2, beta-1 and beta-2 frequency band. Based on bootstrap-analysis, a positive effect of exercise on reliability of wPLI based measures was observed, while exercise induced a negative effect on reliability of Coh-based graph measures. Findings indicate that brain graphs are a reliable tool to analyze brain networks in exercise contexts, which might be related to the neuroregulating effect of exercise inducing functional connections within the connectome. Relative and absolute reliability demonstrated good to excellent reliability after exercise. Chosen graph measures may not only allow analysis of acute, but also longitudinal studies in exercise-scientific contexts.
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Affiliation(s)
- Daniel Büchel
- Department Sport & Health, Exercise Science & Neuroscience Unit, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany.
| | - Tim Lehmann
- Department Sport & Health, Exercise Science & Neuroscience Unit, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
| | - Øyvind Sandbakk
- Department of Neuromedicine and Movement Science, Centre for Elite Sports Research, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jochen Baumeister
- Department Sport & Health, Exercise Science & Neuroscience Unit, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
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20
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Modi S, He X, Chaudhary K, Hinds W, Crow A, Beloor-Suresh A, Sperling MR, Tracy JI. Multiple-brain systems dynamically interact during tonic and phasic states to support language integrity in temporal lobe epilepsy. NEUROIMAGE-CLINICAL 2021; 32:102861. [PMID: 34688143 PMCID: PMC8536775 DOI: 10.1016/j.nicl.2021.102861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/10/2021] [Accepted: 10/13/2021] [Indexed: 11/18/2022]
Abstract
Unique brain dynamics occur during language task in left temporal lobe epilepsy (TLE). Multiple brain systems interact to implement compensated language status in TLE. Tonic/rest dynamics exert influence and may prime the level of phasic/task dynamics. Multi-network integrations are compensatory in patients with lower language skills.
An epileptogenic focus in the dominant temporal lobe can result in the reorganization of language systems in order to compensate for compromised functions. We studied the compensatory reorganization of language in the setting of left temporal lobe epilepsy (TLE), taking into account the interaction of language (L) with key non-language (NL) networks such as dorsal attention (DAN), fronto-parietal (FPN) and cingulo-opercular (COpN), with these systems providing cognitive resources helpful for successful language performance. We applied tools from dynamic network neuroscience to functional MRI data collected from 23 TLE patients and 23 matched healthy controls during the resting state (RS) and a sentence completion (SC) task to capture how the functional architecture of a language network dynamically changes and interacts with NL systems in these two contexts. We provided evidence that the brain areas in which core language functions reside dynamically interact with non-language functional networks to carry out linguistic functions. We demonstrated that abnormal integrations between the language and DAN existed in TLE, and were present both in tonic as well as phasic states. This integration was considered to reflect the entrainment of visual attention systems to the systems dedicated to lexical semantic processing. Our data made clear that the level of baseline integrations between the language subsystems and certain NL systems (e.g., DAN, FPN) had a crucial influence on the general level of task integrations between L/NL systems, with this a normative finding not unique to epilepsy. We also revealed that a broad set of task L/NL integrations in TLE are predictive of language competency, indicating that these integrations are compensatory for patients with lower overall language skills. We concluded that RS establishes the broad set of L/NL integrations available and primed for use during task, but that the actual use of those interactions in the setting of TLE depended on the level of language skill. We believe our analyses are the first to capture the potential compensatory role played by dynamic network reconfigurations between multiple brain systems during performance of a complex language task, in addition to testing for characteristics in both the phasic/task and tonic/resting state that are necessary to achieve language competency in the setting of temporal lobe pathology. Our analyses highlighted the intra- versus inter-system communications that form the basis of unique language processing in TLE, pointing to the dynamic reconfigurations that provided the broad multi-system support needed to maintain language skill and competency.
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Affiliation(s)
- Shilpi Modi
- Department of Neurology, Comprehensive Epilepsy Centre, Thomas Jefferson University, Philadelphia, PA, USA
| | - Xiaosong He
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kapil Chaudhary
- Department of Neurology, Comprehensive Epilepsy Centre, Thomas Jefferson University, Philadelphia, PA, USA
| | - Walter Hinds
- Department of Neurology, Comprehensive Epilepsy Centre, Thomas Jefferson University, Philadelphia, PA, USA
| | - Andrew Crow
- Department of Neurology, Comprehensive Epilepsy Centre, Thomas Jefferson University, Philadelphia, PA, USA
| | - Ashithkumar Beloor-Suresh
- Department of Neurology, Comprehensive Epilepsy Centre, Thomas Jefferson University, Philadelphia, PA, USA
| | - Michael R Sperling
- Department of Neurology, Comprehensive Epilepsy Centre, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joseph I Tracy
- Department of Neurology, Comprehensive Epilepsy Centre, Thomas Jefferson University, Philadelphia, PA, USA.
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21
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Cui X, Ding C, Wei J, Xue J, Wang X, Wang B, Xiang J. Analysis of Dynamic Network Reconfiguration in Adults with Attention-Deficit/Hyperactivity Disorder Based Multilayer Network. Cereb Cortex 2021; 31:4945-4957. [PMID: 34023872 DOI: 10.1093/cercor/bhab133] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/15/2021] [Accepted: 04/15/2021] [Indexed: 11/12/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) has been reported exist abnormal topology structure in the brain network. However, these studies often treated the brain as a static monolithic structure, and dynamic characteristics were ignored. Here, we investigated how the dynamic network reconfiguration in ADHD patients differs from that in healthy people. Specifically, we acquired resting-state functional magnetic resonance imaging data from a public dataset including 40 ADHD patients and 50 healthy people. A novel model of a "time-varying multilayer network" and metrics of recruitment and integration were applied to describe group differences. The results showed that the integration scores of ADHD patients were significantly lower than those of controls at every level. The recruitment scores were lower than healthy people except for the whole-brain level. It is worth noting that the subcortical network and the thalamus in ADHD patients exhibited reduced alliance preference both within and between functional networks. In addition, we also found that recruitment and integration coefficients showed a significant correlation with symptom severity in some regions. Our results demonstrate that the capability to communicate within or between some functional networks is impaired in ADHD patients. These evidences provide a new opportunity for studying the characteristics of ADHD brain networks.
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Affiliation(s)
- Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China
| | - Congli Ding
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China
| | - Jing Wei
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China
| | - Jiayue Xue
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China
| | - Xiaoyue Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China
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22
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Del Pozo SM, Laufs H, Bonhomme V, Laureys S, Balenzuela P, Tagliazucchi E. Unconsciousness reconfigures modular brain network dynamics. CHAOS (WOODBURY, N.Y.) 2021; 31:093117. [PMID: 34598477 DOI: 10.1063/5.0046047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 08/04/2021] [Indexed: 06/13/2023]
Abstract
The dynamic core hypothesis posits that consciousness is correlated with simultaneously integrated and differentiated assemblies of transiently synchronized brain regions. We represented time-dependent functional interactions using dynamic brain networks and assessed the integrity of the dynamic core by means of the size and flexibility of the largest multilayer module. As a first step, we constrained parameter selection using a newly developed benchmark for module detection in heterogeneous temporal networks. Next, we applied a multilayer modularity maximization algorithm to dynamic brain networks computed from functional magnetic resonance imaging (fMRI) data acquired during deep sleep and under propofol anesthesia. We found that unconsciousness reconfigured network flexibility and reduced the size of the largest spatiotemporal module, which we identified with the dynamic core. Our results represent a first characterization of modular brain network dynamics during states of unconsciousness measured with fMRI, adding support to the dynamic core hypothesis of human consciousness.
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Affiliation(s)
- Sofía Morena Del Pozo
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428, Argentina
| | - Helmut Laufs
- Department of Neurology, Christian Albrechts University, Kiel 24118, Germany
| | - Vincent Bonhomme
- Anesthesia and Intensive Care Laboratory, GIGA-Consciousness, GIGA Institute, University of Liège, Liège 4000, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège 4000, Belgium
| | - Pablo Balenzuela
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428, Argentina
| | - Enzo Tagliazucchi
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428, Argentina
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23
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Büchel D, Sandbakk Ø, Baumeister J. Exploring intensity-dependent modulations in EEG resting-state network efficiency induced by exercise. Eur J Appl Physiol 2021; 121:2423-2435. [PMID: 34003363 PMCID: PMC8357751 DOI: 10.1007/s00421-021-04712-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 05/05/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Exhaustive cardiovascular load can affect neural processing and is associated with decreases in sensorimotor performance. The purpose of this study was to explore intensity-dependent modulations in brain network efficiency in response to treadmill running assessed from resting-state electroencephalography (EEG) measures. METHODS Sixteen trained participants were tested for individual peak oxygen uptake (VO2 peak) and performed an incremental treadmill exercise at 50% (10 min), 70% (10 min) and 90% speed VO2 peak (all-out) followed by cool-down running and active recovery. Before the experiment and after each stage, borg scale (BS), blood lactate concentration (BLa), resting heartrate (HRrest) and 64-channel EEG resting state were assessed. To analyze network efficiency, graph theory was applied to derive small world index (SWI) from EEG data in theta, alpha-1 and alpha-2 frequency bands. RESULTS Analysis of variance for repeated measures revealed significant main effects for intensity on BS, BLa, HRrest and SWI. While BS, BLa and HRrest indicated maxima after all-out, SWI showed a reduction in the theta network after all-out. CONCLUSION Our explorative approach suggests intensity-dependent modulations of resting-state brain networks, since exhaustive exercise temporarily reduces brain network efficiency. Resting-state network assessment may prospectively play a role in training monitoring by displaying the readiness and efficiency of the central nervous system in different training situations.
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Affiliation(s)
- Daniel Büchel
- Exercise Science and Neuroscience Unit, Department of Exercise & Health, Faculty of Science, Paderborn University, Paderborn, Germany.
| | - Øyvind Sandbakk
- Department of Neuromedicine and Movement Science, Centre for Elite Sports Research, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jochen Baumeister
- Exercise Science and Neuroscience Unit, Department of Exercise & Health, Faculty of Science, Paderborn University, Paderborn, Germany
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24
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Fan Y, Fan Q, Zhou L, Wang R, Lin P, Wu Y. Cohesive communities in dynamic brain functional networks. Phys Rev E 2021; 104:014302. [PMID: 34412232 DOI: 10.1103/physreve.104.014302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/21/2021] [Indexed: 11/07/2022]
Abstract
In large-scale brain network dynamics, brain nodes switching between modules has been found to correlate with cognition. However, how the brain nodes engage in this kind of reorganization of modules is unclear. Based on a functional magnetic resonance imaging dataset, we construct dynamic brain functional networks and investigate nodal module temporal dynamic behavior by applying the multilayer network analysis approach. We reveal three cohesive communities that are groups of brain nodes linked in the same community during brain module dynamic reorganization. We show that the cohesive communities have higher clustering coefficients and lower characteristic path lengths than the controlled community, indicating cohesive communities are the parts of brain networks with high information processing efficiency. The smaller sample entropy of functional connectivity in cohesive communities also proves their property of being more "static" compared with the controlled community in brain dynamics. Specifically, compared with the controlled community, the functional connectivity of cohesive communities is restricted strictly by structure connectivity and shows more similarity to structure connectivity. More importantly, we find that the cohesive communities are stable not only in the resting state but also when processing cognitive tasks. Our results not only show that cohesive communities may be the fundamental community organization to support brain network dynamics but also provide insights into the intrinsic structural relationship between the resting state and task states of the brain.
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Affiliation(s)
- Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Qiang Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Lv Zhou
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.,National Demonstration Center for Experimental Mechanics Education, Xi'an Jiaotong University, Xi'an 710049, China
| | - Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an 710049, China
| | - Pan Lin
- Department of Psychology and Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Changsha 410081, China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.,National Demonstration Center for Experimental Mechanics Education, Xi'an Jiaotong University, Xi'an 710049, China
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25
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Vlisides PE, Li D, McKinney A, Brooks J, Leis AM, Mentz G, Tsodikov A, Zierau M, Ragheb J, Clauw DJ, Avidan MS, Vanini G, Mashour GA. The Effects of Intraoperative Caffeine on Postoperative Opioid Consumption and Related Outcomes After Laparoscopic Surgery: A Randomized Controlled Trial. Anesth Analg 2021; 133:233-242. [PMID: 33939649 DOI: 10.1213/ane.0000000000005532] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Surgical patients are vulnerable to opioid dependency and related risks. Clinical-translational data suggest that caffeine may enhance postoperative analgesia. This trial tested the hypothesis that intraoperative caffeine would reduce postoperative opioid consumption. The secondary objective was to assess whether caffeine improves neuropsychological recovery postoperatively. METHODS This was a single-center, randomized, placebo-controlled trial. Participants, clinicians, research teams, and data analysts were all blinded to the intervention. Adult (≥18 years old) surgical patients (n = 65) presenting for laparoscopic colorectal and gastrointestinal surgery were randomized to an intravenous caffeine citrate infusion (200 mg) or dextrose 5% in water (40 mL) during surgical closure. The primary outcome was cumulative opioid consumption through postoperative day 3. Secondary outcomes included subjective pain reporting, observer-reported pain, delirium, Trail Making Test performance, depression and anxiety screens, and affect scores. Adverse events were reported, and hemodynamic profiles were also compared between the groups. RESULTS Sixty patients were included in the final analysis, with 30 randomized to each group. The median (interquartile range) cumulative opioid consumption (oral morphine equivalents, milligrams) was 77 mg (33-182 mg) for caffeine and 51 mg (15-117 mg) for placebo (estimated difference, 55 mg; 95% confidence interval [CI], -9 to 118; P = .092). After post hoc adjustment for baseline imbalances, caffeine was associated with increased opioid consumption (87 mg; 95% CI, 26-148; P = .005). There were otherwise no differences in prespecified pain or neuropsychological outcomes between the groups. No major adverse events were reported in relation to caffeine, and no major hemodynamic perturbations were observed with caffeine administration. CONCLUSIONS Caffeine appears unlikely to reduce early postoperative opioid consumption. Caffeine otherwise appears well tolerated during anesthetic emergence.
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Affiliation(s)
- Phillip E Vlisides
- From the Department of Anesthesiology.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan
| | - Duan Li
- From the Department of Anesthesiology.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan
| | | | | | | | | | - Alexander Tsodikov
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | | | | | - Daniel J Clauw
- Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan Medical School, Ann Arbor, Michigan
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine, St Louis, Missouri
| | - Giancarlo Vanini
- From the Department of Anesthesiology.,Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan
| | - George A Mashour
- From the Department of Anesthesiology.,Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan.,Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan
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26
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Petrican R, Graham KS, Lawrence AD. Brain-environment alignment during movie watching predicts fluid intelligence and affective function in adulthood. Neuroimage 2021; 238:118177. [PMID: 34020016 PMCID: PMC8350144 DOI: 10.1016/j.neuroimage.2021.118177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/11/2021] [Accepted: 05/14/2021] [Indexed: 11/29/2022] Open
Abstract
Functional brain connectivity (FC) patterns vary with changes in the environment. Adult FC variability is linked to age-specific network communication profiles. Across adulthood, the younger network interaction profile predicts higher fluid IQ. Yoked FC-concrete environmental changes predict poorer fluid IQ and anxiety. Brain areas linked to episodic memory underpin FC changes at multiple timescales.
BOLD fMRI studies have provided compelling evidence that the human brain demonstrates substantial moment-to-moment fluctuations in both activity and functional connectivity (FC) patterns. While the role of brain signal variability in fostering cognitive adaptation to ongoing environmental demands is well-documented, the relevance of moment-to-moment changes in FC patterns is still debated. Here, we adopt a graph theoretical approach in order to shed light on the cognitive-affective implications of FC variability and associated profiles of functional network communication in adulthood. Our goal is to identify brain communication pathways underlying FC reconfiguration at multiple timescales, thereby improving understanding of how faster perceptually bound versus slower conceptual processes shape neural tuning to the dynamics of the external world and, thus, indirectly, mold affective and cognitive responding to the environment. To this end, we used neuroimaging and behavioural data collected during movie watching by the Cambridge Center for Ageing and Neuroscience (N = 642, 326 women) and the Human Connectome Project (N = 176, 106 women). FC variability evoked by changes to both the concrete perceptual and the more abstract conceptual representation of an ongoing situation increased from young to older adulthood. However, coupling between variability in FC patterns and concrete environmental features was stronger at younger ages. FC variability (both moment-to-moment/concrete featural and abstract conceptual boundary-evoked) was associated with age-distinct profiles of network communication, specifically, greater functional integration of the default mode network in older adulthood, but greater informational flow across neural networks implicated in environmentally driven attention and control (cingulo-opercular, salience, ventral attention) in younger adulthood. Whole-brain communication pathways anchored in default mode regions relevant to episodic and semantic context creation (i.e., angular and middle temporal gyri) supported FC reconfiguration in response to changes in the conceptual representation of an ongoing situation (i.e., narrative event boundaries), as well as stronger coupling between moment-to-moment fluctuations in FC and concrete environmental features. Fluid intelligence/abstract reasoning was directly linked to levels of brain-environment alignment, but only indirectly associated with levels of FC variability. Specifically, stronger coupling between moment-to-moment FC variability and concrete environmental features predicted poorer fluid intelligence and greater affectively driven environmental vigilance. Complementarily, across the adult lifespan, higher fluid (but not crystallised) intelligence was related to stronger expression of the network communication profile underlying momentary and event boundary-based FC variability during youth. Our results indicate that the adaptiveness of dynamic FC reconfiguration during naturalistic information processing changes across the lifespan due to the associated network communication profiles. Moreover, our findings on brain-environment alignment complement the existing literature on the beneficial consequences of modulating brain signal variability in response to environmental complexity. Specifically, they imply that coupling between moment-to-moment FC variability and concrete environmental features may index a bias towards perceptually-bound, rather than conceptual processing, which hinders affective functioning and strategic cognitive engagement with the external environment.
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Affiliation(s)
- Raluca Petrican
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom.
| | - Kim S Graham
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff CF24 4HQ, United Kingdom
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27
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Kesler SR, Sleurs C, McDonald BC, Deprez S, van der Plas E, Nieman BJ. Brain Imaging in Pediatric Cancer Survivors: Correlates of Cognitive Impairment. J Clin Oncol 2021; 39:1775-1785. [PMID: 33886371 DOI: 10.1200/jco.20.02315] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Shelli R Kesler
- School of Nursing, Department of Diagnostic Medicine, Dell School of Medicine, Livestrong Cancer Institutes, Austin, TX
| | - Charlotte Sleurs
- Department of Oncology, Catholic University of Leuven, Leuven, Belgium.,Leuven Cancer Institute, Leuven, Belgium
| | - Brenna C McDonald
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Center for Neuroimaging, Indiana University Melvin and Bren Simon Cancer Center, Indianapolis, IN
| | - Sabine Deprez
- Leuven Cancer Institute, Leuven, Belgium.,Department of Imaging and Pathology, Catholic University of Leuven, Leuven, Belgium
| | - Ellen van der Plas
- Department of Psychiatry, University of Iowa Hospital and Clinics, Iowa City, Iowa
| | - Brian J Nieman
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Ontario Institute for Cancer Research, Toronto, ON, Canada.,Translational Medicine, Hospital for Sick Children, Toronto, ON, Canada
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28
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Mueller JM, Pritschet L, Santander T, Taylor CM, Grafton ST, Jacobs EG, Carlson JM. Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle. Netw Neurosci 2021; 5:125-144. [PMID: 33688609 PMCID: PMC7935041 DOI: 10.1162/netn_a_00169] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 09/07/2020] [Indexed: 12/20/2022] Open
Abstract
Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization. Sex steroid hormones influence the central nervous system across multiple spatiotemporal scales. Estrogen and progesterone concentrations rise and fall throughout the menstrual cycle, but it remains poorly understood whether day-to-day fluctuations in hormones shape human brain dynamics. Here, we assessed the structure and stability of resting-state brain network connectivity in concordance with serum hormone levels from a female who underwent fMRI and venipuncture for 30 consecutive days. Our results reveal that while network structure is largely stable over the course of a menstrual cycle, temporary reorganization of several large-scale functional brain networks occurs during the ovulatory window. In particular, a default mode subnetwork exhibits increased connectivity with itself and with nodes belonging to the temporoparietal and limbic networks, providing novel perspective into brain-hormone interactions.
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Affiliation(s)
- Joshua M Mueller
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Laura Pritschet
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Tyler Santander
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Caitlin M Taylor
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Scott T Grafton
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Emily Goard Jacobs
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Jean M Carlson
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
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29
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Gifford G, Crossley N, Morgan S, Kempton MJ, Dazzan P, Modinos G, Azis M, Samson C, Bonoldi I, Quinn B, Smart SE, Antoniades M, Bossong MG, Broome MR, Perez J, Howes OD, Stone JM, Allen P, Grace AA, McGuire P. Integrated metastate functional connectivity networks predict change in symptom severity in clinical high risk for psychosis. Hum Brain Mapp 2021; 42:439-451. [PMID: 33048435 PMCID: PMC7775992 DOI: 10.1002/hbm.25235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 08/28/2020] [Accepted: 09/29/2020] [Indexed: 01/22/2023] Open
Abstract
The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as 'integrated' FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the 'cartographic profile' of time windows and k-means clustering, and sub-network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub-network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub-network comprised brain areas implicated in bottom-up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk.
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Affiliation(s)
- George Gifford
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Nicolas Crossley
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sarah Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,The Alan Turing Institute, London, UK
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
| | - Gemma Modinos
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Matilda Azis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carly Samson
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ilaria Bonoldi
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
| | - Beverly Quinn
- CAMEO Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Sophie E Smart
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Mathilde Antoniades
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychiatry, Icahn Medical School, Mt Sinai Hospital, New York, New York, USA
| | - Matthijs G Bossong
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Matthew R Broome
- Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Jesus Perez
- CAMEO Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
| | - James M Stone
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK.,Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paul Allen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychology, University of Roehampton, London, UK
| | - Anthony A Grace
- Departments of Neuroscience, Psychiatry and Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,South London and Maudsley NHS Trust, Maudsley Hospital, London, UK
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30
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Wanniarachchi H, Lang Y, Wang X, Pruitt T, Nerur S, Chen KY, Liu H. Alterations of Cerebral Hemodynamics and Network Properties Induced by Newsvendor Problem in the Human Prefrontal Cortex. Front Hum Neurosci 2021; 14:598502. [PMID: 33519401 PMCID: PMC7843457 DOI: 10.3389/fnhum.2020.598502] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 12/14/2020] [Indexed: 01/21/2023] Open
Abstract
While many publications have reported brain hemodynamic responses to decision-making under various conditions of risk, no inventory management scenarios, such as the newsvendor problem (NP), have been investigated in conjunction with neuroimaging. In this study, we hypothesized (I) that NP stimulates the dorsolateral prefrontal cortex (DLPFC) and the orbitofrontal cortex (OFC) joined with frontal polar area (FPA) significantly in the human brain, and (II) that local brain network properties are increased when a person transits from rest to the NP decision-making phase. A 77-channel functional near infrared spectroscopy (fNIRS) system with wide field-of-view (FOV) was employed to measure frontal cerebral hemodynamics in response to NP in 27 healthy human subjects. NP-induced changes in oxy-hemoglobin concentration, Δ[HbO], were investigated using a general linear model (GLM) and graph theory analysis (GTA). Significant activation induced by NP was shown in both DLPFC and OFC+FPA across all subjects. Specifically, higher risk NP with low-profit margins (LM) activated left-DLPFC but deactivated right-DLPFC in 14 subjects, while lower risk NP with high-profit margins (HM) stimulated both DLPFC and OFC+FPA in 13 subjects. The local efficiency, clustering coefficient, and path length of the network metrics were significantly enhanced under NP decision making. In summary, multi-channel fNIRS enabled us to identify DLPFC and OFC+FPA as key cortical regions of brain activations when subjects were making inventory-management risk decisions. We demonstrated that challenging NP resulted in the deactivation within right-DLPFC due to higher levels of stress. Also, local brain network properties were increased when a person transitioned from the rest phase to the NP decision-making phase.
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Affiliation(s)
- Hashini Wanniarachchi
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Yan Lang
- Department of Information Systems and Operations Management, University of Texas at Arlington, Arlington, TX, United States
| | - Xinlong Wang
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Tyrell Pruitt
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Sridhar Nerur
- Department of Information Systems and Operations Management, University of Texas at Arlington, Arlington, TX, United States
| | - Kay-Yut Chen
- Department of Information Systems and Operations Management, University of Texas at Arlington, Arlington, TX, United States
| | - Hanli Liu
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
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31
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Yang Z, Telesford QK, Franco AR, Lim R, Gu S, Xu T, Ai L, Castellanos FX, Yan CG, Colcombe S, Milham MP. Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations. Neuroimage 2021; 225:117489. [PMID: 33130272 PMCID: PMC7829665 DOI: 10.1016/j.neuroimage.2020.117489] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 10/21/2020] [Indexed: 01/16/2023] Open
Abstract
Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization.
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Affiliation(s)
- Zhen Yang
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States.
| | - Qawi K Telesford
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States
| | - Alexandre R Franco
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States; Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Ryan Lim
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States
| | - Shi Gu
- University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Ting Xu
- Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Lei Ai
- Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Francisco X Castellanos
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY 10016, United States
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Stan Colcombe
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Department of Psychiatry, NYU Grossman School of Medicine, 550 1st Avenue, New York, NY 10016, United States
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, 140 Old Orangeburg Rd, Orangeburg, NY 10962, United States; Center for the Developing Brain, The Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States.
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32
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Patil AU, Ghate S, Madathil D, Tzeng OJL, Huang HW, Huang CM. Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition. Sci Rep 2021; 11:165. [PMID: 33420212 PMCID: PMC7794287 DOI: 10.1038/s41598-020-80293-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 12/08/2020] [Indexed: 01/29/2023] Open
Abstract
Creative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.
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Affiliation(s)
- Abhishek Uday Patil
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan
| | - Sejal Ghate
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Deepa Madathil
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Ovid J L Tzeng
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan
- Cognitive Neuroscience Laboratory, Institute of Linguistics, Academia Sinica, Taipei, Taiwan
- College of Humanities and Social Sciences, Taipei Medical University, Taipei, Taiwan
- Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei, Taiwan
- Hong Kong Institute for Advanced Study, City University of Hong Kong, Kowloon, Hong Kong
| | - Hsu-Wen Huang
- Department of Linguistics and Translation, City University of Hong Kong, Kowloon, Hong Kong
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan.
- Cognitive Neuroscience Laboratory, Institute of Linguistics, Academia Sinica, Taipei, Taiwan.
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33
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Sinha N, Berg CN, Yassa MA, Gluck MA. Increased dynamic flexibility in the medial temporal lobe network following an exercise intervention mediates generalization of prior learning. Neurobiol Learn Mem 2021; 177:107340. [PMID: 33186745 PMCID: PMC7861122 DOI: 10.1016/j.nlm.2020.107340] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 07/29/2020] [Accepted: 11/01/2020] [Indexed: 11/28/2022]
Abstract
Recent work has conceptualized the brain as a network comprised of groups of sub-networks or modules. "Flexibility" of brain network(s) indexes the dynamic reconfiguration of comprising modules. Using novel techniques from dynamic network neuroscience applied to high-resolution resting-state functional magnetic resonance imaging (fMRI), the present study investigated the effects of an aerobic exercise intervention on the dynamic rearrangement of modular community structure-a measure of neural flexibility-within the medial temporal lobe (MTL) network. The MTL is one of the earliest brain regions impacted by Alzheimer's disease. It is also a major site of neuroplasticity that is sensitive to the effects of exercise. In a two-group non-randomized, repeated measures and matched control design with 34 healthy older adults, we observed an exercise-related increase in flexibility within the MTL network. Furthermore, MTL network flexibility mediated the beneficial effect aerobic exercise had on mnemonic flexibility, as measured by the ability to generalize past learning to novel task demands. Our results suggest that exercise exerts a rehabilitative and protective effect on MTL function, resulting in dynamically evolving networks of regions that interact in complex communication patterns. These reconfigurations may underlie exercise-induced improvements on cognitive measures of generalization, which are sensitive to subtle changes in the MTL.
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Affiliation(s)
- Neha Sinha
- Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, NJ, USA.
| | - Chelsie N Berg
- Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, NJ, USA.
| | - Michael A Yassa
- Center for the Neurobiology of Learning and Memory, Department of Neurobiology and Behavior, University of California, Irvine, CA, USA.
| | - Mark A Gluck
- Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, NJ, USA.
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34
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Contextual experience modifies functional connectome indices of topological strength and efficiency. Sci Rep 2020; 10:19843. [PMID: 33199790 PMCID: PMC7670469 DOI: 10.1038/s41598-020-76935-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 10/27/2020] [Indexed: 11/08/2022] Open
Abstract
Stimuli presented at short temporal delays before functional magnetic resonance imaging (fMRI) can have a robust impact on the organization of synchronous activity in resting state networks. This presents an opportunity to investigate how sensory, affective and cognitive stimuli alter functional connectivity in rodent models. In the present study we assessed the effect on functional connectivity of a familiar contextual stimulus presented 10 min prior to sedation for imaging. A subset of animals were co-presented with an unfamiliar social stimulus in the same environment to further investigate the effect of familiarity on network topology. Rats were imaged at 11.1 T and graph theory analysis was applied to matrices generated from seed-based functional connectivity data sets with 144 brain regions (nodes) and 10,152 pairwise correlations (after excluding 144 diagonal edges). Our results show substantial changes in network topology in response to the familiar (context). Presentation of the familiar context, both in the absence and presence of the social stimulus, strongly reduced network strength, global efficiency, and altered the location of the highest eigenvector centrality nodes from cortex to the hypothalamus. We did not observe changes in modular organization, nodal cartographic assignments, assortative mixing, rich club organization, and network resilience. We propose that experiential factors, perhaps involving associative or episodic memory, can exert a dramatic effect on functional network strength and efficiency when presented at a short temporal delay before imaging.
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35
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Jalilianhasanpour R, Ryan D, Agarwal S, Beheshtian E, Gujar SK, Pillai JJ, Sair HI. Dynamic Brain Connectivity in Resting State Functional MR Imaging. Neuroimaging Clin N Am 2020; 31:81-92. [PMID: 33220830 DOI: 10.1016/j.nic.2020.09.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Dynamic functional connectivity adds another dimension to resting-state functional MR imaging analysis. In recent years, dynamic functional connectivity has been increasingly used in resting-state functional MR imaging, and several studies have demonstrated that dynamic functional connectivity patterns correlate with different physiologic and pathologic brain states. In fact, evidence suggests that dynamic functional connectivity is a more sensitive marker than static functional connectivity; therefore, it might be a promising tool to add to clinical functional neuroimaging. This article provides a broad overview of dynamic functional connectivity and reviews its general principles, techniques, and potential clinical applications.
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Affiliation(s)
- Rozita Jalilianhasanpour
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Daniel Ryan
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Shruti Agarwal
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Elham Beheshtian
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Sachin K Gujar
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Jay J Pillai
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA; Department of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, MD 21287, USA
| | - Haris I Sair
- Division of Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA; The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
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36
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Zhu Y, Liu J, Ristaniemi T, Cong F. Distinct Patterns of Functional Connectivity During the Comprehension of Natural, Narrative Speech. Int J Neural Syst 2020; 30:2050007. [PMID: 32116090 DOI: 10.1142/s0129065720500070] [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] [Indexed: 11/18/2022]
Abstract
Recent continuous task studies, such as narrative speech comprehension, show that fluctuations in brain functional connectivity (FC) are altered and enhanced compared to the resting state. Here, we characterized the fluctuations in FC during comprehension of speech and time-reversed speech conditions. The correlations of Hilbert envelope of source-level EEG data were used to quantify FC between spatially separate brain regions. A symmetric multivariate leakage correction was applied to address the signal leakage issue before calculating FC. The dynamic FC was estimated based on a sliding time window. Then, principal component analysis (PCA) was performed on individually concatenated and temporally concatenated FC matrices to identify FC patterns. We observed that the mode of FC induced by speech comprehension can be characterized with a single principal component. The condition-specific FC demonstrated decreased correlations between frontal and parietal brain regions and increased correlations between frontal and temporal brain regions. The fluctuations of the condition-specific FC characterized by a shorter time demonstrated that dynamic FC also exhibited condition specificity over time. The FC is dynamically reorganized and FC dynamic pattern varies along a single mode of variation during speech comprehension. The proposed analysis framework seems valuable for studying the reorganization of brain networks during continuous task experiments.
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Affiliation(s)
- Yongjie Zhu
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P. R. China.,Faculty of Information Technology, P. O. Box 35, University of Jyväskylä, FI-40014 University of Jyväskylä, Finland
| | - Jia Liu
- Faculty of Information Technology, P. O. Box 35, University of Jyväskylä, FI-40014 University of Jyväskylä, Finland
| | - Tapani Ristaniemi
- Faculty of Information Technology, P. O. Box 35, University of Jyväskylä, FI-40014 University of Jyväskylä, Finland
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P. R. China.,Faculty of Information Technology, P. O. Box 35, University of Jyväskylä, FI-40014 University of Jyväskylä, Finland
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37
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Garcia JO, Battelli L, Plow E, Cattaneo Z, Vettel J, Grossman ED. Understanding diaschisis models of attention dysfunction with rTMS. Sci Rep 2020; 10:14890. [PMID: 32913263 PMCID: PMC7483730 DOI: 10.1038/s41598-020-71692-6] [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: 11/21/2019] [Accepted: 07/27/2020] [Indexed: 01/18/2023] Open
Abstract
Visual attentive tracking requires a balance of excitation and inhibition across large-scale frontoparietal cortical networks. Using methods borrowed from network science, we characterize the induced changes in network dynamics following low frequency (1 Hz) repetitive transcranial magnetic stimulation (rTMS) as an inhibitory noninvasive brain stimulation protocol delivered over the intraparietal sulcus. When participants engaged in visual tracking, we observed a highly stable network configuration of six distinct communities, each with characteristic properties in node dynamics. Stimulation to parietal cortex had no significant impact on the dynamics of the parietal community, which already exhibited increased flexibility and promiscuity relative to the other communities. The impact of rTMS, however, was apparent distal from the stimulation site in lateral prefrontal cortex. rTMS temporarily induced stronger allegiance within and between nodal motifs (increased recruitment and integration) in dorsolateral and ventrolateral prefrontal cortex, which returned to baseline levels within 15 min. These findings illustrate the distributed nature by which inhibitory rTMS perturbs network communities and is preliminary evidence for downstream cortical interactions when using noninvasive brain stimulation for behavioral augmentations.
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Affiliation(s)
- Javier O Garcia
- US CCDC Army Research Laboratory, 459 Mulberry Pt Rd., Aberdeen Proving Ground, MD, 21005, USA. .,University of Pennsylvania, Philadelphia, PA, USA.
| | - Lorella Battelli
- Center for Neuroscience and Cognitive Systems@UniTn, Istituto Italiano di Tecnologia, Via Bettini 31, 38068, Rovereto, TN, Italy.,Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Ela Plow
- Department of Biomedical Engineering and Department of Physical Medicine and Rehabilitation, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Zaira Cattaneo
- Department of Psychology, University of Milano-Bicocca, 20126, Milan, Italy.,IRCCS Mondino Foundation, Pavia, Italy
| | - Jean Vettel
- US CCDC Army Research Laboratory, 459 Mulberry Pt Rd., Aberdeen Proving Ground, MD, 21005, USA.,University of Pennsylvania, Philadelphia, PA, USA.,University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Emily D Grossman
- Department of Cognitive Sciences, University of California Irvine, Irvine, CA, 92697, USA
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38
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Garcia JO, Ashourvan A, Thurman SM, Srinivasan R, Bassett DS, Vettel JM. Reconfigurations within resonating communities of brain regions following TMS reveal different scales of processing. Netw Neurosci 2020; 4:611-636. [PMID: 32885118 PMCID: PMC7462427 DOI: 10.1162/netn_a_00139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 03/23/2020] [Indexed: 11/23/2022] Open
Abstract
An overarching goal of neuroscience research is to understand how heterogeneous neuronal ensembles cohere into networks of coordinated activity to support cognition. To investigate how local activity harmonizes with global signals, we measured electroencephalography (EEG) while single pulses of transcranial magnetic stimulation (TMS) perturbed occipital and parietal cortices. We estimate the rapid network reconfigurations in dynamic network communities within specific frequency bands of the EEG, and characterize two distinct features of network reconfiguration, flexibility and allegiance, among spatially distributed neural sources following TMS. Using distance from the stimulation site to infer local and global effects, we find that alpha activity (8–12 Hz) reflects concurrent local and global effects on network dynamics. Pairwise allegiance of brain regions to communities on average increased near the stimulation site, whereas TMS-induced changes to flexibility were generally invariant to distance and stimulation site. In contrast, communities within the beta (13–20 Hz) band demonstrated a high level of spatial specificity, particularly within a cluster comprising paracentral areas. Together, these results suggest that focal magnetic neurostimulation to distinct cortical sites can help identify both local and global effects on brain network dynamics, and highlight fundamental differences in the manifestation of network reconfigurations within alpha and beta frequency bands. TMS may be used to probe the causal link between local regional activity and global brain dynamics. Using simultaneous TMS-EEG and dynamic community detection, we introduce what we call “resonating communities” or frequency band-specific clusters in the brain, as a way to index local and global processing. These resonating communities within the alpha and beta bands display both global (or integrating) behavior and local specificity, highlighting fundamental differences in the manifestation of network reconfigurations.
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Affiliation(s)
- Javier O Garcia
- U.S. Army CCDC Army Research Laboratory, Aberdeen Proving Ground, MD, USA
| | - Arian Ashourvan
- U.S. Army CCDC Army Research Laboratory, Aberdeen Proving Ground, MD, USA
| | - Steven M Thurman
- U.S. Army CCDC Army Research Laboratory, Aberdeen Proving Ground, MD, USA
| | - Ramesh Srinivasan
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Jean M Vettel
- U.S. Army CCDC Army Research Laboratory, Aberdeen Proving Ground, MD, USA
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39
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Liu J, Xia M, Wang X, Liao X, He Y. The spatial organization of the chronnectome associates with cortical hierarchy and transcriptional profiles in the human brain. Neuroimage 2020; 222:117296. [PMID: 32828922 DOI: 10.1016/j.neuroimage.2020.117296] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 08/10/2020] [Accepted: 08/18/2020] [Indexed: 02/02/2023] Open
Abstract
The chronnectome of the human brain represents dynamic connectivity patterns of brain networks among interacting regions, but its organization principle and related transcriptional signatures remain unclear. Using task-free fMRI data from the Human Connectome Project (681 participants) and microarray-based gene expression data from the Allen Institute for Brain Science (1791 brain tissue samples from six donors), we conduct a transcriptome-chronnectome association study to investigate the spatial configurations of dynamic brain networks and their linkages with transcriptional profiles. We first classify the dynamic brain networks into four categories of nodes according to their time-varying characteristics in global connectivity and modular switching: the primary sensorimotor regions with large global variations, the paralimbic/limbic regions with frequent modular switching, the frontoparietal cortex with both high global and modular dynamics, and the sensorimotor association cortex with limited dynamics. Such a spatial layout reflects the cortical functional hierarchy, microarchitecture, and primary connectivity gradient spanning from primary to transmodal areas, and the cognitive spectrum from perception to abstract processing. Importantly, the partial least squares regression analysis reveals that the transcriptional profiles could explain 28% of the variation in this spatial layout of network dynamics. The top-related genes in the transcriptional profiles are enriched for potassium ion channel complex and activity and mitochondrial part of the cellular component. These findings highlight the hierarchically spatial arrangement of dynamic brain networks and their coupling with the variation in transcriptional signatures, which provides indispensable implications for the organizational principle and cellular and molecular functions of spontaneous network dynamics.
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Affiliation(s)
- Jin Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xindi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xuhong Liao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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40
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Conrad BN, Wilkey ED, Yeo DJ, Price GR. Network topology of symbolic and nonsymbolic number comparison. Netw Neurosci 2020; 4:714-745. [PMID: 32885123 PMCID: PMC7462424 DOI: 10.1162/netn_a_00144] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 05/08/2020] [Indexed: 12/12/2022] Open
Abstract
Studies of brain activity during number processing suggest symbolic and nonsymbolic numerical stimuli (e.g., Arabic digits and dot arrays) engage both shared and distinct neural mechanisms. However, the extent to which number format influences large-scale functional network organization is unknown. In this study, using 7 Tesla MRI, we adopted a network neuroscience approach to characterize the whole-brain functional architecture supporting symbolic and nonsymbolic number comparison in 33 adults. Results showed the degree of global modularity was similar for both formats. The symbolic format, however, elicited stronger community membership among auditory regions, whereas for nonsymbolic, stronger membership was observed within and between cingulo-opercular/salience network and basal ganglia communities. The right posterior inferior temporal gyrus, left intraparietal sulcus, and two regions in the right ventromedial occipital cortex demonstrated robust differences between formats in terms of their community membership, supporting prior findings that these areas are differentially engaged based on number format. Furthermore, a unified fronto-parietal/dorsal attention community in the nonsymbolic condition was fractionated into two components in the symbolic condition. Taken together, these results reveal a pattern of overlapping and distinct network architectures for symbolic and nonsymbolic number processing.
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Affiliation(s)
- Benjamin N. Conrad
- Psychology and Human Development, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Eric D. Wilkey
- Psychology and Human Development, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Brain & Mind Institute, Western University, London, ON, Canada
| | - Darren J. Yeo
- Psychology and Human Development, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Division of Psychology, School of Social Sciences, Nanyang Technological University, Singapore
| | - Gavin R. Price
- Psychology and Human Development, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
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41
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Ma R, Barnett I. The asymptotic distribution of modularity in weighted signed networks. Biometrika 2020; 108:1-16. [PMID: 34305154 DOI: 10.1093/biomet/asaa059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Modularity is a popular metric for quantifying the degree of community structure within a network. The distribution of the largest eigenvalue of a network's edge weight or adjacency matrix is well studied and is frequently used as a substitute for modularity when performing statistical inference. However, we show that the largest eigenvalue and modularity are asymptotically uncorrelated, which suggests the need for inference directly on modularity itself when the network size is large. To this end, we derive the asymptotic distributions of modularity in the case where the network's edge weight matrix belongs to the Gaussian orthogonal ensemble, and study the statistical power of the corresponding test for community structure under some alternative models. We empirically explore universality extensions of the limiting distribution and demonstrate the accuracy of these asymptotic distributions through Type I error simulations. We also compare the empirical powers of the modularity based tests with some existing methods. Our method is then used to test for the presence of community structure in two real data applications.
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Affiliation(s)
- Rong Ma
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A
| | - Ian Barnett
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A
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42
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Robust dynamic community detection with applications to human brain functional networks. Nat Commun 2020; 11:2785. [PMID: 32503997 PMCID: PMC7275079 DOI: 10.1038/s41467-020-16285-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 04/21/2020] [Indexed: 02/07/2023] Open
Abstract
While current technology permits inference of dynamic brain networks over long time periods at high temporal resolution, the detailed structure of dynamic network communities during human seizures remains poorly understood. We introduce a new methodology that addresses critical aspects unique to the analysis of dynamic functional networks inferred from noisy data. We propose a dynamic plex percolation method (DPPM) that is robust to edge noise, and yields well-defined spatiotemporal communities that span forward and backwards in time. We show in simulation that DPPM outperforms existing methods in accurately capturing certain stereotypical dynamic community behaviors in noisy situations. We then illustrate the ability of this method to track dynamic community organization during human seizures, using invasive brain voltage recordings at seizure onset. We conjecture that application of this method will yield new targets for surgical treatment of epilepsy, and more generally could provide new insights in other network neuroscience applications. Understanding how brain networks evolve in time remains a challenge, with the potential for significant impact to human health and disease. Here, the authors introduce a new methodology to track dynamic functional networks that is robust to edge noise, and yields well-defined spatiotemporal communities that span forward and backwards in time.
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MacDowell CJ, Buschman TJ. Low-Dimensional Spatiotemporal Dynamics Underlie Cortex-wide Neural Activity. Curr Biol 2020; 30:2665-2680.e8. [PMID: 32470366 DOI: 10.1016/j.cub.2020.04.090] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/07/2020] [Accepted: 04/30/2020] [Indexed: 01/04/2023]
Abstract
Cognition arises from the dynamic flow of neural activity through the brain. To capture these dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex of awake mice. We found that the large majority of variance in cortex-wide activity (∼75%) could be explained by a limited set of ∼14 "motifs" of neural activity. Each motif captured a unique spatiotemporal pattern of neural activity across the cortex. These motifs generalized across animals and were seen in multiple behavioral environments. Motif expression differed across behavioral states, and specific motifs were engaged by sensory processing, suggesting the motifs reflect core cortical computations. Together, our results show that cortex-wide neural activity is highly dynamic but that these dynamics are restricted to a low-dimensional set of motifs, potentially allowing for efficient control of behavior.
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Affiliation(s)
- Camden J MacDowell
- Princeton Neuroscience Institute, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Department of Molecular Biology, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Rutgers Robert Wood Johnson Medical School, 125 Paterson St., New Brunswick, NJ 08901, USA.
| | - Timothy J Buschman
- Princeton Neuroscience Institute, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Department of Psychology, Princeton University, Washington Rd., Princeton, NJ 08540, USA.
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He L, Wang X, Zhuang K, Qiu J. Decreased Dynamic Segregation but Increased Dynamic Integration of the Resting-state Functional Networks During Normal Aging. Neuroscience 2020; 437:54-63. [PMID: 32353459 DOI: 10.1016/j.neuroscience.2020.04.030] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 04/19/2020] [Accepted: 04/20/2020] [Indexed: 01/15/2023]
Abstract
A hallmark of the aging process is increased connectivity between networks and decreased connectivity within networks, which to some extent reflects the reorganization of the brain networks during normal aging. Considering the brain as a complex dynamic system, emerging evidence suggests the time-varying connectivity patterns to be more informative of brain functions. However, the age effect on the dynamic reconfiguration of intrinsic resting state networks is still elusive. By tracking the ongoing formation and dissipation of putative functional modules across time and space, we explored the age-related changes of segregation and integration and further elucidated the underlying brain network dynamics mechanism during normal aging. Results showed that aging strongly weakened dynamic global segregation while enhanced dynamic global integration across the whole brain. Aging was associated with decreasing dynamic segregation of most networks (except the cerebellum) while increasing dynamic integration of only a few networks at the large-scale network level. Notably, the fronto-parietal network, the default mode network, the visual network, and a small group of nodes from these networks, whose dynamic segregation and integration, were both modulated by age. These findings provide direct evidence that there are remarkable changes of dynamic network architecture across the human adult lifespan and suggest the age-related modulations of dynamic segregation and integration intuitively reflect the adaptive changes of the functional dedifferentiation and compensation in older adults.
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Affiliation(s)
- Li He
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing 100875, China.
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Petrican R, Palombo DJ, Sheldon S, Levine B. The Neural Dynamics of Individual Differences in Episodic Autobiographical Memory. eNeuro 2020; 7:ENEURO.0531-19.2020. [PMID: 32060035 PMCID: PMC7171291 DOI: 10.1523/eneuro.0531-19.2020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/30/2020] [Accepted: 01/31/2020] [Indexed: 11/24/2022] Open
Abstract
The ability to mentally travel to specific events from one's past, dubbed episodic autobiographical memory (E-AM), contributes to adaptive functioning. Nonetheless, the mechanisms underlying its typical interindividual variation remain poorly understood. To address this issue, we capitalize on existing evidence that successful performance on E-AM tasks draws on the ability to visualize past episodes and reinstate their unique spatiotemporal context. Hence, here, we test whether features of the brain's functional architecture relevant to perceptual versus conceptual processes shape individual differences in both self-rated E-AM and laboratory-based episodic memory (EM) for random visual scene sequences (visual EM). We propose that superior subjective E-AM and visual EM are associated with greater similarity in static neural organization patterns, potentially indicating greater efficiency in switching, between rest and mental states relevant to encoding perceptual information. Complementarily, we postulate that impoverished subjective E-AM and visual EM are linked to dynamic brain organization patterns implying a predisposition towards semanticizing novel perceptual information. Analyses were conducted on resting state and task-based fMRI data from 329 participants (160 women) in the Human Connectome Project (HCP) who completed visual and verbal EM assessments, and an independent gender diverse sample (N = 59) who self-rated their E-AM. Interindividual differences in subjective E-AM were linked to the same neural mechanisms underlying visual, but not verbal, EM, in general agreement with the hypothesized static and dynamic brain organization patterns. Our results suggest that higher E-AM entails more efficient processing of temporally extended information sequences, whereas lower E-AM entails more efficient semantic or gist-based processing.
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Affiliation(s)
- Raluca Petrican
- School of Psychology, Cardiff University, Cardiff, Wales CF10 3AT, UK
| | - Daniela J Palombo
- Department of Psychology, University of British Columbia, Vancouver, British Columbia V6T 1Z4 Canada
| | - Signy Sheldon
- Department of Psychology, McGill University, Montreal, Quebec H3A 1G1, Canada
| | - Brian Levine
- Rotman Research Institute and Departments of Psychology and Neurology, University of Toronto, Toronto, Ontario M6A 2E1, Canada
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Amico E, Dzemidzic M, Oberlin BG, Carron CR, Harezlak J, Goñi J, Kareken DA. The disengaging brain: Dynamic transitions from cognitive engagement and alcoholism risk. Neuroimage 2020; 209:116515. [PMID: 31904492 PMCID: PMC8496455 DOI: 10.1016/j.neuroimage.2020.116515] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 01/01/2020] [Indexed: 10/25/2022] Open
Abstract
Human functional brain connectivity is usually measured either at "rest" or during cognitive tasks, ignoring life's moments of mental transition. We propose a different approach to understanding brain network transitions. We applied a novel independent component analysis of functional connectivity during motor inhibition (stop signal task) and during the continuous transition to an immediately ensuing rest. A functional network reconfiguration process emerged that: (i) was most prominent in those without familial alcoholism risk, (ii) encompassed brain areas engaged by the task, yet (iii) appeared only transiently after task cessation. The pattern was not present in a pre-task rest scan or in the remaining minutes of post-task rest. Finally, this transient network reconfiguration related to a key behavioral trait of addiction risk: reward delay discounting. These novel findings illustrate how dynamic brain functional reconfiguration during normally unstudied periods of cognitive transition might reflect addiction vulnerability, and potentially other forms of brain dysfunction.
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Affiliation(s)
- Enrico Amico
- Purdue Institute for Integrative Neuroscience, Purdue University, USA; School of Industrial Engineering, Purdue University, USA
| | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA
| | - Brandon G Oberlin
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA; Department of Psychiatry, Indiana University School of Medicine, USA
| | - Claire R Carron
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, USA; School of Industrial Engineering, Purdue University, USA; Weldon School of Biomedical Engineering, Purdue University, USA.
| | - David A Kareken
- Department of Neurology, Indiana University School of Medicine, Indiana Alcohol Research Center, USA.
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Gifford G, Crossley N, Kempton MJ, Morgan S, Dazzan P, Young J, McGuire P. Resting state fMRI based multilayer network configuration in patients with schizophrenia. Neuroimage Clin 2020; 25:102169. [PMID: 32032819 PMCID: PMC7005505 DOI: 10.1016/j.nicl.2020.102169] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 12/18/2019] [Accepted: 01/10/2020] [Indexed: 02/06/2023]
Abstract
Novel methods for measuring large-scale dynamic brain organisation are needed to provide new biomarkers of schizophrenia. Using a method for modelling dynamic modular organisation (Mucha et al., 2010), evidence suggests higher 'flexibility' (switching between multilayer network communities) to be a feature of schizophrenia (Braun et al., 2016). The current study compared flexibility between 55 patients with schizophrenia and 72 controls (the COBRE Dataset). In addition, novel methods of 'between resting state network synchronisation' (BRSNS) and the probability of transition from one community to another were used to further describe group differences in dynamic community structure. There was significantly higher schizophrenia group flexibility scores in cerebellar (F (1124) = 9.33, p (FDR) = 0.017), subcortical (F (1124) = 13.14, p (FDR) = 0.005), and fronto-parietal task control (F (1124) = 7.19, p (FDR) = 0.033) resting state networks (RSNs), as well as in the left thalamus (MNI XYZ: -2, -13, 12; F(1, 124) = 17.1, p (FDR) < 0.001) and the right crus I (MNI XYZ: 35, -67, -34; F (1, 124) = 19.65, p (FDR) < 0.001). Flexibility in the left thalamus reflected transitions between communities covering default mode and sensory-somatomotor RSNs. BRSNS scores suggested altered dynamic inter-RSN modular configuration in schizophrenia. This study suggests less stable community structure in a schizophrenia group at an RSN and node level and provides novel methods of exploring dynamic community structure. Mediation of group differences by mean time window correlation did however suggest flexibility to be no better as a schizophrenia biomarker than simpler measures and a range of methodological choices affected results.
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Affiliation(s)
- George Gifford
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK.
| | - Nicolas Crossley
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK; Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, Santiago 8330077, Chile
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Sarah Morgan
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK; The Alan Turing Institute, London NW1 2DB, UK
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Jonathan Young
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
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Vriend C, Wagenmakers MJ, van den Heuvel OA, van der Werf YD. Resting-state network topology and planning ability in healthy adults. Brain Struct Funct 2020; 225:365-374. [PMID: 31865409 PMCID: PMC6957556 DOI: 10.1007/s00429-019-02004-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 12/06/2019] [Indexed: 12/29/2022]
Abstract
Functional magnetic resonance imaging (fMRI) studies have been used extensively to investigate the brain areas that are recruited during the Tower of London (ToL) task. Nevertheless, little research has been devoted to study the neural correlates of the ToL task using a network approach. Here we investigated the association between functional connectivity and network topology during resting-state fMRI and ToL task performance, that was performed outside the scanner. Sixty-two (62) healthy subjects (21-74 years) underwent eyes-closed rsfMRI and performed the task on a laptop. We studied global (whole-brain) and within subnetwork resting-state topology as well as functional connectivity between subnetworks, with a focus on the default-mode, fronto-parietal and dorsal and ventral attention networks. Efficiency and clustering coefficient were calculated to measure network integration and segregation, respectively, at both the global and subnetwork level. Our main finding was that higher global efficiency was associated with slower performance (β = 0.22, Pbca = 0.04) and this association seemed mainly driven by inter-individual differences in default-mode network connectivity. The reported results were independent of age, sex, education-level and motion. Although this finding is contrary to earlier findings on general cognition, we tentatively hypothesize that the reported association may indicate that individuals with a more integrated brain during the resting-state are less able to further increase network efficiency when transitioning from a rest to task state, leading to slower responses. This study also adds to a growing body of literature supporting a central role for the default-mode network in individual differences in cognitive performance.
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Affiliation(s)
- Chris Vriend
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands.
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands.
- Department of Anatomy and Neuroscience, Amsterdam UMC, Location VUmc, p/a sec. ANW O|2, BT, PO Box 7007, 1007 MB, Amsterdam, The Netherlands.
| | - Margot J Wagenmakers
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Odile A van den Heuvel
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Ysbrand D van der Werf
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
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Salehi M, Karbasi A, Barron DS, Scheinost D, Constable RT. Individualized functional networks reconfigure with cognitive state. Neuroimage 2019; 206:116233. [PMID: 31574322 DOI: 10.1016/j.neuroimage.2019.116233] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/22/2019] [Accepted: 09/27/2019] [Indexed: 02/08/2023] Open
Abstract
There is extensive evidence that functional organization of the human brain varies dynamically as the brain switches between task demands, or cognitive states. This functional organization also varies across subjects, even when engaged in similar tasks. To date, the functional network organization of the brain has been considered static. In this work, we use fMRI data obtained across multiple cognitive states (task-evoked and rest conditions) and across multiple subjects, to measure state- and subject-specific functional network parcellation (the assignment of nodes to networks). Our parcellation approach provides a measure of how node-to-network assignment (NNA) changes across states and across subjects. We demonstrate that the brain's functional networks are not spatially fixed, but that many nodes change their network membership as a function of cognitive state. Such reconfigurations are highly robust and reliable to the extent that they can be used to predict cognitive state with up to 97% accuracy. Our findings suggest that if functional networks are to be defined via functional clustering of nodes, then it is essential to consider that such definitions may be fluid and cognitive-state dependent.
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Affiliation(s)
- Mehraveh Salehi
- Department of Electrical Engineering, Yale University, New Haven, CT, 06511, USA; Yale Institute for Network Science (YINS), Yale University, New Haven, CT, 06511, USA.
| | - Amin Karbasi
- Department of Electrical Engineering, Yale University, New Haven, CT, 06511, USA; Yale Institute for Network Science (YINS), Yale University, New Haven, CT, 06511, USA
| | - Daniel S Barron
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA; Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06520, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, 06520, USA
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50
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Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity. Sci Rep 2019; 9:13474. [PMID: 31530857 PMCID: PMC6748940 DOI: 10.1038/s41598-019-49726-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 08/24/2019] [Indexed: 12/31/2022] Open
Abstract
Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.
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