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Chaitanya G, Hinds W, Kragel J, He X, Sideman N, Ezzyat Y, Sperling MR, Sharan A, Tracy JI. Tonic Resting State Hubness Supports High Gamma Activity Defined Verbal Memory Encoding Network in Epilepsy. Neuroscience 2019; 425:194-216. [PMID: 31786346 DOI: 10.1016/j.neuroscience.2019.11.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 10/25/2019] [Accepted: 11/01/2019] [Indexed: 01/06/2023]
Abstract
High gamma activity (HGA) of verbal-memory encoding using invasive-electroencephalogram has laid the foundation for numerous studies testing the integrity of memory in diseased populations. Yet, the functional connectivity characteristics of networks subserving these memory linkages remains uncertain. By integrating this electrophysiological biomarker of memory encoding from IEEG with resting-state BOLD fluctuations, we estimated the segregation and hubness of HGA-memory regions in drug-resistant epilepsy patients and matched healthy controls. HGA-memory regions express distinctly different hubness compared to neighboring regions in health and in epilepsy, and this hubness was more relevant than segregation in predicting verbal memory encoding. The HGA-memory network comprised regions from both the cognitive control and primary processing networks, validating that effective verbal-memory encoding requires integrating brain functions, and is not dominated by a central cognitive core. Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding.
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Affiliation(s)
- Ganne Chaitanya
- Department of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, United States
| | - Walter Hinds
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, United States
| | - James Kragel
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Xiaosong He
- Department of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, United States
| | - Noah Sideman
- Department of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, United States
| | - Youssef Ezzyat
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Michael R Sperling
- Department of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, United States
| | - Ashwini Sharan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107, United States
| | - Joseph I Tracy
- Department of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, United States.
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Bussalb A, Collin S, Barthélemy Q, Ojeda D, Bioulac S, Blasco-Fontecilla H, Brandeis D, Purper Ouakil D, Ros T, Mayaud L. Is there a cluster of high theta-beta ratio patients in attention deficit hyperactivity disorder? Clin Neurophysiol 2019; 130:1387-1396. [PMID: 31176621 DOI: 10.1016/j.clinph.2019.02.021] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 02/15/2019] [Accepted: 02/27/2019] [Indexed: 11/16/2022]
Affiliation(s)
- Aurore Bussalb
- Mensia Technologies SA, 130 rue de Lourmel, 75015 Paris, France; Assistance Publique-Hôpitaux de Paris, Robert Debré Hospital, Child and Adolescent Psychiatry, Paris, France.
| | - Sidney Collin
- Mensia Technologies SA, 130 rue de Lourmel, 75015 Paris, France
| | | | - David Ojeda
- Mensia Technologies SA, 130 rue de Lourmel, 75015 Paris, France
| | - Stephanie Bioulac
- Université de Bordeaux, Sommeil, Addiction et Neuropsychiatrie, USR 3413, F-33000 Bordeaux, France; CNRS, SANPSY, USR 3413, F-33000 Bordeaux, France; CHU Pellegrin, Clinique du Sommeil, F-33076 Bordeaux, France
| | - Hilario Blasco-Fontecilla
- Department of Psychiatry, Puerta de Hierro University Hospital-IDIPHIPSA, Autonoma University of Madrid, CIBERSAM, Madrid, Spain
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany; Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland; Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
| | - Diane Purper Ouakil
- CHU Montpellier-Saint Eloi Hospital, University of Montpellier, Unit of Child and Adolescent Psychiatry (MPEA1), Montpellier, France
| | - Tomas Ros
- Department of Neuroscience, Geneva, Switzerland
| | - Louis Mayaud
- Mensia Technologies SA, 130 rue de Lourmel, 75015 Paris, France
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Li K, Jiang Y, Gong Y, Zhao W, Zhao Z, Liu X, Kendrick KM, Zhu C, Becker B. Functional near-infrared spectroscopy-informed neurofeedback: regional-specific modulation of lateral orbitofrontal activation and cognitive flexibility. NEUROPHOTONICS 2019; 6:025011. [PMID: 31930153 PMCID: PMC6951484 DOI: 10.1117/1.nph.6.2.025011] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 05/13/2019] [Indexed: 06/10/2023]
Abstract
Cognitive flexibility and reward processing critically rely on the orbitofrontal cortex (OFC). Dysregulations in these domains and orbitofrontal activation have been reported in major psychiatric disorders. Hemodynamic brain imaging-informed neurofeedback allows regional-specific control over brain activation and thus may represent an innovative intervention to regulate orbitofrontal dysfunctions. Against this background the present proof-of-concept study evaluates the feasibility and behavioral relevance of functional near-infrared spectroscopy (fNIRS)-assisted neurofeedback training of the lateral orbitofrontal cortex (lOFC). In a randomized sham-controlled between-subject design, 60 healthy participants have undergone four subsequent runs of training to enhance the lOFC activation. Training-induced changes in the lOFC, attentional set-shifting performance, and reward experience have served as primary outcomes. Feedback from the target channel significantly increases the regional-specific lOFC activation over the four training runs in comparison with sham neurofeedback. The real-time OFC neurofeedback group demonstrates a trend for faster responses during the set-shifting relative to the sham neurofeedback group. Within the real-time OFC neurofeedback group, stronger training-induced lOFC increases are associated with higher reward experience. The present results demonstrate that fNIRS-informed neurofeedback allows regional-specific regulation of lOFC activation and may have the potential to modulate the associated behavioral domains. As such fNIRS-informed neurofeedback may represent a promising strategy to regulate OFC dysfunctions in psychiatric disorders.
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Affiliation(s)
- Keshuang Li
- University of Electronic Science and Technology of China, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Chengdu, China
| | - Yihan Jiang
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
| | - Yilong Gong
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
| | - Weihua Zhao
- University of Electronic Science and Technology of China, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Chengdu, China
| | - Zhiying Zhao
- University of Electronic Science and Technology of China, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Chengdu, China
| | - Xiaolong Liu
- University of Electronic Science and Technology of China, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Chengdu, China
| | - Keith M. Kendrick
- University of Electronic Science and Technology of China, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Chengdu, China
| | - Chaozhe Zhu
- Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China
- Beijing Normal University, IDG/McGovern Institute for Brain Research, Beijing, China
- Beijing Normal University, Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing, China
| | - Benjamin Becker
- University of Electronic Science and Technology of China, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Chengdu, China
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Cornblath EJ, Tang E, Baum GL, Moore TM, Adebimpe A, Roalf DR, Gur RC, Gur RE, Pasqualetti F, Satterthwaite TD, Bassett DS. Sex differences in network controllability as a predictor of executive function in youth. Neuroimage 2019; 188:122-134. [PMID: 30508681 PMCID: PMC6401302 DOI: 10.1016/j.neuroimage.2018.11.048] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 10/10/2018] [Accepted: 11/26/2018] [Indexed: 01/06/2023] Open
Abstract
Executive function is a quintessential human capacity that emerges late in development and displays different developmental trends in males and females. Sex differences in executive function in youth have been linked to vulnerability to psychopathology as well as to behaviors that impinge on health, wellbeing, and longevity. Yet, the neurobiological basis of these differences is not well understood, in part due to the spatiotemporal complexity inherent in patterns of brain network maturation supporting executive function. Here we test the hypothesis that sex differences in impulsivity in youth stem from sex differences in the controllability of structural brain networks as they rewire over development. Combining methods from network neuroscience and network control theory, we characterize the network control properties of structural brain networks estimated from diffusion imaging data acquired in males and females in a sample of 879 youth aged 8-22 years. We summarize the control properties of these networks by estimating average and modal controllability, two statistics that probe the ease with which brain areas can drive the network towards easy versus difficult-to-reach states. We find that females have higher modal controllability in frontal, parietal, and subcortical regions while males have higher average controllability in frontal and subcortical regions. Furthermore, controllability profiles in males are negatively related to the false positive rate on a continuous performance task, a common measure of impulsivity. Finally, we find associations between average controllability and individual differences in activation during an n-back working memory task. Taken together, our findings support the notion that sex differences in the controllability of structural brain networks can partially explain sex differences in executive function. Controllability of structural brain networks also predicts features of task-relevant activation, suggesting the potential for controllability to represent context-specific constraints on network state more generally.
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Affiliation(s)
- Eli J Cornblath
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Evelyn Tang
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Graham L Baum
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA, 92521, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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De Vico Fallani F, Bassett DS. Network neuroscience for optimizing brain-computer interfaces. Phys Life Rev 2019; 31:304-309. [PMID: 30642781 DOI: 10.1016/j.plrev.2018.10.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 05/29/2018] [Accepted: 10/10/2018] [Indexed: 01/30/2023]
Abstract
Human-machine interactions are being increasingly explored to create alternative ways of communication and to improve our daily life. Based on a classification of the user's intention from the user's underlying neural activity, brain-computer interfaces (BCIs) allow direct interactions with the external environment while bypassing the traditional effector of the musculoskeletal system. Despite the enormous potential of BCIs, there are still a number of challenges that limit their societal impact, ranging from the correct decoding of a human's thoughts, to the application of effective learning strategies. Despite several important engineering advances, the basic neuroscience behind these challenges remains poorly explored. Indeed, BCIs involve complex dynamic changes related to neural plasticity at a diverse range of spatiotemporal scales. One promising antidote to this complexity lies in network science, which provides a natural language in which to model the organizational principles of brain architecture and function as manifest in its interconnectivity. Here, we briefly review the main limitations currently affecting BCIs, and we offer our perspective on how they can be addressed by means of network theoretic approaches. We posit that the emerging field of network neuroscience will prove to be an effective tool to unlock human-machine interactions.
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Affiliation(s)
- Fabrizio De Vico Fallani
- Inria Paris, Aramis project-team, F-75013, Paris, France; Institut du Cerveau et de la Moelle Epiniere, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, F-75013, Paris, France.
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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Betzel RF, Bassett DS. Generative models for network neuroscience: prospects and promise. J R Soc Interface 2017; 14:20170623. [PMID: 29187640 PMCID: PMC5721166 DOI: 10.1098/rsif.2017.0623] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 11/06/2017] [Indexed: 12/22/2022] Open
Abstract
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here, we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science, broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including Caenorhabditis elegans, Drosophila, mouse, rat, cat, macaque and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modelling approach for network neuroscience.
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Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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Dynamics of large-scale fMRI networks: Deconstruct brain activity to build better models of brain function. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2017. [DOI: 10.1016/j.cobme.2017.09.008] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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