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Farkhondeh Tale Navi F, Heysieattalab S, Ramanathan DS, Raoufy MR, Nazari MA. Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs. Neuroscience 2021; 483:104-126. [PMID: 34902494 DOI: 10.1016/j.neuroscience.2021.12.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 11/27/2022]
Abstract
Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.
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Affiliation(s)
- Farhad Farkhondeh Tale Navi
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran
| | - Soomaayeh Heysieattalab
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran
| | | | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Ali Nazari
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran; Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
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Pan H, Mi W, Song H, Liu F. A universal closed-loop brain–machine interface framework design and its application to a joint prosthesis. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05323-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Corsi MC, Chavez M, Schwartz D, George N, Hugueville L, Kahn AE, Dupont S, Bassett DS, De Vico Fallani F. BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks. J Neural Eng 2021; 18. [PMID: 33725682 DOI: 10.1088/1741-2552/abef39] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 03/16/2021] [Indexed: 11/11/2022]
Abstract
Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the α band was paralleled by a decrease of the integration of visual processing and working memory areas in the β band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the α2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.
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Affiliation(s)
| | - Mario Chavez
- UMR-7225, CNRS, 47, boulevard de l'Hôpital, Paris, 75013, FRANCE
| | - Denis Schwartz
- INSERM, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Nathalie George
- UMR-7225, CNRS, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Laurent Hugueville
- Institut du Cerveau et de la Moelle Epiniere, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Ari E Kahn
- Department of Neuroscience, University of Pennsylvania, 210 S. 33rd Street 240 Skirkanich Hall, Philadelphia, Pennsylvania, 19104-6321, UNITED STATES
| | - Sophie Dupont
- Institut du Cerveau et de la Moelle Epiniere, 47, boulevard de l'Hôpital, Paris, Île-de-France, 75013, FRANCE
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, 210 S. 33rd Street 240 Skirkanich Hall, USA, Philadelphia, Pennsylvania, 19104-6321, UNITED STATES
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Gonzalez-Astudillo J, Cattai T, Bassignana G, Corsi MC, De Vico Fallani F. Network-based brain computer interfaces: principles and applications. J Neural Eng 2020; 18. [PMID: 33147577 DOI: 10.1088/1741-2552/abc760] [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: 06/23/2020] [Accepted: 11/04/2020] [Indexed: 12/17/2022]
Abstract
Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user's mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.
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Affiliation(s)
- Atsushi Nambu
- Division of System Neurophysiology, National Institute for Physiological Sciences, Department of Physiological Sciences, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Aichi 444-8585, Japan.
| | - Ichiro Tsuda
- Chubu University Academy of Emerging Sciences, Kasugai, Aichi 487-8501, Japan
| | - Tatsuya Mima
- The Graduate School of Core Ethics and Frontier Sciences, Ritsumeikan University, Kyoto, 603-8577, Japan
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