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Dong R, Zhang X, Li H, Masengo G, Zhu A, Shi X, He C. EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study. Front Neurosci 2024; 17:1305850. [PMID: 38352938 PMCID: PMC10861750 DOI: 10.3389/fnins.2023.1305850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/28/2023] [Indexed: 02/16/2024] Open
Abstract
Introduction Active rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG. Methods Firstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia. Results Simulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention. Discussion The proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.
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Affiliation(s)
- Runlin Dong
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Xiaodong Zhang
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Hanzhe Li
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Gilbert Masengo
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Aibin Zhu
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Xiaojun Shi
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Chen He
- General Department, AVIC Creative Robotics Co., Ltd., Xi’an, Shaanxi, China
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Goold S, Murphy MJ, Goodale MA, Crewther SG, Laycock R. Faster social attention disengagement in individuals with higher autism traits. J Clin Exp Neuropsychol 2022; 44:755-767. [PMID: 36694386 DOI: 10.1080/13803395.2023.2167943] [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] [Indexed: 01/26/2023]
Abstract
INTRODUCTION Atypical visual and social attention has often been associated with clinically diagnosed autism spectrum disorder (ASD), and with the broader autism phenotype. Atypical social attention is of particular research interest given the importance of facial expressions for social communication, with faces tending to attract and hold attention in neurotypical individuals. In autism, this is not necessarily so, where there is debate about the temporal differences in the ability to disengage attention from a face. METHOD Thus, we have used eye-tracking to record saccadic latencies as a measure of time to disengage attention from a central task-irrelevant face before orienting to a newly presented peripheral nonsocial target during a gap-overlap task. Neurotypical participants with higher or lower autism-like traits (AT) completed the task that included central stimuli with varied expressions of facial emotion as well as an inverted face. RESULTS High AT participants demonstrated faster saccadic responses to detect the nonsocial target than low AT participants when disengaging attention from a face. Furthermore, faster saccadic responses were recorded when comparing disengagement from upright to inverted faces in low AT but not in high AT participants. CONCLUSIONS Together, these results extend findings of atypical social attention disengagement in autism and highlight how differences in attention to faces in the broader autism phenotype can lead to apparently superior task performance under certain conditions. Specifically, autism traits were linked to faster attention orienting to a nonsocial target due to the reduced attentional hold of the task irrelevant face stimuli. The absence of an inversion effect in high AT participants also reinforces the suggestion that they process upright or inverted faces similarly, unlike low AT participants for whom inverted faces are thought to be less socially engaging, thus allowing faster disengagement.
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Affiliation(s)
- Saxon Goold
- School of Psychology and Public Health, La Trobe University, Melbourne, Australia
| | - Melanie J Murphy
- School of Psychology and Public Health, La Trobe University, Melbourne, Australia
| | - Melvyn A Goodale
- Western Institute for Neuroscience, The University of Western Ontario, Ontario, Canada
| | - Sheila G Crewther
- School of Psychology and Public Health, La Trobe University, Melbourne, Australia
| | - Robin Laycock
- School of Psychology and Public Health, La Trobe University, Melbourne, Australia.,School of Health and Biomedical Science, RMIT University, Melbourne, Australia
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Liu S, Zhang J, Wang A, Wu H, Zhao Q, Long J. Subject adaptation convolutional neural network for EEG-based motor imagery classification. J Neural Eng 2022; 19. [PMID: 36270467 DOI: 10.1088/1741-2552/ac9c94] [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: 06/23/2022] [Accepted: 10/21/2022] [Indexed: 01/11/2023]
Abstract
Objective.Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted.Approach.Here, we propose a novel end-to-end deep subject adaptation convolutional neural network (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e. a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy. By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject.Main results.Extensive experiments are carried out on three EEG-based MI datasets, i.e. brain-computer interface (BCI) competition IV dataset IIb, BCI competition III dataset IVa, and BCI competition IV dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN.Significance.This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.
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Affiliation(s)
- Siwei Liu
- College of Information Science and Technology, Jinan University, Guangzhou 510632, People's Republic of China
| | - Jia Zhang
- College of Information Science and Technology, Jinan University, Guangzhou 510632, People's Republic of China
| | - Andong Wang
- Tensor Learning Team, RIKEN AIP, Tokyo, Japan
| | - Hanrui Wu
- College of Information Science and Technology, Jinan University, Guangzhou 510632, People's Republic of China
| | - Qibin Zhao
- Tensor Learning Team, RIKEN AIP, Tokyo, Japan
| | - Jinyi Long
- College of Information Science and Technology, Jinan University, Guangzhou 510632, People's Republic of China.,Guangdong Key Laboratory of Traditional Chinese Medicine Information Technology, Guangzhou 510632, People's Republic of China.,Pazhou Lab, Guangzhou 510335, People's Republic of China
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Ursino M, Ricci G, Astolfi L, Pichiorri F, Petti M, Magosso E. A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models. Brain Sci 2021; 11:brainsci11111479. [PMID: 34827478 PMCID: PMC8615480 DOI: 10.3390/brainsci11111479] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 10/28/2021] [Accepted: 11/03/2021] [Indexed: 11/16/2022] Open
Abstract
Knowledge of motor cortex connectivity is of great value in cognitive neuroscience, in order to provide a better understanding of motor organization and its alterations in pathological conditions. Traditional methods provide connectivity estimations which may vary depending on the task. This work aims to propose a new method for motor connectivity assessment based on the hypothesis of a task-independent connectivity network, assuming nonlinear behavior. The model considers six cortical regions of interest (ROIs) involved in hand movement. The dynamics of each region is simulated using a neural mass model, which reproduces the oscillatory activity through the interaction among four neural populations. Parameters of the model have been assigned to simulate both power spectral densities and coherences of a patient with left-hemisphere stroke during resting condition, movement of the affected, and movement of the unaffected hand. The presented model can simulate the three conditions using a single set of connectivity parameters, assuming that only inputs to the ROIs change from one condition to the other. The proposed procedure represents an innovative method to assess a brain circuit, which does not rely on a task-dependent connectivity network and allows brain rhythms and desynchronization to be assessed on a quantitative basis.
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Affiliation(s)
- Mauro Ursino
- Department of Electrical, Electronic and Information Engineering Guglielmo Marconi, Campus of Cesena, University of Bologna, Via Dell’Università 50, 47521 Cesena, Italy; (G.R.); (E.M.)
- Correspondence:
| | - Giulia Ricci
- Department of Electrical, Electronic and Information Engineering Guglielmo Marconi, Campus of Cesena, University of Bologna, Via Dell’Università 50, 47521 Cesena, Italy; (G.R.); (E.M.)
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, 00185 Roma, Italy; (L.A.); (M.P.)
- Fondazione Santa Lucia, IRCCS Via Ardeatina 306/354, 00179 Roma, Italy;
| | | | - Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, 00185 Roma, Italy; (L.A.); (M.P.)
- Fondazione Santa Lucia, IRCCS Via Ardeatina 306/354, 00179 Roma, Italy;
| | - Elisa Magosso
- Department of Electrical, Electronic and Information Engineering Guglielmo Marconi, Campus of Cesena, University of Bologna, Via Dell’Università 50, 47521 Cesena, Italy; (G.R.); (E.M.)
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Al Harrach M, Pretzel P, Groeschel S, Rousseau F, Dhollander T, Hertz-Pannier L, Lefevre J, Chabrier S, Dinomais M. A connectome-based approach to assess motor outcome after neonatal arterial ischemic stroke. Ann Clin Transl Neurol 2021; 8:1024-1037. [PMID: 33787079 PMCID: PMC8108427 DOI: 10.1002/acn3.51292] [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: 11/03/2020] [Revised: 12/08/2020] [Accepted: 12/09/2020] [Indexed: 12/22/2022] Open
Abstract
Objective Studies of motor outcome after Neonatal Arterial Ischemic Stroke (NAIS) often rely on lesion mapping using MRI. However, clinical measurements indicate that motor deficit can be different than what would solely be anticipated by the lesion extent and location. Because this may be explained by the cortical disconnections between motor areas due to necrosis following the stroke, the investigation of the motor network can help in the understanding of visual inspection and outcome discrepancy. In this study, we propose to examine the structural connectivity between motor areas in NAIS patients compared to healthy controls in order to define the cortical and subcortical connections that can reflect the motor outcome. Methods Thirty healthy controls and 32 NAIS patients with and without Cerebral Palsy (CP) underwent MRI acquisition and manual assessment. The connectome of all participants was obtained from T1‐weighted and diffusion‐weighted imaging. Results Significant disconnections in the lesioned and contra‐lesioned hemispheres of patients were found. Furthermore, significant correlations were detected between the structural connectivity metric of specific motor areas and manuality assessed by the Box and Block Test (BBT) scores in patients. Interpretation Using the connectivity measures of these links, the BBT score can be estimated using a multiple linear regression model. In addition, the presence or not of CP can also be predicted using the KNN classification algorithm. According to our results, the structural connectome can be an asset in the estimation of gross manual dexterity and can help uncover structural changes between brain regions related to NAIS.
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Affiliation(s)
- Mariam Al Harrach
- Université d'Angers, Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS) EA7315, Angers, 49000, France.,Université de Rennes 1, Laboratoire Traitement du Signal et de l'Image (LTSI), INSERM U1099, Rennes, F-35000, France
| | - Pablo Pretzel
- Experimental Paediatric Neuroimaging, Department of Child Neurology, University Hospital Tübingen, Tübingen, Germany
| | - Samuel Groeschel
- Experimental Paediatric Neuroimaging, Department of Child Neurology, University Hospital Tübingen, Tübingen, Germany
| | | | - Thijs Dhollander
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Lucie Hertz-Pannier
- UNIACT, Neurospin, Institut Joliot, CEA-Paris Saclay, Inserm U114, Université de Paris, Gif sur Yvette, F-91191, France
| | - Julien Lefevre
- Institut de Neurosciences de la Timone, UMR 7289, Aix Marseille Université, CNRS, Marseille, 13385, France
| | - Stéphane Chabrier
- INSERM, UMR1059 Sainbiose, Univ Saint-Étienne, Univ Lyon, Saint-Étienne, F-42023, France.,Paediatric Physical and Rehabilitation Medicine Department, CHU Saint-Étienne, French Centre for Paediatric Stroke, INSERM, CIC 1408, Saint-Étienne, F-42055, France
| | - Mickael Dinomais
- Université d'Angers, Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS) EA7315, Angers, 49000, France.,Département de Médecine Physique et de Réadaptions and LUNAM, CHU Angers, Angers, France
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A Neurophysiological Pattern as a Precursor of Work-Related Musculoskeletal Disorders Using EEG Combined with EMG. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18042001. [PMID: 33669544 PMCID: PMC7921951 DOI: 10.3390/ijerph18042001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/12/2021] [Accepted: 02/14/2021] [Indexed: 12/13/2022]
Abstract
We aimed to determine the neurophysiological pattern that is associated with the development of musculoskeletal pain that is induced by biomechanical constraints. Twelve (12) young healthy volunteers (two females) performed two experimental realistic manual tasks for 30 min each: (1) with the high risk of musculoskeletal pain development and (2) with low risk for pain development. During the tasks, synchronized electroencephalographic (EEG) and electromyography (EMG) signals data were collected, as well as pain scores. Subsequently, two main variables were computed from neurophysiological signals: (1) cortical inhibition as Task-Related Power Increase (TRPI) in beta EEG frequency band (β.TRPI) and (2) muscle variability as Coefficient of Variation (CoV) from EMG signals. A strong effect size was observed for pain measurement under the high risk condition during the last 5 min of the task execution; with muscle fatigue, because the CoV has decreased below 18%. An increase in cortical inhibition (β.TRPI >50%) was observed after the 5th min of the task in both experimental conditions. These results suggest the following neurophysiological pattern—β.TRPI ≥ 50% and CoV ≤ 18%—as a possible indicator to monitor the development of musculoskeletal pain in the shoulder in the context of repeated and prolonged exposure to manual tasks.
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Trevisan DA, Parker T, McPartland JC. First-Hand Accounts of Interoceptive Difficulties in Autistic Adults. J Autism Dev Disord 2021; 51:3483-3491. [PMID: 33389300 DOI: 10.1007/s10803-020-04811-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/18/2020] [Indexed: 12/12/2022]
Abstract
Interoceptive awareness refers to one's ability to detect, discriminate, and regulate internal bodily and mental processes. Interoceptive challenges in ASD remain under researched and poorly understood. In this study, we analyzed texts of adults who self-identify as autistic describing their interoceptive challenges. Many individuals described limited awareness of hunger, satiation, or thirst, which contributed to eating disordered behavior in some instances. Others described limited awareness or difficulty understanding affective arousal, pain or illness, and difficulty differentiating benign body signals from signals that represent medical concerns. Findings from this study call for increased research attention on this topic, and a need for valid and objective measures for assessing interoception in ASD.
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Affiliation(s)
- Dominic A Trevisan
- Child Study Center, Yale University, 230 S. Frontage Rd, New Haven, CT, 06511, USA.
| | - Termara Parker
- Child Study Center, Yale University, 230 S. Frontage Rd, New Haven, CT, 06511, USA
| | - James C McPartland
- Child Study Center, Yale University, 230 S. Frontage Rd, New Haven, CT, 06511, USA.
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Ursino M, Ricci G, Magosso E. Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models. Front Comput Neurosci 2020; 14:45. [PMID: 32581756 PMCID: PMC7292208 DOI: 10.3389/fncom.2020.00045] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 04/30/2020] [Indexed: 12/12/2022] Open
Abstract
Objective: Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs). Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified. Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present. Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study.
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Affiliation(s)
- Mauro Ursino
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Giulia Ricci
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Cesena, Italy
| | - Elisa Magosso
- Department of Electrical, Electronic and Information Engineering, University of Bologna, Cesena, Italy
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Kovarski K, Malvy J, Khanna RK, Arsène S, Batty M, Latinus M. Reduced visual evoked potential amplitude in autism spectrum disorder, a variability effect? Transl Psychiatry 2019; 9:341. [PMID: 31852886 PMCID: PMC6920480 DOI: 10.1038/s41398-019-0672-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 10/21/2019] [Accepted: 11/07/2019] [Indexed: 12/27/2022] Open
Abstract
Atypical sensory behaviours represent a core symptom of autism spectrum disorder (ASD). Investigating early visual processing is crucial to deepen our understanding of higher-level processes. Visual evoked potentials (VEPs) to pattern-reversal checkerboards were recorded in ASD children and age-matched controls. Peak analysis of the P100 component and two types of single-trial analyses were carried out. P100 amplitude was reduced in the ASD group, consistent with previous reports. The analysis of the proportion of trials with a positive activity in the latency range of the P100, measuring inter-trial (in)consistency, allowed identifying two subgroups of ASD participants: the first group, as control children, showed a high inter-trial consistency, whereas the other group showed an inter-trial inconsistency. Analysis of median absolute deviation of single-trial P100 (st-P100) latencies revealed an increased latency variability in the ASD group. Both single-trial analyses revealed increased variability in a subset of children with ASD. To control for this variability, VEPs were reconstructed by including only positive trials or trials with homogeneous st-P100 latencies. These control analyses abolished group differences, confirming that the reduced P100 amplitude results from increased inter-trial variability in ASD. This increased variability in ASD supports the neural noise theory. The existence of subgroups in ASD suggests that the neural response variability is not a genuine characteristic of the entire autistic spectrum, but rather characterized subgroups of children. Exploring the relationship between sensory responsiveness and inter-trial variability could provide more precise bioclinical profiles in children with ASD, and complete the functional diagnostic crucial for the development of individualized therapeutical projects.
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Affiliation(s)
- Klara Kovarski
- UMR 1253, iBrain, Université de Tours, Inserm, Tours, France. .,CNRS (Integrative Neuroscience and Cognition Center, UMR 8002), Paris, France. .,Université Paris Descartes, Sorbonne Paris Cité, Paris, France. .,Fondation Ophtalmologique A. de Rothschild, Paris, France.
| | - Joëlle Malvy
- 0000 0001 2182 6141grid.12366.30UMR 1253, iBrain, Université de Tours, Inserm, Tours, France ,0000 0004 1765 1600grid.411167.4CHRU de Tours, Centre Universitaire de Pédopsychiatrie, Tours, France
| | - Raoul K. Khanna
- 0000 0001 2182 6141grid.12366.30UMR 1253, iBrain, Université de Tours, Inserm, Tours, France ,0000 0004 1765 1600grid.411167.4CHRU de Tours, Département d’Ophtalmologie, Tours, France
| | - Sophie Arsène
- 0000 0004 1765 1600grid.411167.4CHRU de Tours, Département d’Ophtalmologie, Tours, France
| | - Magali Batty
- 0000 0001 2353 1689grid.11417.32Université de Toulouse, CERPPS, Toulouse, France
| | - Marianne Latinus
- 0000 0001 2182 6141grid.12366.30UMR 1253, iBrain, Université de Tours, Inserm, Tours, France
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Mondini V, Mangia AL, Cappello A. Single-session tDCS over the dominant hemisphere affects contralateral spectral EEG power, but does not enhance neurofeedback-guided event-related desynchronization of the non-dominant hemisphere's sensorimotor rhythm. PLoS One 2018. [PMID: 29513682 PMCID: PMC5841755 DOI: 10.1371/journal.pone.0193004] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Transcranial direct current stimulation (tDCS) and neurofeedback-guided motor imagery (MI) have attracted considerable interest in neurorehabilitation, given their ability to influence neuroplasticity. As tDCS has been shown to modulate event-related desynchronization (ERD), the neural signature of motor imagery detected for neurofeedback, a combination of the techniques was recently proposed. One limitation of this approach is that the area targeted for stimulation is the same from which the signal for neurofeedback is acquired. As tDCS may interfere with proximal electroencephalographic (EEG) electrodes, in this study our aim was to test whether contralateral tDCS could have interhemispheric effects on the spectral power of the unstimulated hemisphere, possibly mediated by transcallosal connection, and whether such effects could be used to enhance ERD magnitudes. A contralateral stimulation approach would indeed facilitate co-registration, as the stimulation electrode would be far from the recording sites. METHODS Twenty right-handed healthy volunteers (aged 21 to 32) participated in the study: ten assigned to cathodal, ten to anodal versus sham stimulation. We applied stimulation over the dominant (left) hemisphere, and assessed ERD and spectral power over the non-dominant (right) hemisphere. The effect of tDCS was evaluated over time. Spectral power was assessed in theta, alpha and beta bands, under both rest and MI conditions, while ERD was evaluated in alpha and beta bands. RESULTS Two main findings emerged: (1) contralateral alpha-ERD was reduced after anodal (p = 0.0147), but not enhanced after cathodal tDCS; (2) both stimulations had remote effects on the spectral power of the contralateral hemisphere, particularly in theta and alpha (significant differences in the topographical t-value maps). CONCLUSION The absence of contralateral cathodal ERD enhancement suggests that the protocol is not applicable in the context of MI training. Nevertheless, ERD results of anodal and spectral power results of both stimulations complement recent findings on the distant tDCS effects between functionally related areas.
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Affiliation(s)
- Valeria Mondini
- Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Cesena, Italy
- * E-mail:
| | - Anna Lisa Mangia
- Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Cesena, Italy
| | - Angelo Cappello
- Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Cesena, Italy
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