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Nucci L, Miraglia F, Pappalettera C, Micera S, Rossini PM, Vecchio F. Modulation of brain signals during sensorimotor and imaging tasks in a person with an implanted upper-limb prosthesis following amputation of the left hand. Ann Phys Rehabil Med 2024; 67:101802. [PMID: 38118245 DOI: 10.1016/j.rehab.2023.101802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 12/22/2023]
Affiliation(s)
- Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Via Val Cannuta 247, 00166 Rome, Italy; Department of Neuroscience, Sacred Heart Catholic University, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Via Val Cannuta 247, 00166 Rome, Italy; Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate (Como), Italy
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Via Val Cannuta 247, 00166 Rome, Italy; Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate (Como), Italy
| | - Silvestro Micera
- Bertarelli Foundation Chair in Translational Neuroengineering, Neuro-X Institute, School of Engineering, Ecole Polytechnique Federale de Lausanne (EPFL), Rte Cantonale, 1015 Lausanne, Switzerland; The BioRobotics Institute, Health Interdisciplinary Center, and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 33, 56127 Pisa, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Via Val Cannuta 247, 00166 Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Via Val Cannuta 247, 00166 Rome, Italy; Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate (Como), Italy.
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da Silva ALM, Nascimento CP, Azevedo JEC, Vieira LR, Hamoy AO, Tiago ACDS, Martins Rodrigues JC, de Araujo DB, Favacho Lopes DC, de Mello VJ, Hamoy M. Unmasking hidden risks: The surprising link between PDE5 inhibitors and seizure susceptibility. PLoS One 2023; 18:e0294754. [PMID: 38033148 PMCID: PMC10688920 DOI: 10.1371/journal.pone.0294754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 11/08/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Phosphodiesterase 5 inhibitors (PDE5i) are the first line treatment for erectile dysfunction; however, several articles and case reports have shown central nervous system effects, that can cause seizures in susceptible patients. This study aims to describe the changes caused by the use of Sildenafil and Tadalafil through the analysis of abnormalities expressed in the electrocorticogram (ECoG) of rats and evaluate the seizure threshold response and treatment of seizures with anticonvulsants. MATERIALS AND METHODS The study used 108 rats (Wistar). Before surgery for electrode placement in dura mater, the animals were randomly separated into 3 experiments for electrocorticogram analysis. Experiment 1: ECoG response to using PD5i (Sildenafil 20mg/kg and Tadalafil 2.6mg/kg p.o.). Experiment 2: ECoG response to the use of PD5i in association with Pentylenetetrazole (PTZ-30 mg/kg i.p.), a convulsive model. Experiment 3: ECoG response to anticonvulsant treatment (Phenytoin, Phenobarbital and Diazepam) of seizures induced by association IPDE5 + PTZ. All recordings were made thirty minutes after administration of the medication and analyzed for ten minutes, only once. We considered statistical significance level of *p<0.05, **p<0.01 and ***p < 0.001. RESULTS After administration of Sildenafil and Tadalafil, there were increases in the power of recordings in the frequency bands in oscillations in alpha (p = 0.0920) and beta (p = 0.602) when compared to the control group (p<0.001). After the use of Sildenafil and Tadalafil associated with PTZ, greater potency was observed in the recordings during seizures (p<0.001), however, the Sildenafil group showed greater potency when compared to Tadalafil (p<0.05). Phenobarbital and Diazepam showed a better response in controlling discharges triggered by the association between proconvulsant drugs. CONCLUSIONS PDE5i altered the ECoG recordings in the rats' motor cortexes, demonstrating cerebral asynchrony and potentiating the action of PTZ. These findings demonstrate that PDE5i can lower the seizure threshold.
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Affiliation(s)
- Alex Luiz Menezes da Silva
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
| | - Chirlene Pinheiro Nascimento
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
| | - Julianne Elba Cunha Azevedo
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
| | - Luana Rodrigues Vieira
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
| | - Akira Otake Hamoy
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
| | - Allan Carlos da Silva Tiago
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
| | - João Cleiton Martins Rodrigues
- Laboratory of Experimental Neuropathology, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
| | - Daniella Bastos de Araujo
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
| | - Dielly Catrina Favacho Lopes
- Laboratory of Experimental Neuropathology, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
| | - Vanessa Jóia de Mello
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
| | - Moisés Hamoy
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute of Biological Sciences, Federal University of Pará, UFPA, Belém, Pará, Brazil
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Wang H, Zheng H, Yang Y, Fong KNK, Long J. Cortical Contributions to Imagined Power Grip Task: An EEG-Triggered TMS Study. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3813-3822. [PMID: 37729574 DOI: 10.1109/tnsre.2023.3317813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Previous studies have demonstrated that motor imagery leads to desynchronization in the alpha rhythm within the contralateral primary motor cortex. However, the underlying electrophysiological mechanisms responsible for this desynchronization during motor imagery remain unclear. To examine this question, we conducted an investigation using EEG in combination with noninvasive transcranial magnetic stimulation (TMS) during index finger abduction (ABD) and power grip imaginations. The TMS was administered employing diverse coil orientations to selectively stimulate corticospinal axons, aiming to target both early and late synaptic inputs to corticospinal neurons. TMS was triggered based on the alpha power levels, categorized in 20th percentile bins, derived from the individual alpha power distribution during the imagined tasks of ABD and power grip. Our analysis revealed negative correlations between alpha power and motor evoked potential (MEP) amplitude, as well as positive correlations with MEP latency across all coil orientations for each imagined task. Furthermore, we conducted functional network analysis in the alpha band to explore network connectivity during imagined index finger abduction and power grip tasks. Our findings indicate that network connections were denser in the fronto-parietal area during imagined ABD compared to power grip conditions. Moreover, the functional network properties demonstrated potential for effectively classifying between these two imagined tasks. These results provide functional evidence supporting the hypothesis that alpha oscillations may play a role in suppressing MEP amplitude and latency during imagined power grip. We propose that imagined ABD and power grip tasks may activate different populations and densities of axons at the cortical level.
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Carino-Escobar RI, Rodríguez-García ME, Carrillo-Mora P, Valdés-Cristerna R, Cantillo-Negrete J. Continuous versus discrete robotic feedback for brain-computer interfaces aimed for neurorehabilitation. Front Neurorobot 2023; 17:1015464. [PMID: 36925628 PMCID: PMC10011154 DOI: 10.3389/fnbot.2023.1015464] [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: 08/11/2022] [Accepted: 02/01/2023] [Indexed: 03/08/2023] Open
Abstract
Introduction Brain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task. Methods An assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with each feedback. Results Significantly higher BCI performance (65.4 ± 17.9% with the continuous and 62.1 ± 18.6% with the discrete feedback) and pronounced bilateral alpha and ipsilateral beta cortical activations were observed with the continuous feedback. Discussion It was hypothesized that these effects, although heterogenous across participants, were caused by the enhancement of attentional and closed-loop somatosensory processes. This is important, since a continuous feedback timing could increase the number of BCI users that can control a MI-based system or enhance cortical activations associated with neuroplasticity, important for neurorehabilitation applications.
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Affiliation(s)
- Ruben I Carino-Escobar
- Division of Research in Medical Engineering, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City, Mexico
| | - Martín E Rodríguez-García
- Electrical Engineering Department, Universidad Autónoma Metropolitana Unidad Iztapalapa, Mexico City, Mexico
| | - Paul Carrillo-Mora
- Division of Neuroscience, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City, Mexico
| | - Raquel Valdés-Cristerna
- Electrical Engineering Department, Universidad Autónoma Metropolitana Unidad Iztapalapa, Mexico City, Mexico
| | - Jessica Cantillo-Negrete
- Division of Research in Medical Engineering, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City, Mexico
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Kern K, Vukelić M, Guggenberger R, Gharabaghi A. Oscillatory neurofeedback networks and poststroke rehabilitative potential in severely impaired stroke patients. Neuroimage Clin 2023; 37:103289. [PMID: 36525745 PMCID: PMC9791174 DOI: 10.1016/j.nicl.2022.103289] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 12/03/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
Motor restoration after severe stroke is often limited. However, some of the severely impaired stroke patients may still have a rehabilitative potential. Biomarkers that identify these patients are sparse. Eighteen severely impaired chronic stroke patients with a lack of volitional finger extension participated in an EEG study. During sixty-six trials of kinesthetic motor imagery, a brain-machine interface turned event-related beta-band desynchronization of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. A subgroup of eight patients participated in a subsequent four-week rehabilitation training. Changes of the movement extent were captured with sensors which objectively quantified even discrete improvements of wrist movement. Albeit with the same motor impairment level, patients could be differentiated into two groups, i.e., with and without task-related increase of bilateral cortico-cortical phase synchronization between frontal/premotor and parietal areas. This fronto-parietal integration (FPI) was associated with a significantly higher volitional beta modulation range in the ipsilesional sensorimotor cortex. Following the four-week training, patients with FPI showed significantly higher improvement in wrist movement than those without FPI. Moreover, only the former group improved significantly in the upper extremity Fugl-Meyer-Assessment score. Neurofeedback-related long-range oscillatory coherence may differentiate severely impaired stroke patients with regard to their rehabilitative potential, a finding that needs to be confirmed in larger patient cohorts.
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Affiliation(s)
- Kevin Kern
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Germany
| | - Mathias Vukelić
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Germany
| | - Robert Guggenberger
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Germany
| | - Alireza Gharabaghi
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Germany.
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Athanasiou A, Mitsopoulos K, Praftsiotis A, Astaras A, Antoniou P, Pandria N, Petronikolou V, Kasimis K, Lyssas G, Terzopoulos N, Fiska V, Kartsidis P, Savvidis T, Arvanitidis A, Chasapis K, Moraitopoulos A, Nizamis K, Kalfas A, Iakovidis P, Apostolou T, Magras I, Bamidis P. Neurorehabilitation Through Synergistic Man-Machine Interfaces Promoting Dormant Neuroplasticity in Spinal Cord Injury: Protocol for a Nonrandomized Controlled Trial. JMIR Res Protoc 2022; 11:e41152. [PMID: 36099009 PMCID: PMC9516361 DOI: 10.2196/41152] [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: 07/18/2022] [Revised: 07/21/2022] [Accepted: 07/21/2022] [Indexed: 11/29/2022] Open
Abstract
Background Spinal cord injury (SCI) constitutes a major sociomedical problem, impacting approximately 0.32-0.64 million people each year worldwide; particularly, it impacts young individuals, causing long-term, often irreversible disability. While effective rehabilitation of patients with SCI remains a significant challenge, novel neural engineering technologies have emerged to target and promote dormant neuroplasticity in the central nervous system. Objective This study aims to develop, pilot test, and optimize a platform based on multiple immersive man-machine interfaces offering rich feedback, including (1) visual motor imagery training under high-density electroencephalographic recording, (2) mountable robotic arms controlled with a wireless brain-computer interface (BCI), (3) a body-machine interface (BMI) consisting of wearable robotics jacket and gloves in combination with a serious game (SG) application, and (4) an augmented reality module. The platform will be used to validate a self-paced neurorehabilitation intervention and to study cortical activity in chronic complete and incomplete SCI at the cervical spine. Methods A 3-phase pilot study (clinical trial) was designed to evaluate the NeuroSuitUp platform, including patients with chronic cervical SCI with complete and incomplete injury aged over 14 years and age-/sex-matched healthy participants. Outcome measures include BCI control and performance in the BMI-SG module, as well as improvement of functional independence, while also monitoring neuropsychological parameters such as kinesthetic imagery, motivation, self-esteem, depression and anxiety, mental effort, discomfort, and perception of robotics. Participant enrollment into the main clinical trial is estimated to begin in January 2023 and end by December 2023. Results A preliminary analysis of collected data during pilot testing of BMI-SG by healthy participants showed that the platform was easy to use, caused no discomfort, and the robotics were perceived positively by the participants. Analysis of results from the main clinical trial will begin as recruitment progresses and findings from the complete analysis of results are expected in early 2024. Conclusions Chronic SCI is characterized by irreversible disability impacting functional independence. NeuroSuitUp could provide a valuable complementary platform for training in immersive rehabilitation methods to promote dormant neural plasticity. Trial Registration ClinicalTrials.gov NCT05465486; https://clinicaltrials.gov/ct2/show/NCT05465486 International Registered Report Identifier (IRRID) PRR1-10.2196/41152
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Affiliation(s)
- Alkinoos Athanasiou
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Konstantinos Mitsopoulos
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Apostolos Praftsiotis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Alexander Astaras
- Computer Science Department, Division of Science and Technology, American College of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis Antoniou
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Niki Pandria
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileia Petronikolou
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Konstantinos Kasimis
- Department of Physiotherapy, International Hellenic University, Thessaloniki, Greece
| | - George Lyssas
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nikos Terzopoulos
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasilki Fiska
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis Kartsidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Theodoros Savvidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Athanasios Arvanitidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Konstantinos Chasapis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Alexandros Moraitopoulos
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Kostas Nizamis
- Department of Design, Production and Management, University of Twente, Enschede, Netherlands
| | - Anestis Kalfas
- Laboratory of Fluid Mechanics and Turbo-machinery, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Paris Iakovidis
- Department of Physiotherapy, International Hellenic University, Thessaloniki, Greece
| | - Thomas Apostolou
- Department of Physiotherapy, International Hellenic University, Thessaloniki, Greece
| | - Ioannis Magras
- Second Department of Neurosurgery, Ippokrateio General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis Bamidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Naro A, Billeri L, Balletta T, Lauria P, Onesta MP, Calabrò RS. Finding the Way to Improve Motor Recovery of Patients with Spinal Cord Lesions: A Case-Control Pilot Study on a Novel Neuromodulation Approach. Brain Sci 2022; 12:119. [PMID: 35053862 PMCID: PMC8773706 DOI: 10.3390/brainsci12010119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 12/16/2022] Open
Abstract
Robot-assisted rehabilitation (RAR) and non-invasive brain stimulation (NIBS) are interventions that, both individually and combined, can significantly enhance motor performance after spinal cord injury (SCI). We sought to determine whether repetitive transcranial magnetic stimulation (rTMS) combined with active transvertebral direct current stimulation (tvDCS) (namely, NIBS) in association with RAR (RAR + NIBS) improves lower extremity motor function more than RAR alone in subjects with motor incomplete SCI (iSCI). Fifteen adults with iSCI received one daily session of RAR+NIBS in the early afternoon, six sessions weekly, for eight consecutive weeks. Outcome measures included the 6 min walk test (6MWT), the 10 m walk test (10MWT), the timed up and go (TUG) to test mobility and balance, the Walking Index for Spinal Cord Injury (WISCI II), the Functional Independence Measure-Locomotion (FIM-L), the manual muscle testing for lower extremity motor score (LEMS), the modified Ashworth scale for lower limbs (MAS), and the visual analog scale (VAS) for pain. The data of these subjects were compared with those of 20 individuals matched for clinical and demographic features who previously received the same amount or RAR without NIBS (RAR - NIBS). All patients completed the trial, and none reported any side effects either during or following the training. The 10MWT improved in both groups, but the increase was significantly greater following RAR + NIBS than RAR - NIBS. The same occurred for the FIM-L, LEMS, and WISCI II. No significant differences were appreciable concerning the 6MWT and TUG. Conversely, RAR - NIBS outperformed RAR + NIBS regarding the MAS and VAS. Pairing tvDCS with rTMS during RAR can improve lower extremity motor function more than RAR alone can do. Future research with a larger sample size is recommended to determine longer-term effects on motor function and activities of daily living.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo Piemonte, Via Palermo, SS 113, Ctr. Casazza, 98124 Messina, Italy; (A.N.); (L.B.); (T.B.); (P.L.)
| | - Luana Billeri
- IRCCS Centro Neurolesi Bonino Pulejo Piemonte, Via Palermo, SS 113, Ctr. Casazza, 98124 Messina, Italy; (A.N.); (L.B.); (T.B.); (P.L.)
| | - Tina Balletta
- IRCCS Centro Neurolesi Bonino Pulejo Piemonte, Via Palermo, SS 113, Ctr. Casazza, 98124 Messina, Italy; (A.N.); (L.B.); (T.B.); (P.L.)
| | - Paola Lauria
- IRCCS Centro Neurolesi Bonino Pulejo Piemonte, Via Palermo, SS 113, Ctr. Casazza, 98124 Messina, Italy; (A.N.); (L.B.); (T.B.); (P.L.)
| | | | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo Piemonte, Via Palermo, SS 113, Ctr. Casazza, 98124 Messina, Italy; (A.N.); (L.B.); (T.B.); (P.L.)
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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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Kreis SL, Luhmann HJ, Ciolac D, Groppa S, Muthuraman M. Translational Model of Cortical Premotor-Motor Networks. Cereb Cortex 2021; 32:2621-2634. [PMID: 34689188 PMCID: PMC9201593 DOI: 10.1093/cercor/bhab369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 11/17/2022] Open
Abstract
Deciphering the physiological patterns of motor network connectivity is a prerequisite to elucidate aberrant oscillatory transformations and elaborate robust translational models of movement disorders. In the proposed translational approach, we studied the connectivity between premotor (PMC) and primary motor cortex (M1) by recording high-density electroencephalography in humans and between caudal (CFA) and rostral forelimb (RFA) areas by recording multi-site extracellular activity in mice to obtain spectral power, functional and effective connectivity. We identified a significantly higher spectral power in β- and γ-bands in M1compared to PMC and similarly in mice CFA layers (L) 2/3 and 5 compared to RFA. We found a strong functional β-band connectivity between PMC and M1 in humans and between CFA L6 and RFA L5 in mice. We observed that in both humans and mice the direction of information flow mediated by β- and γ-band oscillations was predominantly from PMC toward M1 and from RFA to CFA, respectively. Combining spectral power, functional and effective connectivity, we revealed clear similarities between human PMC-M1 connections and mice RFA-CFA network. We propose that reciprocal connectivity of mice RFA-CFA circuitry presents a suitable model for analysis of motor control and physiological PMC-M1 functioning or pathological transformations within this network.
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Affiliation(s)
- Svenja L Kreis
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz D-55128, Germany
| | - Heiko J Luhmann
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz D-55128, Germany
| | - Dumitru Ciolac
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz D-55131, Germany.,Nicolae Testemitanu State University of Medicine and Pharmacy, Chisinau MD-2001, Republic of Moldova
| | - Sergiu Groppa
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz D-55131, Germany
| | - Muthuraman Muthuraman
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz D-55131, Germany
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10
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Orkan Olcay B, Özgören M, Karaçalı B. On the characterization of cognitive tasks using activity-specific short-lived synchronization between electroencephalography channels. Neural Netw 2021; 143:452-474. [PMID: 34273721 DOI: 10.1016/j.neunet.2021.06.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/04/2021] [Accepted: 06/18/2021] [Indexed: 10/21/2022]
Abstract
Accurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and the complex nature of the brain. The majority of the proposed approaches assume stationarity in brain activity and disregard the systematic timing organization among brain regions during cognitive tasks. In this study, we propose a novel cognitive activity recognition method that captures the activity-specific timing parameters from training data that elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated the characterization power of the activity-specific timing parameter triplets in a motor imagery activity recognition framework. The activity-specific timing parameter triplets consist of latency of the maximally synchronized signal segments from activity onset Δt, the time lag between maximally synchronized signal segments τ, and the duration of the maximally synchronized signal segments w. We used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence value, linearized mutual information, and cross-correntropy to calculate the channel synchronizations at the specific timing parameters. Recognition performances as well as statistical analyses on both BCI Competition-III dataset IVa and PhysioNet Motor Movement/Imagery dataset, indicate that the inter-channel short-lived synchronization calculated using activity-specific timing parameter triplets elicit significantly distinct synchronization profiles for different motor imagery tasks and can thus reliably be used for cognitive task recognition purposes.
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Affiliation(s)
- B Orkan Olcay
- Department of Electrical and Electronics Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
| | - Murat Özgören
- Department of Biophysics, Faculty of Medicine, Near East University, 99138, Nicosia, Cyprus.
| | - Bilge Karaçalı
- Department of Electrical and Electronics Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
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11
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Carino-Escobar RI, Valdés-Cristerna R, Carrillo-Mora P, Rodriguez-Barragan MA, Hernandez-Arenas C, Quinzaños-Fresnedo J, Arias-Carrión O, Cantillo-Negrete J. Prognosis of stroke upper limb recovery with physiological variables using regression tree ensembles. J Neural Eng 2021; 18. [PMID: 33906163 DOI: 10.1088/1741-2552/abfc1e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 04/27/2021] [Indexed: 11/11/2022]
Abstract
Objective.This study assesses upper limb recovery prognosis after stroke with solely physiological information, which can provide an objective estimation of recovery.Approach.Clinical recovery was forecasted using EEG-derived Event-Related Desynchronization/Synchronization and coherence, in addition to Transcranial Magnetic Stimulation elicited motor-evoked potentials and upper limb grip and pinch strength. A Regression Tree Ensemble predicted clinical recovery of a stroke database (n= 10) measured after a two-month intervention with the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT).Main results.There were no significant differences between predicted and actual outcomes with FMA-UE (p= 0.29) and ARAT (p= 0.5). Median prediction error for FMA-UE and ARAT were of 0.3 (IQR = 6.2) and 3.4 (IQR = 9.4) points, respectively. Predictions with the most pronounced errors were due to an underestimation of high upper limb recovery. The best features for FMA-UE prediction included mostly beta activity over the sensorimotor cortex. Best ARAT prediction features were cortical beta activity, corticospinal tract integrity of the unaffected hemisphere, and upper limb strength.Significance.Results highlighted the importance of measuring cortical activity related to motor control processes, the unaffected hemisphere's integrity, and upper limb strength for prognosis. It was also implied that stroke upper limb recovery prediction is feasible using solely physiological variables with a Regression Tree Ensemble, which can also be used to analyze physiological relationships with recovery.
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Affiliation(s)
- Ruben I Carino-Escobar
- Department of Electrical Engineering, Universidad Autónoma Metropolitana Unidad Iztapalapa, Mexico City 09340, Mexico.,Division of Research in Medical Engineering, Instituto Nacional de Rehabilitación 'Luis Guillermo Ibarra Ibarra', Mexico City 14389, Mexico
| | - Raquel Valdés-Cristerna
- Department of Electrical Engineering, Universidad Autónoma Metropolitana Unidad Iztapalapa, Mexico City 09340, Mexico
| | - Paul Carrillo-Mora
- Division of Neuroscience, Instituto Nacional de Rehabilitación 'Luis Guillermo Ibarra Ibarra', Mexico City 14389, Mexico
| | - Marlene A Rodriguez-Barragan
- Division of Neurological Rehabilitation, Instituto Nacional de Rehabilitación 'Luis Guillermo Ibarra Ibarra', Mexico City 14389, Mexico
| | - Claudia Hernandez-Arenas
- Division of Neurological Rehabilitation, Instituto Nacional de Rehabilitación 'Luis Guillermo Ibarra Ibarra', Mexico City 14389, Mexico
| | - Jimena Quinzaños-Fresnedo
- Division of Neurological Rehabilitation, Instituto Nacional de Rehabilitación 'Luis Guillermo Ibarra Ibarra', Mexico City 14389, Mexico
| | - Oscar Arias-Carrión
- Unidad de Trastornos de Movimiento y Sueño (TMS), Hospital General 'Dr Manuel Gea González', Mexico City 14080, Mexico.,Centro de Innovación Médica Aplicada (CIMA), Hospital General 'Dr Manuel Gea González', Mexico City 14080, Mexico
| | - Jessica Cantillo-Negrete
- Division of Research in Medical Engineering, Instituto Nacional de Rehabilitación 'Luis Guillermo Ibarra Ibarra', Mexico City 14389, Mexico
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A Pilot Study of Game Design in the Unity Environment as an Example of the Use of Neurogaming on the Basis of Brain–Computer Interface Technology to Improve Concentration. NEUROSCI 2021. [DOI: 10.3390/neurosci2020007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The article describes the practical use of Unity technology in neurogaming. For this purpose, the article describes Unity technology and brain–computer interface (BCI) technology based on the Emotiv EPOC + NeuroHeadset device. The process of creating the game world and the test results for the use of a device based on the BCI as a control interface for the created game are also presented. The game was created in the Unity graphics engine and the Visual Studio environment in C#. The game presented in the article is called “NeuroBall” due to the player’s object, which is a big red ball. The game will require full focus to make the ball move. The game will aim to improve the concentration and training of the user’s brain in a user-friendly environment. Through neurogaming, it will be possible to exercise and train a healthy brain, as well as diagnose and treat various symptoms of brain disorders. The project was entirely created in the Unity graphics engine in Unity version 2020.1.
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13
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Beppi C, Ribeiro Violante I, Scott G, Sandrone S. EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions. Brain Cogn 2021; 148:105677. [PMID: 33486194 DOI: 10.1016/j.bandc.2020.105677] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 12/26/2020] [Accepted: 12/27/2020] [Indexed: 01/04/2023]
Abstract
Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed.
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Affiliation(s)
- Carolina Beppi
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland; Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland; Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
| | - Inês Ribeiro Violante
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom; School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
| | - Gregory Scott
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom.
| | - Stefano Sandrone
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom.
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14
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Lee SH, Kim SS, Lee BH. Action observation training and brain-computer interface controlled functional electrical stimulation enhance upper extremity performance and cortical activation in patients with stroke: a randomized controlled trial. Physiother Theory Pract 2020; 38:1126-1134. [PMID: 33026895 DOI: 10.1080/09593985.2020.1831114] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE Brain-computer interface (BCI)-functional electronic stimulation (FES) systems are increasingly being explored as potential neuro-rehabilitation tools. Here, we investigate the effect of action observation training (AOT) plus electroencephalogram (EEG)-based BCI-controlled FES system on motor recovery of upper extremity and cortical activation in patients with stroke. METHOD There were a total of 26 patients: an AOT plus BCI-FES group (n = 13) and a control group (n = 13). The control group performed FES treatment and the conventional physical therapy, while the AOT plus BCI-FES group performed AOT plus BCI-FES and the conventional physical therapy. Upper extremity performance was measured using the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Wolf Motor Function Test (WMFT), Motor Activity Log (MAL) and Modified Barthel Index (MBI). Cortical activation was measured using electro-encephalographic recordings from alpha and beta power, concentration, and activation. RESULTS After intervention, there were significant differences between two groups in FMA-UE, WMFT, MAL and MBI and the results of EEG including alpha power, beta power, concentration and activation. CONCLUSIONS This study demonstrated that AOT plus BCI-FES can enhance motor function of upper extremity and cortical activation in patients with stroke. This training method may be feasible and suitable for individuals with stroke.
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Affiliation(s)
- Su-Hyun Lee
- Department of Physical Therapy, Sahmyook University, Seoul, Korea
| | - Seong Sik Kim
- Department of Physical Therapy, Sahmyook University, Seoul, Korea
| | - Byoung-Hee Lee
- Department of Physical Therapy, Sahmyook University, Seoul, Korea
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15
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Robotic Rehabilitation in Spinal Cord Injury: A Pilot Study on End-Effectors and Neurophysiological Outcomes. Ann Biomed Eng 2020; 49:732-745. [PMID: 32918105 DOI: 10.1007/s10439-020-02611-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 09/02/2020] [Indexed: 12/20/2022]
Abstract
Robot-aided gait training (RAGT) has been implemented to provide patients with spinal cord injury (SCI) with a physiological limb activation during gait, cognitive engagement, and an appropriate stimulation of peripheral receptors, which are essential to entrain neuroplasticity mechanisms supporting functional recovery. We aimed at assessing whether RAGT by means of an end-effector device equipped with body weight support could improve functional ambulation in patients with subacute, motor incomplete SCI. In this pilot study, 15 patients were provided with six RAGT sessions per week for eight consecutive weeks. The outcome measures were muscle strength, ambulation, going upstairs, and disease burden. Furthermore, we estimated the activation patterns of lower limb muscles during RAGT by means of surface electromyography and the resting state networks' functional connectivity (RSN-FC) before and after RAGT. Patients achieved a clinically significant improvement in the clinical outcome measures substantially up to six months post-treatment. These data were paralleled by an improvement in the stair-climbing cycle and a potentiating of frequency-specific and area-specific RSN-FC patterns. Therefore, RAGT, by means of an end-effector device equipped with body weight support, is promising in improving gait in patients with subacute, motor incomplete SCI, and it could produce additive benefit for the neuromuscular reeducation to gait in SCI when combined with conventional physiotherapy.
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16
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Menicucci D, Di Gruttola F, Cesari V, Gemignani A, Manzoni D, Sebastiani L. Task-independent Electrophysiological Correlates of Motor Imagery Ability from Kinaesthetic and Visual Perspectives. Neuroscience 2020; 443:176-187. [PMID: 32736068 DOI: 10.1016/j.neuroscience.2020.07.038] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/20/2020] [Accepted: 07/21/2020] [Indexed: 11/19/2022]
Abstract
Motor imagery (MI) ability is highly subjective, as indicated by the individual scores of the MIQ-3 questionnaire, and poor imagers compensate for the difficulty in performing MI with larger cerebral activations, as demonstrated by MI studies involving hands/limbs. In order to identify general, task-independent MI ability correlates, 16 volunteers were stratified with MIQ-3. The scores in the kinaesthetic (K) and 1st-person visual (V) perspectives were associated with EEG patterns obtained during K-MI and V-MI of the same complex MIQ-3 movements during these MI tasks (Spearman's correlation, significance at <0.05, SnPM corrected). EEG measures were relative to rest (relaxation, closed eyes), and based on six electrode clusters both for band spectral content and connectivity (Granger causality). Lower K-MI ability was associated with greater theta decreases during tasks in fronto-central clusters and greater inward information flow to prefrontal clusters for theta, high alpha and beta bands. On the other hand, power band relative decreases were associated with V-MI ability in fronto-central clusters for low alpha and left fronto-central and both centro-parietal clusters for beta bands. The results thus suggest different computational mechanisms for MI-V and MI-K. The association between low alpha/beta desynchronization and V-MIQ scores and between theta changes and K-MIQ scores suggest a cognitive effort with greater cerebral activation in participants with lower V-MI ability. The association between information flow to prefrontal hub and K-MI ability suggest the need for a continuous update of information to support MI-related executive functions in subjects with poor K-MI ability.
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17
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Alanis-Espinosa M, Gutiérrez D. On the Assessment of Functional Connectivity in an Immersive Brain-Computer Interface During Motor Imagery. Front Psychol 2020; 11:1301. [PMID: 32714232 PMCID: PMC7343938 DOI: 10.3389/fpsyg.2020.01301] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 05/18/2020] [Indexed: 11/30/2022] Open
Abstract
New trends on brain-computer interface (BCI) design are aiming to combine this technology with immersive virtual reality in order to provide a sense of realism to its users. In this study, we propose an experimental BCI to control an immersive telepresence system using motor imagery (MI). The system is immersive in the sense that the users can control the movement of a NAO humanoid robot in a first person perspective (1PP), i.e., as if the movement of the robot was his/her own. We analyze functional brain connectivity between 1PP and 3PP during the control of our BCI using graph theory properties such as degree, betweenness centrality, and efficiency. Changes in these metrics are obtained for the case of the 1PP, as well as for the traditional third person perspective (3PP) in which the user can see the movement of the robot as feedback. As proof-of-concept, electroencephalography (EEG) signals were recorded from two subjects while they performed MI to control the movement of the robot. The graph theoretical analysis was applied to the binary directed networks obtained through the partial directed coherence (PDC). In our preliminary assessment we found that the efficiency in the α brain rhythm is greater in 1PP condition in comparison to the 3PP at the prefrontal cortex. Also, a stronger influence of signals measured at EEG channel C3 (primary motor cortex) to other regions was found in 1PP condition. Furthermore, our preliminary results seem to indicate that α and β brain rhythms have a high indegree at prefrontal cortex in 1PP condition, and this could be possibly related to the experience of sense of agency. Therefore, using the PDC combined with graph theory while controlling a telepresence robot in an immersive system may contribute to understand the organization and behavior of brain networks in these environments.
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Affiliation(s)
- Myriam Alanis-Espinosa
- Laboratory of Biomedical Signal Processing, Center for Research and Advanced Studies (Cinvestav) at Monterrey, Apodaca, Mexico
| | - David Gutiérrez
- Laboratory of Biomedical Signal Processing, Center for Research and Advanced Studies (Cinvestav) at Monterrey, Apodaca, Mexico
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18
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Das J, Singh R, Ladol S, Nayak SK, Sharma D. Fisetin prevents the aging-associated decline in relative spectral power of α, β and linked MUA in the cortex and behavioral alterations. Exp Gerontol 2020; 138:111006. [PMID: 32592831 DOI: 10.1016/j.exger.2020.111006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/15/2020] [Accepted: 06/15/2020] [Indexed: 12/22/2022]
Abstract
Mental health in old age is of great concern due to the increased incidence of neurological and psychiatric diseases. With age, probability of cognitive and behavioral deficits increase and the prognosis deteriorates. Although a few in vitro studies have reported that flavonoid fisetin is beneficial for healthy aging, its effect on deteriorating mental health with aging in vivo is very limited and poorly understood. The brain aging is physiologically characterized by electroencephalograph (EEG) wave frequency, power, and distribution. Brain oscillatory waves from neural tissue get altered by various sensory-cognitive inputs. Besides, the fast-wave α(8-12 Hz)- and β(12-28 Hz)-oscillations are associated with coordination and indeed deal with complex behavioral performances. Therefore, the effect of fisetin supplementation on age-associated EEG mean cortical spectral power in α- and β-oscillations, multi-unit activity (MUA) count were studied in vivo which was not addressed so far. Besides, age-associated cognitive and behavioral alterations were also studied. The relative spectral power of α and β declined along with the MUA count in aged rats compared to young. However, supplementing fisetin for four weeks has improved relative α-power, β-power, and MUA count in aged rats. Also, fisetin supplemented aged rats showed significantly improved cognitive and behavioral performances than aged controls. These findings demonstrated the relative cortical spectral power in α-, β-oscillations, and MUA count change in aged rats and that some of these changes and behavioral alterations may be partly as a result of oxidative stress, which was prevented significantly in fisetin supplemented aged rats. Thus, fisetin boosted mental health in the aged animals.
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Affiliation(s)
- Jharana Das
- Neurobiology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
| | - Rameshwar Singh
- Neurobiology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India
| | - Stanzin Ladol
- Department of Zoology, Central University of Jammu, Jammu and Kashmir 181143, India.
| | - Sasmita Kumari Nayak
- Department of Instrumentation and Electronics, College of Engineering and Technology, Bhubaneswar 751003, India
| | - Deepak Sharma
- Neurobiology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
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Gu L, Yu Z, Ma T, Wang H, Li Z, Fan H. EEG-based Classification of Lower Limb Motor Imagery with Brain Network Analysis. Neuroscience 2020; 436:93-109. [PMID: 32283182 DOI: 10.1016/j.neuroscience.2020.04.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 03/06/2020] [Accepted: 04/02/2020] [Indexed: 01/06/2023]
Abstract
This study aims to investigate the difference in cortical signal characteristics between the left and right foot imaginary movements and to improve the classification accuracy of the experimental tasks. Raw signals were gathered from 64-channel scalp electroencephalograms of 11 healthy participants. Firstly, the cortical source model was defined with 62 regions of interest over the sensorimotor cortex (nine Brodmann areas). Secondly, functional connectivity was calculated by phase lock value for α and β rhythm networks. Thirdly, network-based statistics were applied to identify whether there existed stable and significant subnetworks that formed between the two types of motor imagery tasks. Meanwhile, ten graph theory indices were investigated for each network by t-test to determine statistical significance between tasks. Finally, sparse multinomial logistic regression (SMLR)-support vector machine (SVM), as a feature selection and classification model, was used to analyze the graph theory features. The specific time-frequency (α event-related desynchronization and β event-related synchronization) difference network between the two tasks was congregated at the midline and demonstrated significant connections in the premotor areas and primary somatosensory cortex. A few of statistically significant differences in the network properties were observed between tasks in the α and β rhythm. The SMLR-SVM classification model achieved fair discrimination accuracy between imaginary movements of the two feet (maximum 75% accuracy rate in single-trial analyses). This study reveals the network mechanism of the discrimination of the left and right foot motor imagery, which can provide a novel avenue for the BCI system by unilateral lower limb motor imagery.
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Affiliation(s)
- Lingyun Gu
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China
| | - Zhenhua Yu
- College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, Shanxi, PR China
| | - Tian Ma
- College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, Shanxi, PR China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China.
| | - Zhanli Li
- College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, Shanxi, PR China.
| | - Hui Fan
- Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai 264005, Shandong, PR China
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A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery. Brain Sci 2019; 9:brainsci9120372. [PMID: 31847114 PMCID: PMC6955896 DOI: 10.3390/brainsci9120372] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 12/06/2019] [Accepted: 12/07/2019] [Indexed: 12/02/2022] Open
Abstract
Brain–computer interface (BCI) is a technology used to convert brain signals to control external devices. Researchers have designed and built many interfaces and applications in the last couple of decades. BCI is used for prevention, detection, diagnosis, rehabilitation, and restoration in healthcare. EEG signals are analyzed in this paper to help paralyzed people in rehabilitation. The electroencephalogram (EEG) signals recorded from five healthy subjects are used in this study. The sensor level EEG signals are converted to source signals using the inverse problem solution. Then, the cortical sources are calculated using sLORETA methods at nine regions marked by a neurophysiologist. The features are extracted from cortical sources by using the common spatial pattern (CSP) method and classified by a support vector machine (SVM). Both the sensor and the computed cortical signals corresponding to motor imagery of the hand and foot are used to train the SVM algorithm. Then, the signals outside the training set are used to test the classification performance of the classifier. The 0.1–30 Hz and mu rhythm band-pass filtered activity is also analyzed for the EEG signals. The classification performance and recognition of the imagery improved up to 100% under some conditions for the cortical level. The cortical source signals at the regions contributing to motor commands are investigated and used to improve the classification of motor imagery.
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21
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Wang Z, Zhou Y, Chen L, Gu B, Liu S, Xu M, Qi H, He F, Ming D. A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery. J Neural Eng 2019; 16:066012. [DOI: 10.1088/1741-2552/ab377d] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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22
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Cantillo-Negrete J, Carino-Escobar RI, Carrillo-Mora P, Barraza-Madrigal JA, Arias-Carrión O. Robotic orthosis compared to virtual hand for Brain–Computer Interface feedback. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.12.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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23
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Estumano DP, Ferreira LO, Bezerra PAL, da Silva MCP, Jardim GC, Santos GFS, Gustavo KS, Mattos BG, Ramos JAB, Jóia de Mello V, da Costa ET, Lopes DCF, Hamoy M. Alteration of Testosterone Levels Changes Brain Wave Activity Patterns and Induces Aggressive Behavior in Rats. Front Endocrinol (Lausanne) 2019; 10:654. [PMID: 31616380 PMCID: PMC6768956 DOI: 10.3389/fendo.2019.00654] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 09/09/2019] [Indexed: 02/03/2023] Open
Abstract
Testosterone is responsible for several changes in the brain, including behavioral and emotional responses, memory, and cognition. Given this, we investigated changes in the brain wave profile caused by supplementation with exogenous testosterone in both castrated and non-castrated rats. We also investigated the serum testosterone levels, renal and hepatic function, and the lipid and behavioral profiles. We found changes in the spectral wave power in both groups (castrated and non-castrated animals) supplemented with exogenous testosterone, consistent with an aggressive/hostile profile. These changes were observed in the electrocorticographic evaluation associated with increased power in low-frequency (delta and theta) and high-frequency (beta and gamma) activity in the supplemented animals. The castrated animals presented a significant decrease of wave power in the alpha frequency. This correlated with a decrease of the performance of the animals in the elevated plus-maze evaluation, given that the alpha wave is linked to the execution and visualization of motor processes. In the behavioral evaluation, the castrated animals presented a reduced permanence time in the elevated-plus maze, although this was prevented by the supplementation of testosterone. Testosterone supplementation induced aggressive behavior in non-castrated animals, but not in castrated ones. Supplemented animals had significantly elevated serum testosterone levels, while their urea levels were significantly lower, but without clinical significance. Our data indicate that testosterone supplementation in non-castrated rats, but not in castrated ones, causes electrocorticographic changes that could be associated with more aggressive and hostile behavior, in addition to indicating a potential for personality disorder. However, further studies are required to elucidate the cellular and molecular changes caused by acute testosterone supplementation.
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Affiliation(s)
- Daniel Pantoja Estumano
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute Biological Science, Federal University of Pará, Belém, Brazil
| | - Luan Oliveira Ferreira
- Laboratory of Experimental Neuropathology, João de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
| | - Paulo Augusto Lima Bezerra
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute Biological Science, Federal University of Pará, Belém, Brazil
| | - Maria Clara Pinheiro da Silva
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute Biological Science, Federal University of Pará, Belém, Brazil
| | - Giovanna Coutinho Jardim
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute Biological Science, Federal University of Pará, Belém, Brazil
| | - George Francisco Souza Santos
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute Biological Science, Federal University of Pará, Belém, Brazil
| | - Kayo Silva Gustavo
- Laboratory of Experimental Neuropathology, João de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
| | - Bruna Gerrits Mattos
- Laboratory of Experimental Neuropathology, João de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
| | - Jorge Amando Batista Ramos
- Laboratory of Human Cytogenetic, Institute Biological Science, Federal University of Pará, Belém, Brazil
| | - Vanessa Jóia de Mello
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute Biological Science, Federal University of Pará, Belém, Brazil
| | - Edmar Tavares da Costa
- Laboratory of Experimental Neuropathology, João de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
| | - Dielly Catrina Favacho Lopes
- Laboratory of Experimental Neuropathology, João de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil
- *Correspondence: Dielly Catrina Favacho Lopes
| | - Moisés Hamoy
- Laboratory of Pharmacology and Toxicology of Natural Products, Institute Biological Science, Federal University of Pará, Belém, Brazil
- Moisés Hamoy
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Xygonakis I, Athanasiou A, Pandria N, Kugiumtzis D, Bamidis PD. Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:7957408. [PMID: 30154834 PMCID: PMC6092991 DOI: 10.1155/2018/7957408] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 05/30/2018] [Accepted: 06/10/2018] [Indexed: 11/18/2022]
Abstract
Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges include multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble model, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiological knowledge play an important role in developing source space-based BCI algorithms. Feature selection and classifier characteristics of our implementation will be explored to raise performance to current state-of-the-art.
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Affiliation(s)
- Ioannis Xygonakis
- Biomedical Electronics Robotics and Devices (BERD) Group, Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
- Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
| | - Alkinoos Athanasiou
- Biomedical Electronics Robotics and Devices (BERD) Group, Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
| | - Niki Pandria
- Biomedical Electronics Robotics and Devices (BERD) Group, Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
| | - Panagiotis D. Bamidis
- Biomedical Electronics Robotics and Devices (BERD) Group, Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
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25
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Exploring the Neuroplastic Effects of Biofeedback Training on Smokers. Behav Neurol 2018; 2018:4876287. [PMID: 30151058 PMCID: PMC6087614 DOI: 10.1155/2018/4876287] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 05/29/2018] [Accepted: 06/10/2018] [Indexed: 01/17/2023] Open
Abstract
Smoking and stress cooccur in different stages of a nicotine addiction cycle, affecting brain function and showing additive impact on different physiological responses. Resting-state functional connectivity has shown potential in identifying these alterations. Nicotine addiction has been associated with detrimental effects on functional integrity of the central nervous system, including the organization of resting-state networks. Prolonged stress may result in enhanced activation of the default mode network (DMN). Considering that biofeedback has shown promise in alleviating physiological manifestations of stress, we aimed to explore the possible neuroplastic effects of biofeedback training on smokers. Clinical, behavioral, and neurophysiological (resting-state EEG) data were collected from twenty-seven subjects before and after five sessions of skin temperature training. DMN functional cortical connectivity was investigated. While clinical status remained unaltered, the degree of nicotine dependence and psychiatric symptoms were significantly improved. Significant changes in DMN organization and network properties were not observed, except for a significant increase of information flow from the right ventrolateral prefrontal cortex and right temporal pole cortex towards other DMN components. Biofeedback aiming at stress alleviation in smokers could play a protective role against maladaptive plasticity of connectivity. Multiple sessions, individualized interventions and more suitable methods to promote brain plasticity, such as neurofeedback training, should be considered.
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26
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27
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Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury. Neural Plast 2018; 2018:9354207. [PMID: 29853852 PMCID: PMC5954936 DOI: 10.1155/2018/9354207] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 03/06/2018] [Accepted: 03/21/2018] [Indexed: 12/18/2022] Open
Abstract
Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.
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28
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Tamás G, Chirumamilla VC, Anwar AR, Raethjen J, Deuschl G, Groppa S, Muthuraman M. Primary Sensorimotor Cortex Drives the Common Cortical Network for Gamma Synchronization in Voluntary Hand Movements. Front Hum Neurosci 2018; 12:130. [PMID: 29681807 PMCID: PMC5897748 DOI: 10.3389/fnhum.2018.00130] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/20/2018] [Indexed: 11/23/2022] Open
Abstract
Background: Gamma synchronization (GS) may promote the processing between functionally related cortico-subcortical neural populations. Our aim was to identify the sources of GS and to analyze the direction of information flow in cerebral networks at the beginning of phasic movements, and during medium-strength isometric contraction of the hand. Methods: We measured 64-channel electroencephalography in 11 healthy volunteers (age: 25 ± 8 years; four females); surface electromyography detected the movements of the dominant hand. In Task 1, subjects kept a constant medium-strength contraction of the first dorsal interosseus muscle, and performed a superimposed repetitive voluntary self-paced brisk squeeze of an object. In Task 2, brisk, and in Task 3, constant contractions were performed. Time-frequency analysis of the EEG signal was performed with the multitaper method. GS sources were identified in five frequency bands (30–49, 51–75, 76–99, 101–125, and 126–149 Hz) with beamformer inverse solution dynamic imaging of coherent sources. The direction of information flow was estimated by renormalized partial directed coherence for each frequency band. The data-driven surrogate test, and the time reversal technique were performed to identify significant connections. Results: In all tasks, we depicted the first three common sources for the studied frequency bands that were as follows: contralateral primary sensorimotor cortex (S1M1), dorsolateral prefrontal cortex (dPFC) and supplementary motor cortex (SMA). GS was detected in narrower low- (∼30–60 Hz) and high-frequency bands (>51–60 Hz) in the contralateral thalamus and ipsilateral cerebellum in all three tasks. The contralateral posterior parietal cortex was activated only in Task 1. In every task, S1M1 had efferent information flow to the SMA and the dPFC while dPFC had no detected afferent connections to the network in the gamma range. Cortical-subcortical information flow captured by the GS was dynamically variable in the narrower frequency bands for the studied movements. Conclusion: A distinct cortical network was identified for GS in voluntary hand movement tasks. Our study revealed that S1M1 modulated the activity of interconnected cortical areas through GS, while subcortical structures modulated the motor network dynamically, and specifically for the studied movement program.
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Affiliation(s)
- Gertrúd Tamás
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Venkata C Chirumamilla
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Abdul R Anwar
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany.,Biomedical Engineering Centre, University of Engineering and Technology, Lahore, Pakistan
| | - Jan Raethjen
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - Günther Deuschl
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - Sergiu Groppa
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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29
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Zhang Z, Liao M, Yao Z, Hu B, Xie Y, Zheng W, Hu T, Zhao Y, Yang F, Zhang Y, Su L, Li L, Gutknecht J, Majoe D. Frequency-Specific Functional Connectivity Density as an Effective Biomarker for Adolescent Generalized Anxiety Disorder. Front Hum Neurosci 2017; 11:549. [PMID: 29259549 PMCID: PMC5723402 DOI: 10.3389/fnhum.2017.00549] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Accepted: 10/30/2017] [Indexed: 12/11/2022] Open
Abstract
Several neuropsychiatric diseases have been found to influence the frequency-specific spontaneous functional brain organization (SFBO) in resting state, demonstrating that the abnormal brain activities of different frequency bands are associated with various physiological and psychological dysfunctions. However, little is known about the frequency specificities of SFBO in adolescent generalized anxiety disorder (GAD). Here, a novel complete ensemble empirical mode decomposition with adaptive noise method was applied to decompose the time series of each voxel across all participants (31 adolescent patients with GAD and 28 matched healthy controls; HCs) into four frequency-specific bands with distinct intrinsic oscillation. The functional connectivity density (FCD) of different scales (short-range and long-range) was calculated to quantify the SFBO changes related to GAD within each above frequency-specific band and the conventional frequency band (0.01–0.08 Hz). Support vector machine classifier was further used to examine the discriminative ability of the frequency-specific FCD values. The results showed that adolescent GAD patients exhibited abnormal alterations of both short-range and long-range FCD (S-FCD and L-FCD) in widespread brain regions across three frequency-specific bands. Positive correlation between the State Anxiety Inventory (SAI) score and increased L-FCD in the fusiform gyrus in the conventional frequency band was found in adolescents with GAD. Both S-FCD and L-FCD in the insula in the lower frequency band (0.02–0.036 Hz) had the highest classification performance compared to all other brain regions with inter-group difference. Furthermore, a satisfactory classification performance was achieved by combining the discrepant S-FCD and L-FCD values in all frequency bands, with the area under the curve (AUC) value of 0.9414 and the corresponding sensitivity, specificity, and accuracy of 87.15, 92.92, and 89.83%, respectively. This study indicates that the alterations of SFBO in adolescent GAD are frequency dependence and the frequency-specific FCD can potentially serve as a valuable biomarker in discriminating GAD patients from HCs. These findings may provide new insights into the pathophysiological mechanisms of adolescent GAD.
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Affiliation(s)
- Zhe Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Mei Liao
- Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China.,The China National Clinical Research Center for Mental Health Disorders, National Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Changsha, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yuanwei Xie
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Tao Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yu Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Fan Yang
- Guangdong Mental Health Center, Guangdong General Hospital, Guangzhou, China
| | - Yan Zhang
- Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China.,The China National Clinical Research Center for Mental Health Disorders, National Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Changsha, China
| | - Linyan Su
- Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China.,The China National Clinical Research Center for Mental Health Disorders, National Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Changsha, China
| | - Lingjiang Li
- Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China.,The China National Clinical Research Center for Mental Health Disorders, National Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Changsha, China
| | - Jürg Gutknecht
- Computer Systems Institute, ETH Zürich, Zürich, Switzerland
| | - Dennis Majoe
- Computer Systems Institute, ETH Zürich, Zürich, Switzerland
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30
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Athanasiou A, Klados MA, Pandria N, Foroglou N, Kavazidi KR, Polyzoidis K, Bamidis PD. A Systematic Review of Investigations into Functional Brain Connectivity Following Spinal Cord Injury. Front Hum Neurosci 2017; 11:517. [PMID: 29163098 PMCID: PMC5669283 DOI: 10.3389/fnhum.2017.00517] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 10/11/2017] [Indexed: 12/21/2022] Open
Abstract
Background: Complete or incomplete spinal cord injury (SCI) results in varying degree of motor, sensory and autonomic impairment. Long-lasting, often irreversible disability results from disconnection of efferent and afferent pathways. How does this disconnection affect brain function is not so clear. Changes in brain organization and structure have been associated with SCI and have been extensively studied and reviewed. Yet, our knowledge regarding brain connectivity changes following SCI is overall lacking. Methods: In this study we conduct a systematic review of articles regarding investigations of functional brain networks following SCI, searching on PubMed, Scopus and ScienceDirect according to PRISMA-P 2015 statement standards. Results: Changes in brain connectivity have been shown even during the early stages of the chronic condition and correlate with the degree of neurological impairment. Connectivity changes appear as dynamic post-injury procedures. Sensorimotor networks of patients and healthy individuals share similar patterns but new functional interactions have been identified as unique to SCI networks. Conclusions: Large-scale, multi-modal, longitudinal studies on SCI patients are needed to understand how brain network reorganization is established and progresses through the course of the condition. The expected insight holds clinical relevance in preventing maladaptive plasticity after SCI through individualized neurorehabilitation, as well as the design of connectivity-based brain-computer interfaces and assistive technologies for SCI patients.
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Affiliation(s)
- Alkinoos Athanasiou
- Laboratory of Medical Physics, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.,First Department of Neurosurgery, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Manousos A Klados
- Department of Biomedical Engineering, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Niki Pandria
- Laboratory of Medical Physics, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nicolas Foroglou
- First Department of Neurosurgery, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Kyriaki R Kavazidi
- Laboratory of Medical Physics, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Konstantinos Polyzoidis
- First Department of Neurosurgery, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Laboratory of Medical Physics, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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31
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Antonakakis M, Dimitriadis SI, Zervakis M, Papanicolaou AC, Zouridakis G. Altered Rich-Club and Frequency-Dependent Subnetwork Organization in Mild Traumatic Brain Injury: A MEG Resting-State Study. Front Hum Neurosci 2017; 11:416. [PMID: 28912698 PMCID: PMC5582079 DOI: 10.3389/fnhum.2017.00416] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Accepted: 08/03/2017] [Indexed: 12/19/2022] Open
Abstract
Functional brain connectivity networks exhibit “small-world” characteristics and some of these networks follow a “rich-club” organization, whereby a few nodes of high connectivity (hubs) tend to connect more densely among themselves than to nodes of lower connectivity. The Current study followed an “attack strategy” to compare the rich-club and small-world network organization models using Magnetoencephalographic (MEG) recordings from mild traumatic brain injury (mTBI) patients and neurologically healthy controls to identify the topology that describes the underlying intrinsic brain network organization. We hypothesized that the reduction in global efficiency caused by an attack targeting a model's hubs would reveal the “true” underlying topological organization. Connectivity networks were estimated using mutual information as the basis for cross-frequency coupling. Our results revealed a prominent rich-club network organization for both groups. In particular, mTBI patients demonstrated hyper-synchronization among rich-club hubs compared to controls in the δ band and the δ-γ1, θ-γ1, and β-γ2 frequency pairs. Moreover, rich-club hubs in mTBI patients were overrepresented in right frontal brain areas, from θ to γ1 frequencies, and underrepresented in left occipital regions in the δ-β, δ-γ1, θ-β, and β-γ2 frequency pairs. These findings indicate that the rich-club organization of resting-state MEG, considering its role in information integration and its vulnerability to various disorders like mTBI, may have a significant predictive value in the development of reliable biomarkers to help the validation of the recovery from mTBI. Furthermore, the proposed approach might be used as a validation tool to assess patient recovery.
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Affiliation(s)
- Marios Antonakakis
- Institute of Biomagnetism and Biosignal Analysis, Westfalian Wilhelms-University MuensterMuenster, Germany.,Digital Image and Signal Processing Laboratory, School of Electronic and Computer Engineering, Technical University of CreteChania, Greece
| | - Stavros I Dimitriadis
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of MedicineCardiff, United Kingdom.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff UniversityCardiff, United Kingdom.,Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff UniversityCardiff, United Kingdom.,School of Psychology, Cardiff UniversityCardiff, United Kingdom
| | - Michalis Zervakis
- Digital Image and Signal Processing Laboratory, School of Electronic and Computer Engineering, Technical University of CreteChania, Greece
| | - Andrew C Papanicolaou
- Departments of Pediatrics, and Anatomy and Neurobiology, Neuroscience Institute, University of Tennessee Health Science Center, Le Bonheur Children's HospitalMemphis, TN, United States
| | - George Zouridakis
- Biomedical Imaging Lab, Departments of Engineering Technology, Computer Science, Biomedical Engineering, and Electrical and Computer Engineering, University of HoustonHouston, TX, United States
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32
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Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5708937. [PMID: 28948168 PMCID: PMC5602625 DOI: 10.1155/2017/5708937] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2017] [Accepted: 07/05/2017] [Indexed: 12/29/2022]
Abstract
Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.
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33
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Lasaponara S, Mauro F, Carducci F, Paoletti P, Tombini M, Quattrocchi CC, Mallio CA, Errante Y, Scarciolla L, Ben-Soussan TD. Increased Alpha Band Functional Connectivity Following the Quadrato Motor Training: A Longitudinal Study. Front Hum Neurosci 2017; 11:282. [PMID: 28659773 PMCID: PMC5466954 DOI: 10.3389/fnhum.2017.00282] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 05/15/2017] [Indexed: 01/23/2023] Open
Abstract
Quadrato Motor Training (QMT) is a new training paradigm, which was found to increase cognitive flexibility, creativity and spatial cognition. In addition, QMT was reported to enhance inter- and intra-hemispheric alpha coherence as well as Fractional Anisotropy (FA) in a number of white matter pathways including corpus callosum. Taken together, these results seem to suggest that electrophysiological and structural changes induced by QMT may be due to an enhanced interplay and communication of the different brain areas within and between the right and the left hemisphere. In order to test this hypothesis using the exact low-resolution brain electromagnetic tomography (eLORETA), we estimated the current neural density and lagged linear connectivity (LLC) of the alpha band in the resting state electroencephalography (rsEEG) recorded with open (OE) and closed eyes (CE) at three different time points, following 6 and 12 weeks of daily QMT. Significant changes were observed for the functional connectivity. In particular, we found that limbic and fronto-temporal alpha connectivity in the OE condition increased after 6 weeks, while it enhanced at the CE condition in occipital network following 12-weeks of daily training. These findings seem to show that the QMT may have dissociable long-term effects on the functional connectivity depending on the different ways of recording rsEEG. OE recording pointed out a faster onset of Linear Lag Connectivity modulations that tend to decay as quickly, while CE recording showed sensible effect only after the complete 3-months training.
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Affiliation(s)
- Stefano Lasaponara
- Cognitive Neurophysiology Laboratory, Research Institute for Neuroscience, Education and Didactics, Patrizio Paoletti Foundation for Development and CommunicationAssisi, Italy.,Department of Neuropsychology, IRCCS Fondazione Santa LuciaRome, Italy
| | - Federica Mauro
- Cognitive Neurophysiology Laboratory, Research Institute for Neuroscience, Education and Didactics, Patrizio Paoletti Foundation for Development and CommunicationAssisi, Italy.,Department of Psychology, Sapienza Università di RomaRome, Italy
| | - Filippo Carducci
- Department of Physiology and Pharmacology, Sapienza Università di RomaRome, Italy
| | - Patrizio Paoletti
- Cognitive Neurophysiology Laboratory, Research Institute for Neuroscience, Education and Didactics, Patrizio Paoletti Foundation for Development and CommunicationAssisi, Italy
| | - Mario Tombini
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di RomaRome, Italy
| | - Carlo C Quattrocchi
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di RomaRome, Italy
| | - Carlo A Mallio
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di RomaRome, Italy
| | - Yuri Errante
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di RomaRome, Italy
| | - Laura Scarciolla
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di RomaRome, Italy
| | - Tal D Ben-Soussan
- Cognitive Neurophysiology Laboratory, Research Institute for Neuroscience, Education and Didactics, Patrizio Paoletti Foundation for Development and CommunicationAssisi, Italy
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