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Sengupta P, Lakshminarayanan K. Cortical activation and BCI performance during brief tactile imagery: A comparative study with motor imagery. Behav Brain Res 2024; 459:114760. [PMID: 37979923 DOI: 10.1016/j.bbr.2023.114760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/06/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30 ± 3.91 % and MI achieving 81.10 ± 2.96 %, with no significant difference between the two (p = 0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.
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Affiliation(s)
- Puja Sengupta
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Kishor Lakshminarayanan
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
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2
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Lyu S, Cheung RCC. Efficient Multiple Channels EEG Signal Classification Based on Hierarchical Extreme Learning Machine. SENSORS (BASEL, SWITZERLAND) 2023; 23:8976. [PMID: 37960675 PMCID: PMC10649020 DOI: 10.3390/s23218976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 10/29/2023] [Accepted: 11/03/2023] [Indexed: 11/15/2023]
Abstract
The human brain can be seen as one of the most powerful processors in the world, and it has a very complex structure with different kinds of signals for monitoring organics, communicating to neurons, and reacting to different information, which allows large developments in observing human sleeping, revealing diseases, reflecting certain motivations of limbs, and other applications. Relative theory, algorithms, and applications also help us to build brain-computer interface (BCI) systems for different powerful functions. Therefore, we present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification in motor imagery, showing the advantages in both accuracy of classification and training speed compared with conventional machine learning methods. The experiments are performed on software with the dataset of BCI Competition II with fast training time and high accuracy. The final average results show an accuracy of 93.90% as well as a reduction of 75% of the training time as compared to conventional deep learning and machine learning algorithms for EEG signal classification, also showing its prospects of the improvement of the performance of the BCI system.
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Affiliation(s)
| | - Ray C. C. Cheung
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China;
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3
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Hoeferlin GF, Bajwa T, Olivares H, Zhang J, Druschel LN, Sturgill BS, Sobota M, Boucher P, Duncan J, Hernandez-Reynoso AG, Cogan SF, Pancrazio JJ, Capadona JR. Antioxidant Dimethyl Fumarate Temporarily but Not Chronically Improves Intracortical Microelectrode Performance. MICROMACHINES 2023; 14:1902. [PMID: 37893339 PMCID: PMC10609067 DOI: 10.3390/mi14101902] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/24/2023] [Accepted: 10/02/2023] [Indexed: 10/29/2023]
Abstract
Intracortical microelectrode arrays (MEAs) can be used in a range of applications, from basic neuroscience research to providing an intimate interface with the brain as part of a brain-computer interface (BCI) system aimed at restoring function for people living with neurological disorders or injuries. Unfortunately, MEAs tend to fail prematurely, leading to a loss in functionality for many applications. An important contributing factor in MEA failure is oxidative stress resulting from chronically inflammatory-activated microglia and macrophages releasing reactive oxygen species (ROS) around the implant site. Antioxidants offer a means for mitigating oxidative stress and improving tissue health and MEA performance. Here, we investigate using the clinically available antioxidant dimethyl fumarate (DMF) to reduce the neuroinflammatory response and improve MEA performance in a rat MEA model. Daily treatment of DMF for 16 weeks resulted in a significant improvement in the recording capabilities of MEA devices during the sub-chronic (Weeks 5-11) phase (42% active electrode yield vs. 35% for control). However, these sub-chronic improvements were lost in the chronic implantation phase, as a more exacerbated neuroinflammatory response occurs in DMF-treated animals by 16 weeks post-implantation. Yet, neuroinflammation was indiscriminate between treatment and control groups during the sub-chronic phase. Although worse for chronic use, a temporary improvement (<12 weeks) in MEA performance is meaningful. Providing short-term improvement to MEA devices using DMF can allow for improved use for limited-duration studies. Further efforts should be taken to explore the mechanism behind a worsened neuroinflammatory response at the 16-week time point for DMF-treated animals and assess its usefulness for specific applications.
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Affiliation(s)
- George F. Hoeferlin
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA (H.O.); (J.D.)
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Blvd, Cleveland, OH 44106, USA
| | - Tejas Bajwa
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA (H.O.); (J.D.)
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Blvd, Cleveland, OH 44106, USA
| | - Hannah Olivares
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA (H.O.); (J.D.)
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Blvd, Cleveland, OH 44106, USA
| | - Jichu Zhang
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA (H.O.); (J.D.)
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Blvd, Cleveland, OH 44106, USA
| | - Lindsey N. Druschel
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA (H.O.); (J.D.)
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Blvd, Cleveland, OH 44106, USA
| | - Brandon S. Sturgill
- Department of Bioengineering, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, USA (J.J.P.)
| | - Michael Sobota
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA (H.O.); (J.D.)
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Blvd, Cleveland, OH 44106, USA
| | - Pierce Boucher
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA (H.O.); (J.D.)
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Blvd, Cleveland, OH 44106, USA
| | - Jonathan Duncan
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA (H.O.); (J.D.)
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Blvd, Cleveland, OH 44106, USA
| | - Ana G. Hernandez-Reynoso
- Department of Bioengineering, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, USA (J.J.P.)
| | - Stuart F. Cogan
- Department of Bioengineering, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, USA (J.J.P.)
| | - Joseph J. Pancrazio
- Department of Bioengineering, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, USA (J.J.P.)
| | - Jeffrey R. Capadona
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA (H.O.); (J.D.)
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Blvd, Cleveland, OH 44106, USA
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Lin J, Lai D, Wan Z, Feng L, Zhu J, Zhang J, Wang Y, Xu K. Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals. Front Hum Neurosci 2023; 17:1168017. [PMID: 37388414 PMCID: PMC10304012 DOI: 10.3389/fnhum.2023.1168017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/24/2023] [Indexed: 07/01/2023] Open
Abstract
Introduction In the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs). Methods The LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands. Results The frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8-38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What's more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135-300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals. Discussion The results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology. Clinical trial registration https://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.
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Affiliation(s)
- Jiafan Lin
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory for Biomedical Engineering of Ministry of Education, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Dongrong Lai
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory for Biomedical Engineering of Ministry of Education, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zijun Wan
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory for Biomedical Engineering of Ministry of Education, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | | | - Junming Zhu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Jianmin Zhang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Yueming Wang
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
| | - Kedi Xu
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory for Biomedical Engineering of Ministry of Education, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
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Lakshminarayanan K, Shah R, Daulat SR, Moodley V, Yao Y, Sengupta P, Ramu V, Madathil D. Evaluation of EEG Oscillatory Patterns and Classification of Compound Limb Tactile Imagery. Brain Sci 2023; 13:brainsci13040656. [PMID: 37190621 DOI: 10.3390/brainsci13040656] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/10/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Objective: The purpose of this study was to investigate the cortical activity and digit classification performance during tactile imagery (TI) of a vibratory stimulus at the index, middle, and thumb digits within the left hand in healthy individuals. Furthermore, the cortical activities and classification performance of the compound TI were compared with similar compound motor imagery (MI) with the same digits as TI in the same subjects. Methods: Twelve healthy right-handed adults with no history of upper limb injury, musculoskeletal condition, or neurological disorder participated in the study. The study evaluated the event-related desynchronization (ERD) response and brain-computer interface (BCI) classification performance on discriminating between the digits in the left-hand during the imagery of vibrotactile stimuli to either the index, middle, or thumb finger pads for TI and while performing a motor activity with the same digits for MI. A supervised machine learning technique was applied to discriminate between the digits within the same given limb for both imagery conditions. Results: Both TI and MI exhibited similar patterns of ERD in the alpha and beta bands at the index, middle, and thumb digits within the left hand. While TI had significantly lower ERD for all three digits in both bands, the classification performance of TI-based BCI (77.74 ± 6.98%) was found to be similar to the MI-based BCI (78.36 ± 5.38%). Conclusions: The results of this study suggest that compound tactile imagery can be a viable alternative to MI for BCI classification. The study contributes to the growing body of evidence supporting the use of TI in BCI applications, and future research can build on this work to explore the potential of TI-based BCI for motor rehabilitation and the control of external devices.
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Affiliation(s)
- Kishor Lakshminarayanan
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Rakshit Shah
- Department of Chemical and Biomedical Engineering, Cleveland State University, Cleveland, OH 44115, USA
| | - Sohail R Daulat
- Department of Physiology, University of Arizona College of Medicine, Tucson, AZ 85724, USA
| | - Viashen Moodley
- Arizona Center for Hand to Shoulder Surgery, Phoenix, AZ 85004, USA
| | - Yifei Yao
- Soft Tissue Biomechanics Laboratory, Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Puja Sengupta
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Vadivelan Ramu
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Deepa Madathil
- Jindal Institute of Behavioral Sciences, O. P. Jindal Global University, Sonipat 131001, Haryana, India
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Mitchell P, Lee SCM, Yoo PE, Morokoff A, Sharma RP, Williams DL, MacIsaac C, Howard ME, Irving L, Vrljic I, Williams C, Bush S, Balabanski AH, Drummond KJ, Desmond P, Weber D, Denison T, Mathers S, O’Brien TJ, Mocco J, Grayden DB, Liebeskind DS, Opie NL, Oxley TJ, Campbell BCV. Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) Study. JAMA Neurol 2023; 80:270-278. [PMID: 36622685 PMCID: PMC9857731 DOI: 10.1001/jamaneurol.2022.4847] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/18/2022] [Indexed: 01/10/2023]
Abstract
Importance Brain-computer interface (BCI) implants have previously required craniotomy to deliver penetrating or surface electrodes to the brain. Whether a minimally invasive endovascular technique to deliver recording electrodes through the jugular vein to superior sagittal sinus is safe and feasible is unknown. Objective To assess the safety of an endovascular BCI and feasibility of using the system to control a computer by thought. Design, Setting, and Participants The Stentrode With Thought-Controlled Digital Switch (SWITCH) study, a single-center, prospective, first in-human study, evaluated 5 patients with severe bilateral upper-limb paralysis, with a follow-up of 12 months. From a referred sample, 4 patients with amyotrophic lateral sclerosis and 1 with primary lateral sclerosis met inclusion criteria and were enrolled in the study. Surgical procedures and follow-up visits were performed at the Royal Melbourne Hospital, Parkville, Australia. Training sessions were performed at patients' homes and at a university clinic. The study start date was May 27, 2019, and final follow-up was completed January 9, 2022. Interventions Recording devices were delivered via catheter and connected to subcutaneous electronic units. Devices communicated wirelessly to an external device for personal computer control. Main Outcomes and Measures The primary safety end point was device-related serious adverse events resulting in death or permanent increased disability. Secondary end points were blood vessel occlusion and device migration. Exploratory end points were signal fidelity and stability over 12 months, number of distinct commands created by neuronal activity, and use of system for digital device control. Results Of 4 patients included in analyses, all were male, and the mean (SD) age was 61 (17) years. Patients with preserved motor cortex activity and suitable venous anatomy were implanted. Each completed 12-month follow-up with no serious adverse events and no vessel occlusion or device migration. Mean (SD) signal bandwidth was 233 (16) Hz and was stable throughout study in all 4 patients (SD range across all sessions, 7-32 Hz). At least 5 attempted movement types were decoded offline, and each patient successfully controlled a computer with the BCI. Conclusions and Relevance Endovascular access to the sensorimotor cortex is an alternative to placing BCI electrodes in or on the dura by open-brain surgery. These final safety and feasibility data from the first in-human SWITCH study indicate that it is possible to record neural signals from a blood vessel. The favorable safety profile could promote wider and more rapid translation of BCI to people with paralysis. Trial Registration ClinicalTrials.gov Identifier: NCT03834857.
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Affiliation(s)
- Peter Mitchell
- Department of Radiology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Sarah C. M. Lee
- Neurology, Calvary Healthcare Bethlehem, Parkdale, Australia
| | | | - Andrew Morokoff
- Parkville Neurosurgery, The University of Melbourne, Royal Melbourne Hospital, Parkville, Australia
| | - Rahul P. Sharma
- Stanford Healthcare Cardiovascular Medicine, Stanford University, Stanford, California
| | - Daryl L. Williams
- Department of Anaesthesia and Pain Management, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Christopher MacIsaac
- Intensive Care Department, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Mark E. Howard
- Victorian Respiratory Support Service, Austin Health, Heidelberg, Australia
| | - Lou Irving
- Peter MacCallum Cancer Centre, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, Australia
| | - Ivan Vrljic
- Department of Radiology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Cameron Williams
- Department of Neurology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Steven Bush
- Department of Neurology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Anna H. Balabanski
- Department of Neurology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
- Melbourne Brain Centre, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
- Department of Neuroscience, Alfred Brain, Alfred Health, Melbourne, Australia
| | - Katharine J. Drummond
- Department of Neurosurgery, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Patricia Desmond
- Department of Radiology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Douglas Weber
- Department of Biomedical Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Timothy Denison
- Institute of Biomedical Engineering, The University of Oxford, Oxford, United Kingdom
| | - Susan Mathers
- Neurology, Calvary Healthcare Bethlehem, Parkdale, Australia
| | - Terence J. O’Brien
- Department of Neurology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
- Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
- Department of Neuroscience, The Central Clinical School, Monash University and Alfred Health, Melbourne, Australia
| | - J. Mocco
- Department of Neurosurgery, Klingenstein Clinical Center, The Mount Sinai Hospital, New York, New York
| | - David B. Grayden
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, Australia
| | - David S. Liebeskind
- UCLA Comprehensive Stroke Center, Department of Neurology, University of California, Los Angeles
| | - Nicholas L. Opie
- Vascular Bionics Laboratory, Department of Medicine, The University of Melbourne, Melbourne, Australia
- Synchron, Carlton, Australia
| | - Thomas J. Oxley
- Synchron Inc, New York, New York
- Vascular Bionics Laboratory, Department of Medicine, The University of Melbourne, Melbourne, Australia
| | - Bruce C. V. Campbell
- Department of Neurology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
- Melbourne Brain Centre, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
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Bergeron D, Iorio-Morin C, Bonizzato M, Lajoie G, Orr Gaucher N, Racine É, Weil AG. Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations. J Child Neurol 2023; 38:223-238. [PMID: 37116888 PMCID: PMC10226009 DOI: 10.1177/08830738231167736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/11/2023] [Accepted: 03/17/2023] [Indexed: 04/30/2023]
Abstract
Invasive brain-computer interfaces hold promise to alleviate disabilities in individuals with neurologic injury, with fully implantable brain-computer interface systems expected to reach the clinic in the upcoming decade. Children with severe neurologic disabilities, like quadriplegic cerebral palsy or cervical spine trauma, could benefit from this technology. However, they have been excluded from clinical trials of intracortical brain-computer interface to date. In this manuscript, we discuss the ethical considerations related to the use of invasive brain-computer interface in children with severe neurologic disabilities. We first review the technical hardware and software considerations for the application of intracortical brain-computer interface in children. We then discuss ethical issues related to motor brain-computer interface use in pediatric neurosurgery. Finally, based on the input of a multidisciplinary panel of experts in fields related to brain-computer interface (functional and restorative neurosurgery, pediatric neurosurgery, mathematics and artificial intelligence research, neuroengineering, pediatric ethics, and pragmatic ethics), we then formulate initial recommendations regarding the clinical use of invasive brain-computer interfaces in children.
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Affiliation(s)
- David Bergeron
- Division of Neurosurgery, Université de Montréal, Montreal, Québec, Canada
| | | | - Marco Bonizzato
- Electrical Engineering Department, Polytechnique Montréal, Montreal, Québec, Canada
- Neuroscience Department and Centre
interdisciplinaire de recherche sur le cerveau et l’apprentissage (CIRCA), Université de Montréal, Montréal, Québec, Canada
| | - Guillaume Lajoie
- Mathematics and Statistics Department, Université de Montréal, Montreal, Québec, Canada
- Mila - Québec AI Institute, Montréal,
Québec, Canada
| | - Nathalie Orr Gaucher
- Department of Pediatric Emergency
Medicine, CHU Sainte-Justine, Montréal, Québec, Canada
- Bureau de l’Éthique clinique, Faculté
de médecine de l’Université de Montréal, Montreal, Québec, Canada
| | - Éric Racine
- Pragmatic Research Unit, Institute de
Recherche Clinique de Montréal (IRCM), Montreal, Québec, Canada
- Department of Medicine and Department
of Social and Preventative Medicine, Université de Montréal, Montréal, Québec, Canada
| | - Alexander G. Weil
- Division of Neurosurgery, Department
of Surgery, Centre Hospitalier Universitaire Sainte-Justine (CHUSJ), Département de
Pédiatrie, Université de Montréal, Montreal, Québec, Canada
- Department of Neuroscience, Université de Montréal, Montréal, Québec, Canada
- Brain and Development Research Axis,
CHU Sainte-Justine Research Center, Montréal, Québec, Canada
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8
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Valeriani D, Santoro F, Ienca M. The present and future of neural interfaces. Front Neurorobot 2022; 16:953968. [PMID: 36304780 PMCID: PMC9592849 DOI: 10.3389/fnbot.2022.953968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/13/2022] [Indexed: 11/18/2022] Open
Abstract
The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.
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Affiliation(s)
| | - Francesca Santoro
- Institute for Biological Information Processing - Bioelectronics, IBI-3, Forschungszentrum Juelich, Juelich, Germany
- Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany
| | - Marcello Ienca
- College of Humanities, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
- *Correspondence: Marcello Ienca
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Xie YL, Yang YX, Jiang H, Duan XY, Gu LJ, Qing W, Zhang B, Wang YX. Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials. Front Neurosci 2022; 16:949575. [PMID: 35992923 PMCID: PMC9381818 DOI: 10.3389/fnins.2022.949575] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Upper extremity dysfunction after stroke is an urgent clinical problem that greatly affects patients' daily life and reduces their quality of life. As an emerging rehabilitation method, brain-machine interface (BMI)-based training can extract brain signals and provide feedback to form a closed-loop rehabilitation, which is currently being studied for functional restoration after stroke. However, there is no reliable medical evidence to support the effect of BMI-based training on upper extremity function after stroke. This review aimed to evaluate the efficacy and safety of BMI-based training for improving upper extremity function after stroke, as well as potential differences in efficacy of different external devices. Methods English-language literature published before April 1, 2022, was searched in five electronic databases using search terms including “brain-computer/machine interface”, “stroke” and “upper extremity.” The identified articles were screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.4.1 software. The GRADE method was used to assess the quality of the evidence. Results A total of 17 studies with 410 post-stroke patients were included. Meta-analysis showed that BMI-based training significantly improved upper extremity motor function [standardized mean difference (SMD) = 0.62; 95% confidence interval (CI) (0.34, 0.90); I2 = 38%; p < 0.0001; n = 385; random-effects model; moderate-quality evidence]. Subgroup meta-analysis indicated that BMI-based training significantly improves upper extremity motor function in both chronic [SMD = 0.68; 95% CI (0.32, 1.03), I2 = 46%; p = 0.0002, random-effects model] and subacute [SMD = 1.11; 95%CI (0.22, 1.99); I2 = 76%; p = 0.01; random-effects model] stroke patients compared with control interventions, and using functional electrical stimulation (FES) [SMD = 1.11; 95% CI (0.67, 1.54); I2 = 11%; p < 0.00001; random-effects model]or visual feedback [SMD = 0.66; 95% CI (0.2, 1.12); I2 = 4%; p = 0.005; random-effects model;] as the feedback devices in BMI training was more effective than using robot. In addition, BMI-based training was more effective in improving patients' activities of daily living (ADL) than control interventions [SMD = 1.12; 95% CI (0.65, 1.60); I2 = 0%; p < 0.00001; n = 80; random-effects model]. There was no statistical difference in the dropout rate and adverse effects between the BMI-based training group and the control group. Conclusion BMI-based training improved upper limb motor function and ADL in post-stroke patients. BMI combined with FES or visual feedback may be a better combination for functional recovery than robot. BMI-based trainings are well-tolerated and associated with mild adverse effects.
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Affiliation(s)
- Yu-lei Xie
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- Department of Rehabilitation Medicine, Capital Medical University, Beijing, China
| | - Yu-xuan Yang
- Department of Rehabilitation Medicine, The Second Clinical Hospital of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
| | - Hong Jiang
- Department of Rehabilitation Medicine, Xichong County People's Hospital, Nanchong Central Hospital, Nanchong, China
| | - Xing-Yu Duan
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Li-jing Gu
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Wu Qing
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Bo Zhang
- Department of Rehabilitation Medicine, The Second Clinical Hospital of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China
- Bo Zhang
| | - Yin-xu Wang
- Department of Rehabilitation Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
- *Correspondence: Yin-xu Wang
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Smart Device-Driven Corticolimbic Plasticity in Cognitive–Emotional Restructuring of Space-Related Neuropsychiatric Disease and Injury. Life (Basel) 2022; 12:life12020236. [PMID: 35207523 PMCID: PMC8875345 DOI: 10.3390/life12020236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/27/2022] [Accepted: 02/01/2022] [Indexed: 11/16/2022] Open
Abstract
Escalating government and commercial efforts to plan and deploy viable manned near-to-deep solar system exploration and habitation over the coming decades now drives next-generation space medicine innovations. The application of cutting-edge precision medicine, such as brain stimulation techniques, provides powerful clinical and field/flight situation methods to selectively control vagal tone and neuroendocrine-modulated corticolimbic plasticity, which is affected by prolonged cosmic radiation exposure, social isolation or crowding, and weightlessness in constricted operational non-terran locales. Earth-based clinical research demonstrates that brain stimulation approaches may be combined with novel psychotherapeutic integrated memory structure rationales for the corrective reconsolidation of arousing or emotional experiences, autobiographical memories, semantic schema, and other cognitive structures to enhance neuropsychiatric patient outcomes. Such smart cotherapies or countermeasures, which exploit natural, pharmaceutical, and minimally invasive neuroprosthesis-driven nervous system activity, may optimize the cognitive-emotional restructuring of astronauts suffering from space-related neuropsychiatric disease and injury, including mood, affect, and anxiety symptoms of any potential severity and pathophysiology. An appreciation of improved neuropsychiatric healthcare through the merging of new or rediscovered smart theragnostic medical technologies, capable of rendering personalized neuroplasticity training and managed psychotherapeutic treatment protocols, will reveal deeper insights into the illness states experienced by astronauts. Future work in this area should emphasize the ethical role of telemedicine and/or digital clinicians to advance the (semi)autonomous, technology-assisted medical prophylaxis, diagnosis, treatment, monitoring, and compliance of astronauts for elevated health, safety, and performance in remote extreme space and extraterrestrial environments.
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11
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Beauchamp MS, Bosking WH, Oswalt D, Yoshor D. Raising the stakes for cortical visual prostheses. J Clin Invest 2021; 131:154983. [PMID: 34850741 DOI: 10.1172/jci154983] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
In this issue of the JCI, the dream of restoring useful vision to blind individuals with neurotechnology moves one step closer to realization. Fernández et al. implanted an electrode array with 96 penetrating electrodes in the visual cortex of a blind patient who had been without light perception for 16 years due to optic neuropathy. Remarkably, the patient was able to perceive visual patterns created by passing current through array electrodes. The use of a penetrating electrode array meant that action potentials from single neurons could be recorded to study the neural response to stimulation. Compared with electrodes resting on the cortical surface, penetrating electrodes require one-tenth the current to create a visual percept. However, patterned electrical stimulation often fails to produce the expected percept for penetrating and surface electrode arrays, highlighting the need for further research to untangle the relationship between stimulus and perception.
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12
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Wang X, Cavigelli L, Schneider T, Benini L. Sub-100 μW Multispectral Riemannian Classification for EEG-Based Brain-Machine Interfaces. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:1149-1160. [PMID: 34932486 DOI: 10.1109/tbcas.2021.3137290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Motor imagery (MI) brain-machine interfaces (BMIs) enable us to control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the classification of the brain signals is done locally near the sensor using machine learning models embedded on energy-efficient microcontroller units (MCUs), for assured privacy, user comfort, and long-term usage. In this work, we provide practical insights on the accuracy-cost trade-off for embedded BMI solutions. Our multispectral Riemannian classifier reaches 75.1% accuracy on a 4-class MI task. The accuracy is further improved by tuning different types of classifiers to each subject, achieving 76.4%. We further scale down the model by quantizing it to mixed-precision representations with a minimal accuracy loss of 1% and 1.4%, respectively, which is still up to 4.1% more accurate than the state-of-the-art embedded convolutional neural network. We implement the model on a low-power MCU within an energy budget of merely 198 μJ and taking only 16.9 ms per classification. Classifying samples continuously, overlapping the 3.5 s samples by 50% to avoid missing user inputs allows for operation at just 85 μW. Compared to related works in embedded MI-BMIs, our solution sets the new state-of-the-art in terms of accuracy-energy trade-off for near-sensor classification.
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13
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Soekadar SR, Koike Y, Zollo L. Introduction. Int J Neural Syst 2021; 31:2103010. [PMID: 34730076 DOI: 10.1142/s0129065721030106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Surjo R Soekadar
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences (CCM), Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Yasuharu Koike
- Institute of Innovative Research, Tokyo Institute of Technology, 4259-J3-10, Nagatsuta, Midori-ku, Yokohama 226-8503, Japan
| | - Loredana Zollo
- Unit of Advanced Robotics and Human-Centred Technologies (CREO Lab), University Campus Bio-Medico of Rome, Via Álvaro del Portillo, 21, 00128 Roma RM, Italy
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14
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Marasco PD, Hebert JS, Sensinger JW, Beckler DT, Thumser ZC, Shehata AW, Williams HE, Wilson KR. Neurorobotic fusion of prosthetic touch, kinesthesia, and movement in bionic upper limbs promotes intrinsic brain behaviors. Sci Robot 2021; 6:eabf3368. [PMID: 34516746 DOI: 10.1126/scirobotics.abf3368] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Paul D Marasco
- Laboratory for Bionic Integration, Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH 44195, USA.,Advanced Platform Technology Center, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, 10701 East Boulevard 151 W/APT, Cleveland, OH 44106, USA
| | - Jacqueline S Hebert
- Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, Alberta T6G 2E1, Canada.,Glenrose Rehabilitation Hospital, Alberta Health Services, 10230-111 Avenue, Edmonton, Alberta T5G 0B7, Canada
| | - Jonathon W Sensinger
- Institute of Biomedical Engineering, University of New Brunswick, 25 Dineen Drive, Fredericton, New Brunswick E3B 5A3, Canada
| | - Dylan T Beckler
- Laboratory for Bionic Integration, Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH 44195, USA
| | - Zachary C Thumser
- Laboratory for Bionic Integration, Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH 44195, USA.,Research Service, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, 10701 East Boulevard, Research 151, Cleveland, OH 44106, USA
| | - Ahmed W Shehata
- Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, Alberta T6G 2E1, Canada
| | - Heather E Williams
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta T6G 2E1, Canada
| | - Kathleen R Wilson
- Institute of Biomedical Engineering, University of New Brunswick, 25 Dineen Drive, Fredericton, New Brunswick E3B 5A3, Canada
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15
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Orlandi S, House SC, Karlsson P, Saab R, Chau T. Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review. Front Hum Neurosci 2021; 15:643294. [PMID: 34335203 PMCID: PMC8319030 DOI: 10.3389/fnhum.2021.643294] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 05/18/2021] [Indexed: 11/13/2022] Open
Abstract
Brain-computer interfaces (BCIs) represent a new frontier in the effort to maximize the ability of individuals with profound motor impairments to interact and communicate. While much literature points to BCIs' promise as an alternative access pathway, there have historically been few applications involving children and young adults with severe physical disabilities. As research is emerging in this sphere, this article aims to evaluate the current state of translating BCIs to the pediatric population. A systematic review was conducted using the Scopus, PubMed, and Ovid Medline databases. Studies of children and adolescents that reported BCI performance published in English in peer-reviewed journals between 2008 and May 2020 were included. Twelve publications were identified, providing strong evidence for continued research in pediatric BCIs. Research evidence was generally at multiple case study or exploratory study level, with modest sample sizes. Seven studies focused on BCIs for communication and five on mobility. Articles were categorized and grouped based on type of measurement (i.e., non-invasive and invasive), and the type of brain signal (i.e., sensory evoked potentials or movement-related potentials). Strengths and limitations of studies were identified and used to provide requirements for clinical translation of pediatric BCIs. This systematic review presents the state-of-the-art of pediatric BCIs focused on developing advanced technology to support children and youth with communication disabilities or limited manual ability. Despite a few research studies addressing the application of BCIs for communication and mobility in children, results are encouraging and future works should focus on customizable pediatric access technologies based on brain activity.
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Affiliation(s)
- Silvia Orlandi
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Sarah C. House
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Petra Karlsson
- Cerebral Palsy Alliance, The University of Sydney, Sydney, NSW, Australia
| | - Rami Saab
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Tom Chau
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON, Canada
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Bazarek S, Brown JM. The evolution of nerve transfers for spinal cord injury. Exp Neurol 2020; 333:113426. [DOI: 10.1016/j.expneurol.2020.113426] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 07/10/2020] [Accepted: 07/25/2020] [Indexed: 12/15/2022]
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17
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Gilissen SR, Arckens L. Posterior parietal cortex contributions to cross-modal brain plasticity upon sensory loss. Curr Opin Neurobiol 2020; 67:16-25. [PMID: 32777707 DOI: 10.1016/j.conb.2020.07.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 06/30/2020] [Accepted: 07/01/2020] [Indexed: 12/18/2022]
Abstract
Sensory loss causes compensatory behavior, like echolocation upon vision loss or improved visual motion detection upon deafness. This is enabled by recruitment of the deprived cortical area by the intact senses. Such cross-modal plasticity can however hamper rehabilitation via sensory substitution devices. To steer rehabilitation towards the desired outcome for the patient, having control over the cross-modal take-over is essential. Evidence accumulates to support a role for the posterior parietal cortex (PPC) in multimodal plasticity. This area shows increased activity after sensory loss, keeping similar functions but driven by other senses. Patient-specific factors like stress, social situation, age and attention, have a significant influence on the PPC and on cross-modal plasticity. We propose that understanding the response of the PPC to sensory loss and context is extremely important for determining the best possible implant-based therapies, and that mouse research holds potential to help unraveling the underlying anatomical, cellular and neuromodulatory mechanisms.
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Affiliation(s)
- Sara Rj Gilissen
- KU Leuven, Department of Biology & Leuven Brain Institute, 3000 Leuven, Belgium
| | - Lutgarde Arckens
- KU Leuven, Department of Biology & Leuven Brain Institute, 3000 Leuven, Belgium.
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