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Orth L, Meeh J, Gur RC, Neuner I, Sarkheil P. Frontostriatal circuitry as a target for fMRI-based neurofeedback interventions: A systematic review. Front Hum Neurosci 2022; 16:933718. [PMID: 36092647 PMCID: PMC9449529 DOI: 10.3389/fnhum.2022.933718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/08/2022] [Indexed: 11/19/2022] Open
Abstract
Dysregulated frontostriatal circuitries are viewed as a common target for the treatment of aberrant behaviors in various psychiatric and neurological disorders. Accordingly, experimental neurofeedback paradigms have been applied to modify the frontostriatal circuitry. The human frontostriatal circuitry is topographically and functionally organized into the “limbic,” the “associative,” and the “motor” subsystems underlying a variety of affective, cognitive, and motor functions. We conducted a systematic review of the literature regarding functional magnetic resonance imaging-based neurofeedback studies that targeted brain activations within the frontostriatal circuitry. Seventy-nine published studies were included in our survey. We assessed the efficacy of these studies in terms of imaging findings of neurofeedback intervention as well as behavioral and clinical outcomes. Furthermore, we evaluated whether the neurofeedback targets of the studies could be assigned to the identifiable frontostriatal subsystems. The majority of studies that targeted frontostriatal circuitry functions focused on the anterior cingulate cortex, the dorsolateral prefrontal cortex, and the supplementary motor area. Only a few studies (n = 14) targeted the connectivity of the frontostriatal regions. However, post-hoc analyses of connectivity changes were reported in more cases (n = 32). Neurofeedback has been frequently used to modify brain activations within the frontostriatal circuitry. Given the regulatory mechanisms within the closed loop of the frontostriatal circuitry, the connectivity-based neurofeedback paradigms should be primarily considered for modifications of this system. The anatomical and functional organization of the frontostriatal system needs to be considered in decisions pertaining to the neurofeedback targets.
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Affiliation(s)
- Linda Orth
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- *Correspondence: Linda Orth
| | - Johanna Meeh
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany
| | - Pegah Sarkheil
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
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Kumari R, Jarjees M, Susnoschi-Luca I, Purcell M, Vučković A. Effective Connectivity in Spinal Cord Injury-Induced Neuropathic Pain. SENSORS (BASEL, SWITZERLAND) 2022; 22:6337. [PMID: 36080805 PMCID: PMC9460641 DOI: 10.3390/s22176337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/05/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
AIM The aim of this study was to differentiate the effects of spinal cord injury (SCI) and central neuropathic pain (CNP) on effective connectivity during motor imagery of legs, where CNP is typically experienced. METHODS Multichannel EEG was recorded during motor imagery of the legs in 3 groups of people: able-bodied (N = 10), SCI with existing CNP (N = 10), and SCI with no CNP (N = 20). The last group was followed up for 6 months to check for the onset of CNP. Source reconstruction was performed to obtain cortical activity in 17 areas spanning sensorimotor regions and pain matrix. Effective connectivity was calculated using the directed transfer function in 4 frequency bands and compared between groups. RESULTS A total of 50% of the SCI group with no CNP developed CNP later. Statistically significant differences in effective connectivity were found between all groups. The differences between groups were not dependent on the frequency band. Outflows from the supplementary motor area were greater for the able-bodied group while the outflows from the secondary somatosensory cortex were greater for the SCI groups. The group with existing CNP showed the least differences from the able-bodied group, appearing to reverse the effects of SCI. The connectivities involving the pain matrix were different between able-bodied and SCI groups irrespective of CNP status, indicating their involvement in motor networks generally. SIGNIFICANCE The study findings might help guide therapeutic interventions targeted at the brain for CNP alleviation as well as motor recovery post SCI.
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Affiliation(s)
- Radha Kumari
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mohammed Jarjees
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
- Medical Instrumentation Techniques Engineering Department, Northern Technical University, Mosul 41002, Iraq
| | - Ioana Susnoschi-Luca
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mariel Purcell
- Queen Elizabeth National Spinal Injuries Unit, Queen Elizabeth University Hospital, Glasgow G51 4TF, UK
| | - Aleksandra Vučković
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
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Le Franc S, Herrera Altamira G, Guillen M, Butet S, Fleck S, Lécuyer A, Bougrain L, Bonan I. Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives. Front Hum Neurosci 2022; 16:917909. [PMID: 35911589 PMCID: PMC9332194 DOI: 10.3389/fnhum.2022.917909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/20/2022] [Indexed: 11/28/2022] Open
Abstract
Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.
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Affiliation(s)
- Salomé Le Franc
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
- *Correspondence: Salomé Le Franc,
| | | | - Maud Guillen
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
- Neurology Unit, University Hospital of Rennes, Rennes, France
| | - Simon Butet
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | - Stéphanie Fleck
- Université de Lorraine, CNRS, LORIA, Nancy, France
- EA7312 Laboratoire de Psychologie Ergonomique et Sociale pour l’Expérience Utilisateurs (PERSEUS), Metz, France
| | - Anatole Lécuyer
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | | | - Isabelle Bonan
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
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De Filippi E, Marins T, Escrichs A, Gilson M, Moll J, Tovar-Moll F, Deco G. One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity. Cereb Cortex Commun 2022; 3:tgac027. [PMID: 36072710 PMCID: PMC9441014 DOI: 10.1093/texcom/tgac027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 06/28/2022] [Accepted: 07/03/2022] [Indexed: 11/23/2022] Open
Abstract
In the past decade, several studies have shown that Neurofeedback (NFB) by functional magnetic resonance imaging can alter the functional coupling of targeted and non-targeted areas. However, the causal mechanisms underlying these changes remain uncertain. Here, we applied a whole-brain dynamical model to estimate Effective Connectivity (EC) profiles of resting-state data acquired before and immediately after a single-session NFB training for 17 participants who underwent motor imagery NFB training and 16 healthy controls who received sham feedback. Within-group and between-group classification analyses revealed that only for the NFB group it was possible to accurately discriminate between the 2 resting-state sessions. NFB training-related signatures were reflected in a support network of direct connections between areas involved in reward processing and implicit learning, together with regions belonging to the somatomotor, control, attention, and default mode networks, identified through a recursive-feature elimination procedure. By applying a data-driven approach to explore NFB-induced changes in spatiotemporal dynamics, we demonstrated that these regions also showed decreased switching between different brain states (i.e. metastability) only following real NFB training. Overall, our findings contribute to the understanding of NFB impact on the whole brain’s structure and function by shedding light on the direct connections between brain areas affected by NFB training.
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Affiliation(s)
- Eleonora De Filippi
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Carrer de Ramon Trias Fargas , 25-27, 08005 Barcelona, Catalonia, Spain
| | - Theo Marins
- D’Or Institute for Research and Education (IDOR), Rua Diniz Cordeiro 30, Botafogo-Rio de Janeiro , 22281-100, Brazil
- Post-Graduate Program in Morphological Sciences, Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Citade universitaria da Universidade Federal do Rio de Janeiro , 21941-590, Brazil
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Carrer de Ramon Trias Fargas , 25-27, 08005 Barcelona, Catalonia, Spain
| | - Matthieu Gilson
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Carrer de Ramon Trias Fargas , 25-27, 08005 Barcelona, Catalonia, Spain
| | - Jorge Moll
- D’Or Institute for Research and Education (IDOR), Rua Diniz Cordeiro 30, Botafogo-Rio de Janeiro , 22281-100, Brazil
| | - Fernanda Tovar-Moll
- D’Or Institute for Research and Education (IDOR), Rua Diniz Cordeiro 30, Botafogo-Rio de Janeiro , 22281-100, Brazil
- Post-Graduate Program in Morphological Sciences, Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Citade universitaria da Universidade Federal do Rio de Janeiro , 21941-590, Brazil
| | - Gustavo Deco
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig de Lluis Companys , 23, 08010, Barcelona, Catalonia, Spain
- Department of Neuropsychology, Max Planck Institute for human Cognitive and Brain Sciences , Stephanstrasse 1a, 04103, Leipzig, Germany
- Turner Institute for Brain and Mental Health, Monash University level 5 , 18 Innovation Walk, Clayton Campus. Wellington Road, Clayton VIC 3800, Australia
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Madkhali Y, Aldehmi N, Pollick F. Functional Localizers for Motor Areas of the Brain Using fMRI. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7589493. [PMID: 35669664 PMCID: PMC9167083 DOI: 10.1155/2022/7589493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/23/2022] [Accepted: 05/10/2022] [Indexed: 11/17/2022]
Abstract
Neuroimaging researchers increasingly take advantage of the known functional properties of brain regions to localize motor regions in the brain and investigate changes in their activity under various conditions. Using this noninvasive functional MRI (fMRI) method makes it possible to identify and localize brain activation. There are many localizers that can be used to identify brain areas, namely, motor areas such as functional localizer, anatomical localizer, or Atlas mask. Eighteen right-handed participants were recruited for this research to test the reliability of five localizers for primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), motor cerebellum, and motor thalamus. Motor execution task, namely, hand clenching was used to activate M1, SMA, and motor cerebellum. A combined action observation and motor imagery (AOMI) task was used to functionally activate PMC. Finally, a mask based on Talairach coordinates Atlas was created and used to identify the motor thalamus. Our results show that all localizers were successfully activated in the desired regions of interest. Motor execution successfully activated M1, SMA, and motor cerebellum. A novel localizer based on AOMI was successfully activated in PMC, and the motor thalamus mask obtained from the thalamus mask was successfully implemented on each participant. In conclusion, all five localizers tested in this research were reliable and can be used for rt-fMRI neurofeedback research to define the regions of interest.
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Affiliation(s)
- Yahia Madkhali
- Faculty of Applied Medical Sciences, Jazan University, Jizan, Saudi Arabia
| | - Norah Aldehmi
- College of Medical, Veterinary and Life Sciences (MVLS), University of Glasgow, Glasgow, UK
| | - Frank Pollick
- School of Psychology, University of Glasgow, Glasgow, UK
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Ravindran A, Rieke JD, Zapata JDA, White KD, Matarasso A, Yusufali MM, Rana M, Gunduz A, Modarres M, Sitaram R, Daly JJ. Four methods of brain pattern analyses of fMRI signals associated with wrist extension versus wrist flexion studied for potential use in future motor learning BCI. PLoS One 2021; 16:e0254338. [PMID: 34403422 PMCID: PMC8370644 DOI: 10.1371/journal.pone.0254338] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 06/24/2021] [Indexed: 11/21/2022] Open
Abstract
OBJECTIVE In stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults. APPROACH We studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) 'Winner Take All'; and 4) Relative Dominance. RESULTS With these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects. SIGNIFICANCE We encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data.
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Affiliation(s)
- Aniruddh Ravindran
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Jake D. Rieke
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Jose Daniel Alcantara Zapata
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Keith D. White
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
- Department of Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, United States of America
| | - Avi Matarasso
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
- Department of Chemical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - M. Minhal Yusufali
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Mohit Rana
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Aysegul Gunduz
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Mo Modarres
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
| | - Ranganatha Sitaram
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychiatry and Division of Neuroscience, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Janis J. Daly
- J. Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, Florida, United States of America
- Department of Neurology, College of Medicine, University of Florida, Gainesville, United States of America
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Combined real-time fMRI and real time fNIRS brain computer interface (BCI): Training of volitional wrist extension after stroke, a case series pilot study. PLoS One 2021; 16:e0250431. [PMID: 33956845 PMCID: PMC8101762 DOI: 10.1371/journal.pone.0250431] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 04/01/2021] [Indexed: 02/07/2023] Open
Abstract
Objective Pilot testing of real time functional magnetic resonance imaging (rt-fMRI) and real time functional near infrared spectroscopy (rt-fNIRS) as brain computer interface (BCI) neural feedback systems combined with motor learning for motor recovery in chronic severely impaired stroke survivors. Approach We enrolled a four-case series and administered three sequential rt-fMRI and ten rt-fNIRS neural feedback sessions interleaved with motor learning sessions. Measures were: Arm Motor Assessment Tool, functional domain (AMAT-F; 13 complex functional tasks), Fugl-Meyer arm coordination scale (FM); active wrist extension range of motion (ROM); volume of activation (fMRI); and fNIRS HbO concentration. Performance during neural feedback was assessed, in part, using percent successful brain modulations during rt-fNIRS. Main results Pre-/post-treatment mean clinically significant improvement in AMAT-F (.49 ± 0.22) and FM (10.0 ± 3.3); active wrist ROM improvement ranged from 20° to 50°. Baseline to follow-up change in brain signal was as follows: fMRI volume of activation was reduced in almost all ROIs for three subjects, and for one subject there was an increase or no change; fNIRS HbO was within normal range, except for one subject who increased beyond normal at post-treatment. During rt-fNIRS neural feedback training, there was successful brain signal modulation (42%–78%). Significance Severely impaired stroke survivors successfully engaged in spatially focused BCI systems, rt-fMRI and rt-fNIRS, to clinically significantly improve motor function. At the least, equivalency in motor recovery was demonstrated with prior long-duration motor learning studies (without neural feedback), indicating that no loss of motor improvement resulted from substituting neural feedback sessions for motor learning sessions. Given that the current neural feedback protocol did not prevent the motor improvements observed in other long duration studies, even in the presence of fewer sessions of motor learning in the current work, the results support further study of neural feedback and its potential for recovery of motor function in stroke survivors. In future work, expanding the sophistication of either or both rt-fMRI and rt-fNIRS could hold the potential for further reducing the number of hours of training needed and/or the degree of recovery. ClinicalTrials.gov ID:NCT02856035.
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Dynamic Functional Network Connectivity Changes Associated with fMRI Neurofeedback of Right Premotor Cortex. Brain Sci 2021; 11:brainsci11050582. [PMID: 33946251 PMCID: PMC8147082 DOI: 10.3390/brainsci11050582] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 04/27/2021] [Accepted: 04/27/2021] [Indexed: 01/03/2023] Open
Abstract
Neurofeedback of real-time functional magnetic resonance imaging (rtfMRI) can enable people to self-regulate motor-related brain regions and lead to alteration of motor performance and functional connectivity (FC) underlying motor execution tasks. Numerous studies suggest that FCs dynamically fluctuate over time. However, little is known about the impact of neurofeedback training of the motor-related region on the dynamic characteristics of FC underlying motor execution tasks. This study aims to investigate the mechanism of self-regulation of the right premotor area (PMA) on the underlying dynamic functional network connectivity (DFNC) of motor execution (ME) tasks and reveal the relationship between DFNC, training effect, and motor performance. The results indicate that the experimental group spent less time on state 2, with overall weak connections, and more time on state 6, having strong positive connections between motor-related networks than the control group after the training. For the experimental group’s state 2, the mean dwell time after the training showed negative correlation with the tapping frequency and the amount of upregulation of PMA. The findings show that rtfMRI neurofeedback can change the temporal properties of DFNC, and the DFNC changes in state with overall weak connections were associated with the training effect and the improvement in motor performance.
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Soekadar SR, Kohl SH, Mihara M, von Lühmann A. Optical brain imaging and its application to neurofeedback. Neuroimage Clin 2021; 30:102577. [PMID: 33545580 PMCID: PMC7868728 DOI: 10.1016/j.nicl.2021.102577] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 12/30/2020] [Accepted: 01/15/2021] [Indexed: 12/30/2022]
Abstract
Besides passive recording of brain electric or magnetic activity, also non-ionizing electromagnetic or optical radiation can be used for real-time brain imaging. Here, changes in the radiation's absorption or scattering allow for continuous in vivo assessment of regional neurometabolic and neurovascular activity. Besides magnetic resonance imaging (MRI), over the last years, also functional near-infrared spectroscopy (fNIRS) was successfully established in real-time metabolic brain imaging. In contrast to MRI, fNIRS is portable and can be applied at bedside or in everyday life environments, e.g., to restore communication and movement. Here we provide a comprehensive overview of the history and state-of-the-art of real-time optical brain imaging with a special emphasis on its clinical use towards neurofeedback and brain-computer interface (BCI) applications. Besides pointing to the most critical challenges in clinical use, also novel approaches that combine real-time optical neuroimaging with other recording modalities (e.g. electro- or magnetoencephalography) are described, and their use in the context of neuroergonomics, neuroenhancement or neuroadaptive systems discussed.
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Affiliation(s)
- Surjo R Soekadar
- Clinical Neurotechnology Laboratory, Dept. of Psychiatry and Psychotherapy, Neuroscience Research Center, Campus Charité Mitte (CCM), Charité - University Medicine of Berlin, Berlin, Germany.
| | - Simon H Kohl
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany; Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty, RWTH Aachen University, Germany
| | - Masahito Mihara
- Department of Neurology, Kawasaki Medical School, Kurashiki-City, Okayama, Japan
| | - Alexander von Lühmann
- Machine Learning Department, Computer Science, Technische Universität Berlin, Berlin, Germany; Neurophotonics Center, Biomedical Engineering, Boston University, Boston, USA
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Upregulation of Supplementary Motor Area Activation with fMRI Neurofeedback during Motor Imagery. eNeuro 2021; 8:ENEURO.0377-18.2020. [PMID: 33376115 PMCID: PMC7877466 DOI: 10.1523/eneuro.0377-18.2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 12/02/2020] [Accepted: 12/07/2020] [Indexed: 11/21/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) neurofeedback (NF) is a promising tool to study the relationship between behavior and brain activity. It enables people to self-regulate their brain signal. Here, we applied fMRI NF to train healthy participants to increase activity in their supplementary motor area (SMA) during a motor imagery (MI) task of complex body movements while they received a continuous visual feedback signal. This signal represented the activity of participants’ localized SMA regions in the NF group and a prerecorded signal in the control group (sham feedback). In the NF group only, results showed a gradual increase in SMA-related activity across runs. This upregulation was largely restricted to the SMA, while other regions of the motor network showed no, or only marginal NF effects. In addition, we found behavioral changes, i.e., shorter reaction times in a Go/No-go task after the NF training only. These results suggest that NF can assist participants to develop greater control over a specifically targeted motor region involved in motor skill learning. The results contribute to a better understanding of the underlying mechanisms of SMA NF based on MI with a direct implication for rehabilitation of motor dysfunctions.
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Yang H, Hu Z, Imai F, Yang Y, Ogawa K. Effects of neurofeedback on the activities of motor-related areas by using motor execution and imagery. Neurosci Lett 2021; 746:135653. [PMID: 33482311 DOI: 10.1016/j.neulet.2021.135653] [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: 08/08/2020] [Revised: 10/21/2020] [Accepted: 01/08/2021] [Indexed: 12/23/2022]
Abstract
Previous studies have reported that real-time functional magnetic resonance imaging (fMRI) neurofeedback using motor imagery can modulate the activity of several motor-related areas. However, the differences in these modulatory effects on distinct motor-related target regions using the same experimental protocol remain unelucidated. This study aimed to compare neurofeedback effects on the primary motor area (M1) and the ventral premotor cortex (PMv). Of the included participants, 15 received blood oxygenation level-dependent (BOLD) signals from their left M1, and the other 15 received signals from their left PMv. Both groups were instructed to try to increase the neurofeedback score (NF-Score), which reflected the averaged activation level of the target region, by executing or imagining a right-hand clenching movement. The result revealed that during imagery condition, the left M1 was deactivated in the PMv-group but not in the M1-group, whereas the left PMv was activated in the PMv-group but not in the M1-group. Our finding indicates that neurofeedback from distinct motor-related regions has different effects on brain activity regulation.
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Affiliation(s)
- Huixiang Yang
- Department of Psychology, Hokkaido University, Sapporo, Japan
| | - Zhengfei Hu
- Department of Psychology, Hokkaido University, Sapporo, Japan
| | - Fumihito Imai
- Department of Psychology, Hokkaido University, Sapporo, Japan
| | - Yuxiang Yang
- Department of Psychology, Hokkaido University, Sapporo, Japan
| | - Kenji Ogawa
- Department of Psychology, Hokkaido University, Sapporo, Japan.
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12
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Haugg A, Sladky R, Skouras S, McDonald A, Craddock C, Kirschner M, Herdener M, Koush Y, Papoutsi M, Keynan JN, Hendler T, Cohen Kadosh K, Zich C, MacInnes J, Adcock RA, Dickerson K, Chen N, Young K, Bodurka J, Yao S, Becker B, Auer T, Schweizer R, Pamplona G, Emmert K, Haller S, Van De Ville D, Blefari M, Kim D, Lee J, Marins T, Fukuda M, Sorger B, Kamp T, Liew S, Veit R, Spetter M, Weiskopf N, Scharnowski F. Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity? Hum Brain Mapp 2020; 41:3839-3854. [PMID: 32729652 PMCID: PMC7469782 DOI: 10.1002/hbm.25089] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/18/2020] [Accepted: 05/26/2020] [Indexed: 12/31/2022] Open
Abstract
Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.
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Affiliation(s)
- Amelie Haugg
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Ronald Sladky
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Stavros Skouras
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
| | - Amalia McDonald
- Department of PsychologyUniversity of VirginiaCharlottesvilleVirginia
| | - Cameron Craddock
- Department of Diagnostic MedicineThe University of Texas at Austin Dell Medical SchoolAustinTexas
| | - Matthias Kirschner
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- McConnell Brain Imaging CentreMontréal Neurological Institute, McGill UniversityMontrealCanada
| | - Marcus Herdener
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
| | - Yury Koush
- Magnetic Resonance Research Center, Department of Radiology & Biomedical ImagingYale UniversityNew HavenConnecticut
| | - Marina Papoutsi
- UCL Huntington's Disease CentreInstitute of Neurology, University College LondonLondonEngland
| | - Jackob N. Keynan
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | - Talma Hendler
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | | | - Catharina Zich
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordEngland
| | - Jeff MacInnes
- Institute for Learning and Brain SciencesUniversity of WashingtonSeattleWashington
| | - R. Alison Adcock
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Kathryn Dickerson
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Nan‐Kuei Chen
- Department of Biomedical EngineeringUniversity of ArizonaTucsonArizona
| | - Kymberly Young
- Department of Psychiatry, School of MedicineUniversity of PittsburghPittsburghPennsylvania
| | | | - Shuxia Yao
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Benjamin Becker
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordEngland
| | - Renate Schweizer
- Functional Imaging LaboratoryGerman Primate CenterGöttingenGermany
| | - Gustavo Pamplona
- Hôpital and Ophtalmique Jules GoninUniversity of LausanneLausanneSwitzerland
| | - Kirsten Emmert
- Department of NeurologyUniversity Medical Center Schleswig‐Holstein, Kiel UniversityKielGermany
| | - Sven Haller
- Radiology‐Department of Surgical SciencesUppsala UniversityUppsalaSweden
| | - Dimitri Van De Ville
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
- Department of Radiology and Medical Informatics, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
| | - Maria‐Laura Blefari
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
| | - Dong‐Youl Kim
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Jong‐Hwan Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Theo Marins
- D'Or Institute for Research and Education (IDOR)Rio de JaneiroBrazil
| | - Megumi Fukuda
- School of Fundamental Science and EngineeringWaseda UniversityTokyoJapan
| | - Bettina Sorger
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Tabea Kamp
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Sook‐Lei Liew
- Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Ralf Veit
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center MunichUniversity of TübingenTübingenGermany
| | - Maartje Spetter
- School of PsychologyUniversity of BirminghamBirminghamEngland
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Frank Scharnowski
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
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13
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Heunis S, Lamerichs R, Zinger S, Caballero‐Gaudes C, Jansen JFA, Aldenkamp B, Breeuwer M. Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review. Hum Brain Mapp 2020; 41:3439-3467. [PMID: 32333624 PMCID: PMC7375116 DOI: 10.1002/hbm.25010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/13/2020] [Accepted: 04/03/2020] [Indexed: 01/31/2023] Open
Abstract
Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.
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Affiliation(s)
- Stephan Heunis
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | - Rolf Lamerichs
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- Philips ResearchEindhovenThe Netherlands
| | - Svitlana Zinger
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | | | - Jacobus F. A. Jansen
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of RadiologyMaastricht University Medical CentreMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
| | - Bert Aldenkamp
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and NeuropsychologyGhent University HospitalGhentBelgium
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Marcel Breeuwer
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Philips HealthcareBestThe Netherlands
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14
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Pichiorri F, Mattia D. Brain-computer interfaces in neurologic rehabilitation practice. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:101-116. [PMID: 32164846 DOI: 10.1016/b978-0-444-63934-9.00009-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The brain-computer interfaces (BCIs) for neurologic rehabilitation are based on the assumption that by retraining the brain to specific activities, an ultimate improvement of function can be expected. In this chapter, we review the present status, key determinants, and future directions of the clinical use of BCI in neurorehabilitation. The recent advancements in noninvasive BCIs as a therapeutic tool to promote functional motor recovery by inducing neuroplasticity are described, focusing on stroke as it represents the major cause of long-term motor disability. The relevance of recent findings on BCI use in spinal cord injury beyond the control of neuroprosthetic devices to restore motor function is briefly discussed. In a dedicated section, we examine the potential role of BCI technology in the domain of cognitive function recovery by instantiating BCIs in the long history of neurofeedback and some emerging BCI paradigms to address cognitive rehabilitation are highlighted. Despite the knowledge acquired over the last decade and the growing number of studies providing evidence for clinical efficacy of BCI in motor rehabilitation, an exhaustive deployment of this technology in clinical practice is still on its way. The pipeline to translate BCI to clinical practice in neurorehabilitation is the subject of this chapter.
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Affiliation(s)
- Floriana Pichiorri
- Neuroelectrical Imaging and Brain Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - Donatella Mattia
- Neuroelectrical Imaging and Brain Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy.
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15
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Kober SE, Grössinger D, Wood G. Effects of Motor Imagery and Visual Neurofeedback on Activation in the Swallowing Network: A Real-Time fMRI Study. Dysphagia 2019; 34:879-895. [PMID: 30771088 PMCID: PMC6825652 DOI: 10.1007/s00455-019-09985-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/25/2019] [Indexed: 12/01/2022]
Abstract
Motor imagery of movements is used as mental strategy in neurofeedback applications to gain voluntary control over activity in motor areas of the brain. In the present functional magnetic resonance imaging (fMRI) study, we first addressed the question whether motor imagery and execution of swallowing activate comparable brain areas, which has been already proven for hand and foot movements. Prior near-infrared spectroscopy (NIRS) studies provide evidence that this is the case in the outer layer of the cortex. With the present fMRI study, we want to expand these prior NIRS findings to the whole brain. Second, we used motor imagery of swallowing as mental strategy during visual neurofeedback to investigate whether one can learn to modulate voluntarily activity in brain regions, which are associated with active swallowing, using real-time fMRI. Eleven healthy adults performed one offline session, in which they executed swallowing movements and imagined swallowing on command during fMRI scanning. Based on this functional localizer task, we identified brain areas active during both tasks and defined individually regions for feedback. During the second session, participants performed two real-time fMRI neurofeedback runs (each run comprised 10 motor imagery trials), in which they should increase voluntarily the activity in the left precentral gyrus by means of motor imagery of swallowing while receiving visual feedback (the visual feedback depicted one's own fMRI signal changes in real-time). Motor execution and imagery of swallowing activated a comparable network of brain areas including the bilateral pre- and postcentral gyrus, inferior frontal gyrus, basal ganglia, insula, SMA, and the cerebellum compared to a resting condition. During neurofeedback training, participants were able to increase the activity in the feedback region (left lateral precentral gyrus) but also in other brain regions, which are generally active during swallowing, compared to the motor imagery offline task. Our results indicate that motor imagery of swallowing is an adequate mental strategy to activate the swallowing network of the whole brain, which might be useful for future treatments of swallowing disorders.
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Affiliation(s)
- Silvia Erika Kober
- Institute of Psychology, University of Graz, Universitaetsplatz 2/III, 8010 Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Doris Grössinger
- Institute of Psychology, University of Graz, Universitaetsplatz 2/III, 8010 Graz, Austria
| | - Guilherme Wood
- Institute of Psychology, University of Graz, Universitaetsplatz 2/III, 8010 Graz, Austria
- BioTechMed-Graz, Graz, Austria
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16
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Uehara S, Mizuguchi N, Hirose S, Yamamoto S, Naito E. Involvement of human left frontoparietal cortices in neural processes associated with task-switching between two sequences of skilled finger movements. Brain Res 2019; 1722:146365. [PMID: 31400310 DOI: 10.1016/j.brainres.2019.146365] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 07/25/2019] [Accepted: 08/06/2019] [Indexed: 11/30/2022]
Abstract
In this study, we demonstrate the involvement of left frontoparietal cortices in neural processes for task-switching between skilled movements. Functional magnetic resonance imaging was conducted while thirty-two right-handed healthy participants performed two sequential finger-movement tasks with their left hands. One group (n = 16) trained these tasks through random-practice (tasks were either switched or repeated trial by trial) on one day and blocked-practice (successive intensive practice of each task) on the next day, while the remaining participants practiced in the reverse order. On the first day, performance of both tasks improved in all participants, suggesting that the two skilful tasks can be learned in both practice schedules. However, during the random-practice, the performance in the switched trials initially deteriorated and gradually approached to that in the repeated trials as the practice proceeded. The left (mainly inferior) frontoparietal cortices showed greater preparatory activity for the switched trials compared with the repeated trials in a left-hemispheric dominant manner, and the left intraparietal activity decreased as the performance of the switched trials improved. The results indicate that neural processes for task-switching are associated with the greater preparatory activity in the left inferior frontoparietal cortices, and the efficient switching may proceed concomitantly with the left intraparietal activity reduction.
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Affiliation(s)
- Shintaro Uehara
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka 565-0871, Japan; The Japan Society for the Promotion of Science, Tokyo 102-0083, Japan
| | - Nobuaki Mizuguchi
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka 565-0871, Japan; Graduate School of Medicine and Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan
| | - Satoshi Hirose
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka 565-0871, Japan; Graduate School of Medicine and Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan
| | - Shinji Yamamoto
- School of Health and Sport Sciences, Osaka University of Health and Sport Sciences, Osaka 590-0496, Japan
| | - Eiichi Naito
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka 565-0871, Japan; Graduate School of Medicine and Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan.
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17
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Kim HC, Tegethoff M, Meinlschmidt G, Stalujanis E, Belardi A, Jo S, Lee J, Kim DY, Yoo SS, Lee JH. Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback. Neuroimage 2019; 195:409-432. [DOI: 10.1016/j.neuroimage.2019.03.066] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 03/05/2019] [Accepted: 03/27/2019] [Indexed: 12/13/2022] Open
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18
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Marins T, Rodrigues EC, Bortolini T, Melo B, Moll J, Tovar-Moll F. Structural and functional connectivity changes in response to short-term neurofeedback training with motor imagery. Neuroimage 2019; 194:283-290. [PMID: 30898654 DOI: 10.1016/j.neuroimage.2019.03.027] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 03/10/2019] [Accepted: 03/12/2019] [Indexed: 12/31/2022] Open
Abstract
Recent findings have been challenging current understanding of how fast the human brain change its structural and functional connections in response to training. One powerful way to deepen the inner workings of human brain plasticity is using neurofeedback (NFB) by fMRI, a technique that allows self-induced brain plasticity by means of modulating brain activity in real time. In the present randomized, double-blind and sham-controlled study, we use NFB to train healthy individuals to reinforce brain patterns related to motor execution while performing a motor imagery task, with no overt movement. After 1 h of NFB training, participants displayed increased fractional anisotropy (FA) in the sensorimotor segment of corpus callosum and increased functional connectivity of the sensorimotor resting state network. Increased functional connectivity was also observed in the default mode network. These results were not observed in the control group, which was trained with sham feedback. To our knowledge, this is the first demonstration of white matter FA changes following a very short training schedule (<1 h). Our results suggest that NFB by fMRI can be an interesting tool to explore dynamic aspects of brain plasticity and open new venues for investigating brain plasticity in healthy individuals and in neurological conditions.
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Affiliation(s)
- T Marins
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Post-Graduate Program in Morphological Sciences, Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - E C Rodrigues
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Augusto Motta University (Unisuam), Rio de Janeiro, RJ, Brazil
| | - T Bortolini
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - Bruno Melo
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - J Moll
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Post-Graduate Program in Morphological Sciences, Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - F Tovar-Moll
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Post-Graduate Program in Morphological Sciences, Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
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19
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Sorger B, Scharnowski F, Linden DEJ, Hampson M, Young KD. Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies. Neuroimage 2019; 186:256-265. [PMID: 30423429 PMCID: PMC6338498 DOI: 10.1016/j.neuroimage.2018.11.004] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 10/31/2018] [Accepted: 11/05/2018] [Indexed: 12/31/2022] Open
Abstract
fMRI Neurofeedback research employs many different control conditions. Currently, there is no consensus as to which control condition is best, and the answer depends on what aspects of the neurofeedback-training design one is trying to control for. These aspects can range from determining whether participants can learn to control brain activity via neurofeedback to determining whether there are clinically significant effects of the neurofeedback intervention. Lack of consensus over criteria for control conditions has hampered the design and interpretation of studies employing neurofeedback protocols. This paper presents an overview of the most commonly employed control conditions currently used in neurofeedback studies and discusses their advantages and disadvantages. Control conditions covered include no control, treatment-as-usual, bidirectional-regulation control, feedback of an alternative brain signal, sham feedback, and mental-rehearsal control. We conclude that the selection of the control condition(s) should be determined by the specific research goal of the study and best procedures that effectively control for relevant confounding factors.
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Affiliation(s)
- Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Zürich, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Zürich, Switzerland
| | - David E J Linden
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Psychiatry and the Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Kymberly D Young
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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20
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Lee D, Jang C, Park HJ. Neurofeedback learning for mental practice rather than repetitive practice improves neural pattern consistency and functional network efficiency in the subsequent mental motor execution. Neuroimage 2018; 188:680-693. [PMID: 30599191 DOI: 10.1016/j.neuroimage.2018.12.055] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 12/20/2018] [Accepted: 12/27/2018] [Indexed: 10/27/2022] Open
Abstract
During brain modulation, repeated mental practice may not always result in efficient learning. Particularly, the effectiveness of mental motor practice depends on how well one induces neural activity in a desired state consistently across mental trials, which calls for feedbacks to adjust one's performance. We hypothesized that even a brief experience of neurofeedback learning enhances trial-by-trial neural pattern consistency during subsequent mental motor execution and that this experience would change recruitment of functional connectivity in the motor imagery and default mode networks. To test this hypothesis, we conducted an experiment with two sessions of mental motor practice before and after a neurofeedback training session, in which participants conducted four types of first-person mental motor execution tasks (walking forward, turning left, turning right, and touching a tree). During the neurofeedback training session, in which participants conducted a virtual navigation game, 10 experimental participants received real-time fMRI neuro-feedbacks, while 10 control participants simply repeated the same mental task according to given cues without feedbacks. The experimental group showed significantly higher effects of neuro-feedback training on trial-by-trial consistencies and classification accuracies of activated neural patterns than the control group. Task-performing global node strength and network efficiency were increased in the motor imagery network but decreased in the default mode network only in the experimental group. These results demonstrate that even a brief experience of feedback learning is more effective than simple practice repetitions without evaluation, which was reflected in increased neural pattern consistency and task-dependent functional connectivity during a mental motor execution task.
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Affiliation(s)
- Dongha Lee
- Faculty of Psychology and Education Sciences, University of Coimbra, Coimbra, Portugal; Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Changwon Jang
- BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.
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21
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Auer T, Dewiputri WI, Frahm J, Schweizer R. Higher-order Brain Areas Associated with Real-time Functional MRI Neurofeedback Training of the Somato-motor Cortex. Neuroscience 2018; 378:22-33. [PMID: 27133575 PMCID: PMC5953411 DOI: 10.1016/j.neuroscience.2016.04.034] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 03/09/2016] [Accepted: 04/22/2016] [Indexed: 01/22/2023]
Abstract
Neurofeedback (NFB) allows subjects to learn self-regulation of neuronal brain activation based on information about the ongoing activation. The implementation of real-time functional magnetic resonance imaging (rt-fMRI) for NFB training now facilitates the investigation into underlying processes. Our study involved 16 control and 16 training right-handed subjects, the latter performing an extensive rt-fMRI NFB training using motor imagery. A previous analysis focused on the targeted primary somato-motor cortex (SMC). The present study extends the analysis to the supplementary motor area (SMA), the next higher brain area within the hierarchy of the motor system. We also examined transfer-related functional connectivity using a whole-volume psycho-physiological interaction (PPI) analysis to reveal brain areas associated with learning. The ROI analysis of the pre- and post-training fMRI data for motor imagery without NFB (transfer) resulted in a significant training-specific increase in the SMA. It could also be shown that the contralateral SMA exhibited a larger increase than the ipsilateral SMA in the training and the transfer runs, and that the right-hand training elicited a larger increase in the transfer runs than the left-hand training. The PPI analysis revealed a training-specific increase in transfer-related functional connectivity between the left SMA and frontal areas as well as the anterior midcingulate cortex (aMCC) for right- and left-hand trainings. Moreover, the transfer success was related with training-specific increase in functional connectivity between the left SMA and the target area SMC. Our study demonstrates that NFB training increases functional connectivity with non-targeted brain areas. These are associated with the training strategy (i.e., SMA) as well as with learning the NFB skill (i.e., aMCC and frontal areas). This detailed description of both the system to be trained and the areas involved in learning can provide valuable information for further optimization of NFB trainings.
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Affiliation(s)
- Tibor Auer
- Biomedizinische NMR Forschungs GmbH at the Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany; MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.
| | - Wan Ilma Dewiputri
- Biomedizinische NMR Forschungs GmbH at the Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany; Department of Neuroscience, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia; Pusat PERMATApintar Negara, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Malaysia
| | - Jens Frahm
- Biomedizinische NMR Forschungs GmbH at the Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany
| | - Renate Schweizer
- Biomedizinische NMR Forschungs GmbH at the Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany
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Nam HS, Koh S, Beom J, Kim YJ, Park JW, Koh ES, Chung SG, Kim S. Recovery of Proprioception in the Upper Extremity by Robotic Mirror Therapy: a Clinical Pilot Study for Proof of Concept. J Korean Med Sci 2017; 32:1568-1575. [PMID: 28875598 PMCID: PMC5592168 DOI: 10.3346/jkms.2017.32.10.1568] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Accepted: 07/01/2017] [Indexed: 11/20/2022] Open
Abstract
A novel robotic mirror therapy system was recently developed to provide proprioceptive stimulus to the hemiplegic arm during a mirror therapy. Validation of the robotic mirror therapy system was performed to confirm its synchronicity prior to the clinical study. The mean error angle range between the intact arm and the robot was 1.97 to 4.59 degrees. A 56-year-old male who had right middle cerebral artery infarction 11 months ago received the robotic mirror therapy for ten 30-minute sessions during 2 weeks. Clinical evaluation and functional magnetic resonance imaging (fMRI) studies were performed before and after the intervention. At the follow-up evaluation, the thumb finding test score improved from 2 to 1 for eye level and from 3 to 1 for overhead level. The Albert's test score on the left side improved from 6 to 11. Improvements were sustained at 2-month follow-up. The fMRI during the passive motion revealed a considerable increase in brain activity at the lower part of the right superior parietal lobule, suggesting the possibility of proprioception enhancement. The robotic mirror therapy system may serve as a useful treatment method for patients with supratentorial stroke to facilitate recovery of proprioceptive deficit and hemineglect.
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Affiliation(s)
- Hyung Seok Nam
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Sukgyu Koh
- Interdisciplinary Program for Bioengineering, Seoul National University Graduate School, Seoul, Korea
| | - Jaewon Beom
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
- Department of Physical Medicine and Rehabilitation, Chung-Ang University College of Medicine, Seoul, Korea
| | - Yoon Jae Kim
- Interdisciplinary Program for Bioengineering, Seoul National University Graduate School, Seoul, Korea
| | - Jang Woo Park
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Eun Sil Koh
- Department of Rehabilitation Medicine, National Medical Center, Seoul, Korea
| | - Sun Gun Chung
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Sungwan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
- Institute of Medical and Biological Engineering, Seoul National University, Seoul, Korea.
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23
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Wang T, Mantini D, Gillebert CR. The potential of real-time fMRI neurofeedback for stroke rehabilitation: A systematic review. Cortex 2017; 107:148-165. [PMID: 28992948 PMCID: PMC6182108 DOI: 10.1016/j.cortex.2017.09.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 08/02/2017] [Accepted: 09/07/2017] [Indexed: 12/17/2022]
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback aids the modulation of neural functions by training self-regulation of brain activity through operant conditioning. This technique has been applied to treat several neurodevelopmental and neuropsychiatric disorders, but its effectiveness for stroke rehabilitation has not been examined yet. Here, we systematically review the effectiveness of rt-fMRI neurofeedback training in modulating motor and cognitive processes that are often impaired after stroke. Based on predefined search criteria, we selected and examined 33 rt-fMRI neurofeedback studies, including 651 healthy individuals and 15 stroke patients in total. The results of our systematic review suggest that rt-fMRI neurofeedback training can lead to a learned modulation of brain signals, with associated changes at both the neural and the behavioural level. However, more research is needed to establish how its use can be optimized in the context of stroke rehabilitation.
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Affiliation(s)
- Tianlu Wang
- Department of Brain & Cognition, University of Leuven, Leuven, Belgium
| | - Dante Mantini
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom; Research Center for Movement Control and Neuroplasticity, University of Leuven, Leuven, Belgium; Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Celine R Gillebert
- Department of Brain & Cognition, University of Leuven, Leuven, Belgium; Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.
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24
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Bassett DS, Khambhati AN. A network engineering perspective on probing and perturbing cognition with neurofeedback. Ann N Y Acad Sci 2017; 1396:126-143. [PMID: 28445589 PMCID: PMC5446287 DOI: 10.1111/nyas.13338] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition.
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Affiliation(s)
- Danielle S. Bassett
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
- Department of Electrical and Systems EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
| | - Ankit N. Khambhati
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvania
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25
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Abstract
PURPOSE OF REVIEW Recent developments in functional magnetic resonance imaging (fMRI) have catalyzed a new field of translational neuroscience. Using fMRI to monitor the aspects of task-related changes in neural activation or brain connectivity, investigators can offer feedback of simple or complex neural signals/patterns back to the participant on a quasireal-time basis [real-time-fMRI-based neurofeedback (rt-fMRI-NF)]. Here, we introduce some background methodology of the new developments in this field and give a perspective on how they may be used in neurorehabilitation in the future. RECENT FINDINGS The development of rt-fMRI-NF has been used to promote self-regulation of activity in several brain regions and networks. In addition, and unlike other noninvasive techniques, rt-fMRI-NF can access specific subcortical regions and in principle any region that can be monitored using fMRI including the cerebellum, brainstem and spinal cord. In Parkinson's disease and stroke, rt-fMRI-NF has been demonstrated to alter neural activity after the self-regulation training was completed and to modify specific behaviours. SUMMARY Future exploitation of rt-fMRI-NF could be used to induce neuroplasticity in brain networks that are involved in certain neurological conditions. However, currently, the use of rt-fMRI-NF in randomized, controlled clinical trials is in its infancy.
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Affiliation(s)
- David E.J. Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, and Cardiff University Brain Imaging Centre, Cardiff
| | - Duncan L. Turner
- Neurorehabilitation Unit, School of Health, Sport and Bioscience, University of East London, London, UK
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