1
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Yan ZN, Liu PR, Zhou H, Zhang JY, Liu SX, Xie Y, Wang HL, Yu JB, Zhou Y, Ni CM, Huang L, Ye ZW. Brain-computer Interaction in the Smart Era. Curr Med Sci 2024:10.1007/s11596-024-2927-6. [PMID: 39347924 DOI: 10.1007/s11596-024-2927-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 08/18/2024] [Indexed: 10/01/2024]
Abstract
The brain-computer interface (BCI) system serves as a critical link between external output devices and the human brain. A monitored object's mental state, sensory cognition, and even higher cognition are reflected in its electroencephalography (EEG) signal. Nevertheless, unprocessed EEG signals are frequently contaminated with a variety of artifacts, rendering the analysis and elimination of impurities from the collected EEG data exceedingly challenging, not to mention the manual adjustment thereof. Over the last few decades, the rapid advancement of artificial intelligence (AI) technology has contributed to the development of BCI technology. Algorithms derived from AI and machine learning have significantly enhanced the ability to analyze and process EEG electrical signals, thereby expanding the range of potential interactions between the human brain and computers. As a result, the present BCI technology with the help of AI can assist physicians in gaining a more comprehensive understanding of their patients' physical and psychological status, thereby contributing to improvements in their health and quality of life.
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Affiliation(s)
- Zi-Neng Yan
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Peng-Ran Liu
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Hong Zhou
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Jia-Yao Zhang
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Song-Xiang Liu
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yi Xie
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Hong-Lin Wang
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Jin-Bo Yu
- Wuhan Neuracom Technology Development Co., Ltd, Wuhan, 430200, China
| | - Yu Zhou
- Wuhan Neuracom Technology Development Co., Ltd, Wuhan, 430200, China
| | - Chang-Mao Ni
- Wuhan Neuracom Technology Development Co., Ltd, Wuhan, 430200, China
| | - Li Huang
- Wuhan Neuracom Technology Development Co., Ltd, Wuhan, 430200, China.
| | - Zhe-Wei Ye
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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2
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Caria A, Grecucci A. Neuroanatomical predictors of real‐time
fMRI
‐based anterior insula regulation. A supervised machine learning study. Psychophysiology 2022; 60:e14237. [PMID: 36523140 DOI: 10.1111/psyp.14237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/18/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022]
Abstract
Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including fronto-occipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix significantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings highlighted specific neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms.
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Affiliation(s)
- Andrea Caria
- Department of Psychology and Cognitive Science University of Trento Rovereto Italy
| | - Alessandro Grecucci
- Department of Psychology and Cognitive Science University of Trento Rovereto Italy
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3
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Collin SHP, van den Broek PLC, van Mourik T, Desain P, Doeller CF. Inducing a mental context for associative memory formation with real-time fMRI neurofeedback. Sci Rep 2022; 12:21226. [PMID: 36481793 PMCID: PMC9731952 DOI: 10.1038/s41598-022-25799-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
Memory, one of the hallmarks of human cognition, can be modified when humans voluntarily modulate neural population activity using neurofeedback. However, it is currently unknown whether neurofeedback can influence the integration of memories, and whether memory is facilitated or impaired after such neural perturbation. In this study, participants memorized objects while we provided them with abstract neurofeedback based on their brain activity patterns in the ventral visual stream. This neurofeedback created an implicit face or house context in the brain while memorizing the objects. The results revealed that participants created associations between each memorized object and its implicit context solely due to the neurofeedback manipulation. Our findings shed light onto how memory formation can be influenced by synthetic memory tags with neurofeedback and advance our understanding of mnemonic processing.
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Affiliation(s)
- Silvy H. P. Collin
- grid.12295.3d0000 0001 0943 3265Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands
| | - Philip L. C. van den Broek
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Tim van Mourik
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Peter Desain
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Christian F. Doeller
- grid.419524.f0000 0001 0041 5028Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany ,grid.5947.f0000 0001 1516 2393Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer’s Disease, Norwegian University of Science and Technology, Trondheim, Norway ,grid.9647.c0000 0004 7669 9786Institute of Psychology-Wilhelm Wundt, Leipzig University, Leipzig, Germany
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4
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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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5
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Peng Y, Wang Z, Wong CM, Nan W, Rosa A, Xu P, Wan F, Hu Y. Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface. J Neural Eng 2020; 17:045006. [DOI: 10.1088/1741-2552/ab933e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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6
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Shibata K, Lisi G, Cortese A, Watanabe T, Sasaki Y, Kawato M. Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback. Neuroimage 2019; 188:539-556. [PMID: 30572110 PMCID: PMC6431555 DOI: 10.1016/j.neuroimage.2018.12.022] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 12/07/2018] [Accepted: 12/11/2018] [Indexed: 11/19/2022] Open
Abstract
Real-time functional magnetic resonance imaging (fMRI) neurofeedback is an experimental framework in which fMRI signals are presented to participants in a real-time manner to change their behaviors. Changes in behaviors after real-time fMRI neurofeedback are postulated to be caused by neural plasticity driven by the induction of specific targeted activities at the neuronal level (targeted neural plasticity model). However, some research groups argued that behavioral changes in conventional real-time fMRI neurofeedback studies are explained by alternative accounts, including the placebo effect and physiological artifacts. Recently, decoded neurofeedback (DecNef) has been developed as a result of adapting new technological advancements, including implicit neurofeedback and fMRI multivariate analyses. DecNef provides strong evidence for the targeted neural plasticity model while refuting the abovementioned alternative accounts. In this review, we first discuss how DecNef refutes the alternative accounts. Second, we propose a model that shows how targeted neural plasticity occurs at the neuronal level during DecNef training. Finally, we discuss computational and empirical evidence that supports the model. Clarification of the neural mechanisms of DecNef would lead to the development of more advanced fMRI neurofeedback methods that may serve as powerful tools for both basic and clinical research.
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Affiliation(s)
- Kazuhisa Shibata
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Nagoya, 464-0814, Japan
| | - Giuseppe Lisi
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
| | - Aurelio Cortese
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
| | - Takeo Watanabe
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer Street, Providence, RI, 02912, USA
| | - Yuka Sasaki
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer Street, Providence, RI, 02912, USA
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan.
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7
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Data-driven tensor independent component analysis for model-based connectivity neurofeedback. Neuroimage 2019; 184:214-226. [DOI: 10.1016/j.neuroimage.2018.08.067] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/21/2018] [Accepted: 08/28/2018] [Indexed: 12/26/2022] Open
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8
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Abstract
Much remains to be understood about visual system malfunction following injury. The resulting deficits range from dense, visual field scotomas to mild dysfunction of visual perception. Despite the predictive value of anatomical localization studies, much patient-to-patient variability remains regarding (a) perceptual abilities following injury and (b) the capacity of individual patients for visual rehabilitation. Visual field perimetry is used to characterize the visual field deficits that result from visual system injury. However, standard perimetry mapping does not always precisely correspond to underlying anatomical or functional deficits. Functional magnetic resonance imaging can be used to probe the function of surviving visual circuits, allowing us to classify better how the pattern of injury relates to residual visual perception. Identifying pathways that are potentially modifiable by training may guide the development of improved strategies for visual rehabilitation. This review discusses primary visual cortex lesions, which cause dense contralateral scotomas.
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Affiliation(s)
- Stelios M Smirnakis
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts 02115.,Department of Neurology, Jamaica Plain Campus, Veterans Administration Boston Healthcare System, Boston, Massachusetts 02130.,Harvard Medical School, Boston, Massachusetts 02115;
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9
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Vanderwal T, Eilbott J, Finn ES, Craddock RC, Turnbull A, Castellanos FX. Individual differences in functional connectivity during naturalistic viewing conditions. Neuroimage 2017. [PMID: 28625875 DOI: 10.1016/j.neuroimage.2017.06.027] [Citation(s) in RCA: 122] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Naturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task-free rest, and have been used to study both functional connectivity and stimulus-evoked BOLD-signal changes. These task-based hemodynamic changes are synchronized across subjects and involve large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in BOLD-signal based functional connectivity (FC) across 2 distinct movie conditions and eyes-open rest (n=31 healthy adults, 2 scan sessions each). We found that movies have higher within- and between-subject correlations in cluster-wise FC relative to rest. The anatomical distribution of inter-individual variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test-retest matching algorithm that identifies individual subjects from within a group based on FC patterns, quantifying the accuracy of the algorithm across the three conditions. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 61% and 100%. Overall, pairings involving movies outperformed rest, and the social, faster-paced movie attained 100% accuracy. When the parcellation resolution, scan duration, and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are unique at the individual level.
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Affiliation(s)
- Tamara Vanderwal
- Yale University, 230 South Frontage Road, New Haven, CT 06520, USA.
| | - Jeffrey Eilbott
- Yale University, 230 South Frontage Road, New Haven, CT 06520, USA
| | - Emily S Finn
- Yale University, 230 South Frontage Road, New Haven, CT 06520, USA
| | - R Cameron Craddock
- Child Mind Institute, 445 Park Avenue, New York, NY 10022, USA; Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA
| | - Adam Turnbull
- Yale University, 230 South Frontage Road, New Haven, CT 06520, USA
| | - F Xavier Castellanos
- Child Study Center at New York University Langone Medical Center, 1 Park Avenue, New York, NY 10016, USA
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10
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11
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Mateo S, Di Rienzo F, Bergeron V, Guillot A, Collet C, Rode G. Motor imagery reinforces brain compensation of reach-to-grasp movement after cervical spinal cord injury. Front Behav Neurosci 2015; 9:234. [PMID: 26441568 PMCID: PMC4566051 DOI: 10.3389/fnbeh.2015.00234] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 08/19/2015] [Indexed: 01/19/2023] Open
Abstract
Individuals with cervical spinal cord injury (SCI) that causes tetraplegia are challenged with dramatic sensorimotor deficits. However, certain rehabilitation techniques may significantly enhance their autonomy by restoring reach-to-grasp movements. Among others, evidence of motor imagery (MI) benefits for neurological rehabilitation of upper limb movements is growing. This literature review addresses MI effectiveness during reach-to-grasp rehabilitation after tetraplegia. Among articles from MEDLINE published between 1966 and 2015, we selected ten studies including 34 participants with C4 to C7 tetraplegia and 22 healthy controls published during the last 15 years. We found that MI of possible non-paralyzed movements improved reach-to-grasp performance by: (i) increasing both tenodesis grasp capabilities and muscle strength; (ii) decreasing movement time (MT), and trajectory variability; and (iii) reducing the abnormally increased brain activity. MI can also strengthen motor commands by potentiating recruitment and synchronization of motoneurons, which leads to improved recovery. These improvements reflect brain adaptations induced by MI. Furthermore, MI can be used to control brain-computer interfaces (BCI) that successfully restore grasp capabilities. These results highlight the growing interest for MI and its potential to recover functional grasping in individuals with tetraplegia, and motivate the need for further studies to substantiate it.
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Affiliation(s)
- Sébastien Mateo
- ImpAct Team, Lyon Neuroscience Research Center, Université Lyon 1, Université de Lyon, INSERM U1028, CNRS UMR5292 Lyon, France ; Hospices Civils de Lyon, Hôpital Henry Gabrielle, Plateforme Mouvement et Handicap Lyon, France ; Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France ; Ecole Normale Supérieure de Lyon, CNRS UMR5672 Lyon, France
| | - Franck Di Rienzo
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France
| | - Vance Bergeron
- Ecole Normale Supérieure de Lyon, CNRS UMR5672 Lyon, France
| | - Aymeric Guillot
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France ; Institut Universitaire de France Paris, France
| | - Christian Collet
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France
| | - Gilles Rode
- ImpAct Team, Lyon Neuroscience Research Center, Université Lyon 1, Université de Lyon, INSERM U1028, CNRS UMR5292 Lyon, France ; Hospices Civils de Lyon, Hôpital Henry Gabrielle, Plateforme Mouvement et Handicap Lyon, France
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12
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Cisler JM, Bush K, James GA, Smitherman S, Kilts CD. Decoding the Traumatic Memory among Women with PTSD: Implications for Neurocircuitry Models of PTSD and Real-Time fMRI Neurofeedback. PLoS One 2015; 10:e0134717. [PMID: 26241958 PMCID: PMC4524593 DOI: 10.1371/journal.pone.0134717] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Accepted: 07/03/2015] [Indexed: 11/30/2022] Open
Abstract
Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD.
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Affiliation(s)
- Josh M. Cisler
- Brain Imaging Research Center, Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Keith Bush
- Department of Computer Science, University of Arkansas at Little Rock, Little Rock, Arkansas, United States of America
| | - G. Andrew James
- Brain Imaging Research Center, Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Sonet Smitherman
- Brain Imaging Research Center, Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Clinton D. Kilts
- Brain Imaging Research Center, Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
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Ros T, J Baars B, Lanius RA, Vuilleumier P. Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework. Front Hum Neurosci 2014; 8:1008. [PMID: 25566028 PMCID: PMC4270171 DOI: 10.3389/fnhum.2014.01008] [Citation(s) in RCA: 109] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 11/26/2014] [Indexed: 12/03/2022] Open
Abstract
Neurofeedback (NFB) is emerging as a promising technique that enables self-regulation of ongoing brain oscillations. However, despite a rise in empirical evidence attesting to its clinical benefits, a solid theoretical basis is still lacking on the manner in which NFB is able to achieve these outcomes. The present work attempts to bring together various concepts from neurobiology, engineering, and dynamical systems so as to propose a contemporary theoretical framework for the mechanistic effects of NFB. The objective is to provide a firmly neurophysiological account of NFB, which goes beyond traditional behaviorist interpretations that attempt to explain psychological processes solely from a descriptive standpoint whilst treating the brain as a “black box”. To this end, we interlink evidence from experimental findings that encompass a broad range of intrinsic brain phenomena: starting from “bottom-up” mechanisms of neural synchronization, followed by “top-down” regulation of internal brain states, moving to dynamical systems plus control-theoretic principles, and concluding with activity-dependent as well as homeostatic forms of brain plasticity. In support of our framework, we examine the effects of NFB in several brain disorders, including attention-deficit hyperactivity (ADHD) and post-traumatic stress disorder (PTSD). In sum, it is argued that pathological oscillations emerge from an abnormal formation of brain-state attractor landscape(s). The central thesis put forward is that NFB tunes brain oscillations toward a homeostatic set-point which affords an optimal balance between network flexibility and stability (i.e., self-organised criticality (SOC)).
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Affiliation(s)
- Tomas Ros
- Laboratory for Neurology and Imaging of Cognition, Department of Neurosciences, University of Geneva Geneva, Switzerland
| | - Bernard J Baars
- Theoretical Neurobiology, The Neurosciences Institute La Jolla, CA, USA
| | - Ruth A Lanius
- Department of Psychiatry, University of Western Ontario London, ON, Canada
| | - Patrik Vuilleumier
- Laboratory for Neurology and Imaging of Cognition, Department of Neurosciences, University of Geneva Geneva, Switzerland
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14
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White SW, Richey JA, Gracanin D, Bell MA, LaConte S, Coffman M, Trubanova A, Kim I. The Promise of Neurotechnology in Clinical Translational Science. Clin Psychol Sci 2014; 3:797-815. [PMID: 26504676 DOI: 10.1177/2167702614549801] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Neurotechnology is broadly defined as a set of devices used to understand neural processes and applications that can potentially facilitate the brain's ability to repair itself. In the past decade, an increasingly explicit understanding of basic biological mechanisms of brain-related illnesses has produced applications that allow a direct yet noninvasive method to index and manipulate the functioning of the human nervous system. Clinical scientists are poised to apply this technology to assess, treat, and better understand complex socioemotional processes that underlie many forms of psychopathology. In this review, we describe the potential benefits and hurdles, both technical and methodological, of neurotechnology in the context of clinical dysfunction. We also offer a framework for developing and evaluating neurotechnologies that is intended to expedite progress at the nexus of clinical science and neural interface designs by providing a comprehensive vocabulary to describe the necessary features of neurotechnology in the clinic.
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15
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Stoeckel LE, Garrison KA, Ghosh S, Wighton P, Hanlon CA, Gilman JM, Greer S, Turk-Browne NB, deBettencourt MT, Scheinost D, Craddock C, Thompson T, Calderon V, Bauer CC, George M, Breiter HC, Whitfield-Gabrieli S, Gabrieli JD, LaConte SM, Hirshberg L, Brewer JA, Hampson M, Van Der Kouwe A, Mackey S, Evins AE. Optimizing real time fMRI neurofeedback for therapeutic discovery and development. NEUROIMAGE-CLINICAL 2014; 5:245-55. [PMID: 25161891 PMCID: PMC4141981 DOI: 10.1016/j.nicl.2014.07.002] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2014] [Revised: 06/20/2014] [Accepted: 07/05/2014] [Indexed: 11/06/2022]
Abstract
While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain–behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders. Guidelines are proposed for studies of rtfMRI neurofeedback for clinical therapeutics. Evidence-based guidelines are needed for clinical trials of rtfMRI neurofeedback. These guidelines will shape the design of future clinical trials.
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Affiliation(s)
- L E Stoeckel
- Massachusetts General Hospital, Department of Psychiatry, USA ; Harvard Medical School, USA ; Athinoula A. Martinos Center, USA ; McGovern Institute for Brain Research, Massachusetts Institute of Technology, USA
| | - K A Garrison
- Yale University School of Medicine, Department of Psychiatry, USA
| | - S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, USA
| | - P Wighton
- Athinoula A. Martinos Center, USA ; Massachusetts General Hospital, Department of Radiology, USA
| | - C A Hanlon
- Department of Psychiatry, Medical University of South Carolina, USA
| | - J M Gilman
- Massachusetts General Hospital, Department of Psychiatry, USA ; Harvard Medical School, USA ; Athinoula A. Martinos Center, USA
| | - S Greer
- Department of Neuroscience, University of California, Berkeley, USA
| | | | | | - D Scheinost
- Department of Diagnostic Radiology, Yale University School of Medicine, USA
| | | | - T Thompson
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, USA
| | - V Calderon
- Massachusetts General Hospital, Department of Psychiatry, USA
| | - C C Bauer
- Universidad Nacional Autonoma de Mexico, Instituto de Neurobiologia, Mexico
| | - M George
- Department of Psychiatry, Medical University of South Carolina, USA
| | - H C Breiter
- Massachusetts General Hospital, Department of Psychiatry, USA ; Northwestern University Feinberg School of Medicine, Department of Psychiatry and Behavioral Sciences, USA
| | - S Whitfield-Gabrieli
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, USA
| | - J D Gabrieli
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, USA
| | - S M LaConte
- School of Biomedical Engineering and Sciences, Virginia Tech, USA ; Virginia Tech Carilion Research Institute, USA
| | - L Hirshberg
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, USA
| | - J A Brewer
- Yale University School of Medicine, Department of Psychiatry, USA ; Department of Medicine and Psychiatry, University of Massachusetts Medical School, USA
| | - M Hampson
- Department of Diagnostic Radiology, Yale University School of Medicine, USA
| | - A Van Der Kouwe
- Athinoula A. Martinos Center, USA ; Massachusetts General Hospital, Department of Radiology, USA
| | - S Mackey
- Department of Anesthesia, Stanford University School of Medicine, USA
| | - A E Evins
- Massachusetts General Hospital, Department of Psychiatry, USA ; Harvard Medical School, USA
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16
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Moll J, Weingartner JH, Bado P, Basilio R, Sato JR, Melo BR, Bramati IE, de Oliveira-Souza R, Zahn R. Voluntary enhancement of neural signatures of affiliative emotion using FMRI neurofeedback. PLoS One 2014; 9:e97343. [PMID: 24847819 PMCID: PMC4029815 DOI: 10.1371/journal.pone.0097343] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2013] [Accepted: 04/18/2014] [Indexed: 12/25/2022] Open
Abstract
In Ridley Scott's film "Blade Runner", empathy-detection devices are employed to measure affiliative emotions. Despite recent neurocomputational advances, it is unknown whether brain signatures of affiliative emotions, such as tenderness/affection, can be decoded and voluntarily modulated. Here, we employed multivariate voxel pattern analysis and real-time fMRI to address this question. We found that participants were able to use visual feedback based on decoded fMRI patterns as a neurofeedback signal to increase brain activation characteristic of tenderness/affection relative to pride, an equally complex control emotion. Such improvement was not observed in a control group performing the same fMRI task without neurofeedback. Furthermore, the neurofeedback-driven enhancement of tenderness/affection-related distributed patterns was associated with local fMRI responses in the septohypothalamic area and frontopolar cortex, regions previously implicated in affiliative emotion. This demonstrates that humans can voluntarily enhance brain signatures of tenderness/affection, unlocking new possibilities for promoting prosocial emotions and countering antisocial behavior.
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Affiliation(s)
- Jorge Moll
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D’Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Julie H. Weingartner
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D’Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Patricia Bado
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D’Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
- Instituto de Ciências Biomédicas (ICB), Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Rodrigo Basilio
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D’Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - João R. Sato
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D’Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
- Center for Mathematics, Computation, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Bruno R. Melo
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D’Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Ivanei E. Bramati
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D’Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Ricardo de Oliveira-Souza
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D’Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
- Gaffrée e Guinle University Hospital, Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Roland Zahn
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D’Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
- Centre for Affective Disorders, Institute of Psychiatry, King’s College, London, United Kingdom
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