1
|
Using Real-Time fMRI Neurofeedback to Modulate M1-Cerebellum Connectivity. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8744982. [PMID: 36082347 PMCID: PMC9448559 DOI: 10.1155/2022/8744982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/16/2022] [Accepted: 07/25/2022] [Indexed: 11/18/2022]
Abstract
Objective The potential of neurofeedback to alter the M1-cerebellum connectivity was explored using motor imagery-based rt-fMRI. These regions were chosen due to their importance in motor performance and motor rehabilitation. Methods Four right-handed individuals were recruited to examine the potential to change the M1-cerebellum neurofeedback link. The University of Glasgow Cognitive Neuroimaging Centre used a 3T MRI scanner from January 2019 to January 2020 to conduct this prospective study. Everyone participated in each fMRI session, which included six NF training runs. Participants were instructed to imagine complicated hand motions during the NF training to raise a thermometer bar's height. To contrast the correlation coefficients between the initial and last NF runs, a t-test was performed post hoc. Results The neurofeedback connection between M1 and the cerebellum was strengthened in each participant. Motor imagery strategy was a significant task in training M1-cerebellum connectivity as participants used it successfully to enhance the activation level between these regions during M1-cerebellum modulation using real-time fMRI. The t-test and linear regression, on the other hand, showed this increase to be insignificant. Conclusion A novel technique to manipulate M1-cerebellum connectivity was discovered using real-time fMRI NF. This study showed that each participant's neurofeedback connectivity between M1 and cerebellum was enhanced. This increase, on the other hand, was insignificant statistically. The results showed that the connectivity between both areas increased positively. Through the integration of fMRI and neurofeedback, M1-cerebellum connectivity can be positively affected.
Collapse
|
2
|
Pereira JA, Ray A, Rana M, Silva C, Salinas C, Zamorano F, Irani M, Opazo P, Sitaram R, Ruiz S. A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study. Front Hum Neurosci 2022; 16:933559. [PMID: 36092645 PMCID: PMC9452730 DOI: 10.3389/fnhum.2022.933559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the “happiness emotional brain state” of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4–1 Median = 6.563%; Range = 4.10–27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1–15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.
Collapse
Affiliation(s)
- Jaime A. Pereira
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Andreas Ray
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Mohit Rana
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Claudio Silva
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
| | - Cesar Salinas
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
| | - Francisco Zamorano
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile
| | - Martin Irani
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Patricia Opazo
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States
- *Correspondence: Ranganatha Sitaram
| | - Sergio Ruiz
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Sergio Ruiz
| |
Collapse
|
3
|
EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042158. [PMID: 35206341 PMCID: PMC8872045 DOI: 10.3390/ijerph19042158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/11/2022] [Accepted: 02/11/2022] [Indexed: 11/17/2022]
Abstract
Classifying emotional states is critical for brain–computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.
Collapse
|
4
|
Kwak Y, Song WJ, Kim SE. FGANet: fNIRS-guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 2022; 30:329-339. [PMID: 35130163 DOI: 10.1109/tnsre.2022.3149899] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Non-invasive brain-computer interfaces (BCIs) have been widely used for neural decoding, linking neural signals to control devices. Hybrid BCI systems using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention for overcoming the limitations of EEG- and fNIRS-standalone BCI systems. However, most hybrid EEG-fNIRS BCI studies have focused on late fusion because of discrepancies in their temporal resolutions and recording locations. Despite the enhanced performance of hybrid BCIs, late fusion methods have difficulty in extracting correlated features in both EEG and fNIRS signals. Therefore, in this study, we proposed a deep learning-based early fusion structure, which combines two signals before the fully-connected layer, called the fNIRS-guided attention network (FGANet). First, 1D EEG and fNIRS signals were converted into 3D EEG and fNIRS tensors to spatially align EEG and fNIRS signals at the same time point. The proposed fNIRS-guided attention layer extracted a joint representation of EEG and fNIRS tensors based on neurovascular coupling, in which the spatially important regions were identified from fNIRS signals, and detailed neural patterns were extracted from EEG signals. Finally, the final prediction was obtained by weighting the sum of the prediction scores of the EEG and fNIRS-guided attention features to alleviate performance degradation owing to delayed fNIRS response. In the experimental results, the FGANet significantly outperformed the EEG-standalone network. Furthermore, the FGANet has 4.0% and 2.7% higher accuracy than the state-of-the-art algorithms in mental arithmetic and motor imagery tasks, respectively.
Collapse
|
5
|
Vargas P, Sitaram R, Sepúlveda P, Montalba C, Rana M, Torres R, Tejos C, Ruiz S. Weighted neurofeedback facilitates greater self-regulation of functional connectivity between the primary motor area and cerebellum. J Neural Eng 2021; 18. [PMID: 34587606 DOI: 10.1088/1741-2552/ac2b7e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 09/29/2021] [Indexed: 11/12/2022]
Abstract
Objective.Brain-computer interface (BCI) is a tool that can be used to train brain self-regulation and influence specific activity patterns, including functional connectivity, through neurofeedback. The functional connectivity of the primary motor area (M1) and cerebellum play a critical role in motor recovery after a brain injury, such as stroke. The objective of this study was to determine the feasibility of achieving control of the functional connectivity between M1 and the cerebellum in healthy subjects. Additionally, we aimed to compare the brain self-regulation of two different feedback modalities and their effects on motor performance.Approach.Nine subjects were trained with a real-time functional magnetic resonance imaging BCI system. Two groups were conformed: equal feedback group (EFG), which received neurofeedback that weighted the contribution of both regions of interest (ROIs) equally, and weighted feedback group (WFG) that weighted each ROI differentially (30% cerebellum; 70% M1). The magnitude of the brain activity induced by self-regulation was evaluated with the blood-oxygen-level-dependent (BOLD) percent change (BPC). Functional connectivity was assessed using temporal correlations between the BOLD signal of both ROIs. A finger-tapping task was included to evaluate the effect of brain self-regulation on motor performance.Main results.A comparison between the feedback modalities showed that WFG achieved significantly higher BPC in M1 than EFG. The functional connectivity between ROIs during up-regulation in WFG was significantly higher than EFG. In general, both groups showed better tapping speed in the third session compared to the first. For WFG, there were significant correlations between functional connectivity and tapping speed.Significance.The results show that it is possible to train healthy individuals to control M1-cerebellum functional connectivity with rtfMRI-BCI. Besides, it is also possible to use a weighted feedback approach to facilitate a higher activity of one region over another.
Collapse
Affiliation(s)
- Patricia Vargas
- Interdisciplinary Center for Neuroscience, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile.,Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Interdisciplinary Center for Neuroscience, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,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.,Multimodal Functional Brain Imaging Hub, St. Jude Children's Research Hospital, Memphis, TN, United States of America
| | - Pradyumna Sepúlveda
- Institute of Cognitive Neuroscience (ICN), University College London, London, England
| | - Cristian Montalba
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Mohit Rana
- Interdisciplinary Center for Neuroscience, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Rafael Torres
- Interdisciplinary Center for Neuroscience, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristián Tejos
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.,Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sergio Ruiz
- Interdisciplinary Center for Neuroscience, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| |
Collapse
|
6
|
Giulia L, Adolfo V, Julie C, Quentin D, Simon B, Fleury M, Leveque-Le Bars E, Bannier E, Lécuyer A, Barillot C, Bonan I. The impact of neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study. J Neural Eng 2021; 18. [PMID: 34551403 DOI: 10.1088/1741-2552/ac291e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 09/22/2021] [Indexed: 11/12/2022]
Abstract
Objective.In this study, we assessed the impact of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) neurofeedback (NF) on connectivity strength and direction in bilateral motor cortices in chronic stroke patients. Most of the studies using NF or brain computer interfaces for stroke rehabilitation have assessed treatment effects focusing on successful activation of targeted cortical regions. However, given the crucial role of brain network reorganization for stroke recovery, our broader aim was to assess connectivity changes after an NF training protocol targeting localized motor areas.Approach.We considered changes in fMRI connectivity after a multisession EEG-fMRI NF training targeting ipsilesional motor areas in nine stroke patients. We applied the dynamic causal modeling and parametric empirical Bayes frameworks for the estimation of effective connectivity changes. We considered a motor network including both ipsilesional and contralesional premotor, supplementary and primary motor areas.Main results.Our results indicate that NF upregulation of targeted areas (ipsilesional supplementary and primary motor areas) not only modulated activation patterns, but also had a more widespread impact on fMRI bilateral motor networks. In particular, inter-hemispheric connectivity between premotor and primary motor regions decreased, and ipsilesional self-inhibitory connections were reduced in strength, indicating an increase in activation during the NF motor task.Significance.To the best of our knowledge, this is the first work that investigates fMRI connectivity changes elicited by training of localized motor targets in stroke. Our results open new perspectives in the understanding of large-scale effects of NF training and the design of more effective NF strategies, based on the pathophysiology underlying stroke-induced deficits.
Collapse
Affiliation(s)
- Lioi Giulia
- Univ Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France.,IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest, F-29238, France
| | - Veliz Adolfo
- Univ Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France
| | | | - Duché Quentin
- Univ Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France.,Department of Physical and Rehabilitation Medicine, CHU Rennes, Rennes, France
| | - Butet Simon
- Department of Physical and Rehabilitation Medicine, CHU Rennes, Rennes, France
| | - Mathis Fleury
- Univ Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France
| | | | - Elise Bannier
- Univ Rennes, Inria, CNRS, Inserm, IRISA, Rennes, France.,Department of Radiology, CHU Rennes, Rennes, France
| | | | | | - Isabelle Bonan
- Department of Physical and Rehabilitation Medicine, CHU Rennes, Rennes, France
| |
Collapse
|
7
|
Sahonero-Alvarez G, Singh AK, Sayrafian K, Bianchi L, Roman-Gonzalez A. A Functional BCI Model by the P2731 Working Group: Transducer. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1968633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
| | | | - Kamran Sayrafian
- Information Technology Laboratory, National Institute of Standards & Technology, Gaithersburg, USA
| | - Luigi Bianchi
- Civil Engineering and Computer Science Engineering Dept. Tor Vergata University of Rome, Rome, Italy
| | | |
Collapse
|
8
|
Abstract
This paper aims at realizing upper limb rehabilitation training by using an fNIRS-BCI system. This article mainly focuses on the analysis and research of the cerebral blood oxygen signal in the system, and gradually extends the analysis and recognition method of the movement intention in the cerebral blood oxygen signal to the actual brain-computer interface system. Fifty subjects completed four upper limb movement paradigms: Lifting-up, putting down, pulling back, and pushing forward. Then, their near-infrared data and movement trigger signals were collected. In terms of the recognition algorithm for detecting the initial intention of upper limb movements, gradient boosting tree (GBDT) and random forest (RF) were selected for classification experiments. Finally, RF classifier with better comprehensive indicators was selected as the final classification algorithm. The best offline recognition rate was 94.4% (151/160). The ReliefF algorithm based on distance measurement and the genetic algorithm proposed in the genetic theory were used to select features. In terms of upper limb motion state recognition algorithms, logistic regression (LR), support vector machine (SVM), naive Bayes (NB), and linear discriminant analysis (LDA) were selected for experiments. Kappa coefficient was used as the classification index to evaluate the performance of the classifier. Finally, SVM classification got the best performance, and the four-class recognition accuracy rate was 84.4%. The results show that RF and SVM can achieve high recognition accuracy in motion intentions and the upper limb rehabilitation system designed in this paper has great application significance.
Collapse
|
9
|
Kawala-Sterniuk A, Browarska N, Al-Bakri A, Pelc M, Zygarlicki J, Sidikova M, Martinek R, Gorzelanczyk EJ. Summary of over Fifty Years with Brain-Computer Interfaces-A Review. Brain Sci 2021; 11:43. [PMID: 33401571 PMCID: PMC7824107 DOI: 10.3390/brainsci11010043] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/25/2020] [Accepted: 12/27/2020] [Indexed: 11/16/2022] Open
Abstract
Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.
Collapse
Affiliation(s)
- Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (M.P.); (J.Z.)
| | - Natalia Browarska
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (M.P.); (J.Z.)
| | - Amir Al-Bakri
- Department of Biomedical Engineering, College of Engineering, University of Babylon, 51001 Babylon, Iraq;
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (M.P.); (J.Z.)
- Department of Computing and Information Systems, University of Greenwich, London SE10 9LS, UK
| | - Jaroslaw Zygarlicki
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (M.P.); (J.Z.)
| | - Michaela Sidikova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.S.); (R.M.)
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.S.); (R.M.)
| | - Edward Jacek Gorzelanczyk
- Department of Theoretical Basis of BioMedical Sciences and Medical Informatics, Nicolaus Copernicus University, Collegium Medicum, 85-067 Bydgoszcz, Poland;
- Institute of Philosophy, Kazimierz Wielki University, 85-092 Bydgoszcz, Poland
- Babinski Specialist Psychiatric Healthcare Center, Outpatient Addiction Treatment, 91-229 Lodz, Poland
- The Society for the Substitution Treatment of Addiction “Medically Assisted Recovery”, 85-791 Bydgoszcz, Poland
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Camacho MC, King LS, Ojha A, Garcia CM, Sisk LM, Cichocki AC, Humphreys KL, Gotlib IH. Cerebral blood flow in 5- to 8-month-olds: Regional tissue maturity is associated with infant affect. Dev Sci 2020; 23:e12928. [PMID: 31802580 PMCID: PMC8931704 DOI: 10.1111/desc.12928] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/20/2019] [Accepted: 12/02/2019] [Indexed: 12/12/2022]
Abstract
Infancy is marked by rapid neural and emotional development. The relation between brain function and emotion in infancy, however, is not well understood. Methods for measuring brain function predominantly rely on the BOLD signal; however, interpretation of the BOLD signal in infancy is challenging because the neuronal-hemodynamic relation is immature. Regional cerebral blood flow (rCBF) provides a context for the infant BOLD signal and can yield insight into the developmental maturity of brain regions that may support affective behaviors. This study aims to elucidate the relations among rCBF, age, and emotion in infancy. One hundred and seven mothers reported their infants' (infant age M ± SD = 6.14 ± 0.51 months) temperament. A subsample of infants completed MRI scans, 38 of whom produced usable perfusion MRI during natural sleep to quantify rCBF. Mother-infant dyads completed the repeated Still-Face Paradigm, from which infant affect reactivity and recovery to stress were quantified. We tested associations of infant age at scan, temperament factor scores, and observed affect reactivity and recovery with voxel-wise rCBF. Infant age was positively associated with CBF in nearly all voxels, with peaks located in sensory cortices and the ventral prefrontal cortex, supporting the formulation that rCBF is an indicator of tissue maturity. Temperamental Negative Affect and recovery of positive affect following a stressor were positively associated with rCBF in several cortical and subcortical limbic regions, including the orbitofrontal cortex and inferior frontal gyrus. This finding yields insight into the nature of affective neurodevelopment during infancy. Specifically, infants with relatively increased prefrontal cortex maturity may evidence a disposition toward greater negative affect and negative reactivity in their daily lives yet show better recovery of positive affect following a social stressor.
Collapse
Affiliation(s)
| | | | - Amar Ojha
- Stanford University, Stanford, CA, USA
| | | | | | | | | | | |
Collapse
|
12
|
Bagarinao E, Yoshida A, Terabe K, Kato S, Nakai T. Improving Real-Time Brain State Classification of Motor Imagery Tasks During Neurofeedback Training. Front Neurosci 2020; 14:623. [PMID: 32670011 PMCID: PMC7326956 DOI: 10.3389/fnins.2020.00623] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 05/19/2020] [Indexed: 11/24/2022] Open
Abstract
In this study, we investigated the effect of the dynamic changes in brain activation during neurofeedback training in the classification of the different brain states associated with the target tasks. We hypothesized that ongoing activation patterns could change during neurofeedback session due to learning effects and, in the process, could affect the performance of brain state classifiers trained using data obtained prior to the session. Using a motor imagery paradigm, we then examined the application of an incremental training approach where classifiers were continuously updated in order to account for these activation changes. Our results confirmed our hypothesis that neurofeedback training could be associated with dynamic changes in brain activation characterized by an initially more widespread brain activation followed by a more focused and localized activation pattern. By continuously updating the trained classifiers after each feedback run, significant improvement in accurately classifying the different brain states associated with the target motor imagery tasks was achieved. These findings suggest the importance of taking into account brain activation changes during neurofeedback in order to provide more reliable and accurate feedback information to the participants, which is critical for an effective neurofeedback application.
Collapse
Affiliation(s)
| | - Akihiro Yoshida
- Neuroimaging and Informatics Group, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kazunori Terabe
- Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Shohei Kato
- Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Toshiharu Nakai
- Neuroimaging and Informatics Group, National Center for Geriatrics and Gerontology, Obu, Japan.,Department of Radiology, Osaka University Graduate School of Dentistry, Osaka, Japan
| |
Collapse
|
13
|
Correia AI, Branco P, Martins M, Reis AM, Martins N, Castro SL, Lima CF. Resting-state connectivity reveals a role for sensorimotor systems in vocal emotional processing in children. Neuroimage 2019; 201:116052. [DOI: 10.1016/j.neuroimage.2019.116052] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 07/19/2019] [Accepted: 07/23/2019] [Indexed: 11/17/2022] Open
|
14
|
Current progress in real-time functional magnetic resonance-based neurofeedback: Methodological challenges and achievements. Neuroimage 2019; 202:116107. [DOI: 10.1016/j.neuroimage.2019.116107] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 07/26/2019] [Accepted: 08/16/2019] [Indexed: 12/21/2022] Open
|
15
|
Neurofeedback mithilfe funktioneller Magnetresonanztomographie in Echtzeit. PSYCHOTHERAPEUT 2019. [DOI: 10.1007/s00278-019-0352-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
16
|
Camacho MC, Karim HT, Perlman SB. Neural architecture supporting active emotion processing in children: A multivariate approach. Neuroimage 2019; 188:171-180. [PMID: 30537564 PMCID: PMC6401267 DOI: 10.1016/j.neuroimage.2018.12.013] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 11/08/2018] [Accepted: 12/06/2018] [Indexed: 10/27/2022] Open
Abstract
BACKGROUND Adaptive emotion processing is critical for nearly all aspects of social and emotional functioning. There are distinct developmental trajectories associated with improved emotion processing, with a protracted developmental course for negative or complex emotions. The specific changes in neural circuitry that underlie this development, however are still scarcely understood. We employed a multivariate approach in order to elucidate distinctions in complex, naturalistic emotion processing between childhood and adulthood. METHOD Twenty-one adults (M±SD age = 26.57 ± 5.08 years) and thirty children (age = 7.75 ± 1.80 years) completed a free-viewing movie task during BOLD fMRI scanning. This task was designed to assess naturalistic processing of movie clips portraying positive, negative, and neutral emotions. Multivariate support vector machines (SVM) were trained to classify age groups based on neural activation during the task. RESULTS SVMs were able to successfully classify condition (positive, negative, and neutral) across all participants with high accuracy (61.44%). SVMs could successfully distinguish adults and children within each condition (ps < 0.05). Regions that informed the age group SVMs were associated with sensory and socio-emotional processing (inferior parietal lobule), emotion regulation (inferior frontal gyrus), and sensory regions of the temporal and occipital lobes. CONCLUSIONS These results point to distributed differences in activation between childhood and adulthood unique to each emotional condition. In the negative condition specifically, there is evidence for a shift in engagement from regions of sensory and socio-emotional integration to emotion regulation regions between children and adults. These results provide insight into circuitry contributing to maturation of emotional processing across development.
Collapse
Affiliation(s)
- M Catalina Camacho
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Helmet T Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susan B Perlman
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| |
Collapse
|
17
|
Rubia K, Criaud M, Wulff M, Alegria A, Brinson H, Barker G, Stahl D, Giampietro V. Functional connectivity changes associated with fMRI neurofeedback of right inferior frontal cortex in adolescents with ADHD. Neuroimage 2019; 188:43-58. [PMID: 30513395 PMCID: PMC6414400 DOI: 10.1016/j.neuroimage.2018.11.055] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 11/28/2018] [Accepted: 11/29/2018] [Indexed: 11/21/2022] Open
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is associated with poor self-control, underpinned by inferior fronto-striatal deficits. We showed previously that 18 ADHD adolescents over 11 runs of 8.5 min of real-time functional magnetic resonance neurofeedback of the right inferior frontal cortex (rIFC) progressively increased activation in 2 regions of the rIFC which was associated with clinical symptom improvement. In this study, we used functional connectivity analyses to investigate whether fMRI-Neurofeedback of rIFC resulted in dynamic functional connectivity changes in underlying neural networks. Whole-brain seed-based functional connectivity analyses were conducted using the two clusters showing progressively increased activation in rIFC as seed regions to test for changes in functional connectivity before and after 11 fMRI-Neurofeedback runs. Furthermore, we tested whether the resulting functional connectivity changes were associated with clinical symptom improvements and whether they were specific to fMRI-Neurofeedback of rIFC when compared to a control group who had to self-regulate another region. rIFC showed increased positive functional connectivity after relative to before fMRI-Neurofeedback with dorsal caudate and anterior cingulate and increased negative functional connectivity with regions of the default mode network (DMN) such as posterior cingulate and precuneus. Furthermore, the functional connectivity changes were correlated with clinical improvements and the functional connectivity and correlation findings were specific to the rIFC-Neurofeedback group. The findings show for the first time that fMRI-Neurofeedback of a typically dysfunctional frontal region in ADHD adolescents leads to strengthening within fronto-cingulo-striatal networks and to weakening of functional connectivity with posterior DMN regions and that this may be underlying clinical improvement.
Collapse
Affiliation(s)
- K Rubia
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - M Criaud
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - M Wulff
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - A Alegria
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - H Brinson
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - G Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - D Stahl
- Department of Biostatistics & Health Informatics, King's College London, UK
| | - V Giampietro
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| |
Collapse
|
18
|
Van den Boom MA, Jansma JM, Ramsey NF. Rapid acquisition of dynamic control over DLPFC using real-time fMRI feedback. Eur Neuropsychopharmacol 2018; 28:1194-1205. [PMID: 30217551 PMCID: PMC6420021 DOI: 10.1016/j.euroneuro.2018.08.508] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 08/08/2018] [Accepted: 08/14/2018] [Indexed: 12/20/2022]
Abstract
It has been postulated that gaining control over activity in the dorsolateral prefrontal cortex (DLPFC), a key region of the working memory brain network, may be beneficial for cognitive performance and treatment of certain psychiatric disorders. Several studies have reported that, with neurofeedback training, subjects can learn to increase DLPFC activity. However, improvement of dynamic control in terms of switching between low and high activity in DLPFC brain states may potentially constitute more effective self-regulation. Here, we report on feasibility of obtaining dynamic control over DLPFC, meaning the ability to both in- and decrease activity at will, within a single functional MRI scan session. Two groups of healthy volunteers (N = 24) were asked to increase and decrease activity in the left DLPFC as often as possible during fMRI scans (at 7 Tesla), while receiving real-time visual feedback. The experimental group practiced with real-time feedback, whereas the control group received sham feedback. The experimental group significantly increased the speed of intentionally alternating DLPFC activity, while performance of the control group did not change. Analysis of the characteristics of the BOLD signal during successful trials revealed that training with neurofeedback predominantly reduced the time for the DLPFC to return to baseline after activation. These results provide a preliminary indication that people may be able to learn to dynamically down-regulate the level of physiological activity in the DLPFC, and may have implications for psychiatric disorders where DLPFC plays a role.
Collapse
Affiliation(s)
- Max Alexander Van den Boom
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, P.O. Box 85500, Utrecht, The Netherlands.
| | - Johan Martijn Jansma
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands
| | - Nick Franciscus Ramsey
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, P.O. Box 85500, Utrecht, The Netherlands.
| |
Collapse
|
19
|
Thibault RT, MacPherson A, Lifshitz M, Roth RR, Raz A. Neurofeedback with fMRI: A critical systematic review. Neuroimage 2018; 172:786-807. [DOI: 10.1016/j.neuroimage.2017.12.071] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 12/18/2017] [Accepted: 12/21/2017] [Indexed: 10/18/2022] Open
|
20
|
Koush Y, Meskaldji DE, Pichon S, Rey G, Rieger SW, Linden DEJ, Van De Ville D, Vuilleumier P, Scharnowski F. Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback. Cereb Cortex 2018; 27:1193-1202. [PMID: 26679192 DOI: 10.1093/cercor/bhv311] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity-neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top-down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self-regulated the top-down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity-based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity-based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders.
Collapse
Affiliation(s)
- Yury Koush
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1202 Geneva, Switzerland.,Department of Radiology and Medical Informatics
| | - Djalel-E Meskaldji
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1202 Geneva, Switzerland.,Department of Radiology and Medical Informatics
| | - Swann Pichon
- Geneva Neuroscience Center, Department of Neuroscience.,Swiss Center for Affective Sciences.,Faculty of Psychology and Educational Science, University of Geneva, CH-1211 Geneva, Switzerland
| | - Gwladys Rey
- Geneva Neuroscience Center, Department of Neuroscience
| | - Sebastian W Rieger
- Geneva Neuroscience Center, Department of Neuroscience.,Swiss Center for Affective Sciences
| | - David E J Linden
- School of Medicine, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff CF24 4HQ, UK
| | - Dimitri Van De Ville
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1202 Geneva, Switzerland.,Department of Radiology and Medical Informatics
| | - Patrik Vuilleumier
- Geneva Neuroscience Center, Department of Neuroscience.,Swiss Center for Affective Sciences
| | - Frank Scharnowski
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1202 Geneva, Switzerland.,Department of Radiology and Medical Informatics.,Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, 8032 Zürich, Switzerland.,Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, 8032 Zürich, Switzerland
| |
Collapse
|
21
|
Hellrung L, Dietrich A, Hollmann M, Pleger B, Kalberlah C, Roggenhofer E, Villringer A, Horstmann A. Intermittent compared to continuous real-time fMRI neurofeedback boosts control over amygdala activation. Neuroimage 2017; 166:198-208. [PMID: 29100939 DOI: 10.1016/j.neuroimage.2017.10.031] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 10/09/2017] [Accepted: 10/16/2017] [Indexed: 12/20/2022] Open
Abstract
Real-time fMRI neurofeedback is a feasible tool to learn the volitional regulation of brain activity. So far, most studies provide continuous feedback information that is presented upon every volume acquisition. Although this maximizes the temporal resolution of feedback information, it may be accompanied by some disadvantages. Participants can be distracted from the regulation task due to (1) the intrinsic delay of the hemodynamic response and associated feedback and (2) limited cognitive resources available to simultaneously evaluate feedback information and stay engaged with the task. Here, we systematically investigate differences between groups presented with different variants of feedback (continuous vs. intermittent) and a control group receiving no feedback on their ability to regulate amygdala activity using positive memories and feelings. In contrast to the feedback groups, no learning effect was observed in the group without any feedback presentation. The group receiving intermittent feedback exhibited better amygdala regulation performance when compared with the group receiving continuous feedback. Behavioural measurements show that these effects were reflected in differences in task engagement. Overall, we not only demonstrate that the presentation of feedback is a prerequisite to learn volitional control of amygdala activity but also that intermittent feedback is superior to continuous feedback presentation.
Collapse
Affiliation(s)
- Lydia Hellrung
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.
| | - Anja Dietrich
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Maurice Hollmann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Burkhard Pleger
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neurology, BG University Clinic Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Christian Kalberlah
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Elisabeth Roggenhofer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neuroscience Clinique's, University Hospital Genève, Genève, Switzerland
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinics for Cognitive Neurology, University Hospital, Leipzig, Germany; Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany; Mind and Brain Institute, Berlin School of Mind and Brain, Humboldt-University and Charité, Berlin, Germany
| | - Annette Horstmann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany
| |
Collapse
|
22
|
Casimo K, Weaver KE, Wander J, Ojemann JG. BCI Use and Its Relation to Adaptation in Cortical Networks. IEEE Trans Neural Syst Rehabil Eng 2017; 25:1697-1704. [PMID: 28320670 PMCID: PMC5685806 DOI: 10.1109/tnsre.2017.2681963] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In this review, we discuss how BCI designs often take advantage of the brain's motor system infrastructure as sources of command signals. We highlight a growing body of literature examining how this approach leads to changes in activity across cortex, including beyond motor regions, as a result of learning the new skill of BCI control. We discuss the previous research identifying patterns of neural activity associated with BCI skill acquisition and use that closely resembles those associated with learning traditional native motor tasks. We then discuss recent work in animals probing changes in connectivity of the BCI control site, which were linked to BCI skill acquisition, and use this as a foundation for our original work in humans. We present our novel work showing changes in resting state connectivity across cortex following the BCI learning process. We find substantial, heterogeneous changes in connectivity across regions and frequencies, including interactions that do not involve the BCI control site. We conclude from our review and original work that BCI skill acquisition may potentially lead to significant changes in evoked and resting state connectivity across multiple cortical regions. We recommend that future studies of BCIs look beyond motor regions to fully describe the cortical networks involved and long-term adaptations resulting from BCI skill acquisition.
Collapse
|
23
|
Kopel R, Emmert K, Scharnowski F, Haller S, Van De Ville D. Distributed Patterns of Brain Activity Underlying Real-Time fMRI Neurofeedback Training. IEEE Trans Biomed Eng 2017; 64:1228-1237. [DOI: 10.1109/tbme.2016.2598818] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
24
|
Abstract
Autism spectrum disorders (ASDs) are neurodevelopmental disorders with early onset, characterized by deficits in social communication and repetitive and restricted interests and activities. A growing number of studies over the last 10 years support the efficacy of behaviorally based interventions in ASD for the improvement of social communication and behavioral functioning. In contrast, research on neurobiological based therapies for ASD is still at its beginnings. In this article, we will provide a selective overview of both well-established evidence-based treatments and novel interventions and drug treatments based on neurobiological principles aiming at improving core symptoms in ASD. Directions and options for future research on treatment in ASD are discussed.
Collapse
Affiliation(s)
- L Poustka
- Clinic for Child and Adolescent Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
| | - I Kamp-Becker
- Clinic for Child and Adolescent Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| |
Collapse
|
25
|
Sitaram R, Ros T, Stoeckel L, Haller S, Scharnowski F, Lewis-Peacock J, Weiskopf N, Blefari ML, Rana M, Oblak E, Birbaumer N, Sulzer J. Closed-loop brain training: the science of neurofeedback. Nat Rev Neurosci 2016; 18:86-100. [PMID: 28003656 DOI: 10.1038/nrn.2016.164] [Citation(s) in RCA: 540] [Impact Index Per Article: 67.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
26
|
Chaudhary U, Birbaumer N, Ramos-Murguialday A. Brain-computer interfaces for communication and rehabilitation. Nat Rev Neurol 2016; 12:513-25. [PMID: 27539560 DOI: 10.1038/nrneurol.2016.113] [Citation(s) in RCA: 334] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.
Collapse
Affiliation(s)
- Ujwal Chaudhary
- Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstrasse 5, 72076 Tübingen, Germany
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstrasse 5, 72076 Tübingen, Germany.,Wyss-Center for Bio- and Neuro-Engineering, Chenin de Mines 9, Ch 1202, Geneva, Switzerland
| | - Ander Ramos-Murguialday
- Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Silcherstrasse 5, 72076 Tübingen, Germany.,TECNALIA, Health Department, Neural Engineering Laboratory, San Sebastian, Paseo Mikeletegi 1, 20009 San Sebastian, Spain
| |
Collapse
|
27
|
Subramanian L, Morris MB, Brosnan M, Turner DL, Morris HR, Linden DEJ. Functional Magnetic Resonance Imaging Neurofeedback-guided Motor Imagery Training and Motor Training for Parkinson's Disease: Randomized Trial. Front Behav Neurosci 2016; 10:111. [PMID: 27375451 PMCID: PMC4896907 DOI: 10.3389/fnbeh.2016.00111] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 05/23/2016] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) uses feedback of the patient's own brain activity to self-regulate brain networks which in turn could lead to a change in behavior and clinical symptoms. The objective was to determine the effect of NF and motor training (MOT) alone on motor and non-motor functions in Parkinson's Disease (PD) in a 10-week small Phase I randomized controlled trial. METHODS Thirty patients with Parkinson's disease (PD; Hoehn and Yahr I-III) and no significant comorbidity took part in the trial with random allocation to two groups. Group 1 (NF: 15 patients) received rt-fMRI-NF with MOT. Group 2 (MOT: 15 patients) received MOT alone. The primary outcome measure was the Movement Disorder Society-Unified PD Rating Scale-Motor scale (MDS-UPDRS-MS), administered pre- and post-intervention "off-medication". The secondary outcome measures were the "on-medication" MDS-UPDRS, the PD Questionnaire-39, and quantitative motor assessments after 4 and 10 weeks. RESULTS Patients in the NF group were able to upregulate activity in the supplementary motor area (SMA) by using motor imagery. They improved by an average of 4.5 points on the MDS-UPDRS-MS in the "off-medication" state (95% confidence interval: -2.5 to -6.6), whereas the MOT group improved only by 1.9 points (95% confidence interval +3.2 to -6.8). The improvement in the intervention group meets the minimal clinically important difference which is also on par with other non-invasive therapies such as repetitive Transcranial Magnetic Stimulation (rTMS). However, the improvement did not differ significantly between the groups. No adverse events were reported in either group. INTERPRETATION This Phase I study suggests that NF combined with MOT is safe and improves motor symptoms immediately after treatment, but larger trials are needed to explore its superiority over active control conditions.
Collapse
Affiliation(s)
- Leena Subramanian
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff UniversityCardiff, UK
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff, UK
| | - Monica Busse Morris
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff UniversityCardiff, UK
| | - Meadhbh Brosnan
- Trinity College Institute of Neuroscience, Trinity CollegeDublin, Ireland
- Faculty of Psychology and Neuroscience, Maastricht UniversityMaastricht, Netherlands
| | - Duncan L. Turner
- Neurorehabilitation Unit, School of Health, Sport and Bioscience, University of East LondonLondon, UK
| | - Huw R. Morris
- Department of Clinical Neuroscience, Institute of Neurology, University College LondonLondon, UK
| | - David E. J. Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff UniversityCardiff, UK
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff UniversityCardiff, UK
| |
Collapse
|
28
|
Sepulveda P, Sitaram R, Rana M, Montalba C, Tejos C, Ruiz S. How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI. Hum Brain Mapp 2016; 37:3153-71. [PMID: 27272616 DOI: 10.1002/hbm.23228] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Revised: 04/15/2016] [Accepted: 04/18/2016] [Indexed: 02/05/2023] Open
Abstract
The learning process involved in achieving brain self-regulation is presumed to be related to several factors, such as type of feedback, reward, mental imagery, duration of training, among others. Explicitly instructing participants to use mental imagery and monetary reward are common practices in real-time fMRI (rtfMRI) neurofeedback (NF), under the assumption that they will enhance and accelerate the learning process. However, it is still not clear what the optimal strategy is for improving volitional control. We investigated the differential effect of feedback, explicit instructions and monetary reward while training healthy individuals to up-regulate the blood-oxygen-level dependent (BOLD) signal in the supplementary motor area (SMA). Four groups were trained in a two-day rtfMRI-NF protocol: GF with NF only, GF,I with NF + explicit instructions (motor imagery), GF,R with NF + monetary reward, and GF,I,R with NF + explicit instructions (motor imagery) + monetary reward. Our results showed that GF increased significantly their BOLD self-regulation from day-1 to day-2 and GF,R showed the highest BOLD signal amplitude in SMA during the training. The two groups who were instructed to use motor imagery did not show a significant learning effect over the 2 days. The additional factors, namely motor imagery and reward, tended to increase the intersubject variability in the SMA during the course of training. Whole brain univariate and functional connectivity analyses showed common as well as distinct patterns in the four groups, representing the varied influences of feedback, reward, and instructions on the brain. Hum Brain Mapp 37:3153-3171, 2016. © 2016 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Pradyumna Sepulveda
- Biomedical Imaging Center, Pontificia Universidad Católica De Chile, Santiago, Chile.,Department of Electrical Engineering, Pontificia Universidad Católica De Chile, Santiago, Chile.,Laboratory of Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica De Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Laboratory of 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, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica De Chile, Santiago, Chile.,Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Mohit Rana
- Laboratory of Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica De Chile, Santiago, Chile.,Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica De Chile, Santiago, Chile
| | - Cristian Montalba
- Biomedical Imaging Center, Pontificia Universidad Católica De Chile, Santiago, Chile
| | - Cristian Tejos
- Biomedical Imaging Center, Pontificia Universidad Católica De Chile, Santiago, Chile.,Department of Electrical Engineering, Pontificia Universidad Católica De Chile, Santiago, Chile
| | - Sergio Ruiz
- Laboratory of Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica De Chile, Santiago, Chile.,Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica De Chile, Santiago, Chile.,Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| |
Collapse
|
29
|
Cohen Kadosh K, Luo Q, de Burca C, Sokunbi MO, Feng J, Linden DEJ, Lau JYF. Using real-time fMRI to influence effective connectivity in the developing emotion regulation network. Neuroimage 2016; 125:616-626. [PMID: 26475487 PMCID: PMC4692450 DOI: 10.1016/j.neuroimage.2015.09.070] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Revised: 09/29/2015] [Accepted: 09/30/2015] [Indexed: 12/19/2022] Open
Abstract
For most people, adolescence is synonymous with emotional turmoil and it has been shown that early difficulties with emotion regulation can lead to persistent problems for some people. This suggests that intervention during development might reduce long-term negative consequences for those individuals. Recent research has highlighted the suitability of real-time fMRI-based neurofeedback (NF) in training emotion regulation (ER) networks in adults. However, its usefulness in directly influencing plasticity in the maturing ER networks remains unclear. Here, we used NF to teach a group of 17 7-16 year-olds to up-regulate the bilateral insula, a key ER region. We found that all participants learned to increase activation during the up-regulation trials in comparison to the down-regulation trials. Importantly, a subsequent Granger causality analysis of Granger information flow within the wider ER network found that during up-regulation trials, bottom-up driven Granger information flow increased from the amygdala to the bilateral insula and from the left insula to the mid-cingulate cortex, supplementary motor area and the inferior parietal lobe. This was reversed during the down-regulation trials, where we observed an increase in top-down driven Granger information flow to the bilateral insula from mid-cingulate cortex, pre-central gyrus and inferior parietal lobule. This suggests that: 1) NF training had a differential effect on up-regulation vs down-regulation network connections, and that 2) our training was not only superficially concentrated on surface effects but also relevant with regards to the underlying neurocognitive bases. Together these findings highlight the feasibility of using NF in children and adolescents and its possible use for shaping key social cognitive networks during development.
Collapse
Affiliation(s)
- Kathrin Cohen Kadosh
- Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford, OX1 3UD, UK; Psychology Department, Institute of Psychiatry, King's College London, London SE5 8AF, UK.
| | - Qiang Luo
- School of Life Sciences, Fudan University, Shanghai 200433, PR China; Centre for Computational Systems Biology, Fudan University, Shanghai 200433, PR China
| | - Calem de Burca
- Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford, OX1 3UD, UK
| | - Moses O Sokunbi
- MRC Centre for Neuropsychiatric Genetics and Genomics and National Centre for Mental Health, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF10 3AT, United Kingdom; Cognitive Neuroscience Sector, International School for Advanced Studies (SISSA), 34136 Trieste, Italy
| | - Jianfeng Feng
- School of Life Sciences, Fudan University, Shanghai 200433, PR China; Centre for Computational Systems Biology, Fudan University, Shanghai 200433, PR China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - David E J Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics and National Centre for Mental Health, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF10 3AT, United Kingdom
| | - Jennifer Y F Lau
- Psychology Department, Institute of Psychiatry, King's College London, London SE5 8AF, UK
| |
Collapse
|
30
|
Thibault RT, Lifshitz M, Raz A. The self-regulating brain and neurofeedback: Experimental science and clinical promise. Cortex 2016; 74:247-61. [PMID: 26706052 DOI: 10.1016/j.cortex.2015.10.024] [Citation(s) in RCA: 125] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 10/22/2015] [Accepted: 10/29/2015] [Indexed: 12/20/2022]
Affiliation(s)
| | | | - Amir Raz
- McGill University, Montreal, QC, Canada; The Lady Davis Institute for Medical Research, Montreal, QC, Canada.
| |
Collapse
|
31
|
Thibault RT, Lifshitz M, Birbaumer N, Raz A. Neurofeedback, Self-Regulation, and Brain Imaging: Clinical Science and Fad in the Service of Mental Disorders. PSYCHOTHERAPY AND PSYCHOSOMATICS 2015; 84:193-207. [PMID: 26021883 DOI: 10.1159/000371714] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 12/18/2014] [Indexed: 11/19/2022]
Abstract
Neurofeedback draws on multiple techniques that propel both healthy and patient populations to self-regulate neural activity. Since the 1970s, numerous accounts have promoted electroencephalography-neurofeedback as a viable treatment for a host of mental disorders. Today, while the number of health care providers referring patients to neurofeedback practitioners increases steadily, substantial methodological and conceptual caveats continue to pervade empirical reports. And yet, nascent imaging technologies (e.g., real-time functional magnetic resonance imaging) and increasingly rigorous protocols are paving the road towards more effective applications and a better scientific understanding of the underlying mechanisms. Here, we outline common neurofeedback methods, illuminate the tenuous state of the evidence, and sketch out future directions to further unravel the potential merits of this contentious therapeutic prospect.
Collapse
|
32
|
Sherwood MS, Kane JH, Weisend MP, Parker JG. Enhanced control of dorsolateral prefrontal cortex neurophysiology with real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback training and working memory practice. Neuroimage 2015; 124:214-223. [PMID: 26348555 DOI: 10.1016/j.neuroimage.2015.08.074] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Revised: 07/20/2015] [Accepted: 08/22/2015] [Indexed: 12/15/2022] Open
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback can be used to train localized, conscious regulation of blood oxygen level-dependent (BOLD) signals. As a therapeutic technique, rt-fMRI neurofeedback reduces the symptoms of a variety of neurologic disorders. To date, few studies have investigated the use of self-regulation training using rt-fMRI neurofeedback to enhance cognitive performance. This work investigates the utility of rt-fMRI neurofeedback as a tool to enhance human cognition by training healthy individuals to consciously control activity in the left dorsolateral prefrontal cortex (DLPFC). A cohort of 18 healthy participants in the experimental group underwent rt-fMRI neurofeedback from the left DLPFC in five training sessions across two weeks while 7 participants in the control group underwent similar training outside the MRI and without rt-fMRI neurofeedback. Working memory (WM) performance was evaluated on two testing days separated by the five rt-fMRI neurofeedback sessions using two computerized tests. We investigated the ability to control the BOLD signal across training sessions and WM performance across the two testing days. The group with rt-fMRI neurofeedback demonstrated a significant increase in the ability to self-regulate the BOLD signal in the left DLPFC across sessions. WM performance showed differential improvement between testing days one and two across the groups with the highest increases observed in the rt-fMRI neurofeedback group. These results provide evidence that individuals can quickly gain the ability to consciously control the left DLPFC, and this training results in improvements of WM performance beyond that of training alone.
Collapse
Affiliation(s)
- Matthew S Sherwood
- Wright State Research Institute, Wright State University, 4035 Colonel Glenn Hwy, Dayton, OH 45431, USA; Department of Biomedical, Industrial, and Human Factors Engineering, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435, USA.
| | - Jessica H Kane
- Wright State Research Institute, Wright State University, 4035 Colonel Glenn Hwy, Dayton, OH 45431, USA; Department of Neuroscience, Cell Biology, and Physiology, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435, USA
| | - Michael P Weisend
- Wright State Research Institute, Wright State University, 4035 Colonel Glenn Hwy, Dayton, OH 45431, USA
| | - Jason G Parker
- Wright State Research Institute, Wright State University, 4035 Colonel Glenn Hwy, Dayton, OH 45431, USA; Department of Biomedical, Industrial, and Human Factors Engineering, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435, USA
| |
Collapse
|
33
|
Zhang Q, Zhang G, Yao L, Zhao X. Impact of real-time fMRI working memory feedback training on the interactions between three core brain networks. Front Behav Neurosci 2015; 9:244. [PMID: 26388754 PMCID: PMC4559651 DOI: 10.3389/fnbeh.2015.00244] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 08/24/2015] [Indexed: 01/03/2023] Open
Abstract
Working memory (WM) refers to the temporary holding and manipulation of information during the performance of a range of cognitive tasks, and WM training is a promising method for improving an individual’s cognitive functions. Our previous work demonstrated that WM performance can be improved through self-regulation of dorsal lateral prefrontal cortex (PFC) activation using real-time functional magnetic resonance imaging (rtfMRI), which enables individuals to control local brain activities volitionally according to the neurofeedback. Furthermore, research concerning large-scale brain networks has demonstrated that WM training requires the engagement of several networks, including the central executive network (CEN), the default mode network (DMN) and the salience network (SN), and functional connectivity within the CEN and DMN can be changed by WM training. Although a switching role of the SN between the CEN and DMN has been demonstrated, it remains unclear whether WM training can affect the interactions between the three networks and whether a similar mechanism also exists during the training process. In this study, we investigated the dynamic functional connectivity between the three networks during the rtfMRI feedback training using independent component analysis (ICA) and correlation analysis. The results indicated that functional connectivity within and between the three networks were significantly enhanced by feedback training, and most of the changes were associated with the insula and correlated with behavioral improvements. These findings suggest that the insula plays a critical role in the reorganization of functional connectivity among the three networks induced by rtfMRI training and in WM performance, thus providing new insights into the mechanisms of high-level functions and the clinical treatment of related functional impairments.
Collapse
Affiliation(s)
- Qiushi Zhang
- College of Information Science and Technology, Beijing Normal University Beijing, China
| | - Gaoyan Zhang
- School of Computer Science and Technology, Tianjin University Tianjin, China
| | - Li Yao
- College of Information Science and Technology, Beijing Normal University Beijing, China ; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University Beijing, China
| | - Xiaojie Zhao
- College of Information Science and Technology, Beijing Normal University Beijing, China
| |
Collapse
|
34
|
Kim DY, Yoo SS, Tegethoff M, Meinlschmidt G, Lee JH. The Inclusion of Functional Connectivity Information into fMRI-based Neurofeedback Improves Its Efficacy in the Reduction of Cigarette Cravings. J Cogn Neurosci 2015; 27:1552-72. [DOI: 10.1162/jocn_a_00802] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Abstract
Real-time fMRI (rtfMRI) neurofeedback (NF) facilitates volitional control over brain activity and the modulation of associated mental functions. The NF signals of traditional rtfMRI-NF studies predominantly reflect neuronal activity within ROIs. In this study, we describe a novel rtfMRI-NF approach that includes a functional connectivity (FC) component in the NF signal (FC-added rtfMRI-NF). We estimated the efficacy of the FC-added rtfMRI-NF method by applying it to nicotine-dependent heavy smokers in an effort to reduce cigarette craving. ACC and medial pFC as well as the posterior cingulate cortex and precuneus are associated with cigarette craving and were chosen as ROIs. Fourteen heavy smokers were randomly assigned to receive one of two types of NF: traditional activity-based rtfMRI-NF or FC-added rtfMRI-NF. Participants received rtfMRI-NF training during two separate visits after overnight smoking cessation, and cigarette craving score was assessed. The FC-added rtfMRI-NF resulted in greater neuronal activity and increased FC between the targeted ROIs than the traditional activity-based rtfMRI-NF and resulted in lower craving score. In the FC-added rtfMRI-NF condition, the average of neuronal activity and FC was tightly associated with craving score (Bonferroni-corrected p = .028). However, in the activity-based rtfMRI-NF condition, no association was detected (uncorrected p > .081). Non-rtfMRI data analysis also showed enhanced neuronal activity and FC with FC-added NF than with activity-based NF. These results demonstrate that FC-added rtfMRI-NF facilitates greater volitional control over brain activity and connectivity and greater modulation of mental function than activity-based rtfMRI-NF.
Collapse
|
35
|
|
36
|
Xie F, Xu L, Long Z, Yao L, Wu X. Functional connectivity alteration after real-time fMRI motor imagery training through self-regulation of activities of the right premotor cortex. BMC Neurosci 2015; 16:29. [PMID: 25926036 PMCID: PMC4453277 DOI: 10.1186/s12868-015-0167-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2014] [Accepted: 04/21/2015] [Indexed: 11/27/2022] Open
Abstract
Background Real-time functional magnetic resonance imaging technology (real-time fMRI) is a novel method that can be used to investigate motor imagery training, it has attracted increasing attention in recent years, due to its ability to facilitate subjects in regulating the activities of specific brain regions to influence their behaviors. Lots of researchers have demonstrated that the right premotor area play critical roles during real-time fMRI motor imagery training. Thus, it has been hypothesized that modulating the activity of right premotor area may result in an alteration of the functional connectivity between the premotor area and other motor-related regions. Results The results indicated that the functional connectivity between the bilateral premotor area and right posterior parietal lobe significantly decreased during the imagination task. Conclusions This finding is new evidence that real-time fMRI is effective and can provide a theoretical guidance for the alteration of the motor function of brain regions associated with motor imagery training. Electronic supplementary material The online version of this article (doi:10.1186/s12868-015-0167-1) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Fufang Xie
- College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, 100875, Beijing, China.
| | - Lele Xu
- College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, 100875, Beijing, China.
| | - Zhiying Long
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, 100875, Beijing, China.
| | - Li Yao
- College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, 100875, Beijing, China. .,State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, 100875, Beijing, China. .,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, 100875, Beijing, China.
| | - Xia Wu
- College of Information Science and Technology, Beijing Normal University, No. 19 Xin Jie Kou Wai Da Jie, 100875, Beijing, China. .,State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, 100875, Beijing, China. .,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, 100875, Beijing, China. .,State Key Laboratories of Transducer Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
| |
Collapse
|
37
|
Scharnowski F, Veit R, Zopf R, Studer P, Bock S, Diedrichsen J, Goebel R, Mathiak K, Birbaumer N, Weiskopf N. Manipulating motor performance and memory through real-time fMRI neurofeedback. Biol Psychol 2015; 108:85-97. [PMID: 25796342 PMCID: PMC4433098 DOI: 10.1016/j.biopsycho.2015.03.009] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 02/02/2015] [Accepted: 03/10/2015] [Indexed: 02/05/2023]
Abstract
Neurofeedback training of motor cortex shortens reaction times. Self-regulation of parahippocampal cortex activity interferes with memory encoding. Differential neurofeedback reveals double dissociation between neurofeedback target areas.
Task performance depends on ongoing brain activity which can be influenced by attention, arousal, or motivation. However, such modulating factors of cognitive efficiency are unspecific, can be difficult to control, and are not suitable to facilitate neural processing in a regionally specific manner. Here, we non-pharmacologically manipulated regionally specific brain activity using technically sophisticated real-time fMRI neurofeedback. This was accomplished by training participants to simultaneously control ongoing brain activity in circumscribed motor and memory-related brain areas, namely the supplementary motor area and the parahippocampal cortex. We found that learned voluntary control over these functionally distinct brain areas caused functionally specific behavioral effects, i.e. shortening of motor reaction times and specific interference with memory encoding. The neurofeedback approach goes beyond improving cognitive efficiency by unspecific psychological factors such as attention, arousal, or motivation. It allows for directly manipulating sustained activity of task-relevant brain regions in order to yield specific behavioral or cognitive effects.
Collapse
Affiliation(s)
- Frank Scharnowski
- Department of Radiology and Medical Informatics-CIBM, University of Geneva, Rue Gabrielle-Perret-G 4, CH-1211 Geneva 14, Switzerland; Institute of Bioengineering, Swiss Institute of Technology Lausanne (EPFL), STI-IBI Station 17, CH-1015 Lausanne, Switzerland.
| | - Ralf Veit
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Gartenstrasse 29, 72074 Tübingen, Germany
| | - Regine Zopf
- Perception in Action Research Centre, ARC Centre of Excellence in Cognition and its Disorders, Department of Cognitive Science, Macquarie University, Sydney 2109, NSW, Australia
| | - Petra Studer
- Department of Child & Adolescent Mental Health, University Hospital of Erlangen, Schwabachanlage 6+10, 91054 Erlangen, Germany
| | - Simon Bock
- Department of Child & Adolescent Psychiatry and Psychotherapy, Centre for Mental Health, Hospitals of Stuttgart, Prießnitzweg 24, 70374 Stuttgart
| | - Jörn Diedrichsen
- Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1 N 3AR, UK
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht 6200 MD, The Netherlands; Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences (KNAW), 1105 BA Amsterdam, The Netherlands
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Gartenstrasse 29, 72074 Tübingen, Germany; Ospedale San Camillo, Istituto di Ricovero e Cura a Carattere Scientifico, Venezia-Lido, Italy
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, 12 Queen Square, London WC1 N 3BG, UK
| |
Collapse
|
38
|
Zhang G, Yao L, Shen J, Yang Y, Zhao X. Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory. Hum Brain Mapp 2014; 36:1705-15. [PMID: 25545862 DOI: 10.1002/hbm.22731] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Revised: 12/13/2014] [Accepted: 12/18/2014] [Indexed: 11/10/2022] Open
Abstract
Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks.
Collapse
Affiliation(s)
- Gaoyan Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China; School of Computer Science and Technology, Tianjin key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300072, China
| | | | | | | | | |
Collapse
|
39
|
Frühholz S, Trost W, Grandjean D. The role of the medial temporal limbic system in processing emotions in voice and music. Prog Neurobiol 2014; 123:1-17. [PMID: 25291405 DOI: 10.1016/j.pneurobio.2014.09.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Revised: 09/16/2014] [Accepted: 09/29/2014] [Indexed: 01/15/2023]
Abstract
Subcortical brain structures of the limbic system, such as the amygdala, are thought to decode the emotional value of sensory information. Recent neuroimaging studies, as well as lesion studies in patients, have shown that the amygdala is sensitive to emotions in voice and music. Similarly, the hippocampus, another part of the temporal limbic system (TLS), is responsive to vocal and musical emotions, but its specific roles in emotional processing from music and especially from voices have been largely neglected. Here we review recent research on vocal and musical emotions, and outline commonalities and differences in the neural processing of emotions in the TLS in terms of emotional valence, emotional intensity and arousal, as well as in terms of acoustic and structural features of voices and music. We summarize the findings in a neural framework including several subcortical and cortical functional pathways between the auditory system and the TLS. This framework proposes that some vocal expressions might already receive a fast emotional evaluation via a subcortical pathway to the amygdala, whereas cortical pathways to the TLS are thought to be equally used for vocal and musical emotions. While the amygdala might be specifically involved in a coarse decoding of the emotional value of voices and music, the hippocampus might process more complex vocal and musical emotions, and might have an important role especially for the decoding of musical emotions by providing memory-based and contextual associations.
Collapse
Affiliation(s)
- Sascha Frühholz
- Neuroscience of Emotion and Affective Dynamics Lab, Department of Psychology, University of Geneva, Geneva, Switzerland; Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland.
| | - Wiebke Trost
- Neuroscience of Emotion and Affective Dynamics Lab, Department of Psychology, University of Geneva, Geneva, Switzerland; Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
| | - Didier Grandjean
- Neuroscience of Emotion and Affective Dynamics Lab, Department of Psychology, University of Geneva, Geneva, Switzerland; Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
| |
Collapse
|
40
|
Robineau F, Rieger S, Mermoud C, Pichon S, Koush Y, Van De Ville D, Vuilleumier P, Scharnowski F. Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training. Neuroimage 2014; 100:1-14. [DOI: 10.1016/j.neuroimage.2014.05.072] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Revised: 05/06/2014] [Accepted: 05/27/2014] [Indexed: 12/01/2022] Open
|
41
|
Hui M, Zhang H, Ge R, Yao L, Long Z. Modulation of functional network with real-time fMRI feedback training of right premotor cortex activity. Neuropsychologia 2014; 62:111-23. [DOI: 10.1016/j.neuropsychologia.2014.07.012] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Revised: 05/27/2014] [Accepted: 07/14/2014] [Indexed: 11/16/2022]
|
42
|
Schmidt AT, Hanten G, Li X, Wilde EA, Ibarra AP, Chu ZD, Helbling AR, Shah S, Levin HS. Emotional prosody and diffusion tensor imaging in children after traumatic brain injury. Brain Inj 2014; 27:1528-35. [PMID: 24266795 DOI: 10.3109/02699052.2013.828851] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
PRIMARY OBJECTIVE Brain structures and their white matter connections that may contribute to emotion processing and may be vulnerable to disruption by a traumatic brain injury (TBI) occurring in childhood have not been thoroughly explored. RESEARCH DESIGN AND METHODS The current investigation examines the relationship between diffusion tensor imaging (DTI) metrics, including fractional anisotropy (FA) and apparent diffusion coefficient (ADC), and 3-month post-injury performance on a task of emotion prosody recognition and a control task of phonological discrimination in a group of 91 children who sustained either a moderate-to-severe TBI (n = 45) or orthopaedic injury (OI) (n = 46). MAIN OUTCOMES AND RESULTS Brain-behaviour findings within OI participants confirmed relationships between several significant white matter tracts in emotional prosody performance (i.e. the cingulum bundle, genu of the corpus callosum, inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF). The cingulum and genu were also related to phonological discrimination performance. The TBI group demonstrated few strong brain behaviour relationships, with significant findings emerging only in the cingulum bundle for Emotional Prosody and the genu for Phonological Processing. CONCLUSION The lack of clear relationships in the TBI group is discussed in terms of the likely disruption to cortical networks secondary to significant brain injuries.
Collapse
Affiliation(s)
- Adam T Schmidt
- Department of Psychology and Philosophy, Sam Houston State University , Huntsville, TX , USA
| | | | | | | | | | | | | | | | | |
Collapse
|
43
|
Young BM, Nigogosyan Z, Remsik A, Walton LM, Song J, Nair VA, Grogan SW, Tyler ME, Edwards DF, Caldera K, Sattin JA, Williams JC, Prabhakaran V. Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. FRONTIERS IN NEUROENGINEERING 2014; 7:25. [PMID: 25071547 PMCID: PMC4086321 DOI: 10.3389/fneng.2014.00025] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 06/19/2014] [Indexed: 11/26/2022]
Abstract
Brain-computer interface (BCI) technology is being incorporated into new stroke rehabilitation devices, but little is known about brain changes associated with its use. We collected anatomical and functional MRI of nine stroke patients with persistent upper extremity motor impairment before, during, and after therapy using a BCI system. Subjects were asked to perform finger tapping of the impaired hand during fMRI. Action Research Arm Test (ARAT), 9-Hole Peg Test (9-HPT), and Stroke Impact Scale (SIS) domains of Hand Function (HF) and Activities of Daily Living (ADL) were also assessed. Group-level analyses examined changes in whole-brain task-based functional connectivity (FC) to seed regions in the motor network observed during and after BCI therapy. Whole-brain FC analyses seeded in each thalamus showed FC increases from baseline at mid-therapy and post-therapy (p < 0.05). Changes in FC between seeds at both the network and the connection levels were examined for correlations with changes in behavioral measures. Average motor network FC was increased post-therapy, and changes in average network FC correlated (p < 0.05) with changes in performance on ARAT (R2 = 0.21), 9-HPT (R2 = 0.41), SIS HF (R2 = 0.27), and SIS ADL (R2 = 0.40). Multiple individual connections within the motor network were found to correlate in change from baseline with changes in behavioral measures. Many of these connections involved the thalamus, with change in each of four behavioral measures significantly correlating with change from baseline FC of at least one thalamic connection. These preliminary results show changes in FC that occur with the administration of rehabilitative therapy using a BCI system. The correlations noted between changes in FC measures and changes in behavioral outcomes indicate that both adaptive and maladaptive changes in FC may develop with this therapy and also suggest a brain-behavior relationship that may be stimulated by the neuromodulatory component of BCI therapy.
Collapse
Affiliation(s)
- Brittany Mei Young
- Department of Radiology, University of Wisconsin - Madison Madison, WI, USA ; Medical Scientist Training Program, University of Wisconsin - Madison Madison, WI, USA ; Neuroscience Training Program, University of Wisconsin - Madison Madison, WI, USA
| | - Zack Nigogosyan
- Department of Radiology, University of Wisconsin - Madison Madison, WI, USA
| | - Alexander Remsik
- Department of Radiology, University of Wisconsin - Madison Madison, WI, USA
| | - Léo M Walton
- Neuroscience Training Program, University of Wisconsin - Madison Madison, WI, USA ; Department of Biomedical Engineering, University of Wisconsin - Madison Madison, WI, USA
| | - Jie Song
- Department of Radiology, University of Wisconsin - Madison Madison, WI, USA ; Department of Biomedical Engineering, University of Wisconsin - Madison Madison, WI, USA
| | - Veena A Nair
- Department of Radiology, University of Wisconsin - Madison Madison, WI, USA
| | - Scott W Grogan
- Department of Radiology, University of Wisconsin - Madison Madison, WI, USA
| | - Mitchell E Tyler
- Department of Biomedical Engineering, University of Wisconsin - Madison Madison, WI, USA
| | - Dorothy Farrar Edwards
- Departments of Kinesiology and Medicine, University of Wisconsin - Madison Madison, WI, USA
| | - Kristin Caldera
- Department of Orthopedics and Rehabilitation, University of Wisconsin - Madison Madison, WI, USA
| | - Justin A Sattin
- Department of Neurology, University of Wisconsin - Madison Madison, WI, USA
| | - Justin C Williams
- Neuroscience Training Program, University of Wisconsin - Madison Madison, WI, USA ; Department of Biomedical Engineering, University of Wisconsin - Madison Madison, WI, USA ; Department of Neurosurgery, University of Wisconsin - Madison Madison, WI, USA
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin - Madison Madison, WI, USA ; Medical Scientist Training Program, University of Wisconsin - Madison Madison, WI, USA ; Neuroscience Training Program, University of Wisconsin - Madison Madison, WI, USA ; Department of Neurology, University of Wisconsin - Madison Madison, WI, USA ; Departments of Psychology and Psychiatry, University of Wisconsin - Madison Madison, WI, USA
| |
Collapse
|
44
|
Wander JD, Rao RPN. Brain-computer interfaces: a powerful tool for scientific inquiry. Curr Opin Neurobiol 2014; 25:70-5. [PMID: 24709603 PMCID: PMC3980496 DOI: 10.1016/j.conb.2013.11.013] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Revised: 11/25/2013] [Accepted: 11/28/2013] [Indexed: 10/25/2022]
Abstract
Brain-computer interfaces (BCIs) are devices that record from the nervous system, provide input directly to the nervous system, or do both. Sensory BCIs such as cochlear implants have already had notable clinical success and motor BCIs have shown great promise for helping patients with severe motor deficits. Clinical and engineering outcomes aside, BCIs can also be tremendously powerful tools for scientific inquiry into the workings of the nervous system. They allow researchers to inject and record information at various stages of the system, permitting investigation of the brain in vivo and facilitating the reverse engineering of brain function. Most notably, BCIs are emerging as a novel experimental tool for investigating the tremendous adaptive capacity of the nervous system.
Collapse
Affiliation(s)
- Jeremiah D Wander
- Center for Sensorimotor Neural Engineering and Department of Bioengineering, University of Washington, William H. Foege Building, Box 355061, 4000 15th Ave NE, Seattle, WA 98195, United States
| | - Rajesh P N Rao
- Center for Sensorimotor Neural Engineering and Department of Computer Science and Engineering, University of Washington, AC101 Paul G. Allen Center, Box 352350, 185 Stevens Way, Seattle, WA 98195, United States.
| |
Collapse
|
45
|
Aron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Sci 2014; 18:177-85. [PMID: 24440116 DOI: 10.1016/j.tics.2013.12.003] [Citation(s) in RCA: 1301] [Impact Index Per Article: 130.1] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Revised: 12/03/2013] [Accepted: 12/04/2013] [Indexed: 11/16/2022]
Affiliation(s)
- Adam R Aron
- Department of Psychology, University of California, San Diego, CA, USA.
| | - Trevor W Robbins
- Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Russell A Poldrack
- Departments of Psychology and Neuroscience, University of Texas, Austin, TX, USA
| |
Collapse
|
46
|
Scharnowski F, Rosa MJ, Golestani N, Hutton C, Josephs O, Weiskopf N, Rees G. Connectivity changes underlying neurofeedback training of visual cortex activity. PLoS One 2014; 9:e91090. [PMID: 24609065 PMCID: PMC3946642 DOI: 10.1371/journal.pone.0091090] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 02/06/2014] [Indexed: 11/30/2022] Open
Abstract
Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is a new approach that allows training of voluntary control over regionally specific brain activity. However, the neural basis of successful neurofeedback learning remains poorly understood. Here, we assessed changes in effective brain connectivity associated with neurofeedback training of visual cortex activity. Using dynamic causal modeling (DCM), we found that training participants to increase visual cortex activity was associated with increased effective connectivity between the visual cortex and the superior parietal lobe. Specifically, participants who learned to control activity in their visual cortex showed increased top-down control of the superior parietal lobe over the visual cortex, and at the same time reduced bottom-up processing. These results are consistent with efficient employment of top-down visual attention and imagery, which were the cognitive strategies used by participants to increase their visual cortex activity.
Collapse
Affiliation(s)
- Frank Scharnowski
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
- UCL Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- Institute of Bioengineering, Swiss Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology and Medical Informatics – CIBM, University of Geneva, Geneva, Switzerland
- * E-mail:
| | - Maria Joao Rosa
- Department of Neuroscience, Institute of Psychiatry, King’s College London, London, United Kingdom
| | - Narly Golestani
- UCL Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- University Medical School, University of Geneva, Geneva, Switzerland
| | - Chloe Hutton
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Oliver Josephs
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Geraint Rees
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
- UCL Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| |
Collapse
|
47
|
A graphics processing unit accelerated motion correction algorithm and modular system for real-time fMRI. Neuroinformatics 2014; 11:291-300. [PMID: 23319241 DOI: 10.1007/s12021-013-9176-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) has recently gained interest as a possible means to facilitate the learning of certain behaviors. However, rt-fMRI is limited by processing speed and available software, and continued development is needed for rt-fMRI to progress further and become feasible for clinical use. In this work, we present an open-source rt-fMRI system for biofeedback powered by a novel Graphics Processing Unit (GPU) accelerated motion correction strategy as part of the BioImage Suite project ( www.bioimagesuite.org ). Our system contributes to the development of rt-fMRI by presenting a motion correction algorithm that provides an estimate of motion with essentially no processing delay as well as a modular rt-fMRI system design. Using empirical data from rt-fMRI scans, we assessed the quality of motion correction in this new system. The present algorithm performed comparably to standard (non real-time) offline methods and outperformed other real-time methods based on zero order interpolation of motion parameters. The modular approach to the rt-fMRI system allows the system to be flexible to the experiment and feedback design, a valuable feature for many applications. We illustrate the flexibility of the system by describing several of our ongoing studies. Our hope is that continuing development of open-source rt-fMRI algorithms and software will make this new technology more accessible and adaptable, and will thereby accelerate its application in the clinical and cognitive neurosciences.
Collapse
|
48
|
Hwang HJ, Lim JH, Kim DW, Im CH. Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces. JOURNAL OF BIOMEDICAL OPTICS 2014; 19:77005. [PMID: 25036216 DOI: 10.1117/1.jbo.19.7.077005] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2014] [Accepted: 06/20/2014] [Indexed: 05/15/2023]
Abstract
A number of recent studies have demonstrated that near-infrared spectroscopy (NIRS) is a promisingneuroimaging modality for brain-computer interfaces (BCIs). So far, most NIRS-based BCI studies have focusedon enhancing the accuracy of the classification of different mental tasks. In the present study, we evaluated theperformances of a variety of mental task combinations in order to determine the mental task pairs that are bestsuited for customized NIRS-based BCIs. To this end, we recorded event-related hemodynamic responses whileseven participants performed eight different mental tasks. Classification accuracies were then estimated for allpossible pairs of the eight mental tasks (8C2 = 28). Based on this analysis, mental task combinations with relatively high classification accuracies frequently included the following three mental tasks: “mental multiplication,” “mental rotation,” and “right-hand motor imagery.” Specifically, mental task combinations consisting of two of these three mental tasks showed the highest mean classification accuracies. It is expected that our results will be a useful reference to reduce the time needed for preliminary tests when discovering individual-specific mental task combinations.
Collapse
Affiliation(s)
- Han-Jeong Hwang
- Hanyang University, Department of Biomedical Engineering, Seoul 133-791, Republic of KoreabBerlin Institute of Technology, Machine Learning Group, Marchstrasse 23, Berlin 10587, Germany
| | - Jeong-Hwan Lim
- Hanyang University, Department of Biomedical Engineering, Seoul 133-791, Republic of Korea
| | - Do-Won Kim
- Hanyang University, Department of Biomedical Engineering, Seoul 133-791, Republic of Korea
| | - Chang-Hwan Im
- Hanyang University, Department of Biomedical Engineering, Seoul 133-791, Republic of Korea
| |
Collapse
|
49
|
Haller S, Kopel R, Jhooti P, Haas T, Scharnowski F, Lovblad KO, Scheffler K, Van De Ville D. Dynamic reconfiguration of human brain functional networks through neurofeedback. Neuroimage 2013; 81:243-252. [DOI: 10.1016/j.neuroimage.2013.05.019] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Revised: 04/29/2013] [Accepted: 05/05/2013] [Indexed: 11/24/2022] Open
|
50
|
Frühholz S, Grandjean D. Processing of emotional vocalizations in bilateral inferior frontal cortex. Neurosci Biobehav Rev 2013; 37:2847-55. [PMID: 24161466 DOI: 10.1016/j.neubiorev.2013.10.007] [Citation(s) in RCA: 84] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2013] [Revised: 08/09/2013] [Accepted: 10/14/2013] [Indexed: 12/16/2022]
Abstract
A current view proposes that the right inferior frontal cortex (IFC) is particularly responsible for attentive decoding and cognitive evaluation of emotional cues in human vocalizations. Although some studies seem to support this view, an exhaustive review of all recent imaging studies points to an important functional role of both the right and the left IFC in processing vocal emotions. Second, besides a supposed predominant role of the IFC for an attentive processing and evaluation of emotional voices in IFC, these recent studies also point to a possible role of the IFC in preattentive and implicit processing of vocal emotions. The studies specifically provide evidence that both the right and the left IFC show a similar anterior-to-posterior gradient of functional activity in response to emotional vocalizations. This bilateral IFC gradient depends both on the nature or medium of emotional vocalizations (emotional prosody versus nonverbal expressions) and on the level of attentive processing (explicit versus implicit processing), closely resembling the distribution of terminal regions of distinct auditory pathways, which provide either global or dynamic acoustic information. Here we suggest a functional distribution in which several IFC subregions process different acoustic information conveyed by emotional vocalizations. Although the rostro-ventral IFC might categorize emotional vocalizations, the caudo-dorsal IFC might be specifically sensitive to their temporal features.
Collapse
Affiliation(s)
- Sascha Frühholz
- Neuroscience of Emotion and Affective Dynamics Lab, Department of Psychology, University of Geneva, Geneva, Switzerland; Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland.
| | | |
Collapse
|