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Jaeckle T, Williams SCR, Barker GJ, Basilio R, Carr E, Goldsmith K, Colasanti A, Giampietro V, Cleare A, Young AH, Moll J, Zahn R. Self-blame in major depression: a randomised pilot trial comparing fMRI neurofeedback with self-guided psychological strategies. Psychol Med 2023; 53:2831-2841. [PMID: 34852855 PMCID: PMC10235657 DOI: 10.1017/s0033291721004797] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 02/19/2021] [Accepted: 11/02/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Overgeneralised self-blame and worthlessness are key symptoms of major depressive disorder (MDD) and have previously been associated with self-blame-selective changes in connectivity between right superior anterior temporal lobe (rSATL) and subgenual frontal cortices. Another study showed that remitted MDD patients were able to modulate this neural signature using functional magnetic resonance imaging (fMRI) neurofeedback training, thereby increasing their self-esteem. The feasibility and potential of using this approach in symptomatic MDD were unknown. METHOD This single-blind pre-registered randomised controlled pilot trial probed a novel self-guided psychological intervention with and without additional rSATL-posterior subgenual cortex (BA25) fMRI neurofeedback, targeting self-blaming emotions in people with insufficiently recovered MDD and early treatment-resistance (n = 43, n = 35 completers). Participants completed three weekly self-guided sessions to rebalance self-blaming biases. RESULTS As predicted, neurofeedback led to a training-induced reduction in rSATL-BA25 connectivity for self-blame v. other-blame. Both interventions were safe and resulted in a 46% reduction on the Beck Depression Inventory-II, our primary outcome, with no group differences. Secondary analyses, however, revealed that patients without DSM-5-defined anxious distress showed a superior response to neurofeedback compared with the psychological intervention, and the opposite pattern in anxious MDD. As predicted, symptom remission was associated with increases in self-esteem and this correlated with the frequency with which participants employed the psychological strategies in daily life. CONCLUSIONS These findings suggest that self-blame-rebalance neurofeedback may be superior over a solely psychological intervention in non-anxious MDD, although further confirmatory studies are needed. Simple self-guided strategies tackling self-blame were beneficial, but need to be compared against treatment-as-usual in further trials. https://doi.org/10.1186/ISRCTN10526888.
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Affiliation(s)
- Tanja Jaeckle
- Department of Psychological Medicine, Centre for Affective Disorders, London, UK
| | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Rodrigo Basilio
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Ewan Carr
- Department of Biostatistics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Kimberley Goldsmith
- Department of Biostatistics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alessandro Colasanti
- Department of Psychological Medicine, Centre for Affective Disorders, London, UK
| | - Vincent Giampietro
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Anthony Cleare
- Department of Psychological Medicine, Centre for Affective Disorders, London, UK
| | - Allan H. Young
- Department of Psychological Medicine, Centre for Affective Disorders, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Monks Orchard Road, Beckenham, Kent, BR3 3BX, UK
| | - Jorge Moll
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Roland Zahn
- Department of Psychological Medicine, Centre for Affective Disorders, London, UK
- Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Monks Orchard Road, Beckenham, Kent, BR3 3BX, UK
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2
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Davydov N, Peek L, Auer T, Prilepin E, Gninenko N, Van De Ville D, Nikonorov A, Koush Y. Real-time and Recursive Estimators for Functional MRI Quality Assessment. Neuroinformatics 2022; 20:897-917. [PMID: 35297018 DOI: 10.1007/s12021-022-09582-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2022] [Indexed: 12/31/2022]
Abstract
Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.
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Affiliation(s)
- Nikita Davydov
- Aligned Research Group, Los Gatos, USA.,Samara National Research University, Samara, Russia.,Image Processing Systems Institute, Russian Academy of Science, Samara, Russia
| | - Lucas Peek
- Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Tibor Auer
- School of Psychology, University of Surrey, Guildford, UK
| | | | - Nicolas Gninenko
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Artem Nikonorov
- Samara National Research University, Samara, Russia.,Image Processing Systems Institute, Russian Academy of Science, Samara, Russia
| | - Yury Koush
- Department of Radiology and Medical Imaging, Yale University, New Haven, USA.
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3
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Wallace G, Polcyn S, Brooks PP, Mennen AC, Zhao K, Scotti PS, Michelmann S, Li K, Turk-Browne NB, Cohen JD, Norman KA. RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI. Neuroimage 2022; 257:119295. [PMID: 35580808 PMCID: PMC9494277 DOI: 10.1016/j.neuroimage.2022.119295] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/09/2022] [Indexed: 11/21/2022] Open
Abstract
Real-time fMRI (RT-fMRI) neurofeedback has been shown to be effective in treating neuropsychiatric disorders and holds tremendous promise for future breakthroughs, both with regard to basic science and clinical applications. However, the prevalence of its use has been hampered by computing hardware requirements, the complexity of setting up and running an experiment, and a lack of standards that would foster collaboration. To address these issues, we have developed RT-Cloud (https://github.com/brainiak/rt-cloud), a flexible, cloud-based, open-source Python software package for the execution of RT-fMRI experiments. RT-Cloud uses standardized data formats and adaptable processing streams to support and expand open science in RT-fMRI research and applications. Cloud computing is a key enabling technology for advancing RT-fMRI because it eliminates the need for on-premise technical expertise and high-performance computing; this allows installation, configuration, and maintenance to be automated and done remotely. Furthermore, the scalability of cloud computing makes it easier to deploy computationally-demanding multivariate analyses in real time. In this paper, we describe how RT-Cloud has been integrated with open standards, including the Brain Imaging Data Structure (BIDS) standard and the OpenNeuro database, how it has been applied thus far, and our plans for further development and deployment of RT-Cloud in the coming years.
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Affiliation(s)
- Grant Wallace
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Stephen Polcyn
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Paula P Brooks
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Anne C Mennen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Ke Zhao
- Cognitive Science Program, University of Pennsylvania, Philadelphia, PA, United States
| | - Paul S Scotti
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Sebastian Michelmann
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Kai Li
- Department of Computer Science, Princeton University, Princeton, NJ, United States
| | | | - Jonathan D Cohen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States; Department of Psychology, Princeton University, Princeton, NJ, United States
| | - Kenneth A Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States; Department of Psychology, Princeton University, Princeton, NJ, United States.
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4
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Heunis S, Lamerichs R, Zinger S, Caballero‐Gaudes C, Jansen JFA, Aldenkamp B, Breeuwer M. Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review. Hum Brain Mapp 2020; 41:3439-3467. [PMID: 32333624 PMCID: PMC7375116 DOI: 10.1002/hbm.25010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/13/2020] [Accepted: 04/03/2020] [Indexed: 01/31/2023] Open
Abstract
Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.
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Affiliation(s)
- Stephan Heunis
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | - Rolf Lamerichs
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- Philips ResearchEindhovenThe Netherlands
| | - Svitlana Zinger
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | | | - Jacobus F. A. Jansen
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of RadiologyMaastricht University Medical CentreMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
| | - Bert Aldenkamp
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and NeuropsychologyGhent University HospitalGhentBelgium
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Marcel Breeuwer
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Philips HealthcareBestThe Netherlands
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Fede SJ, Dean SF, Manuweera T, Momenan R. A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review. Front Hum Neurosci 2020; 14:60. [PMID: 32161529 PMCID: PMC7052377 DOI: 10.3389/fnhum.2020.00060] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/07/2020] [Indexed: 11/26/2022] Open
Abstract
Background: Although biofeedback using electrophysiology has been explored extensively, the approach of using neurofeedback corresponding to hemodynamic response is a relatively young field. Real time functional magnetic resonance imaging-based neurofeedback (rt-fMRI-NF) uses sensory feedback to operantly reinforce patterns of neural response. It can be used, for example, to alter visual perception, increase brain connectivity, and reduce depression symptoms. Within recent years, interest in rt-fMRI-NF in both research and clinical contexts has expanded considerably. As such, building a consensus regarding best practices is of great value. Objective: This systematic review is designed to describe and evaluate the variations in methodology used in previous rt-fMRI-NF studies to provide recommendations for rt-fMRI-NF study designs that are mostly likely to elicit reproducible and consistent effects of neurofeedback. Methods: We conducted a database search for fMRI neurofeedback papers published prior to September 26th, 2019. Of 558 studies identified, 146 met criteria for inclusion. The following information was collected from each study: sample size and type, task used, neurofeedback calculation, regulation procedure, feedback, whether feedback was explicitly related to changing brain activity, feedback timing, control group for active neurofeedback, how many runs and sessions of neurofeedback, if a follow-up was conducted, and the results of neurofeedback training. Results: rt-fMRI-NF is typically upregulation practice based on hemodynamic response from a specific region of the brain presented using a continually updating thermometer display. Most rt-fMRI-NF studies are conducted in healthy samples and half evaluate its effect on immediate changes in behavior or affect. The most popular control group method is to provide sham signal from another region; however, many studies do not compare use a comparison group. Conclusions: We make several suggestions for designs of future rt-fMRI-NF studies. Researchers should use feedback calculation methods that consider neural response across regions (i.e., SVM or connectivity), which should be conveyed as intermittent, auditory feedback. Participants should be given explicit instructions and should be assessed on individual differences. Future rt-fMRI-NF studies should use clinical samples; effectiveness of rt-fMRI-NF should be evaluated on clinical/behavioral outcomes at follow-up time points in comparison to both a sham and no feedback control group.
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Affiliation(s)
| | | | | | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States
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6
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Zahn R, Weingartner JH, Basilio R, Bado P, Mattos P, Sato JR, de Oliveira-Souza R, Fontenelle LF, Young AH, Moll J. Blame-rebalance fMRI neurofeedback in major depressive disorder: A randomised proof-of-concept trial. NEUROIMAGE-CLINICAL 2019; 24:101992. [PMID: 31505367 PMCID: PMC6737344 DOI: 10.1016/j.nicl.2019.101992] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 08/12/2019] [Accepted: 08/24/2019] [Indexed: 01/25/2023]
Abstract
Previously, using fMRI, we demonstrated lower connectivity between right anterior superior temporal (ATL) and anterior subgenual cingulate (SCC) regions while patients with major depressive disorder (MDD) experience guilt. This neural signature was detected despite symptomatic remission which suggested a putative role in vulnerability. This randomised controlled double-blind parallel group clinical trial investigated whether patients with MDD are able to voluntarily modulate this neural signature. To this end, we developed a fMRI neurofeedback software (FRIEND), which measures ATL-SCC coupling and displays its levels in real time. Twenty-eight patients with remitted MDD were randomised to two groups, each receiving one session of fMRI neurofeedback whilst retrieving guilt and indignation/anger-related autobiographical memories. They were instructed to feel the emotion whilst trying to increase the level of a thermometer-like display on a screen. Active intervention group: The thermometer levels increased with increasing levels of ATL-SCC correlations in the guilt condition. Control intervention group: The thermometer levels decreased when correlation levels deviated from the previous baseline level in the guilt condition, thus reinforcing stable correlations. Both groups also received feedback during the indignation condition reinforcing stable correlations. We confirmed our predictions that patients in the active intervention group were indeed able to increase levels of ATL-SCC correlations for guilt vs. indignation and their self-esteem after training compared to before training and that this differed significantly from the control intervention group. These data provide proof-of-concept for a novel treatment target for MDD patients and are in keeping with the hypothesis that ATL-SCC connectivity plays a key role in self-worth. https://clinicaltrials.gov/ct2/show/results/NCT01920490 Employs real-time fMRI of anterior temporal –subgenual cingulate connectivity Previously decreased for guilt in major depressive disorder (MDD) beyond remission This RCT shows MDD patients can increase connectivity in one neurofeedback session. Active neurofeedback group increase self-esteem vs control neurofeedback group Training-induced self-esteem increases correlate with connectivity increases
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Affiliation(s)
- Roland Zahn
- Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Julie H Weingartner
- Cognitive and Behavioral Neuroscience Unit, Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Rodrigo Basilio
- Cognitive and Behavioral Neuroscience Unit, Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Patricia Bado
- Cognitive and Behavioral Neuroscience Unit, Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; Instituto de Ciências Biomédicas (ICB), Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Paulo Mattos
- Cognitive and Behavioral Neuroscience Unit, Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - João R Sato
- Cognitive and Behavioral Neuroscience Unit, Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; Center for Mathematics, Computation, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Ricardo de Oliveira-Souza
- Cognitive and Behavioral Neuroscience Unit, Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; Gaffrée e Guinle University Hospital, Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Leo F Fontenelle
- Cognitive and Behavioral Neuroscience Unit, Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil
| | - Allan H Young
- Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Jorge Moll
- Cognitive and Behavioral Neuroscience Unit, Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil; Scients Institute, Palo Alto, USA.
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7
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Lorenzetti V, Melo B, Basílio R, Suo C, Yücel M, Tierra-Criollo CJ, Moll J. Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback. Front Neurol 2018; 9:390. [PMID: 30087646 PMCID: PMC6066986 DOI: 10.3389/fneur.2018.00390] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 05/14/2018] [Indexed: 01/15/2023] Open
Abstract
Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants' preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.
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Affiliation(s)
- Valentina Lorenzetti
- School of Psychology, Faculty of Health Sciences, Australian Catholic University, Melbourne, VIC, Australia.,Department of Psychological Sciences, Institute of Psychology Health and Society, University of Liverpool, Liverpool, United Kingdom.,Brain and Mental Health Laboratory, School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, VIC, Australia
| | - Bruno Melo
- D'Or Institute for Research and Education, IDOR, Rio de Janeiro, Brazil.,Biomedical Engineering Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Rodrigo Basílio
- D'Or Institute for Research and Education, IDOR, Rio de Janeiro, Brazil
| | - Chao Suo
- Brain and Mental Health Laboratory, School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, VIC, Australia
| | - Murat Yücel
- Brain and Mental Health Laboratory, School of Psychological Sciences and Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, VIC, Australia
| | - Carlos J Tierra-Criollo
- Biomedical Engineering Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jorge Moll
- D'Or Institute for Research and Education, IDOR, Rio de Janeiro, Brazil
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8
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Gonçalves ÓF, Batistuzzo MC, Sato JR. Real-time functional magnetic resonance imaging in obsessive-compulsive disorder. Neuropsychiatr Dis Treat 2017; 13:1825-1834. [PMID: 28744133 PMCID: PMC5513821 DOI: 10.2147/ndt.s121139] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The current literature provides substantial evidence of brain alterations associated with obsessive-compulsive disorder (OCD) symptoms (eg, checking, cleaning/decontamination, counting compulsions; harm or sexual, symmetry/exactness obsessions), and emotional problems (eg, defensive/appetitive emotional imbalance, disgust, guilt, shame, and fear learning/extinction) and cognitive impairments associated with this disorder (eg, inhibitory control, working memory, cognitive flexibility). Building on this evidence, new clinical trials can now target specific brain regions/networks. Real-time functional magnetic resonance imaging (rtfMRI) was introduced as a new therapeutic tool for the self-regulation of brain-mind. In this review, we describe initial trials testing the use of rtfMRI to target brain regions associated with specific OCD symptoms (eg, contamination), and other mind-brain processes (eg, cognitive - working memory, inhibitory control, emotional - defensive, appetitive systems, fear reduction through counter-conditioning) found impaired in OCD patients. While this is a novel topic of research, initial evidence shows the promise of using rtfMRI in training the self-regulation of brain regions and mental processes associated with OCD. Additionally, studies with healthy populations have shown that individuals can regulate brain regions associated with cognitive and emotional processes found impaired in OCD. After the initial "proof-of-concept" stage, there is a need to follow up with controlled clinical trials that could test rtfMRI innovative treatments targeting brain regions and networks associated with different OCD symptoms and cognitive-emotional impairments.
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Affiliation(s)
- Óscar F Gonçalves
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
- Spaulding Neuromodulation Center, Department of Physical Medicine & Rehabilitation, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Social and Cognitive Neuroscience Laboratory, Center for Health and Biological Sciences, Mackenzie Presbyterian University
| | - Marcelo C Batistuzzo
- Department and Institute of Psychiatry, University of São Paulo Medical School (FMUSP)
| | - João R Sato
- Mathematics, Computing, and Cognition Center, Universidade Federal do ABC – UFABC, São Paulo, Brazil
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9
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Marins TF, Rodrigues EC, Engel A, Hoefle S, Basílio R, Lent R, Moll J, Tovar-Moll F. Enhancing Motor Network Activity Using Real-Time Functional MRI Neurofeedback of Left Premotor Cortex. Front Behav Neurosci 2015; 9:341. [PMID: 26733832 PMCID: PMC4689787 DOI: 10.3389/fnbeh.2015.00341] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Accepted: 11/23/2015] [Indexed: 01/01/2023] Open
Abstract
Neurofeedback by functional magnetic resonance imaging (fMRI) is a technique of potential therapeutic relevance that allows individuals to be aware of their own neurophysiological responses and to voluntarily modulate the activity of specific brain regions, such as the premotor cortex (PMC), important for motor recovery after brain injury. We investigated (i) whether healthy human volunteers are able to up-regulate the activity of the left PMC during a right hand finger tapping motor imagery (MI) task while receiving continuous fMRI-neurofeedback, and (ii) whether successful modulation of brain activity influenced non-targeted motor control regions. During the MI task, participants of the neurofeedback group (NFB) received ongoing visual feedback representing the level of fMRI responses within their left PMC. Control (CTL) group participants were shown similar visual stimuli, but these were non-contingent on brain activity. Both groups showed equivalent levels of behavioral ratings on arousal and MI, before and during the fMRI protocol. In the NFB, but not in CLT group, brain activation during the last run compared to the first run revealed increased activation in the left PMC. In addition, the NFB group showed increased activation in motor control regions extending beyond the left PMC target area, including the supplementary motor area, basal ganglia and cerebellum. Moreover, in the last run, the NFB group showed stronger activation in the left PMC/inferior frontal gyrus when compared to the CTL group. Our results indicate that modulation of PMC and associated motor control areas can be achieved during a single neurofeedback-fMRI session. These results contribute to a better understanding of the underlying mechanisms of MI-based neurofeedback training, with direct implications for rehabilitation strategies in severe brain disorders, such as stroke.
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Affiliation(s)
- Theo F Marins
- D'Or Institute for Research and EducationRio de Janeiro, Brazil; Institute of Biomedical Sciences, Federal University of Rio de JaneiroRio de Janeiro, Brazil
| | - Erika C Rodrigues
- D'Or Institute for Research and EducationRio de Janeiro, Brazil; Augusto Motta University (Unisuam)Rio de Janeiro, Brazil
| | - Annerose Engel
- D'Or Institute for Research and EducationRio de Janeiro, Brazil; Clinic for Cognitive Neurology, University Hospital LeipzigLeipzig, Germany
| | - Sebastian Hoefle
- D'Or Institute for Research and Education Rio de Janeiro, Brazil
| | - Rodrigo Basílio
- D'Or Institute for Research and Education Rio de Janeiro, Brazil
| | - Roberto Lent
- D'Or Institute for Research and EducationRio de Janeiro, Brazil; Institute of Biomedical Sciences, Federal University of Rio de JaneiroRio de Janeiro, Brazil; National Institute for Translational Neuroscience (INNT)Rio de Janeiro, Brazil
| | - Jorge Moll
- D'Or Institute for Research and EducationRio de Janeiro, Brazil; National Institute for Translational Neuroscience (INNT)Rio de Janeiro, Brazil
| | - Fernanda Tovar-Moll
- D'Or Institute for Research and EducationRio de Janeiro, Brazil; Institute of Biomedical Sciences, Federal University of Rio de JaneiroRio de Janeiro, Brazil
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