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Watve A, Haugg A, Frei N, Koush Y, Willinger D, Bruehl AB, Stämpfli P, Scharnowski F, Sladky R. Facing emotions: real-time fMRI-based neurofeedback using dynamic emotional faces to modulate amygdala activity. Front Neurosci 2024; 17:1286665. [PMID: 38274498 PMCID: PMC10808718 DOI: 10.3389/fnins.2023.1286665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction Maladaptive functioning of the amygdala has been associated with impaired emotion regulation in affective disorders. Recent advances in real-time fMRI neurofeedback have successfully demonstrated the modulation of amygdala activity in healthy and psychiatric populations. In contrast to an abstract feedback representation applied in standard neurofeedback designs, we proposed a novel neurofeedback paradigm using naturalistic stimuli like human emotional faces as the feedback display where change in the facial expression intensity (from neutral to happy or from fearful to neutral) was coupled with the participant's ongoing bilateral amygdala activity. Methods The feasibility of this experimental approach was tested on 64 healthy participants who completed a single training session with four neurofeedback runs. Participants were assigned to one of the four experimental groups (n = 16 per group), i.e., happy-up, happy-down, fear-up, fear-down. Depending on the group assignment, they were either instructed to "try to make the face happier" by upregulating (happy-up) or downregulating (happy-down) the amygdala or to "try to make the face less fearful" by upregulating (fear-up) or downregulating (fear-down) the amygdala feedback signal. Results Linear mixed effect analyses revealed significant amygdala activity changes in the fear condition, specifically in the fear-down group with significant amygdala downregulation in the last two neurofeedback runs as compared to the first run. The happy-up and happy-down groups did not show significant amygdala activity changes over four runs. We did not observe significant improvement in the questionnaire scores and subsequent behavior. Furthermore, task-dependent effective connectivity changes between the amygdala, fusiform face area (FFA), and the medial orbitofrontal cortex (mOFC) were examined using dynamic causal modeling. The effective connectivity between FFA and the amygdala was significantly increased in the happy-up group (facilitatory effect) and decreased in the fear-down group. Notably, the amygdala was downregulated through an inhibitory mechanism mediated by mOFC during the first training run. Discussion In this feasibility study, we intended to address key neurofeedback processes like naturalistic facial stimuli, participant engagement in the task, bidirectional regulation, task congruence, and their influence on learning success. It demonstrated that such a versatile emotional face feedback paradigm can be tailored to target biased emotion processing in affective disorders.
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Affiliation(s)
- Apurva Watve
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital, University of Zürich, Zürich, Switzerland
| | - Amelie Haugg
- Department of Child and Adolescent Psychiatry, Psychiatric Hospital, University of Zürich, Zürich, Switzerland
| | - Nada Frei
- Department of Child and Adolescent Psychiatry, Psychiatric Hospital, University of Zürich, Zürich, Switzerland
| | - Yury Koush
- Magnetic Resonance Research Center (MRRC), Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - David Willinger
- Department of Child and Adolescent Psychiatry, Psychiatric Hospital, University of Zürich, Zürich, Switzerland
- Division of Psychodynamics, Department of Psychology and Psychodynamics, Karl Landsteiner University of Health Sciences, Krems an der Donau, Lower Austria, Austria
- Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Zürich, Switzerland
| | - Annette Beatrix Bruehl
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital, University of Zürich, Zürich, Switzerland
- Center for Affective, Stress and Sleep Disorders, Psychiatric University Hospital Basel, Basel, Switzerland
| | - Philipp Stämpfli
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital, University of Zürich, Zürich, Switzerland
| | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital, University of Zürich, Zürich, Switzerland
- Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Zürich, Switzerland
- Zurich Center for Integrative Human Physiology, Faculty of Medicine, University of Zürich, Zürich, Switzerland
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Ronald Sladky
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital, University of Zürich, Zürich, Switzerland
- Social, Cognitive and Affective Neuroscience Unit, Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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3
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Zhao Z, Galiana G, Zillo C, Camarro T, Qiu M, Papademetris X, Hampson M. HALO: A software tool for real-time head alignment in the MR scanner. Magn Reson Med 2023; 89:1506-1513. [PMID: 36426774 PMCID: PMC10753491 DOI: 10.1002/mrm.29535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 10/27/2022] [Accepted: 10/31/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE MRI studies in human subjects often require multiple scanning sessions/visits. Changes in a subject's head position across sessions result in different alignment between brain tissues and the magnetic field which leads to changes in magnetic susceptibility. These changes can have considerable impacts on acquired signals. Head ALignment Optimization (HALO), a software tool was developed by the authors for active head alignment between sessions. METHODS HALO provides real-time visual feedback of a subject's current head position relative to the position in a previous session. The tool was evaluated in a pilot sample of seven healthy human subjects. RESULTS HALO was shown to enable subjects to actively align their head positions to the desired position of their initial sessions. The subjects were able to improve their head alignment significantly using HALO and achieved good alignment with their first session meeting stringent criteria similar to that used for within-run head motion (less than 2 mm translation or 2 degrees rotation in any direction from the desired position). Moreover, we found a negative correlation between the post-alignment rotation and similarity in inter-session BOLD patterns around the air-tissue interface near sinus which further highlighted the impact of tissue-field alignment on BOLD data quality. CONCLUSION Utilization of HALO in longitudinal studies may help to improve data quality by ensuring the consistency of susceptibility gradients in brain tissues across sessions. HALO has been made publicly available.
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Affiliation(s)
- Zhiying Zhao
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine
- Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Gigi Galiana
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine
| | | | - Terry Camarro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine
| | - Maolin Qiu
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine
| | | | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine
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González Méndez PP, Rodino Climent J, Stanley JA, Sitaram R. Real-Time fMRI Neurofeedback Training as a Neurorehabilitation Approach on Depressive Disorders: A Systematic Review of Randomized Control Trials. J Clin Med 2022; 11:jcm11236909. [PMID: 36498484 PMCID: PMC9737316 DOI: 10.3390/jcm11236909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/02/2022] [Accepted: 11/10/2022] [Indexed: 11/25/2022] Open
Abstract
Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-nf) training is an emerging intervention for neurorehabilitation. However, its translation into clinical use on participants with clinical depression is unclear, the effect estimates from randomized control trials and the certainty of the supporting evidence on the effect estimates are unknown. As the number of studies on neurofeedback increases every year, and better quality evidence becomes available, we evaluate the evidence of all randomized control trials available on the clinical application of rt-fMRI-nf training on participants with clinical depression. We performed electronic searches in Pubmed, Embase, CENTRAL, rtFIN database, Epistemonikos, trial registers, reference lists, other systematic reviews, conference abstracts, and cross-citation in Google Scholar. Reviewers independently selected studies, extracted data and evaluated the risk of bias. The certainty of the evidence was judged using the GRADE framework. This review complies with PRISMA guidelines and was submitted to PROSPERO registration. We found 435 results. After the selection process, we included 11 reports corresponding to four RCTs. The effect of rt-fMRI-nf on improving the severity of clinical depression scores demonstrated a tendency to favor the intervention; however, the general effect was not significant. At end of treatment, SMD (standardized mean difference): -0.32 (95% CI -0.73 to 0.10). At follow-up, SMD: -0.33 (95% CI -0.91, 1.25). All the studies showed changes in BOLD fMRI activation after training; however, only one study confirmed regulation success during a transfer run. Whole-brain analyses suggests that rt-fMRI nf may alter activity patterns in brain networks. More studies are needed to evaluate quality of life, acceptability, adverse effects, cognitive tasks, and physiology measures. We conclude that the current evidence on the effect of rt-fMRI-nf training for decision-making outcomes in patients with clinical depression is still based on low certainty of the evidence.
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Affiliation(s)
- Pamela P. González Méndez
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
- Correspondence: (P.P.G.M.); (R.S.)
| | - Julio Rodino Climent
- Brain Dynamics Laboratory, School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso 2362905, Chile
| | - Jeffrey A. Stanley
- Department of Psychiatry and Behavioral Neurosciences, School of Medicine, Wayne State University, Detroit, MI 48202, USA
| | - Ranganatha Sitaram
- Diagnostic Imaging Department, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
- Correspondence: (P.P.G.M.); (R.S.)
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5
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Sanders ZB, Fleming MK, Smejka T, Marzolla MC, Zich C, Rieger SW, Lührs M, Goebel R, Sampaio-Baptista C, Johansen-Berg H. Self-modulation of motor cortex activity after stroke: a randomized controlled trial. Brain 2022; 145:3391-3404. [PMID: 35960166 PMCID: PMC9586541 DOI: 10.1093/brain/awac239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 06/01/2022] [Accepted: 06/20/2022] [Indexed: 11/14/2022] Open
Abstract
Real-time functional MRI neurofeedback allows individuals to self-modulate their ongoing brain activity. This may be a useful tool in clinical disorders that are associated with altered brain activity patterns. Motor impairment after stroke has previously been associated with decreased laterality of motor cortex activity. Here we examined whether chronic stroke survivors were able to use real-time fMRI neurofeedback to increase laterality of motor cortex activity and assessed effects on motor performance and on brain structure and function. We carried out a randomized, double-blind, sham-controlled trial (ClinicalTrials.gov: NCT03775915) in which 24 chronic stroke survivors with mild to moderate upper limb impairment experienced three training days of either Real (n = 12) or Sham (n = 12) neurofeedback. Assessments of brain structure, brain function and measures of upper-limb function were carried out before and 1 week after neurofeedback training. Additionally, measures of upper-limb function were repeated 1 month after neurofeedback training. Primary outcome measures were (i) changes in lateralization of motor cortex activity during movements of the stroke-affected hand throughout neurofeedback training days; and (ii) changes in motor performance of the affected limb on the Jebsen Taylor Test (JTT). Stroke survivors were able to use Real neurofeedback to increase laterality of motor cortex activity within (P = 0.019), but not across, training days. There was no group effect on the primary behavioural outcome measure, which was average JTT performance across all subtasks (P = 0.116). Secondary analysis found improvements in the performance of the gross motor subtasks of the JTT in the Real neurofeedback group compared to Sham (P = 0.010). However, there were no improvements on the Action Research Arm Test or the Upper Extremity Fugl–Meyer score (both P > 0.5). Additionally, decreased white-matter asymmetry of the corticospinal tracts was detected 1 week after neurofeedback training (P = 0.008), indicating that the tracts become more similar with Real neurofeedback. Changes in the affected corticospinal tract were positively correlated with participants neurofeedback performance (P = 0.002). Therefore, here we demonstrate that chronic stroke survivors are able to use functional MRI neurofeedback to self-modulate motor cortex activity in comparison to a Sham control, and that training is associated with improvements in gross hand motor performance and with white matter structural changes.
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Affiliation(s)
- Zeena Britt Sanders
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Melanie K Fleming
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Tom Smejka
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Marilien C Marzolla
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Catharina Zich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Sebastian W Rieger
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, 6229 EV Maastricht, The Netherlands.,Research Department, Brain Innovation B.V., 6229 EV Maastricht, The Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, 6229 EV Maastricht, The Netherlands.,Research Department, Brain Innovation B.V., 6229 EV Maastricht, The Netherlands
| | - Cassandra Sampaio-Baptista
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK.,Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G61 1QH, UK
| | - Heidi Johansen-Berg
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
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Paolini M, Keeser D, Rauchmann BS, Gschwendtner S, Jeanty H, Reckenfelderbäumer A, Yaseen O, Reidler P, Rabenstein A, Engelbregt HJ, Maywald M, Blautzik J, Ertl-Wagner B, Pogarell O, Rüther T, Karch S. Correlations Between the DMN and the Smoking Cessation Outcome of a Real-Time fMRI Neurofeedback Supported Exploratory Therapy Approach: Descriptive Statistics on Tobacco-Dependent Patients. Clin EEG Neurosci 2022; 53:287-296. [PMID: 34878329 PMCID: PMC9174614 DOI: 10.1177/15500594211062703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/28/2021] [Accepted: 10/28/2021] [Indexed: 11/30/2022]
Abstract
The aim of this study was to explore the potential of default mode network (DMN) functional connectivity for predicting the success of smoking cessation in patients with tobacco dependence in the context of a real-time function al MRI (RT-fMRI) neurofeedback (NF) supported therapy.Fifty-four tobacco-dependent patients underwent three RT-fMRI-NF sessions including resting-state functional connectivity (RSFC) runs over a period of 4 weeks during professionally assisted smoking cessation. Patients were randomized into two groups that performed either active NF of an addiction-related brain region or sham NF. After preprocessing, the RSFC baseline data were statistically evaluated using seed-based ROI (SBA) approaches taking into account the smoking status of patients after 3 months (abstinence/relapse).The results of the real study group showed a widespread functional connectivity in the relapse subgroup (n = 10) exceeding the DMN template and mainly low correlations and anticorrelations in the within-seed analysis. In contrast, the connectivity pattern of the abstinence subgroup (n = 8) primarily contained the core DMN in the seed-to-whole-brain analysis and a left lateralized correlation pattern in the within-seed analysis. Calculated Multi-Subject Dictionary Learning (MSDL) matrices showed anticorrelations between DMN regions and salience regions in the abstinence group. Concerning the sham group, results of the relapse subgroup (n = 4) and the abstinence subgroup (n = 6) showed similar trends only in the within-seed analysis.In the setting of a RT-fMRI-NF-assisted therapy, a widespread intrinsic DMN connectivity and a low negative coupling between the DMN and the salience network (SN) in patients with tobacco dependency during early withdrawal may be useful as an early indicator of later therapy nonresponse.
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Affiliation(s)
- Marco Paolini
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
| | - Daniel Keeser
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Sarah Gschwendtner
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Hannah Jeanty
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Arne Reckenfelderbäumer
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Omar Yaseen
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Paul Reidler
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
| | - Andrea Rabenstein
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Hessel Jan Engelbregt
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Hersencentrum Mental Health Institute, Amsterdam, the
Netherlands
| | - Maximilian Maywald
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Janusch Blautzik
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
- Institute for Radiology and Nuclear
Medicine St. Anna, Luzern, Switzerland
| | - Birgit Ertl-Wagner
- Department of Radiology, University
Hospital, LMU Munich, Munich, Germany
- Division of Neuro-Radiology, The Hospital for Sick Children,
University of Toronto, Toronto, Canada
| | - Oliver Pogarell
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Tobias Rüther
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Susanne Karch
- Department of Psychiatry and
Psychotherapy, University Hospital, LMU Munich, Munich, Germany
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Ciarlo A, Russo AG, Ponticorvo S, Di Salle F, Lührs M, Goebel R, Esposito F. Semantic fMRI neurofeedback: A Multi-Subject Study at 3 Tesla. J Neural Eng 2022; 19. [PMID: 35561669 DOI: 10.1088/1741-2552/ac6f81] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Real-time fMRI neurofeedback is a non-invasive procedure allowing the self-regulation of brain functions via enhanced self-control of fMRI based neural activation. In semantic real-time fMRI neurofeedback, an estimated relation between multivariate fMRI activation patterns and abstract mental states is exploited for a multi-dimensional feedback stimulus via real-time representational similarity analysis (rt-RSA). Here, we assessed the performances of this framework in a multi-subject multi-session study on a 3T MRI clinical scanner. APPROACH Eighteen healthy volunteers underwent two semantic real-time fMRI neurofeedback sessions on two different days. In each session, participants were first requested to engage in specific mental states while local fMRI patterns of brain activity were recorded during stimulated mental imagery of concrete objects (pattern generation). The obtained neural representations were to be replicated and modulated by the participants in subsequent runs of the same session under the guidance of a rt-RSA generated visual feedback (pattern modulation). Performance indicators were derived from the rt-RSA output to assess individual abilities in replicating (and maintaining over time) a target pattern. Simulations were carried out to assess the impact of the geometric distortions implied by the low-dimensional representation of patterns' dissimilarities in the visual feedback. MAIN RESULTS Sixteen subjects successfully completed both semantic real-time fMRI neurofeedback sessions. Considering some performance indicators, a significant improvement between the first and the second runs, and within run increasing modulation performances were observed, whereas no improvements were found between sessions. Simulations confirmed that in a small percentage of cases visual feedback could be affected by metric distortions due to dimensionality reduction implicit to the rt-RSA approach. SIGNIFICANCE Our results proved the feasibility of the semantic real-time fMRI neurofeedback at 3T, showing that subjects can successfully modulate and maintain a target mental state, guided by rt-RSA derived feedback. Further development is needed to encourage future clinical applications.
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Affiliation(s)
- Assunta Ciarlo
- University of Salerno - Baronissi Campus, Via S. Allende, Baronissi, Campania, 84081, ITALY
| | | | - Sara Ponticorvo
- University of Salerno - Baronissi Campus, Via S. Allende, Baronissi, Campania, 84081, ITALY
| | - Francesco Di Salle
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno - Baronissi Campus, Via S. Allende, Baronissi, Campania, 84081, ITALY
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, P.O. Box 616, Maastricht, Limburg, 6200 MD, NETHERLANDS
| | - Rainer Goebel
- Faculty of Psychology, University of Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands, Maastricht, 6200 MD, NETHERLANDS
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli School of Medicine and Surgery, Piazza L. Miraglia, Napoli, 80138, ITALY
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Nicholson AA, Rabellino D, Densmore M, Frewen PA, Steryl D, Scharnowski F, Théberge J, Neufeld RWJ, Schmahl C, Jetly R, Lanius RA. Differential mechanisms of posterior cingulate cortex downregulation and symptom decreases in posttraumatic stress disorder and healthy individuals using real-time fMRI neurofeedback. Brain Behav 2022; 12:e2441. [PMID: 34921746 PMCID: PMC8785646 DOI: 10.1002/brb3.2441] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/25/2021] [Accepted: 11/09/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Intrinsic connectivity networks, including the default mode network (DMN), are frequently disrupted in individuals with posttraumatic stress disorder (PTSD). The posterior cingulate cortex (PCC) is the main hub of the posterior DMN, where the therapeutic regulation of this region with real-time fMRI neurofeedback (NFB) has yet to be explored. METHODS We investigated PCC downregulation while processing trauma/stressful words over 3 NFB training runs and a transfer run without NFB (total n = 29, PTSD n = 14, healthy controls n = 15). We also examined the predictive accuracy of machine learning models in classifying PTSD versus healthy controls during NFB training. RESULTS Both the PTSD and healthy control groups demonstrated reduced reliving symptoms in response to trauma/stressful stimuli, where the PTSD group additionally showed reduced symptoms of distress. We found that both groups were able to downregulate the PCC with similar success over NFB training and in the transfer run, although downregulation was associated with unique within-group decreases in activation within the bilateral dmPFC, bilateral postcentral gyrus, right amygdala/hippocampus, cingulate cortex, and bilateral temporal pole/gyri. By contrast, downregulation was associated with increased activation in the right dlPFC among healthy controls as compared to PTSD. During PCC downregulation, right dlPFC activation was negatively correlated to PTSD symptom severity scores and difficulties in emotion regulation. Finally, machine learning algorithms were able to classify PTSD versus healthy participants based on brain activation during NFB training with 80% accuracy. CONCLUSIONS This is the first study to investigate PCC downregulation with real-time fMRI NFB in both PTSD and healthy controls. Our results reveal acute decreases in symptoms over training and provide converging evidence for EEG-NFB targeting brain networks linked to the PCC.
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Affiliation(s)
- Andrew A Nicholson
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada.,Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria
| | - Daniela Rabellino
- Department of Neuroscience, Western University, London, Ontario, Canada.,Imaging, Lawson Health Research Institute, London, Ontario, Canada
| | - Maria Densmore
- Imaging, Lawson Health Research Institute, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada
| | - Paul A Frewen
- Department of Neuroscience, Western University, London, Ontario, Canada.,Department of Psychology, Western University, London, Ontario, Canada
| | - David Steryl
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria
| | - Frank Scharnowski
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria
| | - Jean Théberge
- Imaging, Lawson Health Research Institute, London, Ontario, Canada.,Department of Medical Biophysics, Western University, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada.,Department of Diagnostic Imaging, St. Joseph's Healthcare, London, Ontario, Canada
| | - Richard W J Neufeld
- Department of Neuroscience, Western University, London, Ontario, Canada.,Department of Psychology, Western University, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada.,Department of Psychology, University of British Columbia, Okanagan, Kelowna, British Columbia, Canada
| | - Christian Schmahl
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Heidelberg University, Heidelberg, Germany
| | - Rakesh Jetly
- Canadian Forces, Health Services, Ottawa, Ontario, Canada
| | - Ruth A Lanius
- Department of Neuroscience, Western University, London, Ontario, Canada.,Imaging, Lawson Health Research Institute, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada
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9
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Long Z, Guo Z, Guo Z, Zhang H, Yao L. Dynamic Functional Network Connectivity Changes Associated with fMRI Neurofeedback of Right Premotor Cortex. Brain Sci 2021; 11:582. [PMID: 33946251 DOI: 10.3390/brainsci11050582] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 04/27/2021] [Accepted: 04/27/2021] [Indexed: 01/03/2023] Open
Abstract
Neurofeedback of real-time functional magnetic resonance imaging (rtfMRI) can enable people to self-regulate motor-related brain regions and lead to alteration of motor performance and functional connectivity (FC) underlying motor execution tasks. Numerous studies suggest that FCs dynamically fluctuate over time. However, little is known about the impact of neurofeedback training of the motor-related region on the dynamic characteristics of FC underlying motor execution tasks. This study aims to investigate the mechanism of self-regulation of the right premotor area (PMA) on the underlying dynamic functional network connectivity (DFNC) of motor execution (ME) tasks and reveal the relationship between DFNC, training effect, and motor performance. The results indicate that the experimental group spent less time on state 2, with overall weak connections, and more time on state 6, having strong positive connections between motor-related networks than the control group after the training. For the experimental group’s state 2, the mean dwell time after the training showed negative correlation with the tapping frequency and the amount of upregulation of PMA. The findings show that rtfMRI neurofeedback can change the temporal properties of DFNC, and the DFNC changes in state with overall weak connections were associated with the training effect and the improvement in motor performance.
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10
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Russo AG, Lührs M, Di Salle F, Esposito F, Goebel R. Towards semantic fMRI neurofeedback: navigating among mental states using real-time representational similarity analysis. J Neural Eng 2021; 18. [PMID: 33684900 DOI: 10.1088/1741-2552/abecc3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/08/2021] [Indexed: 11/12/2022]
Abstract
Objective. Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive MRI procedure allowing examined participants to learn to self-regulate brain activity by performing mental tasks. A novel two-step rt-fMRI-NF procedure is proposed whereby the feedback display is updated in real-time based on high-level representations of experimental stimuli (e.g. objects to imagine) via real-time representational similarity analysis of multi-voxel patterns of brain activity.Approach. In a localizer session, the stimuli become associated with anchored points on a two-dimensional representational space where distances approximate between-pattern (dis)similarities. In the NF session, participants modulate their brain response, displayed as a movable point, to engage in a specific neural representation. The developed method pipeline is verified in a proof-of-concept rt-fMRI-NF study at 7 T involving a single healthy participant imagining concrete objects. Based on this data and artificial data sets with similar (simulated) spatio-temporal structure and variable (injected) signal and noise, the dependence on noise is systematically assessed.Main results. The participant in the proof-of-concept study exhibited robust activation patterns in the localizer session and managed to control the neural representation of a stimulus towards the selected target in the NF session. The offline analyses validated the rt-fMRI-NF results, showing that the rapid convergence to the target representation is noise-dependent.Significance. Our proof-of-concept study introduces a new NF method allowing the participant to navigate among different mental states. Compared to traditional NF designs (e.g. using a thermometer display to set the level of the neural signal), the proposed approach provides content-specific feedback to the participant and extra degrees of freedom to the experimenter enabling real-time control of the neural activity towards a target brain state without suggesting a specific mental strategy to the subject.
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Affiliation(s)
- Andrea G Russo
- Department of Political and Communication Sciences, University of Salerno, Fisciano (Salerno), Italy.,Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy
| | - Michael Lührs
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Brain Innovation B.V., Maastricht, The Netherlands
| | - Francesco Di Salle
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy
| | - Fabrizio Esposito
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy.,Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Department of Advanced Medical and Surgical Sciences,University of Campania 'Luigi Vanvitelli', Napoli,Italy
| | - Rainer Goebel
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Brain Innovation B.V., Maastricht, The Netherlands
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11
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Zhao Z, Yao S, Zweerings J, Zhou X, Zhou F, Kendrick KM, Chen H, Mathiak K, Becker B. Putamen volume predicts real-time fMRI neurofeedback learning success across paradigms and neurofeedback target regions. Hum Brain Mapp 2021; 42:1879-1887. [PMID: 33400306 PMCID: PMC7978128 DOI: 10.1002/hbm.25336] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/17/2020] [Accepted: 12/22/2020] [Indexed: 12/11/2022] Open
Abstract
Real-time fMRI guided neurofeedback training has gained increasing interest as a noninvasive brain regulation technique with the potential to modulate functional brain alterations in therapeutic contexts. Individual variations in learning success and treatment response have been observed, yet the neural substrates underlying the learning of self-regulation remain unclear. Against this background, we explored potential brain structural predictors for learning success with pooled data from three real-time fMRI data sets. Our analysis revealed that gray matter volume of the right putamen could predict neurofeedback learning success across the three data sets (n = 66 in total). Importantly, the original studies employed different neurofeedback paradigms during which different brain regions were trained pointing to a general association with learning success independent of specific aspects of the experimental design. Given the role of the putamen in associative learning this finding may reflect an important role of instrumental learning processes and brain structural variations in associated brain regions for successful acquisition of fMRI neurofeedback-guided self-regulation.
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Affiliation(s)
- Zhiying Zhao
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jana Zweerings
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Xinqi Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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12
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Carr SJA, Chen W, Fondran J, Friel H, Sanchez-Gonzalez J, Zhang J, Tatsuoka C. Early Stopping in Experimentation With Real-Time Functional Magnetic Resonance Imaging Using a Modified Sequential Probability Ratio Test. Front Neurosci 2021; 15:643740. [PMID: 34803577 PMCID: PMC8600259 DOI: 10.3389/fnins.2021.643740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 10/13/2021] [Indexed: 11/22/2022] Open
Abstract
Introduction: Functional magnetic resonance imaging (fMRI) often involves long scanning durations to ensure the associated brain activity can be detected. However, excessive experimentation can lead to many undesirable effects, such as from learning and/or fatigue effects, discomfort for the subject, excessive motion artifacts and loss of sustained attention on task. Overly long experimentation can thus have a detrimental effect on signal quality and accurate voxel activation detection. Here, we propose dynamic experimentation with real-time fMRI using a novel statistically driven approach that invokes early stopping when sufficient statistical evidence for assessing the task-related activation is observed. Methods: Voxel-level sequential probability ratio test (SPRT) statistics based on general linear models (GLMs) were implemented on fMRI scans of a mathematical 1-back task from 12 healthy teenage subjects and 11 teenage subjects born extremely preterm (EPT). This approach is based on likelihood ratios and allows for systematic early stopping based on target statistical error thresholds. We adopt a two-stage estimation approach that allows for accurate estimates of GLM parameters before stopping is considered. Early stopping performance is reported for different first stage lengths, and activation results are compared with full durations. Finally, group comparisons are conducted with both early stopped and full duration scan data. Numerical parallelization was employed to facilitate completion of computations involving a new scan within every repetition time (TR). Results: Use of SPRT demonstrates the feasibility and efficiency gains of automated early stopping, with comparable activation detection as with full protocols. Dynamic stopping of stimulus administration was achieved in around half of subjects, with typical time savings of up to 33% (4 min on a 12 min scan). A group analysis produced similar patterns of activity for control subjects between early stopping and full duration scans. The EPT group, individually, demonstrated more variability in location and extent of the activations compared to the normal term control group. This was apparent in the EPT group results, reflected by fewer and smaller clusters. Conclusion: A systematic statistical approach for early stopping with real-time fMRI experimentation has been implemented. This dynamic approach has promise for reducing subject burden and fatigue effects.
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Affiliation(s)
- Sarah J. A. Carr
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Department of Neurology, Case Western Reserve University, Cleveland, OH, United States
| | - Weicong Chen
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Jeremy Fondran
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Harry Friel
- Philips Healthcare, Highland Heights, OH, United States
| | | | - Jing Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Curtis Tatsuoka
- Department of Neurology, Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
- *Correspondence: Curtis Tatsuoka,
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13
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Haugg A, Sladky R, Skouras S, McDonald A, Craddock C, Kirschner M, Herdener M, Koush Y, Papoutsi M, Keynan JN, Hendler T, Cohen Kadosh K, Zich C, MacInnes J, Adcock RA, Dickerson K, Chen N, Young K, Bodurka J, Yao S, Becker B, Auer T, Schweizer R, Pamplona G, Emmert K, Haller S, Van De Ville D, Blefari M, Kim D, Lee J, Marins T, Fukuda M, Sorger B, Kamp T, Liew S, Veit R, Spetter M, Weiskopf N, Scharnowski F. Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity? Hum Brain Mapp 2020; 41:3839-3854. [PMID: 32729652 PMCID: PMC7469782 DOI: 10.1002/hbm.25089] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/18/2020] [Accepted: 05/26/2020] [Indexed: 12/31/2022] Open
Abstract
Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.
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Affiliation(s)
- Amelie Haugg
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Ronald Sladky
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Stavros Skouras
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
| | - Amalia McDonald
- Department of PsychologyUniversity of VirginiaCharlottesvilleVirginia
| | - Cameron Craddock
- Department of Diagnostic MedicineThe University of Texas at Austin Dell Medical SchoolAustinTexas
| | - Matthias Kirschner
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- McConnell Brain Imaging CentreMontréal Neurological Institute, McGill UniversityMontrealCanada
| | - Marcus Herdener
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
| | - Yury Koush
- Magnetic Resonance Research Center, Department of Radiology & Biomedical ImagingYale UniversityNew HavenConnecticut
| | - Marina Papoutsi
- UCL Huntington's Disease CentreInstitute of Neurology, University College LondonLondonEngland
| | - Jackob N. Keynan
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | - Talma Hendler
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | | | - Catharina Zich
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordEngland
| | - Jeff MacInnes
- Institute for Learning and Brain SciencesUniversity of WashingtonSeattleWashington
| | - R. Alison Adcock
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Kathryn Dickerson
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Nan‐Kuei Chen
- Department of Biomedical EngineeringUniversity of ArizonaTucsonArizona
| | - Kymberly Young
- Department of Psychiatry, School of MedicineUniversity of PittsburghPittsburghPennsylvania
| | | | - Shuxia Yao
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Benjamin Becker
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordEngland
| | - Renate Schweizer
- Functional Imaging LaboratoryGerman Primate CenterGöttingenGermany
| | - Gustavo Pamplona
- Hôpital and Ophtalmique Jules GoninUniversity of LausanneLausanneSwitzerland
| | - Kirsten Emmert
- Department of NeurologyUniversity Medical Center Schleswig‐Holstein, Kiel UniversityKielGermany
| | - Sven Haller
- Radiology‐Department of Surgical SciencesUppsala UniversityUppsalaSweden
| | - Dimitri Van De Ville
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
- Department of Radiology and Medical Informatics, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
| | - Maria‐Laura Blefari
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
| | - Dong‐Youl Kim
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Jong‐Hwan Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Theo Marins
- D'Or Institute for Research and Education (IDOR)Rio de JaneiroBrazil
| | - Megumi Fukuda
- School of Fundamental Science and EngineeringWaseda UniversityTokyoJapan
| | - Bettina Sorger
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Tabea Kamp
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Sook‐Lei Liew
- Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Ralf Veit
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center MunichUniversity of TübingenTübingenGermany
| | - Maartje Spetter
- School of PsychologyUniversity of BirminghamBirminghamEngland
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Frank Scharnowski
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
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14
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Vakamudi K, Trapp C, Talaat K, Gao K, Sa De La Rocque Guimaraes B, Posse S. Real-Time Resting-State Functional Magnetic Resonance Imaging Using Averaged Sliding Windows with Partial Correlations and Regression of Confounding Signals. Brain Connect 2020; 10:448-463. [PMID: 32892629 DOI: 10.1089/brain.2020.0758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Background/Introduction: There is considerable interest in using real-time functional magnetic resonance imaging (fMRI) for monitoring functional connectivity dynamics. To date, the majority of real-time resting-state fMRI studies have examined a limited number of brain regions. This is, in part, due to the computational demands of traditional seed- and independent component analysis-based methods, in particular when using increasingly available high-speed fMRI methods. Methods: This study describes a computationally efficient, real-time, seed-based, resting-state fMRI analysis pipeline using moving averaged sliding-windows (ASW) with partial correlations and regression of motion parameters and signals from white matter and cerebrospinal fluid. Results: Analytical and numerical analyses of ASW correlation and sliding-window regression as a function of window width show selectable bandpass filter characteristics and effective suppression of artifactual correlations resulting from signal drifts and transients. The analysis pipeline is compatible with multislab echo-volumar imaging and simultaneous multislice echo-planar imaging with repetition times as short as 136 msec. High-speed, resting-state fMRI data in healthy controls demonstrate the effectiveness of this approach for minimizing artifactual correlations in white and gray matter, which was comparable to conventional regression across the entire scan. Integrating sliding-window averaging (width: W1) within a second-level sliding-window (width: W2) enabled monitoring of intra- and internetwork correlation dynamics of up to 12 resting-state networks with bandpass filter characteristics determined by the first-level sliding-window and temporal resolution W1 + W2. Conclusions: The computational performance and confound tolerance make this seed-based, resting-state fMRI approach suitable for real-time monitoring of data quality and resting-state connectivity dynamics in neuroscience and clinical research studies. Impact statement Using averaged sliding-windows for seed-based correlation and regression of confounding signals provides a powerful model-free approach to increase tolerance to artifactual signal transients in resting-state analysis. The algorithmic efficiency of this sliding-window approach enables real-time, seed-based, resting-state functional magnetic resonance imaging (fMRI) of multiple networks with computation of connectivity matrices and online monitoring of data quality. Integration of a second-level sliding-window enables mapping of resting-state connectivity dynamics. Sensitivity and tolerance to confounding signals compare favorably with conventional correlation and confound regression across the entire scan. This methodological advance has the potential to enhance the clinical utility of resting-state fMRI and facilitate neuroscience applications.
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Affiliation(s)
- Kishore Vakamudi
- Department of Neurology, School of Medicine, The University of New Mexico, Albuquerque, New Mexico, USA
| | - Cameron Trapp
- Department of Neurology, School of Medicine, The University of New Mexico, Albuquerque, New Mexico, USA.,Department of Physics and Astronomy, The University of New Mexico, Albuquerque, New Mexico, USA
| | - Khaled Talaat
- Department of Neurology, School of Medicine, The University of New Mexico, Albuquerque, New Mexico, USA.,Department of Nuclear Engineering, The University of New Mexico, Albuquerque, New Mexico, USA
| | - Kunxiu Gao
- NeurInsight, LLC, Albuquerque, New Mexico, USA
| | | | - Stefan Posse
- Department of Neurology, School of Medicine, The University of New Mexico, Albuquerque, New Mexico, USA.,Department of Physics and Astronomy, The University of New Mexico, Albuquerque, New Mexico, USA
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15
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Liu N, Yao L, Zhao X. Evaluating the amygdala network induced by neurofeedback training for emotion regulation using hierarchical clustering. Brain Res 2020; 1740:146853. [PMID: 32339500 DOI: 10.1016/j.brainres.2020.146853] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 04/08/2020] [Accepted: 04/22/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Previous studies have shown that regulating the target region by real-time fMRI-based neurofeedback training can influence the activation of other regions and the functional connectivity between them. However, it is not clear whether the training effect of neurofeedback, especially in emotion regulation, is manifested in local network specialization or global network integration. In the current study, we chose the left amygdala (LA) as the target region to regulate positive emotion through real-time fMRI training. Average-linkage hierarchical clustering was employed to cluster the fMRI data recorded during the training to construct whole-brain networks and the LA network to which the LA belongs. RESULTS The activation in the LA and those in some other regions were significantly up-regulated during the training. The clustering analysis at group level showed that the feedback training did not affect the number of networks in the whole brain but altered the distribution and functional connectivity in the LA network. CONCLUSION These findings suggested that the feedback training effects in emotion regulation pattern reflected by the activity of the target brain network and the connections within the network were robustly embodied in local network specialization instead of in global network integration.
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Affiliation(s)
- Ning Liu
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
| | - Li Yao
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xiaojie Zhao
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
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16
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Travassos C, Sayal A, Direito B, Castelhano J, Castelo-Branco M. Volitional Modulation of the Left DLPFC Neural Activity Based on a Pain Empathy Paradigm-A Potential Novel Therapeutic Target for Pain. Front Neurol 2020; 11:714. [PMID: 32793103 PMCID: PMC7394699 DOI: 10.3389/fneur.2020.00714] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 06/10/2020] [Indexed: 01/22/2023] Open
Abstract
The ability to perceive and feel another person' pain as if it were one's own pain, e.g., pain empathy, is related to brain activity in the "pain-matrix" network. A non-core region of this network in Dorsolateral Prefrontal Cortex (DLPFC) has been suggested as a modulator of the attentional-cognitive dimensions of pain processing in the context of pain empathy. We conducted a neurofeedback experiment using real-time functional magnetic resonance imaging (rt-fMRI-NF) to investigate the association between activity in the left DLPFC (our neurofeedback target area) and the perspective assumed by the participant ("first-person"/"Self" or "third-person"/"Other" perspective of a pain-inducing stimulus), based on a customized pain empathy task. Our main goals were to assess the participants' ability to volitionally modulate activity in their own DLPFC through an imagery task of pain empathy and to investigate into which extent this ability depends on feedback. Our results demonstrate participants' ability to significantly modulate brain activity of the neurofeedback target area for the "first-person"/"Self" and "third-person"/"Other" perspectives. Results of both perspectives show that the participants were able to modulate (with statistical significance) the activity already in the first run of the session, in spite of being naïve to the task and even in the absence of feedback information. Moreover, they improved modulation throughout the session, particularly in the "Self" perspective. These results provide new insights on the role of DLPFC in pain and pain empathy mechanisms and validate the proposed protocol, paving the way for future interventional studies in clinical populations with empathic deficits.
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Affiliation(s)
- Carolina Travassos
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Siemens Healthineers, Lisbon, Portugal
| | - Alexandre Sayal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Siemens Healthineers, Lisbon, Portugal
| | - Bruno Direito
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - João Castelhano
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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17
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Martz ME, Hart T, Heitzeg MM, Peltier SJ. Neuromodulation of brain activation associated with addiction: A review of real-time fMRI neurofeedback studies. Neuroimage Clin 2020; 27:102350. [PMID: 32736324 PMCID: PMC7394772 DOI: 10.1016/j.nicl.2020.102350] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/07/2020] [Accepted: 07/13/2020] [Indexed: 02/07/2023]
Abstract
Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has emerged in recent years as an imaging modality used to examine volitional control over targeted brain activity. rtfMRI-nf has also been applied clinically as a way to train individuals to self-regulate areas of the brain, or circuitry, involved in various disorders. One such application of rtfMRI-nf has been in the domain of addictive behaviors, including substance use. Given the pervasiveness of substance use and the challenges of existing treatments to sustain abstinence, rtfMRI-nf has been identified as a promising treatment tool. rtfMRI-nf has also been used in basic science research in order to test the ability to modulate brain function involved in addiction. This review focuses first on providing an overview of recent rtfMRI-nf studies in substance-using populations, specifically nicotine, alcohol, and cocaine users, aimed at reducing craving-related brain activation. Next, rtfMRI-nf studies targeting reward responsivity and emotion regulation in healthy samples are reviewed in order to examine the extent to which areas of the brain involved in addiction can be self-regulated using neurofeedback. We propose that future rtfMRI-nf studies could be strengthened by improvements to study design, sample selection, and more robust strategies in the development and assessment of rtfMRI-nf as a clinical treatment. Recommendations for ways to accomplish these improvements are provided. rtfMRI-nf holds much promise as an imaging modality that can directly target key brain regions involved in addiction, however additional studies are needed in order to establish rtfMRI-nf as an effective, and practical, treatment for addiction.
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Affiliation(s)
- Meghan E Martz
- Addiction Center, Department of Psychiatry, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI 48109, USA.
| | - Tabatha Hart
- Addiction Center, Department of Psychiatry, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI 48109, USA
| | - Mary M Heitzeg
- Addiction Center, Department of Psychiatry, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI 48109, USA
| | - Scott J Peltier
- Functional MRI Laboratory, USA; Department of Biomedical Engineering, Bonisteel Interdisciplinary Research Building, 2360 Bonisteel Blvd, Ann Arbor, MI 48109, USA
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18
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Papoutsi M, Magerkurth J, Josephs O, Pépés SE, Ibitoye T, Reilmann R, Hunt N, Payne E, Weiskopf N, Langbehn D, Rees G, Tabrizi SJ. Activity or connectivity? A randomized controlled feasibility study evaluating neurofeedback training in Huntington's disease. Brain Commun 2020; 2:fcaa049. [PMID: 32954301 PMCID: PMC7425518 DOI: 10.1093/braincomms/fcaa049] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/11/2020] [Accepted: 03/27/2020] [Indexed: 12/20/2022] Open
Abstract
Non-invasive methods, such as neurofeedback training, could support cognitive symptom management in Huntington’s disease by targeting brain regions whose function is impaired. The aim of our single-blind, sham-controlled study was to collect rigorous evidence regarding the feasibility of neurofeedback training in Huntington’s disease by examining two different methods, activity and connectivity real-time functional MRI neurofeedback training. Thirty-two Huntington’s disease gene-carriers completed 16 runs of neurofeedback training, using an optimized real-time functional MRI protocol. Participants were randomized into four groups, two treatment groups, one receiving neurofeedback derived from the activity of the supplementary motor area, and another receiving neurofeedback based on the correlation of supplementary motor area and left striatum activity (connectivity neurofeedback training), and two sham control groups, matched to each of the treatment groups. We examined differences between the groups during neurofeedback training sessions and after training at follow-up sessions. Transfer of training was measured by measuring the participants’ ability to upregulate neurofeedback training target levels without feedback (near transfer), as well as by examining change in objective, a priori defined, behavioural measures of cognitive and psychomotor function (far transfer) before and at 2 months after training. We found that the treatment group had significantly higher neurofeedback training target levels during the training sessions compared to the control group. However, we did not find robust evidence of better transfer in the treatment group compared to controls, or a difference between the two neurofeedback training methods. We also did not find evidence in support of a relationship between change in cognitive and psychomotor function and learning success. We conclude that although there is evidence that neurofeedback training can be used to guide participants to regulate the activity and connectivity of specific regions in the brain, evidence regarding transfer of learning and clinical benefit was not robust.
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Affiliation(s)
- Marina Papoutsi
- UCL Huntington’s Disease Centre, Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
- Correspondence to: Marina Papoutsi, PhD UCL Huntington’s Disease Centre, Queen Square Institute of Neurology University College London, Russell Square House, 10–12 Russell Square London WC1B 5EH, UK E-mail:
| | - Joerg Magerkurth
- Birkbeck-UCL Centre for Neuroimaging, University College London, London WC1H 0AP, UK
| | - Oliver Josephs
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Sophia E Pépés
- University of Oxford, Harris Manchester College, Oxford OX1 3TD, UK
| | - Temi Ibitoye
- UCL Huntington’s Disease Centre, Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
| | - Ralf Reilmann
- George Huntington Institute, 48149 Münster, Germany
- Department of Radiology, University of Muenster, 48149 Münster, Germany
- Section for Neurodegeneration and Hertie Institute for Clinical Brain Research, University of Tuebingen, 72076 Tübingen, Germany
| | - Nigel Hunt
- Eastman Dental Institute, University College London, London WC1X 8LD, UK
| | - Edwin Payne
- Eastman Dental Institute, University College London, London WC1X 8LD, UK
| | - Nikolaus Weiskopf
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
- Max Planck Institute for Human Cognitive and Brain Sciences, D-04103 Leipzig, Germany
| | - Douglas Langbehn
- Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Geraint Rees
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, UK
| | - Sarah J Tabrizi
- UCL Huntington’s Disease Centre, Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
- UK Dementia Research Institute at University College London, London WC1E 6BT, UK
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20
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Shiyam Sundar LK, Baajour S, Beyer T, Lanzenberger R, Traub-Weidinger T, Rausch I, Pataraia E, Hahn A, Rischka L, Hienert M, Klebermass EM, Muzik O. Fully Integrated PET/MR Imaging for the Assessment of the Relationship Between Functional Connectivity and Glucose Metabolic Rate. Front Neurosci 2020; 14:252. [PMID: 32269510 PMCID: PMC7111429 DOI: 10.3389/fnins.2020.00252] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 03/06/2020] [Indexed: 01/06/2023] Open
Abstract
In the past, determination of absolute values of cerebral metabolic rate of glucose (CMRGlc) in clinical routine was rarely carried out due to the invasive nature of arterial sampling. With the advent of combined PET/MR imaging technology, CMRGlc values can be obtained non-invasively, thereby providing the opportunity to take advantage of fully quantitative data in clinical routine. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest. To reduce CMRGlc variability associated with these fluctuations, the objective of this study was to determine whether functional connectivity measures derived from resting-state fMRI (rs-fMRI) could be used to correct for these fluctuations in intrinsic brain activity. METHODS We studied 10 healthy volunteers who underwent a test-retest dynamic [18F]FDG-PET study using a fully integrated PET/MR system (Siemens Biograph mMR). To validate the non-invasive derivation of an image-derived input function based on combined analysis of PET and MR data, arterial blood samples were obtained. Using the arterial input function (AIF), parametric images representing CMRGlc were determined using the Patlak graphical approach. Both directed functional connectivity (dFC) and undirected functional connectivity (uFC) were determined between nodes in six major networks (Default mode network, Salience, L/R Executive, Attention, and Sensory-motor network) using either a bivariate-correlation (R coefficient) or a Multi-Variate AutoRegressive (MVAR) model. In addition, the performance of a regional connectivity measure, the fractional amplitude of low frequency fluctuations (fALFF), was also investigated. RESULTS The average intrasubject variability for CMRGlc values between test and retest was determined as (14 ±8%) with an average inter-subject variability of 25% at test and 15% at retest. The average CMRGlc value (umol/100 g/min) across all networks was 39 ±10 at test and increased slightly to 43 ±6 at retest. The R, MVAR and fALFF coefficients showed relatively large test-retest variability in comparison to the inter-subjects variability, resulting in poor reliability (intraclass correlation in the range of 0.11-0.65). More importantly, no significant relationship was found between the R coefficients (for uFC), MVAR coefficients (for dFC) or fALFF and corresponding CMRGlc values for any of the six major networks. DISCUSSION Measurement of functional connectivity within established brain networks did not provide a means to decrease the inter- or intrasubject variability of CMRGlc values. As such, our results indicate that connectivity measured derived from rs-fMRI acquired contemporaneously with PET imaging are not suited for correction of CMRGlc variability associated with intrinsic fluctuations of resting-state brain activity. Thus, given the observed substantial inter- and intrasubject variability of CMRGlc values, the relevance of absolute quantification for clinical routine is presently uncertain.
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Affiliation(s)
- Lalith Kumar Shiyam Sundar
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Shahira Baajour
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ivo Rausch
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | | | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Lucas Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Marius Hienert
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Eva-Maria Klebermass
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Otto Muzik
- Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, United States
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21
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Pereira JA, Sepulveda P, Rana M, Montalba C, Tejos C, Torres R, Sitaram R, Ruiz S. Self-Regulation of the Fusiform Face Area in Autism Spectrum: A Feasibility Study With Real-Time fMRI Neurofeedback. Front Hum Neurosci 2019; 13:446. [PMID: 31920602 PMCID: PMC6933482 DOI: 10.3389/fnhum.2019.00446] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 12/04/2019] [Indexed: 12/27/2022] Open
Abstract
One of the most important and early impairments in autism spectrum disorder (ASD) is the abnormal visual processing of human faces. This deficit has been associated with hypoactivation of the fusiform face area (FFA), one of the main hubs of the face-processing network. Neurofeedback based on real-time fMRI (rtfMRI-NF) is a technique that allows the self-regulation of circumscribed brain regions, leading to specific neural modulation and behavioral changes. The aim of the present study was to train participants with ASD to achieve up-regulation of the FFA using rtfMRI-NF, to investigate the neural effects of FFA up-regulation in ASD. For this purpose, three groups of volunteers with normal I.Q. and fluent language were recruited to participate in a rtfMRI-NF protocol of eight training runs in 2 days. Five subjects with ASD participated as part of the experimental group and received contingent feedback to up-regulate bilateral FFA. Two control groups, each one with three participants with typical development (TD), underwent the same protocol: one group with contingent feedback and the other with sham feedback. Whole-brain and functional connectivity analysis using each fusiform gyrus as independent seeds were carried out. The results show that individuals with TD and ASD can achieve FFA up-regulation with contingent feedback. RtfMRI-NF in ASD produced more numerous and stronger short-range connections among brain areas of the ventral visual stream and an absence of the long-range connections to insula and inferior frontal gyrus, as observed in TD subjects. Recruitment of inferior frontal gyrus was observed in both groups during FAA up-regulation. However, insula and caudate nucleus were only recruited in subjects with TD. These results could be explained from a neurodevelopment perspective as a lack of the normal specialization of visual processing areas, and a compensatory mechanism to process visual information of faces. RtfMRI-NF emerges as a potential tool to study visual processing network in ASD, and to explore its clinical potential.
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Affiliation(s)
- Jaime A. Pereira
- Laboratory for Brain Machine Interfaces and Neuromodulation, Pontifical Catholic University of Chile, Santiago, Chile
- Department of Psychiatry, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
| | - Pradyumna Sepulveda
- Laboratory for Brain Machine Interfaces and Neuromodulation, Pontifical Catholic University of Chile, Santiago, Chile
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Mohit Rana
- Laboratory for Brain Machine Interfaces and Neuromodulation, Pontifical Catholic University of Chile, Santiago, Chile
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Cristian Montalba
- Biomedical Imaging Center, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
| | - Cristian Tejos
- Biomedical Imaging Center, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
- Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile
| | - Rafael Torres
- Department of Psychiatry, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Laboratory for Brain Machine Interfaces and Neuromodulation, Pontifical Catholic University of Chile, Santiago, Chile
- Department of Psychiatry, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
- Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Faculty of Engineering, Pontifical Catholic University of Chile, Santiago, Chile
| | - Sergio Ruiz
- Laboratory for Brain Machine Interfaces and Neuromodulation, Pontifical Catholic University of Chile, Santiago, Chile
- Department of Psychiatry, Faculty of Medicine, Pontifical Catholic University of Chile, Santiago, Chile
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22
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Kaas A, Goebel R, Valente G, Sorger B. Topographic Somatosensory Imagery for Real-Time fMRI Brain-Computer Interfacing. Front Hum Neurosci 2019; 13:427. [PMID: 31920588 PMCID: PMC6915074 DOI: 10.3389/fnhum.2019.00427] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 11/18/2019] [Indexed: 11/23/2022] Open
Abstract
Real-time functional magnetic resonance imaging (fMRI) is a promising non-invasive method for brain-computer interfaces (BCIs). BCIs translate brain activity into signals that allow communication with the outside world. Visual and motor imagery are often used as information-encoding strategies, but can be challenging if not grounded in recent experience in these modalities, e.g., in patients with locked-in-syndrome (LIS). In contrast, somatosensory imagery might constitute a more suitable information-encoding strategy as the somatosensory function is often very robust. Somatosensory imagery has been shown to activate the somatotopic cortex, but it has been unclear so far whether it can be reliably detected on a single-trial level and successfully classified according to specific somatosensory imagery content. Using ultra-high field 7-T fMRI, we show reliable and high-accuracy single-trial decoding of left-foot (LF) vs. right-hand (RH) somatosensory imagery. Correspondingly, higher decoding accuracies were associated with greater spatial separation of hand and foot decoding-weight patterns in the primary somatosensory cortex (S1). Exploiting these novel neuroscientific insights, we developed-and provide a proof of concept for-basic BCI communication by showing that binary (yes/no) answers encoded by somatosensory imagery can be decoded with high accuracy in simulated real-time (in 7 subjects) as well as in real-time (1 subject). This study demonstrates that body part-specific somatosensory imagery differentially activates somatosensory cortex in a topographically specific manner; evidence which was surprisingly still lacking in the literature. It also offers proof of concept for a novel somatosensory imagery-based fMRI-BCI control strategy, with particularly high potential for visually and motor-impaired patients. The strategy could also be transferred to lower MRI field strengths and to mobile functional near-infrared spectroscopy. Finally, given that communication BCIs provide the BCI user with a form of feedback based on their brain signals and can thus be considered as a specific form of neurofeedback, and that repeated use of a BCI has been shown to enhance underlying representations, we expect that the current BCI could also offer an interesting new approach for somatosensory rehabilitation training in the context of stroke and phantom limb pain.
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Affiliation(s)
- Amanda Kaas
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| | - Giancarlo Valente
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
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23
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Vakamudi K, Posse S, Jung R, Cushnyr B, Chohan MO. Real-time presurgical resting-state fMRI in patients with brain tumors: Quality control and comparison with task-fMRI and intraoperative mapping. Hum Brain Mapp 2019; 41:797-814. [PMID: 31692177 PMCID: PMC7268088 DOI: 10.1002/hbm.24840] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 12/11/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with real-time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.
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Affiliation(s)
- Kishore Vakamudi
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico
| | - Stefan Posse
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico.,Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico
| | - Rex Jung
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico
| | - Brad Cushnyr
- Department of Radiology, University of New Mexico, Albuquerque, New Mexico
| | - Muhammad O Chohan
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico
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Abstract
Mental imagery is a promising tool and mechanism of psychological interventions, particularly for mood and anxiety disorders. In parallel developments, neuromodulation techniques have shown promise as add-on therapies in psychiatry, particularly non-invasive brain stimulation for depression. However, these techniques have not yet been combined in a systematic manner. One novel technology that may be able to achieve this is neurofeedback, which entails the self-regulation of activation in specific brain areas or networks (or the self-modulation of distributed activation patterns) by the patients themselves, through real-time feedback of brain activation (for example, from functional magnetic resonance imaging). One of the key mechanisms by which patients learn such self-regulation is mental imagery. Here, we will first review the main mental imagery approaches in psychotherapy and the implicated brain networks. We will then discuss how these networks can be targeted with neuromodulation (neurofeedback or non-invasive or invasive brain stimulation). We will review the clinical evidence for neurofeedback and discuss possible ways of enhancing it through systematic combination with psychological interventions, with a focus on depression, anxiety disorders, and addiction. The overarching aim of this perspective paper will be to open a debate on new ways of developing neuropsychotherapies.
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Affiliation(s)
| | - David E. J. Linden
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
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25
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Sreedharan S, Arun KM, Sylaja PN, Kesavadas C, Sitaram R. Functional Connectivity of Language Regions of Stroke Patients with Expressive Aphasia During Real-Time Functional Magnetic Resonance Imaging Based Neurofeedback. Brain Connect 2019; 9:613-626. [PMID: 31353935 PMCID: PMC6798872 DOI: 10.1089/brain.2019.0674] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Stroke lesions in the language centers of the brain impair the language areas and their connectivity. This article describes the dynamics of functional connectivity (FC) of language areas (FCL) during real-time functional magnetic resonance imaging (RT-fMRI)-based neurofeedback training for poststroke patients with expressive aphasia. The hypothesis is that FCL increases during the upregulation of language areas during neurofeedback training and that the training improves FCL with an increasing number of sessions and restores it toward normalcy. Four test and four control patients with expressive aphasia were recruited for the study along with four healthy volunteers termed as the normal group. The test and normal groups were administered four neurofeedback training sessions in between two test sessions, whereas the control group underwent only the two test sessions. The training session requires the subject to exercise language activity covertly so that it upregulates the feedback signal obtained from the Broca's area (in left inferior frontal gyrus) and amplifies the feedback when it is correlated with the Wernicke's area (in left superior temporal gyrus) using RT-fMRI. FC was measured by Pearson's correlation coefficient. The results indicate that the FC of the test group was weaker in the left hemisphere than that of the normal group, and post-training the connections have strengthened (correlation coefficient increases) in the left hemisphere when compared with the control group. The connections of language areas strengthened in both hemispheres during neurofeedback-based upregulation, and multiple training sessions strengthened new pathways and restored left hemispheric connections toward normalcy.
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Affiliation(s)
- Sujesh Sreedharan
- Division of Artificial Internal Organs, Department of Medical Devices Engineering, Biomedical Technology Wing, Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Trivandrum, India
| | - K M Arun
- Department of Imaging Sciences and Intervention Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Trivandrum, India
| | - P N Sylaja
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Trivandrum, India
| | - Chandrasekharan Kesavadas
- Department of Imaging Sciences and Intervention Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Trivandrum, India
| | - Ranganatha Sitaram
- Institute for Biological and Medical Engineering, Center for Brain-Machine Interfaces and Neuromodulation, and Department of Psychiatry and Division of Neuroscience, Faculties of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
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26
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Chew B, Hauser TU, Papoutsi M, Magerkurth J, Dolan RJ, Rutledge RB. Endogenous fluctuations in the dopaminergic midbrain drive behavioral choice variability. Proc Natl Acad Sci U S A 2019; 116:18732-7. [PMID: 31451671 DOI: 10.1073/pnas.1900872116] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Humans are surprisingly inconsistent in their behavior, often making different choices under identical conditions. Previous research suggests that intrinsic fluctuations in brain activity can influence low-level processes, such as the amount of force applied in a motor response. Here, we show that intrinsic prestimulus brain activity in the dopaminergic midbrain influences how we choose between risky and safe options. Using computational modeling, we demonstrate that endogenous fluctuations alter phasic responses in a decision network and thereby modulate risk taking. Our findings demonstrate that higher-order cognition is influenced by fluctuations in internal brain states, providing a physiological basis for variability in complex human behavior. Human behavior is surprisingly variable, even when facing the same problem under identical circumstances. A prominent example is risky decision making. Economic theories struggle to explain why humans are so inconsistent. Resting-state studies suggest that ongoing endogenous fluctuations in brain activity can influence low-level perceptual and motor processes, but it remains unknown whether endogenous fluctuations also influence high-level cognitive processes including decision making. Here, using real-time functional magnetic resonance imaging, we tested whether risky decision making is influenced by endogenous fluctuations in blood oxygenation level-dependent (BOLD) activity in the dopaminergic midbrain, encompassing ventral tegmental area and substantia nigra. We show that low prestimulus brain activity leads to increased risky choice in humans. Using computational modeling, we show that increased risk taking is explained by enhanced phasic responses to offers in a decision network. Our findings demonstrate that endogenous brain activity provides a physiological basis for variability in complex human behavior.
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Karch S, Paolini M, Gschwendtner S, Jeanty H, Reckenfelderbäumer A, Yaseen O, Maywald M, Fuchs C, Rauchmann BS, Chrobok A, Rabenstein A, Ertl-Wagner B, Pogarell O, Keeser D, Rüther T. Real-Time fMRI Neurofeedback in Patients With Tobacco Use Disorder During Smoking Cessation: Functional Differences and Implications of the First Training Session in Regard to Future Abstinence or Relapse. Front Hum Neurosci 2019; 13:65. [PMID: 30886575 PMCID: PMC6409331 DOI: 10.3389/fnhum.2019.00065] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 02/08/2019] [Indexed: 02/04/2023] Open
Abstract
One of the most prominent symptoms in addiction disorders is the strong desire to consume a particular substance or to show a certain behavior (craving). The strong association between craving and the probability of relapse emphasizes the importance of craving in the therapeutic process. Former studies have demonstrated that neuromodulation using real-time fMRI (rtfMRI) neurofeedback (NF) can be used as a treatment modality in patients with tobacco use disorder. The aim of the present project was to determine whether it is possible to predict the outcome of NF training plus group psychotherapy at the beginning of the treatment. For that purpose, neuronal responses during the first rtfMRI NF session of patients who remained abstinent for at least 3 months were compared to those of patients with relapse. All patients were included in a certified smoke-free course and took part in three NF sessions. During the rtfMRI NF sessions tobacco-associated and neutral pictures were presented. Subjects were instructed to reduce their neuronal responses during the presentation of smoking cues in an individualized region of interest for craving [anterior cingulate cortex (ACC), insula or dorsolateral prefrontal cortex]. Patients were stratified to different groups [abstinence (N = 10) vs. relapse (N = 12)] according to their individual smoking status 3 months after the rtfMRI NF training. A direct comparison of BOLD responses during the first NF-session of patients who had remained abstinent over 3 months after the NF training and patients who had relapsed after 3 months showed that patients of the relapse group demonstrated enhanced BOLD responses, especially in the ACC, the supplementary motor area as well as dorsolateral prefrontal areas, compared to abstinent patients. These results suggest that there is a probability of estimating a successful withdrawal in patients with tobacco use disorder by analyzing the first rtfMRI NF session: a pronounced reduction of frontal responses during NF training in patients might be the functional correlate of better therapeutic success. The results of the first NF sessions could be useful as predictor whether a patient will be able to achieve success after the behavioral group therapy and NF training in quitting smoking or not.
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Affiliation(s)
- Susanne Karch
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Marco Paolini
- Department of Radiology, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Sarah Gschwendtner
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Hannah Jeanty
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Arne Reckenfelderbäumer
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Omar Yaseen
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Maximilian Maywald
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Christina Fuchs
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany.,Department of Radiology, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Agnieszka Chrobok
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Andrea Rabenstein
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Birgit Ertl-Wagner
- Department of Radiology, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Oliver Pogarell
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Daniel Keeser
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany.,Department of Radiology, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
| | - Tobias Rüther
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany
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28
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Bagarinao E, Yoshida A, Ueno M, Terabe K, Kato S, Isoda H, Nakai T. Improved Volitional Recall of Motor-Imagery-Related Brain Activation Patterns Using Real-Time Functional MRI-Based Neurofeedback. Front Hum Neurosci 2018; 12:158. [PMID: 29740302 PMCID: PMC5928248 DOI: 10.3389/fnhum.2018.00158] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 04/05/2018] [Indexed: 11/13/2022] Open
Abstract
Motor imagery (MI), a covert cognitive process where an action is mentally simulated but not actually performed, could be used as an effective neurorehabilitation tool for motor function improvement or recovery. Recent approaches employing brain–computer/brain–machine interfaces to provide online feedback of the MI during rehabilitation training have promising rehabilitation outcomes. In this study, we examined whether participants could volitionally recall MI-related brain activation patterns when guided using neurofeedback (NF) during training. The participants’ performance was compared to that without NF. We hypothesized that participants would be able to consistently generate the relevant activation pattern associated with the MI task during training with NF compared to that without NF. To assess activation consistency, we used the performance of classifiers trained to discriminate MI-related brain activation patterns. Our results showed significantly higher predictive values of MI-related activation patterns during training with NF. Additionally, this improvement in the classification performance tends to be associated with the activation of middle temporal gyrus/inferior occipital gyrus, a region associated with visual motion processing, suggesting the importance of performance monitoring during MI task training. Taken together, these findings suggest that the efficacy of MI training, in terms of generating consistent brain activation patterns relevant to the task, can be enhanced by using NF as a mechanism to enable participants to volitionally recall task-related brain activation patterns.
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Affiliation(s)
| | - Akihiro Yoshida
- Department of Radiological Sciences, Nagoya University Graduate School of Medicine, Nagoya University, Nagoya, Japan.,NeuroImaging and Informatics Lab, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Mika Ueno
- NeuroImaging and Informatics Lab, 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
| | - Haruo Isoda
- Brain & Mind Research Center, Nagoya University, Nagoya, Japan.,Department of Radiological Sciences, Nagoya University Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Toshiharu Nakai
- Department of Radiological Sciences, Nagoya University Graduate School of Medicine, Nagoya University, Nagoya, Japan.,NeuroImaging and Informatics Lab, National Center for Geriatrics and Gerontology, Obu, Japan
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Paret C, Zähringer J, Ruf M, Gerchen MF, Mall S, Hendler T, Schmahl C, Ende G. Monitoring and control of amygdala neurofeedback involves distributed information processing in the human brain. Hum Brain Mapp 2018; 39:3018-3031. [PMID: 29602255 DOI: 10.1002/hbm.24057] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 03/06/2018] [Accepted: 03/13/2018] [Indexed: 01/28/2023] Open
Abstract
Brain-computer interfaces provide conscious access to neural activity by means of brain-derived feedback ("neurofeedback"). An individual's abilities to monitor and control feedback are two necessary processes for effective neurofeedback therapy, yet their underlying functional neuroanatomy is still being debated. In this study, healthy subjects received visual feedback from their amygdala response to negative pictures. Activation and functional connectivity were analyzed to disentangle the role of brain regions in different processes. Feedback monitoring was mapped to the thalamus, ventromedial prefrontal cortex (vmPFC), ventral striatum (VS), and rostral PFC. The VS responded to feedback corresponding to instructions while rPFC activity differentiated between conditions and predicted amygdala regulation. Control involved the lateral PFC, anterior cingulate, and insula. Monitoring and control activity overlapped in the VS and thalamus. Extending current neural models of neurofeedback, this study introduces monitoring and control of feedback as anatomically dissociated processes, and suggests their important role in voluntary neuromodulation.
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Affiliation(s)
- Christian Paret
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Heidelberg, Germany
| | - Jenny Zähringer
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Heidelberg, Germany
| | - Matthias Ruf
- Department Neuroimaging, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Heidelberg, Germany
| | - Martin Fungisai Gerchen
- Department of Clinical Psychology, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Germany and Bernstein Center for Computational Neuroscience Heidelberg/Mannheim, Heidelberg, Germany
| | - Stephanie Mall
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Heidelberg, Germany
| | - Talma Hendler
- Sagol Center for Brain Function, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center and Sagol School of Neuroscience, School of Psychological Sciences and Faculty of Medicine, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Christian Schmahl
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Heidelberg, Germany
| | - Gabriele Ende
- Department Neuroimaging, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Heidelberg, Germany
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30
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Papoutsi M, Weiskopf N, Langbehn D, Reilmann R, Rees G, Tabrizi SJ. Stimulating neural plasticity with real-time fMRI neurofeedback in Huntington's disease: A proof of concept study. Hum Brain Mapp 2018; 39:1339-1353. [PMID: 29239063 PMCID: PMC5838530 DOI: 10.1002/hbm.23921] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 11/14/2017] [Accepted: 12/07/2017] [Indexed: 01/28/2023] Open
Abstract
Novel methods that stimulate neuroplasticity are increasingly being studied to treat neurological and psychiatric conditions. We sought to determine whether real-time fMRI neurofeedback training is feasible in Huntington's disease (HD), and assess any factors that contribute to its effectiveness. In this proof-of-concept study, we used this technique to train 10 patients with HD to volitionally regulate the activity of their supplementary motor area (SMA). We collected detailed behavioral and neuroimaging data before and after training to examine changes of brain function and structure, and cognitive and motor performance. We found that patients overall learned to increase activity of the target region during training with variable effects on cognitive and motor behavior. Improved cognitive and motor performance after training predicted increases in pre-SMA grey matter volume, fMRI activity in the left putamen, and increased SMA-left putamen functional connectivity. Although we did not directly target the putamen and corticostriatal connectivity during neurofeedback training, our results suggest that training the SMA can lead to regulation of associated networks with beneficial effects in behavior. We conclude that neurofeedback training can induce plasticity in patients with Huntington's disease despite the presence of neurodegeneration, and the effects of training a single region may engage other regions and circuits implicated in disease pathology.
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Affiliation(s)
- Marina Papoutsi
- UCL Huntington's Disease Centre, Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Wellcome Trust Centre for NeuroimagingInstitute of Neurology, University College LondonLondonUnited Kingdom
| | | | - Ralf Reilmann
- George Huntington Institute and Department of RadiologyUniversity of MuensterMünsterGermany
- Section for Neurodegeneration and Hertie Institute for Clinical Brain Research, University of TuebingenTübingenGermany
| | - Geraint Rees
- Wellcome Trust Centre for NeuroimagingInstitute of Neurology, University College LondonLondonUnited Kingdom
- Institute of Cognitive Neuroscience, University College LondonLondonUnited Kingdom
| | - Sarah J Tabrizi
- UCL Huntington's Disease Centre, Institute of Neurology, University College LondonLondonUnited Kingdom
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de Pierrefeu A, Fovet T, Hadj-Selem F, Löfstedt T, Ciuciu P, Lefebvre S, Thomas P, Lopes R, Jardri R, Duchesnay E. Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity. Hum Brain Mapp 2018; 39:1777-1788. [PMID: 29341341 DOI: 10.1002/hbm.23953] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 12/14/2017] [Accepted: 01/02/2018] [Indexed: 02/06/2023] Open
Abstract
Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI periods that precede hallucinations versus periods that do not. When applied to whole-brain fMRI data, state-of-the-art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech-related brain regions. The variation in transition-to-hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI-guided therapy for drug-resistant hallucinations, such as fMRI-based neurofeedback.
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Affiliation(s)
| | - Thomas Fovet
- Univ. Lille, CNRS UMR 9193, Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab), PsyCHIC team, Lille, F-59000, France.,CHU Lille, Pôle de Psychiatrie, Unité CURE, Lille, F-59000, France
| | | | - Tommy Löfstedt
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Philippe Ciuciu
- NeuroSpin, CEA, Paris-Saclay, Gif-sur-Yvette, France.,INRIA, CEA, Parietal team, Univ. Paris-Saclay, France
| | - Stephanie Lefebvre
- Univ. Lille, CNRS UMR 9193, Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab), PsyCHIC team, Lille, F-59000, France.,CHU Lille, Pôle de Psychiatrie, Unité CURE, Lille, F-59000, France
| | - Pierre Thomas
- Univ. Lille, CNRS UMR 9193, Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab), PsyCHIC team, Lille, F-59000, France.,CHU Lille, Pôle de Psychiatrie, Unité CURE, Lille, F-59000, France
| | - Renaud Lopes
- Imaging Dpt., Neuroradiology unit, CHU Lille, Lille, F-59000, France.,U1171 - Degenerative and Vascular Cognitive Disorders, Univ. Lille, INSERM, CHU Lille, Lille, F-59000, France
| | - Renaud Jardri
- Univ. Lille, CNRS UMR 9193, Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab), PsyCHIC team, Lille, F-59000, France.,CHU Lille, Pôle de Psychiatrie, Unité CURE, Lille, F-59000, France
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Zweerings J, Pflieger EM, Mathiak KA, Zvyagintsev M, Kacela A, Flatten G, Mathiak K. Impaired Voluntary Control in PTSD: Probing Self-Regulation of the ACC With Real-Time fMRI. Front Psychiatry 2018; 9:219. [PMID: 29899712 PMCID: PMC5989618 DOI: 10.3389/fpsyt.2018.00219] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 05/07/2018] [Indexed: 01/05/2023] Open
Abstract
Background: Post-traumatic stress disorder (PTSD) is characterized by deficits in the self-regulation of cognitions and emotions. Neural networks of emotion regulation may exhibit reduced control mediated by the anterior cingulate cortex (ACC), contributing to aberrant limbic responses in PTSD. Methods: Real-time fMRI neurofeedback (rt-fMRI NF) assessed self-regulation of the ACC in nine patients with PTSD after single trauma exposure and nine matched healthy controls. All participants were instructed to train ACC upregulation on three training days. Results: Both groups achieved regulation, which was associated with wide-spread brain activation encompassing the ACC. Compared to the controls, regulation amplitude and learning rate was lower in patients, correlating with symptom severity. In addition, a frontopolar activation cluster was associated with self-regulation efforts in patients. Conclusions: For the first time, we tested self-regulation of the ACC in patients with PTSD. The observed impairment supports models of ACC-mediated regulation deficits that may contribute to the psychopathology of PTSD. Controlled trials in a larger sample are needed to confirm our findings and to directly investigate whether training of central regulation mechanisms improves emotion regulation in PTSD.
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Affiliation(s)
- Jana Zweerings
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Eliza M Pflieger
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Krystyna A Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Mikhail Zvyagintsev
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany.,Brain Imaging Facility, Interdisciplinary Centre for Clinical Studies (IZKF), Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Anastasia Kacela
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Guido Flatten
- Euregio-Institut für Psychosomatik und Psychotraumatologie, Aachen, Germany
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany.,JARA-Translational Brain Medicine, Aachen, Germany
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33
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Zotev V, Misaki M, Phillips R, Wong CK, Bodurka J. Real-time fMRI neurofeedback of the mediodorsal and anterior thalamus enhances correlation between thalamic BOLD activity and alpha EEG rhythm. Hum Brain Mapp 2017; 39:1024-1042. [PMID: 29181883 DOI: 10.1002/hbm.23902] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 11/16/2017] [Accepted: 11/16/2017] [Indexed: 12/15/2022] Open
Abstract
Real-time fMRI neurofeedback (rtfMRI-nf) with simultaneous EEG allows volitional modulation of BOLD activity of target brain regions and investigation of related electrophysiological activity. We applied this approach to study correlations between thalamic BOLD activity and alpha EEG rhythm. Healthy volunteers in the experimental group (EG, n = 15) learned to upregulate BOLD activity of the target region consisting of the mediodorsal (MD) and anterior (AN) thalamic nuclei using rtfMRI-nf during retrieval of happy autobiographical memories. Healthy subjects in the control group (CG, n = 14) were provided with a sham feedback. The EG participants were able to significantly increase BOLD activities of the MD and AN. Functional connectivity between the MD and the inferior precuneus was significantly enhanced during the rtfMRI-nf task. Average individual changes in the occipital alpha EEG power significantly correlated with the average MD BOLD activity levels for the EG. Temporal correlations between the occipital alpha EEG power and BOLD activities of the MD and AN were significantly enhanced, during the rtfMRI-nf task, for the EG compared to the CG. Temporal correlations with the alpha power were also significantly enhanced for the posterior nodes of the default mode network, including the precuneus/posterior cingulate, and for the dorsal striatum. Our findings suggest that the temporal correlation between the MD BOLD activity and posterior alpha EEG power is modulated by the interaction between the MD and the inferior precuneus, reflected in their functional connectivity. Our results demonstrate the potential of the rtfMRI-nf with simultaneous EEG for noninvasive neuromodulation studies of human brain function.
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Affiliation(s)
- Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, Oklahoma
| | | | - Chung Ki Wong
- Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, Oklahoma.,Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma
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Ramot M, Kimmich S, Gonzalez-Castillo J, Roopchansingh V, Popal H, White E, Gotts SJ, Martin A. Direct modulation of aberrant brain network connectivity through real-time NeuroFeedback. eLife 2017; 6:28974. [PMID: 28917059 PMCID: PMC5626477 DOI: 10.7554/elife.28974] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 08/30/2017] [Indexed: 01/01/2023] Open
Abstract
The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants’ awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns. Even when we are at rest, our brains are always active. For example, areas of the brain involved in vision remain active in complete darkness. Different brain regions that connect together to perform a given task often show coordinated activity at rest. Past studies have shown that these resting connections are different in people with conditions such as autism. Some brain regions are more weakly connected while others are more strongly connected in people with autism spectrum disorder compared to those without. Furthermore, people with more severe symptoms seem to have more abnormal connections. “Neurofeedback training” is a method of changing the resting connections between different brain regions. Scientists measure a brain signal – the connection between different brain regions – from a person in real time. They then provide positive feedback to the person if this signal improves. For example, if a connection that is too weak becomes stronger, the scientists might reinforce this by providing feedback on the success. Previous work has shown that neurofeedback training may even change people’s behaviour. However, it has not yet been explored as a method of treating the abnormal connections seen in people with autism when their brains are at rest. To address this, Ramot et al. used a technique known as “functional magnetic resonance imaging” (or fMRI for short) to measure brain activity in young men with autism. First, certain brain regions were identified as having abnormal resting connections with each other. The participants were then asked to look at a blank screen and to try to reveal a picture hidden underneath. Whenever the connections between the chosen brain regions improved, part of the picture was revealed on the screen, accompanied by an upbeat sound. The participants were unaware that it was their brain signals causing this positive feedback. This form of neurofeedback training successfully changed the abnormal brain connections in most of the participants with autism, making their connections more similar to those seen in the wider population. These effects lasted up to a year after training. Early results also suggest that these changes were related to improvements in symptoms, although further work is needed to see if doctors could reliably use this method as a therapy. These findings show that neurofeedback training could potentially help treat not only autism spectrum disorder, but a range of other disorders that involve abnormal brain connections, including depression and schizophrenia.
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Affiliation(s)
- Michal Ramot
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Sara Kimmich
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Javier Gonzalez-Castillo
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Vinai Roopchansingh
- Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Haroon Popal
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Emily White
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Stephen J Gotts
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Alex Martin
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
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35
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Hohenfeld C, Nellessen N, Dogan I, Kuhn H, Müller C, Papa F, Ketteler S, Goebel R, Heinecke A, Shah NJ, Schulz JB, Reske M, Reetz K. Cognitive Improvement and Brain Changes after Real-Time Functional MRI Neurofeedback Training in Healthy Elderly and Prodromal Alzheimer's Disease. Front Neurol 2017; 8:384. [PMID: 28848488 PMCID: PMC5552678 DOI: 10.3389/fneur.2017.00384] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 07/19/2017] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Cognitive decline is characteristic for Alzheimer's disease (AD) and also for healthy ageing. As a proof-of-concept study, we examined whether this decline can be counteracted using real-time fMRI neurofeedback training. Visuospatial memory and the parahippocampal gyrus (PHG) were targeted. METHODS Sixteen healthy elderly subjects (mean age 63.5 years, SD = 6.663) and 10 patients with prodromal AD (mean age 66.2 years, SD = 8.930) completed the experiment. Four additional healthy subjects formed a sham-feedback condition to validate the paradigm. The protocol spanned five examination days (T1-T5). T1 contained a neuropsychological pre-test, the encoding of a real-world footpath, and an anatomical MRI scan of the brain. T2-T4 included the fMRI neurofeedback training paradigm, in which subjects learned to enhance activation of the left PHG while recalling the path encoded on T1. At T5, the neuropsychological post-test and another anatomical MRI brain scan were performed. The neuropsychological battery included the Montreal Cognitive Assessment (MoCA); the Visual and Verbal Memory Test (VVM); subtests of the Wechsler Memory Scale (WMS); the Visual Patterns Test; and Trail Making Tests (TMT) A and B. RESULTS Healthy elderly and patients with prodromal AD showed improved visuospatial memory performance after neurofeedback training. Healthy subjects also performed better in a working-memory task (WMS backward digit-span) and in the MoCA. Both groups were able to elicit parahippocampal activation during training, but no significant changes in brain activation were found over the course of the training. However, Granger-causality-analysis revealed changes in cerebral connectivity over the course of the training, involving the parahippocampus and identifying the precuneus as main driver of activation in both groups. Voxel-based morphometry showed increases in grey matter volumes in the precuneus and frontal cortex. Neither cognitive enhancements, nor parahippocampal activation were found in the control group undergoing sham-feedback. CONCLUSION These findings suggest that cognitive decline, either related to prodromal AD or healthy ageing, could be counteracted using fMRI-based neurofeedback. Future research needs to determine the potential of this method as a treatment tool.
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Affiliation(s)
- Christian Hohenfeld
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-4, 6), Research Centre Jülich GmbH, Jülich, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Nils Nellessen
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-4, 6), Research Centre Jülich GmbH, Jülich, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Imis Dogan
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-4, 6), Research Centre Jülich GmbH, Jülich, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Hanna Kuhn
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-4, 6), Research Centre Jülich GmbH, Jülich, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Christine Müller
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-4, 6), Research Centre Jülich GmbH, Jülich, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Federica Papa
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-4, 6), Research Centre Jülich GmbH, Jülich, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Simon Ketteler
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-4, 6), Research Centre Jülich GmbH, Jülich, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands.,Maastricht Brain Imaging Centre, Maastricht University, Maastricht, Netherlands.,Brain Innovation, Maastricht, Netherlands
| | | | - N Jon Shah
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-4, 6), Research Centre Jülich GmbH, Jülich, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Jörg B Schulz
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Martina Reske
- Institute of Neuroscience and Medicine (INM-4, 6), Research Centre Jülich GmbH, Jülich, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Kathrin Reetz
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-4, 6), Research Centre Jülich GmbH, Jülich, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
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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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37
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Neyedli HF, Sampaio-Baptista C, Kirkman MA, Havard D, Lührs M, Ramsden K, Flitney DD, Clare S, Goebel R, Johansen-Berg H. Increasing Lateralized Motor Activity in Younger and Older Adults using Real-time fMRI during Executed Movements. Neuroscience 2017; 378:165-174. [PMID: 28214578 PMCID: PMC5953409 DOI: 10.1016/j.neuroscience.2017.02.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 02/06/2017] [Accepted: 02/07/2017] [Indexed: 01/08/2023]
Abstract
Healthy adults performed movements while receiving neurofeedback from real-time fMRI. Two experiments were performed, one with younger and one with older adults. Neurofeedback (NF) represented the laterality of activation in the motor cortices. The NF groups produced more lateralized activity than the sham group.
Neurofeedback training involves presenting an individual with a representation of their brain activity and instructing them to alter the activity using the feedback. One potential application of neurofeedback is for patients to alter neural activity to improve function. For example, there is evidence that greater laterality of movement-related activity is associated with better motor outcomes after stroke; so using neurofeedback to increase laterality may provide a novel route for improving outcomes. However, we must demonstrate that individuals can control relevant neurofeedback signals. Here, we performed two proof-of-concept studies, one in younger (median age: 26 years) and one in older healthy volunteers (median age: 67.5 years). The purpose was to determine if participants could manipulate laterality of activity between the motor cortices using real-time fMRI neurofeedback while performing simple hand movements. The younger cohort trained using their left and right hand, the older group trained using their left hand only. In both studies participants in a neurofeedback group were able to achieve more lateralized activity than those in a sham group (younger adults: F(1,23) = 4.37, p < 0.05; older adults: F(1,15) = 9.08, p < 0.01). Moreover, the younger cohort was able to maintain the lateralized activity for right hand movements once neurofeedback was removed. The older cohort did not maintain lateralized activity upon feedback removal, with the limitation being that they did not train with their right hand. The results provide evidence that neurofeedback can be used with executed movements to promote lateralized brain activity and thus is amenable for testing as a therapeutic intervention for patients following stroke.
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Affiliation(s)
- Heather F Neyedli
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; School of Health and Human Performance, Dalhousie University, Halifax, Nova Scotia, Canada.
| | - Cassandra Sampaio-Baptista
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Matthew A Kirkman
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - David Havard
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, The Netherlands Brain Innovation B.V., Maastricht, The Netherlands
| | - Katie Ramsden
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - David D Flitney
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stuart Clare
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, The Netherlands Brain Innovation B.V., Maastricht, The Netherlands
| | - Heidi Johansen-Berg
- Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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38
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Kirsch M, Gruber I, Ruf M, Kiefer F, Kirsch P. Real-time functional magnetic resonance imaging neurofeedback can reduce striatal cue-reactivity to alcohol stimuli. Addict Biol 2016; 21:982-92. [PMID: 26096546 DOI: 10.1111/adb.12278] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
It has been shown that in alcoholic patients, alcohol-related cues produce increased activation of reward-related brain regions like the ventral striatum (VS), which has been proposed as neurobiological basis of craving. Modulating this activation might be a promising option in the treatment of alcohol addiction. One approach might be real-time functional magnetic resonance imaging neurofeedback (rtfMRI NF). This study was set up to implement and evaluate a rtfMRI approach in a group of non-addicted heavy social drinkers. Thirty-eight heavy drinking students were assigned to a real feedback group (rFB, n = 13), a yoke feedback group (yFB, n = 13) and a passive control group (noFB, n = 12). After conducting a reward task as functional localizer to identify ventral striatal regions, the participants viewed alcohol cues during three NF training blocks in a 3 T MRI scanner. The rFB group received feedback from their own and the yFB from another participants' VS. The noFB group received no feedback. The rFB and the yFB groups were instructed to downregulate the displayed activation. Activation of the VS and prefrontal control regions was compared between the groups. We found significant downregulation of striatal regions specifically in the rFB group. While the rFB and the yFB groups showed significant activation of prefrontal regions during feedback, this activation was only correlated to the reduction of striatal activation in the rFB group. We conclude that rtfMRI NF is a suitable method to reduce striatal activation to alcohol cues. It might be a promising supplement to the treatment of alcoholic patients.
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Affiliation(s)
- Martina Kirsch
- Department of Addiction Behavior and Addiction Medicine; Central Institute of Mental Health; Medical Faculty Mannheim; University of Heidelberg; Germany
| | - Isabella Gruber
- Department of Addiction Behavior and Addiction Medicine; Central Institute of Mental Health; Medical Faculty Mannheim; University of Heidelberg; Germany
| | - Matthias Ruf
- Department of Neuroimaging; Central Institute of Mental Health; Medical Faculty Mannheim; University of Heidelberg; Germany
| | - Falk Kiefer
- Department of Addiction Behavior and Addiction Medicine; Central Institute of Mental Health; Medical Faculty Mannheim; University of Heidelberg; Germany
| | - Peter Kirsch
- Department of Clinical Psychology; Central Institute of Mental Health; Medical Faculty Mannheim; University of Heidelberg; Germany
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39
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Gerin MI, Fichtenholtz H, Roy A, Walsh CJ, Krystal JH, Southwick S, Hampson M. Real-Time fMRI Neurofeedback with War Veterans with Chronic PTSD: A Feasibility Study. Front Psychiatry 2016; 7:111. [PMID: 27445868 PMCID: PMC4914513 DOI: 10.3389/fpsyt.2016.00111] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 06/06/2016] [Indexed: 01/04/2023] Open
Abstract
Many patients with post-traumatic stress disorder (PTSD), especially war veterans, do not respond to available treatments. Here, we describe a novel neurofeedback (NF) intervention using real-time functional magnetic resonance imaging for treating and studying PTSD. The intervention involves training participants to control amygdala activity after exposure to personalized trauma scripts. Three combat veterans with chronic PTSD participated in this feasibility study. All three participants tolerated well the NF training. Moreover, two participants, despite the chronicity of their symptoms, showed clinically meaningful improvements, while one participant showed a smaller symptom reduction. Examination of changes in resting-state functional connectivity patterns revealed a normalization of brain connectivity consistent with clinical improvement. These preliminary results support feasibility of this novel intervention for PTSD and indicate that larger, well-controlled studies of efficacy are warranted.
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Affiliation(s)
- Mattia I. Gerin
- Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Division of Psychology and Language Sciences, University College London (UCL), London, UK
- Anna Freud Centre, London, UK
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Harlan Fichtenholtz
- Department of Veteran Affairs, National Center for PTSD, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Bennington College, Bennington, VT, USA
| | - Alicia Roy
- Department of Veteran Affairs, National Center for PTSD, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Christopher J. Walsh
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - John H. Krystal
- Department of Veteran Affairs, National Center for PTSD, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Steven Southwick
- Department of Veteran Affairs, National Center for PTSD, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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40
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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41
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Marxen M, Jacob MJ, Müller DK, Posse S, Ackley E, Hellrung L, Riedel P, Bender S, Epple R, Smolka MN. Amygdala Regulation Following fMRI-Neurofeedback without Instructed Strategies. Front Hum Neurosci 2016; 10:183. [PMID: 27199706 PMCID: PMC4844623 DOI: 10.3389/fnhum.2016.00183] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 04/11/2016] [Indexed: 11/13/2022] Open
Abstract
Within the field of functional magnetic resonance imaging (fMRI) neurofeedback, most studies provide subjects with instructions or suggest strategies to regulate a particular brain area, while other neuro-/biofeedback approaches often do not. This study is the first to investigate the hypothesis that subjects are able to utilize fMRI neurofeedback to learn to differentially modulate the fMRI signal from the bilateral amygdala congruent with the prescribed regulation direction without an instructed or suggested strategy and apply what they learned even when feedback is no longer available. Thirty-two subjects were included in the analysis. Data were collected at 3 Tesla using blood oxygenation level dependent (BOLD)-sensitivity optimized multi-echo EPI. Based on the mean contrast between up- and down-regulation in the amygdala in a post-training scan without feedback following three neurofeedback sessions, subjects were able to regulate their amygdala congruent with the prescribed directions with a moderate effect size of Cohen's d = 0.43 (95% conf. int. 0.23-0.64). This effect size would be reduced, however, through stricter exclusion criteria for subjects that show alterations in respiration. Regulation capacity was positively correlated with subjective arousal ratings and negatively correlated with agreeableness and susceptibility to anger. A learning effect over the training sessions was only observed with end-of-block feedback (EoBF) but not with continuous feedback (trend). The results confirm the above hypothesis. Further studies are needed to compare effect sizes of regulation capacity for approaches with and without instructed strategies.
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Affiliation(s)
- Michael Marxen
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden Dresden, Germany
| | - Mark J Jacob
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden Dresden, Germany
| | - Dirk K Müller
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden Dresden, Germany
| | - Stefan Posse
- Department of Neurology, School of Medicine, University of New Mexico Albuquerque, NM, USA
| | - Elena Ackley
- Department of Neurology, School of Medicine, University of New Mexico Albuquerque, NM, USA
| | - Lydia Hellrung
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden Dresden, Germany
| | - Philipp Riedel
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden Dresden, Germany
| | - Stephan Bender
- Medical Faculty, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Cologne Cologne, Germany
| | - Robert Epple
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden Dresden, Germany
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42
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Caria A. Self-Regulation of Blood Oxygenation Level Dependent Response: Primary Effect or Epiphenomenon? Front Neurosci 2016; 10:117. [PMID: 27047332 PMCID: PMC4805582 DOI: 10.3389/fnins.2016.00117] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Accepted: 03/09/2016] [Indexed: 11/13/2022] Open
Affiliation(s)
- Andrea Caria
- Department of Psychology and Cognitive Science, University of TrentoRovereto, Italy; Institute of Medical Psychology and Behavioral Neurobiology, Eberhard-Karls-University of TübingenTübingen, Germany
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43
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Auer T, Schweizer R, Frahm J. Training Efficiency and Transfer Success in an Extended Real-Time Functional MRI Neurofeedback Training of the Somatomotor Cortex of Healthy Subjects. Front Hum Neurosci 2015; 9:547. [PMID: 26500521 PMCID: PMC4598802 DOI: 10.3389/fnhum.2015.00547] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Accepted: 09/17/2015] [Indexed: 11/19/2022] Open
Abstract
This study investigated the level of self-regulation of the somatomotor cortices (SMCs) attained by an extended functional magnetic resonance imaging (fMRI) neurofeedback training. Sixteen healthy subjects performed 12 real-time functional magnetic resonance imaging neurofeedback training sessions within 4 weeks, involving motor imagery of the dominant right as well as the non-dominant left hand. Target regions of interests in the SMC were individually localized prior to the training by overt finger movements. The feedback signal (FS) was defined as the difference between fMRI activation in the contra- and ipsilateral SMC and visually presented to the subjects. Training efficiency was determined by an off-line general linear model analysis determining the fMRI percent signal changes in the SMC target areas accomplished during the neurofeedback training. Transfer success was assessed by comparing the pre- and post-training transfer task, i.e., the neurofeedback paradigm without the presentation of the FS. Group results show a distinct increase in feedback performance (FP) in the transfer task for the trained group compared to a matched untrained control group, as well as an increase in the time course of the training, indicating an efficient training and a successful transfer. Individual analysis revealed that the training efficiency was not only highly correlated to the transfer success but also predictive. Trainings with at least 12 efficient training runs were associated with a successful transfer outcome. A group analysis of the hemispheric contributions to the FP showed that it is mainly driven by increased fMRI activation in the contralateral SMC, although some individuals relied on ipsilateral deactivation. Training and transfer results showed no difference between left- and right-hand imagery, with a slight indication of more ipsilateral deactivation in the early right-hand trainings.
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Affiliation(s)
- Tibor Auer
- MRC Cognition and Brain Sciences Unit , Cambridge , UK ; Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie , Göttingen , Germany
| | - Renate Schweizer
- Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie , Göttingen , Germany
| | - Jens Frahm
- Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie , Göttingen , Germany
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44
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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: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Yuan H, Young KD, Phillips R, Zotev V, Misaki M, Bodurka J. Resting-state functional connectivity modulation and sustained changes after real-time functional magnetic resonance imaging neurofeedback training in depression. Brain Connect 2015; 4:690-701. [PMID: 25329241 DOI: 10.1089/brain.2014.0262] [Citation(s) in RCA: 79] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Amygdala hemodynamic responses to positive stimuli are attenuated in major depressive disorder (MDD) and normalize with remission. Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) training with the goal of upregulating amygdala activity during recall of happy autobiographical memories (AMs) has been suggested, and recently explored, as a novel therapeutic approach that resulted in improvement in self-reported mood in depressed subjects. In this study, we assessed the possibility of sustained brain changes as well as the neuromodulatory effects of rtfMRI-nf training of the amygdala during recall of positive AMs in MDD and matched healthy subjects. MDD and healthy subjects went through one visit of rtfMRI-nf training. Subjects were assigned to receive active neurofeedback from the left amygdale (LA) or from a control region putatively not modulated by AM recall or emotion regulation, that is, the left horizontal segment of the intraparietal sulcus. To assess lasting effects of neurofeedback in MDD, the resting-state functional connectivity before and after rtfMRI-nf in 27 depressed subjects, as well as in 27 matched healthy subjects before rtfMRI-nf was measured. Results show that abnormal hypo-connectivity with LA in MDD is reversed after rtfMRI-nf training by recalling positive AMs. Although such neuromodulatory changes are observed in both MDD groups receiving feedback from respective active and control brain regions, only in the active group are larger decreases of depression severity associated with larger increases of amygdala connectivity and a significant, positive correlation is found between the connectivity changes and the days after neurofeedback. In addition, active neurofeedback training of the amygdala enhances connectivity with temporal cortical regions, including the hippocampus. These results demonstrate lasting brain changes induced by amygdala rtfMRI-nf training and suggest the importance of reinforcement learning in rehabilitating emotion regulation in depression.
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Affiliation(s)
- Han Yuan
- 1 Laureate Institute for Brain Research , Tulsa, Oklahoma
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Mathiak KA, Alawi EM, Koush Y, Dyck M, Cordes JS, Gaber TJ, Zepf FD, Palomero-Gallagher N, Sarkheil P, Bergert S, Zvyagintsev M, Mathiak K. Social reward improves the voluntary control over localized brain activity in fMRI-based neurofeedback training. Front Behav Neurosci 2015; 9:136. [PMID: 26089782 PMCID: PMC4452886 DOI: 10.3389/fnbeh.2015.00136] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Accepted: 05/11/2015] [Indexed: 11/17/2022] Open
Abstract
Neurofeedback (NF) based on real-time functional magnetic resonance imaging (rt-fMRI) allows voluntary regulation of the activity in a selected brain region. For the training of this regulation, a well-designed feedback system is required. Social reward may serve as an effective incentive in NF paradigms, but its efficiency has not yet been tested. Therefore, we developed a social reward NF paradigm and assessed it in comparison with a typical visual NF paradigm (moving bar). We trained twenty-four healthy participants, on three consecutive days, to control activation in dorsal anterior cingulate cortex (ACC) with fMRI-based NF. In the social feedback group, an avatar gradually smiled when ACC activity increased, whereas in the standard feedback group, a moving bar indicated the activation level. In order to assess a transfer of the NF training both groups were asked to up-regulate their brain activity without receiving feedback immediately before and after the NF training (pre- and post-test). Finally, the effect of the acquired NF training on ACC function was evaluated in a cognitive interference task (Simon task) during the pre- and post-test. Social reward led to stronger activity in the ACC and reward-related areas during the NF training when compared to standard feedback. After the training, both groups were able to regulate ACC without receiving feedback, with a trend for stronger responses in the social feedback group. Moreover, despite a lack of behavioral differences, significant higher ACC activations emerged in the cognitive interference task, reflecting a stronger generalization of the NF training on cognitive interference processing after social feedback. Social reward can increase self-regulation in fMRI-based NF and strengthen its effects on neural processing in related tasks, such as cognitive interference. A particular advantage of social feedback is that a direct external reward is provided as in natural social interactions, opening perspectives for implicit learning paradigms.
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Affiliation(s)
- Krystyna A Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Behavioral Psychobiology, RWTH Aachen University Aachen, Germany ; Translational Brain Medicine, Jülich-Aachen Research Alliance Jülich, Aachen, Germany ; Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University Aachen, Germany
| | - Eliza M Alawi
- Department of Psychiatry, Psychotherapy and Psychosomatics, Behavioral Psychobiology, RWTH Aachen University Aachen, Germany ; Translational Brain Medicine, Jülich-Aachen Research Alliance Jülich, Aachen, Germany
| | - Yury Koush
- Department of Psychiatry, Psychotherapy and Psychosomatics, Behavioral Psychobiology, RWTH Aachen University Aachen, Germany ; Translational Brain Medicine, Jülich-Aachen Research Alliance Jülich, Aachen, Germany ; Department of Radiology and Medical Informatics, University of Geneva Geneva, Switzerland ; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
| | - Miriam Dyck
- Department of Psychiatry, Psychotherapy and Psychosomatics, Behavioral Psychobiology, RWTH Aachen University Aachen, Germany ; Translational Brain Medicine, Jülich-Aachen Research Alliance Jülich, Aachen, Germany
| | - Julia S Cordes
- Department of Psychiatry, Psychotherapy and Psychosomatics, Behavioral Psychobiology, RWTH Aachen University Aachen, Germany ; Translational Brain Medicine, Jülich-Aachen Research Alliance Jülich, Aachen, Germany
| | - Tilman J Gaber
- Translational Brain Medicine, Jülich-Aachen Research Alliance Jülich, Aachen, Germany ; Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University Aachen, Germany
| | - Florian D Zepf
- Department of Child and Adolescent Psychiatry, School of Psychiatry and Clinical Neurosciences and School of Paediatrics and Child Health, Faculty of Medicine, Dentistry and Health Sciences, The University of Western Australia (M561) Perth, WA, Australia ; Specialised Child and Adolescent Mental Health Services, Department of Health in Western Australia Perth, WA, Australia
| | | | - Pegah Sarkheil
- Department of Psychiatry, Psychotherapy and Psychosomatics, Behavioral Psychobiology, RWTH Aachen University Aachen, Germany ; Translational Brain Medicine, Jülich-Aachen Research Alliance Jülich, Aachen, Germany
| | - Susanne Bergert
- Department of Psychiatry, Psychotherapy and Psychosomatics, Behavioral Psychobiology, RWTH Aachen University Aachen, Germany ; Translational Brain Medicine, Jülich-Aachen Research Alliance Jülich, Aachen, Germany
| | - Mikhail Zvyagintsev
- Department of Psychiatry, Psychotherapy and Psychosomatics, Behavioral Psychobiology, RWTH Aachen University Aachen, Germany ; Translational Brain Medicine, Jülich-Aachen Research Alliance Jülich, Aachen, Germany
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Behavioral Psychobiology, RWTH Aachen University Aachen, Germany ; Translational Brain Medicine, Jülich-Aachen Research Alliance Jülich, Aachen, Germany
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Harmelech T, Friedman D, Malach R. Differential magnetic resonance neurofeedback modulations across extrinsic (visual) and intrinsic (default-mode) nodes of the human cortex. J Neurosci 2015; 35:2588-95. [PMID: 25673851 DOI: 10.1523/JNEUROSCI.3098-14.2015] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Previous advances in magnetic resonance imaging allow the analysis of blood oxygen level-dependent signals in real time, thus opening the possibility of feeding an index of these signals back to scanned human participants. However, it is still not known to what extent different cortical networks may differ in their sensitivity to such internally generated neurofeedback (NF). Here, we compare NF efficacy across six cortical regions including: early and high-order visual areas and the posterior parietal lobe, a prominent node of the default mode network (DMN). Our results reveal a consistent difference in NF activation across these areas. Sham controls ruled out a role of attention/arousal in these effects. These differences are suggestive of a relationship to the relative reliance on intrinsic information, moving from early visual cortex (lowest) to the DMN (highest). Interestingly, the visual parahippocampal place area showed NF activation closer to the DMN node. The results are compatible with the notion of the DMN as an intrinsically oriented system.
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Abstract
A new study trains attention by implementing a closed-loop neurofeedback approach that monitors attention status in the whole brain using real-time fMRI. Offline analyses underscore information carried by the frontoparietal attention network as most relevant for the training-driven behavioral improvements.
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Affiliation(s)
- Jyoti Mishra
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Adam Gazzaley
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA; Departments of Physiology and Psychiatry, University of California, San Francisco, San Francisco, CA, USA.
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Abstract
Autism spectrum disorders (ASDs) comprise a heterogeneous set of neurodevelopmental disorders characterized by dramatic impairments of interpersonal behavior, communication, and empathy. Recent neuroimaging studies suggested that ASD are disorders characterized by widespread abnormalities involving distributed brain network, though clear evidence of differences in large-scale brain network interactions underlying the cognitive and behavioral symptoms of ASD are still lacking. Consistent findings of anterior insula cortex hypoactivation and dysconnectivity during tasks related to emotional and social processing indicates its dysfunctional role in ASD. In parallel, increasing evidence showed that successful control of anterior insula activity can be attained using real-time fMRI paradigms. More importantly, successful regulation of this region was associated with changes in behavior and brain connectivity in both healthy individuals and psychiatric patients. Building on these results we here propose and discuss the use of real-time fMRI neurofeedback in ASD aiming at improving emotional and social behavior.
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Affiliation(s)
- Andrea Caria
- Institut für Medizinische Psychologie und Verhaltensneurobiologie, Eberhard Karls Universität Tübingen Tübingen, Germany ; Fondazione Ospedale San Camillo IRCCS Venezia, Italy
| | - Simona de Falco
- Department of Psychology and Cognitive Science, University of Trento Rovereto, Italy
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Blefari ML, Sulzer J, Hepp-Reymond MC, Kollias S, Gassert R. Improvement in precision grip force control with self-modulation of primary motor cortex during motor imagery. Front Behav Neurosci 2015; 9:18. [PMID: 25762907 PMCID: PMC4327737 DOI: 10.3389/fnbeh.2015.00018] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Accepted: 01/20/2015] [Indexed: 11/13/2022] Open
Abstract
Motor imagery (MI) has shown effectiveness in enhancing motor performance. This may be due to the common neural mechanisms underlying MI and motor execution (ME). The main region of the ME network, the primary motor cortex (M1), has been consistently linked to motor performance. However, the activation of M1 during motor imagery is controversial, which may account for inconsistent rehabilitation therapy outcomes using MI. Here, we examined the relationship between contralateral M1 (cM1) activation during MI and changes in sensorimotor performance. To aid cM1 activity modulation during MI, we used real-time fMRI neurofeedback-guided MI based on cM1 hand area blood oxygen level dependent (BOLD) signal in healthy subjects, performing kinesthetic MI of pinching. We used multiple regression analysis to examine the correlation between cM1 BOLD signal and changes in motor performance during an isometric pinching task of those subjects who were able to activate cM1 during motor imagery. Activities in premotor and parietal regions were used as covariates. We found that cM1 activity was positively correlated to improvements in accuracy as well as overall performance improvements, whereas other regions in the sensorimotor network were not. The association between cM1 activation during MI with performance changes indicates that subjects with stronger cM1 activation during MI may benefit more from MI training, with implications toward targeted neurotherapy.
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Affiliation(s)
- Maria L Blefari
- Rehabilitation Engineering Laboratory, Eidgenössische Technische Hochschule Zürich Zurich, Switzerland ; Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, École polytechnique fédérale de Lausanne Lausanne, Switzerland
| | - James Sulzer
- Rehabilitation Engineering Laboratory, Eidgenössische Technische Hochschule Zürich Zurich, Switzerland ; Department of Mechanical Engineering, University of Texas at Austin Austin, TX, USA
| | - Marie-Claude Hepp-Reymond
- Institute of Neuroinformatics, University of Zurich and Eidgenössische Technische Hochschule Zürich Zurich, Switzerland
| | - Spyros Kollias
- Neuroradiology Clinic, University Hospital Zurich Zurich, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Eidgenössische Technische Hochschule Zürich Zurich, Switzerland
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