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Chaimow D, Lorenz R, Weiskopf N. Closed-loop fMRI at the mesoscopic scale of columns and layers: Can we do it and why would we want to? Philos Trans R Soc Lond B Biol Sci 2024; 379:20230085. [PMID: 39428874 PMCID: PMC11513163 DOI: 10.1098/rstb.2023.0085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/16/2024] [Accepted: 02/19/2024] [Indexed: 10/22/2024] Open
Abstract
Technological advances in fMRI including ultra-high magnetic fields (≥ 7 T) and acquisition methods that increase spatial specificity have paved the way for studies of the human cortex at the scale of layers and columns. This mesoscopic scale promises an improved mechanistic understanding of human cortical function so far only accessible to invasive animal neurophysiology. In recent years, an increasing number of studies have applied such methods to better understand the cortical function in perception and cognition. This future perspective article asks whether closed-loop fMRI studies could equally benefit from these methods to achieve layer and columnar specificity. We outline potential applications and discuss the conceptual and concrete challenges, including data acquisition and volitional control of mesoscopic brain activity. We anticipate an important role of fMRI with mesoscopic resolution for closed-loop fMRI and neurofeedback, yielding new insights into brain function and potentially clinical applications.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.
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Affiliation(s)
- Denis Chaimow
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Romy Lorenz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Cognitive Neuroscience & Neurotechnology Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, LondonWC1N 3AR, UK
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Grössinger D, Spann SM, Stollberger R, Pfeuffer J, Koten JW, Wood G. Real-time fMRI neurofeedback of the anterior insula using arterial spin labelling. Eur J Neurosci 2024; 60:5400-5412. [PMID: 39193617 DOI: 10.1111/ejn.16502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 07/02/2024] [Accepted: 07/28/2024] [Indexed: 08/29/2024]
Abstract
Arterial spin labelling (ASL) is the only non-invasive technique that allows absolute quantification of perfusion and is increasingly used in brain activation studies. Contrary to the blood oxygen level-dependent (BOLD) effect ASL measures the cerebral blood flow (CBF) directly. However, the ASL signal has a lower signal-to-noise ratio (SNR), than the BOLD signal, which constrains its utilization in neurofeedback studies. If successful, ASL neurofeedback can be used to aid in the rehabilitation of health conditions with impaired blood flow, for example, stroke. We provide the first ASL-based neurofeedback study incorporating a double-blind, sham-controlled design. A pseudo-continuous ASL (pCASL) approach with background suppression and 3D GRASE readout was combined with a real-time post-processing pipeline. The real-time pipeline allows to monitor the ASL signal and provides real-time feedback on the neural activity to the subject. In total 41 healthy adults (19-56 years) divided into three groups underwent a neurofeedback-based emotion imagery training of the left anterior insula. Two groups differing only in the explicitness level of instruction received real training and a third group received sham feedback. Only those participants receiving real feedback with explicit instruction showed significantly higher absolute CBF values in the trained region during neurofeedback than participants receiving sham feedback. However, responder analyses of percent signal change values show no differences in activation between the three groups. Persisting limitations, such as the lower SNR, confounding effects of arterial transit time and partial volume effects still impact negatively the implementation of ASL neurofeedback.
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Affiliation(s)
| | - Stefan M Spann
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria
| | - Rudolf Stollberger
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Josef Pfeuffer
- Siemens Healthcare, Application Development, Erlangen, Germany
| | | | - Guilherme Wood
- Institute of Psychology, University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
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3
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Gninenko N, Trznadel S, Daskalou D, Gramatica L, Vanoy J, Voruz F, Robyn CL, Spadazzi A, Yulzari A, Sitaram R, Van De Ville D, Senn P, Haller S. Functional MRI Neurofeedback Outperforms Cognitive Behavioral Therapy for Reducing Tinnitus Distress: A Prospective Randomized Clinical Trial. Radiology 2024; 310:e231143. [PMID: 38349241 DOI: 10.1148/radiol.231143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Background Cognitive behavioral therapy (CBT) is the current standard treatment for chronic severe tinnitus; however, preliminary evidence suggests that real-time functional MRI (fMRI) neurofeedback therapy may be more effective. Purpose To compare the efficacy of real-time fMRI neurofeedback against CBT for reducing chronic tinnitus distress. Materials and Methods In this prospective controlled trial, participants with chronic severe tinnitus were randomized from December 2017 to December 2021 to receive either CBT (CBT group) for 10 weekly group sessions or real-time fMRI neurofeedback (fMRI group) individually during 15 weekly sessions. Change in the Tinnitus Handicap Inventory (THI) score (range, 0-100) from baseline to 6 or 12 months was assessed. Secondary outcomes included four quality-of-life questionnaires (Beck Depression Inventory, Pittsburgh Sleep Quality Index, State-Trait Anxiety Inventory, and World Health Organization Disability Assessment Schedule). Questionnaire scores between treatment groups and between time points were assessed using repeated measures analysis of variance and the nonparametric Wilcoxon signed rank test. Results The fMRI group included 21 participants (mean age, 49 years ± 11.4 [SD]; 16 male participants) and the CBT group included 22 participants (mean age, 53.6 years ± 8.8; 16 male participants). The fMRI group showed a greater reduction in THI scores compared with the CBT group at both 6 months (mean score change, -28.21 points ± 18.66 vs -12.09 points ± 18.86; P = .005) and 12 months (mean score change, -30 points ± 25.44 vs -4 points ± 17.2; P = .01). Compared with baseline, the fMRI group showed improved sleep (mean score, 8.62 points ± 4.59 vs 7.25 points ± 3.61; P = .006) and trait anxiety (mean score, 44 points ± 11.5 vs 39.84 points ± 10.5; P = .02) at 1 month and improved depression (mean score, 13.71 points ± 9.27 vs 6.53 points ± 5.17; P = .01) and general functioning (mean score, 24.91 points ± 17.05 vs 13.06 points ± 10.1; P = .01) at 6 months. No difference in these metrics over time was observed for the CBT group (P value range, .14 to >.99). Conclusion Real-time fMRI neurofeedback therapy led to a greater reduction in tinnitus distress than the current standard treatment of CBT. ClinicalTrials.gov registration no.: NCT05737888; Swiss Ethics registration no.: BASEC2017-00813 © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Nicolas Gninenko
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Stéphanie Trznadel
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Dimitrios Daskalou
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Luca Gramatica
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Julie Vanoy
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - François Voruz
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Claudia Lardi Robyn
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Anne Spadazzi
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Aude Yulzari
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Ranganatha Sitaram
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Dimitri Van De Ville
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Pascal Senn
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
| | - Sven Haller
- From the Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Campus Biotech, Chemin des Mines 9, Geneva 1202, Switzerland (N.G., D.V.D.V.); Department of Radiology and Medical Informatics (N.G., D.V.D.V.) and Department of Medicine (S.H.), University of Geneva, Geneva, Switzerland; Department of Neurology, Psychosomatic Medicine Unit, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland (N.G.); Wyss Center for Bio and Neuroengineering, Campus Biotech, Geneva, Switzerland (S.T., A.Y.); Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences (D.D., L.G., J.V., F.V., P.S.) and Department of Psychiatry (C.L.R., A.S.), Geneva University Hospitals, Geneva, Switzerland; Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tenn (R.S.); Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland (S.H.); and Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden (S.H.)
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Koizumi K, Kunii N, Ueda K, Takabatake K, Nagata K, Fujitani S, Shimada S, Nakao M. Intracranial Neurofeedback Modulating Neural Activity in the Mesial Temporal Lobe During Memory Encoding: A Pilot Study. Appl Psychophysiol Biofeedback 2023; 48:439-451. [PMID: 37405548 PMCID: PMC10581957 DOI: 10.1007/s10484-023-09595-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2023] [Indexed: 07/06/2023]
Abstract
Removal of the mesial temporal lobe (MTL) is an established surgical procedure that leads to seizure freedom in patients with intractable MTL epilepsy; however, it carries the potential risk of memory damage. Neurofeedback (NF), which regulates brain function by converting brain activity into perceptible information and providing feedback, has attracted considerable attention in recent years for its potential as a novel complementary treatment for many neurological disorders. However, no research has attempted to artificially reorganize memory functions by applying NF before resective surgery to preserve memory functions. Thus, this study aimed (1) to construct a memory NF system that used intracranial electrodes to feedback neural activity on the language-dominant side of the MTL during memory encoding and (2) to verify whether neural activity and memory function in the MTL change with NF training. Two intractable epilepsy patients with implanted intracranial electrodes underwent at least five sessions of memory NF training to increase the theta power in the MTL. There was an increase in theta power and a decrease in fast beta and gamma powers in one of the patients in the late stage of memory NF sessions. NF signals were not correlated with memory function. Despite its limitations as a pilot study, to our best knowledge, this study is the first to report that intracranial NF may modulate neural activity in the MTL, which is involved in memory encoding. The findings provide important insights into the future development of NF systems for the artificial reorganization of memory functions.
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Affiliation(s)
- Koji Koizumi
- Department of Mechanical Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
| | - Naoto Kunii
- Department of Neurosurgery, The University of Tokyo, Tokyo, Japan
| | - Kazutaka Ueda
- Department of Mechanical Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
| | | | - Keisuke Nagata
- Department of Neurosurgery, The University of Tokyo, Tokyo, Japan
| | - Shigeta Fujitani
- Department of Neurosurgery, The University of Tokyo, Tokyo, Japan
| | - Seijiro Shimada
- Department of Neurosurgery, The University of Tokyo, Tokyo, Japan
| | - Masayuki Nakao
- Department of Mechanical Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
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5
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Kleinjung T, Meyer M, Neff P. Neurofeedback for tinnitus treatment: an innovative method with promising potential. Brain Commun 2023; 5:fcad209. [PMID: 37577379 PMCID: PMC10414139 DOI: 10.1093/braincomms/fcad209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 06/20/2023] [Accepted: 07/26/2023] [Indexed: 08/15/2023] Open
Abstract
This scientific commentary refers to 'Does it matter what is trained? A randomized controlled trial evaluating the specificity of alpha/delta ratio neurofeedback in reducing tinnitus symptoms 'by Jensen et al. (https://doi.org/10.1093/braincomms/fcad185).
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Affiliation(s)
- Tobias Kleinjung
- Department of Otorhinolaryngology—Head and Neck Surgery, University Hospital of Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Martin Meyer
- Department of Comparative Language Science, University of Zurich, 8050 Zurich, Switzerland
| | - Patrick Neff
- Department of Otorhinolaryngology—Head and Neck Surgery, University Hospital of Zurich, University of Zurich, 8091 Zurich, Switzerland
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany
- Centre for Cognitive Neuroscience, University of Salzburg, 5020 Salzburg, Austria
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6
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Haugg A, Frei N, Menghini M, Stutz F, Steinegger S, Röthlisberger M, Brem S. Self-regulation of visual word form area activation with real-time fMRI neurofeedback. Sci Rep 2023; 13:9195. [PMID: 37280217 DOI: 10.1038/s41598-023-35932-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/25/2023] [Indexed: 06/08/2023] Open
Abstract
The Visual Word Form Area (VWFA) is a key region of the brain's reading network and its activation has been shown to be strongly associated with reading skills. Here, for the first time, we investigated whether voluntary regulation of VWFA activation is feasible using real-time fMRI neurofeedback. 40 adults with typical reading skills were instructed to either upregulate (UP group, N = 20) or downregulate (DOWN group, N = 20) their own VWFA activation during six neurofeedback training runs. The VWFA target region was individually defined based on a functional localizer task. Before and after training, also regulation runs without feedback ("no-feedback runs") were performed. When comparing the two groups, we found stronger activation across the reading network for the UP than the DOWN group. Further, activation in the VWFA was significantly stronger in the UP group than the DOWN group. Crucially, we observed a significant interaction of group and time (pre, post) for the no-feedback runs: The two groups did not differ significantly in their VWFA activation before neurofeedback training, but the UP group showed significantly stronger activation than the DOWN group after neurofeedback training. Our results indicate that upregulation of VWFA activation is feasible and that, once learned, successful upregulation can even be performed in the absence of feedback. These results are a crucial first step toward the development of a potential therapeutic support to improve reading skills in individuals with reading impairments.
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Affiliation(s)
- Amelie Haugg
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Nada Frei
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Milena Menghini
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Felizia Stutz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Sara Steinegger
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martina Röthlisberger
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
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7
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Ma X, Chen N, Wang F, Zhang C, Dai J, Ding H, Yan C, Shen W, Yang S. Surface-based functional metrics and auditory cortex characteristics in chronic tinnitus. Heliyon 2022; 8:e10989. [PMID: 36276740 PMCID: PMC9582700 DOI: 10.1016/j.heliyon.2022.e10989] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 06/11/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
Abnormal auditory cortex (AC) neuronal activity is thought to be a primary cause of the auditory disturbances perceived by individuals suffering from tinnitus. The present study was designed to test that possibility by evaluating auditory cortical characteristics (volume, curvature, surface area, thickness) and surface-based functional metrics in chronic tinnitus patients. In total, 63 chronic tinnitus patients and 36 age-, sex- and education level-matched healthy control (HC) patients were enrolled in this study. Hearing levels in these two groups were comparable, and following magnetic resonance imaging (MRI) of these individuals, the DPABISurf software was used to compute cerebral cortex curvature, thickness, and surface area as well as surface-based functional metrics. The Tinnitus Handicap Inventory (THI), Tinnitus Handicap Questionary (THQ), and Visual Analogue Scales (VAS) were used to gauge participant tinnitus severity, while correlation analyses were conducted to evaluate associations between these different analyzed parameters. A significant increase in the regional homogeneity (ReHo) of the right secondary AC was detected in the tinnitus group relative to the HC group. There were also significant reductions in the cortical volume and surface area of the right secondary AC in the tinnitus group relative to the HC group (all P < 0.05). In addition, significant negative correlations between tinnitus pitch and the cortical area and volume of the right secondary AC were observed in the tinnitus group.
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Affiliation(s)
- Xiaoyan Ma
- The First Affiliated Hospital of Xi'an, Jiaotong University, Shanxi, China,Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China
| | - Ningxuan Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China,International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, China,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China,Center for Cognitive Science of Language, Beijing Language and Culture University, Beijing, China
| | - Fangyuan Wang
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China
| | - Chi Zhang
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China
| | - Jing Dai
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China
| | - Haina Ding
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China,International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, China,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China,Center for Cognitive Science of Language, Beijing Language and Culture University, Beijing, China,Department of Child and Adolescent Psychiatry, Hassenfeld Children's Hospital at NYU Langone, New York, NY, USA,Corresponding author.
| | - Weidong Shen
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China,Corresponding author.
| | - Shiming Yang
- Medical School of Chinese PLA, Beijing, China,Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China,National Clinical Research Center for Otolaryngologic Diseases, Beijing, China,Key Lab of Hearing Science, Ministry of Education, Beijing, China,Beijing Key Lab of Hearing Impairment Prevention and Treatment, Beijing, China,Corresponding author.
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8
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Abouzari M, Tawk K, Lee D, Djalilian HR. Migrainous Vertigo, Tinnitus, and Ear Symptoms and Alternatives. Otolaryngol Clin North Am 2022; 55:1017-1033. [PMID: 36150941 PMCID: PMC9580398 DOI: 10.1016/j.otc.2022.06.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Migraine headaches frequently coexist with vestibular symptoms such as vertigo, motion sickness, and gait instability. Migraine-related vasospasm can also damage the inner ear, which results in symptoms such as sudden sensorineural hearing loss and resultant tinnitus. The pathophysiology of these symptoms is not yet fully understood, and despite their prevalence, there is no universally approved management. This review summarizes the data on complementary and integrative medicine in treating patients with migrainous ear disorders.
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Affiliation(s)
- Mehdi Abouzari
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, 19182 Jamboree Road, Otolaryngology-5386, Irvine, CA 92697, USA
| | - Karen Tawk
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, 19182 Jamboree Road, Otolaryngology-5386, Irvine, CA 92697, USA
| | - Darlene Lee
- Susan Samueli Integrative Health Institute, University of California, 5141 California Avenue, Suite 200B, Irvine, CA 92617, USA
| | - Hamid R Djalilian
- Division of Neurotology and Skull Base Surgery, Department of Otolaryngology-Head and Neck Surgery, University of California Irvine, 19182 Jamboree Road, Otolaryngology-5386, Irvine, CA 92697, USA; Department of Biomedical Engineering, University of California, Irvine, USA.
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9
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Mennen AC, Nastase SA, Yeshurun Y, Hasson U, Norman KA. Real-time neurofeedback to alter interpretations of a naturalistic narrative. NEUROIMAGE: REPORTS 2022; 2. [PMID: 36081469 PMCID: PMC9451129 DOI: 10.1016/j.ynirp.2022.100111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
We explored the potential of using real-time fMRI (rt-fMRI) neurofeedback training to bias interpretations of naturalistic narrative stimuli. Participants were randomly assigned to one of two possible conditions, each corresponding to a different interpretation of an ambiguous spoken story. While participants listened to the story in the scanner, neurofeedback was used to reward neural activity corresponding to the assigned interpretation. After scanning, final interpretations were assessed. While neurofeedback did not change story interpretations on average, participants with higher levels of decoding accuracy during the neurofeedback procedure were more likely to adopt the assigned interpretation; additional control conditions are needed to establish the role of individualized feedback in driving this result. While naturalistic stimuli introduce a unique set of challenges in providing effective and individualized neurofeedback, we believe that this technique holds promise for individualized cognitive therapy.
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Affiliation(s)
- Anne C. Mennen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540-1010, USA
- Corresponding author. Princeton Neuroscience Institute, Princeton University, USA. (A.C. Mennen)
| | - Samuel A. Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540-1010, USA
| | - Yaara Yeshurun
- School of Psychological Sciences, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Uri Hasson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540-1010, USA
- Department of Psychology, Princeton University, Princeton, NJ, 08540-1010, USA
| | - Kenneth A. Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540-1010, USA
- Department of Psychology, Princeton University, Princeton, NJ, 08540-1010, USA
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10
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Orth L, Meeh J, Gur RC, Neuner I, Sarkheil P. Frontostriatal circuitry as a target for fMRI-based neurofeedback interventions: A systematic review. Front Hum Neurosci 2022; 16:933718. [PMID: 36092647 PMCID: PMC9449529 DOI: 10.3389/fnhum.2022.933718] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/08/2022] [Indexed: 11/19/2022] Open
Abstract
Dysregulated frontostriatal circuitries are viewed as a common target for the treatment of aberrant behaviors in various psychiatric and neurological disorders. Accordingly, experimental neurofeedback paradigms have been applied to modify the frontostriatal circuitry. The human frontostriatal circuitry is topographically and functionally organized into the "limbic," the "associative," and the "motor" subsystems underlying a variety of affective, cognitive, and motor functions. We conducted a systematic review of the literature regarding functional magnetic resonance imaging-based neurofeedback studies that targeted brain activations within the frontostriatal circuitry. Seventy-nine published studies were included in our survey. We assessed the efficacy of these studies in terms of imaging findings of neurofeedback intervention as well as behavioral and clinical outcomes. Furthermore, we evaluated whether the neurofeedback targets of the studies could be assigned to the identifiable frontostriatal subsystems. The majority of studies that targeted frontostriatal circuitry functions focused on the anterior cingulate cortex, the dorsolateral prefrontal cortex, and the supplementary motor area. Only a few studies (n = 14) targeted the connectivity of the frontostriatal regions. However, post-hoc analyses of connectivity changes were reported in more cases (n = 32). Neurofeedback has been frequently used to modify brain activations within the frontostriatal circuitry. Given the regulatory mechanisms within the closed loop of the frontostriatal circuitry, the connectivity-based neurofeedback paradigms should be primarily considered for modifications of this system. The anatomical and functional organization of the frontostriatal system needs to be considered in decisions pertaining to the neurofeedback targets.
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Affiliation(s)
- Linda Orth
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Johanna Meeh
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany
| | - Pegah Sarkheil
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
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11
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Lubianiker N, Paret C, Dayan P, Hendler T. Neurofeedback through the lens of reinforcement learning. Trends Neurosci 2022; 45:579-593. [PMID: 35550813 DOI: 10.1016/j.tins.2022.03.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/11/2022] [Accepted: 03/24/2022] [Indexed: 11/29/2022]
Abstract
Despite decades of experimental and clinical practice, the neuropsychological mechanisms underlying neurofeedback (NF) training remain obscure. NF is a unique form of reinforcement learning (RL) task, during which participants are provided with rewarding feedback regarding desired changes in neural patterns. However, key RL considerations - including choices during practice, prediction errors, credit-assignment problems, or the exploration-exploitation tradeoff - have infrequently been considered in the context of NF. We offer an RL-based framework for NF, describing different internal states, actions, and rewards in common NF protocols, thus fashioning new proposals for characterizing, predicting, and hastening the course of learning. In this way we hope to advance current understanding of neural regulation via NF, and ultimately to promote its effectiveness, personalization, and clinical utility.
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Affiliation(s)
- Nitzan Lubianiker
- School of Psychological Sciences, Gershon H. Gordon Faculty of Social Sciences, Tel Aviv University, Tel Aviv, Israel; Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
| | - Christian Paret
- School of Psychological Sciences, Gershon H. Gordon Faculty of Social Sciences, Tel Aviv University, Tel Aviv, Israel; Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany; University of Tübingen, Tübingen, Germany
| | - Talma Hendler
- School of Psychological Sciences, Gershon H. Gordon Faculty of Social Sciences, Tel Aviv University, Tel Aviv, Israel; Sagol Brain Institute, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sagol school of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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12
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Jaroszynski C, Job A, Jedynak M, David O, Delon-Martin C. Tinnitus Perception in Light of a Parietal Operculo-Insular Involvement: A Review. Brain Sci 2022; 12:334. [PMID: 35326290 PMCID: PMC8946618 DOI: 10.3390/brainsci12030334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/21/2022] [Accepted: 02/24/2022] [Indexed: 12/07/2022] Open
Abstract
In tinnitus literature, researchers have increasingly been advocating for a clearer distinction between tinnitus perception and tinnitus-related distress. In non-bothersome tinnitus, the perception itself can be more specifically investigated: this has provided a body of evidence, based on resting-state and activation fMRI protocols, highlighting the involvement of regions outside the conventional auditory areas, such as the right parietal operculum. Here, we aim to conduct a review of available investigations of the human parietal operculo-insular subregions conducted at the microscopic, mesoscopic, and macroscopic scales arguing in favor of an auditory-somatosensory cross-talk. Both the previous literature and new results on functional connectivity derived from cortico-cortical evoked potentials show that these subregions present a dense tissue of interconnections and a strong connectivity with auditory and somatosensory areas in the healthy brain. Disrupted integration processes between these modalities may thus result in erroneous perceptions, such as tinnitus. More precisely, we highlight the role of a subregion of the right parietal operculum, known as OP3 according to the Jülich atlas, in the integration of auditory and somatosensory representation of the orofacial muscles in the healthy population. We further discuss how a dysfunction of these muscles could induce hyperactivity in the OP3. The evidence of direct electrical stimulation of this area eliciting auditory hallucinations further suggests its involvement in tinnitus perception. Finally, a small number of neuroimaging studies of therapeutic interventions for tinnitus provide additional evidence of right parietal operculum involvement.
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Affiliation(s)
- Chloé Jaroszynski
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; (C.J.); (M.J.); (O.D.)
| | - Agnès Job
- Institut de Recherche Biomédicale des Armées, IRBA, 91220 Brétigny-sur-Orge, France;
| | - Maciej Jedynak
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; (C.J.); (M.J.); (O.D.)
- Aix Marseille University, Inserm, INS, Inst Neurosci Syst, 13005 Marseille, France
| | - Olivier David
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; (C.J.); (M.J.); (O.D.)
- Aix Marseille University, Inserm, INS, Inst Neurosci Syst, 13005 Marseille, France
| | - Chantal Delon-Martin
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; (C.J.); (M.J.); (O.D.)
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13
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Xu ZG, Xu JJ, Hu J, Wu Y, Wang D. Arterial Spin Labeling Cerebral Perfusion Changes in Chronic Tinnitus With Tension-Type Headache. Front Neurol 2021; 12:698539. [PMID: 34512515 PMCID: PMC8427518 DOI: 10.3389/fneur.2021.698539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 07/09/2021] [Indexed: 11/28/2022] Open
Abstract
Purpose: Tinnitus is along with tension-type headache that will influence the cerebral blood flow (CBF) and accelerate the tinnitus severity. However, the potential associations between tension-type headache and tinnitus is still unknown. The current study will explore whether abnormal CBF exists in tinnitus patients and examine the effects of headache on CBF in tinnitus patients. Materials and Methods: Resting-state perfusion magnetic resonance imaging was performed in 40 chronic tinnitus patients and 50 healthy controls using pseudocontinuous arterial spin labeling. Regions with CBF differences between tinnitus patients and healthy controls were investigated. The effects of headache on tinnitus for CBF changes were further explored. Correlation analyses revealed the relationship between CBF values and tinnitus distress as well as CBF values and headache degree. Results: Relative to healthy controls, chronic tinnitus showed decreased CBF, mainly in right superior temporal gyrus (STG), left middle frontal gyrus (MFG), and left superior frontal gyrus (SFG); the CBF in the right STG and the left MFG was negatively correlated with THQ scores (r = −0.553, p = 0.001; r = −0.399, p = 0.017). We also observed a significant effect of headache on tinnitus for CBF in the right STG. Furthermore, the headache degree was correlated positively with tinnitus distress (r = 0.594, p = 0.020). Conclusion: Decreased CBF in auditory and prefrontal cortex was observed in chronic tinnitus patients. Headache may accelerate CBF reductions in tinnitus, which may form the basis for the neurological mechanism in chronic tinnitus with tension-type headache.
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Affiliation(s)
- Zhen-Gui Xu
- Department of Otolaryngology, Nanjing Pukou Central Hospital, Pukou Branch Hospital of Jiangsu Province Hospital, Nanjing, China
| | - Jin-Jing Xu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jinghua Hu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yuanqing Wu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Dan Wang
- Department of Otolaryngology, Nanjing Pukou Central Hospital, Pukou Branch Hospital of Jiangsu Province Hospital, Nanjing, China
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14
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Xu ZG, Xu JJ, Chen YC, Hu J, Wu Y, Xue Y. Aberrant cerebral blood flow in tinnitus patients with migraine: a perfusion functional MRI study. J Headache Pain 2021; 22:61. [PMID: 34187358 PMCID: PMC8240196 DOI: 10.1186/s10194-021-01280-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/02/2021] [Indexed: 12/26/2022] Open
Abstract
PURPOSE Migraine is often accompanied with chronic tinnitus that will affect the cerebral blood flow (CBF) and exacerbate the tinnitus distress. However, the potential relationship between migraine and tinnitus remains unclear. This study will investigate whether aberrant CBF patterns exist in migraine patients with tinnitus and examine the influence of migraine on CBF alterations in chronic tinnitus. MATERIALS AND METHODS Participants included chronic tinnitus patients (n = 45) and non-tinnitus controls (n = 50), matched for age, sex, education, and hearing thresholds. CBF images were collected and analyzed using arterial spin labeling (ASL) perfusion functional magnetic resonance imaging (fMRI). Regions with major CBF differences between tinnitus patients and non-tinnitus controls were first detected. The effects of migraine on tinnitus for CBF alterations were further examined. Correlation analyses illustrated the association between CBF values and tinnitus severity as well as between CBF and severity of migraine. RESULTS Compared with non-tinnitus controls, chronic tinnitus patients without migraine exhibited decreased CBF, primarily in right superior temporal gyrus (STG), bilateral middle frontal gyrus (MFG), and left superior frontal gyrus (SFG); decreased CBF in these regions was correlated with tinnitus distress. There was a significant effect of migraine on tinnitus for CBF in right STG and MFG. Moreover, the severity of migraine correlated negatively with CBF in tinnitus patients. CONCLUSIONS Chronic tinnitus patients exhibited reduced CBF in the auditory and prefrontal cortex. Migraine may facilitate a CBF decrease in the setting of tinnitus, which may underlie the neuropathological mechanisms of chronic tinnitus comorbid with migraine.
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Affiliation(s)
- Zhen-Gui Xu
- Department of Otolaryngology, Nanjing Pukou Central Hospital, Pukou Branch Hospital of Jiangsu Province Hospital, No.166, Shanghe Road, 211899, Nanjing, China
| | - Jin-Jing Xu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006, Nanjing, China
| | - Jinghua Hu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, China
| | - Yuanqing Wu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, China.
| | - Yuan Xue
- Department of Otolaryngology, Nanjing Pukou Central Hospital, Pukou Branch Hospital of Jiangsu Province Hospital, No.166, Shanghe Road, 211899, Nanjing, China.
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15
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Dewiputri WI, Schweizer R, Auer T. Brain Networks Underlying Strategy Execution and Feedback Processing in an Efficient Functional Magnetic Resonance Imaging Neurofeedback Training Performed in a Parallel or a Serial Paradigm. Front Hum Neurosci 2021; 15:645048. [PMID: 34113243 PMCID: PMC8185020 DOI: 10.3389/fnhum.2021.645048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/06/2021] [Indexed: 11/13/2022] Open
Abstract
Neurofeedback (NF) is a complex learning scenario, as the task consists of trying out mental strategies while processing a feedback signal that signifies activation in the brain area to be self-regulated and acts as a potential reward signal. In an attempt to dissect these subcomponents, we obtained whole-brain networks associated with efficient self-regulation in two paradigms: parallel, where the task was performed concurrently, combining feedback with strategy execution; and serial, where the task was performed consecutively, separating feedback processing from strategy execution. Twenty participants attempted to control their anterior midcingulate cortex (aMCC) using functional magnetic resonance imaging (fMRI) NF in 18 sessions over 2 weeks, using cognitive and emotional mental strategies. We analyzed whole-brain fMRI activations in the NF training runs with the largest aMCC activation for the serial and parallel paradigms. The equal length of the strategy execution and the feedback processing periods in the serial paradigm allows a description of the two task subcomponents with equal power. The resulting activation maps were spatially correlated with functionally annotated intrinsic connectivity brain maps (BMs). Brain activation in the parallel condition correlates with the basal ganglia (BG) network, the cingulo-opercular network (CON), and the frontoparietal control network (FPCN); brain activation in the serial strategy execution condition with the default mode network (DMN), the FPCN, and the visual processing network; while brain activation in the serial feedback processing condition predominantly with the CON, the DMN, and the FPCN. Additional comparisons indicate that BG activation is characteristic to the parallel paradigm, while supramarginal gyrus (SMG) and superior temporal gyrus (STG) activations are characteristic to the serial paradigm. The multifaceted view of the subcomponents allows describing the cognitive processes associated with strategy execution and feedback processing independently in the serial feedback task and as combined processes in the multitasking scenario of the conventional parallel feedback task.
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Affiliation(s)
- Wan Ilma Dewiputri
- International Max Planck Research School for Neurosciences, Georg-August-University, Göttingen, Göttingen, Germany
| | - Renate Schweizer
- Functional Imaging Laboratory, German Primate Center, Göttingen, Germany.,Leibniz Science Campus Primate Cognition, Göttingen, Germany
| | - Tibor Auer
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
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16
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Haugg A, Renz FM, Nicholson AA, Lor C, Götzendorfer SJ, Sladky R, Skouras S, McDonald A, Craddock C, Hellrung L, Kirschner M, Herdener M, Koush Y, Papoutsi M, Keynan J, Hendler T, Cohen Kadosh K, Zich C, Kohl SH, Hallschmid M, MacInnes J, Adcock RA, Dickerson KC, Chen NK, Young K, Bodurka J, Marxen M, Yao S, Becker B, Auer T, Schweizer R, Pamplona G, Lanius RA, Emmert K, Haller S, Van De Ville D, Kim DY, Lee JH, Marins T, Megumi F, Sorger B, Kamp T, Liew SL, Veit R, Spetter M, Weiskopf N, Scharnowski F, Steyrl D. Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis. Neuroimage 2021; 237:118207. [PMID: 34048901 DOI: 10.1016/j.neuroimage.2021.118207] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 05/14/2021] [Accepted: 05/24/2021] [Indexed: 12/12/2022] Open
Abstract
Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
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Affiliation(s)
- Amelie Haugg
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria.
| | - Fabian M Renz
- Faculty of Psychology, University of Vienna, Austria
| | | | - Cindy Lor
- Faculty of Psychology, University of Vienna, Austria
| | | | - Ronald Sladky
- Faculty of Psychology, University of Vienna, Austria
| | - Stavros Skouras
- Department of Biological and Medical Psychology, University of Bergen, Norway
| | - Amalia McDonald
- Department of Psychology, University of Virginia, United States
| | - Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, United States
| | - Lydia Hellrung
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Switzerland
| | - Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Canada
| | - Marcus Herdener
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland
| | - Yury Koush
- Department of Radiology and Biomedical Imaging, Yale University, United States
| | - Marina Papoutsi
- UCL Huntington's Disease Centre, Institute of Neurology, University College London, United Kingdom; IXICO plc, United Kingdom
| | - Jackob Keynan
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
| | - Talma Hendler
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
| | | | - Catharina Zich
- Nuffiled Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Simon H Kohl
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Germany
| | - Manfred Hallschmid
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Germany; German Center for Diabetes Research (DZD), Germany
| | - Jeff MacInnes
- Institute for Learning and Brain Sciences, University of Washington, United States
| | - R Alison Adcock
- Duke Institute for Brain Sciences, Duke University, United States; Department of Psychiatry and Behavioral Sciences, Duke University, United States
| | - Kathryn C Dickerson
- Department of Psychiatry and Behavioral Sciences, Duke University, United States
| | - Nan-Kuei Chen
- Department of Biomedical Engineering, University of Arizona, United States
| | - Kymberly Young
- Department of Psychiatry, School of Medicine, University of Pittsburgh, United States
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, United States; Stephenson School of Biomedical Engineering, University of Oklahoma, United States
| | - Michael Marxen
- Department of Psychiatry, Technische Universität Dresden, Germany
| | - Shuxia Yao
- Clinical Hospital of the Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, China
| | - Benjamin Becker
- Clinical Hospital of the Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, China
| | - Tibor Auer
- School of Psychology, University of Surrey, United Kingdom
| | | | - Gustavo Pamplona
- Department of Ophthalmology, University of Lausanne and Fondation Asile des Aveugles, Switzerland
| | - Ruth A Lanius
- Department of Psychiatry, University of Western Ontario, Canada
| | - Kirsten Emmert
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel University, Germany
| | - Sven Haller
- Department of Surgical Sciences, Radiology, Uppsala University, Sweden
| | - Dimitri Van De Ville
- Center for Neuroprosthetics, Ecole polytechnique féderale de Lausanne, Switzerland; Faculty of Medicine, University of Geneva, Switzerland
| | - Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Korea
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Korea
| | - Theo Marins
- D'Or Institute for Research and Education, Brazil
| | | | - Bettina Sorger
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | - Tabea Kamp
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | | | - Ralf Veit
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Germany; German Center for Diabetes Research (DZD), Germany; High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Germany
| | - Maartje Spetter
- School of Psychology, University of Birmingham, United Kingdom
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Germany
| | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria
| | - David Steyrl
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria
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17
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Hu J, Cui J, Xu JJ, Yin X, Wu Y, Qi J. The Neural Mechanisms of Tinnitus: A Perspective From Functional Magnetic Resonance Imaging. Front Neurosci 2021; 15:621145. [PMID: 33642982 PMCID: PMC7905063 DOI: 10.3389/fnins.2021.621145] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 01/05/2021] [Indexed: 12/18/2022] Open
Abstract
Tinnitus refers to sound perception in the absence of external sound stimulus. It has become a worldwide problem affecting all age groups especially the elderly. Tinnitus often accompanies hearing loss and some mood disorders like depression and anxiety. The comprehensive adverse effects of tinnitus on people determine the severity of tinnitus. Understanding the mechanisms of tinnitus and related discomfort may be beneficial to the prevention and treatment, and then getting patients out of tinnitus distress. Functional magnetic resonance imaging (fMRI) is a powerful technique for characterizing the intrinsic brain activity and making us better understand the tinnitus neural mechanism. In this article, we review fMRI studies published in recent years on the neuroimaging mechanisms of tinnitus. The results have revealed various neural network alterations in tinnitus patients, including the auditory system, limbic system, default mode network, attention system, and some other areas involved in memory, emotion, attention, and control. Moreover, changes in functional connectivity and neural activity in these networks are related to the perception, persistence, and severity of tinnitus. In summary, the neural mechanism of tinnitus is a complex regulatory mechanism involving multiple networks. Future research is needed to study these neural networks more accurately to refine the tinnitus models.
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Affiliation(s)
- Jinghua Hu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jinluan Cui
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jin-Jing Xu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yuanqing Wu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jianwei Qi
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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18
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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: 3.5] [Reference Citation Analysis] [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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19
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Tursic A, Eck J, Lührs M, Linden DEJ, Goebel R. A systematic review of fMRI neurofeedback reporting and effects in clinical populations. Neuroimage Clin 2020; 28:102496. [PMID: 33395987 PMCID: PMC7724376 DOI: 10.1016/j.nicl.2020.102496] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 10/29/2020] [Accepted: 11/02/2020] [Indexed: 12/22/2022]
Abstract
Real-time fMRI-based neurofeedback is a relatively young field with a potential to impact the currently available treatments of various disorders. In order to evaluate the evidence of clinical benefits and investigate how consistently studies report their methods and results, an exhaustive search of fMRI neurofeedback studies in clinical populations was performed. Reporting was evaluated using a limited number of Consensus on the reporting and experimental design of clinical and cognitive-behavioral neurofeedback studies (CRED-NF checklist) items, which was, together with a statistical power and sensitivity calculation, used to also evaluate the existing evidence of the neurofeedback benefits on clinical measures. The 62 found studies investigated regulation abilities and/or clinical benefits in a wide range of disorders, but with small sample sizes and were therefore unable to detect small effects. Most points from the CRED-NF checklist were adequately reported by the majority of the studies, but some improvements are suggested for the reporting of group comparisons and relations between regulation success and clinical benefits. To establish fMRI neurofeedback as a clinical tool, more emphasis should be placed in the future on using larger sample sizes determined through a priori power calculations and standardization of procedures and reporting.
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Affiliation(s)
- Anita Tursic
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands.
| | - Judith Eck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands.
| | - Michael Lührs
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands.
| | - David E J Linden
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Department of Neuroimaging and Neuromodeling, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands.
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20
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Pei G, Yang R, Shi Z, Guo G, Wang S, Liu M, Qiu Y, Wu J, Go R, Han Y, Yan T. Enhancing Working Memory Based on Mismatch Negativity Neurofeedback in Subjective Cognitive Decline Patients: A Preliminary Study. Front Aging Neurosci 2020; 12:263. [PMID: 33132892 PMCID: PMC7550626 DOI: 10.3389/fnagi.2020.00263] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 08/03/2020] [Indexed: 01/16/2023] Open
Abstract
Mismatch negativity (MMN) is suitable for studies of preattentive auditory discriminability and the auditory memory trace. Subjective cognitive decline (SCD) is an ideal target for early therapeutic intervention because SCD occurs at preclinical stages many years before the onset of Alzheimer’s disease (AD). According to a novel lifespan-based model of dementia risk, hearing loss is considered the greatest potentially modifiable risk factor of dementia among nine health and lifestyle factors, and hearing impairment is associated with cognitive decline. Therefore, we propose a neurofeedback training based on MMN, which is an objective index of auditory discriminability, to regulate sensory ability and memory as a non-pharmacological intervention (NPI) in SCD patients. Seventeen subjects meeting the standardized clinical evaluations for SCD received neurofeedback training. The auditory frequency discrimination test, the visual digital N-back (1-, 2-, and 3-back), auditory digital N-back (1-, 2-, and 3-back), and auditory tone N-back (1-, 2-, and 3-back) tasks were used pre- and post-training in all SCD patients. The intervention schedule comprised five 60-min training sessions over 2 weeks. The results indicate that the subjects who received neurofeedback training had successfully improved the amplitude of MMN at the parietal electrode (Pz). A slight decrease in the threshold of auditory frequency discrimination was observed after neurofeedback training. Notably, after neurofeedback training, the working memory (WM) performance was significantly enhanced in the auditory tone 3-back test. Moreover, improvements in the accuracy of all WM tests relative to the baseline were observed, although the changes were not significant. To the best of our knowledge, our preliminary study is the first to investigate the effects of MMN neurofeedback training on WM in SCD patients, and our results suggest that MMN neurofeedback may represent an effective treatment for intervention in SCD patients and the elderly with aging memory decline.
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Affiliation(s)
- Guangying Pei
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Ruoshui Yang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Zhongyan Shi
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Guoxin Guo
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Shujie Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Miaomiao Liu
- Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan
| | - Yuxiang Qiu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jinglong Wu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.,Faculty of Engineering, Okayama University, Okayama, Japan
| | - Ritsu Go
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
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21
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Pamplona GS, Heldner J, Langner R, Koush Y, Michels L, Ionta S, Scharnowski F, Salmon CE. Network-based fMRI-neurofeedback training of sustained attention. Neuroimage 2020; 221:117194. [DOI: 10.1016/j.neuroimage.2020.117194] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 07/07/2020] [Accepted: 07/20/2020] [Indexed: 11/29/2022] Open
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22
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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: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/18/2020] [Accepted: 05/26/2020] [Indexed: 12/31/2022] Open
Abstract
Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.
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Affiliation(s)
- Amelie Haugg
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Ronald Sladky
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Stavros Skouras
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
| | - Amalia McDonald
- Department of PsychologyUniversity of VirginiaCharlottesvilleVirginia
| | - Cameron Craddock
- Department of Diagnostic MedicineThe University of Texas at Austin Dell Medical SchoolAustinTexas
| | - Matthias Kirschner
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- McConnell Brain Imaging CentreMontréal Neurological Institute, McGill UniversityMontrealCanada
| | - Marcus Herdener
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
| | - Yury Koush
- Magnetic Resonance Research Center, Department of Radiology & Biomedical ImagingYale UniversityNew HavenConnecticut
| | - Marina Papoutsi
- UCL Huntington's Disease CentreInstitute of Neurology, University College LondonLondonEngland
| | - Jackob N. Keynan
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | - Talma Hendler
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | | | - Catharina Zich
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordEngland
| | - Jeff MacInnes
- Institute for Learning and Brain SciencesUniversity of WashingtonSeattleWashington
| | - R. Alison Adcock
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Kathryn Dickerson
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Nan‐Kuei Chen
- Department of Biomedical EngineeringUniversity of ArizonaTucsonArizona
| | - Kymberly Young
- Department of Psychiatry, School of MedicineUniversity of PittsburghPittsburghPennsylvania
| | | | - Shuxia Yao
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Benjamin Becker
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordEngland
| | - Renate Schweizer
- Functional Imaging LaboratoryGerman Primate CenterGöttingenGermany
| | - Gustavo Pamplona
- Hôpital and Ophtalmique Jules GoninUniversity of LausanneLausanneSwitzerland
| | - Kirsten Emmert
- Department of NeurologyUniversity Medical Center Schleswig‐Holstein, Kiel UniversityKielGermany
| | - Sven Haller
- Radiology‐Department of Surgical SciencesUppsala UniversityUppsalaSweden
| | - Dimitri Van De Ville
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
- Department of Radiology and Medical Informatics, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
| | - Maria‐Laura Blefari
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
| | - Dong‐Youl Kim
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Jong‐Hwan Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Theo Marins
- D'Or Institute for Research and Education (IDOR)Rio de JaneiroBrazil
| | - Megumi Fukuda
- School of Fundamental Science and EngineeringWaseda UniversityTokyoJapan
| | - Bettina Sorger
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Tabea Kamp
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Sook‐Lei Liew
- Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Ralf Veit
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center MunichUniversity of TübingenTübingenGermany
| | - Maartje Spetter
- School of PsychologyUniversity of BirminghamBirminghamEngland
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Frank Scharnowski
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
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23
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Heunis S, Lamerichs R, Zinger S, Caballero‐Gaudes C, Jansen JFA, Aldenkamp B, Breeuwer M. Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review. Hum Brain Mapp 2020; 41:3439-3467. [PMID: 32333624 PMCID: PMC7375116 DOI: 10.1002/hbm.25010] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/13/2020] [Accepted: 04/03/2020] [Indexed: 01/31/2023] Open
Abstract
Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.
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Affiliation(s)
- Stephan Heunis
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | - Rolf Lamerichs
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- Philips ResearchEindhovenThe Netherlands
| | - Svitlana Zinger
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | | | - Jacobus F. A. Jansen
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of RadiologyMaastricht University Medical CentreMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
| | - Bert Aldenkamp
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and NeuropsychologyGhent University HospitalGhentBelgium
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Marcel Breeuwer
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Philips HealthcareBestThe Netherlands
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24
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Klöbl M, Michenthaler P, Godbersen GM, Robinson S, Hahn A, Lanzenberger R. Reinforcement and Punishment Shape the Learning Dynamics in fMRI Neurofeedback. Front Hum Neurosci 2020; 14:304. [PMID: 32792929 PMCID: PMC7393482 DOI: 10.3389/fnhum.2020.00304] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 07/08/2020] [Indexed: 12/22/2022] Open
Abstract
Introduction Neurofeedback (NF) using real-time functional magnetic resonance imaging (fMRI) has proven to be a valuable neuroscientific tool for probing cognition and promising therapeutic approach for several psychiatric disorders. Even though learning constitutes an elementary aspect of NF, the question whether certain training schemes might positively influence its dynamics has largely been neglected. Methods To address this issue, participants were trained to exert control on their subgenual anterior cingulate cortex (sgACC) blood-oxygenation-level-dependent signal, receiving either exclusively positive reinforcement (PR, “positive feedback”) or also positive punishment (PP, “negative feedback”). The temporal dynamics of the learning process were investigated by individually modeling the feedback periods and trends, offering the possibility to assess activation changes within and across blocks, runs and sessions. Results The results show faster initial learning of the PR + PP group by significantly lower deactivations of the sgACC in the first session and stronger regulation trends during the first runs. Independent of the group, significant control over the sgACC could further be shown with but not without feedback. Conclusion The beneficial effect of PP is supported by previous findings of multiple research domains suggesting that error avoidance represents an important motivational factor of learning, which complements the reward spectrum. This hypothesis warrants further investigation with respect to NF, as it could offer a way to generally facilitate the process of gaining volitional control over brain activity.
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Affiliation(s)
- Manfred Klöbl
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Paul Michenthaler
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | | | - Simon Robinson
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.,Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia.,Department of Neurology, Medical University of Graz, Graz, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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25
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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: 2.2] [Reference Citation Analysis] [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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26
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Fede SJ, Dean SF, Manuweera T, Momenan R. A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review. Front Hum Neurosci 2020; 14:60. [PMID: 32161529 PMCID: PMC7052377 DOI: 10.3389/fnhum.2020.00060] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/07/2020] [Indexed: 11/26/2022] Open
Abstract
Background: Although biofeedback using electrophysiology has been explored extensively, the approach of using neurofeedback corresponding to hemodynamic response is a relatively young field. Real time functional magnetic resonance imaging-based neurofeedback (rt-fMRI-NF) uses sensory feedback to operantly reinforce patterns of neural response. It can be used, for example, to alter visual perception, increase brain connectivity, and reduce depression symptoms. Within recent years, interest in rt-fMRI-NF in both research and clinical contexts has expanded considerably. As such, building a consensus regarding best practices is of great value. Objective: This systematic review is designed to describe and evaluate the variations in methodology used in previous rt-fMRI-NF studies to provide recommendations for rt-fMRI-NF study designs that are mostly likely to elicit reproducible and consistent effects of neurofeedback. Methods: We conducted a database search for fMRI neurofeedback papers published prior to September 26th, 2019. Of 558 studies identified, 146 met criteria for inclusion. The following information was collected from each study: sample size and type, task used, neurofeedback calculation, regulation procedure, feedback, whether feedback was explicitly related to changing brain activity, feedback timing, control group for active neurofeedback, how many runs and sessions of neurofeedback, if a follow-up was conducted, and the results of neurofeedback training. Results: rt-fMRI-NF is typically upregulation practice based on hemodynamic response from a specific region of the brain presented using a continually updating thermometer display. Most rt-fMRI-NF studies are conducted in healthy samples and half evaluate its effect on immediate changes in behavior or affect. The most popular control group method is to provide sham signal from another region; however, many studies do not compare use a comparison group. Conclusions: We make several suggestions for designs of future rt-fMRI-NF studies. Researchers should use feedback calculation methods that consider neural response across regions (i.e., SVM or connectivity), which should be conveyed as intermittent, auditory feedback. Participants should be given explicit instructions and should be assessed on individual differences. Future rt-fMRI-NF studies should use clinical samples; effectiveness of rt-fMRI-NF should be evaluated on clinical/behavioral outcomes at follow-up time points in comparison to both a sham and no feedback control group.
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Affiliation(s)
| | | | | | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States
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27
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Abstract
Brain-computer interfaces (BCIs) based on functional magnetic resonance imaging (fMRI) provide an important complement to other noninvasive BCIs. While fMRI has several disadvantages (being nonportable, methodologically challenging, costly, and noisy), it is the only method providing high spatial resolution whole-brain coverage of brain activation. These properties allow relating mental activities to specific brain regions and networks providing a transparent scheme for BCI users to encode information and for real-time fMRI BCI systems to decode the intents of the user. Various mental activities have been used successfully in fMRI BCIs so far that can be classified into the four categories: (a) higher-order cognitive tasks (e.g., mental calculation), (b) covert language-related tasks (e.g., mental speech and mental singing), (c) imagery tasks (motor, visual, auditory, tactile, and emotion imagery), and (d) selective attention tasks (visual, auditory, and tactile attention). While the ultimate spatial and temporal resolution of fMRI BCIs is limited by the physiologic properties of the hemodynamic response, technical and analytical advances will likely lead to substantially improved fMRI BCIs in the future using, for example, decoding of imagined letter shapes at 7T as the basis for more "natural" communication BCIs.
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Affiliation(s)
- Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center (M-BIC), Maastricht, The Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center (M-BIC), Maastricht, The Netherlands.
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28
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Cai WW, Li ZC, Yang QT, Zhang T. Abnormal Spontaneous Neural Activity of the Central Auditory System Changes the Functional Connectivity in the Tinnitus Brain: A Resting-State Functional MRI Study. Front Neurosci 2019; 13:1314. [PMID: 31920484 PMCID: PMC6932986 DOI: 10.3389/fnins.2019.01314] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 11/25/2019] [Indexed: 01/20/2023] Open
Abstract
Objective An abnormal state of the central auditory system (CAS) likely plays a large role in the occurrence of phantom sound of tinnitus. Various tinnitus studies using resting-state functional MRI (RS-fMRI) have reported aberrant spontaneous brain activity in the non-auditory system and altered functional connectivity between the CAS and non-auditory system. This study aimed to investigate abnormal functional connections between the aberrant spontaneous activity in the CAS and the whole brain in tinnitus patients, compared to healthy controls (HC) using RS-fMRI. Materials and Methods RS-fMRI from 16 right-ear tinnitus patients with normal hearing (TNHs) and 15 HC individuals was collected, and the time series were extracted from different clusters of a CAS template, supplied by the Anatomy Toolbox of the Statistical Parametric Mapping software. These data were used to derive the smoothed mean amplitude of low-frequency fluctuation (smALFF) values and calculate the relationship between such values and the corresponding clinical data. In addition, clusters in the CAS identified by the smALFF maps were set as seed regions for calculating and comparing the brain-wide connectivity between TNH and HC. Results We identified the different clusters located in the left higher auditory cortex (HAC) and the right inferior colliculus (IC) from the smALFF maps that contained increased (HAC) and decreased (IC) activity when the TNH group was compared to the HC group, respectively. The value of increased smALFF cluster in the HAC was positively correlated with the tinnitus score, but the decreased smALFF cluster in the IC was not correlated with any clinical characters of tinnitus. The TNH group displayed increased connectivity, compared to the HC group, in brain regions that encompassed the left IC, bilateral Heschl gyrus, bilateral supplementary motor area, right insula, bilateral superior temporal gyrus, right middle temporal gyrus, left hippocampus, left amygdala, and right supramarginal gyrus. Conclusion Tinnitus may be linked to abnormal spontaneous activity in the HAC, which can arise from the neural plasticity induced from the increased functional connectivity between the auditory network, cerebellum, and limbic system.
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Affiliation(s)
- Wei-Wei Cai
- Department of Otolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Department of Otolaryngology-Head and Neck Surgery, Panyu Central Hospital, Guangzhou, China
| | - Zhi-Cheng Li
- Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qin-Tai Yang
- Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tao Zhang
- Department of Otolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China
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29
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Affiliation(s)
- Michelle Hampson
- Department of Radiology and Biomedical Imaging, Department of Psychiatry, and the Child Study Center, Yale University School of Medicine, New Haven, CT, USA.
| | - Sergio Ruiz
- Department of Psychiatry, Medicine School, and Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Junichi Ushiba
- Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Japan.
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30
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Current progress in real-time functional magnetic resonance-based neurofeedback: Methodological challenges and achievements. Neuroimage 2019; 202:116107. [DOI: 10.1016/j.neuroimage.2019.116107] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 07/26/2019] [Accepted: 08/16/2019] [Indexed: 12/21/2022] Open
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31
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Conversations and connections: improving real-time health data on behalf of public interest. HEALTH AND TECHNOLOGY 2019. [DOI: 10.1007/s12553-019-00296-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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32
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Lubianiker N, Goldway N, Fruchtman-Steinbok T, Paret C, Keynan JN, Singer N, Cohen A, Kadosh KC, Linden DEJ, Hendler T. Process-based framework for precise neuromodulation. Nat Hum Behav 2019; 3:436-445. [DOI: 10.1038/s41562-019-0573-y] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 03/05/2019] [Indexed: 12/20/2022]
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33
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Sherwood MS, Parker JG, Diller EE, Ganapathy S, Bennett KB, Esquivel CR, Nelson JT. Self-directed down-regulation of auditory cortex activity mediated by real-time fMRI neurofeedback augments attentional processes, resting cerebral perfusion, and auditory activation. Neuroimage 2019; 195:475-489. [PMID: 30954710 DOI: 10.1016/j.neuroimage.2019.03.078] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 02/23/2019] [Accepted: 03/31/2019] [Indexed: 12/18/2022] Open
Abstract
In this work, we investigated the use of real-time functional magnetic resonance imaging (fMRI) with neurofeedback training (NFT) to teach volitional down-regulation of the auditory cortex (AC) using directed attention strategies as there is a growing interest in the application of fMRI-NFT to treat neurologic disorders. Healthy participants were separated into two groups: the experimental group received real feedback regarding activity in the AC; the control group was supplied sham feedback yoked from a random participant in the experimental group and matched for fMRI-NFT experience. Each participant underwent five fMRI-NFT sessions. Each session contained 2 neurofeedback runs where participants completed alternating blocks of "rest" and "lower" conditions while viewing a continuously-updated bar representing AC activation and listening to continuous noise. Average AC deactivation was extracted from each closed-loop neuromodulation run and used to quantify the control over AC (AC control), which was found to significantly increase across training in the experimental group. Additionally, behavioral testing was completed outside of the MRI on sessions 1 and 5 consisting of a subjective questionnaire to assess attentional control and two quantitative tests of attention. No significant changes in behavior were observed; however, there was a significant correlation between changes in AC control and attentional control. Also, in a neural assessment before and after fMRI-NFT, AC activity in response to continuous noise stimulation was found to significantly decrease across training while changes in AC resting perfusion were found to be significantly greater in the experimental group. These results may be useful in formulating effective therapies outside of the MRI, specifically for chronic tinnitus which is often characterized by hyperactivity of the primary auditory cortex and altered attentional processes. Furthermore, the modulation of attention may be useful in developing therapies for other disorders such as chronic pain.
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Affiliation(s)
- Matthew S Sherwood
- Department of Biomedical, Industrial & Human Factors Engineering, Wright State University, Dayton, OH, USA.
| | - Jason G Parker
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University, IN, USA
| | - Emily E Diller
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University, IN, USA; College of Health and Human Services, Purdue University, West Lafayette, IN, USA
| | - Subhashini Ganapathy
- Department of Biomedical, Industrial & Human Factors Engineering, Wright State University, Dayton, OH, USA; Department of Trauma Care, Boonshoft School of Medicine, Wright State University, Dayton, OH, USA
| | - Kevin B Bennett
- Department of Psychology, Wright State University, Dayton, OH, USA
| | - Carlos R Esquivel
- Department of Defense Hearing Center of Excellence, JBSA-Lackland, USA
| | - Jeremy T Nelson
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University, IN, USA; Department of Defense Hearing Center of Excellence, JBSA-Lackland, USA; Ho-Chunk Inc., Alexandria, VA, USA
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Skottnik L, Sorger B, Kamp T, Linden D, Goebel R. Success and failure of controlling the real-time functional magnetic resonance imaging neurofeedback signal are reflected in the striatum. Brain Behav 2019; 9:e01240. [PMID: 30790474 PMCID: PMC6422826 DOI: 10.1002/brb3.1240] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 01/24/2019] [Accepted: 01/25/2019] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION Over the last decades, neurofeedback has been applied in variety of research contexts and therapeutic interventions. Despite this extensive use, its neural mechanisms are still under debate. Several scientific advances have suggested that different networks become jointly active during neurofeedback, including regions generally involved in self-regulation, regions related to the specific mental task driving the neurofeedback and regions generally involved in feedback learning (Sitaram et al., 2017, Nature Reviews Neuroscience, 18, 86). METHODS To investigate the neural mechanisms specific to neurofeedback but independent from general effects of self-regulation, we compared brain activation as measured with functional magnetic resonance imaging (fMRI) across different mental tasks involving gradual self-regulation with and without providing neurofeedback. Ten participants freely chose one self-regulation task and underwent two training sessions during fMRI scanning, one with and one without receiving neurofeedback. During neurofeedback sessions, feedback signals were provided in real-time based on activity in task-related, individually defined target regions. In both sessions, participants aimed at reaching and holding low, medium, or high brain-activation levels in the target region. RESULTS During gradual self-regulation with neurofeedback, a network of cortical control regions as well as regions implicated in reward and feedback processing were activated. Self-regulation with feedback was accompanied by stronger activation within the striatum across different mental tasks. Additional time-resolved single-trial analysis revealed that neurofeedback performance was positively correlated with a delayed brain response in the striatum that reflected the accuracy of self-regulation. CONCLUSION Overall, these findings support that neurofeedback contributes to self-regulation through task-general regions involved in feedback and reward processing.
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Affiliation(s)
- Leon Skottnik
- Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, Netherlands.,Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands.,Brain Innovation BV, Maastricht, Netherlands
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Tabea Kamp
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - David Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom.,School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands.,Brain Innovation BV, Maastricht, Netherlands.,Department of Neuroimaging and Neuromodeling, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, Netherlands
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35
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Thibault RT, MacPherson A, Lifshitz M, Roth RR, Raz A. Neurofeedback with fMRI: A critical systematic review. Neuroimage 2018; 172:786-807. [DOI: 10.1016/j.neuroimage.2017.12.071] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 12/18/2017] [Accepted: 12/21/2017] [Indexed: 10/18/2022] Open
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36
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Jacob Y, Or-Borichev A, Jackont G, Lubianiker N, Hendler T. Network Based fMRI Neuro-Feedback for Emotion Regulation; Proof-of-Concept. COMPLEX NETWORKS & THEIR APPLICATIONS VI 2018. [DOI: 10.1007/978-3-319-72150-7_101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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37
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Güntensperger D, Thüring C, Meyer M, Neff P, Kleinjung T. Neurofeedback for Tinnitus Treatment - Review and Current Concepts. Front Aging Neurosci 2017; 9:386. [PMID: 29249959 PMCID: PMC5717031 DOI: 10.3389/fnagi.2017.00386] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 11/09/2017] [Indexed: 12/18/2022] Open
Abstract
An effective treatment to completely alleviate chronic tinnitus symptoms has not yet been discovered. However, recent developments suggest that neurofeedback (NFB), a method already popular in the treatment of other psychological and neurological disorders, may provide a suitable alternative. NFB is a non-invasive method generally based on electrophysiological recordings and visualizing of certain aspects of brain activity as positive or negative feedback that enables patients to voluntarily control their brain activity and thus triggers them to unlearn typical neural activity patterns related to tinnitus. The purpose of this review is to summarize and discuss previous findings of neurofeedback treatment studies in the field of chronic tinnitus. In doing so, also an overview about the underlying theories of tinnitus emergence is presented and results of resting-state EEG and MEG studies summarized and critically discussed. To date, neurofeedback as well as electrophysiological tinnitus studies lack general guidelines that are crucial to produce more comparable and consistent results. Even though neurofeedback has already shown promising results for chronic tinnitus treatment, further research is needed in order to develop more sophisticated protocols that are able to tackle the individual needs of tinnitus patients more specifically.
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Affiliation(s)
- Dominik Güntensperger
- Neuroplasticity and Learning in the Healthy Aging Brain (HAB LAB), Department of Psychology, University of Zurich, Zurich, Switzerland.,University Research Priority Program 'Dynamics of Healthy Aging', University of Zurich, Zurich, Switzerland
| | - Christian Thüring
- Department of Otorhinolaryngology, University Hospital of Zurich, Zurich, Switzerland
| | - Martin Meyer
- Neuroplasticity and Learning in the Healthy Aging Brain (HAB LAB), Department of Psychology, University of Zurich, Zurich, Switzerland.,University Research Priority Program 'Dynamics of Healthy Aging', University of Zurich, Zurich, Switzerland
| | - Patrick Neff
- Neuroplasticity and Learning in the Healthy Aging Brain (HAB LAB), Department of Psychology, University of Zurich, Zurich, Switzerland.,University Research Priority Program 'Dynamics of Healthy Aging', University of Zurich, Zurich, Switzerland
| | - Tobias Kleinjung
- Department of Otorhinolaryngology, University Hospital of Zurich, Zurich, Switzerland
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38
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Kleinjung T, Thüring C, Güntensperger D, Neff P, Meyer M. [Neurofeedback for the treatment of chronic tinnitus : Review and future perspectives]. HNO 2017; 66:198-204. [PMID: 29143096 DOI: 10.1007/s00106-017-0432-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Neurofeedback is a noninvasive neuromodulation technique employing real-time display of brain activity in terms of electroencephalography (EEG) signals to teach self-regulation of distinct patterns of brain activity or influence brain activity in a targeted manner. The benefit of this approach for control of symptoms in attention deficit disorders, hyperactivity, depression, and migraine has been proven. Studies in recent years have also repeatedly shown this treatment to improve tinnitus symptoms, although it has not become established as routine therapy. The primary focus of this review is the rational of EEG neurofeedback for tinnitus treatment and the currently available data from published studies. Furthermore, alternative neurofeedback protocols using real-time functional magnetic resonance imaging (fMRI) measurements for tinnitus control are considered. Finally, this article highlights how modern EEG analysis (source localization, connectivity) and the improving understanding of tinnitus pathology can contribute to development of more focused neurofeedback protocols for more sustainable control of tinnitus.
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Affiliation(s)
- T Kleinjung
- Klinik für Ohren‑, Nasen‑, Hals- und Gesichtschirurgie, UniversitätsSpital Zürich, Frauenklinikstraße 24, 8091, Zürich, Schweiz.
| | - C Thüring
- Klinik für Ohren‑, Nasen‑, Hals- und Gesichtschirurgie, UniversitätsSpital Zürich, Frauenklinikstraße 24, 8091, Zürich, Schweiz
| | - D Güntensperger
- Psychologisches Institut, Universität Zürich, Zürich, Schweiz
| | - P Neff
- Psychologisches Institut, Universität Zürich, Zürich, Schweiz
| | - M Meyer
- Psychologisches Institut, Universität Zürich, Zürich, Schweiz
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Hellrung L, Dietrich A, Hollmann M, Pleger B, Kalberlah C, Roggenhofer E, Villringer A, Horstmann A. Intermittent compared to continuous real-time fMRI neurofeedback boosts control over amygdala activation. Neuroimage 2017; 166:198-208. [PMID: 29100939 DOI: 10.1016/j.neuroimage.2017.10.031] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 10/09/2017] [Accepted: 10/16/2017] [Indexed: 12/20/2022] Open
Abstract
Real-time fMRI neurofeedback is a feasible tool to learn the volitional regulation of brain activity. So far, most studies provide continuous feedback information that is presented upon every volume acquisition. Although this maximizes the temporal resolution of feedback information, it may be accompanied by some disadvantages. Participants can be distracted from the regulation task due to (1) the intrinsic delay of the hemodynamic response and associated feedback and (2) limited cognitive resources available to simultaneously evaluate feedback information and stay engaged with the task. Here, we systematically investigate differences between groups presented with different variants of feedback (continuous vs. intermittent) and a control group receiving no feedback on their ability to regulate amygdala activity using positive memories and feelings. In contrast to the feedback groups, no learning effect was observed in the group without any feedback presentation. The group receiving intermittent feedback exhibited better amygdala regulation performance when compared with the group receiving continuous feedback. Behavioural measurements show that these effects were reflected in differences in task engagement. Overall, we not only demonstrate that the presentation of feedback is a prerequisite to learn volitional control of amygdala activity but also that intermittent feedback is superior to continuous feedback presentation.
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Affiliation(s)
- Lydia Hellrung
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.
| | - Anja Dietrich
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Maurice Hollmann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Burkhard Pleger
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neurology, BG University Clinic Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Christian Kalberlah
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Elisabeth Roggenhofer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neuroscience Clinique's, University Hospital Genève, Genève, Switzerland
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinics for Cognitive Neurology, University Hospital, Leipzig, Germany; Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany; Mind and Brain Institute, Berlin School of Mind and Brain, Humboldt-University and Charité, Berlin, Germany
| | - Annette Horstmann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany
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40
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Watanabe T, Sasaki Y, Shibata K, Kawato M. Advances in fMRI Real-Time Neurofeedback. Trends Cogn Sci 2017; 21:997-1010. [PMID: 29031663 DOI: 10.1016/j.tics.2017.09.010] [Citation(s) in RCA: 127] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 09/01/2017] [Accepted: 09/18/2017] [Indexed: 12/22/2022]
Abstract
Functional magnetic resonance imaging (fMRI) neurofeedback is a type of biofeedback in which real-time online fMRI signals are used to self-regulate brain function. Since its advent in 2003 significant progress has been made in fMRI neurofeedback techniques. Specifically, the use of implicit protocols, external rewards, multivariate analysis, and connectivity analysis has allowed neuroscientists to explore a possible causal involvement of modified brain activity in modified behavior. These techniques have also been integrated into groundbreaking new neurofeedback technologies, specifically decoded neurofeedback (DecNef) and functional connectivity-based neurofeedback (FCNef). By modulating neural activity and behavior, DecNef and FCNef have substantially advanced both basic and clinical research.
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Affiliation(s)
- Takeo Watanabe
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, 190 Thayer Street, Providence, RI 02912, USA; Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Equal contributions
| | - Yuka Sasaki
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, 190 Thayer Street, Providence, RI 02912, USA; Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Equal contributions
| | - Kazuhisa Shibata
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Nagoya 464-0814, Japan; Equal contributions
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan.
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Oblak EF, Lewis-Peacock JA, Sulzer JS. Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment. PLoS Comput Biol 2017; 13:e1005681. [PMID: 28753639 PMCID: PMC5550007 DOI: 10.1371/journal.pcbi.1005681] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 08/09/2017] [Accepted: 07/13/2017] [Indexed: 01/15/2023] Open
Abstract
Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic) and feedback timing (continuous or intermittent). Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results suggest that different self-regulation mechanisms prefer different feedback timings, and that these factors can be effectively explored and optimized via simulation prior to deployment in the MRI scanner. By repeatedly stimulating fine-grained patterns of neural activity, it is possible to manipulate behaviors associated with these patterns. While millimeter-scale patterns cannot yet be targeted with noninvasive brain stimulation, some people can learn to self-stimulate these activity patterns if they receive real-time feedback of their own recorded brain activity through a procedure known as fMRI neurofeedback. Other ‘non-responders’ are, for reasons unknown, unable to learn how to self-regulate these patterns. Here, we investigate how the properties of the fMRI signal, feedback timing, and self-regulation strategies may lead to this non-responder effect. The signal recorded by fMRI is related to blood flow in the brain and can be delayed by up to six seconds relative to underlying neural activity, which makes it difficult to learn. Because experiments in the MRI scanner are costly and time-consuming, we created a simulated neurofeedback environment to compare continuous versus intermittent feedback timing and cognitive versus automatic self-regulation strategies. In a cognitive experiment with human participants playing a simple game with the simulated neurofeedback signal, we found continuous feedback led to faster learning. However, in a computer model of automatic reward-based learning, we found that intermittent feedback was more reliable. These results will help improve future fMRI neurofeedback experiments and treatments by improving the efficacy of neurofeedback training procedures.
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Affiliation(s)
- Ethan F. Oblak
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, USA
- * E-mail:
| | - Jarrod A. Lewis-Peacock
- Department of Psychology, The University of Texas at Austin, Austin, Texas, USA
- Institute for Neuroscience, The University of Texas at Austin, Austin, Texas, USA
| | - James S. Sulzer
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, USA
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Gonçalves ÓF, Batistuzzo MC, Sato JR. Real-time functional magnetic resonance imaging in obsessive-compulsive disorder. Neuropsychiatr Dis Treat 2017; 13:1825-1834. [PMID: 28744133 PMCID: PMC5513821 DOI: 10.2147/ndt.s121139] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The current literature provides substantial evidence of brain alterations associated with obsessive-compulsive disorder (OCD) symptoms (eg, checking, cleaning/decontamination, counting compulsions; harm or sexual, symmetry/exactness obsessions), and emotional problems (eg, defensive/appetitive emotional imbalance, disgust, guilt, shame, and fear learning/extinction) and cognitive impairments associated with this disorder (eg, inhibitory control, working memory, cognitive flexibility). Building on this evidence, new clinical trials can now target specific brain regions/networks. Real-time functional magnetic resonance imaging (rtfMRI) was introduced as a new therapeutic tool for the self-regulation of brain-mind. In this review, we describe initial trials testing the use of rtfMRI to target brain regions associated with specific OCD symptoms (eg, contamination), and other mind-brain processes (eg, cognitive - working memory, inhibitory control, emotional - defensive, appetitive systems, fear reduction through counter-conditioning) found impaired in OCD patients. While this is a novel topic of research, initial evidence shows the promise of using rtfMRI in training the self-regulation of brain regions and mental processes associated with OCD. Additionally, studies with healthy populations have shown that individuals can regulate brain regions associated with cognitive and emotional processes found impaired in OCD. After the initial "proof-of-concept" stage, there is a need to follow up with controlled clinical trials that could test rtfMRI innovative treatments targeting brain regions and networks associated with different OCD symptoms and cognitive-emotional impairments.
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Affiliation(s)
- Óscar F Gonçalves
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
- Spaulding Neuromodulation Center, Department of Physical Medicine & Rehabilitation, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Social and Cognitive Neuroscience Laboratory, Center for Health and Biological Sciences, Mackenzie Presbyterian University
| | - Marcelo C Batistuzzo
- Department and Institute of Psychiatry, University of São Paulo Medical School (FMUSP)
| | - João R Sato
- Mathematics, Computing, and Cognition Center, Universidade Federal do ABC – UFABC, São Paulo, Brazil
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Urbach H. Emerging Challenges for Neuroradiologists. Clin Neuroradiol 2017; 27:133. [DOI: 10.1007/s00062-017-0587-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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