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Krause F, Linden DEJ, Hermans EJ. Getting stress-related disorders under control: the untapped potential of neurofeedback. Trends Neurosci 2024:S0166-2236(24)00150-4. [PMID: 39261131 DOI: 10.1016/j.tins.2024.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 07/05/2024] [Accepted: 08/16/2024] [Indexed: 09/13/2024]
Abstract
Stress-related disorders are among the biggest global health challenges. Despite significant progress in understanding their neurocognitive basis, the promise of applying insights from fundamental research to prevention and treatment remains largely unfulfilled. We argue that neurofeedback - a method for training voluntary control over brain activity - has the potential to fill this translational gap. We provide a contemporary perspective on neurofeedback as endogenous neuromodulation that can target complex brain network dynamics, is transferable to real-world scenarios outside a laboratory or treatment facility, can be trained prospectively, and is individually adaptable. This makes neurofeedback a prime candidate for a personalized preventive neuroscience-based intervention strategy that focuses on the ecological momentary neuromodulation of stress-related brain networks in response to actual stressors in real life.
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Affiliation(s)
- Florian Krause
- Donders Institute for Brain, Cognition and Behaviour, Radboud university medical center, Nijmegen, The Netherlands.
| | - David E J Linden
- Faculty of Health, Medicine and Life Sciences, Mental Health and Neuroscience Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Erno J Hermans
- Donders Institute for Brain, Cognition and Behaviour, Radboud university medical center, Nijmegen, The Netherlands
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2
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Meissner SN, Bächinger M, Kikkert S, Imhof J, Missura S, Carro Dominguez M, Wenderoth N. Self-regulating arousal via pupil-based biofeedback. Nat Hum Behav 2024; 8:43-62. [PMID: 37904022 PMCID: PMC10810759 DOI: 10.1038/s41562-023-01729-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 09/20/2023] [Indexed: 11/01/2023]
Abstract
The brain's arousal state is controlled by several neuromodulatory nuclei known to substantially influence cognition and mental well-being. Here we investigate whether human participants can gain volitional control of their arousal state using a pupil-based biofeedback approach. Our approach inverts a mechanism suggested by previous literature that links activity of the locus coeruleus, one of the key regulators of central arousal and pupil dynamics. We show that pupil-based biofeedback enables participants to acquire volitional control of pupil size. Applying pupil self-regulation systematically modulates activity of the locus coeruleus and other brainstem structures involved in arousal control. Furthermore, it modulates cardiovascular measures such as heart rate, and behavioural and psychophysiological responses during an oddball task. We provide evidence that pupil-based biofeedback makes the brain's arousal system accessible to volitional control, a finding that has tremendous potential for translation to behavioural and clinical applications across various domains, including stress-related and anxiety disorders.
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Affiliation(s)
- Sarah Nadine Meissner
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
| | - Marc Bächinger
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Sanne Kikkert
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Jenny Imhof
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland
| | - Silvia Missura
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Manuel Carro Dominguez
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland
| | - Nicole Wenderoth
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland.
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.
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3
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Engeli EJE, Russo AG, Ponticorvo S, Zoelch N, Hock A, Hulka LM, Kirschner M, Preller KH, Seifritz E, Quednow BB, Esposito F, Herdener M. Accumbal-thalamic connectivity and associated glutamate alterations in human cocaine craving: A state-dependent rs-fMRI and 1H-MRS study. Neuroimage Clin 2023; 39:103490. [PMID: 37639901 PMCID: PMC10474092 DOI: 10.1016/j.nicl.2023.103490] [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: 02/03/2023] [Revised: 07/21/2023] [Accepted: 08/04/2023] [Indexed: 08/31/2023]
Abstract
Craving is a core symptom of cocaine use disorder and a major factor for relapse risk. To date, there is no pharmacological therapy to treat this disease or at least to alleviate cocaine craving as a core symptom. In animal models, impaired prefrontal-striatal signalling leading to altered glutamate release in the nucleus accumbens appear to be the prerequisite for cocaine-seeking. Thus, those network and metabolic changes may constitute the underlying mechanisms for cocaine craving and provide a potential treatment target. In humans, there is recent evidence for corresponding glutamatergic alterations in the nucleus accumbens, however, the underlying network disturbances that lead to this glutamate imbalance remain unknown. In this state-dependent randomized, placebo-controlled, double-blinded, cross-over multimodal study, resting state functional magnetic resonance imaging in combination with small-voxel proton magnetic resonance spectroscopy (voxel size: 9.4 × 18.8 × 8.4 mm3) was applied to assess network-level and associated neurometabolic changes during a non-craving and a craving state, induced by a custom-made cocaine-cue film, in 18 individuals with cocaine use disorder and 23 healthy individuals. Additionally, we assessed the potential impact of a short-term challenge of N-acetylcysteine, known to normalize disturbed glutamate homeostasis and to thereby reduce cocaine-seeking in animal models of addiction, compared to a placebo. We found increased functional connectivity between the nucleus accumbens and the dorsolateral prefrontal cortex during the cue-induced craving state. However, those changes were not linked to alterations in accumbal glutamate levels. Whereas we additionally found increased functional connectivity between the nucleus accumbens and a midline part of the thalamus during the cue-induced craving state. Furthermore, obsessive thinking about cocaine and the actual intensity of cocaine use were predictive of cue-induced functional connectivity changes between the nucleus accumbens and the thalamus. Finally, the increase in accumbal-thalamic connectivity was also coupled with craving-related glutamate rise in the nucleus accumbens. Yet, N-acetylcysteine had no impact on craving-related changes in functional connectivity. Together, these results suggest that connectivity changes within the fronto-accumbal-thalamic loop, in conjunction with impaired glutamatergic transmission, underlie cocaine craving and related clinical symptoms, pinpointing the thalamus as a crucial hub for cocaine craving in humans.
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Affiliation(s)
- Etna J E Engeli
- Centre for Addictive Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.
| | - Andrea G Russo
- Department of Advanced Medical and Surgical Sciences, School of Medicine and Surgery, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Sara Ponticorvo
- Center for Magnetic Resonance Research, University of Minnesota, Minnesota, United States
| | - Niklaus Zoelch
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland; Institute of Forensic Medicine, Department of Forensic Medicine and Imaging, University of Zurich, Zurich, Switzerland
| | - Andreas Hock
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland; Institute for Biomedical Engineering, University and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Lea M Hulka
- Centre for Addictive Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Matthias Kirschner
- Transdiagnostic and Multimodal Neuroimaging, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Katrin H Preller
- Pharmaco-Neuroimaging and Cognitive-Emotional Processing, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland; Neuroscience Centre Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Boris B Quednow
- Neuroscience Centre Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland; Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, School of Medicine and Surgery, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Marcus Herdener
- Centre for Addictive Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
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4
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Bezmaternykh DD, Mel'nikov ME, Petrovskii ED, Mazhirina KG, Savelov AA, Kalgin KV, Shtark MB, Koush YA. Effective Connectivity of the Bilateral Amygdala, Dorsomedial Prefrontal, and Subgenual Anterior Cingulate Cortices: Feasibility of Positive Social Emotion Regulation Models for Real-Time Functional Magnetic Resonance Imaging. Bull Exp Biol Med 2023; 175:487-491. [PMID: 37768449 DOI: 10.1007/s10517-023-05892-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Indexed: 09/29/2023]
Abstract
Effective connectivity based on functional magnetic resonance imaging (fMRI) allows assessing directions of interaction between brain regions. For real-time fMRI, we compared models of positive social emotion regulation based on a network involving the bilateral amygdala, dorsomedial prefrontal, and subgenual anterior cingulate cortex. The top-down regulation model implied modulation of the dorsomedial prefrontal cortex exerted onto other regions, while the bottom-up model implied the inverse modulation. The validity of model calculations was tested using the data from three healthy volunteers who imagined positive interactions with people in presented photos (stimuli). We confirmed the dominance of the top-down model and evaluated the number and duration of iterations required for model estimations. The study shows the applicability of the four-node effective connectivity models for regulation of positive social emotions using real-time fMRI, e.g., for neurofeedback applications.
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Affiliation(s)
- D D Bezmaternykh
- Research Institute of Molecular Biology and Biophysics, Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia
| | - M E Mel'nikov
- Research Institute of Molecular Biology and Biophysics, Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia
| | - E D Petrovskii
- International Tomography Center, Siberian Division of the Russian Academy of Sciences, Novosibirsk, Russia
| | - K G Mazhirina
- Research Institute of Molecular Biology and Biophysics, Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia
| | - A A Savelov
- International Tomography Center, Siberian Division of the Russian Academy of Sciences, Novosibirsk, Russia
| | - K V Kalgin
- Research Institute of Molecular Biology and Biophysics, Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia
| | - M B Shtark
- Research Institute of Molecular Biology and Biophysics, Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia
| | - Y A Koush
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA.
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5
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Sacu S, Wackerhagen C, Erk S, Romanczuk-Seiferth N, Schwarz K, Schweiger JI, Tost H, Meyer-Lindenberg A, Heinz A, Razi A, Walter H. Effective connectivity during face processing in major depression - distinguishing markers of pathology, risk, and resilience. Psychol Med 2023; 53:4139-4151. [PMID: 35393001 PMCID: PMC10317809 DOI: 10.1017/s0033291722000824] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 02/20/2022] [Accepted: 03/09/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Aberrant brain connectivity during emotional processing, especially within the fronto-limbic pathway, is one of the hallmarks of major depressive disorder (MDD). However, the methodological heterogeneity of previous studies made it difficult to determine the functional and etiological implications of specific alterations in brain connectivity. We previously reported alterations in psychophysiological interaction measures during emotional face processing, distinguishing depressive pathology from at-risk/resilient and healthy states. Here, we extended these findings by effective connectivity analyses in the same sample to establish a refined neural model of emotion processing in depression. METHODS Thirty-seven patients with MDD, 45 first-degree relatives of patients with MDD and 97 healthy controls performed a face-matching task during functional magnetic resonance imaging. We used dynamic causal modeling to estimate task-dependent effective connectivity at the subject level. Parametric empirical Bayes was performed to quantify group differences in effective connectivity. RESULTS MDD patients showed decreased effective connectivity from the left amygdala and left lateral prefrontal cortex to the fusiform gyrus compared to relatives and controls, whereas patients and relatives showed decreased connectivity from the right orbitofrontal cortex to the left insula and from the left orbitofrontal cortex to the right fusiform gyrus compared to controls. Relatives showed increased connectivity from the anterior cingulate cortex to the left dorsolateral prefrontal cortex compared to patients and controls. CONCLUSIONS Our results suggest that the depressive state alters top-down control of higher visual regions during face processing. Alterations in connectivity within the cognitive control network present potential risk or resilience mechanisms.
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Affiliation(s)
- Seda Sacu
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Carolin Wackerhagen
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Nina Romanczuk-Seiferth
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Kristina Schwarz
- Systems Neuroscience in Psychiatry, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Janina I. Schweiger
- Systems Neuroscience in Psychiatry, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Heike Tost
- Systems Neuroscience in Psychiatry, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Systems Neuroscience in Psychiatry, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas Heinz
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Adeel Razi
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
- Turner Institute for Brain and Mental Health & Monash Biomedical Imaging, Monash University, Clayton, Australia
| | - Henrik Walter
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
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6
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Di Pietro SV, Willinger D, Frei N, Lutz C, Coraj S, Schneider C, Stämpfli P, Brem S. Disentangling influences of dyslexia, development, and reading experience on effective brain connectivity in children. Neuroimage 2023; 268:119869. [PMID: 36639004 DOI: 10.1016/j.neuroimage.2023.119869] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 12/29/2022] [Accepted: 01/09/2023] [Indexed: 01/12/2023] Open
Abstract
Altered brain connectivity between regions of the reading network has been associated with reading difficulties. However, it remains unclear whether connectivity differences between children with dyslexia (DYS) and those with typical reading skills (TR) are specific to reading impairments or to reading experience. In this functional MRI study, 132 children (M = 10.06 y, SD = 1.46) performed a phonological lexical decision task. We aimed to disentangle (1) disorder-specific from (2) experience-related differences in effective connectivity and to (3) characterize the development of DYS and TR. We applied dynamic causal modeling to age-matched (ndys = 25, nTR = 35) and reading-level-matched (ndys = 25, nTR = 22) groups. Developmental effects were assessed in beginning and advanced readers (TR: nbeg = 48, nadv = 35, DYS: nbeg = 24, nadv = 25). We show that altered feedback connectivity between the inferior parietal lobule and the visual word form area (VWFA) during print processing can be specifically attributed to reading impairments, because these alterations were found in DYS compared to both the age-matched and reading-level-matched TR. In contrast, feedforward connectivity from the VWFA to parietal and frontal regions characterized experience in TR and increased with age and reading skill. These directed connectivity findings pinpoint disorder-specific and experience-dependent alterations in the brain's reading network.
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Affiliation(s)
- Sarah V Di Pietro
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Switzerland; URPP Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zurich, Zurich, Switzerland
| | - David Willinger
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Switzerland; Department of Psychology and Psychodynamics, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Nada Frei
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Switzerland
| | - Christina Lutz
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Switzerland
| | - Seline Coraj
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Switzerland
| | - Chiara Schneider
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland
| | - Philipp Stämpfli
- MR-Center of the Department of Psychiatry, Psychotherapy and Psychosomatics and the Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Switzerland; URPP Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zurich, Zurich, Switzerland; MR-Center of the Department of Psychiatry, Psychotherapy and Psychosomatics and the Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.
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7
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Dehghani A, Soltanian-Zadeh H, Hossein-Zadeh GA. Probing fMRI brain connectivity and activity changes during emotion regulation by EEG neurofeedback. Front Hum Neurosci 2023; 16:988890. [PMID: 36684847 PMCID: PMC9853008 DOI: 10.3389/fnhum.2022.988890] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 12/13/2022] [Indexed: 01/09/2023] Open
Abstract
Despite the existence of several emotion regulation studies using neurofeedback, interactions among a small number of regions were evaluated, and therefore, further investigation is needed to understand the interactions of the brain regions involved in emotion regulation. We implemented electroencephalography (EEG) neurofeedback with simultaneous functional magnetic resonance imaging (fMRI) using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. Then, an explorative analysis of whole brain regions was done to understand the effect of neurofeedback on brain activity and the interaction of whole brain regions involved in emotion regulation. The participants in the control and experimental groups were asked to do emotion regulation while viewing positive images of autobiographical memories and getting sham or real (based on alpha asymmetry) EEG neurofeedback, respectively. The proposed multimodal approach quantified the effects of EEG neurofeedback in changing EEG alpha power, fMRI blood oxygenation level-dependent (BOLD) activity of prefrontal, occipital, parietal, and limbic regions (up to 1.9% increase), and functional connectivity in/between prefrontal, parietal, limbic system, and insula in the experimental group. New connectivity links were identified by comparing the brain functional connectivity between experimental conditions (Upregulation and View blocks) and also by comparing the brain connectivity of the experimental and control groups. Psychometric assessments confirmed significant changes in positive and negative mood states in the experimental group by neurofeedback. Based on the exploratory analysis of activity and connectivity among all brain regions involved in emotion regions, we found significant BOLD and functional connectivity increases due to EEG neurofeedback in the experimental group, but no learning effect was observed in the control group. The results reveal several new connections among brain regions as a result of EEG neurofeedback which can be justified according to emotion regulation models and the role of those regions in emotion regulation and recalling positive autobiographical memories.
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Affiliation(s)
- Amin Dehghani
- School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran,Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States,*Correspondence: Amin Dehghani, ,
| | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran,Medical Image Analysis Lab, Department of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States,School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Gholam-Ali Hossein-Zadeh
- School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran,School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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8
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Kerick SE, Asbee J, Spangler DP, Brooks JB, Garcia JO, Parsons TD, Bannerjee N, Robucci R. Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study. PLoS One 2023; 18:e0283418. [PMID: 36952490 PMCID: PMC10035884 DOI: 10.1371/journal.pone.0283418] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 03/08/2023] [Indexed: 03/25/2023] Open
Abstract
Previous neurofeedback research has shown training-related frontal theta increases and performance improvements on some executive tasks in real feedback versus sham control groups. However, typical sham control groups receive false or non-contingent feedback, making it difficult to know whether observed differences between groups are associated with accurate contingent feedback or other cognitive mechanisms (motivation, control strategies, attentional engagement, fatigue, etc.). To address this question, we investigated differences between two frontal theta training groups, each receiving accurate contingent feedback, but with different top-down goals: (1) increase and (2) alternate increase/decrease. We hypothesized that the increase group would exhibit greater increases in frontal theta compared to the alternate group, which would exhibit lower frontal theta during down- versus up-modulation blocks over sessions. We also hypothesized that the alternate group would exhibit greater performance improvements on a Go-NoGo shooting task requiring alterations in behavioral activation and inhibition, as the alternate group would be trained with greater task specificity, suggesting that receiving accurate contingent feedback may be the more salient learning mechanism underlying frontal theta neurofeedback training gains. Thirty young healthy volunteers were randomly assigned to increase or alternate groups. Training consisted of an orientation session, five neurofeedback training sessions (six blocks of six 30-s trials of FCz theta modulation (4-7 Hz) separated by 10-s rest intervals), and six Go-NoGo testing sessions (four blocks of 90 trials in both Low and High time-stress conditions). Multilevel modeling revealed greater frontal theta increases in the alternate group over training sessions. Further, Go-NoGo task performance increased at a greater rate in the increase group (accuracy and reaction time, but not commission errors). Overall, these results reject our hypotheses and suggest that changes in frontal theta and performance outcomes were not explained by reinforcement learning afforded by accurate contingent feedback. We discuss our findings in terms of alternative conceptual and methodological considerations, as well as limitations of this research.
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Affiliation(s)
- Scott E Kerick
- U.S. Combat Capabilities Development Command, Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States of America
| | - Justin Asbee
- The Institute for Integrative & Innovative Research, University of Arkansas, Fayetteville, AR, United States of America
| | - Derek P Spangler
- U.S. Combat Capabilities Development Command, Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States of America
- Department of Biobehavioral Health, Penn State University, University Park, PA, United States of America
| | - Justin B Brooks
- U.S. Combat Capabilities Development Command, Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States of America
- D-Prime, Washington, DC, United States of America
- Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, United States of America
| | - Javier O Garcia
- U.S. Combat Capabilities Development Command, Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States of America
| | - Thomas D Parsons
- Computational Neuropsychology and Simulation (CNS) Laboratory, Edson College, Arizona State University, Phoenix, AZ, United States of America
| | - Nilanjan Bannerjee
- Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, United States of America
| | - Ryan Robucci
- Department of Computer Science and Electrical Engineering, University of Maryland at Baltimore County, Baltimore, MD, United States of America
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9
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Shah-Basak P, Boukrina O, Li XR, Jebahi F, Kielar A. Targeted neurorehabilitation strategies in post-stroke aphasia. Restor Neurol Neurosci 2023; 41:129-191. [PMID: 37980575 PMCID: PMC10741339 DOI: 10.3233/rnn-231344] [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] [Indexed: 11/21/2023]
Abstract
BACKGROUND Aphasia is a debilitating language impairment, affecting millions of people worldwide. About 40% of stroke survivors develop chronic aphasia, resulting in life-long disability. OBJECTIVE This review examines extrinsic and intrinsic neuromodulation techniques, aimed at enhancing the effects of speech and language therapies in stroke survivors with aphasia. METHODS We discuss the available evidence supporting the use of transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation, and functional MRI (fMRI) real-time neurofeedback in aphasia rehabilitation. RESULTS This review systematically evaluates studies focusing on efficacy and implementation of specialized methods for post-treatment outcome optimization and transfer to functional skills. It considers stimulation target determination and various targeting approaches. The translation of neuromodulation interventions to clinical practice is explored, emphasizing generalization and functional communication. The review also covers real-time fMRI neurofeedback, discussing current evidence for efficacy and essential implementation parameters. Finally, we address future directions for neuromodulation research in aphasia. CONCLUSIONS This comprehensive review aims to serve as a resource for a broad audience of researchers and clinicians interested in incorporating neuromodulation for advancing aphasia care.
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Affiliation(s)
| | - Olga Boukrina
- Kessler Foundation, Center for Stroke Rehabilitation Research, West Orange, NJ, USA
| | - Xin Ran Li
- School of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Fatima Jebahi
- Department of Speech, Languageand Hearing Sciences, University of Arizona, Tucson, AZ, USA
| | - Aneta Kielar
- Department of Speech, Languageand Hearing Sciences, University of Arizona, Tucson, AZ, USA
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10
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Pindi P, Houenou J, Piguet C, Favre P. Real-time fMRI neurofeedback as a new treatment for psychiatric disorders: A meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry 2022; 119:110605. [PMID: 35843369 DOI: 10.1016/j.pnpbp.2022.110605] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/12/2022] [Accepted: 07/11/2022] [Indexed: 10/17/2022]
Abstract
Neurofeedback using real-time functional MRI (RT-fMRI-NF) is an innovative technique that allows to voluntarily modulate a targeted brain response and its associated behavior. Despite promising results in the current literature, its effectiveness on symptoms management in psychiatric disorders is not yet clearly demonstrated. Here, we provide 1) a state-of-art qualitative review of RT-fMRI-NF studies aiming at alleviating clinical symptoms in a psychiatric population; 2) a quantitative evaluation (meta-analysis) of RT-fMRI-NF effectiveness on various psychiatric disorders and 3) methodological suggestions for future studies. Thirty-one clinical trials focusing on psychiatric disorders were included and categorized according to standard diagnostic categories. Among the 31 identified studies, 22 consisted of controlled trials, of which only eight showed significant clinical improvement in the experimental vs. control group after the training. Nine studies found an effect at follow-up on ADHD symptoms, emotion dysregulation, facial emotion processing, depressive symptoms, hallucinations, psychotic symptoms, and specific phobia. Within-group meta-analysis revealed large effects of the NF training on depressive symptoms right after the training (g = 0.81, p < 0.01) and at follow-up (g = 1.19, p < 0.01), as well as medium effects on anxiety (g = 0.44, p = 0.01) and emotion regulation (g = 0.48, p < 0.01). Between-group meta-analysis showed a medium effect on depressive symptoms (g = 0.49, p < 0.01) and a large effect on anxiety (g = 0.77, p = 0.01). However, the between-studies heterogeneity is very high. The use of RT-fMRI-NF as a treatment for psychiatric symptoms is promising, however, further double-blind, multicentric, randomized-controlled trials are warranted.
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Affiliation(s)
- Pamela Pindi
- Paris Est Créteil University (UPEC), INSERM U955, IMRB, Translational Neuro-psychiatry Team, AP-HP, DMU IMPACT, Mondor University Hospitals, FondaMental Foundation, F-94010 Créteil, France; Paris-Saclay University, Neurospin, CEA, UNIACT Lab, PsyBrain Team, F-91191 Gif-sur-Yvette, France
| | - Josselin Houenou
- Paris Est Créteil University (UPEC), INSERM U955, IMRB, Translational Neuro-psychiatry Team, AP-HP, DMU IMPACT, Mondor University Hospitals, FondaMental Foundation, F-94010 Créteil, France; Paris-Saclay University, Neurospin, CEA, UNIACT Lab, PsyBrain Team, F-91191 Gif-sur-Yvette, France.
| | - Camille Piguet
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Campus Biotech, Geneva, Switzerland
| | - Pauline Favre
- Paris Est Créteil University (UPEC), INSERM U955, IMRB, Translational Neuro-psychiatry Team, AP-HP, DMU IMPACT, Mondor University Hospitals, FondaMental Foundation, F-94010 Créteil, France; Paris-Saclay University, Neurospin, CEA, UNIACT Lab, PsyBrain Team, F-91191 Gif-sur-Yvette, France
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11
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Davydov N, Peek L, Auer T, Prilepin E, Gninenko N, Van De Ville D, Nikonorov A, Koush Y. Real-time and Recursive Estimators for Functional MRI Quality Assessment. Neuroinformatics 2022; 20:897-917. [PMID: 35297018 DOI: 10.1007/s12021-022-09582-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2022] [Indexed: 12/31/2022]
Abstract
Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.
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Affiliation(s)
- Nikita Davydov
- Aligned Research Group, Los Gatos, USA.,Samara National Research University, Samara, Russia.,Image Processing Systems Institute, Russian Academy of Science, Samara, Russia
| | - Lucas Peek
- Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Tibor Auer
- School of Psychology, University of Surrey, Guildford, UK
| | | | - Nicolas Gninenko
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Artem Nikonorov
- Samara National Research University, Samara, Russia.,Image Processing Systems Institute, Russian Academy of Science, Samara, Russia
| | - Yury Koush
- Department of Radiology and Medical Imaging, Yale University, New Haven, USA.
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12
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De Filippi E, Marins T, Escrichs A, Gilson M, Moll J, Tovar-Moll F, Deco G. One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity. Cereb Cortex Commun 2022; 3:tgac027. [PMID: 36072710 PMCID: PMC9441014 DOI: 10.1093/texcom/tgac027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 06/28/2022] [Accepted: 07/03/2022] [Indexed: 11/23/2022] Open
Abstract
In the past decade, several studies have shown that Neurofeedback (NFB) by functional magnetic resonance imaging can alter the functional coupling of targeted and non-targeted areas. However, the causal mechanisms underlying these changes remain uncertain. Here, we applied a whole-brain dynamical model to estimate Effective Connectivity (EC) profiles of resting-state data acquired before and immediately after a single-session NFB training for 17 participants who underwent motor imagery NFB training and 16 healthy controls who received sham feedback. Within-group and between-group classification analyses revealed that only for the NFB group it was possible to accurately discriminate between the 2 resting-state sessions. NFB training-related signatures were reflected in a support network of direct connections between areas involved in reward processing and implicit learning, together with regions belonging to the somatomotor, control, attention, and default mode networks, identified through a recursive-feature elimination procedure. By applying a data-driven approach to explore NFB-induced changes in spatiotemporal dynamics, we demonstrated that these regions also showed decreased switching between different brain states (i.e. metastability) only following real NFB training. Overall, our findings contribute to the understanding of NFB impact on the whole brain’s structure and function by shedding light on the direct connections between brain areas affected by NFB training.
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Affiliation(s)
- Eleonora De Filippi
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Carrer de Ramon Trias Fargas , 25-27, 08005 Barcelona, Catalonia, Spain
| | - Theo Marins
- D’Or Institute for Research and Education (IDOR), Rua Diniz Cordeiro 30, Botafogo-Rio de Janeiro , 22281-100, Brazil
- Post-Graduate Program in Morphological Sciences, Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Citade universitaria da Universidade Federal do Rio de Janeiro , 21941-590, Brazil
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Carrer de Ramon Trias Fargas , 25-27, 08005 Barcelona, Catalonia, Spain
| | - Matthieu Gilson
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Carrer de Ramon Trias Fargas , 25-27, 08005 Barcelona, Catalonia, Spain
| | - Jorge Moll
- D’Or Institute for Research and Education (IDOR), Rua Diniz Cordeiro 30, Botafogo-Rio de Janeiro , 22281-100, Brazil
| | - Fernanda Tovar-Moll
- D’Or Institute for Research and Education (IDOR), Rua Diniz Cordeiro 30, Botafogo-Rio de Janeiro , 22281-100, Brazil
- Post-Graduate Program in Morphological Sciences, Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Citade universitaria da Universidade Federal do Rio de Janeiro , 21941-590, Brazil
| | - Gustavo Deco
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig de Lluis Companys , 23, 08010, Barcelona, Catalonia, Spain
- Department of Neuropsychology, Max Planck Institute for human Cognitive and Brain Sciences , Stephanstrasse 1a, 04103, Leipzig, Germany
- Turner Institute for Brain and Mental Health, Monash University level 5 , 18 Innovation Walk, Clayton Campus. Wellington Road, Clayton VIC 3800, Australia
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13
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Kvamme TL, Ros T, Overgaard M. Can neurofeedback provide evidence of direct brain-behavior causality? Neuroimage 2022; 258:119400. [PMID: 35728786 DOI: 10.1016/j.neuroimage.2022.119400] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 01/01/2023] Open
Abstract
Neurofeedback is a procedure that measures brain activity in real-time and presents it as feedback to an individual, thus allowing them to self-regulate brain activity with effects on cognitive processes inferred from behavior. One common argument is that neurofeedback studies can reveal how the measured brain activity causes a particular cognitive process. The causal claim is often made regarding the measured brain activity being manipulated as an independent variable, similar to brain stimulation studies. However, this causal inference is vulnerable to the argument that other upstream brain activities change concurrently and cause changes in the brain activity from which feedback is derived. In this paper, we outline the inference that neurofeedback may causally affect cognition by indirect means. We further argue that researchers should remain open to the idea that the trained brain activity could be part of a "causal network" that collectively affects cognition rather than being necessarily causally primary. This particular inference may provide a better translation of evidence from neurofeedback studies to the rest of neuroscience. We argue that the recent advent of multivariate pattern analysis, when combined with implicit neurofeedback, currently comprises the strongest case for causality. Our perspective is that although the burden of inferring direct causality is difficult, it may be triangulated using a collection of various methods in neuroscience. Finally, we argue that the neurofeedback methodology provides unique advantages compared to other methods for revealing changes in the brain and cognitive processes but that researchers should remain mindful of indirect causal effects.
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Affiliation(s)
- Timo L Kvamme
- Cognitive Neuroscience Research Unit, CFIN/MINDLab, Aarhus University, Universitetsbyen 3, Aarhus, Denmark; Centre for Alcohol and Drug Research (CRF), Aarhus University, Aarhus, Denmark.
| | - Tomas Ros
- Departments of Neuroscience and Psychiatry, University of Geneva, Campus Biotech, Geneva, Switzerland
| | - Morten Overgaard
- Cognitive Neuroscience Research Unit, CFIN/MINDLab, Aarhus University, Universitetsbyen 3, Aarhus, Denmark
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14
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Orendáčová M, Kvašňák E. Possible Mechanisms Underlying Neurological Post-COVID Symptoms and Neurofeedback as a Potential Therapy. Front Hum Neurosci 2022; 16:837972. [PMID: 35431842 PMCID: PMC9010738 DOI: 10.3389/fnhum.2022.837972] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/26/2022] [Indexed: 12/13/2022] Open
Abstract
Theoretical considerations related to neurological post-COVID complications have become a serious issue in the COVID pandemic. We propose 3 theoretical hypotheses related to neurological post-COVID complications. First, pathophysiological processes responsible for long-term neurological complications caused by COVID-19 might have 2 phases: (1) Phase of acute Sars-CoV-2 infection linked with the pathogenesis responsible for the onset of COVID-19-related neurological complications and (2) the phase of post-acute Sars-CoV-2 infection linked with the pathogenesis responsible for long-lasting persistence of post-COVID neurological problems and/or exacerbation of another neurological pathologies. Second, post-COVID symptoms can be described and investigated from the perspective of dynamical system theory exploiting its fundamental concepts such as system parameters, attractors and criticality. Thirdly, neurofeedback may represent a promising therapy for neurological post-COVID complications. Based on the current knowledge related to neurofeedback and what is already known about neurological complications linked to acute COVID-19 and post-acute COVID-19 conditions, we propose that neurofeedback modalities, such as functional magnetic resonance-based neurofeedback, quantitative EEG-based neurofeedback, Othmer's method of rewarding individual optimal EEG frequency and heart rate variability-based biofeedback, represent a potential therapy for improvement of post-COVID symptoms.
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Affiliation(s)
- Mária Orendáčová
- Department of Medical Biophysics and Medical Informatics, Third Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | - Eugen Kvašňák
- Department of Medical Biophysics and Medical Informatics, Third Faculty of Medicine, Charles University in Prague, Prague, Czechia
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15
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Taylor JE, Yamada T, Kawashima T, Kobayashi Y, Yoshihara Y, Miyata J, Murai T, Kawato M, Motegi T. Depressive symptoms reduce when dorsolateral prefrontal cortex-precuneus connectivity normalizes after functional connectivity neurofeedback. Sci Rep 2022; 12:2581. [PMID: 35173179 PMCID: PMC8850610 DOI: 10.1038/s41598-022-05860-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 01/18/2022] [Indexed: 11/09/2022] Open
Abstract
Depressive disorders contribute heavily to global disease burden; This is possibly because patients are often treated homogeneously, despite having heterogeneous symptoms with differing underlying neural mechanisms. A novel treatment that can directly influence the neural circuit relevant to an individual patient's subset of symptoms might more precisely and thus effectively aid in the alleviation of their specific symptoms. We tested this hypothesis in a proof-of-concept study using fMRI functional connectivity neurofeedback. We targeted connectivity between the left dorsolateral prefrontal cortex/middle frontal gyrus and the left precuneus/posterior cingulate cortex, because this connection has been well-established as relating to a specific subset of depressive symptoms. Specifically, this connectivity has been shown in a data-driven manner to be less anticorrelated in patients with melancholic depression than in healthy controls. Furthermore, a posterior cingulate dominant state-which results in a loss of this anticorrelation-is expected to specifically relate to an increase in rumination symptoms such as brooding. In line with predictions, we found that, with neurofeedback training, the more a participant normalized this connectivity (restored the anticorrelation), the more related (depressive and brooding symptoms), but not unrelated (trait anxiety), symptoms were reduced. Because these results look promising, this paradigm next needs to be examined with a greater sample size and with better controls. Nonetheless, here we provide preliminary evidence for a correlation between the normalization of a neural network and a reduction in related symptoms. Showing their reproducibility, these results were found in two experiments that took place several years apart by different experimenters. Indicative of its potential clinical utility, effects of this treatment remained one-two months later.Clinical trial registration: Both experiments reported here were registered clinical trials (UMIN000015249, jRCTs052180169).
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Affiliation(s)
- Jessica Elizabeth Taylor
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan
| | - Takashi Yamada
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan.,Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, USA.,Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Takahiko Kawashima
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuko Kobayashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuo Kawato
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan
| | - Tomokazu Motegi
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan. .,Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.
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16
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Girges C, Vijiaratnam N, Zrinzo L, Ekanayake J, Foltynie T. Volitional Control of Brain Motor Activity and Its Therapeutic Potential. Neuromodulation 2022; 25:1187-1196. [DOI: 10.1016/j.neurom.2022.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 12/08/2021] [Accepted: 12/28/2021] [Indexed: 12/01/2022]
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17
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Weiss F, Zhang J, Aslan A, Kirsch P, Gerchen MF. Feasibility of training the dorsolateral prefrontal-striatal network by real-time fMRI neurofeedback. Sci Rep 2022; 12:1669. [PMID: 35102203 PMCID: PMC8803939 DOI: 10.1038/s41598-022-05675-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/17/2022] [Indexed: 12/17/2022] Open
Abstract
Real-time fMRI neurofeedback (rt-fMRI NF) is a promising non-invasive technique that enables volitional control of usually covert brain processes. While most rt-fMRI NF studies so far have demonstrated the ability of the method to evoke changes in brain activity and improve symptoms of mental disorders, a recently evolving field is network-based functional connectivity (FC) rt-fMRI NF. However, FC rt-fMRI NF has methodological challenges such as respirational artefacts that could potentially bias the training if not controlled. In this randomized, double-blind, yoke-controlled, pre-registered FC rt-fMRI NF study with healthy participants (N = 40) studied over three training days, we tested the feasibility of an FC rt-fMRI NF approach with online global signal regression (GSR) to control for physiological artefacts for up-regulation of connectivity in the dorsolateral prefrontal-striatal network. While our pre-registered null hypothesis significance tests failed to reach criterion, we estimated the FC training effect at a medium effect size at the end of the third training day after rigorous control of physiological artefacts in the offline data. This hints at the potential of FC rt-fMRI NF for the development of innovative transdiagnostic circuit-specific interventional approaches for mental disorders and the effect should now be confirmed in a well-powered study.
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Affiliation(s)
- Franziska Weiss
- Department of Clinical Psychology, Central Institute of Mental Health (ZI), Heidelberg University/Medical Faculty Mannheim, J5, 68159, Mannheim, Germany
| | - Jingying Zhang
- Department of Clinical Psychology, Central Institute of Mental Health (ZI), Heidelberg University/Medical Faculty Mannheim, J5, 68159, Mannheim, Germany
| | - Acelya Aslan
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Heidelberg University/Medical Faculty Mannheim, Mannheim, Germany
| | - Peter Kirsch
- Department of Clinical Psychology, Central Institute of Mental Health (ZI), Heidelberg University/Medical Faculty Mannheim, J5, 68159, Mannheim, Germany.,Bernstein Center for Computational Neuroscience Heidelberg/Mannheim, Mannheim, Germany.,Department of Psychology, Heidelberg University, Heidelberg, Germany
| | - Martin Fungisai Gerchen
- Department of Clinical Psychology, Central Institute of Mental Health (ZI), Heidelberg University/Medical Faculty Mannheim, J5, 68159, Mannheim, Germany. .,Bernstein Center for Computational Neuroscience Heidelberg/Mannheim, Mannheim, Germany. .,Department of Psychology, Heidelberg University, Heidelberg, Germany.
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18
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Taschereau-Dumouchel V, Cushing C, Lau H. Real-Time Functional MRI in the Treatment of Mental Health Disorders. Annu Rev Clin Psychol 2022; 18:125-154. [DOI: 10.1146/annurev-clinpsy-072220-014550] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Multiple mental disorders have been associated with dysregulation of precise brain processes. However, few therapeutic approaches can correct such specific patterns of brain activity. Since the late 1960s and early 1970s, many researchers have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim to modulate brain activity directly. However, neurofeedback never gained mainstream acceptance in mental health, in part due to methodological considerations. In this review, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging neurofeedback and discuss current and future applications to mental health. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Vincent Taschereau-Dumouchel
- Department of Psychiatry and Addictology, Université de Montréal, Montréal, Québec, Canada
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Québec, Canada
| | - Cody Cushing
- Department of Psychology, University of California, Los Angeles, California, USA
| | - Hakwan Lau
- RIKEN Center for Brain Science, Wakoshi, Saitama, Japan
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19
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Sladky R, Hahn A, Karl IL, Geissberger N, Kranz GS, Tik M, Kraus C, Pfabigan DM, Gartus A, Lanzenberger R, Lamm C, Windischberger C. Dynamic Causal Modeling of the Prefrontal/Amygdala Network During Processing of Emotional Faces. Brain Connect 2021; 12:670-682. [PMID: 34605671 DOI: 10.1089/brain.2021.0073] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: The importance of the amygdala/medial orbitofrontal cortex (OFC) network during processing of emotional stimuli, emotional faces in particular, is well established. This premise is supported by converging evidence from animal models, human neuroanatomical results, and neuroimaging studies. However, there is missing evidence from human brain connectivity studies that the OFC and no other prefrontal brain areas such as the dorsolateral prefrontal cortex (DLPFC) or ventrolateral prefrontal cortex (VLPFC) are responsible for amygdala regulation in the functional context of emotional face stimuli. Methods: Dynamic causal modeling of ultrahigh-field functional magnetic resonance imaging data acquired at 7 Tesla in 38 healthy subjects and a well-established paradigm for emotional face processing were used to assess the central role of the OFC to provide empirical validation for the assumed network architecture. Results: Using Bayesian model selection, it is demonstrated that indeed the OFC, and not the VLPFC and the DLPFC, downregulates amygdala activation during the emotion discrimination task. In addition, Bayesian model averaging group results were rigorously tested using bootstrapping, further corroborating these findings and providing an estimator for robustness and optimal sample sizes. Discussion: While it is true that VLPFC and DLPFC are relevant for the processing of emotional faces and are connected to the OFC, the OFC appears to be a central hub for the prefrontal/amygdala interaction.
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Affiliation(s)
- Ronald Sladky
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.,Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Inga-Lisa Karl
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Nicole Geissberger
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Georg S Kranz
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.,Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.,The State Key Laboratory of Brain and Cognitive Science, The University of Hong Kong, Hong Kong, China
| | - Martin Tik
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Christoph Kraus
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Daniela M Pfabigan
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Andreas Gartus
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Claus Lamm
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Christian Windischberger
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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20
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Finn ES. Is it time to put rest to rest? Trends Cogn Sci 2021; 25:1021-1032. [PMID: 34625348 PMCID: PMC8585722 DOI: 10.1016/j.tics.2021.09.005] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/08/2021] [Accepted: 09/10/2021] [Indexed: 12/29/2022]
Abstract
The so-called resting state, in which participants lie quietly with no particular inputs or outputs, represented a paradigm shift from conventional task-based studies in human neuroimaging. Our foray into rest was fruitful from both a scientific and methodological perspective, but at this point, how much more can we learn from rest on its own? While rest still dominates in many subfields, data from tasks have empirically demonstrated benefits, as well as the potential to provide insights about the mind in addition to the brain. I argue that we can accelerate progress in human neuroscience by de-emphasizing rest in favor of more grounded experiments, including promising integrated designs that respect the prominence of self-generated activity while offering enhanced control and interpretability.
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Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
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21
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Initial Results of Tests Using GSR Biofeedback as a New Neurorehabilitation Technology Complementing Pharmacological Treatment of Patients with Schizophrenia. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5552937. [PMID: 34222472 PMCID: PMC8213473 DOI: 10.1155/2021/5552937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 05/22/2021] [Accepted: 06/04/2021] [Indexed: 11/24/2022]
Abstract
Galvanic skin response (GSR) Biofeedback uses training to reduce tension and anxiety and improve concentration and self-regulation. The study was aimed to evaluate this method as a form of rehabilitation and quantify the outcomes achieved by patients undergoing training using this technique. Six schizophrenic patients were enrolled in the study and underwent training based on the relaxation training module (CENTER), concentration training module (BALANCE), and self-regulation training module (INSECTS). Training sessions were held twice a week for 6 weeks. From the total group of subjects involved in the study, two patients had a statistically significant increase in measured values after the CENTER exercise, indicating that relaxation was achieved. Four patients showed a statistically significant decrease in measured values after the BALANCE exercise, which was reflective of an improvement in concentration. Three patients had a statistically significant decrease in measured values after the INSECTS exercise, which indicated an improvement in self-regulation. GSR Biofeedback may be used to complement the pharmacological treatment of patients diagnosed with schizophrenia.
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22
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Koush Y, de Graaf RA, Kupers R, Dricot L, Ptito M, Behar KL, Rothman DL, Hyder F. Metabolic underpinnings of activated and deactivated cortical areas in human brain. J Cereb Blood Flow Metab 2021; 41:986-1000. [PMID: 33472521 PMCID: PMC8054719 DOI: 10.1177/0271678x21989186] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 11/04/2020] [Accepted: 12/11/2020] [Indexed: 11/16/2022]
Abstract
Neuroimaging with functional MRI (fMRI) identifies activated and deactivated brain regions in task-based paradigms. These patterns of (de)activation are altered in diseases, motivating research to understand their underlying biochemical/biophysical mechanisms. Essentially, it remains unknown how aerobic metabolism of glucose to lactate (aerobic glycolysis) and excitatory-inhibitory balance of glutamatergic and GABAergic neuronal activities vary in these areas. In healthy volunteers, we investigated metabolic distinctions of activating visual cortex (VC, a task-positive area) using a visual task and deactivating posterior cingulate cortex (PCC, a task-negative area) using a cognitive task. We used fMRI-guided J-edited functional MRS (fMRS) to measure lactate, glutamate plus glutamine (Glx) and γ-aminobutyric acid (GABA), as indicators of aerobic glycolysis and excitatory-inhibitory balance, respectively. Both lactate and Glx increased upon activating VC, but did not change upon deactivating PCC. Basal GABA was negatively correlated with BOLD responses in both brain areas, but during functional tasks GABA decreased in VC upon activation and GABA increased in PCC upon deactivation, suggesting BOLD responses in relation to baseline are impacted oppositely by task-induced inhibition. In summary, opposite relations between BOLD response and GABAergic inhibition, and increases in aerobic glycolysis and glutamatergic activity distinguish the BOLD response in (de)activated areas.
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Affiliation(s)
- Yury Koush
- Magnetic Resonance Research Center, Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Robin A de Graaf
- Magnetic Resonance Research Center, Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Ron Kupers
- BRAINlab, Department of Neuroscience, Panum Institute, University of Copenhagen, Copenhagen, Denmark
| | - Laurence Dricot
- Institute of NeuroScience (IoNS), Université catholique de Louvain (UCLouvain), Belgium
| | - Maurice Ptito
- School of Optometry, Université de Montreal, Montreal, Canada
| | - Kevin L Behar
- Magnetic Resonance Research Center, Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Douglas L Rothman
- Magnetic Resonance Research Center, Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Fahmeed Hyder
- Magnetic Resonance Research Center, Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
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23
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Russo AG, Lührs M, Di Salle F, Esposito F, Goebel R. Towards semantic fMRI neurofeedback: navigating among mental states using real-time representational similarity analysis. J Neural Eng 2021; 18. [PMID: 33684900 DOI: 10.1088/1741-2552/abecc3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/08/2021] [Indexed: 11/12/2022]
Abstract
Objective. Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive MRI procedure allowing examined participants to learn to self-regulate brain activity by performing mental tasks. A novel two-step rt-fMRI-NF procedure is proposed whereby the feedback display is updated in real-time based on high-level representations of experimental stimuli (e.g. objects to imagine) via real-time representational similarity analysis of multi-voxel patterns of brain activity.Approach. In a localizer session, the stimuli become associated with anchored points on a two-dimensional representational space where distances approximate between-pattern (dis)similarities. In the NF session, participants modulate their brain response, displayed as a movable point, to engage in a specific neural representation. The developed method pipeline is verified in a proof-of-concept rt-fMRI-NF study at 7 T involving a single healthy participant imagining concrete objects. Based on this data and artificial data sets with similar (simulated) spatio-temporal structure and variable (injected) signal and noise, the dependence on noise is systematically assessed.Main results. The participant in the proof-of-concept study exhibited robust activation patterns in the localizer session and managed to control the neural representation of a stimulus towards the selected target in the NF session. The offline analyses validated the rt-fMRI-NF results, showing that the rapid convergence to the target representation is noise-dependent.Significance. Our proof-of-concept study introduces a new NF method allowing the participant to navigate among different mental states. Compared to traditional NF designs (e.g. using a thermometer display to set the level of the neural signal), the proposed approach provides content-specific feedback to the participant and extra degrees of freedom to the experimenter enabling real-time control of the neural activity towards a target brain state without suggesting a specific mental strategy to the subject.
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Affiliation(s)
- Andrea G Russo
- Department of Political and Communication Sciences, University of Salerno, Fisciano (Salerno), Italy.,Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy
| | - Michael Lührs
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Brain Innovation B.V., Maastricht, The Netherlands
| | - Francesco Di Salle
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy
| | - Fabrizio Esposito
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Baronissi (Salerno), Italy.,Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Department of Advanced Medical and Surgical Sciences,University of Campania 'Luigi Vanvitelli', Napoli,Italy
| | - Rainer Goebel
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands.,Brain Innovation B.V., Maastricht, The Netherlands
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24
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Trambaiolli LR, Kohl SH, Linden DEJ, Mehler DMA. Neurofeedback training in major depressive disorder: A systematic review of clinical efficacy, study quality and reporting practices. Neurosci Biobehav Rev 2021; 125:33-56. [PMID: 33587957 DOI: 10.1016/j.neubiorev.2021.02.015] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 02/05/2021] [Accepted: 02/06/2021] [Indexed: 12/28/2022]
Abstract
Major depressive disorder (MDD) is the leading cause of disability worldwide. Neurofeedback training has been suggested as a potential additional treatment option for MDD patients not reaching remission from standard care (i.e., psychopharmacology and psychotherapy). Here we systematically reviewed neurofeedback studies employing electroencephalography, or functional magnetic resonance-based protocols in depressive patients. Of 585 initially screened studies, 24 were included in our final sample (N = 480 patients in experimental and N = 194 in the control groups completing the primary endpoint). We evaluated the clinical efficacy across studies and attempted to group studies according to the control condition categories currently used in the field that affect clinical outcomes in group comparisons. In most studies, MDD patients showed symptom improvement superior to the control group(s). However, most articles did not comply with the most stringent study quality and reporting practices. We conclude with recommendations on best practices for experimental designs and reporting standards for neurofeedback training.
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Affiliation(s)
- Lucas R Trambaiolli
- Division of Basic Neuroscience, McLean Hospital, Harvard Medical School, Boston, USA.
| | - Simon H Kohl
- JARA Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Germany; Department of Child and Adolescent Psychiatry, Medical Faculty, RWTH Aachen University, Germany
| | - David E J Linden
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, the Netherlands
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25
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Tsuchiyagaito A, Misaki M, Zoubi OA, Paulus M, Bodurka J. Prevent breaking bad: A proof of concept study of rebalancing the brain's rumination circuit with real-time fMRI functional connectivity neurofeedback. Hum Brain Mapp 2020; 42:922-940. [PMID: 33169903 PMCID: PMC7856643 DOI: 10.1002/hbm.25268] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/06/2020] [Accepted: 10/20/2020] [Indexed: 12/14/2022] Open
Abstract
Rumination, repetitively thinking about the causes, consequences, and one's negative affect, has been considered as an important factor of depression. The intrusion of ruminative thoughts is not easily controlled, and it may be useful to visualize one's neural activity related to rumination and to use that information to facilitate one's self‐control. Real‐time fMRI neurofeedback (rtfMRI‐nf) enables one to see and regulate the fMRI signal from their own brain. This proof‐of concept study utilized connectivity‐based rtfMRI‐nf (cnf) to normalize brain functional connectivity (FC) associated with rumination. Healthy participants were instructed to brake or decrease FC between the precuneus and the right temporoparietal junction (rTPJ), associated with high levels of rumination, while engaging in a self‐referential task. The cnf group (n = 14) showed a linear decrease in the precuneus‐rTPJ FC across neurofeedback training (trend [112] = −0.180, 95% confidence interval [CI] −0.330 to −0.031, while the sham group (n = 14) showed a linear increase in the target FC (trend [112] = 0.151, 95% CI 0.017 to 0.299). Although the cnf group showed a greater reduction in state‐rumination compared to the sham group after neurofeedback training (p < .05), decoupled precuneus‐rTPJ FC did not predict attenuated state‐rumination. We did not find any significant aversive effects of rtfMRI‐nf in all study participants. These results suggest that cnf has the capacity to influence FC among precuneus and rTPJ of a ruminative brain circuit. This approach can be applied to mood and anxiety patients to determine the clinical benefits of reduction in maladaptive rumination.
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Affiliation(s)
- Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,Japan Society for the Promotion of Science, Tokyo, Japan
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,Department of Electrical and Computer Engineering, University of Oklahoma, Tulsa, Oklahoma, USA
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- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,Department of Community Medicine, Oxley Health Sciences, University of Tulsa, Tulsa, Oklahoma, USA
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.,Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, USA
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26
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Haugg A, Sladky R, Skouras S, McDonald A, Craddock C, Kirschner M, Herdener M, Koush Y, Papoutsi M, Keynan JN, Hendler T, Cohen Kadosh K, Zich C, MacInnes J, Adcock RA, Dickerson K, Chen N, Young K, Bodurka J, Yao S, Becker B, Auer T, Schweizer R, Pamplona G, Emmert K, Haller S, Van De Ville D, Blefari M, Kim D, Lee J, Marins T, Fukuda M, Sorger B, Kamp T, Liew S, Veit R, Spetter M, Weiskopf N, Scharnowski F. Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity? Hum Brain Mapp 2020; 41:3839-3854. [PMID: 32729652 PMCID: PMC7469782 DOI: 10.1002/hbm.25089] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/18/2020] [Accepted: 05/26/2020] [Indexed: 12/31/2022] Open
Abstract
Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.
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Affiliation(s)
- Amelie Haugg
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Ronald Sladky
- Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Stavros Skouras
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
| | - Amalia McDonald
- Department of PsychologyUniversity of VirginiaCharlottesvilleVirginia
| | - Cameron Craddock
- Department of Diagnostic MedicineThe University of Texas at Austin Dell Medical SchoolAustinTexas
| | - Matthias Kirschner
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- McConnell Brain Imaging CentreMontréal Neurological Institute, McGill UniversityMontrealCanada
| | - Marcus Herdener
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
| | - Yury Koush
- Magnetic Resonance Research Center, Department of Radiology & Biomedical ImagingYale UniversityNew HavenConnecticut
| | - Marina Papoutsi
- UCL Huntington's Disease CentreInstitute of Neurology, University College LondonLondonEngland
| | - Jackob N. Keynan
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | - Talma Hendler
- Functional Brain CenterWohl Institute for Advanced Imaging, Tel‐Aviv Sourasky Medical Center, Tel‐Aviv UniversityTel AvivIsrael
| | | | - Catharina Zich
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordEngland
| | - Jeff MacInnes
- Institute for Learning and Brain SciencesUniversity of WashingtonSeattleWashington
| | - R. Alison Adcock
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Kathryn Dickerson
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth Carolina
| | - Nan‐Kuei Chen
- Department of Biomedical EngineeringUniversity of ArizonaTucsonArizona
| | - Kymberly Young
- Department of Psychiatry, School of MedicineUniversity of PittsburghPittsburghPennsylvania
| | | | - Shuxia Yao
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Benjamin Becker
- Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordEngland
| | - Renate Schweizer
- Functional Imaging LaboratoryGerman Primate CenterGöttingenGermany
| | - Gustavo Pamplona
- Hôpital and Ophtalmique Jules GoninUniversity of LausanneLausanneSwitzerland
| | - Kirsten Emmert
- Department of NeurologyUniversity Medical Center Schleswig‐Holstein, Kiel UniversityKielGermany
| | - Sven Haller
- Radiology‐Department of Surgical SciencesUppsala UniversityUppsalaSweden
| | - Dimitri Van De Ville
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
- Department of Radiology and Medical Informatics, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
| | - Maria‐Laura Blefari
- Center for NeuroprostheticsEcole Polytechnique Féderale de LausanneLausanneSwitzerland
| | - Dong‐Youl Kim
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Jong‐Hwan Lee
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
| | - Theo Marins
- D'Or Institute for Research and Education (IDOR)Rio de JaneiroBrazil
| | - Megumi Fukuda
- School of Fundamental Science and EngineeringWaseda UniversityTokyoJapan
| | - Bettina Sorger
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Tabea Kamp
- Department Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Sook‐Lei Liew
- Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Ralf Veit
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center MunichUniversity of TübingenTübingenGermany
| | - Maartje Spetter
- School of PsychologyUniversity of BirminghamBirminghamEngland
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Frank Scharnowski
- Psychiatric University Hospital ZurichUniversity of ZurichZürichSwitzerland
- Faculty of PsychologyUniversity of ViennaViennaAustria
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27
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Vakamudi K, Trapp C, Talaat K, Gao K, Sa De La Rocque Guimaraes B, Posse S. Real-Time Resting-State Functional Magnetic Resonance Imaging Using Averaged Sliding Windows with Partial Correlations and Regression of Confounding Signals. Brain Connect 2020; 10:448-463. [PMID: 32892629 DOI: 10.1089/brain.2020.0758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Background/Introduction: There is considerable interest in using real-time functional magnetic resonance imaging (fMRI) for monitoring functional connectivity dynamics. To date, the majority of real-time resting-state fMRI studies have examined a limited number of brain regions. This is, in part, due to the computational demands of traditional seed- and independent component analysis-based methods, in particular when using increasingly available high-speed fMRI methods. Methods: This study describes a computationally efficient, real-time, seed-based, resting-state fMRI analysis pipeline using moving averaged sliding-windows (ASW) with partial correlations and regression of motion parameters and signals from white matter and cerebrospinal fluid. Results: Analytical and numerical analyses of ASW correlation and sliding-window regression as a function of window width show selectable bandpass filter characteristics and effective suppression of artifactual correlations resulting from signal drifts and transients. The analysis pipeline is compatible with multislab echo-volumar imaging and simultaneous multislice echo-planar imaging with repetition times as short as 136 msec. High-speed, resting-state fMRI data in healthy controls demonstrate the effectiveness of this approach for minimizing artifactual correlations in white and gray matter, which was comparable to conventional regression across the entire scan. Integrating sliding-window averaging (width: W1) within a second-level sliding-window (width: W2) enabled monitoring of intra- and internetwork correlation dynamics of up to 12 resting-state networks with bandpass filter characteristics determined by the first-level sliding-window and temporal resolution W1 + W2. Conclusions: The computational performance and confound tolerance make this seed-based, resting-state fMRI approach suitable for real-time monitoring of data quality and resting-state connectivity dynamics in neuroscience and clinical research studies. Impact statement Using averaged sliding-windows for seed-based correlation and regression of confounding signals provides a powerful model-free approach to increase tolerance to artifactual signal transients in resting-state analysis. The algorithmic efficiency of this sliding-window approach enables real-time, seed-based, resting-state functional magnetic resonance imaging (fMRI) of multiple networks with computation of connectivity matrices and online monitoring of data quality. Integration of a second-level sliding-window enables mapping of resting-state connectivity dynamics. Sensitivity and tolerance to confounding signals compare favorably with conventional correlation and confound regression across the entire scan. This methodological advance has the potential to enhance the clinical utility of resting-state fMRI and facilitate neuroscience applications.
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Affiliation(s)
- Kishore Vakamudi
- Department of Neurology, School of Medicine, The University of New Mexico, Albuquerque, New Mexico, USA
| | - Cameron Trapp
- Department of Neurology, School of Medicine, The University of New Mexico, Albuquerque, New Mexico, USA.,Department of Physics and Astronomy, The University of New Mexico, Albuquerque, New Mexico, USA
| | - Khaled Talaat
- Department of Neurology, School of Medicine, The University of New Mexico, Albuquerque, New Mexico, USA.,Department of Nuclear Engineering, The University of New Mexico, Albuquerque, New Mexico, USA
| | - Kunxiu Gao
- NeurInsight, LLC, Albuquerque, New Mexico, USA
| | | | - Stefan Posse
- Department of Neurology, School of Medicine, The University of New Mexico, Albuquerque, New Mexico, USA.,Department of Physics and Astronomy, The University of New Mexico, Albuquerque, New Mexico, USA
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28
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Pandria N, Athanasiou A, Konstantara L, Karagianni M, Bamidis PD. Advances in biofeedback and neurofeedback studies on smoking. Neuroimage Clin 2020; 28:102397. [PMID: 32947225 PMCID: PMC7502375 DOI: 10.1016/j.nicl.2020.102397] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 08/02/2020] [Accepted: 08/19/2020] [Indexed: 11/19/2022]
Abstract
Smoking is a leading cause of morbidity and premature death constituting a global health challenge. Although, pharmacological and behavioral approaches comprise the mainstay of smoking cessation interventions, the efficacy and safety of pharmacotherapy is not demonstrated for some populations. Non-pharmacological approaches, such as biofeedback (BF) and neurofeedback (NF) could facilitate self-regulation of predisposing factors of relapse such as craving and stress. The current review aims to aggregate the existing evidence regarding the effects of BF and NF training on smokers. Relevant studies were identified through searching in Scopus, PubMed and Cochrane Library, and through hand-searching the references of screened articles. Peer-reviewed controlled and uncontrolled studies, where BF and/or NF training was administered, were included and evaluated according to PICOS framework. Narrative qualitative synthesis of ten eligible studies was performed, aggregated into three categories according to training provided. BF outcomes seem to be affected by smoking behavior prior to training; individualized EEG NF training holds promise for modulating craving-related response while minimizing the required number of sessions. Real-time fMRI NF studies concluded that nicotine-dependent individuals could modulate craving-related brain responses, while mixed results were revealed regarding smokers' ability to modulate brain responses related to resistance towards the urge to smoke. BF and NF training seem to facilitate modulation of autonomous and/or central nervous system activity while also transferring this learned self-regulation to behavioral outcomes. BF and NF training should a) address remaining issues on specificity and scientific validity, b) target diverse demographics, and c) produce robust reproducible methodologies and clinical guidelines for relevant health care providers, in order to be considered as viable complementary tools to standard smoking cessation care.
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Affiliation(s)
- N Pandria
- Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece; Northern Greece Neurofeedback Center, Thessaloniki, Greece.
| | - A Athanasiou
- Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.
| | - L Konstantara
- Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.
| | - M Karagianni
- Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.
| | - P D Bamidis
- Lab of Medical Physics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.
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29
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Heunis S, Lamerichs R, Zinger S, Caballero‐Gaudes C, Jansen JFA, Aldenkamp B, Breeuwer M. Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review. Hum Brain Mapp 2020; 41:3439-3467. [PMID: 32333624 PMCID: PMC7375116 DOI: 10.1002/hbm.25010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/13/2020] [Accepted: 04/03/2020] [Indexed: 01/31/2023] Open
Abstract
Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.
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Affiliation(s)
- Stephan Heunis
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | - Rolf Lamerichs
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- Philips ResearchEindhovenThe Netherlands
| | - Svitlana Zinger
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | | | - Jacobus F. A. Jansen
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of RadiologyMaastricht University Medical CentreMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
| | - Bert Aldenkamp
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and NeuropsychologyGhent University HospitalGhentBelgium
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Marcel Breeuwer
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Philips HealthcareBestThe Netherlands
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30
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Scharnowski F, Nicholson AA, Pichon S, Rosa MJ, Rey G, Eickhoff SB, Van De Ville D, Vuilleumier P, Koush Y. The role of the subgenual anterior cingulate cortex in dorsomedial prefrontal-amygdala neural circuitry during positive-social emotion regulation. Hum Brain Mapp 2020; 41:3100-3118. [PMID: 32309893 PMCID: PMC7336138 DOI: 10.1002/hbm.25001] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 03/23/2020] [Accepted: 03/24/2020] [Indexed: 01/10/2023] Open
Abstract
Positive-social emotions mediate one's cognitive performance, mood, well-being, and social bonds, and represent a critical variable within therapeutic settings. It has been shown that the upregulation of positive emotions in social situations is associated with increased top-down signals that stem from the prefrontal cortices (PFC) which modulate bottom-up emotional responses in the amygdala. However, it remains unclear if positive-social emotion upregulation of the amygdala occurs directly through the dorsomedial PFC (dmPFC) or indirectly linking the bilateral amygdala with the dmPFC via the subgenual anterior cingulate cortex (sgACC), an area which typically serves as a gatekeeper between cognitive and emotion networks. We performed functional MRI (fMRI) experiments with and without effortful positive-social emotion upregulation to demonstrate the functional architecture of a network involving the amygdala, the dmPFC, and the sgACC. We found that effortful positive-social emotion upregulation was associated with an increase in top-down connectivity from the dmPFC on the amygdala via both direct and indirect connections with the sgACC. Conversely, we found that emotion processes without effortful regulation increased network modulation by the sgACC and amygdala. We also found that more anxious individuals with a greater tendency to suppress emotions and intrusive thoughts, were likely to display decreased amygdala, dmPFC, and sgACC activity and stronger connectivity strength from the sgACC onto the left amygdala during effortful emotion upregulation. Analyzed brain network suggests a more general role of the sgACC in cognitive control and sheds light on neurobiological informed treatment interventions.
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Affiliation(s)
- Frank Scharnowski
- Department of Cognition, Emotion and Methods in Psychology, Faculty of PsychologyUniversity of ViennaViennaAustria
- Department of Psychiatry, Psychotherapy and PsychosomaticsPsychiatric Hospital, University of ZürichZürichSwitzerland
- Neuroscience Center ZürichUniversity of Zürich and Swiss Federal Institute of TechnologyZürichSwitzerland
- Zürich Center for Integrative Human Physiology (ZIHP)University of ZürichZürichSwitzerland
| | - Andrew A. Nicholson
- Department of Cognition, Emotion and Methods in Psychology, Faculty of PsychologyUniversity of ViennaViennaAustria
| | - Swann Pichon
- Geneva Neuroscience Center, Department of NeuroscienceUniversity of GenevaGenevaSwitzerland
- NCCR Affective SciencesUniversity of GenevaGenevaSwitzerland
- Faculty of Psychology and Educational ScienceUniversity of GenevaGenevaSwitzerland
| | - Maria J. Rosa
- Department of Computer ScienceCentre for Computational Statistics and Machine Learning, University College LondonLondonUK
| | - Gwladys Rey
- Geneva Neuroscience Center, Department of NeuroscienceUniversity of GenevaGenevaSwitzerland
- Institute of BioengineeringEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Simon B. Eickhoff
- Institute of Neuroscience and MedicineBrain & Behaviour (INM‐7), Research Center JülichJülichGermany
- Institute of Systems Neuroscience, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Dimitri Van De Ville
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
- Institute of BioengineeringEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Patrik Vuilleumier
- Geneva Neuroscience Center, Department of NeuroscienceUniversity of GenevaGenevaSwitzerland
- NCCR Affective SciencesUniversity of GenevaGenevaSwitzerland
| | - Yury Koush
- Department of Radiology and Biomedical ImagingYale UniversityNew HavenConnecticutUSA
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31
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Bagarinao E, Yoshida A, Terabe K, Kato S, Nakai T. Improving Real-Time Brain State Classification of Motor Imagery Tasks During Neurofeedback Training. Front Neurosci 2020; 14:623. [PMID: 32670011 PMCID: PMC7326956 DOI: 10.3389/fnins.2020.00623] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 05/19/2020] [Indexed: 11/24/2022] Open
Abstract
In this study, we investigated the effect of the dynamic changes in brain activation during neurofeedback training in the classification of the different brain states associated with the target tasks. We hypothesized that ongoing activation patterns could change during neurofeedback session due to learning effects and, in the process, could affect the performance of brain state classifiers trained using data obtained prior to the session. Using a motor imagery paradigm, we then examined the application of an incremental training approach where classifiers were continuously updated in order to account for these activation changes. Our results confirmed our hypothesis that neurofeedback training could be associated with dynamic changes in brain activation characterized by an initially more widespread brain activation followed by a more focused and localized activation pattern. By continuously updating the trained classifiers after each feedback run, significant improvement in accurately classifying the different brain states associated with the target motor imagery tasks was achieved. These findings suggest the importance of taking into account brain activation changes during neurofeedback in order to provide more reliable and accurate feedback information to the participants, which is critical for an effective neurofeedback application.
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Affiliation(s)
| | - Akihiro Yoshida
- Neuroimaging and Informatics Group, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Kazunori Terabe
- Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Shohei Kato
- Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Toshiharu Nakai
- Neuroimaging and Informatics Group, National Center for Geriatrics and Gerontology, Obu, Japan.,Department of Radiology, Osaka University Graduate School of Dentistry, Osaka, Japan
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Zich C, Johnstone N, Lührs M, Lisk S, Haller SP, Lipp A, Lau JY, Kadosh KC. Modulatory effects of dynamic fMRI-based neurofeedback on emotion regulation networks in adolescent females. Neuroimage 2020; 220:117053. [PMID: 32574803 PMCID: PMC7573536 DOI: 10.1016/j.neuroimage.2020.117053] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 06/08/2020] [Accepted: 06/10/2020] [Indexed: 01/01/2023] Open
Abstract
Research has shown that difficulties with emotion regulation abilities in childhood and adolescence increase the risk for developing symptoms of mental disorders, e.g anxiety. We investigated whether functional magnetic resonance imaging (fMRI)-based neurofeedback (NF) can modulate brain networks supporting emotion regulation abilities in adolescent females. We performed three experiments (Experiment 1: N = 18; Experiment 2: N = 30; Experiment 3: N = 20). We first compared different NF implementations regarding their effectiveness of modulating prefrontal cortex (PFC)-amygdala functional connectivity (fc). Further we assessed the effects of fc-NF on neural measures, emotional/metacognitive measures and their associations. Finally, we probed the mechanism underlying fc-NF by examining concentrations of inhibitory and excitatory neurotransmitters. Results showed that NF implementations differentially modulate PFC-amygdala fc. Using the most effective NF implementation we observed important relationships between neural and emotional/metacognitive measures, such as practice-related change in fc was related with change in thought control ability. Further, we found that the relationship between state anxiety prior to the MRI session and the effect of fc-NF was moderated by GABA concentrations in the PFC and anterior cingulate cortex. To conclude, we were able to show that fc-NF can be used in adolescent females to shape neural and emotional/metacognitive measures underlying emotion regulation. We further show that neurotransmitter concentrations moderate fc–NF–effects. Neurofeedback implementations differentially modulate PFC-amygdala connectivity. Functional connectivity neurofeedback affect measures of emotion regulation. Neurotransmitter concentrations moderate neurofeedback effects.
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Affiliation(s)
- Catharina Zich
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK.
| | - Nicola Johnstone
- School of Psychology, University of Surrey, Guildford, GU2 7XH, UK
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, 6211 LK, the Netherlands; Department of Research and Development, Brain Innovation, Maastricht, 6229 EV, the Netherlands
| | - Stephen Lisk
- Psychology Department, Institute of Psychiatry, King's College London, London, SE5 8AF, UK
| | - Simone Pw Haller
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK
| | - Annalisa Lipp
- School of Psychology, University of Surrey, Guildford, GU2 7XH, UK
| | - Jennifer Yf Lau
- Psychology Department, Institute of Psychiatry, King's College London, London, SE5 8AF, UK
| | - Kathrin Cohen Kadosh
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK; School of Psychology, University of Surrey, Guildford, GU2 7XH, UK
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33
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Simultaneous EEG-fMRI during a neurofeedback task, a brain imaging dataset for multimodal data integration. Sci Data 2020; 7:173. [PMID: 32523031 PMCID: PMC7287136 DOI: 10.1038/s41597-020-0498-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 04/22/2020] [Indexed: 11/08/2022] Open
Abstract
Combining EEG and fMRI allows for integration of fine spatial and accurate temporal resolution yet presents numerous challenges, noticeably if performed in real-time to implement a Neurofeedback (NF) loop. Here we describe a multimodal dataset of EEG and fMRI acquired simultaneously during a motor imagery NF task, supplemented with MRI structural data. The study involved 30 healthy volunteers undergoing five training sessions. We showed the potential and merit of simultaneous EEG-fMRI NF in previous work. Here we illustrate the type of information that can be extracted from this dataset and show its potential use. This represents one of the first simultaneous recording of EEG and fMRI for NF and here we present the first open access bi-modal NF dataset integrating EEG and fMRI. We believe that it will be a valuable tool to (1) advance and test methodologies for multi-modal data integration, (2) improve the quality of NF provided, (3) improve methodologies for de-noising EEG acquired under MRI and (4) investigate the neuromarkers of motor-imagery using multi-modal information.
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34
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Weiss F, Zamoscik V, Schmidt SN, Halli P, Kirsch P, Gerchen MF. Just a very expensive breathing training? Risk of respiratory artefacts in functional connectivity-based real-time fMRI neurofeedback. Neuroimage 2020; 210:116580. [DOI: 10.1016/j.neuroimage.2020.116580] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/15/2020] [Accepted: 01/20/2020] [Indexed: 10/25/2022] Open
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Nicholson AA, Ros T, Jetly R, Lanius RA. Regulating posttraumatic stress disorder symptoms with neurofeedback: Regaining control of the mind. JOURNAL OF MILITARY, VETERAN AND FAMILY HEALTH 2020. [DOI: 10.3138/jmvfh.2019-0032] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Neurofeedback is emerging as a psychophysiological treatment where self-regulation is achieved through online feedback of neural states. Novel personalized medicine approaches are particularly important for the treatment of posttraumatic stress disorder (PTSD), as symptom presentation of the disorder, as well as responses to treatment, are highly heterogeneous. Learning to achieve control of specific neural substrates through neurofeedback has been shown to display therapeutic evidence in patients with a wide variety of psychiatric disorders, including PTSD. This article outlines the neural mechanisms underlying neurofeedback and examines converging evidence for the efficacy of neurofeedback as an adjunctive treatment for PTSD via both electroencephalography (EEG) and real-time functional magnetic resonance imaging (fMRI) modalities. Further, implications for the treatment of PTSD via neurofeedback in the military member and Veteran population is examined.
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Affiliation(s)
- Andrew A. Nicholson
- Department of Psychological Research and Research Methods, University of Vienna, Vienna, Austria
- Neurology and Imaging of Cognition Lab, University of Geneva, Geneva, Switzerland
- Canadian Forces Health Services Group, Department of National Defence, Government of Canada, Ottawa
- Department of Psychology, Western University, London, Ontario
| | - Tomas Ros
- Department of Psychological Research and Research Methods, University of Vienna, Vienna, Austria
- Neurology and Imaging of Cognition Lab, University of Geneva, Geneva, Switzerland
- Canadian Forces Health Services Group, Department of National Defence, Government of Canada, Ottawa
- Department of Psychology, Western University, London, Ontario
| | - Rakesh Jetly
- Department of Psychological Research and Research Methods, University of Vienna, Vienna, Austria
- Neurology and Imaging of Cognition Lab, University of Geneva, Geneva, Switzerland
- Canadian Forces Health Services Group, Department of National Defence, Government of Canada, Ottawa
- Department of Psychology, Western University, London, Ontario
| | - Ruth A. Lanius
- Department of Psychological Research and Research Methods, University of Vienna, Vienna, Austria
- Neurology and Imaging of Cognition Lab, University of Geneva, Geneva, Switzerland
- Canadian Forces Health Services Group, Department of National Defence, Government of Canada, Ottawa
- Department of Psychology, Western University, London, Ontario
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36
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Scheinost D, Hsu TW, Avery EW, Hampson M, Constable RT, Chun MM, Rosenberg MD. Connectome-based neurofeedback: A pilot study to improve sustained attention. Neuroimage 2020; 212:116684. [PMID: 32114151 DOI: 10.1016/j.neuroimage.2020.116684] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/23/2020] [Accepted: 02/24/2020] [Indexed: 12/27/2022] Open
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMRI, connectome-based neurofeedback, in which intermittent feedback is based on the strength of complex functional networks spanning hundreds of regions and thousands of functional connections. We first demonstrate the technical feasibility of calculating whole-brain functional connectivity in real-time and provide resources for implementing connectome-based neurofeedback. We next show that this approach can be used to provide accurate feedback about the strength of a previously defined connectome-based model of sustained attention, the saCPM, during task performance. Although, in our initial pilot sample, neurofeedback based on saCPM strength did not improve performance on out-of-scanner attention tasks, future work characterizing effects of network target, training duration, and amount of feedback on the efficacy of rt-fMRI can inform experimental or clinical trial designs.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.
| | - Tiffany W Hsu
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Emily W Avery
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Marvin M Chun
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Monica D Rosenberg
- Department of Psychology, Yale University, New Haven, CT, USA; Department of Psychology, University of Chicago, Chicago, IL, USA.
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37
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Fede SJ, Dean SF, Manuweera T, Momenan R. A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review. Front Hum Neurosci 2020; 14:60. [PMID: 32161529 PMCID: PMC7052377 DOI: 10.3389/fnhum.2020.00060] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/07/2020] [Indexed: 11/26/2022] Open
Abstract
Background: Although biofeedback using electrophysiology has been explored extensively, the approach of using neurofeedback corresponding to hemodynamic response is a relatively young field. Real time functional magnetic resonance imaging-based neurofeedback (rt-fMRI-NF) uses sensory feedback to operantly reinforce patterns of neural response. It can be used, for example, to alter visual perception, increase brain connectivity, and reduce depression symptoms. Within recent years, interest in rt-fMRI-NF in both research and clinical contexts has expanded considerably. As such, building a consensus regarding best practices is of great value. Objective: This systematic review is designed to describe and evaluate the variations in methodology used in previous rt-fMRI-NF studies to provide recommendations for rt-fMRI-NF study designs that are mostly likely to elicit reproducible and consistent effects of neurofeedback. Methods: We conducted a database search for fMRI neurofeedback papers published prior to September 26th, 2019. Of 558 studies identified, 146 met criteria for inclusion. The following information was collected from each study: sample size and type, task used, neurofeedback calculation, regulation procedure, feedback, whether feedback was explicitly related to changing brain activity, feedback timing, control group for active neurofeedback, how many runs and sessions of neurofeedback, if a follow-up was conducted, and the results of neurofeedback training. Results: rt-fMRI-NF is typically upregulation practice based on hemodynamic response from a specific region of the brain presented using a continually updating thermometer display. Most rt-fMRI-NF studies are conducted in healthy samples and half evaluate its effect on immediate changes in behavior or affect. The most popular control group method is to provide sham signal from another region; however, many studies do not compare use a comparison group. Conclusions: We make several suggestions for designs of future rt-fMRI-NF studies. Researchers should use feedback calculation methods that consider neural response across regions (i.e., SVM or connectivity), which should be conveyed as intermittent, auditory feedback. Participants should be given explicit instructions and should be assessed on individual differences. Future rt-fMRI-NF studies should use clinical samples; effectiveness of rt-fMRI-NF should be evaluated on clinical/behavioral outcomes at follow-up time points in comparison to both a sham and no feedback control group.
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Affiliation(s)
| | | | | | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States
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Morgenroth E, Saviola F, Gilleen J, Allen B, Lührs M, W Eysenck M, Allen P. Using connectivity-based real-time fMRI neurofeedback to modulate attentional and resting state networks in people with high trait anxiety. NEUROIMAGE-CLINICAL 2020; 25:102191. [PMID: 32044712 PMCID: PMC7013190 DOI: 10.1016/j.nicl.2020.102191] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 01/17/2020] [Accepted: 01/19/2020] [Indexed: 11/25/2022]
Abstract
Controlled experiment on functional connectivity-based real-time fMRI neurofeedback. Reduced anxiety levels in the experimental group after neurofeedback training. Altered activity and connectivity in neurofeedback ROIs in the experimental group. Increased resting state functional connectivity in the PCC in the experimental group.
High levels of trait anxiety are associated with impaired attentional control, changes in brain activity during attentional control tasks and altered network resting state functional connectivity (RSFC). Specifically, dorsolateral prefrontal cortex to anterior cingulate cortex (DLPFC – ACC) functional connectivity, thought to be crucial for effective and efficient attentional control, is reduced in high trait anxious individuals. The current study examined the potential of connectivity-based real-time functional magnetic imaging neurofeedback (rt-fMRI-nf) for enhancing DLPFC – ACC functional connectivity in trait anxious individuals. We specifically tested if changes in DLPFC - ACC connectivity were associated with reduced anxiety levels and improved attentional control. Thirty-two high trait anxious participants were assigned to either an experimental group (EG), undergoing veridical rt-fMRI-nf, or a control group (CG) that received sham (yoked) feedback. RSFC (using resting state fMRI), anxiety levels and Stroop task performance were assessed pre- and post-rt-fMRI-nf training. Post-rt-fMRI-nf training, relative to the CG, the EG showed reduced anxiety levels and increased DLPFC-ACC functional connectivity as well as increased RSFC in the posterior default mode network. Moreover, in the EG, changes in DLPFC – ACC functional connectivity during rt-fMRI-nf training were associated with reduced anxiety levels. However, there were no group differences in Stroop task performance. We conclude that rt-fMRI-nf targeting DLPFC – ACC functional connectivity can alter network connectivity and interactions and is a feasible method for reducing trait anxiety.
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Affiliation(s)
- Elenor Morgenroth
- Ecole Polytechnique Federale, Lausanne, Switzerland; Department of Psychology, University of Roehampton, Whitelands College, Hollybourne Avenue, London SW15 4JD, UK.
| | - Francesca Saviola
- CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto (Trento), Italy
| | - James Gilleen
- Department of Psychology, University of Roehampton, Whitelands College, Hollybourne Avenue, London SW15 4JD, UK
| | - Beth Allen
- Department of Psychology, Royal Holloway University of London, London, UK
| | - Michael Lührs
- Research Department, Brain Innovation B.V., Maastricht, Netherlands; Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Michael W Eysenck
- Department of Psychology, University of Roehampton, Whitelands College, Hollybourne Avenue, London SW15 4JD, UK; Department of Psychology, Royal Holloway University of London, London, UK
| | - Paul Allen
- Department of Psychology, University of Roehampton, Whitelands College, Hollybourne Avenue, London SW15 4JD, UK; Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Combined Universities Brain Imaging Centre, London, UK; Icahn School of Medicine, Mount Sinai Hospital, New York, NY, USA
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Pereira J, Direito B, Sayal A, Ferreira C, Castelo-Branco M. Self-Modulation of Premotor Cortex Interhemispheric Connectivity in a Real-Time Functional Magnetic Resonance Imaging Neurofeedback Study Using an Adaptive Approach. Brain Connect 2019; 9:662-672. [DOI: 10.1089/brain.2019.0697] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- João Pereira
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- CIBIT, Coimbra Institute for Biomedical Imaging, Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Bruno Direito
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- CIBIT, Coimbra Institute for Biomedical Imaging, Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Alexandre Sayal
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- CIBIT, Coimbra Institute for Biomedical Imaging, Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Carlos Ferreira
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- CIBIT, Coimbra Institute for Biomedical Imaging, Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- CIBIT, Coimbra Institute for Biomedical Imaging, Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
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40
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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: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 07/26/2019] [Accepted: 08/16/2019] [Indexed: 12/21/2022] Open
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41
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Linhartová P, Látalová A, Kóša B, Kašpárek T, Schmahl C, Paret C. fMRI neurofeedback in emotion regulation: A literature review. Neuroimage 2019; 193:75-92. [DOI: 10.1016/j.neuroimage.2019.03.011] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 03/03/2019] [Accepted: 03/05/2019] [Indexed: 12/23/2022] Open
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42
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Kopel R, Sladky R, Laub P, Koush Y, Robineau F, Hutton C, Weiskopf N, Vuilleumier P, Van De Ville D, Scharnowski F. No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI. Neuroimage 2019; 191:421-429. [PMID: 30818024 PMCID: PMC6503944 DOI: 10.1016/j.neuroimage.2019.02.058] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 02/19/2019] [Accepted: 02/22/2019] [Indexed: 01/15/2023] Open
Abstract
As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.
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Affiliation(s)
- R Kopel
- Department of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - R Sladky
- Department of Psychiatric, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland; Social, Cognitive and Affective Neuroscience Unit, Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria.
| | - P Laub
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Y Koush
- Department of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Imaging, Yale University, New Haven, USA
| | - F Robineau
- Laboratory for Behavioral Neurology and Imaging of Cognition, Department of Neuroscience, University Medical Center, Geneva, Switzerland; Geneva Neuroscience Center, Geneva, Switzerland
| | - C Hutton
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK
| | - N Weiskopf
- Geneva Neuroscience Center, Geneva, Switzerland; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - P Vuilleumier
- Laboratory for Behavioral Neurology and Imaging of Cognition, Department of Neuroscience, University Medical Center, Geneva, Switzerland; Geneva Neuroscience Center, Geneva, Switzerland
| | - D Van De Ville
- Department of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - F Scharnowski
- Department of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Psychiatric, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Winterthurerstr. 190, 8057, Zürich, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, 8057, Zürich, Switzerland
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43
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Zhao Z, Yao S, Li K, Sindermann C, Zhou F, Zhao W, Li J, Lührs M, Goebel R, Kendrick KM, Becker B. Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety. PSYCHOTHERAPY AND PSYCHOSOMATICS 2019; 88:5-15. [PMID: 30699438 DOI: 10.1159/000496057] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 12/03/2018] [Indexed: 11/19/2022]
Abstract
BACKGROUND Deficient emotion regulation and exaggerated anxiety represent a major transdiagnostic psychopathological marker. On the neural level these deficits have been closely linked to impaired, yet treatment-sensitive, prefrontal regulatory control over the amygdala. Gaining direct control over these pathways could therefore provide an innovative and promising intervention to regulate exaggerated anxiety. To this end the current proof-of-concept study evaluated the feasibility, functional relevance and maintenance of a novel connectivity-informed real-time fMRI neurofeedback training. METHODS In a randomized crossover sham-controlled design, 26 healthy subjects with high anxiety underwent real-time fMRI-guided neurofeedback training to enhance connectivity between the ventrolateral prefrontal cortex (vlPFC) and the amygdala (target pathway) during threat exposure. Maintenance of regulatory control was assessed after 3 days and in the absence of feedback. Training-induced changes in functional connectivity of the target pathway and anxiety ratings served as primary outcomes. RESULTS Training of the target, yet not the sham control, pathway significantly increased amygdala-vlPFC connectivity and decreased levels of anxiety. Stronger connectivity increases were significantly associated with higher anxiety reduction on the group level. At the follow-up, volitional control over the target pathway was maintained in the absence of feedback. CONCLUSIONS The present results demonstrate for the first time that successful self-regulation of amygdala-prefrontal top-down regulatory circuits may represent a novel intervention to control anxiety. As such, the present findings underscore both the critical contribution of amygdala-prefrontal circuits to emotion regulation and the therapeutic potential of connectivity-informed real-time neurofeedback.
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Affiliation(s)
- Zhiying Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, 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, University of Electronic Science and Technology of China, Chengdu, China
| | - Keshuang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, 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, University of Electronic Science and Technology of China, Chengdu, China
| | - Weihua Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China,
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44
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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: 8.6] [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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45
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Guggisberg AG, Koch PJ, Hummel FC, Buetefisch CM. Brain networks and their relevance for stroke rehabilitation. Clin Neurophysiol 2019; 130:1098-1124. [PMID: 31082786 DOI: 10.1016/j.clinph.2019.04.004] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 03/04/2019] [Accepted: 04/08/2019] [Indexed: 12/21/2022]
Abstract
Stroke has long been regarded as focal disease with circumscribed damage leading to neurological deficits. However, advances in methods for assessing the human brain and in statistics have enabled new tools for the examination of the consequences of stroke on brain structure and function. Thereby, it has become evident that stroke has impact on the entire brain and its network properties and can therefore be considered as a network disease. The present review first gives an overview of current methodological opportunities and pitfalls for assessing stroke-induced changes and reorganization in the human brain. We then summarize principles of plasticity after stroke that have emerged from the assessment of networks. Thereby, it is shown that neurological deficits do not only arise from focal tissue damage but also from local and remote changes in white-matter tracts and in neural interactions among wide-spread networks. Similarly, plasticity and clinical improvements are associated with specific compensatory structural and functional patterns of neural network interactions. Innovative treatment approaches have started to target such network patterns to enhance recovery. Network assessments to predict treatment response and to individualize rehabilitation is a promising way to enhance specific treatment effects and overall outcome after stroke.
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Affiliation(s)
- Adrian G Guggisberg
- Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital Geneva, Switzerland.
| | - Philipp J Koch
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), 1202 Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology Valais (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland; Department of Clinical Neuroscience, University Hospital Geneva, 1202 Geneva, Switzerland
| | - Cathrin M Buetefisch
- Depts of Neurology, Rehabilitation Medicine, Radiology, Emory University, Atlanta, GA, USA
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46
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Byeon K, Park BY, Park H. Spatially guided functional correlation tensor: A new method to associate body mass index and white matter neuroimaging. Comput Biol Med 2019; 107:137-144. [DOI: 10.1016/j.compbiomed.2019.02.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 01/28/2019] [Accepted: 02/14/2019] [Indexed: 12/01/2022]
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47
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Marins T, Rodrigues EC, Bortolini T, Melo B, Moll J, Tovar-Moll F. Structural and functional connectivity changes in response to short-term neurofeedback training with motor imagery. Neuroimage 2019; 194:283-290. [PMID: 30898654 DOI: 10.1016/j.neuroimage.2019.03.027] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 03/10/2019] [Accepted: 03/12/2019] [Indexed: 12/31/2022] Open
Abstract
Recent findings have been challenging current understanding of how fast the human brain change its structural and functional connections in response to training. One powerful way to deepen the inner workings of human brain plasticity is using neurofeedback (NFB) by fMRI, a technique that allows self-induced brain plasticity by means of modulating brain activity in real time. In the present randomized, double-blind and sham-controlled study, we use NFB to train healthy individuals to reinforce brain patterns related to motor execution while performing a motor imagery task, with no overt movement. After 1 h of NFB training, participants displayed increased fractional anisotropy (FA) in the sensorimotor segment of corpus callosum and increased functional connectivity of the sensorimotor resting state network. Increased functional connectivity was also observed in the default mode network. These results were not observed in the control group, which was trained with sham feedback. To our knowledge, this is the first demonstration of white matter FA changes following a very short training schedule (<1 h). Our results suggest that NFB by fMRI can be an interesting tool to explore dynamic aspects of brain plasticity and open new venues for investigating brain plasticity in healthy individuals and in neurological conditions.
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Affiliation(s)
- T Marins
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Post-Graduate Program in Morphological Sciences, Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - E C Rodrigues
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Augusto Motta University (Unisuam), Rio de Janeiro, RJ, Brazil
| | - T Bortolini
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - Bruno Melo
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - J Moll
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Post-Graduate Program in Morphological Sciences, Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - F Tovar-Moll
- D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Post-Graduate Program in Morphological Sciences, Institute of Biomedical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
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48
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Gotts SJ, Ramot M, Jasmin K, Martin A. Altered resting-state dynamics in autism spectrum disorder: Causal to the social impairment? Prog Neuropsychopharmacol Biol Psychiatry 2019; 90:28-36. [PMID: 30414457 DOI: 10.1016/j.pnpbp.2018.11.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 11/01/2018] [Accepted: 11/02/2018] [Indexed: 10/27/2022]
Abstract
Autism spectrum disorder (ASD) is characterized by profound impairments in social abilities and by restricted interests and repetitive behaviors. Much work in the past decade has been dedicated to understanding the brain-bases of ASD, and in the context of resting-state functional connectivity fMRI in high-functioning adolescents and adults, the field has established a set of reliable findings: decreased cortico-cortical interactions among brain regions thought to be engaged in social processing, along with a simultaneous increase in thalamo-cortical and striato-cortical interactions. However, few studies have attempted to manipulate these altered patterns, leading to the question of whether such patterns are actually causally involved in producing the corresponding behavioral impairments. We discuss a few such recent attempts in the domains of fMRI neurofeedback and overt social interaction during scanning, and we conclude that the evidence of causal involvement is somewhat mixed. We highlight the potential role of the thalamus and striatum in ASD and emphasize the need for studies that directly compare scanning during multiple cognitive states in addition to the resting-state.
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Affiliation(s)
- Stephen J Gotts
- Section on Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bldg 10, Rm 4C-217, Bethesda, MD 20892-1366, United States.
| | - Michal Ramot
- Section on Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bldg 10, Rm 4C-217, Bethesda, MD 20892-1366, United States
| | - Kyle Jasmin
- Section on Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bldg 10, Rm 4C-217, Bethesda, MD 20892-1366, United States; Department of Psychological Sciences, Birkbeck University of London, London, UK
| | - Alex Martin
- Section on Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bldg 10, Rm 4C-217, Bethesda, MD 20892-1366, United States
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49
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Kohl SH, Veit R, Spetter MS, Günther A, Rina A, Lührs M, Birbaumer N, Preissl H, Hallschmid M. Real-time fMRI neurofeedback training to improve eating behavior by self-regulation of the dorsolateral prefrontal cortex: A randomized controlled trial in overweight and obese subjects. Neuroimage 2019; 191:596-609. [PMID: 30798010 DOI: 10.1016/j.neuroimage.2019.02.033] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 01/30/2019] [Accepted: 02/13/2019] [Indexed: 01/17/2023] Open
Abstract
Obesity is associated with altered responses to food stimuli in prefrontal brain networks that mediate inhibitory control of ingestive behavior. In particular, activity of the dorsolateral prefrontal cortex (dlPFC) is reduced in obese compared to normal-weight subjects and has been linked to the success of weight-loss dietary interventions. In a randomized controlled trial in overweight/obese subjects, we investigated the effect on eating behavior of volitional up-regulation of dlPFC activity via real-time functional magnetic resonance imaging (fMRI) neurofeedback training. Thirty-eight overweight or obese subjects (BMI 25-40 kg/m2) took part in fMRI neurofeedback training with the aim of increasing activity of the left dlPFC (dlPFC group; n = 17) or of the visual cortex (VC/control group; n = 21). Participants were blinded to group assignment. The training session took place on a single day and included three training runs of six trials of up-regulation and passive viewing. Food appraisal and snack intake were assessed at screening, after training, and in a follow-up session four weeks later. Participants of both groups succeeded in up-regulating activity of the targeted brain area. However, participants of the control group also showed increased left dlPFC activity during up-regulation. Functional connectivity between dlPFC and ventromedial PFC, an area that processes food value, was generally increased during up-regulation compared to passive viewing. At follow-up compared to baseline, both groups rated pictures of high-, but not low-calorie foods as less palatable and chose them less frequently. Actual snack intake remained unchanged but palatability and choice ratings for chocolate cookies decreased after training. We demonstrate that one session of fMRI neurofeedback training enables individuals with increased body weight to up-regulate activity of the left dlPFC. Behavioral effects were observed in both groups, which might have been due to dlPFC co-activation in the control group and, in addition, unspecific training effects. Improved dlPFC-vmPFC functional connectivity furthermore suggested enhanced food intake-related control mechanisms. Neurofeedback training might support therapeutic strategies aiming at improved self-control in obesity, although the respective contributions of area-specific mechanisms and general regulation effects are in need of further investigation.
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Affiliation(s)
- Simon H Kohl
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Center, Jülich, Germany
| | - 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; German Center for Diabetes Research (DZD), Tübingen, Germany; High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Maartje S Spetter
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; School of Psychology, University of Birmingham, Edgbaston, Birmingham, UK
| | - Astrid Günther
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany
| | - Andriani Rina
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Lührs
- Brain Innovation B.V, Research Department, Maastricht, Netherlands; Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Netherlands
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Wyss Center for Bio and Neuroengineering, Geneva, 1202, Switzerland
| | - Hubert Preissl
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen; German Center for Diabetes Research (DZD), Tübingen, Germany; Institute of Pharmaceutical Sciences, Interfaculty Centre for Pharmacogenomics and Pharma Research, Department of Pharmacy and Biochemistry, University of Tübingen, Tübingen, 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; German Center for Diabetes Research (DZD), Tübingen, Germany
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50
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Sorger B, Scharnowski F, Linden DEJ, Hampson M, Young KD. Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies. Neuroimage 2019; 186:256-265. [PMID: 30423429 PMCID: PMC6338498 DOI: 10.1016/j.neuroimage.2018.11.004] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 10/31/2018] [Accepted: 11/05/2018] [Indexed: 12/31/2022] Open
Abstract
fMRI Neurofeedback research employs many different control conditions. Currently, there is no consensus as to which control condition is best, and the answer depends on what aspects of the neurofeedback-training design one is trying to control for. These aspects can range from determining whether participants can learn to control brain activity via neurofeedback to determining whether there are clinically significant effects of the neurofeedback intervention. Lack of consensus over criteria for control conditions has hampered the design and interpretation of studies employing neurofeedback protocols. This paper presents an overview of the most commonly employed control conditions currently used in neurofeedback studies and discusses their advantages and disadvantages. Control conditions covered include no control, treatment-as-usual, bidirectional-regulation control, feedback of an alternative brain signal, sham feedback, and mental-rehearsal control. We conclude that the selection of the control condition(s) should be determined by the specific research goal of the study and best procedures that effectively control for relevant confounding factors.
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Affiliation(s)
- Bettina Sorger
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Zürich, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Zürich, Switzerland
| | - David E J Linden
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Michelle Hampson
- Department of Radiology and Biomedical Imaging, Psychiatry and the Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Kymberly D Young
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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