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Murphy E, Poudel G, Ganesan S, Suo C, Manning V, Beyer E, Clemente A, Moffat BA, Zalesky A, Lorenzetti V. Real-time fMRI-based neurofeedback to restore brain function in substance use disorders: A systematic review of the literature. Neurosci Biobehav Rev 2024; 165:105865. [PMID: 39197715 DOI: 10.1016/j.neubiorev.2024.105865] [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: 05/01/2024] [Revised: 08/16/2024] [Accepted: 08/25/2024] [Indexed: 09/01/2024]
Abstract
INTRODUCTION Real-time functional magnetic resonance based-neurofeedback (fMRI-neurofeedback) is a neuromodulation tool where individuals self-modulate brain function based on real-time feedback of their brain activity. fMRI-neurofeedback has been used to target brain dysfunction in substance use disorders (SUDs) and to reduce craving, but a systematic synthesis of up-to-date literature is lacking. METHOD Following PRISMA guidelines, we conducted a systematic review of all the literature that examined the effects of fMRI-neurofeedback on individuals with regular psychoactive substance use (PROSPERO pre-registration = CRD42023401137). RESULTS The literature included 16 studies comprising 446 participants with SUDs involving alcohol, tobacco, and cocaine. There is consistent between-condition (e.g., fMRI-neurofeedback versus control), less consistent pre-to-post fMRI-neurofeedback, and little intervention-by-time effects on brain function in prefrontal-striatal regions and craving. CONCLUSION The evidence for changes in brain function/craving was early and inconsistent. More rigorous experiments including repeated measure designs with placebo control conditions, are required to confirm the efficacy of fMRI-neurofeedback in reducing brain alterations and craving in SUDs.
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Affiliation(s)
- Ethan Murphy
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Australia
| | - Govinda Poudel
- Mary MacKillop Institute for Health Research, Australian Catholic University, Australia
| | - Saampras Ganesan
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Australia; Contemplative Studies Centre, Melbourne School of Psychological Sciences, The University of Melbourne, Australia
| | - Chao Suo
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Australia; BrainPark, Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia
| | - Victoria Manning
- Monash Addiction Research Centre, Eastern Health Clinical School, Monash University, Clayton, Australia; Turning Point, Eastern Health, Melbourne, Victoria, Australia
| | - Emillie Beyer
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Australia
| | - Adam Clemente
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Australia
| | - Bradford A Moffat
- Melbourne Brain Centre Imaging Unit, Department of Radiology, The University of Melbourne, Australia
| | - Andrew Zalesky
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Australia
| | - Valentina Lorenzetti
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Australia.
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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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Trambaiolli L, Maffei C, Dann E, Biazoli C, Bezgin G, Yendiki A, Haber S. Translation of monosynaptic circuits underlying amygdala fMRI neurofeedback training. Neuropsychopharmacology 2024:10.1038/s41386-024-01944-w. [PMID: 39103495 DOI: 10.1038/s41386-024-01944-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/16/2024] [Accepted: 07/23/2024] [Indexed: 08/07/2024]
Abstract
fMRI neurofeedback using autobiographical memory recall to upregulate the amygdala is associated with resting-state functional connectivity (rsFC) changes between the amygdala and the salience and default mode networks (SN and DMN, respectively). We hypothesize the existence of anatomical circuits underlying these rsFC changes. Using a cross-species brain parcellation, we identified in non-human primates locations homologous to the regions of interest (ROIs) from studies showing pre-to-post-neurofeedback changes in rsFC with the left amygdala. We injected bidirectional tracers in the basolateral, lateral, and central amygdala nuclei of adult macaques and used bright- and dark-field microscopy to identify cells and axon terminals in each ROI (SN: anterior cingulate, ventrolateral, and insular cortices; DMN: temporal pole, middle frontal gyrus, angular gyrus, precuneus, posterior cingulate cortex, parahippocampal gyrus, hippocampus, and thalamus). We also performed additional injections in specific ROIs to validate the results following amygdala injections and delineate potential disynaptic pathways. Finally, we used high-resolution diffusion MRI data from four post-mortem macaque brains and one in vivo human brain to translate our findings to the neuroimaging domain. Different amygdala nuclei had significant monosynaptic connections with all the SN and DMN ipsilateral ROIs. Amygdala connections with the DMN contralateral ROIs are disynaptic through the hippocampus and parahippocampal gyrus. Diffusion MRI in both species benefitted from using the ground-truth tracer data to validate its findings, as we identified false-negative ipsilateral and false-positive contralateral connectivity results. This study provides the foundation for future causal investigations of amygdala neurofeedback modulation of the SN and DMN through these anatomic connections.
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Affiliation(s)
- Lucas Trambaiolli
- McLean Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pharmacology and Physiology, University of Rochester, Rochester, NY, USA.
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Evan Dann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Claudinei Biazoli
- Center for Mathematics Computation and Cognition, Federal University of ABC, Santo André, Brazil
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Gleb Bezgin
- Neuroinformatics for Personalized Medicine lab, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Suzanne Haber
- McLean Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pharmacology and Physiology, University of Rochester, Rochester, NY, USA.
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Morfini F, Bauer CCC, Zhang J, Whitfield-Gabrieli S, Shinn AK, Niznikiewicz MA. Targeting the superior temporal gyrus with real-time fMRI neurofeedback: A pilot study of the indirect effects on self-referential processes in schizophrenia. Schizophr Res 2024; 270:358-365. [PMID: 38968807 DOI: 10.1016/j.schres.2024.06.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 06/20/2024] [Accepted: 06/22/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND Individuals with schizophrenia (SZ) and auditory hallucinations (AHs) display a distorted sense of self and self-other boundaries. Alterations of activity in midline cortical structures such as the prefrontal cortex (mPFC) and anterior cingulate cortex (ACC) during self-reference as well as in the superior temporal gyrus (STG) have been proposed as neuromarkers of SZ and AHs. METHODS In this randomized, participant-blinded, sham-controlled trial, 22 adults (18 males) with SZ spectrum disorders (SZ or schizoaffective disorder) and frequent medication-resistant AHs received one session of real-time fMRI neurofeedback (NFB) either from the STG (n = 11; experimental group) or motor cortex (n = 11; control group). During NFB, participants were instructed to upregulate their STG activity by attending to pre-recorded sentences spoken in their own voice and downregulate it by ignoring unfamiliar voices. Before and after NFB, participants completed a self-reference task where they evaluated if trait adjectives referred to themselves (self condition), Abraham Lincoln (other condition), or whether adjectives had a positive valence (semantic condition). FMRI activation analyses of self-reference task data tested between-group changes after NFB (self>semantic, post>pre-NFB, experimental>control). Analyses were pre-masked within a self-reference network. RESULTS Activation analyses revealed significantly (p < 0.001) greater activation increase in the experimental, compared to the control group, after NFB within anterior regions of the self-reference network (mPFC, ACC, superior frontal cortex). CONCLUSIONS STG-NFB was associated with activity increase in the mPFC, ACC, and superior frontal cortex during self-reference. Modulating the STG is associated with activation changes in other, not-directly targeted, regions subserving higher-level cognitive processes associated with self-referential processes and AHs psychopathology in SZ. CLINICALTRIALS GOV: Rt-fMRI Neurofeedback and AH in Schizophrenia; https://clinicaltrials.gov/study/NCT03504579.
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Affiliation(s)
- Francesca Morfini
- Northeastern University, Department of Psychology, Boston, MA 02115, USA.
| | - Clemens C C Bauer
- Northeastern University, Department of Psychology, Boston, MA 02115, USA; Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, Cambridge, MA 02139, USA
| | - Jiahe Zhang
- Northeastern University, Department of Psychology, Boston, MA 02115, USA
| | - Susan Whitfield-Gabrieli
- Northeastern University, Department of Psychology, Boston, MA 02115, USA; Harvard Medical School, Department of Psychiatry, Boston, MA 02115, USA; Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA; Massachusetts Institute of Technology, McGovern Institute for Brain Research, Cambridge, MA 02139, USA
| | - Ann K Shinn
- Harvard Medical School, Department of Psychiatry, Boston, MA 02115, USA; McLean Hospital, Psychotic Disorders Division, Belmont, MA 02478, USA
| | - Margaret A Niznikiewicz
- Harvard Medical School, Department of Psychiatry, Boston, MA 02115, USA; Veterans Affairs Boston Healthcare System, Department of Psychiatry, Brockton, MA 02301, USA; Boston VA Research Institute, Boston, MA 02130, USA
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Morrissey G, Tsuchiyagaito A, Takahashi T, McMillin J, Aupperle RL, Misaki M, Khalsa SS. Could neurofeedback improve therapist-patient communication? Considering the potential for neuroscience informed examinations of the psychotherapeutic relationship. Neurosci Biobehav Rev 2024; 161:105680. [PMID: 38641091 DOI: 10.1016/j.neubiorev.2024.105680] [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: 03/31/2023] [Revised: 03/22/2024] [Accepted: 04/16/2024] [Indexed: 04/21/2024]
Abstract
Empathic communication between a patient and therapist is an essential component of psychotherapy. However, finding objective neural markers of the quality of the psychotherapeutic relationship have been elusive. Here we conceptualize how a neuroscience-informed approach involving real-time neurofeedback, facilitated via existing functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) technologies, could provide objective information for facilitating therapeutic rapport. We propose several neurofeedback-assisted psychotherapy (NF-AP) approaches that could be studied as a way to optimize the experience of the individual patient and therapist across the spectrum of psychotherapeutic treatment. Finally, we consider how the possible strengths of these approaches are balanced by their current limitations and discuss the future prospects of NF-AP.
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Affiliation(s)
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK, USA
| | - Toru Takahashi
- Laureate Institute for Brain Research, Tulsa, OK, USA; Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo, Japan
| | - John McMillin
- Advocate Medical Group, Downers Grove, IL, USA; Department of Psychiatry, University of Oklahoma-Tulsa, Tulsa, OK, USA
| | - Robin L Aupperle
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK, USA.
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Popovova J, Mazloum R, Macauda G, Stämpfli P, Vuilleumier P, Frühholz S, Scharnowski F, Menon V, Michels L. Enhanced attention-related alertness following right anterior insular cortex neurofeedback training. iScience 2024; 27:108915. [PMID: 38318347 PMCID: PMC10839684 DOI: 10.1016/j.isci.2024.108915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/15/2023] [Accepted: 01/11/2024] [Indexed: 02/07/2024] Open
Abstract
The anterior insular cortex, a central node of the salience network, plays a critical role in cognitive control and attention. Here, we investigated the feasibility of enhancing attention using real-time fMRI neurofeedback training that targets the right anterior insular cortex (rAIC). 56 healthy adults underwent two neurofeedback training sessions. The experimental group received feedback from neural responses in the rAIC, while control groups received sham feedback from the primary visual cortex or no feedback. Cognitive functioning was evaluated before, immediately after, and three months post-training. Our results showed that only the rAIC neurofeedback group successfully increased activity in the rAIC. Furthermore, this group showed enhanced attention-related alertness up to three months after the training. Our findings provide evidence for the potential of rAIC neurofeedback as a viable approach for enhancing attention-related alertness, which could pave the way for non-invasive therapeutic strategies to address conditions characterized by attention deficits.
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Affiliation(s)
- Jeanette Popovova
- Department of Neuroradiology, University Hospital of Zurich, 8091 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
- Department of Psychology, University of Zurich, 8050 Zurich, Switzerland
| | - Reza Mazloum
- Department of Neuroradiology, University Hospital of Zurich, 8091 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
- Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
| | - Gianluca Macauda
- Department of Neuroradiology, University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Philipp Stämpfli
- MR-Center of the Department of Psychiatry, Psychotherapy and Psychosomatics and the Department of Child and Adolescent Psychiatry, Psychiatric Hospital, University of Zurich, 8032 Zurich, Switzerland
| | - Patrik Vuilleumier
- Department of Neurosciences and Clinic of Neurology, Laboratory for Neurology and Imaging of Cognition, University of Geneva, 1211 Geneva, Switzerland
| | - Sascha Frühholz
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
- Department of Psychology, University of Oslo, 0851 Oslo, Norway
| | - Frank Scharnowski
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, 1010 Vienna, Austria
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Lars Michels
- Department of Neuroradiology, University Hospital of Zurich, 8091 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, 8057 Zurich, Switzerland
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Fagerland SM, Berntsen HR, Fredriksen M, Endestad T, Skouras S, Rootwelt-Revheim ME, Undseth RM. Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2024; 15:41-62. [PMID: 38827812 PMCID: PMC11141335 DOI: 10.2478/joeb-2024-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Indexed: 06/05/2024]
Abstract
Objective The goal of this study was to explore the development and implementation of a protocol for real-time fMRI neurofeedback (rtfMRI-nf) and to assess the potential for enhancing the selective brain activation using stimuli from Virtual Reality (VR). In this study we focused on two specific brain regions, supplementary motor area (SMA) and right inferior frontal gyrus (rIFG). Publications by other study groups have suggested impaired function in these specific brain regions in patients with the diagnoses Attention Deficit Hyperactivity Disorder (ADHD) and Tourette's Syndrome (TS). This study explored the development of a protocol to investigate if attention and contextual memory may be used to systematically strengthen the procedure of rtfMRI-nf. Methods We used open-science software and platforms for rtfMRI-nf and for developing a simulated repetition of the rtfMRI-nf brain training in VR. We conducted seven exploratory tests in which we updated the protocol at each step. During rtfMRI-nf, MRI images are analyzed live while a person is undergoing an MRI scan, and the results are simultaneously shown to the person in the MRI-scanner. By focusing the analysis on specific regions of the brain, this procedure can be used to help the person strengthen conscious control of these regions. The VR simulation of the same experience involved a walk through the hospital toward the MRI scanner where the training sessions were conducted, as well as a subsequent simulated repetition of the MRI training. The VR simulation was a 2D projection of the experience.The seven exploratory tests involved 19 volunteers. Through this exploration, methods for aiming within the brain (e.g. masks/algorithms for coordinate-system control) and calculations for the analyses (e.g. calculations based on connectivity versus activity) were updated by the project team throughout the project. The final procedure involved three initial rounds of rtfMRI-nf for learning brain strategies. Then, the volunteers were provided with VR headsets and given instructions for one week of use. Afterward, a new session with three rounds of rtfMRI-nf was conducted. Results Through our exploration of the indirect effect parameters - brain region activity (directed oxygenated blood flow), connectivity (degree of correlated activity in different regions), and neurofeedback score - the volunteers tended to increase activity in the reinforced brain regions through our seven tests. Updates of procedures and analyses were always conducted between pilots, and never within. The VR simulated repetition was tested in pilot 7, but the role of the VR contribution in this setting is unclear due to underpowered testing. Conclusion This proof-of-concept protocol implies how rtfMRI-nf may be used to selectively train two brain regions (SMA and rIFG). The method may likely be adapted to train any given region in the brain, but readers are advised to update and adapt the procedure to experimental needs.
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Affiliation(s)
- Steffen Maude Fagerland
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Department of Cognitive and Neuropsychology, Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Psychology, University of Oslo, Norway
| | - Henrik Røsholm Berntsen
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
| | - Mats Fredriksen
- Neuropsychatric Outpatient Clinic, Vestfold Hospital Trust, Tønsberg, Norway
| | - Tor Endestad
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Psychology, University of Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Norway
| | - Stavros Skouras
- Department of Fundamental Neurosciences, Faculty of Medicine, University of Geneva, Geneva, CH-1202, Switzerland
- Department of Biological and Medical Psychology, University of Bergen, Bergen, NO-5020, Norway
- Department of Neurology, Inselspital University Hospital Bern, Bern, CH-3010, Switzerland
| | - Mona Elisabeth Rootwelt-Revheim
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Ragnhild Marie Undseth
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Department of Cognitive and Neuropsychology, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Radiology Research, The Intervention Centre, Oslo University Hospital, Oslo, Norway
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Wang X, Zhou X, Li J, Gong Y, Feng Z. A feasibility study of goal-directed network-based real-time fMRI neurofeedback for anhedonic depression. Front Psychiatry 2023; 14:1253727. [PMID: 38125285 PMCID: PMC10732355 DOI: 10.3389/fpsyt.2023.1253727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 11/06/2023] [Indexed: 12/23/2023] Open
Abstract
Anhedonia is a hallmark symptom of depression that often lacks adequate interventions. The translational gap remains in clinical treatments based on neural substrates of anhedonia. Our pilot study found that depressed individuals depended less on goal-directed (GD) reward learning (RL), with reduced reward prediction error (RPE) BOLD signal. Previous studies have found that anhedonia is related to abnormal activities and/or functional connectivities of the central executive network (CEN) and salience network (SN), both of which belong to the goal-directed system. In addition, it was found that real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) could improve the balance between CEN and SN in healthy individuals. Therefore, we speculate that rt-fMRI NF of the CEN and SN associated with the GD system may improve depressive and/or anhedonic symptoms. Therefore, this study (1) will examine individuals with anhedonic depression using GD-RL behavioral task, combined with functional magnetic resonance imaging and computational modeling to explore the role of CEN/SN deficits in anhedonic depression; and (2) will utilize network-based rt-fMRI NF to investigate whether it is feasible to regulate the differential signals of brain CEN/SN of GD system through rt-fMRI NF to alleviate depressive and/or anhedonic symptoms. This study highlights the need to elucidate the intervention effects of rt-fMRI NF and the underlying computational network neural mechanisms.
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Affiliation(s)
- Xiaoxia Wang
- Department of Basic Psychology, School of Psychology, Army Medical University, Chongqing, China
| | - Xiaoyan Zhou
- Chongqing City Mental Health Center, Southwest University, Chongqing, China
| | - Jing Li
- Department of Radiology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Yushun Gong
- Department of Medical Equipment and Metrology, College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Zhengzhi Feng
- School of Psychology, Army Medical University, Chongqing, China
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Zhang J, Raya J, Morfini F, Urban Z, Pagliaccio D, Yendiki A, Auerbach RP, Bauer CCC, Whitfield-Gabrieli S. Reducing default mode network connectivity with mindfulness-based fMRI neurofeedback: a pilot study among adolescents with affective disorder history. Mol Psychiatry 2023; 28:2540-2548. [PMID: 36991135 PMCID: PMC10611577 DOI: 10.1038/s41380-023-02032-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 03/02/2023] [Accepted: 03/10/2023] [Indexed: 03/31/2023]
Abstract
Adolescents experience alarmingly high rates of major depressive disorder (MDD), however, gold-standard treatments are only effective for ~50% of youth. Accordingly, there is a critical need to develop novel interventions, particularly ones that target neural mechanisms believed to potentiate depressive symptoms. Directly addressing this gap, we developed mindfulness-based fMRI neurofeedback (mbNF) for adolescents that aims to reduce default mode network (DMN) hyperconnectivity, which has been implicated in the onset and maintenance of MDD. In this proof-of-concept study, adolescents (n = 9) with a lifetime history of depression and/or anxiety were administered clinical interviews and self-report questionnaires, and each participant's DMN and central executive network (CEN) were personalized using a resting state fMRI localizer. After the localizer scan, adolescents completed a brief mindfulness training followed by a mbNF session in the scanner wherein they were instructed to volitionally reduce DMN relative to CEN activation by practicing mindfulness meditation. Several promising findings emerged. First, mbNF successfully engaged the target brain state during neurofeedback; participants spent more time in the target state with DMN activation lower than CEN activation. Second, in each of the nine adolescents, mbNF led to significantly reduced within-DMN connectivity, which correlated with post-mbNF increases in state mindfulness. Last, a reduction of within-DMN connectivity mediated the association between better mbNF performance and increased state mindfulness. These findings demonstrate that personalized mbNF can effectively and non-invasively modulate the intrinsic networks associated with the emergence and persistence of depressive symptoms during adolescence.
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Affiliation(s)
- Jiahe Zhang
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA.
| | - Jovicarole Raya
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Francesca Morfini
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Zoi Urban
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - David Pagliaccio
- Department of Psychiatry, Columbia University, New York, NY, 10032, USA
- New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02129, USA
| | - Randy P Auerbach
- Department of Psychiatry, Columbia University, New York, NY, 10032, USA
- New York State Psychiatric Institute, New York, NY, 10032, USA
- Division of Clinical Developmental Neuroscience, Sackler Institute, New York, NY, 10032, USA
| | - Clemens C C Bauer
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02129, USA
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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Zafar A, Hussain SJ, Ali MU, Lee SW. Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23073714. [PMID: 37050774 PMCID: PMC10098559 DOI: 10.3390/s23073714] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 06/01/2023]
Abstract
In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset's dimensionality, increase the computing effectiveness, and enhance the BCI's performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.
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Affiliation(s)
- Amad Zafar
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Shaik Javeed Hussain
- Department of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, Oman
| | - Muhammad Umair Ali
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Seung Won Lee
- Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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Zafar A, Dad Kallu K, Atif Yaqub M, Ali MU, Hyuk Byun J, Yoon M, Su Kim K. A Hybrid GCN and Filter-Based Framework for Channel and Feature Selection: An fNIRS-BCI Study. INT J INTELL SYST 2023. [DOI: 10.1155/2023/8812844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
In this study, a channel and feature selection methodology is devised for brain-computer interface (BCI) applications using functional near-infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter-based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left- and right-hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two-class BCI applications (i.e., left- vs. right-hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four-class BCI applications (i.e., left- vs. right-hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two-class and four-class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four-class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four-class) yielded high average classification accuracy
. However, the results of the ReliefF algorithm are more stable and significant.
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Stefanov K, Al-Wasity S, Parkinson JT, Waiter GD, Cavanagh J, Basu N. Brain mapping inflammatory-arthritis-related fatigue in the pursuit of novel therapeutics. THE LANCET. RHEUMATOLOGY 2023; 5:e99-e109. [PMID: 38251542 DOI: 10.1016/s2665-9913(23)00007-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 01/26/2023]
Abstract
Despite developments in pharmacological treatments, chronic fatigue is an unresolved issue for most people with inflammatory arthritis that severely disrupts their personal and working lives. Fatigue in these patients is not strongly linked with peripheral disease activity but is associated with CNS-derived symptoms such as chronic pain, sleep disturbance, and depression. Therefore, a neurobiological basis should be considered when pursuing novel fatigue-specific therapeutics. In this Review, we focus on clinical imaging biomarkers that map candidate brain regions and are crucial in fatigue pathophysiology. We then evaluate neuromodulation techniques that could affect these candidate brain regions and are potential treatment strategies for fatigue in patients with inflammatory arthritis. We delineate work that is still required for neuroimaging and neuromodulation to eventually become part of a clinical pathway to treat and manage fatigue.
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Affiliation(s)
- Kristian Stefanov
- School of Infection and Immunity, University of Glasgow, Glasgow, UK.
| | - Salim Al-Wasity
- School of Infection and Immunity, University of Glasgow, Glasgow, UK; College of Engineering, University of Wasit, Al Kūt, Iraq
| | - Joel T Parkinson
- School of Infection and Immunity, University of Glasgow, Glasgow, UK
| | - Gordon D Waiter
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Jonathan Cavanagh
- School of Infection and Immunity, University of Glasgow, Glasgow, UK
| | - Neil Basu
- School of Infection and Immunity, University of Glasgow, Glasgow, UK
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Hong J, Park JH. Efficacy of Neuro-Feedback Training for PTSD Symptoms: A Systematic Review and Meta-Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13096. [PMID: 36293673 PMCID: PMC9603735 DOI: 10.3390/ijerph192013096] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
If the negative emotions experienced in life become trauma, they affect daily life. Neuro-feedback technology has recently been introduced as a treatment, but many different neuro-feedback protocols and methods exits. This study conducted a meta-analysis of neuro-feedback training for post-traumatic stress disorder (PTSD) symptoms to evaluate the effects of functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG)-based neuro-feedback training. A search of PubMed, the Cochrane Library, Web of Science, Science Direct, and ClinicalTrials.gov was conducted from January 2011 to December 2021. The studies' quality was assessed using the Cochrane risk of bias tool and publication bias was assessed by Egger's regression test. Seven studies that met the inclusion criteria were used for the systematic review and meta-analysis. EEG was more effective than fMRI for PTSD symptoms, and the effect on PTSD symptoms was higher than on anxiety and depression. There was no difference in the effectiveness of the training sessions. Our findings showed that EEG-based neuro-feedback training was more helpful for training PTSD symptoms. Additionally, the methods were also shown to be valid for evaluating clinical PTSD diagnoses. Further research is needed to establish a gold standard protocol for the EEG-based neuro-feedback training (EEG-NFT) method for PTSD symptoms.
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Affiliation(s)
- Jian Hong
- Department of ICT Convergence, The Graduate School, Soonchunhyang University, Asan 31538, Korea
| | - Jin-Hyuck Park
- Department of Occupational Therapy, Soonchunhyang University, Asan 31538, Korea
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Kalokairinou L, Choi R, Nagappan A, Wexler A. Opportunity Cost or Opportunity Lost: An Empirical Assessment of Ethical Concerns and Attitudes of EEG Neurofeedback Users. NEUROETHICS-NETH 2022; 15:28. [PMID: 36249541 PMCID: PMC9555209 DOI: 10.1007/s12152-022-09506-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/15/2022] [Indexed: 10/14/2022]
Abstract
Background Electroencephalography (EEG) neurofeedback is a type of biofeedback that purportedly teaches users how to control their brainwaves. Although neurofeedback is currently offered by thousands of providers worldwide, its provision is contested, as its effectiveness beyond a placebo effect is unproven. While scholars have voiced numerous ethical concerns about neurofeedback-regarding opportunity cost, physical and psychological harms, financial cost, and informed consent-to date these concerns have remained theoretical. This pilot study aimed to provide insights on whether these issues were supported by empirical data from the experiences of neurofeedback users. Methods Semi-structured telephone interviews were conducted with individuals who had used EEG neurofeedback for themselves and/or for a child. Results The majority of respondents (N = 36) were female (75%), white (92%), and of higher socioeconomic status relative to the U.S. population. Among adult users (n = 33), most (78.8%) resorted to neurofeedback after having tried other therapies and were satisfied with treatment (81.8%). The majority paid for neurofeedback out-of-pocket (72.7%) and considered it to be good value for money (84.8%). More than half (57.6%) considered neurofeedback to be a scientifically well-established therapy. However, of those, 78.9%were using neurofeedback for indications not adequately supported by scientific evidence. Conclusion Concerns regarding opportunity cost, physical and psychological harms, and financial cost are not substantiated by our findings. Our results partially support concerns regarding insufficient understanding of limitations. This study underlines the disconnect between some of the theoretical concerns raised by scholars regarding the use of non-validated therapies and the lived experiences of users.
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Affiliation(s)
- Louiza Kalokairinou
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Rebekah Choi
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ashwini Nagappan
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anna Wexler
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Using Real-Time fMRI Neurofeedback to Modulate M1-Cerebellum Connectivity. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8744982. [PMID: 36082347 PMCID: PMC9448559 DOI: 10.1155/2022/8744982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/16/2022] [Accepted: 07/25/2022] [Indexed: 11/18/2022]
Abstract
Objective The potential of neurofeedback to alter the M1-cerebellum connectivity was explored using motor imagery-based rt-fMRI. These regions were chosen due to their importance in motor performance and motor rehabilitation. Methods Four right-handed individuals were recruited to examine the potential to change the M1-cerebellum neurofeedback link. The University of Glasgow Cognitive Neuroimaging Centre used a 3T MRI scanner from January 2019 to January 2020 to conduct this prospective study. Everyone participated in each fMRI session, which included six NF training runs. Participants were instructed to imagine complicated hand motions during the NF training to raise a thermometer bar's height. To contrast the correlation coefficients between the initial and last NF runs, a t-test was performed post hoc. Results The neurofeedback connection between M1 and the cerebellum was strengthened in each participant. Motor imagery strategy was a significant task in training M1-cerebellum connectivity as participants used it successfully to enhance the activation level between these regions during M1-cerebellum modulation using real-time fMRI. The t-test and linear regression, on the other hand, showed this increase to be insignificant. Conclusion A novel technique to manipulate M1-cerebellum connectivity was discovered using real-time fMRI NF. This study showed that each participant's neurofeedback connectivity between M1 and cerebellum was enhanced. This increase, on the other hand, was insignificant statistically. The results showed that the connectivity between both areas increased positively. Through the integration of fMRI and neurofeedback, M1-cerebellum connectivity can be positively affected.
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Pereira JA, Ray A, Rana M, Silva C, Salinas C, Zamorano F, Irani M, Opazo P, Sitaram R, Ruiz S. A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study. Front Hum Neurosci 2022; 16:933559. [PMID: 36092645 PMCID: PMC9452730 DOI: 10.3389/fnhum.2022.933559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the “happiness emotional brain state” of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4–1 Median = 6.563%; Range = 4.10–27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1–15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.
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Affiliation(s)
- Jaime A. Pereira
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Andreas Ray
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Mohit Rana
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Claudio Silva
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
| | - Cesar Salinas
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
| | - Francisco Zamorano
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile
| | - Martin Irani
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Patricia Opazo
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States
- *Correspondence: Ranganatha Sitaram
| | - Sergio Ruiz
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Sergio Ruiz
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Orth L, Meeh J, Gur RC, Neuner I, Sarkheil P. Frontostriatal circuitry as a target for fMRI-based neurofeedback interventions: A systematic review. Front Hum Neurosci 2022; 16:933718. [PMID: 36092647 PMCID: PMC9449529 DOI: 10.3389/fnhum.2022.933718] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 08/08/2022] [Indexed: 11/19/2022] Open
Abstract
Dysregulated frontostriatal circuitries are viewed as a common target for the treatment of aberrant behaviors in various psychiatric and neurological disorders. Accordingly, experimental neurofeedback paradigms have been applied to modify the frontostriatal circuitry. The human frontostriatal circuitry is topographically and functionally organized into the "limbic," the "associative," and the "motor" subsystems underlying a variety of affective, cognitive, and motor functions. We conducted a systematic review of the literature regarding functional magnetic resonance imaging-based neurofeedback studies that targeted brain activations within the frontostriatal circuitry. Seventy-nine published studies were included in our survey. We assessed the efficacy of these studies in terms of imaging findings of neurofeedback intervention as well as behavioral and clinical outcomes. Furthermore, we evaluated whether the neurofeedback targets of the studies could be assigned to the identifiable frontostriatal subsystems. The majority of studies that targeted frontostriatal circuitry functions focused on the anterior cingulate cortex, the dorsolateral prefrontal cortex, and the supplementary motor area. Only a few studies (n = 14) targeted the connectivity of the frontostriatal regions. However, post-hoc analyses of connectivity changes were reported in more cases (n = 32). Neurofeedback has been frequently used to modify brain activations within the frontostriatal circuitry. Given the regulatory mechanisms within the closed loop of the frontostriatal circuitry, the connectivity-based neurofeedback paradigms should be primarily considered for modifications of this system. The anatomical and functional organization of the frontostriatal system needs to be considered in decisions pertaining to the neurofeedback targets.
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Affiliation(s)
- Linda Orth
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Johanna Meeh
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany
| | - Pegah Sarkheil
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
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Ciarlo A, Russo AG, Ponticorvo S, Di Salle F, Lührs M, Goebel R, Esposito F. Semantic fMRI neurofeedback: A Multi-Subject Study at 3 Tesla. J Neural Eng 2022; 19. [PMID: 35561669 DOI: 10.1088/1741-2552/ac6f81] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Real-time fMRI neurofeedback is a non-invasive procedure allowing the self-regulation of brain functions via enhanced self-control of fMRI based neural activation. In semantic real-time fMRI neurofeedback, an estimated relation between multivariate fMRI activation patterns and abstract mental states is exploited for a multi-dimensional feedback stimulus via real-time representational similarity analysis (rt-RSA). Here, we assessed the performances of this framework in a multi-subject multi-session study on a 3T MRI clinical scanner. APPROACH Eighteen healthy volunteers underwent two semantic real-time fMRI neurofeedback sessions on two different days. In each session, participants were first requested to engage in specific mental states while local fMRI patterns of brain activity were recorded during stimulated mental imagery of concrete objects (pattern generation). The obtained neural representations were to be replicated and modulated by the participants in subsequent runs of the same session under the guidance of a rt-RSA generated visual feedback (pattern modulation). Performance indicators were derived from the rt-RSA output to assess individual abilities in replicating (and maintaining over time) a target pattern. Simulations were carried out to assess the impact of the geometric distortions implied by the low-dimensional representation of patterns' dissimilarities in the visual feedback. MAIN RESULTS Sixteen subjects successfully completed both semantic real-time fMRI neurofeedback sessions. Considering some performance indicators, a significant improvement between the first and the second runs, and within run increasing modulation performances were observed, whereas no improvements were found between sessions. Simulations confirmed that in a small percentage of cases visual feedback could be affected by metric distortions due to dimensionality reduction implicit to the rt-RSA approach. SIGNIFICANCE Our results proved the feasibility of the semantic real-time fMRI neurofeedback at 3T, showing that subjects can successfully modulate and maintain a target mental state, guided by rt-RSA derived feedback. Further development is needed to encourage future clinical applications.
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Affiliation(s)
- Assunta Ciarlo
- University of Salerno - Baronissi Campus, Via S. Allende, Baronissi, Campania, 84081, ITALY
| | | | - Sara Ponticorvo
- University of Salerno - Baronissi Campus, Via S. Allende, Baronissi, Campania, 84081, ITALY
| | - Francesco Di Salle
- Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno - Baronissi Campus, Via S. Allende, Baronissi, Campania, 84081, ITALY
| | - Michael Lührs
- Department of Cognitive Neuroscience, Maastricht University, P.O. Box 616, Maastricht, Limburg, 6200 MD, NETHERLANDS
| | - Rainer Goebel
- Faculty of Psychology, University of Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands, Maastricht, 6200 MD, NETHERLANDS
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli School of Medicine and Surgery, Piazza L. Miraglia, Napoli, 80138, ITALY
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Li X, Li Z, Zou Z, Wu X, Gao H, Wang C, Zhou J, Qi F, Zhang M, He J, Qi X, Yan F, Dou S, Zhang H, Tong L, Li Y. Real-Time fMRI Neurofeedback Training Changes Brain Degree Centrality and Improves Sleep in Chronic Insomnia Disorder: A Resting-State fMRI Study. Front Mol Neurosci 2022; 15:825286. [PMID: 35283729 PMCID: PMC8904428 DOI: 10.3389/fnmol.2022.825286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundChronic insomnia disorder (CID) is considered a major public health problem worldwide. Therefore, innovative and effective technical methods for studying the pathogenesis and clinical comprehensive treatment of CID are urgently needed.MethodsReal-time fMRI neurofeedback (rtfMRI-NF), a new intervention, was used to train 28 patients with CID to regulate their amygdala activity for three sessions in 6 weeks. Resting-state fMRI data were collected before and after training. Then, voxel-based degree centrality (DC) method was used to explore the effect of rtfMRI-NF training. For regions with altered DC, we determined the specific connections to other regions that most strongly contributed to altered functional networks based on DC. Furthermore, the relationships between the DC value of the altered regions and changes in clinical variables were determined.ResultsPatients with CID showed increased DC in the right postcentral gyrus, Rolandic operculum, insula, and superior parietal gyrus and decreased DC in the right supramarginal gyrus, inferior parietal gyrus, angular gyrus, middle occipital gyrus, and middle temporal gyrus. Seed-based functional connectivity analyses based on the altered DC regions showed more details about the altered functional networks. Clinical scores in Pittsburgh sleep quality index, insomnia severity index (ISI), Beck depression inventory, and Hamilton anxiety scale decreased. Furthermore, a remarkable positive correlation was found between the changed ISI score and DC values of the right insula.ConclusionsThis study confirmed that amygdala-based rtfMRI-NF training altered the intrinsic functional hubs, which reshaped the abnormal functional connections caused by insomnia and improved the sleep of patients with CID. These findings contribute to our understanding of the neurobiological mechanism of rtfMRI-NF in insomnia treatment. However, additional double-blinded controlled clinical trials with larger sample sizes need to be conducted to confirm the effect of rtfMRI-NF from this initial study.
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Affiliation(s)
- Xiaodong Li
- Department of Radiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhonglin Li
- Department of Radiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhi Zou
- Department of Radiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaolin Wu
- Department of Nuclear Medicine, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui Gao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Caiyun Wang
- Department of Radiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Jing Zhou
- Health Management Center, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Fei Qi
- Department of Radiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Miao Zhang
- Department of Radiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Junya He
- Department of Radiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Qi
- Department of Radiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Fengshan Yan
- Department of Radiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Shewei Dou
- Department of Radiology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Hongju Zhang
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Li Tong
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
- *Correspondence: Li Tong,
| | - Yongli Li
- Health Management Center, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
- Yongli Li,
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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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21
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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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22
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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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23
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Li Z, Liu J, Chen B, Wu X, Zou Z, Gao H, Wang C, Zhou J, Qi F, Zhang M, He J, Qi X, Yan F, Dou S, Tong L, Zhang H, Han X, Li Y. Improved Regional Homogeneity in Chronic Insomnia Disorder After Amygdala-Based Real-Time fMRI Neurofeedback Training. Front Psychiatry 2022; 13:863056. [PMID: 35845454 PMCID: PMC9279663 DOI: 10.3389/fpsyt.2022.863056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Chronic insomnia disorder (CID) is a highly prevalent sleep disorder, which influences people's daily life and is even life threatening. However, whether the resting-state regional homogeneity (ReHo) of disrupted brain regions in CID can be reshaped to normal after treatment remains unclear. METHODS A novel intervention real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) was used to train 28 CID patients to regulate the activity of the left amygdala for three sessions in 6 weeks. The ReHo methodology was adopted to explore its role on resting-state fMRI data, which were collected before and after training. Moreover, the relationships between changes of clinical variables and ReHo value of altered regions were determined. RESULTS Results showed that the bilateral dorsal medial pre-frontal cortex, supplementary motor area (SMA), and left dorsal lateral pre-frontal cortex had decreased ReHo values, whereas the bilateral cerebellum anterior lobe (CAL) had increased ReHo values after training. Some clinical scores markedly decreased, including Pittsburgh Sleep Quality Index, Insomnia Severity Index, Beck Depression Inventory, and Hamilton Anxiety Scale (HAMA). Additionally, the ReHo values of the left CAL were positively correlated with the change in the Hamilton depression scale score, and a remarkable positive correlation was found between the ReHo values of the right SMA and the HAMA score. CONCLUSION Our study provided an objective evidence that amygdala-based rtfMRI-NF training could reshape abnormal ReHo and improve sleep in patients with CID. The improved ReHo in CID provides insights into the neurobiological mechanism for the effectiveness of this intervention. However, larger double-blinded sham-controlled trials are needed to confirm our results from this initial study.
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Affiliation(s)
- Zhonglin Li
- Department of Radiology, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Jiao Liu
- Department of Nuclear Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Medical Key Laboratory of Molecular Imaging, Zhengzhou, China
| | - Bairu Chen
- Department of Medical Imaging, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoling Wu
- Department of Nuclear Medicine, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhi Zou
- Department of Radiology, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui Gao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Caiyun Wang
- Department of Radiology, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Jing Zhou
- Health Management Center, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Fei Qi
- Department of Radiology, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Miao Zhang
- Department of Radiology, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Junya He
- Department of Radiology, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Qi
- Department of Radiology, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Fengshan Yan
- Department of Radiology, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Shewei Dou
- Department of Radiology, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Li Tong
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Hongju Zhang
- Department of Neurology, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Xingmin Han
- Department of Nuclear Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Medical Key Laboratory of Molecular Imaging, Zhengzhou, China
| | - Yongli Li
- Health Management Center, Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, China
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24
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Tursic A, Eck J, Lührs M, Linden D, Goebel R. Response to Commentary of Dr. Robert T. Thibault and Dr. Hugo Pedder entitled: “Excess significance and power miscalculations in neurofeedback research”. NEUROIMAGE: CLINICAL 2022; 35:103088. [PMID: 35780661 PMCID: PMC9254157 DOI: 10.1016/j.nicl.2022.103088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Anita Tursic
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands
| | - Judith Eck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands
| | - Michael Lührs
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands
| | - David Linden
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands; Brain Innovation B.V, Oxfordlaan 55, 6229 EV Maastricht, The Netherlands
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25
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Editorial: Clinical Neurofeedback. NEUROIMAGE: CLINICAL 2022; 35:102905. [PMID: 34866039 PMCID: PMC9421443 DOI: 10.1016/j.nicl.2021.102905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
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26
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Kim DY, Tegethoff M, Meinlschmidt G, Yoo SS, Lee JH. Cigarette craving modulation is more feasible than resistance modulation for heavy cigarette smokers: empirical evidence from functional MRI data. Neuroreport 2021; 32:762-770. [PMID: 33901056 DOI: 10.1097/wnr.0000000000001653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Modulation of cigarette craving and neuronal activations from nicotine-dependent cigarette smokers using real-time functional MRI (rtfMRI)-based neurofeedback (rtfMRI-NF) has been previously reported. OBJECTIVES The aim of this study was to evaluate the efficacy of rtfMRI-NF training in reducing cigarette cravings using fMRI data acquired before and after training. METHODS Treatment-seeking male heavy cigarette smokers (N = 14) were enrolled and randomly assigned to two conditions related to rtfMRI-NF training aiming at resisting the urge to smoke. In one condition, subjects underwent conventional rtfMRI-NF training using neuronal activity as the neurofeedback signal (activity-based) within regions-of-interest (ROIs) implicated in cigarette craving. In another condition, subjects underwent rtfMRI-NF training with additional functional connectivity information included in the neurofeedback signal (functional connectivity-added). Before and after rtfMRI-NF training at each of two visits, participants underwent two fMRI runs with cigarette smoking stimuli and were asked to crave or resist the urge to smoke without neurofeedback. Cigarette craving-related or resistance-related regions were identified using a general linear model followed by paired t-tests and were evaluated using regression analysis on the basis of neuronal activation and subjective craving scores (CRSs). RESULTS Visual areas were mainly implicated in craving, whereas the superior frontal areas were associated with resistance. The degree of (a) CRS reduction and (b) the correlation between neuronal activation and CRSs were statistically significant (P < 0.05) in the functional connectivity-added neurofeedback group for craving-related ROIs. CONCLUSION Our study demonstrated the feasibility of altering cigarette craving in craving-related ROIs but not in resistance-related ROIs via rtfMRI-NF training.
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Affiliation(s)
- Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
| | - Marion Tegethoff
- Institute of Psychology, RWTH Aachen, Jägerstrasse, Aachen, Germany
- Division of Clinical Psychology and Psychiatry, Department of Psychology, University of Basel, Missionsstrasse, Basel, Switzerland
| | - Gunther Meinlschmidt
- Division of Clinical Psychology and Cognitive Behavioral Therapy, International Psychoanalytic University, Stromstrasse, Berlin, Germany
- Department of Psychosomatic Medicine, University Hospital Basel and University of Basel, Hebelstrasse, Basel, Switzerland
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Missionsstrasse, Basel, Switzerland
| | - Seung-Schik Yoo
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Anam-ro, Seongbuk-gu, Seoul, Republic of Korea
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27
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Trambaiolli LR, Cassani R, Mehler DMA, Falk TH. Neurofeedback and the Aging Brain: A Systematic Review of Training Protocols for Dementia and Mild Cognitive Impairment. Front Aging Neurosci 2021; 13:682683. [PMID: 34177558 PMCID: PMC8221422 DOI: 10.3389/fnagi.2021.682683] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 05/03/2021] [Indexed: 11/24/2022] Open
Abstract
Dementia describes a set of symptoms that occur in neurodegenerative disorders and that is characterized by gradual loss of cognitive and behavioral functions. Recently, non-invasive neurofeedback training has been explored as a potential complementary treatment for patients suffering from dementia or mild cognitive impairment. Here we systematically reviewed studies that explored neurofeedback training protocols based on electroencephalography or functional magnetic resonance imaging for these groups of patients. From a total of 1,912 screened studies, 10 were included in our final sample (N = 208 independent participants in experimental and N = 81 in the control groups completing the primary endpoint). We compared the clinical efficacy across studies, and evaluated their experimental designs and reporting quality. In most studies, patients showed improved scores in different cognitive tests. However, data from randomized controlled trials remains scarce, and clinical evidence based on standardized metrics is still inconclusive. In light of recent meta-research developments in the neurofeedback field and beyond, quality and reporting practices of individual studies are reviewed. We conclude with recommendations on best practices for future studies that investigate the effects of neurofeedback training in dementia and cognitive impairment.
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Affiliation(s)
- Lucas R Trambaiolli
- Basic Neuroscience Division, McLean Hospital - Harvard Medical School, Boston, MA, United States
| | - Raymundo Cassani
- Institut National de la Recherche Scientifique - Energy, Materials, and Telecommunications Centre (INRS-EMT), University of Québec, Montréal, QC, Canada
| | - David M A Mehler
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tiago H Falk
- Institut National de la Recherche Scientifique - Energy, Materials, and Telecommunications Centre (INRS-EMT), University of Québec, Montréal, QC, Canada
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28
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Trambaiolli LR, Tiwari A, Falk TH. Affective Neurofeedback Under Naturalistic Conditions: A Mini-Review of Current Achievements and Open Challenges. FRONTIERS IN NEUROERGONOMICS 2021; 2:678981. [PMID: 38235228 PMCID: PMC10790905 DOI: 10.3389/fnrgo.2021.678981] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 04/28/2021] [Indexed: 01/19/2024]
Abstract
Affective neurofeedback training allows for the self-regulation of the putative circuits of emotion regulation. This approach has recently been studied as a possible additional treatment for psychiatric disorders, presenting positive effects in symptoms and behaviors. After neurofeedback training, a critical aspect is the transference of the learned self-regulation strategies to outside the laboratory and how to continue reinforcing these strategies in non-controlled environments. In this mini-review, we discuss the current achievements of affective neurofeedback under naturalistic setups. For this, we first provide a brief overview of the state-of-the-art for affective neurofeedback protocols. We then discuss virtual reality as a transitional step toward the final goal of "in-the-wild" protocols and current advances using mobile neurotechnology. Finally, we provide a discussion of open challenges for affective neurofeedback protocols in-the-wild, including topics such as convenience and reliability, environmental effects in attention and workload, among others.
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Affiliation(s)
- Lucas R. Trambaiolli
- Basic Neuroscience Division, McLean Hospital–Harvard Medical School, Belmont, MA, United States
| | - Abhishek Tiwari
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC, Canada
| | - Tiago H. Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC, Canada
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29
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Dudek E, Dodell-Feder D. The efficacy of real-time functional magnetic resonance imaging neurofeedback for psychiatric illness: A meta-analysis of brain and behavioral outcomes. Neurosci Biobehav Rev 2021; 121:291-306. [PMID: 33370575 PMCID: PMC7856210 DOI: 10.1016/j.neubiorev.2020.12.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 12/01/2020] [Accepted: 12/18/2020] [Indexed: 12/13/2022]
Abstract
Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) has gained popularity as an experimental treatment for a variety of psychiatric illnesses. However, there has yet to be a quantitative review regarding its efficacy. Here, we present the first meta-analysis of rtfMRI-NF for psychiatric disorders, evaluating its impact on brain and behavioral outcomes. Our literature review identified 17 studies and 105 effect sizes across brain and behavioral outcomes. We find that rtfMRI-NF produces a medium-sized effect on neural activity during training (g = .59, 95 % CI [.44, .75], p < .0001), a large-sized effect after training when no neurofeedback is provided (g = .84, 95 % CI [.37, 1.31], p = .005), and small-sized effects for behavioral outcomes (symptoms g = .37, 95 % CI [.16, .58], p = .002; cognition g = .23, 95 % CI [-.33, .78], p = .288). Mixed-effects analyses revealed few moderators. Together, these data suggest a positive impact of rtfMRI-NF on brain and behavioral outcomes, although more research is needed to determine how rtfMRI-NF works, for whom, and under what circumstances.
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Affiliation(s)
- Emily Dudek
- Department of Psychology, University of Rochester, United States
| | - David Dodell-Feder
- Department of Psychology, University of Rochester, United States; Department of Neuroscience, University of Rochester Medical Center, United States.
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