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Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback.

作者信息

Fagerland Steffen Maude, Berntsen Henrik Røsholm, Fredriksen Mats, Endestad Tor, Skouras Stavros, Rootwelt-Revheim Mona Elisabeth, Undseth Ragnhild Marie

机构信息

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.

出版信息

J Electr Bioimpedance. 2024 May 31;15(1):41-62. doi: 10.2478/joeb-2024-0006. eCollection 2024 Jan.


DOI:10.2478/joeb-2024-0006
PMID:38827812
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11141335/
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.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/1b4b9097cc8b/j_joeb-2024-0006_fig_008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/ecd30e87ade1/j_joeb-2024-0006_fig_001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/09b08dd43269/j_joeb-2024-0006_fig_002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/b97144655803/j_joeb-2024-0006_fig_003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/24503074783e/j_joeb-2024-0006_fig_004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/4e2fed7a0e94/j_joeb-2024-0006_fig_005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/fda2aa3633ec/j_joeb-2024-0006_fig_006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/cec4e3dc30e6/j_joeb-2024-0006_fig_007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/1b4b9097cc8b/j_joeb-2024-0006_fig_008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/ecd30e87ade1/j_joeb-2024-0006_fig_001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/09b08dd43269/j_joeb-2024-0006_fig_002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/b97144655803/j_joeb-2024-0006_fig_003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/24503074783e/j_joeb-2024-0006_fig_004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/4e2fed7a0e94/j_joeb-2024-0006_fig_005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/fda2aa3633ec/j_joeb-2024-0006_fig_006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/cec4e3dc30e6/j_joeb-2024-0006_fig_007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da2e/11141335/1b4b9097cc8b/j_joeb-2024-0006_fig_008.jpg

相似文献

[1]
Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback.

J Electr Bioimpedance. 2024-5-31

[2]
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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Method for Using Functional Near-Infrared Spectroscopy (fNIRS) to Explore Music-Induced Brain Activation in Orchestral Musicians in Concert.

Sensors (Basel). 2025-3-14

本文引用的文献

[1]
Clinical efficacy of neurofeedback protocols in treatment of Attention Deficit/Hyperactivity Disorder (ADHD): A systematic review.

Psychiatry Res Neuroimaging. 2023-10

[2]
Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress.

Chronic Stress (Thousand Oaks). 2023-9-28

[3]
A functional MRI pre-processing and quality control protocol based on statistical parametric mapping (SPM) and MATLAB.

Front Neuroimaging. 2023-1-10

[4]
Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning.

Brain Inform. 2023-6-21

[5]
An investigation of the effectiveness of neurofeedback training on motor performance in healthy adults: A systematic review and meta-analysis.

Neuroimage. 2023-4-15

[6]
Multimodal assessment of the spatial correspondence between fNIRS and fMRI hemodynamic responses in motor tasks.

Sci Rep. 2023-2-8

[7]
Home-based portable fNIRS-derived cortical laterality correlates with impairment and function in chronic stroke.

Front Hum Neurosci. 2022-12-9

[8]
Inducing a mental context for associative memory formation with real-time fMRI neurofeedback.

Sci Rep. 2022-12-8

[9]
Double-Blind, Sham-Controlled Randomized Trial Testing the Efficacy of fMRI Neurofeedback on Clinical and Cognitive Measures in Children With ADHD.

Am J Psychiatry. 2022-12-1

[10]
Heart Rate Variability Biofeedback in Cancer Patients: A Scoping Review.

Behav Sci (Basel). 2022-10-11

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