Cargando…
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
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 gui...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452730/ https://www.ncbi.nlm.nih.gov/pubmed/36092645 http://dx.doi.org/10.3389/fnhum.2022.933559 |
_version_ | 1784784975687581696 |
---|---|
author | Pereira, Jaime A. Ray, Andreas Rana, Mohit Silva, Claudio Salinas, Cesar Zamorano, Francisco Irani, Martin Opazo, Patricia Sitaram, Ranganatha Ruiz, Sergio |
author_facet | Pereira, Jaime A. Ray, Andreas Rana, Mohit Silva, Claudio Salinas, Cesar Zamorano, Francisco Irani, Martin Opazo, Patricia Sitaram, Ranganatha Ruiz, Sergio |
author_sort | Pereira, Jaime A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9452730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94527302022-09-09 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 Pereira, Jaime A. Ray, Andreas Rana, Mohit Silva, Claudio Salinas, Cesar Zamorano, Francisco Irani, Martin Opazo, Patricia Sitaram, Ranganatha Ruiz, Sergio Front Hum Neurosci Human Neuroscience 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. Frontiers Media S.A. 2022-08-25 /pmc/articles/PMC9452730/ /pubmed/36092645 http://dx.doi.org/10.3389/fnhum.2022.933559 Text en Copyright © 2022 Pereira, Ray, Rana, Silva, Salinas, Zamorano, Irani, Opazo, Sitaram and Ruiz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Human Neuroscience Pereira, Jaime A. Ray, Andreas Rana, Mohit Silva, Claudio Salinas, Cesar Zamorano, Francisco Irani, Martin Opazo, Patricia Sitaram, Ranganatha Ruiz, Sergio 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | real-time fmri neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: a proof of principle study |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452730/ https://www.ncbi.nlm.nih.gov/pubmed/36092645 http://dx.doi.org/10.3389/fnhum.2022.933559 |
work_keys_str_mv | AT pereirajaimea arealtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT rayandreas arealtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT ranamohit arealtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT silvaclaudio arealtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT salinascesar arealtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT zamoranofrancisco arealtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT iranimartin arealtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT opazopatricia arealtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT sitaramranganatha arealtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT ruizsergio arealtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT pereirajaimea realtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT rayandreas realtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT ranamohit realtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT silvaclaudio realtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT salinascesar realtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT zamoranofrancisco realtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT iranimartin realtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT opazopatricia realtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT sitaramranganatha realtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy AT ruizsergio realtimefmrineurofeedbacksystemfortheclinicalalleviationofdepressionwithasubjectindependentclassificationofbrainstatesaproofofprinciplestudy |