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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Real-Time fMRI Pattern Decoding and Neurofeedback Using FRIEND: An FSL-Integrated BCI Toolbox

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Sato, Joao R. [1, 2, 3] ; Basilio, Rodrigo [2, 3] ; Paiva, Fernando F. [2, 3, 4] ; Garrido, Griselda J. [2, 3] ; Bramati, Ivanei E. [5, 2, 3] ; Bado, Patricia [5, 2, 3] ; Tovar-Moll, Fernanda [5, 2, 3] ; Zahn, Roland [2, 3, 6] ; Moll, Jorge [5, 2, 3]
Total Authors: 9
[1] Univ Fed ABC, Ctr Math Computat & Cognit, Santo Andre - Brazil
[2] DOr Inst Res & Educ IDOR, Cognit & Behav Neurosci Unit, Rio De Janeiro - Brazil
[3] DOr Inst Res & Educ IDOR, Neuroinformat Workgrp, Rio De Janeiro - Brazil
[4] Univ Sao Paulo, Inst Phys Sao Carlos, Sao Carlos, SP - Brazil
[5] Univ Fed Rio de Janeiro, Inst Biomed Sci, Rio De Janeiro - Brazil
[6] Kings Coll London, Inst Psychiat, Dept Psychol Med, London WC2R 2LS - England
Total Affiliations: 6
Document type: Journal article
Source: PLoS One; v. 8, n. 12 DEC 2 2013.
Web of Science Citations: 13

The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e. g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity) and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages, such as FSL and libSVM. In addition, a user-defined visual neurofeedback module allows users to easily design and run fMRI neurofeedback experiments using ROI-based or multivariate classification approaches. FRIEND is open-source and free for non-commercial use. Processing tutorials and extensive documentation are available. (AU)

FAPESP's process: 13/10498-6 - Machine learning in neuroimaging: development of methods and clinical applications in psychiatric disorders
Grantee:João Ricardo Sato
Support type: Regular Research Grants