OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis

Yury Koush,John Ashburner,Evgeny Prilepin,Ronald Sladky,Peter Zeidman,Sergei Bibikov,Frank Scharnowski,Artem Nikonorov,Dimitri Van De Ville

doi:10.1016/j.neuroimage.2017.06.039

Abstract

Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user’s needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.

Highlights

About two decades ago, real-time functional magnetic resonance imaging was introduced (Cox et al, 1995) and turned into a rapidly developing discipline
Neurofeedback based on real-time functional magnetic resonance imaging is a novel and rapidly developing research field
We provide a brief overview of the core neurofeedback data processing steps required to perform activity, connectivity- and classification-based neurofeedback studies, and introduce an open-source framework, termed Open NeuroFeedback Training (OpenNFT)

Summary

Introduction

Real-time functional magnetic resonance imaging (rt-fMRI) was introduced (Cox et al, 1995) and turned into a rapidly developing discipline. With the help of neurofeedback, participants can learn voluntary control over the own brain activity Such neurofeedback training has been shown to cause behavioral consequences, providing a scientific tool for investigating the relationship between brain function and behavior (Sitaram et al, 2017; Sulzer et al, 2013). Neurofeedback allows neurological and psychiatric patients to normalize abnormal levels of brain activity that are associated with their disorder It holds great promises as a drug-free and non-invasive experimental therapy that has been shown to be effective in depression, addiction, stroke, chronic pain, Parkinson’s disease, and tinnitus (Haller et al, 2010; Hartwell et al, 2013; Li et al, 2013; Liew et al, 2016; Linden et al, 2012; Subramanian et al, 2011; Young et al, 2014)

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