Optimizing real time fMRI neurofeedback for therapeutic discovery and development.

While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain-behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders.

Real-time fMRI neurofeedback: progress and challenges.

In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.

Wavelet transform-based Wiener filtering of event-related fMRI data.

The advent of event-related functional magnetic resonance imaging (fMRI) has resulted in many exciting studies that have exploited its unique capability. However, the utility of event-related fMRI is still limited by several technical difficulties. One significant limitation in event-related fMRI is the low signal-to-noise ratio (SNR). In this work, a method based on Wiener filtering in the wavelet domain is developed and demonstrated for denoising event-related fMRI data. Application of the technique to simulated and experimental data demonstrates that the technique is effective in reducing noise while preserving neuronal activity-induced response.

Temporal filtering of event-related fMRI data using cross-validation.

To circumvent the problem of low signal-to-noise ratio (SNR) in event-related fMRI data, the fMRI experiment is typically designed to consist of repeated presentations of the stimulus and measurements of the response, allowing for subsequent averaging of the resulting data. Due to factors such as time limitation, subject motion, habituation, and fatigue, practical constraints on the number of repetitions exist. Thus, filtering is commonly applied to further improve the SNR of the averaged data. Here, a time-varying filter based on theoretical work by Nowak is employed. This filter operates under the stationary wavelet transform framework and is demonstrated to lead to good estimates of the true signals in simulated data. The utility of the filter is also shown using experimental data obtained with a visual-motor paradigm.