Semantic fMRI neurofeedback: a multi-subject study at 3 tesla

Abstract

Objective. Real-time fMRI neurofeedback is a non-invasive procedure allowing the self-regulation of brain functions via enhanced self-control of fMRI based neural activation. In semantic real-time fMRI neurofeedback, an estimated relation between multivariate fMRI activation patterns and abstract mental states is exploited for a multi-dimensional feedback stimulus via real-time representational similarity analysis (rt-RSA). Here, we assessed the performances of this framework in a multi-subject multi-session study on a 3T MRI clinical scanner. Approach. Eighteen healthy volunteers underwent two semantic real-time fMRI neurofeedback sessions on two different days. In each session, participants were first requested to engage in specific mental states while local fMRI patterns of brain activity were recorded during stimulated mental imagery of concrete objects (pattern generation). The obtained neural representations were to be replicated and modulated by the participants in subsequent runs of the same session under the guidance of a rt-RSA generated visual feedback (pattern modulation). Performance indicators were derived from the rt-RSA output to assess individual abilities in replicating (and maintaining over time) a target pattern. Simulations were carried out to assess the impact of the geometric distortions implied by the low-dimensional representation of patterns’ dissimilarities in the visual feedback. Main results. Sixteen subjects successfully completed both semantic real-time fMRI neurofeedback sessions. Considering some performance indicators, a significant improvement between the first and the second runs, and within run increasing modulation performances were observed, whereas no improvements were found between sessions. Simulations confirmed that in a small percentage of cases visual feedback could be affected by metric distortions due to dimensionality reduction implicit to the rt-RSA approach. Significance. Our results proved the feasibility of the semantic real-time fMRI neurofeedback at 3T, showing that subjects can successfully modulate and maintain a target mental state, guided by rt-RSA derived feedback. Further development is needed to encourage future clinical applications.

Clinical Response to Neurofeedback in Major Depression Relates to Subtypes of Whole-Brain Activation Patterns During Training

“…As NF methodologies continue to evolve, focusing on the process of regulating affective states, rather than activating specific brain regions, might yield more effective treatments. In this context, the semantic NF approach, which emphasizes navigating positions within the representational state space over focusing on specific brain activities, offers a promising direction for refining training protocols 75, 76 .…”

“…As NF methodologies continue to evolve, focusing on the process of regulating affective states rather than merely activating specific brain regions might yield more effective treatments. In this context, pattern-based NF approaches such as DecNef 77 , connectome-based neurofeedback 78 , and semantic neurofeedback 79, 80 , which emphasize extracting feedback signals from multivariate brain activation patterns, offer a promising direction for refining training protocols.…”

Retrieving fMRI data in real-time: difficulties and pitfalls

“…A lot of different studies have shown the possible applications of real-time functional magnetic resonance imaging (rt-fMRI) and facilitate the importance of this method (Ciarlo et al, 2022; Lührs et al, 2019; Pandria, Athanasiou, Konstantara, Karagianni, & Bamidis, 2020; Russo, Lührs, Salle, Esposito, & Goebel, 2020; Sorger et al, 2009; Tursic, Eck, Lührs, Linden, & Goebel, 2020; Weber, Ethofer, & Ehlis, 2020; Weiskopf, 2012). The main prerequisite for successful rt-fMRI is the ability to get access to the fMRI data in real-time.…”