Data-driven analysis for fMRI during naturalistic music listening

Abstract

Interest towards higher ecological validity in functional magnetic resonance imaging (fMRI) experiments has been steadily growing since the turn of millennium. The trend is reflected in increasing amount of naturalistic experiments, where participants are exposed to the real-world complex stimulus and/or cognitive tasks such as watching movie, playing video games, or listening to music. Multifaceted stimuli forming parallel streams of input information, combined with reduced control over experimental variables introduces number of methodological challenges associated with isolating brain responses to individual events. This exploratory work demonstrated some of those methodological challenges by applying widely used data-driven methods to real fMRI data elicited from continuous music listening experiment. Under the general goal of finding functional networks of brain regions involved in music processing, this work contributed to improvement of the methodology from two perspectives. One is to produce a set of representative features for stimulus audio that can capture different aspects of music, such as timbre and tonality. Another is to improve reliability and quality of separation of the observed brain activations into independent spatial patterns. Improved separation in turn enables better differentiation of stimulus-related activations from the ones originating from unrelated physiological, cognitive, or technical processes. More specifically, part of the research explored an application of a nonlinear method for generating perceptually relevant stimulus features representing high-level concepts in music. Another part addressed dimensionality reduction and model order estimation problem before subjecting fMRI data to source separation and offered few methodological developments in this regard.