Data-driven analysis for fMRI during naturalistic music listening
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.
...
Publisher
University of JyväskyläISBN
978-951-39-7240-0ISSN Search the Publication Forum
1456-5390Keywords
Metadata
Show full item recordCollections
- Väitöskirjat [3589]
License
Related items
Showing items with similar title or keywords.
-
Brain integrative function driven by musical training during real-world music listening
Burunat Pérez, Iballa (University of Jyväskylä, 2017)The present research investigated differences in the brain dynamics of continuous, real-world music listening between listeners with and without professional musical training, using functional magnetic resonance imaging ... -
On application of kernel PCA for generating stimulus features for fMRI during continuous music listening
Tsatsishvili, Valeri; Burunat, Iballa; Cong, Fengyu; Toiviainen, Petri; Alluri, Vinoo; Ristaniemi, Tapani (Elsevier BV, 2018)Background There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, ... -
Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data
Hu, Guoqiang; Li, Huanjie; Zhao, Wei; Hao, Yuxing; Bai, Zonglei; Nickerson, Lisa D.; Cong, Fengyu (Elsevier, 2022)The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component ... -
Processing of an Audiobook in the Human Brain Is Shaped by Cultural Family Background
Hakonen, Maria; Ikäheimonen, Arsi; Hultèn, Annika; Kauttonen, Janne; Koskinen, Miika; Lin, Fa-Hsuan; Lowe, Anastasia; Sams, Mikko; Jääskeläinen, Iiro P. (MDPI AG, 2022)Perception of the same narrative can vary between individuals depending on a listener’s previous experiences. We studied whether and how cultural family background may shape the processing of an audiobook in the human ... -
Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression
Zhu, Yongjie; Wang, Xiaoyu; Mathiak, Klaus; Toiviainen, Petri; Ristaniemi, Tapani; Xu, Jing; Chang, Yi; Cong, Fengyu (World Scientific, 2021)To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing ...