Towards Tunable Consensus Clustering for Studying Functional Brain Connectivity During Affective Processing

Abstract

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package "UNCLES" available on http://cran.rproject.org/web/packages/UNCLES/index.html.

Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening

“…Recently, the brain state under the naturalistic stimuli including music and movie has been investigated through functional magnetic resonance imaging (fMRI) (Alluri et al 2012a, b;Alluri et al 2013;Burunat et al 2014Burunat et al , 2016aLiu et al 2017;Toiviainen et al 2014), MEG (Koskinen et al 2013;Lankinen et al 2014) and EEG (Cong et al 2013a, b;Daly et al 2014Daly et al , 2015Schaefer et al 2013;Sturm et al 2015;Zhu et al 2019Zhu et al , 2020. Alluri et al explored the neural correlates of music feature processing as it occurs in a realistic or naturalistic environment, where eleven participants attentively listened to the whole piece of music (Alluri et al 2012a, b;Burunat et al 2016b, a).…”

The early adolescent brain on music: analysis of functional dynamics reveals engagement of orbitofrontal cortex reward system

“…Similarly to other pleasant activities, music listening has been associated with a cyclical time course of pleasure including: a phase of expectation or wanting for a specific rewarding musical structure; a phase of consummation or liking of the music reward, which can have a peak level of pleasure (e.g., musical chills); a satiety or learning phase, where one learns and updates musical predictions changing both the wanting phase and the liking phase for future listening experiences (Gebauer et al 2012;Georgiadis and Kringelbach 2012;Kringelbach et al 2012;Brattico et al 2013;Brattico 2015Brattico , 2019a. These musical pleasure cycles, driven by different mechanisms including musical expectancy, memory associations, evaluative conditions (for a review, see Gebauer et al 2012;Brattico 2019b), involve the reward brain system, in particular the OFC, the ventral tegmental area, and the nucleus accumbens (Blood and Zatorre 2001;Brown et al 2004;Menon and Levitin 2005;Koelsch et al 2006;Suzuki et al 2008;Osuch et al 2009;Brattico et al 2015;Liu et al 2016Liu et al , 2017Reybrouck et al, 2018). All these areas are similarly engaged in other pleasurable experiences involving food or sex (Georgiadis & Kringelbach, 2012).…”

Global Sensory Qualities and Aesthetic Experience in Music

“…Although it allows researchers to use realistic music stimuli, the listening conditions are less than optimal, especially in a fMRI setting, in which noise saturation, low temporal resolution and the risk of false positives in the results (Eklund et al, 2016; Liu et al, 2017) pose considerable methodological challenges to our approach. Even if replicability of brain responses to musical features using the naturalistic paradigm has been shown (mainly for timbral features; Burunat et al, 2016), the concerns for applying the current approach to fMRI data might present bottlenecks that are hard to circumvent.…”