Towards Tunable Consensus Clustering for Studying Functional Brain Connectivity During Affective Processing.
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.r-project.org/web/packages/UNCLES/index.html .
* Department of Electronic and Computer Engineering, Brunel University London, London, UK.
Chao
C
Liu
* Department of Electronic and Computer Engineering, Brunel University London, London, UK.
Basel
B
Abu-Jamous
† Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.|‡ The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark.
Elvira
E
Brattico
* Department of Electronic and Computer Engineering, Brunel University London, London, UK.|§ The Key Laboratory of Embedded Systems and Service Computing, College of Electronic and Information Engineering, Tongji University, Shanghai, P. R. China.
Asoke K
AK
Nandi
D000339:Affect / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D002560:Cerebrovascular Circulation / Q000502:physiology; D016000:Cluster Analysis; D066264:Datasets as Topic; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009146:Music; D009434:Neural Pathways / Q000502:physiology; D010100:Oxygen / Q000097:blood; D011361:Professional Competence; D012201:Reward; D014796:Visual Perception / Q000502:physiology
D010100:Oxygen
Bi-CoPam; Consensus clustering; ective processing; fMRI; functional connectivity; model-free analysis