Towards tunable consensus clustering for studying functional brain connectivity during affective processing

TY - JOUR

T1 - Towards tunable consensus clustering for studying functional brain connectivity during affective processing

AU - Liu, Chao

AU - Abu-Jamous, Basel

AU - Brattico, Elvira

AU - Nandi, Asoke K.

PY - 2017/3

Y1 - 2017/3

N2 - 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.

AB - 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.

KW - Bi-CoPam

KW - Consensus clustering

KW - affective processing

KW - fMRI

KW - functional connectivity

KW - model-free analysis

UR - http://www.scopus.com/inward/record.url?scp=84986571443&partnerID=8YFLogxK

U2 - 10.1142/S0129065716500428

DO - 10.1142/S0129065716500428

M3 - Journal article

C2 - 27596928

AN - SCOPUS:84986571443

SN - 0129-0657

VL - 27

SP - 1650042

JO - International Journal of Neural Systems

JF - International Journal of Neural Systems

IS - 2

M1 - 1650042

ER -