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DC Field | Value | Language |
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dc.contributor.author | Liu, C | - |
dc.contributor.author | Abu-Jamous, B | - |
dc.contributor.author | Brattico, E | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2016-10-13T14:51:48Z | - |
dc.date.available | 2016-10-13T14:51:48Z | - |
dc.date.issued | 2016-09-05 | - |
dc.identifier | ORCiD: Asoke K. Nandi https://orcid.org/0000-0001-6248-2875 | - |
dc.identifier | 1650042 | - |
dc.identifier.citation | Liu, C. et al. (2016) 'Towards Tunable Consensus Clustering for Studying Functional Brain Connectivity During Affective Processing', International Journal of Neural Systems, 27 (2), 1650042, pp. 1 - 16. doi: 10.1142/S0129065716500428. | en_US |
dc.identifier.issn | 0129-0657 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/13346 | - |
dc.description.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.r-project.org/web/packages/UNCLES/index.html. | en_US |
dc.description.sponsorship | Chao Liu would like to thank Brunel University London for the funding of research studentship. Elvira Brattico would like to thank the Danish National Research Foundation DNRF117 and Academy of Finland (Project No. 133673) for funding her research and the data acquisition. This work was partly supported by the National Science Foundation of China grant number 61520106006. | - |
dc.format.extent | 1 - 16 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en | en_US |
dc.rights | Copyright © The Author(s) 2016. This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License (https://creativecommons.org/licenses/by/4.0/). Further distribution of this work is permitted, provided the original work is properly cited. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | consensus clustering | en_US |
dc.subject | Bi-CoPam | en_US |
dc.subject | model-free analysis | en_US |
dc.subject | fMRI | en_US |
dc.subject | affective processing | en_US |
dc.subject | functional connectivity | en_US |
dc.title | Towards tunable consensus clustering for studying functional brain connectivity during affective processing | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1142/S0129065716500428 | - |
dc.relation.isPartOf | International Journal of Neural Systems | - |
pubs.issue | 2 | - |
pubs.publication-status | Published | - |
pubs.volume | 27 | - |
dc.identifier.eissn | 1793-6462 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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