Consensus-based feature extraction in rs-fMRI data analysis
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The high-dimensional nature of resting state functional MRI (fMRI) data implies the need of suitable feature selection techniques. Traditional univariate techniques are fast and straightforward to interpret, but are unable to unveil relationships among multiple features. The aim of this work is to evaluate the applicability of clustering based techniques to the problem of feature extraction in resting state fMRI data analysis. More specifically, we devise a methodology based on consensus clustering, a particular approach to the clustering problem that consists in combining different partitions of the same data set in a final solution. Our approach was validated on a real-word data set, deriving from multiple clinical studies on Parkinson’s disease and amyotrophic lateral sclerosis. Our results show that the adoption of consensus-based techniques can indeed lead to an improvement of the results, not only in terms of feature discriminability, but also from the point of view of interpretability.
KeywordsResting state fMRI Feature extraction Consensus clustering Independent component analysis Default-mode network
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.
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