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Soft Computing

, Volume 22, Issue 11, pp 3785–3795 | Cite as

Consensus-based feature extraction in rs-fMRI data analysis

  • Paola GaldiEmail author
  • Michele Fratello
  • Francesca Trojsi
  • Antonio Russo
  • Gioacchino Tedeschi
  • Roberto Tagliaferri
  • Fabrizio Esposito
Methodologies and Application

Abstract

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.

Keywords

Resting state fMRI Feature extraction Consensus clustering Independent component analysis Default-mode network 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.NeuRoNe Lab, Department of Management and Innovation SystemsUniversity of SalernoFisciano, SalernoItaly
  2. 2.Department of Medical, Surgical, Neurological, Metabolic and Aging SciencesSecond University of NaplesNaplesItaly
  3. 3.Department of Medicine, Surgery and Dentistry ‘Scuola Medica Salernitana’University of SalernoBaronissi, SalernoItaly

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