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Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining.

Hadeel K Aljobouri1, Hussain A Jaber2, Orhan M Koçak3

  • 1Electrical and Electronics Engineering Department, Graduate School of Natural Science, Ankara Yıldırım Beyazıt University, Ankara, Turkey; Biomedical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, Iraq.

Journal of Neuroscience Methods
|February 23, 2018
PubMed
Summary

A novel Robust Growing Neural Gas (RGNG) algorithm effectively clusters functional magnetic resonance imaging (fMRI) data, outperforming traditional methods. RGNG accurately identifies brain activity and determines the optimal number of clusters.

Keywords:
Clustering techniqueData miningGrowing neural gas (GNG)Robust growing neural gas (RGNG)

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Data Analysis

Background:

  • Clustering algorithms in functional magnetic resonance imaging (fMRI) aim to delineate brain regions with similar activity patterns.
  • Selecting appropriate clustering algorithms for fMRI data remains a significant challenge in neuroscience research.

Purpose of the Study:

  • To introduce and evaluate the Robust Growing Neural Gas (RGNG) algorithm for fMRI data analysis.
  • To compare the performance of RGNG against the standard Growing Neural Gas (GNG) algorithm in a medical context.

Main Methods:

  • The study applied the RGNG algorithm to real, free auditory fMRI datasets.
  • The RGNG algorithm's performance was benchmarked against the GNG algorithm, which has no prior medical applications.
  • Performance was quantitatively assessed using Minimum Description Length (MDL) and Receiver Operating Characteristic (ROC) analysis.

Main Results:

  • The RGNG algorithm successfully identified active brain areas within the auditory cortices, consistent with expected outcomes.
  • RGNG demonstrated superior performance compared to other methods, showing robustness to initialization variations and outliers.
  • The algorithm accurately determined the optimal number of clusters in the fMRI datasets.

Conclusions:

  • The Robust Growing Neural Gas (RGNG) algorithm is a powerful tool for analyzing fMRI data.
  • RGNG can effectively detect active brain zones, analyze brain function, and determine the optimal number of clusters.
  • The algorithm's ability to define cluster centers corresponding to minimal MDL values enhances its utility in neuroimaging research.