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Connectome sorting by consensus clustering increases separability in group neuroimaging studies.

Javier Rasero1, Ibai Diez2, Jesus M Cortes1

  • 1Biocruces Health Research Institute, Hospital Universitario de Cruces, Barakaldo, Spain.

Network Neuroscience (Cambridge, Mass.)
|February 23, 2019
PubMed
Summary
This summary is machine-generated.

Consensus clustering enhances brain connectivity analysis by identifying subject subgroups with lower variability. This method improves the separability of connectomes as biomarkers and reveals unique brain regions for clinical insights.

Keywords:
Brain connectivityClassificationConsensus clusteringUnsupervised learning

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • Increasing the signal-to-noise ratio is crucial for neuroimaging data analysis.
  • Preprocessing pipelines often require advanced techniques for effective data handling.

Purpose of the Study:

  • To introduce consensus clustering as an additional step in connectome processing.
  • To enhance the identification of subgroups within neuroimaging datasets.
  • To improve the clinical relevance of connectomes as biomarkers.

Main Methods:

  • Application of consensus clustering to brain connectivity matrices.
  • Partitioning neuroimaging data prior to group comparisons.
  • Analysis of unique brain regions emerging from clustered data.

Main Results:

  • Consensus clustering effectively reduces intragroup variability in subject subgroups.
  • The approach increases the separability of distinct subgroups based on connectomes.
  • Identification of unique brain regions within clusters offers novel clinical information.

Conclusions:

  • Consensus clustering is a valuable method for connectome processing.
  • This technique can improve the utility of connectomes as biomarkers.
  • The identified unique regions may hold significant clinical relevance for differentiating populations.