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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical inference in brain graphs using threshold-free network-based statistics.

Hugo C Baggio1, Alexandra Abos1, Barbara Segura1

  • 1Medical Psychology Unit, Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Catalonia, Spain.

Human Brain Mapping
|February 17, 2018
PubMed
Summary
This summary is machine-generated.

Threshold-Free Network-Based Statistics (TFNBS) offers a new way to analyze brain networks. This method effectively controls false positives in neuroimaging, providing reliable statistical assessment of brain graphs.

Keywords:
connectomicsfunctional connectivitynetwork-based statisticstructural connectivitythreshold-free cluster enhancement

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Area of Science:

  • Neuroimaging
  • Graph Theory
  • Statistical Inference

Background:

  • Brain networks are increasingly modeled as graphs in neuroimaging research.
  • Statistical inference for edge-wise connectivity in brain graphs faces challenges with false positives.

Purpose of the Study:

  • To assess the properties of Threshold-Free Network-Based Statistics (TFNBS) using simulated data.
  • To evaluate TFNBS's ability to control false-positive rates in brain graph analysis.

Main Methods:

  • TFNBS combines threshold-free cluster enhancement with Network-Based Statistics (NBS).
  • The study utilized simulated data to test TFNBS performance.
  • Unlike NBS, TFNBS provides edge-wise significance without a predefined threshold.

Main Results:

  • TFNBS can be configured to detect strong, clustered effects.
  • The method demonstrates effective control over false-positive rates.
  • TFNBS proved sensitive to topological effects within brain networks.

Conclusions:

  • TFNBS is a suitable technique for statistical assessment of brain graphs.
  • The findings support TFNBS as a valuable tool for neuroimaging studies.
  • Careful parameter selection is important for optimal TFNBS performance.