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Statistical power in network neuroscience.

Koen Helwegen1, Ilan Libedinsky1, Martijn P van den Heuvel2

  • 1Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Trends in Cognitive Sciences
|February 1, 2023
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Summary
This summary is machine-generated.

Statistical power is crucial for detecting real effects in network neuroscience studies of brain connectivity. Understanding factors like sample size and network topology can improve the reliability of connectome findings.

Keywords:
brain networkconnectivityconnectomefunctional connectivitynetwork-based inferencestatistical powerstructural connectivity

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Area of Science:

  • Neuroscience
  • Network Science
  • Statistical Analysis

Background:

  • Network neuroscience is a key method for studying brain connectivity.
  • The validity of findings relies on accurate connectivity mapping and statistical power.
  • Statistical power is essential for detecting true effects in neuroimaging data.

Purpose of the Study:

  • To review the current state of statistical power in network neuroscience.
  • To identify key factors influencing statistical power in brain connectivity investigations.
  • To introduce the concept of 'differential power' and its implications.

Main Methods:

  • Review of existing literature on statistical power in network neuroscience.
  • Discussion of factors influencing power: sample size, effect size, measurement error, and network topology.
  • Conceptualization of 'differential power' across network components and metrics.

Main Results:

  • Statistical power varies significantly within brain connectivity studies.
  • Factors such as sample size, effect size, measurement error, and network topology critically impact power.
  • 'Differential power' can manifest in both positive and negative connectome results.
  • Power differences can be observed across network nodes, edges, and graph metrics.

Conclusions:

  • Acknowledging and addressing statistical power is vital for robust network neuroscience research.
  • Strategies for managing power, rather than ignoring it, are necessary for reliable connectome findings.
  • Understanding differential power can help interpret both significant and non-significant results in connectivity analyses.