Statistical power in network neuroscience

Koen Helwegen; Ilan Libedinsky; Martijn P. van den Heuvel

doi:10.1016/j.tics.2022.12.011

Statistical power in network neuroscience

Koen Helwegen, Ilan Libedinsky, Martijn P. van den Heuvel

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Abstract

Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.

Original language	English
Pages (from-to)	282-301
Number of pages	20
Journal	Trends in Cognitive Sciences
Volume	27
Issue number	3
Early online date	30 Jan 2023
DOIs	https://doi.org/10.1016/j.tics.2022.12.011
Publication status	Published - Mar 2023

Bibliographical note

Publisher Copyright:
Copyright © 2023 Elsevier Ltd. All rights reserved.

Funding

We thank Sophie van der Sluis for fruitful discussions, and Bernardo Maciel, Elleke Tissink, and Sara Seoane for helping to evaluate papers for the literature survey. The work performed for this study was supported by a European Research Council (ERC) Consolidator Grant (ID 101001062 ) to M.P.v.d.H. We thank Sophie van der Sluis for fruitful discussions, and Bernardo Maciel, Elleke Tissink, and Sara Seoane for helping to evaluate papers for the literature survey. The work performed for this study was supported by a European Research Council (ERC) Consolidator Grant (ID 101001062) to M.P.v.d.H. The authors declare no conflicts of interest.

Funders	Funder number
Bernardo Maciel
Elleke Tissink
European Research Council	101001062

Keywords

brain network
connectivity
connectome
functional connectivity
network-based inference
statistical power
structural connectivity

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.tics.2022.12.011

Helwegen 2023_Statistical power in network neuroscienceFinal published version, 2.34 MBLicence: Article 25fa Dutch Copyright Act

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Statistical power in network neuroscience

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

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