Connectivity differences in brain networks
Abstract
The scenario considered here is one where brain connectivity is represented as a network and an experimenter wishes to assess the evidence for an experimental effect at each of the typically thousands of connections comprising the network. To do this, a univariate model is independently fitted to each connection. It would be unwise to declare significance based on an uncorrected threshold of α=0.05, since the expected number of false positives for a network comprising N=90 nodes and N(N-1)/2=4005 connections would be 200. Control of Type I errors over all connections is therefore necessary. The network-based statistic (NBS) and spatial pairwise clustering (SPC) are two distinct methods that have been used to control family-wise errors when assessing the evidence for an experimental effect with mass univariate testing. The basic principle of the NBS and SPC is the same as supra-threshold voxel clustering. Unlike voxel clustering, where the definition of a voxel cluster is unambiguous, 'clusters' formed among supra-threshold connections can be defined in different ways. The NBS defines clusters using the graph theoretical concept of connected components. SPC on the other hand uses a more stringent pairwise clustering concept. The purpose of this article is to compare the pros and cons of the NBS and SPC, provide some guidelines on their practical use and demonstrate their utility using a case study involving neuroimaging data.
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Currently, there is no consensus about which brain parcellation technique could be most useful.
“Currently, there is no consensus about which brain parcellation technique could be most useful (de Reus and Van den Heuvel, 2013; Yeh et al., 2020; Zalesky et al., 2012).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Zalesky et al., 2012
Selection of a gray matter parcellation scheme is the first essential consideration when constructing a structural connectivity matrix and has been shown to be highly consequential in connectome analysis.
“Selection of a gray matter parcellation scheme (as described in Section 4.2) is the first essential consideration when constructing a structural connectivity matrix, and it has been shown to be highly consequential in connectome analysis (de Reus and Van den Heuvel, 2013; Yeh et al., 2020; Zalesky et al., 2012).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Zalesky et al., 2012
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Self-citation
- Authors
- Zalesky, A., Cocchi, L., Fornito, A., Murray, M. M., & Bullmore, E.
- Affiliations
- The University of Melbourne (AU)
- The University of Queensland (AU)
- Centre Hospitalier Universitaire Vaudois (CH)
- Corresponding author
- Zalesky, Andrew
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-04
- Volume
- 60
- Issue
- 2
- Pages
- 1055-1062
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2156372649
- Cited by
- 260
- References
- 62
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 13 · 2023: 21 · 2022: 23 · 2021: 18 · 2020: 17 · 2019: 17 · 2018: 16 · 2017: 16 · 2016: 19 · 2015: 26 · 2014: 19 · 2013: 20 · 2012: 7
- FWCI
- 11.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- Professor Trevor Kilpatrick
- the Australian Research Council (DP0986320)
- the Melbourne Neuroscience Institute
- the National Health and Medical Research Council of Australia
- the Swiss National Science Foundation (310030B_133136)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- brain connectivity, network-based statistics, multiple testing correction, connected components, false positives, EEG, brain networks
- MeSH terms
- Brain, Brain Mapping, Humans, Nerve Net
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