ArticleNetwork neuroscience (Cambridge, Mass.)2021
Exploratory factor analysis with structured residuals for brain network data.
Article in Network neuroscience (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Neural Topologies of Reinforcement Sensitivity Theory: A Latent Variable Approach to Magnetic Resonance Imaging Data.Biological psychiatry global open science · 2025Article
- Longitudinal relationships between lifestyle risk factors and neurodevelopment in early adolescence.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2023Article
- Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex.eLife · 2023Article
- Latent functional connectivity underlying multiple brain states.Network neuroscience (Cambridge, Mass.) · 2022Article
- An Exploratory Analysis of the Internal Structure of Test Through a Multimethods Exploratory Approach of the ASQ:SE in Brazil.Journal of neurosciences in rural practice · 2022Article
- Alteration of brain functional networks induced by electroacupuncture stimulation in rats with ischemia-reperfusion: An independent component analysis.Frontiers in neuroscience · 2022Article
- Constraining functional coactivation with a cluster-based structural connectivity network.Network neuroscience (Cambridge, Mass.) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high-dimensional data to a small number of underlying variables. Techniques such as exploratory factor analysis (EFA) are used by neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA by using structured residuals (EFAST), and (c) apply this technique to three large and varied brain-imaging network datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets.
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Registered trials
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