ArticleFrontiers in neuroscience2020
Multivariate Brain Functional Connectivity Through Regularized Estimators.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Mental time travel and insight in schizophrenia.Psychological medicine · 2025Review
- Multivariate linear time-series modeling and prediction of cerebral physiologic signals: review of statistical models and implications for human signal analytics.Frontiers in network physiology · 2025Review
- Global brain connectivity: Test-retest stability and association with biological and neurocognitive variables.Journal of neuroscience methods · 2024Article
- Brain Network Analysis: A Review on Multivariate Analytical Methods.Brain connectivity · 2023Review
- Regularized Functional Connectivity in Schizophrenia.Frontiers in human neuroscience · 2022Article
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Authors and funding
10 authors.
Funding
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Abstract
Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of
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