ReviewJournal of neuroscience methods2020
Statistical model for dynamically-changing correlation matrices with application to brain connectivity.
Review in Journal of neuroscience methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Article
- Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.Scientific reports · 2024Article
- Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy.ArXiv · 2024Article
- Topological state-space estimation of functional human brain networks.PLoS computational biology · 2024Article
- Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance.NeuroImage · 2023Article
- Hodge Laplacian of Brain Networks.IEEE transactions on medical imaging · 2023Article
- TOPOLOGICAL LEARNING FOR BRAIN NETWORKS.The annals of applied statistics · 2023Article
- Topological analysis of functional connectivity in Parkinson's disease.Frontiers in neuroscience · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundRecent studies have indicated that functional connectivity is dynamic even during rest. A common approach to modeling the dynamic functional connectivity in whole-brain resting-state fMRI is to compute the correlation between anatomical regions via sliding time windows. However, the direct use of the sample correlation matrices is not reliable due to the image acquisition and processing noises in resting-sate fMRI. NEW
methodTo overcome these limitations, we propose a new statistical model that smooths out the noise by exploiting the geometric structure of correlation matrices. The dynamic correlation matrix is modeled as a linear combination of symmetric positive-definite matrices combined with cosine series representation. The resulting smoothed dynamic correlation matrices are clustered into disjoint brain connectivity states using the k-means clustering algorithm.
resultsThe proposed model preserves the geometric structure of underlying physiological dynamic correlation, eliminates unwanted noise in connectivity and obtains more accurate state spaces. The difference in the estimated dynamic connectivity states between males and females is identified. COMPARISON WITH EXISTING
methodsWe demonstrate that the proposed statistical model has less rapid state changes caused by noise and improves the accuracy in identifying and discriminating different states.
conclusionsWe propose a new regression model on dynamically changing correlation matrices that provides better performance over existing windowed correlation and is more reliable for the modeling of dynamic connectivity.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.