Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariat...

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Bibliographic Details
Published in:Human brain mapping 2020-10, Vol.41 (15), p.4264-4287
Main Authors: Maleki Balajoo, Somayeh, Asemani, Davud, Khadem, Ali, Soltanian‐Zadeh, Hamid
Format: Article
Language:English
Subjects:
Algorithms
Bivariate analysis
Brain
Brain mapping
Computer simulation
dynamic connection detectability
dynamic functional connectivity
Functional magnetic resonance imaging
hypothesis testing
Magnetic resonance imaging
multivariate dependencies
Neural networks
Neuroimaging
Observational weighting
resting state functional magnetic resonance imaging
Similarity
Statistical analysis
surrogate data
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Summary:To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel‐reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered‐SWC (T‐SWC) with different window lengths) based on both simulated and real rs‐fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T‐SWC (p‐value = .001) and SWC (p‐value
ISSN:1065-9471
1097-0193
DOI:10.1002/hbm.25124