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

ABSTRACT: 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 w

SUBMITTER: Maleki Balajoo S 

PROVIDER: S-EPMC7502846 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature