Assessment of dynamic functional connectivity in resting‐state fMRI using the sliding window technique. Issue 4 (18th March 2019)

• Record Type: Journal Article
• Authors: Savva, Antonis D.; Mitsis, Georgios D.; Matsopoulos, George K.
• Is Part Of: Brain and behavior. Volume 9:Issue 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-03-18
• Subjects: functional connectivity metrics -- information based metrics -- Multiplication of Temporal Derivatives -- partial correlation metrics -- surrogate data -- window size; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.1255
• Languages: English
• ISSNs: 2162-3279

Abstract

Abstract: Introduction: Recent studies related to assessing functional connectivity (FC) in resting‐state functional magnetic resonance imaging have revealed that the resulting connectivity patterns exhibit considerable fluctuations (dynamic FC [dFC]). A widely applied method for quantifying dFC is the sliding window technique. According to this method, the data are divided into segments with the same length (window size) and a correlation metric is employed to assess the connectivity within these segments, whereby the window size is often empirically chosen. Methods: In this study, we rigorously investigate the assessment of dFC using the sliding window approach. Specifically, we perform a detailed comparison between different correlation metrics, including Pearson, Spearman and Kendall correlation, Pearson and Spearman partial correlation, Mutual Information (MI), Variation of Information (VI), Kullback–Leibler divergence, Multiplication of Temporal Derivatives and Inverse Covariance. Results: Using test–retest datasets, we show that MI and VI yielded the most consistent results by achieving high reliability with respect to dFC estimates for different window sizes. Subsequent hypothesis testing, based on multivariate phase randomization surrogate data generation, allowed the identification of dynamic connections between the posterior cingulate cortex and regions in the frontal lobe and inferior parietal lobes, which were overall in agreement with previous studies. Conclusions: In the case of MI and VI, a window size of at least 120 s was found to be necessary for detecting dFC for some of the previously identified dynamically connected regions. Abstract : Dynamic functional connectivity (dFC) was assessed in the Default Mode Network by employing the sliding window technique using a variety of functional connectivity metrics over a broad range of window sizes. The results suggest that Mutual Information (MI) and Variation of Information (VI) yielded dFC estimates which were more reproducible between the test and retest datasets for all window sizes. Simultaneously, MI and VI identified previously reported dynamic connections involving the posterior cingulate cortex using a window size larger than 120 s.