Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states

By Sadia Shakil, Chin-Hui Lee, Shella Dawn Keilholz. NeuroImage, 2016

295 citations recorded by the source corpus.

A promising recent development in the study of brain function is the dynamic analysis of resting-state functional MRI scans, which can enhance understanding of normal cognition and alterations that result from brain disorders. One widely used method of capturing the dynamics of functional connectivity is sliding window correlation (SWC). However, in the absence of a “gold standard” for comparison, evaluating the performance of the SWC in typical resting-state data is challenging. This study uses simulated networks (SNs) with known transitions to examine the effects of parameters such as window length, window offset, window type, noise, filtering, and sampling rate on the SWC performance. The SWC time course was calculated for all node pairs of each SN and then clustered using the k-means algorithm to determine how resulting brain states match known configurations and transitions in the SNs. The outcomes show that the detection of state transitions and durations in the SWC is most strongly influenced by the window length and offset, followed by noise and filtering parameters. The effect of the image sampling rate was relatively insignificant. Tapered windows provide less sensitivity to state transitions than rectangular windows, which could be the result of the sharp transitions in the SNs. Overall, the SWC gave poor estimates of correlation for each brain state. Clustering based on the SWC time course did not reliably reflect the underlying state transitions unless the window length was comparable to the state duration, highlighting the need for new adaptive window analysis techniques.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c77382a74abbbe55075d96204f0bf178d7731058

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Citations in the corpus

Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, unknown): “…a speciﬁc system (Hindriks et al., 2016), biophysical models that simulate different classes of dynamics in the brain (Heitmann & Breakspear, 2018), and techniques that are designed to test the properties of speciﬁc methods used to estimate TVFC (Allen et al., 2014; Shakil, Lee, & Keilholz, 2016).”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, methodology): “The sliding time-window technique (Sakoglu et al., 2010; Hutchison et al., 2013; Hindriks et al., 2016; Shakil et al., 2016) is the most widely used.”