Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?

By Rikkert Hindriks, Mohit H. Adhikari, Yusuke Murayama, Marco Ganzetti, Dante Mantini, Nikos K. Logothetis, Gustavo Deco. NeuroImage, 2016

602 citations recorded by the source corpus.

During the last several years, the focus of research on resting-state functional magnetic resonance imaging (fMRI) has shifted from the analysis of functional connectivity averaged over the duration of scanning sessions to the analysis of changes of functional connectivity within sessions. Although several studies have reported the presence of dynamic functional connectivity (dFC), statistical assessment of the results is not always carried out in a sound way and, in some studies, is even omitted. In this study, we explain why appropriate statistical tests are needed to detect dFC, we describe how they can be carried out and how to assess the performance of dFC measures, and we illustrate the methodology using spontaneous blood-oxygen level-dependent (BOLD) fMRI recordings of macaque monkeys under general anesthesia and in human subjects under resting-state conditions. We mainly focus on sliding-window correlations since these are most widely used in assessing dFC, but also consider a recently proposed non-linear measure. The simulations and methodology, however, are general and can be applied to any measure. The results are twofold. First, through simulations, we show that in typical resting-state sessions of 10 min, it is almost impossible to detect dFC using sliding-window correlations. This prediction is validated by both the macaque and the human data: in none of the individual recording sessions was evidence for dFC found. Second, detection power can be considerably increased by session- or subject-averaging of the measures. In doing so, we found that most of the functional connections are in fact dynamic. With this study, we hope to raise awareness of the statistical pitfalls in the assessment of dFC and how they can be avoided by using appropriate statistical methods.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/58eafb31d15956e14f8f1e5778518860f31fc795

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Citations in the corpus

Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, background): “…network ‘‘dynamics’’ (i.e., time-varying changes in functional networks) within a scan, finding little evidence for time-varying changes in functional networks at rest, once artifacts and state changes (drowsiness, task) are accounted for (see also Hindriks et al., 2016; Liégeois et al., 2017).”
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.”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “Other work has called into question some of the common statistical approaches to assessing dynamic FC (Hindriks et al., 2016; Leonardi and Van De Ville, 2015), which may not test against null models that properly account for random noise.”
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, methodology): “(Hindriks et al., 2015) reported that the mere presence of fluctuations in the SWC time series cannot be taken as the evidence of dynamic functional connectivity and that it is important to select an appropriate null hypothesis when performing the dynamic FC analysis and (Shine et al., 2015)…”
Brain network dynamics are hierarchically organized in time (2017, methodology): “This approach has access to temporal scales as fast as the data modality allows and overcomes the statistical limitations of the sliding windows technique when applied to the analysis of brain dynamics (14, 15, 16, 17).”
Discovering dynamic brain networks from big data in rest and task (2017, methodology): “The sliding windows approach (and methods built upon it) suffers from a number of limitations (Hindriks et al., 2016) that can undermine any conclusions.”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, unknown): “Multiple methods have been developed to generate surrogate data, including methods that represent a null model based on 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…”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, methodology): “…different phenomenological aspects of resting-state activity not considered in the current review (Petkoski and Jirsa, Freyer et al., 2011, Deco et al., 2012, Senden et al., 2012, Nakagawa et al., 2014, Hindriks et al., 2015b, Vasa et al., 2015, Vuksanovic and Hovel, 2015, Robinson et al., 2016).”
Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks (2019, unknown): “Controversies surrounding the interpretation of dynamic functional connectivity notwithstanding (Hindriks et al. 2016; Laumann et al. 2017; Liégeois et al. 2017), consideration of brain dynamics remains an intriguing direction for future research aimed at network taxonomy delineation.”
Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, background): “Parsing the contribution of real, brain-derived dynamics underlying variability at shorter timescales remains a challenge (Hutchison et al. 2013; Tagliazucchi and Laufs 2014; Hindriks et al. 2016; Laumann et al. 2016).”