title: Towards a statistical test for functional connectivity dynamics.
abstract: Sliding-window correlation is an emerging method for mapping time-resolved, resting-state functional connectivity. To avoid mapping spurious connectivity fluctuations (false positives), Leonardi and Van De Ville recently recommended choosing a window length exceeding the longest wavelength composing the BOLD signal, usually assumed to be ~100s. Here, we provide further statistical support for this rule of thumb. However, we demonstrate that non-stationary fluctuations in functional connectivity can in theory be detected with much shorter window lengths (e.g. 40s), while maintaining nominal control of false positives. We find that statistical power is near-maximal for window lengths chosen according to Leonardi and Van De Ville's rule of thumb. Furthermore, we lay some foundations for a parametric test to identify non-stationary fluctuations in functional connectivity, also noting limitations of the sinusoidal model upon which our work, and the work of Leonardi and Van De Ville, is based. Most notably, our analytical results pertain to covariances, as does our statistical test, whereas functional connectivity is more commonly measured using correlations.
authors.affiliation: Melbourne Neuropsychiatry Centre and Melbourne Health, The University of Melbourne, Victoria 3010, Australia; Melbourne School of Engineering, The University of Melbourne, Victoria 3010, Australia. Electronic address: azalesky@unimelb.edu.au.
authors.forename: Andrew
authors.initials: A
authors.lastname: Zalesky
authors.affiliation: QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4029, Australia; The Royal Brisbane and Womens Hospital, Brisbane, Queensland 4029, Australia. Electronic address: michael.breakspear@qimrberghofer.edu.au.
authors.forename: Michael
authors.initials: M
authors.lastname: Breakspear
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009434:Neural Pathways / Q000502:physiology; D013997:Time Factors
keywords: Dynamic connectivity; Functional connectivity; Non-stationarity; Sliding window; Time-resolved networks