Assessing dynamic functional connectivity in heterogeneous samples.
MetadataShow full item record
Lehmann, B., White, S., Henson, R., Cam-Can,, & Geerligs, L. (2017). Assessing dynamic functional connectivity in heterogeneous samples.. NeuroImage, 157 635-647. https://doi.org/10.1016/j.neuroimage.2017.05.065
Several methods have been developed to measure dynamic functional connectivity (dFC) in fMRI data. These methods are often based on a sliding-window analysis, which aims to capture how the brain's functional organization varies over the course of a scan. The aim of many studies is to compare dFC across groups, such as younger versus older people. However, spurious group differences in measured dFC may be caused by other sources of heterogeneity between people. For example, the shape of the haemodynamic response function (HRF) and levels of measurement noise have been found to vary with age. We use a generic simulation framework for fMRI data to investigate the effect of such heterogeneity on estimates of dFC. Our findings show that, despite no differences in true dFC, individual differences in measured dFC can result from other (non-dynamic) features of the data, such as differences in neural autocorrelation, HRF shape, connectivity strength and measurement noise. We also find that common dFC methods such as k-means and multilayer modularity approaches can detect spurious group differences in dynamic connectivity due to inappropriate setting of their hyperparameters. fMRI studies therefore need to consider alternative sources of heterogeneity across individuals before concluding differences in dFC.
Dynamic functional connectivity, FMRI, Resting state, Group studies
Is supplemented by: https://doi.org/10.1016/j.neuroimage.2017.05.065
BL, SRW and RNH are supported by the UK Medical Research Council [Programme numbers U105292687 MC-A060-5PR10]. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) was supported by the Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1). LG is funded by a Rubicon grant [446-13-013] and a Veni grant [451-16-013] from the Netherlands Organization for Scientific Research.
WELLCOME TRUST (103838/Z/14/Z)
Medical Research Council (MC_U105597119)
Medical Research Council (MC_UU_00005/8)
Medical Research Council (MC_U105579226)
External DOI: https://doi.org/10.1016/j.neuroimage.2017.05.065
This record's URL: https://www.repository.cam.ac.uk/handle/1810/266656
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/