The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

Abstract

PurposeMultiple computational studies have demonstrated that essentially all current analytical approaches to determine effective connectivity perform poorly when applied to synthetic functional Magnetic Resonance Imaging (fMRI) datasets. In this study, we take a theoretical approach to investigate the potential factors facilitating and hindering effective connectivity research in fMRI.Materials and MethodsIn this work, we perform a simulation study with use of Dynamic Causal Modeling generative model in order to gain new insights on the influence of factors such as the slow hemodynamic response, mixed signals in the network and short time series, on the effective connectivity estimation in fMRI studies.ResultsFirst, we perform a Linear Discriminant Analysis study and find that not the hemodynamics itself but mixed signals in the neuronal networks are detrimental to the signatures of distinct connectivity patterns. This result suggests that for statistical methods (which do not involve lagged signals), deconvolving the BOLD responses is not necessary, but at the same time, functional parcellation into Regions of Interest (ROIs) is essential. Second, we study the impact of hemodynamic variability on the inference with use of lagged methods. We find that the local hemodynamic variability provide with an upper bound on the success rate of the lagged methods. Furthermore, we demonstrate that upsampling the data to TRs lower than the TRs in state‐of‐the‐art datasets does not influence the performance of the lagged methods.ConclusionsFactors such as background scale‐free noise and hemodynamic variability have a major impact on the performance of methods for effective connectivity research in functional Magnetic Resonance Imaging.

Thresholding functional connectomes by means of mixture modeling

“…Since the original atlas has twice higher spatial resolution than the HCP data, this 3D map was subsampled to 91 × 109 x 91 voxels. As known from previous computational studies, mixing signals within ROIs is very detrimental to the connectivity research in fMRI ( Smith et al., 2011 , Bielczyk et al., 2017a ). Therefore in case voxels within a given block of 2 × 2 x 2 voxels belonged to two or more separate ROIs from Wang's atlas, we did not assign any label to that block in the downsampled atlas.…”

Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches

“…What is more, the hemodynamic response function (HRF) may well vary across regions (David et al, 2008; Handwerker, Ollinger, & D’Esposito, 2004), revealing spurious causal connections: when the HRF in one region is faster than in another, the temporal precedence of the peak will easily be mistaken for causation. The estimated directionality can in the worst case, even be reversed, when the region with the slower HRF in fact causes the region with the faster HRF (Bielczyk, et al, 2017). Furthermore, the BOLD signal might be noninvertible into the neuronal time series (Seth et al, 2015), which can affect GC analysis regardless of whether it is performed on the BOLD time series or the deconvolved signal.…”

“…The hemodynamic response thus acts as a low-pass filter, which results in high correlations between activity in consecutive frames (J. D. Ramsey et al, 2010). Since the hemodynamic lags (understood as the peaks of the hemodynamic response) are region- and subject-specific (Devonshire et al, 2012) and vary over time (Glomb, Ponce-Alvarez, Gilson, Ritter, & Deco, 2017), it is difficult to infer causality between two time series with potentially different hemodynamic lags (Bielczyk, Llera, Buitelaar, Glennon, & Beckmann, 2017). Computational work by Seth, Chorley, and Barnett (2013) suggests that upsampling the signal to low repetition times (TRs) (<0.1[ s ]) might potentially overcome this issue.…”

Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions

“…Furthermore, blood oxygen level–dependent (BOLD) activity is delayed with respect to neuronal firing, with a delay of 3–6 s in the adult human brain (Arichi et al, 2012). The delayed hemodynamic response can also induce spurious cross-correlations between two BOLD time series (Ramsey et al, 2010; Bielczyk, Llera, Buitelaar, Glennon, & Beckmann, 2017). Both subject-to-subject and region-to-region variability in the shape of hemodynamic response (Devonshire et al, 2012) provide a general limitation to the methods for effective connectivity research in fMRI: when the hemodynamic response in one region is faster than in another, the temporal precedence of the peak of the hemodynamic response can easily be mistaken for causation.…”