The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI. Issue 8 (20th July 2017)

• Record Type: Journal Article
• Authors: Bielczyk, Natalia Z.; Llera, Alberto; Buitelaar, Jan K.; Glennon, Jeffrey C.; Beckmann, Christian F.
• Is Part Of: Brain and behavior. Volume 7:Issue 8(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-07-20
• Subjects: brain parcellation -- causal discovery -- Dynamic Causal Modeling -- effective connectivity -- functional Magnetic Resonance Imaging -- neuronal noise; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.777
• Languages: English
• ISSNs: 2162-3279

Abstract

Abstract: Purpose: Multiple 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 Methods: In 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. Results: First, 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. Conclusions: Factors 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. Abstract : In this work, we perform a simulation study in order to gain new insights on the influence of potential confounders on the effective connectivity estimation in functional Magnetic Resonance Imaging studies. We find that factors such as the magnitude of the background scale‐free noise and a local hemodynamic variability have a major influence on the performance of methods for effective connectivity research in fMRI.