Dataset Information

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

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.

SUBMITTER: Bielczyk NZ

PROVIDER: S-EPMC5561328 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Bielczyk Natalia Z NZ Llera Alberto A Buitelaar Jan K JK Glennon Jeffrey C JC Beckmann Christian F CF

Brain and behavior 20170720 8

<h4>Purpose</h4>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.<h4>Materials and methods</h4>In this work, we perform a simulation study with use of Dynamic Causal M ...[more]

PMID: 28828228

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Dataset Information

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

Purpose

Materials and methods

Results

Conclusions

Publications

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

Similar Datasets

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