Neuroimaging methods

Functional magnetic resonance imaging (fMRI) data is typically collected with gradient-echo echo-planar imaging (GE-EPI) sequences, which are particularly prone to the susceptibility artifact as a result of B₀ field inhomogeneity.

Here, we compared two different distortion correction approaches, by acquiring additional: (1) EPI data with reversed phase encoding direction (TOPUP), and (2) standard (and undistorted) GE data at two different echo times (GRE).

GRE estimated the largest voxel shifts and more positively impacted the quality of the analyses, in terms of the (significantly lower) cost function of the registration, the (higher) spatial overlap between the RSNs and appropriate templates, and the (significantly higher) sensitivity of the task-related mapping based on the Z-score values of the associated activation maps, although also evident when considering TOPUP.

fMRI data should thus be corrected for geometric distortions, with the choice of the approach having a modest, albeit positive, impact on the fMRI analyses.

Abreu & Duarte (2021). DOI: 10.3389/fnins.2021.642808.

Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem.

Here we compared four different inversion algorithms (MN, LORETA, EBB and MSP) under a Bayesian framework, each with three different sets of priors consisting of: 1) those specific to the algorithm (S1); 2) S₁ plus fMRI task activation maps and resting-state networks (S₂); and 3) S₂ plus network modules of task-related dynamic functional connectivity (dFC) states estimated from the dFC fluctuations (S₃).

Our results pave the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be crucial in future studies.

Abreu et al. (2021). DOI: 10.1101/2021.02.19.431976.

There are two main types of head motion affecting fMRI data: gradual head shifts and sudden movements of the head known as motion outliers.

We aimed at applying different techniques to tackle head motion in fMRI data in order to reach a consensus on the best strategies to use. We compared common approaches to correct head motion effects such as motion regression, motion censoring and data interpolation.

Our results pave the way towards finding an optimal motion correction strategy, which is required to improve the accuracy of fMRI analyses, crucially in clinical studies .

Soares et al. (2021). DOI: 10.5220/0010327803060313.