Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex.

Magnetic Resonance Imaging (MRI) datasets from epidemiological studies often show a lower prevalence of motion artifacts than what is encountered in clinical practice. These artifacts can be unevenly distributed between subject groups and studies which introduces a bias that needs addressing when augmenting data for machine learning purposes. Since unreconstructed multi-channel k-space data is typically not available for population-based MRI datasets, motion simulations must be performed using signal magnitude data. There is thus a need to systematically evaluate how realistic such magnitude-based simulations are. We performed magnitude-based motion simulations on a dataset (MR-ART) from 148 subjects in which real motion-corrupted reference data was also available. The similarity of real and simulated motion was assessed by using image quality metrics (IQMs) including Coefficient of Joint Variation (CJV), Signal-to-Noise-Ratio (SNR), and Contrast-to-Noise-Ratio (CNR). An additional comparison was made by investigating the decrease in the Dice-Sørensen Coefficient (DSC) of automated segmentations with increasing motion severity. Segmentation of the cerebral cortex was performed with 6 freely available tools: FreeSurfer, BrainSuite, ANTs, SAMSEG, FastSurfer, and SynthSeg+. To better mimic the real subject motion, the original motion simulation within an existing data augmentation framework (TorchIO), was modified. This allowed a non-random motion paradigm and phase encoding direction. The mean difference in CJV/SNR/CNR between the real motion-corrupted images and our modified simulations (0.004±0.054/-0.7±1.8/-0.09±0.55) was lower than that of the original simulations (0.015±0.061/0.2±2.0/-0.29±0.62). Further, the mean difference in the DSC between the real motion-corrupted images was lower for our modified simulations (0.03±0.06) compared to the original simulations (-0.15±0.09). SynthSeg+ showed the highest robustness towards all forms of motion, real and simulated. In conclusion, reasonably realistic synthetic motion artifacts can be induced on a large-scale when only magnitude MR images are available to obtain unbiased data sets for the training of machine learning based models.

Somatosensory BOLD fMRI reveals close link between salient blood pressure changes and the murine neuromatrix.

The neuromatrix, or "pain matrix", is a network of cortical brain areas which is activated by noxious as well as salient somatosensory stimulation. This has been studied in mice and humans using blood oxygenation level-dependent (BOLD) fMRI. Here we demonstrate that BOLD effects observed in the murine neuromatrix in response to salient somatosensory stimuli are prone to reflect mean arterial blood pressure (MABP) changes, rather than neural activity. We show that a standard electrostimulus typically used in murine somatosensory fMRI can induce substantial elevations in MABP. Equivalent drug-induced MABP changes - without somatosensory stimulation - evoked BOLD patterns in the neuromatrix strikingly similar to those evoked by electrostimulation. This constitutes a serious caveat for murine fMRI. The regional specificity of these BOLD patterns can be attributed to the co-localization of the neuromatrix with large draining veins. Based on these findings we propose a cardiovascular support mechanism whereby abrupt elevations in MABP provide additional energy supply to the neuromatrix and other essential brain areas in fight-or-flight situations.