NeuroImage
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Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. ⋯ Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size.
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The lenticulostriate arteries (LSAs) with small diameters of a few hundred microns take origin directly from the high flow middle cerebral artery (MCA), making them especially susceptible to damage (e.g. by hypertension). This study aims to present high resolution (isotropic ∼0.5 mm), black blood MRI for the visualization and characterization of LSAs at both 3 T and 7 T. ⋯ The high-resolution black-blood 3D T1w TSE-VFA sequence offers a new method for the visualization and quantification of LSAs at both 3 T and 7 T, which may be applied for a number of pathological conditions related to the damage of LSAs.
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Brain arteriovenous malformations (AVMs) are congenital vascular anomalies characterized by arteriovenous shunting through a network of coiled and tortuous vessels. Because of this anatomy, the venous drainage of an AVM is hypothesized to contain more oxygenated, arterialized blood than healthy veins. By exploiting the paramagnetic properties of deoxygenated hemoglobin in venous blood using magnetic resonance imaging (MRI) quantitative susceptibility mapping (QSM), we aimed to explore venous density and oxygen saturation (SvO2) in patients with a brain AVM. ⋯ Therefore, QSM can be used to detect SvO2 alterations in brain AVMs. However, since factors such as flow-induced signal dephasing or the presence of hemosiderin deposits also strongly affect QSM image contrast, AVM vein segmentation must be performed based on alternative MRI acquisitions, e.g., time of flight magnetic resonance angiography or T1-weighted images. This is the first study to show, non-invasively, that AVM draining veins have a significantly larger SvO2 than healthy veins, which is a finding congruent with arteriovenous shunting.
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With myelin playing a vital role in normal brain integrity and function and thus in various neurological disorders, myelin sensitive magnetic resonance imaging (MRI) techniques are of great importance. In particular, multi-exponential T2 relaxation was shown to be highly sensitive to myelin. The myelin water imaging (MWI) technique allows to separate the T2 decay into short components, specific to myelin water, and long components reflecting the intra- and extracellular water. ⋯ In MRI, iron extraction lead to a decrease in MWF by 26%-28% in WM. Thus, a change in MWF does not necessarily reflect a change in myelin content. This observation has important implications for the interpretation of MWI findings in previously published studies and future research.