Data_Sheet_1_Altered metabolites in the periaqueductal gray of COVID-19 patients experiencing headaches: a longitudinal MRS study.pdf

BackgroundHeadache is one of the most common symptoms of acute COVID-19 infection. However, its mechanisms remain poorly understood, and there is a lack of studies investigating changes in the periaqueductal gray (PAG) in COVID-19 patients exhibiting headaches.PurposeThe study aimed to explore the alterations in metabolites of the PAG pre- and post-COVID-19 infection in individuals who suffered from headaches during the acute phase of the disease using proton magnetic resonance spectroscopy (1H-MRS).MethodsFifteen participants who experienced headaches during the acute phase of COVID-19 were recruited. All subjects underwent two proton magnetic resonance spectroscopy (1H-MRS) examinations focusing on the PAG before and after they were infected. Metabolite changes were assessed between the pre- and post-infection groups.ResultsThe combined glutamine and glutamate/total creatine ratio (Glx/tCr) was increased in the PAG following COVID-19 infection. The total choline/total creatine ratio (tCho/tCr) in the pre-infection group was negatively correlated with the duration of headache during the COVID-19 acute phase.ConclusionThe present study indicates that PAG plays a pivotal role in COVID-19 headaches, thereby supporting the involvement of trigeminovascular system activation in the pathophysiology of COVID-19 headaches.</p

Transportation of Aqueous and Alcoholic Solutions through the Nanochannel of MCM-41: A Spin Probe Electron Spin Resonance Study

Real-time ESR observations have been made on the aqueous solutions of di-tert-butyl nitroxide and 2,2,6,6-tetramethylpiperidine-1-oxyl-4-ol flowing in a quartz column of 0.81 mm ⌀ packed with well-dried MCM-41. In both systems, the ESR spectrum is composed of two signals, the major signal (>98% of the total) is a very broad one assigned to the radical in the nanochannel of MCM-41, and the minor signal is a sharp one due to the radical in the bulk space between the MCM-41 particles. Although the spin probes are highly condensed deep in the cylindrical nanospace of MCM-41, they are transported downstream in a rather short time. The analysis of these observations led us to conclude that the aqueous solution is transported through the nanochannel of MCM-41 at a small rate but still much larger than that predicted by the conventional law. The same type of experiment was made with ethanol solutions of the same spin probes, whose ESR spectra also show different shapes in the two spaces at a high concentration but not a distinct adsorption. In this case, the transportation must be smooth for both components, since the time profile of the ESR signal for the flow in the MCM-41-packed column is almost homologous with that observed in the open column. Since the translational diffusion of the individual molecules is quenched in the nanochannel, as being reported in earlier studies, the solute as well as the solvent molecules should move collectively through the nanochannel. The present technique to study the fluid flow in the nanospaces may be called “spin probe nano flowmetry”

ADHD-200, personal characteristic data with structural images.

ADHD-200, personal characteristic data with structural images.</p

Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism

<div><p>A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets. The algorithm is able to distinguish ADHD from control with hold-out accuracy of 69.6% (over baseline 55.0%) using personal characteristics and structural brain scan features when trained on the ADHD-200 dataset (769 participants in training set, 171 in test set). It is able to distinguish autism from control with hold-out accuracy of 65.0% (over baseline 51.6%) using functional images with personal characteristic data when trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (889 participants in training set, 222 in test set). These results outperform all previously presented methods on both datasets. To our knowledge, this is the first demonstration of a single automated learning process that can produce classifiers for distinguishing patients vs. controls from brain imaging data with above-chance accuracy on large datasets for two different psychiatric illnesses (ADHD and autism). Working toward clinical applications requires robustness against real-world conditions, including the substantial variability that often exists among data collected at different institutions. It is therefore important that our algorithm was successful with the large ADHD-200 and ABIDE datasets, which include data from hundreds of participants collected at multiple institutions. While the resulting classifiers are not yet clinically relevant, this work shows that there is a signal in the (f)MRI data that a learning algorithm is able to find. We anticipate this will lead to yet more accurate classifiers, over these and other psychiatric disorders, working toward the goal of a clinical tool for high accuracy differential diagnosis.</p></div

5-fold cross validation accuracies on the training set.

The accuracies are obtained using RBF SVM (with various sigma values), on the training portion of the ADHD-200 dataset using functional images plus personal characteristic data. This figure is best viewed in color.</p

Preprocessing pipeline.

The preprocessing pipeline for functional and structural magnetic resonance images is summarized in the figure. Orange shapes in the image show the steps of preprocessing necessary for both fMRI and structural MRI scans. Green shapes show the preprocessing steps only needed for fMRI scans. This figure is best viewed in color.</p

Input and output of 2D HOG on a brain image.

The left panel shows an axial slice of a structural MR image of a brain. The right panel shows the HOG features of the same slice. Here, we represent the HOG features by an 8-sided “star”, where the length of each arm is the size of the histogram in that direction. This representation is generated using VLFeat [32].</p

Details of Holdout Dataset participants, ABIDE.

<p>Details of Holdout Dataset participants, ABIDE.</p

ADHD-200, personal characteristic data.

<p>ADHD-200, personal characteristic data.</p

ADHD-200, personal characteristic data with functional images.

<p>ADHD-200, personal characteristic data with functional images.</p