Biology subjects

Cho, T.-H.

Publications and source records attributed to Cho, T.-H..

3 recordsLinked to original sources

bioRxiv2024

Head down tilt 15° increases cerebral perfusion before recanalization in acute ischemic stroke: a pre-clinical MRI study.

We investigated the therapeutic effect of head down positioning at -15{degrees} (head down tilt; HDT15) on cerebral collateral flow and infarct growth in a rat model of large vessel occlusion (LVO) stroke, using multi-modal MRI. Twenty-eight Wistar rats were randomly assigned to HDT15 or flat position for 60 minutes, starting 30 minutes after occlusion of the middle cerebral artery, followed by reperfusion. The perfusion shift analysis, comparing post- versus pre-treatment voxel-level changes in time-to-peak perfusion maps, showed a significant increase in cerebral perfusion in the HDT15 group (common odds ratio 1.50; 95% CI 1.41-1.60; p < 0.0001), but not in the flat group (common odds ratio 0.97; 95% CI 0.92-1.03; p = 0.3503). Infarct growth at 24 hours was + 31.4% in the flat group (343 versus 250 mm3; 95% CI 2.4 to 165.1; p = 0.0447) and + 15.4% in the HDT15 group (224 versus 192 mm3; 95% CI -26.9 to 85.9; p = 0.2272). Our findings indicate that HDT15 acutely increases cerebral perfusion in LVO acute ischemic stroke and provides a tissue-saving effect before recanalization. Further research is needed to develop HDT15 as an emergency therapy to acutely increase collateral flow in ischemic stroke prior to recanalization therapy.

neuroscience↗

bioRxiv2023

PREMISE: A database of 20 Macaca Fascicularis PET/MRI brain imaging available for research

Non-human primate (NHP) studies are unique in translational research, especially in neurosciences and neuroimaging approaches are a preferred method for scaling cross-species comparative neurosciences. In this regard, neuroimaging database development and sharing are encouraged to increase the number of subjects available to the community while limiting the number of animals used in research. We present here a simultaneous PET/MR dataset of 20 Macaca Fascicularis structured according to the Brain Imaging Data Structure (BIDS) standards. This database contains multiple MRI sequences (anatomical, diffusion and perfusion imaging notably), as well as PET perfusion and inflammation using respectively [15O]H2O and [11C]PK11195 radiotracers. We describe the pipeline method to assemble baseline data from various cohorts and qualitatively assessed all the data using signal-to-noise and contrast-to-noise ratios as well as the median of intensity. The database is stored and available through the PRIME-DE consortium repository.

scientific communication and education↗

bioRxiv2019

Comparison of classification methods for tissue outcome after ischemic stroke

In acute ischemic stroke, identifying brain tissue at high risk of infarction is important for clinical decision-making. This tissue may be identified with suitable classification methods from magnetic resonance imaging (MRI) data. The aim of the present study was to assess comparatively the performance of five popular classification methods (Adaptive Boosting (ADA), Logistic Regression (LR), Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machine (SVM)) in identifying tissue at high risk of infarction on human voxel-based brain imaging data. The classification methods were used with eight MRI parameters including diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) obtained in 55 patients. Sensitivity, specificity, the area under the receiver operating curve (ROC) as well as the area under the precision-recall curve criteria were used to compare the method performances. The methods performed equally in terms of sensitivity and specificity while the results of the area under the ROC were significantly better for ADA, LR, ANN and RF. However, there was no statistically significant difference between the performances of these five classification methods regarding the area under the precision-recall curve, which was the main comparison metric.

neuroscience↗