Pourrezaei, K. | bioRxiv Science

bioRxiv2022

Near infrared spectroscopy detection of hemispheric cerebral ischemia following middle cerebral artery occlusion in rats

Timely and sensitive in vivo estimation of ischemic stroke-induced brain infarction are necessary to guide diagnosis and evaluation of treatments efficacy. The gold standard for estimation of the cerebral infarction volume is magnetic resonance imaging (MRI), which is expensive and not readily accessible. Measuring regional cerebral blood flow (rCBF) with Laser Doppler flowmetry (LDF) is the status quo for confirming reduced blood flow in experimental ischemic stroke models. However, rCBF reduction following cerebral artery occlusion often does not correlate with subsequent infarct volume. In the present study, we employed the continuous-wave near infrared spectroscopy (NIRS) technique to monitor cerebral oxygenation during 90 min of the intraluminal middle cerebral artery occlusion (MCAO) in Sprague-Dawley rats (n=8, male). The NIRS device consisted of a controller module and an optical sensor with two LED light sources and two photodiodes making up two parallel channels for monitoring left and right cerebral hemispheres. Optical intensity measurements were converted to deoxyhemoglobin (Hb) and oxyhemoglobin (HbO2) changes relative to a 2-min window prior to MCAO. Area under the curve (auc) for Hb and HbO2 was calculated for the 90-min occlusion period for each hemisphere (ipsilateral and contralateral). To obtain a measure of total ischemia, auc of the contralateral side was subtracted from the ipsilateral side resulting in {Delta}Hb and {Delta}HbO2 parameters. Infarct volume (IV) was calculated by triphenyl tetrazolium chloride (TTC) staining at 24h reperfusion. Results showed a significant negative correlation (r = -0.81, p = 0.03) between {Delta}Hb and infarct volume. In conclusion, our results show feasibility of using a noninvasive optical imaging instrument, namely NIRS, in monitoring cerebral ischemia in a rodent stroke model. This cost-effective, non-invasive technique may improve the rigor of experimental models of ischemic stroke by enabling in vivo longitudinal assessment of cerebral oxygenation and ischemic injury.

neuroscience↗

bioRxiv2021

Functional Near Infrared Spectroscopy (fNIRS) based Odor Detection and Classification using Functional Data Analysis

An objective approach for odor detection is to analyze the brain activity using imaging techniques during the odor stimulation. In this study, Functional Near Infrared Spectroscopy (fNIRS) is used to record hemodynamic response from the frontal region of the brain by using a 4-channel fNIRS system. The fNIRs data is collected during the odor detection task in which the subjects were asked to press a button when they detect the given odor. Functional Data Analysis (FDA) was applied on fNIRs data to convert discrete measured samples of data to continuous smooth curves. The FDA method enables us to use the bases coefficients of fNIRS smoothed curves for features that represent the shape of the raw fNIRS signal. With the learning algorithm that we proposed, these features were used to train the support vector machine classifier. We evaluated the odor detection problem, in two binary classification cases: odorant vs. non-odorant and odorant vs. fingertapping. The model achieved a classification accuracy of 94.12% and 97.06% over the stimulus condition in the two cases, respectively. Moreover to find the actual predictors we used the extracted defined features (slope, standard deviation, and delta) to train our classifier. We achieved an average accuracy of 91.18 % on classifying odorant vs. non-odorant and an accuracy of 94.12% for odorant vs. fingertapping on the stimulus condition. The results determined that fNIRs signals of odorant and non-odorant are distinguishable without being affected by the motor activity during the experiment. These findings suggest that fNIRs measurement on the forehead could be potentially used for objective and comparably inexpensive assessment of odor detection in cases that the subjective report is unreliable.

bioinformatics↗