Moradi, F. | bioRxiv Science

bioRxiv2022

Early Electrophysiological Aberrations in the Hippocampus of the TgF344-AD Rat Model as a Potential Biomarker for Alzheimer's Disease Prognosis

The hippocampus is thought to guide navigation and has an essential contribution to learning and memory. Hippocampus is one of the brain regions impaired in Alzheimers disease (AD), a neurodegenerative disease with progressive memory impairments and cognitive decline. Although successful treatments for AD are still not available, developing new strategies to detect AD at early stages before clinical manifestation is crucial for timely interventions. Here, we investigated in the TgF344-AD rat model the classification of AD-transgenic rats versus Wild-type littermates (WT) from electrophysiological activity recorded in the hippocampus of freely moving subjects at an early, pre-symptomatic stage of the disease (6 months old). To this end, recorded signals were filtered in two separate frequency regimes namely low frequency LFP signals and high frequency spiking activity and passed to machine learning (ML) classifiers to identify the genotype of the rats (TG vs. WT). For the low frequency analysis, we first filtered the signals and extracted the power spectra in different frequency bands known to carry differential information in the hippocampus (delta, theta, slow- and fast-gamma) while for the high frequency analysis, we extracted spike-trains of neurons and calculated different distance metrics between them, including Van Rossum (VR), Inter Spike Interval (ISI), and Event Synchronization (ES). These measures were then used as features for classification with different ML classifiers. We found that both low and high frequency signals were able to classify the rat genotype with a high accuracy with specific signals such as the gamma band power, providing an important fraction of information. In addition, when we combined information from both low and high frequency the classification was boosted indicating that independent information is present across the two bands. The results of this study offer a better insight into how different regions of the hippocampus are affected in earlier stages of AD.

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↗