Spontaneously hypertensive rats manifest deficits in emotional response to 22-kHz and 50-kHz ultrasonic playback

Many symptoms used routinely for human psychiatric diagnosis cannot be directly observed in animals which cannot describe their internal states. However, the ultrasonic vocalizations (USV) rodents use to communicate their emotional states can be measured. USV have therefore become a particularly useful tool in brain disease models. Spontaneously hypertensive rats (SHR) are considered an animal model of attention deficit hyperactivity disorder (ADHD) and schizophrenia. However, the specifics of SHR's behavior have not been fully described and there is very little data on their USV. Recently, we developed a communication model, in which Wistar rats are exposed to pre-recorded playbacks of aversive (22-kHz) or appetitive (50-kHz) USV, and their vocal responses depend on the extent of prior fear conditioning (0, 1, 6 or 10 shocks). Here, we investigated SHR's behavior and heart rate (HR) in our communication model, in comparison to Wistar rats employed as controls. In general, SHR emitted typical USV categories, however, they contained more short 22-kHz and less 50-kHz USV overall. Moreover, fewer SHR, in comparison with Wistar rats, emitted long 22-kHz USV after fear conditioning. SHR did not show a 50-kHz playback-induced HR increase, while they showed a profound 22-kHz playback-induced HR decrease. Finally, the number of previously delivered conditioning shocks appeared to have no effect on the investigated vocal, locomotor and HR responses of SHR. The phenomena observed in SHR are potentially attributable to deficits in emotional perception and processing. A lower number of 50-kHz USV emitted by SHR may reflect observations of speech impairments in human patients and further supports the usefulness of SHR to model ADHD and schizophrenia.

Sensory and Motor Conduction Velocity in Spontaneously Hypertensive Rats: Sex and Aging Investigation

The literature is extensive on how hypertension affects the morphology and function of the central nervous system (CNS) and is being focused on multiple organ damage involving the kidneys, heart, endothelium and retina. Hypertension damage to the peripheral nervous system is less explored in the literature. We have previously shown morphometric alterations in large and small caliber myelinated fibers of nerves in the adult spontaneously hypertensive rat (SHR). However, the functional correlation of these findings has not been explored. We performed an electrophysiological investigation of hind limb nerves in SHR of both genders in different ages. Normotensive Wistar-Kyoto (WKY) rats were used as controls. Electrophysiological recordings and determination of motor (MCV) and sensory (SCV) nerve conduction velocity were performed in the same animals at four different ages: 5, 8, 20 and 40 weeks after birth. Comparisons were made between ages, genders and animal strain. We showed a continuous body weight increase in adult life in all animals studied. MCV got stable at 20-week old hypertensive animals and continued to increase in normotensive ones. The SCV was constant between the ages of 20 and 40 weeks old in female SHR and decreased in male SHR while it continued to increase in WKY animals. The electrophysiological investigation of the nerves in WKY and SHR from both genders and different ages, associated with morphological and morphometric data from the literature suggest that hypertension affects the nerve function and might corroborate the development of a peripheral neuropathy.

Identification and segmentation of myelinated nerve fibers in a cross-sectional optical microscopic image using a deep learning model

BACKGROUND

The morphometric analysis of myelinated nerve fibers of peripheral nerves in cross-sectional optical microscopic images is valuable. Several automated methods for nerve fiber identification and segmentation have been reported. This paper presents a new method that uses a deep learning model of a convolutional neural network (CNN). We tested it for human sural nerve biopsy images.

METHODS

The method comprises four steps: normalization, clustering segmentation, myelinated nerve fiber identification, and clump splitting. A normalized sample image was separated into individual objects with clustering segmentation. Each object was applied to a CNN deep learning model that labeled myelinated nerve fibers as positive and other structures as negative. Only positives proceeded to the next step. For pretraining the model, 70,000 positive and negative data each from 39 samples were used. The accuracy of the proposed algorithm was evaluated using 10 samples that were not part of the training set. A P-value of <0.05 was considered statistically significant.

RESULTS

The total true-positive rate (TPR) for the detection of myelinated fibers was 0.982, and the total false-positive rate was 0.016. The defined total area similarity (AS) and area overlap error of segmented myelin sheaths were 0.967 and 0.068, respectively. In all but one sample, there were no significant differences in estimated morphometric parameters obtained from our method and manual segmentation.

COMPARISON WITH EXISTING METHODS

The TPR and AS were higher than those obtained using previous methods.

CONCLUSIONS

High-performance automated identification and segmentation of myelinated nerve fibers were achieved using a deep learning model.