Microdosing ketamine in Drosophila does not block serotonin reuptake, but causes complex behavioral changes mediated by glutamate and serotonin receptors

Microdosing ketamine is a novel antidepressant for treatment-resistant depression. Traditional antidepressants, like selective serotonin reuptake inhibitors (SSRIs), inhibit serotonin reuptake, but it is not clear if ketamine shows a similar mechanism. Here, we tested the effects of feeding ketamine and SSRIs to Drosophila melanogaster larvae, which has a similar serotonin system to mammals and is a good model to track depressive behaviors, such as locomotion and feeding. Fast-scan cyclic voltammetry (FSCV) was used to measure optogenetically stimulated serotonin changes, and locomotion tracking software and blue dye feeding to monitor behavior. We fed larvae various doses (1-100 mM) of antidepressants for 24 h and found that 1 mM ketamine did not affect serotonin, but increased locomotion and feeding. Low doses (≤10 mM) of escitalopram and fluoxetine inhibited dSERT and also increased feeding and locomotion behaviors. At 100 mM, ketamine inhibited dSERT and increased serotonin concentrations, but decreased locomotion and feeding because of its anesthetic properties. Since microdosing ketamine causes behavioral effects, we further investigated behavioral changes with a SERT16 mutant and low doses of other NMDA receptor antagonists and 5-HT1A and 2 agonists. Feeding and locomotion changes were similar to ketamine in the mutant, and we found NMDA receptor antagonism increased feeding, while serotonin receptor agonism increased locomotion, which could explain these effects with ketamine. Ultimately, this work shows that Drosophila is a good model to discern antidepressant mechanisms, and that ketamine does not work on dSERT like SSRIs, but effects behavior with other mechanisms that should be investigated further.

Microdosing ketamine in Drosophila does not inhibit SERT like SSRIs, but causes behavioral changes mediated by glutamate and serotonin receptors

Recently, the FDA approved microdosing ketamine for treatment resistant depression. Traditional antidepressants, like serotonin selective reuptake inhibitors (SSRIs), block serotonin reuptake, but it is not clear if ketamine blocks serotonin reuptake. Here, we tested the effects of feeding ketamine and SSRIs to Drosophila melanogaster larvae, which has a similar serotonin system to mammals, and is a good model to track depression behaviors, such as locomotion and feeding. Fast-scan cyclic voltammetry (FSCV) was used to measure optogenetically-stimulated serotonin changes, and locomotion tracking software and blue dye feeding to monitor behavior. We fed larvae various doses (1-100 mM) of antidepressants for 24 hours and found that 1 mM ketamine did not affect serotonin, but increased locomotion and feeding. Low doses (≤ 10 mM) of escitalopram and fluoxetine inhibited dSERT and also increased feeding and locomotion behaviors. At 100 mM, ketamine inhibited dSERT and increased serotonin concentrations, but decreased locomotion and feeding due to its anesthetic properties. Since microdosing ketamine causes behavioral effects, we also investigated behavior changes with low doses of other NMDA receptor antagonists and 5-HT1A and 2 agonists, which are other possible sites for ketamine action. NMDA receptor antagonism increased feeding, while serotonin receptor agonism increased locomotion, which could explain these effects with ketamine. Ultimately, this work shows that Drosophila is a good model to discern antidepressant mechanisms, and that ketamine does not work on dSERT like SSRIs at microdoses, but affects behavior with other mechanisms.

0584 Detecting Sleep Disordered Breathing Using Sub-Terahertz Radio-Frequency Micro-Radar

Introduction

New sensor technologies are entering sleep testing at a rapid pace; Neteera™ developed a novel sensor and algorithm for sleep apnea detection utilizing a contact-free, radar-based sensor system. The system utilizes a high-frequency, low-power, directional micro-radar which operates at ~120GHz and a sampling rate of 2500Hz as well as algorithms which are able to detect both pulse and respiratory activity of subjects during sleep.

Methods

Adult subjects undergoing diagnostic PSG for clinical purposes were simultaneously assessed with the novel micro-radar system with sensors under the mattress. Disordered breathing events (DBEs) were scored from the PSG using AASM scoring guidelines and were compared with those detected by the micro-radar sensor. Test data were grouped into three sets: 1. Single under mattress sensor; 2. Two under mattress sensors on each side of the bed (to improve signal capture); 3. After software optimization. The micro-radar sensor detected DBEs but software to describe the type of DBEs (obstructive apnea/central apnea/hypopnea) is still under development. Detection rate of DBEs was compared between the two methodologies and the development sets.

Results

n=22 (12 F, 10 M), Age=50.8±12.4 years, BMI=35.32±7.37 kg/m2. Diagnostic PSG AHI: 19.7±29.4/hr, T90=15.8±25.7%. Percent DBEs missed by the micro-radar sensor: 1st set=14.6±10.6%; 2nd set=9.4±8.3%; 3rd set=1.2±2.6%. Number of DBEs assessed for each set was 646, 1144, 125 events, respectively. With each successive set, the detection rate improved.

Conclusion

A novel micro-radar, non-contact sensor technology can be used to detect DBEs during sleep. Detection rate improved with utilization of two sensors per bed and software optimization. Future software development is expected to improve detection rate and facilitate breathing event classification into obstructive apneas/central apneas/hypopneas.

Support

None.

PEAR: PEriodic and ApeRiodic signal separation for fast FMRI

Undersampling of functional MRI (fMRI) data leads to increased temporal resolution, as it allows shorter acquisition time per frame. High quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the fMRI data. Recent publications suggest that the fMRI signal is a superposition of periodic and aperiodic signals. In this paper we develop an fMRI reconstruction approach based on this modeling. The fMRI data is assumed to be composed of two components: a component that holds a sum of periodic signals which is sparse in the temporal Fourier domain and an component that holds the remaining imaging information (consisting of the background and aperiodic signals) which has low rank. Data reconstruction is done by solving a constrained problem that enforces a fixed, moderate rank on one of the components, and a limited number of temporal frequencies on the other. Our approach is coined PEAR - PEriodic and ApeRiodic signal separation for fast fMRI. Experimental results are based on fMRI reconstruction using realistic timecourses. Evaluation was performed both quantitatively and visually versus ground truth. Results demonstrate PEAR's improvement in estimating the realistic timecourses versus state-of-the-art approaches at acceleration ratio of R=16.6.

Fast reference based MRI

In many clinical MRI scenarios, existing imaging information can be used to significantly shorten acquisition time or improve Signal to Noise Ratio (SNR). In those cases, a previously acquired image can serve as a reference image, that may exhibit similarity in some sense to the image being acquired. Examples include similarity between adjacent slices in high resolution MRI, similarity between various contrasts in the same scans and similarity between different scans of the same patients. In this paper we present a general framework for utilizing reference images for fast MRI. We take into account that the reference image may exhibit low similarity with the acquired image and develop a hybrid adaptive-weighted approach for sampling and reconstruction. Experiments demonstrate the performance of the method in three different clinical MRI scenarios: SNR improvement in high resolution brain MRI, utilizing similarity between T2-weighted and fluid-attenuated inversion recovery (FLAIR) for fast FLAIR scanning and utilizing similarity between baseline and follow-up scans for fast follow-up scanning.

Tumor burden evaluation in NF1 patients with plexiform neurofibromas in daily clinical practice

BACKGROUND

Existing volumetric measurements of plexiform neurofibromas (PNs) are time consuming and error prone, as they require delineation of PN boundaries, a procedure that is not practical in the typical clinical setting. The aim of this study is to assess the Plexiform Neurofibroma Instant Segmentation Tool (PNist), a novel semi-automated segmentation program that we developed for PN delineation in a clinical context. PNist was designed to greatly simplify volumetric assessment of PNs through use of an intuitive user interface while providing objectively consistent results with minimal interobserver and intraobserver variabilities in reasonable time.

MATERIALS AND METHODS

PNs were measured in 30 magnetic resonance imaging (MRI) scans from 12 patients with neurofibromatosis 1. Volumetric measurements were performed using PNist and compared to a standard semi-automated volumetric method (Analyze 9.0).

RESULTS

High correlation was detected between PNist and the semi-automated method (R(2) = 0.996), with a mean volume overlap error of 9.54 % and low intraobserver and interobserver variabilities. The segmentation time required for PNist was 60 % of the time required for Analyze 9.0 (360 versus 900 s, respectively). PNist was also reliable when assessing changes in tumor size over time, compared to the existing commercial method.

CONCLUSIONS

Our study suggests that the new PNist method is accurate, intuitive, and less time consuming for PN segmentation compared to existing commercial volumetric methods. The workflow is simple and user-friendly, making it an important clinical tool to be used by radiologists, neurologists and neurosurgeons on a daily basis, helping them deal with the complex task of evaluating PN burden and progression.

Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI

This paper presents an automatic method for the segmentation, internal classification and follow-up of optic pathway gliomas (OPGs) from multi-sequence MRI datasets. Our method starts with the automatic localization of the OPG and its core with an anatomical atlas followed by a binary voxel classification with a probabilistic tissue model whose parameters are estimated from the MR images. The method effectively incorporates prior location, tissue characteristics, and intensity information for the delineation of the OPG boundaries in a consistent and repeatable manner. Internal classification of the segmented OPG volume is then obtained with a robust method that overcomes grey-level differences between learning and testing datasets. Experimental results on 25 datasets yield a mean surface distance error of 0.73 mm as compared to manual segmentation by experienced radiologists. Our method exhibits reliable performance in OPG growth follow-up MR studies, which are crucial for monitoring disease progression. To the best of our knowledge, this is the first method that addresses automatic segmentation, internal classification, and follow-up of OPG.

Acoustic monitoring of lung sounds for the detection of one-lung intubation

INTRODUCTION

Monitoring methods for the early diagnosis of one-lung intubation (OLI) are nonspecific and controversial. In this study, we evaluated a new acoustic monitoring system for the detection of OLI.

METHODS

Lung sounds were collected from 24 adult surgical patients scheduled for routine surgical procedures. Four piezoelectric microphones attached to the patients' backs were used to sample lung sounds during induction of anesthesia and endotracheal tube positioning. To achieve OLI, the endotracheal tube was inserted and advanced down the airway so that diminished or no breath sounds were heard on the left side of the chest. The tube was then withdrawn stepwise until equal breath sounds were heard. Fiberoptic bronchoscopy confirmed the tube's final position. Acoustic analyses were preformed by a new algorithm which assumes a Multiple Input Multiple Output system, in which a multidimensional Auto-Regressive model relates the input (lungs) and the output (recorded sounds) and a classifier, based on a Generalized Likelihood Ratio Test, indicates the number of ventilated lungs without reconstructing the original lung sounds from the recorded samples.

RESULTS

This algorithm achieved an OLI detection probability of 95.2% with a false alarm probability of 4.8%.

CONCLUSION

Higher detection values can be achieved at the price of a higher incidence of false alarms.

Detection of one lung intubation by monitoring lungs sounds

Analysis of lungs sounds for monitoring and diagnosis of pulmonary function is well known. One of the applications of this method is detection of one lung intubation (OLI) during anesthesia or intensive care. An algorithm for detection the one-lung ventilation situation from the lungs sounds is presented. The algorithm assumes a MIMO (Multiple Input Multiple Output) system, in which a multidimensional AR (Auto-Regressive) model relates the input (lungs) and the output (recorded sounds). The unknown AR parameters are estimated, and a detector based on the estimated eigenvalues of the source covariance matrix is developed, in order to detect one lung ventilation situation. Testing the algorithm on real breathing sounds, which were recorded in a surgery room, shows more than 90% accuracy in OLI detection.