Automatic detection and removal of fast phases from nystagmographic recordings by optimal thresholding

Hybrid clustering system using Nystagmus parameters discrimination for vestibular disorder diagnosis

BACKGROUD AND OBJECTIVE

The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition.

METHODS

In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 normal subjects.

RESULTS

The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy.

CONCLUSIONS

The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers.

The mammalian efferent vestibular system plays a crucial role in the high-frequency response and short-term adaptation of the vestibuloocular reflex

Although anatomically well described, the functional role of the mammalian efferent vestibular system (EVS) remains unclear. Unlike in fish and reptiles, the mammalian EVS does not seem to play a role in modulation of primary afferent activity in anticipation of active head movements. However, it could play a role in modulating long-term mechanisms requiring plasticity such as vestibular adaptation. We measured the efficacy of vestibuloocular reflex (VOR) adaptation in α9-knockout mice. These mice carry a missense mutation of the gene encoding the α9 nicotinic acetylcholine receptor (nAChR) subunit. The α9 nAChR subunit is expressed in the vestibular and auditory periphery, and its loss of function could compromise peripheral input from the predominantly cholinergic EVS. We measured the VOR gain (eye velocity/head velocity) in 26 α9-knockout mice and 27 cba129 control mice. Mice were randomly assigned to one of three groups: gain-increase adaptation (1.5×), gain-decrease adaptation (0.5×), or no adaptation (baseline, 1×). After adaptation training (horizontal rotations at 0.5 Hz with peak velocity 20°/s), we measured the sinusoidal (0.2-10 Hz, 20-100°/s) and transient (1,500-6,000°/s(2)) VOR in complete darkness. α9-Knockout mice had significantly lower baseline gains compared with control mice. This difference increased with stimulus frequency (∼ 5% <1 Hz to ∼ 25% >1 Hz). Moreover, vestibular adaptation (difference in VOR gain of gain-increase and gain-decrease adaptation groups as % of gain increase) was significantly reduced in α9-knockout mice (17%) compared with control mice (53%), a reduction of ∼ 70%. Our results show that the loss of α9 nAChRs moderately affects the VOR but severely affects VOR adaptation, suggesting that the EVS plays a crucial role in vestibular plasticity.

Automatic Classification of the Vestibulo-Ocular Reflex Nystagmus: Integration of Data Clustering and System Identification

The vestibulo-ocular reflex (VOR) plays an important role in our daily activities by enabling us to fixate on objects during head movements. Modeling and identification of the VOR improves our insight into the system behavior and improves diagnosis of various disorders. However, the switching nature of eye movements (nystagmus), including the VOR, makes dynamic analysis challenging. The first step in such analysis is to segment data into its subsystem responses (here slow and fast segment intervals). Misclassification of segments results in biased analysis of the system of interest. Here, we develop a novel three-step algorithm to classify the VOR data into slow and fast intervals automatically. The proposed algorithm is initialized using a K-means clustering method. The initial classification is then refined using system identification approaches and prediction error statistics. The performance of the algorithm is evaluated on simulated and experimental data. It is shown that the new algorithm performance is much improved over the previous methods, in terms of higher specificity.

Glycine receptor deficiency and its effect on the horizontal vestibulo-ocular reflex: a study on the SPD1J mouse

Inhibition is critical in the pathways controlling the vestibulo-ocular reflex (VOR) and plays a central role in the precision, accuracy and speed of this important vestibular-mediated compensatory eye movement. While γ-aminobutyric acid is the common fast inhibitory neurotransmitter in most of the VOR microcircuits, glycine is also found in key elements. For example, the omnidirectional pause neurons (OPNs) and inhibitory burst neurons in the horizontal VOR both use glycine as their preferred inhibitory neurotransmitter. Determining the precise contribution of glycine to the VOR pathway has been difficult due to the lack of selective tools; however, we used spasmodic mice that have a naturally occurring defect in the glycine receptor (GlyR) that reduces glycinergic transmission. Using this animal model, we compared the horizontal VOR in affected animals with unaffected controls. Our data showed that initial latency and initial peak velocity as well as slow-phase eye movements were unaffected by reduced glycinergic transmission. Importantly however, there were significant effects on quick-phase activity, substantially reducing their number (30-70 %), amplitude (~55 %) and peak velocity (~38 %). We suggest that the OPNs were primarily responsible for the reduced quick-phase properties, since they are part of an unmodifiable, or more 'hard-wired', microcircuit. In contrast, the effects of reduced glycinergic transmission on slow-phases were likely ameliorated by the intrinsically modifiable nature of this pathway. Our results also suggested there is a 'threshold' in GlyR-affected animals, below which the effects of reduced glycinergic transmission were undetected.

Automatic classification and robust identification of vestibulo-ocular reflex responses: from theory to practice: introducing GNL-HybELS

The Vestibulo-Ocular Reflex (VOR) stabilizes images of the world on our retinae when our head moves. Basic daily activities are thus impaired if this reflex malfunctions. During the past few decades, scientists have modeled and identified this system mathematically to diagnose and treat VOR deficits. However, traditional methods do not analyze VOR data comprehensively because they disregard the switching nature of nystagmus; this can bias estimates of VOR dynamics. Here we propose, for the first time, an automated tool to analyze entire VOR responses (slow and fast phases), without a priori classification of nystagmus segments. We have developed GNL-HybELS (Generalized NonLinear Hybrid Extended Least Squares), an algorithmic tool to simultaneously classify and identify the responses of a multi-mode nonlinear system with delay, such as the horizontal VOR and its alternating slow and fast phases. This algorithm combines the procedures of Generalized Principle Component Analysis (GPCA) for classification, and Hybrid Extended Least Squares (HybELS) for identification, by minimizing a cost function in an optimization framework. It is validated here on clean and noisy VOR simulations and then applied to clinical VOR tests on controls and patients. Prediction errors were less than 1 deg for simulations and ranged from .69 deg to 2.1 deg for the clinical data. Nonlinearities, asymmetries, and dynamic parameters were detected in normal and patient data, in both fast and slow phases of the response. This objective approach to VOR analysis now allows the design of more complex protocols for the testing of oculomotor and other hybrid systems.