A longitudinal study of pupillary light reflex in 6- to 24-month children.

Pupillary light reflex (PLR) is an involuntary response where the pupil size changes with luminance. Studies have shown that PLR response was altered in children with autism spectrum disorders (ASDs) and other neurological disorders. However, PLR in infants and toddlers is still understudied. We conducted a longitudinal study to investigate PLR in children of 6-24 months using a remote pupillography device. The participants are categorized into two groups. The 'high risk' (HR) group includes children with one or more siblings diagnosed with ASDs; whereas the 'low risk' (LR) group includes children without an ASD diagnosis in the family history. The participants' PLR was measured every six months until the age of 24 months. The results indicated a significant age effect in multiple PLR parameters including resting pupil radius, minimal pupil radius, relative constriction, latency, and response time. In addition, the HR group had a significantly larger resting and minimal pupil size than the LR group. The experimental data acquired in this study revealed not only general age-related PLR changes in infants and toddlers, but also different PLRs in children with a higher risk of ASD.

High resolution imaging of the fibrous microstructure in bovine common carotid artery using optical polarization tractography.

The biomechanical properties of artery are primarily determined by the fibrous structures in the vessel wall. Many vascular diseases are associated with alternations in the orientation and alignment of the fibrous structure in the arterial wall. Knowledge on the structural features of the artery wall is crucial to our understanding of the biology of vascular diseases and the development of novel therapies. Optical coherence tomography (OCT) and polarization-sensitive OCT have shown great promise in imaging blood vessels due to their high resolution, fast acquisition, good imaging depth, and large field of view. However, the feasibility of using OCT based methods for imaging fiber orientation and distribution in the arterial wall has not been investigated. Here we show that the optical polarization tractography (OPT), a technology developed from Jones matrix OCT, can reveal the fiber orientation and alignment in the bovine common carotid artery. The fiber orientation and alignment data obtained in OPT provided a robust contrast marker to clearly resolve the intima and media boundary of the carotid artery wall. Optical polarization tractography can visualize fiber orientation and alignment in carotid artery.

Optimizing dynamical similarity index extraction window for seizure detection.

This paper addresses an optimization problem in choosing optimum window length for feature extraction in automatic seizure detection. The processing window length plays an important role in reducing the false positive and false negative rates and decreasing required processing time for seizure detection. This study presents an approach for selecting the optimum window length toward the extraction of dynamical similarity index (DSI) feature. Then, the optimal window value in DSI extraction was used to detect seizure onset automatically. The algorithm was applied to electroencephalogram (EEG) signals from European Epilepsy Database. Although the main purpose of this study was not the seizure detection and mainly focuses on proposing an approach for finding an optimum window length for feature extraction towards the early seizure detection, the results showed that the proposed method achieves 83.99% of sensitivity in seizure detection. The low false positive rate per hour (FPR/h) was also significant due to continuous EEG analysis. The method showed fast computation speed which promises a potential for the real time applications. The proposed method for the window optimization in feature extraction of DSI can be implemented for other features to further improve the performance of seizure detection.

Seizure prediction using adaptive neuro-fuzzy inference system.

In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour.