Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework.

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.

Physiological constraints of visual pathway lead to more efficient coding of information in retina.

Nowadays, numerous studies have investigated the modeling of efficient neural encoding processes in the retina of the eye to encode the sensory data. Retina, as the innermost coat of the eye, is the first and the most important area of the visual neural system of mammalians, which is responsible for neural processes. Retina encodes the information of light intensity into a sequence of spikes, and sends them to retinal ganglion cells (RGCs) for further processing. An appropriate retinal encoding model should be adapted to the real retina as much as possible by considering the physiological constraints of the visual pathway to transfer most of the information of the input signal to the brain without too much redundancy of the channel. In this paper, inspired from the existing linear models of retinal encoding process, which have employed input noise and the spatial locality of the RGCs receptive fields (RFs) in the calculation of the encoding matrix, two extra physiological constraints, adapted from the real retina are taken into account so as to achieve a more realistic model for themammalian retina. These new constraints that are the correlation between RGCs and the spatial locality of the photoreceptors' projective fields (PFs), are modeled in a mathematical form and analyzed in detail. To quantify fidelity of the proposed encoding matrix and prove its superiority over existing models, various parameters of the models are calculated and presented in this paper: mean square error between the original and reconstructed image (MSE), the redundancy of the channel, the amount of information transferred through the channel, and the amount of wasted capacity for carrying input noise, to name a few. The results of these calculations show that the proposed model transfers input information with less redundancy of the channel. In other words, it reduces a portion of channel capacity which is wasted for carrying the input noise in comparison to the existing models. Also, due to considering extra physiological constraints in the proposed model, it is acceptable to have a slightly higher amount of MSE value in order to become similar to the real retina.

Peptide functionalized polyhydroxyalkanoate nanofibrous scaffolds enhance Schwann cells activity.

Interactions between Schwann cells (SCs) and scaffolds are important for tissue development during nerve regeneration, because SCs physiologically assist in directing the growth of regenerating axons. In this study, we prepared electrospun scaffolds combining poly (3-hydroxybutyrate) (PHB) and poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) functionalized with either collagen I, H-Gly-Arg-Gly-Asp-Ser-OH (GRGDS), H-Tyr-Ile-Gly-Ser-Arg-NH2 (YIGSR), or H-Arg-Asn-Ile-Ala-Glu-Ile-Ile-Lys-Asp-Ile-OH (p20) neuromimetic peptides to mimic naturally occurring ECM motifs for nerve regeneration. Cells cultured on fibrous mats presenting these biomolecules showed a significant increase in metabolic activity and proliferation while exhibiting unidirectional orientation along the orientation of the fibers. Real-time PCR showed cells cultured on peptide-modified scaffolds had a significantly higher neurotrophin expression compared to those on untreated nanofibers. Our study suggests that biofunctionalized aligned PHB/PHBV nanofibrous scaffolds may elicit essential cues for SCs activity and could serve as a potential scaffold for nerve regeneration. From the clinical editor: Nanotechnology-based functionalized scaffolds represent one of the most promising approaches in peripheral nerve recovery, as well as spinal cord recovery. In this study, bio-functionalized and aligned PHB/PHBV nanofibrous scaffolds were found to elicit essential cues for Schwann cell activity, therefore could serve as a potential scaffold for nerve regeneration.

A Comparison Study of Point-Process Filter and Deep Learning Performance in Estimating Rat Position Using an Ensemble of Place Cells.

The emergence of deep learning techniques has provided new tools for the analysis of complex data in the field of neuroscience. In parallel, advanced statistical approaches like point-process modeling provide powerful tools for analyzing the spiking activity of neural populations. How statistical and machine learning techniques compare when applied to neural data remains largely unclear. In this research, we compare the performance of a point-process filter and a long short-term memory (LSTM) network in decoding the 2D movement trajectory of a rat using the neural activity recorded from an ensemble of hippocampal place cells. We compute the least absolute error (LAE), a measure of accuracy of prediction, and the coefficient of determination (R2), a measure of prediction consistency, to compare the performance of these two methods. We show that the LSTM and point-process filter provide comparable accuracy in predicting the position; however, the point-process provides further information about the prediction which is unavailable for LSTM. Though previous results report better performance using deep learning techniques, our results indicate that this is not universally the case. We also investigate how these techniques encode information carried by place cell activity and compare the computational efficiency of the two methods. While the point-process model is built using the receptive field for each place cell, we show that LSTM does not necessarily encode receptive fields, but instead decodes the movement trajectory using other features of neural activity. Although it is less robust, LSTM runs more than 7 times faster than the fastest point-process filter in this research, providing a strong advantage in computational efficiency. Together, these results suggest that the point-process filters and LSTM approaches each provide distinct advantages; the choice of model should be informed by the specific scientific question of interest.

Detection of Pulmonary Nodules in Low-dose Computed Tomography Using Localized Active Contours and Shape Features.

BACKGROUND: Pulmonary nodules are symptoms of lung cancer. The shape and size of these nodules are used to diagnose lung cancer in computed tomography (CT) images. In the early stages, nodules are very small, and radiologist has to refer to many CT images to diagnose the disease, causing operator mistakes. Image processing algorithms are used as an aid to detect and localize nodules. METHODS: In this paper, a novel lung nodules detection scheme is proposed. First, in the preprocessing stage, our algorithm segments two lung lobes to increase processing speed and accuracy. Second, template-matching is applied to detect the suspicious nodule candidates, including both nodules and some blood vessels. Third, the suspicious nodule candidates are segmented by localized active contours. Finally, the false-positive errors produced by vessels are reduced using some two-/three-dimensional geometrical features in three steps. In these steps, the size, long and short diameters and sphericity are used to decrease the false-positive rate. RESULTS: In the first step, some vessels that are parallel to CT cross-plane are identified. In the second step, oblique vessels are detected using shift of center of gravity in two successive slices. In step three, vessels vertical to CT cross-plane are identified. Using these steps, vessels are separated from nodules. Early Lung Cancer Action Project is used as a popular dataset in this work. CONCLUSIONS: Our algorithm achieved a sensitivity of 90.1% and a specificity of 92.8%, quite acceptable in comparison to other related works.

Nonrigid image registration in digital subtraction angiography using multilevel B-spline.

We address the problem of motion artifact reduction in digital subtraction angiography (DSA) using image registration techniques. Most of registration algorithms proposed for application in DSA, have been designed for peripheral and cerebral angiography images in which we mainly deal with global rigid motions. These algorithms did not yield good results when applied to coronary angiography images because of complex nonrigid motions that exist in this type of angiography images. Multiresolution and iterative algorithms are proposed to cope with this problem, but these algorithms are associated with high computational cost which makes them not acceptable for real-time clinical applications. In this paper we propose a nonrigid image registration algorithm for coronary angiography images that is significantly faster than multiresolution and iterative blocking methods and outperforms competing algorithms evaluated on the same data sets. This algorithm is based on a sparse set of matched feature point pairs and the elastic registration is performed by means of multilevel B-spline image warping. Experimental results with several clinical data sets demonstrate the effectiveness of our approach.

Computer aided measurement of sub-epithelial collagen band in colon biopsies for collagenous colitis diagnosis.

This paper presents a novel computer aided technique for screening of Collagenous Colitis (CC). CC is a type of microscopic colitis mostly characterized by chronic watery diarrhea which is a common feature with a range of other etiologies. Routine paraclinical tests from CC patients such as endoscopic and radiographic studies are usually normal, and diagnosis must be made by biopsy. The gold standard for a confirmative diagnosis of CC is to measure the thickness of the sub-epithelial collagen (SEC) in colon tissue samples. Visual inspection of microscopic samples is often time-consuming, cumbersome and subject to human errors. This fact demonstrates the necessity of developing an automated method which assists pathologists in evaluating histopathological samples more accurately in the busy clinical environment. To the best of our knowledge, this is the first time that a computer-assisted diagnosis algorithm has been applied to CC detection. The proposed method uses a pre-trained Multi-Layer Perceptron neural network to segment SEC band in colon tissue images. We compared a variety of different color and texture descriptors and explore the best set of features for this task. The investigation of the proposed method shows 94.5% specificity and 95.6% sensitivity rate.

Fabrication, characterization and cellular compatibility of poly(hydroxy alkanoate) composite nanofibrous scaffolds for nerve tissue engineering.

Tissue engineering techniques using a combination of polymeric scaffolds and cells represent a promising approach for nerve regeneration. We fabricated electrospun scaffolds by blending of Poly (3-hydroxybutyrate) (PHB) and Poly (3-hydroxy butyrate-co-3- hydroxyvalerate) (PHBV) in different compositions in order to investigate their potential for the regeneration of the myelinic membrane. The thermal properties of the nanofibrous blends was analyzed by differential scanning calorimetry (DSC), which indicated that the melting and glass temperatures, and crystallization degree of the blends decreased as the PHBV weight ratio increased. Raman spectroscopy also revealed that the full width at half height of the band centered at 1725 cm(-1) can be used to estimate the crystalline degree of the electrospun meshes. Random and aligned nanofibrous scaffolds were also fabricated by electrospinning of PHB and PHBV with or without type I collagen. The influence of blend composition, fiber alignment and collagen incorporation on Schwann cell (SCs) organization and function was investigated. SCs attached and proliferated over all scaffolds formulations up to 14 days. SCs grown on aligned PHB/PHBV/collagen fibers exhibited a bipolar morphology that oriented along the fiber direction, while SCs grown on the randomly oriented fibers had a multipolar morphology. Incorporation of collagen within nanofibers increased SCs proliferation on day 14, GDNF gene expression on day 7 and NGF secretion on day 6. The results of this study demonstrate that aligned PHB/PHBV electrospun nanofibers could find potential use as scaffolds for nerve tissue engineering applications and that the presence of type I collagen in the nanofibers improves cell differentiation.

Impulse noise cancellation of medical images using wavelet networks and median filters.

This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage. The second step is removing impulse noise with a median filter. The wavelet network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively, and at the same time preserves chromaticity and image details very well.

A Brief Survey of Computational Models of Normal and Epileptic EEG Signals: A Guideline to Model-based Seizure Prediction.

In recent decades, seizure prediction has caused a lot of research in both signal processing and the neuroscience field. The researches have tried to enhance the conventional seizure prediction algorithms such that the rate of the false alarms be appropriately small, so that seizures can be predicted according to clinical standards. To date, none of the proposed algorithms have been sufficiently adequate. In this article we show that in considering the mechanism of the generation of seizures, the prediction results may be improved. For this purpose, an algorithm based on the identification of the parameters of a physiological model of seizures is introduced. Some models of electroencephalographic (EEG) signals that can also be potentially considered as models of seizure and some developed seizure models are reviewed. As an example the model of depth-EEG signals, proposed by Wendling, is studied and is shown to be a suitable model.