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.

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.

The problem of unrecognized acromegaly: surgeries patients undergo prior to diagnosis of acromegaly

PURPOSE

To reveal the variety of symptoms experienced by patients before acromegaly diagnosis and to emphasize unneeded surgeries that patients undergo related to acromegaly prior to diagnosis of the disease.

METHODS

In total, 490 consecutive adult patients with acromegaly who were treated at our institution between 1998 and 2014 were included in this cross-sectional study, of which 313 could be contacted. Participants were questioned about their complaints at initial consultation and at the time of diagnosis, the first medical professional who they consulted, interval between onset and diagnosis, and surgeries they had undergone.

RESULTS

This study included 313 participants, of whom 181 were women. The mean age was 48.8 ± 12.0 years. Patients most frequently presented with acral growth of hands and feet (32.6%) and headache (26.2%). Internists were the medical specialists who were most frequently first consulted (29.4%) then neurosurgeons (11.8%). Acromegaly was generally diagnosed by endocrinologists (55%), followed by neurosurgeons (23%). The median elapsed period prior to diagnosis was 24 months, interquartile range 6.0-48.0 months. Some 45.7% had undergone surgery; 35.2% were related to acromegaly symptoms: head and throat surgery (12.8%), nose surgery (9.3%), thyroidectomy (6.4%), carpal tunnel surgery (4.8%). The delay period for patients who had an operation prior to acromegaly diagnosis was significantly longer than for those who had no operations (p < 0.001).

CONCLUSIONS

Acromegaly patients mostly present to internal medicine professionals. Surgeries related to acromegaly complications and symptoms before diagnosis cause a long delay period before diagnosis. Medical staff must be more aware of the clinical aspects of acromegaly.

A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals

In order to predict epileptic seizures many precursory features, extracted from the EEG signals, have been introduced. Before checking out the performance of features in detection of pre-seizure state, it is required to see whether these features are accurately extracted. Evaluation of feature estimation methods has been less considered, mainly due to the lack of a ground truth for the real EEG signals' features. In this paper, some simulated long-term depth-EEG signals, with known state spaces, are generated via a realistic neural mass model with physiological parameters. Thanks to the known ground truth of these synthetic signals, they are suitable for evaluating different algorithms used to extract the features. It is shown that conventional methods of estimating correlation dimension, the largest Lyapunov exponent, and phase coherence have non-negligible errors. Then, a parameter identification-based method is introduced for estimating the features, which leads to better estimation results for synthetic signals. It is shown that the neural mass model is able to reproduce real depth-EEG signals accurately; thus, assuming this model underlying real depth-EEG signals, can improve the accuracy of features' estimation.

Proposing a two-level stochastic model for epileptic seizure genesis

By assuming the brain as a multi-stable system, different scenarios have been introduced for transition from normal to epileptic state. But, the path through which this transition occurs is under debate. In this paper a stochastic model for seizure genesis is presented that is consistent with all scenarios: a two-level spontaneous seizure generation model is proposed in which, in its first level the behavior of physiological parameters is modeled with a stochastic process. The focus is on some physiological parameters that are essential in simulating different activities of ElectroEncephaloGram (EEG), i.e., excitatory and inhibitory synaptic gains of neuronal populations. There are many depth-EEG models in which excitatory and inhibitory synaptic gains are the adjustable parameters. Using one of these models at the second level, our proposed seizure generator is complete. The suggested stochastic model of first level is a hidden Markov process whose transition matrices are obtained through analyzing the real parameter sequences of a seizure onset area. These real parameter sequences are estimated from real depth-EEG signals via applying a parameter identification algorithm. In this paper both short-term and long-term validations of the proposed model are done. The long-term synthetic depth-EEG signals simulated by this model can be taken as a suitable tool for comparing different seizure prediction algorithms.

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.

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.

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.

Estimating the mutual information between bilateral breast in thermograms using nonparametric windows

Comparison between contra lateral breast images is one of the effective methods in breast cancer detection. Asymmetric temperature distribution can be an indicator of abnormality. The mutual information is a good measure of nonlinear correlation. It is a measure that captures linear and nonlinear dependencies, without requiring the specification of any kind of model of dependence. Therefore, it is suitable for our abnormality indicator. Although nonparametric windows is a numerically expensive technique but it is accurate. The reason is that nonparametric windows incorporate an interpolation model which enhances the resolution to a highly oversampled image. For our purposes we worked with sixty simulated breast thermal images. It is shown that the more similar the thermal image of right breast to the thermal image of left breast, the closer the normalized mutual information value to one.

Aerobic treatment of landfill leachate using a submerged membrane bioreactor--prospects for on-site use

Submerged membrane bioreactor (MBR) technology for aerobic treatment of landfill leachate was studied in laboratory scale to evaluate its potential for on-site use. Three combinations of solid retention time (SRT) - hydraulic retention time (HRT) of 60 days - 3.5 days, 60 days - 2 days and 30 days - 1 day, were examined to evaluate reactor performance under varying loading and biomass conditions. Chemical Oxygen Demand (COD) removal ranged from 54 to 78%, depending on the influent leachate source and loading conditions. The MBR showed excellent Biological Oxygen Demand (BOD5) removal of 97% and higher, even at HRT as low as 1 day. Complete suspended solids retention and full nitrification of the incoming ammonia was observed despite highly variable loading. Significant removal of iron, lead, manganese, cadmium and aluminum was observed. No significant changes in the removal efficiency of metals, ammonia, and BOD5 were observed at different SRT-HRT. Toxicity removal decreased with increasing HRT. The produced effluent met current water quality guidelines for discharge into natural streams in Manitoba.

Weight control and calorie expenditure: thermogenic effects of pre-prandial and post-prandial exercise

The thermogenic effects of pre- and postprandial exercise was examined in seven lean active females. Energy expenditure was measured for 3 h via open circuit indirect calorimetry after four separate treatments: Exercise Only (25 min treadmill run at 60% VO2 max), Meal Only (910 kcal mixed meal), Exercise-Meal and Meal-Exercise. The thermogenic response to the Exercise-Meal treatment was similar to the Meal Only treatment. However, the Meal-Exercise treatment resulted in a greater energy expenditure than the Meal Only and Exercise-Meal treatments. The Exercise Only treatment showed the lowest thermogenic response. These data suggest that exercise following a meal would be more beneficial than exercise before a meal in increasing and maintaining an elevated energy expenditure.

INTRATHECAL ADMINISTRATION OF HYPERBARIC MORPHINE FOR THE RELIEF OF PAIN IN LABOUR

Thirty healthy women in active labour received an intrathecal injection of morphine 0.5 mg (n = 12) or 1 mg (n = 18) in 7.5% dextrose. Both doses provided excellent analgesia for labour, 93% of patients obtaining at least 50% pain relief. Analgesia began 15-60 min after injection and did not decrease until 6-8 h after injection. Analgesia was satisfactory until distension of the perineum, either by forceps or the infant's head. The intrathecal injection of morphine did not adversely affect the condition of the infant. Eighty per cent of patients developed pruritus; 53%, nausea or vomiting, or both; 43%, urinary retention; and 43%, drowsiness. These side effects were decreased by naloxone, which did not affect the degree of analgesia. There was no significant depression of ventilation in any patient. These results suggest that morphine 0.5 mg or 1 mg, administered intrathecally, effectively decreases the pain of labour, and that i.v. administration of naloxone can alleviate the common side effects.

Effects of inhaled ammonium sulfate on benzo[a]pyrene carcinogenesis

The effect of inhaled ammonium sulfate on benzo[a]pyrene carcinogenesis in the lungs of Syrian golden hamsters was studied. Exposure to ammonium sulfate at an airborne concentration 20 times average United States ambient levels resulted in a significant depression (p less than 0.05) of benzo[a]pyrene carcinogenesis in the first 6 mo of the study. However, at 2 yr, the termination of the study, there were no differences in cancer incidence between groups receiving benzo[a]pyrene and benzo[a]pyrene plus ammonium sulfate. In addition, at the concentration studied, inhaled ammonium sulfate did not significantly increase the incidence or severity of pneumonitis or pulmonary fibrosis in the hamster. However, this inhalation did increase the incidence of emphysema but not the severity. The decreased incidence of cancer during the first 6 mo of this study in animals receiving both benzo[a]pyrene and ammonium sulfate suggests that interaction between sulfate and benzo[a]pyrene does occur, but is insufficient to afford long-term protection against the development of cancer. No enhancement of carcinogenesis by benzo[a]pyrene occurs in the presence of inhaled sulfate.

Fetal and maternal cardiovascular effects of atropine and glycopyrrolate

This study compared the effects of intravenous atropine and glycopyrrolate on maternal and fetal heart rates and variability, maternal blood pressure, and uterine activity in 20 normal full-term parturients in labor. Group 1 (N = 10) received 0.005 mg/kg of glycopyrrolate, and group 2 (N = 10) received 0.01 mg/kg of atropine. There were no statistically significant changes in fetal heart rates or variability in either group. Uterine activity increased in a normal manner as labor progressed. Maternal heart rate increased significantly and essentially equally in all patients in both groups ranging from 15 to 78 beats/min and a mean (+/- SD) of 36 +/- 16 in group 1 and from 15 to 65 beats/min with a mean of 35 +/- 17 in group 2. There were no statistically significant changes in maternal blood pressure in either group.

Colorectal polyps and carcinoma in Southern Iran

A study was made of the prevalence of colorectal polyps (excluding juvenile polyps, familial polyposis coli and villous adenomas) and the minimum frequency of colorectal carcinoma in Southern Iran. A total of 801 large intestines from necropsies performed on individuals 20 years or older was examined with or without magnifying lens for presence of polyps. Our prevalence rate was then compared with those reported on postmortem material from the USA, Australia, South Africa (Bantus), Colombia, and Hawaii (Japanese immigrants). Our surgical pathology files were reviewed for colorectal polyps over a 22-year period (1952-1973). Colorectal carcinomas diagnosed in the Department of Pathology were reviewed over 11 years (1963-1973) and the minimum frequency rates compared with corresponding age- and sex-specific incidence rates from Connecticut, USA. The data indicate 1) a very low prevalence rate for colorectal polyps as well as carcinoma in our region and 2) a rather striking predominance of right-sided colonic carcinoma.