A six stage approach for the diagnosis of the Alzheimer’s disease based on fMRI data

AbstractThe aim of this work is to present an automated method that assists in the diagnosis of Alzheimer’s disease and also supports the monitoring of the progression of the disease. The method is based on features extracted from the data acquired during an fMRI experiment. It consists of six stages: (a) preprocessing of fMRI data, (b) modeling of fMRI voxel time series using a Generalized Linear Model, (c) feature extraction from the fMRI data, (d) feature selection, (e) classification using classical and improved variations of the Random Forests algorithm and Support Vector Machines, and (f) conversion of the trees, of the Random Forest, to rules which have physical meaning. The method is evaluated using a dataset of 41 subjects. The results of the proposed method indicate the validity of the method in the diagnosis (accuracy 94%) and monitoring of the Alzheimer’s disease (accuracy 97% and 99%)

Decreased Deceleration Capacity of Heart Rate Detects Heart Failure Patients at Risk for Malignant Ventricular Arrhythmias

BACKGROUND: Deceleration capacity (DC) of the heart rate has proved an independent predictor of total mortality in post-myocardial infarction (post-MI) patients but it is unknown whether DC predicts the arrhythmic risk as well. OBJECTIVE: Our aim was to investigate whether DC can predict the arrhythmic sudden cardiac death (SCD) surrogate in patients with heart failure (HF). PATIENTS AND METHODS: We prospectively screened 145 HF patients with electrocardiogram (ECG), signal averaged ECG, echocardiography, and 24-hour Holter ECG. After 41.2 months, patients were divided into high (n=43) and low risk (n=102) groups according to three arrhythmic surrogates: clinical ventricular tachyarrhythmia (ventricular tachycardia -VT/ ventricular fibrillation-VF) (n=18), appropriate activation of the implantable cardioverter defibrillator (ICD) device (n=23) and confirmed SCD (n=2). RESULTS: High risk patients had impaired DC with significantly lower values (3.2±1.8 ms vs 4.0±2.1 ms, p=0.025). In the Cox regression analysis model adjusted for age, gender, diabetes, left ventricular ejection fraction (LVEF), filtered QRS, QTc, nonsustained VT episode(s) ≥ 1/24 h, ventricular premature beats ≥240/24 and DC, DC emerged as an important SCD surrogate predictor with a hazard ratio of 0.804, (95% confidence intervals-CI: 0.671- 0.963, p = 0.018). The cutoff point of DC≤3.352 ms (median) presented a hazard ratio of 2.885 (95% CI: 1.342 - 6.199, p=0.007, log rank test: p=0.003) for SCD surrogate. CONCLUSION: Decreased DC was found to be an important and independent SCD surrogate predictor. The cutoff point of DC≤3.352 ms detects HF patients at increased arrhythmic risk.

High-resolution volumetric imaging constrains compartmental models to explore synaptic integration and temporal processing by cochlear nucleus globular bushy cells

Globular bushy cells (GBCs) of the cochlear nucleus play central roles in the temporal processing of sound. Despite investigation over many decades, fundamental questions remain about their dendrite structure, afferent innervation, and integration of synaptic inputs. Here, we use volume electron microscopy (EM) of the mouse cochlear nucleus to construct synaptic maps that precisely specify convergence ratios and synaptic weights for auditory- nerve innervation and accurate surface areas of all postsynaptic compartments. Detailed biophysically-based compartmental models can help develop hypotheses regarding how GBCs integrate inputs to yield their recorded responses to sound. We established a pipeline to export a precise reconstruction of auditory nerve axons and their endbulb terminals together with high-resolution dendrite, soma, and axon reconstructions into biophysically-detailed compartmental models that could be activated by a standard cochlear transduction model. With these constraints, the models predict auditory nerve input profiles whereby all endbulbs onto a GBC are subthreshold (coincidence detection mode), or one or two inputs are suprathreshold (mixed mode). The models also predict the relative importance of dendrite geometry, soma size, and axon initial segment length in setting action potential threshold and generating heterogeneity in sound-evoked responses, and thereby propose mechanisms by which GBCs may homeostatically adjust their excitability. Volume EM also reveals new dendritic structures and dendrites that lack innervation. This framework defines a pathway from subcellular morphology to synaptic connectivity, and facilitates investigation into the roles of specific cellular features in sound encoding. We also clarify the need for new experimental measurements to provide missing cellular parameters, and predict responses to sound for further in vivo studies, thereby serving as a template for investigation of other neuron classes

Contribution of Deep Learning in the Investigation of Possible Dual LOX-3 Inhibitors/DPPH Scavengers: The Case of Recently Synthesized Compounds

Even though non-steroidal anti-inflammatory drugs are the most effective treatment for inflammatory conditions, they have been linked to negative side effects. A promising approach to mitigating potential risks, is the development of new compounds able to combine anti-inflammatory with antioxidant activity to enhance activity and reduce toxicity. The implication of reactive oxygen species in inflammatory conditions has been extensively studied, based on the pro-inflammatory properties of generated free radicals. Drugs with dual activity (i.e., inhibiting inflammation related enzymes, e.g., LOX-3 and scavenging free radicals, e.g., DPPH) could find various therapeutic applications, such as in cardiovascular or neurodegenerating disorders. The challenge we embarked on using deep learning was the creation of appropriate classification and regression models to discriminate pharmacological activity and selectivity as well as to discover future compounds with dual activity prior to synthesis. An accurate filter algorithm was established, based on knowledge from compounds already evaluated in vitro, that can separate compounds with low, moderate or high activity. In this study, we constructed a customized highly effective one dimensional convolutional neural network (CONV1D), with accuracy scores up to 95.2%, that was able to identify dual active compounds, being LOX-3 inhibitors and DPPH scavengers, as an indication of simultaneous anti-inflammatory and antioxidant activity. Additionally, we created a highly accurate regression model that predicted the exact value of effectiveness of a set of recently synthesized compounds with anti-inflammatory activity, scoring a root mean square error value of 0.8. Eventually, we succeeded in observing the manner in which those newly synthesized compounds differentiate from each other, regarding a specific pharmacological target, using deep learning algorithms

A Multithreaded Algorithm for the Computation of Sample Entropy

Many popular entropy definitions for signals, including approximate and sample entropy, are based on the idea of embedding the time series into an m-dimensional space, aiming to detect complex, deeper and more informative relationships among samples. However, for both approximate and sample entropy, the high computational cost is a severe limitation. Especially when large amounts of data are processed, or when parameter tuning is employed premising a large number of executions, the necessity of fast computation algorithms becomes urgent. In the past, our research team proposed fast algorithms for sample, approximate and bubble entropy. In the general case, the bucket-assisted algorithm was the one presenting the lowest execution times. In this paper, we exploit the opportunities given by the multithreading technology to further reduce the computation time. Without special requirements in hardware, since today even our cost-effective home computers support multithreading, the computation of entropy definitions can be significantly accelerated. The aim of this paper is threefold: (a) to extend the bucket-assisted algorithm for multithreaded processors, (b) to present updated execution times for the bucket-assisted algorithm since the achievements in hardware and compiler technology affect both execution times and gain, and (c) to provide a Python library which wraps fast C implementations capable of running in parallel on multithreaded processors

Simulations of cochlear nucleus bushy cells reconstructed from serial blockface electron microscopy (V1.0)

This repository contains simulation results based on biophysical representations of mouse ventral cochlear nucleus bushy cells reconstructed in 3D from serial blockface (volume) electron microscopy. Included in the dataset are the morphology files for use in NEURON (complete reconstructions in SWC format; original and manipulated reconstructions in .hoc format), and simulation result files in Python pickle format. Data are included for 10 fully-reconstructed cells. The software and documentation used to generate the simulations (and use the morphology files) can be found on GitHub (see References below).</p