Search results for " Learning"

The Severity of Acute Stress Is Represented by Increased Synchronous Activity and Recruitment of Hypothalamic CRH Neurons

2016

The hypothalamo-pituitary-adrenocortical (HPA) axis regulates stress physiology and behavior. To achieve an optimally tuned adaptive response, it is critical that the magnitude of the stress response matches the severity of the threat. Corticotropin-releasing hormone (CRH) released from the paraventricular nucleus of the hypothalamus is a major regulator of the HPA axis. However, how CRH-producing neurons in an intact animal respond to different stressor intensities is currently not known. Using two-photon calcium imaging on intact larval zebrafish, we recorded the activity of CRH cells, while the larvae were exposed to stressors of varying intensity. By combining behavioral and physiologic…

Unraveling the Molecular Mechanism of Action of Empagliflozin in Heart Failure With Reduced Ejection Fraction With or Without Diabetes

2019

Visual Abstract

Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures

2019

Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide re…

Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

2017

Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential f…

Frequency-specific after-effects of transcranial alternating current stimulation (tACS) on motor learning: Preliminary data of a simultaneous tACS-EE…

2017

Machine learning–XGBoost analysis of language networks to classify patients with epilepsy

2017

Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing ‘atypical’ (compared to ‘typical’ in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures. The neurosurgeon should only remove the zone generating seizures and must preserve cognitive functions to avoid deficits. To preserve functions, one sho…

Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction

2020

Introduction: Parkinson's disease (PD) is a progressive movement disorder characterized by heterogenous motor dysfunction with fluctuations in severity. Objective, short-timescale characterization of this dysfunction is necessary as therapies become increasingly adaptive. Objectives: This study aims to characterize a novel, naturalistic, and goal-directed tablet-based task and complementary analysis protocol designed to characterize the motor features of PD. Methods: A total of 26 patients with PD and without deep brain stimulation (DBS), 20 control subjects, and eight patients with PD and with DBS completed the task. Eight metrics, each designed to capture an aspect of motor dysfunction in…

Modulation of GABAA receptors by neurosteroids. A new concept to improve cognitive and motor alterations in hepatic encephalopathy

2016

Hepatic encephalopathy (HE) is a complex neuropsychiatric syndrome affecting patients with liver diseases, mainly those with liver cirrhosis. The mildest form of HE is minimal HE (MHE), with mild cognitive impairment, attention deficit, psychomotor slowing and impaired visuo-motor and bimanual coordination. MHE may progress to clinical HE with worsening of the neurological alterations which may lead to reduced consciousness and, in the worse cases, may progress to coma and death. HE affects several million people in the world and is a serious health, social and economic problem. There are no specific treatments for the neurological alterations in HE. The mechanisms underlying the cognitive …

Somatosensory Training Improves Proprioception and Untrained Motor Function in Parkinsons Disease

2018

Background: Proprioceptive impairment is a common feature of Parkinson's disease (PD). Proprioceptive function is only partially restored with anti-parkinsonian medication or deep brain stimulation. Behavioral exercises focusing on somatosensation have been promoted to overcome this therapeutic gap. However, conclusive evidence on the effectiveness of such somatosensory-focused behavioral training for improving somatosensory function is lacking. Moreover, it is unclear, if such training has any effect on motor performance in PD.Objective: To investigate, whether proprioception improves with a somatosensory focused, robot-aided training in people with PD (PWPs), and whether enhanced proprioc…

Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting

2020

Background Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. Objective The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. Methods Three hundred twenty-two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsoni…