AIMC Topic: Brain Stem
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Brainstem noradrenergic modulation of the kisspeptin neuron GnRH pulse generator in mice.
Nature communications
Jul 1, 2025
Brainstem noradrenaline (NA) neurons modulate the activity of many neural networks including those responsible for the control of fertility. Using brain slice electrophysiology, we demonstrate that the arcuate nucleus kisspeptin (ARN) neurons, recent...
Imitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept.
Journal of clinical monitoring and computing
Aug 20, 2024
Artificial neural networks (ANNs) are versatile tools capable of learning without prior knowledge. This study aims to evaluate whether ANN can calculate minute volume during spontaneous breathing after being trained using data from an animal model of...
Encoding of speech in convolutional layers and the brain stem based on language experience.
Scientific reports
Apr 20, 2023
Comparing artificial neural networks with outputs of neuroimaging techniques has recently seen substantial advances in (computer) vision and text-based language models. Here, we propose a framework to compare biological and artificial neural computat...
A Retrospective Study of Puncture and Drainage for Primary Brainstem Hemorrhage With the Assistance of a Surgical Robot.
The neurologist
Mar 1, 2023
BACKGROUND: Whether primary brainstem hemorrhage (PBH) should be treated with a conservative treatment or with surgical intervention (such as craniotomy, puncture, and drainage) is still controversial. The aim of this study was to assess the feasibil...
Machine learning approaches used to analyze auditory evoked responses from the human auditory brainstem: A systematic review.
Computer methods and programs in biomedicine
Sep 9, 2022
BACKGROUND: The application of machine learning algorithms for assessing the auditory brainstem response has gained interest over recent years with a considerable number of publications in the literature. In this systematic review, we explore how mac...
Frameless robot-assisted stereotactic biopsies for lesions of the brainstem-a series of 103 consecutive biopsies.
Journal of neuro-oncology
Jan 27, 2022
PURPOSE: Targeted treatment for brainstem lesions requires above all a precise histopathological and molecular diagnosis. In the current technological era, robot-assisted stereotactic biopsies represent an accurate and safe procedure for tissue diagn...
Development of a Novel Prognostic Model to Predict 6-Month Swallowing Recovery After Ischemic Stroke.
Stroke
Dec 30, 2019
Background and Purpose- The aim of this study was to explore clinical and radiological prognostic factors for long-term swallowing recovery in patients with poststroke dysphagia and to develop and validate a prognostic model using a machine learning ...
Algorithms
Prognosis
Female
Reproducibility of Results
Aged
Male
Middle Aged
Magnetic Resonance Imaging
Humans
Deglutition Disorders
Machine Learning
White Matter
Enteral Nutrition
Brain Stem
Basal Ganglia
Brain Ischemia
Recovery of Function
Internal Capsule
Proportional Hazards Models
Fluoroscopy
Multivariate Analysis
Stroke
Cerebral Cortex
Sex Factors
Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation.
International journal of radiation oncology, biology, physics
Mar 2, 2019
PURPOSE: Organ-at-risk (OAR) delineation is a key step in treatment planning but can be time consuming, resource intensive, subject to variability, and dependent on anatomical knowledge. We studied deep learning (DL) for automated delineation of mult...
Clinical applications of control systems models: The neural integrators for eye movements.
Progress in brain research
Feb 8, 2019
The first models that were proposed to account for the neural control of eye movements applied a classic control systems approach, including feedback, and measured system responses to sinusoidal and transient stimuli. Although such models provided ma...
Objective auditory brainstem response classification using machine learning.
International journal of audiology
Jan 21, 2019
OBJECTIVE: The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: 'clear response', 'inconclusive' or 'response absent'.
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