Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Neurotransmitters

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Multivariate Poisson cokriging: A geostatistical model for health count data.

Statistical methods in medical research·2024

Same author

Spatio-temporal modeling of traffic accidents incidence on urban road networks based on an explicit network triangulation.

Journal of applied statistics·2023

Same author

Route effects in city-based survey knowledge estimates.

Cognitive processing·2023

Same author

Spatio-temporal stochastic differential equations for crime incidence modeling.

Stochastic environmental research and risk assessment : research journal·2023

Same author

Modeling noisy time-series data of crime with stochastic differential equations.

Stochastic environmental research and risk assessment : research journal·2022

Same author

A stochastic Bayesian bootstrapping model for COVID-19 data.

Stochastic environmental research and risk assessment : research journal·2022

Search research articles

Related Experiment Video

Updated: Aug 19, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Spatially informed Bayesian neural network for neurodegenerative diseases classification.

David Payares-Garcia^1,2, Jorge Mateu³, Wiebke Schick²

¹ITC Faculty Geo-Information Science and Earth Observation, University of Twente, Enschede.

Statistics in Medicine

|November 28, 2022

Summary

This study introduces a Spatially Informed Bayesian Neural Network (SBNN) for improved neurodegenerative disease classification using MRI. The SBNN enhances diagnostic accuracy by incorporating spatial brain information and accounting for diagnostic uncertainty.

Keywords:

Bayesian inference classification deep learning magnetic resonance imaging neurodegenerative diseases spatial information

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Aug 19, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Neuroimaging
Artificial Intelligence in Medicine
Neurology

Background:

Magnetic Resonance Imaging (MRI) is crucial for diagnosing neurodegenerative diseases.
Current MRI classification methods often lack realism, probabilistic outputs, and spatial considerations.
Neurodegenerative disorders present with heterogeneous symptoms and spatial patterns of brain alteration.

Purpose of the Study:

To develop an advanced MRI classification technique for neurodegenerative diseases.
To incorporate uncertainty and spatial brain information into automated diagnosis.
To improve the accuracy and clinical relevance of neurodegenerative disease classification.

Main Methods:

Proposed a Spatially Informed Bayesian Neural Network (SBNN) model.
Utilized a 3D neural network for feature extraction from MRI scans.
Integrated Bayesian inference for uncertainty quantification and Hidden Markov Random Fields for spatial encoding.

Main Results:

The SBNN model achieved up to a 25% increase in classification accuracy when incorporating spatial MRI information.
The SBNN provided robust probabilistic diagnoses, mimicking clinical decision-making.
The model effectively handled the heterogeneous presentations of neurodegenerative disorders.

Conclusions:

Spatially informed Bayesian neural networks offer a significant advancement in MRI-based neurodegenerative disease classification.
This approach enhances diagnostic accuracy and provides clinically relevant probabilistic outputs.
The SBNN model is adaptable to the complex and varied nature of neurodegenerative conditions.