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Dementia

Dementia

Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....

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Related Experiment Video

Updated: Sep 22, 2025

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

Brain simulation augments machine-learning-based classification of dementia.

Paul Triebkorn^1,2,3, Leon Stefanovski^1,2, Kiret Dhindsa^1,2

¹Berlin Institute of Health at Charité - Universitätsmedizin Berlin Berlin Germany.

Alzheimer'S & Dementia (New York, N. Y.)

|May 23, 2022

Summary

This summary is machine-generated.

Machine learning combined with brain simulation improves Alzheimer's disease (AD) diagnosis. Integrating amyloid beta (Aβ) PET data with simulated brain activity enhanced classification accuracy, offering new diagnostic insights.

Keywords:

Alzheimer's disease The Virtual Brain machine learning multi‐scale brain simulation positron emission tomography

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Last Updated: Sep 22, 2025

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Published on: August 12, 2019

Area of Science:

Neuroscience
Computational Biology
Medical Imaging

Background:

Alzheimer's disease (AD) diagnosis relies on complex data.
Computational brain network modeling offers novel approaches.

Purpose of the Study:

To enhance Alzheimer's disease (AD) diagnostics using The Virtual Brain (TVB) simulation and machine learning (ML).
To integrate multi-modal neuroimaging with brain simulation for improved classification accuracy.

Main Methods:

Utilized TVB for whole-brain simulation, incorporating a cause-and-effect model of amyloid beta (Aβ) and altered excitability.
Combined Aβ positron emission tomography (PET) and magnetic resonance imaging (MRI) data from 33 ADNI3 participants with simulated local field potentials (LFPs) for ML classification.

Main Results:

The combined approach significantly improved classification accuracy by approximately 10% (weighted F1-score: 74.28%) compared to empirical data alone (64.34%).
Identified informative features with high biological plausibility related to AD's spatial distribution.

Conclusions:

Integrating Aβ-induced hyperexcitation models into ML enhances AD classification.
Demonstrates TVB's capability in decoding empirical data through connectivity-based brain simulation for AD research.