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Alzheimer's Disease: Overview

Alzheimer's Disease: Overview

Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...

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Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach.

Danilo Pena^1,2, Arko Barman^1,2, Jessika Suescun³

¹School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States.

Frontiers in Neuroscience

|October 23, 2019

Summary

A new deep learning method, DeepSymNet, tracks Alzheimer's disease (AD) progression using brain MRI. This AI approach identifies changes without predefined regions, offering faster, more accurate monitoring for early diagnosis and neurodegeneration tracking.

Keywords:

ADNI Alzheimer's disease biomarkers deep learning longitudinal magnetic resonance imaging progression

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Area of Science:

Neuroimaging
Artificial Intelligence
Neurodegenerative Diseases

Background:

Alzheimer's disease (AD) is a leading cause of death, with variable progression making monitoring challenging.
Current brain MRI analysis for AD relies on complex, time-consuming methods prone to errors.
Existing techniques require predefined regions of interest and non-linear registration, hindering clinical translation.

Purpose of the Study:

To develop a novel, data-driven deep learning method for quantifying longitudinal changes in Alzheimer's disease.
To overcome limitations of current neuroimaging analysis pipelines in AD progression monitoring.
To create a more efficient and accurate tool for early AD diagnosis and patient assessment.

Main Methods:

An extended deep learning architecture, DeepSymNet, was developed for end-to-end training.
The model analyzes longitudinal brain MRI data directly from raw voxels, avoiding atlases and non-linear registration.
Performance was validated against Freesurfer longitudinal pipelines and voxel-based methods using the ADNI database.

Main Results:

DeepSymNet achieved comparable results to Freesurfer longitudinal pipelines in identifying AD progression, but with significantly reduced processing time.
The model demonstrated statistically significant performance improvements over other voxel-based methods.
DeepSymNet successfully differentiated between healthy subjects and those with mild cognitive impairment (MCI).

Conclusions:

The proposed deep learning approach offers a powerful, efficient method for monitoring neurodegeneration in Alzheimer's disease.
DeepSymNet's ability to analyze longitudinal changes without prior assumptions has the potential to enhance early diagnosis and clinical practice.
Model interpretability revealed key brain regions (pallidum, putamen, superior temporal gyrus) driving predictions, offering insights into AD pathology.