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Review
. 2023 Oct;18(10):2134-2140.
doi: 10.4103/1673-5374.367840.

Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning

Iroshan Aberathne  1 , Don Kulasiri  1 , Sandhya Samarasinghe  1
Affiliations
Review

Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning

Iroshan Aberathne et al. Neural Regen Res. 2023 Oct.

Abstract

The scientists are dedicated to studying the detection of Alzheimer's disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer's disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer's disease onset.

Keywords: deep learning; image processing; linear mixed effect model; neuroimaging; neuroimaging data sources; onset of Alzheimer’s disease detection; pattern recognition.

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Conflict of interest statement

None

Figures

Figure 1
Figure 1
Search strategy and study selection process.
Figure 2
Figure 2
Potential brain regions which have the greatest impact on AD (top row), pMCI (middle row), and sMCI (bottom row). AD: Alzheimer’s disease; pMCI: progressive mild cognitive impairment; sMCI: stable mild cognitive impairment. Reprinted with permission from Feng et al. (2019) © 2019 IEEE.
Figure 3
Figure 3
Ventricle enlargement and hippocampus changes can be observed at different stages of AD via MRI images. CN (top), MCI (middle), and AD (bottom). AD: Alzheimer’s disease; CN: cognitive normal; MCI: mild cognitive impairment; MRI: magnetic resonance imaging. Reprinted from Basheera and Sai Ram (2019) under the terms of the Creative Commons Attribution License (CC BY).
Figure 4
Figure 4
The correlation of volume trajectories of the ventricles and hippocampus of the left hemisphere with age. The ventricle expansion shows a non-linear positive relationship whereas hippocampus reduction has a linear but negative relationship with age even though the results show a higher variability. Reprinted from Mofrad et al. (2021) under the terms of the Creative Commons Attribution License (CC BY).
Figure 5
Figure 5
The atrophy of the hippocampus (left) and precuneus (right) with age. CN (blue) and AD (red) show a linear correlation with age in both brain regions. AD: Alzheimer’s disease; CN: cognitive normal. Reprinted from Möller et al. (2013) with permission from Elsevier.
See this image and copyright information in PMC

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