Machine learning of brain structural biomarkers for Alzheimer's disease (AD) diagnosis, prediction of disease progression, and amyloid beta deposition in the Japanese population

AbstractBackgroundAlzheimer’s disease (AD) is a common, age-related, neurodegenerative disease that impairs a person’s ability to perform day-to-day activities. Diagnosing AD is challenging, especially in the early stages. Many patients still go undiagnosed, partly due to the complex heterogeneity in disease progression. This highlights a need for early prediction of the disease course to assist its treatment and tailor therapy options to the disease progression rate. Recent developments in machine learning techniques provide the potential to not only predict disease progression and trajectory of AD but also to classify the disease into different etiological subtypes.Methods and findingsThe work shown here clusters participants in distinct and multifaceted progression subgroups of AD and discusses an approach to predict the progression rate from baseline diagnosis. We observed that the myriad of clinically reported symptoms summarized in the proposed AD progression space corresponds directly with memory and cognitive measures, which are routinely used to monitor disease onset and progression. Our analysis demonstrated accurate prediction of disease progression after four years from the first 12 months of post-diagnosis clinical data (Area Under the Curve of 0.96 (95% confidence interval (CI), 0.92-1.0), 0.81 (95% CI, 0.74-0.88) and 0.98 (95% CI, 0.96-1.0) for slow, moderate and fast progression rate patients respectively). Further, we explored the long short-term memory (LSTM) neural networks to predict the trajectory of an individual patient’s progression.ConclusionThe machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries. With additional information about the progression rate of AD at hand, providers may further individualize the treatment plans. The predictive tests discussed in this study not only allow for early AD diagnosis but also facilitate the characterization of distinct AD subtypes relating to trajectories of disease progression. These findings are a crucial step forward for early disease detection. These models can be used to design improved clinical trials for AD research.

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Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Alzheimer s & Dementia ◽

10.1002/alz.12032 ◽

2020 ◽

Vol 16 (3) ◽

pp. 501-511 ◽

Cited By ~ 2

Author(s):

Nicolai Franzmeier ◽

Nikolaos Koutsouleris ◽

Tammie Benzinger ◽

Alison Goate ◽

Celeste M. Karch ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Disease Progression ◽

Sporadic Alzheimer’S Disease

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P3-032: Identification of Predictors of Disease Progression in an Amyloid-Beta 42 (Aβ42)-Positive, Prodromal Alzheimer’s Disease Cohort: Analysis of the Scarlet Road Study

Alzheimer s & Dementia ◽

10.1016/j.jalz.2016.06.1689 ◽

2016 ◽

Vol 12 ◽

pp. P829-P829

Author(s):

Shuguang Sun ◽

Fabian Model ◽

Tania Nikolcheva ◽

Robert A. Lasser

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Disease Progression ◽

Amyloid Beta ◽

Cohort Analysis ◽

Prodromal Alzheimer’S Disease ◽

Amyloid Beta 42 ◽

Road Study

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PET measurement of longitudinal amyloid load identifies the earliest stages of amyloid-beta accumulation during Alzheimer's disease progression in Down syndrome

NeuroImage ◽

10.1016/j.neuroimage.2021.117728 ◽

2021 ◽

pp. 117728

Author(s):

Matthew D. Zammit ◽

Dana L. Tudorascu ◽

Charles M. Laymon ◽

Sigan L. Hartley ◽

Shahid H. Zaman ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Down Syndrome ◽

Disease Progression ◽

Amyloid Beta ◽

Amyloid Load

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Machine learning for comprehensive forecasting of Alzheimer’s Disease progression

Scientific Reports ◽

10.1038/s41598-019-49656-2 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 19

Author(s):

Charles K. Fisher ◽

◽

Aaron M. Smith ◽

Jonathan R. Walsh ◽

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Disease Progression ◽

Synthetic Data ◽

Patient Characteristics ◽

Patient Data ◽

Word Recall ◽

Boltzmann Machine ◽

Machine Learning Model

Abstract Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer’s Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer’s Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.

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Amyloid Beta-Related Alterations to Glutamate Signaling Dynamics During Alzheimer’s Disease Progression

ASN NEURO ◽

10.1177/1759091419855541 ◽

2019 ◽

Vol 11 ◽

pp. 175909141985554 ◽

Cited By ~ 16

Author(s):

Caleigh A. Findley ◽

Andrzej Bartke ◽

Kevin N. Hascup ◽

Erin R. Hascup

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Disease Progression ◽

Amyloid Beta ◽

Cognitive Deficits ◽

Senile Plaques ◽

Neuronal Loss ◽

Long Term Potentiation ◽

Term Potentiation

Alzheimer’s disease (AD) ranks sixth on the Centers for Disease Control and Prevention Top 10 Leading Causes of Death list for 2016, and the Alzheimer’s Association attributes 60% to 80% of dementia cases as AD related. AD pathology hallmarks include accumulation of senile plaques and neurofibrillary tangles; however, evidence supports that soluble amyloid beta (Aβ), rather than insoluble plaques, may instigate synaptic failure. Soluble Aβ accumulation results in depression of long-term potentiation leading to cognitive deficits commonly characterized in AD. The mechanisms through which Aβ incites cognitive decline have been extensively explored, with a growing body of evidence pointing to modulation of the glutamatergic system. The period of glutamatergic hypoactivation observed alongside long-term potentiation depression and cognitive deficits in later disease stages may be the consequence of a preceding period of increased glutamatergic activity. This review will explore the Aβ-related changes to the tripartite glutamate synapse resulting in altered cell signaling throughout disease progression, ultimately culminating in oxidative stress, synaptic dysfunction, and neuronal loss.

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Multi-scale brain simulation with integrated positron emission tomography yields hidden local field potential activity that augments machine-learning classification of Alzheimer's disease

10.1101/2021.02.27.433161 ◽

2021 ◽

Author(s):

Paul Triebkorn ◽

Leon Stefanovski ◽

Kiret Dhindsa ◽

Margarita-Arimatea Diaz-Cortes ◽

Patrik Bey ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Amyloid Beta ◽

Support Vector ◽

Machine Learning Classification ◽

Multi Scale ◽

Positron Emission ◽

Vector Machines ◽

The Right

Introduction: While the prevalence of neurodegenerative diseases and dementia increases, our knowledge of the underlying pathomechanisms and related diagnostic biomarkers, outcome predictors, or therapeutic targets remains limited. In this article, we show how computational multi-scale brain network modeling using The Virtual Brain (TVB) simulation platform supports revealing potential disease mechanisms and can lead to improved diagnostics. Methods: TVB allows standardized large-scale structural connectivity (SC)-based modeling and simulation of whole-brain dynamics. We combine TVB with a cause-and-effect model for amyloid-beta, and machine-learning classification with support vector machines and random forests. The amyloid-beta burden as quantified from individual AV-45 PET scans informs parameters of local excitation/inhibition balance. We use magnetic resonance imaging (MRI), positron emission tomography (PET, specifically Amyloid-beta (Abeta) binding tracer AV-45-PET, and Tau-protein (Tau) binding AV-1451-PET) from 33 participants of Alzheimer's Disease Neuroimaging Initiative study 3 (ADNI3). The frequency compositions of simulated local field potentials (LFP) are under investigation for their potential to classify individuals between Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and healthy controls (HC) using support vector machines and random forest classifiers. Results: The combination of empirical features (subcortical volumetry, AV-45- and AV-1451- PET standard uptake value ratio, SUVR per region) and simulated features (mean LFP frequency per brain region) significantly outperformed the classification accuracy of empirical data alone by about 10% in the accuracy index of weighted F1-score (empirical 64.34% vs. combined 74.28%). There was no significant difference between empirical and simulated features alone. The features with the highest feature importance showed high biological plausibility with respect to the AD-typical spatial distribution of the features. This was demonstrated for all feature types, e.g., increased importance indices for the left entorhinal cortex as the most important Tau-feature, the left dorsal temporopolar cortex for Abeta, the right thalamus for LFP frequency, and the right putamen for volume. Discussion: In summary, here we suggest a strategy and provide proof of concept for TVB-inferred mechanistic biomarkers that are direct indicators of pathogenic processes in neurodegenerative disease. We show how the cause-and-effect implementation of local hyperexcitation caused by Abeta can improve the machine-learning-driven classification of AD. This proves TVBs ability to decode information in empirical data by means of SC-based brain simulation.

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Proteolytic processing of the amyloid-beta protein precursor of Alzheimer's disease

Essays in Biochemistry ◽

10.1042/bse0380037 ◽

2002 ◽

Vol 38 ◽

pp. 37-49 ◽

Cited By ~ 27

Author(s):

Janelle Nunan ◽

David H Small

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Amyloid Beta ◽

Alternative Pathway ◽

Protein A ◽

Proteolytic Processing ◽

Gamma Secretase ◽

Amyloid Beta Protein ◽

Protein Precursor ◽

Amyloid Beta Protein Precursor

The proteolytic processing of the amyloid-beta protein precursor plays a key role in the development of Alzheimer's disease. Cleavage of the amyloid-beta protein precursor may occur via two pathways, both of which involve the action of proteases called secretases. One pathway, involving beta- and gamma-secretase, liberates amyloid-beta protein, a protein associated with the neurodegeneration seen in Alzheimer's disease. The alternative pathway, involving alpha-secretase, precludes amyloid-beta protein formation. In this review, we describe the progress that has been made in identifying the secretases and their potential as therapeutic targets in the treatment or prevention of Alzheimer's disease.

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