Identification and Temporal Characterization of Features Associated with the Conversion from Mild Cognitive Impairment to Alzheimer’s Disease

Martínez-Torteya, Antonio; Gómez-Rueda, Hugo; Treviño, Víctor; Farber, Joshua M.; Tamez-Peña, José G.

doi:10.2174/1567205015666180202095616

Cited by 7 publications

(12 citation statements)

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“…This change in selection criteria resulted in the ROC curve presented by Figure 6. We clearly see that Cox based conversion risk prediction had a similar performance (ROC ACU= 0.79) to previous works [47].…”

Section: A ML Methods Validationsupporting

confidence: 79%

Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

Orozco-Sanchez

Treviño

Martinez-Ledesma

et al. 2019

Preprint

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Several studies have documented that structural MRI findings are associated with the presence of earlystage Alzheimer Disease (AD). However, the association of each MRI feature with the rate of conversion from mild cognitive impairment (MCI) to AD in a multivariate setting has not been studied fully. The objective of this work is the comprehensive exploration of four different machine learning (ML) strategies to build MRI-based multivariate Cox regression models. These models evaluated the association of MRI features with the time of MCI to clinical AD conversion. We used 442 MCI subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI) study. Each subject was described by 346 MRI features and time to AD conversion. Cox regression models then estimated the rate of conversion. Models were built using four ML methodologies in a cross-validation (CV) setting. All the ML methods returned successful Cox models with different CV performances. The best model exhibited a concordance index of 0.84 (95% CI: 0.82-0.86). The final analysis described the hazard ratios (HR) of the top ten MRI features associated with MCI to AD conversion. Our results suggest ML exploration is a viable strategy for building and analyzing survival models that predict subjects at risk of AD conversion.

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Section: A ML Methods Validationsupporting

confidence: 79%

Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

Orozco-Sanchez

Treviño

Martinez-Ledesma

et al. 2019

Preprint

Self Cite

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“…Therefore, ADNI is a well-planned study that produces the required data to be data mined by ML. There have been several machine learning strategies that have been used to explore early stages of AD [11–13]. Most of the ML approaches are based on exploring univariate associations with MCI to AD conversions [13], and some efforts have been made in building predictive multivariate models based on merging clinical, MRI, laboratory and PET imaging [14].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking machine learning models for late-onset alzheimer’s disease prediction from genomic data

et al. 2019

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BackgroundLate-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.ResultsWe conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve.ConclusionsMachine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.

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“…Declining cognition, one of the AD symptoms, has been researched extensively. Episodic memory deficits are prevalent in MCI patients, but other cognitive functions are also impaired with disease progression 44,45 . Specifically, the executive function has shown to be a good predictor of the speed of AD conversion 44,56 .…”

Section: Discussionmentioning

confidence: 99%

Effects of risk factors on longitudinal changes in brain structure and function in the progression of AD

Hrast

Khachatryan

Šuput

et al. 2023

Alzheimer's & Dementia

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Introduction Past research on Alzheimer's disease (AD) has focused on biomarkers, cognition, and neuroimaging as primary predictors of its progression, albeit additional ones have recently gained attention. When turning to the prediction of the progression from one stage to another, one could benefit from the joint assessment of imaging‐based biomarkers and risk/protective factors. Methods We included 86 studies that fulfilled our inclusion criteria. Results Our review summarizes and discusses the results of 30 years of longitudinal research on brain changes assessed with neuroimaging and the risk/protective factors and their effect on AD progression. We group results into four sections: genetic, demographic, cognitive and cardiovascular, and lifestyle factors. Discussion Given the complex nature of AD, including risk factors could prove invaluable for a better understanding of AD progression. Some of these risk factors are modifiable and could be targeted by potential future treatments.

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Identification and Temporal Characterization of Features Associated with the Conversion from Mild Cognitive Impairment to Alzheimer’s Disease

Abstract: Our results characterized features associated with disease progression from MCI to AD, and, in addition, the log-rank test identified features which are associated with the risk of early conversion.

Cited by 7 publications

References 0 publications

Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

Benchmarking machine learning models for late-onset alzheimer’s disease prediction from genomic data

Effects of risk factors on longitudinal changes in brain structure and function in the progression of AD

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