Improved multimodal biomarkers for Alzheimer's disease and mild cognitive impairment diagnosis: data from ADNI

Abstract

The accurate diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) confers many clinical research and patient care benefits. Studies have shown that multimodal biomarkers provide better diagnosis accuracy of AD and MCI than unimodal biomarkers, but their construction has been based on traditional statistical approaches. The objective of this work was the creation of accurate AD and MCI diagnostic multimodal biomarkers using advanced bioinformatics tools. The biomarkers were created by exploring multimodal combinations of features using machine learning techniques. Data was obtained from the ADNI database. The baseline information (e.g. MRI analyses, PET analyses and laboratory essays) from AD, MCI and healthy control (HC) subjects with available diagnosis up to June 2012 was mined for case/controls candidates. The data mining yielded 47 HC, 83 MCI and 43 AD subjects for biomarker creation. Each subject was characterized by at least 980 ADNI features. A genetic algorithm feature selection strategy was used to obtain compact and accurate cross-validated nearest centroid biomarkers. The biomarkers achieved training classification accuracies of 0.983, 0.871 and 0.917 for HC vs. AD, HC vs. MCI and MCI vs. AD respectively. The constructed biomarkers were relatively compact: from 5 to 11 features. Those multimodal biomarkers included several widely accepted univariate biomarkers and novel image and biochemical features. Multimodal biomarkers constructed from previously and non-previously AD associated features showed improved diagnostic performance when compared to those based solely on previously AD associated features.

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer’s disease progression

“…The segmentation masks were provided by Heckemann et al 13 using multiatlas propagation enhanced registration, an automatic whole-brain multiregion segmentation method. In order to continue studying the same population as in previous works, 14 only data and images uploaded up to June 2012 were considered. The 98 MCI subjects studied in such works were considered for this study, where those with a reported progression to AD within the 24 months following their baseline visit (BL) were considered as cases and those who maintained a cognitive stability during the same period of time were considered as controls.…”

An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks

“…Features that statistically describe sound waves have previously been found to be of importance in solving similar problems [22]. Thus, the 16 statistical features listed in the following list were extracted from each sample.…”

Improved Diagnostic Multimodal Biomarkers for Alzheimer’s Disease and Mild Cognitive Impairment

“…Our objective was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between subjects with AD, MCI, and healthy controls (HC), building on preliminary versions [28]. …”

“…In this paper, we explore additional information from imaging sources (e.g., cortical thickness and hypometabolic convergence index), biological tests (e.g., complement component 3 concentrations and TOMM40 poly-T variable length), and clinical records (e.g., blood pressure, drug sensitivities, and presence of a depressed mood). Our objective was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between subjects with AD, MCI, and healthy controls (HC), building on preliminary versions [ 28 ].…”