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

Antonio Martinez-Torteya; Víctor Treviño-Alvarado; José Tamez-Peña

doi:10.1117/12.2008100

28 February 2013 Improved multimodal biomarkers for Alzheimer's disease and mild cognitive impairment diagnosis: data from ADNI

Antonio Martinez-Torteya, Víctor Treviño-Alvarado, José Tamez-Peña

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86700S (2013) https://doi.org/10.1117/12.2008100
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Antonio Martinez-Torteya, Víctor Treviño-Alvarado, and José Tamez-Peña "Improved multimodal biomarkers for Alzheimer's disease and mild cognitive impairment diagnosis: data from ADNI", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700S (28 February 2013); https://doi.org/10.1117/12.2008100

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