Classification of mild cognitive impairment and Alzheimer's Disease with machine-learning techniques using H-1 Magnetic Resonance Spectroscopy data

C. Munteanu, C. Fernandez-Lozano*, V. Mato Abad, S. Pita Fernandez, J. Alvarez-Linera, J. Antonio Hernandez-Tamames, A. Pazos

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Several magnetic resonance techniques have been proposed as non-invasive imaging biomarkers for the evaluation of disease progression and early diagnosis of Alzheimer's Disease (AD). This work is the first application of the Proton Magnetic Resonance Spectroscopy H-1-MRS data and machine-learning techniques to the classification of AD. A gender-matched cohort of 260 subjects aged between 57 and 99 years from the Alzheimer's Disease Research Unit, of the Fundacion CIEN-Fundacion Reina Sofia has been used. A single-layer perceptron was found for AD prediction with only two spectroscopic voxel volumes (Tvol and CSFvol) in the left hippocampus, with an AUROC value of 0.866 (with TPR 0.812 and FPR 0.204) in a filter feature selection approach. These results suggest that knowing the composition of white and grey matter and cerebrospinal fluid of the spectroscopic voxel is essential in a H-1-MRS study to improve the accuracy of the quantifications and classifications, particularly in those studies involving elder patients and neurodegenerative diseases.
Original languageEnglish
Pages (from-to)6205-6214
JournalExpert Systems with Applications
Issue number15-16
Publication statusPublished - 1 Jan 2015

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