An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease
| Autor: | Philippe Maeder, Cristina Granziera, Alexis Roche, Stefan Klöppel, Alessandro Daducci, Delphine Ribes, Bénédicte Maréchal, Reto Meuli, Gunnar Krueger, Meritxell Bach-Cuadra, Ahmed Abdulkadir, Daniel Schmitter |
|---|---|
| Přispěvatelé: | Alzheimer's Disease Neuroimaging Initiative |
| Rok vydání: | 2015 |
| Předmět: |
Support vector machine
Disease computer.software_genre lcsh:RC346-429 030218 nuclear medicine & medical imaging 0302 clinical medicine Computer-Assisted Voxel 80 and over Aged 80 and over Image segmentation medicine.diagnostic_test Brain Regular Article Cognition Alzheimer's disease Middle Aged Classification Magnetic Resonance Imaging Neurology Cardiology lcsh:R858-859.7 Psychology Algorithms medicine.medical_specialty Cognitive Neuroscience Brain morphometry lcsh:Computer applications to medicine. Medical informatics 03 medical and health sciences Atrophy Neuroimaging Magnetic resonance imaging Mild cognitive impairment Aged Alzheimer Disease Case-Control Studies Cognitive Dysfunction Humans Image Interpretation Computer-Assisted Reproducibility of Results Internal medicine medicine Radiology Nuclear Medicine and imaging Image Interpretation lcsh:Neurology. Diseases of the nervous system medicine.disease Neurology (clinical) computer Neuroscience 030217 neurology & neurosurgery |
| Zdroj: | Neuroimage. Clinical, vol. 7, pp. 7-17 NeuroImage: Clinical, Vol 7, Iss C, Pp 7-17 (2015) NeuroImage : Clinical |
| ISSN: | 2213-1582 |
| DOI: | 10.1016/j.nicl.2014.11.001 |
| Popis: | Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease. |
| Databáze: | OpenAIRE |
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