| 1 |
Review Magnetic resonance imaging of Alzheimer's disease. 2007
Lehéricy S, Marjanska M, Mesrob L, Sarazin M, Kinkingnehun S. · Department of Neuroradiology, Université Pierre et Marie Curie-Paris 6, Groupe Hospitalier Pitié-Salpêtrière, 47-83 boulevard de l'Hôpital, Paris 75651, Cedex 13, France. · Eur Radiol. · Pubmed #16865367 No free full text.
Abstract: A modern challenge for neuroimaging techniques is to contribute to the early diagnosis of neurodegenerative diseases, such as Alzheimer's disease (AD). Early diagnosis includes recognition of pre-demented conditions, such as mild cognitive impairment (MCI) or having a high risk of developing AD. The role of neuroimaging therefore extends beyond its traditional role of excluding other conditions such as neurosurgical lesions. In addition, early diagnosis would allow early treatment using currently available therapies or new therapies in the future. Structural imaging can detect and follow the time course of subtle brain atrophy as a surrogate marker for pathological processes. New MR techniques and image analysis software can detect subtle brain microstructural, perfusion or metabolic changes that provide new tools to study the pathological processes and detect pre-demented conditions. This review focuses on markers of macro- and microstructural, perfusion, diffusion and metabolic MR imaging and spectroscopy in AD.
|
| 2 |
Article Support vector machine-based classification of Alzheimer's disease from whole-brain anatomical MRI. 2009
Magnin B, Mesrob L, Kinkingnéhun S, Pélégrini-Issac M, Colliot O, Sarazin M, Dubois B, Lehéricy S, Benali H. · UMR-S 678, Inserm, Paris, France. · Neuroradiology. · Pubmed #18846369 No free full text.
Abstract: PURPOSE: We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer's disease (AD) and elderly control subjects. MATERIALS AND METHODS: We studied 16 patients with AD [mean age +/- standard deviation (SD) = 74.1 +/- 5.2 years, mini-mental score examination (MMSE) = 23.1 +/- 2.9] and 22 elderly controls (72.3 +/- 5.0 years, MMSE = 28.5 +/- 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results. RESULTS: We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%). CONCLUSIONS: Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD.
|
|
|