کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
3074895 1580956 2016 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease
ترجمه فارسی عنوان
ترکیب آناتومیک، انتشار و تصویرسازی تشدید مغناطیسی کارکردی در حالت استراحت برای طبقه بندی منحصر به فرد از بیماری خفیف و متوسط آلزایمر
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی روانپزشکی بیولوژیکی
چکیده انگلیسی


• We use machine learning classification to classify Alzheimer's disease.
• For classification we use anatomical MRI, diffusion MRI, and resting state fMRI.
• Grey matter density is most successful for single modality classification.
• Combining multiple modalities improves classification of Alzheimer's disease.

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage: Clinical - Volume 11, 2016, Pages 46–51
نویسندگان
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