دورية أكاديمية
Novel ThickNet features for the discrimination of amnestic MCI subtypes
| العنوان: | Novel ThickNet features for the discrimination of amnestic MCI subtypes |
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| المؤلفون: | Pradeep Reddy Raamana, Wei Wen, Nicole A. Kochan, Henry Brodaty, Perminder S. Sachdev, Lei Wang, Mirza Faisal Beg |
| المصدر: | NeuroImage: Clinical, Vol 6, Iss C, Pp 284-295 (2014) |
| بيانات النشر: | Elsevier, 2014. |
| سنة النشر: | 2014 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Neurology. Diseases of the nervous system |
| مصطلحات موضوعية: | Mild cognitive impairment, Cortical thickness, Network, ThickNet, Early detection, Alzheimer, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429 |
| الوصف: | Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage between healthy aging and Alzheimer's disease (AD), and consists of two subtypes: single-domain aMCI (sd-aMCI) and multi-domain aMCI (md-aMCI). Individuals with md-aMCI are found to exhibit higher risk of conversion to AD. Accurate discrimination among aMCI subtypes (sd- or md-aMCI) and controls could assist in predicting future decline. Methods: We apply our novel thickness network (ThickNet) features to discriminate md-aMCI from healthy controls (NC). ThickNet features are extracted from the properties of a graph constructed from inter-regional co-variation of cortical thickness. We fuse these ThickNet features using multiple kernel learning to form a composite classifier. We apply the proposed ThickNet classifier to discriminate between md-aMCI and NC, sd-aMCI and NC and; and also between sd-aMCI and md-aMCI, using baseline T1 MR scans from the Sydney Memory and Ageing Study. Results: ThickNet classifier achieved an area under curve (AUC) of 0.74, with 70% sensitivity and 69% specificity in discriminating md-aMCI from healthy controls. The same classifier resulted in AUC = 0.67 and 0.67 for sd-aMCI/NC and sd-aMCI/md-aMCI classification experiments respectively. Conclusions: The proposed ThickNet classifier demonstrated potential for discriminating md-aMCI from controls, and in discriminating sd-aMCI from md-aMCI, using cortical features from baseline MRI scan alone. Use of the proposed novel ThickNet features demonstrates significant improvements over previous experiments using cortical thickness alone. This result may offer the possibility of early detection of Alzheimer's disease via improved discrimination of aMCI subtypes. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2213-1582 |
| العلاقة: | http://www.sciencedirect.com/science/article/pii/S2213158214001417Test; https://doaj.org/toc/2213-1582Test |
| DOI: | 10.1016/j.nicl.2014.09.005 |
| الوصول الحر: | https://doaj.org/article/6c8a32ab829840078491a80b3ca650d0Test |
| رقم الانضمام: | edsdoj.6c8a32ab829840078491a80b3ca650d0 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 22131582 |
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| DOI: | 10.1016/j.nicl.2014.09.005 |