Neuroimage Biomarker Identification of the Conversion of Mild Cognitive Impairment to Alzheimer's Disease
| Autor: | Te-Han Kung, Tzu-Cheng Chao, Yi-Ru Xie, Ming-Chyi Pai, Yu-Min Kuo, Gwo Giun Chris Lee |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.diagnostic_test
business.industry General Neuroscience Magnetic resonance imaging Feature selection Sulcus medicine.disease lcsh:RC321-571 hippocampal subfields Random forest mild cognitive impairment medicine.anatomical_structure Atrophy Gyrus medicine magnetic resonance imaging Biomarker (medicine) multilayer perceptron business lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Alzheimer’s disease Neuroscience Cognitive load Original Research |
| Zdroj: | Frontiers in Neuroscience Frontiers in Neuroscience, Vol 15 (2021) |
| ISSN: | 1662-4548 |
| Popis: | An efficient method to identify whether mild cognitive impairment (MCI) has progressed to Alzheimer’s disease (AD) will be beneficial to patient care. Previous studies have shown that magnetic resonance imaging (MRI) has enabled the assessment of AD progression based on imaging findings. The present work aimed to establish an algorithm based on three features, namely, volume, surface area, and surface curvature within the hippocampal subfields, to model variations, including atrophy and structural changes to the cortical surface. In this study, a new biomarker, the ratio of principal curvatures (RPC), was proposed to characterize the folding patterns of the cortical gyrus and sulcus. Along with volumes and surface areas, these morphological features associated with the hippocampal subfields were assessed in terms of their sensitivity to the changes in cognitive capacity by two different feature selection methods. Either the extracted features were statistically significantly different, or the features were selected through a random forest model. The identified subfields and their structural indices that are sensitive to the changes characteristic of the progression from MCI to AD were further assessed with a multilayer perceptron classifier to help facilitate the diagnosis. The accuracy of the classification based on the proposed method to distinguish whether a MCI patient enters the AD stage amounted to 79.95%, solely using the information from the features selected by a logical feature selection method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|