Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI
| Autor: | Seyed Hani Hojjati, Ata Ebrahimzadeh, Alzheimer's Disease Neuroimaging Initiative, Ali Khazaee, Abbas Babajani-Feremi |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Male Support Vector Machine Computer science Health Informatics 03 medical and health sciences 0302 clinical medicine Alzheimer Disease Image Interpretation Computer-Assisted medicine Humans Cognitive Dysfunction Diagnosis Computer-Assisted Mild cognitive impairment (MCI) Cognitive impairment Aged Aged 80 and over Brain Mapping Resting state fMRI business.industry Functional connectivity Brain Pattern recognition medicine.disease Magnetic Resonance Imaging Computer Science Applications Support vector machine 030104 developmental biology Female Artificial intelligence business 030217 neurology & neurosurgery Algorithms |
| Zdroj: | Computers in biology and medicine. 102 |
| ISSN: | 1879-0534 |
| Popis: | Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) have provided promising results in the diagnosis of Alzheimer's disease (AD), though the utility of integrating sMRI with rs-fMRI has not been explored thoroughly. We investigated the performances of rs-fMRI and sMRI in single modality and multi-modality approaches for classifying patients with mild cognitive impairment (MCI) who progress to probable AD-MCI converter (MCI-C) from those with MCI who do not progress to probable AD-MCI non-converter (MCI-NC). The cortical and subcortical measurements, e.g. cortical thickness, extracted from sMRI and graph measures extracted from rs-fMRI functional connectivity were used as features in our algorithm. We trained and tested a support vector machine to classify MCI-C from MCI-NC using rs-fMRI and sMRI features. Our algorithm for classifying MCI-C and MCI-NC utilized a small number of optimal features and achieved accuracies of 89% for sMRI, 93% for rs-fMRI, and 97% for the combination of sMRI with rs-fMRI. To our knowledge, this is the first study that investigated integration of rs-fMRI and sMRI for identification of the early stage of AD. Our findings shed light on integration of sMRI with rs-fMRI for identification of the early stages of AD. |
| Databáze: | OpenAIRE |
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