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Classifying cognitive impairment based on FDG-PET and combined T1-MRI and rs-fMRI: An ADNI study
Iman Jahani1, Ali Jahani1, Mehdi Delrobaei1,2
1Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Background:
Mild cognitive impairment (MCI) refers to a memory impairment among non-demented adults. It is a condition that increases the risk of dementia, notably due to Alzheimer's disease (AD). MCI is heterogeneous and there is a need for novel diagnostic approaches. Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging provides robust AD biomarker characteristics, while anatomical and functional magnetic resonance imaging (MRI) offer complementary information.
Objective:
Classify MCI and cognitively normal (CN) adults using FDG-PET images; predict individuals with MCI that convert to AD dementia; determine if MRI can achieve comparable performance to FDG-PET classification.
Methods:
Four ADNI cohorts were created. Cohort 1: 805 participants (MCI n = 455; CN n = 350) that underwent FDG-PET. FDG-PET images were inputs to a one-channel 3-dimensional (3D) DenseNet deep learning model. Cohort 2: 348 participants (MCI n = 174; CN n = 174) with MRI and functional MRI. Cohort 3: overlapping cases from cohorts 1 and 2 (MCI n = 70; CN n = 70). Cohort 4: 336 participants (MCI-converters n = 168; MCI-stable n = 168) with FDG-PET from cohort 1. The one/two-channel models' inputs were T1-weighted MRI and/or amplitude of low-frequency fluctuations images, with classification metrics evaluated through 10-fold cross-validation.
Results:
The FDG-PET model achieved 88.02%±3.82 accuracy for MCI versus CN classification, with 88.70%±4.70 sensitivity and 87.14%±5.03 specificity. Neither MRI model outperformed the FDG-PET model, as the highest MRI-based accuracy was 76.86%±1.95. The FDG-PET model achieved 63.23%±4.68 accuracy in classifying MCI-converters versus MCI-stable.
Conclusions:
FDG-PET images produced the highest accuracy in classifying MCI versus CN. While MRI-based approaches were inferior to FDG-PET, multi-contrast MRI still offers value for neurodegeneration classification.
Insights
Fluorodeoxyglucose positron emission tomography (FDG-PET) effectively classifies mild cognitive impairment (MCI) versus cognitively normal (CN) adults. While MRI showed lower accuracy, it remains valuable for neurodegeneration assessment.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Neurology
Background:
- Mild cognitive impairment (MCI) is a precursor to dementia, necessitating advanced diagnostic tools.
- Heterogeneity of MCI requires novel approaches for accurate classification and prediction.
- Fluorodeoxyglucose positron emission tomography (FDG-PET) offers robust Alzheimer's disease (AD) biomarkers.
Purpose of the Study:
- To classify individuals with MCI versus cognitively normal (CN) adults using FDG-PET.
- To predict MCI individuals who will convert to AD dementia.
- To compare the diagnostic performance of MRI against FDG-PET for MCI classification.
Main Methods:
- A 3D DenseNet deep learning model was trained on FDG-PET images from 805 participants (MCI/CN).
- MRI and functional MRI data from 348 participants were used to train alternative classification models.
- Performance was evaluated using 10-fold cross-validation across four ADNI cohorts.
Main Results:
- The FDG-PET model achieved 88.02% accuracy in classifying MCI vs. CN.
- MRI-based models yielded lower accuracy, with the highest reaching 76.86%.
- The FDG-PET model showed 63.23% accuracy in predicting MCI converters versus stable MCI.
Conclusions:
- FDG-PET imaging demonstrates superior accuracy for classifying MCI versus CN.
- Although less accurate than FDG-PET, multi-contrast MRI provides valuable insights for neurodegeneration classification.
- Deep learning models show promise for analyzing neuroimaging data in MCI and AD research.
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