Evaluation of Functional Decline in Alzheimer's Dementia Using 3D Deep Learning and Group ICA for rs fMRI Measurements
Purpose: To perform automatic assessment of dementia severity using a deep learning framework applied to resting state functional magnetic resonance imaging (rs fMRI) data. Method: We divided 133 Alzheimer's disease (AD) patients with clinical dementia rating (CDR) scores from 0.5 to 3 into two groups based on dementia severity; the groups with very mild/mild (CDR: 0.5 1) and moderate to severe (CDR: 2 3) dementia consisted of 77 and 56 subjects, respectively. We used rs fMRI to extract functional connectivity features, calculated using independent component analysis (ICA) and performed automated severity classification with three dimensional convolutional neural networks (3D CNNs) based on deep learning. Results: The mean balanced classification accuracy was 0.923 0.042 (p 0.001) with a specificity of 0.946 0.019 and sensitivity of 0.896 0.077. The rs fMRI data indicated that the medial frontal, sensorimotor, executive control, dorsal attention, and visual related networks mainly correlated with dementia severity. Conclusions: Our CDR based novel classification using rs fMRI is an acceptable objective severity indicator. In the absence of trained neuropsychologists, dementia severity can be objectively and accurately classified using a 3D deep learning framework with rs fMRI independent components.
Front. Aging Neurosci.