Evaluation of Functional Decline in Alzheimer's Dementia Using 3D Deep Learning and Group ICA for rs-fMRI Measurements

Frontiers in Aging Neuroscience
Muhammad Naveed Iqbal QureshiBoreom Lee

Abstract

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 ...Continue Reading

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Citations

Jul 1, 2020·Frontiers in Neuroinformatics·Gloria CastellazziClaudia A M Gandini Wheeler-Kingshott
Oct 24, 2020·APL Bioengineering·Alice SegatoElena De Momi
Nov 2, 2021·ACS Chemical Neuroscience·Ying ZhouXiaoying Tang

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Software Mentioned

Multivariate Exploratory Linear Optimized Decomposition into I...
FMRIB Software Library ( FSL
FSLNet
FSLNets
fMRI
FSL
TensorFlow
VGG

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