Early detection of mild cognitive impairment with convolutional neural network based on brain structural connectivity. (31st December 2021)

• Record Type: Journal Article
• Authors: Chen, Qianyun; Yan, Taiyu; Abrigo, Jill; Chu, Winnie C.W.
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.052845
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Structural connectivity derived from diffusion tensor imaging (DTI) reflects white matter network changes during Alzheimer's Disease (AD) progression, which can potentially serve as a biomarker for prediction. In this study we explored the ability of structural connectivity to predict mild cognitive impairment (MCI) by implementing a convolutional neural network (CNN). Method: 613 DTI scans (194 controls, 291 MCI and 128 AD) from 206 patients (113 male, 93 female; age range: 55‐91 years) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) Phase GO and 2 were included in this study. The clinical diagnosis at last ADNI follow‐up visit served as reference for prediction (average: 44.4 ± 26.7 months). DTI scans were acquired with 5 b0 and 41 diffusion weighted volumes with b=1000 s/mm 2 at 2.7×2.7×2.7mm 3 resolution. Preprocessing included eddy current correction and co‐registration to normalized T1‐weighted scans. Whole brain white matter fiber was tracked by deterministic tractography using DSI Studio under Q‐space diffeomorphic reconstruction scheme, which calculates the quantitative anisotropy mapping prior to normalization to standard space. An Automated Anatomical Labeling atlas 2 with 120 region‐of‐interests (ROI) was used to parcellate the brain, where each ROI represented a node of the network. The weight of the edge was calculated by number of tracts connecting two ROIs and normalized by medial length. Therefore a 120×120 structural connectivity matrix was generated for each scan. 80% of data was used for training and 10% of data for validation. The parameters that achieved highest accuracy on validation set was applied to the remaining 10% (hold‐out test set) for final evaluation. A seven‐layer CNN model was constructed for differentiating three groups. The performance was evaluated by accuracy and sensitivity. Result: On an average of 3.7 years before the diagnosis, the prediction accuracy was 86.9% in validation set and 85.7% in hold‐out test set. The sensitivity to predict controls and MCI were 95.0% and 93.3%, respectively. Conclusion: Using CNN, structural connectivity allowed good ability to predict MCI from cohorts at an early stage before diagnosis.