Improved Diagnostic Accuracy of Alzheimer’s Disease by Combining Regional Cortical Thickness and Default Mode Network Functional Connectivity: Validated in the Alzheimer’s Disease Neuroimaging Initiative Set
- Resource Type
- Article
- Authors
- 박지은; 박범우; 김상준; 김호성; 최충곤; 정승채; 오주영; 이재홍; 노지훈; 심우현; Alzheimer’s Disease Neuroimaging Initiative
- Source
- Korean Journal of Radiology, 18(6), pp.983-991 Dec, 2017
- Subject
- 방사선과학
- Language
- English
- ISSN
- 2005-8330
1229-6929
Objective: To identify potential imaging biomarkers of Alzheimer’s disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model’s diagnostic accuracy in a validation set. Materials and Methods: Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer’s Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging. Cortical regions with significant differences between patients and healthy controls in the correlation of CThk and functional connectivity were identified in the study set. The diagnostic accuracy of functional connectivity measures combined with CThk in the identified regions was evaluated against that in the medial temporal lobes using the validation set and application of a support vector machine. Results: Group-wise differences in the correlation of CThk and default mode network functional connectivity were identified in the superior temporal (p < 0.001) and supramarginal gyrus (p = 0.007) of the left cerebral hemisphere. Default mode network functional connectivity combined with the CThk of those two regions were more accurate than that combined with the CThk of both medial temporal lobes (91.7% vs. 75%). Conclusion: Combining functional information with CThk of the superior temporal and supramarginal gyri in the left cerebral hemisphere improves diagnostic accuracy, making it a potential imaging biomarker for Alzheimer’s disease.