A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

Medientyp: E-Artikel

Titel: A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

Beteiligte: Zhang, Ying; Xue, Le; Zhang, Shuoyan; Yang, Jiacheng; Zhang, Qi; Wang, Min; Wang, Luyao; Zhang, Mingkai; Jiang, Jiehui; Li, Yunxia; Weiner, Michael W.; Aisen, Paul; Petersen, Ronald; Jack, Clifford R.; Jagust, William; Trojanowski, John Q.; Toga, Arthur W.; Beckett, Laurel; Green, Robert C.; Saykin, Andrew J.; Morris, John; Shaw, Leslie M.; Khachaturian, Zaven; Sorensen, Greg; [...]

Erschienen: Springer Science and Business Media LLC, 2024

Sprache: Englisch

DOI: 10.1186/s13195-024-01425-8

ISSN: 1758-9193

Schlagwörter: Cognitive Neuroscience ; Neurology (clinical) ; Neurology

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Beschreibung: Abstract Background Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD. Methods This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers. Results The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status. Conclusions This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

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