Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer's disease using deep learning-derived saliency maps.
Authors
Affiliations (11)
Affiliations (11)
- School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia.
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, 201807, China.
- School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia. [email protected].
- University of Sydney Association of Professors (USAP), University of Sydney, Sydney, NSW, Australia.
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA.
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China. [email protected].
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, 201807, China. [email protected].
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China. [email protected].
Abstract
Alzheimer's disease (AD) is a heterogeneous neurodegenerative disorder in which tau neurofibrillary tangles are a pathological hallmark closely associated with cognitive dysfunction and neurodegeneration. In this study, we used brain tau data to investigate AD heterogeneity by identifying and characterizing the subpopulations among patients. We included 615 cognitively normal and 159 AD brain <sup>18</sup>F-flortaucipr PET scans, along with T1-weighted MRI from the Alzheimer Disease Neuroimaging Initiative database. A three dimensional-convolutional neural network model was employed for AD detection using standardized uptake value ratio (SUVR) images. The model-derived saliency maps were generated and employed as informative image features for clustering AD participants. Among the identified subpopulations, statistical analysis of demographics, neuropsychological measures, and SUVR were compared. Correlations between neuropsychological measures and regional SUVRs were assessed. A generalized linear model was utilized to investigate the sex and APOE ε4 interaction effect on regional SUVRs. Two distinct subpopulations of AD patients were revealed, denoted as S<sub>Hi</sub> and S<sub>Lo</sub>. Compared to the S<sub>Lo</sub> group, the S<sub>Hi</sub> group exhibited a significantly higher global tau burden in the brain, but both groups showed similar cognition distribution levels. In the S<sub>Hi</sub> group, the associations between the neuropsychological measurements and regional tau deposition were weakened. Moreover, a significant interaction effect of sex and APOE ε4 on tau deposition was observed in the S<sub>Lo</sub> group, but no such effect was found in the S<sub>Hi</sub> group. Our results suggest that tau tangles, as shown by SUVR, continue to accumulate even when cognitive function plateaus in AD patients, highlighting the advantages of PET in later disease stages. The differing relationships between cognition and tau deposition, and between gender, APOE4, and tau deposition, provide potential for subtype-specific treatments. Targeting gender-specific and genetic factors influencing tau deposition, as well as interventions aimed at tau's impact on cognition, may be effective.