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. 2023 Jul 15:275:120147.
doi: 10.1016/j.neuroimage.2023.120147. Epub 2023 May 6.

Identifying sex-specific risk architectures for predicting amyloid deposition using neural networks

Linghai Wang  1 , Antonija Kolobaric  2 , Howard Aizenstein  3 , Brian Lopresti  2 , Dana Tudorascu  4 , Beth Snitz  2 , William Klunk  5 , Minjie Wu  6
Affiliations

Identifying sex-specific risk architectures for predicting amyloid deposition using neural networks

Linghai Wang et al. Neuroimage. .

Abstract

In older adults without dementia, White Matter Hyperintensities (WMH) in MRI have been shown to be highly associated with cerebral amyloid deposition, measured by the Pittsburgh compound B (PiB) PET. However, the relation to age, sex, and education in explaining this association is not well understood. We use the voxel counts of regional WMH, age, one-hot encoded sex, and education to predict the regional PiB using a multilayer perceptron with only rectilinear activations using mean squared error. We then develop a novel, robust metric to understand the relevance of each input variable for prediction. Our observations indicate that sex is the most relevant predictor of PiB and that WMH is not relevant for prediction. These results indicate that there is a sex-specific risk architecture for Aβ deposition.

Keywords: Alzheimer's disease; Beta-amyloid; Machine learning; Sex differences; Small vessel disease.

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Conflict of interest statement

Declaration of Competing Interest GE Healthcare holds a license agreement with the University of Pittsburgh based on the technology described in this manuscript. Dr. Klunk is a co-inventor of PiB and, as such, has a financial interest in this license agreement. GE Healthcare provided no grant support for this study and had no role in the design or interpretation of results or preparation of this manuscript. All other authors have no conflicts of interest with this work and had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Figures

Figure 1:
Figure 1:
Diagram of model input and outputs
Figure 2
Figure 2
a: Permuted feature importance box plot for predicting regional PiB shows no significant predictors. b: The W-table of permuted feature importance value comparisons reflects the lack of significant predictors in 2a.
Figure 2
Figure 2
a: Permuted feature importance box plot for predicting regional PiB shows no significant predictors. b: The W-table of permuted feature importance value comparisons reflects the lack of significant predictors in 2a.
Figure 3
Figure 3
a: Feature relevance box plot for predicting regional PiB shows a strong effect for females. b: W-table of feature relevance value comparisons reflects the strong effect in females.
Figure 3
Figure 3
a: Feature relevance box plot for predicting regional PiB shows a strong effect for females. b: W-table of feature relevance value comparisons reflects the strong effect in females.
See this image and copyright information in PMC

References

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