| 1. |
- Wachinger, Christian, et al.
(författare)
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Latent processes governing neuroanatomical change in aging and dementia
- 2017
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Ingår i: Medical image computing and computer assisted intervention, MICCAI 2017. - Cham : Springer. - 9783319661780 ; , s. 30-37
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Konferensbidrag (refereegranskat)abstract
- Clinically normal aging and pathological processes cause structural changes in the brain. These changes likely occur in overlapping regions that accommodate neural systems with high susceptibility to deleterious factors. Due to the overlap, the separation between aging and pathological processes is challenging when analyzing brain structures independently. We propose to identify multivariate latent processes that govern cross-sectional and longitudinal neuroanatomical changes across the brain in aging and dementia. A discriminative representation of neuroanatomy is obtained from spectral shape descriptors in the BrainPrint. We identify latent factors by maximizing the covariance between morphological change and response variables of age and a proxy for dementia. Our results reveal cross-sectional and longitudinal patterns of change inneuroanatomy that distinguishes aging processes from disease processes. Finally, latent processes do not only yield a parsimonious model but also a significantly improved prediction accuracy.
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| 2. |
- Wachinger, Christian, et al.
(författare)
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Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
- 2019
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Ingår i: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. - Cham : Springer. - 9783030322519 - 9783030322502 ; , s. 484-492
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Konferensbidrag (refereegranskat)abstract
- Neuroimaging datasets keep growing in size to address increasingly complex medical questions. However, even the largest datasets today alone are too small for training complex machine learning models. A potential solution is to increase sample size by pooling scans from several datasets. In this work, we combine 12,207 MRI scans from 15 studies and show that simple pooling is often ill-advised due to introducing various types of biases in the training data. First, we systematically define these biases. Second, we detect bias by experimentally showing that scans can be correctly assigned to their respective dataset with 73.3% accuracy. Finally, we propose to tell causal from confounding factors by quantifying the extent of confounding and causality in a single dataset using causal inference. We achieve this by finding the simplest graphical model in terms of Kolmogorov complexity. As Kolmogorov complexity is not directly computable, we employ the minimum description length to approximate it. We empirically show that our approach is able to estimate plausible causal relationships from real neuroimaging data.
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