Browsing by Author "Wachinger, Christian"

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    Alzheimer’s Disease Diagnosis via Deep Factorization Machine Models
    (Springer, 2021) Ronge, Raphael; Nho, Kwangsik; Wachinger, Christian; Pölsterl, Sebastian; Radiology and Imaging Sciences, School of Medicine
    The current state-of-the-art deep neural networks (DNNs) for Alzheimer’s Disease diagnosis use different biomarker combinations to classify patients, but do not allow extracting knowledge about the interactions of biomarkers. However, to improve our understanding of the disease, it is paramount to extract such knowledge from the learned model. In this paper, we propose a Deep Factorization Machine model that combines the ability of DNNs to learn complex relationships and the ease of interpretability of a linear model. The proposed model has three parts: (i) an embedding layer to deal with sparse categorical data, (ii) a Factorization Machine to efficiently learn pairwise interactions, and (iii) a DNN to implicitly model higher order interactions. In our experiments on data from the Alzheimer’s Disease Neuroimaging Initiative, we demonstrate that our proposed model classifies cognitive normal, mild cognitive impaired, and demented patients more accurately than competing models. In addition, we show that valuable knowledge about the interactions among biomarkers can be obtained.
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    A Longitudinal Imaging Genetics Study of Neuroanatomical Asymmetry in Alzheimer's Disease
    (Elsevier, 2018-10-01) Wachinger, Christian; Nho, Kwangsik; Saykin, Andrew J.; Reuter, Martin; Rieckmann, Anna; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Neuroanatomical asymmetries have recently been associated with the progression of Alzheimer’s disease (AD) but the biological basis of asymmetric brain changes in disease remains unknown. Methods We investigated genetic influences on brain asymmetry by identifying associations between MRI-derived measures of asymmetry and candidate single-nucleotide polymorphisms (SNPs) that have previously been identified in genome-wide association studies (GWAS) for AD diagnosis and for brain subcortical volumes. For the longitudinal neuroimaging data (1,241 individuals; 6,395 scans), we use a mixed effects model with interaction between genotype and diagnosis. Results We found significant associations between asymmetry of amygdala, hippocampus, and putamen and SNPs in the genes BIN1, CD2AP, ZCWPW1, ABCA7, TNKS, and DLG2. For AD candidate SNPs, we demonstrated an asymmetric effect on subcortical brain structures. Conclusions The associations between SNPs in the genes TNKS and DLG2 and AD-related increases in shape asymmetry are of particular interest; these SNPs have previously been associated with subcortical volumes of amygdala and putamen but have not yet been associated with Alzheimer’s pathology. This provides novel evidence about the biological underpinnings of brain asymmetry as a disease marker. Contralateral brain structures represent a unique, within-patient, reference element for disease and asymmetries can provide a personalized measure of the accumulation of past disease processes.