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Session: Behavior & Monitoring
Session starts: Thursday 24 January, 13:30
Presentation starts: 14:00
Room: Lecture room 535

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Leontine Ham (Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands)
Vikram Venkatraghavan (Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands)
Alle Meije Wink (VU University Medical Center, Amsterdam, The Netherlands)
Wiesje van der Flier (VU University Medical Center, Amsterdam, The Netherlands)
Rebecca Steketee (Radiology, Erasmus MC, Rotterdam, The Netherlands)
Marion Smits (Radiology, Erasmus MC, Rotterdam, The Netherlands)
Wiro Niessen (Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands)
Stefan Klein (Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands)
Esther Bron (Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands)

Abstract:
Understanding the progression of dementia is important for early diagnosis. Event-based models (EBMs) [Fonteijn, 2012] estimate disease progression, which can be represented as a sequence of events, using cross-sectional data. In addition, they output a patient stage which can be used for diagnosis and prognosis. EBMs use mixture modelling, e.g. Gaussian Mixture Model (GMM), to fit the patient- and control distributions of each biomarker. In small and heterogeneous datasets, GMM fitting may fail and thus impede the reliability of EBM. Therefore, we propose a transfer learning solution that enables EBM of small and heterogeneous datasets by adding biomarker data for a similar disease. Data from a well-defined cohort of a similar disease is expected to aid more accurate estimation of Gaussian parameters. We jointly fit GMM such that Gaussian parameters are shared, while the mixing parameters are estimated independently for the different datasets. We use discriminative EBM (DEBM) [Venkatraghavan, 2018] to estimate the event sequence. We applied the proposed solution to vascular cognitive impairment (VCI). VCI presents itself with cognitive decline of cerebrovascular origin, has high heterogeneity and often co-occurs with Alzheimer’s disease (AD). VCI datasets are generally small, without a homogeneously diagnosed group. Since AD has similar grey matter (GM) atrophy effects, we used data from AD-subjects to aid the GMM fitting in VCI-subjects. We included subjects from the Heart-Brain Connection study [Hooghiemstra, 2017]; 148 VCI patients, of which 35 with ≥2 cognitive domains affected (VCI2), and 124 cognitively normals (CN). External biomarker data [Bron, 2017; Binnewijzend, 2015] was used in our transfer learning approach: 88 AD, 22 mild cognitive impairment and 125 CN. For both datasets, global- and 74 region GM volumes were computed [Bron, 2014]. We selected 30 significantly different GM volumes (29 ROIs + entire brain) (VCI2-CN; t-test; p<0.05). We evaluated patient staging classification performance (VCI2-CN) using 50-repetition 5-fold cross-validation. AUC was slightly higher for the novel approach (0.866 ± 0.084) than for HBC alone (0.847 ± 0.098) and standard deviation was smaller. The proposed transfer learning approach improved and stabilized AUC. Hence, DEBM combined with transfer learning is promising for small and heterogeneous datasets.