CompAge 2020
With thanks to all participants, please enjoy the available content below!
Day 1 - Tuesday 01/09/2020
Times are from Paris, France.
| Presentation time | Title | Authors |
|---|---|---|
| 0845 – 0900 | Welcome message | S. Durrleman (Paris Brain Institute – ICM) |
| 0900 – 0930 | EuroPOND project highlights | D. Alexander (UCL) |
| 0930 – 1000 | Piloting a novel screening tool for reducing heterogeneity in clinical trials in Alzheimer’s disease | N. Oxtoby (UCL) |
| 1030 – 1130 | Keynote 1 | Mihaela van der Schaar (University of Cambridge & The Alan Turing Institute) |
| 1130 – 1200 | Prediction of biomarkers’ trajectory in Huntington’s disease: application to precise clinical trial design | I. Koval (Paris Brain Institute – ICM); T. Dighiero; R. Scahill; A. Durr; S. Durrleman |
| 1200 – 1230 | Event-based modelling of multimodal biomarkers in multiple sclerosis | V. Wottschel (Amsterdam UMC); I. Dekker; M. Schoonheim ; V. Venkatraghavan; A. Eijlers; I. Brouwer; E. Bron; S. Klein; M. Wattjes; J. Geurts; B. Uitdehaag; N. Oxtoby; D. Alexander; H. Vrenken; J. Killestein; F. Barkhof |
| 1400 – 1430 | Application of Variational Autoencoder Modular Bayesian Networks (VAMBN) in assessing role of functional and cognitive decline in observational cohorts | M. Sood (Fraunhofer SCAI); M. Hofmann-Apitius; H. Fröhlich |
| 1430 – 1500 | Deep learning for clustering of multivariate longitudinal clinical patient data with missing values | J. de Jong (UCB Biosciences GmbH) |
| 1530 – 1600 | Generating Images that Mimic Disease Progression | D. Ravì (UCL); D. Alexander; N. Oxtoby |
| 1600 – 1630 | Aging Human Avatar: a computational modeling platform to study neural correlates of aging | D. Sheynikhovich (Institut de la Vision) |
| 1630 – 1730 | Day 1 posters (see below) | Interactive session |
Day 1 Posters
1. Luigi Lorenzini (VUmc Amsterdam)Atypical development of subcortico-cortical effective connectivity in autism spectrum disorders
2. Damiano Archetti (IRCCS Centro San Giovanni di Dio-Fatebenefratelli)
Inter-cohort validation of SuStaIn model for Alzheimer's disease
3. Maura Bellio (University College London)
A framework to translate computational approaches for chronic conditions into a clinical tool: the icompass case.
4. Ksenia Sokolova (King's College London)
Convolutional Neural Networks for Assessing Population-wide Whole Brain Ageing Effects from Structural MRI Scans
5. Riccardo Pascuzzo (Fondazione IRCCS Istituto Neurologico Carlo Besta)
Strain-specific disease progression patterns of sporadic Creutzfeldt-Jakob disease revealed by Subtype and Stage Inference model
6. Carinna Torgerson (USC)
Machine learning of WM diffusion changes shows robust brain age prediction and different regional aging
7. Vikram Venkatraghavan (Erasmus MC)
Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations
8. Elisabeth Vinke (Erasmus MC)
Predicting the incidence of Alzheimer's disease in the general elderly population using event-based modelling
9. Mengting Liu (University of Southern California)
Deep learning of brain MRI reveals sex and ethnicity-dependent brain aging led by metabolic syndromes
10. Neil Oxtoby (University College London)
Model-based subtypes of disease progression in Parkinson's
Day 2 - Wednesday 02/09/2020
| Presentation time | Title | Authors |
|---|---|---|
| 0900 – 0930 | Combining magnetic resonance imaging and magnetoencephalography enhances modeling of brain age | D. Engemann (INRIA); O. Kozynets; D. Sabbagh; G. Lemaître; G. Varoquaux; F. Liem; A. Gramfort |
| 0930 – 1000 | Brain-age predicts subsequent dementia in memory clinic patients | F. Biondo (King’s College London/UCL); J. Cole |
| 1030 – 1100 | Forecast of the MMSE score up to 6 years ahead, with cross-cohort replications | E. Maheux (INRIA / Paris Brain Institute – ICM); I. Koval; S. Durrleman |
| 1100 – 1130 | Comparison of Alzheimer’s Disease Progression Patterns across Multiple Cohort Study Datasets | Y. Salimi (Fraunhofer Institute for Algorithms and Scientific Computing); C. Birkenbihl; M. Hofmann-Apitius; H. Fröhlich |
| 1130 – 1230 | Day 2 Posters (see below) | Interactive session |
| 1400 – 1430 | Radar-AD highlights | H. Frölich (for M. Hofmann-Apitius, Fraunhofer SCAI, Bonn-Aachen International Center for Information Technology) |
| 1430 – 1500 | Reliable MRI volumetry for Alzheimer’s disease: diagnostic performance of icobrain dm on real-world data | M. Wittens (Uantwerpen); A. Ribbens; D. Sima; W. van Hecke; E. de la Rosa; D. Smeets; S. Engelborghs |
| 1530 – 1630 | Keynote 2 | Adam Schwarz (Takeda Pharmaceuticals) |
| 1630 – 1730 | Industry Round Table | Interactive session (was not recorded) |
Day 2 Posters
11. Thomas Linden (Fraunhofer SCAI, Bonn-Aachen International Center for Information Technology )Predicting comorbidities of epilepsy patients using big data from Electronic Health Records augmented with biomedical knowledge
12. Ashar Ahmad(University of Bonn)
Identifying, predicting and validating subtypes of Parkinson Disease progression using machine learning
13. Philipp Wendland (Fraunhofer SCAI, University of Applied Sciences Koblenz)
Generative Artificial Intelligence Approaches for Modeling of Multimodal Longitudinal Clinical Studies and Simulation of Virtual Cohorts
14. Mohamed Aborageh (Fraunhofer SCAI)
Machine learning classification of Alzheimer’s disease: Diagnostic Prediction Using Cognitive and Functional Domains
15. David Sabbagh (INRIA)
Robust prediction of age from MEG/EEG signals without biophysical source modeling
16. Angelo Arleo (CNRS Vision Institute)
Lack of support for a common cause hypothesis of visuo-cognitive aging: multivariate statistical analysis on the SilverSight follow-up cohort study
17. Lara Dular (University of Ljubljana, Faculty of Electrical Engineering, Laboratory of Imaging Technologies)
Deep learning models for brain age estimation
18. Alexandra Young (Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London)
The scored events model: Subtype and Stage Inference (SuStaIn) for visual ratings, clinical scores and other ordinal data
19. Pierre-Emmanuel Poulet (ARAMIS Lab)
Modelling longitudinal binary data in Parkinson
20. Raphaël Couronné (UPMC/INRIA)
Modeling the progression of Parkinson’s Disease: comparison of subjects with and without Sleep Disorders
21. Stijn Denissen (UZ Brussels/icometrix)
Predicted brain age as a cognitive biomarker in Multiple Sclerosis