Alzheimer’s disease (AD) is a neurodegenarative disorder that is increasingly affecting the worldwide population. There are currently no disease-modifying treatments available, however, promising drugs are being tested in multi-cohort clinical trials. Accurate early-stage forecasts of the development of AD progression are helpful in improving cohort selection. In this study, I investigate the performance of a multi-input convolutional neural network (CNN) for predictions of AD progression. I specifically focus on the generalisability of these forecasts to previously unseen individuals. The multiclass predictions distinguish between cognitively normal (CN) patients, patients with a mild cognitive impairment (MCI), and AD patients. The model is based on grey matter density maps of brain MRI scans and two non-image features, i.e. current diagnosis and follow-up time. One of the main challenges of predicting future diagnosis is to outperform a simple model that always predicts current diagnosis. I therefore introduce a custom loss function that differentiates between non-converters, converters to MCI, and converters to AD. Furthermore, the Grad-CAM tool is used to visualise model activations in the brain and the external performance of the methods is validated on a separate dataset. This study shows that the progression of Alzheimer’s disease can be predicted quite accurately based on a single MRI scan and corresponding current diagnosis. When converters are of interest, a custom loss function is beneficial for tackling the imbalance in the data. A model that uses such a custom loss function generalises quite well to predictions further in the future and to an external dataset. The Grad-CAM results show that model activations in the brain are not always consistent, however, the cerebellum appears to be an important brain region for MRIbased predictions of Alzheimer’s disease progression.

Additional Metadata
Thesis Advisor Groenen, P.J.F.
Persistent URL hdl.handle.net/2105/52263
Series Econometrie
Citation
Michelotti, T. (2020, June 8). Convolutional Neural Networks for Multiclass Predictions of Alzheimer's Disease Progression. Econometrie. Retrieved from http://hdl.handle.net/2105/52263