DeepAtrophy: Teaching a Neural Network to Differentiate Progressive Changes from Noise on Longitudinal MRI in Alzheimer's Disease

Abstract

Volume change measures derived from longitudinal MRI (e.g. hippocampal atrophy) are a well-studied biomarker of disease progression in Alzheimer's Disease (AD) and are used in clinical trials to track the therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures can be confounded by non-biological factors, such as different degrees of head motion and susceptibility artifact between pairs of MRI scans. We hypothesize that deep learning methods applied directly to pairs of longitudinal MRI scans can be trained to differentiate between biological changes and non-biological factors better than conventional approaches based on deformable image registration. To achieve this, we make a simplifying assumption that biological factors are associated with time (i.e. the hippocampus shrinks overtime in the aging population) whereas non-biological factors are independent of time. We then formulate deep learning networks to infer the temporal order of same-subject MRI scans input to the network in arbitrary order; as well as to infer ratios between interscan intervals for two pairs of same-subject MRI scans. In the test dataset, these networks perform better in tasks of temporal ordering (89.3%) and interscan interval inference (86.1%) than a state-of-the-art deformation-based morphometry method ALOHA (76.6% and 76.1% respectively) (Das et al., 2012). Furthermore, we derive a disease progression score from the network that is able to detect a group difference between 58 preclinical AD and 75 beta-amyloid-negative cognitively normal individuals within one year, compared to two years for ALOHA. This suggests that deep learning can be trained to differentiate MRI changes due to biological factors (tissue loss) from changes due to non-biological factors, leading to novel biomarkers that are more sensitive to longitudinal changes at the earliest stages of AD.

arXiv commentSubmitted to a journal, IF ~ 6

References in corpus (10)

• Deep Residual Learning for Image Recognition
• Self-Attention Generative Adversarial Networks
• Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
• Med3D: Transfer Learning for 3D Medical Image Analysis
• Disease Progression Modeling and Prediction through Random Effect Gaussian Processes and Time Transformation
• Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
• TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data
• Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images
• Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling
• Early Prediction of Alzheimer's Disease Dementia Based on Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks