DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease

Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based o...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Vol. 243; p. 118514
Main Authors: Dong, Mengjin, Xie, Long, Das, Sandhitsu R., Wang, Jiancong, Wisse, Laura E.M., deFlores, Robin, Wolk, David A., Yushkevich, Paul A.
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01.11.2021
Elsevier Limited
Elsevier
Subjects:
Aged
Aged, 80 and over
Aging
Algorithms
Alzheimer Disease - pathology
Alzheimer's disease
Atrophy
Biomarkers
Clinical Medicine
Clinical trials
Cognitive Dysfunction - pathology
Deep learning
Disease Progression
Female
Hippocampus
Hippocampus - pathology
Hippocampus area
Humans
Interscan interval
Klinisk medicin
Longitudinal analysis
Longitudinal Studies
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical and Health Sciences
Medicin och hälsovetenskap
Morphometry
Neural networks
Neural Networks, Computer
Neurodegenerative diseases
Neuroimaging
Neuroimaging - methods
Neurologi
Neurology
Radiologi och bildbehandling
Radiology and Medical Imaging
Registration
Substantia grisea
T1-weighted MRI
Teaching
Longitudinal analysis
Hippocampus area
T1-weighted MRI
Interscan interval
Alzheimer's disease
Disease progression
ISSN:1053-8119, 1095-9572, 1095-9572
Online Access:Get full text
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Summary:Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.
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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2021.118514