Longitudinal Neuroimaging Hippocampal Markers for Diagnosing Alzheimer’s Disease

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 17; no. 1; pp. 43 - 61
• Main Authors: Platero, Carlos, Lin, Lin, Tobar, M. Carmen
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2019; Springer Nature B.V
• Subjects: Aged; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer's disease; Atrophy; Bioinformatics; Biomedical and Life Sciences; Biomedicine; Classification; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - pathology; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Differential diagnosis; Disease Progression; Female; Hippocampus; Hippocampus - diagnostic imaging; Hippocampus - pathology; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Neuroimaging; Neuroimaging - methods; Neurology; Neurosciences; NMR; Nuclear magnetic resonance; Original Article; Reproducibility of Results; ROC Curve; Segmentation; Hippocampal segmentation; Longitudinal analysis; Alzheimer’s disease; MRI
• ISSN: 1539-2791; 1559-0089; 1559-0089
• DOI: 10.1007/s12021-018-9380-2

Hippocampal atrophy measures from magnetic resonance imaging (MRI) are powerful tools for monitoring Alzheimer’s disease (AD) progression. In this paper, we introduce a longitudinal image analysis framework based on robust registration and simultaneous hippocampal segmentation and longitudinal marker classification of brain MRI of an arbitrary number of time points. The framework comprises two innovative parts: a longitudinal segmentation and a longitudinal classification step. The results show that both steps of the longitudinal pipeline improved the reliability and the accuracy of the discrimination between clinical groups. We introduce a novel approach to the joint segmentation of the hippocampus across multiple time points; this approach is based on graph cuts of longitudinal MRI scans with constraints on hippocampal atrophy and supported by atlases. Furthermore, we use linear mixed effect (LME) modeling for differential diagnosis between clinical groups. The classifiers are trained from the average residue between the longitudinal marker of the subjects and the LME model. In our experiments, we analyzed MRI-derived longitudinal hippocampal markers from two publicly available datasets (Alzheimer’s Disease Neuroimaging Initiative, ADNI and Minimal Interval Resonance Imaging in Alzheimer’s Disease, MIRIAD). In test/retest reliability experiments, the proposed method yielded lower volume errors and significantly higher dice overlaps than the cross-sectional approach (volume errors: 1.55% vs 0.8%; dice overlaps: 0.945 vs 0.975). To diagnose AD, the discrimination ability of our proposal gave an area under the receiver operating characteristic (ROC) curve (AUC) = 0.947 for the control vs AD, AUC = 0.720 for mild cognitive impairment (MCI) vs AD, and AUC = 0.805 for the control vs MCI.