Network Neurodegeneration in Alzheimer's Disease via MRI Based Shape Diffeomorphometry and High-Field Atlasing.
This paper examines MRI analysis of neurodegeneration in Alzheimer's Disease (AD) in a network of structures within the medial temporal lobe using diffeomorphometry methods coupled with high-field atlasing in which the entorhinal cortex is partitioned into eight subareas. The morphometry markers for three groups of subjects (controls, preclinical AD, and symptomatic AD) are indexed to template coordinates measured with respect to these eight subareas. The location and timing of changes are examined within the subareas as it pertains to the classic Braak and Braak staging by comparing the three groups. We demonstrate that the earliest preclinical changes in the population occur in the lateral most sulcal extent in the entorhinal cortex (alluded to as transentorhinal cortex by Braak and Braak), and then proceeds medially which is consistent with the Braak and Braak staging. We use high-field 11T atlasing to demonstrate that the network changes are occurring at the junctures of the substructures in this medial temporal lobe network. Temporal progression of the disease through the network is also examined via changepoint analysis, demonstrating earliest changes in entorhinal cortex. The differential expression of rate of atrophy with progression signaling the changepoint time across the network is demonstrated to be signaling in the intermediate caudal subarea of the entorhinal cortex, which has been noted to be proximal to the hippocampus. This coupled to the findings of the nearby basolateral involvement in amygdala demonstrates the selectivity of neurodegeneration in early AD.
Center for Imaging Science, Johns Hopkins University , Baltimore, MD , USA ; Institute for Computational Medicine, Johns Hopkins University , Baltimore, MD , USA ; Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD , USA.
Michael I
MI
Miller
Center for Imaging Science, Johns Hopkins University , Baltimore, MD , USA ; Institute for Computational Medicine, Johns Hopkins University , Baltimore, MD , USA ; Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD , USA.
J Tilak
JT
Ratnanather
Center for Imaging Science, Johns Hopkins University , Baltimore, MD , USA ; Institute for Computational Medicine, Johns Hopkins University , Baltimore, MD , USA ; Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD , USA.
Daniel J
DJ
Tward
Center for Imaging Science, Johns Hopkins University , Baltimore, MD , USA.
Timothy
T
Brown
Center for Imaging Science, Johns Hopkins University , Baltimore, MD , USA ; Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD , USA.
David S
DS
Lee
Center for Imaging Science, Johns Hopkins University , Baltimore, MD , USA ; Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD , USA.
Michael
M
Ketcha
Center for Imaging Science, Johns Hopkins University , Baltimore, MD , USA.
Kanami
K
Mori
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University , Baltimore, MD , USA.
Mei-Cheng
MC
Wang
Department of Radiology, Johns Hopkins University School of Medicine , Baltimore, MD , USA.
Susumu
S
Mori
Department of Neurology, Johns Hopkins University School of Medicine , Baltimore, MD , USA.
Marilyn S
MS
Albert
Center for Imaging Science, Johns Hopkins University , Baltimore, MD , USA ; Institute for Computational Medicine, Johns Hopkins University , Baltimore, MD , USA ; Department of Applied Mathematics and Statistics, Johns Hopkins University , Baltimore, MD , USA.
Laurent
L
Younes
cell–cell hypothesis; diffeomorphometry; entorhinal cortex; preclinical Alzheimer’s disease; shape