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Sex and Hemispheric Dependent DTI-based Network Analysis of an Alzheimer’s Disease Mouse Model
David C Hike1,2, Casey P Weiner3,4, Scott E Boebinger5,6, Tara N Palin3, and Samuel Colles Grant1,2

1National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, United States, 2Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL, United States, 3Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, 4Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD, United States, 5Wallace H. Cooulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States, 6Wallace H. Coulter Department of Biomedical Engineeing, Emory University, Atlanta, GA, United States

Synopsis

This study utilizes DTI and graph theory as a novel way for early detection of pathology and connectivity changes related to Alzheimer’s Disease. As a function of phenotype, age and sex, DTI studies were performed on APP/PS1 mouse brains and age-matched wild type controls at 11.75 T. Current hemisphere-dependentdata shows differences between hemispheres within age and phenotype for the parameters observed.

Introduction

Alzheimer’s Disease (AD) is the most common form of dementia, characterized by memory loss and changes in behavior1. The most prevalent preclinical model of AD is the APP/PS1 mouse expressing human genes for amyloid precursor protein (APP) and presenilin-1 (PS1). Clinically, MRI is used to diagnose AD by means of volumetrics, mainly focusing on hippocampal atrophy2. In this study, diffusion tensor imaging (DTI) is applied to the 5xFAD variant of the APP/PS1 mouse model. At multiple early time points, network theory was employed to examine alterations in structural connectivity as a function of age, sex and across hemispheres compared to wild-type controls. The investigation of hemispheric dependence in the loss of neural connectivity was partially motivated by a previous study that showed possible hemispheric differences in vascular pathology related to AD3.

Methods

Image datasets were acquired using preserved mouse brains (4% paraformaldehyde) from male and female specimens either expressing the APP/PS1 phenotype or an age-matched wild type. Brains were harvested at 1, 2, 4 and 6 months (N=5 per age, gender and phenotype). Using an 11.75-T magnet, DTI data was acquired with a multi-slice 2D spin echo sequence using 18 diffusion encoding directions (nominal b = 400 s/mm2) and four unweighted acquisitions for which Δ=21 ms and δ=3 ms. Two to three brains were imaged simultaneously with an in-plane resolution of 100x100 mm, matrix size of 256x256, slice thickness of 500mm, repetition time of 2 s and echo time of 30 ms. With 15 averages and a 47-hr total acquisition time, high signal-to-noise ratios of 60:1 were obtained. DTI data were analyzed using DSI Studio4 to generate tracts using the following parameters: Threshold=0.10, Angular Threshold=60o, Step Size=0.05mm, Minimum Length=1mm, Maximum Length=25mm, and termination after 106 seeds. Adjacency matrices were created by running whole brain tracking and placing regions of interest (ROIs) within developed tracts and generating a pass through matrix. Network matrices then were utilized by GEPHI to obtain network properties. A hemisphere-dependent analysis was completed to indicate any changes that may exist. A one-way ANOVA with a least significant difference post-hoc test (p<0.05) was used to determine statistical significance among samples.

Results & Discussion

Four main areas of focus were the Piriform Area, Temporal Lobe, Parietal Lobe and Hippocampus. These regions divided into left and right hemispheres increased the number of regions to eight. Structural connectivity work was assessed using 14 equally spaced prolate spheroidal ROIs that were positioned in cortical regions, as well as manually segmented nodes representing the left and right hippocampus, to quantify connectivity both locally and globally [Figure 1]. Tracts were measured between each individual node in order to derive quantifiable adjacency matrices that correspond to neural graphs. The analysis was done using a weighted approach to study the structural connectivity changes in the models. Impacts of aging can be seen in closeness and harmonic centrality when hemispheric differences are not considered. Weighted degree and clustering in older samples show impacts of phenotype. In younger samples, impacts of phenotype can be seen in eccentricity as well as closeness and harmonic centrality. When comparing left and right hemisphere differences for certain network metrics, a divergent pattern with age for female transgenic (decreases) versus wild-type(increases) developed, localized to the left hemisphere [Figures 2,3]. Male specimens generally did not show this hemispheric trend, instead wild-type network metrics either remained stable or decreased until 4 months before rebounding while transgenic metrics increased until 4 months then decreased [Figure 4].

Conclusion

This research utilizes network theory and MRI to detect and classify the progression of Alzheimer’s Disease, potentially providing early hallmarks of structural connectivity changes that may be impacted by sex. Such classifications may assist in defining treatment regiments earlier in disease progression, possibly before hallmark symptoms present. Additionally, this work will help to expand the application of DTI and network theory to identification and assessment of progression of other neurodegenerative diseases.

Acknowledgements

This work was funded by NSF (DMR-1157490 & DMR-1644779), the State of Florida, the National High Magnetic Field Laboratory User Collaborations Grant Program, and NIH (R01 NS102395).

References

1. U.S Department of Health & Human Services.www.alzheimers.gov.

2. Tang, X., Qin, Y., Wu, J., Zhang, M., Zhu, W., & Miller, M. I. 2016. Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer's disease. Magnetic resonance imaging. 34(8):1087-1099.

3. Giannakopoulos, P., Kövari, E., Herrmann, F. R., Hof, P. R., & Bouras, C. 2008. Interhemispheric distribution of Alzheimer disease and vascular pathology in brain aging. Stroke, 40(3), 983-6.

4. Yeh F, Verstynen TD, Wang Y, Fernández-Miranda JC, Tseng WI. 2013. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS One. 8(11): e80713.

Figures

Figure 1: DICOM image showing positions of 14 ROIs placed around the cortex and 2 ROIs representing the hippocampus with a division in the middle to preserve the midline of the brain. Allows for separation of left and right hemispheres for analysis.

Figure 2: Graphs showing left-right hemisphere differences across phenotype in female samples for eigenvector centrality. Red lines show longitudinal changes in wild-type models. Blue lines show longitudinal changes in transgenic model.

Figure 3: Graphs showing left-right hemisphere differences across phenotype in female samples for changes in eccentricity. Red lines show longitudinal changes in wild-type models. Blue lines show longitudinal changes in transgenic models.

Figure 4: Graph showing directional change in network metric across phenotype in male samples for changes in eccentricity. Red lines show longitudinal changes in wild-type models. Blue lines show longitudinal changes in transgenic models.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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