Brain volumetric analysis across the lifespan of a non-human primate model of sporadic CAA pathology
Poster No:
210
Submission Type:
Abstract Submission
Authors:
Muhammad Soliman1, Suleiman Khan1, Charles Kingsley2, Jody Swain2, Michele Mulholland3, William Hopkins3, Thomas Wisniewski1, Youssef Wadghiri1, Jelle Veraart1, Henrieta Scholtzova1
Institutions:
1NYU Grossman School of Medicine, New York, NY, 2UT MD Anderson Cancer Center, SAIF, Houston, TX, 3UT MD Anderson Cancer Center, Michael E. Keeling Center for Comparative Medicine and Research, Bastrop, TX
First Author:
Co-Author(s):
Michele Mulholland
UT MD Anderson Cancer Center, Michael E. Keeling Center for Comparative Medicine and Research
Bastrop, TX
UT MD Anderson Cancer Center, Michael E. Keeling Center for Comparative Medicine and Research
Bastrop, TX
William Hopkins
UT MD Anderson Cancer Center, Michael E. Keeling Center for Comparative Medicine and Research
Bastrop, TX
UT MD Anderson Cancer Center, Michael E. Keeling Center for Comparative Medicine and Research
Bastrop, TX
Introduction:
The squirrel monkey (SQM), a New World non-human primate species, has been highlighted for its importance in Alzheimer's disease (AD) and dementia research due to its sporadic AD pathology with a unique propensity for cerebral amyloid angiopathy (CAA) (Elfenbein, 2007). Our previous studies have demonstrated SQMs as an opportune platform for evaluating the therapeutic efficacy and safety of our novel immunomodulatory approaches (Nehete, 2020; Patel, 2021). New FDA approved AD treatments have introduced unforeseen challenges, including the emergence of amyloid-related imaging abnormalities (ARIA) (Sperling, 2011). These abnormalities manifest as vasogenic edema/effusion (ARIA-E) and cerebral microhemorrhage (ARIA-H). We have observed multiple instances of naturally occurring ARIA-E and ARIA-H-like abnormalities in geriatric SQMs. Moreover, we employed quantitative R2* (1/T2* mapping to examine brain regions at different stages of aging in these monkeys (Murray, 2022; 2023).
Our current study extends this work by assessing in-vivo brain volumetrics using T2*-weighted multi-gradient echo scans across the SQM lifespan. The presence of amyloid-related pathology, particularly CAA, in all geriatric SQMs offers a valuable opportunity to study the impact of CAA and vascular dysfunction on brain volumetric changes.
Our current study extends this work by assessing in-vivo brain volumetrics using T2*-weighted multi-gradient echo scans across the SQM lifespan. The presence of amyloid-related pathology, particularly CAA, in all geriatric SQMs offers a valuable opportunity to study the impact of CAA and vascular dysfunction on brain volumetric changes.
Methods:
This study assessed 46 SQMs, with an age range of 6-23 years old. In vivo MRI was performed at UT/MD Anderson Cancer Center on a 7-Tesla Bruker 7030 Biospec scanner interfaced to an Avance 3-HD console. A CPTx/Rx birdcage RF coil (ID=86mm) enabled full head coverage. Scan protocols included multigradient echo sequence (MGE, 8 Echoes, TE/ES=2.96/4.0ms TR=41ms, FA=13°, FOV=38.4 × 25.6 × 25.6mm, Matrix=384 × 256 × 256; Acq. Time=45min, NRep=3).
Brain volumetric changes were quantified as a function of age using the Jacobian of local deformation fields. Following affine registration, the deformation fields between individual subjects and a template were computed using a diffeomorphic registration algorithm of the ANTs Toolbox. Age-dependency of the Jacobian of the deformation field voxelwise was then evaluated using quadratic fitting. The determinant of the Jacobian provides a metric for the local shrinkage and expansion. Following the fitting, age-specific trajectories of structural changes as the derivative of observed trend can be computed.
Brain volumetric changes were quantified as a function of age using the Jacobian of local deformation fields. Following affine registration, the deformation fields between individual subjects and a template were computed using a diffeomorphic registration algorithm of the ANTs Toolbox. Age-dependency of the Jacobian of the deformation field voxelwise was then evaluated using quadratic fitting. The determinant of the Jacobian provides a metric for the local shrinkage and expansion. Following the fitting, age-specific trajectories of structural changes as the derivative of observed trend can be computed.
Results:
Comparing young to geriatric SQMs, preliminary analysis of deformation masks depicted a decrease of the prefrontal cortex (1B), putamen and caudate nucleus (1D), and the thalamus (1F) volume. These results mirrored previously reported findings in rhesus macaques (Dash, 2023). Negative volumetric correlations with age were observed in the putamen and thalamus (1E, G), and to a lesser extent in the prefrontal cortex (1C) when comparing middle aged to geriatric SQMs. Furthermore, age related decreases in white matter (WM) volume were more apparent when comparing middle age to geriatric SQMs (1C, E, G), mirroring nonlinear trends observed by Dash et al. Previous studies in humans have described non-demented sporadic CAA patients with globally thinner cortices compared to controls (Fotiadis, 2016). Lobular microbleeds, a marker of CAA severity, have also been correlated with WM atrophy in humans (Fotiadis, 2020). Follow up analysis of volumetric changes with age using atlas-based quantification is currently underway. It is unclear what underpins volumetric differences between young, middle age, and geriatric SQMs detected by MRI.
·Figure 1: Brain volumetric analysis using Jacobian of local deformation fields, Coronal view.
Conclusions:
The current study further promotes the advancement of neuroimaging tools for a more sensitive and specific brain volume evaluation, potentially linked to age-associated cerebrovascular dysfunction. Our preliminary findings portray the importance of this species to enhance the understanding of CAA-related pathology.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Image Registration and Computational Anatomy
Neuroinformatics and Data Sharing:
Brain Atlases
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
ANIMAL STUDIES
Cerebrovascular Disease
MRI
Neurological
STRUCTURAL MRI
1|2Indicates the priority used for review
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Elfenbein, H. A. (2007). Cerebral beta-amyloid angiopathy in aged squirrel monkeys. Histol Histopathol, 22(2), 155-167. https://doi.org/10.14670/hh-22.155
Fotiadis, P. (2020). White matter atrophy in cerebral amyloid angiopathy. Neurology, 95(5), e554-e562. https://doi.org/10.1212/wnl.0000000000010017
Fotiadis, P. (2016). Cortical atrophy in patients with cerebral amyloid angiopathy: a case-control study. Lancet Neurol, 15(8), 811-819. https://doi.org/10.1016/s1474-4422(16)30030-8
Murray, S. (2022). Multi-parametric Neuroimaging in a Non-Human Primate Model of Sporadic Cerebral Amyloid Angiopathy (Saimiri boliviensis boliviensis). Alzheimer's & Dementia, 18(S5), e066977. https://doi.org/https://doi.org/10.1002/alz.066977
Murray, S. (2023). Development of neuroimaging biomarkers in non-human primate model of sporadic Alzheimer’s disease pathology. Alzheimer's & Dementia, 19(S17), e079140. https://doi.org/https://doi.org/10.1002/alz.079140
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Patel, A. G. (2021). Innate immunity stimulation via CpG oligodeoxynucleotides ameliorates Alzheimer's disease pathology in aged squirrel monkeys. Brain, 144(7), 2146-2165. https://doi.org/10.1093/brain/awab129
Sperling, R. A. (2011). Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: recommendations from the Alzheimer's Association Research Roundtable Workgroup. Alzheimers Dement, 7(4), 367-385. https://doi.org/10.1016/j.jalz.2011.05.2351