TY - JOUR
T1 - Age-Related Regional Network Covariance of Magnetic Resonance Imaging Gray Matter in the Rat
AU - Alexander, Gene E.
AU - Lin, Lan
AU - Yoshimaru, Eriko S.
AU - Bharadwaj, Pradyumna K.
AU - Bergfield, Kaitlin L.
AU - Hoang, Lan T.
AU - Chawla, Monica K.
AU - Chen, Kewei
AU - Moeller, James R.
AU - Barnes, Carol A.
AU - Trouard, Theodore P.
N1 - Funding Information:
We are grateful for support from the National Institute on Aging at the National Institutes of Health, the state of Arizona and Arizona Department of Health Services, the Arizona Advanced Research Institute for Biomedical Imaging, and the McKnight Brain Research Foundation. Funding. This research was supported by National Institute on Aging at the National Institutes of Health (R01AG03376, R01AG025526, P30AG019610, R01AG049464, and R01AG049465).
Publisher Copyright:
© Copyright © 2020 Alexander, Lin, Yoshimaru, Bharadwaj, Bergfield, Hoang, Chawla, Chen, Moeller, Barnes and Trouard.
PY - 2020/8/26
Y1 - 2020/8/26
N2 - Healthy human aging has been associated with brain atrophy in prefrontal and selective temporal regions, but reductions in other brain areas have been observed. We previously found regional covariance patterns of gray matter with magnetic resonance imaging (MRI) in healthy humans and rhesus macaques, using multivariate network Scaled Subprofile Model (SSM) analysis and voxel-based morphometry (VBM), supporting aging effects including in prefrontal and temporal cortices. This approach has yet to be applied to neuroimaging in rodent models of aging. We investigated 7.0T MRI gray matter covariance in 10 young and 10 aged adult male Fischer 344 rats to identify, using SSM VBM, the age-related regional network gray matter covariance pattern in the rodent. SSM VBM identified a regional pattern that distinguished young from aged rats, characterized by reductions in prefrontal, temporal association/perirhinal, and cerebellar areas with relative increases in somatosensory, thalamic, midbrain, and hippocampal regions. Greater expression of the age-related MRI gray matter pattern was associated with poorer spatial learning in the age groups combined. Aging in the rat is characterized by a regional network pattern of gray matter reductions corresponding to aging effects previously observed in humans and non-human primates. SSM MRI network analyses can advance translational aging neuroscience research, extending from human to small animal models, with potential for evaluating mechanisms and interventions for cognitive aging.
AB - Healthy human aging has been associated with brain atrophy in prefrontal and selective temporal regions, but reductions in other brain areas have been observed. We previously found regional covariance patterns of gray matter with magnetic resonance imaging (MRI) in healthy humans and rhesus macaques, using multivariate network Scaled Subprofile Model (SSM) analysis and voxel-based morphometry (VBM), supporting aging effects including in prefrontal and temporal cortices. This approach has yet to be applied to neuroimaging in rodent models of aging. We investigated 7.0T MRI gray matter covariance in 10 young and 10 aged adult male Fischer 344 rats to identify, using SSM VBM, the age-related regional network gray matter covariance pattern in the rodent. SSM VBM identified a regional pattern that distinguished young from aged rats, characterized by reductions in prefrontal, temporal association/perirhinal, and cerebellar areas with relative increases in somatosensory, thalamic, midbrain, and hippocampal regions. Greater expression of the age-related MRI gray matter pattern was associated with poorer spatial learning in the age groups combined. Aging in the rat is characterized by a regional network pattern of gray matter reductions corresponding to aging effects previously observed in humans and non-human primates. SSM MRI network analyses can advance translational aging neuroscience research, extending from human to small animal models, with potential for evaluating mechanisms and interventions for cognitive aging.
KW - aging
KW - behavior
KW - perirhinal cortex
KW - prefrontal cortex
KW - scaled subprofile model
KW - structural covariance
UR - http://www.scopus.com/inward/record.url?scp=85090760360&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090760360&partnerID=8YFLogxK
U2 - 10.3389/fnagi.2020.00267
DO - 10.3389/fnagi.2020.00267
M3 - Article
AN - SCOPUS:85090760360
SN - 1663-4365
VL - 12
JO - Frontiers in Aging Neuroscience
JF - Frontiers in Aging Neuroscience
M1 - 267
ER -