Associations of subcortical shapes with age‐related neuropathologies in community‐based older adults

Abstract

AbstractBackgroundAge‐related neuropathologies lead to substantial atrophy of subcortical brain regions. However, the effects of age‐related neuropathologies on the shape of subcortical brain structures are not well understood because a) definitive diagnosis of most neuropathologies is only possible at autopsy and b) multiple neuropathologies often coexist in the brain of older adults. The purpose of this work was to integrate ex‐vivo MRI and detailed neuropathologic examination in a large number of community‐based older adults.Method Participants and Data Cerebral hemispheres from 842 older adults participating in four longitudinal, clinical‐pathologic cohort studies of aging: the Rush Memory and Aging Project (MAP), the Religious Orders Study (ROS), the Minority Aging Research Study (MARS) and the Clinical Core (CC) of the Rush Alzheimer’s Disease Research Center (ADRC)1,2 (Fig.1) were included in this work. All hemispheres were imaged at room temperature while immersed in 4% formaldehyde solution using clinical 3T MRI scanners approximately 30 days postmortem. After ex‐vivo MRI, each hemisphere underwent detailed neuropathologic examination by a board‐certified neuropathologist (Fig.1). Ex‐vivo MRI and Shape Estimation The subcortical structures were segmented in ex‐vivo T2‐weighted MR images from all participants. Shape analysis was performed using SPHARM‐PDM3. For each subcortical structure, signed shape differences were computed from the average mesh for all vertices and all participants. Statistical Analysis Vertex‐wise linear regression was performed for each subcortical structure, modeling the signed shape difference at each vertex (dependent variable) as a function of neuropathologies (specifically amyloid‐β plaques, neurofibrillary tangles, limbic‐predominant age‐related TDP‐43 encephalopathy neuropathological change (LATE‐NC), Lewy bodies, arteriolosclerosis, atherosclerosis, gross and microscopic infarcts, and cerebral amyloid angiopathy), and controlling for age at death, sex, years of education, postmortem interval to fixation, postmortem interval to imaging, and scanner. The statistical analysis was conducted using PALM4 with family‐wise error rate correction.ResultThe results are shown in Figure 2. These findings for tangles and LATE‐NC were in good agreement with our previous work5,6. Finally, although MRI was conducted ex‐vivo, we expect the results to translate well to in‐vivo7.ConclusionThe differences in spatial patterns of the effects of various neuropathologies may contribute towards the development of tools for in‐vivo prediction of these neuropathologies.