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Mariam Mojtabai

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2 papers

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YNICL Journal 2025 Journal Article

Association of quantitative susceptibility mapping signal in gray matter with clinical characteristics and aging-related MRI markers: The Multi-Ethnic Study of Atherosclerosis

Susan R. Heckbert
Paul N. Jensen
Tanweer Rashid
David H. Wang
Colleen M. Sitlani
Crystal G. Franklin
Mariam Mojtabai
Ana Navas-Acien

BACKGROUND AND OBJECTIVES: In neuropathologic studies, iron accumulation in gray matter (GM) is associated with aging and specific neurological diseases, but less is known about its correlates in community-based populations. METHODS: In the Multi-Ethnic Study of Atherosclerosis, brain MRI was conducted in 2018-2019. To estimate iron content, we derived the median quantitative susceptibility mapping (QSM) signal from four regions: the basal ganglia and cortical GM of the frontal, temporal, and parietal lobes. We examined cross-sectional associations with demographic and clinical characteristics, cognitive test performance, gait speed, and brain MRI measures of atrophy and small vessel disease. RESULTS: We studied 943 participants (14 % Chinese, 25 % Black, 20 % Hispanic, 41 % White; mean age 74 years; 48 % men). In multivariable models, higher left basal ganglia QSM signal was associated with older age (7.2 ppb per 10 years; 95 %CI 4.6,9.9), smoking (7.1; 3.4,10.9), and diabetes (7.4; 2.5,12.3). Lower QSM signal was associated with Black race (-15.3; -20.6,-10, relative to White) and higher high-density lipoprotein cholesterol (-3.4 per 20 mg/dL; -5.8,-0.9). In cortical GM, QSM signal was associated with greater waist circumference, lifetime alcohol use, and log-transformed white matter hyperintensity (WMH) volume (0.08-0.12 SD units per SD, all p ≤ 0.002), but not with cognitive test performance or gait speed. DISCUSSION: In cross-sectional analyses in a community-based cohort, older age, White race, smoking, diabetes, and greater WMH volume were associated with higher QSM signal in basal ganglia and/or cortical GM. Longitudinal studies are needed to further explore GM QSM signal in relation to cognition and gait in older individuals.

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YNICL Journal 2025 Journal Article

Multi-atlas multi-modality morphometry analysis of the South Texas Alzheimer’s Disease Research Center postmortem repository

Nicolas Honnorat
Mariam Mojtabai
Karl Li
Jinqi Li
David Michael Martinez
Tanweer Rashid
Morgan Smith
Margaret E Flanagan

Histopathology provides critical insights into the neurological processes inducing neurodegenerative diseases and their impact on the brain, but brain banks combining histology and neuroimaging data are difficult to create. As part of an ongoing global effort to establish new brain banks providing both high-quality neuroimaging scans and detailed histopathology examinations, the South Texas Alzheimer's Disease Re- search Center postmortem repository was recently created with the specific purpose of studying comorbid dementias. As the repository is reaching a milestone of two hundred brain donations and a hundred curated MRI sessions are ready for processing, robust statistical analyses can now be conducted. In this work, we report the very first morphometry analysis conducted with this new data set. We describe the processing pipelines that were specifically developed to exploit the available MRI sequences, and we explain how we addressed several postmortem neuroimaging challenges, such as the separation of brain tissues from fixative fluids, the need for updated brain atlases, and the tissue contrast changes induced by brain fixation. In general, our results establish that a combination of structural MRI sequences can provide enough informa- tion for state-of-the-art Deep Learning algorithms to almost perfectly separate brain tissues from a formalin buffered solution. Regional brain volumes are challenging to measure in postmortem scans, but robust estimates sensitive to sex differences and age trends, reflecting clinical diagnosis, neuropathology findings, and the shrinkage induced by tissue fixation can be obtained. We hope that the new processing methods developed in this work, such as the lightweight Deep Networks we used to identify the formalin signal in multimodal MRI scans and the MRI synthesis tools we used to fix our anisotropic resolution brain scans, will inspire other research teams working with postmortem MRI scans.

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