To understand the associations of quantitative susceptibility mapping with age-related neuropathologies, it is essential to combine QSM with direct assessments of age-related brain pathologies on the same individuals. Using ex-vivo QSM for this purpose may be more advantageous than in-vivo QSM, since ex-vivo QSM overcomes several of the obstacles that complicate MRI-pathology investigations and can provide magnetic susceptibility measurements that are linked to those collected in-vivo. Therefore, our goal was to investigate the associations of magnetic susceptibility with the pathology of multiple age-related diseases by combining ex-vivo QSM with histology.
Participants
223 participants were recruited from the Rush Memory and Aging Project and Religious Orders Study, two longitudinal epidemiologic clinical-pathologic cohort studies of aging3,4.
MRI Data Acquisition
The postmortem hemisphere of each participant was imaged while submerged in formaldehyde. Due to MR hardware updates throughout the two cohort studies, the postmortem tissue of each participant was imaged at one of three 3T MR scanners. For all scanners, 3D gradient-echo and 2D spin-echo data were collected with similar imaging protocols (Table 1); The voxel size of the gradient-echo and spin-echo data across all scanners was 1mm isotropic and no larger than 0.65x0.65x1.5 mm, respectively.
Quantitative Susceptibility Mapping (QSM)
Total field maps were produced for the in-vivo and ex-vivo QSM data. Local fields were calculated using PDF5. Magnetic susceptibility maps were created with MEDI6, and processed with consistency on cone data to suppress streaking artifacts7. The reference values as chosen as the mean susceptibility value of the formaldehyde solution.
Histopathological Evaluation
Within 2 weeks after ex-vivo MRI, the imaged hemisphere was dissected and evaluated for both neurodegenerative and vascular disease pathologies: AD, Lewy bodies, hippocampal sclerosis, TDP43, arteriosclerosis, atherosclerosis, cerebral amyloid angiopathy gross infarcts and microinfarcts. The evaluation was performed by a board-certified neuropathologist, who was blinded to age and clinical diagnoses.
Voxelwise correlations of magnetic susceptibility and pathology
The 223 participants were chosen based on having similar hemisphere orientations across left and right hemispheres. The spin-echo data from all participants were registered to a study-specific template generated using the “unbiased atlas construction via group-wise DRAMMS registration” tool8. For each participant, the magnetic susceptibility map was transferred to the template space by combining the corresponding transformations. The susceptibility map was median filtered and then low pass filtered.
At each voxel of the template, linear regression of the participants’ magnetic susceptibility values was conducted with the corresponding measurements for the pathologies mentioned above and the following covariates: hemisphere side, scanner, age at death, sex, years of education, and postmortem interval to fixation. The overall registration quality throughout the template was assessed using a patch-based method. Voxels with poor registration quality or with less than 130 participants in the regression were excluded. False discovery rate (FDR) was applied, and statistical significance was achieved at p<0.05 for the FDR-adjusted p-values.
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3. Bennett DA, Schneider JA, Buchman AS, et al. Overview and findings from the Rush Memory and Aging Project. Curr Alzheimer Res 2012;9:646–663. 2.
4. Bennett DA, Wilson RS, Arvanitakis Z, et al. Selected findings from the Religious Orders Study and Rush Memory and Aging Project. J Alzheimers Dis 2013;33 Suppl 1:S397-403.
5. Liu T, Khalidov I, de Rochefort L et al. A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR Biomed. 2011 Nov;24(9):1129-36.
6. Liu J, Liu T, de Rochefort L et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. Neuroimage. 2012 Feb 1;59(3):2560-8.
7. Wen Y, Wang Y, Liu T. Enhancing k-space quantitative susceptibility mapping by enforcing consistency on the cone data (CCD) with structural priors. Magn Reson Med. 2016 Feb;75(2):823-30.
8. Ou Y, Sotiras A, Paragios N, Davatzikos C. DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting. Med Image Anal. 2011 Aug;15(4):622-39.
9. Evia A, Kotrotsou A, Tamhane A, et al. Ex-vivo quantitative susceptibility mapping of human brain hemispheres. PLoS One. In Press.