Schwarz, Christopher G

Abstract Background Use of software to “de‐face” research brain images has grown as advances in automated face recognition have made it increasingly possible to re‐identify participants. Imaging studies of Alzheimer's Disease and related diseases have included more participants from historically underrepresented groups, but de‐facing software have primarily been validated using images from mostly non‐Hispanic White participants, and its performance has not been directly compared across ethnoracial groups. Method From The Health and Aging Brain Study: Health Disparities (HABS‐HD) study, we constructed a sample of 305 T1‐weighted MRI from HABS‐HD baseline visits, matched across three ethnoracial groups (non‐Hispanic White (NHW, n = 104), non‐Hispanic African American (AA, n = 104), Mexican American (MA, n = 97)) according to age‐decade, sex, cognitive status (impaired/unimpaired), and MRI scanner model. We ran mri_reface 0.3.5 on each image, and we ran FreeSurfer 7.4.1 on each unmodified image and on each de‐faced output. We then compared regional gray matter (GM) volume and cortical thickness measurements by subtracting each de‐faced image measurement from the corresponding unmodified image measurement. We compared these differences between de‐faced and unmodified images across the three matched groups using GLM analyses adjusted for age, sex, and cognitive status, and Bonferroni corrected. Result When aggregating across all brain regions, differences in cortical thickness measures due to de‐facing were significantly different between the NHW and MA groups, and between AA and MA, but not between NHW and AA (Figure 1). Differences between these group means were <0.005mm. For GM volume, differences were significant between AA and MA (magnitude < 0.2%), but not between NHW and AA or NHW and MA (Figure 2). Examining each region individually (Figure 3), cortical thickness differences were significant for only 4 regions (all with magnitudes <0.03mm), and GM volume differences were significant for only 6 regions (all with magnitudes <2%). Conclusion Effects of mri_reface on FreeSurfer brain measurements were significantly different across ethnoracial groups. Our future work will optimize mri_reface to eliminate these differences. However, the magnitudes of these differences are sufficiently small that they are unlikely to have a practical effect on analyses of data using current versions.

Abstract Background In addition to visuospatial and visuoperceptual deficits, most PCA patients also report deficits in visual attention, episodic memory, and working memory. The dorsal attention network is known to play a critical role in modulating these clinical functions. However, little is known about the relationship of the dorsal attention network with other core networks and relationships to volume loss and memory functions in PCA. Method Fifty‐seven PCA patients were recruited by the Neurodegenerative Research group, Mayo Clinic, and compared to 60 cognitively unimpaired (CU) individuals from the Mayo Clinic Study of Aging. The CONN functional connectivity toolbox was used to calculate within‐ and between‐network connectivity. Functional connectivity within the key regions (i.e., inter‐hemisphere connectivity) of the dorsal attention network, namely frontal eye field (FEF) and intraparietal sulcus (IPS), and the entire network was generated. Functional connectivity between the dorsal attention network and the frontoparietal, visual, and default mode networks (DMN) were also generated using CONN's network parcellation atlas. Multivariate linear regression models were fit i) to compare within‐ and between‐network connectivity between both groups and ii) to assess relationships between within‐network connectivity and both gray matter volumes and clinical test scores. Result PCA showed reduced within‐network connectivity in the dorsal attention network, specifically within the IPS and the entire network compared to CU. An increase in between‐network connectivity between the frontoparietal network and both FEF and the entire dorsal attention network was noted in PCA compared to CU (Figure 1). Lower dorsal attention within‐network connectivity was associated with a trend for lower volumes in the entire network (Figure 2) and significantly lower scores on the auditory verbal learning test‐recognition percent correct (AVLT‐RCP) and Wechsler Memory Scale (WMS) III‐digit span backward in PCA (Figure 3). Conclusion These findings suggest that within‐ and between‐network connectivity of the dorsal attention network, both within key regions and the entire network, was disrupted in PCA patients. However, this network did not show disrupted connectivity with the core PCA networks. Within‐network connectivity disruptions in the dorsal attention network are associated with aspects of memory function, and to some extent, with volume loss.

Abstract Background Aging and dementia are generally associated with lower metabolism in gray matter on FDG‐PET and have been widely studied. However, white matter (WM) metabolic changes remain understudied. We investigated the metabolic changes in WM associated with aging and dementia and their relationships with established Alzheimer's Disease (AD) biomarkers and cognitive outcomes. Method We analyzed data from 3,169 participants (72 ± 9 years; 53% male; 65% cognitively unimpaired) enrolled in the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). All participants underwent baseline MRI, amyloid‐PET, and FDG‐PET scans, as well as neuropsychological evaluations, with at least one clinical follow‐up. Voxel‐wise multiple regression analysis identified WM metabolic changes associated with global cognition in cognitively unimpaired MCSA participants ( n = 1,754). Univariate analyses and structural equation models were applied to elucidate the driving factors underlying the WM metabolic changes in MCSA data. Multiple regression and linear mixed‐effects models evaluated the explanatory utility of WM metabolism for baseline global cognition and cognitive decline in both cohorts. In a subset of MCSA participants, we evaluated neurite density index from diffusion MRI as a surrogate of WM health and glial fibrillary acidic protein (GFAP) as a surrogate of neuroinflammation to understand underlying mechanisms. Result We identified an atypical WM signature (AWM‐Signature) in the anterior and superior corona radiata, where glucose metabolism increased as cognitive performance worsened (Figure 1). Hypermetabolism in AWM‐Signature was related to increased age, amyloidosis, vascular disease, and neuroinflammation, as well as decreasing WM neurite density in the same regions (Figure 2). Hypermetabolism in AWM‐Signature regions predicted faster future cognitive decline, even after accounting for demographics, amyloid, and vascular disease metrics. The results observed in the AWM‐Signature regions were consistently in the opposite direction of the associations seen with gray matter and typical WM regions. Conclusion We found unexpected metabolism increases in corona radiata, a hub that carries significant portion of the brain's neural traffic, with aging and disease which likely indicates a compensatory response. These changes may be useful cognitive resilience measures and require further investigation to understand their role in maintaining cognitive function.