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Scott A. Przybelski

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11 papers

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YNICL Journal 2024 Journal Article

Assessing network degeneration and phenotypic heterogeneity in genetic frontotemporal lobar degeneration by decoding FDG-PET

Nick Corriveau-Lecavalier
Leland R. Barnard
Scott A. Przybelski
Venkatsampath Gogineni
Hugo Botha
Jonathan Graff-Radford
Vijay K. Ramanan
Leah K. Forsberg

Fluorodeoxyglucose-positron emission tomography (FDG-PET) data from 39 patients with genetic FTLD, including 11 carrying the C9orf72 hexanucleotide expansion, 16 carrying a MAPT mutation and 12 carrying a GRN mutation. We performed a spectral covariance decomposition analysis between FDG-PET images to yield unbiased latent patterns reflective of whole brain patterns of metabolism ("eigenbrains" or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB-based predictions of genetic mutation and predominant clinical phenotype (i.e., behavior/personality, language, asymptomatic). Five EBs were significant and explained 58.52 % of the covariance between FDG-PET images. EBs indicative of hypometabolism in left frontotemporal and temporo-parietal areas distinguished GRN mutation carriers from other genetic mutations and were associated with predominant language phenotypes. EBs indicative of hypometabolism in prefrontal and temporopolar areas with a right hemispheric predominance were mostly associated with predominant behavioral phenotypes and distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 79.5 % and 76.9 % in predicting genetic status and predominant clinical phenotype, respectively. A small number of EBs explained a high proportion of covariance in patterns of network degeneration across FTLD-related genetic mutations. These EBs contained biological information relevant to the variability in the pathophysiological and clinical aspects of genetic FTLD, and for offering valuable guidance in complex clinical decision-making, such as decisions related to genetic testing.

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YNICL Journal 2024 Journal Article

Complex relationships of socioeconomic status with vascular and Alzheimer’s pathways on cognition

Dror Shir
Jonathan Graff-Radford
Angela J. Fought
Timothy G. Lesnick
Scott A. Przybelski
Maria Vassilaki
Val J. Lowe
David S. Knopman

INTRODUCTION: AD and CVD, which frequently co-occur, are leading causes of age-related cognitive decline. We assessed how demographic factors, socioeconomic status (SES) as indicated by education and occupation, vascular risk factors, and a range of biomarkers associated with both CVD (including white matter hyperintensities [WMH], diffusion MRI abnormalities, infarctions, and microbleeds) and AD (comprising amyloid-PET and tau-PET) collectively influence cognitive function. METHODS: In this cross-sectional population study, structural equation models were utilized to understand these associations in 449 participants (mean age (SD) = 74.5 (8.4) years; 56% male; 7.5% cognitively impaired). RESULTS: (1) Higher SES had a protective effect on cognition with mediation through the vascular pathway. (2) The effect of amyloid directly on cognition and through tau was 11-fold larger than the indirect effect of amyloid on cognition through WMH. (3) There is a significant effect of vascular risk on tau deposition. DISCUSSION: The utilized biomarkers captured the impact of CVD and AD on cognition. The overall effect of vascular risk and SES on these biomarkers are complex and need further investigation.

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YNICL Journal 2023 Journal Article

Effects of de-facing software mri_reface on utility of imaging biomarkers used in Alzheimer’s disease research

Christopher G. Schwarz
Walter K. Kremers
Stephen D. Weigand
Carl M. Prakaashana
Matthew L. Senjem
Scott A. Przybelski
Val J. Lowe
Jeffrey L. Gunter

Brain imaging research studies increasingly use "de-facing" software to remove or replace facial imagery before public data sharing. Several works have studied the effects of de-facing software on brain imaging biomarkers by directly comparing automated measurements from unmodified vs de-faced images, but most research brain images are used in analyses of correlations with cognitive measurements or clinical statuses, and the effects of de-facing on these types of imaging-to-cognition correlations has not been measured. In this work, we focused on brain imaging measures of amyloid (A), tau (T), neurodegeneration (N), and vascular (V) measures used in Alzheimer's Disease (AD) research. We created a retrospective sample of participants from three age- and sex-matched clinical groups (cognitively unimpaired, mild cognitive impairment, and AD dementia, and we performed region- and voxel-wise analyses of: hippocampal volume (N), white matter hyperintensity volume (V), amyloid PET (A), and tau PET (T) measures, each from multiple software pipelines, on their ability to separate cognitively defined groups and their degrees of correlation with age and Clinical Dementia Rating (CDR)-Sum of Boxes (CDR-SB). We performed each of these analyses twice: once with unmodified images and once with images de-faced with leading de-facing software mri_reface, and we directly compared the findings and their statistical strengths between the original vs. the de-faced images. Analyses with original and with de-faced images had very high agreement. There were no significant differences between any voxel-wise comparisons. Among region-wise comparisons, only three out of 55 correlations were significantly different between original and de-faced images, and these were not significant after correction for multiple comparisons. Overall, the statistical power of the imaging data for AD biomarkers was almost identical between unmodified and de-faced images, and their analyses results were extremely consistent.

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YNICL Journal 2022 Journal Article

Causal structure discovery identifies risk factors and early brain markers related to evolution of white matter hyperintensities

Xinpeng Shen
Sheelakumari Raghavan
Scott A. Przybelski
Timothy G. Lesnick
Sisi Ma
Robert I. Reid
Jonathan Graff-Radford
Michelle M. Mielke

Our goal was to understand the complex relationship between age, sex, midlife risk factors, and early white matter changes measured by diffusion tensor imaging (DTI) and their role in the evolution of longitudinal white matter hyperintensities (WMH). We identified 1564 participants (1396 cognitively unimpaired, 151 mild cognitive impairment and 17 dementia participants) with age ranges of 30-90 years from the population-based sample of Mayo Clinic Study of Aging. We used computational causal structure discovery and regression analyses to evaluate the predictors of WMH and DTI, and to ascertain the mediating effect of DTI on WMH. We further derived causal graphs to understand the complex interrelationships between midlife protective factors, vascular risk factors, diffusion changes, and WMH. Older age, female sex, and hypertension were associated with higher baseline and progression of WMH as well as DTI measures (P ≤ 0.003). The effects of hypertension and sex on WMH were partially mediated by microstructural changes measured on DTI. Higher midlife physical activity was predictive of lower WMH through a direct impact on better white matter tract integrity as well as an indirect effect through reducing the risk of hypertension by lowering BMI. This study identified key risks factors, early brain changes, and pathways that may lead to the evolution of WMH.

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YNIMG Journal 2022 Journal Article

Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging

Krishnakant V. Saboo
Chang Hu
Yogatheesan Varatharajah
Scott A. Przybelski
Robert I. Reid
Christopher G. Schwarz
Jonathan Graff-Radford
David S. Knopman

Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adults at risk of cognitive decline. Using deep learning models augmented with a model-interpretation technique on data from 1432 Mayo Clinic Study of Aging participants, we identified a subset of brain structures that were most predictive of individualized cognitive trajectories and indicative of cognitively resilient vs. vulnerable individuals. Specifically, these structures explained why some participants were resilient to the deleterious effects of elevated brain amyloid and poor vascular health. Of these, medial temporal lobe and fornix, reflective of age and pathology-related degeneration, and corpus callosum, reflective of inter-hemispheric disconnection, accounted for 60% of the heterogeneity explained by the most predictive structures. Our results are valuable for identifying cognitively vulnerable individuals and for developing interventions for cognitive decline.

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YNICL Journal 2021 Journal Article

FDG PET metabolic signatures distinguishing prodromal DLB and prodromal AD

Kejal Kantarci
Bradley F. Boeve
Scott A. Przybelski
Timothy G. Lesnick
Qin Chen
Julie Fields
Christopher G. Schwarz
Matthew L. Senjem

BACKGROUND AND PURPOSE: F-fluorodeoxyglucose (FDG) PET. Whether this pattern of hypometabolism is present as early as the prodromal stage of DLB is unknown. We investigated the pattern of hypometabolism in patients with mild cognitive impairment (MCI) who progressed to probable DLB compared to MCI patients who progressed to Alzheimer's disease (AD) dementia and clinically unimpaired (CU) controls. METHODS: Patients with MCI from the Mayo Clinic Alzheimer's Disease Research Center who underwent FDG PET at baseline and progressed to either probable DLB (MCI-DLB; n = 17) or AD dementia (MCI-AD; n = 41) during follow-up, and a comparison cohort of CU controls (n = 100) were included. RESULTS: Patients with MCI-DLB had hypometabolism in the parieto-occipital cortex extending into temporal lobes, substantia nigra and thalamus. When compared to MCI-AD, medial temporal and posterior cingulate metabolism were preserved in patients with MCI-DLB, accompanied by greater hypometabolism in the substantia nigra in MCI-DLB compared to MCI-AD. In distinguishing MCI-DLB from MCI-AD at the maximum value of Youden's index, CIS ratio was highly specific (90%) but not sensitive (59%), but a higher medial temporal to substantia nigra ratio was both sensitive (94%) and specific (83%). CONCLUSION: FDG PET is a potential biomarker for the prodromal stage of DLB. A higher medial temporal metabolism and CIS ratio, and lower substantia nigra metabolism have additive value in distinguishing prodromal DLB and AD.

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YNIMG Journal 2021 Journal Article

Selecting software pipelines for change in flortaucipir SUVR: Balancing repeatability and group separation

Christopher G. Schwarz
Terry M. Therneau
Stephen D. Weigand
Jeffrey L. Gunter
Val J. Lowe
Scott A. Przybelski
Matthew L. Senjem
Hugo Botha

Since tau PET tracers were introduced, investigators have quantified them using a wide variety of automated methods. As longitudinal cohort studies acquire second and third time points of serial within-person tau PET data, determining the best pipeline to measure change has become crucial. We compared a total of 415 different quantification methods (each a combination of multiple options) according to their effects on a) differences in annual SUVR change between clinical groups, and b) longitudinal measurement repeatability as measured by the error term from a linear mixed-effects model. Our comparisons used MRI and Flortaucipir scans of 97 Mayo Clinic study participants who clinically either: a) were cognitively unimpaired, or b) had cognitive impairments that were consistent with Alzheimer's disease pathology. Tested methods included cross-sectional and longitudinal variants of two overarching pipelines (FreeSurfer 6.0, and an in-house pipeline based on SPM12), three choices of target region (entorhinal, inferior temporal, and a temporal lobe meta-ROI), five types of partial volume correction (PVC) (none, two-compartment, three-compartment, geometric transfer matrix (GTM), and a tau-specific GTM variant), seven choices of reference region (cerebellar crus, cerebellar gray matter, whole cerebellum, pons, supratentorial white matter, eroded supratentorial WM, and a composite of eroded supratentorial WM, pons, and whole cerebellum), two choices of region masking (GM or GM and WM), and two choices of statistic (voxel-wise mean vs. median). Our strongest findings were: 1) larger temporal-lobe target regions greatly outperformed entorhinal cortex (median sample size estimates based on a hypothetical clinical trial were 520-526 vs. 1740); 2) longitudinal processing pipelines outperformed cross-sectional pipelines (median sample size estimates were 483 vs. 572); and 3) reference regions including supratentorial WM outperformed traditional cerebellar and pontine options (median sample size estimates were 370 vs. 559). Altogether, our results favored longitudinally SUVR methods and a temporal-lobe meta-ROI that includes adjacent (juxtacortical) WM, a composite reference region (eroded supratentorial WM + pons + whole cerebellum), 2-class voxel-based PVC, and median statistics.

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YNICL Journal 2018 Journal Article

Regional cortical perfusion on arterial spin labeling MRI in dementia with Lewy bodies: Associations with clinical severity, glucose metabolism and tau PET

Zuzana Nedelska
Matthew L. Senjem
Scott A. Przybelski
Timothy G. Lesnick
Val J. Lowe
Bradley F. Boeve
Arvin Arani
Prashanthi Vemuri

Visually preserved metabolism in posterior cingulate cortex relative to hypometabolism in precuneus and cuneus, the cingulate island sign, is a feature of dementia with Lewy bodies (DLB) on FDG-PET. Lower cingulate island sign ratio (posterior cingulate cortex/cuneus+precuneus; FDG-CISr) values have been associated with a higher Braak neurofibrillary tangle stage in autopsied DLB. Using voxel-wise analysis, we assessed the patterns of regional cortical perfusion and metabolism, and using an atlas-based approach, we measured perfusion cingulate island sign ratio on arterial spin labeling MRI (ASL-CISr), and its associations with FDG-CISr, uptake on tau-PET and clinical severity in DLB. Our study sample (n = 114) included clinically probable DLB patients (n = 19), age-matched patients with probable Alzheimer's disease dementia (AD; n = 19) and matched controls (n = 76) who underwent MRI with 3-dimensional pseudo-continuous arterial spin labeling, 18F-FDG-PET and 18F-AV-1451 tau PET. Patterns of cortical perfusion and metabolism were derived from quantitative maps using Statistical Parametric Mapping. DLB patients showed hypoperfusion on ASL-MRI in precuneus, cuneus and posterior parieto-occipital cortices, compared to controls, and relatively spared posterior cingulate gyrus, similar to pattern of hypometabolism on FDG-PET. DLB patients had higher ASL-CISr and FDG-CISr than AD patients (p <0. 001). ASL-CISr correlated with FDG-CISr in DLB patients (r = 0. 67; p =0. 002). Accuracy of distinguishing DLB from AD patients was 0. 80 for ASL-CISr and 0. 91 for FDG-CISr. Lower ASL-CISr was moderately associated with a higher composite medial temporal AV-1451 uptake (r = −0. 50; p =0. 03) in DLB. Lower perfusion in precuneus and cuneus was associated with worse global clinical scores. In summary, the pattern of cortical hypoperfusion on ASL-MRI is similar to hypometabolism on FDG-PET, and respective cingulate island sign ratios correlate with each other in DLB. Non-invasive and radiotracer-free ASL-MRI may be further developed as a tool for the screening and diagnostic evaluation of DLB patients in a variety of clinical settings where FDG-PET is not accessible.

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YNICL Journal 2016 Journal Article

A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity

Christopher G. Schwarz
Jeffrey L. Gunter
Heather J. Wiste
Scott A. Przybelski
Stephen D. Weigand
Chadwick P. Ward
Matthew L. Senjem
Prashanthi Vemuri

Alzheimer's disease (AD) researchers commonly use MRI as a quantitative measure of disease severity. Historically, hippocampal volume has been favored. Recently, “AD signature” measurements of gray matter (GM) volumes or cortical thicknesses have gained attention. Here, we systematically evaluate multiple thickness- and volume-based candidate-methods side-by-side, built using the popular FreeSurfer, SPM, and ANTs packages, according to the following criteria: (a) ability to separate clinically normal individuals from those with AD; (b) (extent of) correlation with head size, a nuisance covariatel (c) reliability on repeated scans; and (d) correlation with Braak neurofibrillary tangle stage in a group with autopsy. We show that volume- and thickness-based measures generally perform similarly for separating clinically normal from AD populations, and in correlation with Braak neurofibrillary tangle stage at autopsy. Volume-based measures are generally more reliable than thickness measures. As expected, volume measures are highly correlated with head size, while thickness measures are generally not. Because approaches to statistically correcting volumes for head size vary and may be inadequate to deal with this underlying confound, and because our goal is to determine a measure which can be used to examine age and sex effects in a cohort across a large age range, we thus recommend thickness-based measures. Ultimately, based on these criteria and additional practical considerations of run-time and failure rates, we recommend an AD signature measure formed from a composite of thickness measurements in the entorhinal, fusiform, parahippocampal, mid-temporal, inferior-temporal, and angular gyrus ROIs using ANTs with input segmentations from SPM12.

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YNIMG Journal 2014 Journal Article

Improved DTI registration allows voxel-based analysis that outperforms Tract-Based Spatial Statistics

Christopher G. Schwarz
Robert I. Reid
Jeffrey L. Gunter
Matthew L. Senjem
Scott A. Przybelski
Samantha M. Zuk
Jennifer L. Whitwell
Prashanthi Vemuri

Tract-Based Spatial Statistics (TBSS) is a popular software pipeline to coregister sets of diffusion tensor Fractional Anisotropy (FA) images for performing voxel-wise comparisons. It is primarily defined by its skeleton projection step intended to reduce effects of local misregistration. A white matter “skeleton” is computed by morphological thinning of the inter-subject mean FA, and then all voxels are projected to the nearest location on this skeleton. Here we investigate several enhancements to the TBSS pipeline based on recent advances in registration for other modalities, principally based on groupwise registration with the ANTS-SyN algorithm. We validate these enhancements using simulation experiments with synthetically-modified images. When used with these enhancements, we discover that TBSS's skeleton projection step actually reduces algorithm accuracy, as the improved registration leaves fewer errors to warrant correction, and the effects of this projection's compromises become stronger than those of its benefits. In our experiments, our proposed pipeline without skeleton projection is more sensitive for detecting true changes and has greater specificity in resisting false positives from misregistration. We also present comparative results of the proposed and traditional methods, both with and without the skeleton projection step, on three real-life datasets: two comparing differing populations of Alzheimer's disease patients to matched controls, and one comparing progressive supranuclear palsy patients to matched controls. The proposed pipeline produces more plausible results according to each disease's pathophysiology.

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YNIMG Journal 2011 Journal Article

Antemortem differential diagnosis of dementia pathology using structural MRI: Differential-STAND

Prashanthi Vemuri
Gyorgy Simon
Kejal Kantarci
Jennifer L. Whitwell
Matthew L. Senjem
Scott A. Przybelski
Jeffrey L. Gunter
Keith A. Josephs

The common neurodegenerative pathologies underlying dementia are Alzheimer's disease (AD), Lewy body disease (LBD) and frontotemporal lobar degeneration (FTLD). Our aim was to identify patterns of atrophy unique to each of these diseases using antemortem structural MRI scans of pathologically confirmed dementia cases and build an MRI-based differential diagnosis system. Our approach of creating atrophy maps using structural MRI and applying them for classification of new incoming patients is labeled Differential-STAND (Differential Diagnosis Based on Structural Abnormality in Neurodegeneration). Pathologically confirmed subjects with a single dementing pathologic diagnosis who had an MRI at the time of clinical diagnosis of dementia were identified: 48 AD, 20 LBD, 47 FTLD-TDP (pathology-confirmed FTLD with TDP-43). Gray matter density in 91 regions-of-interest was measured in each subject and adjusted for head size and age using a database of 120 cognitively normal elderly. The atrophy patterns in each dementia type when compared to pathologically confirmed controls mirrored known disease-specific anatomic patterns: AD-temporoparietal association cortices and medial temporal lobe; FTLD-TDP-frontal and temporal lobes and LBD-bilateral amygdalae, dorsal midbrain and inferior temporal lobes. Differential-STAND based classification of each case was done based on a mixture model generated using bisecting k-means clustering of the information from the MRI scans. Leave-one-out classification showed reasonable performance compared to the autopsy gold standard and clinical diagnosis: AD (sensitivity: 90. 7%; specificity: 84%), LBD (sensitivity: 78. 6%; specificity: 98. 8%) and FTLD-TDP (sensitivity: 84. 4%; specificity: 93. 8%). The proposed approach establishes a direct a priori relationship between specific topographic patterns on MRI and “gold standard” of pathology which can then be used to predict underlying dementia pathology in new incoming patients.

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