Characterization of individual tau-PET networks in Alzheimer’s disease and related dementia
Poster No:
1267
Submission Type:
Late-Breaking Abstract Submission
Authors:
Evgeny Chumin1, Mario Dzemidzic1, Shannon Risacher2, Liana Apostolova1, Martin Farlow1, Brenna McDonald1, Yu-Chien Wu1, Kwangsik Nho1, Andrew Saykin1, Olaf Sporns3
Institutions:
1Indiana University School of Medicine, Indianapolis, IN, 2Wake Forest University School of Medicine, Winston-Salem, NC, 3Indiana University, Bloomington, IN
First Author:
Co-Author(s):
Late Breaking Reviewer(s):
Rosanna Olsen
Rotman Research Institute, Baycrest Academy for Research and Education
Toronto, Ontario
Rotman Research Institute, Baycrest Academy for Research and Education
Toronto, Ontario
Introduction:
One hallmark of Alzheimer's disease (AD) is accumulation of hyperphosphorylated tau in the brain, the spread of which occurs preferentially within functional systems (Vogel 2020). Network neuroscience uses functional MRI (fMRI) connectivity (FC) to estimate participant-level networks from time-varying data, but tau-PET imaging yields a static snapshot of the tau distribution. Therefore, tau-PET networks are most commonly generated by using inter-subject correlation to estimate a groupwise covariance network (Veronese 2019). Here we used a sample of 82 older adults with varying AD-related diagnoses from the Indiana AD Research Center who underwent resting state functional MRI (rsfMRI) and [18F]flortaucipir PET to generate participant-level tau similarity networks and assess relationships between FC and tau-PET covariance networks.
Methods:
MRI data from 29 cognitively normal (CN, mean age 71yo, 22 Females), 16 subjective cognitive decline (SCD, 70yo, 9F), 20 mild cognitive impairment (MCI, 71yo, 11F), and 17 dementia (DEM, 66yo, 11F) participants were processed using a publicly available pipeline (Chumin 2021). Anatomical T1-weighted images were denoised, skull-stripped, subdivided using a 200-node Schaefer (2018) parcellation, and registered to each subject's T1 and rsfMRI data. Parcel-averaged time series were extracted from rsfMRI data after standard preprocessing, including nuisance and global signal regression (ICA-AROMA, aCompCor). PET was collected 80-100min post injection in 4x5min frames that were motion-corrected, registered to standard space, and smoothed with an isotropic 8mm FWHM kernel. Mean regional values were extracted from standardized uptake value ratio (SUVr) images, with cerebellar crus as the reference region. FC and group-level tau covariance networks were computed with Pearson and Spearman correlation, respectively. Participant similarity networks were assembled by: 1) computing the absolute difference in SUVr among all regions pairs to generate a distance matrix, (2) normalizing this matrix by the maximum distance, to yield 0-1 weights, where 1 corresponded to maximum difference in SUVR, and (3) computing the final tau similarity matrix (1-the normalized distance, i.e., higher weights correspond to greater similarity). Network nodes were ordered by 7 resting state networks (RSN; Yeo, 2011). Associations among network types at group and individual levels for whole networks were computed as Spearman correlation of edge weights.
Results:
Fig. 1 illustrates group average FC networks (panels A-D), Tau-PET group covariance networks (panels E-H), and group averages of the proposed tau-PET similarity networks (panels I-L), which are generated for each participant unlike covariance networks. Both PET-derived network types were qualitatively similar. The similarity was greater within modality, i.e., between the novel tau-PET similarity networks and group covariance (rho range 0.4-0.54 across groups, Fig. 2E-F) as compared to FC networks (rho range 0.1-0.26, Fig. 2A-D). Spearman rho between FC and tau-PET covariance networks ranged from 0.16-0.26. As expected, median SUVr across all nodes was higher with greater diagnostic severity (Fig. 2I). At the participant level, coupling between FC and tau-PET similarity networks (Spearman's rho, Fig. 2J) increased in diagnostic groups, and that relationship was not related to overall greater tau in the brain (Fig. 2K, rho=0.11, p>0.3).
·Figure 1
·Figure 2
Conclusions:
Here we present a method for generating participant-level PET networks by using distance-based similarity and applying it in a clinical tau-PET dataset. These networks are moderately similar to the group-level covariance methods currently used, but are instead obtained at the participant-level, making them more suitable for multilayered network model and precision medicine approaches. Future characterization of these networks and associated metrics as well as their generalizability to other PET tracers is needed to understand their diagnostic/clinical utility.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Methods Development
PET Modeling and Analysis
Keywords:
Computational Neuroscience
Data analysis
Degenerative Disease
FUNCTIONAL MRI
Positron Emission Tomography (PET)
1|2Indicates the priority used for review
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2. Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex, 28(9), 3095-3114.
3. Veronese, M., Moro, L., Arcolin, M., Dipasquale, O., Rizzo, G., Expert, P., Khan, W., Fisher, P. M., Svarer, C., Bertoldo, A., Howes, O., & Turkheimer, F. E. (2019). Covariance statistics and network analysis of brain PET imaging studies. Scientific Reports, 9(1), 2496.
4. Vogel, J. W., Iturria-Medina, Y., Strandberg, O. T., Smith, R., Levitis, E., Evans, A. C., & Hansson, O. (2020). Spread of pathological tau proteins through communicating neurons in human Alzheimer's disease. Nat Commun, 11(1).
5. Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125-1165.