Please use this identifier to cite or link to this item: https://doi.org/10.1212/WNL.0000000000010362
Title: Latent atrophy factors related to phenotypical variants of posterior cortical atrophy
Authors: Colin Groot
YEO BOON THYE THOMAS 
Jacob W. Vogel
Xiuming Zhang
Nanbo Sun
Elizabeth C. Mormino
Yolande A.L. Pijnenburg
Bruce L. Miller
Howard J. Rosen
Renaud La Joie
Frederik Barkhof
Philip Scheltens
Wiesje M. van der Flier
Gil D. Rabinovici
Rik Ossenkoppele
Issue Date: 22-Sep-2020
Citation: Colin Groot, YEO BOON THYE THOMAS, Jacob W. Vogel, Xiuming Zhang, Nanbo Sun, Elizabeth C. Mormino, Yolande A.L. Pijnenburg, Bruce L. Miller, Howard J. Rosen, Renaud La Joie, Frederik Barkhof, Philip Scheltens, Wiesje M. van der Flier, Gil D. Rabinovici, Rik Ossenkoppele (2020-09-22). Latent atrophy factors related to phenotypical variants of posterior cortical atrophy. Neurology. ScholarBank@NUS Repository. https://doi.org/10.1212/WNL.0000000000010362
Rights: Attribution 4.0 International
Abstract: Objective To determine whether atrophy relates to phenotypical variants of posterior cortical atrophy (PCA) recently proposed in clinical criteria (i.e., dorsal, ventral, dominant-parietal, and caudal) we assessed associations between latent atrophy factors and cognition. Methods We employed a data-driven Bayesian modeling framework based on latent Dirichlet allocation to identify latent atrophy factors in a multicenter cohort of 119 individuals with PCA (age 64 ± 7 years, 38% male, Mini-Mental State Examination 21 ± 5, 71% β-amyloid positive, 29% β-amyloid status unknown). The model uses standardized gray matter density images as input (adjusted for age, sex, intracranial volume, MRI scanner field strength, and whole-brain gray matter volume) and provides voxelwise probabilistic maps for a predetermined number of atrophy factors, allowing every individual to express each factor to a degree without a priori classification. Individual factor expressions were correlated to 4 PCA-specific cognitive domains (object perception, space perception, nonvisual/parietal functions, and primary visual processing) using general linear models. Results The model revealed 4 distinct yet partially overlapping atrophy factors: right-dorsal, right-ventral, left-ventral, and limbic. We found that object perception and primary visual processing were associated with atrophy that predominantly reflects the right-ventral factor. Furthermore, space perception was associated with atrophy that predominantly represents the right-dorsal and right-ventral factors. However, individual participant profiles revealed that the large majority expressed multiple atrophy factors and had mixed clinical profiles with impairments across multiple domains, rather than displaying a discrete clinical–radiologic phenotype. Conclusion Our results indicate that specific brain behavior networks are vulnerable in PCA, but most individuals display a constellation of affected brain regions and symptoms, indicating that classification into 4 mutually exclusive variants is unlikely to be clinically useful.
Source Title: Neurology
URI: https://scholarbank.nus.edu.sg/handle/10635/189260
ISSN: 1526632X
DOI: 10.1212/WNL.0000000000010362
Rights: Attribution 4.0 International
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Groot_PCA_variants_resubmission_wo_tracked_changes.pdf6.04 MBAdobe PDF

OPEN

Post-printView/Download

SCOPUSTM   
Citations

6
checked on May 13, 2022

WEB OF SCIENCETM
Citations

4
checked on Oct 7, 2021

Page view(s)

127
checked on May 12, 2022

Download(s)

3
checked on May 12, 2022

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons