Please use this identifier to cite or link to this item: https://doi.org/10.1212/WNL.0000000000010362
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dc.titleLatent atrophy factors related to phenotypical variants of posterior cortical atrophy
dc.contributor.authorColin Groot
dc.contributor.authorYEO BOON THYE THOMAS
dc.contributor.authorJacob W. Vogel
dc.contributor.authorXiuming Zhang
dc.contributor.authorNanbo Sun
dc.contributor.authorElizabeth C. Mormino
dc.contributor.authorYolande A.L. Pijnenburg
dc.contributor.authorBruce L. Miller
dc.contributor.authorHoward J. Rosen
dc.contributor.authorRenaud La Joie
dc.contributor.authorFrederik Barkhof
dc.contributor.authorPhilip Scheltens
dc.contributor.authorWiesje M. van der Flier
dc.contributor.authorGil D. Rabinovici
dc.contributor.authorRik Ossenkoppele
dc.date.accessioned2021-04-14T02:44:12Z
dc.date.available2021-04-14T02:44:12Z
dc.date.issued2020-09-22
dc.identifier.citationColin 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
dc.identifier.issn1526632X
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/189260
dc.description.abstractObjective 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.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.typeArticle
dc.contributor.departmentDEPT OF ELECTRICAL & COMPUTER ENGG
dc.description.doi10.1212/WNL.0000000000010362
dc.description.sourcetitleNeurology
dc.published.statePublished
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