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Latent atrophy factors related to phenotypical variants of posterior cortical atrophy

Groot, Colin ; Yeo, B. T.Thomas ; Vogel, Jacob W. LU ; Zhang, Xiuming ; Sun, Nanbo ; Mormino, Elizabeth C. ; Pijnenburg, Yolande A.L. ; Miller, Bruce L. ; Rosen, Howard J. and La Joie, Renaud , et al. (2020) In Neurology 95(12). p.1672-1685
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Neurology
volume
95
issue
12
pages
1672 - 1685
publisher
American Academy of Neurology
external identifiers
  • pmid:32675078
  • scopus:85091469982
ISSN
1526-632X
DOI
10.1212/WNL.0000000000010362
language
English
LU publication?
yes
id
61e6b0cc-e23f-4e7e-b64e-e55e674eec0e
date added to LUP
2020-10-26 12:05:50
date last changed
2021-06-08 05:21:04
@article{61e6b0cc-e23f-4e7e-b64e-e55e674eec0e,
  abstract     = {<p>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.</p>},
  author       = {Groot, Colin and Yeo, B. T.Thomas and Vogel, Jacob W. and Zhang, Xiuming and Sun, Nanbo and Mormino, Elizabeth C. and Pijnenburg, Yolande A.L. and Miller, Bruce L. and Rosen, Howard J. and La Joie, Renaud and Barkhof, Frederik and Scheltens, Philip and van der Flier, Wiesje M. and Rabinovici, Gil D. and Ossenkoppele, Rik},
  issn         = {1526-632X},
  language     = {eng},
  number       = {12},
  pages        = {1672--1685},
  publisher    = {American Academy of Neurology},
  series       = {Neurology},
  title        = {Latent atrophy factors related to phenotypical variants of posterior cortical atrophy},
  url          = {http://dx.doi.org/10.1212/WNL.0000000000010362},
  doi          = {10.1212/WNL.0000000000010362},
  volume       = {95},
  year         = {2020},
}