Latent atrophy factors related to phenotypical variants of posterior cortical atrophy

Colin Groot, B T Thomas Yeo, 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

Research output: Contribution to journalArticleAcademicpeer-review


OBJECTIVE: To determine whether atrophy relates to phenotypical variants of posterior cortical atrophy (PCA) recently proposed in clinical criteria; dorsal, ventral, dominant-parietal and caudal, we assessed associations between latent atrophy factors and cognition.

METHODS: We employed a data-driven Bayesian modelling framework based on latent Dirichlet allocation to identify latent atrophy factors in a multi-center cohort of 119 individuals with PCA (age:64±7, 38% male, MMSE:21±5, 71% amyloid-β-positive, 29% amyloid-β status unknown). The model uses standardized gray matter density images as input (adjusted for age, sex, intracranial volume, 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 four PCA-specific cognitive domains (object-perception, space-perception, non-visual/parietal functions and primary visual processing) using general linear models.

RESULTS: The model revealed four 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 vast majority expressed multiple atrophy factors and had mixed clinical profiles with impairments across multiple domains, rather than displaying a discrete clinical-radiological phenotype.

CONCLUSION: Our results indicate that particular brain-behavior networks are vulnerable in PCA, but most individuals display a constellation of affected brain-regions and symptoms, indicating that classification into four mutually exclusive variants is unlikely to be clinically useful.

Original languageEnglish
Publication statusE-pub ahead of print - 16 Jul 2020

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