Colin Groot1, B T Thomas Yeo2, Jacob W Vogel2, Xiuming Zhang2, Nanbo Sun2, Elizabeth C Mormino2, Yolande A L Pijnenburg2, Bruce L Miller2, Howard J Rosen2, Renaud La Joie2, Frederik Barkhof2, Philip Scheltens2, Wiesje M van der Flier2, Gil D Rabinovici2, Rik Ossenkoppele2. 1. From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden. c.groot3@amsterdamumc.nl. 2. From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden.
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.
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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Authors: Colin Groot; Michel J Grothe; Shubhabrata Mukherjee; Irina Jelistratova; Iris Jansen; Anna Catharina van Loenhoud; Shannon L Risacher; Andrew J Saykin; Christine L Mac Donald; Jesse Mez; Emily H Trittschuh; Gregor Gryglewski; Rupert Lanzenberger; Yolande A L Pijnenburg; Frederik Barkhof; Philip Scheltens; Wiesje M van der Flier; Paul K Crane; Rik Ossenkoppele Journal: Neuroimage Clin Date: 2021-04-03 Impact factor: 4.881
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