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001     280925
005     20250918102642.0
020 _ _ |a 978-3-319-66178-0 (print)
020 _ _ |a 978-3-319-66179-7 (electronic)
024 7 _ |a 10.1007/978-3-319-66179-7_4
|2 doi
024 7 _ |a 0302-9743
|2 ISSN
024 7 _ |a 1611-3349
|2 ISSN
037 _ _ |a DZNE-2025-01008
100 1 _ |a Descoteaux, Maxime
|0 0000-0002-8191-2129
|b 0
|e Editor
111 2 _ |a Medical Image Computing and Computer Assisted Intervention
|g MICCAI 2017
|c Quebec City
|d 2017-09-11 - 2017-09-13
|w Canada
245 _ _ |a Latent Processes Governing Neuroanatomical Change in Aging and Dementia
260 _ _ |a Cham
|c 2017
|b Springer International Publishing
295 1 0 |a Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 / Descoteaux, Maxime (Editor) [https://orcid.org/0000-0002-8191-2129] ; Cham : Springer International Publishing, 2017, Chapter 4 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-319-66178-0=978-3-319-66179-7 ; doi:10.1007/978-3-319-66179-7
300 _ _ |a 30 - 37
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336 7 _ |a Contribution to a book
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490 0 _ |a Lecture Notes in Computer Science
|v 10435
520 _ _ |a Clinically normal aging and pathological processes cause structural changes in the brain. These changes likely occur in overlapping regions that accommodate neural systems with high susceptibility to deleterious factors. Due to the overlap, the separation between aging and pathological processes is challenging when analyzing brain structures independently. We propose to identify multivariate latent processes that govern cross-sectional and longitudinal neuroanatomical changes across the brain in aging and dementia. A discriminative representation of neuroanatomy is obtained from spectral shape descriptors in the BrainPrint. We identify latent factors by maximizing the covariance between morphological change and response variables of age and a proxy for dementia. Our results reveal cross-sectional and longitudinal patterns of change in neuroanatomy that distinguishes aging processes from disease processes. Finally, latent processes do not only yield a parsimonious model but also a significantly improved prediction accuracy.
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588 _ _ |a Dataset connected to CrossRef Book Series, Journals: pub.dzne.de
700 1 _ |a Maier-Hein, Lena
|b 1
|e Editor
700 1 _ |a Franz, Alfred
|b 2
|e Editor
700 1 _ |a Jannin, Pierre
|0 0000-0002-7415-071X
|b 3
|e Editor
700 1 _ |a Collins, D. Louis
|b 4
|e Editor
700 1 _ |a Duchesne, Simon
|b 5
|e Editor
700 1 _ |a Wachinger, Christian
|b 6
700 1 _ |a Rieckmann, Anna
|b 7
700 1 _ |a Reuter, Martin
|0 P:(DE-2719)2812134
|b 8
|e Last author
|u dzne
773 _ _ |a 10.1007/978-3-319-66179-7_4
856 4 _ |u https://pub.dzne.de/record/280925/files/DZNE-2025-01008.pdf
856 4 _ |u https://pub.dzne.de/record/280925/files/DZNE-2025-01008.pdf?subformat=pdfa
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909 C O |o oai:pub.dzne.de:280925
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910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
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915 _ _ |a Nationallizenz
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|d 2024-12-28
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|d 2024-12-28
920 1 _ |0 I:(DE-2719)1040310
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|l Artificial Intelligence in Medicine
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980 _ _ |a contrib
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980 _ _ |a contb
980 _ _ |a I:(DE-2719)1040310
980 _ _ |a UNRESTRICTED

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