Model fit for in-sample and out-of-sample data.

Model fit for in-sample and out-of-sample data.

<p>(A) Goodness of fit for four models, local FKPP, global FKPP, diffusion and logistic. For all panels, each point represents a region in the connectome model, averaged over subjects per scan. Top row shows estimated vs observed SUVR values. Bottom row shows estimated change vs observed chang...

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Main Author: Pavanjit Chaggar (9515394) (author)
Other Authors: Jacob W. Vogel (17601848) (author), Alexa Pichet Binette (17282919) (author), Travis B. Thompson (9515391) (author), Olof Strandberg (5612342) (author), Niklas Mattsson-Carlgren (15238039) (author), Linda Karlsson (368463) (author), Erik Stomrud (371678) (author), Saad Jbabdi (117812) (author), Stefano Magon (513040) (author), Gregory Klein (4673425) (author), Oskar Hansson (233854) (author), Alain Goriely (1669849) (author)
Published: 2025
Subjects:
Genetics
Molecular Biology
Neuroscience
Biotechnology
Science Policy
Mental Health
Infectious Diseases
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
xlink "> aggregation
two independent cohorts
predict accurately subject
new clinical tool
alzheimer &# 8217
tau pet data
pathological tau seeds
hyperphosphorylated tau protein
rich spatiotemporal pattern
local tau production
disease exhibiting transport
pathologic tau
spatiotemporal process
local production
transport dynamics
sequentially progressing
regional level
potentially providing
early stage
disease timeline
central driver
brain regions
accumulation exhibits
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Summary:<p>(A) Goodness of fit for four models, local FKPP, global FKPP, diffusion and logistic. For all panels, each point represents a region in the connectome model, averaged over subjects per scan. Top row shows estimated vs observed SUVR values. Bottom row shows estimated change vs observed change in SUVR. The local FKPP, global FKPP and logistic models provide the best fit to longitudinal data, while the diffusion model is unable to capture longitudinal change since it does not describe production. (B) Out-of-sample fits for four models of proteopathy. Top row: predicted vs observed out-of-sample SUVR. Bottom row: predicted change vs observed change from first in-sample scan to last out-of-sample scan. Each point represents a region in the connectome model, averaged over subjects.</p>

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