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Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort

Authors: Cury, Claire; Durrleman, Stanley; Cash, David; Lorenzi, Marco; Nicholas, Jennifer,; Bocchetta, Martina; Van Swieten, John,; +56 Authors

Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort

Abstract

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.

Highlights • Clustering shape parametrisation allows local shape analysis. • Thalamic shape changes appear 5 years before onset of fronto temporal dementia. • Shape changes seem to occur before volume changes. • Pre-symptomatic shape changes in thalamus are dorsofrontal, where connecting to temporal lobes.

Graphical abstract Image 1

Countries
France, Netherlands, France, France, France, France, Italy, Switzerland
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Dewey Decimal Classification: ddc:618.97

Keywords

Male, Cognitive Neuroscience, EMC OR-01, Prodromal Symptoms, Neuroimaging, Article, Clustering, Cohort Studies, Spatio-Temporal Analysis, Thalamus, Settore BIO/13 - Biologia Applicata, Clustering; Computational anatomy; Parallel transport; Shape analysis; Spatiotemporal geodesic regression; Thalamus; Neurology; Cognitive Neuroscience, Parallel transport, Humans, Spatiotemporal geodesic regression, info:eu-repo/classification/ddc/618.97, Middle Aged, Magnetic Resonance Imaging, Shape analysis, Computational anatomy, Neurology, Frontotemporal Dementia, Settore MED/26 - Neurologia, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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