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Sökning: id:"swepub:oai:lup.lub.lu.se:d99ed342-0667-4767-8b89-cee23148dec1" > An automated, geome...

An automated, geometry-based method for hippocampal shape and thickness analysis

Diers, Kersten (författare)
German Center for Neurodegenerative Diseases (DZNE), Bonn
Baumeister, Hannah (författare)
German Center for Neurodegenerative Diseases (DZNE), Bonn
Jessen, Frank (författare)
University of Cologne,University Hospital of Cologne,German Center for Neurodegenerative Diseases (DZNE), Bonn
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Düzel, Emrah (författare)
German Center for Neurodegenerative Diseases (DZNE), Bonn,Otto von Guericke University Magdeburg
Berron, David (författare)
Lund University,Lunds universitet,Klinisk minnesforskning,Forskargrupper vid Lunds universitet,LU profilområde: Proaktivt åldrande,Lunds universitets profilområden,Clinical Memory Research,Lund University Research Groups,LU Profile Area: Proactive Ageing,Lund University Profile areas,German Center for Neurodegenerative Diseases (DZNE), Bonn
Reuter, Martin (författare)
Massachusetts General Hospital,German Center for Neurodegenerative Diseases (DZNE), Bonn,Harvard Medical School
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 (creator_code:org_t)
2023
2023
Engelska.
Ingår i: NeuroImage. - 1053-8119. ; 276
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • The hippocampus is one of the most studied neuroanatomical structures due to its involvement in attention, learning, and memory as well as its atrophy in ageing, neurological, and psychiatric diseases. Hippocampal shape changes, however, are complex and cannot be fully characterized by a single summary metric such as hippocampal volume as determined from MR images. In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature. Starting from an automated segmentation of hippocampal subfields, we create a 3D tetrahedral mesh model as well as a 3D intrinsic coordinate system of the hippocampal body. From this coordinate system, we derive local curvature and thickness estimates as well as a 2D sheet for hippocampal unfolding. We evaluate the performance of our algorithm with a series of experiments to quantify neurodegenerative changes in Mild Cognitive Impairment and Alzheimer's disease dementia. We find that hippocampal thickness estimates detect known differences between clinical groups and can determine the location of these effects on the hippocampal sheet. Further, thickness estimates improve classification of clinical groups and cognitively unimpaired controls when added as an additional predictor. Comparable results are obtained with different datasets and segmentation algorithms. Taken together, we replicate canonical findings on hippocampal volume/shape changes in dementia, extend them by gaining insight into their spatial localization on the hippocampal sheet, and provide additional, complementary information beyond traditional measures. We provide a new set of sensitive processing and analysis tools for the analysis of hippocampal geometry that allows comparisons across studies without relying on image registration or requiring manual intervention.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Neurosciences (hsv//eng)

Nyckelord

Flattening
Hippocampus
Neuroimaging
Shape analysis
Thickness

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