TY  - JOUR
AU  - Singh, Devesh
AU  - Grazia, Alice
AU  - Reiz, Achim
AU  - Hermann, Andreas
AU  - Altenstein, Slawek
AU  - Beichert, Lukas
AU  - Bernhardt, Alexander
AU  - Buerger, Katharina
AU  - Butryn, Michaela
AU  - Dechent, Peter
AU  - Duezel, Emrah
AU  - Ewers, Michael
AU  - Fliessbach, Klaus
AU  - Freiesleben, Silka D
AU  - Glanz, Wenzel
AU  - Hetzer, Stefan
AU  - Janowitz, Daniel
AU  - Kilimann, Ingo
AU  - Kimmich, Okka
AU  - Laske, Christoph
AU  - Levin, Johannes
AU  - Lohse, Andrea
AU  - Lüsebrink-Rindsland, Jann Falk Silvester
AU  - Munk, Matthias
AU  - Perneczky, Robert
AU  - Peters, Oliver
AU  - Preis, Lukas
AU  - Priller, Josef
AU  - Prudlo, Johannes
AU  - Rauchmann, Boris Stephan
AU  - Rostamzadeh, Ayda
AU  - Roy-Kluth, Nina
AU  - Scheffler, Klaus
AU  - Schneider, Anja
AU  - Schneider, Luisa-Sophie
AU  - Schott, Björn H
AU  - Spottke, Annika
AU  - Spruth, Eike Jakob
AU  - Synofzik, Matthis
AU  - Wiltfang, Jens
AU  - Jessen, Frank
AU  - Teipel, Stefan J
AU  - Dyrba, Martin
TI  - A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans.
JO  - Journal of Alzheimer's disease
VL  - 108
IS  - 1_suppl
SN  - 1387-2877
CY  - Amsterdam
PB  - IOS Press
M1  - DZNE-2025-01230
SP  - S258 - S273
PY  - 2025
AB  - BackgroundConvolutional neural network (CNN) based volumetry of MRI data can help differentiate Alzheimer's disease (AD) and the behavioral variant of frontotemporal dementia (bvFTD) as causes of cognitive decline and dementia. However, existing CNN-based MRI volumetry tools lack a structured hierarchical representation of brain anatomy, which would allow for aggregating regional pathological information and automated computational inference.ObjectiveDevelop a computational ontology pipeline for quantifying hierarchical pathological abnormalities and visualize summary charts for brain atrophy findings, aiding differential diagnosis.MethodsUsing FastSurfer, we segmented brain regions and measured volume and cortical thickness from MRI scans pooled across multiple cohorts (N = 3433; ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD), including healthy controls, prodromal and clinical AD cases, and bvFTD cases. Employing the Web Ontology Language (OWL), we built a semantic model encoding hierarchical anatomical information. Additionally, we created summary visualizations based on sunburst plots for visual inspection of the information stored in the ontology.ResultsOur computational framework dynamically estimated and aggregated regional pathological deviations across different levels of neuroanatomy abstraction. The disease similarity index derived from the volumetric and cortical thickness deviations achieved an AUC of 0.88 for separating AD and bvFTD, which was also reflected by distinct atrophy profile visualizations.ConclusionsThe proposed automated pipeline facilitates visual comparison of atrophy profiles across various disease types and stages. It provides a generalizable computational framework for summarizing pathologic findings, potentially enhancing the physicians' ability to evaluate brain pathologies robustly and interpretably.
KW  - Humans
KW  - Magnetic Resonance Imaging: methods
KW  - Alzheimer Disease: pathology
KW  - Alzheimer Disease: diagnostic imaging
KW  - Brain: pathology
KW  - Brain: diagnostic imaging
KW  - Male
KW  - Female
KW  - Aged
KW  - Frontotemporal Dementia: pathology
KW  - Frontotemporal Dementia: diagnostic imaging
KW  - Atrophy: pathology
KW  - Neural Networks, Computer
KW  - Middle Aged
KW  - Alzheimer's disease (Other)
KW  - brain volumetry (Other)
KW  - computer graphics (Other)
KW  - frontotemporal dementia (Other)
KW  - magnetic resonance imaging (Other)
KW  - neuroanatomy (Other)
KW  - ontology (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40255031
C2  - pmc:PMC12583655
DO  - DOI:10.1177/13872877251331222
UR  - https://pub.dzne.de/record/281857
ER  -