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            <funder>This research was supported by Mr Jean-Paul Baudecroux and The Big Brain Theory Program - Paris Brain Institute (ICM). The human samples were obtained from the Neuro-CEB brain bank (https://www.neuroceb.org/en/) (BRIF Number 0033-00011), partly funded by the patients’ associations ARSEP, ARSLA, “Connaître les Syndromes Cérébelleux,” France-DFT, France Parkinson and by Vaincre Alzheimer Foundation, to which we express our gratitude. We are also grateful to the patients and their families. This work was granted access to the HPC resources of IDRIS under the allocation 2023-AD011013250 made by GENCI.</funder>
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              <p>In this study, we proposed and evaluated a graph-based framework to assess variations in Alzheimer’s disease (AD) neuropathologies, focusing on classic (cAD) and rapid (rpAD) progression forms. Histopathological images are converted into tau-pathology-based (i.e., amyloid plaques and tau tangles) graphs, and derived metrics are used in a machine-learning classifier. This classifier incorporates SHAP value explainability to differentiate be- tween cAD and rpAD. Furthermore, we tested graph neural networks (GNNs) to extract topological embeddings from the graphs and use them in classifying the progression forms of AD. The analysis demonstrated denser networks in rpAD and a distinctive impact on brain cortical layers: rpAD predominantly affects middle layers, whereas cAD influences both superficial and deep layers of the same cortical regions. These results suggest a unique neuropathological network organization for each AD variant.</p>
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          <orgName>Institut National de Recherche en Informatique et en Automatique</orgName>
          <orgName type="acronym">Inria</orgName>
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              <addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.inria.fr/en/</ref>
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