Depth-resolved fiber photometry of amyloid plaque signals in freely behaving Alzheimer's disease mice

This work was supported by the Medical Research Council (Grant Nos. MR/V033964/1 and MR/Y004051/1 to S.S.), the Alzheimer’s Research UK (Grant Nos. PPG2023B-019 to S.S.), the European Union’s Horizon 2020 (Grant Nos. H2020-ICT, DEEPER, 101016787 to N.M., Fi.P., M.P., K.M., M.D.V., Fe.P. and S.S.; MINING, 101125498 to M.D.V. and Fe.P.), the European Union’s Horizon Europe (Grant No. ERC, NEUROLIDAR, 101163003 to Fi.P.), and the PARD 2024 from the University of Padua to Fi.P. K.M. was supported through an RAEng Chair in Emerging Technologies. C.M., M.D.V., and Fe.P. acknowledge funding from the Project “RAISE (Robotics and AI for Socio-economic Empowerment) (code ECS00000035) funded by the European Union – NextGenerationEU PNRR MUR – M4C2 – Investimento 1.5 – Avviso ‘Ecosistemi dell’Innovazione” CUP J33C22001220001. Concerning ERC funding, views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. The preprint has been posted to bioRxiv ( https://doi.org/10.1101/2025.04.08.647900).

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-23
DOI
https://doi.org/10.5281/zenodo.17293711
Primary Topic
Dental materials and restorations
Type
article
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article

Depth-resolved fiber photometry of amyloid plaque signals in freely behaving Alzheimer's disease mice

Zenodo (CERN European Organization for Nuclear Research)
Dental materials and restorations
article

Depth-resolved fiber photometry of amyloid plaque signals in freely behaving Alzheimer's disease mice

article en

Abstract

This work was supported by the Medical Research Council (Grant Nos. MR/V033964/1 and MR/Y004051/1 to S.S.), the Alzheimer’s Research UK (Grant Nos. PPG2023B-019 to S.S.), the European Union’s Horizon 2020 (Grant Nos. H2020-ICT, DEEPER, 101016787 to N.M., Fi.P., M.P., K.M., M.D.V., Fe.P. and S.S.; MINING, 101125498 to M.D.V. and Fe.P.), the European Union’s Horizon Europe (Grant No. ERC, NEUROLIDAR, 101163003 to Fi.P.), and the PARD 2024 from the University of Padua to Fi.P. K.M. was supported through an RAEng Chair in Emerging Technologies. C.M., M.D.V., and Fe.P. acknowledge funding from the Project “RAISE (Robotics and AI for Socio-economic Empowerment) (code ECS00000035) funded by the European Union – NextGenerationEU PNRR MUR – M4C2 – Investimento 1.5 – Avviso ‘Ecosistemi dell’Innovazione” CUP J33C22001220001. Concerning ERC funding, views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. The preprint has been posted to bioRxiv ( https://doi.org/10.1101/2025.04.08.647900).

Zenodo (CERN European Organization for Nuclear Research)
University of Padua (IT), University of Strathclyde (GB), TiGenix (Spain) (ES), Italian Institute of Technology (IT), Center for Biomolecular Nanotechnologies (IT), Danmarks Nationalbank (DK)
Partnerships for the goals
Openalex Percentile: Top 41%
Dental materials and restorations
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