{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16"],"submitter":["Yin X"],"pubmed_abstract":["Simultaneously visualizing Amyloid-β (Aβ) plaque with its surrounding brain structures at the subcellular level in the intact brain is essential for understanding the complex pathology of Alzheimer's disease, but is still rarely achieved due to the technical limitations. Combining the micro-optical sectioning tomography (MOST) system, whole-brain Nissl staining, and customized image processing workflow, we generated a whole-brain panorama of Alzheimer's disease mice without specific labeling. The workflow employed the steps that include virtual channel splitting, feature enhancement, iso-surface rendering, direct volume rendering, and feature fusion to extract and reconstruct the different signals with distinct gray values and morphologies. Taking advantage of this workflow, we found that "],"journal":["Frontiers in neuroscience"],"pagination":["870520"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9067162"],"repository":["biostudies-literature"],"pubmed_title":["High-Resolution Digital Panorama of Multiple Structures in Whole Brain of Alzheimer's Disease Mice."],"pmcid":["PMC9067162"],"pubmed_authors":["Yin X","Yang W","Zhang J","Zhang X","Zhang H","Jiang H","Gao Z","Sun X"],"additional_accession":[]},"is_claimable":false,"name":"High-Resolution Digital Panorama of Multiple Structures in Whole Brain of Alzheimer's Disease Mice.","description":"Simultaneously visualizing Amyloid-β (Aβ) plaque with its surrounding brain structures at the subcellular level in the intact brain is essential for understanding the complex pathology of Alzheimer's disease, but is still rarely achieved due to the technical limitations. Combining the micro-optical sectioning tomography (MOST) system, whole-brain Nissl staining, and customized image processing workflow, we generated a whole-brain panorama of Alzheimer's disease mice without specific labeling. The workflow employed the steps that include virtual channel splitting, feature enhancement, iso-surface rendering, direct volume rendering, and feature fusion to extract and reconstruct the different signals with distinct gray values and morphologies. Taking advantage of this workflow, we found that ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-26T04:42:20.911Z","creation":"2025-04-06T11:13:11.88Z"},"accession":"S-EPMC9067162","cross_references":{"pubmed":["35516801"],"doi":["10.3389/fnins.2022.870520"]}}