DOI: 10.5061/dryad.wstqjq30t
title: Part 2: Data from: Clinical and pathologic correlations of machine learning quantification of Aβ deposits across three brain Regions of decedents with Alzheimer's disease

description: The advent of machine learning enables scalable quantification of
 neuropathology, offering deeper phenotyping of Alzheimer's disease
 (AD). In this study, we quantified amyloid-beta (Aβ) deposits across
 multiple brain regions and examined their associations with clinical,
 demographic, and genetic factors in persons pathologically diagnosed with
 AD. We analyzed densities (#/mm2) of cored plaques, diffuse plaques, and
 cerebral amyloid angiopathy (CAA) in 273 individuals from three
 Alzheimer’s Disease Research Centers. Formalin-fixed paraffin-embedded
 sections of frontal, temporal, and parietal cortices were immunostained
 and digitized, generating 799 whole slide images (WSIs). Following log
 transformation, mixed-effects modeling revealed that the parietal cortex
 had the highest cored plaque densities (p &lt; 0.001), while the
 temporal cortex had the highest diffuse plaque densities (p &lt;
 0.001); CAA showed no regional differences. Wilcoxon rank-sum tests and
 covariate-adjusted linear models showed ApoE ε4− status was associated
 with higher cored plaque densities in the temporal lobe (p = 0.04), while
 ApoE ε4+ status was associated with higher diffuse plaque densities in the
 temporal lobe (p = 0.001) and increased CAA in the frontal lobe (p =
 0.004). These findings support deeper phenotyping to define generalizable
 patterns of disease heterogeneity and neuroanatomical distribution in AD,
 providing insights that may guide precision medicine–based approaches.
description: # Part 2: Data from: Clinical and pathologic correlations of machine
 learning quantification of Aβ deposits across three brain Regions of
 decedents with Alzheimer's disease Dataset DOI:
 [10.5061/dryad.wstqjq30t](https://doi.org/10.5061/dryad.wstqjq30t) ##
 Description of the data and file structure # **UC Davis ADRC RO1 Whole
 Slide Image (WSI) Dataset (Part 2)** ## **Dataset Description** This
 dataset contains whole slide images (WSIs) of Aβ-immunostained human brain
 tissue used in the study: **Clinical and Pathologic Correlations of
 Machine Learning Quantification of Aβ Deposits Across Three Brain Regions
 of Decedents with Alzheimer's Disease.** Because of file size
 constraints, the full WSI dataset is provided in **two parts**: * **Part 1
 - Batches 1 to 9** * **Part 2 - Batches - 10 to 12** The content of each
 part is non-overlapping. Users should download **both Part 1 and Part 2**
 to access the complete WSI collection. --- ## **Whole Slide Image
 Details** * **Total WSIs:** 799 * **Tissue source:** 273 decedents with a
 neuropathologic diagnosis of intermediate or high Alzheimer's disease
 * **Brain regions included:** * Frontal cortex * Parietal cortex *
 Temporal cortex * **Stain:** Aβ immunohistochemistry using 4G8 monoclonal
 antibody * **Section thickness:** 5 μm * **Scanner:** Zeiss Axio Scan Z.1
 * **Magnification:** 40× * **Resolution:** 0.11 μm/pixel * **File
 format:** `.czi` * **Compression:** 60% All WSIs were stained at a single
 site to minimize staining variability across centers. --- ## **Notes for
 Users** * Each part of the dataset contains a portion of the WSIs. *
 Together, **Part 1 + Part 2** represent the full dataset used in the
 study. * These files contain only the **raw WSIs**. ### **Viewing and
 Loading** **`.czi`** **Whole Slide Images** Due to known limitations in
 some open-source tools, successful viewing depends on the operating system
 and software used. #### **Recommended Software** **Zeiss ZEN (Free Viewer)
 – Recommended** Software: **ZEISS ZEN Lite / ZEN 3.6** * Platform: Windows
 * This software natively supports `.czi` WSIs and was used by the authors
 to verify all files. * 
 
 * **Linux / High-Performance Workflows** For large-scale processing,
 tiling, and visualization, users may use the open-source pipeline
 available at:\
 [https://github.com/ucdrubinet/BrainSec](https://github.com/ucdrubinet/BrainSec) * Validated environment: * Ubuntu 16.04 / 18.04 (AMD64) * System RAM ≥ 128 GB (less might be okay depending on usage) * NVIDIA GPU with CUDA ≥ 10.1 * Docker ≥ 19.03 * nvidia-container-toolkit --- 
 
 ### Files and variables #### File: Batch10_4G8.zip **Description:** Whole
 slide images immunostained in **Batch 10**, including **frontal, parietal,
 and temporal lobe sections** for the volunteers assigned to this staining
 batch #### File: Batch11_4G8.zip **Description:** Whole slide images
 immunostained in **Batch 11**, including **frontal, parietal, and temporal
 lobe sections** for the volunteers assigned to this staining batch ####
 File: Batch12_4G8.zip **Description:** Whole slide images immunostained in
 **Batch 12**, including **frontal, parietal, and temporal lobe sections**
 for the volunteers assigned to this staining batch #### File:
 Batch_Remanants.zip **Description:**\ Contains remaining whole slide
 images (05-310-Parietal_4G8.czi, 05-912-Temporal_4G8.czi and
 06-554-MFG_4G8.czi), not included in the main numbered batch archives.
 These files follow the same staining protocol, scanner settings, and file
 format as the other batches and should be viewed using the same software
 described above. ## Code/software Please refer
 to [https://github.com/ucdrubinet/BrainSec](https://github.com/ucdrubinet/BrainSec) for loading, tiling and using ML pipeline on this dataset ## Human subjects data We kept de-identification as a necessity by design for generating the data for this study.