DOI: 10.5061/dryad.7h44j107j
title: 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: # 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.7h44j107j](https://doi.org/10.5061/dryad.7h44j107j) ##
 Description of the data and file structure # **UC Davis ADRC RO1 Whole
 Slide Image (WSI) Dataset (Part 1)** ## **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** * **Part 2** 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: Batch1_4G8.zip **Description:** Whole
 slide images immunostained in **Batch 1**, including **frontal, parietal,
 and temporal lobe sections** for the volunteers assigned to this staining
 batch #### File: Batch2_4G8.zip **Description:** Whole slide images
 immunostained in **Batch 2**, including **frontal, parietal, and temporal
 lobe sections** for the volunteers assigned to this staining batch ####
 File: Batch3_4G8.zip **Description:** Whole slide images immunostained in
 **Batch 3**, including **frontal, parietal, and temporal lobe sections**
 for the volunteers assigned to this staining batch #### File:
 Batch4_4G8.zip **Description:** Whole slide images immunostained in
 **Batch 4**, including **frontal, parietal, and temporal lobe sections**
 for the volunteers assigned to this staining batch #### File:
 Batch5_4G8.zip **Description:** Whole slide images immunostained in
 **Batch 5**, including **frontal, parietal, and temporal lobe sections**
 for the volunteers assigned to this staining batch #### File:
 Batch6_4G8.zip **Description:** Whole slide images immunostained in
 **Batch 6**, including **frontal, parietal, and temporal lobe sections**
 for the volunteers assigned to this staining batch #### File:
 Batch7_4G8.zip **Description:** Whole slide images immunostained in
 **Batch 7**, including **frontal, parietal, and temporal lobe sections**
 for the volunteers assigned to this staining batch #### File:
 Batch8_4G8.zip **Description:** Whole slide images immunostained in
 **Batch 8**, including **frontal, parietal, and temporal lobe sections**
 for the volunteers assigned to this staining batch #### File:
 Batch9_4G8.zip **Description:** Whole slide images immunostained in
 **Batch 9**, including **frontal, parietal, and temporal lobe sections**
 for the volunteers assigned to this staining batch ## Code/software Please
 refer
 to [https://github.com/ucdrubinet/BrainSec](https://github.com/ucdrubinet/BrainSec) for loading, tiling and using the ML pipeline on the associated WSIs. ## Human subjects data We kept de-identification as a necessity by design for generating the data for this study.