DementiaBank is a medical domain task. It contains 117 people diagnosed with Alzheimer Disease, and 93 healthy people, reading a description of an image, and the task is to classify these groups. This release contains only the audio part of this dataset, without the text features.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('dementiabank', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

This statistic shows the forecast of the global population affected with dementia from 2015 to 2050, according to the World Bank income classification. The upper middle and high income countries have higher proportion of people affected by dementia. In 2050, the number of people affected in upper middle and high income countries will be around 53.4 and 42.2 million, respectively.

The United Nations declareds 2021–2030 the ‘Decade of Healthy Ageing ’. Both individuals and society suffer from increasing rates of Alzheimer’s disease and other types of dementia (ADs). In 2019, these diseases contributed to a loss of 33.1 million years of healthy life globally. However, existing research has not fully analyzed the relationship among socioeconomic data and ADs. This study was designed to explore the relationship between Alzheimer’s disease rates and socioeconomic conditions in 120 countries. We used mixed effect models to investigate the relationship between the rates of ADs and socioeconomic data. The data was obtained from global databases, including from The Global Burden of Disease and World Bank. The socioeconomic data included information onf gender inequality, wealth inequality, and countries’ overall wealth. This study is among the first studies to put forward statistical evidence of a significant association between AD and other dementias among the elderly and socioeconomic inequality. These findings could help to inform the policies to be designed to improve the quality of interventions for ADs. Date Submitted: 2022-08-01

Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations.

Paper: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: https://doi.org/10.1101/454793.

Details: A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows:

1. Development (Phases I-II). 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).
2. Hold-out (Phase III). 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.
3. CERAD-like hold-out. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.

Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented.

Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files.

Slide source and preparation: All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (https://www.ucdmc.ucdavis.edu/alzheimers/). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification.

Code: Please visit https://github.com/keiserlab/plaquebox-paper

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration as a result of abnormal neuronal loss. To elucidate the molecular systems associated with AD, we characterized the gene expression changes associated with multiple clinical and neuropathological traits in 1,053 postmortem brain samples across 19 brain regions from 125 persons dying with varying severities of dementia and variable AD-neuropathology severities. 125 human brains were accessed from the Mount Sinai/JJ Peters VA Medical Center Brain Bank (MSBB). This brain resource was assembled after applying stringent inclusion/exclusion criteria and represents the full spectrum of clinical and neuropathological disease severity in the absence of discernable non-AD neuropathology. RNA samples from 19 brain regions isolated from the 125 MSBB specimens were collected and profiled using Affymetrix Genechip microarrays. There were 50 to 60 subjects per brain region with varying degrees of AD pathological abnormalities.

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration as a result of abnormal neuronal loss. To elucidate the molecular systems associated with AD, we characterized the gene expression changes associated with multiple clinical and neuropathological traits in 1,053 postmortem brain samples across 19 brain regions from 125 persons dying with varying severities of dementia and variable AD-neuropathology severities. 125 human brains were accessed from the Mount Sinai/JJ Peters VA Medical Center Brain Bank (MSBB). This brain resource was assembled after applying stringent inclusion/exclusion criteria and represents the full spectrum of clinical and neuropathological disease severity in the absence of discernable non-AD neuropathology. RNA samples from 19 brain regions isolated from the 125 MSBB specimens were collected and profiled using Affymetrix Genechip microarrays. There were 50 to 60 subjects per brain region with varying degrees of AD pathological abnormalities.

yr = year; NK = not known. Human fibroblasts were purchased from Coriell and brains from NICHD Brain and Tissue Bank for Developmental Disorders. Independent T-test revealed no age difference between NPC and control brain donors (20.9±8.6 vs. 17.3±11.1, p = 0.61).

With the exception of ApoE4, genome-wide association studies have failed to identify strong genetic risk factors for late-onset Alzheimer’s disease, despite strong evidence of heritability, suggesting that many low penetrance genes may be involved. Additionally, the nature of the identified genetic risk factors and their relation to disease pathology is also largely obscure. Previous studies have found that a cancer-associated variant of the cell cycle inhibitor gene p21cip1 is associated with increased risk of Alzheimer’s disease. The aim of this study was to confirm this association and to elucidate the effects of the variant on protein function and Alzheimer-type pathology. We examined the association of the p21cip1 variant with Alzheimer’s disease and Parkinson’s disease with dementia. The genotyping studies were performed on 719 participants of the Oxford Project to Investigate Memory and Ageing, 225 participants of a Parkinson’s disease DNA bank, and 477 participants of the Human Random Control collection available from the European Collection of Cell Cultures. The post mortem studies were carried out on 190 participants. In the in-vitro study, human embryonic kidney cells were transfected with either the common or rare p21cip1 variant; and cytometry was used to assess cell cycle kinetics, p21cip1 protein expression and sub-cellular localisation. The variant was associated with an increased risk of Alzheimer’s disease, and Parkinson’s disease with dementia, relative to age matched controls. Furthermore, the variant was associated with an earlier age of onset of Alzheimer’s disease, and a more severe phenotype, with a primary influence on the accumulation of tangle pathology. In the in-vitro study, we found that the SNPs reduced the cell cycle inhibitory and anti-apoptotic activity of p21cip1. The results suggest that the cancer-associated variant of p21cip1 may contribute to the loss of cell cycle control in neurons that may lead to Alzheimer-type neurodegeneration.

The DRIVES Project collects and processes low frequency and high frequency naturalistic driving data in order to study their association with cognitive decline in older drivers. Both sets of data are obtained daily from an off-the-shelf telematics datalogger that is installed our participants' vehicles. The low frequency data is collected at 1 Hz in 30 second intervals, whereas the high frequency data is collected at 24 Hz in one second intervals. The low frequency data is collected in the form of four CSV files: 1) A breadcrumbs file that contains the periodic driving data, 2) An activity file that provides detailed trip information, 3) An events file that provides detailed information on all adverse events 4) A a summary file that aggregates all daily trips carried out by each vehicle a day. The high frequency data is collected in the form of JSON files; each JSON file contains data for a single trip taken by a single vehicle on a given day. Each JSON is processed into four data tables: 1) A trip_info table that provides the periodic driving data 2) An activity table that details all adverse events that occurred during the trip (i.e. speeding, hard braking, idling etc.) 3) A braking table that details all hard braking events that occurred during the trip, and 4) A idling table that details each time the vehicle was idle during a trip.In addition to naturalistic driving data, the DRIVES Project collects clinical and neuropsychological data from our enrolled participants. Our participants undergo a variety of neuropsychological evaluations from which the DRIVES Project derives this data from (see attached data descriptor for more details). The DRIVES Project also collects data related to social determinants of health (SDoH). In particular, the DRIVES Project uses our participants' primary home addresses to obtain their Area of Deprivation Index (ADI) and Social Vulnerability Index (SVI) rankings. These rankings are provided by the Center of Health Disparities Research at the University of Wisconsin, Madison and the Center for Disease Control’s Agency for Toxic Substances and Disease Registry.The DRIVES Project uses two Python scripts to process the raw data files for the LFD and HFD. The scripts remove data and transforms the raw data files as needed to create the processed tables. In this repository, we provide a short demo of how our scripts processes our raw data in preparation for subsequent analysis or data storage. The demo code provides a walkthrough on how our scripts process 4 LFD CSV files that the DRIVES Project collected on March 31st, 2023 and a single HFD trip JSON that the project collected on March 31st, 2023. In the raw_data folder, we have provided four 'Spring2023' CSV files that contain the combined daily files that we download for the breadcrumbs, activity, events, and summary LFD data from March 1st, 2023 to May 31st, 2023. We've also provided three tarballs (.tar.gz files) that contain all of the HFD trip JSONs that we downloaded during the same time period; each tarball corresponds to the HFD trip JSONs we downloaded in a month (i.e. March, April, May). We've included these comprehensive files in case users would like to experiment with our scripts on more data.See attached metadata file for an explanation on the features for each table.

(AD: Alzheimer’s disease; OD: Other dementia; AD/PD: Alzheimer’s disease with Parkinson’s disease; MCI: Mild cognitive impairment; PD: Parkinson’s disease; PD D: Parkinson’s disease with dementia; OPTIMA: Oxford Project to Investigate Memory and Ageing; PD GEN: Parkinson’s disease DNA bank).The total number of subjects in each diagnostic category.