Genetics of Early Neurological InStability after Ischemic Stroke (GENISIS) - USA
Description
The Genetics of Early Neurological InStability after Ischemic Stroke (GENISIS) project is a multi-center, prospective cohort study aimed at identifying genetic contributors to neurological changes occurring within the first 24 hours after acute ischemic stroke. For each enrolled participant, NIH Stroke Scale (NIHSS) scores were recorded both within six hours of symptom onset and again at the 24-hour mark to capture early neurological dynamics. This dataset represents the GENISIS-USA cohort, consisting of 871 participants enrolled at U.S. sites as part of a larger international effort spanning seven countries. Prior to release, all data were thoroughly de-identified in accordance with best practices. Measures included application of the mri_reface tool to obscure facial features in imaging, removal of direct identifiers, recoding of sensitive clinical variables, and date shifting of scan timestamps to preserve participant privacy.The shared imaging dataset includes 1,821 CT scans and 57 MR scans, available in both DICOM and NIfTI file formats. The complete dataset occupies approximately 142.6 GB.This dataset is hosted on the Imaging Cerebrovascular Disease Knowledge Portal (iCDKP), which is supported in part by the National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), under Grant No. 1U24NS132940-01. Users are required to acknowledge both iCDKP and its federal funding source in any presentations or publications that utilize the dataset, following the citation guidelines provided on the dataset’s iCDKP page.To request access, users must register for an account and complete a Data Request License Agreement available via the iCDKP request portal: https://sites.wustl.edu/icdkp/request_data/.
Citations (0)
No citations found
Mentions (0)
No mentions found
Metrics Over Time
Publication Details
Subfield
Epidemiology
Field
Medicine
Domain
Health Sciences
Confidence Score
46%
Source
Scholar Data Model