B(RAIN)²-BRAIN integrated Resource for Anatomy and Intracranial Neurophysiology.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-03-15 DOI:10.1038/s41597-025-04784-5

Amirreza Alijanpourotaghsara, Koorosh Mirpour, Jeong Woo Choi, Krishna Kanth Chitta, Ahmed Shalaby, Misque Boswell, Sahil Chilukuri, Samantha L Cohen, Ryan Byon, Mohsen Benam, Saar Kariv, Jeon Lee, Dominique Duncan, Nader Pouratian

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Abstract

The Brain Integrated Resource for Human Anatomy and Intracranial Neurophysiology (B(RAIN)²) is a multi-center data resource that provides standardized imaging and intracranial neurophysiological data from patients who have undergone deep brain stimulation (DBS) implantation with intracranial electroencephalography (iEEG) recordings. The database includes patients' electrophysiological recordings from the cerebral cortex and deeper brain structures, alongside high-resolution neurological imaging scans taken at preoperative, intraoperative, and postoperative stages. The data are systematically organized using the Brain Imaging Data Structure (BIDS) format for imaging data and iEEG-BIDS for intracranial electrophysiological recordings. This standardized structure facilitates data integration, sharing, and analysis across research institutions. Additionally, the database features detailed metadata, such as diagnostic information and neurological assessment scores, providing a comprehensive profile of each patient's data.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.