The Neuroimaging Data Model Linear Regression Tool (nidm_linreg): PyNIDM Project
Ashmita Kumar, Albert Crowley, Nazek Queder, JB Poline, Satrajit S. Ghosh, David Kennedy, Jeffrey S. Grethe, Karl G. Helmer, David B. Keator, Karsten Specht, Ashmita Kumar, Adam G. Thomas, Eric Earl, Ashmita Kumar

TL;DR
The paper introduces a linear regression tool in the PyNIDM toolbox for analyzing neuroimaging data described by the NIDM standard.
Contribution
A new linear regression tool is introduced that operates directly on NIDM documents to support statistical analysis across studies.
Findings
The linear regression tool enables high-level statistical analysis on NIDM documents.
The tool allows users to specify variables and perform regression with optional contrast and regularization.
The tool integrates with PyNIDM's query capabilities to facilitate data combination across studies.
Abstract
The Neuroimaging Data Model (NIDM) is a series of specifications for describing all aspects of the neuroimaging data lifecycle from raw data to analyses and provenance. NIDM uses community-driven terminologies along with unambiguous data dictionaries within a Resource Description Framework (RDF) document to describe data and metadata for integration and query. Data from different studies, using locally defined variable names, can be retrieved by linking them to higher-order concepts from established ontologies and terminologies. Through these capabilities, NIDM documents are expected to improve reproducibility and facilitate data discovery and reuse. PyNIDM is a Python toolbox supporting the creation, manipulation, and querying of NIDM documents. Using the query tools available in PyNIDM, users are able interrogate datasets to find studies that have collected variables measuring similar…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsFunctional Brain Connectivity Studies · Biomedical Text Mining and Ontologies · Bioinformatics and Genomic Networks
