Evidence map›Paper›PMID 39185142›Full record

ArticleF1000Research2022

The Neuroimaging Data Model Linear Regression Tool (nidm_linreg): PyNIDM Project.

Ashmita Kumar, Albert Crowley, Nazek Queder, J B Poline, Satrajit S Ghosh, David Kennedy, Jeffrey S Grethe, Karl G Helmer, David B Keator

Abstract read
In one paragraph

Article in F1000Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ashmita KumarTroy High School, Fullerton, California, USA.ORCID https://orcid.org/0000-0001-8076-7620
Albert CrowleyTCG, Inc., Washington, DC, USA.ORCID https://orcid.org/0000-0002-5006-7065
Nazek QuederPsychiatry and Human Behavior, University of California, Irvine, Irvine, California, USA.ORCID https://orcid.org/0000-0001-6268-862X
J B PolineDepartment of Neurology and Neurosurgery, McConnell Brain Imaging Centre, McGill University, Faculty of Medicine and Health Sciences, Montreal, Canada.
Satrajit S GhoshMcGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.ORCID https://orcid.org/0000-0002-5312-6729
David KennedyDepartment of Psychiatry, Eunice Kennedy Shriver Center, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA.ORCID https://orcid.org/0000-0002-9377-0797
Jeffrey S GretheDepartment of Neuroscience, Harvard Medical School, San Diego, California, USA.ORCID https://orcid.org/0000-0002-9377-0797
Karl G HelmerDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0001-5212-7052
David B KeatorPsychiatry and Human Behavior, University of California, Irvine, Irvine, California, USA.

Funding

Resource DiscoveryP41EB019936 · NIBIB · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI David Nelson Kennedy · 2016 to 2026
$15.2M
The Neuroimaging Data Model: FAIR descriptors of Brain Initiative Imaging ExperimentsRF1MH120021 · NIMH · UNIVERSITY OF CALIFORNIA-IRVINE · PI KEATOR, DAVID BRYANT · 2019 to 2019
$944k
NIBIB NIH HHS P41 EB019936NIMH NIH HHS RF1 MH120021
6 · The paper itself

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 phenotypic properties. This, in turn, facilitates the transformation and combination of data across multiple studies. The focus of this manuscript is the linear regression tool which is a part of the PyNIDM toolbox and works directly on NIDM documents. It provides a high-level statistical analysis that aids researchers in gaining more insight into the data that they are considering combining across studies. This saves researchers valuable time and effort while showing potential relationships between variables. The linear regression tool operates through a command-line interface integrated with the other tools (pynidm linear-regression) and provides the user with the opportunity to specify variables of interest using the rich query techniques available for NIDM documents and then conduct a linear regression with optional contrast and regularization.

Indexed as

Linear RegressionMachine LearningNeuroimagingNeuroimaging Data ModelPyNIDM

Identifiers

PMID39185142
PMCPMC11344200

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.