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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: Jan 18, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Mining the neuroimaging literature.

Jérome Dockès¹, Kendra M Oudyk², Mohammad Torabi²

¹National Institute for Research in Digital Science and Technology (INRIA), Paris, France.

|September 11, 2025

Summary

This summary is machine-generated.

Researchers can now easily collect, process, and annotate biomedical literature using new tools. These resources streamline literature mining and meta-science, enhancing accessibility and reproducibility in biomedical research.

Keywords:

meta-research meta-science natural language processing neuroimaging neuroscience none text mining

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Last Updated: Jan 18, 2026

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Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Published on: July 12, 2012

Area of Science:

Biomedical Informatics
Computational Biology
Text Mining

Background:

Automated analysis of biomedical literature (literature mining) is valuable but hindered by challenges in data collection and processing.
Existing methods for collecting and annotating large volumes of scientific articles are often time-consuming and difficult to implement.

Purpose of the Study:

To introduce a suite of tools designed to simplify the collection, processing, and annotation of biomedical literature.
To enhance the accessibility, effectiveness, and reproducibility of text mining and meta-science projects in the biomedical domain.

Main Methods:

Utilized pubget, a command-line tool for bulk downloading and processing articles from PubMed Central, including metadata and specific information like stereotactic brain coordinates.
Employed Labelbuddy, a local application for text annotation, to facilitate complex information extraction and the creation of ground-truth labels for validating automated methods.
Described repositories for sharing analysis code and manual annotations to promote reuse and collaboration.

Main Results:

Demonstrated a streamlined workflow for biomedical literature analysis using the described tools.
Successfully illustrated the application of these tools through several example projects, showcasing their practical utility.
Provided a framework for researchers to more easily engage in literature mining and meta-science.

Conclusions:

The developed tools significantly lower the barrier to entry for biomedical literature mining and meta-science.
These resources promote more efficient, effective, and reproducible research by simplifying data handling and annotation.
The described workflow and tools empower researchers to unlock deeper insights from the biomedical literature.