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Related Concept Videos

Brain Imaging01:14

Brain Imaging

589
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...
589

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NeuroQuery, comprehensive meta-analysis of human brain mapping.

Jérôme Dockès1, Russell A Poldrack2, Romain Primet3

  • 1Inria, CEA, Université Paris-Saclay, Essonne, France.

Elife
|March 5, 2020
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Neuroscience research faces challenges linking diverse brain concepts. A new predictive model analyzes text to map brain activity, overcoming limitations of traditional meta-analysis for rare terms and complex data.

Keywords:
brain imagingcognitive ontologieshumanmeta analysisneurosciencepredictive models

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Medical Informatics

Background:

  • Global brain organization understanding requires integrating diverse mental processes and mechanisms.
  • Varying neuroscience terminology hinders relating brain imaging results across studies.
  • Current meta-analysis methods are limited to frequently occurring terms.

Purpose of the Study:

  • To develop a novel paradigm for large-scale meta-analysis of neuroimaging data.
  • To predict the spatial distribution of neurological observations from experimental text.
  • To overcome limitations of traditional methods in handling rare terms and complex data.

Main Methods:

  • A multivariate predictive model was developed to map text descriptions to spatial brain activity.
  • The model analyzes text of arbitrary length, including rare neuroscience terms.
  • The approach was applied to 13,459 neuroimaging publications, covering 7,547 neuroscience terms.

Main Results:

  • The model successfully predicts the spatial distribution of neurological observations based on textual input.
  • It captures relationships and neural correlates for a broad range of neuroscience terms.
  • The developed tool, neuroquery.org, provides a comprehensive view of published brain findings.

Conclusions:

  • The predictive approach offers a powerful new method for neuroscience meta-analysis.
  • It enables hypothesis generation and data-analysis priors grounded in extensive published literature.
  • Neuroquery.org facilitates a more integrated understanding of brain organization and function.