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Brain Imaging01:14

Brain Imaging

209
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...
209
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

4.9K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: Jun 5, 2025

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
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3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

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Inferring neurocognition using artificial intelligence on brain MRIs.

Mohammad Arafat Hussain1, Patricia Ellen Grant1,2, Yangming Ou1,2,3

  • 1Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.

Frontiers in Neuroimaging
|December 12, 2024
PubMed
Summary
This summary is machine-generated.

Magnetic resonance imaging (MRI) studies brain structure and cognition. Big data and artificial intelligence (AI) offer new ways to understand individual differences in intelligence, but careful consideration of methods is needed.

Keywords:
P-FIT modelartificial intelligencebrain MRIintelligenceneurocognition

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Artificial Intelligence

Background:

  • Brain magnetic resonance imaging (MRI) provides insights into the neuroanatomic basis of human neurocognition.
  • Understanding individual differences in neurocognition and intelligence using MRI remains a significant challenge.
  • Limitations in sample size and population-level studies hinder individual-level explanations.

Purpose of the Study:

  • To review advancements in using MRI to study neurocognition.
  • To explore the potential of big data and artificial intelligence (AI) in this field.
  • To discuss challenges and facilitate future research on AI-driven neurocognitive inference.

Main Methods:

  • Review of existing literature on MRI and neurocognition.
  • Discussion of big data and AI methodologies.
  • Analysis of data harmonization, study design, and interpretation considerations.

Main Results:

  • Significant progress has been made over the past four decades in studying the neuroanatomic basis of neurocognition.
  • Big data and AI present novel opportunities for advancing individual-level understanding.
  • Careful consideration of data sources, harmonization, study design, and interpretation is crucial for successful AI implementation.

Conclusions:

  • AI holds significant promise for inferring human neurocognition from neuroimaging data.
  • Addressing methodological challenges is key to unlocking the full potential of AI in cognitive neuroscience.
  • Further interdisciplinary research is needed to bridge AI and neuroimaging for a deeper understanding of intelligence.