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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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...
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are slanted or...
Brain Imaging01:14

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 Stimulation (TMS).
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...

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Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions
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Revealing representational content with pattern-information fMRI--an introductory guide.

Marieke Mur1, Peter A Bandettini, Nikolaus Kriegeskorte

  • 1Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA. mariekemur@gmail.com

Social Cognitive and Affective Neuroscience
|January 20, 2009
PubMed
Summary

Pattern-information analysis reveals brain region representational content by examining multivoxel activity patterns, going beyond traditional whole-region activation detection in functional magnetic resonance imaging (fMRI). This advanced technique offers deeper insights into cognitive processes.

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Last Updated: Jun 26, 2026

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions
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Published on: July 6, 2011

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Data Analysis

Background:

  • Functional magnetic resonance imaging (fMRI) conventionally detects whole-brain region activation.
  • This approach overlooks detailed activity patterns within regions, which may hold crucial information.
  • Neuronal population codes are thought to be reflected in these multivoxel patterns.

Purpose of the Study:

  • To introduce pattern-information analysis for fMRI data.
  • To explain the assumptions and methods of pattern-information analysis.
  • To guide researchers in applying these techniques to uncover representational content.

Main Methods:

  • Pattern-information analysis investigates multivoxel activity patterns.
  • This contrasts with traditional methods focusing on overall regional activation.
  • The framework interprets multivariate pattern information as representational content.

Main Results:

  • Pattern-information analysis provides a novel way to investigate brain activity.
  • It allows for the detection of representational content within brain regions.
  • This method complements traditional activation analysis.

Conclusions:

  • Pattern-information analysis is a powerful tool for understanding cognitive processes.
  • It enhances the interpretability of fMRI data by considering detailed activity patterns.
  • This tutorial aims to facilitate the adoption of pattern-information analysis in neuroscience research.