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Related Concept Videos

Brain Imaging01:14

Brain Imaging

258
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...
258

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Related Experiment Video

Updated: Jul 19, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

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Decoding the Brain's Surface to Track Deeper Activity.

Mark L Tenzer1, Jonathan M Lisinski1, Stephen M LaConte1,2

  • 1Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, United States.

Frontiers in Neuroimaging
|August 9, 2023
PubMed
Summary
This summary is machine-generated.

Scalp-based brain recordings can now track deeper brain activity. This study shows functional magnetic resonance imaging (fMRI) surface data can predict activity in deeper brain regions and networks.

Keywords:
cerebral cortexfunctional magnetic resonance imagingmultimodalresting state connectivitysupport vector machine

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Scalp-based neural recording techniques (electromagnetic, optical) offer non-invasive brain activity monitoring but suffer from depth-dependent sensitivity limitations.
  • The brain's intricate connectivity, particularly the cortex's links to deeper structures, presents a potential avenue to overcome these depth limitations.
  • Functional magnetic resonance imaging (fMRI) can model surface-to-deep brain connectivity, potentially enhancing the depth capabilities of other neuroimaging modalities.

Purpose of the Study:

  • To investigate if surface-limited functional magnetic resonance imaging (fMRI) data, using support vector regression, can accurately track activity in deeper brain regions and distributed networks.
  • To establish a proof-of-concept for using surface-based neuroimaging data to infer activity in non-superficial brain areas.

Main Methods:

  • Utilized resting-state fMRI data.
  • Applied surface-limited support vector regression (SVR) to analyze the relationship between cortical surface activity and deeper brain signals.
  • Validated the predictive model using independent datasets to assess its generalizability.

Main Results:

  • Demonstrated that depth-limited fMRI signals, when analyzed with SVR, can be successfully calibrated to reflect ongoing activity in deeper brain structures.
  • Confirmed the ability of surface-derived models to track distributed brain networks beyond the immediate cortical surface.
  • Showcased the potential of multivariate analysis of surface signals for inferring deeper brain states.

Conclusions:

  • Surface-based neuroimaging, specifically fMRI, can be leveraged to infer activity in deeper brain regions, overcoming intrinsic depth limitations.
  • The connectivity patterns within the brain allow surface recordings to serve as proxy signals for deeper neural processes.
  • Future research can build upon these findings to develop advanced neuroimaging techniques that integrate surface and deep brain activity information for comprehensive brain monitoring.