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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
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Related Experiment Video

Updated: May 14, 2026

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals
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Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals

Published on: January 19, 2024

Spatiotemporal imaging of complexity.

Stephen E Robinson1, Arnold J Mandell, Richard Coppola

  • 1MEG Core Facility, National Institute of Mental Health, National Institutes of Health Bethesda, MD, USA.

Frontiers in Computational Neuroscience
|January 29, 2013
PubMed
Summary
This summary is machine-generated.

New complexity measures reveal hidden dynamics in brain activity. Rank Vector Entropy (RVE) applied to magnetoencephalography (MEG) data uncovers independent electrophysiological state changes, offering deeper insights beyond traditional energy utilization metrics.

Keywords:
beamformercognitivecomplexitymagnetoencephalographymixingneurosciencenonlinearturbulence

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Related Experiment Videos

Last Updated: May 14, 2026

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Published on: August 21, 2019

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Functional neuroimaging aims to characterize neocortical activity, often assuming energy utilization (glucose/oxygen) reflects this.
  • Magnetoencephalography (MEG) measures electrophysiological activity, showing correlations with BOLD fMRI, but completeness is questioned.
  • Existing measures may not capture all state changes in cortical activity independent of amplitude or metabolic rates.

Purpose of the Study:

  • To explore state changes in electrophysiological cortical activity.
  • To investigate if these changes occur independently of amplitude, power, or metabolic rate indices.
  • To introduce and validate a new complexity measure, Rank Vector Entropy (RVE), for analyzing these changes.

Main Methods:

  • Applied Rank Vector Entropy (RVE), a non-parametric symbolic dynamic informational entropy measure, to source waveform estimates.
  • Utilized beamformer-processed magnetoencephalography (MEG) data for analysis.
  • RVE represents measurements by rank values, overcoming partitioning issues and signal compression, making it amplitude-independent and robust.

Main Results:

  • Demonstrated that electrophysiological state changes can occur independently of averaged amplitude, source power, or metabolic rate indices.
  • Showcased RVE's ability to describe these independent state changes.
  • Task-free and task-dependent MEG examples revealed RVE uncovers hidden dynamical structure in spontaneous cortical activity.

Conclusions:

  • Rank Vector Entropy (RVE) offers a novel approach to characterizing brain activity beyond traditional neuroimaging methods.
  • RVE provides new information by revealing complex dynamics in spontaneous cortical activity.
  • This complexity measure is robust, amplitude-independent, and valuable for understanding brain states.