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Magnetic Resonance Imaging01:24

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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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The Use of Magnetic Resonance Spectroscopy as a Tool for the Measurement of Bi-hemispheric Transcranial Electric Stimulation Effects on Primary Motor Cortex Metabolism
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Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke.

Elena Jiménez-Xarrié1, Myriam Davila2,3, Ana Paula Candiota2,3,4

  • 1Stroke Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, Sant Antoni Maria Claret 167, 08025, Barcelona, Spain.

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|January 15, 2017
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Summary

SpectraClassifier software accurately identified metabolic changes in rat brains to track stroke evolution and map brain regions. This non-invasive magnetic resonance spectroscopy approach aids in understanding ischemic stroke progression.

Keywords:
Animal modelMagnetic resonance spectroscopyMetabolomicsPattern recognitionStroke

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Area of Science:

  • Neuroscience
  • Biochemistry
  • Medical Imaging

Background:

  • Magnetic resonance spectroscopy (MRS) offers non-invasive in vivo metabolic profiling of brain tissue.
  • SpectraClassifier software enables objective pattern recognition in MRS data by identifying key spectral features (metabolites).

Purpose of the Study:

  • Develop an Infarct Evolution Classifier to monitor stroke progression.
  • Develop a Brain Regions Classifier to differentiate brain tissue types using MRS data.
  • Utilize SpectraClassifier in a rat model of focal ischemic stroke.

Main Methods:

  • Analyzed 164 single-voxel proton spectra from Sprague-Dawley rats (healthy and stroke models) using a 7 Tesla magnet.
  • Applied SpectraClassifier to spectra from non-infarcted, subventricular, and infarcted parenchyma at different stroke phases (acute and subacute).

Main Results:

  • The Infarct Evolution Classifier used lactate + mobile lipids, total creatine, and mobile lipids to distinguish between non-infarcted, acute, and subacute infarct phases with high sensitivity and specificity.
  • The Brain Regions Classifier utilized myoinositol and total creatine to differentiate infarcted parenchyma, non-infarcted parenchyma, and subventricular zones with high accuracy.

Conclusions:

  • SpectraClassifier successfully identified spectral features indicative of infarct evolution, such as mobile lipid accumulation.
  • Candidate biomarkers for differentiating brain regions, including myoinositol content, were identified.
  • This study demonstrates the utility of SpectraClassifier for objective metabolic analysis in stroke research.