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Los Alamos Team of Scientists Develops New Brain Imaging Technology
Published: March 29, 2004
Posted: April 21, 2004

A Los Alamos National Laboratory (LANL) team of scientists led by Dr. Robert H. Kraus, Jr., has demonstrated a new, non-invasive approach to imaging human brain structure and function that enables researchers and clinicians to directly observe the electrical activity of neurons while simultaneously acquiring physical images of the brain structure (anatomy). The LANL team, using 'SQUID' (Superconducting QUantum Interference Device) sensors, the most sensitive magnetic field sensors known, has for the first time ever, demonstrated the ability to measure the MEG signal from brain activity simultaneously with measuring the magnetic resonance signal at ultra-low magnetic fields that can be used to generate MRI images. The LANL team has for more than a decade led in developing Magnetoencephalography (MEG) instrumentation. MEG probes tiny magnetic fields in the brain generated from the currents that flow in the neuronal network, and is best among currently available techniques at directly observing the electrical activity of neurons in the human brain on a millisecond time frame. MEG does not provide direct correlation of measured brain activity with physical structures in the brain. This is commonly accomplished by combining brain activity measured by MEG with a magnetic resonance image (MRI) of the brain structure. However, accurately combining two data sets taken at different times with MEG and MRI is very difficult. This approach may also prove important for people who cannot be subjected to the huge magnetic fields necessary to make a traditional MRI image. Ultra low field MRI uses magnetic fields similar to the natural magnetic field of the earth, thus, almost anyone could have this new type of brain scan.

Contact: Prem Srivastava, SC-73, (301) 903-4071
Topic Areas:

  • Research Area: Research Technologies and Methodologies
  • Research Area: Human Subjects Research

Division: SC-33.2 Biological Systems Science Division, BER
      (formerly SC-73 Medical Sciences Division, OBER)

 

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