A previously unknown relationship between brain structure and brain function has been uncovered by scientists at The University of Nottingham. The finding builds on our knowledge of how the brain works and could help us understand how communication in the brain breaks down in diseases such as multiple sclerosis and mental disorders such as schizophrenia.
The research team, led by PhD student Ben Hunt, previously pioneered new ways to measure the primary pathways of electrical transmission between brain regions. They then wanted to understand how underlying myelin structure supports those pathways. Ben said:
“The brain can be thought of as a big bunch of electrical wires. Communication between different areas of the brain is achieved by sending electrical transmissions down these wires. The speed at which these transmissions flow is largely governed by the amount of electrical insulation around these wires.
However, unlike typical plastic insulation on electrical cables, the brain uses fat to insulate the wires. This is called myelin and it is essential for the proper functioning of the nervous system."
The team used Magnetoencephalography (MEG), a non-invasive functional neuroimaging technique which measures magnetic fields produced outside the head by electrical activity in the wires of the human brain.
Using advanced mathematical modelling, the research team was able to reconstruct pathways showing electrical communication between brain areas. With state of the art Magnetic Resonance Imaging (MRI) they measured the amount of myelin in those areas and matched this information with the MEG measurements.
The results showed a clear relationship with the brain’s myelin structure mirroring the strength of electrical communication.
The study was funded as part of a £1.5 million initiative from the Medical Research Council (MRC), and a further £600,000 (MRC) grant to develop new ways to understand communication in the human brain.
Benjamin A. E. Hunt, et al. Relationships between cortical myeloarchitecture and electrophysiological networks PNAS; doi: 10.1073/pnas.1608587113
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