Validating diffusion tractography derived connectivity measures of the rat red nucleus and rubrospinal tract using neuroanatomical tracing
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Our bodies and brains are full of water, which is constantly moving around. When water molecules move around freely, they will diffuse randomly and evenly in all directions. In the brain, on the other hand, water molecules tend to move along the fibres of neurons that connect each part of the brain. When an MRI machine is set up in a specific way, it becomes sensitive to the movement of those water molecules. This technique is called diffusion weighted imaging (DWI). The images that are obtained using DWI can then be used to infer the direction of the fibres along which the molecules move. This is called diffusion tractography. Diffusion tractography is very valuable in neuroscientific research. As an example, consider the case of stroke patients. Many people suffer motoric impairments after having a stroke and while some of them recover quite well, others never do. It is suggested that the integrity of a few small neuron fibres such as the rubrospinal tract (RST) might help to explain this difference in outcomes. Diffusion tractography is uniquely qualified as a technique to investigate this in living patients. There is one issue though; tractography algorithms can’t be blindly trusted. They need to be validated using other methods. This is were animal models come in handy. Research on animals provides the opportunity to investigate the same sample with neuroanatomical tracing methods, which are considered a golden standard for validating tractography. It entails injecting a solution in the neuron-fibres of interest, which can then be visualised through chemical processes. This study used neuroanatomical tracing to validate diffusion tractography derived connectivity measures of the red nucleus, the point of origin of the RST. The tracing techniques were successful, which provided a database to compare the tractography methods to. This comparison suggests that the algorithms that were used are not quite accurate yet. Although there were some limitations, this finding means that more research should be done before these algorithms can be deemed trustworthy enough for pre-clinical research into recovery after stroke.