3 ms·

I am not a brain scientist and I'm sure they've considered this at least at some level of depth: But what are the chances that we are just seeing this as a side

I am not a brain scientist and I'm sure they've considered this at least at some level of depth: But what are the chances that we are just seeing this as a side-effect of the tool being used to observe the brain?

ricardobeat 14y ago
This technology is being explored since the 80s [1] and has been the source of similar images since at least 2008 [1]. Each line is the path traced by a water molecule (layman's explanation, please correct me). DSI is a variant of MRI that seems to have been developed precisely to map small crossing fibers, so that would be a huge failure. [1] http://en.wikipedia.org/wiki/Diffusion_MRI http://en.wikipedia.org/wiki/Diffusion_MRI [2] http://www.technologyreview.com/news/410560/the-brain-unmasked/ http://www.technologyreview.com/news/410560/the-brain-unmask...
- maebert 14y ago
  Ex-Neuroscientist here. Your explanation is quite accurate, though I believe they use a more modern variant called Diffusion Tensor Imaging. The idea of MRI is that you put the brain into a huge magnetic field, switch the magnet of and count how long it takes for the nuclear spin to jump back to random states in different areas of the brain - this time is specific to each different atom and allows for an estimate of the concentration of different atoms (like water). In DTI, the hypothesis is that water molecules line up with the neurons axons (the long cables that connect neurons to distant other neurons). If change the direction of the magnetic field gradient a couple of times, you don't just get one value per voxel (how much water is in there), but slightly different values for each direction. The highest value is _probably_ the direction of alignment of the majority of axons in that voxel. The problem is not the tool, but the statistical analysis of the data, which depends on a lot of parameters, and the number of different gradient directions, which are usually orthogonal, hence favouring orthogonal neuronal connections and discounting those that are diagonal to the gradient directions. You have to correct for this in the analysis, but it's very hard to do, and mostly guesswork. Which coincides my personal evaluation of the entire study: guesswork.

ricardobeat 14y ago

This technology is being explored since the 80s [1] and has been the source of similar images since at least 2008 [1]. Each line is the path traced by a water molecule (layman's explanation, please correct me). DSI is a variant of MRI that seems to have been developed precisely to map small crossing fibers, so that would be a huge failure. [1] http://en.wikipedia.org/wiki/Diffusion_MRI http://en.wikipedia.org/wiki/Diffusion_MRI [2] http://www.technologyreview.com/news/410560/the-brain-unmasked/ http://www.technologyreview.com/news/410560/the-brain-unmask...

maebert 14y ago
Ex-Neuroscientist here. Your explanation is quite accurate, though I believe they use a more modern variant called Diffusion Tensor Imaging. The idea of MRI is that you put the brain into a huge magnetic field, switch the magnet of and count how long it takes for the nuclear spin to jump back to random states in different areas of the brain - this time is specific to each different atom and allows for an estimate of the concentration of different atoms (like water). In DTI, the hypothesis is that water molecules line up with the neurons axons (the long cables that connect neurons to distant other neurons). If change the direction of the magnetic field gradient a couple of times, you don't just get one value per voxel (how much water is in there), but slightly different values for each direction. The highest value is _probably_ the direction of alignment of the majority of axons in that voxel. The problem is not the tool, but the statistical analysis of the data, which depends on a lot of parameters, and the number of different gradient directions, which are usually orthogonal, hence favouring orthogonal neuronal connections and discounting those that are diagonal to the gradient directions. You have to correct for this in the analysis, but it's very hard to do, and mostly guesswork. Which coincides my personal evaluation of the entire study: guesswork.