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

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.