Editing Magnetoencephalography (section)

=== Distributed source models ===
Unlike multiple-dipole modeling, distributed source models divide the source space into a grid containing a large number of dipoles. The inverse problem is to obtain the dipole moments for the grid nodes.<ref>{{cite journal | vauthors = Hämäläinen MS, Ilmoniemi RJ | title = Interpreting magnetic fields of the brain: minimum norm estimates | journal = Medical & Biological Engineering & Computing | volume = 32 | issue = 1 | pages = 35–42 | date = January 1994 | pmid = 8182960 | doi = 10.1007/BF02512476 | s2cid = 6796187 }}</ref> As the number of unknown dipole moments is much greater than the number of MEG sensors, the inverse solution is highly underdetermined, so additional constraints are needed to reduce ambiguity of the solution. The primary advantage of this approach is that no prior specification of the source model is necessary. However, the resulting distributions may be difficult to interpret, because they only reflect a "blurred" (or even distorted) image of the true neuronal source distribution. The matter is complicated by the fact that spatial resolution depends strongly on various parameters such as brain area, depth, orientation, number of sensors etc.<ref>{{cite journal | vauthors = Molins A, Stufflebeam SM, Brown EN, Hämäläinen MS | title = Quantification of the benefit from integrating MEG and EEG data in minimum ℓ<sub>2</sub>-norm estimation | journal = NeuroImage | volume = 42 | issue = 3 | pages = 1069–77 | date = September 2008 | pmid = 18602485 | doi = 10.1016/j.neuroimage.2008.05.064 | s2cid = 6462818 }}</ref>