Clarification on Depth Weighting and dSPM in MNE

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1

Hello MNE users,

I have a question regarding the processing flow for inverse solvers in MNE-python, particularly in relation to depth weighting, wMNE, and dSPM.

From my understanding:

  1. Inverse Operator and Depth Weighting: When creating the inverse operator, MNE applies a depth weighting (default is 0.8 for MEG). This depth weighting scales the leadfield matrix to adjust for the bias toward superficial sources, particularly helping to emphasize deeper sources.
  2. wMNE Calculation: Once the inverse operator is created (with depth weighting), wMNE is computed. As I understand it, wMNE involves normalizing the source estimates, including noise and depth-weighting corrections (from the inverse operator).
  3. Foundation of dSPM: Then, the dSPM (dynamic Statistical Parametric Mapping) is computed, which further normalizes the wMNE results by dividing by a noise estimate. This noise estimate is often derived from the noise covariance matrix, which can be computed from baseline recordings or other noise sources.

Therefore, with depth weighting factor of 0.8 in inverse operator - dSPM solution is build upon wMNE? with Is this understanding correct?

Thanks for your help!
Dip

2

Hi @richard I can use some help. Thank you :slight_smile: