# Applying the fMRI weighted w file on the inverse operator **URL:** \ **Category:** Mailing List Archive (read-only)\ **Tags:** list-archive\ **Created:** [June 25, 2014, 8:42pm UTC](https://mne.discourse.group/t/applying-the-fmri-weighted-w-file-on-the-inverse-operator/747 "2014-06-25T20:42:03Z")\ **Posts on this page:** 6\ **Page:** 1
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [June 25, 2014, 8:42pm UTC](https://mne.discourse.group/t/applying-the-fmri-weighted-w-file-on-the-inverse-operator/747/1 "2014-06-25T20:42:03Z")
Hi MNE users, I'm currently trying to applying a a prior source constraints via the fMRI weighting file. But according to MNE documentation: "It turns out that the fMRI weighting has a strong influence on the MNE but the noise-normalized estimates are much less affected by it." ([http://martinos.org/mne/stable/manual/mne.html#cbbdijhi](http://martinos.org/mne/stable/manual/mne.html#cbbdijhi)) Can anyone please explain to me why this is such a case, maybe in both mathematical and intuitive sense. Thank you very much in advance. Regards, Thinh Nguyen Research Assistant University of Houston - Biomedical Engineering department -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20140625/ad4dec8b/attachment.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20140625/ad4dec8b/attachment.html) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [June 26, 2014, 3:01pm UTC](https://mne.discourse.group/t/applying-the-fmri-weighted-w-file-on-the-inverse-operator/747/2 "2014-06-26T15:01:00Z")
hi Thinh, real quick and hand waving: dSPM / sLORETA use scaling to downweight locations where the noise is strong / highly amplified. This amplification factor is proportional to the fMRI weight. So if you normalize by division you pretty much cancel the main effect of the fMRI weight. HTH Alex ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [July 1, 2014, 9:54pm UTC](https://mne.discourse.group/t/applying-the-fmri-weighted-w-file-on-the-inverse-operator/747/3 "2014-07-01T21:54:57Z")
Thanks for your answer, Is there anyway to use the dSPM or sLORETA algorithm and still be able to reliably apply the fMRI prior weighting constraints? Because it seems to me that, with fMRI constraints, WMNE algorithm performs well with reasonable, as expected results, but after the noise normalization steps (dSPM/sLORETA), the results become unexpected, if not inaccurate. Thank you MNE users Thinh Nguyen ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [July 1, 2014, 10:06pm UTC](https://mne.discourse.group/t/applying-the-fmri-weighted-w-file-on-the-inverse-operator/747/4 "2014-07-01T22:06:59Z")
Hi Thinh,    Could you share any plots/images of your unexpected/inaccurate results? Also, could you motivate why you want to do a dSPM (on a whole brain basis) given that you have fMRI priors? As mentioned by Alex, the dSPM/sLORETA normalizations make it such that everywhere on the brain, the baseline is of unit variance.. This would indeed negate the fMRI prior weighting because the fMRI prior scales down some vertices relative to the others (whereas dSPM tends to equalize them by making them unity).. In my mind, given that you have an fMRI prior, if you are still interested in obtaining a z-score type metric, rather than currents, then perhaps one way to do that would be to mask out the vertices that are not active in the fMRI before converting to z-score.. HTH, Hari ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [July 1, 2014, 10:29pm UTC](https://mne.discourse.group/t/applying-the-fmri-weighted-w-file-on-the-inverse-operator/747/5 "2014-07-01T22:29:17Z")
Hi Hari, Thank you for the reply, I guess it makes little sense trying to use both fMRI prior and dSPM/sLORETA algorithm. I was just concern about the accuracy of the results using WMNE alone, as it is accepted that dSPM/sLORETA out perform WMNE. I don't have any reliable way to validate my results so I was just trying things out. Maybe I don't fully understand the concept, intuition behind the normalizations done in dSPM/sLORETA, if you have any good literature/papers in mind that you could point me to, it would be very much appreciated. Best, Thinh Nguyen ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [July 2, 2014, 3:44am UTC](https://mne.discourse.group/t/applying-the-fmri-weighted-w-file-on-the-inverse-operator/747/6 "2014-07-02T03:44:25Z")
Hi Thinh, The dSPM sLORETA outperform MNE is because they normalize noise at each voxels. To validate your results, you can do enough simulations. You can put simulated sources at your expected locations and then using WMNE to reconstuct them under specific conditions. To some degree, this methods can validate your results. In fact, for real MEG, no gold truth to proof your results or you simultaneously recording EcoG at each voxels. 2014-07-02 junpeng.zhang ???Thinh Nguyen \ ???2014-07-02 06:29 ???Re: [Mne\_analysis] Applying the fMRI weighted w file on the inverse operator ???"Discussion and support forum for the users of MNE Software"\ ??? Hi Hari, Thank you for the reply, I guess it makes little sense trying to use both fMRI prior and dSPM/sLORETA algorithm. I was just concern about the accuracy of the results using WMNE alone, as it is accepted that dSPM/sLORETA out perform WMNE. I don't have any reliable way to validate my results so I was just trying things out. Maybe I don't fully understand the concept, intuition behind the normalizations done in dSPM/sLORETA, if you have any good literature/papers in mind that you could point me to, it would be very much appreciated. Best, Thinh Nguyen Hi Thinh,    Could you share any plots/images of your unexpected/inaccurate results? Also, could you motivate why you want to do a dSPM (on a whole brain basis) given that you have fMRI priors? As mentioned by Alex, the dSPM/sLORETA normalizations make it such that everywhere on the brain, the baseline is of unit variance.. This would indeed negate the fMRI prior weighting because the fMRI prior scales down some vertices relative to the others (whereas dSPM tends to equalize them by making them unity).. In my mind, given that you have an fMRI prior, if you are still interested in obtaining a z-score type metric, rather than currents, then perhaps one way to do that would be to mask out the vertices that are not active in the fMRI before converting to z-score.. HTH, Hari