[FieldTrip] Spatial Bias Dominates Variance in Distributed Source Modeling

Matti Stenroos matti.stenroos at aalto.fi
Tue Oct 16 12:16:11 CEST 2018

Dear Alexander,

Also I could not fully follow the pipeline. The terms "sensor spectral 
matrix" and "contrast" however, caught my eye. Is your "data vector" 
that you try to map on the brain a result of only linear processing of 
the measurement?


On 2018-10-16 10:51, Schoffelen, J.M. (Jan Mathijs) wrote:
> Dear Alexander,
> I am not sure whether I completely follow your diagnostic steps and your 
> conclusions, but I would also check:
> - the alignment between the electrodes, volume conduction model, and 
> source model.
> - are the electrode positions expressed in the same coordinate system as 
> the volume conductor/source model?
> - are the leadfields well-behaved? For instance: if the dipole locations 
> are too close to the innermost mesh, numerical problems may arise.
> Best wishes,
> Jan-Mathijs
>> On 12 Oct 2018, at 18:34, Nakhnikian, Alexander 
>> <Alexander_Nakhnikian at hms.harvard.edu 
>> <mailto:Alexander_Nakhnikian at hms.harvard.edu>> wrote:
>> Dear All,
>> Apologies for the long post, I've tried to be as succinct as possible 
>> while describing my problem is sufficient detail.
>> I've been trying to solve a problem with source modeling for sometime. 
>> I've found that distributed source estimates (MNE, sLORETA, eLORETA) 
>> are heavily biased towards the ventral temporal lobe. This is the case 
>> with multiple data sets analyzed using both built-in field trip 
>> functions and imaging kernels generated by my own code. It occurs in 
>> within group grand averages and statistical contrasts between controls 
>> and patients. I've confirmed that sLORETA and eLORETA are unbiased for 
>> noiseless data by filtering point sources through the resolution 
>> kernel. I'm working on a Mac running OS 10.11.6 and the latest version 
>> of Field Trip. I'm using a standard 10/10 electrode layout (no 
>> individual sensor locations) with 4 custom locations (PO9/PO10, M1/M2) 
>> and Field Trip's template BEM. The forward model is restricted to the 
>> cortical mantle (I've had similar problems with whole brain forward 
>> models as well).
>> I recently ran PCA at the source level to explore the issue. The data 
>> were collecting during quiet rest and bandpassed to isolate a peak 
>> that accounts for a significant difference between controls and 
>> patients at the sensor level. The imaging kernel was applied to the 
>> sensor spectral matrix. To obtain the PCs, I analyzed the covariance 
>> of power among voxels. The rank of the voxel covariance matrix was 31 
>> with the first 3 eigenvalues account for approximately 95% of the 
>> variance. Interestingly, the spatial distribution of the first three 
>> principal components exhibited the same bias as the sensor data. When 
>> I reconstructed the source data/omitting/the first 3 components, the 
>> control-patient contrast localized to a collection of canonical DMN 
>> nodes.
>> It seems extremely odd to me that removing so much of the information 
>> present in the original data returns a reasonable source-level 
>> contrast while the majority of variance is accounted for by what is 
>> clearly bias. I cannot complete this analysis and submit the results 
>> unless I can isolate the problem and correct it so I can run the 
>> analysis without reducing the rank of the source data. If anyone can 
>> speculate on possible reasons for this problem and/or potential 
>> solutions I would be grateful.
>> Thank you,
>> Alexander
>> Alexander Nakhnikian, Ph.D.
>> Research Investigator
>> VA Boston Healthcare System
>> Instructor in Psychiatry, Harvard Medical School
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>> https://doi.org/10.1371/journal.pcbi.1002202
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