Briefly, we have acquired SCR simultaneously during an fMRI fear learning task (@ 3T). The task goes for around 16 minutes. SCR was recorded from fingers on the left hand.
Our data was acquired in using the Labchart Pro V8 from ADI instruments and at the beginning of the task we ‘subject zero’.
“...The subject zero gives an absolute measure of the skin conductivity of the subject, the baseline conductivity from which relative changes are measured. The absolute conductivity value is shown when everything is zeroed. Normal baseline conductivity values should be in the range of 10 to 50 μS, depending on the individual and the air humidity level (which affects skin conductivity). A scale measuring the change in conductivity (also in μS) is used for the final record. This scale corresponds to the GSR readout in a traditional polygraph…”
Our data is full of negative values. We are wondering what effect this would have on the way that the programme interprets the values. Does PsPM assume absolute values relative to an open circuit? Do you know of a way to get around this issue?
To use PsPM I export this data into a Matlab file (converts the units to S not mcS), and negative values are still present. As per your previous advice on artefact removal, I have applied a median filter (3 consecutive points) to remove much of the scanner noise. Is there any problem with applying the median filter as an artefact removal step and then later applying the default butterworts filters when creating the GLM - could we be removing too much real signal?
I have attached 10 mpspm example files below for you to see the negative values and our condition file if that would help you to understand and solve our problem.
Further, we have SCR data during resting state fMRI for the same population that we would like to do DCM analysis on; this data also has negative values (also subject zero’d etc.). Will these negatives be an issue when using DCM modelling instead of GLM?
Thanks for your help!
Hannah
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negative numbers in the data are not an issue. PsPM treats the data to be on an interval scale, i.e. with no absolute zero. Offset (positive and negative) is fitted with an intercept in GLM, and for DCM the minimum in the data is subtracted before fitting.
I guess it is ok to first median filter and then use the standard PsPM filters. The data still appear rather noisy, and I spotted some remaining artefacts. You can also mark them manually (using the data editor) and remove that from model inversion (by specifying 'missing data' epochs).
Dominik
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Hi Dominik and Tobias
Briefly, we have acquired SCR simultaneously during an fMRI fear learning task (@ 3T). The task goes for around 16 minutes. SCR was recorded from fingers on the left hand.
Our data was acquired in using the Labchart Pro V8 from ADI instruments and at the beginning of the task we ‘subject zero’.
“...The subject zero gives an absolute measure of the skin conductivity of the subject, the baseline conductivity from which relative changes are measured. The absolute conductivity value is shown when everything is zeroed. Normal baseline conductivity values should be in the range of 10 to 50 μS, depending on the individual and the air humidity level (which affects skin conductivity). A scale measuring the change in conductivity (also in μS) is used for the final record. This scale corresponds to the GSR readout in a traditional polygraph…”
Our data is full of negative values. We are wondering what effect this would have on the way that the programme interprets the values. Does PsPM assume absolute values relative to an open circuit? Do you know of a way to get around this issue?
To use PsPM I export this data into a Matlab file (converts the units to S not mcS), and negative values are still present. As per your previous advice on artefact removal, I have applied a median filter (3 consecutive points) to remove much of the scanner noise. Is there any problem with applying the median filter as an artefact removal step and then later applying the default butterworts filters when creating the GLM - could we be removing too much real signal?
I have attached 10 mpspm example files below for you to see the negative values and our condition file if that would help you to understand and solve our problem.
Further, we have SCR data during resting state fMRI for the same population that we would like to do DCM analysis on; this data also has negative values (also subject zero’d etc.). Will these negatives be an issue when using DCM modelling instead of GLM?
Thanks for your help!
Hannah
Example files:
Hi Hannah
negative numbers in the data are not an issue. PsPM treats the data to be on an interval scale, i.e. with no absolute zero. Offset (positive and negative) is fitted with an intercept in GLM, and for DCM the minimum in the data is subtracted before fitting.
I guess it is ok to first median filter and then use the standard PsPM filters. The data still appear rather noisy, and I spotted some remaining artefacts. You can also mark them manually (using the data editor) and remove that from model inversion (by specifying 'missing data' epochs).
Dominik