Following is a list of features requested for HomER:

August 25, 2006
Question: Well, we have the continuous record of the fNIRS signal of a group of subjects while they perform a cognitive task lasting 20 seconds. We pretend to get the OxyHb and DeoxyHb averages of this period of time, and compare between different subgroups of subjects. So, after making one nirs file for each subject, we select and load all subjects with the "Import data | to current Session" option. After that, we set LPF to 0.8 and HPF to 0.1., and click in "update all files". After that, we tick the "Use DPF correction" and "Part. vol. correction" options in the advanced filtering options (keeping the default values). Then we go to the "Averaging" tab, set the "preTime" in 0, and the "postTime" in 20, and click on "calculate average all". When is done, we uncheck the "display Average all" (if it is checked, we have no figure in the upper side, and a blank figure in the lower side). Then, we go through subject by subject rightclicking in the lower figure and selecting the option "Export all channels to file" (in any moment we have to check some channel in the probe scheme to avoid some errors). When it's done, we open those files and average the HbO and HbR variables across time for each subject and channel, and make between-subjects ANOVAs for each channel (or group of channels). Are we doing it well? Can we interpret that there is a different neural processing between subjects if we obtain significant differences (assuming that confounding variables are controlled).

Response: That sounds good for the analysis approach.  One detail is that the pre/post time used to calculate the hemodynamic response should be longer then the task.  I.e. for a 20-sec task, set the post-time to (i.e.) 30sec and verify that the response returns to baseline  (maybe this is not actually necessary).  Also, the HPF at 0.1 will remove any signals which are SLOWER then 1/10sec.  If your task is 20-sec long, it may have slow temporal components to it  (I don't know what the task is)... consider using a smaller number (or even not using the HPF at all).  Otherwise, the averaging protocols sound fine (although statistics are most correct when no filtering is done at all).

The ANOVA tests between subjects is the way I would first approach it for a publication.

You should probably do multi-way ANOVA where the subject variability is considered an additional dimension  (you might be already doing this in the analysis).  Possibly consider channels in the region of interest as another degree of freedom as well.  The variability between subjects (or channels) will make it harder to reach significance (i.e p<0.05).  If you are able to meet this criterion- great.  If not, you may need to better control the factors that are responsible for the  variability of the response amplitude across subjects (i.e. partial volume/pathlength).

The partial volume/ pathlength factors in the Modified Beer Lambert Law (i..e the calculation of HbO2 and HbR from optical density) are subject dependent and are probably variable across the probe (depending on the thickness of bone (etc) below the optodes).  There are several groups whom have argued that this prevents meaningful comparisons of the amplitude of signals across subjects and even across regions of the probe.  I would agree.  For example, if you were to test the same subject several times, the results may vary depending on how reproducibly you placed the optode probe.  However, I would argue that for a large enough sample size of subjects, this averages out (and there is some support for this in the literature).  This will be controlled for in the multi-way ANOVA.

However, the result of this is that a) at the minimum this should be a fact that you consider when you draw conclusions from such an ANOVA tests.  b)  consider experimental ways to control for this.

There aren't really a lot of experimental studies yet, which have addressed inter-subject ANOVA tests with NIRS for this reason.

Possible examples of a way to control for variability in the partial pathlength factors:

1) Run an experiment with a positive control task that both subject groups respond to equally in addition to the task that you are testing for differences in.  For example,  both subject group A and B are known to perform working memory task #1 the same (i.e. previous literature supports).  However, you expect (and wish to test) if group A is significantly different then group B in the performance of task #2 (a different working memory task).

Run both group A and B while they perform both tasks.  Then in the analysis perform your ANOVA or paired T-tests on Group A:(difference of Task 2 to Task 1) verses Group B:(diff Task 2 to Task 1).  {i.e. normalize all subjects to their task 1 response}.   This would allow you to normalize out probe effects on amplitude and directly test if group A's response to task 2 is different from group B's response.  Of course, this assumes that a task can be found that both groups respond to equally.

2) Consider comparing the timing of the responses rather than their amplitudes.  I'm not sure the best way to do this in practice.

March 6, 2006
Question: I would like to understand how to load Oxy & Deoxy concentration changes data  into HomER. The user manual clearly shows how to load intensity changes at channels. But it is not clear how one could load oxy & deoxy values if they are already available from a commercial NIRS system.

Response: As far as I recall, Homer currently doesn't have the ability to load concentration data instead of intensity data.  We should add that to our feature request list and add it to the next release.