[Eeglablist] ICA and Connectivity On Combined Data From Multiple Subjects

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Mon Apr 1 17:58:23 PDT 2019

Dear Nathan,

Good question!
First of all, it is NOT wrong to concatenate all subject's data first then
run a single ICA: this approach is called group ICA. The assumption is that
ICA gives you a group's grand-mean spatial filter. Meanwhile, SCCN's
approach is to run ICA for each individual: The assumption is that the
resultant ICA weight matrix represents individual subject's spatial filter
i.e., functionally independent stationary cortical region to scalp sensors.
In a sense, SCCN's approach allows individual differences to be maximally
manifested. For comparison, consider Fz, Cz, and Pz monitoring some brain
electric activity. A spatial relation between an active brain region and a
scalp electrode varies from subject to subject, because of anatomical
variance across population. But ICA can scoop the brain region that emits a
unique signal than others. This is as if ICA would provide a tailor-made
stethoscope for each individual to monitor the active cortical region.

However, even if you use an ICA for individual subjects, in the end you'll
need to summarize all the individual data into much lower dimensional data
that represents the group's representative values (typically average). This
dimension reduction is the whole purpose of the group level analysis.
SCCN's approach keeps individual differences all the way down to the final
clustering stage. Other approaches, like group ICA, collapse individual
differences in earlier stages. Again, these approaches are equally valid,
and not that one is good and the other is bad/wrong.

This is related to a meta-level question of what we want to do with a
group-level analysis. One of my colleagues criticized the current SCCN's
approach for separating individual subject analysis and the group-level
analysis. His point is that if we know we will collapse all the subject's
data in the end, why don't we optimize individual data in terms of
group-level summary. I think such a criticism is valid. This idea will be
demonstrated soon.

An even larger problem here is what is individual difference to us. The
current inferential statistics treats it as an error. However, geneticists
take it granted that there are always subtypes/subgroups in a group, so
individual differences are readily justified. If you follow SCCN
IC-clustering approach, there is a chance that you encounter an emerging
subtypes/subgroups in your result (if you have a large database), which
makes an interesting advantage of this approach. But, if you have rather
small (n=15 or less) data sets, then you may suffer from the individual
differences, since you may find only 60% of unique subjects in a given
cluster, for example...


On Mon, Apr 1, 2019 at 5:21 PM Nathan Sanders <nesander at ncsu.edu> wrote:

> Hey Group,
> My understanding based on reading the SCCN wiki is that ICA should be
> performed on a per subject (per session) basis. For example, Makoto's
> preprocessing pipeline says that "To apply a single ICA, your subject
> cannot take off the electrode cap!
> <https://sccn.ucsd.edu/wiki/Makoto%27s_preprocessing_pipeline#General_tips_for_performing_ICA_.2806.2F26.2F2018_updated.29>".
> However, one approach to group connectivity analysis that I have run across
> involves first combining data sets from all subjects, then performing ICA
> on the aggregate data. This doesn't seem right to me, but I am having a
> hard time articulating why, mostly because I am new to the field and still
> only have a basic understanding of how ICA works. Can anyone speak to the
> validity of applying ICA to combined data sets? Can you recommend any
> papers to help me understand?
> Thanks,
> Nathan
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Makoto Miyakoshi
Assistant Project Scientist, Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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