[Eeglablist] Lack of convergence in AMICA solution

[Farhan Ali (Asst Prof)]

Thanks, Scott. Some quick responses below:
>> Then I might well expect to find some different 'brain' ICs in the different models...  as we do.  (p.s. How many channels were recorded?)
14. After ASR, typically only 10-11 channels remain “good” using default settings.
>> Wouldn't this be expected?  Were the ICs strikingly non-dipolar (high resid. var.)?
I expect different ICs across models of the same run. But the issue is for the same dataset, when AMICA is run twice, the ICs for say model 1, run 1, cannot be found in model 1 run 2, or model 2 run 2, etc. This was the original problem I posted to the board about.
And the ICs are highly dipolar. When I run dipfit for a single dipole source, the residual variance is typically less than 10%.
>> Might you try computing the mutual information reduction (MIR) in the data by single-model ICA vs multimodel ICA (after breaking the data into the different model ranges).
Good idea. I compared AMICA with 1 model vs. AMICA with 5 models of the same exact dataset and ran the MIR with the post-AMICA utility. The multi-model ICA produced 2-3 times higher IC to IC MIR (averaged across all pairwise IC comparisons and models) than the single-model ICA for a few of the datasets I checked. So, multi-model ICA is somewhat justified? But multi-model ICA has many more parameters. From a model-fitting perspective, should I penalize for that? Say using AIC or BIC? However, a priori there is no reason to think a more complex model will necessarily produce more independent sources unlike say regression in the context of over-fitting, so am no sure any penalty is necessary. Any advice?

>> It doesn't sound surprising, since ICA has already 'honed in' on stable IC processes. Thus, unlikely that multi-model ICA would highlight interesting differences e.g. between conditions. (p.s. conditions = what here?)
Conditions = subjects undergoing different activities in a real-world classroom. In one condition, they were listening to a lecture by a teacher; in another condition, they were watching a video projected to the front of the class, etc. It’s basically the Dikker et al., (2017). Current Biology dataset that we got our hands on and are analysing.


Regards,
Farhan

From: Scott Makeig <smakeig at ucsd.edu>
Sent: 30 September 2020 04:31
To: Farhan Ali (Asst Prof) <farhan.ali at nie.edu.sg>
Cc: Jason Palmer <japalmer29 at gmail.com>; Shawn Hsu <goodshawn12 at gmail.com>; eeglablist at sccn.ucsd.edu; Zeynep Akalin Acar <zeynep at sccn.ucsd.edu>
Subject: Re: [Eeglablist] Lack of convergence in AMICA solution

Farhan - See my >> comment below.  -Scott

On Tue, Sep 29, 2020 at 4:06 AM Farhan Ali (Asst Prof) <farhan.ali at nie.edu.sg<mailto:farhan.ali at nie.edu.sg>> wrote:
Thanks Scott for the insightful comments. Some responses below.

 - Perhaps there were no meaningful state transitions in your data, thus the 'extra' multiple models latched onto various noise data features.   See Hsu et al. 2018 on interpretation of multiple models in datasets involving state changes.
Possible. However, the data is a concatenation of 4 blocks representing 4 different conditions that the subjects underwent and in some (but not all), we do see different models being dominant in different conditions (somewhat similar to Hsu et al. 2018, NeuroImage), so we think at least in some subjects, multiple models may have some basis.
>> Then I might well expect to find some different 'brain' ICs in the different models...  as we do.  (p.s. How many channels were recorded?)
>> Note: By 'brain' ICs above I mean those with resid. var. to the (single or dual symmetric) dipole model < ~15%.

- How did you see that the solutions were 'very different'?  Different time domains for the 3 models? Different IC maps?  The post-AMICA menu invokes tools to plot the domains of the different models.
Different IC scalp maps by visual inspection. The differences across runs were quite obvious, so I didn’t even bother doing statistical comparisons.
>> Wouldn't this be expected?  Were the ICs strikingly non-dipolar (high resid. var.)?
>> Might you try computing the mutual information reduction (MIR) in the data by single-model ICA vs multimodel ICA (after breaking the data into the different model ranges).
>> Does multimodel AMICA decomposition effect stronger MIR than single model? If not, then there is likely to be no meaningful changes in source-space configuration revealed by multi-model decomposition.

- One should expect that most ICs should be shared by all models (e.g., ICs accounting for eyeblink artifact? etc.).
It’s true that some ICs are common, particularly horizontal eye movements, but the majority of the ICs are not shared across models within an individual run, at least in our data.
 >> Again, without saying what differences in which kinds of IC maps, it is hard to say anything...

- Perhaps the dimension of your recording (10-12 channels) was insufficient to create a stable decomposition -- more independent sources than channels...
I find this explanation more likely and related to my earlier point. When we do an initial ICA decomposition assuming stationarity, then select only brain ICs, re-project them, followed by subsequent AMICA, the final solutions we get are very stable. All runs on the same data produce almost identical AMICA solutions. This pipeline likely reduced the dimensionality of the data (less number of independent components) to then allow AMICA to converge. That’s my interpretation. What do you think of this pipeline?
>> It doesn't sound surprising, since ICA has already 'honed in' on stable IC processes. Thus, unlikely that multi-model ICA would highlight interesting differences e.g. between conditions. (p.s. conditions = what here?)

 -  What ASR processes did you employ? Data correction?
I used the Reject data using Clean RawData and ASR option in EEGLab
>> I'll leave suggestions on this to others.  -Scott

--
Scott Makeig, Research Scientist and Director, Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla CA 92093-0961, http://sccn.ucsd.edu/~scott
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