[FieldTrip] Clustering algorithms, large and long clusters, and watershed?

[Baris Demiral]

Hi all,

I am testing clustering based correction algorithms on a TF power data in a
predefined frequent band; theta. I have four conditions. I used F
statistic. I defined neighbors moderately so that the number of neighbors
is not very small or extremely large. In some analyses I used pairwise
t-test statistic to compare between conditions as well. I have a-priory
expectations, such that some conditions will increase the centro-frontal
theta, and some will increase the posterior theta. I use maxsum and wcm
approaches.

I heave the following questions:

-Why do I observe that very distant electrodes are clustered together? I
noticed that FCZ is clustered with occipital electrodes and belong to the
same cluster written as in stat.cfg.posclusterlabel (label 1). In some ways
I can understand that because my task produces highly posteriorized theta
power. The centro-frontal power is weaker. This leads to my next question:
"Is there a watershed type of algorithm to separate these activities?"

- Are the electrodes I see in the plotting (marked by *,x,+) the peak
electrodes in the clusters, or do these electrodes form the significant
clusters (with smaller p values < .01, .05 etc)? Because, if the cluster is
formed between distant electrodes as mentioned above, I would expect to see
the intermediate electrodes (such as CZ etc.) in the cluster electrode list
as well.

-Can you implement in plotting function where color can represent the
cluster number? The *,+,x signs represent thresholds, but I cannot see
which electrode belongs to which cluster. If you color code electrodes, it
will be very helpful.

-Is there a range of weight values for the weighted cluster mass (wcm)
approach? I looked at the paper, and seems like 0.45-.055 seems to be the
weight parameter. Is this correct?

Thanks,


-- 
S. Baris Demiral
NIH/NIDCD
10 Center Drive
Building 10, 5C410
Bethesda, 20892
MD
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