I am doing a research where I am trying to identify the functional connectivity of the cortical region in the brain during a learning task. I have a dataset which has Power Spectral Density values of alpha, eta, theta, gamma and delta bands.
However I am still new to this subject. I am planning to conduct a graph theoretical analysis using the dataset? Is it possible? If yes, can you advice me on the steps to conduct the research.
Thanks in Advance
if you want to use spectral density values and look at functional connectivity, have you looked into using eLORETA? have a read of this paper that did something similar. Article Reproducibility of power spectrum, functional connectivity a...
Does your dataset have the raw data for the individual electrode sites or only aggregate PSD data? You will need data for each site in order to do some sort of inverse source localization. eLORETA (or some other variation of the approach) can be used to identify a neural region (along with the associated controversy that comes with the approach) but I think you'll need to figure out how you want to represent the 'activity' of each of those regions.
The following paper looks like it's doing something similar to what you're trying to do. Good luck!
Article Graph Theory on Brain Cortical Sources in Parkinson’s Diseas...
Hi Eric Andrews my dataset have PSD values obtained from the cortical region of the brain. I am not aware whether we can compute graph theoretical analysis for a particular region to identify functional connectivity
Hi Dasuni. Sorry if I'm not understanding you correctly. I'm not sure if your last post was a question or not. If so, no, you won't be able to create a functional graph if you only have aggregate PSD values for the cortex. Since you only have values for a single 'region' there's nothing to compare.
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