I'm currently investigating connectivity patterns between the anterior and posterior insula and specific pain-related brain regions, including the anterior, mid, and posterior cingulate cortex. Specifically, I aim to discern these patterns in individuals with chronic pain during electrical stimulation.
Having undergone Mike Cohen's course and delved into several papers authored by him and others, I've surmised that the Phase Locking Value (PLV) is the most suitable metric for my study. This is because my research question is driven by a specific hypothesis rather than being exploratory in nature.
Moreover, I'm keen to explore the impact of the stimulus on brain connectivity among participants. This has led me to consider a dynamic connectivity analysis. While I've noted that Brainstorm may not directly support a sliding window approach for PLV, I'm contemplating a manual implementation of the same:
- Segment the continuous data based on a predefined window position. For instance, in a 2-second continuous recording with a 500 ms window and 100 ms step, the data segments would be from 0-500 ms, 100-600 ms, 200-700 ms, etc.
- Compute the PLV for each segment using Brainstorm's connectivity tools.
- Record the PLV value and proceed by moving the window according to the step size, repeating the process.
I'd love to hear your insights on this approach. Alternatively, would you recommend any other time-resolved phase-based metric such as correlation or coherence?
On a related note, if you're looking to self-educate on connectivity, I highly recommend Mike Cohen's YouTube lectures. You can access them https://www.youtube.com/watch?v=ardi0hO6lOU&list=PLn0OLiymPak1wp4wMQ7tbYrtyFUatMVJs.
Thank you for your time and input!