Hi folks,
I have read the following interesting paper, using Dynamic Time Warping as a way of improving the measure of time series similarity between subjects:
Is this possible to do within Nilearn?
If anyone has any experience in this please do reach out.
Hi Bertrand,
I have been researching this more in depth, and it seems to be another connectivity parameterization method. It is best explained here: https://ieeexplore.ieee.org/document/7760247
Using DTW would account for small time delay differences between activated regions, therefore creating a more accurate picture of the correlation between regions in contrast to the standard pearson correlation.
If you have any thoughts on this I would love to hear them.
This is interesting, even though I tend to be cautious with short, single-experiment paper.
Can you reproduce on one of the resting-state datasets of Nilearn ?
Best,
It achieves a better accuracy than standard correlation with 88%.
Please find the Jupyter Notebook on github here: https://github.com/FL33TW00D/DTW
I did not implement it in the ConnectivityMeasure object yet as this was a short experiment.
Iām afraid that the gain is quite limited. I would suggest to make it a side project that is compatible with Nilearn, and consider it if we observe systematic gains in many settings and support from the community.