longitudinal resting-state

Dear AFNI experts,
I am writing to ask your opinion and suggestions about longitudinal resting-state analyses.
In the literature I found several studies that just analyzed resting-state data acquired at different time points as independent data, and then “took care” of the longitudinal nature of the analysis only at the group analysis stage.
I would like your opinion about that, since in my opinion this approach does not take into account the autocorrelation of the time-series and the temporal “dimension” of this type of acquisition.
A recent paper used a novel longitudinal functional connectivity model using a variance components approach (https://www.sciencedirect.com/science/article/pii/S105381191830497X?via%3Dihub). This method “seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI time series data, while restricting the number of parameters in order to make the method computationally feasible and stable”.

I work with neurodegenerative patients and a longitudinal approach with resting-state data would be important in studying this type of populations.

Do you have any suggestions or opinion in running this type of analysis?

Thank you very much for your help.


Giovanni, I don’t have time reading the literature at the moment. Your questions seem too generic. Instead of worrying about modeling specifics, it would be more helpful to first spill out your research focus. For example, with the resting-state dataset, what is your research goal/interest/hypothesis about those multiple time points? How are you analyzing the data: whole-brain voxel-wise, seed-based correlation, ICA, or correlation analysis among a list of ROIs? etc…