Activation likelihood estimation (ALE) is a very good method for task fMRI meta-analysis studies (including both within-group between-condition activation difference and between-group activation difference). Although the design of task fMRI studies varies a lot from study to study, most task fMRI studies use very similar GLM method and focus on the local activity of, rather than the relationship between, brain regions. There have been many meta-analysis task fMRI studies.
The design of resting-state fMRI is very similar across studies, therefore it is should be better for meta-analysis, especially on brain disorders. There have been many many resting-state fMRI papers on brain disorders. Unfortunately, few meta-analysis papers have been published.
One big problem is that the analytic methods are very different for most resting-state functional connectivity studies. This is especially a problem for seed-region-based functional connectivity studies. Virtually, I can write hundreds of papers on a single resting-state fMRI dataset by using seed-region-based functional connectivity analysis because I have many many seed regions, multiplying the various nuisance regressors. And actually, I have had a few resting-state functional connectity papers on a single dataset of ADHD. Each sounds like an interesting story.
The situation seems better for complex network analyses, e.g., ICA and graph. While we know the complex intergration of brain function is critical, more attention should be paid on simpler local activity, e.g., amplitude of low frequency fluctuation (almost the same as root mean square and standard deviation, equal to the square root of power) and regional homogeneity (local synchronization, similar to short range functional connectivity density). Although papers on local activity seem to have less novelty than complex network, it is more helpful to meta-analysis, and therefore to provide strong evidences for clinical medicine.
Thanks, Dr. Zang! This is an
Thanks, Dr. Zang! This is an important topic.
I agree that ALE could be performed for regional measures such as ALFF, ReHo, degree centrality, but is more problematic for seed based correlation analysis.
Seed based correlation analysis is highly depend on where the seed sit, also highly depend on the spatial normalization algorithm. If the subjects are not coregistered with each other well, then seed based correlation analysis can be biased.
Here I proposed a quick idea to archive functional connectivity analysis has a characteristic of seed based correlation analysis, but also take some idea from ICA (especially dual regression for ICA). I haven't checked literature for this quick idea yet, nor implemented it (but should be pretty easy).
It's essentially to create a subject specific network integration map, together with a time course of how each time point contribute to that network.
1. Do a seed based correlation analysis, for example, choose PCC seed for default mode network analysis. This step resulted a functional connectivity map. Alternatively, could be started with Step 2 if using an existing network template (e.g., use network template from Smith et al., 2009, PNAS).
2. For each time point, calculate it's spatial correlation with the FC map acquired in Step 1 or a network template, resulting in a time course. This time course indicate the contribution (involvement, or integration) of that time point to that network.
3. Use the time course acquired in Step 2 as a seed course, re-calculate a FC map.
4. Repeat Steps 2 and 3, until convergent, e.g., the correlation between the FC map at the current iteration and FC map at the previous iteration is greater than a setting number, e.g., r>0.99999.
5. The final FC map means a subject-specific network, the value at each voxel means how that voxel contributed (involved) in that network. The final time course means how the network fluctuates across time.
I hope this strategy can be more robust than simple seed based correlation analysis. Also hope some researchers interested in this idea can follow up, and even conduct a study.
Thanks,
Chao-Gan