Meta-analysis is a critical tool for clinical neuroimaging studies. Activation likelihood estimation (ALE) approach has been widely used in the meta-analysis on task fMRI studies (including both within-group activation and between-group comparison of activation), albeit that the task design varies a lot across studies. Although resting-state fMRI is more applicable to clinical studies, meta-analysis has rarely been applied. One big problem is that the analytic methods are quite different for most resting-state fMRI studies. This is especially a problem for the functional connectivity analysis based on a seed region of interest (ROI) because the results are very sensitive to the location of the seed ROI.

This special issue encourages resting-state fMRI studies on human brain disorders by using either blood oxygenation level dependent (BOLD) or arterial spin labeling (ASL) techniques. Studies on normal population with direct application to brain disorders are welcome as well. The analytic methods should facilitate future meta-analysis. It is therefore helpful to create a model for the prediction or diagnosis of brain disorders. One example of applications is accurate localization of abnormal spontaneous brain activity and to guide further intervention therapies (e.g., deep brain stimulation and transcranial magnetic stimulation).

Reanalyses with different methods from published data are highly encouraged. Seed-ROI-based functional connectivity alone should be avoided in a paper for this special issue, regarding the fact that very few studies will use completely the same ROI and therefore it is not applicable to meta-analysis on such results.

Potential topics include, but are not limited to:

• Amplitude of low frequency fluctuation (ALFF, a measure very similar with root mean square and standard deviation and equal to the square root of power) or fractional ALFF
• Regional homogeneity, network centrality
• Full brain correlation with well-known template (e.g., Harvard-Oxford, AAL, Craddock-200, and Yeo-2011)
• Spatial independent component analysis