Hi everyone,
I’m currently working with fMRI data from an n-back task and I’ve been thinking about quality control procedures specific to this type of paradigm.
As far as I can tell, there isn’t a well-established or standardized QC pipeline in the literature specifically tailored for n-back fMRI tasks, beyond general fMRI preprocessing and QC steps. Because of this, I would really appreciate hearing about your experiences and approaches.
For context, I have preprocessed my data using FSL, including standard steps (motion correction, spatial normalization, smoothing, etc.).
I have come across a few relevant papers:
-
Editorial: Demonstrating quality control (QC) procedures in fMRI
-
Efficient evaluation of the Open QC task fMRI dataset
-
Quality control in functional MRI studies with MRIQC and fMRIPrep
However, they generally suggest that there is no task-specific QC standard for n-back paradigms. Only one of them mentions concrete exclusion criteria, such as discarding participants with performance below 40% and framewise displacement (FD) greater than 0.9.
Given this, I’m unsure whether additional task-specific QC criteria should be applied, particularly considering aspects like:
-
Performance-related exclusions (e.g., accuracy thresholds)
-
Motion thresholds
-
Motion-related spikes
-
Signal to noise ratios
-
Inspection of activation patterns
I would be very interested to know:
-
What QC steps do you typically apply for n-back fMRI data?
-
Do you use any specific thresholds or criteria for excluding participants?
-
Any recommended papers, guidelines, or best practices?
Thanks in advance for your insights!