We recently implemented an image-reconstruction method that uses AFNI tools to analyze our fNIRS data (validated in an fNIRS/fMRI paper: Wijeakumar et al. 2017). One reviewer for our recent manuscript provided feedback stating that we may be misusing the AFNI tools, so I'm here to get some feedback from the AFNI community. I'm not sure how much detail is necessary so I apologize for the length, and please ask for clarification where needed.

In this study, we used 14 channels to measure the hemodynamic response over the left temporal and frontal cortices in an event-related listening task. A GLM was used to estimate the beta coefficient of the hemodynamic response for each condition, channel, participant, and chromophore (oxy and deoxyhemoglobin). Each subject head volume and position of the nirs probe were measured with Polhemus Digitizer. We use 3dslicer to convert subject head volumes to nifti images aligned to Colin's atlas. We take the probe geometry projections of each channel and run monte carlo simulations with 1,000,000 photons to yield a sensitivity profile for each channel. These sensitivity profiles are thresholded at 0.0001 and summed together to create a mask that reflected the cortical volume from which the NIRS channels were recording. Tokhonov regularization method was applied to estimate oxy and doexy concentration changes for each condition. Data were analyzed within a composite mask that was restricted to only include voxels in which all participants contributed data. We ran 3dclustsim on this composite mask to estimate minimum voxel cluster size (3dClustSim -mask compositemask.nii -pthr 0.05).

The reviewer indicated that 3dclustsim is not appropriate for NIRS reconstructions bc it makes assumptions about that the spatial covariance that are only true for MRI. Is this correct? Reviewer then suggested a proper cluster statistical analysis, which I am still unsure on how to implement. I'd appreciate any thoughts, comments, or ideas to help me figure out how to proceed with my data analyses.

-Jess