Hi Joakim
yes, I believe your first point is true as you are implictly only smoothing within gray matter on the surface, as opposed to volume smoothing that includes CSF and even skull at larger kernel sizes.
W.r.t the optimal kernel size, that's a harder question. In general you smooth to (1) reduce noise, and (2) account for residual misregistration. The matched filter theorem says that your kernel should match the hypothesized size of the effect you are looking for, so there is no general right answer. The best kernel size is smaller if you are expecting focal effects, and bigger if they are more diffuse.
sorry, I wish there was a better answer than that.
cheers Bruce
On Thu, 9 Dec 2010, Joakim Vinberg wrote:
Hi FreeSurfer community:
I wanted to ask for general thoughts on spatial smoothing of data.
In particular, some searching of the archives reveals that a bit of smoothing can help the quality of the data, as well as the inter-subject registration of significance volumes.
FreeSurfer can perform both volume-based smoothing and surface-based smoothing. Theoretical concerns and some testing seem to indicate that smoothing along the surface yields more robust results and more consistent results on the data in practice-is this correct?
Secondly, I have read some of the general thoughts about smoothing (more localized region, less smoothing; more subjects, less smoothing). Does anybody have some more practical insight into appropriate smoothing kernels for anatomical data (e.g. thickness), and functional data (typically BOLD, typically recorded at 3 x 3 x 4mm)? I'm interested in subject pool sizes between 10 and 50.
Thanks very much in advance for all of the help and thoughts-
Joakim