[color=#000000]Hi Daniel,[/color]
[color=#000000]Typically the first dimension is the one being most commonly used/reported to characterize each ROI. In particular, when extracting multiple dimensions from an ROI the first dimension always represents the average BOLD timeseries across all voxels within the ROI, while the following dimensions represent the principal components of the timeseries variability across all voxels within the ROI. If the ROI is relatively small and homogeneous then the average timeseries should adequately characterize the BOLD response within this ROI, while if the ROI is larger and non-homogeneous then the average timeseries might fail to capture the variablity in BOLD responses within the ROI (and in those cases using a multivariate representation instead of a single dimension helps capture that additional variability).[/color]
[color=#000000]Hope this helps[/color]
[color=#000000]Alfonso [/color]
[i]Originally posted by Daniel Kopala-Sibley:[/i][quote]Hi, Hopefully this isn't redundant with another question. I'm new to Conn, and am I'm running resting state connectivity analyses, with a focus on the DMN, and asked for seeds in the mPFC, PCC, and left and right parietal cortex. I then had it extract two dimensions for each at the first level. I've been running connectivity analyses with a covariate (parenting behaviors), and find that results differ quite a bit depending on whether I use the first or second dimensions for each seed. Are there any guidelines on which dimension to use?
Thanks in advance
Daniel[/quote]
[color=#000000]Typically the first dimension is the one being most commonly used/reported to characterize each ROI. In particular, when extracting multiple dimensions from an ROI the first dimension always represents the average BOLD timeseries across all voxels within the ROI, while the following dimensions represent the principal components of the timeseries variability across all voxels within the ROI. If the ROI is relatively small and homogeneous then the average timeseries should adequately characterize the BOLD response within this ROI, while if the ROI is larger and non-homogeneous then the average timeseries might fail to capture the variablity in BOLD responses within the ROI (and in those cases using a multivariate representation instead of a single dimension helps capture that additional variability).[/color]
[color=#000000]Hope this helps[/color]
[color=#000000]Alfonso [/color]
[i]Originally posted by Daniel Kopala-Sibley:[/i][quote]Hi, Hopefully this isn't redundant with another question. I'm new to Conn, and am I'm running resting state connectivity analyses, with a focus on the DMN, and asked for seeds in the mPFC, PCC, and left and right parietal cortex. I then had it extract two dimensions for each at the first level. I've been running connectivity analyses with a covariate (parenting behaviors), and find that results differ quite a bit depending on whether I use the first or second dimensions for each seed. Are there any guidelines on which dimension to use?
Thanks in advance
Daniel[/quote]