If you really wanted to extract a specific time period, you could use `fslroi` for NIFTI volumes or `wb_command -cifti-merge` for CIFTI volumes. You could also use the FSL matlab libraries (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation/MacOsX?highlight=%28matlab%29#Using_FSL_MATLAB_libraries) or ciftiopen.m (https://wiki.humanconnectome.org/display/PublicData/HCP+Users+FAQ#HCPUsersFAQ-2.HowdoyougetCIFTIfilesintoMATLAB?) functions to read the data into matlab, and analyze whichever time periods you wish.
However, I think that there will be a number of difficulties with this approach. First and foremost, you will get very unstable estimates of functional connectivity using only 27.5 s of data, or even 110 s of data from all four face blocks. Second, you will need to account for the hemodynamic response to focus on the actual BOLD response corresponding to the face blocks. Third, BOLD activation will be a combination of underlying functional connectivity between regions and activation driven by the task stimuli, which may make it difficult to isolate what you’re interested in. I would advise looking at the following papers: Cisler, J. M., Bush, K., & Steele, J. S. (2014). A comparison of statistical methods for detecting context-modulated functional connectivity in fMRI. NeuroImage, 84, 1042–1052. http://doi.org/10.1016/j.neuroimage.2013.09.018 McLaren, D. G., Ries, M. L., Xu, G., & Johnson, S. C. (2012). A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches. NeuroImage, 61(4), 1277–1286. http://doi.org/10.1016/j.neuroimage.2012.03.068 Repovš, G., & Barch, D. M. (2012). Working memory related brain network connectivity in individuals with schizophrenia and their siblings. Frontiers in Human Neuroscience, 6, 137. http://doi.org/10.3389/fnhum.2012.00137 --Greg ____________________________________________________________________ Greg Burgess, Ph.D. Staff Scientist, Human Connectome Project Washington University School of Medicine Department of Neuroscience Phone: 314-362-7864 Email: [email protected] > On May 22, 2016, at 3:11 PM, Vasilis Pezoulas <[email protected]> wrote: > > Thanks for the reply Greg. > > However, I am not trying to re-establish a new GLM model. I only want to use > the time-series of the WM task and specifically for the face condition in > order to perform functional connectivity analyses. The file > tfMRI_WM_LR.nii.gz contains the 402 frames/sec time-series. Is there a > specific way to combine the results of HCP's FSF analysis with these > time-series in order to spot the face task as well as the > correct/error/no-response trials of this task (based on their > onsets/durations from the txt files) ? I mean those 402 frames, in their > current form, can be used to identify the face condition task intervals and > the correct/error/no-response blocks? > > > > 2016-05-20 18:24 GMT+03:00 Burgess, Greg <[email protected]>: > > On May 18, 2016, at 1:25 PM, Manousos Klados <[email protected]> wrote: > > > > > > > > Dear HCP community, > > > > I send you a question of one of my students, because he cannot subscribe in > > the mailing list. His email is the following: > > > > I am currently studying tfMRI data and especially I am interested in the > > Working Memory task. However, I came up with a question regarding the > > onsets of the WM condition blocks. For example, in Subject 100307 the EV > > directory (path:100307/MNINonLinear/Results/tfMRI_WM_LR/ EVs) contains the > > .txt files with the onsets and durations of various conditions and trials > > (as already stated in the manual). Lets assume that we want to study the > > 2-bk face task (2bk_faces.txt). The onset is at 79.208 sec with a duration > > of 27.5 sec. > > > > The first part of my question is how to retrieve this information from the > > BOLD time-series (405 frames/run)? I mean, is there a fixed way to retrieve > > those frames of interest in the specific interval? > > > Traditionally, a GLM is used to estimate activation in response to each task > condition. We used FSL’s FEAT (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT) to > estimate activation. We have provided the parameter estimates for each > subject (averaged over the two task runs) available for download in the > ConnectomeDB. > > > > The last part of my question concerns the onsets of the correct trials in > > this task. More specifically, the file 2bk_cor.txt contains the onsets of > > the correct trials however I would expect the onsets to be on the interval > > [79.208, 79.208+27.5] since those trials are executed within the condition > > block. Is there a way to match this information according to the 2-bk face > > onset? > > If you want to run your own GLM using different predictors, you will need to > create those predictors and GLMs on your own. You could extract the > 2bk-face-specific correct trials from the file containing all correct 2bk > trials (i.e., 2bk_cor.txt). Or you could code your predictors in any variety > of ways by processing the information in TAB.txt files directly. See the > documentation at: > http://www.humanconnectome.org/documentation/S900/HCP_S900_Release_Appendix_VI.pdf > . > > Running a custom GLM will be a little trickier, since it will require some > custom modification to the TaskfMRIAnalysis pipeline within the HCP > Pipelines. The pipelines and some documentation is located in GitHub. > https://github.com/Washington-University/Pipelines > > An example of how these scripts might be modified was provided in Practical > #7 at last year’s HCP Course > https://www.humanconnectome.org/courses/2015/exploring-the-human-connectome.php > > Hope this helps! > --Greg > > ____________________________________________________________________ > Greg Burgess, Ph.D. > Staff Scientist, Human Connectome Project > Washington University School of Medicine > Department of Neuroscience > Phone: 314-362-7864 > Email: [email protected] > > > > > > Best regards, > > Bill. > > Manousos Klados, PhD > > Max Planck Institute for Human Cognitive & Brain Sciences > > Research Group of Neuroanatomy and Connectivity > > Phone: +49(0)-341-9940-2507 > > Mobile: +49(0)-176-6988-1781 > > Email: [email protected] > > Website: http://www.mklados.com > > Skype: mklados | Twitter: @mklados > > Address: Stephanstraße 1a PC D-04103 Leipzig Germany > > > > Join us at our next event:SAN 2016 Conferece > > > > > > > > _______________________________________________ > > HCP-Users mailing list > > [email protected] > > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > > > > > _______________________________________________ HCP-Users mailing list [email protected] http://lists.humanconnectome.org/mailman/listinfo/hcp-users
