Thanks once more for the reply Greg, I will certaintly look those papers before attempting any extractions from the time-series.
2016-05-24 19:10 GMT+03:00 Burgess, Greg <[email protected]>: > 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
