I think that Steve didn’t concatenate across runs. Peace,
Matt. From: Sang-Yun Oh <san...@gmail.com<mailto:san...@gmail.com>> Date: Saturday, May 13, 2017 at 2:02 PM To: Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>> Subject: Re: [HCP-Users] Replicating "Resting-state fMRI in the Human Connectome Project" Hi Matt, Does that mean matrix G is subjects ICA+FIX is concatenated? Thank you, Sang On Fri, May 12, 2017, 5:56 PM Glasser, Matthew <glass...@wustl.edu<mailto:glass...@wustl.edu>> wrote: ICA+FIX precedes FSLNets and the appropriate data is distributed by the HCP. Peace, Matt. From: "Harms, Michael" <mha...@wustl.edu<mailto:mha...@wustl.edu>> Date: Saturday, May 13, 2017 at 8:08 AM To: Sang-Yun Oh <san...@gmail.com<mailto:san...@gmail.com>>, Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>>, "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>>, "st...@fmrib.ox.ac.uk<mailto:st...@fmrib.ox.ac.uk>" <st...@fmrib.ox.ac.uk<mailto:st...@fmrib.ox.ac.uk>> Subject: Re: [HCP-Users] Replicating "Resting-state fMRI in the Human Connectome Project" FSLNets takes subjects x space x time. If what you want is the group average dense connectome (space x space), then a pre-computed version of that is available for download at db.humanconnectome.org<http://db.humanconnectome.org>. Note that it is 33 GB. If you just want to visualize the dense connectome, you can do that interactively through Workbench (wb_view) without needing to download the actual data. cheers, -MH -- Michael Harms, Ph.D. ----------------------------------------------------------- Conte Center for the Neuroscience of Mental Disorders Washington University School of Medicine Department of Psychiatry, Box 8134 660 South Euclid Ave.Tel: 314-747-6173 St. Louis, MO 63110Email: mha...@wustl.edu<mailto:mha...@wustl.edu> From: Sang-Yun Oh <san...@gmail.com<mailto:san...@gmail.com>> Date: Friday, May 12, 2017 at 4:44 PM To: Michael Harms <mha...@wustl.edu<mailto:mha...@wustl.edu>>, "Glasser, Matthew" <glass...@wustl.edu<mailto:glass...@wustl.edu>>, "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>>, Stephen Smith <st...@fmrib.ox.ac.uk<mailto:st...@fmrib.ox.ac.uk>> Subject: Re: [HCP-Users] Replicating "Resting-state fMRI in the Human Connectome Project" Dear Michael, Please let me know if my understanding is correct. I am trying to get my hands on the input matrix G (Group time series data) in Figure 2 I took Matt's reply to mean that Steve Smith generated G, and Matt's ICA+FIX would be downstream from that (i.e. not G). If that is not correct, are you saying ICA+FIX data is matrix G? (of course taking each subject and concatenating them row-wise) My understanding is FSLNets would take G^T G as input Thank you, Sang On Fri, May 12, 2017 at 2:27 PM Harms, Michael <mha...@wustl.edu<mailto:mha...@wustl.edu>> wrote: No, ICA+FIX is data cleanup that is applied to the minimally preprocessed (MPP) data. I suggest you start here https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLNets and in particular the file “nets_examples.m” cheers, -MH -- Michael Harms, Ph.D. ----------------------------------------------------------- Conte Center for the Neuroscience of Mental Disorders Washington University School of Medicine Department of Psychiatry, Box 8134 660 South Euclid Ave.Tel: 314-747-6173<tel:(314)%20747-6173> St. Louis, MO 63110Email: mha...@wustl.edu<mailto:mha...@wustl.edu> From: <hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of Sang-Yun Oh <san...@gmail.com<mailto:san...@gmail.com>> Date: Friday, May 12, 2017 at 4:10 PM To: "Glasser, Matthew" <glass...@wustl.edu<mailto:glass...@wustl.edu>>, "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>>, Stephen Smith <st...@fmrib.ox.ac.uk<mailto:st...@fmrib.ox.ac.uk>> Subject: Re: [HCP-Users] Replicating "Resting-state fMRI in the Human Connectome Project" Dear Matt, Thank you for your quick response. Would that mean matrix G is derived from minimally preprocessed data? If so, how can I reconstruct G from minimally preprocessed data? Thank you, Sang On Fri, May 12, 2017 at 1:55 PM Glasser, Matthew <glass...@wustl.edu<mailto:glass...@wustl.edu>> wrote: Steve Smith would have generated that matrix. I would have been after running ICA+FIX, which would remove linear trends, 24 movement regressors and ICA noise component timeseries. The matrix would have been made with FSLNets. Peace, Matt. From: <hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of Sang-Yun Oh <san...@gmail.com<mailto:san...@gmail.com>> Date: Saturday, May 13, 2017 at 5:38 AM To: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: [HCP-Users] Replicating "Resting-state fMRI in the Human Connectome Project" Dear HCP users, I am new to this area and I would like to do a replication exercise to learn more about HCP dataset and neuroscience tools. What would be the simplest way to get my hands on matrix G (Figure 2) in this paper? https://doi.org/10.1016/j.neuroimage.2013.05.039 I would imagine G is after regressing out any motion and white matter signals (is this true?) I tried to read the supplemental material; however, it was very complex and way over my head. Is there a script available for computing G from HCP released data that a newbie like me can use? I would appreciate any guidance Best, Sang _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto:HCP-Users@humanconnectome.org> http://lists.humanconnectome.org/mailman/listinfo/hcp-users ________________________________ The materials in this message are private and may contain Protected Healthcare Information or other information of a sensitive nature. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited. 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