If you want to make a cluster table, I think percent overlaps with areas is a very reasonable way to do it. I would recommend you follow Tim’s suggestion with vertex areas as well. I would strongly recommend sharing your data as well (if it is CIFTI/GIFTI/NIFTI, the balsa.wustl.edu database is designed for it).
Matt. From: <hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Date: Thursday, April 25, 2019 at 1:55 PM To: "Stevens, Michael" <michael.stev...@hhchealth.org<mailto:michael.stev...@hhchealth.org>> Cc: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: Re: [HCP-Users] Reporting dense analysis results For just finding the overlap of some (positive-only) map with the parcels, the script would likely be a lot simpler if you used -cifti-parcellate with the "-method SUM" option (when doing so, I would also recommend using vertex areas, so that the resulting numbers are surface-area integrals rather than based on number of vertices). You can then use -cifti-stats SUM to get the total, and divide by that in -cifti-math to get percentages. Sharing the data files of the results means that to some extent, tables may not be as necessary. I don't have a strong opinion here. Personally, I like figures, but I haven't done/used meta-analysis. Tim On Thu, Apr 25, 2019 at 8:50 AM Stevens, Michael <michael.stev...@hhchealth.org<mailto:michael.stev...@hhchealth.org>> wrote: Hi folks, Yesterday’s question/replies on reporting tables of pscalar results prompted us to ask about a related question – I’m wondering what HCP folks recommend in terms of the format of tabulating/reporting straightforward “activation results” for DENSE data? I couldn’t find a prior listserv post that exactly addressed this question, nor did a couple passes through recently published literature using HCP methodology turn up a good example to follow. Could be I’m just missing stuff… We’re finishing up analyses on a somewhat conceptually novel analysis that we think might be received at peer review better if we report the dense results. So we sorta envision reporting a table of clusters/cluster peaks where we refer to the 2017 parcellation paper for annotations, e.g., “Cluster 1 – Left IFSp (72%), Left IFJa (26%), Left IFSa (2%)”. To get there, I’m picturing a do-able, yet somewhat awkward combination of cluster finding calls, label file references, ROI definitions, finding peaks/center-of-mass, and then a whole a bunch of –cifti-math operations to determine overlap of clusters vs. parcels… The number of steps/operations that would go into this is enough that I’m just brought up short thinking, “Wait, am I possibly missing something…” Before I start going down this path in coding something like this up, I thought I’d check two things: A) Is there a different conceptual approach altogether that you’d recommend considering for showcasing dense analysis results? Our goal ultimately is to simply reinforce our results are fairly compatible with the demarcations of the 360-parcel atlas to remove a potential reviewer criticism (this analysis is some weird stuff… using spontaneous fluctuations of electrodermal signals as event-onsets for fMRI timeseries analyses… amazingly, it seemed to work, with pretty interesting results that mirror our connectivity analyses on the same data). But if HCP has an entirely different approach to tabulating/summarizing dense results, we’d welcome being brought up-to-speed. B) The lazy part of me wonders… Has someone already coded up workbench function call or even a script for the various wb_commands needed that might already do this sort of thing with dense data? Again, this seems so meat-and-potatoes for fMRI that we don’t want to re-invent the wheel here. 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