Scene: Figure 13
WU-Minn HCP Consortium Open Access Data Use Terms
1. I will not attempt to establish the identity of or attempt to contact any of the included human subjects.
2. I understand that under no circumstances will the code that would link these data to Protected Health Information be given to me, nor will any additional information about individual human subjects be released to me under these Open Access Data Use Terms.
3. I will comply with all relevant rules and regulations imposed by my institution. This may mean that I need my research to be approved or declared exempt by a committee that oversees research on human subjects, e.g. my IRB or Ethics Committee. The released HCP data are not considered de-identified, insofar as certain combinations of HCP Restricted Data (available through a separate process) might allow identification of individuals. Different committees operate under different national, state and local laws and may interpret regulations differently, so it is important to ask about this. If needed and upon request, the HCP will provide a certificate stating that you have accepted the HCP Open Access Data Use Terms.
4. I may redistribute original WU-Minn HCP Open Access data and any derived data as long as the data are redistributed under these same Data Use Terms.
5. I will acknowledge the use of WU-Minn HCP data and data derived from WU-Minn HCP data when publicly presenting any results or algorithms that benefitted from their use.
1. Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from HCP data should contain the following wording in the acknowledgments section: "Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University."
2. Authors of publications or presentations using WU-Minn HCP data should cite relevant publications describing the methods used by the HCP to acquire and process the data. The specific publications that are appropriate to cite in any given study will depend on what HCP data were used and for what purposes. An annotated and appropriately up-to-date list of publications that may warrant consideration is available at http://www.humanconnectome.org/about/acknowledgehcp.html
3. The WU-Minn HCP Consortium as a whole should not be included as an author of publications or presentations if this authorship would be based solely on the use of WU-Minn HCP data.
6. Failure to abide by these guidelines will result in termination of my privileges to access WU-Minn HCP data.
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Figure 13 shows the group average full covariance matrices after sICA+FIX, sICA+FIX + tICA, and sICA+FIX + MGTR in Panels 1-3. We use covariance here because, like variances, covariances are additive and represent the absolute amount of variance shared by any two pairs of ROIs (scaled from -0.1 to 0.1 in percent BOLD for Panels 1-6). As in other figures, a global positive bias is removed by tICA cleanup (Panel 4 shows the difference between Panel 2 and Panel 1), but MGTR also removes additional signal in the bottom right quadrant of the matrix relative to the upper left quadrant with the off-diagonal quadrants in between (Panel 5 shows the difference between Panel 3 and Panel 1). Importantly, the difference between the tICA cleanup and MGTR (Panel 6 shows the difference between Panel 3 and Panel 2) is highly network specific, including small increases in cognitive/task-negative regions (bottom row, parcels shown in red) and large decreases in primarily non-cognitive/task positive regions (bottom row, blue parcels), with connections between the parcels of these two broad groups of regions showing smaller decreases. Panels 7 and 8 show that mean across subjects partial correlation regularized with ridge regression (rho=0.23, which was optimal in matching the individual matrices to the group matrix computed with no regularization; scaled Z=+/-5) is much less affected by tICA cleanup, as it already controls for global artifacts (Glasser et al., 2016b). Thus, Panel 9 (difference between Panel 8 and Panel 7) does not reveal substantial differences. The 360 cortical areas are ordered according to the same hierarchical clustering as the grey plots, and the first split, into cognitive/task negative (red) and non-cognitive and task positive (blue) regions, is shown in the bottom row and noted by a star on the netmats, with red parcels in the upper left quadrant of the netmats, and the blue parcels in the lower right quadrant. Note that it would be inappropriate to use partial correlation after MGTR, as any dataset that has zero global signal is rank deficient, because each parcel's timeseries equals the negated sum of all other parcels' timeseries.
Modality:T1-weighted, Modality:Myelin Map, Surface Mesh:32k fs LR, Registration:MSMAll, Modality:T2-weighted, Species:Human