Scene: Figure 6
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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Visualization of the mean grey signal. The mean grey timeseries beta map (top rows) and the ratio of the variance of the mean grey timeseries to the total BOLD variance (i.e., variance classified as signal by ICA+FIX; bottom rows). The absolute magnitude of the mean grey signal is highest in sensory regions including visual cortex, early somatosensory cortex (particularly of the face), early auditory cortex, and several thalamic nuclei including the LGN/MGN. Visual, somatosensory, auditory, and likely vestibular cortical areas are highlighted with black outlines. Data based on averaging across 210 HCP subjects, aligned using MSMAll for the cortical surface. When the global signal is strong in individual subjects, it closely matches the group average pattern, however when it is weaker, it may or may not match the group pattern. In these cases, the global signal often looks like one or another widespread RSN (e.g., the task positive or task negative (default mode) network). This is further evidence that removing the global signal as a preprocessing step may distort resting state functional connectivity and hence that we need a more selective way to clean global noise out of the data. The bottom row is a relative measure of how much the fMRI timeseries will be altered by regressing out the mean grey timeseries (on average across subjects), as it is a measure of the proportion of the total BOLD signal represented by the mean grey timeseries. Regressing out the mean grey signal will also tend to cause resting state gradient boundaries to move somewhat in the regions where this map has sharp gradients.
Surface Mesh:32k fs LR, Registration:MSMAll, Species:Human, Modality:Myelin Map, Modality:T2-weighted, Modality:T1-weighted