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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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Fig. 8. Language network evaluation. A & B) Overlap between the language network (LAN, teal outline) from our resting-state based network partition and activations from an independent language processing task (collected in the same sample of 337 subjects) in cortical and subcortical regions. Pink areas indicate overlap between LAN and task activation. Underlay shows task activation t-statistics from the ‘Story versus Baseline’ contrast of the LANGUAGE task, replicating the analysis conducted by Glasser et al. (2016). Note that t-scores are shown here because the high statistics resulted in infinity values when converting to Z-scores. Percentage overlap between LAN and task activation in the language processing task is much greater than would be expected by chance. C) Cortical map displaying the asymmetry of parcels. The teal outline indicates the language network, which is highly asymmetric compared to the other networks, with left hemisphere dominance. D & E) Network asymmetry in the subcortex. Colored bars in Panel E show the proportion of subcortical voxels in each network that do not overlap when comparing left and right hemispheres. Complete asymmetry (no overlap) is indicated by dotted line at 100% for reference. gray bars indicate chance asymmetry calculated given the size of each network. Solid and dashed red lines indicate 95% upper and lower confidence intervals for chance respectively. Panel E displays the proportion of total voxels in left and right hemispheres for each network. Chance level for this measure is 50% for all networks; confidence intervals are calculated given the total number of voxels in each network. Because functional representation of left and right is reversed in the cerebellum relative to the rest of the brain (due to the midline crossing of projecting fibers (van Baarsen et al., 2016)), left and right cerebellar hemispheres were exchanged in calculating this measure. Like the cortical networks, panel E&F show that subcortical networks are symmetric in general, with a left lateralized LAN. In subcortex, VMM is also significantly asymmetric. F & G) Functional connectivity seed maps for left and right perisylvian language areas (PSL) based on resting-state data in 337 subjects. Both the left and right language seed area show strongest connections to ipsilateral regions.
Surface Mesh:32k fs LR, Modality:T1-weighted, Other Data:gene, Species:Human