Rest-task modulation of fMRI-derived global signal topography is mediated by transient co-activation patterns
Cite this dataset
Zhang, Jianfeng; Huang, Zirui; Tumati, Shankar; Northoff, Georg (2020). Rest-task modulation of fMRI-derived global signal topography is mediated by transient co-activation patterns [Dataset]. Dryad. https://doi.org/10.5061/dryad.xsj3tx9bw
Recent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.
The data set was from the Human Connectome Project (HCP). The 3 Tesla dataset was served for main analyses, and 7 Tesla served for replication. All subjects from the HCP 1200-subject data release (Mar 01, 2017) having completed fMRI sessions were included (837 subjects in 3 Tesla and 145 subjects in7 Tesla, respectively).
The filename describes to which figure the data is used for, for example, 'fig_4B_s2.mat' means this data is used for Fig 4B and S2 Fig.
All datasets are saved in .mat. For gifti strcuture in '.mat', the vaule can be found in '.cdata' field, and this brain map can be visualized by Connectome Workbench.
For more detials about visualization, please visit https://www.humanconnectome.org/software/connectome-workbench
If you have any other questions, feel free to contact me (Jianfeng Zhang): firstname.lastname@example.org