In brain connectomics, connectome network's connectivity data are usually visualized as weighted graphs.
Brain connectivity data can be integrated from different sources.
Connectivity data is reported uncorrected for multiple comparisons.
To put into context why the intrinsic geometry may be a better space to understand brain connectivity data, we can look at the field of cartography.
The network plot on a multi-layer visualization (Fig. 14) renders the brain connectivity data more clearly and effectively.
Brain connectivity data consist of information about one brain region projecting nerve fibers to another region and forming synaptic connections.
Although preprocessing of the original fMRI time series data attempts to transform the imaging data into a common space across subjects, formation of the brain connectivity network is likely influenced by subject variability.
We agree that whole brain connectivity maps are valuable for illustrating the connectivity profiles of the tEC and sEC.
Intrinsic brain connectivity may be important for maintenance of synaptic connectivity and, as such, modulates the efficiency and extent of neuronal transmission between brain regions.
Furthermore, brain connectivity networks can be constructed from the tensor data, embedding subtle interactions between brain regions.
Magnetoencephalography (MEG) provides a temporally rich source of information on brain network dynamics and represents one source of functional connectivity data to be provided by the HCP.
