Local gradient analysis of human brain function using the Vogt-Bailey Index
The VB/ReHo maps are the result of generating synthetic fMRI data with the R software package neuRosim [Welvaert et al., 2011], and then feeding the data into a modified version of the VB toolbox. The synthetic data consists of five hard-edged spherical regions of activation superimposed on a background of noise (SNR=3) and is available with this data set*, while the VB toolbox may be downloaded from GitHub: https://github.com/VBIndex/py_vb_toolbox/tree/Local-gradients-paper
*Volumetric data: SNR_3_hard_edge.nii.gz
Volumetric mask: SNR_3_hard_edge_full_volmask.nii.gz
The items listed below were constructed from resting-state fMRI data obtained from the minimally preprocessed HCP Young Adult data set [Van Essen et al., 2013, Glasser et al., 2013]:
1) The baseline image (for producing the synthetic data)
2) The volumetric mask (as above. Also required to run this particular version of the VB toolbox)
and where used in conjunction with the following (these, too, were obtained from HCP):
3) 32k midthickness surface
4) 32k cortical mask
M. Welvaert, J. Durnez, B. Moerkerke, et al., neuRosim: An R package for generating fMRI
data. J. Stat. Softw., 44 (10):1, 2011.
D. C. Van Essen, S. M. Smith, D. M. Barch, et al., The WU-Minn Human Connectome
Project: An overview. NeuroImage, 80:62, 2013.
M. F. Glasser, S. N. Sotiropoulos, J. A. Wilson, et al., The minimal preprocessing pipelines for
the Human Connectome Project. NeuroImage, 80:105, 2013.
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.
In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in [Bajada et al., NeuroImage 221:117140, 2020] as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a scaled cut-set weight that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its nearest neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of small-scale similarity in brain activity: the greater the similarity, the ‘heavier’ the edges and the more difficult it is to disconnect the network, hence the higher the value of the minimum ratio cut. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm [Zang et al., NeuroImage 22(1):394, 2004], commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional organization of the human cortex.
BioRxiv - DOI: 10.1101/2022.10.14.511925
- Christine Farrugia
- Paola Galdi
- Irati Arenzana Irazu
- Kenneth Scerri
- Claude J. Bajada
- Mondragon Unibertsitatea
- The University of Edinburgh
- University of Malta