@misc{item_3243939,
  title = {{Bipartite connectivity mapping (BCM)}},
  booktitle = {{26th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2020)}},
  abstract = {{Introduction: Here we present a new approach called \textasciigrave\textasciigraveBipartite connectivity mapping (BCM)\textquotesingle\textquotesingle for analyzing connectivity in rs-fMRI between two brain regions. The two regions can be of any size, but should be non-overlapping. The main idea is to represent connectivity between the regions as a bipartite graph and analyse it using bipartite network projections. The advantage of this approach is that it allows to fully preserve spatial precision in both ROIs. This sets it apart from traditional seed-based connectivity mapping where the seed ROI must be averaged so that its spatial information is lost. Here we show an application of this idea to rs-fMRI data acquired at 9.4 Tesla. Methods: A bipartite graph is a graph whose edges have endpoints connecting the two regions, but that do not connect vertices within the same region. Our proposed method BCM is a general framework that offers several options for analysing bipartite graphs. The first and easiest technique is to compute the hubs of the bipartite graph using either degree centrality or eigenvector centrality mapping [1] and map them to each voxel. The second technique is to project the bipartite graph onto one of the ROIs and compute the hubs of the projected network. There are several choices for projections of bipartite graphs [2], one of which is currently implemented in BCM. Results: Resting state fMRI data of a healthy female subject were acquired at a 9.4 Tesla scanner (Siemens). A gradient echo EPI sequence with TR\textequals2.03 sec, (1.2)\textasciicircum3 mm isotropic resolution was used. The scan time was 12 minutes (rs-fMRI with eyes open). Preprocessing consisted of corrections for motion and removal of base line drifts via a highpass filter. Two ROIs were manually defined with one ROI (A) corresponding to left and right thalamus, and ROI B corresponding to right-hemispheric grey matter. The size of A was 10618 voxels, the size of B was 120209 voxels. The hubs of the bipartite graph are shown below (Figure 1). The BCM results in the two ROIs are scaled independently for better visualization. Note that LGN shows up prominently in A, while V1 is clearly visible in B. Using the same data as above, we applied a BCM network projection. Here we projected the above bipartite graph onto ROI A (thalamus). Below (Figure 2) we show the degree centrality mapping of the projected network. Conclusions: The new BCM algorithm provides a method for analyzing connectivity between two ROIs. It can for instance be used for detecting nuclei in subcortical regions that are characterized by their connectivity to other brain areas. BCM is a general framework for handling bipartite graphs. At present, two definitions of hubs are implemented, namely degree centrality and eigenvector centrality. Furthermore, a method for network projection is implemented. We believe that BCM offers an alternative route for ROI-based connectivity analysis in fMRI. Its main advantage is that it preserves spatial specificity in both ROIs.}},
  pages = {92},
  year = {2020},
  slug = {item_3243939},
  author = {Lohmann, G and Stelzer, J and Scheffler, K}
}