2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDBIO-3.2
Paper Title IDENTIFYING COHERENT SUBGRAPHS IN DYNAMIC BRAIN NETWORKS
Authors Vikram Ravindra, Purdue University, United States; Geoffrey Sanders, Lawrence Livermore National Labs, United States; Ananth Grama, Purdue University, United States
SessionBIO-3: Biomedical Signal Processing 3
LocationArea C
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Biomedical Signal Processing: Medical image analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Dynamic graphs are natural abstractions for modeling correlations in brain activity. These correlation graphs are constructed from time-series signals corresponding to neuronal activity in different regions of the brain over suitably selected time windows, by linking correlated regions via edges. An important problem in the context of these dynamic correlation graphs is the discovery of sets of regions of the brain, whose activity level is temporally coherent. These manifest as temporally persistent sub-graphs that are strongly connected, referred to as \emph{coherent subgraphs}. In this paper, we present a model and method for identifying coherent subgraphs in dynamic correlation graphs. We show that densely connected components in correlation graphs can be effectively modeled as low-rank sub-matrices derived from the time series signals. Specifically, we derive theoretical results showing that quasi cliques in a correlation graph can be inferred from rows of the left singular matrix of its time-series, and can be tracked in time to identify coherent subgraphs. We apply our proposed method to real-world time-series data from functional MRIs. We show that signals corresponding to nodes in coherent subgraphs can accurately predict whether the subject was actively performing a cognitive task, or was at rest. Furthermore, we also show that the same set of nodes can predict task outcomes/ conditions (such as win v/s loss in a gambling task). To the best of our knowledge, our work is the first in theoretically modeling and analyzing dynamic brain networks using spectral decomposition of the windowed time-series.