Call for Papers

This workshop is a forum for exchanging ideas and methods for brain connectome analysis (network or image data), particularly using neural network models, via developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. The goal is to bring together researchers from academia, industry, and government, to create a forum for discussing recent advances in brain connectome analysis. In doing so, we aim to better understand the overarching principles and the limitations of our current methods and to inspire research on new algorithms and techniques for brain connectome analysis.

To reflect the broad scope of work on brain connectome analysis , we encourage submissions that span the spectrum from theoretical analysis to algorithms and implementation, to applications, empirical studies and reflection papers. As an example, the growth of various cohort studies has given rise to a host of new opportunities for brain connectome analysis towards mental disorders. More recently, the advent of neural network models has spurred numerous works in neuralimage computing and graph representation learning. We encourage submissions on theory, methods, and applications focusing on a broad range of neural network-based approaches in various domains.

Topics of interest include, but are not limited to:

  • Shallow or deep neural networks for neuroimage computing
  • Geometric deep learning and graph neural networks (GNNs) for brain connectome analysis
  • Multi-modal neuroimage fusion and integration with neural network models
  • Spatial-temporal neuroimage analysis with neural network models
  • Novel applications of neural network models in neuroimage acquisition, reconstruction, and analysis
  • New principles of deep learning in neuroimaging such as transfer learning, pre-training, knowledge distillation, meta-learning
  • Interpretable neural network models for mental disorder analysis and biomarker discovery
  • New datasets, benchmarks, and empirical studies of neural network models for brain connectome analysis and neuroimage computing
  • Ethics, privacy, fairness and robustness of neural network models for brain connectome analysis

We welcome many kinds of papers, such as, but not limited to:

  • Novel research papers
  • Demo papers
  • Work-in-progress papers
  • Visionary papers (white papers)
  • Appraisal papers of existing methods and tools (e.g., lessons learned)
  • Evaluatory papers which revisit validity of domain assumptions
  • Relevant work that has been previously published
  • Work that will be presented at the main conference

Authors should clearly indicate in their abstracts the kinds of submissions that the papers belong to, to help reviewers better understand their contributions.

All papers will be single-blinded and peer-reviewed. Submissions must be in PDF, no more than 8 pages long (including all references and appendices) — shorter papers are welcome — and formatted according to the latest standard double-column IEEE Proceedings Style.

The accepted papers will be published on the workshop’s website and will not be considered archival for resubmission purposes. However, high-quality accepted papers will have the opportunity to be invited for publication in a special issue in the IEEE Transactions on Big Data (subject to additional reviews). While all accepted papers will be presented with posters, high-quality accepted papers will also have the opportunity to participate in the oral/spotlight presentation, and win our Best Paper Award(s).

For paper submission, please proceed to the submission website. Please send enquiries to organizers@brainnn.us.