Traditional analysis of the brain has centered on localized properties, such as segmenting a specific structure, quantifying changes in volume, or pinpointing functional activation. Recently, the focus has shifted to brain connectivity, which measures the relationship between regions rather than characteristics of an individual locale. These interactions are key to developing a comprehensive understanding of the brain and to guide the development of imaging biomarkers.

Despite being an active area of research, most connectivity results are difficult to interpret and to validate. In part, this is because our knowledge of the brain is organized around region properties, and we know little about pairwise relationships. Furthermore, conventional methods are designed to identify, but not to explain, the significant patterns.

This workshop focuses on novel mathematical methods that address the practical applications of brain connectivity. Essay topics include, but are not limited to, the following:

  • Multi-Modal Analysis: While anatomical and functional connectivity provide complementary viewpoints of the brain, our measurements of these variables rely on completely different physiological processes. Hence, the relationship between anatomy and function is necessarily indirect and convoluted.
  • Clinical Applications: Connectivity analysis is particularly attractive for clinical populations, as patients are not required to perform challenging experimental paradigms. However, our ability to develop sophisticated algorithms for this application is limited by uncertainty in quantifying individual and group connectivity.
  • Functional/Structural Organization of the Brain: Connectivity measures inform us about the organization of the brain. For example, temporal correlations in fMRI data reveal a functional architecture that can be observed during activation and rest. Similarly, anatomical measures can be used to map complex neural pathways. Here, the goal is to develop robust methods that learn the functional/structural organization of the brain.
  • Quantifying Brain Connectivity: Measures of connectivity are only meaningful if they reflect intrinsic biological properties of the brain. The acquisition principles underlying both anatomical and functional connectivity cannot directly recover such properties, but rather, provide surrogate observations. Determining which statistics provide the best models of brain connectivity for a given application is an open area of research.
  • Model Validation: Since connectivity analysis lacks a ground-truth baseline, the neuroscience community relies on surrogate measures of accuracy, such as statistical significance or a qualitative evaluation. However, this has led to widely inconsistent results in the literature, particularly for clinical studies. Therefore, designing robust validation procedures is an important research direction in this field.