SPP 2041 Computational Connectomics
Sacha van Albada, together with Timo Dickscheid (INM-1) and Claus Hilgetag (UMC Hamburg-Eppendorf), received a DFG grant in the priority program "Computational Connectomics" to develop a model of human visual cortex. In the project, layer- and area-specific neuron densities in human cortex will be measured and used to help predict cortical connectivity, and the resulting model will be simulated to investigate relationships between structure and dynamics.
The computational functions of the human cerebral cortex result from the distributed network interactions of a multitude of specialized local neuronal populations. In order to understand the emergent computational properties of this complex, multi-scale cortical connectome, one needs to quantitatively characterize the structural organization of the local cortical populations, along with their specific intrinsic circuitry as well as their characteristic extrinsic connections with other populations, in order to develop an accurate structural basis for simulating the complex distributed cortical activity patterns.
This formidable challenge is addressed in the present project, which combines three complementary state-of-the-art approaches in large-scale connectivity analysis and modeling. In particular, we will use advanced image processing to quantify the histology and cytoarchitecture of human cortical brain regions. We will use such data in multivariate statistical approaches, to predict the characteristic structural connectivity among human cortical regions, based on wiring rules derived from systematic studies of mammalian brain architecture and connectivity.
Finally, we will integrate the structural connectivity and brain architecture data in a multi-scale, supercomputational network model that simulates the distributed neural activation patterns of the human cerebral cortex at single-cell resolution. The project provides a worked example for integrating experimental studies of human brain connectivity with neuroinformatics and computational neuroscience and delivers a multi-scale computational model that can be used as a versatile platform for addressing diverse aspects of structure-function relationships in the human brain.