Constrain your data: relating detailed animal studies to spatial templates in neuroimaging

Techniques such as EEG, fMRI, PET and DTI are widely utilized to extract information on functional and anatomical connectivity in the human brain. For each analyzed brain, these methods provide a complete picture of the measured quantity at a limited resolution. This is in sharp contrast with invasive animal studies, carried out primarily in non-human primates. In particular, the most unambiguous information on long-range interareal connectivity is provided by axonal fibre tract tracing studies. Obtained data are precise up to axonal level, albeit typically apply only to a particular part of the analyzed brain. Hence, linking these two kinds of resources is mutually beneficial.

We describe an approach for spatially registering all cortical brain regions from the textual database CoCoMac (Kӧtter, 2004). Currently, it contains 458 studies, mainly tract tracing experiments, many of which define their own cortical (sub)parcellation. Direct registration to a surface-based Macaque cortex is applied to 9 core parcellations using the tool Caret (Van Essen and Dierker, 2007). The rest of the database is semantically linked to these core parcellations using previously developed algebraic and machine learning techniques (Stephan et al., 2000; Bezgin et al., 2008; Bakker et al., ongoing work). For the translation to the human cortex we rely on Van Essen's landmark-based macaque to human warpings. As a result, one can query CoCoMac for a given spatial coordinate in any of the Caret-supported macaque and human cortical templates.

Connectivity was analyzed using multiple graph-theoretical measures to capture global properties of the derived network using the new brain network visualization and analysis tool ConJUNGtion developed on the basis of JUNG software (Madadhain et al., 2005). As a next step, the spatial connectivity maps can be used either as a direct validation of neuroimaging data, or indirectly by constraining generative models of brain activity with a plausible anatomical connectivity assignment.

References

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Kӧtter R. (2004) Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. Neuroinformatics 2(2), 127-44.

Madadhain J., Fisher D., Smyth P., White S., Boey Y.B. (2005) Analysis and visualization of network data using JUNG. Journal of Statistical Software, Vol. 10, p. 1--35.

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