Multimodal characterisation of cortical areas by multivariate analyses of receptor binding and connectivity data.

Cortical areas are regarded as fundamental structural and functional units within the information processing networks of the brain. Their properties have been described extensively by cyto-, myelo- and chemoarchitectonics, cortical and extracortical connectivity patterns, receptive field mapping, activation properties, lesion effects, and other structural and functional characteristics. Systematic integrative approaches aiming at multimodal characterisations of cortical areas or at the delineation of global features of the cortical network, however, are still scarce and usually limited to a single data modality, such as cytoarchitectonical or tract tracing data. Here we describe a methodological framework for the systematic evaluation, comparison and integration of different data modalities from the brain and demonstrate its practical application and significance in the analysis of receptor binding and connectivity data within the motor and visual cortices of macaque monkeys. The framework builds on algorithmic methods to convert data between different cortical parcellation schemes, as well as on statistical techniques for the exploration of multivariate data sets comprising data of different types and scales. Thereby, we establish a relationship between intrinsic area properties as expressed by quantitative receptor binding, and extrinsic inter-area communication, which relies on anatomical connectivity. Our analyses provide preliminary evidence for a good correspondence of these two data types in the motor cortex, and their partial discrepancy in the visual cortex, raising hypotheses about the different organisational aspects highlighted by receptors and connectivity. The methodological framework presented here is flexible enough to accommodate a wide range of further data modalities, and is specific enough to permit novel insights and predictions concerning brain organisation. Thus, this approach promises to be very useful in the endeavour to characterise multimodal structure-function relationships in the brain.