The majority of neurons in the human brain process signals from neurons elsewhere in the brain. Connective Field (CF) modelling is a biologically-grounded method to describe this essential aspect of the brain’s circuitry. It allows characterizing the response of a population of neurons in terms of the activity in another part of the brain. CF modelling translates the concept of the receptive field (RF) into the domain of connectivity by assessing, at the voxel level, the spatial dependency between signals in distinct cortical visual field areas. Thus, the approach enables to characterize the functional cortical circuitry of the human cortex. At present, we have two flavors, Bayesian Connective field modeling (more powerful, but also computationally more demanding) and the standard version.

Bayesian Connective field modeling using a Markov Chain Monte Carlo approach

Standard Connective field modeling

Understanding the relationships between responses in distinct locations in the visual system is essential to clarify this network of cortical signaling pathways. Connective field modeling is a model-based analysis for estimating the dependence between signals in distinct cortical regions using functional magnetic resonance imaging (fMRI). Just as the receptive field of a visual neuron predicts its response as a function of stimulus position, the connective field of a neuron predicts its response as a function of activity in another part of the brain. Connective field modeling opens up a wide range of research opportunities to study information processing in the visual system and other topographically organized cortices.

Haak, K. V., Winawer, J., Harvey, B. M., Renken, R., Dumoulin, S. O., Wandell, B. A. & Cornelissen, F. W. Connective field modeling. NeuroImage (2013), 66, pp. 376-384.

Standard Connective Field modeling is integrated in mrVista, which can be found [here].