Abstract
Cortical network structure has been extensively characterized at the level of local circuits and in terms of long-range connectivity, but seldom in a manner that integrates both of these scales. Furthermore, while the connectivity of cortex is known to be related to its architecture, this knowledge has not been used to derive a comprehensive cortical connectivity map. In this study, we integrate data on cortical architecture and axonal tracing data into a consistent multi-scale framework of the structure of one hemisphere of macaque vision-related cortex. The connectivity model predicts the connection probability between any two neurons based on their types and locations within areas and layers. Our analysis reveals regularities of cortical structure. We confirm that cortical thickness decays with cell density. A gradual reduction in neuron density together with the relative constancy of the volume density of synapses across cortical areas yields denser connectivity in visual areas more remote from sensory inputs and of lower structural differentiation. Further, we find a systematic relation between laminar patterns on source and target sides of cortical projections, extending previous findings from combined anterograde and retrograde tracing experiments. Going beyond the classical schemes, we statistically assign synapses to target neurons based on anatomical reconstructions, which suggests that layer 4 neurons receive substantial feedback input. Our derived connectivity exhibits a community structure that corresponds more closely with known functional groupings than previous connectivity maps and identifies layer-specific directional differences in cortico-cortical pathways. The resulting network can form the basis for studies relating structure to neural dynamics in mammalian cortex at multiple scales.
Highlights
Connectivity maps allow insights into the structure of the brain, for instance through graph-theoretical analyses (Jouve et al 1998; Rubinov and Sporns 2010), and help to create hypotheses on neural processing strategies (Maunsell and Newsome 1987; Felleman and Van Essen 1991; Nassi and Callaway 2009)
Cortical network structure has been extensively characterized at the level of local circuits and in terms of long-range connectivity, but seldom in a manner that integrates both of these scales
We describe the definition of the network structure
Summary
Connectivity maps allow insights into the structure of the brain, for instance through graph-theoretical analyses (Jouve et al 1998; Rubinov and Sporns 2010), and help to create hypotheses on neural processing strategies (Maunsell and Newsome 1987; Felleman and Van Essen 1991; Nassi and Callaway 2009). Connectivity maps provide a structural basis for dynamical models of the brain. They have been derived at different levels of detail and for different species such as the mouse (Oh et al 2014) and macaque monkey (Stephan et al 2001; Bakker et al 2012). There is an ongoing need for improvement, gradual refinement, and theoretical integration
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