Functional connectivity-based parcellation and connectome of cortical midline structures in the mouse: a perfusion autoradiography study

Rodent cortical midline structures (CMS) are involved in emotional, cognitive and attentional processes. Tract tracing has revealed complex patterns of structural connectivity demonstrating connectivity-based integration and segregation for the prelimbic, cingulate area 1, retrosplenial dysgranular cortices dorsally, and infralimbic, cingulate area 2, and retrosplenial granular cortices ventrally. Understanding of CMS functional connectivity (FC) remains more limited. Here we present the first subregion-level FC analysis of the mouse CMS, and assess whether fear results in state-dependent FC changes analogous to what has been reported in humans. Brain mapping using [14C]-iodoantipyrine was performed in mice during auditory-cued fear conditioned recall and in controls. Regional cerebral blood flow (CBF) was analyzed in 3-D images reconstructed from brain autoradiographs. Regions-of-interest were selected along the CMS anterior-posterior and dorsal-ventral axes. In controls, pairwise correlation and graph theoretical analyses showed strong FC within each CMS structure, strong FC along the dorsal-ventral axis, with segregation of anterior from posterior structures. Seed correlation showed FC of anterior regions to limbic/paralimbic areas, and FC of posterior regions to sensory areas–findings consistent with functional segregation noted in humans. Fear recall increased FC between the cingulate and retrosplenial cortices, but decreased FC between dorsal and ventral structures. In agreement with reports in humans, fear recall broadened FC of anterior structures to the amygdala and to somatosensory areas, suggesting integration and processing of both limbic and sensory information. Organizational principles learned from animal models at the mesoscopic level (brain regions and pathways) will not only critically inform future work at the microscopic (single neurons and synapses) level, but also have translational value to advance our understanding of human brain architecture.

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