No abstract

We investigated the synaptic innervation of apical dendrites of cortical pyramidal cells in a region between layers (L) 1 and 2 using 3-D electron microscopy applied to four cortical regions in mouse. We found the relative inhibitory input at the apical dendrite’s main bifurcation to be more than 2-fold larger for L2 than L3 and L5 thick-tufted pyramidal cells. Towards the distal tuft dendrites in upper L1, the relative inhibitory input was at least about 2-fold larger for L5 pyramidal cells than for all others. Only L3 pyramidal cells showed homogeneous inhibitory input fraction. The inhibitory-to-excitatory synaptic ratio is thus specific for the types of pyramidal cells. Inhibitory axons preferentially innervated either L2 or L3/5 apical dendrites, but not both. These findings describe connectomic principles for the control of pyramidal cells at their apical dendrites and support differential computational properties of L2, L3 and subtypes of L5 pyramidal cells in cortex.

Much of our understanding of the neuronal mechanisms of spatial navigation is derived from chronic recordings in rodents in which headdirection, place, and grid cells have all been described. However, despite the proposed importance of self-reference information to these internal representations of space, their congruence with vestibular signaling remains unclear. Here we have undertaken brain-wide functional mapping using both fMRI and electrophysiological methods to directly determine the spatial extent, strength, and time course of vestibular signaling across the rat forebrain. We find distributed activity throughout thalamic, limbic, and particularly primary sensory cortical areas in addition to known head-direction pathways. We also observe activation of frontal regions, including infralimbic and cingulate cortices, indicating integration of vestibular information throughout functionally diverse cortical regions. These whole-brain activity maps therefore suggest a widespread contribution of vestibular signaling to a self-centered framework for multimodal sensorimotor integration in support of movement planning, execution, spatial navigation, and autonomic responses to gravito-inertial changes.

The labeling and identification of long-range axonal inputs from multiple sources within densely reconstructed electron microscopy (EM) datasets from mammalian brains has been notoriously difficult because of the limited color label space of EM. Here, we report FluoEM for the identification of multi-color fluorescently labeled axons in dense EM data without the need for artificial fiducial marks or chemical label conversion. The approach is based on correlated tissue imaging and computational matching of neurite reconstructions, amounting to a virtual color labeling of axons in dense EM circuit data. We show that the identification of fluorescent light- microscopically (LM) imaged axons in 3D EM data from mouse cortex is faithfully possible as soon as the EM dataset is about 40–50 µm in extent, relying on the unique trajectories of axons in dense mammalian neuropil. The method is exemplified for the identification of long-distance axonal input into layer 1 of the mouse cerebral cortex.