The problem of reconstructing synaptic connectivity in neural circuits has recently attracted much attention [1], [2], [3], [4], [5], and a few projects for reconstructing connectivity in different systems, such as C. Elegans, Drosophila, or mouse, had been suggested or are already under way.
Using very high-spatial resolution DW data (90 micra cubic voxels) on formaldehyde-fixed specimens, researchers from the same lab were able to reconstruct connectivity maps between the insula and ACC in mouse lemur, macaques, and gorillas (Park et al., personal communication).
In our opinion, the most promising approach for using synaptic Brainbow at current time is to combine it with the method such as in [31] for assembling connectivity matrix from multiple animals, which may allow reconstructing the connectivity matrix statistically, using a smaller number of fluorophores but imaging many animals.
It may still be possible to use this strategy for certain purposes, such as reconstructing the connectivity backbone made of larger synapses, etc.
Based on these findings, we propose a new approach for reconstructing neural connectivity optically, by tagging synapses with arrays of spectrally distinct fluorescent markers, expressed in different combinations in different neurons using Cre/Lox system Brainbow (i.e., synaptic Brainbow) or libraries of genetic promoters.
By applying and enhancing recently-developed methods of modelling route networks in dynamic lowlands, this study reconstructs connectivity patterns in the Rhine-Meuse delta.
By localizing fluorescent synaptic puncta optically, and identifying the patterns of pre- and post-synaptic fluorophores at different synapses, one can determine the pre- and post-synaptic cells for each synaptic connection, and, thus, reconstruct the connectivity matrix without tracing neural projections – a task presenting formidable challenge both for conventional serial EM and Brainbow LM.
The strong relationship between the physical connectivity and the functional dynamics suggests an ability to solve one of inverse problems: reconstruct physical connectivity from observed functional dynamics.
Statistical models that predict neuron spike occurrence from the earlier spiking activity of the whole recorded network are promising tools to reconstruct functional connectivity graphs.
Using structured light techniques for 3D reconstruction, surface patches can be compressed as a 2D image together with 3D calibration parameters, transmitted over a network and remotely reconstructed (geometry, connectivity and texture map) at the receiving end with the same resolution as the original data [13, 14].
Four ratios are used to reconstruct the connectivity patterns and their temporal dynamics (Fig. 2).
