Reconstruction Data

Brain Regions

Experimental data on the multi-scale organization of brain regions is sparse. Here, we extracted the maximal possible information from a sparse dataset on the cellular, synaptic and microcircuit organization of the rat SSCx to exploit interdependencies in the experimental data to build a dense tissue level model. We developed algorithmic procedures that extrapolate the available data to fill in knowledge gaps.

Brain Regions
Brain region generalisation

The infographic depicts how sparse experimental data was extrapolated to yield a dense, multi-scale reconstruction of 8 sub-regions of the rat primary somatosensory cortex.

We procured the following sparse experimental data from the hind limb representation of juvenile rat primary somatosensory cortex (S1 HL):

  1. Layer heights
  2. Neuron densities
  3. Synapse densities
  4. Morphological reconstructions
  5. Layer-wise proportions of morphological types (m-types)
  6. Electrophysiological recordings (e-types)
  7. Morpho-electrical proportions (me-types)
  8. M-type specific bouton densities
  9. No. of synapses/connection and patterns of their axo-dendritic innervation
  10. synapse types (s-types) based on release probability, time constants for recovery from depression/facilitation, quantal conductances, and reversal potentials recorded at 2 mM [Ca2+]o

We then extrapolated these data to 7 other sub-regions of rat S1: S1FL, "Fore Limb", S1Sh, "Shoulder", S1Tr, "Trunk", S1J, “Jaw", S1ULp, "Upper lip", S1DZ, "Disgranular zone", S1DZO, "Oral disgranular zone" to yield a dense, multi-scale reconstruction of the entire S1.

Brain region generalization illustration