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A null model of the mouse whole-neocortex micro-connectome Michael W. Reimann, Michael Gevaert, Ying Shi, Huanxiang Lu, Henry Markram & Eilif Muller Blue Brain Project Layer 23 Layer 4 Layer 5IT Layer 5PT Layer 6 PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp -3 -1 -2 -2.5 -1.5 ipsi-lateral contra-lateral ipsi-lateral contra-lateral log 10 synapses / um 3 PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp PF AL SoM Vis Med Temp source region target region VISa VISrl VISal VISli VISpl VISpor VISpm VISam VISl Source region Raw projection strengths Not every projection completely covers both source or target region. This is captured and quantified by the model. FRP MOs ACAd ACAv PL ILA ORBl ORBm ORBvl AId AIv AIp GU VISC SSs SSp-bfd SSp-tr SSp-ll SSp-ul SSp-un SSp-n SSp-m MOp VISal VISl VISp VISpl VISli VISpor VISrl VISa VISam VISpm RSPagl RSPd RSPv AUDd AUDp AUDpo AUDv TEa PERI ECT FRP MOs ACAd ACAv PL ILA ORBl ORBm ORBvl AId AIv AIp GU VISC SSs SSp-bfd SSp-tr SSp-ll SSp-ul SSp-un SSp-n SSp-m MOp VISal VISl VISp VISpl VISli VISpor VISrl VISa VISam VISpm RSPagl RSPd RSPv AUDd AUDp AUDpo AUDv TEa PERI ECT 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.00 0.25 0.50 0.75 Predicted innervation prob. 0.0 0.2 0.4 0.6 Observed innervation prob. MOs - L5 MOs - L2/3 MOs - L6a 2 4 6 8 10 12 14 IPSI IPSI CONTRA CONTRA n.s no data 10 20 30 40 50 Proj. density IPSI CONTRA Axon number FRP MOs ACAd ACAv PL ILA ORBl ORBm ORBvl AId AIv AIp GU VISC SSs SSp-bfd SSp-tr SSp-ll SSp-ul SSp-un SSp-n SSp-m MOp VISal VISl VISp VISpl VISli VISpor VISrl VISa VISam VISpm RSPagl RSPd RSPv AUDd AUDp AUDpo AUDv TEa PERI ECT FRP MOs ACAd ACAv PL ILA ORBl ORBm ORBvl AId AIv AIp GU VISC SSs SSp-bfd SSp-tr SSp-ll SSp-ul SSp-un SSp-n SSp-m MOp VISal VISl VISp VISpl VISli VISpor VISrl VISa VISam VISpm RSPagl RSPd RSPv AUDd AUDp AUDpo AUDv TEa PERI ECT First order innervation prob. predicted from projection density Pairs of regions tend to be innervated more often together A tree-based model creates statistical predictions of individual region innervation profiles Validation of the region innervation model 1 target 74% 2 targets 24% 3 targets 2% Model-based neurons (6 visual areas) Han et al., 2018 1 target 44% 2 targets 38% 3 targets 10% 4 targets 6% 5 targets 2% 1 target 53% 2 targets 28% 3 targets 14% 4 targets 5% 5 targets <1% 1 target 35% 2 targets 33% 3 targets 20% 4 targets 9% 5 targets+ 3% Fluorescence-based MAPseq-based 1 target 56% 2 targets 36% 3 targets 8% 4 targets <1% 5 targets 0% 1 target 50% 2 targets 38% 3 targets 4% 4 targets 8% 5 targets 0% L2/3 L4 L5 L6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 VISli VISl VISal VISpm VISam VISrl VISli VISl VISal VISpm VISam VISrl Area A Area B P(B|A) Model P(B|A) VISli VISl VISal VISpm VISam VISrl Area A VISli VISl VISal VISpm VISam VISrl Area B Han et al., 2018 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 L2/3 L2/3 within-region modules VISa within-region modules VISam within-region modules VISa within-region modules VISam 0 100 200 300 400 500 0 2 4 6 8 10 Unidirectional con. prob. (%) 0.0 0.2 0.4 0.6 0.8 Bidirectional con. prob. (%) Sampling offset (um) 200 400 600 Region radius (um) 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 Reciprocal overexpression Model instances Mean 0 100 200 300 400 500 1 2 3 4 5 6 7 Reciprocal overexpression Model instances Mean Sampling offset (um) 200 400 600 Region radius (um) 2 4 6 8 10 12 14 16 Unidirectional con. prob. (%) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Bidirectional con. prob. (%) VISam->VISa VISa->VISam (VISa->VISam * VISam->VISa) VISa<->VISam 10 -6 10 -5 10 -4 10 -3 10 -1 10 -2 10 0 0 50 100 150 200 250 300 350 Count (model) log edge density 0 500 1000 1500 2000 2500 3000 3500 Count (control) 25 20 15 10 5 0 Edge density (%) source: prefrontal source: medial source: somatomotor source: temporal source: anterolateral source: visual intra-module inter-module log control half-width 1.2 1.0 0.8 0.6 0.4 log model half-width 1 2 3 4 5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 [...] [...] Model Expected Log-count MOs MOp FRP Neuron triplets FRP MOp MOs Sampling radius 150 um The model predicts over- expression of certain long- range motifs model data Relative frequency profile number Per projection class Per module: intra-module Per module: inter-module 1 2 3 4 5 6 5pt 1 2 3 4 5 6 5it 1 2 3 4 5 6 4 1 2 3 4 5 6 23 1 2 3 4 5 6 6 1 2 3 4 5 6 visual 1 2 3 4 5 6 somatomotor 1 2 3 4 5 6 anterolateral 1 2 3 4 5 6 temporal 1 2 3 4 5 6 medial 1 2 3 4 5 6 prefrontal 1 2 3 4 5 6 visual 1 2 3 4 5 6 somatomotor 1 2 3 4 5 6 anterolateral 1 2 3 4 5 6 prefrontal 1 2 3 4 5 6 temporal 1 2 3 4 5 6 medial S o u r c e T a r g e t s o u r c e t a r g e t Wang and Burkhalter (2007) VISpl VISpor VISl VISli VISal VISrl VISa VISam VISpm Yes 90 deg None 180 deg Yes None None None None 180 deg Yes None None 90 deg None 90 deg Yes None Yes 90 deg None 180 deg Yes None None None None 180 deg Yes None None 90 deg None 90 deg Yes None reflection? rotation? reflection? rotation? reflection? rotation? reflection? rotation? reflection? rotation? reflection? rotation? reflection? rotation? reflection? rotation? reflection? rotation? Yes - None - Yes - None - None - Yes - - - None - Yes - --- Juavinett et al. (2017) our mapping Contact: [email protected] // bluebrain.epfl.ch Strengths of individual projections Layer profiles of synapse densities Validation of layer profiles Validation of the mapping FRP MOs ACAd ACAv PL ILA ORBl ORBm ORBvl AId AIv AIp GU VISC SSs SSp-bfd SSp-tr SSp-ll SSp-ul SSp-un SSp-n SSp-m MOp VISal VISl VISp VISpl VISli VISpor VISrl VISa VISam VISpm RSPagl RSPd RSPv AUDd AUDp AUDpo AUDv TEa PERI ECT FRP MOs ACAd ACAv PL ILA ORBl ORBm ORBvl AId AIv AIp GU VISC SSs SSp-bfd SSp-tr SSp-ll SSp-ul SSp-un SSp-n SSp-m MOp VISal VISl VISp VISpl VISli VISpor VISrl VISa VISam VISpm RSPagl RSPd RSPv AUDd AUDp AUDpo AUDv TEa PERI ECT 1 5 10 15 Statistical interaction for innervations from MOs P(MOs->A|MOs->B) / P(MOs->A) Predicted innervation Which combination of regions is innervated by individual axons? Analyze innervation patterns of reconstructed axons from Janelia Mouselight Topographical mapping of projections Region innervation profiles of individual axons Incomplete region coverage We model a topographical mapping between brain regions by defining local coordinate systems in the source and target regions. Fit to projection data from the voxelized model of Knox et al., 2018 We first validate the model against the input data: How much is lost by modelling only linear mapping? Next, we validate against the literature. This is functional, not anatomical data! Predicted motifs and micro- connectivity We predict the full micro-connectivity for over 10 million neurons Reciprocal connectivity between regions Multi-region connectivity motif counts Long-range connectivity shaped by local clusters We predict a connectivity principle described by Taylor et al., 2017 ex- tends to single-neuron scale. Julie A Harris, Stefan Mihalas, et al. The organization of intracortical connections by layer and cell class in the mouse brain. April 2018. doi: 10.1101/292961. URL http://biorxiv.org/lookup/doi/10.1101/292961. Almut Schüz and Günther Palm. Density of neurons and synapses in the cerebral cortex of the mouse. Journal of Comparative Neurology, 286(4):442–455, August 1989. Joseph E. Knox, et al. High resolution data-driven model of the mouse connectome. April 2018. doi: 10.1101/293019. URL http://biorxiv.org/lookup/ doi/10.1101/293019. Charles R. Gerfen, Michael N. Economo, and Jayaram Chandrashekar. Long distance projections of cortical pyramidal neurons. Journal of Neuroscience Research, 96(9):1467–1475, September 2018. doi: 10.1002/jnr.23978. Ashley L Juavinett, Ian Nauhaus, Marina E Garrett, Jun Zhuang, and Edward M Callaway. Automated identification of mouse visual areas with intrinsic signal imaging. Nature Protocols, 12(1):32–43, Quanxin Wang and Andreas Burkhalter. Area map of mouse visual cortex. The Journal of Comparative Neurology, 502(3):339–357, May 2007. doi: 10.1002/ cne.21286. Yunyun Han, et al. The logic of single-cell projections from visual cortex. Nature, 556(7699):51–56, March 2018. doi: 10.1038/nature26159. Peter N. Taylor, Yujiang Wang, and Marcus Kaiser. Within brain area tractography suggests local modularity using high resolution connectomics. Scientific Reports, 7 (1):39859, December 2017. doi: 10.1038/srep39859. References Availability Both the code, parameterized connectome constraints (recipe) and connectome instances are available at: https://portal.bluebrain.epfl.ch/ resources/models/mouse-projections/ Motivation: Connectomics takes place in two separate worlds: Micro-connectomics, studying connections between individual neurons, but at comparatively small scales; and macro- connectomics, studying connections at large scales, but only between regions of hundreds of neurons. We take a first step towards consolidating the two approaches. We start by considering the micro-connectivity implied by the macro- connectivity models, that of unstructured region-to-region connections, then apply a number of additional biological constraints: We then find that these simple constraints lead to highly nonrandom micro-connectivity between regions. Projections have a certain layer profile of synapse density in the target region Projections implement a topographical mapping between regions Individual neurons innervate only a subset of the regions targeted by their source region. We start with macro- or meso-scale connectomics. Harris et al., 2018 report the strengths of projections between regions in the Allen Common Coordinate Framework. They provide strengths for five different projection classes. We convert the reported strengths to average synapse densities in terms of synapses um-3 using a predicted total number of synapses (88 billion, Schuz & Palm, 1989). We predict and apply layer profiles of synapse density in the target region based on data from Harris et al., 2018 We validate the prevalence of profiles and the resulting densities against raw data of the volumetric connectome model of Knox et al., 2018
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