Number and Laminar Distribution of Neurons in a Thalamocortical Projection Column of Rat Vibrissal Cortex

This is the second article in a series of three studies that investigate the anatomical determinants of thalamocortical (TC) input to excitatory neurons in a cortical column of rat primary somatosensory cortex (S1). Here, we report the number and distribution of NeuN-positive neurons within the C2, D2, and D3 TC projection columns in P27 rat somatosensory barrel cortex based on an exhaustive identification of 89 834 somata in a 1.15 mm3 volume of cortex. A single column contained 19 109 ± 444 neurons (17 560 ± 399 when normalized to a standard-size projection column). Neuron density differences along the vertical column axis delineated “cytoarchitectonic” layers. The resulting neuron numbers per layer in the average column were 63 ± 10 (L1), 2039 ± 524 (L2), 3735 ± 905 (L3), 4447 ± 439 (L4), 1737 ± 251 (L5A), 2235 ± 99 (L5B), 3786 ± 168 (L6A), and 1066 ± 170 (L6B). These data were then used to derive the layer-specific action potential (AP) output of a projection column. The estimates confirmed previous reports suggesting that the ensembles of spiny L4 and thick-tufted pyramidal neurons emit the major fraction of APs of a column. The number of APs evoked in a column by a sensory stimulus (principal whisker deflection) was estimated as 4441 within 100 ms post-stimulus

Cell Type–Specific Thalamic Innervation in a Column of Rat Vibrissal Cortex

This is the concluding article in a series of 3 studies that investigate the anatomical determinants of thalamocortical (TC) input to excitatory neurons in a cortical column of rat primary somatosensory cortex (S1). We used viral synaptophysin-enhanced green fluorescent protein expression in thalamic neurons and reconstructions of biocytin-labeled cortical neurons in TC slices to quantify the number and distribution of boutons from the ventral posterior medial (VPM) and posteromedial (POm) nuclei potentially innervating dendritic arbors of excitatory neurons located in layers (L)2–6 of a cortical column in rat somatosensory cortex. We found that 1) all types of excitatory neurons potentially receive substantial TC input (90–580 boutons per neuron); 2) pyramidal neurons in L3–L6 receive dual TC input from both VPM and POm that is potentially of equal magnitude for thick-tufted L5 pyramidal neurons (ca. 300 boutons each from VPM and POm); 3) L3, L4, and L5 pyramidal neurons have multiple (2–4) subcellular TC innervation domains that match the dendritic compartments of pyramidal cells; and 4) a subtype of thick-tufted L5 pyramidal neurons has an additional VPM innervation domain in L4. The multiple subcellular TC innervation domains of L5 pyramidal neurons may partly explain their specific action potential patterns observed in vivo. We conclude that the substantial potential TC innervation of all excitatory neuron types in a cortical column constitutes an anatomical basis for the initial near-simultaneous representation of a sensory stimulus in different neuron types

Neuropathic pain caused by miswiring and abnormal end organ targeting

Nerve injury leads to chronic pain and exaggerated sensitivity to gentle touch (allodynia) as well as a loss of sensation in the areas in which injured and non-injured nerves come together1-3. The mechanisms that disambiguate these mixed and paradoxical symptoms are unknown. Here we longitudinally and non-invasively imaged genetically labelled populations of fibres that sense noxious stimuli (nociceptors) and gentle touch (low-threshold afferents) peripherally in the skin for longer than 10 months after nerve injury, while simultaneously tracking pain-related behaviour in the same mice. Fully denervated areas of skin initially lost sensation, gradually recovered normal sensitivity and developed marked allodynia and aversion to gentle touch several months after injury. This reinnervation-induced neuropathic pain involved nociceptors that sprouted into denervated territories precisely reproducing the initial pattern of innervation, were guided by blood vessels and showed irregular terminal connectivity in the skin and lowered activation thresholds mimicking low-threshold afferents. By contrast, low-threshold afferents-which normally mediate touch sensation as well as allodynia in intact nerve territories after injury4-7-did not reinnervate, leading to an aberrant innervation of tactile end organs such as Meissner corpuscles with nociceptors alone. Genetic ablation of nociceptors fully abrogated reinnervation allodynia. Our results thus reveal the emergence of a form of chronic neuropathic pain that is driven by structural plasticity, abnormal terminal connectivity and malfunction of nociceptors during reinnervation, and provide a mechanistic framework for the paradoxical sensory manifestations that are observed clinically and can impose a heavy burden on patients.The research leading to these results has received funding from the following sources: an ERC Advanced Investigator grant to R.K. (Pain Plasticity 294293); grants from the Deutsche Forschungsgemeinschaft to R.K. (SFB1158, projects B01, B06), to T.K. (SFB1158, project B08), to S.G.L. (SFB1158, project A01) and to V.G. (SFB1158, project A03); a grant to B.O. (project number 371923335); and grant CIDEGENT/2020/052 from Generalitat Valenciana to F.J.T. R.K. is a member of the Molecular Medicine Partnership Unit of the European Molecular Biology Laboratory and Medical Faculty Heidelberg. V.G. and T.A.N. were partially supported by a post-doctoral fellowship and physician scientist fellowship, respectively, from the Medical Faculty Heidelberg. D.M. was partially supported by a post-doctoral fellowship from Excellence Cluster CellNetworks. We acknowledge support from the Interdisciplinary Neurobehavioral Core (INBC) for the behavioural experiments, the data storage service SDS@hd and bwMLS&WISO HPC supported by the state of Baden-Württemberg and the German Research Foundation (DFG) through grants INST 35/1314-1 FUGG and INST 35/1134-1 FUGG, respectively.Peer reviewe

Large-Scale Automated Histology in the Pursuit of Connectomes

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain's computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity

A barrel-related interneuron in layer 4 of rat somatosensory cortex with a high intra-barrel connectivity

Synaptic connections between identified fast-spiking (FS), parvalbu-min (PV)-positive interneurons, and excitatory spiny neurons in layer 4 (L4) of the barrel cortex were investigated using patch-clamp re-cordings and simultaneous biocytin fillings. Three distinct clusters of FS L4 interneurons were identified based on their axonal morphology relative to the barrel column suggesting that these neurons do not constitute a homogeneous interneuron population. One L4 FS inter-neuron type had an axonal domain strictly confined to a L4 barrel and was therefore named “barrel-confined inhibitory interneuron ” (BIn). BIns established reliable inhibitory synaptic connections with L4 spiny neurons at a high connectivity rate of 67%, of which 69 % were reci-procal. Unitary IPSPs at these connections had a mean amplitude of 0.9 ± 0.8 mV with little amplitude variation and weak short-term sy-naptic depression. We found on average 3.7 ± 1.3 putative inhibitory synaptic contacts that were not restricted to perisomatic areas. I

The Mutual Inspirations of Machine Learning and Neuroscience

Neuroscientists are generating data sets of enormous size, which are matching the complexity of real-world classification tasks. Machine learning has helped data analysis enormously but is often not as accurate as human data analysis. Here, Helmstaedter discusses the challenges and promises of neuroscience-inspired machine learning that lie ahead