Soma location, dendrite morphology, and synaptic innervation may represent key determinants of functional responses of individual neurons, such as sensory-evoked spiking. Here, we reconstruct the 3D circuits formed by thalamocortical afferents from the lemniscal pathway and excitatory neurons of an anatomically defined cortical column in rat vibrissal cortex. We objectively classify 9 cortical cell types and estimate the number and distribution of their somata, dendrites, and thalamocortical synapses. Somata and dendrites of most cell types intermingle, while thalamocortical connectivity depends strongly upon the cell type and the 3D soma location of the postsynaptic neuron. Correlating dendrite morphology and thalamocortical connectivity to functional responses revealed that the lemniscal afferents can account for some of the cell type- and location-specific subthreshold and spiking responses after passive whisker touch (e.g., in layer 4, but not for other cell types, e.g., in layer 5). Our data provides a quantitative 3D prediction of the cell type–specific lemniscal synaptic wiring diagram and elucidates structure–function relationships of this physiologically relevant pathway at single-cell resolution.

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.

To understand sensory representation in cortex, it is crucial to identify its constituent cellular components based on cell-type-specific criteria. With the identification of cell types, an important question can be addressed: to what degree does the cellular properties of neurons depend on cortical location? We tested this question using pyramidal neurons in layer 5 (L5) because of their role in providing major cortical output to subcortical targets. Recently developed transgenic mice with cell-type-specific enhanced green fluorescent protein labeling of neuronal subtypes allow reliable identification of 2 cortical cell types in L5 throughout the entire neocortex. A comprehensive investigation of anatomical and functional properties of these 2 cell types in visual and somatosensory cortex demonstrates that, with important exceptions, most properties appear to be cell-type-specific rather than dependent on cortical area. This result suggests that although cortical output neurons share a basic layout throughout the sensory cortex, fine differences in properties are tuned to the cortical area in which neurons reside.

Although physiological data on microcircuits involving a few inhibitory neurons in the mammalian cerebral cortex are available, data on the quantitative relation between inhibition and excitation in cortical circuits involving thousands of neurons are largely missing. Because the distribution of neurons is very inhomogeneous in the cerebral cortex, it is critical to map all neurons in a given volume rather than to rely on sparse sampling methods. Here, we report the comprehensive mapping of interneurons (INs) in cortical columns of rat somatosensory cortex, immunolabeled for neuron-specific nuclear protein and glutamate decarboxylase. We found that a column contains ∼2,200 INs (11.5% of ∼19,000 neurons), almost a factor of 2 less than previously estimated. The density of GABAergic neurons was inhomogeneous between layers, with peaks in the upper third of L2/3 and in L5A. IN density therefore defines a distinct layer 2 in the sensory neocortex. In addition, immunohistochemical markers of IN subtypes were layer-specific. The "hot zones" of inhibition in L2 and L5A match the reported low stimulus-evoked spiking rates of excitatory neurons in these layers, suggesting that these inhibitory hot zones substantially suppress activity in the neocortex.F or a quantitative understanding of the interaction between excitatory and inhibitory synaptic transmission in the neocortex, it is crucial to obtain data on the absolute numbers and the relative distribution of excitatory and inhibitory (mostly GABAergic) neurons [interneurons (INs)] in a cortical column. Only on the basis of such prevalence numbers is it possible to interpret data on single-cell physiology (1-8) and synaptic connections of pairs of neurons (9-12) at the circuit level. The distribution of cortical neurons and INs has therefore been the objective of several studies over the past decades (13-15). Statistical sampling methods were used because mapping tens of thousands of neurons was not feasible. Although the nominal error bounds of such methods are in the range of 10-20%, the reported absolute numbers differed strongly among studies, especially for sparse neuron populations, such as INs and their subtypes. The ratio of INs reported for the somatosensory cortex varied between 15% and 25%, thus by almost a factor of 2 (13,16,17). Data on possible differences in IN density between layers in a cortical column were contradictory (13,15,16,18,19).To resolve these substantial discrepancies, we undertook the complete mapping of INs in entire cortical columns using the cytoarchitectonic barrels in L4 as a reference frame for the thalamocortical innervation volume (20) in postnatal day (P) 25-36 rat vibrissal cortex. Our data show that the overall prevalence of inhibitory neurons was previously overestimated by almost a factor of 2. We find a unique substructure of the distribution of INs in a cortical column that can explain the layer-specific differences in the excitability in neocortex. The distribution of INs defines layer 2 as a distinct neocortical layer.

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.

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.

The cellular organization of the cortex is of fundamental importance for elucidating the structural principles that underlie its functions. It has been suggested that reconstructing the structure and synaptic wiring of the elementary functional building block of mammalian cortices, the cortical column, might suffice to reverse engineer and simulate the functions of entire cortices. In the vibrissal area of rodent somatosensory cortex, whisker-related "barrel" columns have been referred to as potential cytoarchitectonic equivalents of functional cortical columns. Here, we investigated the structural stereotypy of cortical barrel columns by measuring the 3D neuronal composition of the entire vibrissal area in rat somatosensory cortex and thalamus. We found that the number of neurons per cortical barrel column and thalamic "barreloid" varied substantially within individual animals, increasing by ∼2.5-fold from dorsal to ventral whiskers. As a result, the ratio between whisker-specific thalamic and cortical neurons was remarkably constant. Thus, we hypothesize that the cellular architecture of sensory cortices reflects the degree of similarity in sensory input and not columnar and/or cortical uniformity principles.T wo major concepts of cortical neuronal organization have been proposed. Structurally, correlations between stereologybased measurements (1) of neuron density and cortical thickness resulted in the hypothesis of structural uniformity, arguing that the number of neurons beneath a square millimeter of cortical surface is constant and independent of cortical area and species (2, 3). Functionally, cortex is organized in a columnar fashion, reflecting similar neuronal activity along the vertical cortex axis in response to peripheral stimuli (4-8). Similar spatial extents of functional cortical columns in the horizontal plane, combined with the idea of cortical uniformity, resulted in the notion that a stereotypic columnar network may also represent the elementary structural building block of sensory cortices (9). In combination, the two concepts thus suggested a common organization of all sensory cortices, which led to reverse engineering and simulation efforts that build up large-scale network models of repeatedly occurring identical cortical circuits (10, 11).The ideal model system for investigating columnar structure and function is the vibrissal area of rodent somatosensory cortex. There, "barrels" of neurons in layer 4 (L4) have been identified as somatotopically organized structural correlates of peripheral receptor organs (i.e., facial whiskers). Whisker/barrel columns have thus been regarded as both structural and functional elementary cortical units (12)(13)(14). To investigate the structural stereotypy of cortical barrel columns, independent of the drawbacks associated with stereology (i.e., extrapolations from small sampling regions), we decided to locate each excitatory and inhibitory neuron soma within the entire volume of interest. Using high-resolution, large-scale confocal microscopy (15) and a...

Diffusion weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique based on measure of water diffusion in tissues. This diffusion can be quantified by apparent diffusion coefficient (ADC). Some reports indicated that ADC can reflect tumor proliferation potential. The purpose of this meta-analysis was to provide evident data regarding associations between ADC and KI 67 in different tumors. Studies investigating the relationship between ADC and KI 67 in different tumors were identified.MEDLINE library was screened for associations between ADC and KI 67 in different tumors up to April 2017. Overall, 42 studies with 2026 patients were identified. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients.Associations between ADC and KI 67 were analyzed by Spearman's correlation coefficient. The reported Pearson correlation coefficients in some studies were converted into Spearman correlation coefficients.The pooled correlation coefficient between ADCmean and KI 67 for all included tumors was ρ = −0.44. Furthermore, correlation coefficient for every tumor entity was calculated. The calculated correlation coefficients were as follows: ovarian cancer: ρ = −0.62, urothelial carcinomas: ρ = −0.56, cerebral lymphoma: ρ = −0.55, neuroendocrine tumors: ρ = −0.52, glioma: ρ = −0.51, lung cancer: ρ = −0.50, prostatic cancer: ρ = −0.43, rectal cancer: ρ = −0.42, pituitary adenoma:ρ = −0.44, meningioma, ρ = −0.43, hepatocellular carcinoma: ρ = −0.37, breast cancer: ρ = −0.22.