Search for computational modules in the C. elegans brain

BACKGROUND: Does the C. elegans nervous system contain multi-neuron computational modules that perform stereotypical functions? We attempt to answer this question by searching for recurring multi-neuron inter-connectivity patterns in the C. elegans nervous system's wiring diagram. RESULTS: Our statistical analysis reveals that some inter-connectivity patterns containing two, three and four (but not five) neurons are significantly over-represented relative to the expectations based on the statistics of smaller inter-connectivity patterns. CONCLUSIONS: Over-represented patterns (or motifs) are candidates for computational modules that may perform stereotypical functions in the C. elegans nervous system. These modules may appear in other species and need to be investigated further

Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits

How different is local cortical circuitry from a random network? To answer this question, we probed synaptic connections with several hundred simultaneous quadruple whole-cell recordings from layer 5 pyramidal neurons in the rat visual cortex. Analysis of this dataset revealed several nonrandom features in synaptic connectivity. We confirmed previous reports that bidirectional connections are more common than expected in a random network. We found that several highly clustered three-neuron connectivity patterns are overrepresented, suggesting that connections tend to cluster together. We also analyzed synaptic connection strength as defined by the peak excitatory postsynaptic potential amplitude. We found that the distribution of synaptic connection strength differs significantly from the Poisson distribution and can be fitted by a lognormal distribution. Such a distribution has a heavier tail and implies that synaptic weight is concentrated among few synaptic connections. In addition, the strengths of synaptic connections sharing pre- or postsynaptic neurons are correlated, implying that strong connections are even more clustered than the weak ones. Therefore, the local cortical network structure can be viewed as a skeleton of stronger connections in a sea of weaker ones. Such a skeleton is likely to play an important role in network dynamics and should be investigated further

Next-generation sequencing of immunoglobulin gene rearrangements for clonality assessment: a technical feasibility study by EuroClonality-NGS

One of the hallmarks of B lymphoid malignancies is a B cell clone characterized by a unique footprint of clonal immunoglobulin (IG) gene rearrangements that serves as a diagnostic marker for clonality assessment. The EuroClonality/BIOMED-2 assay is currently the gold standard for analyzing IG heavy chain (IGH) and κ light chain (IGK) gene rearrangements of suspected B cell lymphomas. Here, the EuroClonality-NGS Working Group presents a multicentre technical feasibility study of a novel approach involving next-generation sequencing (NGS) of IGH and IGK loci rearrangements that is highly suitable for detecting IG gene rearrangements in frozen and formalin-fixed paraffin-embedded tissue specimens. By employing gene-specific primers for IGH and IGK amplifying smaller amplicon sizes in combination with deep sequencing technology, this NGS-based IG clonality analysis showed robust performance, even in DNA samples of suboptimal DNA integrity, and a high clinical sensitivity for the detection of clonal rearrangements. Bioinformatics analyses of the high-throughput sequencing data with ARResT/Interrogate, a platform developed within the EuroClonality-NGS Working Group, allowed accurate identification of clonotypes in both polyclonal cell populations and monoclonal lymphoproliferative disorders. This multicentre feasibility study is an important step towards implementation of NGS-based clonality assessment in clinical practice, which will eventually improve lymphoma diagnostics

Illustration of a Quadruple Whole-Cell Recording

<div><p>(A) Dodt contrast image showing four thick-tufted L5 neurons before patching on.</p> <p>(B) Fluorescent image of the same four cells in whole-cell configuration.</p> <p>(C) Average EPSP waveform measured in the postsynaptic neuron (bottom) while evoking action potentials in the presynaptic neuron (top).</p> <p>(D) Diagram of detected synaptic connections and their strengths for this quadruple recording.</p></div

Probability of Connection among Adjacent Neurons Does Not Depend Strongly on the Interneuron Distance

<div><p>(A) Relative location of labeled neurons in the plane of the section. Positive direction of y-axis is aligned with apical dendrite. Potentially presynaptic neuron is located at the origin. Red—bidirectionally connected pairs; blue—unidirectionally connected pairs; green—unconnected pairs.</p> <p>(B) Histogram showing the numbers of pairs in the three classes as a function of distance between neurons (Euclidian distance was calculated from relative <i>X, Y, Z</i> coordinates).</p> <p>(C) Probability of connection versus interneuron distance. Error bars are 95% confidence intervals estimated from binomial distribution.</p></div

Bidirectionally Connected Pairs Contain Connections That Are Stronger and Correlated

<div><p>(A) Synaptic connections in bidirectionally connected pairs are on average stronger than those in unidirectionally connected pairs. The probability density distribution for both the reciprocal (red solid, p(w) = 0.41exp(−(ln w + 0.60)²/(2 × 0.976²)/w) and nonreciprocal (blue dashed, p(w) = 0.47exp(−(ln w + 0.81)²/(2 × 0.834²)/w) connections are shown.</p> <p>(B) In bidirectionally connected pairs synaptic connection strengths are moderately but significantly correlated (<i>R</i> = 0.36, <i>p</i> < 0.0001).</p> <p>(C) Scatter plot of the strength of synaptic connections that shared no pre- and postsynaptic neurons in the same quadruple recording. There might be other connections in the quadruplet besides these two connections. No significant correlation is observed (<i>R</i> = 0.068, <i>p</i> = 0.48). All correlations calculated using Pearson's <i>R</i> method in log space.</p> <p>(D) Average connection strength for bidirectional connections does not vary systemically with interneuron distance (one-way ANOVA, <i>p</i> = 0.068). Numbers on top of data points are the number of connections. Error bars are standard errors of the mean.</p></div

Several Three-Neuron Patterns Are Overrepresented as Compared to the Random Network

<div><p>(A) Null hypothesis for three-neuron patterns assumes independent combinations of connection probabilities of two kinds of two-neuron patterns.</p> <p>(B) Ratio of actual counts (numbers above bars) to that predicted by the null hypothesis. Error bars are standard deviations estimated by bootstrap method.</p> <p>(C) Raw (open bars) and multiple-hypothesis testing corrected (filled bars) <i>p</i>-values. <i>p</i>-values above 0.5 are not shown.</p></div

Two-Neuron Connectivity Patterns Are Nonrandom

<div><p>(A) Null hypothesis is generated by assuming independent probabilities of connection.</p> <p>(B) Reciprocal connections are four times more likely than predicted by the null hypothesis (<i>p</i> < 0.0001, Monte Carlo simulation to test for overrepresentation). Numbers on top of bars are actual counts. Error bars are standard deviations estimated by bootstrap method.</p></div