The effects of aging on neuropil structure in mouse somatosensory cortex-A 3D electron microscopy analysis of layer 1.

This study has used dense reconstructions from serial EM images to compare the neuropil ultrastructure and connectivity of aged and adult mice. The analysis used models of axons, dendrites, and their synaptic connections, reconstructed from volumes of neuropil imaged in layer 1 of the somatosensory cortex. This shows the changes to neuropil structure that accompany a general loss of synapses in a well-defined brain region. The loss of excitatory synapses was balanced by an increase in their size such that the total amount of synaptic surface, per unit length of axon, and per unit volume of neuropil, stayed the same. There was also a greater reduction of inhibitory synapses than excitatory, particularly those found on dendritic spines, resulting in an increase in the excitatory/inhibitory balance. The close correlations, that exist in young and adult neurons, between spine volume, bouton volume, synaptic size, and docked vesicle numbers are all preserved during aging. These comparisons display features that indicate a reduced plasticity of cortical circuits, with fewer, more transient, connections, but nevertheless an enhancement of the remaining connectivity that compensates for a generalized synapse loss.

NeuroMorph: a toolset for the morphometric analysis and visualization of 3D models derived from electron microscopy image stacks.

Serialelectron microscopy imaging is crucial for exploring the structure of cells and tissues. The development of block face scanning electron microscopy methods and their ability to capture large image stacks, some with near isotropic voxels, is proving particularly useful for the exploration of brain tissue. This has led to the creation of numerous algorithms and software for segmenting out different features from the image stacks. However, there are few tools available to view these results and make detailed morphometric analyses on all, or part, of these 3D models. We have addressed this issue by constructing a collection of software tools, called NeuroMorph, with which users can view the segmentation results, in conjunction with the original image stack, manipulate these objects in 3D, and make measurements of any region. This approach to collecting morphometric data provides a faster means of analysing the geometry of structures, such as dendritic spines and axonal boutons. This bridges the gap that currently exists between rapid reconstruction techniques, offered by computer vision research, and the need to collect measurements of shape and form from segmented structures that is currently done using manual segmentation methods.

Enhanced tunneling conductivity induced by gelation of attractive colloids.

We show that the formation of a gel by conducting colloidal particles leads to a dramatic enhancement in bulk conductivity, due to interparticle electron tunneling, combining predictions from molecular-dynamics simulations with structural measurements in an experimental colloid system. Our results show how colloidal gelation can be used as a general route to huge enhancements of conductivity, and suggest a feasible way for developing cheap materials with novel properties and low metal content.

Quasiuniversal connectedness percolation of polydisperse rod systems.

The connectedness percolation threshold (η(c)) and critical coordination number (Z(c)) of systems of penetrable spherocylinders characterized by a length polydispersity are studied by way of Monte Carlo simulations for several aspect ratio distributions. We find that (i) η(c) is a nearly universal function of the weight-averaged aspect ratio, with an approximate inverse dependence that extends to aspect ratios that are well below the slender rod limit and (ii) that percolation of impenetrable spherocylinders displays a similar quasiuniversal behavior. For systems with a sufficiently high degree of polydispersity, we find that Z(c) can become smaller than unity, in analogy with observations reported for generalized and complex networks.