Kenneth J. Hayworth

Distinct Profiles of Myelin Distribution Along Single Axons of Pyramidal Neurons in the Neocortex

Patchy Insulation Myelin insulates neuronal axons such that their electrical signals travel faster and more efficiently. However, not all axons are myelinated equally. Tomassy et al. (p. 319; see the Perspective by Fields) obtained detailed images from two snippets of the adult mouse brain and generated three-dimensional reconstructions of individual neurons and their myelination patterns. The images show that some axons have long, unmyelinated stretches, which might offer sites for building new connections. Thus, myelination is not an all-or-none phenomenon but rather is a characteristic of what may be a specific dialogue between the neuron and the surrounding myelin-producing cells. Mouse neurons display different and distinctive patterns of myelination. [Also see Perspective by Fields] Myelin is a defining feature of the vertebrate nervous system. Variability in the thickness of the myelin envelope is a structural feature affecting the conduction of neuronal signals. Conversely, the distribution of myelinated tracts along the length of axons has been assumed to be uniform. Here, we traced high-throughput electron microscopy reconstructions of single axons of pyramidal neurons in the mouse neocortex and built high-resolution maps of myelination. We find that individual neurons have distinct longitudinal distribution of myelin. Neurons in the superficial layers displayed the most diversified profiles, including a new pattern where myelinated segments are interspersed with long, unmyelinated tracts. Our data indicate that the profile of longitudinal distribution of myelin is an integral feature of neuronal identity and may have evolved as a strategy to modulate long-distance communication in the neocortex.

Automating the Collection of Ultrathin Serial Sections for Large Volume TEM Reconstructions

TEM serial section reconstructions have proven invaluable for mapping the complex neural circuitry of tiny invertebrate animals such as C. Elegans [1], as well as small pieces of the vertebrate nervous system. If such reconstructions could be applied to larger volumes of neural tissue (on the order of many cubic millimeters) they could provide answers to many lingering questions of vertebrate neuroanatomy. Unfortunately, such reconstructions are currently limited to several thousand sections and correspondingly small tissue volumes due, in part, to the manual nature of the ultramicrotomy and tissue collection process [2]. Here we introduce a new type of ultramicrotome we are developing, an Automatic Tape-collecting Lathe Ultramicrotome (ATLUM). The ATLUM is designed to automate not only the sectioning process, but also the collection of ultrathin sections from the knife’s waterboat (currently an exclusively manual process). The ATLUM’s mechanism collects ultrathin sections onto a specially prepared copper tape that can subsequently be patternetched to generate TEM slot grids with the collected tissue already attached and ready for imaging.

Boundary Learning by Optimization with Topological Constraints

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topo-logical differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.

High-contrast en bloc staining of neuronal tissue for field emission scanning electron microscopy

Conventional heavy metal poststaining methods on thin sections lend contrast but often cause contamination. To avoid this problem, we tested several en bloc staining techniques to contrast tissue in serial sections mounted on solid substrates for examination by field emission scanning electron microscopy (FESEM). Because FESEM section imaging requires that specimens have higher contrast and greater electrical conductivity than transmission electron microscopy (TEM) samples, our technique uses osmium impregnation (OTO) to make the samples conductive while heavily staining membranes for segmentation studies. Combining this step with other classic heavy metal en bloc stains, including uranyl acetate (UA), lead aspartate, copper sulfate and lead citrate, produced clean, highly contrasted TEM and scanning electron microscopy (SEM) samples of insect, fish and mammalian nervous systems. This protocol takes 7–15 d to prepare resin-embedded tissue, cut sections and produce serial section images.

Neural evidence for intermediate representations in object recognition

The lateral occipital complex (LOC), a cortical region critical for human object recognition, has been shown to primarily code the shape, rather than the surface properties, of an object. But what aspects of shape? Using an fMRI-adaptation (fMRI-a) paradigm in which subjects judged whether two contour-deleted images of objects were the same or different exemplars, virtually all the adaptation in LOC [especially in LOCs most anterior portion (pFs)] could be attributed to repetition of the parts, almost none to the repetition of local image features, such as lines or vertices, templates, or basic- or subordinate-level concepts of the object. These results support the hypothesis that the neural representation of shape in LOC is an intermediate one, encoding the parts of an object.

Stacked Endoplasmic Reticulum Sheets Are Connected by Helicoidal Membrane Motifs

The endoplasmic reticulum (ER) often forms stacked membrane sheets, an arrangement that is likely required to accommodate a maximum of membrane-bound polysomes for secretory protein synthesis. How sheets are stacked is unknown. Here, we used improved staining and automated ultrathin sectioning electron microscopy methods to analyze stacked ER sheets in neuronal cells and secretory salivary gland cells of mice. Our results show that stacked ER sheets form a continuous membrane system in which the sheets are connected by twisted membrane surfaces with helical edges of left- or right-handedness. The three-dimensional structure of tightly stacked ER sheets resembles a parking garage, in which the different levels are connected by helicoidal ramps. A theoretical model explains the experimental observations and indicates that the structure corresponds to a minimum of elastic energy of sheet edges and surfaces. The structure allows the dense packing of ER sheets in the restricted space of a cell.

Pervasive Synaptic Branch Removal in the Mammalian Neuromuscular System at Birth

VIDEO ABSTRACT Using light and serial electron microscopy, we show profound refinements in motor axonal branching and synaptic connectivity before and after birth. Embryonic axons become maximally connected just before birth when they innervate ∼10-fold more muscle fibers than in maturity. In some developing muscles, axons innervate almost every muscle fiber. At birth, each neuromuscular junction is coinnervated by approximately ten highly intermingled axons (versus one in adults). Extensive die off of terminal branches occurs during the first several postnatal days, leading to much sparser arbors that still span the same territory. Despite the extensive pruning, total axoplasm per neuron increases as axons elongate, thicken, and add more synaptic release sites on their remaining targets. Motor axons therefore initially establish weak connections with nearly all available postsynaptic targets but, beginning at birth, massively redistribute synaptic resources, concentrating many more synaptic sites on many fewer muscle fibers. Analogous changes in connectivity may occur in the CNS.

Adaptation to objects in the lateral occipital complex (LOC): Shape or semantics?

A change in the basic-level class when viewing a sequence of two objects produces a large release from adaptation in LOC compared to when the images are identical. Is this due to a change in semantics or shape? In an fMRI-adaptation experiment, subjects viewed a sequence of two objects and judged whether the stimuli were identical in shape. Different-shaped stimuli could be from the same or different basic-level classes, where the physical similarities of the pairs in the two conditions were equated by a model of simple cell similarity. BOLD responses in LOC for the two conditions were equivalent, and higher than that of the identical condition, indicating that LOC is sensitive to shape rather than to basic-level semantics.

Development of High-Throughput, High-Resolution 3D Reconstruction of Large-Volume Biological Tissue Using Automated Tape Collection Ultramicrotomy and Scanning Electron Microscopy

A full understanding of brain function requires extensive knowledge of the intricate patterns of axons and dendrites that connect neurons at synapses. Such wiring diagrams (“connectomes”) are in short supply owing to the enormous number of synaptic connectivities that need to be catalogued and the very high resolution necessary to trace them [1]. The key therefore is to have an approach that both allows large volumes (cubic millimeters or larger) to be analyzed but at a level of resolution of several nanometers. One approach to this problem is to slice the brain into many thousands of very thin sections and then reconstruct the connectome by tracing nerve cell processes (axons and dendrites) from one section to the next. Obviously the sheer number of sections, digital images and the millions or more processes that need to be traced requires an automated approach. We have approached this problem by automating a number of steps with the ultimate aim of having a fully automated pipeline from tissue sample to wiring diagram.

Evolvable Hardware for Space Applications

This paper focuses on characteristics and applications of evolvable hardware (EHW) to space systems. The motivation for looking at EHW originates in the need for more autonomous adaptive space systems. The idea of evolvable hardware becomes attractive for long missions when the hardware looses optimality, and uploading new software only partly alleviates the problem if the computing hardware becomes obsolete or the sensing hardware faces needs outside original design specifications. The paper reports the first intrinsic evolution on an analog ASIC (a custom analog neural chip), suggests evolution of dynamical systems in state-space representations, and demonstrates evolution of compression algorithms with results better than the best-known compression algorithms.