Bruce H. McCormick

Visualization: expanding scientific and engineering research opportunities

The use of scientific visualization to represent the solutions obtained in computational science and engineering is discussed. The short- and long-term needs of those who use visualization tools and those who create them are addressed. For the user a three-tiered model environment is beginning to emerge that categorizes visualization systems by such factors as power, cost, and software support. Workstations with access to supercomputers are also required. The need to educate the scientific and engineering research communities about the available equipment is also discussed, and the available software and hardware are described.<<ETX>>

Knife-edge scanning microscopy for imaging and reconstruction of three-dimensional anatomical structures of the mouse brain

Anatomical information at the cellular level is important in many fields, including organ systems development, computational biology and informatics. Creating data sets at resolutions that provide enough detail to reconstruct cellular structures across tissue volumes from 1 to 100 mm3 has proven to be difficult and time‐consuming. In this paper, we describe a new method for staining and imaging large volumes of tissue at sub‐micron resolutions. Serial sections are cut using an automated ultra‐microtome, whereas concurrently each section is imaged through a light microscope with a high‐speed line‐scan camera. This technique, knife‐edge scanning microscopy, allows us to view and record large volumes of tissue in a relatively small amount of time (approximately 7 mm2 s−1).

L-system modeling of neurons

A formal representation of neuron morphology, adequate for the geometric modeling of manually-traced neurons, is presented. The concept of a stochastic L-system is then introduced and the critical distribution functions governing the stochastic generation of dendritic and axonal trees are defined. Experiments with various stochastic L-system models for pyramidal, motoneuron, and Purkinje cells are reported which generate synthetic neurons with promising proximity to neurons in the neurobiology literature. Work is in progress to improve this degree of proximity, but more importantly to validate the derived stochastic models against available databases of manually-traced neurons. To this end a neuron morphology modeler is described which provides a methodology for iterative refinement of the stochastic L-system model.

Preattentive considerations for gaze-contingent image processing

Gaze-contingent interfaces must provide adequate peripheral information to the viewer to preserve perceptual fidelity. specifically, it has been shown that preattentive (or preview) benefit must be preserved so that scene perception is not disrupted. In this paper we review recent attempts at peripheral degradation of digital imagery. We suggest that to be successful such degradation must preserve potential future visual attractors and, furthermore, not introduce artificial ones. We present a simple multiresolution image processing approach that can be utilized for this purpose. The feasibility of gaze-contingent processing has recently been questioned. In their paper, Stelmach and Tam processed images by low-pass filtering, effectively smoothing extrafoveal regions. The authors then quantized DCT coefficients in the periphery, introducing blocking artifacts. In our paper, we simulate these results and claim that neither of these methods is suitable for GC interfaces. Alternatively, we implement a simple multiple region of interest (ROI) multiresolution scheme in an attempt to degrade the periphery while preserving attentional cues. We evaluate three variants of this approach: a linear degradation function, a nonlinear function, and a function matching human visual system (HVS) acuity. The HVS-matching multiple-ROI algorithm gives good compression and alleviates the degradation of potential visual attractors. Furthermore, MIP mapping offers efficient implementation of the algorithm, making it a good candidate for gaze-contingent applications.

Exploring the brain forest

Abstract Exploring the Brain Forest, a virtual environment currently in design, presents hierarchical views of the brain at several levels of scale from a global overview to immersion within its forest of neurons and glial cells. The virtual environment provides a 3D graphical model of brain data sets drawn from microscopy of human brain tissue mapped at the limit of optical resolution. The virtual environment is framed in a finite element model of the cerebral cortex. This solid model is implanted with a database of neurons, either traced biological neurons or synthetically generated neurons.

NOISE AND ARTIFACT REMOVAL IN KNIFE-EDGE SCANNING MICROSCOPY

Knife-edge scanning microscopy (KESM) is a recently developed technique that allows fast and automated imaging of several hundred cubic millimeters of tissue at sub-micron resolution. Successive sections are captured in registration by imaging the specimen concurrently with cutting by a diamond-knife ultramicrotome. Because this imaging technique is relatively new, we are currently investigating ways to improve image quality and data rate. In addition, certain imaging artifacts are unique to this technology and the time required to perform corrective image processing is a concern due to the high rate of image capture. In this paper, we describe algorithms that can be used to process KESM images in order to obtain the quality necessary for subsequent segmentation and modeling. There is also emphasis on making these algorithms independent of global information within the image so that they can be more easily parallelized.

Distributed, web-based microstructure database for brain tissue

Abstract A finite element model of the cerebral cortex enables a structured visualization of its gross anatomy and provides access to the neuronal databases associated with each finite element of tissue. Partitioned by finite elements, the distributed, web-based microstructure database serves as a tool for organizing neurons and neuronal forests, and for modeling local cortical microstructure by wiring up the forests. Embedding the database in XML adds structure and web accessibility to the inherent information. When integrated with the brain tissue scanner, the distributed, web-based microstructure database serves as a comprehensive infrastructure for organizing brain tissue at three different hierarchical levels: volume, neuronal morphology, and network.

Brain microstructure database system: an exoskeleton to 3D reconstruction and modeling

Abstract To explore and understand the topology and geometry of brain architecture at a neuronal level of detail requires mapping brain microstructure by 3D reconstruction of mammalian brain. The Brain Tissue Scanner (BTS) allows the reconstruction of mammalian brain architectures by scanning and generating 1 Tbyte of volumetric brain data per day. We are building a large-scale, distributed database system to store and process this massive data. The database system provides an exoskeleton to the 3D reconstruction and modeling software, and provides a seamless integration with HTML/XML for user interface and with XML for data exchange. The database exoskeleton can be isolated from inevitable changes in the reconstruction and modeling software.

Finite element decomposition of human neocortex

Modeling brain morphology at both cellular and tissue levels brings richness to our understanding of brain organization that both complements and transcends knowledge derived exclusively from neuron tracing and brain atlases.

Construction of anatomically correct models of mouse brain networks

The Mouse Brain Web (MBW), a web-organized database, provides for the construction of anatomically correct models of mouse brain networks. Each web page in this database provides the position, orientation, morphology, and putative synapses for each biologically observed neuron. The MBW has been designed to support (1) mapping of the spatial distribution and morphology of neurons by type; (2) wiring of the network–synaptic assembly; (3) projection of neuron morphology and synapses to geometric multi-compartmental models; (4) search for motifs and basic circuits in the brain networks using customized web-crawlers; and (5) the mapping of anatomically correct networks to physiologically correct network simulations. c