Server-based approach to web visualization of integrated three-dimensional brain imaging data.

The authors describe a client-server approach to three-dimensional (3-D) visualization of neuroimaging data, which enables researchers to visualize, manipulate, and analyze large brain imaging datasets over the Internet. All computationally intensive tasks are done by a graphics server that loads and processes image volumes and 3-D models, renders 3-D scenes, and sends the renderings back to the client. The authors discuss the system architecture and implementation and give several examples of client applications that allow visualization and analysis of integrated language map data from single and multiple patients.

Particle chaos in the Earth's magnetotail.

Nonlinear particle dynamics is studied both in current sheets and near neutral lines. The parameter governing particle chaos in a current sheet with a constant normal component, B(n), is kappa=(R(min)/rho(max))(1/2), where R(min) is the minimum field line radius of curvature and rho(max) is the maximum gyroradius. In such a current sheet, motion can be viewed as a combination of a component normal to the current sheet and a tangential component. The parameter kappa represents the ratio of the characteristic time scale of the normal component to the tangential, and thus, particle chaos is maximized for kappa approximately 1. For kappa<<1, the slow motion preserves the action integral of the fast motion, J(z), except near the separatrix, the phase space boundary separating motion that crosses the current sheet midplane from that which does not. Near a linear neutral line, it is found that the parameter b(n), which is the ratio of the characteristic vertical and horizontal field strengths, rather than kappa governs particle chaos. In the limit b(n)<<1, the slow motion again preserves J(z), and J(z) has the same analytic form as in a constant B(n) current sheet. In the limit of b(n)<<1, the structure of x-p(x) phase space is controlled by the stable and unstable manifolds associated with the unstable fixed point orbit at (x,p(x))=(0,0), and this structure lies along a contour of constant J(z).

Problems and solutions with integrating terminologies into evolving knowledge bases.

We have merged two established anatomical terminologies with an evolving ontology of biological structure: the Foundational Model of Anatomy. We describe the problems we have encountered and the solutions we have developed. We believe that both the problems and solutions generalize to the integration of any legacy terminology with a disciplined ontology within the same domain.

Analysis of naming errors during cortical stimulation mapping: implications for models of language representation.

This study reports on the characteristics and distribution of naming errors of patients undergoing cortical stimulation mapping (CSM). During the procedure, electrical stimulation is used to induce temporary functional lesions and locate 'essential' language areas for preservation. Under stimulation, patients are shown slides of common objects and asked to name them. Cortical stimulation can lead to a variety of naming errors. In the present study, we aggregate errors across patients to examine the neuroanatomical correlates and linguistic characteristics of six common errors: semantic paraphasias, circumlocutions, phonological paraphasias, neologisms, performance errors, and no-response errors. Aiding analysis, we relied on a suite of web-based querying and imaging tools that enabled the summative mapping of normalized stimulation sites. Errors were visualized and analyzed by type and location. We provide descriptive statistics to characterize the commonality of errors across patients and location. The errors observed suggest a widely distributed and heterogeneous cortical network that gives rise to differential patterning of paraphasic errors. Data are discussed in relation to emerging models of language representation that honor distinctions between frontal, parietal, and posterior temporal dorsal implementation systems and ventral-temporal lexical semantic and phonological storage and assembly regions; the latter of which may participate both in language comprehension and production.

Challenges in reconciling different views of neuroanatomy in a reference ontology of anatomy.

A fundamental requirement for integrating neuroscience data is a well-structured ontology that can incorporate, accommodate and reconcile different neuroanatomical views. Here we describe the challenges in creating such ontology, and, because of its principled design, illustrate the potential of the Foundational Model of Anatomy to be that ontology.

Shape-based cortical surface segmentation for visualization brain mapping.

We describe a knowledge-based approach to cortical surface segmentation that uses learned knowledge of the overall shape and range of variation of the cortex (excluding the detailed gyri and sulci) to guide the search for the grey-CSF boundary in a structural MRI image volume. The shape knowledge is represented by a radial surface model, which is a type of geometric constraint network (GCN) that we hypothesize can represent shape by networks of locally interacting constraints. The shape model is used in a protocol for visualization-based mapping of cortical stimulation mapping (CSM) sites onto the brain surface, prior to integration with other mapping modalities or as input to existing surface analysis and reconfiguration programs. Example results are presented for CSM data related to language organization in the cortex, but the methods should be applicable to other situations where a realistic visualization of the brain surface, as seen at neurosurgery, is desired.

The evolving neuroanatomical component of the Foundational Model of Anatomy.

In order to meet the need for an expressive ontology in neuroinformatics, we have integrated the extensive terminologies of NeuroNames and Terminologia Anatomica into the Foundational Model of Anatomy (FMA). We have enhanced the FMA to accommodate information unique to neuronal structures, such as axonal input/output relationships.