Progress and perspectives in computational neuroanatomy.
The tremendous increase in processing power of personal computers has recently allowed the construction of highly sophisticated models of neuronal function and behavior. Anatomy plays a fundamental role in supporting and shaping nervous system activity, yet to date most details of such a role have escaped the efforts of experimental and theoretical neuroscientists, mainly because of the problem's complexity. When accurate cellular morphologies are included in electrophysiological computer simulations, quantitative and qualitative effects of dendritic structure on firing properties can be extensively characterized. Complete models of dendritic morphology can be implemented to allow the computer generation of virtual neurons that model the anatomical characteristics of their real counterparts to a great degree of approximation. From a restricted and already available experimental database, stochastic and statistical algorithms can create an unlimited number of non-identical virtual neurons within several mammalian morphological classes, storing them in a compact and parsimonious format. When modeled neurons are distributed in three-dimensional and biologically plausible rules governing axonal navigation and connectivity are added to the simulations, entire portions of the nervous system can be "grown" as anatomically realistic neural networks. These computational constructs are useful to determine the influence of local geometry on system neuroanatomy, and to investigate systematically the mutual interactions between anatomical parameters and electrophysiological activity at the network level. A detailed computer model of a "virtual brain" that was truly equivalent to the biological structure could in principle allow scientists to carry out experiments that could not be performed on real nervous systems because of physical constraints. The computational approach to neuroanatomy is just at its beginning, but has a great potential to enhance the intuition of investigators and to aid neuroscience education. Anat Rec (New Anat): 257:195-207, 1999. (+info)
Professor Edward George Gray, FRS (1924-1999).
Professor George Gray, who died in August 1999, had a notable career as a pioneer electron microscopist of neural tissues. His name is still attached to synapses, which can be classified as Gray type 1 (symmetric) or type 2 (asymmetric), and in addition he made a number of other profound contributions to our knowledge of synaptic structures.He started his academic career late, having worked before the second World War as a bank clerk, and then serving in the Navy, patrolling for U-boats in the North Sea and Atlantic for 4 years during the latter part of the war. He had an early interest in zoology, particularly in marine biology and microscopy and when he left the Navy he took the opportunity to work for a degree in Zoology at the University of Wales in Aberystwyth. A first class honours degree was followed by a PhD on melanophores in teleosts. It was fortunate that the external examiner for the thesis was J. Z. Young, who was impressed by the work and by George, and who invited George to work as his assistant in the preparation of The Life of the Mammals in the Anatomy Department at University College London. (+info)
A four-dimensional probabilistic atlas of the human brain.
The authors describe the development of a four-dimensional atlas and reference system that includes both macroscopic and microscopic information on structure and function of the human brain in persons between the ages of 18 and 90 years. Given the presumed large but previously unquantified degree of structural and functional variance among normal persons in the human population, the basis for this atlas and reference system is probabilistic. Through the efforts of the International Consortium for Brain Mapping (ICBM), 7,000 subjects will be included in the initial phase of database and atlas development. For each subject, detailed demographic, clinical, behavioral, and imaging information is being collected. In addition, 5,800 subjects will contribute DNA for the purpose of determining genotype- phenotype-behavioral correlations. The process of developing the strategies, algorithms, data collection methods, validation approaches, database structures, and distribution of results is described in this report. Examples of applications of the approach are described for the normal brain in both adults and children as well as in patients with schizophrenia. This project should provide new insights into the relationship between microscopic and macroscopic structure and function in the human brain and should have important implications in basic neuroscience, clinical diagnostics, and cerebral disorders. (+info)
BrainImageJ: a Java-based framework for interoperability in neuroscience, with specific application to neuroimaging.
The Human Brain Project consortium continues to struggle with effective sharing of tools. To facilitate reuse of its tools, the Stanford Psychiatry Neuroimaging Laboratory (SPNL) has developed BrainImageJ, a new software framework in Java. The framework consists of two components-a set of four programming interfaces and an application front end. The four interfaces define extension pathways for new data models, file loaders and savers, algorithms, and visualization tools. Any Java class that implements one of these interfaces qualifies as a BrainImageJ plug-in-a self-contained tool. After automatically detecting and incorporating new plug-ins, the application front end transparently generates graphical user interfaces that provide access to plug-in functionality. New plug-ins interoperate with existing ones immediately through the front end. BrainImageJ is used at the Stanford Psychiatry Neuroimaging Laboratory to develop image-analysis algorithms and three-dimensional visualization tools. It is the goal of our development group that, once the framework is placed in the public domain, it will serve as an interlaboratory platform for designing, distributing, and using interoperable tools. (+info)
An integrated software suite for surface-based analyses of cerebral cortex.
The authors describe and illustrate an integrated trio of software programs for carrying out surface-based analyses of cerebral cortex. The first component of this trio, SureFit (Surface Reconstruction by Filtering and Intensity Transformations), is used primarily for cortical segmentation, volume visualization, surface generation, and the mapping of functional neuroimaging data onto surfaces. The second component, Caret (Computerized Anatomical Reconstruction and Editing Tool Kit), provides a wide range of surface visualization and analysis options as well as capabilities for surface flattening, surface-based deformation, and other surface manipulations. The third component, SuMS (Surface Management System), is a database and associated user interface for surface-related data. It provides for efficient insertion, searching, and extraction of surface and volume data from the database. (+info)
Generation, description and storage of dendritic morphology data.
It is generally assumed that the variability of neuronal morphology has an important effect on both the connectivity and the activity of the nervous system, but this effect has not been thoroughly investigated. Neuroanatomical archives represent a crucial tool to explore structure-function relationships in the brain. We are developing computational tools to describe, generate, store and render large sets of three-dimensional neuronal structures in a format that is compact, quantitative, accurate and readily accessible to the neuroscientist. Single-cell neuroanatomy can be characterized quantitatively at several levels. In computer-aided neuronal tracing files, a dendritic tree is described as a series of cylinders, each represented by diameter, spatial coordinates and the connectivity to other cylinders in the tree. This 'Cartesian' description constitutes a completely accurate mapping of dendritic morphology but it bears little intuitive information for the neuroscientist. In contrast, a classical neuroanatomical analysis characterizes neuronal dendrites on the basis of the statistical distributions of morphological parameters, e.g. maximum branching order or bifurcation asymmetry. This description is intuitively more accessible, but it only yields information on the collective anatomy of a group of dendrites, i.e. it is not complete enough to provide a precise 'blueprint' of the original data. We are adopting a third, intermediate level of description, which consists of the algorithmic generation of neuronal structures within a certain morphological class based on a set of 'fundamental', measured parameters. This description is as intuitive as a classical neuroanatomical analysis (parameters have an intuitive interpretation), and as complete as a Cartesian file (the algorithms generate and display complete neurons). The advantages of the algorithmic description of neuronal structure are immense. If an algorithm can measure the values of a handful of parameters from an experimental database and generate virtual neurons whose anatomy is statistically indistinguishable from that of their real counterparts, a great deal of data compression and amplification can be achieved. Data compression results from the quantitative and complete description of thousands of neurons with a handful of statistical distributions of parameters. Data amplification is possible because, from a set of experimental neurons, many more virtual analogues can be generated. This approach could allow one, in principle, to create and store a neuroanatomical database containing data for an entire human brain in a personal computer. We are using two programs, L-NEURON and ARBORVITAE, to investigate systematically the potential of several different algorithms for the generation of virtual neurons. Using these programs, we have generated anatomically plausible virtual neurons for several morphological classes, including guinea pig cerebellar Purkinje cells and cat spinal cord motor neurons. These virtual neurons are stored in an online electronic archive of dendritic morphology. This process highlights the potential and the limitations of the 'computational neuroanatomy' strategy for neuroscience databases. (+info)
A graphical anatomical database of neural connectivity.
We describe a graphical anatomical database program, called XANAT (so named because it was developed under the X window system in UNIX), that allows the results of numerous studies on neuroanatomical connections to be stored, compared and analysed in a standardized format. Data are entered into the database by drawing injection and label sites from a particular tracer study directly onto canonical representations of the neuroanatomical structures of interest, along with providing descriptive text information. Searches may then be performed on the data by querying the database graphically, for example by specifying a region of interest within the brain for which connectivity information is desired, or via text information, such as keywords describing a particular brain region, or an author name or reference. Analyses may also be performed by accumulating data across multiple studies and displaying a colour-coded map that graphically represents the total evidence for connectivity between regions. Thus, data may be studied and compared free of areal boundaries (which often vary from one laboratory to the next), and instead with respect to standard landmarks, such as the position relative to well-known neuro-anatomical substrates or stereotaxic coordinates. If desired, areal boundaries may also be defined by the user to facilitate the interpretation of results. We demonstrate the application of the database to the analysis of pulvinar-cortical connections in the macaque monkey, for which the results of over 120 neuro-anatomical experiments were entered into the database. We show how these techniques can be used to elucidate connectivity trends and patterns that may otherwise go unnoticed. (+info)
Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac).
The need to integrate massively increasing amounts of data on the mammalian brain has driven several ambitious neuroscientific database projects that were started during the last decade. Databasing the brain's anatomical connectivity as delivered by tracing studies is of particular importance as these data characterize fundamental structural constraints of the complex and poorly understood functional interactions between the components of real neural systems. Previous connectivity databases have been crucial for analysing anatomical brain circuitry in various species and have opened exciting new ways to interpret functional data, both from electrophysiological and from functional imaging studies. The eventual impact and success of connectivity databases, however, will require the resolution of several methodological problems that currently limit their use. These problems comprise four main points: (i) objective representation of coordinate-free, parcellation-based data, (ii) assessment of the reliability and precision of individual data, especially in the presence of contradictory reports, (iii) data mining and integration of large sets of partially redundant and contradictory data, and (iv) automatic and reproducible transformation of data between incongruent brain maps. Here, we present the specific implementation of the 'collation of connectivity data on the macaque brain' (CoCoMac) database (http://www.cocomac.org). The design of this database addresses the methodological challenges listed above, and focuses on experimental and computational neuroscientists' needs to flexibly analyse and process the large amount of published experimental data from tracing studies. In this article, we explain step-by-step the conceptual rationale and methodology of CoCoMac and demonstrate its practical use by an analysis of connectivity in the prefrontal cortex. (+info)