- Email: [email protected]
Modeling neuron spatial distribution and morphology in the developing human cerebral cortex
Neurocomputing 32}33 (2000) 897}904
Modeling neuron spatial distribution and morphology in the developing human cerebral cortex B.H. McCormick *, R.W. DeVaul , W.R. Shankle, J.H. Fallon Scientixc Visualization Laboratory, Department of Computer Science, Texas A&M University, College Station, TX 77843-3112, USA Department of Cognitive Sciences, University of California, Irvine, Irvine CA 92697, USA Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine CA 92697, USA Accepted 13 January 2000
Abstract Forests of synthetic neurons can be generated in graphical form that are both visually and statistically indistinguishable from equivalent populations of biological neurons selected by cytoarchitectural area, layer, age, and neurological diagnosis from the developing human cerebral cortex. Representative populations of several neuron types (pyramidal, spiny stellate, basket cells, chandelier cells, etc.) have been modeled. The long-range goal of these studies, anatomical realism, transcends the traditional statistical view that models the neuron database exclusively as a set of parameters and their variances. Anatomical realism requires that su$cient parameters be captured that a credible forest of neurons can be grown synthetically within the computer and displayed graphically. 2000 Published by Elsevier Science B.V. All rights reserved. Keywords: Cortex; Neuroanatomy; Neuron morphology; Stochastic generation of neurons/ forests; Finite element modeling
1. Introduction Our objective is to stochastically generate neuron populations which are visually and statistically indistinguishable in morphology from speci"ed neuron populations
* Corresponding author. Tel.: #1-979-845-8870; fax: #1-979-847-8578. E-mail address: [email protected] (B.H. McCormick). 0925-2312/00/$ - see front matter 2000 Published by Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 2 3 1 2 ( 0 0 ) 0 0 2 5 8 - 7
898
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
in the neuron/forest database. The purpose of such analysis is several-fold: E Quantitative neuroanatomical modeling of the human cerebral cortex (and other species) down to the synaptic level. Although synaptic level detail is computationally intractable, it can be statistically modeled from knowledge of neuron spatial distribution and morphology measured at the limit of optical resolution [9]. E Phylogenetic and ontogenetic understanding of brain evolution and development. Only when systemic quantitative microscopic brain data have been gathered, and their morphology modeled, are we assured that our understanding of these processes is internally consistent and reasonably complete. E Understanding variants of normal brain architecture, from genius to neurological disease. Building species-wide data on variants is a critical prerequisite to advancement of knowledge in these "elds. E Characterization of connectivity of brains of any species. Today there are no quantitative microscopic data characterizing the connectivity of the human cerebral cortex (or most other species). E Realistic computational models of the brain of any species. At present, most computational models ignore neuroanatomical realism because of the lack of data.
2. Stochastic models of dendritic morphology Stochastic models of dendritic morphology have evolved over the past several years [4,8,10}12,17]. These models are increasingly developmental (ontogenetically) in nature and driven by our knowledge of growth cone dynamics [5,17]. These models also give a stochastic description of axonal arborization. The use of stochastic L-systems (Lindenmayer systems) [7] is mandated by the non-deterministic nature of neuron morphology. No two neurons, even of the same type, are identical; yet they are su$ciently similar to be classi"ed as being members of the same type. This property points to the existence of a common family of rules, or grammar, where the rules have stochastic actions. These rules presumably mirror the genotype of the neuron. The N# # language [4,8], an open stochastic L-system, provides a representational framework for the description of neuron development and morphology. The N# # language was designed with three key features in mind: E Accurate structural representation. The representation's syntax should re#ect neuron morphology. E Accurate growth modeling. The system must accurately model growing neurons within the con"nes of neural tissue. E Statistical modeling. The syntax should facilitate statistical methods used in stochastic modeling. The N# # language produces well-formed sentences, each of which may be geometrically interpreted as a neuron. In the N# # language, cell typology de"nes the grammar. The normal pattern of growth is de"ned by the conditional probabilities
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
899
governing rule selection in the N# # language. The normal morphology of a cell type is de"ned by the turtle geometry interpretation of N# # language productions, in combination with the N# # language grammar and the stochastic generation process. Statistical di!erentiation between two cell populations is facilitated by the string representation of N# # neuron models. Context-sensitive features have extended the N# # language (e.g., an open Lsystem allows dependence on chemical concentrations in the environment). Additional context-sensitive features to accommodate segment retraction, di!erential growth rates in adjacent dendritic arbors, and the synchronized growth of neuronal forests are being examined.
3. Computational framework for modeling neuron morphology A computational framework for modeling neuron morphology is presented in [2,4,8,10] that is adequate both for the quantitative description and stochastic generation of neuron populations. At the data level, an object-oriented description of cells (their dendritic and axonal arbors, soma, and spines) forms the basis for a neuron morphology database. A neuron visualizer allows a 3D interactive display of neurons in the database, either singly or in juxtaposition. At the knowledge level, the components for neuron morphology modeling are described. These components summarize neuron population data into normative stochastic L-system models of neuron morphology. The stochastic L-system models for pyramidal, stellate, and motor neurons have produced synthetic neurons with promising proximity to neurons described in the neurobiology literature. A neuron visualizer [2,6,10] is provided that reads the object model format of a neuron's morphology and displays the neuron in 3D. Input "les recreate a neuron's morphology and display it in 3D using one of three display types. The stick type displays all segments as lines of integral thickness and gives nearly instantaneous response when navigating or editing features. The cylindrical type draws segments as colored, lighted cylinders with diameters and tapers proportional to the measured segment diameters, and provides more realistic views of neuron morphology. A third display type, the hybrid, uses cylindrical representation for thicker segments, and wire frame representation for more terminal segments, to minimize display times. The user interactively controls the camera's view of the scene, including xeld of view, eye position, and gaze direction.
4. Framing microstructure data into 5nite element models of the cortex Finite elements provide a frame for visualizing and modeling neuronal forests [1,2]. Each reconstructed neuron in the neuron morphology database is assigned to the "nite element containing its soma. The cerebral cortex, so modeled (Fig. 1), can be viewed as a giant `chest of drawersa where a `drawera (any selected "nite element or
900
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
Fig. 1. Finite element model for part of the neocortical shell [2].
Fig. 2. Finite element populated with synthetic neurons [2].
cluster of neighboring "nite elements) can be `openeda as a "le and its population of neurons visualized, as illustrated in Fig. 2. These "nite elements therefore de"ne a "le structure isomorphic to the cerebral cortex as modeled and visualized at the cellular and tissue level. Approximately 2300 "nite elements of the scale and complexity shown in Fig. 1 (typically 4 mm;4 mm;3 mm) are needed to model a hemisphere of the human cerebral cortex.
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
901
Neuron morphology is highly dependent upon the shape and size of the local environment, that is, the "nite elements within which the neurons develop. Moreover, chemical concentration gradient "elds (e.g., Netrin) are needed to shape the growing pyramidal cells such that their terminal apical "bers fold over gracefully and run parallel to the cortical surface [10]. The "nite elements of the immediate neuronal neighborhood are required to grow simulated neuron populations that (ideally) are indistinguishable from those seen microscopically.
5. Web-based neuron/forest database To support these studies we have speci"ed a distributed brain tissue database [6], which stores the position and morphology of all reconstructed soma, dendritic arbors, and "bers of brain tissue generated in the course of neuroanatomical studies and which provides a graphical interface to this virtual environment [2]. The prototype database system is designed to run on an Intranet within the Department of Computer Science, Texas A&M University. When integrated with the brain tissue scanner, the distributed, web-based brain tissue database serves as a comprehensive infrastructure for organizing brain tissue at three di!erent hierarchical levels: volume data (i.e., the image stack derived from scanning consecutive physical or optical sections from the tissue block), neuron/forest data, and network data.
6. Neuroanatomical sources of data The above neuroanatomical modeling and visualization tools are designed to support a broadly based examination of the neuroanatomical development of the human brain. The research described here has natural application to infants, children and adolescents with both normal and abnormal brain development. With our recent discovery that the human brain doubles its number of neurons in the cerebral cortex after birth [14,15], there is an emerging opportunity to understand in much greater detail how the human and other mammalian cerebral cortices structure themselves. These advances will be greatly facilitated by a better understanding of the microstructure of the developing human brain and how it a!ects the information processing that ultimately determines human behavior. Several sources of data are described next. 6.1. The UCI brain tissue repository The material for our neuroanatomical studies will be drawn from the UCI Brain Tissue Repository, which consists of good-quality formalin-"xed material from 4000 autopsies performed at Children's Hospital Los Angeles over the last 25 years. The UCI Brain Tissue Repository covers more than 50 pediatric neurological disorders plus normal development from the 3rd trimester of gestation to 21 years old.
902
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
6.2. The CYBERCHILD database We have developed a large, quantitative database on the microscopic features of &73% of the laminar development of the postnatal human cerebral cortex from 0 to 72 months [13]. These data, based on six million individual measurements of Conel [3], comprise the most complete quantitative microstructure survey of the developing human postnatal cerebral cortex, and will serve as an initial basis of comparison for data derived from the described methods. The "ndings from these data [13}16] support the need for methods to model the spatial distribution and morphology of neurons in the developing human cerebral cortex. CYBERCHILD will be interfaced with GENESIS to facilitate its use in computational modeling.
Acknowledgements This work was supported in part by Texas Advanced Technology Program grant 999903-124 (McCormick) from the Texas Higher Education Coordinating Board.
References [1] D.A. Batte, T.S. Chow, B.H. McCormick, Finite element decomposition of human neocortex, in: J.M. Bower (Ed.), Computational Neuroscience: Trends in Research, Plenum Press, New York, 1998, pp. 573}578. [2] B.P. Burton, T.S. Chow, A.T. Duchowski, W. Koh, B.H. McCormick, Exploring the brain forest, Neurocomputing 26}27 (2000) 971}980. [3] J. Conel, Postnatal Development of the Human Cerebral Cortex, 8 Vols., Harvard University Press, Cambridge, MA, 1939}1967. [4] R.W. DeVaul, B.H. McCormick, Neuron developmental modeling and structural representation I: introduction to the N# # language, Technical Report, Scienti"c Visualization Laboratory, Department of Computer Science, Texas A&M University, College Station, TX, December 1996. [5] S.B. Kater, R.W. Davenport, P.B. Githrie, Filopodia as detectors of environmental cues: signal integration through changes in growth cone calcium levels,, in: J. van Pelt, M.A. Comer, H.B.M. Uylinger, F.H. Lopes da Siva (Eds.), The Self-Organizing Brain: From Growth Cones to Functional Networks, Elsevier, Amsterdam, 1994, pp. 49}60. [6] W. Koh, B.H. McCormick, Distributed, web-based microstructure database for brain tissue, Neurocomputing (2000), this issue. [7] A. Lindenmayer, Mathematical models for cellular interaction in development, Parts I and II, J. Theoret. Biol. 18 (1968) 280}315. [8] B.H. McCormick, R.W. DeVaul, Neuron developmental modeling and structural representation II: the stochastic model, Technical Report, Scienti"c Visualization Laboratory, Department of Computer Science, Texas A&M University, College Station, TX, 1996. [9] B.H. McCormick, W. Koh, W.R. Shankle, J.H. Fallon, Geometric modeling of local cortical circuits, Neurocomputing (2000), this issue. [10] B.H. McCormick, K. Mulchandani, L-system modeling of neurons, in: Proceedings of Visualization in Biomedical Computing, SPIE, Vol. 2359, 1994, pp. 693}705. [11] J. van Pelt, A.E. Dityatev, H.B.M. Uylings, Natural variability in the number of dendritic segments: model-based inferences about branching during neurite outgrowth, J. Comput. Neurol. 387 (1997) 325}340.
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
903
[12] J. van Pelt, H.B.M. Uylings, Natural variability in the geometry of dendritic branching patterns, in: Modeling in the Neurosciences, From Ionic Channels to Neural Networks, P. Poznanski (ed.), Harwood Academic Publisher, Newark, NJ, 1998 (Chapter 4). [13] W.R. Shankle, B.H. Landing, M. Ra"i, J. Hara, J.H. Fallon, A.K. Romney, J.P. Boyd, CYBERCHILD: a database of the microscopic development of the postnatal human cerebral cortex from birth to 72 months, Neurocomputing (2000), this issue. [14] W.R. Shankle, B.H. Landing, M.S. Ra"i, A.V.R. Schiano, J.M. Chen, J. Hara, Numbers of neurons per column in the developing human cerebral cortex from birth to 72 months: evidence for an apparent post-natal increase in neuron numbers, J. Theor. Biol. 191 (1998) 115}140. [15] W.R. Shankle, M.S. Ra"i, B.H. Landing, J.H. Fallon, Approximate doubling of the numbers of neurons in the postnatal human cerebral cortex and in 35 speci"c cytoarchitectural areas from birth to 72 months, Pediatr. Dev. Pathol. 2 (1999) 244}259. [16] W.R. Shankle, A. Romney, B. Landing, J. Hara, Developmental patterns in the cytoarchitecture of the human cerebral cortex from birth to 6 years examined by correspondence analysis, Proc. Natl. Acad. Sci. USA 95 (1998) 4023}4028. [17] M.P. van Veen, J. van Pelt, Dynamic mechanisms of neuronal outgrowth, in: J. van Pelt, M.A. Cotner, H.B.M. Uylinger, F.H. Lopes da Siva (Eds.), The Self-Organizing Brain From Growth Cones to Functional Networks, Elsevier, Amsterdam, 1994, pp. 95}108.
Bruce H. McCormick is Professor of Computer Science and the Director of the Scienti"c Visualization Laboratory at Texas A&M University. His research interests include scienti"c visualization, brain mapping, computational neuroscience, and neural networks. He received his BS and Ph.D. degrees in Physics from MIT and Harvard University, respectively. He was Professor of Computer Science and Physics at the University of Illinois at Urbana-Champaign. At the University of Illinois at Chicago he served as head of the Department of Information Engineering, and at Texas A&M University, as the "rst head of the Department of Computer Science.
Richard W. DeVaul is pursuing his doctorate at the Media Lab, MIT, Cambridge, MA. He has an MS from the Visualization Program, Department of Architecture, Texas A&M University. The work described here was done as a graduate research assistant in the Scienti"c Visualization Laboratory, Department of Computer Science, Texas A&M University. His research interests include scienti"c visualization, physically based graphical modeling of clothing, and ubiquitous computing.
William R. Shankle is an Associate Professor of Cognitive Science at UC Irvine. He trained as a statistician and neurologist. His research focuses on data analysis of brain development, aging and degeneration. With B.H. Landing, he overturned the dogma of no postnatal neurogenesis in human cerebral cortex.
904
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904 James H. Fallon is Professor of Anatomy and Neurobiology in the College of Medicine at the University of California, Irvine. His areas of interest are in the study of the function of neurotrophic factors in neurodegenerative disorders, aging and development, and their use in neural stem cell therapies. He was the "rst to localize a characterized growth factor in the brain, and the "rst to localize EGF, TGF, and aFGF in the brain. He has a long-standing interest in neurochemical anatomy of monoamine, opioid, and other neurotransmitter systems in the mammalian brain. He has collaborated extensively as the systems neuroanatomist on numerous PET and MRI imaging studies in human. He is now collaborating with Junko Hara and Rod Shankle on analyses of developing human cortex, as well as novel analyses of art and the artist's brain, and with these scientists and Bruce McCormick on geometric modeling of human cortex.
Modeling neuron spatial distribution and morphology in the developing human cerebral cortex B.H. McCormick *, R.W. DeVaul , W.R. Shankle, J.H. Fallon Scientixc Visualization Laboratory, Department of Computer Science, Texas A&M University, College Station, TX 77843-3112, USA Department of Cognitive Sciences, University of California, Irvine, Irvine CA 92697, USA Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine CA 92697, USA Accepted 13 January 2000
Abstract Forests of synthetic neurons can be generated in graphical form that are both visually and statistically indistinguishable from equivalent populations of biological neurons selected by cytoarchitectural area, layer, age, and neurological diagnosis from the developing human cerebral cortex. Representative populations of several neuron types (pyramidal, spiny stellate, basket cells, chandelier cells, etc.) have been modeled. The long-range goal of these studies, anatomical realism, transcends the traditional statistical view that models the neuron database exclusively as a set of parameters and their variances. Anatomical realism requires that su$cient parameters be captured that a credible forest of neurons can be grown synthetically within the computer and displayed graphically. 2000 Published by Elsevier Science B.V. All rights reserved. Keywords: Cortex; Neuroanatomy; Neuron morphology; Stochastic generation of neurons/ forests; Finite element modeling
1. Introduction Our objective is to stochastically generate neuron populations which are visually and statistically indistinguishable in morphology from speci"ed neuron populations
* Corresponding author. Tel.: #1-979-845-8870; fax: #1-979-847-8578. E-mail address: [email protected] (B.H. McCormick). 0925-2312/00/$ - see front matter 2000 Published by Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 2 3 1 2 ( 0 0 ) 0 0 2 5 8 - 7
898
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
in the neuron/forest database. The purpose of such analysis is several-fold: E Quantitative neuroanatomical modeling of the human cerebral cortex (and other species) down to the synaptic level. Although synaptic level detail is computationally intractable, it can be statistically modeled from knowledge of neuron spatial distribution and morphology measured at the limit of optical resolution [9]. E Phylogenetic and ontogenetic understanding of brain evolution and development. Only when systemic quantitative microscopic brain data have been gathered, and their morphology modeled, are we assured that our understanding of these processes is internally consistent and reasonably complete. E Understanding variants of normal brain architecture, from genius to neurological disease. Building species-wide data on variants is a critical prerequisite to advancement of knowledge in these "elds. E Characterization of connectivity of brains of any species. Today there are no quantitative microscopic data characterizing the connectivity of the human cerebral cortex (or most other species). E Realistic computational models of the brain of any species. At present, most computational models ignore neuroanatomical realism because of the lack of data.
2. Stochastic models of dendritic morphology Stochastic models of dendritic morphology have evolved over the past several years [4,8,10}12,17]. These models are increasingly developmental (ontogenetically) in nature and driven by our knowledge of growth cone dynamics [5,17]. These models also give a stochastic description of axonal arborization. The use of stochastic L-systems (Lindenmayer systems) [7] is mandated by the non-deterministic nature of neuron morphology. No two neurons, even of the same type, are identical; yet they are su$ciently similar to be classi"ed as being members of the same type. This property points to the existence of a common family of rules, or grammar, where the rules have stochastic actions. These rules presumably mirror the genotype of the neuron. The N# # language [4,8], an open stochastic L-system, provides a representational framework for the description of neuron development and morphology. The N# # language was designed with three key features in mind: E Accurate structural representation. The representation's syntax should re#ect neuron morphology. E Accurate growth modeling. The system must accurately model growing neurons within the con"nes of neural tissue. E Statistical modeling. The syntax should facilitate statistical methods used in stochastic modeling. The N# # language produces well-formed sentences, each of which may be geometrically interpreted as a neuron. In the N# # language, cell typology de"nes the grammar. The normal pattern of growth is de"ned by the conditional probabilities
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
899
governing rule selection in the N# # language. The normal morphology of a cell type is de"ned by the turtle geometry interpretation of N# # language productions, in combination with the N# # language grammar and the stochastic generation process. Statistical di!erentiation between two cell populations is facilitated by the string representation of N# # neuron models. Context-sensitive features have extended the N# # language (e.g., an open Lsystem allows dependence on chemical concentrations in the environment). Additional context-sensitive features to accommodate segment retraction, di!erential growth rates in adjacent dendritic arbors, and the synchronized growth of neuronal forests are being examined.
3. Computational framework for modeling neuron morphology A computational framework for modeling neuron morphology is presented in [2,4,8,10] that is adequate both for the quantitative description and stochastic generation of neuron populations. At the data level, an object-oriented description of cells (their dendritic and axonal arbors, soma, and spines) forms the basis for a neuron morphology database. A neuron visualizer allows a 3D interactive display of neurons in the database, either singly or in juxtaposition. At the knowledge level, the components for neuron morphology modeling are described. These components summarize neuron population data into normative stochastic L-system models of neuron morphology. The stochastic L-system models for pyramidal, stellate, and motor neurons have produced synthetic neurons with promising proximity to neurons described in the neurobiology literature. A neuron visualizer [2,6,10] is provided that reads the object model format of a neuron's morphology and displays the neuron in 3D. Input "les recreate a neuron's morphology and display it in 3D using one of three display types. The stick type displays all segments as lines of integral thickness and gives nearly instantaneous response when navigating or editing features. The cylindrical type draws segments as colored, lighted cylinders with diameters and tapers proportional to the measured segment diameters, and provides more realistic views of neuron morphology. A third display type, the hybrid, uses cylindrical representation for thicker segments, and wire frame representation for more terminal segments, to minimize display times. The user interactively controls the camera's view of the scene, including xeld of view, eye position, and gaze direction.
4. Framing microstructure data into 5nite element models of the cortex Finite elements provide a frame for visualizing and modeling neuronal forests [1,2]. Each reconstructed neuron in the neuron morphology database is assigned to the "nite element containing its soma. The cerebral cortex, so modeled (Fig. 1), can be viewed as a giant `chest of drawersa where a `drawera (any selected "nite element or
900
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
Fig. 1. Finite element model for part of the neocortical shell [2].
Fig. 2. Finite element populated with synthetic neurons [2].
cluster of neighboring "nite elements) can be `openeda as a "le and its population of neurons visualized, as illustrated in Fig. 2. These "nite elements therefore de"ne a "le structure isomorphic to the cerebral cortex as modeled and visualized at the cellular and tissue level. Approximately 2300 "nite elements of the scale and complexity shown in Fig. 1 (typically 4 mm;4 mm;3 mm) are needed to model a hemisphere of the human cerebral cortex.
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
901
Neuron morphology is highly dependent upon the shape and size of the local environment, that is, the "nite elements within which the neurons develop. Moreover, chemical concentration gradient "elds (e.g., Netrin) are needed to shape the growing pyramidal cells such that their terminal apical "bers fold over gracefully and run parallel to the cortical surface [10]. The "nite elements of the immediate neuronal neighborhood are required to grow simulated neuron populations that (ideally) are indistinguishable from those seen microscopically.
5. Web-based neuron/forest database To support these studies we have speci"ed a distributed brain tissue database [6], which stores the position and morphology of all reconstructed soma, dendritic arbors, and "bers of brain tissue generated in the course of neuroanatomical studies and which provides a graphical interface to this virtual environment [2]. The prototype database system is designed to run on an Intranet within the Department of Computer Science, Texas A&M University. When integrated with the brain tissue scanner, the distributed, web-based brain tissue database serves as a comprehensive infrastructure for organizing brain tissue at three di!erent hierarchical levels: volume data (i.e., the image stack derived from scanning consecutive physical or optical sections from the tissue block), neuron/forest data, and network data.
6. Neuroanatomical sources of data The above neuroanatomical modeling and visualization tools are designed to support a broadly based examination of the neuroanatomical development of the human brain. The research described here has natural application to infants, children and adolescents with both normal and abnormal brain development. With our recent discovery that the human brain doubles its number of neurons in the cerebral cortex after birth [14,15], there is an emerging opportunity to understand in much greater detail how the human and other mammalian cerebral cortices structure themselves. These advances will be greatly facilitated by a better understanding of the microstructure of the developing human brain and how it a!ects the information processing that ultimately determines human behavior. Several sources of data are described next. 6.1. The UCI brain tissue repository The material for our neuroanatomical studies will be drawn from the UCI Brain Tissue Repository, which consists of good-quality formalin-"xed material from 4000 autopsies performed at Children's Hospital Los Angeles over the last 25 years. The UCI Brain Tissue Repository covers more than 50 pediatric neurological disorders plus normal development from the 3rd trimester of gestation to 21 years old.
902
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
6.2. The CYBERCHILD database We have developed a large, quantitative database on the microscopic features of &73% of the laminar development of the postnatal human cerebral cortex from 0 to 72 months [13]. These data, based on six million individual measurements of Conel [3], comprise the most complete quantitative microstructure survey of the developing human postnatal cerebral cortex, and will serve as an initial basis of comparison for data derived from the described methods. The "ndings from these data [13}16] support the need for methods to model the spatial distribution and morphology of neurons in the developing human cerebral cortex. CYBERCHILD will be interfaced with GENESIS to facilitate its use in computational modeling.
Acknowledgements This work was supported in part by Texas Advanced Technology Program grant 999903-124 (McCormick) from the Texas Higher Education Coordinating Board.
References [1] D.A. Batte, T.S. Chow, B.H. McCormick, Finite element decomposition of human neocortex, in: J.M. Bower (Ed.), Computational Neuroscience: Trends in Research, Plenum Press, New York, 1998, pp. 573}578. [2] B.P. Burton, T.S. Chow, A.T. Duchowski, W. Koh, B.H. McCormick, Exploring the brain forest, Neurocomputing 26}27 (2000) 971}980. [3] J. Conel, Postnatal Development of the Human Cerebral Cortex, 8 Vols., Harvard University Press, Cambridge, MA, 1939}1967. [4] R.W. DeVaul, B.H. McCormick, Neuron developmental modeling and structural representation I: introduction to the N# # language, Technical Report, Scienti"c Visualization Laboratory, Department of Computer Science, Texas A&M University, College Station, TX, December 1996. [5] S.B. Kater, R.W. Davenport, P.B. Githrie, Filopodia as detectors of environmental cues: signal integration through changes in growth cone calcium levels,, in: J. van Pelt, M.A. Comer, H.B.M. Uylinger, F.H. Lopes da Siva (Eds.), The Self-Organizing Brain: From Growth Cones to Functional Networks, Elsevier, Amsterdam, 1994, pp. 49}60. [6] W. Koh, B.H. McCormick, Distributed, web-based microstructure database for brain tissue, Neurocomputing (2000), this issue. [7] A. Lindenmayer, Mathematical models for cellular interaction in development, Parts I and II, J. Theoret. Biol. 18 (1968) 280}315. [8] B.H. McCormick, R.W. DeVaul, Neuron developmental modeling and structural representation II: the stochastic model, Technical Report, Scienti"c Visualization Laboratory, Department of Computer Science, Texas A&M University, College Station, TX, 1996. [9] B.H. McCormick, W. Koh, W.R. Shankle, J.H. Fallon, Geometric modeling of local cortical circuits, Neurocomputing (2000), this issue. [10] B.H. McCormick, K. Mulchandani, L-system modeling of neurons, in: Proceedings of Visualization in Biomedical Computing, SPIE, Vol. 2359, 1994, pp. 693}705. [11] J. van Pelt, A.E. Dityatev, H.B.M. Uylings, Natural variability in the number of dendritic segments: model-based inferences about branching during neurite outgrowth, J. Comput. Neurol. 387 (1997) 325}340.
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904
903
[12] J. van Pelt, H.B.M. Uylings, Natural variability in the geometry of dendritic branching patterns, in: Modeling in the Neurosciences, From Ionic Channels to Neural Networks, P. Poznanski (ed.), Harwood Academic Publisher, Newark, NJ, 1998 (Chapter 4). [13] W.R. Shankle, B.H. Landing, M. Ra"i, J. Hara, J.H. Fallon, A.K. Romney, J.P. Boyd, CYBERCHILD: a database of the microscopic development of the postnatal human cerebral cortex from birth to 72 months, Neurocomputing (2000), this issue. [14] W.R. Shankle, B.H. Landing, M.S. Ra"i, A.V.R. Schiano, J.M. Chen, J. Hara, Numbers of neurons per column in the developing human cerebral cortex from birth to 72 months: evidence for an apparent post-natal increase in neuron numbers, J. Theor. Biol. 191 (1998) 115}140. [15] W.R. Shankle, M.S. Ra"i, B.H. Landing, J.H. Fallon, Approximate doubling of the numbers of neurons in the postnatal human cerebral cortex and in 35 speci"c cytoarchitectural areas from birth to 72 months, Pediatr. Dev. Pathol. 2 (1999) 244}259. [16] W.R. Shankle, A. Romney, B. Landing, J. Hara, Developmental patterns in the cytoarchitecture of the human cerebral cortex from birth to 6 years examined by correspondence analysis, Proc. Natl. Acad. Sci. USA 95 (1998) 4023}4028. [17] M.P. van Veen, J. van Pelt, Dynamic mechanisms of neuronal outgrowth, in: J. van Pelt, M.A. Cotner, H.B.M. Uylinger, F.H. Lopes da Siva (Eds.), The Self-Organizing Brain From Growth Cones to Functional Networks, Elsevier, Amsterdam, 1994, pp. 95}108.
Bruce H. McCormick is Professor of Computer Science and the Director of the Scienti"c Visualization Laboratory at Texas A&M University. His research interests include scienti"c visualization, brain mapping, computational neuroscience, and neural networks. He received his BS and Ph.D. degrees in Physics from MIT and Harvard University, respectively. He was Professor of Computer Science and Physics at the University of Illinois at Urbana-Champaign. At the University of Illinois at Chicago he served as head of the Department of Information Engineering, and at Texas A&M University, as the "rst head of the Department of Computer Science.
Richard W. DeVaul is pursuing his doctorate at the Media Lab, MIT, Cambridge, MA. He has an MS from the Visualization Program, Department of Architecture, Texas A&M University. The work described here was done as a graduate research assistant in the Scienti"c Visualization Laboratory, Department of Computer Science, Texas A&M University. His research interests include scienti"c visualization, physically based graphical modeling of clothing, and ubiquitous computing.
William R. Shankle is an Associate Professor of Cognitive Science at UC Irvine. He trained as a statistician and neurologist. His research focuses on data analysis of brain development, aging and degeneration. With B.H. Landing, he overturned the dogma of no postnatal neurogenesis in human cerebral cortex.
904
B.H. McCormick et al. / Neurocomputing 32}33 (2000) 897}904 James H. Fallon is Professor of Anatomy and Neurobiology in the College of Medicine at the University of California, Irvine. His areas of interest are in the study of the function of neurotrophic factors in neurodegenerative disorders, aging and development, and their use in neural stem cell therapies. He was the "rst to localize a characterized growth factor in the brain, and the "rst to localize EGF, TGF, and aFGF in the brain. He has a long-standing interest in neurochemical anatomy of monoamine, opioid, and other neurotransmitter systems in the mammalian brain. He has collaborated extensively as the systems neuroanatomist on numerous PET and MRI imaging studies in human. He is now collaborating with Junko Hara and Rod Shankle on analyses of developing human cortex, as well as novel analyses of art and the artist's brain, and with these scientists and Bruce McCormick on geometric modeling of human cortex.