Computational Neuroanatomy: Principles and Methods

Publisher: Humana Press Inc.

Editor: Giorgio Ascoli

(To appear in Spring 2002.)

Table of Contents


Chapter 1: Computing the brain and the computing brain. Giorgio A. Ascoli
1.1 Introduction
1.2 Computing the brain
1.3 From neurons to networks
1.4 The computing brain
1.5 Conclusions


Chapter 2: Some approaches to quantitative dendritic morphology. Robert E. Burke and William B. Marks
2.1 Introduction
2.2 Two-dimensional analysis of dendrites in isolated neurons
2.3 How good is good enough?
2.4 Neurons in three dimensions
 2.4.1 Building three-dimensional dendrites
 2.4.2 The problem of neuronal packing
2.5 Concluding comments

Chapter 3: Generation and description of neuronal morphology using L-Neuron: a case study. Duncan E. Donohue, Ruggero Scorcioni, and Giorgio A. Ascoli
3.1 Introduction
3.2 Methods
 3.2.1 Experimental data
 3.2.2 L-Neuron and the modified Hillman algorithm
 3.2.3 Extraction of basic and emergent parameters with L-Measure
 3.2.4 Data analysis
 3.2.5 Visualization of neuronal morphology and dendrograms
3.3 Results
3.4 Discussion
3.5 Spatial orientation
3.6 Conclusion

Chapter 4: Optimal-wiring models of neuroanatomy. Christopher Cherniak, Zekeria Mokhtarzda, and Uri Nodelman
4.1 Introduction
4.2 Conceptual background
4.3 Network optimization theory
4.4 Optimization mechanisms
4.5 Functional role of neural optimization

Chapter 5: The Modeler's Workspace: Making model-based studies of the nervous system more accessible. Michael Hucka, Kavita Shankar, David Beeman, and James M. Bower
5.1 Introduction
5.2 The need for model-based approaches
 5.2.1 The role of structurally realistic models
 5.2.2 Data evaluation and functional assessment
5.3 Overview of the Modeler’s Workspace
 5.3.1 The Modeler’s Workspace user interface and workspace datatabase
 5.3.2 Elements of the user interface
 5.3.3 The site browser
 5.3.4 Access to neural simulation packages
 5.3.5 An example usage scenario
5.4 The underlying architecture
 5.4.1 Layered framework
 5.4.2 Highly modular, extensible architecture
5.5 Representation of models and data
 5.5.1 Template hierarchy
 5.5.2 Advantages of the approach
5.6 Interacting with databases
 5.6.1 Workspace databases
 5.6.2 Foreign databases
 5.6.3 Template-driven search interface
 5.6.4 The Modeler’s Workspace directory server
5.7 Conclusion

Chapter 6: The relationship between neuronal shape and neuronal activity. Jeffrey L. Krichmar and Slawomir J. Nasuto
6.1 Introduction
6.2 Experimental studies of morphological variability
6.3 Computational studies of morphological variability
 6.3.1 Neuronal modeling and simulation
 6.3.2 Measuring morphological data
 6.3.3 Archives of neuroanatomy
 6.3.4 Artificially generated neurons
 6.3.5 Testing the “morphology influences physiology” hypothesis
6.4 Conclusions

Chapter 7: Practical aspects in anatomically accurate simulations of neuronal electrophysiology. Maciej T. Lazarewicz, Sybrand Boer-Iwema, and Giorgio A. Ascoli
7.1 Introduction
7.2 Computational implementation
7.3 Anatomical representation
 7.3.1 Rule of 1/3
 7.3.2 Passive case
 7.3.3 Active case
 7.3.4 Additional practical aspects on compartmentalization
7.4 From morphological reconstructions to electrophysiological compartmental models: tools and algorithms
7.5 Conclusions


Chapter 8: Predicting emergent properties of neuronal ensembles using a database of individual neurons. Gwen A. Jacobs and Colin S. Pittendrigh
8.1 Introduction
8.2 A model sensory system for studying ensemble encoding of sensory information
 8.2.1 Physiological characteristics of neurons in the cercal system
 8.2.2 Anatomical characteristics of neurons in the system
8.3 Using NeuroSys to study emergent properties of neuronal ensembles
 8.3.1 Neural maps of direction and frequency in the cricket cercal system
8.3.2 Predicting spatio-temporal patterns of activity within an ensemble of sensory neurons
 8.3.3 Predicting spatio-temporal patterns of activity within neural ensembles
8.4 Transfer of information between ensembles of neurons
8.5 General applications of NeuroSys

Chapter 9: Computational anatomical analysis of the basal forebrain corticopetal system. Laszlo Zaborszky, Attila Csordas, Derek L. Buhl, Alvaro Duque, Jozsef Somogyi, and Zoltan Nadasdy
9.1 Introduction
9.2 Association and segregation of different hodologically identified neural populations
 9.2.1 Overlap analysis
 9.2.2 Iso-density surface rendering
9.3 Inhomogeneous distribution of chemically identified cell populations
 9.3.1 Differential density 3D scatter plot
 9.3.2 Iso-relational surface rendering
9.4 Cholinergic cell groups show regionally selective dendritic orientation
 9.4.1 Mean 3D vector of dendritic processes
 9.4.2 2D dendritic stick analysis (polar histogram)
9.5 Various afferents in the BF show regionally restricted localization
9.6 Probability of connections
9.7 Merging datafiles containing neurons of different complexities
9.8 Concluding remarks
9.9 Appendix
 9.9.1 Animals and tissue processing
 9.9.2 Data acquisition
 9.9.3 Selection of neurons for dendritic tracing
 9.9.4 Analysis of the data

Chapter 10: Architecture of sensory map transformations: axonal tracing in combination with 3-D reconstruction, geometric modeling, and quantitative analyses. Trygve B. Leergaard and Jan G. Bjaalie
10.1 Introduction
10.2 Map transformations in cerebro-cerebellar and auditory systems
10.3 Neural tracing techniques
10.4 Image-combining microscopy for data acquisition
10.5 3-D reconstruction
10.6 Local coordinate systems for individual brain stem nuclei
10.6.1 Comparison of results
10.6.2 Databasing
10.7 Visualization and quantitative analyses of the distribution of labeled axons and cells
10.7.1 Slicing of 3-D reconstructions
10.7.2 Surface modeling of labeled structures
10.7.3 Density gradient analysis
10.7.4 Stereo-imaging
10.7.5 Analysis of spatial overlap
10.8 Conclusions

Chapter 11: Competition in neuronal morphogenesis and the development of nerve connections. Arjen van Ooyen and Jaap van Pelt
11.1 Introduction
11.2 Development of dendritic morphology: a stochastic model
11.3 Neurite elongation and branching: cell biological mechanisms
 11.3.1 Neurite elongation as a result of tubulin polymerization
11.3.2 The role of microtubule-associated proteins in neurite elongation and branching
11.4 Competition between axons in the refinement of neural circuits
11.4.1 Competition through synaptic normalization and modified Hebbian learning rules
11.4.2 Competition through dependence on shared, target-derived resources
11.5 Discussion

Chapter 12: Axonal navigation through voxel substrates: a strategy for reconstructing brain circuitry. Stephen L. Senft
12.1 Introduction
 12.1.1 Volume data
 12.1.2 Network data
 12.1.3 Histochemical data
 12.1.4 Integrative aims
12.2 Methods and results
 12.2.1 Mouse atlas
 12.2.2 ArborVitae
 12.2.3 In Voxo tissue culture
 12.2.4 Navigation
 12.2.5 Arborization
 12.2.6 Approximations
12.3 Discussion
 12.3.1 Biological navigation
 12.3.2 Biological branching
 12.3.3 Growth algorithms
 12.3.4 Future directions

Chapter 13: Principle and applications of diffusion tensor imaging: a new MRI technique for neuroanatomical studies. Susumo Mori
13.1 Background on Diffusion Tensor Imaging
 13.1.1 Conventional MRI and DTI
 13.1.2 Diffusion process
 13.1.3 Importance of studying the water diffusion process in the brain
13.2 Measurement and calculation
13.3 Two-dimensional DTI data analysis and visualization techniques and their application in brain studies
13.3.1 Trace image: an orientation-independent visualization technique for the size of the diffusion ellipsoid
13.3.2 Application of trace image: Stroke studies
13.3.3 Anisotropy map: an orientation-independent visualization technique for anisotropy
13.3.4 Color map: visualization technique for orientation
13.4 3D-based DTI techniques and their applications
13.5 Future directions and summary


Chapter 14: Computational methods for the analysis of brain connectivity. Claus Hilgetag, Rolf Kotter, Klaas Stephan, and Olaf Sporns
14.1 Introduction
14.2 Description of neural connectivity
 14.2.1 Experimental identification of connectivity
 14.2.2 Computational treatment of experimental data
 14.2.3 Formal description
14.3 Graph-theoretical analysis
 14.3.1 Average degree of connectivity
 14.3.2 Local connectivity indices
 14.3.3 Paths and cycles
 14.3.4 Reachability matrix and connectedness
 14.3.5 Distance matrix and diameter
 14.3.6. Disjoint paths, edge and vertex connectivity
 14.3.7 Random graphs
 14.3.8 Small-worlds attributes: characteristic path length and cluster index
 14.3.9 Scale-free attributes
 14.3.10 Conclusions and perspectives
14.4 Statistical exploration of connectivity
 14.4.1 General considerations
 14.4.2 Non-metric multidimensional scaling (NMDS)
 14.4.3 Factor analysis / Principal component analysis (PCA)
14.4.4 Multiple correspondence analysis (MCA)
14.4.5 Cluster analysis
14.4.6 Combined approaches
14.5 Conceptual hypothesis testing
 14.5.1 Wiring principles
 14.5.2 Optimization analyses
14.6 Conclusions

Chapter 15: Development of columnar structures in visual cortex. Miguel A. Carreira-Perpinan and Geoffrey J. Goodhill
15.1 Introduction
15.2 Structure of adult maps
15.3 Map development: role of activity
15.4 Theoretical models: coverage and continuity
 15.4.1 Mathematical formulation of coverage uniformity and continuity
15.5 The elastic net algorithm
15.6 Discussion
 15.6.1 Retinotopy distortions
 15.6.2 Activity-independent mechanisms in column development

Chapter 16: Multi-level neuron and network modeling in computational neuroanatomy. Rolf Kotter, Pernille Nielse, Jonas Dyhrfjeld-Johnsen, Friedricj T. Sommer, and Georg Northoff
16.1 Introduction
16.2 Models of neurons and neuronal populations
 16.2.1 Neuron models
 16.2.2 Neuronal population and area models
 16.2.3 Interfacing different models
16.3 Multi-level modeling of visual cortex
 16.3.1 Stimulus representation
 16.3.2 Microcircuit representation of primary visual cortex
 16.3.3 Network implementation
16.4 Results
16.5 Discussion

Chapter 17: Quantitative neurotoxicity. David S. Lester, Joseph P. Hanig and P. Scott Pine
17.1 Introduction
17.2 Imaging
17.3 Imaging and toxicology
17.3.1 Magnetic resonance imaging (MRI)
17.3.2 MRI as a tool for preclinical neurotoxicology
17.3.3 MRI as a tool for detecting multiple sclerosis (MS)
17.3.4 Midinfrared spectral imaging of brain sections
17.4 Conclusions

Chapter 18: How the brain develops and how it functions: application of neuroanatomical data of the developing human cerebral cortex to computational models. William Rodman Shankle, Junko Hara, James Fallon, Benjamin H. Landing
18.1 Introduction: the Conel data
 18.1.1 The microscopic, neuroanatomic features
 18.1.2 Cortical layers and fiber systems
 18.1.3 Cytoarchitectonic regions
18.2 Analysis of developmental changes: birth to 72 months
 18.2.1 Global analysis of microscopic neuroanatomic changes
 18.2.2 Numbers of neurons per 1mm2 column per cytoarchitectonic area
 18.2.3 Numbers of neurons per cytoarchitectonic area
 18.2.4 Total numbers of neurons in the cerebral cortex
 18.2.5 Relationship of total cortical neuron numbers to acquiring new behaviors
 18.2.6 Numbers of neurons per layer per cytoarchitectonic area
18.3 Conclusions

Chapter 19: Towards virtual brains. Alexei Samsonovich and Giorgio A. Ascoli
19.1 A brief historical introduction
19.2 Old machines, new goals
19.3 Implementing the brain
19.4 From structure to function
19.5 Implementing the mind
19.6 Concluding remarks