- Email: [email protected]
CYBERCHILD: A database of the microscopic development of the postnatal human cerebral cortex from birth to 72 months
Neucom=1150=Chan=Venkatachala=BG
Neurocomputing 32}33 (2000) 1109}1114
CYBERCHILD: A database of the microscopic development of the postnatal human cerebral cortex from birth to 72 months William R. Shankle *, Benjamin H. Landing, Michael S. Ra"i, Junko Hara, James H. Fallon, A. Kimball Romney, John P. Boyd Department of Cognitive Science and Pharmacology, SSPB, University of California, Irvine, CA 92697-5100, USA Department of Pathology, Children's Hospital, Los Angeles, CA, USA School of Medicine, Brown University, Providence, RI, USA Department of Information and Computer Science, University of California, Irvine, CA 92697-3425, USA Department of Anatomy and Neurobiology, University of California, Irvine, CA 92697, USA School of Social Sciences, University of California, Irvine, CA 92697, USA Accepted 13 January 2000
Abstract We introduce a database of the microscopic, laminar development of &73% of postnatal human cerebral cortical areas from 0 to 72 months. These data have yielded important "ndings, such as overturning the dogma of no postnatal neurogenesis in humans. To facilitate their use in computational models, the data are being interfaced with GENESIS. 2000 Elsevier Science B.V. All rights reserved. Keywords: Microscopic neuroanatomy; Computational modeling; Neurogenesis
Postnatal
development;
Cortical
columns;
1. Introduction The purpose of this article is to (1) introduce a database (CYBERCHILD) on the microscopic structure of the developing postnatal human cerebral cortex; (2) discuss the quality and principle "ndings of the data; (3) discuss why and how it may be useful to computational modelers. * Corresponding author. Tel.: #1-949-723-4106; fax #1-949-723-9686. E-mail address: [email protected] (W.R. Shankle). 0925-2312/00/$ - see front matter 2000 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 2 3 1 2 ( 0 0 ) 0 0 2 8 5 - X
Neucom=1150=Chan=VVC=BG
1110
W.R. Shankle et al. / Neurocomputing 32}33 (2000) 1109}1114
2. The database (CYBERCHILD) The CYBERCHILD database is derived, in large part, from the data of Conel [1]. The full details regarding the data collected by Conel are described elsewhere [9]. Conel reported the mean values of six microscopic features for each layer of 34}53 cytoarchitectural areas (&73% of the cortex) at eight postnatal age points (0, 1, 3, 6, 15, 24, 48 and 72 months) based on 54 grossly and microscopically normal human brains at autopsy (5}9 per age point). The microscopic features Conel measured and available data in CYBERCHILD are given in Table 1. Each reported datum represents the mean value of 150}270 randomly selected measures (30 per brain). The Von Economo and equivalent Brodmann cytoarchitectural area classi"cations that Conel used are provided in CYBERCHILD. We corrected Conel's original data for tissue shrinkage due to tissue preparation methods and, where appropriate, for stereologic errors a!ecting counts [9].
3. The quality of Conel's data Conel's data, at age 72 months, on the number of neurons in a column of cortex under 1 mm of cortical surface for "ve Brodmann areas [9], were within 10% of values reported by contemporary authors for adult human cortex [3,7,8]. Also, correspondence analysis gave three orthogonal factors that explained 80% of the total variance of the Conel data, indicating very little `noisea [11]. 4. Principal 5ndings arising from the CYBERCHILD database The number of neurons in the postnatal human cerebral cortex approximately doubles from 24 to 72 months of age (Fig. 1), and, postnatal neuron number increases of at least 60% occurred in all 35 cortical areas examined. This "nding [9,10]
Table 1 Available data in the CYBERCHILD database Microscopic feature
Units
Hemisphere
Stain used
Number of areas
Cortical layer thickness Neuron packing density Neuronal somal height Neuronal somal breadth Axon density Proximal dendrite density Cortical surface area Synaptic density Neurons/layer/mm col. Neurons/layer/ctx. area
mm mm\ um um mm\ mm\ mm mm\ mm mm
Left Left Left Left Right Right Both Left Left Left
Cresyl violet Cresyl violet Cresyl violet Cresyl violet Weigert Mod. cajal silver N/a PTA Cresyl violet Cresyl violet
42 43 46 46 46 46 35 3 35 35
Neucom=1150=Chan=VVC=BG
W.R. Shankle et al. / Neurocomputing 32}33 (2000) 1109}1114
1111
Fig. 1. Total neuron number (shrinkage and stereologically corrected) in postnatal human cerebral cortex from birth to 72 Months Table 2 Rank ordering of the number of neurons in the six cortical layers for each area at each age. The shading in each cell indicates which two layers have the most neurons for that area and age.
overturned the longstanding dogma of no postnatal neurogenesis in humans. The computational power, in terms of neuron number, is therefore signi"cantly greater at 72 months than at 24 months of age. From birth to 72 months, in terms of laminar neuron number, most cortical areas shift from emphasizing short cortico-cortical (Layer 2) and thalamo-cortical (Layer 4) processing to emphasizing long cortico-cortical (Layer 3) and cortico-thalamic (Layer 6) processing (Table 2; [5,6]). For association cortical areas, this shift occurs at 15 or 24 months.
Neucom=1150=Chan=VVC=BG
1112
W.R. Shankle et al. / Neurocomputing 32}33 (2000) 1109}1114
A common developmental program characterizes the developmental of all neocortical areas [11]. Using correspondence analysis, we showed that the 99% con"dence ellipses of the microscopic features of all neocortical areas Conel examined strongly overlap, suggesting that their relative changes are quite similar and could be modelled by a single program with di!erent initial starting values at birth.
5. Relevance of the CYBERCHILD database to computational modeling Microscopic, structural ontogenetic changes during postnatal development are likely to a!ect cortical function, such that modeling human cortical development without capturing these dynamics would uncouple structure}function relations. The relatively small variations (coe$cients of variation from 11}25%) in age-speci"c values of the microscopic features reported by Conel and others (e.g., [2,8]) suggests that genetics can account for a large amount of the observed changes in cortical microscopic neuroanatomy during postnatal development. Because Conel reported mean values, the large laminar charges observed in the cortical areas Conel studied cannot be related to environmental exposures of the individuals he examined. Incorporating his microscopic, structural data into computational models of cortical development should therefore bring us closer to understanding structure}function relationships. The computing power of a speci"c cortical area is at least partly determined by its neuron number. This was recently demonstrated in a large-scale simulation of cerebellar cortex using PGENESIS [4]. Using CYBERCHILD to incorporate the observed changes of a cortical area's neuron number into computational models could give us a better understanding of the relation between its computational/electrophysiologic properties and the appearance of related behaviors. The function of a cortical area at a speci"c age is at least partly determined by the relative computing power of each of its cortical layers at that age. The abrupt shift at 15 or 24 months in many association cortical areas, from emphasizing short-distance cortico-cortical and thalamo-cortical signal processing to emphasizing long-distance cortico-cortical and cortico-thalamic signal processing, coincides with a dramatic increase in behavioral abilities observed in the 15}24 month human infant. Computational models incorporating the changes in laminar computing power of speci"c cortical areas from birth to 72 months using the CYBERCHILD database may provide a greater understanding of how cortical areas can alter their functional capacity during development. Those interested in using the data should contact [email protected] for details on availability of the CYBERCHILD database.
Acknowledgements This work was supported by Dr. B.H. Landing.
Neucom=1150=Chan=VVC=BG
W.R. Shankle et al. / Neurocomputing 32}33 (2000) 1109}1114
1113
References [1] J.L. Conel, Postnatal Development of the Human Cerebral Cortex, Vols. 1}8. Harvard University Press, Cambridge, MA, 1939, 1941, 1947, 1951, 1955, 1959, 1963, 1967. [2] H. Haug, Brain sizes, surfaces and neuronal sizes of the cortex cerebri, Am. J. Anat. 180 (1987) 126}142. [3] S. Hendry, H. Schwark, E.G. Jones, J. Fan, Numbers and proportions of GABA immunoreactive neurons in di!erent areas of monkey cerebral cortex, J. Neurosci. 7 (1987) 1503}1519. [4] F. Howell, J. Dyhrfjeld-Johnsen, M. Reinhoud, N. Goddard, E. De Schutter, A Large Scale Simulation Model of the Cerebellar Cortex using PGENESIS, Proceedings of the Eighth Annual Computational Neuroscience Conference, 1999, p. 95. [5] B.H. Landing, W.R. Shankle, J.P. Boyd, Quantitative microscopic anatomy, illustrated by its potential role in furthering understanding of the processes of structuring the developing human cerebral cortex, Acta Paediatr. Japan 40 (1998) 400}418. [6] B.H. Landing, W.R. Shankle, J. Hara, Constructing the human cerebral cortex during infancy and childhood: types and numbers of cortical columns and numbers of neurons in such columns at di!erent age-points, Acta Paediatr. Japan 40 (1998) 530}543. [7] J. O'Kusky, M. Colonnier, A laminar analysis of the number of neurons, glia, and synapses in the visual cortex (area 17) of adult macaque monkeys, J. Comput Neurol. 210 (1982) 278}290. [8] A.J. Rockel, R.W. Hiorns, T.P. Powell, The basic uniformity in structure of the neocortex, Brain 103 (1980) 221}244. [9] 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. Theoret. Biol. 191 (1998) 115}140. [10] W.R. Shankle, M.S. Ra"i, B.H. Landing, J.H. Fallon, Approximate doubling of the numbers of neurons in the postnatal human cerbral cortex and in 35 speci"c cytoarchitectural areas from birth to 72 months, Pediatr. Dev. Pathol. 2 (1999) 244}259. [11] W.R. Shankle, A.K. Romney, B.H. Landing, J. Hara, Developmental patterns in the cytoarchitecture of the human cerebral cortex from birth to six years examined by correspondence analysis, Proc. Natl. Acad. Sci. USA 95 (1998) 4023}4028.
Dr. Shankle is a statistician and neurologist whose research focusses in data analysis of brain development and degeneration. He is currently an Associate Professor at UC Irvine.
Dr. Hara, trained in biomedical engineering and bioinformatics at Keio University, currently researches EEG and computational models of cortical development and Alzheimer's disease.
Dr. Fallon, full Professor at UC Irvine, is an accomplished systems neuroanatomist who researches both Neurophysiologic and Neuroanatomical Changes during development, aging, and brain degeneration.
Dr. Ra5i is completing his MD/PhD training at Brown University and does molecular neurobiological research and computational modeling.
Neucom=1150=Chan=VVC=BG
1114
W.R. Shankle et al. / Neurocomputing 32}33 (2000) 1109}1114
Dr. A.K. Romney is an Emeritus Professor of Anthropology at UC Irvine, and member of the National Academy of Science. He uses advanced analytical methods to examine e!ect of gene and environment on various human abilities. Dr. J.P. Boyd is a Professor of Anthropology at UC Irvine and researches in mathematical methods applied to a wide array of data. Dr. B.H. Landing is Emeritus Professor of Pathology and paediatrics at USC and researches structuralfunctional relations in brain, skeletal muscle, liver, and enteric nervous system development, as well as evolutionary development of complex adaptive behaviors in humans, insects and butter#ies.
Neurocomputing 32}33 (2000) 1109}1114
CYBERCHILD: A database of the microscopic development of the postnatal human cerebral cortex from birth to 72 months William R. Shankle *, Benjamin H. Landing, Michael S. Ra"i, Junko Hara, James H. Fallon, A. Kimball Romney, John P. Boyd Department of Cognitive Science and Pharmacology, SSPB, University of California, Irvine, CA 92697-5100, USA Department of Pathology, Children's Hospital, Los Angeles, CA, USA School of Medicine, Brown University, Providence, RI, USA Department of Information and Computer Science, University of California, Irvine, CA 92697-3425, USA Department of Anatomy and Neurobiology, University of California, Irvine, CA 92697, USA School of Social Sciences, University of California, Irvine, CA 92697, USA Accepted 13 January 2000
Abstract We introduce a database of the microscopic, laminar development of &73% of postnatal human cerebral cortical areas from 0 to 72 months. These data have yielded important "ndings, such as overturning the dogma of no postnatal neurogenesis in humans. To facilitate their use in computational models, the data are being interfaced with GENESIS. 2000 Elsevier Science B.V. All rights reserved. Keywords: Microscopic neuroanatomy; Computational modeling; Neurogenesis
Postnatal
development;
Cortical
columns;
1. Introduction The purpose of this article is to (1) introduce a database (CYBERCHILD) on the microscopic structure of the developing postnatal human cerebral cortex; (2) discuss the quality and principle "ndings of the data; (3) discuss why and how it may be useful to computational modelers. * Corresponding author. Tel.: #1-949-723-4106; fax #1-949-723-9686. E-mail address: [email protected] (W.R. Shankle). 0925-2312/00/$ - see front matter 2000 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 2 3 1 2 ( 0 0 ) 0 0 2 8 5 - X
Neucom=1150=Chan=VVC=BG
1110
W.R. Shankle et al. / Neurocomputing 32}33 (2000) 1109}1114
2. The database (CYBERCHILD) The CYBERCHILD database is derived, in large part, from the data of Conel [1]. The full details regarding the data collected by Conel are described elsewhere [9]. Conel reported the mean values of six microscopic features for each layer of 34}53 cytoarchitectural areas (&73% of the cortex) at eight postnatal age points (0, 1, 3, 6, 15, 24, 48 and 72 months) based on 54 grossly and microscopically normal human brains at autopsy (5}9 per age point). The microscopic features Conel measured and available data in CYBERCHILD are given in Table 1. Each reported datum represents the mean value of 150}270 randomly selected measures (30 per brain). The Von Economo and equivalent Brodmann cytoarchitectural area classi"cations that Conel used are provided in CYBERCHILD. We corrected Conel's original data for tissue shrinkage due to tissue preparation methods and, where appropriate, for stereologic errors a!ecting counts [9].
3. The quality of Conel's data Conel's data, at age 72 months, on the number of neurons in a column of cortex under 1 mm of cortical surface for "ve Brodmann areas [9], were within 10% of values reported by contemporary authors for adult human cortex [3,7,8]. Also, correspondence analysis gave three orthogonal factors that explained 80% of the total variance of the Conel data, indicating very little `noisea [11]. 4. Principal 5ndings arising from the CYBERCHILD database The number of neurons in the postnatal human cerebral cortex approximately doubles from 24 to 72 months of age (Fig. 1), and, postnatal neuron number increases of at least 60% occurred in all 35 cortical areas examined. This "nding [9,10]
Table 1 Available data in the CYBERCHILD database Microscopic feature
Units
Hemisphere
Stain used
Number of areas
Cortical layer thickness Neuron packing density Neuronal somal height Neuronal somal breadth Axon density Proximal dendrite density Cortical surface area Synaptic density Neurons/layer/mm col. Neurons/layer/ctx. area
mm mm\ um um mm\ mm\ mm mm\ mm mm
Left Left Left Left Right Right Both Left Left Left
Cresyl violet Cresyl violet Cresyl violet Cresyl violet Weigert Mod. cajal silver N/a PTA Cresyl violet Cresyl violet
42 43 46 46 46 46 35 3 35 35
Neucom=1150=Chan=VVC=BG
W.R. Shankle et al. / Neurocomputing 32}33 (2000) 1109}1114
1111
Fig. 1. Total neuron number (shrinkage and stereologically corrected) in postnatal human cerebral cortex from birth to 72 Months Table 2 Rank ordering of the number of neurons in the six cortical layers for each area at each age. The shading in each cell indicates which two layers have the most neurons for that area and age.
overturned the longstanding dogma of no postnatal neurogenesis in humans. The computational power, in terms of neuron number, is therefore signi"cantly greater at 72 months than at 24 months of age. From birth to 72 months, in terms of laminar neuron number, most cortical areas shift from emphasizing short cortico-cortical (Layer 2) and thalamo-cortical (Layer 4) processing to emphasizing long cortico-cortical (Layer 3) and cortico-thalamic (Layer 6) processing (Table 2; [5,6]). For association cortical areas, this shift occurs at 15 or 24 months.
Neucom=1150=Chan=VVC=BG
1112
W.R. Shankle et al. / Neurocomputing 32}33 (2000) 1109}1114
A common developmental program characterizes the developmental of all neocortical areas [11]. Using correspondence analysis, we showed that the 99% con"dence ellipses of the microscopic features of all neocortical areas Conel examined strongly overlap, suggesting that their relative changes are quite similar and could be modelled by a single program with di!erent initial starting values at birth.
5. Relevance of the CYBERCHILD database to computational modeling Microscopic, structural ontogenetic changes during postnatal development are likely to a!ect cortical function, such that modeling human cortical development without capturing these dynamics would uncouple structure}function relations. The relatively small variations (coe$cients of variation from 11}25%) in age-speci"c values of the microscopic features reported by Conel and others (e.g., [2,8]) suggests that genetics can account for a large amount of the observed changes in cortical microscopic neuroanatomy during postnatal development. Because Conel reported mean values, the large laminar charges observed in the cortical areas Conel studied cannot be related to environmental exposures of the individuals he examined. Incorporating his microscopic, structural data into computational models of cortical development should therefore bring us closer to understanding structure}function relationships. The computing power of a speci"c cortical area is at least partly determined by its neuron number. This was recently demonstrated in a large-scale simulation of cerebellar cortex using PGENESIS [4]. Using CYBERCHILD to incorporate the observed changes of a cortical area's neuron number into computational models could give us a better understanding of the relation between its computational/electrophysiologic properties and the appearance of related behaviors. The function of a cortical area at a speci"c age is at least partly determined by the relative computing power of each of its cortical layers at that age. The abrupt shift at 15 or 24 months in many association cortical areas, from emphasizing short-distance cortico-cortical and thalamo-cortical signal processing to emphasizing long-distance cortico-cortical and cortico-thalamic signal processing, coincides with a dramatic increase in behavioral abilities observed in the 15}24 month human infant. Computational models incorporating the changes in laminar computing power of speci"c cortical areas from birth to 72 months using the CYBERCHILD database may provide a greater understanding of how cortical areas can alter their functional capacity during development. Those interested in using the data should contact [email protected] for details on availability of the CYBERCHILD database.
Acknowledgements This work was supported by Dr. B.H. Landing.
Neucom=1150=Chan=VVC=BG
W.R. Shankle et al. / Neurocomputing 32}33 (2000) 1109}1114
1113
References [1] J.L. Conel, Postnatal Development of the Human Cerebral Cortex, Vols. 1}8. Harvard University Press, Cambridge, MA, 1939, 1941, 1947, 1951, 1955, 1959, 1963, 1967. [2] H. Haug, Brain sizes, surfaces and neuronal sizes of the cortex cerebri, Am. J. Anat. 180 (1987) 126}142. [3] S. Hendry, H. Schwark, E.G. Jones, J. Fan, Numbers and proportions of GABA immunoreactive neurons in di!erent areas of monkey cerebral cortex, J. Neurosci. 7 (1987) 1503}1519. [4] F. Howell, J. Dyhrfjeld-Johnsen, M. Reinhoud, N. Goddard, E. De Schutter, A Large Scale Simulation Model of the Cerebellar Cortex using PGENESIS, Proceedings of the Eighth Annual Computational Neuroscience Conference, 1999, p. 95. [5] B.H. Landing, W.R. Shankle, J.P. Boyd, Quantitative microscopic anatomy, illustrated by its potential role in furthering understanding of the processes of structuring the developing human cerebral cortex, Acta Paediatr. Japan 40 (1998) 400}418. [6] B.H. Landing, W.R. Shankle, J. Hara, Constructing the human cerebral cortex during infancy and childhood: types and numbers of cortical columns and numbers of neurons in such columns at di!erent age-points, Acta Paediatr. Japan 40 (1998) 530}543. [7] J. O'Kusky, M. Colonnier, A laminar analysis of the number of neurons, glia, and synapses in the visual cortex (area 17) of adult macaque monkeys, J. Comput Neurol. 210 (1982) 278}290. [8] A.J. Rockel, R.W. Hiorns, T.P. Powell, The basic uniformity in structure of the neocortex, Brain 103 (1980) 221}244. [9] 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. Theoret. Biol. 191 (1998) 115}140. [10] W.R. Shankle, M.S. Ra"i, B.H. Landing, J.H. Fallon, Approximate doubling of the numbers of neurons in the postnatal human cerbral cortex and in 35 speci"c cytoarchitectural areas from birth to 72 months, Pediatr. Dev. Pathol. 2 (1999) 244}259. [11] W.R. Shankle, A.K. Romney, B.H. Landing, J. Hara, Developmental patterns in the cytoarchitecture of the human cerebral cortex from birth to six years examined by correspondence analysis, Proc. Natl. Acad. Sci. USA 95 (1998) 4023}4028.
Dr. Shankle is a statistician and neurologist whose research focusses in data analysis of brain development and degeneration. He is currently an Associate Professor at UC Irvine.
Dr. Hara, trained in biomedical engineering and bioinformatics at Keio University, currently researches EEG and computational models of cortical development and Alzheimer's disease.
Dr. Fallon, full Professor at UC Irvine, is an accomplished systems neuroanatomist who researches both Neurophysiologic and Neuroanatomical Changes during development, aging, and brain degeneration.
Dr. Ra5i is completing his MD/PhD training at Brown University and does molecular neurobiological research and computational modeling.
Neucom=1150=Chan=VVC=BG
1114
W.R. Shankle et al. / Neurocomputing 32}33 (2000) 1109}1114
Dr. A.K. Romney is an Emeritus Professor of Anthropology at UC Irvine, and member of the National Academy of Science. He uses advanced analytical methods to examine e!ect of gene and environment on various human abilities. Dr. J.P. Boyd is a Professor of Anthropology at UC Irvine and researches in mathematical methods applied to a wide array of data. Dr. B.H. Landing is Emeritus Professor of Pathology and paediatrics at USC and researches structuralfunctional relations in brain, skeletal muscle, liver, and enteric nervous system development, as well as evolutionary development of complex adaptive behaviors in humans, insects and butter#ies.