Three-dimensional computerised atlas of the rat brain stem precerebellar system: approaches for mapping, visualization, and comparison of spatial distribution data

Brevik, Asgeir; Leergaard, Trygve B.; Svanevik, Marius; Bjaalie, Jan G.

doi:10.1007/s004290100202

Cited by 31 publications

(36 citation statements)

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“…Placing data into an atlas-based coordinate systems provides one method by which data taken across scales and distributed across multiple resources can reliably be compared. 55 Through the use of computer-based atlases and associated tools for warping and registration, it is possible to express the location of anatomical features or signals in terms of a standardized coordinate system. While there may be disagreement among neuroscientists about the identity of a brain area giving rise to a signal, its location in terms of spatial coordinates is at least quantifiable.…”

Section: A Case Study: the Cell Centered Database 45mentioning

confidence: 99%

“…These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis." 55 Simulation models can also be used to test design ideas for engineering networks in cells. For example, very simple models have been used to provide insight into a genetic oscillator and a switch in E. coli.…”

Section: On Inferring Qualitative Connectionsmentioning

confidence: 99%

“…A biological instantiation of integral feedback is contained in bacterial chemotaxis. 55 In addition to the filters described above, other mechanisms attenuate noise in systems. These include the following:…”

Section: Noise In Biological Phenomenamentioning

confidence: 99%

“…For example, it is well known from the agricultural context that monocultures are less robust than ecosystems that involve multiple species-the first can be wiped out by a disease that targets the specific crop in question, whereas the second cannot. Thus, some degree of variation in a populating species is desirable, and noise is one mechanism for introducing variation that results in population heteroge- 55 The size of a single bacterium is so small that the bacterium is unable to sense a spatial gradient across the length of its body. Thus, to sense a spatial gradient, the bacterium moves around and senses chemical concentrations in different locations at different times; the result is a motion bias toward attractants.…”

Section: Noise In Biological Phenomenamentioning

confidence: 99%

“…Sussman, et al, "Amorphous Computing," available at http://www.swiss.ai.mit.edu/projects/ amorphous/white-paper/amorph-new/amorph-new.html. 55 The discussion in Section 8.3.1 owes much to Melanie Mitchell, now at Portland State University in Oregon. 56 Evolutionary computation is a generic name for techniques that are based loosely on evolutionary principles.…”

Section: What Is Evolutionary Computation?mentioning

confidence: 99%

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Catalyzing Inquiry at the Interface of Computing and Biology

Wooley¹,

Lin²

2005

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Preface viiIn the last decade of the 20th century, computer science and biology both emerged as fields capable of remarkable and rapid change. Moreover, they evolved as fields of inquiry in ways that draw attention to their areas of intersection. The continuing advancements in technology and the pace of scientific research present the means for computing to help answer fundamental questions in the biological sciences and for biology to demonstrate that new approaches to computing are possible.Advances in the power and ease of use of computing and communications systems have fueled computational biology (e.g., genomics) and bioinformatics (e.g., database development and analysis). Modeling and simulation of biological entities such as cells have joined biologists and computer scientists (and mathematicians, physicists, and statisticians too) to work together on activities from pharmaceutical design to environmental analysis.On the other side, computer scientists have pondered the significance of biology for their field. For example, computer scientists have explored the use of DNA as a substrate for new computing hardware and the use of biological approaches in solving hard computing problems. Exploration of biological computation suggests a potential for insight into the nature of and alternative processes for computation, and it also gives rise to questions about hybrid systems that achieve some kind of synergy of biological and computational systems. And there is also the fact that biological systems exhibit characteristics such as adaptability, self-healing, evolution, and learning that would be desirable in the information technologies that humans use.Making the most of the research opportunities at the interface of computing and biology-what we are calling the BioComp interface-requires illuminating what they are and effectively engaging people from both computing and biology. As in other contexts, the challenges of interdisciplinary education and of collaboration are significant, and each will require attention, together with substantive work from both policy makers and researchers. At the start of the 1990s, attempts were made to stimulate mutual interest and collaboration among young researchers in computing and biology. Those early efforts yielded nontrivial successes, but in retrospect represented a Version 1.0 prototype for the potential in bringing the two fields together. Circumstances today seem much more favorable for progress. New research teams and training programs have been formed as individual investigators from the respective communities, government agencies, and private foundations have become increasingly engaged. Similarly, some larger groups of investigators from different backgrounds have been able to viii PREFACE obtain funding to work together to address cross-disciplinary research problems. It is against this background that the committee sees a Version 2.0 of the BioComp interface emerging that will yield unprecedented progress and advance.The range of possible activities at the Bio...

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Section: A Case Study: the Cell Centered Database 45mentioning

confidence: 99%

Section: On Inferring Qualitative Connectionsmentioning

confidence: 99%

Section: Noise In Biological Phenomenamentioning

confidence: 99%

Section: Noise In Biological Phenomenamentioning

confidence: 99%

Section: What Is Evolutionary Computation?mentioning

confidence: 99%

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Catalyzing Inquiry at the Interface of Computing and Biology

Wooley¹,

Lin²

2005

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Three‐dimensional topography of corticopontine projections from rat sensorimotor cortex: Comparisons with corticostriatal projections reveal diverse integrative organization

Leergaard

Alloway

Pham

et al. 2004

J of Comparative Neurology

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The major cortical-subcortical re-entrant pathways through the basal ganglia and cerebellum are considered to represent anatomically segregated channels for information originating in different cortical areas. A capacity for integrating unique combinations of cortical inputs has been well documented in the basal ganglia circuits but is largely undefined in the precerebellar circuits. To compare and quantify the amount of overlap that occurs in the first link of the cortico-ponto-cerebellar pathway, a dual tracing approach was used to map the spatial relationship between projections originating from the primary somatosensory cortex (SI), the secondary somatosensory cortex (SII), and the primary motor cortex (MI). The anterograde tracers biotinylated dextran amine and Fluoro-Ruby were injected into homologous whisker representations of either SI and SII, or SI and MI. The ensuing pontine labeling patterns were analyzed using a computerized three-dimensional reconstruction approach. The results demonstrate that whisker-related projections from SI and MI are largely segregated. At some locations, the two projections are adjoining and partly overlapping. Furthermore, SI contributes significantly more corticopontine projections than MI. By comparison, projections from corresponding representations in SI and SII terminate in similar parts of the pontine nuclei and display considerable amounts of spatial overlap. Finally, comparison of corticopontine and corticostriatal projections in the same experimental animals reveals that SI-SII overlap is significantly larger in the pontine nuclei than in the neostriatum. These structural differences indicate a larger capacity for integration of information within the same sensory modality in the pontocerebellar system compared to the basal ganglia.

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Organization of pontocerebellar projections to identified climbing fiber zones in the rat

Pijpers

Ruigrok

2006

J of Comparative Neurology

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The organization of pontocerebellar projections to the paravermis and hemisphere of the posterior cerebellum of the rat was studied in relation to the organization of climbing fibers. Small injections of cholera toxin subunit B were placed in the cerebellar cortex at locations predetermined by evoked climbing fiber potentials from selected body parts or based on coordinates. The injection site was characterized with respect to the zebrin pattern and by the distribution of retrogradely labeled neurons in the inferior olive. The following zones were studied: hindlimb-related zones C1 and C2 of lobule VIII; forelimb-related zones C1, C2, and D0/D1 of the paramedian lobule; and face-related zones A2 of the paramedian lobule and C2 and D0 of crus 2B. The results show that the distribution of pontine neurons is closely related to the climbing fiber somatotopy. Injections centered on face-related zones result in distribution of pontine neurons within the pontine core region. Forelimb regions surround this core, whereas hindlimb regions are mostly supplied by caudal pontine regions and by a single patch of more rostrally located neurons. This distribution fits well with published data on the somatotopy of the corticopontine projection from the rat primary somatosensory cortex. However, apart from differences in the participation of ipsilaterally projecting cells, the distribution of pontine neurons does not change significantly when the injection covers different zones of the same lobule such as C1 and C2 of lobule VIII; C1, C2, and D0/D1 of the paramedian lobule; A2 of the paramedian lobule; and C2 and D0 of crus 2B.

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Three-dimensional computerised atlas of the rat brain stem precerebellar system: approaches for mapping, visualization, and comparison of spatial distribution data

Cited by 31 publications

References 58 publications

Catalyzing Inquiry at the Interface of Computing and Biology

Catalyzing Inquiry at the Interface of Computing and Biology

Three‐dimensional topography of corticopontine projections from rat sensorimotor cortex: Comparisons with corticostriatal projections reveal diverse integrative organization

Organization of pontocerebellar projections to identified climbing fiber zones in the rat

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