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Algorithmic Extraction of Morphological Statistics from Electronic Archives of Neuroanatomy

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Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence (IWANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2084))

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Abstract

A large amount of digital data describing the 3D structure of neuronal cells has been collected by many laboratories worldwide in the past decade. Part of these data is made available to the scientific community through internet-accessible archives. The potential of such data sharing is great, in that the experimental acquisition of high-resolution and complete neuronal tracing reconstructions is extremely time consuming. Through electronic databases, scientists can reanalyze and mine archived data in a fast and inexpensive way. However, the lack of software tools available for this purpose has so far limited the use of shared neuroanatomical data. Here we introduce L-Measure (LM), a free software package for the extraction of morphological data from digitized neuronal reconstructions. LM consists of a user-friendly graphical interface and a flexible core engine. LM allows both single-neuron study and the statistical analysis of sets of neurons. Studies can be limited to specific regions of the dendrites; morphological statistics can be returned as raw data, as frequency histograms, or as cross-parameter dependencies. The current version of LM (v1.0) runs under Windows and its output is compatible with MS-Excel.

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Author information

Authors and Affiliations

  1. Krasnow Institute for Advanced Study and Department of Psychology, George Mason University, mail Stop 2A1, 4400 University Dr., Fairfax, VA, 22030, USA

    Ruggero Scorcioni & Giorgio A. Ascoli

Authors
  1. Ruggero Scorcioni
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  2. Giorgio A. Ascoli
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Editor information

Editors and Affiliations

  1. Departamento de Inteligencia Artificial Sanda del Rey, Universidad Nacional de Educación a Distancia, s/n., Madrid, 28040, Spain

    José Mira

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Campus Fuentenueva, 18071, Granada, Spain

    Alberto Prieto

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© 2001 Springer-Verlag Berlin Heidelberg

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Scorcioni, R., Ascoli, G.A. (2001). Algorithmic Extraction of Morphological Statistics from Electronic Archives of Neuroanatomy. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_4

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  • DOI: https://doi.org/10.1007/3-540-45720-8_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42235-8

  • Online ISBN: 978-3-540-45720-6

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