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Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells

Lookup NU author(s): Professor Marcus Kaiser


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We report a morphology-based approach for the automatic identification of outlier neurons, as well as its application to the database, with more than 5,000 neurons. Each neuron in a given analysis is represented by a feature vector composed of 20 measurements, which are then projected into a two-dimensional space by applying principal component analysis. Bivariate kernel density estimation is then used to obtain the probability distribution for the group of cells, so that the cells with highest probabilities are understood as archetypes while those with the smallest probabilities are classified as outliers. The potential of the methodology is illustrated in several cases involving uniform cell types as well as cell types for specific animal species. The results provide insights regarding the distribution of cells, yielding single and multi-variate clusters, and they suggest that outlier cells tend to be more planar and tortuous. The proposed methodology can be used in several situations involving one or more categories of cells, as well as for detection of new categories and possible artifacts.

Publication metadata

Author(s): Zawadzki K, Feenders C, Viana MP, Kaiser M, Costa LD

Publication type: Article

Publication status: Published

Journal: Neuroinformatics

Year: 2012

Volume: 10

Issue: 4

Pages: 379-389

Print publication date: 22/05/2012

ISSN (print): 1539-2791

ISSN (electronic): 1559-0089

Publisher: Springer


DOI: 10.1007/s12021-012-9150-5


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Funder referenceFunder name
R32-10142Ministry of Education, Science and Technology
CARMEN e-science Neuroinformatics project
WCU program of the National Research Foundation of Korea