Software that performs a morphology-based approach for the automatic identification of outlier neurons based on neuronal tree structures. This tool was used by Zawadzki et al. (2012), who reported on and its application to the NeuroMorpho database. For the analysis, each neuron is represented by a feature vector composed of 20 measurements, which are projected into lower dimensional space with PCA. Bivariate kernel density estimation is then used to obtain a probability distribution for cells. Cells with high probabilities are understood as archetypes, while those with the small probabilities are classified as outliers. Further details about the method and its application in other domains can be found in Costa et al. (2009) and Echtermeyer et al. (2011).
This version requires Matlab (Mathworks Inc, Natick, USA) and allows the user to apply the workflow using a graphical user interface.
* L. d. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser. Beyond the average: Detecting global singular nodes from local features in complex networks, Europhysics Letters, 87, 1 (2009)
* C. Echtermeyer, L. d. Fontoura Costa, F. A. Rodrigues, M. Kaiser. Automatic Network Fingerprinting Through Single-Node Motifs, PLoS ONE 6, 9 (2011)
* K. d. Zawadzki, C. Feenders, M. P. Viana, M. Kaiser, and L. d. Fontoura Costa. Morphological Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells, Neuroinformatics (2012)
Resource Type: Resource
Version: Latest Version
neuron, feature-space, archetype, outlier, matlab, neuromorphometry, computational neuroscience
FAPESP, CNPq, EPSRC, Code Analysis Repository and Modelling for e-Neuroscience, Korean Ministry of Education Science and Technology, 05/00587-5, 301303/06-1, 2010/01994-1, 2010/16310-0, 573583/2008-0, EP/G03950X/1, R32-10142
Detection of Outlier NEurons
Additional Resource Types
GNU General Public License
Created 3 years ago by Anonymous
- Zawadzki K
- 2012 11
We report a morphology-based approach for the automatic identification of outlier neurons, as well as its application to the NeuroMorpho.org 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.