A new software tool to generate graphical representations of neurons

Researchers from Universidad Politécnica de Madrid are involved in the development of a new method of graphic representation of neurons that allows us to visualize the data developed by neuroscientists.

A team of researchers from Universidad Politécnica de Madrid (UPM), Universidad Rey Juan Carlos (URJC) and the Cajal Institute (IC) -from the Spanish National Research Council (CSIC)− has developed a new model of symbolic representation of neurons that helps the analysis of their morphologies for neuroscience research.

This research project has been published in the Frontiers in Neuroanatomy journal and includes a software tool to generate graphical representations of neurons from 3D computer-aided reconstruction files.

The increasing number of cells reconstructed thanks to the improvement of techniques and equipment is shifting the bottleneck from the acquisition of data to the analysis of data. Therefore, the symbolic representation of data is a great leap forward in knowledge.

Every specific area has developed different visual models that are used both for analysis and for transfer of information and knowledge allowing access to these valuable scientific resources by the multidisciplinary teams who process such information.

Following this approach, UPM researchers in collaboration with researchers from URJC and CI have designed a graphical tool to better understand and detect the morphological characteristics of neuronal cells. In addition, this model can be also used for other representations such as the distribution of bronchi in a lung and the vascularization of the brain.

José Aliaga, a UPM researcher, says "we have not only developed a representation model with multiple graphical possibilities but also developed a study that validates its usability. This study contrasts with traditional methods of two-dimensional data projection".

The results suggest the proposed representation helps understand the main morphological characteristics of neurons, reducing the number of errors in their classification while improving the speed of reading.

The developed method, based on a symbolic representation that can be tailored to enhance a particular range of features of a neuron or neuron set, has shown to be useful for highlighting particular geometries that may be hidden due to the complexity of the analysis tasks and the richness of neuronal morphologies.

The increasing number of cells reconstructed thanks to the improvement of techniques and equipment is shifting the bottleneck from the acquisition of data to the analysis of data. Therefore, the symbolic representation of data is a great leap forward in knowledge.

Every specific area has developed different visual models that are used both for analysis and for transfer of information and knowledge allowing access to these valuable scientific resources by the multidisciplinary teams who process such information.

Following this approach, UPM researchers in collaboration with researchers from URJC and CI have designed a graphical tool to better understand and detect the morphological characteristics of neuronal cells. In addition, this model can be also used for other representations such as the distribution of bronchi in a lung and the vascularization of the brain.

José Aliaga, a UPM researcher, says "we have not only developed a representation model with multiple graphical possibilities but also developed a study that validates its usability. This study contrasts with traditional methods of two-dimensional data projection".

The results suggest the proposed representation helps understand the main morphological characteristics of neurons, reducing the number of errors in their classification while improving the speed of reading.

The developed method, based on a symbolic representation that can be tailored to enhance a particular range of features of a neuron or neuron set, has shown to be useful for highlighting particular geometries that may be hidden due to the complexity of the analysis tasks and the richness of neuronal morphologies.

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