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New insights into the classification and nomenclature of cortical GABAergic interneurons

Key Points

  • A feature-based classification and agreed-upon nomenclature of GABAergic interneurons of the cerebral cortex is much needed but currently lacking.

  • We designed a web-based interactive system that allowed 42 neuroscience experts to classify a representative sample of 320 cortical neurons and a selected set of simple morphology features based on reconstructions of their axonal arbors.

  • The consensus on and usefulness of these features and neuron names were investigated using agreement analysis, clustering algorithms, Bayesian networks and supervised classification on the resulting data.

  • The results quantitatively confirm the impression that different investigators use their own, mutually inconsistent classification schemes based on morphological criteria. However, the analyses also demonstrate that the community may be reaching consensus for a practical approach to the naming of certain anatomical terms that are useful for neuronal characterization and classification.

  • State-of-the-art machine learning approaches were shown to achieve discrimination capability equivalent to or better than human performance, opening the possibility of creating an objective computer tool for automatic classification of neurons, a Neuroclassifier.

Abstract

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.

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Figure 1: The web-based interactive system.
Figure 2: Schematics of the morphological features.
Figure 3: Agreement analysis.
Figure 4: Examples of inter-expert agreement and disagreement.
Figure 5: Clustering of neurons considering all features.
Figure 6: Examples of Bayesian networks.

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Acknowledgements

We thank the experts who took part in testing neuron classification using the web-based interactive system (in addition to the authors of the article), in alphabetical order: L. Alonso-Nanclares, C. Dávid, H. Geoffroy, M. Inan, V. Garcia-Marín, Á. Merchán-Pérez, L. McGarry, A. Muñoz, C. Palazzetti, N. Povysheva, D. Rotaru, R. Scott, R. Tremblay and A. Zaitsev. This work was supported by funding from the Spanish Ministry of Economy and Competitiveness (grants TIN2010-20900-C04-04 (to P.L.), SAF2009-09394 (to J.DF.) and the Cajal Blue Brain Project, Spanish partner of the Blue Brain Project initiative from EPFL (to J.DF. and P.L.)) and the National Institutes of Health under Grant R01-39600 (to G.A.A.).

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Correspondence to Javier DeFelipe, Pedro Larrañaga or Giorgio A. Ascoli.

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Supplementary information

Supplementary Information S1

A set of experts on cortical interneurons from different laboratories were asked to classify morphological reconstructions of interneurons (http://cajalbbp.cesvima.upm.es/gardenerclassification). (PDF 474 kb)

Supplementary Information S2

Analysis of raw data (PDF 3589 kb)

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FURTHER INFORMATION

Gardener Classification

ICNF Program on Ontologies of Neural Structures

NeuroLex

NeuroMorpho

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DeFelipe, J., López-Cruz, P., Benavides-Piccione, R. et al. New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat Rev Neurosci 14, 202–216 (2013). https://doi.org/10.1038/nrn3444

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