New insights into the classification and nomenclature of cortical GABAergic interneurons

• Published in: Nature reviews. Neuroscience Vol. 14; no. 3; pp. 202 - 216
• Main Authors: DeFelipe, Javier, López-Cruz, Pedro L., Benavides-Piccione, Ruth, Bielza, Concha, Larrañaga, Pedro, Anderson, Stewart, Burkhalter, Andreas, Cauli, Bruno, Fairén, Alfonso, Feldmeyer, Dirk, Fishell, Gord, Fitzpatrick, David, Freund, Tamás F., González-Burgos, Guillermo, Hestrin, Shaul, Hill, Sean, Hof, Patrick R., Huang, Josh, Jones, Edward G., Kawaguchi, Yasuo, Kisvárday, Zoltán, Kubota, Yoshiyuki, Lewis, David A., Marín, Oscar, Markram, Henry, McBain, Chris J., Meyer, Hanno S., Monyer, Hannah, Nelson, Sacha B., Rockland, Kathleen, Rossier, Jean, Rubenstein, John L. R., Rudy, Bernardo, Scanziani, Massimo, Shepherd, Gordon M., Sherwood, Chet C., Staiger, Jochen F., Tamás, Gábor, Thomson, Alex, Wang, Yun, Yuste, Rafael, Ascoli, Giorgio A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.03.2013; Nature Publishing Group
• Subjects: 631/378/2571/1696; 87; Algorithms; analysis; Animal Genetics and Genomics; Animals; Bayes Theorem; Behavioral Sciences; Biological Techniques; Biomedical and Life Sciences; Biomedicine; Cerebral cortex; Cerebral Cortex - cytology; Cerebral Cortex - metabolism; Classification; Cluster Analysis; GABA; gamma-Aminobutyric Acid - metabolism; Humans; Interneurons; Interneurons - classification; Interneurons - cytology; Interneurons - metabolism; Life Sciences; Medicine; Morphology; Neurobiology; Neurons; Neurons and Cognition; Neurosciences; Ontology; Physiological aspects; Terminology as Topic; France; United States; Spain; New York; Aachen Germany; United States > US; Florida; Madrid Spain; Germany; Pennsylvania
• ISSN: 1471-003X; 1471-0048; 1471-0048; 1469-3178
• DOI: 10.1038/nrn3444

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. The classification of cortical neurons, including interneurons, remains a thorny issue in neuroscience. This Analysis article presents and tests a possible taxonomical solution for classifying cortical GABAergic interneurons based on a web-based interactive system that allows experts to classify neurons with pre-determined morphological criteria. 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.