Classification of GABAergic interneurons by leading neuroscientists

Published in Research Data
Classification of GABAergic interneurons by leading neuroscientists
Like

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

There is currently no unique catalogue of cortical GABAergic interneuron types, and building one is a major goal in neuroscience. In 2013, we asked 48 prominent neuroscientists to classify 320 interneurons by inspecting images of their morphology (see the paper here). That study was the first to quantify the degree of agreement among neuroscientists in morphology-based interneuron classification.

In our paper, out today in Scientific Data, we present the data set containing the classification choices provided by the 48 neuroscientists. These data can be used as training labels for learning supervised machine learning classifiers of interneurons, or pinpoint anatomical characteristics that make an interneuron especially difficult or especially easy to classify.

The idea of seeking a consensus on interneuron classification came following the meeting of the Petilla interneuron nomenclature Group, held in in 2005 in Petilla de Aragón, a small village in Navarra (Northern Spain), where Santiago Ramón y Cajal was born (see figure). We gathered a set of interneurons, built a web application for classification, and asked 48 neuroscientists, many of them members of the Petilla group, to classify the interneurons according to a proposed taxonomy. The data gathered is available here while an R package to simplify analysis is available here.

High-throughput generation of data is expected to enable learning a systematic taxonomy within a decade from data, by considering molecular, morphological, and electro-physiological features. Hopefully, our data can contribute towards that goal by allowing researchers to leverage the knowledge of leading neuroscientists.

Petilla de Aragón (Navarra), birthplace of Cajal. A, photograph of Main Street. The church of San Millán (XII-XIII century) can be seen in the background. B, the house where Cajal was born, located on Main Street. C, image taken from inside the Church of San Millán during one of the scientific sessions of the international meeting on classification of cortical neurons “Petilla terminology” (Ascoli et al., 2006). First row: Bernardo Rudy, György Buzsáki and Giorgio Ascoli. Second row: Carl Petersen, Andreas Burkhalter, Tamás Freund and Gábor Tamás. Third row: Ruth Benavides-Piccione and Lidia Alonso-Nanclares. Taken from DeFelipe, 2018 (Cajal's Neuronal Forest: Science and Art, Oxford University Press, New York).


References:

Mihaljević, B. et al. Classification of GABAergic interneurons by leading neuroscientists, Scientific Data 6, Article number: 221 (2019)

DeFelipe, J. et al. New insights into the classification and nomenclature of cortical GABAergic interneurons. Nature Reviews Neuroscience 14, 202–216 (2013)

Ascoli, G. A. et al. Petilla Terminology: Nomenclature of features of GABAergic interneurons of the cerebral cortex. Nature Reviews Neuroscience 9, 557-568 (2008).


Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Go to the profile of Bojan Mihaljević
almost 5 years ago

My e-phys and modelling friends alike will definitely appreciate this. Anything to make working with neurons a little easier, it's hard work!

Follow the Topic

Research Data
Research Communities > Community > Research Data

Related Collections

With collections, you can get published faster and increase your visibility.

Epidemiological data

This Collection presents a series of articles describing epidemiological datasets spanning diverse populations, ecosystems, and disease contexts. Data are presented without hypotheses or significant analyses, and can be derived from population surveys, health registries, electronic health records, field sampling, or other sources.

Publishing Model: Open Access

Deadline: Dec 22, 2024

Metabolomics

This collection presents a series of articles describing metabolomics datasets, covering data from any organism type, collected via any valid metabolomic technique, and for any application.

Publishing Model: Open Access

Deadline: Nov 28, 2024