Dataset Information

Classification of electrophysiological and morphological neuron types in the mouse visual cortex.

ABSTRACT: Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.

SUBMITTER: Gouwens NW

PROVIDER: S-EPMC8078853 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

Classification of electrophysiological and morphological neuron types in the mouse visual cortex.

Gouwens Nathan W NW Sorensen Staci A SA Berg Jim J Lee Changkyu C Jarsky Tim T Ting Jonathan J Sunkin Susan M SM Feng David D Anastassiou Costas A CA Barkan Eliza E Bickley Kris K Blesie Nicole N Braun Thomas T Brouner Krissy K Budzillo Agata A Caldejon Shiella S Casper Tamara T Castelli Dan D Chong Peter P Crichton Kirsten K Cuhaciyan Christine C Daigle Tanya L TL Dalley Rachel R Dee Nick N Desta Tsega T Ding Song-Lin SL Dingman Samuel S Doperalski Alyse A Dotson Nadezhda N Egdorf Tom T Fisher Michael M de Frates Rebecca A RA Garren Emma E Garwood Marissa M Gary Amanda A Gaudreault Nathalie N Godfrey Keith K Gorham Melissa M Gu Hong H Habel Caroline C Hadley Kristen K Harrington James J Harris Julie A JA Henry Alex A Hill DiJon D Josephsen Sam S Kebede Sara S Kim Lisa L Kroll Matthew M Lee Brian B Lemon Tracy T Link Katherine E KE Liu Xiaoxiao X Long Brian B Mann Rusty R McGraw Medea M Mihalas Stefan S Mukora Alice A Murphy Gabe J GJ Ng Lindsay L Ngo Kiet K Nguyen Thuc Nghi TN Nicovich Philip R PR Oldre Aaron A Park Daniel D Parry Sheana S Perkins Jed J Potekhina Lydia L Reid David D Robertson Miranda M Sandman David D Schroedter Martin M Slaughterbeck Cliff C Soler-Llavina Gilberto G Sulc Josef J Szafer Aaron A Tasic Bosiljka B Taskin Naz N Teeter Corinne C Thatra Nivretta N Tung Herman H Wakeman Wayne W Williams Grace G Young Rob R Zhou Zhi Z Farrell Colin C Peng Hanchuan H Hawrylycz Michael J MJ Lein Ed E Ng Lydia L Arkhipov Anton A Bernard Amy A Phillips John W JW Zeng Hongkui H Koch Christof C

Nature neuroscience 20190617 7

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these ...[more]

PMID: 31209381

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Project description:IntroductionThe visual cortex is a key region in the mouse brain, responsible for processing visual information. Comprised of six distinct layers, each with unique neuronal types and connections, the visual cortex exhibits diverse decoding properties across its layers. This study aimed to investigate the relationship between visual stimulus decoding properties and the cortical layers of the visual cortex while considering how this relationship varies across different decoders and brain regions.MethodsThis study reached the above conclusions by analyzing two publicly available datasets obtained through two-photon microscopy of visual cortex neuronal responses. Various types of decoders were tested for visual cortex decoding.ResultsOur findings indicate that the decoding accuracy of neuronal populations with consistent sizes varies among visual cortical layers for visual stimuli such as drift gratings and natural images. In particular, layer 4 neurons in VISp exhibited significantly higher decoding accuracy for visual stimulus identity compared to other layers. However, in VISm, the decoding accuracy of neuronal populations with the same size in layer 2/3 was higher than that in layer 4, despite the overall accuracy being lower than that in VISp and VISl. Furthermore, SVM surpassed other decoders in terms of accuracy, with the variation in decoding performance across layers being consistent among decoders. Additionally, we found that the difference in decoding accuracy across different imaging depths was not associated with the mean orientation selectivity index (OSI) and the mean direction selectivity index (DSI) neurons, but showed a significant positive correlation with the mean reliability and mean signal-to-noise ratio (SNR) of each layer's neuron population.DiscussionThese findings lend new insights into the decoding properties of the visual cortex, highlighting the role of different cortical layers and decoders in determining decoding accuracy. The correlations identified between decoding accuracy and factors such as reliability and SNR pave the way for more nuanced understandings of visual cortex functioning.

Dataset Information

Classification of electrophysiological and morphological neuron types in the mouse visual cortex.

Publications

Classification of electrophysiological and morphological neuron types in the mouse visual cortex.

Similar Datasets

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