ReviewCurrent opinion in neurobiology2019
Generative models and abstractions for large-scale neuroanatomy datasets.
Review in Current opinion in neurobiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction.Advances in neural information processing systems · 2022Article
- Creating artificial human genomes using generative neural networks.PLoS genetics · 2021Article
- A three-dimensional thalamocortical dataset for characterizing brain heterogeneity.Scientific data · 2020Article
- Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data.Neuroinformatics · 2020Article
- Current Challenges in Translational and Clinical fMRI and Future Directions.Frontiers in psychiatry · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Neural datasets are increasing rapidly in both resolution and volume. In neuroanatomy, this trend has been accelerated by innovations in imaging technology. As full datasets are impractical and unnecessary for many applications, it is important to identify abstractions that distill useful features of neural structure, organization, and anatomy. In this review article, we discuss several such abstractions and highlight recent algorithmic advances in working with these models. In particular, we discuss the use of generative models in neuroanatomy; such models may be considered 'meta-abstractions' that capture distributions over other abstractions.
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Registered trials
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