ArticleNeuroinformatics2020
A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination.
Article in Neuroinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 36 citations in OpenAlex.
- Functional KCC2 expression marks an evolutionarily conserved population of early-maturing interneurons in the perinatal cortex.Nature communications · 2025Article
- Article
- High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy.Nature communications · 2024Article
- Multi-level feature fusion network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Single-Cell and Subcellular Analysis Using Ultrahigh Resolution 21 T MALDI FTICR Mass Spectrometry.Analytical chemistry · 2023Article
- Loss or gain of function? Effects of ion channel mutations on neuronal firing depend on the neuron type.Frontiers in neurology · 2023Article
- Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test.Neuroinformatics · 2023Article
- Polymer Physics-Based Classification of Neurons.Neuroinformatics · 2023Article
- Olfactory responses ofeLife · 2022Article
- Large scale similarity search across digital reconstructions of neural morphology.Neuroscience research · 2022Article
- nGauge: Integrated and Extensible Neuron Morphology Analysis in Python.Neuroinformatics · 2022Article
- Topological Sholl descriptors for neuronal clustering and classification.PLoS computational biology · 2022Article
- Article
- Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas.Nature communications · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Quantitative analysis of neuronal morphologies usually begins with choosing a particular feature representation in order to make individual morphologies amenable to standard statistics tools and machine learning algorithms. Many different feature representations have been suggested in the literature, ranging from density maps to intersection profiles, but they have never been compared side by side. Here we performed a systematic comparison of various representations, measuring how well they were able to capture the difference between known morphological cell types. For our benchmarking effort, we used several curated data sets consisting of mouse retinal bipolar cells and cortical inhibitory neurons. We found that the best performing feature representations were two-dimensional density maps, two-dimensional persistence images and morphometric statistics, which continued to perform well even when neurons were only partially traced. Combining these feature representations together led to further performance increases suggesting that they captured non-redundant information. The same representations performed well in an unsupervised setting, implying that they can be suitable for dimensionality reduction or clustering.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.