ArticleScientific reports2023
Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 7 citations in OpenAlex.
- VENturing into machine learning for the morphological analysis of von Economo neurons.Scientific reports · 2025Article
- A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.Neuroinformatics · 2024Article
- A novel approach to cytoarchitectonics: developing an objective framework for the morphological analysis of the cerebral cortex.Frontiers in neuroanatomy · 2024Article
- Cell density quantification of high resolution Nissl images of the juvenile rat brain.Frontiers in neuroanatomy · 2024Article
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Authors and funding
5 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The complexity of the cerebral cortex underlies its function and distinguishes us as humans. Here, we present a principled veridical data science methodology for quantitative histology that shifts focus from image-level investigations towards neuron-level representations of cortical regions, with the neurons in the image as a subject of study, rather than pixel-wise image content. Our methodology relies on the automatic segmentation of neurons across whole histological sections and an extensive set of engineered features, which reflect the neuronal phenotype of individual neurons and the properties of neurons' neighborhoods. The neuron-level representations are used in an interpretable machine learning pipeline for mapping the phenotype to cortical layers. To validate our approach, we created a unique dataset of cortical layers manually annotated by three experts in neuroanatomy and histology. The presented methodology offers high interpretability of the results, providing a deeper understanding of human cortex organization, which may help formulate new scientific hypotheses, as well as to cope with systematic uncertainty in data and model predictions.
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