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Pregled bibliografske jedinice broj: 1275042

Interpretable machine learning approach for neuron- centric analysis of human cortical cytoarchitecture


Štajduhar, Andrija; Lipić, Tomislav; Lončarić, Sven; Judaš, Miloš; Sedmak, Goran
Interpretable machine learning approach for neuron- centric analysis of human cortical cytoarchitecture // Scientific Reports, 13 (2023), 1; 5567, 12 doi:10.1038/s41598-023-32154-x (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1275042 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Interpretable machine learning approach for neuron- centric analysis of human cortical cytoarchitecture

Autori
Štajduhar, Andrija ; Lipić, Tomislav ; Lončarić, Sven ; Judaš, Miloš ; Sedmak, Goran

Izvornik
Scientific Reports (2045-2322) 13 (2023), 1; 5567, 12

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
N/A

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Temeljne medicinske znanosti, Javno zdravstvo i zdravstvena zaštita



POVEZANOST RADA


Projekti:
--KK.01.1.1.01.0007 - Znanstveni centar izvrnosti - Eksperimentalna i klinička istraživanja hipoksijsko-ishemijskog oštećenja mozga u perinatalnoj i odrasloj dobi (ZCI - NEURO) (Judaš, Miloš) ( CroRIS)

Ustanove:
Medicinski fakultet, Zagreb

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

Štajduhar, Andrija; Lipić, Tomislav; Lončarić, Sven; Judaš, Miloš; Sedmak, Goran
Interpretable machine learning approach for neuron- centric analysis of human cortical cytoarchitecture // Scientific Reports, 13 (2023), 1; 5567, 12 doi:10.1038/s41598-023-32154-x (međunarodna recenzija, članak, znanstveni)
Štajduhar, A., Lipić, T., Lončarić, S., Judaš, M. & Sedmak, G. (2023) Interpretable machine learning approach for neuron- centric analysis of human cortical cytoarchitecture. Scientific Reports, 13 (1), 5567, 12 doi:10.1038/s41598-023-32154-x.
@article{article, author = {\v{S}tajduhar, Andrija and Lipi\'{c}, Tomislav and Lon\v{c}ari\'{c}, Sven and Juda\v{s}, Milo\v{s} and Sedmak, Goran}, year = {2023}, pages = {12}, DOI = {10.1038/s41598-023-32154-x}, chapter = {5567}, keywords = {N/A}, journal = {Scientific Reports}, doi = {10.1038/s41598-023-32154-x}, volume = {13}, number = {1}, issn = {2045-2322}, title = {Interpretable machine learning approach for neuron- centric analysis of human cortical cytoarchitecture}, keyword = {N/A}, chapternumber = {5567} }
@article{article, author = {\v{S}tajduhar, Andrija and Lipi\'{c}, Tomislav and Lon\v{c}ari\'{c}, Sven and Juda\v{s}, Milo\v{s} and Sedmak, Goran}, year = {2023}, pages = {12}, DOI = {10.1038/s41598-023-32154-x}, chapter = {5567}, keywords = {N/A}, journal = {Scientific Reports}, doi = {10.1038/s41598-023-32154-x}, volume = {13}, number = {1}, issn = {2045-2322}, title = {Interpretable machine learning approach for neuron- centric analysis of human cortical cytoarchitecture}, keyword = {N/A}, chapternumber = {5567} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • Social Science Citation Index (SSCI)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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