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In silico neuronal morphology classification: A systematic review.

Fábio Lobato1, Jéssica Leite2, Antonio Jacob3

  • 1Federal University of Western Pará, Institute of Engineering and Geosciences, Santarém, Pará, Brazil; State University of Maranhão, Department of Computer Engineering, São Luís, Maranhão, Brazil; University of São Paulo, Institute of Mathematics and Computer Sciences, São Carlos, São Paulo, Brazil.

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Summary
This summary is machine-generated.

This review highlights Artificial Neural Networks as the primary method for classifying neuronal morphology using microscopy images. Accuracy is the most common evaluation metric in this rapidly advancing field.

Keywords:
Computational intelligenceDeep learningMachine learningNeuromorphologyNeuronNeuroscience

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Bioinformatics

Background:

  • Understanding brain function relies on connectomics and neuronal diversity.
  • Classifying neuronal morphology is crucial but computationally complex.
  • A systematic review is needed to map current methods and future trends.

Purpose of the Study:

  • To review state-of-the-art computational methods for neuronal morphology classification.
  • To identify common data representations and evaluation metrics.
  • To provide an updated perspective guiding future neuroscience research.

Main Methods:

  • Systematic review of peer-reviewed studies from 2018-2024.
  • Searched five databases, collecting 840 papers.
  • Assessed 35 studies for quality, methodology, and reporting.

Main Results:

  • Artificial Neural Networks are the dominant computational method (21 papers).
  • Microscopy images are the most common data representation (30 papers).
  • Accuracy is the predominant evaluation measure (29 papers).

Conclusions:

  • Artificial Neural Networks and image-based features are key in current neuronal classification.
  • Standardized evaluation metrics are crucial for progress.
  • This review offers a roadmap for future research in neuronal morphology classification.