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3D Modeling of Dendritic Spines with Synaptic Plasticity
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A data-driven framework for modeling the dendritic spine continuum using dimensionality reduction and clustering

Uma Shashi Sharma1, Philip R LeDuc1, Yongjie Jessica Zhang1

  • 1Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

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Choosing the right computational methods is crucial for understanding dendritic spine morphology and its role in memory. This study presents a framework to systematically evaluate analysis strategies for accurate biological interpretation.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Biophysics

Background:

  • Dendritic spines are crucial for neuronal plasticity, memory, and learning.
  • Computational methods are vital for analyzing spine morphology, but analysis choices can impact biological interpretations.
  • Current approaches often lack systematic evaluation of dimensionality reduction and clustering strategies.

Purpose of the Study:

  • To present a decision-based visual characterization framework for systematically evaluating dimensionality reduction and clustering strategies in dendritic spine morphometry.
  • To assess how different computational methods influence low-dimensional representations of spine shape and their biological interpretations.
  • To introduce a Biological Transition Score (BTS) for quantifying the biological relevance of these representations.

Main Methods:

  • Compared PCA, ISOMAP, t-SNE, UMAP, and PCUMAP for dimensionality reduction.
  • Evaluated hierarchical clustering, Fuzzy C-Means, and Gaussian Mixture Models for probabilistic clustering.
  • Utilized labeled two-photon laser scanning microscopy (2PLSM) and a secondary dataset to assess generalization.
  • Introduced and applied the Biological Transition Score (BTS) to evaluate biological relevance.

Main Results:

  • Dimensionality reduction methods capture complementary aspects of spine morphology.
  • Nonlinear methods (e.g., PCUMAP) better preserve fine-scale structure on high-resolution data, while PCA is more robust to noise in lower-resolution data.
  • The optimal dimensionality reduction strategy is dataset-dependent.
  • Probabilistic clustering reveals a morphological continuum bridging traditional spine categories, resolving intra-class heterogeneity.

Conclusions:

  • A systematic, data-driven approach to method selection is essential for biologically grounded interpretations of dendritic spine morphology.
  • The proposed framework enables consistent and quantitative analysis of spine shape.
  • Weakly supervised representations can uncover biological insights beyond discrete manual classifications.