A data-driven framework for modeling the dendritic spine continuum using dimensionality reduction and clustering...

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.