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Updated: Jun 24, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Mapping morphological cortical networks with joint probability distributions from multiple morphological features.

Yuqi Wang1, Junle Li1, Suhui Jin1

  • 1Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China.

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|June 8, 2024
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Integrating multiple brain imaging features creates more reliable and behaviorally relevant human brain connectivity networks (MCNs) than using single features alone.

Keywords:
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Area of Science:

  • Neuroimaging
  • Network Neuroscience
  • Computational Anatomy

Background:

  • Structural magnetic resonance imaging (sMRI) provides morphological features to infer human brain connectivity.
  • Integrating multiple morphological features for enhanced precision in morphological connectivity networks (MCNs) is theoretically beneficial but empirically understudied.
  • Methods for incorporating diverse morphological features into MCN construction remain an open research question.

Purpose of the Study:

  • To propose and validate a novel method for constructing cortical MCNs using multiple morphological features.
  • To assess the advantages of multi-feature MCNs over single-feature MCNs in terms of network topology, reliability, biological plausibility, and behavioral relevance.
  • To investigate the reproducibility and cross-species generalizability of the proposed method.

Main Methods:

  • Developed a method utilizing multi-dimensional kernel density estimation to model joint probability distributions of multiple morphological features.
  • Quantified inter-regional similarity using Jensen-Shannon divergence on estimated joint probability distributions.
  • Compared MCNs derived from combinations of four morphological features against single-feature MCNs.

Main Results:

  • Multi-feature MCNs exhibited a more integrated, less segregated network architecture with distinct hubs compared to single-feature MCNs.
  • MCNs constructed from multiple features demonstrated higher test-retest reliability and greater biological plausibility (more inter-hemispheric and intra-class connections).
  • Multi-feature MCNs explained greater inter-individual variance in behavioral and cognitive measures, with findings robust across different brain atlases and reproducible in macaque models.

Conclusions:

  • The proposed method effectively integrates multiple morphological features for constructing robust and informative MCNs.
  • Multi-feature MCNs offer superior topological organization, reliability, and explanatory power for behavior and cognition compared to single-feature approaches.
  • This work provides a valuable framework for advancing MCN research and understanding brain connectivity through multi-modal feature integration.