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Spacetime concordance in the primate cortex.

Ping Wang1, Xinli Luo1, Xi-Nian Zuo1,2

  • 1State Key Lab of Cognitive Neuroscience and Learning, Beijing Normal University, No 19 Xinjiekouwai Street, Beijing, 100875, Beijing, China.

Biorxiv : the Preprint Server for Biology
|November 19, 2025
PubMed
Summary
This summary is machine-generated.

A new Regional Functional Affinity (RFA) metric analyzes brain connectivity in large datasets. This method reveals functional organization principles conserved across primate species, aiding comparative neuroscience.

Keywords:
functional brain parcellationfunctional diversityfunctional uniformityprimate cortexregional functional affinityspacetime concordance

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Area of Science:

  • Neuroscience
  • Comparative Neuroscience
  • Brain Imaging Analysis

Background:

  • Functional brain image datasets are growing in scale and complexity.
  • Existing methods require advanced space-time analytics for efficient processing.
  • Primate connectome analysis demands robust frameworks for understanding brain function.

Purpose of the Study:

  • To introduce a novel metric, Regional Functional Affinity (RFA), for quantifying functional diversity and uniformity in primate brain connectomes.
  • To develop a high-speed, adaptive framework (Spacetime Concordance - STC) for analyzing large-scale functional brain image data.
  • To validate the RFA metric using human and marmoset brain datasets for comparative neuroscience.

Main Methods:

  • Development of the Spacetime Concordance (STC) framework incorporating the Regional Functional Affinity (RFA) metric.
  • Application of STC and RFA to wakeful fMRI data from the Human Connectome Project (N = 1,003) and NIH Marmoset Brain Mapping Project (N = 26).
  • Analysis of functional heterogeneity and uniformity in relation to established brain parcellation boundaries.

Main Results:

  • Striking correspondence observed between Human Connectome Project atlas boundaries and functional heterogeneity, with low RFA values at boundaries.
  • Higher-order association networks in humans showed lower RFA (greater diversity), while sensorimotor networks showed higher RFA (greater uniformity).
  • Cross-species analysis revealed conserved organizational principles and species-specific adaptations in primate brain functional organization.

Conclusions:

  • The RFA metric effectively quantifies functional diversity and uniformity, bridging discrete and continuous brain models.
  • STC framework provides a data-driven, optimized solution for high-speed analysis of complex functional brain data.
  • Findings offer significant insights into primate brain organization and evolution, with broad relevance for comparative neuroscience.