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Related Experiment Video

Updated: Jan 31, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Biological substrates of structure-function coupling in brain networks.

Panagiotis Fotiadis¹, Amy F T Arnsten², Linden Parkes³

¹Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA.

Neuroscience and Biobehavioral Reviews

|January 29, 2026

Summary

This summary is machine-generated.

The human brain

Keywords:

Computational neuroscience Cytoarchitecture Evolution Functional connectivity Glia Myeloarchitecture Neuromodulation Neurotransmission Structural connectivity Structure-function coupling

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Area of Science:

Neuroscience
Computational Neuroscience

Background:

Structure-function coupling varies across the human cortex.
Biological factors like evolution, myeloarchitecture, cytoarchitecture, and neuromodulation influence this variability.
Individual differences in structure-function coupling are significant.

Purpose of the Study:

To explore the reasons behind varying structure-function correlations in the human brain.
To integrate neurobiological insights with computational modeling.
To propose future directions for personalized brain modeling.

Main Methods:

Review of empirical studies on structure-function coupling.
Analysis of biological factors influencing brain connectivity.
Investigation of biologically inspired computational models.
Simulation of perturbations and lesions in brain networks.

Main Results:

Structure-function coupling is heterogeneous across cortical regions and individuals.
Evolution, myeloarchitecture, cytoarchitecture, and neuromodulation play key roles.
Computational models reveal causal mechanisms linking structure to function.
Simulations of lesions provide insights into network dynamics.

Conclusions:

Bridging neurobiology and computational modeling is crucial for accurate brain models.
Individualized, multi-layered network models incorporating biological gradients show promise.
Experimental validation of personalized models could lead to connectome-based clinical treatments.