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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Functional brain networks reflect spatial and temporal autocorrelation.

Maxwell Shinn^1,2, Amber Hu³, Laurel Turner³

¹Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.

Nature Neuroscience

|April 24, 2023

Summary

This summary is machine-generated.

Simple brain activity statistics, spatial and temporal autocorrelation, explain complex network topology measures. These findings link neuroimaging complexity to underlying biological processes.

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Area of Science:

Neuroscience
Network Neuroscience
Computational Neuroscience

Background:

High-throughput neuroscience methods generate vast, complex data.
Understanding emergent phenomena from simpler statistics is a key challenge.

Purpose of the Study:

To investigate if low-dimensional statistics explain complex network topology measures in resting-state functional magnetic resonance imaging (rs-fMRI).
To determine the neurobiological underpinnings of network topology variations.

Main Methods:

Analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data.
Application of complex topology measures from network neuroscience.
Generation of surrogate time series matched for spatial and temporal autocorrelation.

Main Results:

Spatial and temporal autocorrelation reliably explain numerous network topology measures.
Surrogate time series capture most individual and regional variation in topology measures.
Aging-related network topology changes are driven by spatial autocorrelation.
Serotonergic drugs induce topographic changes in temporal autocorrelation.

Conclusions:

Widely used complexity measures in network neuroscience can be reduced to simpler autocorrelation statistics.
This reductionistic approach offers a potential link between complex neuroimaging measures and neurobiology.