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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Inferences on the Watts-Strogatz Model: A Study on Brain Functional Connectivity.

Allan Falconi-Souto¹, Rodrigo M Cabral-Carvalho², André Fujita^3,4

¹Postgraduate Program in Neuroscience and Cognition, Federal University of ABC, Alameda da Universidade, SN, São Bernardo do Campo, São Paulo, SP, 09606-045, Brazil. allan.falconi@ufabc.edu.br.

Neuroinformatics

|November 27, 2025

Summary

This summary is machine-generated.

Researchers developed a deep neural network to estimate the Watts-Strogatz model parameter (p) for brain networks. Age significantly predicted this parameter, highlighting neurodevelopment

Keywords:

ADHD-200 Deep neural network Functional connectivity Watts-Strogatz model

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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Area of Science:

Network Science
Computational Neuroscience
Machine Learning

Background:

Real-world network modeling is crucial for understanding complex systems.
The Watts-Strogatz model generates small-world networks but its parameter (p) is difficult to manipulate.
Investigating brain network structure requires robust modeling techniques.

Purpose of the Study:

To propose a novel deep neural network method for estimating the Watts-Strogatz model's rewiring probability (p).
To apply this method to resting-state functional magnetic resonance imaging (fMRI) data.
To explore the relationship between estimated network properties and neurodevelopmental factors.

Main Methods:

A deep neural network was trained to estimate the Watts-Strogatz parameter (p).
Resting-state fMRI data from the ADHD-200 database (ADHD patients and typically developing children) were utilized.
Generalized linear models were employed to analyze the relationship between estimated 'p' and participant characteristics.

Main Results:

The neural network successfully estimated the 'p' parameter for functional brain connectivity graphs.
Estimated 'p' values indicated small-world network structures (mean ± s.e.m.: 0.804 ± 0.003).
Age was a significant predictor of 'p' (mean ± s.e.m.: 4.410 ± 0.877; p < 0.001), but gender and diagnosis were not.

Conclusions:

The proposed deep learning approach offers an efficient framework for estimating the Watts-Strogatz model parameter.
Neurodevelopment, indicated by age, significantly influences brain network structure.
This method has the potential to enhance investigations into brain connectivity and network dynamics.