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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

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Published on: August 7, 2017

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder.

Adèle Helena Ribeiro¹, Maciel Calebe Vidal², João Ricardo Sato³

¹Data Science Institute, Columbia University, New York, NY 10027, USA.

Entropy (Basel, Switzerland)

|September 28, 2021

Summary

This study introduces a new method to measure information flow between dynamic networks using Granger causality (G-causality). The findings reveal differences in brain hemisphere communication in children with Autism Spectrum Disorder (ASD).

Keywords:

Granger causality autism spectrum disorder brain connectivity random graphs spectral radius

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Last Updated: Oct 18, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

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Area of Science:

Network science
Time-series analysis
Computational neuroscience

Background:

Dynamic networks and time-evolving data are increasingly common.
Understanding information flow in these networks is crucial.
Standard methods like vector autoregressive models are insufficient for time-varying graphs.

Purpose of the Study:

To develop a theoretical framework for modeling time-varying graphs.
To infer Granger causality (G-causality) between network time series.
To apply this framework to understand brain hemisphere communication in Autism Spectrum Disorder (ASD).

Main Methods:

Proposed a mathematical graph model with time-varying parameters.
Defined G-causality between graphs based on G-causality of their parameters.
Utilized the spectral radius as an estimator for graph model parameters, even when the model is unknown.

Main Results:

Demonstrated that the spectral radius can estimate parameters of random graph models.
Successfully applied the G-causality framework to analyze brain hemisphere interactions.
Identified significant differences in G-causality intensity from the right to the left brain hemisphere between ASD and control groups.

Conclusions:

The proposed method enables G-causality inference between time-varying graphs.
This approach offers new insights into network dynamics and information flow.
The study highlights distinct patterns of interhemispheric communication in ASD.