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Updated: Sep 20, 2025

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Functional Connectome-Based Predictive Modeling in Autism.

Corey Horien¹, Dorothea L Floris², Abigail S Greene¹

¹Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut.

Biological Psychiatry

|June 11, 2022

Summary

This summary is machine-generated.

Predictive modeling of brain activity using functional magnetic resonance imaging (fMRI) offers key insights into autism spectrum disorder (ASD). This approach helps understand brain network alterations and potential clinical applications for autism.

Keywords:

Clinical translation Development Fingerprinting Individual differences Machine learning Resting-state fMRI

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Last Updated: Sep 20, 2025

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Published on: October 13, 2023

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Published on: November 1, 2019

Area of Science:

Neuroscience
Developmental Neuroscience
Computational Neuroscience

Background:

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with varied presentations.
Functional magnetic resonance imaging (fMRI) studies have illuminated alterations in brain network activity associated with ASD.

Purpose of the Study:

To review the application of predictive modeling in understanding autism spectrum disorder (ASD).
To explore how predictive modeling, utilizing functional connectivity and symptom measures, reveals insights into ASD.
To discuss the potential clinical utility of predictive models in ASD.

Main Methods:

Review of predictive modeling frameworks applied to fMRI data in autism research.
Analysis of functional connectivity and symptom data within prediction models.
Consideration of interpretation challenges like data decay and sampling biases.

Main Results:

Predictive modeling has provided significant insights into brain-based features underlying autism symptomatology.
Different prediction frameworks offer varied perspectives on ASD neurobiology.
Potential for predictive models in clinical settings for autism is being explored.

Conclusions:

Predictive modeling is a valuable tool for advancing the understanding of autism spectrum disorder (ASD).
Careful interpretation of model results, considering biases, is crucial.
Future directions in predictive modeling hold promise for autism research and clinical practice.