Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Brain age gap as a diffusion MRI-based marker of traumatic brain injury-related brain changes and associated outcomes.

Brain communications·2026

Same author

Perceived Sensitivity of Sensor-Based Digital Health Data: Qualitative Interview Study.

JMIR mHealth and uHealth·2026

Same author

PIGMENT: A deep learning framework for Porcine Immunohistochemistry seGMENTation.

bioRxiv : the preprint server for biology·2026

Same author

A transdiagnostic AI-based measure of interpersonal coordination in autism and other conditions.

Molecular autism·2026

Same author

Automatic measurement of social gaze during naturalistic conversations in autism.

Journal of psychiatric research·2026

Same author

Personalized machine learning-guided radiation dose escalation in newly diagnosed glioblastoma: prospective pilot study.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Feb 19, 2026

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

Whole brain white matter connectivity analysis using machine learning: An application to autism.

Fan Zhang¹, Peter Savadjiev¹, Weidong Cai²

¹Harvard Medical School, Boston MA, USA.

|October 29, 2017

Summary

This study introduces an automated method to analyze white matter connectivity for classifying autism spectrum disorder (ASD). The machine learning approach identified specific fiber tracts crucial for differentiating ASD from typically developing controls.

Keywords:

Autism spectrum disorder Fiber clustering Machine learning White matter connectivity

More Related Videos

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

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

Published on: November 8, 2012

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

Related Experiment Videos

Last Updated: Feb 19, 2026

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

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

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

Published on: November 8, 2012

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

Area of Science:

Neuroimaging
Machine Learning
Connectomics

Background:

White matter abnormalities are implicated in neurodevelopmental disorders.
Accurate characterization of white matter connectivity is essential for understanding brain function.

Purpose of the Study:

To develop and validate an automated method for white matter connectivity analysis using machine learning.
To identify discriminative fiber tracts for classifying autism spectrum disorder (ASD).

Main Methods:

Utilized diffusion MRI tractography and a data-driven approach to cluster white matter fiber tracts.
Extracted diffusion properties from fiber clusters as features for machine learning classification.
Applied a two-tensor fiber tracking model to investigate discriminative diffusion features.

Main Results:

Achieved 78.33% classification accuracy in differentiating children with ASD from typically developing controls (TDC).
Identified mean fractional anisotropy from the second tensor (crossing fibers) as significantly affected in ASD.
Found localized along-tract differences in diffusion properties within key white matter tracts.

Conclusions:

The proposed machine learning pipeline effectively identifies discriminative white matter tracts in ASD.
Key affected tracts include the corpus callosum, arcuate fasciculus, and others related to the cerebellum and brain stem.
This automated approach shows potential for characterizing white matter alterations in ASD.