Related Experiment Video
Updated: Mar 25, 2026
Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
Published on: September 12, 2011
Identification of Infants at Risk for Autism Using Multi-parameter Hierarchical White Matter Connectomes
Yan Jin1, Chong-Yaw Wee1, Feng Shi1
1Department of Radiology and BRIC, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Insights
Researchers developed a new method to identify infants at high risk for autism spectrum disorder (ASD) at 6 months old using brain white matter connectivity. This approach improves early detection of ASD, enabling timely interventions.
Area of Science:
- Neuroscience
- Developmental Psychology
- Medical Imaging
Background:
- Autism spectrum disorder (ASD) presents lifelong communication and social challenges, with early signs detectable within the first year of life.
- Abnormalities in white matter (WM) connectivity have been observed in infants later diagnosed with ASD.
- Current ASD diagnosis is typically made around 2 years of age, missing crucial early developmental windows.
Purpose of the Study:
- To introduce a novel multi-kernel support vector machine (SVM) framework for early identification of infants at high risk for ASD.
- To utilize diffusion parameters from a hierarchical set of WM connectomes for risk assessment at 6 months of age.
- To enhance the accuracy of ASD risk prediction by integrating information from multiple levels of brain connectivity.
Main Methods:
- Development of a multi-kernel support vector machine (SVM) framework.
- Analysis of diffusion parameters derived from a hierarchical set of white matter (WM) connectomes.
- Classification of infants at 6 months old based on WM connectivity patterns.
Main Results:
- The proposed multi-kernel SVM framework achieved a classification accuracy of 76% for identifying infants at high risk for ASD.
- This performance surpasses the accuracy of 70% obtained using a single connectome.
- Complementary information from hierarchical networks significantly improved classification, with key discriminative connections aligning with existing research.
Conclusions:
- The novel framework provides crucial imaging connectomic markers for early ASD risk evaluation.
- This method enables the assessment of ASD risks as early as 6 months of age.
- Early identification through advanced neuroimaging analysis can facilitate timely interventions and support for affected infants.
Abstract:
Autism spectrum disorder (ASD) is a variety of developmental disorders that cause life-long communication and social deficits. However, ASD could only be diagnosed at children as early as 2 years of age, while early signs may emerge within the first year. White matter (WM) connectivity abnormalities have been documented in the first year of lives of ASD subjects. We introduce a novel multi-kernel support vector machine (SVM) framework to identify infants at high-risk for ASD at 6 months old, by utilizing the diffusion parameters derived from a hierarchical set of WM connectomes. Experiments show that the proposed method achieves an accuracy of 76%, in comparison to 70% with the best single connectome. The complementary information extracted from hierarchical networks enhances the classification performance, with the top discriminative connections consistent with other studies. Our framework provides essential imaging connectomic markers and contributes to the evaluation of ASD risks as early as 6 months.
