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Related Experiment Video

Updated: May 16, 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

Subgrouping autism and ADHD based on structural MRI population modelling centiles.

Clara Pecci-Terroba¹, Meng-Chuan Lai^2,3,4,5,6, Michael V Lombardo⁷

¹Department of Psychology, University of Cambridge, Cambridge, UK.

Molecular Autism

|June 4, 2025

Summary

This summary is machine-generated.

Population modeling identified distinct subgroups in autism and attention deficit hyperactivity disorder (ADHD), revealing varied neuroanatomical patterns. Method selection significantly impacts subgroup identification in these neurodevelopmental conditions.

Keywords:

ADHD Autism Neuroimaging Population modelling Structural MRI Subgrouping

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Last Updated: May 16, 2026

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Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

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Published on: May 7, 2014

Area of Science:

Neuroscience
Developmental Psychology
Biostatistics

Background:

Autism and ADHD are heterogeneous neurodevelopmental conditions with complex, variable neurobiology.
Existing neuroimaging studies show inconsistent results regarding characteristic neuroanatomical profiles.
Identifying homogeneous subgroups within or across these conditions is crucial for clinical insight.

Purpose of the Study:

To apply population modeling for clustering neurodevelopmental heterogeneity in autism and ADHD.
To compare a novel semi-supervised machine learning algorithm (HYDRA) with traditional clustering approaches.
To investigate neuroanatomical differences and subgroup characteristics within autism and ADHD datasets.

Main Methods:

Utilized a multi-site dataset with global and regional centile scores of cortical thickness, surface area, and grey matter volume.
Employed HYDRA, a novel semi-supervised machine learning algorithm, for population modeling and clustering.
Compared HYDRA's performance against a traditional clustering method.

Main Results:

Identified distinct subgroups within autism and ADHD, and across diagnoses, exhibiting varied neuroanatomical alterations compared to controls.
Subgroups were characterized by unique combinations of increased or decreased morphometric patterns.
No significant clinical differences were found across the identified subgroups.

Conclusions:

Population modeling is a valuable tool for exploring heterogeneity and subgrouping in autism and ADHD.
Distinct subgroups with specific neuroanatomical alteration patterns were identified.
Results underscore the critical influence of algorithm choice and feature selection in subgroup analysis, emphasizing the need for detailed methodological reporting.