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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: Jun 5, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Developing a multi-modal neuroimaging-based BrainAge model across childhood.

Shi Yu Chan¹, Pei Huang¹, Ai Ling Teh^1,2

¹Institute for Human Development and Potential, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.

Biorxiv : the Preprint Server for Biology

|June 4, 2026

Summary

Phase-specific BrainAge models using structural and functional neuroimaging best predict age and distinguish healthy from symptomatic children. This advances developmental brain health biomarkers for early intervention.

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Last Updated: Jun 5, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

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Published on: June 30, 2020

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Published on: June 30, 2014

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Published on: May 7, 2014

Area of Science:

Neuroscience
Developmental Biology
Biomarkers

Background:

BrainAge models are promising biomarkers for childhood developmental brain health.
Accurate models must capture dynamic neurodevelopmental changes across childhood.
Distinguishing normative variance from pathological divergence is crucial for early intervention.

Purpose of the Study:

To develop and evaluate BrainAge models reflecting distinct childhood developmental phases.
To compare phase-specific and full-span models using multi-modal neuroimaging data.
To identify optimal neuroimaging features for predicting age and clinical status in children.

Main Methods:

Utilized multi-modal neuroimaging data from three pediatric cohorts (ages 4-13 years, n=1005).
Developed twelve sex-stratified BrainAge models (Full-Span and Phase-Specific) using structural and functional features.
Compared model performance in age prediction and subgroup discrimination, benchmarking against existing models.

Main Results:

Phase-specific models incorporating structural (cortical thickness, subcortical volumes) and functional (network integration) features demonstrated superior age prediction.
The best-performing model effectively distinguished between healthy and symptomatic pediatric subgroups.
Findings support the development of higher temporal resolution BrainAge models aligned with developmental phases.

Conclusions:

Phase-specific, multi-modal BrainAge models offer improved accuracy for assessing childhood neurodevelopment.
These models serve as a proof-of-concept for advanced developmental biomarkers.
Optimized BrainAge models can enhance early detection and intervention for developmental brain health.