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Related Concept Videos

Brain Imaging01:14

Brain Imaging

199
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...
199

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Related Experiment Video

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Reliable Multimodal Brain Signatures Predict Mental Health Outcomes in Children.

Kathryn Y Manning1, Alberto Llera2, Catherine Lebel1

  • 1Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Developmental Neuroimaging Lab, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.

Biological Psychiatry. Cognitive Neuroscience and Neuroimaging
|March 19, 2025
PubMed
Summary
This summary is machine-generated.

Early childhood brain differences predict later mental health. Multimodal brain signatures identified in children aged 9-10 years linked to depression and anxiety symptoms, offering targets for early intervention.

Keywords:
AdolescenceBrainChildMRIMental healthMultimodal

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Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Psychiatry

Background:

  • Individual brain variations may precede mood and anxiety disorders, but specific alterations are not fully understood.
  • Previous research often used single imaging modalities, limiting comprehensive neurobiological insights.
  • Multimodal image analysis offers a more holistic approach to understanding mental health neurobiology.

Purpose of the Study:

  • To identify linked variations in brain structure and microstructure that predict longitudinal mental health symptoms in children.
  • To investigate multimodal brain signatures associated with depression, anxiety, and behavioral outcomes.
  • To examine brain differences in twins discordant for at-risk behaviors.

Main Methods:

  • Utilized data-driven linked independent component analysis on a large cohort (N > 10,000) from the Adolescent Brain Cognitive Development (ABCD) Study.
  • Applied multimodal analysis to cortical structure and white matter microstructure data.
  • Examined brain differences in a subsample of twins with at-risk behaviors.

Main Results:

  • Two multimodal brain signatures at ages 9-10 predicted longitudinal mental health symptoms up to age 12.
  • Cortical variations linked with white matter microstructure predicted higher depression and anxiety symptoms.
  • Specific brain patterns predicted behavioral inhibition, sensation seeking, and psychosis symptoms in males and differed in twins with self-injurious behavior.

Conclusions:

  • Reliable, multimodal brain patterns in childhood predict long-term mental health outcomes.
  • These findings highlight potential targets for early identification of at-risk children before disorder onset.
  • Multimodal neuroimaging provides valuable insights into the developmental trajectories of mental health.