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Related Concept Videos

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings.

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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Abnormal brain connectivity patterns in adults with ADHD: a coherence study.

João Ricardo Sato1, Marcelo Queiroz Hoexter, Xavier Francisco Castellanos

  • 1Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo Andre, Brazil. jrsatobr@gmail.com

Plos One
|October 11, 2012
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Summary

Attention-Deficit/Hyperactivity Disorder (ADHD) in adults shows abnormal brain connectivity patterns similar to young typically developing individuals. This suggests ADHD may be linked to brain maturation deficits, not just adult-specific changes.

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Area of Science:

  • Neuroscience
  • Developmental Neuroscience
  • Cognitive Neuroscience

Background:

  • Resting-state functional magnetic resonance imaging (fMRI) studies indicate reduced functional connectivity between the dorsal anterior cingulate cortex (dACC) and Default Mode Network (DMN) in adults with Attention-Deficit/Hyperactivity Disorder (ADHD) compared to typically developing (TD) individuals.
  • Traditional analyses often rely on Pearson correlation coefficients, which offer a limited view of complex brain connectivity.

Purpose of the Study:

  • To investigate functional connectivity between the dACC and posterior cingulate cortex (PCC) in adult ADHD patients and TD subjects using a more advanced machine learning approach.
  • To explore potential differences in connectivity patterns related to brain maturation in ADHD.

Main Methods:

  • Employed unsupervised machine learning, specifically a one-class support vector machine (OC-SVM), to quantify an abnormality index for functional connectivity between dACC and PCC.
  • Analyzed three groups: adult ADHD patients (n=21), age-matched TD subjects (n=21), and young TD subjects (n=21).
  • Validated the approach using fMRI data from the ADHD-200 Competition.

Main Results:

  • Adult ADHD patients exhibited a significantly higher median abnormality index compared to age-matched TD controls (p=0.014).
  • Connectivity patterns in adult ADHD patients were comparable to those in young TD subjects, but distinct from older TD subjects.
  • Low-frequency bands (below 0.05 Hz and around 0.20 Hz) were crucial for differentiating between ADHD patients and TD controls, and between older and younger TD groups.

Conclusions:

  • Abnormal brain coherence patterns in adult ADHD patients resemble those in young TD individuals, supporting the hypothesis that ADHD is associated with brain maturation deficits.
  • The OC-SVM approach provides a more comprehensive measure of functional connectivity abnormalities than traditional methods.
  • Findings highlight the importance of considering developmental trajectories in understanding ADHD pathophysiology.