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Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity.

Xun-Heng Wang1, Yun Jiao2, Lihua Li1

  • 1College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China.

Neuroscience Letters
|July 23, 2018
PubMed
Summary

Attention-deficit hyperactivity disorder (ADHD) diagnosis is improved using novel brain connectivity models. This study identified unique brain connectivity patterns in children with ADHD, offering potential biomarkers for improved classification and understanding of ADHD brain networks.

Keywords:
ADHDAnatomical MRIDiagnostic modelMorphological connectivity

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Psychiatry

Background:

  • Previous diagnostic models for ADHD relied on regional brain features.
  • Modeling individual interregional morphological connectivity presents significant challenges.

Purpose of the Study:

  • To identify children with ADHD using a novel interregional morphological connectivity model.
  • To discover discriminative patterns in ADHD patients.
  • To enhance diagnostic model performance using connectivity features.

Main Methods:

  • Extracted novel interregional morphological patterns using surface-based analysis.
  • Employed a hybrid machine learning method with leave-one-out cross-validation (LOOCV).
  • Trained and tested interregional morphological features for diagnostic modeling.

Main Results:

  • Interregional morphological connectivity significantly improved diagnostic model performance over regional features.
  • The optimized model achieved 74.65% total accuracy, 75% sensitivity, and 74.29% specificity.
  • Altered morphological connectivity was observed in regions including the insula, caudal anterior cingulate cortex, frontal pole, and postcentral cortex, correlating with clinical symptoms.

Conclusions:

  • Patients with ADHD exhibit altered morphological brain connectivity.
  • Altered connectivity patterns may serve as potential biomarkers for ADHD classification.
  • Identified discriminative features can benefit future research on ADHD brain network mechanisms.