Keywords: Neuro, Quantitative Susceptibility mapping, attention deficit hyperactivity disorder; susceptibility source separation; machine learning
Motivation: The relationship between brain iron homeostasis and metabolism in attention deficit hyperactivity disorder (ADHD).
Goal(s): To explore whether the paramagnetic iron abnormalities in the deep gray matter nuclei of individuals with ADHD.
Approach: APART-QSM was adopted to separate paramagnetic and diamagnetic distributions. The paramagnetic susceptibility values were used to quantify brain iron levels and to classify ADHD from healthy controls using machine learning algorithms.
Results: Lower paramagnetic susceptibility values in the left caudate nucleus and bilateral pallidum were found in ADHD. The paramagnetic susceptibility values of deep nuclei achieved promising classification performance.
Impact: The advanced approach to separate paramagnetic and diamagnetic substances susceptibility shows the ability to reveal brain iron deficiency in ADHD, and provides a potential biomarker for early diagnosis and a better understanding the pathological mechanism of ADHD.
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