In Frontiers in psychiatry
Attention-deficit hyperactivity disorder (ADHD) is a debilitating disorder with apparent roots in abnormal brain development. Here, we quantified the level of individual brain maturation in children with ADHD using structural neuroimaging and a recently developed machine learning algorithm. More specifically, we compared the BrainAGE index between three groups matched for chronological age (mean ± SD: 11.86 ± 3.25 years): 89 children diagnosed with ADHD, 34 asymptomatic siblings of those children with ADHD, and 21 unrelated healthy control children. Brains of children with ADHD were estimated significantly younger (-0.85 years) than brains of healthy controls (Cohen's d = -0.33; p = 0.028, one-tailed), while there were no significant differences between unaffected siblings and healthy controls. In addition, more severe ADHD symptoms were significantly associated with younger appearing brains. Altogether, these results are in line with the proposed delay of individual brain maturation in children with ADHD. However, given the relatively small sample size (N = 144), the findings should be considered preliminary and need to be confirmed in future studies.
Kurth Florian, Levitt Jennifer G, Gaser Christian, Alger Jeffry, Loo Sandra K, Narr Katherine L, O’Neill Joseph, Luders Eileen
2022
ADHD, BrainAGE, brain, development, machine learning, relevance vector, sex, siblings
