Regional brain volume predicts response to methylphenidate treatment in individuals with ADHD

Abstract

Background: Despite the effectiveness of methylphenidate for treating ADHD, up to 30% of individuals with ADHD show poor responses to methylphenidate. Neuroimaging biomarkers to predict medication responses remain elusive. This study characterized neuroanatomical features that differentiated between clinically good and poor methylphenidate responders with ADHD.

Methods: Using a naturalistic observation design selected from a larger cohort, we included 79 drug-naive individuals (aged 6-42 years) with ADHD without major psychiatric comorbidity, who had acceptable baseline structural MRI data quality. Based on a retrospective chart review, we defined responders by individuals' responses to at least one-month treatment with methylphenidate. A nonparametric mass-univariate voxel-based morphometric analysis was used to compare regional gray matter volume differences between good and poor responders. A multivariate pattern recognition based on the support vector machine was further implemented to identify neuroanatomical indicators to predict an individual's response.

Results: 63 and 16 individuals were classified in the good and poor responder group, respectively. Using the small-volume correction procedure based on the hypothesis-driven striatal and default-mode network masks, poor responders had smaller regional volumes of the left putamen as well as larger precuneus volumes compared to good responders at baseline. The machine learning approach identified that volumetric information among these two regions alongside the left frontoparietal regions, occipital lobes, and posterior/inferior cerebellum could predict clinical responses to methylphenidate in individuals with ADHD.

Conclusion: Our results suggest regional striatal and precuneus gray matter volumes play a critical role in mediating treatment responses in individuals with ADHD.

Keywords: ADHD; Methylphenidate; Striatum; Support vector machine; Treatment response; VBM.

Fig. 1

Flow diagram of the procedure. One hundred and forty ADHD participants without previous drug exposure were enrolled initially. Nineteen participants were excluded due to poor quality of T1-weighted images, and 42 individuals were excluded due to comorbidity, received no medication or treatment with atomoxetine, diagnosed with the major psychiatric disorders later, or had the loss to follow-up at the clinic within one month. The final sample of 79 medication-naïve participants with ADHD was divided into good responder group and poor responder groups based on the Clinical Global Impressions–Improvement Scale and then proceeded with further image analysis

Fig. 2

Mass-univariate analysis of relative regional gray matter volumes between participants with ADHD with good and poor methylphenidate response. a Using a small-volume correction within the striatum, the good responders had significantly larger GM volumes in the left putamen cluster (1738 mm3, FWE-p = 0.032) than the poor responders. b Within the DMN mask, the good responders had a significantly smaller GM volume in the bilateral precuneus than the poor responders (3642 mm3, FWE-p = 0.012)

Fig. 3

The top 17 areas recognized by machine learning with leave-one-out and 5-folds cross-validation. The bilateral occipital lobes, cerebellar vermis and posterior/inferior cerebellum, posterior cingulate/precuneus, left putamen, and left parietal lobe, and bilateral lateral prefrontal cortex were recognized as the most informative regions for classification between good and poor responders. The color range displayed represents the weight of each ROI, contributing to pattern classification