Attention deficit hyperactivity disorder (ADHD) is a mental disorder that often occurs in early childhood. Typical features of ADHD are inattention, impulsivity and hyperactivity. Preschool children with ADHD have increased motor activity and destructive behavior, and older children and adolescents with ADHD experience learning difficulties, aggressive behavior, clumsiness, impulsivity, difficulty in maintaining attention and risk-taking behaviors. About 5 to 10 percent of school-age children are diagnosed with ADHD. Besides, according to the study, 30 to 60 percent of children continue to show ADHD symptoms as adults. In the treatment of ADHD, education, behavioral therapy and medication of parents and teachers are the main components.
The most widely used drug to treat ADHD is psycho-stimulants such as methylphenidate and amphetamine. Pharmacological treatment fundamentally improves ADHD symptoms in the short term, while the efficacy data of psychosocial interventions is scarce and the inconsistent. Currently, drug treatment is more and more popular, and new drugs to treat ADHD are also on the market.
Case Study
Patient enrollment is a key challenge in most clinical trials. In order to solve this problem, all of our registered participants' regulations underwent clinical, neuropsychological, and imaging assessments. In addition, all ADHD participants completed the pretreatment and post-treatment MRI scans and other required enrollment without any missing data.
Another challenge is statistical analysis. We used IBM SPSS Statistics for statistical analyses. The alpha value was pre-selected at P<0.05.
One-way ANOVA was used to determine differences in clinical symptoms, neuropsychological performance, vital signs and measures of in-scanner head motion at baseline. One-way repeated-measures ANOVA was used to compare differences within subjects between Weeks 8-10 and baseline in each treatment arm of the clinical trial. Mixed-effect ANOVA was used to test for treatment time interaction effect. To control the risks of false-positives, all significant clusters in neuroimaging related statistical analyses were corrected for multiple comparisons at the cluster level by controlling topological family-wise error (FWE) calculated based on Random Field Theory implemented in SPM8, using a cluster-forming voxel-level height threshold of P< 0.01 and a spatial extent threshold (corrected for non-stationarity) that ensures a cluster-wise FWE at P< 0.05. For baseline comparisons, we analyzed rs-fMRI data in SPM8 using 2-sample t tests to determine significant differences in RSFC between ADHD and controls. For treatment effects, we used SPM8 to input the seed-based connectivity map of each ADHD participant into the 2×2 repeat measurement factor model.
Design:
A double-blind, placebo-controlled clinical trial.
Participants:
A total of 48 people participated in this experiment, including 24 patients with ADHD and 24 healthy controls (Figure 1).
All Participants Must Satisfy the Following Conditions:
- Age 18 to 52 years old.
- No major medical problems.
- Accepting the same treatment of clinical, psychiatric, neuropsychological, and MRI.
The 24 Patients with ADHD Need to Meet Those Conditions:
- Fulfilling the DSM-IV-TR criteria for childhood and current ADHD diagnosed by the corresponding author.
- Confirming with the semi-structured Conners’ Adult ADHD Diagnostic Interview as described in the DSM-IV (CAADID, Multi-Health Systems Inc).
Exclusion Criteria for Participants:
- Had any systemic medical illness.
- Had the history of bipolar disorder, psychosis, major depression, substance use disorder, pervasive developmental disorder.
- Had depressive or anxiety symptoms or suicidal ideations currently.
- Had been treated with any psychotropic agents, including medications for ADHD.
- IQ less than 80 as assessed by the Wechsler Adult Intelligence Scale.
Figure 1. Participant flow
Length of Enrollment Period:
8 weeks
Interventions:
Adults with ADHD (n=24) were randomly assigned to double-blind treatment with atomoxetine (n = 12) or placebo (n = 12) according to computer-generated random sequencing. Participants with ADHD were initially administered atomoxetine 0.5mg/kg/day in the morning at baseline and would titrate the drug dosage at week 2 (usually reaching the optimal dose, 1.2mg/kg/day), and at week 4 depending on clinical response and adverse effects (maximum daily dosage of atomoxetine=1.2 mg/kg).
Main Outcomes:
- Follow-up scans - primary outcome measures
- Changes in intrinsic resting-state functional connectivity (RSFC) with treatment response - primary outcome measures
- Baseline scans
Results:
Participants who received atomoxetine treatment showed changes in both primary outcomes compared with placebo.
Adults with ADHD, relative to the controls, did not have significant hyper connectivity in the cognitive control, ventral attention, affective, and default mode networks. In follow-up scans, we found significant time × treatment interactions throughout all major brain networks (Table 1). After treatment with atomoxetine, we detected stronger negative connections in the cognitive control network between the left dorsolateral prefrontal cortex (DLPFC) and left superior frontal gyrus, medial part (corresponding to the medial prefrontal cortex; P = 0.006); in the DMN between the left precuneus (PRE) and right middle frontal gyrus, lateral part (corresponding to the DLPFC; P = 0.042), and between the posterior cingulate cortex (PCC) and left inferolateral temporal lobe (P = 0.024); and in the dorsal attention network between the bilateral frontal eye field (FEF) (P = 0.008) and orbitofrontal cortex/medial prefrontal cortex (mPFC) (P = 0.002), respectively. After treatment with atomoxetine, the post-hoc analyses demonstrated increased connectivity strength in the default mode network (DMN) between the mPFC and right middle occipital/temporal gyrus (P< 0.001); in the affective network between the left subgenual anterior cingulate cortex (ACC) and right inferior temporal/middle occipital gyrus (P = 0.005); in the cognitive network between the left DLPFC and right hippocampus (P = 0.008); in the right ventral attention network between the right temporoparietal junction (TPJ) and left middle occipital gyrus (P = 0.037).
Table 1. Connections displaying treatment × time interactions
†The normalized voxel was resampled to the size of isotropic 3mm.
‡The cluster-forming threshold was set at voxel-level p<0.01
Abbreviations: ACC= anterior cingulate cortex; TPJ= temporoparietal junction; VFC= ventral frontal cortex; IPS= inferior parietal sulcus; FEF= frontal eye field; DLPFC= dorsolateral prefrontal cortex; PRE= precuneus; mPFC= medial prefrontal cortex; PCC= posterior cingulate cortex; MNI= Montreal Neurological Institute; BA= Brodmann area; FWE=Family-wise error; Rz= z-transformed Pearson’s correlational coefficient; SD= standard deviation.
We found that greater reductions in inattention symptoms showed a positive correlation with increased connectivity between the left TPJ and left middle temporal gyrus (MTG) (P< 0.001), between the left VFC and left TPJ (P = 0.001), between the right VFC and MTG (P = 0.046), and between the right ventral frontal cortex (VFC) and left TPJ (P = 0.017) in the ventral attention network. As hyperactivity/impulsivity improved, increased RSFC was observed in the ventral attention network between the left VFC and TPJ (P = 0.014), and in the DMN between the PCC and left middle/inferior occipital gyrus (P = 0.007). We identified negative correlations between symptom improvement and changes in RSFC between the right inferior parietal sulcus (IPS) and PRE in the dorsal attention network (inattention P = 0.022; hyperactivity/impulsivity P = 0.026). We also observed no significant associations between changes in behaviors and RSFC in the affective network in the) atomoxetine-treated adults.
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References:
1. Lin H Y, Gau S F. (2016) "Atomoxetine Treatment Strengthens an Anti-Correlated Relationship between Functional Brain Networks in Medication-Naïve Adults with Attention-Deficit Hyperactivity Disorder: A Randomized Double-Blind Placebo-Controlled Clinical Trial", International Journal of Neuropsychopharmacology, 19(3).
2. Kulkarni M. (2015) "Attention Deficit Hyperactivity Disorder", Indian Journal of Pediatrics, 82(3):267-271.
3. Matthews M, Nigg J T, Fair D A. (2014) "Attention Deficit Hyperactivity Disorder", Current Topics in Behavioral Neurosciences, 16(2):235.