Investigation of Brain Functional Networks in Children Suffering from Attention Deficit Hyperactivity Disorder

Abstract

ADHD defects the recognition of facial emotions. This study assesses the neurophysiological differences between children with ADHD and matched healthy controls during a face emotional recognition task. The study also explores how brain connectivity is affected by ADHD. Electroencephalogram (EEG) signals were recorded from 64 scalp electrodes. Event-related phase coherence (ERPCOH) method was applied to pre-processed signals, and functional connectivity between any pair of electrodes was computed in different frequency bands. A logistic regression (LR) classifier with elastic net regularization (ENR) was trained to classify ADHD and HC participants using the functional connectivity of frequency bands as a potential biomarker. Subsequently, the brain network is constructed using graph-theoretic techniques, and graph indices such as clustering coefficient (C) and shortest path length (L) were calculated. Significant intra-hemispheric and the inter-hemispheric discrepancy between ADHD and healthy control (HC) groups in the beta band was observed. The graph features indicate that the clustering coefficient is significantly higher in the ADHD group than that in the HC group. At the same time, the shortest path length is significantly lower in the beta band. ADHD’s brain networks have a problem in transferring information among various neural regions, which can cause a deficiency in the processing of facial emotions. The beta band seems better to reflect the differences between ADHD and HC. The observed functional connectivity and graph differences could also be helpful in ADHD investigations.

Appendices

Appendix 1

In this Appendix, details of corrected p-values as well as size effect for significant main effect “Disease” in Table1 and interaction effect “Disease × Emotions” (in four emotions) in Tables 2, 3, 4 and 5 for the features of connectivity are provided. These details are related to Fig. 3b and c.

See Tables 4, 5, 6, 7, 8

Table 4 Details for significant main effect “Disease”

Table 5 Details for significant interaction effect “Disease × Emotions” (Angry face emotion)

Table 6 Details for significant interaction effect (Happy face emotion)

Table 7 Details for significant interaction effect “Disease × Emotions” (Neutral face emotion)

Table 8 Details for interaction effect “Disease × Emotions” (Sad face emotion)

Appendix 2

In this Appendix, details of corrected p-values as well as size effect for significant main effect “Disease” in Table1 and interaction effect “Disease × Emotions” (in four emotions) in Tables 2, 3, 4 and 5 for the features of shortest path lenght (L) are provided. These details are related to Fig. 4a.

See Tables 9, 10, 11, 12, 13

Table 9 details for significant main effect “Disease”

Table 10 Details for significant interaction effect “Disease × Emotions” (Angry face emotion)

Table 11 Details for significant interaction effect “Disease × Emotions” (Happy face emotion)

Table 12 Details for significant interaction effect “Disease × Emotions” (Neutral face emotion)

Table 13 Details for significant interaction effect “Disease × Emotions” (Sad face emotion)

Appendix 3

In this Appendix, details of corrected p-values as well as size effect for significant main effect “Disease” in Table1 and interaction effect “Disease × Emotions” (in four emotions) in Tables 2, 3, 4 and 5 for the features of clustering coefficient (C) are provided. These details are related to Fig. 4b.

See Tables 14, 15, 16, 17, 18

Table 14 details for significant main effect “Disease”

Table 15 Details for significant interaction effect “Disease × Emotions” (Angry face emotion)

Table 16 Details for significant interaction effect “Disease × Emotions” (Happy face emotion)

Table 17 Details for significant interaction effect “Disease × Emotions” (Neutral face emotion)

Table 18 Details for significant interaction effect “Disease × Emotions” (Sad face emotion)

Appendix 4

In this part, the rationale behind sample size (enrolling 49 participants) is provided. The sample size is calculated by the following formula:

$$n = \frac{{(Z_{{1 - \frac{\alpha }{2}}} )^{2} \times p(1 - p)}}{{d^{2} }}$$

where α is the type I error, p refers to proportion estimation, and d is the acceptable error in estimating the desired ratio.

In this study, we considered α = 0.05 so ${Z}_{1-\frac{\alpha }{2}}=1.96$. Due to the literature review (Polanczyk, Willcutt et al. 2014), the ADHD’s proportion (prevalence of ADHD) is estimated as 5%, so p = 0.05. Finally, the accepted error is considered as d = 0.1 in this study. Therefore, the sample size calculated as follows:

$$n = \frac{{(1.96)^{2} \times 0.05(0.095)}}{{(0.1)^{2} }} = 18.25 \approx 19$$

So we had to have 19 participants (with the 10% acceptable error) in each group. If we wanted to be more precise, we had to put the acceptable error as 5%. Then, the sample size would be:

$$n = \frac{{(1.96)^{2} \times 0.05(0.095)}}{{(0.05)^{2} }} = 72.99 \approx 73$$

Based on the limitations (time and budget), the acceptable error is considered to be 10%. SO, in the ADHD group, we had 24, and in the HC group, we had 25 participants.”