Sleep Spindle-Related EEG Connectivity in Children with Attention-Deficit/Hyperactivity Disorder: An Exploratory Study

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a neurobehavioral disorder with known brain abnormalities but no biomarkers to support clinical diagnosis. Recently, EEG analysis methods such as functional connectivity have rekindled interest in using EEG for ADHD diagnosis. Most studies have focused on resting-state EEG, while connectivity during sleep and spindle activity has been underexplored. Here we present the results of a preliminary study exploring spindle-related connectivity as a possible biomarker for ADHD. We compared sensor-space connectivity parameters in eight children with ADHD and nine age/sex-matched healthy controls during sleep, before, during, and after spindle activity in various frequency bands. All connectivity parameters were significantly different between the two groups in the delta and gamma bands, and Principal Component Analysis (PCA) in the gamma band distinguished ADHD from healthy subjects. Cluster coefficient and path length values in the sigma band were also significantly different between epochs, indicating different spindle-related brain activity in ADHD.

Keywords: ADHD; EEG; connectivity; graph analysis; sleep spindle.

Figure 1

The top panel represents the EEG of a spindle in the Fz channel. Red vertical lines identify the interval of the E1 pre-spindle epoch, green vertical lines the E2 spindle epoch and blue lines the E3 post-spindle epoch. The bottom panel shows the power of the same EEG signal with colors corresponding to power amplitude. The black ellipse highlights the frequency band (11–15 Hz) related to the spindle, in which a peak of power value is present.

Figure 2

Steps of sleep EEG epochs data processing (MVAR = multivariate autoregression, GPDC = generalized Partial Direct Coherence).

Figure 3

Bars indicate the mean number of connections for each band in the three epochs: E1 (pre-spindle), E2 (spindle) and E3 (post-spindle), with the standard deviations, in children with ADHD and in Controls. The symbol * highlights significant statistical differences between the two groups; horizontal lines indicate differences among epochs within each group.

Figure 4

Representations of “in-degree” (K_iin) values plotted versus K_iout in low (delta), middle (sigma) and high (gamma) frequency bands for the three epochs: on the left, the pre-spindle epoch (E1), at the center the spindle epoch (E2), on the right the post-spindle epoch (E3). Red triangles represent channels in the Controls group; blue circles represent channels in the ADHD group.

Figure 5

Representation of the first two components resulted from Principal Component Analysis (PCA) in the gamma band. Red triangles represent Controls group, blue circles ADHD group. Black line is the result of the application of the Fisher linear discriminant rule evaluated as K + L(1)x₁ + L(2)x₂ = 0.

Figure 6

(a) Mean values of C and L parameters, for each epoch, in delta, sigma and gamma bands. Red triangles represent the Control group, and blue circles the ADHD group. Vertical bars on the markers show the standard deviation of the values. The symbol * represents the significant difference between the two groups; black lines above (Controls significances) or below (ADHD significances) the markers show statistical significances between epochs in the two groups (b). Significant differences of single node clustering coefficient (C_i) between the two groups in all three epochs (pre-spindle E1, spindle E2 and post-spindle E3) in Delta, Sigma and Gamma bands. Fulfilled circles represent the channels with p < 0.05. Blue circles mean ADHD values greater than Controls, and red color vice versa.