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    • Compared to fMRI BOLD signals

    2018-10-25

    Compared to fMRI BOLD signals, which map neural activity by imaging haemodynamic responses, DC-EEG offers a more direct measure of spontaneous VLF oscillations (VLFOs), albeit with relatively limited spatial resolution. While the functional significance of VLFOs and its relation to BOLD signals continue to be debated, recent DC-EEG studies have also identified a temporally and spatially stable resting VLF EEG network in healthy young adults with maximal power distributed across midline frontal and posterior scalp regions (Helps et al., 2008). The attenuation of VLF EEG power within this network following the transition from rest to the performance of cognitive demanding tasks has been replicated a number of times (Helps et al., 2009). The intra-cranial sources of this scalp activity have been localized and appear to overlap to some degree with DMN heat shock protein inhibitors regions (Broyd et al., 2011). Moreover, children and adolescents with ADHD display reduced attenuation when working on attention demanding tasks with this reduction correlated with their attentional performance (Helps et al., 2010). In an apparently unrelated way, individuals with ADHD also have difficulty waiting for future outcomes and prefer to choose smaller sooner (SS) over larger later rewards (LL) even when this leads to less reward overall (Marco et al., 2009). Explanations for this “impulsive choice” in ADHD (Robbins et al., 2012) have focused on: (i) a reduced ability to resist temptation linked to executive dysfunction (Barkley et al., 2001); (ii) increased discounting of the value of future rewards (Scheres et al., 2010), reflecting hypo-activation of reward brain centres (e.g., ventral striatum; Plichta and Scheres, 2014), and; (iii) negative affect generated by the experience of delay (i.e. delay aversion; Sonuga-Barke, 2002) mediated by hyper-activation within the brain\'s emotion centres (e.g. insula and amygdala; Lemiere et al., 2012; Plichta et al., 2009; Wilbertz et al., 2013). Interestingly, the potential role of intrinsic brain activity during the process of waiting in individuals with ADHD has not been investigated. A lot is known about the resting brain in ADHD; but nothing about the waiting brain. Hsu and colleagues recently drew a parallel between waiting and resting brain states – highlighting some similarities and also some important differences (Hsu et al., 2013). In particular, they pointed out how both states involve the experience of a period of idle time. In other ways, they argued, these states are different, as waiting is always directed to a specified outcome in the future while the goal of resting may be purely recuperative. In this sense, waiting and resting can be seen as similar activities framed motivationally in different ways. Interestingly a comparison of EEG activity, made by the authors, revealed that in typically developing adults the VLFO signature for waiting, especially when this was freely chosen and rewarded, was more similar to that displayed while working (on a simple cognitive task) than during resting – with VLFO power attenuation seen in anterior and posterior medial scalp regions in both states (Hsu et al., 2013).
    Materials and methods
    Results Individuals with ADHD had higher delay aversion and temporal discounting scores on the QDQ (Table 1). The levels of mean VLFO power within the network of each condition are shown in Fig. 1. There was a significant main effect of Group (F(1, 37)=16.92, p<.001) and Condition (F(3, 111)=21.22, p<.001) and a significant Group by Condition interaction (F(3, 111)=9.02, p<.001). Controls displayed significantly and substantially lower levels of VLFO activity in WORK, F-WAIT and C-WAIT compared to REST (Control: Cohen\'s dWORK=1.41; dC-WAIT=1.17; dF-WAIT=1.21). In the ADHD group the VLFO power was reduced in WORK compared to REST, but not in C-WAIT or F-WAIT (ADHD: Cohen\'s dWORK=0.82; dC-WAIT=−0.16; dF-WAIT=−0.34). Moreover, the REST-to-WORK attenuation effect in ADHD was significantly smaller than seen in controls (t (38)=2.30; p<.03). Adding IQ as a covariate reduced the Condition effect (F<1.50; p>20), but other effects remained significant (FGroup>4.84, p<.05; FGroup by Condition>4.94, p<.05). The correlation between the REST-to-WORK attenuation in scalp VLFO power and performance error on the 2CRT task was not significant (r=−.14, p>.05). However, the REST-to-WAIT but not REST-to-WORK difference in scalp VLFO power negatively correlated with parents’ combined QDQ ratings (rF-WAIT=−.53, p<.01; rC-WAIT=−.42; p<.01; rWORK=−.19, p=.23), suggesting the less REST-to-WAIT attenuation the higher levels of delay aversion and temporal discounting.