The primary objective of this study was to assess whether analyses of spectral measures, such as RT LFO, provide further information in explaining parent ratings of ADHD than RT-CV alone. As hypothesized, we found that the RT LFO in all four computer tasks significantly explained ratings of Conners subscales in a moderately large convenience sample of mostly Latino children. We also found that adding RT LFO as a predictor significantly increased the proportions of variance explained in subscales of both inattention and hyperactivity/impulsivity, beyond the effects of RT-CV in several instances.
Our results are consistent with a prior report that spectral measures provide additional information beyond time domain analyses in characterizing categorically diagnosed ADHD [8
]. In the present study, this relationship was also observed across a dimensional spectrum of symptom severity. Further, in contrast with prior studies [4
] that used only one task paradigm with durations ranging from 5.5 to 18 min, here we used a battery of brief (~ 3 min 20 s) cognitive tasks designed to assess aspects of executive function. RT LFO during the simple choice reaction time task (0B) explained only two ADHD subscales in contrast to LFO on the remaining more complex tasks that explained all five subscales. Adding RT LFO to RT-CV during the 0BI and 1BI tasks significantly increased the proportion of explained variance for the Cognitive Problems/Inattention subscale; RT LFO during 1BI also significantly increased the proportion of explained variance for the two Hyperactivity subscales. Further research would analyze why the inclusion of an inhibitory component in the executive task had a larger effect on the proportion of parent ratings explained by RT LFO. We could hypothesize this is due to a relationship between abilities of inhibition and attention. Because the tasks were presented in a fixed rather than counterbalanced order, we cannot disentangle the extent to which cognitive load may interact with RT LFO. Although future studies are needed to clarify this point, our results suggest that the relation between RT LFO and ADHD symptoms can be observed in a range of tasks including a brief simple-choice RT task. These results contrast with the lack of a relationship between ADHD diagnosis and RT variability reported by Geurts et al. despite the use of three analytical methods that included FFT in large samples of children with ADHD and typically developing controls [13
]. One possible explanation for this divergence is that FFT, which assumes stationarity of time series, may have missed time-varying fluctuations characteristic of LFO that we noted even in our brief tasks. Additionally, the ADHD subjects in the Geurts et al. study were carefully diagnosed and relatively free of comorbid conditions; our subjects were explicitly not diagnosed and were selected to provide a naturalistic range of symptom severities. These factors may also have contributed to the differences in our results.
Exploratory analyses on specific frequency bands within and above the LFO frequency range indicate that all the low frequency bands examined were strongly related to the ADHD rating scales. Slow-3 showed the most pervasive results, explaining all five rating subscales across all four tasks. Slow-4 and Slow-5 were almost as strongly related, each with significant relationships across three tasks (all but 1BI for Slow-4 and 0B for Slow-5). These findings contrast with Di Martino et al. [8
], who showed Slow-4 to be more related to variability than Slow-3 and Slow-5.
In general we did not discern prominent differences among the specific slow frequency bands – all were strongly related to Conners ratings and to performance measures such as error rates. To a first approximation, this resembles the finding of Monto et al. that all six electroencephalographic frequency bands were nested within LFO and equivalently related to fluctuations in sensory detection [25
One of the limitations of the study is that we used a variable inter-trial interval, jittered between 1750, 2000 and 2250 ms which we approximated with the mean of 2000 ms as our sampling rate. This only minimally reduced our frequency resolution above 0.22 Hz, so we excluded frequencies over 0.20 Hz. Another limitation is the lack of diagnostic interviews. The study was designed with a dimensional approach, with a main focus on continuous ratings of ADHD features and avoiding dichotomized classifications of the disorder. An advantage of our approach was that the brevity of the tasks made it feasible to conduct the study in a busy clinical setting in which we were able to recruit a clinic-based sample of mainly Latino children, who tend to be underrepresented in research studies [1
The potential link to pathophysiological mechanisms possibly implicated in ADHD rests on the observation that similar slow fluctuations observed with brain fMRI might account for intermittent lapses of attention [2
]. Spontaneous low frequency oscillations appear to regulate reciprocal relationships between anti-correlated networks in the brain, task positive networks and task negative networks [9
] The default network is a resting state network synchronized by spontaneous low-frequency oscillations [7
] which is argued to represent a physiological baseline, showing greater activity at rest than during the performance of goal-directed tasks [29
]. This circuit includes the medial and lateral parietal, the medial prefrontal cortex and the precuneus and posterior cingulate cortex. Failure to modulate activity in the default network may underlie intermittent lapses of attention [19
]. Data from fMRI studies demonstrate that LFO are related to behavioral trial-to-trial variability [33
] and variations in EEG [25
In conclusion, RT LFO explain a significant proportion of the variation found in the parent ratings of ADHD symptoms. This finding suggests that frequency analyses might be a suitable methodology to find links between behavioral performance and underlying physiological processes.