The purpose of this study was to determine whether measuring response time variability in children with ADHD within the Slow-4 frequency band (i.e., 0.027-0.073 Hz) would provide greater diagnostic information than RT-SD alone. Having confirmed prior reports (6
) that children with ADHD exhibit greater RT-SD than age-matched TDC, we also found that they showed significantly greater variability specifically in the Slow-4 frequency range. Furthermore, when variability in the Slow-4 frequency band was isolated by normalizing the spectral density functions, it was the only low frequency band tested which significantly contributed to RT-SD in differentiating the ADHD from the TDC group.
Our results are in broad agreement with recent findings by Johnson et al. (46
). Using fixed and random order versions of the Sustained Attention Response Task, with instructions to respond to all digits from 1 to 9 except to the digit 3, presented every 1.4 s, they found that children with ADHD show greater spectrum in frequencies from 0.004 to 0.35 Hz. Their task allowed them to differentiate a “fast” variability component (0.0772-0.35 Hz), which they suggested reflects sustained attention mechanisms, and a slower variability component (below 0.0772 Hz), ascribed to arousal processes. While the increased magnitude in the lower frequency band is broadly in agreement with our findings, differences in task designs do not allow us to determine the extent of convergence or disagreement. For instance, we did not find group differences in frequencies higher than Slow-4. However, our fixed 3 s inter-trial interval limited the extent to which we could examine the entire range of Slow-3 and the even faster frequencies analyzed by Johnson et al. (46
). On the other hand, because our task duration was nearly three times longer (15 min), we could explore the slower frequency bands, Slow-5 and Slow-6. The groups did not differ significantly in these two frequency bands.
The present work confirms our previous findings (4
) despite several methodological refinements. First, we performed frequency analysis in residualized RT timeseries demonstrating that group differences in Slow-4 are not due to RT fluctuations driven by the trial types. Second, by normalizing the spectrum by total variance, we measured the proportional contribution of each frequency band separately from global spectrally non-specific variability. Thus, our findings highlight a more delimited low frequency band, Slow-4. Third, we decomposed variability into both time and frequency domains using the Morlet CWT, thus avoiding violations of the assumption of stationarity of the RT timeseries required by FFT. We found equivalent nonstationarity in both diagnostic groups; thus, analyzing relative spectrum over time did not lead to a loss of information comparing groups. By contrast, computing spectral density via FFT under the assumption that the RT timeseries are stationary, is strictly speaking, not appropriate in this case. Still, FFT measures provided for comparability with the literature showed rough agreement with our CWT results.
In contrast with the frequency domain and other time domain measures, children with ADHD worsened over time in number of directional errors, suggesting a possible additional impairment in sustained attention. RT Slow-4 was positively correlated with the number of omission and directional errors, except in TDC where directional errors were minimal. Better characterization of the relation between errors and RT spectral measures, as well as of the frequency pattern with which errors occur, would require designs that can elicit more frequent errors.
Our results should be interpreted in light of study limitations. The two groups, although matched for age, socio-economic status, ethnicity and IQ, were not matched for sex distribution. Accordingly, all our analyses were adjusted for sex. However, comparisons limited to the 26 boys with ADHD and 12 TDC boys showed identically significant results for Slow-4 (F(36)=5.98, p=0.02, Cohen’s d = 0.80). We excluded children who omitted over 15% of responses. The four excluded children with ADHD were among the most severely affected, and conservatively removing them reduced our statistical power. Further our sample size was not adequate to test the possible effects of specific clinical characteristics such as ADHD group subtype, comorbidity, history of medication treatment, current medication status, and whether effects may have been exacerbated by pharmacological rebound.
Increased ISV in ADHD has been a recent focus of active study (45
). Leth-Steensen et al. suggested that exponentially prolonged RTs contributed to increased RT-ISV and are uniquely responsible for the group differences observed in such tasks (48
). The contribution of such prolonged RTs can be measured by analyzing ex-Gaussian distributions (49
), which decompose the RT distribution into a Gaussian normal component (indexed by mu
, representing the mean and SD of the normal distribution) and an exponential component (indexed by tau
representing both mean and SD of the exponential distribution). Hervey et al. (41
) confirmed that children with ADHD differ markedly in having prolonged RTs, indexed by larger tau
). Increased variability can also include a higher proportion of extremely rapid RTs (45
From a theoretical perspective, increased RT-ISV in ADHD has been attributed to a deficient allocation of effort in accordance with the cognitive energetic state regulation deficit model (50
). Independent confirmation that Slow-4 fluctuations in RT contribute independently to differentiating individuals with ADHD would support focusing on this easily collected measure as an objective index that could be linkable to underlying neurophysiological processes (19
). Independent neuronal oscillation bands have been defined from ultra fast (1-4 ms/cycle) to very slow (15-40 s/cycle) frequencies (19
). Although their nature, relation, and specific physiological functions have yet to be fully clarified, in general high frequency oscillations are hypothesized to provide high spatial resolution, whereas slow oscillations involve larger neuronal areas and are better suited for regulating dynamic relationships between and within brain networks (19
Episodic prolongations of RT were predicted by periodically decreased BOLD fMRI signal in any of three loci, including right dorsal anterior cingulate (52
). In our present data, the association between the magnitude of Slow-4 fluctuations and errors supports the interpretation that these fluctuations in RT reflect episodic lapses of attention which may result from the interplay of intrinsic brain rhythms fluctuating at low frequencies and spanning large expanses of brain (9
). In a recent resting state fMRI study, we found that the temporal coherence between the right dorsal anterior cingulate cortex (52
) and precuneus/posterior cingulate cortex was significantly decreased in adults with ADHD (13
). A planned study will test whether increased Slow-4 fluctuations in RT are linked to decreased functional connectivity of this circuit in ADHD. Additional study designs, electrophysiological approaches, and pharmacological probes are needed to examine whether increased Slow-4 variability may have a greater effect on incongruent trials requiring inhibitory control, or on presumably more boring neutral or congruent trials (14
In summary, our findings indicate that fluctuations in Slow-4 RT variability predict the diagnosis of ADHD beyond the effects associated with differences in global variability. Future studies will examine whether such spectrally specific fluctuations in behavioral responses are linked to intrinsic regional cerebral hemodynamic oscillations (12
) occurring in similar frequency bands.