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. Author manuscript; available in PMC: 2023 Dec 1.
Published in final edited form as: J Child Psychol Psychiatry. 2022 May 27;63(12):1615–1621. doi: 10.1111/jcpp.13645

Absence of Dynamic Neural Oscillatory Response to Environmental Conditions Marks Childhood Attention Deficit Hyperactivity Disorder

Anne B Arnett 1, Margaret Fearey 1, Virginia Peisch 1, April R Levin 2
PMCID: PMC9691533  NIHMSID: NIHMS1819872  PMID: 35620850

Abstract

Background.

Prior research suggests that symptoms of attention deficit hyperactivity disorder (ADHD) and related neurodevelopmental disorders may derive from alterations in the brain’s ability to flexibly tune the balance between information integration and segregation and global versus local processing. This balance allows the brain to optimally filter salient stimuli in the environment and can be measured with electroencephalography (EEG) via calculation of the aperiodic spectral slope. Steeper aperiodic slope increases capacity of global neural networks to process low-salience stimuli, while flatter aperiodic slope reflects an emphasis on local neural networks that respond preferentially to high-salience input. Although aperiodic slope differences have been reported in ADHD, prior studies have not accounted for differing levels of stimulus input in experimental paradigms. There is evidence to suggest that dynamic shifts in neural oscillation patterns in response to changing environmental conditions could be critical for attention regulation.

Methods.

Using high-density resting EEG, we measured aperiodic spectral slope during low contrast (lights off) and high contrast (lights on) environmental conditions in a sample of 88 7–11-year-old children diagnosed with ADHD and 29 controls (30% female).

Results.

While controls showed flatter aperiodic slope during the high contrast (lights on) as compared to low contrast (lights off) environmental condition, children with ADHD did not show any change in aperiodic slope across conditions.

Conclusions.

This study presents a novel etiological model of biological mechanisms associated with ADHD. Children with ADHD show suboptimal modulation of intrinsic neural activity in response to changing environmental input. Dynamic spectral slope is a promising candidate biomarker for ADHD. The possibility that dynamic spectral slope is associated with cognitive-behavioral regulation more broadly merits further investigation.

Keywords: ADHD, EEG, Cognition, Neurodevelopmental Disorders

Integration and segregation of information across temporal and spatial domains during neural processing is a critical component of healthy brain functioning. Many neurodevelopmental disorders, including attention deficit hyperactivity disorder (ADHD), are characterized by symptoms of impaired cognitive-behavioral control, which may result from alterations in top-down cortical regulation of information processing. Despite ample evidence for a neurobiological etiology for ADHD (Kasparek, Theiner, & Filova, 2015), the diagnosis is currently made based on subjective report of behavioral symptoms related to cognitive dysregulation and associated functional impairment (Association, 2013). There is growing recognition that neurocognitive biomarkers, such as those derived from electroencephalography (EEG), could be useful for objective diagnostic and precision medicine treatment approaches (Faraone, Bonvicini, & Scassellati, 2014; McLoughlin, Makeig, & Tsuang, 2014).

EEG can be used to measure the spectral density power distribution (i.e., aperiodic spectral slope, or 1/f distribution) across oscillatory frequencies. Broadly speaking, oscillations allow the brain to rhythmically sample the environment, providing windows of time during which the brain determines which stimuli are connected to one another. Stimuli that occur within the timeframe of a single oscillation are integrated (i.e., assumed to be meaningfully related), whereas stimuli that occur across distinct oscillations are segregated. Slower oscillations thus tend to allow for integration across longer periods of time and larger areas of cortex (i.e., more “global” processing”), whereas faster oscillations integrate across shorter periods of time and smaller areas of cortex (i.e., more “local” processing) (Fries, 2005). Because environmental input occurs across a wide range of temporal and spatial frequencies, the brain’s intrinsic activity across a similarly wide range of oscillatory frequencies optimizes its ability to process a variety of external stimuli (Palva & Palva, 2018). Aperiodic activity, which reflects the relative strengths of slower and faster oscillations, is therefore a potential marker of the balance between integration versus segregation of neural information processing. This phenomenon has been recognized across many (biological and non-biological) scientific fields using terms such as self-organized criticality, long-range temporal correlation, scale-free dynamics, pink noise, and the sand pile effect (Jensen, Bonnefond, & VanRullen, 2012; Zimmern, 2020). To be consistent with prior investigations of this concept in neurodevelopmental disorders, the term aperiodic slope is used in the current study.

Human studies have documented a developmental progression wherein the aperiodic slope flattens with age. Infants show declining aperiodic slope within the first seven months of life (Schaworonkow & Voytek, 2021). Similarly, older adults exhibit flatter aperiodic slope than younger adults, particularly in the context of cognitive decline (Voytek et al., 2015). In addition to this age effect, aperiodic slope is moderated by cognitive arousal, with steeper slopes documented during sleep as compared to awake states (Lendner et al., 2020) and during resting as opposed to active tasks (He, Zempel, Snyder, & Raichle, 2010). This is thought to reflect integration of information across longer temporal and spatial regions during rest. Altogether, fluctuations in the aperiodic slope allow the brain to self-modulate how it perceives, attends to, and processes various stimuli in its environment.

Given these multiple contributing factors, it is perhaps unsurprising that research on aperiodic slope in pediatric ADHD reveals inconsistent findings. Robertson and colleagues (Robertson et al., 2019) reported steeper aperiodic slopes among preschool-age children with ADHD, while others have found flatter slopes among school-age and adolescent ADHD samples (Ostlund, Alperin, Drew, & Karalunas, 2021; Pertermann, Bluschke, Roessner, & Beste, 2019). Ostlund et al. (Ostlund et al., 2021) reported a positive association between aperiodic exponent and variability of reaction time, underscoring the link between aperiodic slope and cognitive control more broadly. Several studies have reported greater theta-beta ratio (TBR) and theta-alpha ratio among children with ADHD (Arns, Conners, & Kraemer, 2013), which could imply a steeper aperiodic slope. However, traditional TBR and related frequency ratio analyses do not differentiate between periodic and aperiodic power contributions, which reflect distinct underlying neurocognitive processes (Donoghue, Haller, et al., 2020).

The aperiodic spectral slope may be moderated by pharmacological treatment of ADHD. Robertson et al. (Robertson et al., 2019) reported that among preschool-aged children with ADHD, those who were medication naïve had steeper resting EEG aperiodic slopes than those who had previously taken stimulant medications, even following a 24-hour medication washout. In contrast, Pertermann et al. (Pertermann et al., 2019) found that concurrent, optimized methylphenidate treatment was associated with a steeper aperiodic slope during inhibitory trials of a go-no-go task. In both studies, medication exposure was associated with normalization of the aperiodic slope to match that of controls.

Importantly, the majority of studies investigating aperiodic slope among ADHD youth have focused on a single experimental state, or have combined across eyes-open and eyes-closed resting conditions (Robertson et al., 2019). Yet, there is substantial evidence to suggest that the brain self-tunes or adapts its oscillatory dynamics (i.e., aperiodic slope) depending on the characteristics of incoming visual information (Shew et al., 2015). Dynamic self-tuning by the brain may be related to the concept of “arousal,” which is frequently cited in the ADHD literature. Atypical modulation of alpha band oscillatory activity in response to increased visual input has previously been interpreted as evidence of reduced neurocognitive arousal in ADHD samples (Bellato, Arora, Kochhar, Hollis, & Groom, 2020; Fonseca, Tedrus, Bianchini, & Silva, 2013). However, investigation of alpha band power in isolation provides limited information about the neural dynamics that support flexible response to shifting environmental conditions. For example, while high-salience stimuli may be detected regardless of oscillatory phase, low-salience stimuli are only processed during higher-excitability stages of an oscillation. These higher-excitability stages last for only a small fraction of a high-amplitude oscillation, but a larger fraction of a low-amplitude oscillation (Jensen et al., 2012). Therefore, in a low-contrast environment (i.e., with the lights off), steeper intrinsic aperiodic slope facilitates a flexible neural response wherein the brain can amplify or dampen down its responses, as needed, to optimize characterization of salient versus nonsalient stimuli. On the other hand, in a high-contrast environment (i.e., with the lights on) the brain does not need to ramp up or down its responses to the same extent; a flatter slope is adequate because the environment itself is already differentiating between salient and non-salient stimuli. In this way, the nervous system is constantly modulating its activity to optimize detection of aspects of the environment it deems most relevant.

No study to date has investigated change in aperiodic slope across varying levels of environmental stimulation as a potential marker of atypical neurodevelopment. We hypothesize that children with ADHD will show reduced change in aperiodic slope across low (lights off) and high (lights on) visual contrast conditions, reflecting inefficient neural adaptation to shifting environmental features.

Methods

Participants

Children with a diagnosis of ADHD (n = 107) and controls (n = 34) between the ages of 7–11 years were recruited from the greater Seattle, WA area. Exclusion criteria were a diagnosis of autism spectrum disorder, intellectual disability, gestational age < 32 weeks, genetic disorder, prenatal exposure to alcohol or drugs, or colorblindness. Control children did not have an immediate family history of ADHD and had fewer than two DSM-5 ADHD symptoms in either symptom domain, as rated by a caregiver on the Strengths and Weakness of ADHD and Normative behaviors (SWAN) rating scale (Swanson et al., 2012). ADHD diagnoses were confirmed by a licensed clinical psychologist using a combination of sources that included direct observation, caregiver report on the computerized version of the Kiddie Schedule for Affective Disorders and Schizophrenia (Townsend et al., 2019), review of medical records, and/or caregiver report of at least six inattentive or hyperactive/impulsive symptoms on the SWAN. Teacher ratings were not obtained due to the high number of participants who were regularly taking ADHD medications during the school day.

Participants were excluded from the current analyses following enrollment due to failing to meet ADHD diagnostic criteria (n = 10), subsequent diagnosis of autism spectrum disorder (n =4), suspicion of ADHD or ADHD family history in a control (n = 2), failure to abstain from medications (n = 2), seizure activity during the EEG (n = 1), technical error during the EEG (n = 2), or IQ < 80 (n = 2). The medicated ADHD participants were prescribed methylphenidate (n = 40), amphetamines (n = 14) or non-stimulants (n = 2). There were no differences in proportions of females or non-White participants across groups (Table 1). The medicated ADHD group was older (mean difference = 0.96 years, 95% CI = 0.27 – 1.66, p = .004) and had higher caregiver-rated hyperactivity/impulsivity symptom severity (mean difference = 0.53, 95% CI = 0.14 – 0.91, p = .005) than the unmedicated ADHD group. The control group had a higher average IQ score compared to medicated ADHD (mean difference = 9.34, 95% CI 3.10 – 15.58, p = .001) and unmedicated ADHD (mean difference = 11.74, 95% CI 4.79 – 18.70, p < .001) participants, as measured by the two-subtest version of the Wechsler Abbreviated Scales of Intelligence, 2nd Edition (Wechsler, 2011) during the research visit. The medicated and unmedicated ADHD groups did not differ on IQ (mean difference = 2.40, 95% CI 8.45 – 3.65, p = .614).

Table 1.

Participant Characteristics

Control Unmedicated ADHD Medicated ADHD Group Differences
N 29 32 56 n/a
Female 34% 31% 28% ns
Age in years (SD) 8.83 (1.23) 8.50 (1.24) 9.46 (1.38) Medicated ADHD > Unmedicated ADHD**
Non-White Race 42% 32% 42% ns
IQ 117.50 (10.45) 105.76 (13.62) 108.16 (10.91) Control > ADHD**
Inattention Severity (SD) −0.57 (0.59) 1.43 (0.73) 1.66 (0.80) ADHD > Control***
Hyperactivity/Impulsivity Severity (SD) −0.75 (0.75) 0.97 (0.73) 1.5 (0.72) Medicated ADHD > Unmedicated ADHD** > Control***
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Note:

**

p<.01,

***

p<.001,

ns = not significant

Ethical Considerations

Informed consent and assent were obtained from caregivers and children at the start of the visit in compliance with the approved University of Washington IRB protocol (STUDY00004534).

Procedures

ADHD participants abstained from prescribed medications at least 48 hours prior to the research visit. Participants visited a university laboratory with a caregiver for a single 3-hour research session that included a 1-hour EEG, neuropsychological assessment, and parent-report questionnaires. Children were seated comfortably for the duration of EEG acquisition. Lights on and lights off resting paradigms were completed first, followed by 30–40 minutes of additional experiments not analyzed as part of the current study. Visual stimuli for the lights on condition were presented using E-Prime 2.0 on a computer monitor placed 70cm in front of the participant, and consisted of six 30-second, silent, abstract color videos (Webb et al., 2018). During the lights off condition, the participant room was reduced to near-total darkness. Participants were instructed to sit quietly with their eyes open during both experiments. The lights off condition was selected over an “eyes-closed” paradigm due to young children’s difficulty keeping their eyes shut for extended periods of time, and the muscle artifact that is often introduced by squinting.

EEG Processing

Continuous EEG was collected with a high-density,128-channel Magstim-EGI Hydrocel geodesic sensor net and Netstation Acquisition software version 4.5.6 integrated with a 400-series high impedance amplifier (Magstim-EGI; Plymouth, MN). Electrode impedances were reduced to below 50 kOhms at the start of the session. EEG signals were referenced to the vertex electrode, analog filtered (0.1 Hz high-pass, 100 Hz elliptical low-pass), amplified and digitized with a sampling rate of 1000 Hz. Data were subsequently processed offline in MATLAB R2018b using EEGLAB 15 and ERPLab v8.0 functions. To aid with artifact detection, initial processing included all six minutes of resting EEG data as well as 25 minutes of event related potential paradigms (not analyzed as part of the current study). Eye electrodes and 14 rim channels were excluded from analyses. Data were downsampled to 250 hz and bandpass filtered at 0.3–80 hz. Electrical line noise from 55–65 hz was removed using the Cleanline plugin for EEGLAB. Bad channels were automatically detected and subsequently interpolated back into the dataset prior to average referencing, following methods outlined in Gabard-Durnham, et al. (Gabard-Durnam, Mendez Leal, Wilkinson, & Levin, 2018) Extended independent component analysis (ICA) was run with primary component analysis dimension reduction to identify and subsequently remove artifactual independent components (e.g., eye blinks, line noise or cardiac signal), consistent with published pipelines (Levin, Méndez Leal, Gabard-Durnam, & O’Leary, 2018). Welch’s method was used to perform fast Fourier transformation (FFT) on continuous EEG data with a 50% overlap and 1-second Hamming window. Next, the Fitting Oscillations and One-Over-f (FOOOF) MATLAB toolbox (Donoghue, Dominguez, & Voytek, 2020; Donoghue, Haller, et al., 2020) was used to compute the aperiodic exponent across a frequency range of 1–50 Hz, at each electrode for each individual. Consistent with prior literature (Ostlund et al., 2021; Robertson et al., 2019), we specified a fixed aperiodic slope calculation after visual inspection did not indicate a “knee” in individual power spectral density distributions. Other parameters were specified as follows: peak_width_limits = [2,12], max_n_peaks = 8, min_peak_height = 0.5, peak_threshold = 2.0. The aperiodic exponent was extracted for analysis. Lower exponents indicate flatter aperiodic slope, while higher values indicate a steeper aperiodic slope. Region average aperiodic exponents were calculated for anterior frontal (Afz, Af3, Af4), frontal (Fz, F3, F4), central (Cz, C3, C4), parietal (Pz, P3, P4) and occipital (Oz, O3, O4) electrode clusters; values ranged from 0.22 – 2.30.

Statistical Analysis

Statistical analyses were executed in R Studio version 1.4.1717. Multilevel linear models were estimated using the lme4 package. The aperiodic slope exponent was modeled as the dependent variable, with condition (lights on vs. lights off), diagnostic group (ADHD vs. control), region (anterior frontal, frontal, central, parietal, or occipital), and age as predictors. Interaction terms among ADHD, condition, and medication status were also included in the models. A random intercept was specified for individual. Post-hoc pairwise comparisons were computed using estimated marginal means.

Results

Omnibus Effects

Results of the multilevel linear model indicated a main effect of condition, wherein aperiodic slope was flatter during lights on as compared to lights off (B = −0.04, SE = 0.02, p = .030), indicating reduced slow and increased fast oscillations in the context of higher visual input. Older age was also associated with flatter aperiodic slope: B = −0.03, SE = 0.01, p = .005. There was a main effect of region, driven by flatter aperiodic slope in the occipital relative to anterior frontal electrode cluster: B = −0.05, SE = 0.01, p < .001. Contrary to expectations, there was no main effect of ADHD diagnosis on aperiodic slope: B = −0.02, SE = 0.04, p = .646.

Group × Condition Interaction

The multilevel model indicated a two-way interaction between ADHD diagnosis and condition (B = −6.45, SE = 2.26, p = .004). Post-hoc pairwise analyses revealed that controls had flatter aperiodic slope during the lights on relative to lights off condition (mean difference = −0.04, 95% CI −0.07 – −0.004, p = .030) while the ADHD group did not show a difference across environmental conditions (mean difference = 0.01, 95% CI −0.01 – 0.03, p = 0.450; see Figure 1). Within condition, the control group had steeper aperiodic slope than the ADHD group during the lights off (0.09, 95% CI 0.02 – 0.16, p = .042) but not lights on (0.05, 95% CI −0.02 – 0.11, p = .185) condition. This result is consistent with our theory that control children are modulating neural dynamics to optimize processing of environmental input, while ADHD children are not, and underscores the importance of considering environmental conditions in studies of aperiodic slope.

Figure 1.

Figure 1.

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While the control group showed greater aperiodic slope in the lights off relative to lights on condition, the ADHD groups did not show a dynamic aperiodic slope response to shifting levels of visual contrast in the environment.

Group × Condition × Medication Status Interaction

A three-way interaction between condition, ADHD diagnosis and medication status was indicated by the multilevel model (B = 4.27, SE = 1.96, p = .030). Post-hoc comparisons revealed that during the lights off condition, the control group had steeper aperiodic slope than both the unmedicated ADHD (mean difference = 0.08, 95% CI 0.001 – 0.17, p = .047) and medicated ADHD (mean difference = 0.10, 95% CI 0.02 – 0.17, p = .012) groups. In contrast, during the lights on condition, the control group had a steeper aperiodic slope than the medicated ADHD group only (unmedicated ADHD: mean difference = 0.02, 95% CI −0.06 – 0.10, p = .642; medicated ADHD: mean difference = 0.08, 95% CI 0.00 – 0.15, p = .049). The two ADHD groups did not differ from one another during either condition (lights off: mean difference = 0.01, 95% CI −0.06 – 0.09, p = .726; lights on: mean difference = 0.06, 95% CI −0.02 – 0.13, p = .140). This interaction suggests that atypical neural modulation is more apparent during low contrast environmental input, and children who are prescribed stimulant medications have more abnormal aperiodic slope relative to controls across conditions.

Discussion

This is the first study to demonstrate that children with ADHD do not modulate their intrinsic neural activity (measured here via aperiodic spectral slope) based on the conditions of the external environment. Consistent with our hypothesis, the average aperiodic slope of control children was steeper during low- as compared to high-visual contrast environmental conditions, which is believed to be an adaptive phenomenon that supports a flexible response to low salience stimuli. In contrast, overall, the ADHD group demonstrated lack of this adaptive dynamic aperiodic slope response.

Our findings offer an opportunity to bridge concepts across multiple fields of research. In physics, the concept of “self-organized criticality” is ubiquitous across natural and human-made phenomena (Bak, 1996). Recent conceptual, computational, and clinical studies suggest that self-organized criticality is likewise relevant in neuroscience, where the brain adjusts its own intrinsic activity in response to features of the environment (Zimmern, 2020). While the semantics of this concept differ across fields (e.g., scale-free dynamics (He et al., 2010; Palva & Palva, 2018), neural noise (Pertermann et al., 2019; Voytek et al., 2015), arousal (Lendner et al., 2020), the theoretical underpinnings are constant. Specifically, the healthy brain is capable of modulating its neural dynamics by shifting the balances of integration versus segregation and local versus global information processing, in order to optimally process incoming stimuli from a given environment (Zimmern, 2020). Aperiodic slope is a quantification of this self-modulation (He et al., 2010; Palva & Palva, 2018) and provides a metric by which to measure atypical neural dynamics across a variety of disease states (Zimmern, 2020). In the current study, by evaluating the aperiodic slope across two different types of visual input (lights on versus lights off), we find that the brains of children with ADHD do not adequately modulate their intrinsic neural activity in response to altered environmental inputs. We propose that this translates into reduced signal-to-noise ratio in information processing in this clinical sample, making it difficult for children with ADHD to orient to environmental stimuli that are most relevant in a given situation. At a behavioral level, this could be described as impaired attention regulation.

Unexpectedly, medication status moderated the condition-by-group interaction, even though the medicated ADHD group had undergone a 48-hour washout. Unlike prior work, in which medication exposure normalized the aperiodic slope of children with ADHD (Pertermann et al., 2019; Robertson et al., 2019), the medicated ADHD group in our study had flatter aperiodic slope than controls during both conditions, while the unmedicated ADHD group differed from controls only during the lights off condition. Notably, the two ADHD groups did not differ from one another; however, this may be due to lack of power to detect a statistically significant difference between these smaller subgroups. Alternately, this finding could be due to higher hyperactivity/impulsivity ratings among the medicated ADHD group, which would suggest that reduced aperiodic slope is associated with a more severe ADHD phenotype. Although the medicated group was also older than the unmedicated group, this is a less likely explanation for these differences because age was included as a covariate in the model and all three groups included the full range of ages (7–11 years).

On the other hand, while the effect of condition on aperiodic slope was not statistically significant for either ADHD group, the medicated ADHD group showed a potential correction of dynamic aperiodic slope across conditions, such that they had marginally steeper aperiodic slope during the lights off relative to lights on condition. Possibly, optimized and concurrent medication use might fully normalize the dynamic aperiodic slope. If so, we would propose that dynamic aperiodic slope is a candidate biomarker for ADHD state, while flatter aperiodic slope during a low contrast condition could be a marker of ADHD trait. This theory builds on a study by Mattfield and colleagues (Mattfeld et al., 2014), which found that among adults, ADHD symptom persistence (i.e., state) was associated with reduced functional connectivity in the default mode network, while diagnosis (i.e., trait) was associated with atypical segregation of task-related networks.

A limitation of our study is that we did not have access to historical data on medication use; thus, children in the unmedicated ADHD group were not necessarily medication naïve. Additionally, we did not have power to control for class, dose, and duration of medication in the current analyses. In future studies, we plan to examine these moderators in more detail, as well as test for alternative contributing factors that might explain the result, such as baseline severity of ADHD and co-occurring symptoms. In future work, we plan to examine the potential utility of dynamic aperiodic slope as an index of neurodevelopmental symptom dimensions, including attention, impulsivity, cognitive control, and emotion regulation.

The clinical objective of our work was to advance etiological models of atypical neurodevelopment, with the ultimate aim of identifying neurodevelopmental biomarkers. ADHD has long been described as a disorder of hypo-arousal using single experimental conditions (Satterfield, Cantwell, & Satterfield, 1974). However, our results support more recent work suggesting that static neurocognitive indices are not as powerful as dynamic measures of transitions between cognitive states (Fonseca et al., 2013; Saad, Kohn, Clarke, Lagopoulos, & Hermens, 2018). Further, we hypothesize that a lack of dynamic aperiodic slope is not specific to ADHD or the visual environment, but rather represents a cross-diagnostic deficit in bottom-up neural modulation associated with atypical attention to sensory information across visual, auditory and tactile modalities. If this is true, dynamic aperiodic slope could be a potential treatment target, with behavioral and pharmacological therapies targeting this neural index across different psychiatric nosologies (Beauchaine & Hinshaw, 2020). To this extent, we encourage future research to examine aperiodic slope differences in other clinical populations to determine whether it represents a marker of reduced cognitive-behavioral control more broadly.

Key Points.

  • Aperiodic spectral slope as measured with EEG reflects intrinsic brain activity that supports integration and segregation of neural information across time and space.

  • A few past studies have found that children with ADHD have atypical aperiodic slope relative to controls.

  • The current study finds that the brains of children with ADHD do not adjust aperiodic slope in response to changing levels of visual contrast; i.e., they lack a dynamic aperiodic slope response.

  • Lack of dynamic aperiodic slope may underly behavioral difficulties associated with ADHD, such as problems filtering relevant information in the environment.

  • Dynamic spectral slope is a promising neural marker for ADHD diagnosis and possibly treatment response.

Acknowledgments

This research was funded by grants to A.B.A. from the National Institute of Mental Health (K99MH116064-01A1 and R00MH116064-01A1). The funder approved the study design. The funder was not involved in data collection, management, analysis, or interpretation of the data. The funder was not involved in the preparation, review, approval of the manuscript, or the decision to submit the manuscript for publication.

Footnotes

Disclosures

The authors have no conflicts of interest to declare.

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