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. Author manuscript; available in PMC: 2020 Dec 1.
Published in final edited form as: Int J Psychophysiol. 2019 Oct 24;146:101–106. doi: 10.1016/j.ijpsycho.2019.08.012

Dopaminergic Hypo-activity and Reduced Theta-band Power in Autism Spectrum Disorder: A Resting-state EEG Study

Taylor Hornung 1, Wen-Hsuan Chan 2, Ralph-Axel Müller 3, Jeanne Townsend 2, Brandon Keehn 4,5
PMCID: PMC6933439  NIHMSID: NIHMS1543986  PMID: 31669326

Abstract

Background:

Prior studies using a variety of methodologies have reported inconsistent dopamine (DA) findings in individuals with autism spectrum disorder (ASD), ranging from dopaminergic hypo- to hyper-activity. Theta-band power derived from scalp-recorded electroencephalography (EEG), which may be associated with dopamine levels in frontal cortex, has also been shown to be atypical in ASD. The present study examined spontaneous eye-blink rate (EBR), an indirect, non-invasive measure of central dopaminergic activity, and theta power in children with ASD to determine: 1) whether ASD may be associated with atypical DA levels, and 2) whether dopaminergic dysfunction may be associated with aberrant theta-band activation.

Method:

Participants included thirty-two children with ASD and thirty-two age-, IQ-, and sex-matched typically developing (TD) children. Electroencephalography and eye-tracking data were acquired while participants completed an eyes-open resting-state session. Blinks were counted and EBR was determined by dividing blink frequency by session duration and theta power (4-7.5 Hz) was extracted from midline leads.

Results:

Eye-blink rate and theta-band activity were significantly reduced in children with ASD as compared to their TD peers. For all participants, greater midline theta power was associated with increased EBR (related to higher DA levels).

Conclusions:

These results suggest that ASD may be associated with dopaminergic hypo-activity, and that this may contribute to atypical theta-band power. Lastly, EBR may be a useful tool to non-invasively index dopamine levels in ASD and could potentially have many clinical applications, including selecting treatment options and monitoring treatment response.

Keywords: autism spectrum disorder, blink rate, dopamine, EEG, eye tracking, theta

1. Introduction

The ascending dopaminergic pathways play a critical role in a number of complex cognitive functions, including reward processing, motivation, and executive control abilities (Schultz, 2007). Autism spectrum disorder (ASD) is a complex, highly heritable neurodevelopmental disability diagnosed on the basis of impairments on social communication and the presence of restricted and repetitive behaviors (APA, 2013). Dopaminergic dysfunction in ASD has been an area of intense speculation but modest empirical consensus. For example, reduced social orienting and motivation (Chevallier et al., 2012) as well as the presence of repetitive behaviors (Lewis & Bodfish, 1998) have been hypothesized to result, in part, from atypical dopamine-related circuitry. However, studies investigating dopamine (DA) in ASD using assays of blood, urine, and cerebrospinal fluid have produced mixed results (see Lam et al., 2006, for review). Likewise, studies using positron emission tomography (PET), which measure DA synthesis, transporters, and receptor availability, have also produced inconsistent findings (Zurcher et al., 2015). Thus, the status of dopaminergic function in ASD remains uncertain.

Spontaneous eye-blink rate (EBR) is considered to be an indirect, non-invasive measure of central dopaminergic activity (Jongkees & Colzato, 2016). Prior studies have demonstrated that pharmacological manipulation of DA levels affect EBR. For example, DA agonists increase EBR (Karson, Staub, et al., 1981), whereas 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), which destroys DA-synthesizing cells in the brain, decreases EBR (Lawrence & Redmond, 1991). In vivo measurement using PET has also revealed an association between EBR and DA receptor availability (Groman et al., 2014; although see Dang et al., 2017; Sescousse et al., 2018). Atypical EBRs are also present in conditions associated with dopaminergic dysfunction such as schizophrenia, Parkinson’s disease, and attention deficit hyperactivity disorder (ADHD; Jongkees & Colzato, 2016). Moreover, DA levels, as indexed by EBRs in typically developing (TD) individuals, have been used to indirectly link theory of mind abilities (Lackner et al., 2010), executive functions (Zhang et al., 2015), and reward processing (Barkley-Levenson & Galvan, 2017) to dopaminergic activity. To date, only one study has examined spontaneous EBR in ASD (Goldberg et al., 1987). These authors found that low-ability children with ASD had the highest EBR compared to TD children and children with intellectual disability, and suggested that elevated EBR in ASD was indicative of dopaminergic hyperactivity.

The neural substrates underlying dopamine-mediated cognitive processes – for example, reward anticipation (Gruber et al., 2013) and executive control (Nigbur et al., 2011) – include the prefrontal cortex and have been measured in the form of midline frontal theta-band power (approximately 4-8 Hz) using scalp-recorded electroencephalography (EEG). Further, attenuation of this theta-band activity is associated with decreased DA levels in frontal cortex (Parker et al., 2014, 2015), whereas administration of a DA agonist (L-Dopa) has been shown to increase theta power (Eckart et al., 2014). Findings from prior studies have also shown atypical resting (Wang et al., 2013) and task-related changes in theta power (e.g., Larrain-Valenzuela et al., 2017; Yeung et al., 2016) in ASD. However, it remains unclear whether differences in theta-band activation shown in individuals with ASD are related to aberrant DA levels.

Therefore, the objectives of this exploratory study were to further investigate dopaminergic activity, as indexed by spontaneous EBR, in high-ability children with ASD to determine: 1) whether ASD is associated with atypical DA levels, and 2) whether dopaminergic dysfunction may be associated with aberrant theta-band activation. Lastly, because DA has been hypothesized to contribute to sociocommunicative impairments and repetitive behaviors in ASD (Chevallier et al., 2012; Lewis & Bodfish, 1998), we examined the relationship between EBR, theta-band activation, and measures of ASD symptomatology.

2. Methods and Materials

2.1. Participants

Participants included 32 children and adolescents with ASD and 32 age-, sex-, and IQ-matched TD individuals (Table 1). Clinical diagnoses were confirmed using the Autism Diagnostic Interview – Revised (ADI-R; Rutter, Le Couteur, et al., 2003) and/or the Autism Diagnostic Observation Schedule (ADOS; Lord et al., 1999), and expert clinical judgment according to DSM-IV criteria (ADOS, ADI-R, DSM-IV = 28; ADOS, DSM-IV = 3; and DSM-IV = 1). IQ was measured using the Wechsler Abbreviated Scales of Intelligence (WASI; Wechsler, 1999), and children with non-verbal IQ (NVIQ) below average (< 80) were excluded. Children with ASD-related medical conditions (e.g., Fragile-X syndrome) were also excluded. TD participants had no reported family history of ASD and were confirmed via parent report to be free of clinically significant ASD-related symptoms (based on standardized parent-report measures of sociocommunicative abilities: the Social Responsiveness Scale, SRS [cutoff: total t-score ≥ 75]; Constantino & Gruber, 2005, and the Social Communication Questionnaire, SCQ [cutoff: score > 15]; Rutter et al., 2003) or any other neurological or psychiatric conditions (based on family and medical history questionnaire). Only participants tested between 9A.M. and 7 P.M. were included as EBR may be affected by fatigue (Barbato et al., 2000); testing time did not differ across groups (ASD = 1:52pm; TD = 1:54pm), t(62) = 0.55, p = 0.956. Informed assent and consent were obtained from all participants and their caregivers in accordance with university Institutional Review Board.

Table 1.

Participant Characteristics

ASD TD t-value p
n (females) 32 (4) 32 (4) - -
Age (years) 12.7 (2.6); 7.7-17.1 12.4 (2.7); 7.4-16.5 0.332 0.741
Hispanic ethnicity 15.6% 12.5% 0.391 0.822
Race American Indian 6.3% 3.1% 0.611 0.962
Asian 3.1% 3.1%
White 65.6% 68.8%
More than one race 15.6% 12.5%
Not stated 9.4% 12.5%
Verbal IQ 108 (17); 72-147 112 (11); 87-133 −1.159 0.251
Nonverbal IQ 107 (14); 81-140 107 (11); 88-129 −0.088 0.931
EEG duration (seconds) 336 (74)
78-438		 358 (50)
182-429		 1.416 0.162
EBR 13.1 (6.8); 0.5-29.8 19.7 (14.0); 2.9-50.7 2.391 0.020
SRS Total score 77 (10); 57-94 44 (7); 35-66 15.010 < 0.001
ADOS (n = 31) Communication 3 (1); 0-5 - - -
Social Interaction 8 (2); 4-14 - - -
Repetitive Behavior 3 (2); 0-6 - - -
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Mean (SD); range. Differences in race and ethnicity were examined using Chi-Squared tests. Social Responsiveness Scale, SRS; Eye-blink rate, EBR; Autism Diagnostic Observation Schedule, ADOS

2.2. Resting State

Participants were seated approximately 57cm from the display and completed between approximately 1.5 to 7-minutes of eyes-open resting state. There was no group difference in EEG recording duration, t(62) = −1.416, p = 0.162, nor was there an association between recording duration and eye-blink rate, r(63) = −0.091, p = 0.476, or theta power, r(63) = −0.125, p = 0.325. During resting state, a black central crosshair was presented on a grey background, and participants were instructed to relax, remain still, and to look at the crosshair.

2.3. Electroencephalography (EEG)

EEG was recorded using a Biosemi Active Two system with 68 Ag/AgCl active electrodes at a sampling rate of 256Hz. Sixty-four electrodes were mounted in an elastic cap according to locations in the modified International 10-20 system, and the remaining four electrodes were placed below the right eye (to monitor blinks) and on the outer canthus of the left eye (to monitor saccadic eye movements), and over the left and right mastoids (reference electrodes). EEG data were preprocessed and analyzed using EEGLAB (Delorme et al., 2011). EEG data were referenced to the average reference and high-pass filtered at 1Hz.

2.3.1. Spontaneous Eye-Blink Rate.

Spontaneous EBR was measured using vertical electrooculogram (EOG), which recorded the voltage difference between an electrode placed below participant’s right eye and the channel Fpz located above the eyes. A trained research assistant, blind to group membership, identified blinks and EBR was determined by dividing blink frequency by the recording duration (Figure 1a).

Figure 1.

Figure 1.

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(a) Example of EEG from a subset of channels including vertical electrooculogram (EOG; red line) used to code blinks. (b) Mean spontaneous eye-blink rate (EBR) for ASD (white) and TD (gray). Error bars represent ± 1 SEM. (c) Scalp maps of resting-state theta-band power (4-7.5 Hz) power for the ASD and TD groups. Absolute theta power shown in the left column and relative theta power on the right. Channels examined are displayed as black dots (Fz, Cz, Pz). Color bar displays absolute power in decibels (dB; left) and percentage of total power (i.e., relative power; right)

2.3.2. Theta-Band Power.

EEG data were decomposed using extended infomax Independent Component Analysis (ICA). Next, two phases of offline artifact rejection and correction were completed. First, component activations were visually inspected for gross motor artifact and artifact-contaminated data were rejected. In the second phase, the resulting component activations and their scalp topographies were visually inspected to identify and correct oculomotor artifacts (i.e., blinks and saccades) (Jung et al., 2000). After artifact rejection and correction, noisy channels were interpolated (ASD: M = 2.3 channels, SD = 1.8; TD: M = 1.6 channels, SD = 1.8, t(62) = −1.60, p = .115). Two-second windows from artifact-free EEG were analyzed using the EEGLAB function spectopo, which applies a hamming window and computes power spectral density with the frequency resolution of 0.25Hz. Average absolute theta power (4-7.5Hz), expressed as decibels (dB), was extracted from midline channels Fz, Cz, and Pz (see Figure 1c). Relative theta power was also calculated for these channels as the percentage of total power (1 – 50Hz) accounted for by theta-band activity.

2.4. Eye-tracking

Eye movements were recorded at a sampling rate of 500Hz using a SR Research Eyelink 1000 remote eye-tracking system. For the Eyelink system, the onset/offset of blinks trigger a saccade parser, which classifies these events as blink saccades. Eye blinks were coded as blink saccades with a duration between 100 and 500ms.

2.5. Measures

2.5.1. Social Responsiveness Scale (SRS; Constantino & Gruber, 2005).

The SRS is a 65-item parent-report questionnaire used to measure ASD symptomatology in children aged 4 to 18 years. The SRS Total t-score was used for correlational analyses.

3. Results

3.1. Spontaneous Eye-Blink Rate

EEG was used as the primary measure of EBR as only a subset of participants (n = 36; ASD = 18; TD = 18) completed simultaneous EEG and eye-tracking, and of those 18, two participants with ASD did not provide usable eye-tracking data (i.e., calibration issues and had less than 70% useable eye-tracking data). EEG-measured spontaneous EBR was entered into an independent-samples t-test. As illustrated in Figure 1b, the mean EBR in the ASD group was significantly lower than the TD group, t(62) = 2.391, p = 0.020, d = 0.60. Additionally, four participants in the ASD group were currently taking psychotropic medication (Tenex, Sertraline, Risperidone, Concerta, Vyvanse, Oxcarbazepine, Seroquel, Lithium). Analyses excluding these individuals showed a similar significant group difference, t(58) = 2.177, p = 0.034, d = 0.58. Lastly, significant group differences in EBR remained unchanged when both age (p = 0.021) and NVIQ (p = 0.019) were included as covariates (separately).

To confirm that EBR EEG measures corresponded with eye-tracking values, correlations between EEG and eye-tracking data were conducted. There was a strong positive correlation, r(33) = 0.964, p < 0.001, between the EBR obtained by both measures. However, paired-samples t-test for all participants showed that rates were significantly higher for EEG (M=14.9 blinks/min, SD=11.8) compared to eye-tracking (M=12.9 blinks/min, SD=11.5), t(33) = 3.673, p < 0.001, d = 0.63.

3.2. Theta Power

3.2.1. Absolute Power

Midline absolute theta power was entered into a mixed-model repeated-measures ANOVA with within-subjects factor channel (Fz, Cz, Pz) and between-subjects factor group (ASD, TD). There was a significant main effect of channel, F(2, 124) = 14.85, p < 0.001 ηp2 = 0.19. Follow-up paired-samples t-tests showed that theta power was greater at Fz compared to both Cz, t(63) = 3.58, p = 0.001, d = 0.45, and Pz, t(63) = 5.0, p < .001, d = 0.56, and at Cz compared to Pz, t(63) = 2.37, p = 0.021, d = 0.30 (Fz > Cz > Pz; Table 2). There was no significant interaction between channel and group, F(2, 124) = 0,043, p = 0.958; however, as illustrated in Figure 1c, theta was significantly reduced in the ASD (M=3.99 dB; SD = 2.74) compared to the TD group (M=5.56 dB; SD = 3.49), F(1, 62) = 4.01, p = 0.0496, ηp2 = 0.06. Exclusion of participants on psychotropic medications revealed a similar group difference, F(1, 58) = 3.663, p = 0.061, ηp2 = 0.06. Lastly, inclusion of age and NVIQ (separately) as covariates in ANOVA did not change outcome of main effect of group (all p < 0.05 for both ANCOVAs), however, within-subject effect of channel location was no longer significant (p > 0.2).

Table 2.

Absolute (dB) and Relative (%) Theta Power at Fz, Cz, and Pz for ASD and TD Groups

Frontal (Fz) Central (Cz) Posterior (Pz)
ASD Absolute 4.50 (2.6) 3.99 (2.9) 3.48 (3.0)
Relative 21.7% (4.2) 23.4% (5.2) 19.1% (5.8)
TD Absolute 6.11 (3.3) 5.49 (3.3) 5.56 (4.2)
Relative 25.0% (7.5) 24.4% (5.4) 19.4% (6.8)
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Mean (SD)

3.2.2. Relative Power

Midline relative theta power was entered into an identical mixed-model repeated-measures ANOVA. Similar to absolute power, there was a significant main effect of channel, F(2, 124) = 37.57, p < 0.001 ηp2 = 0.38. Follow-up paired-samples t-tests showed that relative theta power was greater at Fz and Cz compared to Pz (p < 0.001), but Fz and Cz did not differ significantly, t(63) = −0.97, p = 0.336 (Fz = Cz > Pz; Table 2). There was no significant main effect of group, F(1, 62) = 1.32, p = 0.254, ηp2 = 0.02, however, there was a significant interaction between group and channel, F(2, 124) = 3.35, p = 0.038, ηp2 = 0.05. Follow-up independent-samples t-tests showed that the ASD group had significantly lower relative frontal theta, t(62) = 2.15, p = 0.035, d = 0.54, but not central or posterior theta (p > 0.4) compared to the TD group. Exclusion of participants on psychotropic medications and inclusion of age and NVIQ (separately) as covariates in ANOVA did not change outcome of analyses.

3.3. Associations between Theta Power, Blink Rate, and ASD Symptomatology

Pearson correlations were used to examine the relationship between EBR, theta-band power, and SRS Total scores. As there was no significant interaction between channel and group and to reduce the number of correlations, absolute theta power was averaged across frontal, central, and posterior channels. Further, since relative power represents the relationship between frequency bands and not the degree of atypical activity within a given frequency band, absolute rather than relative power was used for correlational analyses. For both groups combined, there was a significant positive correlation between EBR and average absolute theta power, r(63) = 0.270, p = 0.031. Separate correlations for each group showed that EBR and theta were not significant correlated within the ASD, r(31) = 0.024, p = 0.896, or TD, r(31) = 0.295, p = 0.102, groups. Within the ASD group, SRS Total score was not correlated with EBR, r(31) = 0,035, p = 0.848, however, there was a significant negative correlation with average absolute theta power, r(31) = −0.601, p < 0.001 (Figure 2c). Results were unchanged for partial correlations that included age and non-verbal IQ as covariates. Specifically, SRS Total score was not correlated with EBR when age, r(29) = 0.153, p = 0.412, or NVIQ, r(29) = 0.028, p = 0.880, were included as covariates. However, SRS Total score remained significantly correlated with average theta power when age, r(29) = −0.459, p = 0.009, and NVIQ, r(29) = −0.581, p = 0.001, were included as covariates. Finally, correlational results were unchanged with the exclusion of participants currently taking psychotropic medications.

Figure 2.

Figure 2.

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For the ASD group, scatterplots showing: (a) spontaneous eye-blink rate (EBR) and average theta power, (b) EBR and SRS Total score, and (c) average theta power and SRS Total score.

Based on the potential nonlinear associations between DA levels and DA-mediated cognitive processes (Cools & D’Esposito, 2011), quadratic associations were also examined in addition to linear associations via three hierarchical linear regression models for the ASD group. In the first model, which examined relationship between average absolute theta power and EBR, EBR was entered as a first step, and a quadratic EBR (EBR*EBR) term in the second step. For models predicting SRS Total score, EBR or average absolute theta power were entered as a first step, and a quadratic EBR (EBR*EBR) or average absolute theta power (theta*theta) term in the second step (Table 3). Of particular interest was R-square change value (ΔR2) for step 2 (the increase in variability in theta power or SRS Total score accounted for by the parabolic effect). In the first model, an additional 20% of the variance in absolute theta power was accounted for by an inverted U-shaped parabolic relationship with EBR (Figure 2a). In model 2, an additional 14% of the variance in SRS Total score was accounted for the by U-shaped parabolic relationship with EBR (Figure 2b). However, for model 3, no significant change in R2 (< 1%) in the SRS Total score was accounted for by the quadratic theta term (Figure 2c).

Table 3.

Hierarchical Linear Regression of Linear and Curvilinear Relationships for Children and Adolescents with ASD

Outcome
Model 1: B (SE) ß t-value ΔR2
Theta Power Step 1 0.001
 EBR 0.01 (0.07) 0.02 0.13
Step 2 0.20*
 EBR 0.65 (0.24) 1.60 2.66*
 Quadratic EBR −0.02 (0.01) −1.64 −2.73*
Model 2: Step 1 0.001
SRS Total  EBR 0.05 (0.27) 0.04 0.19
Step 2 0.142*
 EBR −1.93 (0.94) −1.28 −2.05*
 Quadratic EBR 0.07 (0.03) 1.37 2.19*
Model 3: Step 1 0.361**
SRS Total  Absolute Theta −2.24 (0.54) −0.60 −4.12**
Step 2 0.003
 Absolute Theta −2.69(1.30) −0.71 −2.01*
 Quadratic Theta 0.06 (0.17) 0.13 0.38
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*

p < 0.05;

**

p < 0.01

4. Discussion

Contrary to the only prior report of EBR in ASD (Goldberg et al., 1987), results of the present study suggest that spontaneous EBR is lower in ASD, which is indicative of dopaminergic hypo-activity. Children with ASD also exhibited decreased theta-band power compared to their TD peers. Furthermore, across both groups, inter-individual differences in dopaminergic function were associated with levels of theta activity. Lastly, a U-shaped relationship between EBR and ASD symptomatology was present, whereas decreased theta-band activation was linearly associated with increased ASD symptomology.

In the only previously published report of EBR in ASD (Goldberg et al., 1987), EBR was determined by visually counting blinks while participants were engaged in an interview. However, prior studies have shown that EBR increases while individuals are engaged in conversation compared to resting conditions (Karson, Berman, et al., 1981), and, furthermore it remains unclear how this may differentially affect individuals with ASD given the social nature of the task. In the present study, children were not engaged in a socially-demanding task, but were asked to relax and look at a computer screen. In addition, the current study examined high-ability children, whereas Goldberg and colleagues (1987) investigated low-ability children with ASD. These methodological differences may help to explain why the present findings diverged from prior results. Our finding of reduced EBR suggests that children with ASD may have lower levels of DA compared to their TD peers. Although findings have been mixed (Lam et al., 2006; Zurcher et al., 2015), prior studies have reported decreased DA levels in ASD based on blood and urine samples (Martineau et al., 1992), PET imaging (Ernst, 1998), and in a mouse models of ASD (Hara et al., 2015). Moreover, recent evidence suggests that dopamine risk genotypes, related to less efficient dopaminergic functioning, are associated with reduced levels of initiating joint attention in infants at high risk for ASD (Gangi et al., 2016).

In addition to EBR findings, children with ASD also showed reduced theta-band activity compared to their TD peers. This finding is in agreement with Dawson and colleagues (1995), who showed reduced theta power in ASD, but is inconsistent with more recent reports of increased (Cornew et al., 2012) and equivalent (Coben et al., 2008) absolute theta-band activity in ASD. Additionally, in accord with prior pharmacological (Eckart et al., 2014) and animal studies (Parker et al., 2014), across all participants greater midline theta power was associated with increased DA levels (related to higher EBR). However, prior research also suggests that an inverted-U shaped relationship exists between DA levels and DA-mediated cognitive processes, with both excessive and insufficient levels resulting in impaired performance (Cools & D’Esposito, 2011). In agreement with this, curvilinear relationships between EBR and both theta-band power and SRS Total score were present in the ASD group. Both low and high EBR were associated with decreased theta-band power and increased ASD symptomatology. Thus, mixed findings related to both DA levels and theta-band power may reflect heterogeneity and differences in cohort compositions with hypo- and hyper-activity of the DA system. In sum, findings from the present study suggest decreased EBR and theta-band activity in ASD, and that these measures show curvilinear (EBR) and linear (theta) associations with ASD symptomology.

While findings from the present study indicate that reduced EBR and theta power are present in ASD, it should be noted that atypical eye-blink rates (Jongkees & Colzato, 2016) and theta-band power (Newson & Thiagarajan, 2018) have also been reported in other neurodevelopmental disorders and psychiatric conditions. Thus, differences in these measures and the dopaminergic activity that they may reflect may not be specific to ASD. Be that as it may, there is likely genetic overlap between ASD and these other disorders (e.g., ADHD, schizophrenia) (Cristino et al., 2014; Geschwind, 2011), including common variants associated with the DA system (Gadow et al., 2008; Rommelse et al., 2010). Therefore, future studies may wish to examine blink rates, theta-band power, and the cognitive processes associated with dopaminergic activity (e.g., executive control) in a transdiagnostic sample of individuals to see whether indirect measures of DA are associated with phenotypic characteristics across these clinical populations.

Lastly, the present study employed two methods of recording blinks, which were highly correlated; however, EBRs measured using eye-tracking were lower compared to EEG. Lower eye-tracking values are surprising given that true blinks recorded using eye-tracking reflect signal loss (i.e., due to pupil occlusion), and yet signal loss may result from many other sources (e.g., participant movement, loss of pupil/corneal reflection). Although participant movement and other artifacts affect EEG as well, eye blinks are recorded within the EEG signal rather than as the absence of signal being interpreted as the presence of a blink (as is the case with eye tracking). To account for the difference in the way blinks are recorded using eye tracking, the current study measured only events parsed as blink saccades (with a duration of 100-500ms). These criteria may have been too conservative, excluding some true blinks. Regardless, studies investigating blink rates using solely eye tracking methods should be cautious when selecting the parameters used to define blinks. Future studies using video observation paired with eye-tracking and/or EEG may helpful for demonstrating the most accurate criteria for measuring blinks.

The present study is not without limitations. In particular, although our artifact correction procedure attempts to remove ocular activity from the EEG, residual signal from blinks and/or saccades may remain. Given that the presence of these artifacts affects broadband spectral power measurements, particularly theta (Hagemann & Naumann, 2001; McEvoy et al., 2015), correlations between blink rates and theta power may result from this residual activity and therefore should be interpreted with caution.

In conclusion, children with ASD showed lower EBRs and decreased theta-band activation compared to their TD peers, potentially indicative of reduced dopaminergic activity and atypical function of dopaminergic pathways. Preliminary findings suggest that, similar to previous reports (Larrain-Valenzuela et al., 2017), this reduction in theta power may be associated with increased levels of ASD symptomology, whereas both increased and decreased DA-levels (as indexed by EBR) may be associated with greater levels of sociocommunicative impairment. Lastly, our findings suggest that EBR may be a useful tool to non-invasively index DA levels in ASD, and that EBR and theta power could potentially have many clinical applications, including selecting treatment options, monitoring treatment response, and, perhaps, even as a future neuro-behavioral marker.

Highlights.

  • Eye blink rate, an indirect measure of dopamine, was examined in children with ASD

  • Blink rate was significantly lower in children with ASD compared to their TD peers

  • Findings suggest that ASD may be associated with dopaminergic hypo-activity

  • Theta power, which may be linked with dopamine levels, was also reduced in ASD

  • Reduced theta power was associated with greater ASD symptomatology

Acknowledgements

Special thanks to the children and families who generously participated. This research was supported by R01-MH081023 (RAM) and R01-NS42639 (JT). All authors report no biomedical financial interests or potential conflicts of interest.

Footnotes

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