Resting-State Neurophysiological Activity Patterns in Young People with ASD, ADHD, and ASD + ADHD
Resting-State Neurophysiological Activity Patterns in Young People with ASD, ADHD, and ASD + ADHD
Elizabeth Shephard 0 1 2
Charlotte Tye 0 1 2
Karen L. Ashwood 0 1 2
Bahar Azadi 0 1 2
Philip Asherson 0 1 2
Patrick F. Bolton 0 1 2
Grainne McLoughlin 0 1 2
0 Present Address: Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London , De Crespigny Park, London , UK
1 Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London , De Crespigny Park, London SE5 8AF , UK
2 MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London , De Crespigny Park, London SE5 8AF , UK
3 Charlotte Tye
Altered power of resting-state neurophysiological activity has been associated with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which commonly co-occur. We compared resting-state neurophysiological power in children with ASD, ADHD, co-occurring ASD + ADHD, and typically developing controls. Children with ASD (ASD/ASD + ADHD) showed reduced theta and alpha power compared to children without ASD (controls/ADHD). Children with ADHD (ADHD/ASD + ADHD) displayed decreased delta power compared to children without ADHD (ASD/controls). Children with ASD + ADHD largely presented as an additive cooccurrence with deficits of both disorders, although reduced theta compared to ADHD-only and reduced delta compared to controls suggested some unique markers. Identifying specific neurophysiological profiles in ASD and ADHD may assist in characterising more homogeneous subgroups to inform treatment approaches and aetiological investigations.
ASD; ADHD; Co-occurring ASD + ADHD; Resting-state; EEG; Spectral power
Elizabeth Shephard and Charlotte Tye are Joint first authors.
Autism spectrum disorder (ASD) and attention-deficit/
hyperactivity disorder (ADHD) are two of the most
common and impairing neurodevelopmental disorders which
frequently co-occur and share genetic mechanisms
(Grzadzinski et al. 2016; Ronald et al. 2008; Simonoff et al. 2008)
It is not well understood whether the presence of both ASD
and ADHD in one individual reflects a third distinct clinical
entity, or if ASD and ADHD are different manifestations
of a single entity. In order to understand the mechanisms
underlying this overlap, it is important to characterise shared
and/or distinct pathophysiological underpinnings. Both ASD
and ADHD have been associated with atypicalities in brain
structure and function
(Ecker 2017; Friedman and
. An ideal method of investigating the temporal
dynamics of brain function in developmental psychiatric
populations is through the measurement of
electro-encephalographic (EEG) activity on the scalp. Resting-state EEG
(activity recorded while the brain is not engaged in a specific
task) is particularly well-suited to investigating brain
function in developmental disorders due to the low cognitive
demands required of the child. Neural indices obtained from
resting-state EEG include the power of oscillations in
different frequency bands, i.e. delta (0.5–3.5 Hz), theta (4–8 Hz),
alpha (8–12 Hz), beta (12–30 Hz), and gamma (>30 Hz),
which reveal information about baseline neurophysiological
states such as motivation and neural excitability
et al. 2007; Knyazev 2007)
Alterations in resting-state power of different frequency
bands have been associated with ASD and ADHD. In turn,
these alterations have been interpreted as reflecting
neurophysiological disturbances core to the symptoms of ASD
and ADHD. In ASD, both children and adults have been
reported to show increased resting-state power in the slow
delta and theta frequencies
(Cantor et al. 1986; Chan et al.
2007; Cornew et al. 2012; Machado et al. 2015; Mathewson
et al. 2012; Murias et al. 2007; Pop-Jordanova et al. 2010)
reduced power in the middle-range alpha frequency
et al. 1986; Chan et al. 2007; Dawson et al. 1995; Machado
et al. 2015; Murias et al. 2007)
, and increased power at
high beta and gamma frequencies
(Machado et al. 2015;
Mathewson et al. 2012; Stroganova et al. 2007)
to typically developing controls. This U-shaped EEG profile
has been proposed to reflect an imbalance in cortical
inhibition and excitability, such that reduced GABA-mediated
cortical inhibition results in increased cortical excitation or
(Wang et al. 2013)
. In line with these
interpretations, a cortical excitatory/inhibitory imbalance has
been proposed to disrupt functional brain organisation in
ASD, which in turn leads to the diverse social cognition,
language, and emotional impairments characteristic of this
(Rubenstein and Merzenich 2003)
. Still, findings
are inconsistent and may reflect clinical heterogeneity and
In contrast, children and adults with ADHD have been
reported to show increased resting-state activity in the
slow frequencies, particularly the theta range
(Barry et al.
2009; Bresnahan et al. 1999; Kitsune et al. 2015; Koehler
et al. 2009; Tye et al. 2014)
, and less consistently, in the
(Bresnahan et al. 1999; Kitsune et al. 2015)
compared to typically developing controls. The increased
slow-wave activity is accompanied by decreased fast-wave
activity in the beta range
(Barry et al. 2009; Bresnahan
et al. 1999; Buyck and Wiersema 2014; Clarke et al. 2006;
Kitsune et al. 2015)
, and less reliably, in the alpha range
(Barry et al. 2009; Clarke et al. 2006; Loo et al. 2009)
atypicalities have been interpreted as reflecting delayed brain
maturation in ADHD
(Barry et al. 2003)
, since delta/theta
activity decreases with age in typical development
. Increased theta has also been suggested to reflect
hypo-arousal and an under-focused, suboptimal energetic
or the action of a top-down attentional
control network that regulates arousal level
(Sergeant et al.
. In line with this interpretation, excessive
restingstate theta and increased theta/beta ratio are associated with
poor cognitive task performance in ADHD
(Hermens et al.
2005; van Dongen-Boomsma et al. 2010)
. Nevertheless, the
increased slow-wave + decreased fast-wave activity pattern
may only present in a subset of individuals with ADHD
(Arns 2012; Arns et al. 2008; Clarke et al. 2013)
patterns of resting-state alterations, such as reduced alpha
power, may characterise other individuals
(Arns et al. 2008;
Loo et al. 2009)
While the previous work in ASD and ADHD has
contributed to understanding the neurobiological mechanisms
involved in these disorders, one limitation is that few studies
have controlled for comorbidity. A large proportion of
individuals with ASD have co-occurring clinical or sub-clinical
symptoms of ADHD and vice versa for individuals with
(Grzadzinski et al. 2016; Simonoff et al. 2008; Tick
et al. 2016)
. It is possible that some of the heterogeneity
in resting-state EEG profiles in ASD and ADHD reflects
unmeasured symptoms of the other disorder. An
investigation of resting-state power in children with “pure” ASD
(i.e. without co-occurring ADHD), “pure” ADHD (without
co-occurring ASD), and children with co-occurring ASD
and ADHD (ASD + ADHD) is needed to clarify the
restingstate power atypicalities associated with ASD and ADHD,
and to examine how atypicalities manifest in children with
both disorders, that is, whether atypicalities are summed
(“additive”) or whether there are more interactive effects
of ASD and ADHD. An additive model suggests the single
disorders (ASD-only, ADHD-only) can be differentiated
from each other, but when the comorbid (ASD + ADHD)
condition is considered the manifestations converge, such
that the unique features are observed. Interactive models of
ASD and ADHD may reflect the presence of independent
subtypes, such that each disorder displays its own unique
deficits with qualitatively distinct EEG profiles, or
alternatively a symptomatic phenocopy, whereby ASD + ADHD
presents with the same behavioural manifestation, but the
EEG profile is similar to ASD and not ADHD
(or vice versa;
Banaschewski and Brandeis 2007)
. Accordingly, a direct
comparison across the three patient groups enables a test
of the model of comorbidity, which can provide insight into
the brain-behaviour pathways in each disorder and inform
previous inconsistent associations between EEG profiles and
behaviour. There are limited comparisons of resting-state
neurophysiological activity in children with ASD + ADHD.
Previous inconsistent studies have reported elevated beta
power in children with ADHD and co-occurring ASD traits
compared to children with ADHD-only
(Clarke et al. 2011)
or increased resting-state theta power in adolescents with
ADHD-only compared to adolescents with ASD + ADHD,
interpreted as reflecting hypoarousal in the adolescents with
ADHD without ASD compared to those with ASD + ADHD
(Bink et al. 2015)
. However, these study designs do not
enable a test of whether differences in the comorbid group
reflect co-occurring ASD symptoms or rather the effects of
multiple neurodevelopmental pathophysiologies.
In the current study we aimed to address the limitations
with the previous work by examining resting-state
neurophysiological activity in children with pure ASD, pure
ADHD, ASD + ADHD, and typically developing children.
We aimed to clarify the profile of resting-state atypicalities
in ASD, ADHD, and ASD + ADHD. Further, we sought to
assess how ASD- and ADHD-related resting-state
atypicalities manifest in children with both disorders, that is, whether
the atypicalities are additive or interactive in children with
ASD + ADHD. We hypothesised that, firstly, children with
ASD would show a U-shaped pattern of resting-state
neurophysiological abnormality, with increased delta, theta, and
beta power but decreased alpha power compared to
controls. Secondly, children with ADHD would show increased
slow-wave (delta and theta) activity and decreased fast-wave
(alpha and beta) activity compared to controls. Finally, we
predicted that children with ASD + ADHD would show
additive ASD- and ADHD-related atypicalities in
restingstate power, characterised by increased delta and theta
power and reduced alpha power compared to controls, as
well as decreased beta power compared to the ASD group
but increased beta power compared to the ADHD group.
This would suggest that the co-occurring symptoms reflect
true overlap between ASD and ADHD at the
Participants were boys aged 8–13 years with ASD
(ASD group: n = 19), ADHD (ADHD group: n = 18), or
ASD + ADHD (ASD + ADHD group: n = 29), and typically
developing boys (Control group: n = 26). We included only
male participants to reduce sample heterogeneity. Group
characteristics are presented in Table 1. All participants
had normal or corrected-to-normal vision, IQ scores in the
(>69 on the Wechsler Abbreviated Scale of
Intelligence; Wechsler 1999)
, and were without neurological
conditions or co-occurring neurodevelopmental/psychiatric
conditions other than ASD/ADHD (excluding oppositional
defiant disorder). Participants were excluded from the study
if they were receiving medications other than stimulants.
Six boys with ADHD and six boys with ASD + ADHD were
receiving stimulant medication; all 12 children refrained
from taking their medication for 48 h prior to testing. Boys
with ASD and/or ADHD were recruited from South London
neurodevelopmental outpatient clinics and held a DSM-IV
(American Psychiatric Association 2000)
Health Organisation 1993)
clinical diagnosis of one or both
disorders. Research diagnoses were confirmed by trained
researchers using the social communication questionnaire
(Rutter et al. 2003)
, autism diagnostic
(Lord et al. 1994)
and autism diagnostic
observation schedule-generic (ADOS-G)
(Lord et al. 2000)
for ASD, and the conners 3 parent short form
and parental account of childhood symptoms (PACS)
(Taylor et al. 1986)
for ADHD. Typically developing boys
without neurodevelopmental or psychiatric diagnoses and
without siblings with ASD or ADHD were recruited from
local schools and forums for the control group. All control
participants were screened for subclinical symptoms using
the strengths and difficulties questionnaire (SDQ)
, SCQ, and Conners 3. Ethical approval for the study
was obtained from the NHS National Research Ethics
Service (NHS RES Wandsworth REC 08/H0903/161) and
London Research and Development Departments. In accordance
with the declaration of Helsinki, parental written informed
consent was obtained prior to completion of study measures.
Eyes-Open Resting-State Paradigm
Participants completed 6 min of eyes-open (EO) resting-state
EEG during a 1-h EEG task battery, which also included
6 min of eyes-closed resting state and a series of
experimental tasks (data not presented here). During the EO
restingstate, participants fixated on a dot on the opposite wall and
were encouraged to minimise ocular and other movements.
EEG Acquisition and Processing
EEG was recorded continuously from 62 Ag/AgCl active
(actiCAP) scalp electrodes placed according to the extended
10–20 system using an ActiCHamp (active channel
amplifier) DC-coupled Brain Products recording system (Brain
Groups marked with different superscript letters (a–c) differed significantly with Bonferroni correction applied (p < .05)
WASI FSIQ Wechsler Abbreviated Scale of Intelligence-Full-Scale IQ, Hyp/Imp and Inattention conners 3 parent-rated short form hyperactivity/
impulsivity and inattentive T-scores, SCQ social communication questionnaire total score
Products, Munich, Germany). The data were referenced
online to electrode FCz and sampled at 500 Hz. Electrode
impedances were kept below 10 kΩ. Vertical and horizontal
eye movements were recorded from electrodes placed above
and below the left eye and at the outer canthi. EEG data
were processed offline using Brain Vision Analyzer v2.03
(Brain Products, Munich, Germany). Flat or noisy channels
were removed and interpolated using spherical spline
interpolation prior to re-referencing to the average reference and
filtering with 0.1 Hz high-pass, 30 Hz low-pass, 50 Hz notch
Butterworth 24dB/Oct filters. Independent components
analysis (ICA) was used to identify and remove ocular artefact
components after which the data were segmented into
2-second non-overlapping epochs within the EO condition.
Automated artefact-detection was used to exclude any epochs
with remaining artefacts, defined as those with amplitudes
exceeding ±90 µv or a peak-to-peak amplitude change of
200 µv; this resulted in the exclusion of between 1 and 166
epochs (2–332 s/0.6–92% of the EO data) across participants
(mean number of epochs removed in the sample = 51.36,
SD = 44.92). Clean epochs were subjected to Fast Fourier
Transform (FFT) with a 10% Hanning window taper to
obtain absolute spectral power in the delta (0.5–3.5 Hz),
theta (4–8 Hz), alpha (8–12 Hz) and beta (12–20 Hz)
frequency bands. Following previous research
(Liechti et al.
2013; Loo and Smalley 2008)
, absolute power in each band/
condition was averaged over clusters of electrodes at frontal
(F1–F8, Fz), central (C1–C6, Cz), parietal (P3–4, P7–8, Pz),
and occipital (O1–2, Oz) scalp locations for analysis. Power
data were log-transformed to approximate a normal
distribution prior to statistical analysis.
Participants were excluded from analyses if they had
<40 s of artefact-free data (20 epochs) according to the
artefact-rejection criteria described above, or had power
values (prior to log transform) 3.5SD outside of their
group mean. Three control boys, one boy with ASD, three
boys with ADHD, and four boys with ASD + ADHD were
excluded due to having insufficient artefact-free epochs for
analysis (<15 epochs). The EEG data from these children
was characterised by excessive muscular artefact and slow
waves. A further one control boy, two boys with ASD, and
four boys with ASD + ADHD were excluded for having
outlying power values in multiple frequency bands. The
analysis of resting-state power was therefore conducted on
a final sample of 22 Controls, 16 ASD, 15 ADHD, and 21
ASD + ADHD. The number of epochs included in
analysis did not differ between groups [F(3, 70) = 0.68, p = .58,
ηp2 = 0.027; ASD mean (SD) = 123.94 (45.88), ADHD mean
(SD) = 126.80 (47.57), ASD + ADHD mean (SD) = 140.76
(42.78), Control mean (SD) = 139.95 (45.88)]. The
majority of the children included in the final analysis had at least
45 epochs (90 s) of artefact-free data for analysis; one child
with ASD + ADHD had only 23 epochs. Statistical analyses
were repeated without this child and results are reported
wherever they differ from the main analyses.
Statistical analyses were conducted in SPSS v22
. We tested the hypothesised atypicalities in
resting-state power in ASD, ADHD, and ASD + ADHD in
two ways. Firstly, to assess the profile of resting-state EEG
power in each participant group, we used ANCOVA to
compare power in each frequency band between the four groups
(ASD, ADHD, ASD + ADHD, Controls). A separate model
was used for each frequency band (delta, theta, alpha, beta).
All models included electrode cluster (frontal, central,
parietal, occipital) as a within-subjects factor. Significant main
effects of group and cluster, and interactions between these
factors, were further investigated using planned pairwise
contrasts between pairs of groups/clusters with Bonferroni
correction applied to control for multiple comparisons.
Secondly, we used a factorial approach to allow us to test for
effects of ADHD (both ADHD groups compared to both
non-ADHD groups) and ASD (both ASD groups compared
to both non-ASD groups) and the interaction between these
factors on resting-state power. The test for the interaction
between ASD and ADHD factors was crucial for testing the
hypothesis that ASD + ADHD reflects additive comorbidity.
For this analysis, power in each frequency band was entered
into 2 × 2 factorial ANCOVAs with the between-subjects
factors ASD (ASD-yes: ASD and ASD + ADHD groups;
ASD-no: ADHD and Control groups) and ADHD
(ADHDyes: ADHD and ASD + ADHD groups; ADHD-no: ASD and
Control groups). Electrode cluster (frontal, central, parietal,
occipital) was entered as a within subjects factor in all
models. A separate model was used for power in each of the four
frequency bands. Significant main effects of ASD, ADHD,
and cluster, and significant interactions between these
factors, were further investigated using planned pairwise
contrasts with Bonferroni correction applied to control for
multiple comparisons. IQ and age were included as covariates
in all models given known effects of these variables on EEG
(Kitsune et al. 2015; Michels et al. 2013)
Finally, we conducted a dimensional analysis to
investigate how symptoms of ASD and ADHD were associated
with resting-state power in the whole sample. Pearson
correlation coefficients were computed between SCQ scores
(ASD symptoms), Conners Hyperactive/Impulsive and
Inattentive T-scores (ADHD symptoms) and resting-state
power values. Only power in frequency bands that differed
significantly between groups were included in dimensional
analysis to limit the number of tests conducted.
Absolute power values (prior to log transform) are presented
by group in Table 2. Grand averaged absolute power values
(prior to log transform) are presented by group in Figs. 1
There was a significant main effect of electrode cluster
[F(2.46, 167.52) = 3.16, p = .04, ηp2 = 0.044] and a
significant group*cluster interaction [F(7.39, 167.52) = 4.03,
p < .001, ηp2 = 0.151] on absolute delta power. The main
effect of cluster reflected significant differences in delta
power between all pairs of electrode clusters (all p < .001),
with power greatest at occipital and frontal scalp. The
group*cluster interaction reflected significantly lower delta
power in the ADHD group than the ASD group at the frontal
cluster (p = .03, d = 0.85), significantly lower power in both
ADHD and ASD + ADHD groups than the ASD group at
the central cluster (both p < .001, d ≥ 0.80), and significantly
lower power in the ASD + ADHD group than the Control
group at the parietal cluster (p = .03, d = 0.96) (Figs. 1, 2).
When combined by ASD/ADHD diagnosis, there was a
significant main effect of ADHD [F(1, 68) = 6.40, p = .01,
Table 2 Mean (SD) absolute power values (µ2) by group
(n = 16)
(n = 15)
ASD + ADHD
(n = 21)
(n = 22)
Frontal 7.14 (1.87) 5.58 (1.78) 6.09 (1.80)
Central 4.28 (1.36) 3.13 (1.50) 3.04 (0.78)
Parietal 4.81 (1.89) 4.83 (2.04) 4.11 (0.98)
Occipital 8.49 (4.37) 7.47 (2.98) 7.13 (1.72)
Frontal 0.85 (0.25) 1.07 (0.51) 0.84 (0.33)
Central 0.65 (0.22) 0.85 (0.54) 0.58 (0.20)
Parietal 0.75 (0.32) 1.19 (0.80) 0.73 (0.23)
Occipital 1.11 (0.46) 1.51 (0.80) 1.08 (0.37)
Frontal 0.49 (0.20) 0.63 (0.35) 0.44 (0.15)
Central 0.45 (0.29) 0.67 (0.53) 0.39 (0.21)
Parietal 0.61 (0.33) 0.98 (0.67) 0.53 (0.21)
Occipital 0.85 (0.37) 1.25 (0.95) 0.74 (0.29)
Frontal 0.20 (0.07) 0.19 (0.11) 0.18 (0.06)
Central 0.13 (0.07) 0.14 (0.10) 0.10 (0.04)
Parietal 0.18 (0.11) 0.20 (0.11) 0.15 (0.05)
Occipital 0.28 (0.13) 0.28 (0.11) 0.21 (0.07)
Absolute power values (µ2) in the EO resting-state are presented
by group, prior to log-transform. Delta: 0.5–3.5 Hz, theta: 4–8 Hz,
alpha: 8–12 Hz, beta: 12–20 Hz
ηp2 = 0.086], reflecting significantly lower delta power in
boys with ADHD than those without ADHD. This effect was
qualified by a significant ADHD*cluster interaction [F(2.46,
167.52) = 7.31, p < .001, ηp2 = 0.097], which showed that the
reduction in delta power in boys with ADHD was significant
at frontal (p = .008, d = 0.49), central (p < .001, d = 0.75),
and parietal (p = .03, d = 0.47) clusters. There was also a
significant ASD*cluster interaction [F(2.46, 167.52) = 4.74,
p = .006, ηp2 = 0.065], which revealed a trend for increased
delta power in boys with ASD compared to boys without
ASD at the frontal cluster (p = .07, d = 0.18). The interaction
between the ASD and ADHD factors was non-significant
[F(1, 68) = 1.04, p = .31, ηp2 = 0.015] supporting additive
effects of ASD and ADHD. Age [F(1, 68) = 9.09, p = .004,
ηp2 = .118] but not IQ (p = .11, ηp2 = .036) was a significant
covariate in these models.
There was a significant main effect of cluster on absolute
theta power [F(2.49, 168.98) = 4.40, p = .009, ηp2 = 0.061],
reflecting significant differences between all pairs of
electrodes (all p ≤ .05) with power largest at frontal and occipital
scalp, and a marginal main effect of group [F(3, 68) = 2.71,
p = .052, ηp2 = 0.107], reflecting a trend for greater theta
power in the ADHD than ASD + ADHD group (p = .06,
d = 0.71) (Figs. 1, 2). These main effects were qualified by a
significant group*cluster interaction [F(7.46, 168.98) = 3.01,
p = .004, ηp2 = 0.117], which showed that the ADHD group
had significantly greater theta power than the ASD+ ADHD
group at parietal scalp (p = .03, d = 0.78). Combining the
groups by ASD/ADHD diagnosis, there was a significant
main effect of ASD [F(1, 68) = 4.81, p = .03, ηp2 = 0.066],
which reflected significantly lower theta power in boys with
ASD than boys without ASD. There was also a significant
ADHD*cluster interaction [F(2.49, 168.98) = 6.20, p = .001,
ηp2 = 0.084], but follow-up Bonferroni-corrected pairwise
group contrasts at each electrode cluster separately revealed
no significant differences between boys with ADHD and
those without ADHD (all p ≥ .16). The ASD*ADHD
interaction was non-significant [F(1, 68) = 2.45, p = .12,
ηp2 = 0.035], supporting additive effects. Age was a
significant covariate in these models [F(1, 68) = 17.86, p < .001,
ηp2 = 0.208], while IQ was non-significant (p = .19,
ηp2 = 0.025).
There was a significant main effect of group on absolute
alpha power (F(3, 68) = 3.84, p = .01, ηp2 = 0.145),
reflecting a trend for lower power in the ASD + ADHD group than
in the ADHD group (p = .06, d = 0.86) (Figs. 1, 2).
Combined by ASD/ADHD diagnosis, there was a significant
main effect of ASD (F(1, 68) = 10.91, p = .002, ηp2 = 0.138),
reflecting significantly lower alpha power in boys with ASD
than boys without ASD. The ASD*ADHD interaction was
non-significant (F(1, 68) = 0.218, p = .64, ηp2 = 0.003),
suggestive of additive effects. Age and IQ were non-significant
covariates in these models (both p ≥ .48, ηp2 ≤ 0.008).
There was a significant main effect of cluster [F(2.29,
155.52) = 3.01, p = .045, ηp2 = 0.042] on absolute beta
power, reflecting greater power at the central cluster than
all other clusters (all p ≤ .001), as well as a significant
group*cluster interaction [F(6.86, 155.52) = 2.26, p = .03,
ηp2 = 0.090]. However, further investigation of this
interaction revealed no significant differences between the four
groups at any of the clusters (all p ≥ .46). There were no
significant main effects of ASD or ADHD and no
interaction between these factors when combined by diagnosis
(all F ≤ 2.86, p ≥ .10, ηp2 ≤ 0.040). Age and IQ were
nonsignificant covariates (both F≤ 0.896, p ≥ .35, ηp2 ≤ 0.013).
Associations Between Resting-State Power and Symptoms of ASD and ADHD
SCQ scores were significantly negatively correlated with
delta power at the parietal cluster [r(74) = −0.334, p = .004,
r2 = 0.11], theta power at frontal [r(74) = −0.239, p = .04,
r2 = 0.06], central [r(74) = −0.301, p = .009, r2 = 0.09],
and parietal [r(74) = −0.332, p = .004, r2 = 0.11] clusters,
and with alpha power at frontal [r(74) = −0.381, p = .001,
r2 = 0.15], central [r(74) = −0.468, p < .001, r2 = 0.22],
parietal [r(74) = −0.440, p < .001, r2 = 0.19], and occipital
[r(74) = −0.387, p = .001, r2 = 0.15] clusters, indicating
children with higher levels of ASD symptoms or traits had lower
delta, theta, and alpha power. There were no significant
associations between hyperactive/impulsive or inattentive
symptoms of ADHD and power at any frequency or cluster (all
r ≤ −.209, all p ≥ .07, all r2 ≤ 0.04).
This study examined neurophysiological activity during the
resting-state in children with ASD, ADHD, and
co-occurring ASD + ADHD compared to typically developing
controls. The findings appear to dissociate ASD and ADHD on
the basis of different neurophysiological power profiles.
Specifically in relation to our hypotheses, (1) children with ASD
showed reduced theta and alpha power compared to children
without ASD; (2) children with ADHD showed decreased
delta power compared to children without ADHD; and (3),
children with ASD + ADHD displayed a largely additive
profile with the unique deficits of both ASD and ADHD,
although specific differences compared to “pure” cases of
ASD and ADHD were also observed.
Children with ASD (ASD/ASD + ADHD) demonstrated
a unique EEG profile of reduced power in the theta and
alpha frequencies compared to children without ASD. This
pattern partially contrasts with previous suggestions of a
U-shaped profile in ASD with increased power at low (delta,
theta) and high (beta) frequencies and reduced alpha power
(Wang et al. 2013)
. Nevertheless, our finding of reduced
alpha power is in line with several previous resting-state
studies of children
(Cantor et al. 1986; Chan et al. 2007;
Cornew et al. 2012)
and adults with ASD
(Mathewson et al.
2012; Murias et al. 2007)
, suggesting this atypicality may
be a robust characteristic of individuals with ASD. Further,
alpha power at all electrode clusters was negatively
associated with SCQ scores, indicating that children with more
severe ASD symptoms or traits had greater reductions in
alpha power. We interpret these findings as being in line
with the excitatory/inhibitory imbalance hypothesis of ASD
(Rubenstein and Merzenich 2003) since alpha
de-synchronisation (decreased alpha power) is associated with decreased
tonic neural inhibition/increased neural excitability
(Klimesch et al. 2007)
and the idling state of alpha oscillatory
activity has been directly linked with GABAergic circuitry
(Jensen and Mazaheri 2010)
, which modulates excitatory
cell activity. Further, inhibitory interneurons, which are
likely abnormal in ASD
(Casanova et al. 2002)
, play a role
in maintaining alpha oscillations
(Lőrincz et al. 2009)
Longitudinal studies will be necessary to investigate whether
the excitatory/inhibitory imbalance occurs early in
development and if this pattern reflects a core pathophysiology in
ASD. It will also be important for future work to investigate
relationships between resting-state alpha power and social
cognition, emotion processing, and language ability in ASD
to fully test the proposed causal links between alpha
oscillations, excitatory/inhibitory imbalance, functional brain
disruption, and cognition
(Rubenstein and Merzenich 2003;
Thatcher et al. 2009; Wang et al. 2013)
. Nevertheless, the
robust reductions in alpha power in the current and previous
studies, as well as the strong negative association between
ASD symptoms and alpha reductions, suggest that this
neurophysiological atypicality may be a useful target for treating
ASD symptoms, for example via neurofeedback training or
as a biomarker in clinical drug trials.
Reduced theta power in children with ASD is
consistent with some previous research on children
(Dawson et al.
1995; Machado et al. 2015)
, although the majority of
previous studies have found increased theta power in children and
adults with ASD
(Coben et al. 2008; Cornew et al. 2012;
Mathewson et al. 2012; Murias et al. 2007)
alterations in resting-state theta activity are more
heterogeneous in ASD than are atypical alpha oscillations. Since we
observed the reduced theta in children with ASD with and
without co-occurring ADHD symptoms, it is unlikely that
the inconsistency in theta alterations across studies reflects
the influence of unmeasured comorbidity with ADHD.
Theta at frontal, central, and parietal scalp was negatively
correlated with SCQ scores, indicating that, in our sample,
reduced theta was associated with increased ASD symptoms
dimensionally as well as at the group level. Decreases in
theta power have been associated with increases in arousal
levels in typically developing children
(Barry et al. 2009)
One interpretation of our theta finding is therefore that it
reflects hyper-arousal. This interpretation is consistent with
previous findings of hyper-arousal in ASD as indexed by
skin conductance and pupillometry measures, and with
models that propose some of the symptoms of ASD,
particularly sensory abnormalities, reflect attempts to control
(Hirstein et al. 2001; Martineau et al. 2011;
Prince et al. 2016)
. Since not all children with ASD exhibit
significant sensory abnormalities, this might explain the
heterogeneity in theta alterations across studies. It will
be important for future research to test this hyper-arousal
interpretation further by examining associations between
theta power, skin conductance or pupillometry measures of
arousal, and sensory symptoms in ASD.
Children with ADHD (ADHD/ASD + ADHD) showed
reduced delta power in fronto-central and parietal regions
compared to children without ADHD. Previous work has
found either no differences in delta between children and
adults with ADHD and controls
(Buyck and Wiersema 2014;
Clarke et al. 2006; Koehler et al. 2009)
, or increased delta
in adolescents and adults with ADHD compared to controls
(Bresnahan et al. 1999; Kitsune et al. 2015)
. Our four-group
analysis indicated reduced delta power in the ADHD groups
compared to the ASD-only group across fronto-central scalp
regions (in line with the full-factorial results), thus group
effects may reflect differences between clinical groups rather
than case-control differences. In support, a trend towards
elevated delta power in children with ASD diagnosis at
frontal scalp regions was indicated, compared to children
without ASD diagnosis, in addition to effects of ADHD
diagnosis. Delta oscillations have been associated with function of
the default mode network (DMN), the idling network of the
brain which is prominent during rest and becomes
deactivated during cognitive tasks. For instance, using
simultaneous EEG-fMRI, Hlinka, Alexakis, Diukova, Liddle, and
Auer (2010) reported a negative association between delta
power and connectivity within the DMN in typical adults.
In line with this, our reduced delta finding could reflect
alterations in functional connectivity within the DMN in
children with ADHD. This interpretation is consistent with
MRI research indicating that DMN connectivity is altered
in ADHD (Uddin et al. 2008) and models proposing that
atypical DMN connectivity is involved in causing attentional
problems in ADHD
(Sonuga-Barke and Castellanos 2007)
However, we did not find that delta power was associated
with inattentive symptoms in dimensional analysis. Delta
activity has also been proposed to index baseline activity
in dopaminergic reward/reinforcement circuitry
. Reduced delta power in ADHD may therefore reflect
tonic hypo-activity in this circuitry, which is in line with
models proposing that hypo-dopaminergia and impaired
reinforcement/reward processing are core to the pathology of
(Sagvolden et al. 2005)
as well as empirical findings
of impaired behavioural performance and atypical
neurophysiological correlates of reinforcement learning in ADHD
(Frank et al. 2007; Shephard et al. 2016)
. Further research is
needed to clarify the role of atypical resting-state delta
oscillations in ADHD, for example by correlating delta power
during rest with DMN connectivity assessed with fMRI and
with reinforcement learning task performance.
In contrast to our hypotheses and many previous
(Barry et al. 2009; Bresnahan et al. 1999; Kitsune et al.
2015; Koehler et al. 2009; Tye et al. 2014)
, we found limited
evidence for increased theta activity in children with ADHD
compared to typically developing children. However,
several recent studies of both children and adults with ADHD
have failed to replicate past findings of increased theta in
(Buyck and Wiersema 2014; Liechti et al. 2013;
Loo et al. 2009; van Dongen-Boomsma et al. 2010)
together with the current findings questions the legitimacy of
increased resting-state theta as a marker of ADHD. A recent
study demonstrated that theta power is comparable between
adults with ADHD and controls during the resting-state, but
changes (increases) in theta power from the resting-state to
cognitive task-states is diminished in ADHD compared to
controls, and this pattern normalises with methylphenidate
treatment and associated improvements in ADHD
(Skirrow et al. 2015)
. Thus, it may be the case that
alterations in task-related theta activity, rather than baseline
theta oscillations, are associated with the ADHD phenotype
(McLoughlin et al. 2014)
Our findings converge to suggest a dissociation between
ASD and ADHD on the basis of their cortical EEG profiles,
whereby children with ASD display a high delta, low theta
and low alpha pattern and children with ADHD display a
low delta pattern. Importantly, children with ASD + ADHD
largely present as an additive co-occurrence of both ASD
and ADHD, with low delta, low theta and low alpha
patterns, rather than presenting as a distinct entity with unique
patterns of EEG power. The finding that the ASD+ ADHD
group presents with the unique deficits of both disorders
suggests it cannot be assumed that the correlates and aetiology
of ASD are the same regardless of the presence of absence
or ADHD, and vice versa
(Caron and Rutter 1991)
has implications both for assessing and treating individuals
with both conditions (as treatment of ADHD may not reduce
ASD symptoms), and in the identification of more
homogenous subgroups to further understand genetic and
biological underpinnings and to target specific treatments. However,
the children with ASD + ADHD also displayed reduced theta
power compared to children with pure ADHD at parietal
(in line with Bink et al. 2015)
delta power compared to typically developing children in
parietal regions, which indicates some unique patterns of
EEG power compared to the single disorders. The
reduction of power across all frequency bands in ASD + ADHD
may suggest qualitative differences in resting brain activity
across pure and comorbid cases. Still, there was no evidence
of non-additive statistical interactions between ASD and
ADHD diagnosis to support the comorbid condition as a
qualitatively distinct entity, although this may reflect limited
power to detect significant interactions. This suggests that
resting-state EEG profiles in ASD and in ADHD are not
dependent on or exacerbated by having the comorbidity, but
rather EEG profiles in the comorbid group are the product
of both conditions.
Several limitations should be taken into consideration.
The small sample size limits firm conclusions and along
with heterogeneity in EEG profiles may have contributed
to null findings. For example, lack of group differences on
beta power may reflect the presence of distinct EEG
subtypes that have been described in children with ADHD, that
differ on deficiency versus excess beta power
(Clarke et al.
and behavioural subtypes within children with ASD
that differ on alpha power
(Dawson et al. 1995)
. It will be
important to investigate changes in power and group
differences under different conditions, including real-world
contexts. For example, part of the EEG here was collected
at the beginning of the testing session and therefore may
reflect the potential anxious state of the child (e.g. reduced
alpha) in a new clinical environment. In support, EEG
findings are different when recorded at the beginning compared
to the end of a testing session in adolescents and adults
(Kitsune et al. 2015)
. The topographical
differences observed in the delta and theta bands warrant
further research using advanced source analysis of EEG data
(McLoughlin et al. 2014)
. This may, for example, reflect
group differences in connectivity between brain regions that
are not captured by absolute power indices at selected scalp
regions. An additional consideration is differing
developmental trajectories in relation to EEG power (and potential
compensatory processes in the examination of associated
changes in EEG power). For example, a recent review
indicates developmental subtypes of ASD and ADHD may be
related to changing connectivity in frontal brain regions
(Rommelse et al. 2017)
. Future longitudinal
studies that track oscillatory power at frequent intervals will
help to characterise the developmental changes to disorder
specificity in EEG profiles, across a range of disorders
associated with altered EEG profiles. A final consideration is
that the power values we report (see Table 2) are smaller
than those reported in some studies
(e.g. Barry et al. 2009;
Liechti et al. 2013; Loo et al. 2009)
, although other
previous studies have reported similarly low power values
Kitsune et al. 2015; Tye et al. 2014; van Dongen-Boomsma
et al. 2010)
. We could not identify a systematic difference in
EEG recording or processing parameters that could explain
the variation in power values across studies, and we do not
have reason to believe that our lower power values
contributed to group differences in power, but it might be helpful
for future research to systematically explore the effects of
different amplifier and processing settings on resting-state
power to resolve this inconsistency in the field. This would
be particularly helpful for multi-centre studies comparing
EEG data collected with different recording systems across
In conclusion, this study extends previous studies of
EEG power in ASD and ADHD by identifying distinct
profiles, while demonstrating that children with comorbid
ASD + ADHD largely demonstrate the unique deficits of
both disorders. Examination of EEG power at rest is
therefore useful in elucidating the basis of these overlapping
neurodevelopmental disorders and the potential biological
pathways that underlie comorbidity. Such findings are likely
to show clinical value by aiding in evaluating the validity of
non-invasive EEG in the diagnosis and targeted treatment of
more homogenous subgroups of neurodevelopmental
disorders, and informing aetiological investigations.
Acknowledgments We are grateful to the participating families and
all staff involved in this study. This work was supported by grants from
Action Medical Research (GN2301), the National Institute for Health
Research (NIHR) Biomedical Research Centre for Mental Health
(BRC), the Waterloo Foundation (G686984), and the Steel Charitable
Author Contributions ES conducted the analyses and drafted the
manuscript. CT designed the EEG study and collected all EEG data,
assisted with analyses, and drafted the manuscript. KLA and BA
recruited participants, collected all clinical data, assigned research
diagnoses, and helped to draft the manuscript. GM designed the EEG
study and helped draft the manuscript. PA and PFB conceived of the
study, designed the clinical and EEG components, reviewed research
diagnoses, and helped draft the manuscript.
Compliance with Ethical Standards
Conflict of interest The authors have no conflict of interest.
Research Involving Human Participants Ethical approval for the
study was obtained from the NHS National Research Ethics Service
(NHS RES Wandsworth REC 08/H0903/161) and London Research
and Development Departments. In accordance with the declaration
of Helsinki, parental written informed consent was obtained prior to
completion of study measures.
Informed Consent Informed consent was obtained from all
individual participants included in the study.
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
American Psychiatric Association ( 2000 ). Diagnostic and statistical manual of mental disorders (DSM-IV-TR) (4th ed .). Washington, DC: American Psychiatric Association.
Arns , M. ( 2012 ). EEG-based personalized medicine in ADHD: Individual alpha peak frequency as an endophenotype associated with nonresponse . Journal of Neurotherapy , 16 ( 2 ), 123 - 141 .
Arns , M. , Gunkelman , J. , Breteler , M. , & Spronk , D. ( 2008 ). EEG phenotypes predict treatment outcome to stimulants in children with ADHD . Journal of Integrative Neuroscience , 7 ( 03 ), 421 - 438 .
Banaschewski , T. , & Brandeis , D. ( 2007 ). Annotation: What electrical brain activity tells us about brain function that other techniques cannot tell us-a child psychiatric perspective . Journal of Child Psychology and Psychiatry , 48 ( 5 ), 415 - 435 .
Barry , R. J. , Clarke , A. R. , & Johnstone , S. J. ( 2003 ). A review of electrophysiology in attentiondeficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography . Clinical Neurophysiology , 114 ( 2 ), 171 - 183 .
Barry , R. J. , Clarke , A. R. , Johnstone , S. J. , Brown , C. R. , Bruggemann , J. M. , & van Rijbroek , I. ( 2009 ). Caffeine effects on resting-state arousal in children . International Journal of Psychophysiology , 73 ( 3 ), 355 - 361 .
Barry , R. J. , Clarke , A. R. , Johnstone , S. J. , McCarthy , R. , & Selikowitz , M. ( 2009 ). Electroencephalogram theta/beta ratio and arousal in attention-deficit/hyperactivity disorder: Evidence of independent processes . Biological Psychiatry , 66 ( 4 ), 398 - 401 .
Bink , M., van Boxtel, G. , Popma , A. , Bongers , I. , Denissen , A. , & van Nieuwenhuizen , C. ( 2015 ). EEG theta and beta power spectra in adolescents with ADHD versus adolescents with ASD + ADHD . European Child & Adolescent Psychiatry , 24 ( 8 ), 873 - 886 .
Bresnahan , S. M. , Anderson , J. W. , & Barry , R. J. ( 1999 ). Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder . Biological Psychiatry , 46 ( 12 ), 1690 - 1697 .
Buyck , I. , & Wiersema , J. R. ( 2014 ). Resting electroencephalogram in attention deficit hyperactivity disorder: Developmental course and diagnostic value . Psychiatry Research , 216 ( 3 ), 391 - 397 .
Cantor , D. S. , Thatcher , R. W. , Hrybyk , M. , & Kaye , H. ( 1986 ). Computerized EEG analyses of autistic children . Journal of Autism and Developmental Disorders , 16 ( 2 ), 169 - 187 .
Caron , C. , & Rutter , M. ( 1991 ). Comorbidity in child psychopathology: Concepts, issues and research strategies . Journal of Child Psychology and Psychiatry , 32 ( 7 ), 1063 - 1080 .
Casanova , M. F. , Buxhoeveden , D. P. , Switala , A. E. , & Roy , E. ( 2002 ). Minicolumnar pathology in autism . Neurology , 58 ( 3 ), 428 - 432 .
Chan , A. , Sze , S. , & Cheung , M. ( 2007 ). Quantitative electroencephalographic profiles for children with autistic spectrum disorder . Neuropsychology , 21 ( 1 ), 74 - 81 .
Clarke , A. R. , Barry , R. J. , Dupuy , F. E. , McCarthy , R. , Selikowitz , M. , & Johnstone , S. J. ( 2013 ). Excess beta activity in the EEG of children with attention-deficit/hyperactivity disorder: A disorder of arousal? International Journal of Psychophysiology , 89 ( 3 ), 314 - 319 .
Clarke , A. R. , Barry , R. J. , Irving , A. M. , McCarthy , R. , & Selikowitz , M. ( 2011 ). Children with attention-deficit/hyperactivity disorder and autistic features: EEG evidence for comorbid disorders . Psychiatry Research , 185 ( 1-2 ), 225 - 231 .
Clarke , A. R. , Barry , R. J. , McCarthy , R. , & Selikowitz , M. ( 2001 ). EEG-defined subtypes of children with attention-deficit/hyperactivity disorder . Clinical Neurophysiology , 112 ( 11 ), 2098 - 2105 .
Clarke , A. R. , Barry , R. J. , McCarthy , R. , Selikowitz , M. , Magee , C. A. , Johnstone , S. J. , & Croft , R. J. ( 2006 ). Quantitative EEG in low-IQ children with attention-deficit/hyperactivity disorder . Clinical Neurophysiology , 117 ( 8 ), 1708 - 1714 . doi: 10 .1016/j. clinph. 2006 . 04 .015.
Coben , R. , Clarke , A. R. , Hudspeth , W. , & Barry , R. J. ( 2008 ). EEG power and coherence in autistic spectrum disorder . Clinical Neurophysiology , 119 ( 5 ), 1002 - 1009 .
Conners , C. ( 2008 ). Conners' rating scales 3rd edition . North Tonawanda, NY: Multi Health Systems.
Cornew , L. , Roberts , T. P. L. , Blaskey , L. , & Edgar , J. C. ( 2012 ). Resting-state oscillatory activity in autism spectrum disorders . Journal of Autism and Developmental Disorders , 42 ( 9 ), 1884 - 1894 . doi: 10 .1007/s10803-011-1431-6.
Dawson , G. , Klinger , L. G. , Panagiotides , H. , Lewy , A. , & Castelloe , P. ( 1995 ). Subgroups of autistic children based on social behavior display distinct patterns of brain activity . Journal of Abnormal Child Psychology , 23 ( 5 ), 569 - 583 . doi: 10 .1007/bf01447662.
Ecker , C. ( 2017 ). The neuroanatomy of autism spectrum disorder: An overview of structural neuroimaging findings and their translatability to the clinical setting . Autism: The International Journal of Research and Practice , 21 ( 1 ), 18 - 28 . doi: 10 .1177/1362361315627136.
Frank , M. J. , Santamaria , A. , O'Reilly , R. C. , & Willcutt , E. ( 2007 ). Testing computational models of dopamine and noradrenaline dysfunction in attention deficit/hyperactivity disorder . Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology , 32 ( 7 ), 1583 - 1599 .
Friedman , L. A. , & Rapoport , J. L. ( 2015 ). Brain development in ADHD . Current Opinion in Neurobiology, 30 , 106 - 111 . doi: 10 .1016/j.conb. 2014 . 11 .007.
Goodman , R. ( 1997 ). The strengths and difficulties questionnaire: A research note . Journal of Child Psychology and Psychiatry , 38 , 581 - 586 .
Grzadzinski , R. , Dick , C. , Lord , C. , & Bishop , S. ( 2016 ). Parentreported and clinician-observed autism spectrum disorder (ASD) symptoms in children with attention deficit/hyperactivity disorder (ADHD): Implications for practice under DSM-5 . Molecular Autism, 7 ( 1 ), 7. doi: 10 .1186/s13229-016-0072-1.
Hermens , D. F. , Soei , E. X. , Clarke , S. D. , Kohn , M. R. , Gordon , E. , & Williams , L. M. ( 2005 ). Resting EEG theta activity predicts cognitive performance in attention-deficit hyperactivity disorder . Pediatric Neurology , 32 ( 4 ), 248 - 256 .
Hirstein , W. , Iversen , P. , & Ramachandran , V. ( 2001 ). Autonomic responses of autistic children to people and objects . Proceedings of the Royal Society of London B: Biological Sciences , 268 ( 1479 ), 1883 - 1888 .
Hlinka , J. , Alexakis , C. , Diukova , A. , Liddle , P. F. , & Auer , D. P. ( 2010 ). Slow EEG pattern predicts reduced intrinsic functional connectivity in the default mode network: An intersubject analysis . NeuroImage , 53 ( 1 ), 239 - 246 . doi: 10 .1016/j. neuroimage. 2010 . 06 .002.
IBM Corp ( 2013 ). IBM SPSS Statistics for Windows, Version 22 .0. Armonk, NY: IBM Corp.
Jensen , O. , & Mazaheri , A. ( 2010 ). Shaping functional architecture by oscillatory alpha activity: Gating by inhibition . Frontiers in Human Neuroscience , 4 , 186 .
Kitsune , G. L. , Cheung , C. H. , Brandeis , D. , Banaschewski , T. , Asherson , P. , McLoughlin , G. , & Kuntsi , J. ( 2015 ). A matter of time: The influence of recording context on EEG spectral power in adolescents and young adults with ADHD . Brain Topography , 28 ( 4 ), 580 - 590 .
Klimesch , W. ( 1999 ). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis . Brain Research Reviews , 29 ( 2 ), 169 - 195 .
Klimesch , W. , Sauseng , P. , & Hanslmayr , S. ( 2007 ). EEG alpha oscillations: The inhibition-timing hypothesis . Brain Research Reviews , 53 ( 1 ), 63 - 88 .
Knyazev , G. G. ( 2007 ). Motivation, emotion, and their inhibitory control mirrored in brain oscillations . Neuroscience & Biobehavioral Reviews , 31 ( 3 ), 377 - 395 . doi: 10 .1016/j.neubiorev. 2006 . 10 .004.
Koehler , S. , Lauer , P. , Schreppel , T. , Jacob , C. , Heine , M. , BoreattiHümmer , A., … Herrmann , M. ( 2009 ). Increased EEG power density in alpha and theta bands in adult ADHD patients . Journal of Neural Transmission , 116 ( 1 ), 97 - 104 .
Liechti , M. D. , Valko , L. , Müller , U. C., Döhnert , M. , Drechsler , R. , Steinhausen , H.-C., & Brandeis , D. ( 2013 ). Diagnostic value of resting electroencephalogram in attention-deficit/hyperactivity disorder across the lifespan . Brain Topography , 26 ( 1 ), 135 - 151 .
Loo , S. , Hale , T. S. , Macion , J. , Hanada , G. , McGough , J. J. , McCracken , J. T. , & Smalley , S. L. ( 2009 ). Cortical activity patterns in ADHD during arousal, activation and sustained attention . Neuropsychologia , 47 ( 10 ), 2114 - 2119 .
Loo , S. K. , & Smalley , S. L. ( 2008 ). Preliminary report of familial clustering of EEG measures in ADHD . American Journal of Medical Genetics Part B: Neuropsychiatric Genetics , 147 ( 1 ), 107 - 109 .
Lord , C. , Risi , S. , Lambrecht , L. , Cook , E. , Levethal , B. , Dilavore , P. , … Rutter , M. ( 2000 ). The ADOS-G (autism diagnostic observation schedule-generic): A standard measure of social-communication deficits associated with autism spectrum disorders . Journal of Autism and Developmental Disorders , 30 , 205 - 223 .
Lord , C. , Rutter , M. , & Couteur , A. ( 1994 ). Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders . Journal of Autism and Developmental Disorders , 24 ( 5 ), 659 - 685 .
Lőrincz , M. L. , Kékesi , K. A. , Juhász , G. , Crunelli , V. , & Hughes , S. W. ( 2009 ). Temporal framing of thalamic relay-mode firing by phasic inhibition during the alpha rhythm . Neuron , 63 ( 5 ), 683 - 696 .
Machado , C. , Estévez , M. , Leisman , G. , Melillo , R. , Rodríguez , R. , DeFina, P., … Beltrán , C. ( 2015 ). QEEG spectral and coherence assessment of autistic children in three different experimental conditions . Journal of Autism and Developmental Disorders , 45 , 406 - 424 . doi: 10 .1007/s10803-013-1909-5.
Martineau , J. , Hernandez , N. , Hiebel , L. , Roché , L. , Metzger , A. , & Bonnet-Brilhault , F. ( 2011 ). Can pupil size and pupil responses during visual scanning contribute to the diagnosis of autism spectrum disorder in children? Journal of Psychiatric Research , 45 ( 8 ), 1077 - 1082 .
Mathewson , K. J. , Jetha , M. K. , Drmic , I. E. , Bryson , S. E. , Goldberg , J. O. , & Schmidt , L. A. ( 2012 ). Regional EEG alpha power, coherence, and behavioral symptomatology in autism spectrum disorder . Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology. doi:10 .1016/j. clinph. 2012 . 02 .061.
McLoughlin , G. , Palmer , J. A. , Rijsdijk , F. , & Makeig , S. ( 2014 ). Genetic overlap between evoked frontocentral theta-band phase variability, reaction time variability, and attention-deficit/hyperactivity disorder symptoms in a twin study . Biological Psychiatry , 75 ( 3 ), 238 - 247 . doi: 10 .1016/j.biopsych. 2013 . 07 .020.
Michels , L. , Muthuraman , M. , Lüchinger , R. , Martin , E. , Anwar , A. R. , Raethjen , J. , … Siniatchkin , M. ( 2013 ). Developmental changes of functional and directed resting-state connectivities associated with neuronal oscillations in EEG . NeuroImage, 81 , 231 - 242 .
Murias , M. , Webb , S. J. , Greenson , J. , & Dawson , G. ( 2007 ). Resting state cortical connectivity reflected in EEG coherence in individuals with autism . Biological Psychiatry , 62 ( 3 ), 270 - 273 .
Pop-Jordanova , N. , Zorec , T. , Demerdzieva , A. , & Gucev , Z. ( 2010 ). QEEG characteristics and spectrum weighted frequency for children diagnosed as autistic spectrum disorder . Non-Linear Biomedical Physics , 4 , 4 .
Prince , E. B. , Kim , E. S. , Wall , C. A. , Gisin , E. , Goodwin , M. S. , Simmons , E. S. , … Shic , F. ( 2016 ). The relationship between autism symptoms and arousal level in toddlers with autism spectrum disorder, as measured by electrodermal activity . Autism: The International Journal of Research and Practice. doi:10.1177/1362361316648816
Rommelse , N. , Buitelaar , J. K. , & Hartman , C. A. ( 2017 ). Structural brain imaging correlates of ASD and ADHD across the lifespan: A hypothesis-generating review on developmental ASD-ADHD subtypes . Journal of Neural Transmission , 124 ( 2 ), 259 - 271 . doi: 10 .1007/s00702-016-1651-1.
Ronald , A. , Happé , F. , & Plomin , R. ( 2008 ). A twin study investigating the genetic and environmental aetiologies of parent, teacher and child ratings of autistic-like traits and their overlap . European Child & Adolescent Psychiatry , 17 ( 8 ), 473 - 483 . doi: 10 .1007/ s00787-008-0689-5.
Rubenstein , J. L. R. , & Merzenich , M. M. ( 2003 ). Model of autism: Increased ratio of excitation/inhibition in key neural systems . Genes, Brain and Behavior , 2 ( 5 ), 255 - 267 .
Rutter , M. , Bailey , A. , & Lord , C. ( 2003 ). SCQ: The social communication questionnaire manual . Los Angeles, CA: Western Psychological Services.
Sagvolden , T. , Johansen , E. b. , Aase , H. , & Russell , V. a . ( 2005 ). A dynamic developmental theory of attention-deficit/hyperactivity disorder (adhd) predominantly hyperactive/impulsive and combined subtypes . Behavioral and Brain Sciences , 28 ( 3 ), 397 - 419 . doi: 10 .1017/S0140525X05000075.
Sergeant , J. A. ( 2000 ). The cognitive-energetic model: An empirical approach to attention-deficit hyperactivity disorder . Neuroscience & Biobehavioral Reviews , 24 ( 1 ), 7 - 12 .
Sergeant , J. A. , Geurts , H. , Huijbregts , S. , Scheres , A. , & Oosterlaan , J. ( 2003 ). The top and the bottom of ADHD: A neuropsychological perspective . Neuroscience and Biobehavioral Reviews , 27 , 583 - 592 .
Shephard , E. , Jackson , G. M. , & Groom , M. J. ( 2016 ). Electrophysiological correlates of reinforcement learning in young people with Tourette syndrome with and without co-occurring ADHD symptoms . International Journal of Developmental Neuroscience , 51 , 17 - 27 .
Simonoff , E. , Pickles , A. , Charman , T. , Chandler , S. , Loucas , T. , & Baird , G. ( 2008 ). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a population-derived sample . Journal of the American Academy of Child & Adolescent Psychiatry , 47 ( 8 ), 921 - 929 .
Skirrow , C. , McLoughlin , G. , Banaschewski , T. , Brandeis , D. , Kuntsi , J. , & Asherson , P. ( 2015 ). Normalisation of frontal theta activity following methylphenidate treatment in adult attention-deficit/ hyperactivity disorder . European Neuropsychopharmacology , 25 ( 1 ), 85 - 94 .
Sonuga-Barke , E. J. , & Castellanos , F. X. ( 2007 ). Spontaneous attentional fluctuations in impaired states and pathological conditions: A neurobiological hypothesis . Neuroscience & Biobehavioral Reviews , 31 ( 7 ), 977 - 986 .
Stroganova , T. A. , Nygen , G. , Tsetlin , M. M. , Posikera , I. N. , Gillberg , C. , Elam , M. , & Orekhova , E. V. ( 2007 ). Abnormal EEG lateralization in boys with autism . Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology. doi:10 .1016/j.clinph. 2007 . 05 .005.
Taylor , E., Schachar , R. , Thorley , G. , & Wieselberg , M. ( 1986 ). Conduct disorder and hyperactivity: I. Separation of hyperactivity and antisocial conduct in British child psychiatric patients . The British Journal of Psychiatry , 149 ( 6 ), 760 - 767 . doi: 10 .1192/ bjp.149.6.760.
Thatcher , R. W. , North, D. M. , Neubrander , J. , Biver , C. J. , Cutler , S. , & DeFina , P. ( 2009 ). Autism and EEG phase reset: Deficient GABA mediated inhibition in thalamo-cortical circuits . Developmental Neuropsychology , 34 ( 6 ), 780 - 800 .
Tick , B. , Colvert , E. , McEwen , F. , Stewart , C. , Woodhouse , E. , Gillan , N. , … Simonoff , E. ( 2016 ). Autism Spectrum Disorders and other mental health problems: Exploring etiological overlaps and phenotypic causal associations . Journal of the American Academy of Child & Adolescent Psychiatry , 55 ( 2 ), 106 - 113 . e104 .
Tye , C. , Rijsdijk , F. , & McLoughlin , G. ( 2014 ). Genetic overlap between ADHD symptoms and EEG theta power . Brain and Cognition , 87 , 168 - 172 .
Uddin , L. Q. , Kelly , A. M. C. , Biswal , B. B. , Margulies , D. S. , Shehzad , Z. , Shaw , D. , … Milham , M. P. ( 2008 ). Network homogeneity reveals decreased integrity of default-mode network in ADHD . Journal of neuroscience Methods , 169 ( 1 ), 249 - 254 .
van Dongen-Boomsma , M. , Lansbergen , M. M. , Bekker , E. M. , Kooij , J. S., van der Molen, M. , Kenemans , J. L. , & Buitelaar , J. K. ( 2010 ). Relation between resting EEG to cognitive performance and clinical symptoms in adults with attention-deficit/hyperactivity disorder . Neuroscience Letters , 469 ( 1 ), 102 - 106 .
Wang , J. , Barstein , J. , Ethridge , L. , Mosconi , M. , Takarae , Y. , & Sweeney , J. ( 2013 ). Resting state EEG abnormalities in autism spectrum disorders . Journal of Neurodevelopmental Disorders , 5 ( 1 ), 24 .
Wechsler , D. ( 1999 ). Wechsler abbreviated scale of intelligence (WASI) . San Antonio, TX: Harcourt Assessment.
World Health Organisation ( 1993 ). Mental disorders: A glossary and guide to their classification in accordance with the 10th revision of the international classification of disease-research diagnostic criteria: ICD-10 . Geneva: World Health Organisation.