ArticlePDF Available

Abstract and Figures

Autism spectrum disorders (ASD) are a group of complex and heterogeneous developmental disorders involving multiple neural system dysfunctions. In an effort to understand neurophysiological substrates, identify etiopathophysiologically distinct subgroups of patients, and track outcomes of novel treatments with translational biomarkers, EEG (electroencephalography) studies offer a promising research strategy in ASD. Resting-state EEG studies of ASD suggest a U-shaped profile of electrophysiological power alterations, with excessive power in low-frequency and high-frequency bands, abnormal functional connectivity, and enhanced power in the left hemisphere of the brain. In this review, we provide a summary of recent findings, discuss limitations in available research that may contribute to inconsistencies in the literature, and offer suggestions for future research in this area for advancing the understanding of ASD.
Content may be subject to copyright.
R E V I E W Open Access
Resting state EEG abnormalities in autism
spectrum disorders
Jun Wang
, Jamie Barstein
, Matthew W Mosconi
, Yukari Takarae
and John A Sweeney
Autism spectrum disorders (ASD) are a group of complex and heterogeneous developmental disorders
involving multiple neural system dysfunctions. In an effort to understand neurophysiological substrates, identify
etiopathophysiologically distinct subgroups of patients, and track outcomes of novel treatments with translational
biomarkers, EEG (electroencephalography) studies offer a promising research strategy in ASD. Resting-state EEG
studies of ASD suggest a U-shaped profile of electrophysiological power alterations, with excessive power in
low-frequency and high-frequency bands, abnormal functional connectivity, and enhanced power in the left
hemisphere of the brain. In this review, we provide a summary of recent findings, discuss limitations in available
research that may contribute to inconsistencies in the literature, and offer suggestions for future research in this
area for advancing the understanding of ASD.
Keywords: Autism, Resting-state, EEG, Electroencephalography
Autism spectrum disorders (ASD) are characterized by
social and communication impairments, and by restricted
and stereotyped behaviors [1]. ASD affect approximately 1
in 88 children and 1 in 54 males [2]. These disorders
are highly heritable, with estimates ranging from 70 to
90% [3], and are known to have a high recurrence rate
in siblings (10 to 20% [4]), yet, progress in identifying
pathophysiological and etiological mechanisms has been
It is likely that there are many causes of ASD. Several
single-gene disorders (for example, Fragile X, tuberous
sclerosis) and rare copy number variants (CNVs) (for
example, 16p11 deletions, 15q13 duplications) appear to
be strongly associated with ASD, but genetic syndromes,
mutations, and single-gene etiologies account for only 10
to 20% of ASD cases, and many individuals with these
genetic syndromes do not have ASD [5]. The majority
of affected individuals appear to have more complex
underlying genetic and epigenetic abnormalities, involving
highly penetrant yet undiscovered rare mutations, or
combinations of less penetrant and more common variants.
The highly variable clinical presentation of ASD reflects
this heterogeneity. Affected individuals vary greatly in the
course of the disorder (around a quarter show significant
developmental regression), associated medical conditions,
behavioral challenges (for example, sensory issues, hyper-
activity), and degree of intellectual impairment [6,7].
Many studies have attempted to characterize the
neural system abnormalities associated with ASD [8,9].
Post-mortem studies have most consistently noted
abnormalities in the limbic system and cerebellum [10].
Neuroimaging studies have identified abnormalities in
brain and head size, and in cerebellum and limbic
structures [11-13], with some individuals showing a
pattern of early brain overgrowth [11,14-16]. Functional
MRI (fMRI) studies have reported abnormalities in
individuals with ASD when performing various tasks
involving language comprehension [17], working memory
[18], face recognition [19,20], and eye movements [9].
Compared with typically developing subjects, individuals
with ASD usually express a diffuse network pattern with
diminished activity in task-related regions and increased
activity in task-unrelated regions [9,20,21]. When there is
no task involved, individuals with ASD show functional
underconnectivity in anterior-posterior connections [22]
and reduced connectivity involving the medial prefrontal
* Correspondence:
Equal contributors
Department of Psychiatry, University of Texas Southwestern, Dallas, TX, USA
Full list of author information is available at the end of the article
© 2013 Wang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24
cortex and the left angular gyrus [23]. Moreover, a lack
of deactivation in task-related regions during rest has
also been reported in individuals with ASD [24]. Although
post-mortem and structural MRI studies of ASD have
provided promising insights, abnormalities often fail to
provide a direct link to the clinical symptomatology of
the disorder, with few exceptions [13,25], a challenge for
which neurophysiological studies offer advantages.
Electroencephalography (EEG), which primarily mea-
sures neurophysiological changes related to postsynaptic
activity in the neocortex [26], has proven to be a powerful
tool for studying complex neuropsychiatric disorders
[27-30]. EEG has been the primary measure used to
capture and characterize epileptiform and abnormal par-
oxysmal activity through the detection of focal spikes,
which occur with increased frequency in ASD [31,32].
Resting EEG studies have shown that 20% of individuals
with ASD show epileptiform discharges at rest, typically
without the presence of clinical seizures [33,34]. Higher
rates of epileptiform activityhavealsobeenreportedin
sleep studies; for example, Chez, et al.[35]reported
that 61% of individuals with ASD and no clinical history
of seizures displayed epileptiform abnormalities.
The most common way to characterize resting EEG is
by breaking down the oscillatory patterns into bands of
frequencies that share physiological properties. The typical
clinically relevant frequency bands of EEG range from 0.3
to 100 Hz. Within the scope of the current paper, we focus
on five frequency bands ranging from 1 to 100 Hz: delta
(1 to 3 Hz), theta (4 to 7 Hz), alpha (8 to 12 Hz), beta
(13 to 35 Hz), and gamma (>35 Hz). These historically
documented frequency bands have attracted rapidly
growing interest in clinical and cognitive neuroscience
fields, and are believed to govern different cognitive
processes [36]. Delta dominates deep sleep, and is thought
to underlie the event-related slow waves seen in tasks
for detection of attention and salience [37]. Theta is most
commonly studied in relation to memory processes [38].
Alpha waves are present in relaxed awake individuals,
and are associated with precise timing of sensory and
cognitive inhibition [39]. Beta waves are associated with
alertness, active task engagement, and motor behavior
[40]. Finally, gamma waves, present during working-
memory matching [41] and a variety of early sensory
responses, are believed to facilitate feature binding in
sensory processing [42,43].
Additionally, EEG recordings can be used to assess
functional connectivity between different brain regions
over time via EEG coherence, and quantitative measure-
ment of the relationship of frequency spectra between two
EEG signals [44]. This advantageous feature can further
our understanding of the impaired interactions between
brain regions of individuals with ASD that have been
suggested by functional MRI studies [45-51].
Advantages of resting-state EEG for studying brain
dysfunction in ASD
Resting-state EEG studies are used to monitor brain acti-
vity in the absence of overt task performance or sensory
stimulation. These measurements can identify abnorma-
lities for which evoked potential studies, the most widely
used approach in EEG research with ASD, are not well
suited [52]. Indeed, task-dependent changes in brain
function are difficult to interpret without fundamental
knowledge of functional differences in individuals with
ASD at rest. In task-based evoked potential studies,
only time-locked neural responses to events of interest
are studied; all other spontaneous activity is typically
considered background noise [53-55]. Multiple studies
have suggested that the brain is a system that operates
intrinsically, with intrinsic resting-state integration. Ex-
ternal sensory information interacts with, rather than
determines, the operation of brain systems [55-57]. For
example, many studies have shown that pre-stimulus
EEG activities predict the event-related potentials for
visual stimuli [58] or motor responses [59].
There are also several practical advantages of using
EEG to study brain function in developmental disorders
such as ASD. Compared with MRI, EEG can be used
across a wider range of age groups and developmental
abilities to study brain physiology, has a higher relative
tolerance for movement, has higher temporal resolution,
is more clinically available, and can be used to collect
repeated measurements because (compared with positron
emission tomography) it is non-invasive. Resting-state
approaches do not require subjects to make a response.
This element is particularly promising for studying more
severely impaired and/or younger patients who may not
be able to perform tasks accurately because of cognitive,
physical, or developmental challenges. This is crucial
for studying the abnormal maturational trajectory in
ASD through early childhood. The literature on resting-
state EEG in healthy individuals shows increased alpha
power and coherence in individuals with ASD [60], as well
as reduced power in low-frequency bands (delta, theta)
[61] in adults relative to children. These findings reflect
maturation of long-range cortico-cortical connections into
Quantitative EEG of resting-state data also has promise
as an approach for monitoring treatment outcomes.
Pineda et al. [62] reported that individuals with ASD
who received neurofeedback training on controlling neural
oscillatory activity in the alpha or mu band displayed
decreased mu power and coherence, as well as im-
proved performance on an attention test and decreased
scores on the Autism Treatment Evaluation Checklist
[63]. Neurofeedback training (aiming at reducing theta
activity while increasing beta activity) has also been
reported to improve executive test performance in indi
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 2 of 14
viduals with ASD (including attentional control, cognitive
flexibility, and goal-setting) for up to 12 months [64,65].
Despite these unique advantages, relatively few studies
have used EEG to study resting-state brain alterations in
ASD. In the present article, we review the existing litera-
ture on EEG resting-state abnormalities in ASD, discuss
potential causes of inconsistencies between studies, and
offer suggestions for future studies utilizing resting-state
EEG to understand the pathophysiological mechanisms
involved in ASD.
Resting-state EEG findings in ASD
Early resting-state EEG studies of ASD failed to identify
consistent patterns of atypical neural activity [66-70]. The
recognized prevalence of EEG abnormalities in patients
varied greatly between studies, which may be attributable
to the lack of standardized diagnostic approaches at the
time or to limitations in EEG recording technology (for
example, small numbers of electrodes) and analysis (for
example, qualitative judgments, different approaches to
quantification). We limited our review to EEG studies that
used spectral analysis to investigate activity in different
power bands and coherence between hemispheres and
brain regions (Table 1).
Abnormal power
EEG power can be measured as either relative power or
absolute power. Relative power is the amount of EEG
activity in an individual frequency band divided by the
amount of activity in all frequency bands. Absolute power
is the amount of EEG activity in one band independent
of activity in other bands. Relative power thus reflects
the relationship between frequency bands, but does not
yield an indication of the degree to which abnormal
electrophysiological activity is present in a specific fre-
quency band. Studies of ASD vary widely in the extent
to which they present absolute power findings (Table 1),
so interpretation of atypical inter-relationships between
different frequency bands (relative power) can be advanta-
geous for comparing frequency bands, but also confounds
measurement of activity in the target band within any
alterations that may occur in other frequency bands.
Absolute power is in many ways preferable for developing
an understanding of electrophysiological alterations in
ASD, and for the interpretation of differences in relative
power in this population [71].
In addition to differences between studies in approaches
for characterizing EEG activity, studies of ASD are also
confounded by the extreme behavioral and putative
neurophysiological heterogeneity that characterizes this
disorder. Studies vary widely in the demographic char-
acteristics of their samples, and factors such as the age of
subjects studied, and whether or not subjects with intel-
lectual disability (ID) were included, may significantly
affect study findings. Despite these important concerns, a
relatively consistent and unique profile of electrophysio-
logical abnormalities has emerged from resting-state EEG
studies, which appears to be present across diverse patient
populations. Specifically, excessive power at low-frequency
(delta, theta) and high-frequency (beta, gamma) bands,
but reduced power in the middle-range frequency band
(alpha) (Figure 1) has been found at all stages of devel-
opment and in children with and children without
comorbid ID [72-75]. The excess in delta power has been
found in both relative [72,73] and absolute [73-75] powers,
and in multiple brain regions, including the dorsal mid-
line, parietal, right temporal [73] and frontal cortical
[74,75] areas, suggesting a widely distributed pattern of
abnormality [72]. Similarly, enhanced low frequency rela-
tive [76,77] and absolute [75,78] theta (4 to 7 Hz) activity
has been seen in both adults [76,78] and children [75,77]
with ASD, primarily in the frontal and right posterior
cortex. Enhanced power has also been reported in
high-frequency relative beta(13to35Hz)andabsolute
gamma (>35 Hz) bands in both adults [76] and children
[77,79] with ASD. Within the higher-frequency bands,
the most significant alterations have been found in
gamma power over occipital, parietal [76] and midline
[77,79] regions.
In contrast to the excess power displayed in low-fre-
quency (delta, theta) and high-frequency (beta, gamma)
bands, individuals with ASD show reduced relative
[72,73,76] and absolute [80] power in middle-range
(alpha) frequencies across many brain regions [72,73],
including the frontal, [76,80], occipital, parietal [76],
and temporal [80] cortex. This pattern indicates a U-
shaped profile of electrophysiological power alter-
ations in ASD in which the extremities of the power
spectrum are abnormally increased, while power in the
middle frequencies is reduced. Available evidence for
this model is mostly supportive, but more hypothesis-
driven work is needed to confirm and validate it.
We speculate that the etiology for this U-shaped profile
gamma-aminobutryic acid (GABA)ergic tone in inhibitory
circuitry, which influences the functional and develop-
mental plasticity of the brain and is thought to modulate
power in high-frequency and low-frequency bands while
increasing the power of middle-range frequencies (alpha
band) [81]. Activity in the gamma band that is visible in
EEG seizure recordings has been linked to impairment of
dendritic GABAergic inhibition [82]. However, increasing
GABA concentration by administrating the GABA antag-
onist vigibatrin has been shown to increase resting delta
power in both rats [83] and humans [84], suggesting
that a simple decrease in GABA does not fully explain
the U-shaped spectral profile in ASD. For example,
thalamocortical delta oscillations are produced by an
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 3 of 14
Table 1 Power and coherence effects in ASD compared with typically developing individuals
Frequency band Brain region(s) Effect Ref(s)
Absolute power
Delta Frontal Enhanced [74,75]
Frontal Reduced [77,80]
Central/parietal Enhanced [73]
Temporal Reduced [80]
Theta Frontal/prefrontal Enhanced [75,78]
Frontal Reduced [80]
Temporal Reduced [80]
Parietal Reduced [80]
Alpha All regions No effect [77,105]
Frontal/prefrontal Reduced [80]
Frontal Enhanced [106]
Parietal Enhanced [106]
Central Enhanced [106]
Temporal Reduced [80]
Beta All regions No effect [80]
All regions Reduced [77]
Gamma Midline/central and parietal Enhanced [79]
Relative power
Delta All regions Enhanced [72]
Frontal Reduced [77]
Central/parietal Enhanced [73]
Theta Frontal/prefrontal Enhanced [76]
Right posterior Enhanced [77]
Alpha All regions Reduced [72,73]
All regions No effect [77]
Frontal/prefrontal Reduced [76]
Occipital/parietal Reduced [76]
Beta Occipital/parietal Enhanced [76]
Delta Short/long intrahemispheric Reduced [77]
Lateral-frontal intrahemispheric Enhanced [118]
Middle frontal Reduced [118]
Occipital Reduced [118]
Frontal Reduced interhemispheric [77]
Temporal Reduced interhemispheric [77]
Central/parietal/occipital Reduced interhemispheric [77]
Theta Short/long intrahemispheric Reduced [77,119]
Short/long intrahemispheric Enhanced [76]
Frontal Reduced interhemispheric [77]
Temporal Reduced interhemispheric [77]
Central/parietal/occipital Reduced interhemispheric [77]
Alpha Frontal Reduced [76]
Between frontal and all other regions Reduced [76]
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 4 of 14
interaction between GABAergic interneurons and N-
methyl-D-aspartate receptors on glutamatergic neurons,
which are in turn modulated by dopaminergic neurons
in the thalamus [85]. ASD power abnormalities may result
from a complex pattern of neurochemical alterations
that affect the physiology of inhibitory GABAergic inter-
neurons and their modulation of excitatory activity in py-
ramidal cells.
There is evidence that GABAergic interneuron deve-
lopment and connectivity is disrupted in the prefrontal and
temporal cortices in ASD [86], and that this disruption is
relevant to excitatory/inhibitory balance [87]. The GABA
Table 1 Power and coherence effects in ASD compared with typically developing individuals (Continued)
Temporal Reduced interhemispheric [77]
Short/long intrahemispheric Reduced [119]
Beta Central/parietal/occipital Reduced interhemispheric [77],
Frontal-temporal Reduced [119]
Short/long intrahemispheric Reduced [119]
Hemispheric asymmetry
Delta Frontal Reduced power in left hemisphere [80]
Frontal Enhanced power in left compared with right hemisphere [74]
Temporal Reduced power in left hemisphere [80]
Temporal Enhanced power in left compared with right hemisphere [74]
Parietal Enhanced power in left compared with right hemisphere [74]
Posterior-temporal Enhanced power in left compared with right hemisphere [72]
Central Enhanced power in left compared with right hemisphere [72]
Occipital Enhanced power in left compared with right hemisphere [72]
Occipital No difference between left and right hemispheres [105]
Theta Frontal Reduced power in left hemisphere [80]
Frontal Enhanced power in left compared with right hemisphere [74,78]
Temporal Reduced power in left hemisphere [80
Temporal Enhanced power in left compared with right hemisphere [74]
Parietal Enhanced power in left compared with right hemisphere [74]
Right posterior Enhanced power in right hemisphere [77]
Posterior-temporal Enhanced power in left compared with right hemisphere [72]
Central Enhanced power in left compared with right hemisphere [72,74]
Occipital Enhanced power in left compared with right hemisphere [72, 74]
Occipital No difference between left and right hemispheres [105]
Alpha Frontal Reduced power in left hemisphere [80]
Mid-frontal Enhanced power in left compared with right hemisphere [106,107]
Temporal Reduced power in left hemisphere [80]
Temporal Enhanced power in left compared with right hemisphere [74]
Parietal Enhanced power in left compared with right hemisphere [74]
Posterior-temporal Enhanced power in left compared with right hemisphere [72]
Central Enhanced power in left compared with right hemisphere [72]
Occipital Enhanced power in left compared with right hemisphere [72]
Occipital No difference between left and right hemispheres [105]
Beta Posterior-temporal Enhanced power in left compared with right hemisphere [72]
Central Enhanced power in left compared with right hemisphere [72]
Occipital Enhanced power in left compared with right hemisphere [72]
Occipital No difference between left and right hemispheres [105]
Mu Central No difference between left and right hemispheres [74]
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 5 of 14
agonist lorazepam has been shown to increase long-range
cortical functional connectivity in the alpha and low beta
ranges [88], suggesting an association between GABA tone
and large scale cortico-cortical connectivity. The middle
alpha frequencies have commonly been associated with
an idlingstate, or more recently with active inhibition
[39,89], a state that has been associated with GABAergic
circuitry [90]. GABAergic abnormalities can also have
early developmental consequences, as GABA acts as an
excitatory trophic factor prenatally, guiding growth and
connectivity of dendrites [91]. Abnormal embryonic GABA
concentrations could lead to development of abnormal
excitatory/inhibitory circuitry, causing long-term alterations
in the entire oscillatory activity at multiple frequencies.
GABA abnormalities could bias neural networks away
from the state of active inhibition (alpha) and towards
greater excitation (higher frequencies). Intermittent theta-
burst stimulation has been shown to increase cortical
inhibition in rat neocortex by reducing parvalbumin
expression in fast-spiking interneurons [92]. Stimulation
in both the delta and theta frequency bands also increases
expression of GABA precursors in inhibitory cortical
systems [93]. In such cases, increased low-frequency acti-
vity could be a compensatory mechanism in ASD to halt
the proliferation of high-frequency excitatory activation
produced by GABAergic dysfunction. This hypothesis is
also consistent with several studies that have documented
GABAergic abnormalities in individuals with ASD [94].
Fatemi et al. [95,96] showed reductions in GABA receptor
density in cerebellum and Brodmannsareas9and40
[95,96]. At the genetic level, it has been suggested that
an interaction between GABA receptor subunit genes
(GABRA4 and GABRB1) could be directly involved in the
etiology of ASD by contributing to increased neuronal
excitability, particularly during development, when GAB
RA4 mRNA levels in brain tissues are at their highest
[94]. Increased excitation/inhibition ratios reflecting
glutamatergic/GABAergic balance [97], induced by en-
dogenously suppressed GABAergic inhibition [98-101],
has been shown in some individuals with ASD. Alpha
band power is thought to play an important role in
top-down control of sensorimotor responses, including
successful voluntary inhibition of contextually inappro-
priate responses [39]. Many children with ASD show
increased levels of inattention and impulsivity [102,103],
which may be linked to increased rates of inhibitory
control errors in affected individuals [104]. Although
associations between alpha power and inhibitory control
deficits in ASD have not been directly examined, the
potential role of this neurophysiological mechanism in this
domain of behavioral impairment merits investigation.
There arre inconsistencies between studies regarding
the U-shaped pattern of power in ASD. Reduced frontal
low-frequency delta has been reported in children with
ASD without ID [77] and reduced delta has been seen
in the frontal and temporal regions in children with
high-functioning and low-functioning ASD [80]. A few
studies reported no differences in alpha frequency bands
in children with ASD [77,105] and enhanced alpha power
over frontal/parietal/midline regions in high-functioning
individuals with ASD [106]. Dawson and colleagues [80]
found reduced theta band activity in the frontal, temporal,
and parietal regions, withnoeffectinthebetaband.
Differences in participant characteristics such as IQ
and age might account for the inconsistencies in these
findings. Within the delta band, all enhanced delta find-
ings (supporting the U-shaped curve hypothesis) have
been most robust in relatively low-functioning children
(mean IQ = 37.[72]), those with over 20% mental age delay
[74], and those with 28% lower intelligence score in
ASD [73]. However, reduced delta power was reported
in high-functioning children with ASD (mean Full Scale
Intelligence Quotient of 93) by Coben et al. [77]. Findings
of reduced alpha power (again supporting the U-shaped
curve hypothesis) have been most consistent in low-
functioning children with ASD [72,73,80], but there are
examples of this pattern in relatively high-functioning
adults with ASD [76]. Studies reporting enhanced or un-
affected alpha power were reported for high-functioning
children with ASD [77,105,106]. The relation between
resting EEG abnormalities and level of intellectual disabi-
lity, impairments in various behavioral domains, history
of regression, and other clinical features of ASD will be
an important focus of future research in this area as
larger, well-characterized cohorts are studied.
Abnormal hemispheric asymmetry
In addition to spectral power differences in individuals
with ASD, changes have been reported in the hemispheric
asymmetry of brain neurophysiology. The majority of
the existing resting-state EEG literature reports enhanced
Figure 1 Illustration of the U-shaped profile of abnormal power
pattern in autism spectrum disorders (ASD).
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 6 of 14
power in the left compared with the right hemisphere
in individuals with ASD, across all frequency bands
[72,74,78,106,107]. This asymmetry is generally much
larger than the mild individually variable asymmetries
seen in typically developing humans [106].
Cantor and colleagues [72] reported that subjects with
ASD had enhanced power in the delta band, in the
posterior-temporal, midline, and occipital regions of
the left hemisphere. Similarly, Stroganova et al.[74]found
enhanced delta power in the left hemisphere of individuals
with ASD in the frontal, temporal, and parietal regions.
In the theta band, left-hemisphere dominance in ASD
was seen in frontal [74,78], parietal [74], temporal [72,74],
and occipital [72,74] regions. In the alpha band, left-
hemisphere dominance in ASD was reported in multiple
studies in mid-frontal [106,107], temporal, parietal [72,74],
midline [72,106], and occipital [72] regions. Finally, Cantor
et al. [72] replicated the left-hemisphere dominance
pattern in the beta band in posterior-temporal, midline,
and occipital regions.
Left-hemisphere asymmetry in ASD is of clinical inter-
est, given the common language abnormalities seen in
ASD [108-111]. Increased resting power in the left hemi-
sphere may contribute to left-hemisphere performance
deficits by decreasing the signal-to-noise ratio during
active tasks, similar to reports of increased background
noise and behavioral performance impairment in the
literature on schizophrenia [112,113]. Left-hemisphere
dysfunction may also be dependent on the task that
subjects are performing. When performing tasks of ex-
ecutive functioning (for example, Go/No-go and Stroop
tests), high-functioning adults with ASD had significantly
increased activation restricted to the left hemisphere
[114]. Left-hemisphere dysfunction has also been iden-
tified in smooth pursuit eye movements in individuals
with ASD [115], as have left-lateralized alterations during
an oculomotor serial reaction time task [116].
Nevertheless, as in many neuropsychiatric disorders,
evidence of lateralized abnormalities has been inconsistent
[74]. Dawson et al. [80] reported reduced delta power
in the left mid-temporal cortex, and Lazarev et al. [105]
reported no left/right-hemisphere differences frequency
bands in the occipital cortex. However, in the same and
subsequent reports, the same authors noted hypercon-
nectivity within the left hemisphere [117] and reduced
power in the right hemisphere [105] in children with
ASD during presentation of photic driving stimulation.
Dawson and colleagues [80] utilized a relatively short
(1 second) window to measure delta power, which may
affect reliability of measured low-frequency activity. Lazarev
and colleagues [105,117] measured activity only in 14
relatively heterogeneous subjects with ASD, so those stu-
dies may have lacked statistical power to detect effects.
Photic driving is a robust response, and would potentially
be more sensitive to small pathological alterations in stu-
dies with small patient cohorts.
Abnormal coherence
Resting-state EEG studies of ASD have also documented
reduced long-range coherence patterns [76,77,118,119].
Weaker coherence between frontal and occipital regions
was reported for delta [77,118] and theta [77] bands,
whereas Murias and colleagues [76] reported signifi-
cantly reduced alpha coherence between the frontal
cortex and the temporal, parietal, and occipital cortices.
Duffy and Als [119] reported weaker left frontal-temporal
connectivity within the beta band. This finding is similar
to results from multiple fMRI studies that have shown
reduced left frontotemporal connectivity during resting
state [18,46,120]. These findings parallel those from
Horwitz and colleagues [121], who used positron emission
tomography to show reduced correlations in glucose
metabolism between frontal and other cortical areas in
resting adults with ASD. Generalized decreases in front-
oparietal and fronto-occipital connectivity have also
been reported in ASD during resting fMRI [22,50]. These
findings converge to suggest weakened long-range
connectivity between the frontal lobe and other cor-
tical regions. The frontal lobe plays a crucial role in
higher-order cognitive, language, social, and emotional
functioning [122]. Thus, it is not surprising that clear
deficits in frontal lobe connectivity have been reported
in ASD, as frontal lobe abnormalities have been pro-
posed to play a key role in regulating a wide range of
cognitive, sensory, and motor processes [123-125].
Findings on short-range connection patterns in resting
EEG studies are less consistent. Both intrahemispheric
and interhemispheric local coherences in all brain re-
gions have been reported to be reduced in delta and
theta bands [77], while a reduced local coherence over
mid-frontal regions has been found in both the delta
[118] and alpha band [76]. By contrast, enhanced local
coherence has been found over the lateral-frontal region
in the delta band [118], and over left frontal and tem-
poral regions in the theta band [76]. Furthermore, these
functional frontal deficits have been linked to structural
abnormalities in the frontal lobe in ASD [126]. Although
several studies have suggested excessive short-range con-
nectivity in ASD, due to increased density of cortical
mini-columns [123] and disproportionately increased white
matter [127,128], some diffusion tensor imaging (DTI)
studies failed to report this pattern [49]. Although short-
range coherence studies will require more investigation
with newer high-resolution DTI techniques, in parallel
with resting-state measures of frontal connectivity using
EEG, weaker long-range coherence between frontal and
other brain regions found in resting EEG studies suggests
that the frontal lobe is less well integrated with other local
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 7 of 14
cortical areas, in ways likely to have considerable neurobe-
havioral significance [123]. Further examination of this
model is necessary to understand how these functional
abnormalities relate to clinical phenotypes.
Crucial considerations
Although previous studies of resting-state EEG in ASD
have identified abnormalities in low-frequency and high-
frequency band power, connectivity, and lateralization of
brain functions, there are multiple crucial methodological
and clinical issues that warrant attention in reviewing this
literature and in planning future research.
Small sample size with narrow range of subject characteristics
Many previous studies were conducted with small sample
sizes (often with <20 subjects per group), as well as with
subjects displaying a narrow range of demographic/clinical
characteristics including age, intellectual ability, history
of seizures, and severity of behavioral problems. Deve-
lopmental variations in power at different frequencies,
coherence, and lateralization of function are important
considerations when studying a developmental disorder
such as ASD, in which behavioral and cognitive presen-
tations are diverse and can change over the age span
[129,130]. Additionally, individuals with ASD vary widely
in the extent to which their intellectual abilities are
affected. IQ and educational levels were not consistent
between previous studies of resting-state EEG, and at
times, not even between patient and control groups. Other
clinical aspects could also affect profiles of neuro-
physiological alterations in ASD, including comorbid
medical and psychiatric conditions.
Many investigators did not conduct correlational ana-
lyses to relate abnormal EEG patterns to severity of
various clinical aspects of ASD. This limits understanding
of the clinical relevance of EEG observations. Of those
studies reporting clinical correlations, Orekhova et al.
[79] found a positive correlation between gamma activity
and cognitive delay in ASD. Sutton et al. [106] reported
that abnormal left frontal asymmetry, defined by greater
activation in the left frontal regions, was related to higher
levels of social anxiety and social stress, as was abnormal
right frontal asymmetry. Stroganova et al.[74]showed
that increased prefrontal delta power was related to
cognitive delay in ASD. Burnett et al. [107] reported an
association of left frontal EEG asymmetry with parental
reports of later onset of ASD symptoms, and increased
instances of aggressive outbursts and obsessive compulsive
behavior. Finally, Barttfeld et al. [118] described a positive
correlation between short-distance synchronization and
Autism Diagnostic Observation Schedule (ADOS) scores
[131], with a negative correlation between long-distance
synchronization and ADOS scores, suggesting that there
may be a clinically relevant excess of short-range func-tional
connectivity coupled with a reduction in long-distance
connectivity across the brain in ASD.
In future studies, it will be of great value to utilize
large samples with a wide range of subject characteristics
to increase the range of brain alterations and to better
establish clinicopathological associations.
Resting-state conditions
Although the literature reviewed above referred to testing
of subjects in the resting state, different studies have
used eyes-closed (EC) and eyes-open (EO) conditions.
In the EO condition, subjects typically viewed calming
stimuli such as bubbles moving across a screen. Distinct
EEG patterns in these two conditions were recently
reported. Barry et al. [132] reported significant amplitude
reductions in delta (lateral-frontal), theta (posterior), alpha
(posterior), and beta (posterior) frequency bands in EO
conditions relative to EC conditions. By contrast, increased
frontal beta was found in EC conditions. Furthermore,
skin conductance levels were higher in EO conditions, and
were negatively correlated with alpha power, indicating
a higher level of arousal. Chen et al. [133] reported en-
hanced prefrontal delta and reduced frontal-midline
theta in EO states. Finally, reduced low alpha and low
beta (13 to 23 Hz) in the posterior region was reported
during the EO condition, whereas high beta (24 to 34 Hz)
and gamma failed to show any difference between condi-
tions. In evaluating the use of EO versus EC conditions in
ASD, a recent study by Mathewson et al. [134] reported
that adults with ASD did not differ from healthy controls
on alpha power levels in the EC condition but displayed
less alpha suppression during the EO condition. Although
Barry and colleagues later replicated their resting EEG
results for healthy adults [132] and children [135], a direct
comparison of resting-state conditions has not been
done in children with ASD, to our knowledge, so it is
unclear whether a similar pattern would be seen in this
EEG analysis methods: confounds and suggestions
EEG studies need to be concerned about blurring sources
of neural activity on EEG scalp recordings, due to the
highly conductive nature of the scalp and differences in
electrical conductivities between the brain, cerebrospinal
fluid, and skull. Inhomogeneities in conductivity between
tissues can change patterns of volume conduction (trans-
mission of the electrical signal from the source to the
measurement electrode), particularly when source models
are calculated based on standard assumptions of tissue
thickness and position, rather than the more realistic
but not always available individual structural MRI mea-
surements. These blurred recordings make it difficult
to identify the source of atypical spectral activity, espe-
cially when fewer electrode leads are used in studies.
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 8 of 14
Although the most consistent pattern of findings in the
ASD literature is a U-shaped pattern of spectral power
relative to healthy controls, the reported topographical
locations of frequency band alterations in ASD have been
disparate. For instance, in the delta band, significant power
difference was presented in frontal [74,75], midline/par-
ietal [73], temporal [80] or even all regions [72]. These
widespread differences in topography could be due to
widely distributed deficits within each frequency band, or
due to data blurring, an issue that could be improved by
comparing source densities between groups. In fact, in a
source localization study on EEG oscillations [136], focal
sources were reported for different frequency bands (for
example, the most anterior source for delta and the most
posterior source for alpha). However, resting frequency
bands often show a distributed source network during
simultaneous EEG-fMRI [137,138], so the possibility that
multiple sources within each frequency band may be
contributing to differential findings between studies needs
to be considered.
In addition to its effect on power, data blurring also
has a large effect on coherence analysis, especially for
short-range coherence. This could contribute to the mixed
results for short-range coherence patterns that exist
between resting-state EEG studies. Two techniques may
help to resolve this blurring problem. One technique
includes the use of surface Laplacian transformation to
estimate current source density (CSD) based on EEG
potentials, from which power and coherence can then
be evaluated. CSD transformation computes the second
spatial derivative of voltage between nearby electrode
sites. This approach can enhance contributions from
local electrical activity while attenuating contributions
from remote activity (although care should be taken
interpreting results if deep sources are of interest, as
this technique is necessarily biased toward superficial
cortical tissue). A recent resting EEG study with a large
sample size utilized this technique to overcome the
spatial blurring limitation, and found an overall reduction
in short-range connectivity in ASD [119]. The second
technique was proposed by Hoechstetter and colleagues
[139], and in this technique, surface potentials are first
transferred to source space by using multiple discrete
equivalent current dipoles or regional sources. Coherence
analysis is then calculated between source regions instead
of electrodes. Cornew and colleagues [140] applied this
technique in a resting-state magnetoencephalography
study in high-functioning children with ASD, although
they then quantified local oscillatory activity rather
than coherence between regions.
The method of calculation of coherence itself is an-
other important issue. As indicated by Murias et al. [76],
coherence measured in short distances can be biased by
power due to the classic coherence calculation frequently
used in resting-state EEG studies. This calculation is a
product of complex power spectrum decomposition, and
it is sensitive to both amplitude and phase relationships
between two signals. Strong power modulation at single
sources can be detected by multiple nearby electrodes,
inflating local connectivity measurements between these
electrodes without reflecting the true coherence between
separate but adjacent neural sources [141]. This induces
the confounding factor of local source strength, limiting
the certainty of the real cause of the relationship when
concurrent power modulations are detected (although
phase relationship usually has a larger contribution than
amplitude [60]). To overcome this limitation, phase syn-
chrony analysis methods represent an approach to assess
the phase relationship independently. Lachaux and col-
leagues [142] proposed calculating a phase-locking value
(PLV) to measure phase synchrony. In their method,
two EEG signals were first narrow band-passed (target
frequency ± 2 Hz), then convolved with the Gabor wavelet
function. Finally, the phase outputs from wavelet de-
composition between two signals were compared. PLV
is a metric bound between 0 and 1, with 1 indicating
that phase difference varies little between trials (ERP)
or segments (resting EEG), and 0 indicating a complete
lack of phase synchrony. In addition to measuring phase
relationships independently, another important feature
is that PLV does not rest on the assumption of stationarity
(between trials or segments) as in classic coherence calcu-
lation. Stationarity refers to the similarity of spectral prop-
erties between measurements, which can be more easily
assumed with multiple trials that have identical stimula-
tion periods, as in ERP tasks. In resting EEG, there are no
clear breakpoints between segments of continuous data,
and segment lengths are often based on the best tradeoff
between frequency and temporal resolution, that is, how
short each measurement segment can be while still
affording accurate coverage of a number of oscillations in
the frequency bands of interest. In the case of traditional
coherence, non-stationarity of the power in a frequency
band across time with no change in phase may present
as changes in coherence values. Taking the confound
between power and coherence into account is particularly
important in studies of ASD, given reports of differences
in resting-state spectral power in this population.
EEG recordings acquired in resting-state paradigms
include both neural activity and non-neuronal activity
such as muscular and cardiac activity, and ocular arti-
facts (for example, eye movement and eye blink). The
conventional visual inspection and epoch rejection used in
reviewed resting-state EEG studies may not be sufficient
to completely remove these and other artifacts [143,144].
In this scenario, alternative solutions such as independent
component analysis (ICA) [53] can serve as a complemen-
tary method. ICA is a linear decomposition method that
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 9 of 14
separates a multivariate EEG signal into temporally inde-
pendent signals available in the raw EEG channel data
[145]. Each separate component can be treated like a
virtual channel. Within each channel, noise components
can be identified through component properties such
as topography and spectral characteristics. The application
of ICA to remove artifacts has been used with other
clinical populations [28,29,146], and can generate rela-
tively artifact-free resting EEG data [119].
Finally, statistical methods such as principal components
analysis have proven to be a useful method to distill multi-
dimensional and complex EEG data into more manageable
representative components of neural activity patterns
[147,148]. This technique has been used to great advan-
tage in coherence modeling in recent investigations of
resting EEG in ASD [119].
Future directions
The maturational trajectory of resting-state activity in ASD
Few studies have compared individuals with ASD and
healthy individuals, either cross-sectionally between age
groups, or longitudinally. A recent longitudinal study of
infants at high and low risk for ASD reported changes in
developmental trajectory, that is, the slope of the power
curve across time, related to risk status from 6 to 24 months
of age, particularly in the delta, beta, and gamma frequency
ranges [81]. This same group also reported changes in
the developmental trajectory of overall EEG complexity
(entropy) for high-risk infants as compared with typically
developing controls across the same time window [149].
One 3-month longitudinal study in children with ASD
indicated that EEG characteristics are relatively stable
across short time intervals [150]. Longer-term longitudinal
studies are needed to understand whether individuals with
ASD show similar trajectories of functional connectivity
maturation, or whether these processes are disrupted and/
or delayed. Behavioral longitudinal studies of children
with ASD indicate an early improvement in language
and cognitive skills in some affected individuals (ages
12 to 13 years) followed by considerable and abnormal
decrease in the rate of gains through adolescence (ages
19 to 20 years) [151]. These findings raise the possibility
that developmental lag or deviance becomes more pro-
found during late childhood and adolescence as the long-
range connections to prefrontal cortex are optimized [152].
Reports of resting-state EEG data in younger ASD cohorts
are beginning to appear, but studies on a long-term and/or
adolescent sample would provide potentially important
information about functional connectivity changes ac-
companying ASD through childhood and adolescence.
Early detection and possible biomarkers for ASD
Retrospective studies of infants later diagnosed with ASD
have shown that features of ASD are present as early as
12 months. However, many children are not diagnosed
until the age of 4 years or later [153,154]. This highlights
the need for biomarkers for early detection in order to
implement early intervention [155]. It is difficult to reli-
ably identify individuals with ASD within the first year of
life based only on behavioral observation, but studies of
resting-state EEG suggest that selective alterations may
be identifiable as early as 6 months [81,149]. Globally
reduced power of delta, theta, alpha, beta and gamma
frequency bands have been found in high-risk infants with
siblings with ASD compared with low-risk 6-month-old
control infants [81]. However, no group difference in
hemispheric asymmetry was reported. Elsabbagh et al.
[156] reported increased gamma band activity in the
midline anterior and right temporal cortex of high-risk
infants. Currently, there are few studies to determine the
utility of these measures in at risk children for identifying
individuals with a high likelihood of developing ASD
later in life.
Resting-state EEG studies in ASD with genetic etiology
The heterogeneous nature of idiopathic ASD can make
studies of the underlying etiology difficult. However, a
small subset of individuals with ASD (10 to 20%) is be-
lieved to have simplergenetics, possessing identifiable
chromosomal abnormalities or rare mutations found in
higher ratios in the ASD population. High proportions
of individuals with Fragile X syndrome, Rett syndrome
[157], tuberous sclerosis [158], and Phelan-McDermid
syndrome [159] have ASD, and each of these disorders
has been linked to known abnormalities of individual genes
(for example, FMRI1,MECP2,TSC1,TSC2). Further,
several rare de novo mutations, some of which converge
on pathways that overlap with those associated with
Fragile X and tuberous sclerosis, and are linked to synapse
formation and function, have been found to be more
common in this population [5]. Using resting-state
EEG, it may be possible to connect distinct patterns of
altered electrophysiological activity with symptoms found
in identified single-gene disorders related to ASD. To date,
few studies have utilized ERP in subjects with Fragile X
syndrome [160] and primarily epileptiform activity has
been examined in resting-state EEG in this population
[132,161]; however, there is some evidence for increased
resting theta activity in Fragile X [162-164]. The use of
EEG to examine Rett syndrome has followed a similar
path, with the majority of resting EEG studies dedicated
to describing seizure activity (but see studies by
Ishizaki [165] and Niedermeyer et al. [166] for evidence
of prominent resting theta activity in Rett syndrome).
Currently, there are no published accounts of resting
EEG in Phelan-McDermid syndrome, although changes in
excitatory/inhibitory balance in the brains of knockout
mouse models suggest measurable electrophysiological
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 10 of 14
changes in this disorder [167]. It would be of particular
interest to study resting EEG in these known single-gene
conditions, particularly Fragile X, one of the better charac-
terized single-gene disorders, potentially serving as refer-
ence for other subpopulations in ASD and shedding
light on biological mechanisms shared across the autism
spectrum. Whether resting EEG studies can be useful
for parsing biological heterogeneity of idiopathic ASD
remains another important question to be addressed in
future studies.
In this review, we have addressed resting-state EEG
studies in ASD with an emphasis on three aspects: spec-
tral power, coherence, and hemispheric asymmetry. The
existing literature suggests a U-shaped pattern of power
abnormalities, overall local overconnectivity and long-
range underconnectivity, and enhanced power in the left
hemisphere of the brain in individuals with ASD. There
are important considerations for EEG methodology and
clinical assessment that both need consideration for
designing the most informative future studies. Recent
advances in quantitative EEG analytic methodology and
scientific findings from work in this area are encouraging.
Future work linking EEG studies of animal models with
patient-oriented studies are promising, especially for
rare genetic variants for which animal models are most
directly relevant. Because of a combination of advan-
tages including its non-invasive nature, high temporal
resolution, and relative ease of use across the lifespan,
resting-state EEG studies have the potential to make
important contributions to the understanding of the
pathophysiology of ASD.
Competing interests
JS serves as a member of advisory boards to Takeda, Lilly, BMS, Roche and
Pfizer, and has received support from Janssen that is unrelated to the work
presented in this manuscript.
JW, JB, MW and JS made substantial contributions to design of this study.
JW and JB wrote the first draft of the manuscript, and LE, MW, YT and JS
revised the manuscript. All authors contributed to writing the manuscript.
All authors read and approved the final manuscript.
This study was funded by the NIMH Autism Center of Excellence
1P50HD055751-01, K23MH092696, K01MH087720, Department of the Army
award AR100276, and Autism Speaks.
Author details
Department of Psychiatry, University of Texas Southwestern, Dallas, TX, USA.
Department of Pediatrics, University of Texas Southwestern, Dallas, TX, USA.
Center for Autism Spectrum Disorders, Bond University, Gold Coast,
Received: 13 May 2013 Accepted: 4 September 2013
Published: 16 September 2013
1. American Psychiatric Association: Diagnostic and statistical manual of mental
disorders-4th edition; 2000.
2. Centers for Disease Control and Prevention: Prevalence of autism spectrum
disorders--Autism and Developmental Disabilities Monitoring Network,
14 sites; 2012.
3. Muhle R, Trentacoste SV, Rapin I: The genetics of autism. Pediatrics 2004,
4. Ozonoff S, Young GS, Carter A, Messinger D, Yirmiya N, Zwaigenbaum L,
Bryson S, Carver LJ, Constantino JN, Dobkins K, et al:Recurrence risk for
autism spectrum disorders: a baby siblings research consortium study.
Pediatrics 2011, 128:e488e495.
5. Abrahams BS, Geschwind DH: Advances in autism genetics: on the
threshold of a new neurobiology. Nat Rev Genet 2008, 9:341355.
6. Chakrabarti S, Fombonne E: Pervasive developmental disorders in
preschool children: confirmation of high prevalence. Am J Psychiatry
2005, 162:11331141.
7. Parr JR, Le Couteur A, Baird G, Rutter M, Pickles A, Fombonne E, Bailey AJ:
International molecular genetic study of autism consortium M: early
developmental regression in autism spectrum disorder: evidence from
an international multiplex sample. J Autism Dev Disord 2011, 41:332340.
8. Akshoomoff N, Pierce K, Courchesne E: The neurobiological basis of
autism from a developmental perspective. Dev Psychopathol 2002,
9. Takarae Y, Minshew NJ, Luna B, Sweeney JA: Atypical involvement of
frontostriatal systems during sensorimotor control in autism. Psychiatry
Res 2007, 156:117127.
10. Bauman ML, Kemper TL: Neuroanatomic observations of the brain in
autism: a review and future directions. Int J Dev Neurosci 2005, 23:183187.
11. Courchesne E, Karns CM, Davis HR, Ziccardi R, Carper RA, Tigue ZD, Chisum
HJ, Moses P, Pierce K, Lord C, et al:Unusual brain growth patterns in early
life in patients with autistic disorder: an MRI study. Neurology 2001,
12. Courchesne E, Pierce K: Brain overgrowth in autism during a critical time
in development: implications for frontal pyramidal neuron and
interneuron development and connectivity. Int J Dev Neurosci 2005,
13. Mosconi MW, Cody-Hazlett H, Poe MD, Gerig G, Gimpel-Smith R, Piven J:
Longitudinal study of amygdala volume and joint attention in
2- to 4-year-old children with autism. Arch Gen Psychiatry 2009, 66:509516.
14. Aylward EH, Minshew NJ, Field K, Sparks BF, Singh N: Effects of age on brain
volume and head circumference in autism. Neurology 2002, 59:175183.
15. Redcay E, Courchesne E: When is the brain enlarged in autism? a
meta-analysis of all brain size reports. Biol Psychiatry 2005, 58:19.
16. Mosconi MW, Zwaigenbaum L, Piven J: Structural MRI in autism: findings
and future directions. Clin Neurosci Res 2006, 6:135144.
17. Knaus TA, Silver AM, Lindgren KA, Hadjikhani N, Tager-Flusberg H: fMRI
activation during a language task in adolescents with ASD. JINS 2008,
18. Koshino H, Kana RK, Keller TA, Cherkassky VL, Minshew NJ, Just MA: fMRI
investigation of working memory for faces in autism: visual coding and
underconnectivity with frontal areas. Cereb Cortex 2008, 18:289300.
19. Schultz RT, Gauthier I, Klin A, Fulbright RK, Anderson AW, Volkmar F,
Skudlarski P, Lacadie C, Cohen DJ, Gore JC: Abnormal ventral temporal
cortical activity during face discrimination among individuals with
autism and Asperger syndrome. Arch Gen Psychiatry 2000, 57:331340.
20. Pierce K, Muller RA, Ambrose J, Allen G, Courchesne E: Face processing
occurs outside the fusiform face areain autism: evidence from
functional MRI. Brain 2001, 124:20592073.
21. Muller RA, Pierce K, Ambrose JB, Allen G, Courchesne E: Atypical patterns
of cerebral motor activation in autism: a functional magnetic resonance
study. Biol Psychiatry 2001, 49:665676.
22. Cherkassky VL, Kana RK, Keller TA, Just MA: Functional connectivity in a
baseline resting-state network in autism. Neuroreport 2006, 17:16871690.
23. Kennedy DP, Courchesne E: The intrinsic functional organization of the
brain is altered in autism. Neuroimage 2008, 39:18771885.
24. Kennedy DP, Redcay E, Courchesne E: Failing to deactivate: resting
functional abnormalities in autism. Proc Natl Acad Sci U S A 2006,
25. Schumann CM, Bloss CS, Barnes CC, Wideman GM, Carper RA, Akshoomoff
N, Pierce K, Hagler D, Schork N, Lord C, Courchesne E: Longitudinal
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 11 of 14
magnetic resonance imaging study of cortical development through
early childhood in autism. J Neurosci 2010, 30:44194427.
26. Regan D: Human Brain Electrophysiology: Evoked Potentials and Evoked
Magnetic Fields in Science and Medicine. New York: McGraw-Hill; 1989.
27. Mann CA, Lubar JF, Zimmerman AW, Miller CA, Muenchen RA: Quantitative
analysis of EEG in boys with attention-deficit-hyperactivity disorder:
controlled study with clinical implications. Pediatr Neurol 1992, 8:3036.
28. Wang J, Brown R, Dobkins KR, McDowell JE, Clementz BA: Diminished
parietal cortex activity associated with poor motion direction discrimination
performance in schizophrenia. Cereb Cortex 2010, 20:17491755.
29. Ethridge LE, Hamm JP, Shapiro JR, Summerfelt AT, Keedy SK, Stevens MC,
Pearlson G, Tamminga CA, Boutros NN, Sweeney JA, et al:Neural
activations during auditory oddball processing discriminating
schizophrenia and psychotic bipolar disorder. Biol Psychiatry 2012,
30. Coben R: The importance of electroencephalogram assessment for
autistic disorders. Biofeedback 2009, 37:7180.
31. Tuchman R, Rapin I: Epilepsy in autism. Lancet Neurol 2002, 1:352358.
32. Tuchman RF, Rapin I: Regression in pervasive developmental disorders:
seizures and epileptiform electroencephalogram correlates. Pediatrics
1997, 99:560566.
33. Rossi PG, Parmeggiani A, Bach V, Santucci M, Visconti P: EEG features and
epilepsy in patients with autism. Brain Dev 1995, 17:169174.
34. Hughes JR, Melyn M: EEG and seizures in autistic children and
adolescents: further findings with therapeutic implications. Clinical EEG
Neuroscience 2005, 36:1520.
35. Chez MG, Chang M, Krasne V, Coughlan C, Kominsky M, Schwartz A:
Frequency of epileptiform EEG abnormalities in a sequential screening
of autistic patients with no known clinical epilepsy from 1996 to 2005.
Epilepsy Behav 2006, 8:267271.
36. Basar E, Basar-Eroglu C, Karakas S, Schurmann M: Gamma, alpha, delta, and
theta oscillations govern cognitive processes. Int J Psychophysiol 2001,
37. Knyazev GG: EEG delta oscillations as a correlate of basic homeostatic
and motivational processes. Neurosci Biobehav Rev 2012, 36:677695.
38. Klimesch W: Memory processes, brain oscillations and EEG
synchronization. Int J Psychophysiol 1996, 24:61100.
39. Klimesch W, Sauseng P, Hanslmayr S: EEG alpha oscillations: the
inhibition-timing hypothesis. Brain Res Rev 2007, 53:6388.
40. Neuper C, Pfurtscheller G: Event-related dynamics of cortical rhythms:
frequency-specific features and functional correlates. Int J Psychophysiol
2001, 43:4158.
41. Tallon-Baudry C: Oscillatory synchrony and human visual cognition.
J Physiol Paris 2003, 97:355363.
42. Skinner JE, Molnar M, Kowalik ZJ: The role of the thalamic reticular
neurons in alpha- and gamma-oscillations in neocortex: a mechanism
for selective perception and stimulus binding. Acta Neurobiol Exp 2000,
43. Singer W, Gray CM: Visual feature integration and the temporal
correlation hypothesis. Annu Rev Neurosci 1995, 18:555586.
44. Olejniczak P: Neurophysiologic basis of EEG. J Clin Neurophysiol 2006,
45. Barnea-Goraly N, Kwon H, Menon V, Eliez S, Lotspeich L, Reiss AL: White
matter structure in autism: preliminary evidence from diffusion tensor
imaging. Biol Psychiatry 2004, 55:323326.
46. Just MA, Cherkassky VL, Keller TA, Minshew NJ: Cortical activation and
synchronization during sentence comprehension in high-functioning
autism: evidence of underconnectivity. Brain 2004, 127:18111821.
47. Herbert MR, Ziegler DA, Deutsch CK, OBrien LM, Kennedy DN, Filipek PA,
Bakardjiev AI, Hodgson J, Takeoka M, Makris N, Caviness VS Jr: Brain
asymmetries in autism and developmental language disorder: a nested
whole-brain analysis. Brain 2005, 128:213226.
48. Alexander AL, Lee JE, Lazar M, Boudos R, DuBray MB, Oakes TR, Miller JN, Lu
J, Jeong EK, McMahon WM, et al:Diffusion tensor imaging of the corpus
callosum in Autism. Neuroimage 2007, 34:6173.
49. Sundaram SK, Kumar A, Makki MI, Behen ME, Chugani HT, Chugani DC:
Diffusion tensor imaging of frontal lobe in autism spectrum disorder.
Cerebral cortex 2008, 18:26592665.
50. Monk CS, Peltier SJ, Wiggins JL, Weng SJ, Carrasco M, Risi S, Lord C:
Abnormalities of intrinsic functional connectivity in autism spectrum
disorders. Neuroimage 2009, 47:764772.
51. Wass S: Distortions and disconnections: disrupted brain connectivity in
autism. Brain Cogn 2011, 75:1828.
52. Fox MD, Greicius M: Clinical applications of resting state functional
connectivity. Front Syst Neurosci 2010, 4:19.
53. Makeig S, Delorme A, Westerfield M, Jung TP, Townsend J, Courchesne E,
Sejnowski TJ: Electroencephalographic brain dynamics following
manually responded visual targets. PLoS Biol 2004, 2:e176.
54. Fox MD, Snyder AZ, Zacks JM, Raichle ME: Coherent spontaneous activity
accounts for trial-to-trial variability in human evoked brain responses.
Nat Neurosci 2006, 9:2325.
55. Fox MD, Raichle ME: Spontaneous fluctuations in brain activity observed
with functional magnetic resonance imaging. Nat Rev Neurosci 2007,
56. Olshausen BA, Field DJ: How close are we to understanding v1? Neural
Comput 2005, 17:16651699.
57. Raichle ME, Snyder AZ: A default mode of brain function: a brief history
of an evolving idea. Neuroimage 2007, 37:10831090. discussion
58. Gruber WR, Klimesch W, Sauseng P, Doppelmayr M: Alpha phase
synchronization predicts P1 and N1 latency and amplitude size.
Cereb Cortex 2005, 15:371377.
59. Mazaheri A, Nieuwenhuis IL, van Dijk H, Jensen O: Prestimulus alpha and
mu activity predicts failure to inhibit motor responses. Hum Brain Mapp
2009, 30:17911800.
60. Srinivasan R, Nunez PL, Silberstein RB: Spatial filtering and neocortical
dynamics: estimates of EEG coherence. IEEE Trans Biomed Eng 1998,
61. Whitford TJ, Rennie CJ, Grieve SM, Clark CR, Gordon E, Williams LM: Brain
maturation in adolescence: concurrent changes in neuroanatomy and
neurophysiology. Hum Brain Mapp 2007, 28:228237.
62. Pineda JA, Brang D, Hecht E, Edwards L, Carey S, Bacon M, Futagaki C, Suk
D, Tom J, Birnbaum C, Rork A: Positive behavioral and electrophysiological
changes following neurofeedback training in children with autism. Res
Autism Spectrum Disord 2008, 2:557581.
63. Rimland B, Edelson M: Autism Treatment Evaluation Checklist (ATEC); 1999.
64. Kouijzer M, De Moor J, Gerrits B, Buitelaar JK, Van Schie H: Long-term
effects of neurofeedback treatment in autism. Res Autism Spectrum Disord
2009, 3:496501.
65. Kouijzer M, De Moor J, Gerrits B, Congedo M, Van Schie H: Research in
Autism Spectrum Disorders. 3 Neurofeedback improves executive functioning in
chidlren with autism spectrum disorders; 2009:145162.
66. White PT, Demyer W, Demyer M: Eeg abnormalities in early childhood
schizophrenia: a double-blind study of psychiatrically disturbed and
normal children during promazine sedation. Am J Psychiatry 1964,
67. Hutt SJ, Hutt C, Lee D, Ounsted C: A behavioural and electroencephalographic
study of autistic children. J Psychiatr Res 1965, 3:181197.
68. Hermelin B, OConnor N: Measures of the occipital alpha rhythm in
normal, subnormal and autistic children. Br J Psychiatry 1968, 114:603610.
69. Creak M, Pampiglione G: Clinical and EEG studies on a group of 35
psychotic children. Dev Med Child Neurol 1969, 11:218227.
70. Small JG: EEG and neurophysiological studies of early infantile autism.
Biol Psychiatry 1975, 10:385397.
71. Pivik RT, Broughton RJ, Coppola R, Davidson RJ, Fox N, Nuwer MR:
Guidelines for the recording and quantitative analysis of
electroencephalographic activity in research contexts. Psychophysiology
1993, 30:547558.
72. Cantor DS, Thatcher RW, Hrybyk M, Kaye H: Computerized EEG analyses of
autistic children. J Autism Dev Disord 1986, 16:169187.
73. Chan AS, Sze SL, Cheung MC: Quantitative electroencephalographic
profiles for children with autistic spectrum disorder. Neuropsychology
2007, 21:7481.
74. Stroganova TA, Nygren G, Tsetlin MM, Posikera IN, Gillberg C, Elam M,
Orekhova EV: Abnormal EEG lateralization in boys with autism. Clin
Neurophysiol 2007, 118:18421854.
75. Pop-Jordanova N, Zorcec T, Demerdzieva A, Gucev Z: QEEG characteristics
and spectrum weighted frequency for children diagnosed as autistic
spectrum disorder. Nonlinear Biomedical Physics 2010, 4:4.
76. Murias M, Webb SJ, Greenson J, Dawson G: Resting state cortical
connectivity reflected in EEG coherence in individuals with autism.
Biol Psychiatry 2007, 62:270273.
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 12 of 14
77. Coben R, Clarke AR, Hudspeth W, Barry RJ: EEG power and coherence in
autistic spectrum disorder. Clin Neurophysiol 2008, 119:10021009.
78. Daoust AM, Limoges E, Bolduc C, Mottron L, Godbout R: EEG spectral
analysis of wakefulness and REM sleep in high functioning autistic
spectrum disorders. Clin Neurophysiol 2004, 115:13681373.
79. Orekhova EV, Stroganova TA, Nygren G, Tsetlin MM, Posikera IN, Gillberg C,
Elam M: Excess of high frequency electroencephalogram oscillations in
boys with autism. Biol Psychiatry 2007, 62:10221029.
80. Dawson G, Klinger LG, Panagiotides H, Lewy A, Castelloe P: Subgroups of
autistic children based on social behavior display distinct patterns of
brain activity. J Abnorm Child Psychol 1995, 23:569583.
81. Tierney AL, Gabard-Durnam L, Vogel-Farley V, Tager-Flusberg H, Nelson CA:
Developmental trajectories of resting EEG power: an endophenotype of
autism spectrum disorder. PloS one 2012, 7:e39127.
82. Wendling F, Bartolomei F, Bellanger JJ, Chauvel P: Epileptic fast activity can
be explained by a model of impaired GABAergic dendritic inhibition.
Eur J Neurosci 2002, 15:14991508.
83. Halonen T, Pitkanen A, Koivisto E, Partanen J, Riekkinen PJ: Effect of vigabatrin
on the electroencephalogram in rats. Epilepsia 1992, 33:122127.
84. Marciani MG, Stanzione P, Maschio M, Spanedda F, Bassetti MA, Mattia D,
Bernardi G: EEG changes induced by vigabatrin monotherapy in focal
epilepsy. Acta Neurol Scand 1997, 95:115120.
85. Zhang Y, Llinas RR, Lisman JE: Inhibition of NMDARs in the nucleus
reticularis of the thalamus produces delta frequency bursting. Frontiers in
Neural Circuits 2009, 3:20.
86. Casanova MF, Buxhoeveden DP, Switala AE, Roy E: Minicolumnar
pathology in autism. Neurology 2002, 58:428432.
87. Levitt P: Disruption of interneuron development. Epilepsia 2005,
46(Suppl 7):2228.
88. Fingelkurts AA, Kivisaari R, Pekkonen E, Ilmoniemi RJ, Kahkonen S: The
interplay of lorazepam-induced brain oscillations: microstructural
electromagnetic study. Clin Neurophysiol 2004, 115:674690.
89. Mathewson KE, Lleras A, Beck DM, Fabiani M, Ro T, Gratton G: Pulsed out of
awareness: EEG alpha oscillations represent a pulsed-inhibition of
ongoing cortical processing. Front Psychol 2011, 2:99.
90. Jensen O, Mazaheri A: Shaping functional architecture by oscillatory alpha
activity: gating by inhibition. Front Hum Neurosci 2010, 4:186.
91. Owens DF, Kriegstein AR: Is there more to GABA than synaptic inhibition?
Nat Rev Neurosci 2002, 3:715727.
92. Benali A, Trippe J, Weiler E, Mix A, Petrasch-Parwez E, Girzalsky W, Eysel UT,
Erdmann R, Funke K: Theta-burst transcranial magnetic stimulation alters
cortical inhibition. J Neurosci 2011, 31:11931203.
93. Trippe J, Mix A, Aydin-Abidin S, Funke K, Benali A: Theta burst and
conventional low-frequency rTMS differentially affect GABAergic
neurotransmission in the rat cortex. Experimental Brain Research
Experimentelle Hirnforschung Experimentation cerebrale 2009, 199:411421.
94. Ma DQ, Whitehead PL, Menold MM, Martin ER, Ashley-Koch AE, Mei H,
Ritchie MD, Delong GR, Abramson RK, Wright HH, et al:Identification of
significant association and gene-gene interaction of GABA receptor
subunit genes in autism. Am J Hum Genet 2005, 77:377388.
95. Fatemi SH, Reutiman TJ, Folsom TD, Thuras PD: GABA(A) receptor
downregulation in brains of subjects with autism. J Autism Dev Disord
2009, 39:223230.
96. Fatemi SH, Folsom TD, Reutiman TJ, Thuras PD: Expression of GABA(B)
receptors is altered in brains of subjects with autism. Cerebellum 2009,
97. Rubenstein JL, Merzenich MM: Model of autism: increased ratio of
excitation/inhibition in key neural systems. Genes Brain Behav 2003,
98. Hussman JP: Suppressed GABAergic inhibition as a common factor in
suspected etiologies of autism. J Autism Dev Disord 2001, 31:247248.
99. Schmitz C, van Kooten IA, Hof PR, van Engeland H, Patterson PH, Steinbusch
HW: Autism: neuropathology, alterations of the GABAergic system, and
animal models. Int Rev Neurobiol 2005, 71:126.
100. Delong R: GABA(A) receptor alpha5 subunit as a candidate gene for
autism and bipolar disorder: a proposed endophenotype with parent-of-
origin and gain-of-function features, with or without oculocutaneous
albinism. Autism 2007, 11:135147.
101. Thatcher RW, North DM, Neubrander J, Biver CJ, Cutler S, Defina P: Autism
and EEG phase reset: deficient GABA mediated inhibition in
thalamo-cortical circuits. Dev Neuropsychol 2009, 34:780800.
102. Goldstein S, Schwebach AJ: The comorbidity of pervasive
developmental disorder and attention deficit hyperactivity disorder:
results of a retrospective chart review. JAutismDevDisord2004,
103. Yoshida Y, Uchiyama T: The clinical necessity for assessing attention
deficit/hyperactivity disorder (AD/HD) symptoms in children with
high-functioning pervasive developmental disorder (PDD). Eur Child
Adolesc Psychiatry 2004, 13:307314.
104. Mosconi MW, Kay M, DCruz AM, Seidenfeld A, Guter S, Stanford LD,
Sweeney JA: Impaired inhibitory control is associated with higher-order
repetitive behaviors in autism spectrum disorders. Psychol Med 2009,
105. Lazarev VV, Pontes A, De Azevedo LC: EEG photic driving:
right-hemisphere reactivity deficit in childhood autism. A pilot study.
Int J Psychophysiol 2009, 71:177183.
106. Sutton SK, Burnette CP, Mundy PC, Meyer J, Vaughan A, Sanders C, Yale M:
Resting cortical brain activity and social behavior in higher functioning
children with autism. J Child Psychol Psychiatry 2005, 46:211222.
107. Burnette CP, Henderson HA, Inge AP, Zahka NE, Schwartz CB, Mundy PC:
Anterior EEG asymmetry and the modifier model of autism. J Autism Dev
Disord 2011, 41:11131124.
108. Dawson G, Finley C, Phillips S, Lewy A: A comparison of hemispheric
asymmetries in speech-related brain potentials of autistic and dysphasic
children. Brain Lang 1989, 37:2641.
109. Herbert MR, Harris GJ, Adrien KT, Ziegler DA, Makris N, Kennedy DN, Lange
NT, Chabris CF, Bakardjiev A, Hodgson J, et al:Abnormal asymmetry in
language association cortex in autism. Ann Neurol 2002, 52:588596.
110. Rojas DC, Bawn SD, Benkers TL, Reite ML, Rogers SJ: Smaller left
hemisphere planum temporale in adults with autistic disorder. Neurosci
Lett 2002, 328:237240.
111. De Fosse L, Hodge SM, Makris N, Kennedy DN, Caviness VS Jr, McGrath L,
Steele S, Ziegler DA, Herbert MR, Frazier JA, et al:Language-association
cortex asymmetry in autism and specific language impairment. Ann
Neurol 2004, 56:757766.
112. Winterer G, Ziller M, Dorn H, Frick K, Mulert C, Wuebben Y, Herrmann WM,
Coppola R: Schizophrenia: reduced signal-to-noise ratio and impaired
phase-locking during information processing. Clin Neurophysiol 2000,
113. Winterer G, Weinberger DR: Genes, dopamine and cortical signal-to-noise
ratio in schizophrenia. Trends Neurosci 2004, 27:683690.
114. Schmitz N, Rubia K, Daly E, Smith A, Williams S, Murphy DG: Neural
correlates of executive function in autistic spectrum disorders.
Biol Psychiatry 2006, 59:716.
115. Takarae Y, Minshew NJ, Luna B, Krisky CM, Sweeney JA: Pursuit eye
movement deficits in autism. Brain 2004, 127:25842594.
116. DCruz AM, Mosconi MW, Steele S, Rubin LH, Luna B, Minshew N, Sweeney
JA: Lateralized response timing deficits in autism. Biol Psychiatry 2009,
117. Lazarev VV, Pontes A, Mitrofanov AA, De Azevedo LC: Interhemispheric
asymmetry in EEG photic driving coherence in childhood autism.
Clin Neurophysiol 2010, 121:145152.
118. Barttfeld P, Wicker B, Cukier S, Navarta S, Lew S, Sigman M: A big-world
network in ASD: dynamical connectivity analysis reflects a deficit in
long-range connections and an excess of short-range connections.
Neuropsychologia 2011, 49:254263.
119. Duffy FH, Als H: A stable pattern of EEG spectral coherence distinguishes
children with autism from neuro-typical controls - a large case control
study. BMC Med 2012, 10:64.
120. Sato W, Toichi M, Uono S, Kochiyama T: Impaired social brain network for
processing dynamic facial expressions in autism spectrum disorders.
BMC Neurosci 2012, 13:99.
121. Horwitz B, Rumsey JM, Grady CL, Rapoport SI: The cerebral metabolic
landscape in autism. Intercorrelations Reg Glucose Util Arch Neurol 1988,
122. Stuss DT, Knight RT: Principles of Frontal Lobe Function. Oxford: Oxford
University Press; 2002.
123. Courchesne E, Pierce K: Why the frontal cortex in autism might be talking
only to itself: local over-connectivity but long-distance disconnection.
Curr Opin Neurobiol 2005, 15:225230.
124. Geschwind DH, Levitt P: Autism spectrum disorders: developmental
disconnection syndromes. Curr Opin Neurobiol 2007, 17:103111.
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 13 of 14
125. Shalom DB: The medial prefrontal cortex and integration in autism.
Neuroscientist 2009, 15:589598.
126. Carper RA, Moses P, Tigue ZD, Courchesne E: Cerebral lobes in autism: early
hyperplasia and abnormal age effects. Neuroimage 2002, 16:10381051.
127. Herbert MR, Ziegler DA, Deutsch CK, OBrien LM, Lange N, Bakardjiev A,
Hodgson J, Adrien KT, Steele S, Makris N, et al:Dissociations of cerebral
cortex, subcortical and cerebral white matter volumes in autistic boys.
Brain 2003, 126:11821192.
128. Herbert MR, Ziegler DA, Makris N, Filipek PA, Kemper TL, Normandin JJ,
Sanders HA, Kennedy DN, Caviness VS Jr: Localization of white matter
volume increase in autism and developmental language disorder.
Ann Neurol 2004, 55:530540.
129. Anderson DK, Oti RS, Lord C, Welch K: Patterns of growth in adaptive
social abilities among children with autism spectrum disorders.
J Abnorm Child Psychol 2009, 37:10191034.
130. Seltzer MM, Shattuck P, Abbeduto L, Greenberg JS: Trajectory of
development in adolescents and adults with autism. Ment Retard Dev
Disabil Res Rev 2004, 10:234247.
131. Lord C, Risi S, Lambrecht L, Cook EH Jr, Leventhal BL, DiLavore PC, Pickles A,
Rutter M: The autism diagnostic observation schedule-generic: a
standard measure of social and communication deficits associated with
the spectrum of autism. J Autism Dev Disord 2000, 30:205223.
132. Barry RJ, Clarke AR, Johnstone SJ, Magee CA, Rushby JA: EEG differences
between eyes-closed and eyes-open resting conditions. Clin Neurophysiol
2007, 118:27652773.
133. Chen AC, Feng W, Zhao H, Yin Y, Wang P: EEG default mode network in
the human brain: spectral regional field powers. Neuroimage 2008,
134. Mathewson KJ, Jetha MK, Drmic IE, Bryson SE, Goldberg JO, Schmidt LA:
Regional EEG alpha power, coherence, and behavioral symptomatology
in autism spectrum disorder. Clin Neurophysiol 2012, 123:17981809.
135. Barry RJ, Clarke AR, Johnstone SJ, Brown CR: EEG differences in children
between eyes-closed and eyes-open resting conditions. Clin Neurophysiol
2009, 120:18061811.
136. Michel CM, Lehmann D, Henggeler B, Brandeis D: Localization of the sources
of EEG delta, theta, alpha and beta frequency bands using the FFT dipole
approximation. Electroencephalogr Clin Neurophysiol 1992, 82:3844.
137. Hlinka J, Alexakis C, Diukova A, Liddle PF, Auer DP: Slow EEG pattern
predicts reduced intrinsic functional connectivity in the default mode
network: an inter-subject analysis. Neuroimage 2010, 53:239246.
138. Mo J, Liu Y, Huang H, Ding M: Coupling between visual alpha oscillations
and default mode activity. Neuroimage 2013, 68:112118.
139. Hoechstetter K, Bornfleth H, Weckesser D, Ille N, Berg P, Scherg M: BESA
source coherence: a new method to study cortical oscillatory coupling.
Brain Topogr 2004, 16:233238.
140. Cornew L, Roberts TP, Blaskey L, Edgar JC: Resting-state oscillatory activity
in autism spectrum disorders. J Autism Dev Disord 2012, 42:18841894.
141. Nunez PL, Srinivasan R: Electric fields of the brain: the neurophysics of EEG.
2nd edition. New York: Oxford University Press; 2006.
142. Lachaux JP, Rodriguez E, Martinerie J, Varela FJ: Measuring phase
synchrony in brain signals. Hum Brain Mapp 1999, 8:194208.
143. Duffy FH, Jones K, Bartels P, McAnulty G, Albert M: Unrestricted principal
components analysis of brain electrical activity: issues of data
dimensionality, artifact, and utility. Brain Topogr 1992, 4:291307.
144. Duffy FH, Denckla MB, McAnulty GB, Holmes JA: Neurophysiological
studies in dyslexia. Res Publ Assoc Res Nerv Ment Dis 1988, 66:149170.
145. Makeig S, Jung TP, Bell AJ, Ghahremani D, Sejnowski TJ: Blind separation of
auditory event-related brain responses into independent components.
Proc Natl Acad Sci U S A 1997, 94:1097910984.
146. Hamm JP, Gilmore CS, Clementz BA: Augmented gamma band auditory
steady-state responses: support for NMDA hypofunction in
schizophrenia. Schizophr Res 2012, 138:17.
147. Carroll CA, Kieffaber PD, Vohs JL, ODonnell BF, Shekhar A, Hetrick WP:
Contributions of spectral frequency analyses to the study of P50 ERP
amplitude and suppression in bipolar disorder with or without a history
of psychosis. Bipolar Disord 2008, 10:776787.
148. Clementz BA, Blumenfeld LD: Multichannel electroencephalographic
assessment of auditory evoked response suppression in schizophrenia.
Experimental Brain Research Experimentelle Hirnforschung Experimentation
Cerebrale 2001, 139:377390.
149. Bosl W, Tierney A, Tager-Flusberg H, Nelson C: EEG complexity as a
biomarker for autism spectrum disorder risk. BMC Med 2011, 9:18.
150. Chan AS, Leung WW: Differentiating autistic children with quantitative
encephalography: A 3-month longitudinal study. J Child Neurol 2006,
151. Sigman M, McGovern CW: Improvement in cognitive and language skills
from preschool to adolescence in autism. J Autism Dev Disord 2005, 35:1523.
152. Qiu M, Li Q, Liu G, Xie B, Wang J: Voxel-based analysis of white matter
during adolescence and young adulthood. Brain Dev 2010, 32:531537.
153. Gray KM, Tonge BJ: Screening for autism in infants and preschool
children with developmental delay. Aust N Z J Psychiatry 2005, 39:378386.
154. Rogers SJ: Diagnosis of autism before the age of 3. Int Rev Res Ment Retard
2000, 23:131.
155. Dawson G: Recent advances in research on early detection, causes,
biology, and treatment of autism spectrum disorders. Curr Opin Neurol
2010, 23:9596.
156. Elsabbagh M, Volein A, Csibra G, Holmboe K, Garwood H, Tucker L, Krljes S,
Baron-Cohen S, Bolton P, Charman T, et al:Neural correlates of eye gaze
processing in the infant broader autism phenotype. Biol Psychiatry 2009,
157. Percy AK: Rett syndrome: exploring the autism link. Arch Neurol 2011,
158. Wiznitzer M: Autism and tuberous sclerosis. J Child Neurol 2004, 19:675679.
159. Bonaglia MC, Giorda R, Beri S, De Agostini C, Novara F, Fichera M, Grillo L,
Galesi O, Vetro A, Ciccone R, et al:Molecular mechanisms generating and
stabilizing terminal 22q13 deletions in 44 subjects with Phelan/McDermid
syndrome. PLoS Genet 2011, 7:e1002173.
160. Knoth IS, Lippe S: Event-related potential alterations in fragile X
syndrome. Front Hum Neurosci 2012, 6:264.
161. Musumeci SA, Hagerman RJ, Ferri R, Bosco P, Dalla Bernardina B, Tassinari
CA, De Sarro GB, Elia M: Epilepsy and EEG findings in males with fragile X
syndrome. Epilepsia 1999, 40:10921099.
162. Gorbachevskaia NL, Denisova LV: Brain bioelectrical activity in patients
with the fragile X-chromosome syndrome and in their mothers. Zhurnal
nevrologii i psikhiatrii imeni SS Korsakova / Ministerstvo zdravookhraneniia i
meditsinskoi promyshlennosti Rossiiskoi Federatsii, Vserossiiskoe obshchestvo
nevrologov [i] Vserossiiskoe obshchestvo psikhiat 1997, 97:3337.
163. Iznak AF, Gorbachevskaia NL, Zhigulskaia SE, GrigorEva NV, Grachev VV,
VasilEva AG, Chaianov NV, Gavrilova SI, Roshchina IF, Kolykhalov IV:
Quantitative EEG correlates of the human frontal lobe dysfunction.
Vestnik Rossiiskoi akademii meditsinskikh nauk / Rossiiskaia akademiia
meditsinskikh nauk 2001, 7:4853.
164. Sabaratnam M, Vroegop PG, Gangadharan SK: Epilepsy and EEG findings in
18 males with fragile X syndrome. Seizure 2001, 10:6063.
165. Ishizaki A: Electroencephalographical study of the Rett syndrome with
special reference to the monorhythmic theta activities in adult patients.
Brain Dev 1992, 14(Suppl):S31S36.
166. Niedermeyer E, Naidu SB, Plate C: Unusual EEG theta rhythms over central
region in Rett syndrome: considerations of the underlying dysfunction.
Clinical EEG 1997, 28:3643.
167. Uchino S, Waga C: SHANK3 as an autism spectrum disorder-associated
gene. Brain Dev 2013, 35:106110.
Cite this article as: Wang et al.:Resting state EEG abnormalities in
autism spectrum disorders. Journal of Neurodevelopmental Disorders
2013 5:24.
Wang et al. Journal of Neurodevelopmental Disorders 2013, 5:24 Page 14 of 14
... Many studies suggest that ASD is a connectivity disorder [27]. Electroencephalography, which primarily measures neurophysiological changes related to postsynaptic activity in the neocortex [26], has proven to be a powerful tool for studying complex neuropsychiatric disorders [28,29]. It is thus reasonable to conjecture that EEG investigations in different power bands and coherence between hemispheres and brain regions may demonstrate discernible patterns, reflecting information about the underlying neural networks that highlight changes in intellectual and behavioral ASD impairments. ...
... The interaction of ASD, intellectual disability, and other comorbidities led to the hypothesis of general dysregulation of E/I balance, caused by defects in GABAergic fibers, particularly GABAergic interneurons maturation, or GABA receptor function [46]. In their review of resting-state EEG studies in ASD, Wang and colleagues [29] reported a potential "U-shaped" profile of EEG power spectra in ASD as compared to typically developing controls, with excess power in low-and high-frequency bands and decreased power in middle-ranged frequency band. This resting EEG profile highlights the hypothesis that ASD oscillatory dysfunctions could be attributed to affected GABAergic interneurons that have a modulating role on excitatory pyramidal cells. ...
... This resting EEG profile highlights the hypothesis that ASD oscillatory dysfunctions could be attributed to affected GABAergic interneurons that have a modulating role on excitatory pyramidal cells. Although Wang and colleagues [29] identify this U-shaped profile as an EEG biomarker observed in resting state, on the contrary, a recent comprehensive review [47] underscores that no general pattern can be inferred within EEG findings among patients with ASD. On the other hand, interestingly, this "U-shaped" profile in a resting condition is in line with our preliminary findings based on repetitive visual stimulation technique, representing a potential biomarker of disrupted oscillatory synchronization in ASD. ...
Full-text available
Citation: Vetri, L.; Maniscalco, L.; Diana, P.; Guidotti, M.; Matranga, D.; Bonnet-Brilhault, F.; Tripi, G. A Preliminary Study on Photic Driving in the Electroencephalogram of Children with Autism across a Wide Cognitive and Behavioral Range. J. Clin. Med. 2022, 11, 3568. https://
... As shown in Tables 1, 2, power abnormalities, overconnectivity, and underconnectivity across frequency bands and brain regions are implicated in ASD and FXS. Yet these differences are far from consistent in the literature and do not appear to fall neatly into one model (e.g., the "U-shaped profile" of ASD to describe excessive power in low-frequency and high-frequency bands) (Wang et al., 2013). Only significant differences are reported in the tables, but many of the studies found no differences between the ASD/FXS and control groups for a given frequency band and brain area. ...
... Delta power is elevated globally in ASD (and insufficiently studied in FXS). Enhanced delta power is commonly observed among low-functioning children with ASD in studies that involve doing a task (Wang et al., 2013), as well as children with learning disabilities (Fonseca et al., 2006) and those born preterm (Rommel et al., 2017). The delta band plays roles ranging from sustained attention to decision making to motivation, and it has been proposed that increased resting delta power is a general marker of brain trauma, pathology, or neurotransmitter disturbances (Başar-Eroglu et al., 1992;Kirmizi-Alsan et al., 2006;Knyazev, 2012;Rommel et al., 2017). ...
... The data on alpha power in ASD are mixed. The U-shaped profile of power, whereby alpha power is reduced in individuals with ASD, is a popular model in the literature (Wang et al., 2013). However, several studies found an excess of alpha power instead. ...
Full-text available
Autism Spectrum Disorder (ASD) and Fragile X Syndrome (FXS) are neurodevelopmental disorders with similar clinical and behavior symptoms and partially overlapping and yet distinct neurobiological origins. It is therefore important to distinguish these disorders from each other as well as from typical development. Examining disruptions in functional connectivity often characteristic of neurodevelopment disorders may be one approach to doing so. This review focuses on EEG and MEG studies of resting state in ASD and FXS, a neuroimaging paradigm frequently used with difficult-to-test populations. It compares the brain regions and frequency bands that appear to be impacted, either in power or connectivity, in each disorder; as well as how these abnormalities may result in the observed symptoms. It argues that the findings in these studies are inconsistent and do not fit neatly into existing models of ASD and FXS, then highlights the gaps in the literature and recommends future avenues of inquiry.
... Indeed, EEG measures are widely used to diagnose and monitor treatment effects in epilepsy. More recently, quantitative EEG has been increasingly used for monitoring neuropsychiatric disorders, including depression (Rajpurkar et al., 2020;Wu et al., 2020;Zhdanov et al., 2020) and in ASD trials (Wang et al., 2013;Jeste et al., 2015;Heunis et al., 2016). For instance, we and others have shown that functional E/I ratios (f E/I) in neuronal networks may be quantified using EEG Donoghue et al., 2020). ...
... We focused on alpha-band oscillations (8-13 Hz) due to their relevance for healthy neuronal network development and cognitive function and their clear disruption in neurodevelopmental disorders (Wang et al., 2013;Dickinson et al., 2019). Computational neuronal network models generating alpha-band oscillations have shown that measures such as amplitude, frequency, temporal correlations, and f E/I are sensitive to changes in excitation/inhibition ratios (Poil et al., 2020;Bruining et al., 2020). ...
... alpha-band power in TSC compared to TDC-an observation that has been described also in ASD (Kulandaivel and Holmes, 2011;Tierney et al., 2012;Wang et al., 2013;Bruining et al., 2020) and in attention deficit and hyperactivity disorder (Deiber et al., 2020). Furthermore, in TSC studies, it has been associated with delayed cognitive and motor development (Dickinson et al., 2019;De Ridder et al., 2021). ...
Full-text available
Neuronal excitation-inhibition (E/I) imbalances are considered an important pathophysiological mechanism in neurodevelopmental disorders. Preclinical studies on tuberous sclerosis complex (TSC), suggest that altered chloride homeostasis may impair GABAergic inhibition and thereby E/I-balance regulation. Correction of chloride homeostasis may thus constitute a treatment target to alleviate behavioral symptoms. Recently, we showed that bumetanide—a chloride-regulating agent—improved behavioral symptoms in the open-label study Bumetanide to Ameliorate Tuberous Sclerosis Complex Hyperexcitable Behaviors trial (BATSCH trial; Eudra-CT: 2016-002408-13). Here, we present resting-state EEG as secondary analysis of BATSCH to investigate associations between EEG measures sensitive to network-level changes in E/I balance and clinical response to bumetanide. EEGs of 10 participants with TSC (aged 8–21 years) were available. Spectral power, long-range temporal correlations (LRTC), and functional E/I ratio ( f E/I) in the alpha-frequency band were compared before and after 91 days of treatment. Pre-treatment measures were compared against 29 typically developing children (TDC). EEG measures were correlated with the Aberrant Behavioral Checklist-Irritability subscale (ABC-I), the Social Responsiveness Scale-2 (SRS-2), and the Repetitive Behavior Scale-Revised (RBS-R). At baseline, TSC showed lower alpha-band absolute power and f E/I than TDC. Absolute power increased through bumetanide treatment, which showed a moderate, albeit non-significant, correlation with improvement in RBS-R. Interestingly, correlations between baseline EEG measures and clinical outcomes suggest that most responsiveness might be expected in children with network characteristics around the E/I balance point. In sum, E/I imbalances pointing toward an inhibition-dominated network are present in TSC. We established neurophysiological effects of bumetanide although with an inconclusive relationship with clinical improvement. Nonetheless, our results further indicate that baseline network characteristics might influence treatment response. These findings highlight the possible utility of E/I-sensitive EEG measures to accompany new treatment interventions for TSC. Clinical Trial Registration EU Clinical Trial Register, EudraCT 2016-002408-13 ( ). Registered 25 July 2016.
... Several neural metrics can be derived from resting-state EEG but the majority of EEG studies to date examining group differences between autistic individuals 1 and age-matched peers have utilized absolute or relative EEG power. Absolute power is the summation of neural activity integrated over a frequency band of interest independent of neural activity in other bands, whereas relative power is neural activity within a frequency band of interest divided by the activity in all other frequency bands (Wang et al., 2013). A review article determined that despite significant heterogeneity in sample demographics such as age of participants and the presence or absence of comorbid intellectual disability, autistic individuals demonstrate a "U shaped" electrophysiological profile of increased absolute or relative low-frequency EEG power (delta and theta), reduced absolute or relative alpha EEG power, and increased absolute or relative high-frequency EEG power (beta and gamma) (Wang et al., 2013). ...
... Absolute power is the summation of neural activity integrated over a frequency band of interest independent of neural activity in other bands, whereas relative power is neural activity within a frequency band of interest divided by the activity in all other frequency bands (Wang et al., 2013). A review article determined that despite significant heterogeneity in sample demographics such as age of participants and the presence or absence of comorbid intellectual disability, autistic individuals demonstrate a "U shaped" electrophysiological profile of increased absolute or relative low-frequency EEG power (delta and theta), reduced absolute or relative alpha EEG power, and increased absolute or relative high-frequency EEG power (beta and gamma) (Wang et al., 2013). This electrophysiological profile may be linked to increased GABAergic activity underlying a shift in the E/I balance (Nelson & Valakh, 2015;Wang et al., 2013). ...
... A review article determined that despite significant heterogeneity in sample demographics such as age of participants and the presence or absence of comorbid intellectual disability, autistic individuals demonstrate a "U shaped" electrophysiological profile of increased absolute or relative low-frequency EEG power (delta and theta), reduced absolute or relative alpha EEG power, and increased absolute or relative high-frequency EEG power (beta and gamma) (Wang et al., 2013). This electrophysiological profile may be linked to increased GABAergic activity underlying a shift in the E/I balance (Nelson & Valakh, 2015;Wang et al., 2013). ...
Approximately 7% of preterm infants receive an autism spectrum disorder (ASD) diagnosis. Yet, there is a significant gap in the literature in identifying prospective markers of neurodevelopmental risk in preterm infants. The present study examined two electroencephalography (EEG) parameters during infancy, absolute EEG power and aperiodic activity of the power spectral density (PSD) slope, in association with subsequent autism risk and cognitive ability in a diverse cohort of children born preterm in South Africa. Participants were 71 preterm infants born between 25 and 36 weeks gestation (34.60 ± 2.34 weeks). EEG was collected during sleep between 39 and 41 weeks postmenstrual age adjusted (40.00 ± 0.42 weeks). The Bayley Scales of Infant Development and Brief Infant Toddler Social Emotional Assessment (BITSEA) were administered at approximately 3 years of age adjusted (34 ± 2.7 months). Aperiodic activity, but not the rhythmic oscillatory activity, at multiple electrode sites was associated with subsequent increased autism risk on the BITSEA at three years of age. No associations were found between the PSD slope or absolute EEG power and cognitive development. Our findings highlight the need to examine potential markers of subsequent autism risk in high‐risk populations other than infants at familial risk.
... Welch's method applies to extract power spectral density of LR EEG and the corresponding SR EEG reconstructed by Deep-EEGSR at 5 frequency bands respectively [27], such as delta (0.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), gamma (30-40 Hz or higher) [41], each sample corresponding to n × 5 features (n: the number of channels). Support Vector Machine (SVM) then distinguishes the brain states (ASD & TD) based on these features. ...
... Table 6 depicts the statistical results and the corresponding brain regions identified via the decompo-sition of HR EEG features (ground truth). The typical ASD characteristics at each frequency band are roughly consistent with the conclusions in the literature [41]. In particular, the power spectrum of delta in ASD EEG is excessive, and the related brain regions include the central/parietal lobe and the frontal lobe. ...
Electroencephalogram (EEG) excels in portraying rapid neural dynamics at the level of milliseconds, but its spatial resolution has often been lagging behind the increasing demands in neuroscience research or subject to limitations imposed by emerging neuro-engineering scenarios, especially those centering on consumer EEG devices. Current super-resolution methods generally do not suffice in the reconstruction of high-resolution EEG as it remains a grand challenge to properly handle the connection relationship amongst EEG electrodes (channels) and the intensive individuality of subjects. This study proposes a deep EEG super-resolution framework correlating brain structural and functional connectivities (Deep-EEGSR), which consists of a compact convolutional network and an auxiliary fully-connected network for filter generation (FGN). Deep-EEGSR applies graph convolution adapting to the structural connectivity amongst EEG channels when coding super-resolution EEG. Sample-specific dynamic convolution is designed with filter parameters adjusted by FGN conforming to functional connectivity of intensive subject individuality. Overall, Deep-EEGSR operates on low-resolution EEG and reconstructs the corresponding high-resolution acquisitions through an end-to-end super-resolution course. Experimental results on three EEG datasets (Autism Spectrum Disorder, Emotion, Motor Imagery) indicate that 1) Deep-EEGSR significantly outperforms the state-of-the-art counterparts with NMSE decreased by 1% ∼ 6% and the improvement of SNR up to 1.2 dB; 2) the super-resolution EEG manifests superiority to the low-resolution alternative in ASD discrimination and spatial localization of typical ASD EEG characteristics, and this superiority even increases with the scale of super-resolution.
... Improvement in autism severity, which eventually corresponds to an improved cohabitation with their relatives, as a consequence of tPBM, as reported here, could also be explained due to the potential effect over electrophysiological oscillations. Indeed, EEG power abnormalities in autism have been reported (Wang et al., 2013) [49] and recently, tPBM has been shown to modulate neural oscillations (Wang et al., 2019;Zomorrodi et al., 2019) [50,51]. Future studies might deeply investigate this point, by studying the potential correlation between improvements in autism severity and EEG changes. ...
... Improvement in autism severity, which eventually corresponds to an improved cohabitation with their relatives, as a consequence of tPBM, as reported here, could also be explained due to the potential effect over electrophysiological oscillations. Indeed, EEG power abnormalities in autism have been reported (Wang et al., 2013) [49] and recently, tPBM has been shown to modulate neural oscillations (Wang et al., 2019;Zomorrodi et al., 2019) [50,51]. Future studies might deeply investigate this point, by studying the potential correlation between improvements in autism severity and EEG changes. ...
Full-text available
Children with Autism Spectrum Disorder (ASD) face several challenges due to deficits in social function and communication along with restricted patterns of behaviors. Often, they also have difficult-to-manage and disruptive behaviors. At the moment, there are no pharmacological treatments for ASD core features. Recently, there has been a growing interest in non-pharmacological interventions for ASD, such as neuromodulation. In this retrospective study, data are reported and analyzed from 21 patients (13 males, 8 females) with ASD, with an average age of 9.1 (range 5–15), who received six months of transcranial photobiomodulation (tPBM) at home using two protocols (alpha and gamma), which, respectively, modulates the alpha and gamma bands. They were evaluated at baseline, after three and six months of treatment using the Childhood Autism Rating Scale (CARS), the Home Situation Questionnaire-ASD (HSQ-ASD), the Autism Parenting Stress Index (APSI), the Montefiore Einstein Rigidity Scale–Revised (MERS–R), the Pittsburgh Sleep Quality Index (PSQI) and the SDAG, to evaluate attention. Findings show that tPBM was associated with a reduction in ASD severity, as shown by a decrease in CARS scores during the intervention (p < 0.001). A relevant reduction in noncompliant behavior and in parental stress have been found. Moreover, a reduction in behavioral and cognitive rigidity was reported as well as an improvement in attentional functions and in sleep quality. Limitations were discussed as well as future directions for research.
... However, no clear picture has emerged yet. Various theories for PS alterations in ASD have been put forward, including a U-shaped profile with excessive power in low and high frequencies [8], but many findings are in conflict to each other. For example, for spectral power in the alpha band, there are various reports of increase [9][10][11], decrease [12][13][14] and no effects [15,16] in ASD compared to NT (neurotypicals). ...
Full-text available
Background: Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed. Methods: We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split). Results: In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset. Limitations: The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects. Conclusions: This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects.
Full-text available
Autism is a pervasive neurodevelopmental disorder of multifactorial causation and phenotypical variation. The nature of the disorder together with the difficulty in planning and implementing highly effective treatment models, have directed the scientific research towards discovering and implementing new intervention or/and therapeutic models, of different type and philosophy. Brain – computer interface systems as intervention tools in autism comprise an approach consistent with the demands of the new era. The dissertation aims at examining applied, non-invasive research protocols of this kind, placing emphasis on the way they were implemented and their effectiveness. The review was conducted on published research in the last decade, concerning ages 4-21.
Learning the subtype of dyslexia may help shorten the rehabilitation process and focus more on the relevant special education or diet for children with dyslexia. For this purpose, the resting-state eyes-open 2-min QEEG measurement data were collected from 112 children with dyslexia (84 male, 28 female) between 7 and 11 years old for 96 sessions per subject on average. The z-scores are calculated for each band power and each channel, and outliers are eliminated afterward. Using the k-Means clustering method, three different clusters are identified. Cluster 1 (19% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 2 (76% of the cases) has negative z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 3 (5% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers at AF3, F3, FC5, and T7 channels and mostly negative z-scores for other channels. In Cluster 3, there is temporal disruption which is a typical description of dyslexia. In Cluster 1, there is a general brain inflammation as both slow and fast waves are detected in the same channels. In Cluster 2, there is a brain maturation delay and a mild inflammation. After Auto Train Brain training, most of the cases resemble more of Cluster 2, which may mean that inflammation is reduced and brain maturation delay comes up to the surface which might be the result of inflammation. Moreover, Cluster 2 center values at the posterior parts of the brain shift toward the mean values at these channels after 60 sessions. It means, Auto Train Brain training improves the posterior parts of the brain for children with dyslexia, which were the most relevant regions to be strengthened for dyslexia.
Full-text available
Autism is a neurodevelopmental disorder involving dysmaturation of widely distributed brain systems. Accordingly, behaviors that depend on distributed systems, such as higher level cognition and sensorimotor control, are compromised in the disorder. The current study investigated alterations in neural systems underlying sensorimotor disturbances in autism. An fMRI investigation was conducted using saccadic and. pursuit eye movement paradigms with 13 high functioning individuals with autism and 14 age- and IQ-matched typically developing individuals. Individuals with autism had reduced activation in cortical eye fields and cerebellar hemispheres during both eye movement tasks. When executing visually guided saccades, individuals with autism had greater activation bilaterally in a frontostriatal circuit including dorsolateral prefrontal cortex, caudate nucleus, medial thalamus, anterior and posterior cingulate cortex, and right dentate nucleus. The increased activation in prefrontal-striatal-thalamocortical circuitry during visually guided saccades indicates that systems typically dedicated to cognitive control may need to compensate for disturbances in lower-level sensorimotor systems. Reduced activation throughout visual sensorimotor systems may contribute to saccadic and pursuit disturbances that have been reported in autism. These findings document that neurodevelopmental disturbances in autism affect widely distributed brain systems beyond those mediating language and social cognition.
This book provides a review of historical and current research on the function of the frontal lobes and frontal systems of the brain. The content spans frontal lobe functions from birth to old age, from biochemistry and anatomy to rehabilitation, and from normal to disrupted function. The book covers a variety of disciplines including neurology, neuroscience, psychiatry, psychology, and health care.
Problem/Condition: Autism spectrum disorders (ASDs) are a group of developmental disabilities characterized by impairments in social interaction and communication and by restricted, repetitive, and stereotyped patterns of behavior. Symptoms typically are apparent before age 3 years. The complex nature of these disorders, coupled with a lack of biologic markers for diagnosis and changes in clinical definitions over time, creates challenges in monitoring the prevalence of ASDs. Accurate reporting of data is essential to understand the prevalence of ASDs in the population and can help direct research. Period Covered: 2008. Description of System: The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that estimates the prevalence of ASDs and describes other characteristics among children aged 8 years whose parents or guardians reside within 14 ADDM sites in the United States. ADDM does not rely on professional or family reporting of an existing ASD diagnosis or classification to ascertain case status. Instead, information is obtained from children's evaluation records to determine the presence of ASD symptoms at any time from birth through the end of the year when the child reaches age 8 years. ADDM focuses on children aged 8 years because a baseline study conducted by CDC demonstrated that this is the age of identified peak prevalence. A child is included as meeting the surveillance case definition for an ASD if he or she displays behaviors (as described on a comprehensive evaluation completed by a qualified professional) consistent with the American Psychiatric Association's Diagnostic and Statistical Manual-IV, Text Revision (DSM-IV-TR) diagnostic criteria for any of the following conditions: Autistic Disorder; Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS, including Atypical Autism); or Asperger Disorder. The first phase of the ADDM methodology involves screening and abstraction of comprehensive evaluations completed by professional providers at multiple data sources in the community. Multiple data sources are included, ranging from general pediatric health clinics to specialized programs for children with developmental disabilities. In addition, many ADDM sites also review and abstract records of children receiving special education services in public schools. In the second phase of the study, all abstracted evaluations are reviewed by trained clinicians to determine ASD case status. Because the case definition and surveillance methods have remained consistent across all ADDM surveillance years to date, comparisons to results for earlier surveillance years can be made. This report provides updated ASD prevalence estimates from the 2008 surveillance year, representing 14 ADDM areas in the United States. In addition to prevalence estimates, characteristics of the population of children with ASDs are described, as well as detailed comparisons of the 2008 surveillance year findings with those for the 2002 and 2006 surveillance years. Results: For 2008, the overall estimated prevalence of ASDs among the 14 ADDM sites was 11.3 per 1,000 (one in 88) children aged 8 years who were living in these communities during 2008. Overall ASD prevalence estimates varied widely across all sites (range: 4.8-21.2 per 1,000 children aged 8 years). ASD prevalence estimates also varied widely by sex and by racial/ethnic group. Approximately one in 54 boys and one in 252 girls living in the ADDM Network communities were identified as having ASDs. Comparison of 2008 findings with those for earlier surveillance years indicated an increase in estimated ASD prevalence of 23% when the 2008 data were compared with the data for 2006 (from 9.0 per 1,000 children aged 8 years in 2006 to 11.0 in 2008 for the 11 sites that provided data for both surveillance years) and an estimated increase of 78% when the 2008 data were compared with the data for 2002 (from 6.4 per 1,000 children aged 8 years in 2002 to 11.4 in 2008 for the 13 sites that provided data for both surveillance years). Because the ADDM Network sites do not make up a nationally representative sample, these combined prevalence estimates should not be generalized to the United States as a whole. Interpretation: These data confirm that the estimated prevalence of ASDs identified in the ADDM network surveillance populations continues to increase. The extent to which these increases reflect better case ascertainment as a result of increases in awareness and access to services or true increases in prevalence of ASD symptoms is not known. ASDs continue to be an important public health concern in the United States, underscoring the need for continued resources to identify potential risk factors and to provide essential supports for persons with ASDs and their families. Public Health Action: Given substantial increases in ASD prevalence estimates over a relatively short period, overall and within various subgroups of the population, continued monitoring is needed to quantify and understand these patterns. With 5 biennial surveillance years completed in the past decade, the ADDM Network continues to monitor prevalence and characteristics of ASDs and other developmental disabilities for the 2010 surveillance year. Further work is needed to evaluate multiple factors contributing to increases in estimated ASD prevalence over time. ADDM Network investigators continue to explore these factors, with a focus on understanding disparities in the identification of ASDs among certain subgroups and on how these disparities have contributed to changes in the estimated prevalence of ASDs. CDC is partnering with other federal and private partners in a coordinated response to identify risk factors for ASDs and to meet the needs of persons with ASDs and their families.
Objective: This study aimed to identify emotional and behavioural problems specific to young children with autism using the Developmental Behaviour Checklist (DBC-P) and thus evaluate the efficacy of this checklist as a screening tool for autism in children with developmental delay aged 18–48 months. Method: Subjects were 60 children with autism and developmental delay and 60 children with developmental delay without autism. Results: Features were identified which differentiated the children with autism from those with developmental delay without autism. Analyses revealed that a 17-item version of the DBC-P performed well as a screening tool for autism, with an ‘area under the curve’ of 0.874, sensitivity of 0.8750, and specificity of 0.6909. Conclusions: The DBC-P offers a potential simple and inexpensive method of screening at risk populations of preschool children with developmental delay for autism, thus facilitating timely referral to scarce specialist autism diagnostic services.
Guidelines for submitting commentsPolicy: Comments that contribute to the discussion of the article will be posted within approximately three business days. We do not accept anonymous comments. Please include your email address; the address will not be displayed in the posted comment. Cell Press Editors will screen the comments to ensure that they are relevant and appropriate but comments will not be edited. The ultimate decision on publication of an online comment is at the Editors' discretion. Formatting: Please include a title for the comment and your affiliation. Note that symbols (e.g. Greek letters) may not transmit properly in this form due to potential software compatibility issues. Please spell out the words in place of the symbols (e.g. replace “α” with “alpha”). Comments should be no more than 8,000 characters (including spaces ) in length. References may be included when necessary but should be kept to a minimum. Be careful if copying and pasting from a Word document. Smart quotes can cause problems in the form. If you experience difficulties, please convert to a plain text file and then copy and paste into the form.