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Using quantitative and analytic EEG methods in the understanding of connectivity in autism spectrum disorders: A theory of mixed over- and under-connectivity

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Neuroimaging technologies and research has shown that autism is largely a disorder of neuronal connectivity. While advanced work is being done with fMRI, MRI-DTI, SPECT and other forms of structural and functional connectivity analyses, the use of EEG for these purposes is of additional great utility. Cantor et al. (1986) were the first to examine the utility of pairwise coherence measures for depicting connectivity impairments in autism. Since that time research has shown a combination of mixed over and under-connectivity that is at the heart of the primary symptoms of this multifaceted disorder. Nevertheless, there is reason to believe that these simplistic pairwise measurements under represent the true and quite complicated picture of connectivity anomalies in these persons. We have presented three different forms of multivariate connectivity analysis with increasing levels of sophistication (including one based on principle components analysis, sLORETA source coherence, and Granger causality) to present a hypothesis that more advanced statistical approaches to EEG coherence analysis may provide more detailed and accurate information than pairwise measurements. A single case study is examined with findings from MR-DTI, pairwise and coherence and these three forms of multivariate coherence analysis. In this case pairwise coherences did not resemble structural connectivity, whereas multivariate measures did. The possible advantages and disadvantages of different techniques are discussed. Future work in this area will be important to determine the validity and utility of these techniques.
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HYPOTHESIS AND THEORY ARTICLE
published: 26 February 2014
doi: 10.3389/fnhum.2014.00045
Using quantitative and analytic EEG methods in the
understanding of connectivity in autism spectrum
disorders: a theory of mixed over- and under-connectivity
Robert Coben1,2*, Iman Mohammad-Rezazadeh3,4 and Rex L. Cannon5
1Neurorehabilitation and Neuropsychological Services, Massapequa Park, NY, USA
2Integrated Neuroscience Services, Fayetteville, AR, USA
3Center for Mind and Brain, University of California, Davis, CA, USA
4Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
5Psychoeducational Network, Knoxville, TN, USA
Edited by:
Tal Kenet, Massachusetts General
Hospital, USA
Reviewed by:
Sheraz Khan, Massachusetts
General Hospital, USA
Catherine Chu, Massachusetts
General Hospital, USA
*Correspondence:
Robert Coben, Integrated
Neuroscience Services, 86 West
Sunbridge Drive, Fayetteville,
AR 72703, USA
e-mail: drcoben@integratedneuro
scienceservices.com
Neuroimaging technologies and research has shown that autism is largely a disorder of
neuronal connectivity. While advanced work is being done with fMRI, MRI-DTI, SPECT and
other forms of structural and functional connectivity analyses, the use of EEG for these
purposes is of additional great utility. Cantor et al. (1986) were the first to examine the
utility of pairwise coherence measures for depicting connectivity impairments in autism.
Since that time research has shown a combination of mixed over and under-connectivity
that is at the heart of the primary symptoms of this multifaceted disorder. Nevertheless,
there is reason to believe that these simplistic pairwise measurements under represent
the true and quite complicated picture of connectivity anomalies in these persons. We
have presented three different forms of multivariate connectivity analysis with increasing
levels of sophistication (including one based on principle components analysis, sLORETA
source coherence, and Granger causality) to present a hypothesis that more advanced
statistical approaches to EEG coherence analysis may provide more detailed and accurate
information than pairwise measurements. A single case study is examined with findings
from MR-DTI, pairwise and coherence and these three forms of multivariate coherence
analysis. In this case pairwise coherences did not resemble structural connectivity,
whereas multivariate measures did. The possible advantages and disadvantages of
different techniques are discussed. Future work in this area will be important to determine
the validity and utility of these techniques.
Keywords: autism spectrum disorders, EEG/MEG, connectivity analysis, coherence analysis, sLORETA, granger
causation analysis
INTRODUCTION
Autistic Spectrum Disorders (ASD) are a heterogeneous group
of pervasive developmental disorders including Autistic Disorder,
Childhood Disintegrative Disorder, Pervasive Developmental
Disorder-Not Otherwise Specified (PDD-NOS), and Asperger
Disorder. Children with ASD demonstrate impairment in social
interaction, verbal and nonverbal communication, and behaviors
or interests (DSM-IV-TR; APA, 2000). ASD may be comorbid
with sensory integration difficulties, mental retardation or seizure
disorders. Children with ASD may have severe sensitivity to
sounds, textures, tastes, and smells. Cognitive deficits are often
associated with impaired communication skills. Repetitive stereo-
typed behaviors, perseveration, and obsessionality, common in
ASD, are associated with executive deficits. Executive dysfunction
in inhibitory control and set shifting have been attributed to ASD
(Schmitz et al., 2006). Seizure disorders may occur in one out of
four children with ASD; frequently beginning in early childhood
or adolescence (NIMH, 2006).
Research reviewing the epidemiology of autism (Center for
Disease Control and Prevention; CDC, 2009)reportedbetween
1 in 80 and 1 in 240 children in the United States diagnosed with
the disorder. A report of just 3 years ago (CDC, 2009) suggested a
prevalence of 1 in 110, and as high as 1 in 70 boys. In their most
recent report, the CDC (2012) suggests that the rate has risen to 1
in 88. ASDs are five times more likely in boys for which it is seen
in 1 out of 54 male children. According to Blaxill (2004),therates
of ASD were reported to be <3 per 10,000 children in the 1970s
and rose to >30 per 10,000 in the 1990s. This rise in the rate of
ASD constituted a 10-fold increase over a 20 year interval in the
United States. These findings make accurate assessment of autistic
individuals and their underlying neurophysiology a priority.
EEG ASSESSMENT IN AUTISM
Multiple neuroimaging studies have demonstrated brain anoma-
lies in autistics compared to healthy controls (McAlonan et al.,
2004; Page et al., 2006). The electroencephalogram (EEG) was one
of the earliest techniques used to investigate the neurobiology of
autism (Minshew, 1991). The recognition of a high instance of
EEG abnormalities and of seizure disorders in the autistic popu-
lation was among the earliest evidence of a biologic basis for the
disorder (Minshew, 1991). Moreover, the EEG is a premiere tool
to assess neural dysfunctions related to autism and seizures due
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HUMAN NEUROSCIENCE
Coben et al. EEG connectivity in autism
to its’ noninvasive nature, availability and utility in detailing these
types of difficulties.
Recent analyses have estimated the prevalence of seizure disor-
ders in autistic series at anywhere from 20 to 46%. Based on recent
analyses, the prevalence of seizure disorders in autistic series is
estimated at about 36% (Danielsson et al., 2005; Hughes and
Melyn, 2005; Hara, 2007; Parmeggiani et al., 2007). In fact, it has
been reported that the autistic population has about 3- to 22-
fold increased risk of developing seizure disorders as compared
to the normal population (Volkmar and Nelson, 1989). Sub-
clinical seizure activity or paroxysmal discharges occur in an even
higher proportion of autistics, but the significance of these remain
uncertain (Hughes and Melyn, 2005; Parmeggiani et al., 2007).
Ray et al. (2007) have suggested that the initial phase of corti-
cal spikes may relate to underlying intracranial foci. Other work
has suggested that EEG spikes may reflect underlying morpho-
logical brain abnormalities (Shelley et al., 2008) and/or metabolic
disturbances (Kobayashi et al., 2006).
In a recent study, Parmeggiani et al. (2010) demonstrated that
in a large inpatient sample 58% of adults with autism aged 20 or
older had experienced epilepsy or a seizure during their lifetime.
For these reasons, experts in the field have recommended the use
of routine and sleep EEGs in the evaluation of autistic disorders,
especially when there has been regression or there are signs of pos-
sible seizures. In fact, seizure detection has been the primary role
of the EEG for decades. When EEG assessment is processed and
analyzed with the most advanced techniques it can be invaluable
for screening for possible seizures, evaluation of autistic disorders,
and assessing the neurophysiological challenges of children with
ASD. While brain structural imaging may reveal interesting find-
ings, assessment of regional brain dysfunction is more revealing
and usually requires functional brain imaging techniques. This
would include techniques such as functional MRI, PET, single
photon emission computed tomography, magnoencephalography
(MEG), and even EEG. Some of these techniques require sedation
or injection of radioactive material so as to make participation
difficult for a typical autistic child. EEG, however, appears to be
the most clinically available and again least invasive of these tech-
niques. Further, it has been found that unique patterns of regional
dysfunction could be discerned through the quantitative analysis
of the EEG.
QUANTITATIVE EEG FINDINGS AND ASD
A review of the existing literature identified 14 studies that
used quantitative techniques to analyze differences in EEG
(QEEG) activity between children with ASD and normal con-
trols with conflicting results. Two studies showed decreased delta
frontally (Dawson et al., 1982; Coben et al., 2008), while one
found increased activity in the delta frequency range (Murias
et al., 2007). Two studies reported increased generalized delta or
described “slowing” (Cantor et al., 1986; Stroganova et al., 2007).
Two studies showed theta increases (Small et al., 1975; Coben
et al., 2008), while one study reported reduced theta (Dawson
et al., 1982). By contrast, findings have been quite consistent
within the alpha through gamma frequency range. All studies
reported reduced alpha power (Dawson et al., 1982; Cantor et al.,
1986) and increased beta (Rossi et al., 1995; Chan and Leung,
2006; Coben et al., 2008) and gamma power (Orekhova et al.,
2006). Multiple studies report a lack of hemispheric differences
in QEEG spectral power in autistic samples compared to findings
of hemispheric differences in normal controls. Autistic children
showed decreased power asymmetry when compared to normal
or mentally handicapped controls (Dawson et al., 1982; Ogawa
et al., 1982). Three studies investigated cortical connectivity in
ASD samples using QEEG coherence measures, with all report-
ing reduced connectivity, especially over longer distances (Cantor
et al., 1986; Lazarev et al., 2004; Coben et al., 2008). One con-
cern has been that sample sizes by and large have not been large
enough to allow for investigation of the observed inconsistencies
in findings reported above.
In the largest study of its’ kind, we (Coben et al., 2013)
included a total of 182 children, 91 on the autistic spectrum
and 91 healthy controls. Findings indicated an absolute delta
deficit over frontal and central brain regions and theta excesses
over frontal, temporal and posterior regions for the ASD sam-
ple. There were significant relative theta excesses over frontal and
temporal regions, alpha and beta excesses over multiple regions.
Interestingly, cluster analytic techniques were used and able to
delineate qeeg subtypes of ASD. Furthermore, a discriminant
function analysis was able to correctly identify ASD children
atarateof95%.Despitepowersubtypeshavingbeenshown,
VA R E TA ( di Michele et al., 2005) revealed similar sources of acti-
vation including temporal, posterior cortical and various limbic
regions. These findings raise the likelihood that the study of neu-
ronal networks in autism may lead to a greater understanding
of ASD than localization of brain activity. Power asymmetry and
coherence findings were also significant consistent with evidence
supporting the notion of frontal hypercoherence and anterior to
posterior temporal hypocoherences. These findings suggest that
the brain dysfunction in autistic disorders is often bilateral and
impacts both anterior and posterior axes. Alternatively, one could
view the brain dysfunction in autism as an abnormality in con-
nectivity that disrupts function in multiple regions (Minshew
and Williams, 2007). This would suggest that such connectivity
impairments are prevalent in autistic children. This is consistent
with the findings of Coben et al. (2008). Such an interpreta-
tion is also supported by the literature suggesting that autism is
primarily a disorder of neural connectivity.
AUTISM AS A DISORDER OF NEURAL CONNECTIVITY
There is increasing evidence that the cardinal disruptions in
autism are represented by disruptions in brain connectivity
(Courchesne and Pierce, 2005; Minshew and Williams, 2007;
Mak-Fan et al., 2012). There is mounting evidence of head
enlargement as a result of brain overgrowth early in life (first 1–2
years) (Courchesne et al., 2001, 2003) as a result of enhancements
in frontal white matter and minicolumn pathology (Casanova
et al., 2002; Herbert et al., 2004; Carper and Courchesne, 2005;
Vargas et al., 2005). This overgrowth, then, leads frontal over-
connectivity (Courchesne and Pierce, 2005; Coben and Myers,
2008; Rinaldi et al., 2008) which interferes with the normal devel-
opmental trajectory. This disruption, theoretically, then halts the
natural developmental progression in which anterior to pos-
terior brain regions would enhance their synchronization and
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Coben et al. EEG connectivity in autism
specialization of fucntions (Damasio, 1989; Supekar et al., 2009).
This pattern, in fact, was observed in our data above showing
frontal hypercoherence and bilateral temporal hypocoherences
(Coben et al., 2013).
Other data support this hypothesis as well. For example, Mak-
Fan et al. (2012) examined changes in diffusivity with age within
frontal, long distant, longitudinal and interhemispheric tracts
across ages 6–14. Their findings showed that while typically devel-
oping controls change and evolve on such measures children with
autism did not. This suggests that such connectivity difficulty
exist and persist in such children. More specifically, frontal and
local (short neuronal paths) hyperconnectivity has been shown to
be present in autistic samples (Wass, 2011; Li et al., 2014). In addi-
tion, there is other recent data showing hypoconnectivity in long
distance and posterior to anterior or temporal regions in autistics.
Isler et al. (2010) have shown low interhemispheric coherence in
visual evoked potentials in such children. Studies of functional
connectivity related to visuospatial processing and the social-
emotional processing networks have also shown reduced connec-
tivity compared to healthy controls (Ameis et al., 2011; McGrath
et al., 2012; von dem Hagen et al., 2013). Similarly, low functional
connectivity has been shown to relate to poor language processing
in autistic children (Kana et al., 2006). Many of these studies used
3-dimensional imaging techniques such as MRI, fMRI or DTI
(diffusion tensor imaging). Interestingly, EEG/QEEG studies of
coherence have shown similar findings. Coben et al. (2013) have
recently shown findings consistent with frontal hypercoherence
and bilateral posterior-temporal hypocoherences. Similarly, high
frontal coherence has been observed in other studies (Coben and
Padolsky, 2007; Murias et al., 2007). In addition, EEG technol-
ogy has been able to demonstrate long range, anterior to posterior
and temporal hypocoherences (Murias et al., 2007; Coben et al.,
2008). All of these coherence findings have been based on mea-
surements between pairs of electrodes. There is reason to believe
that more advanced statistical approaches to EEG coherence may
provide more detailed and accurate information.
PAIRWISE vs. MULTIVARIATE COHERENCE ESTIMATES
Traditionally and historically EEG coherence estimates have
arisen from cross correlations between pairs of electrodes (Bendat
and Piersol, 1980). Such a calculation is often performed within
a given frequency range and is normalized for amplitude or mag-
nitude. As such the following equation serves as the operational
definition:
τ2
xy(f)=Gxy (f)2
Gxx(f)Gyy (f)(1)
Where: Gxy(f) =cross power spectral density and
Gxx(f)and Gyy(f) =auto power spectral densities
The final normalized coherence value is given by Equation (2):
τ2
xy(f)=r2
xy +q2
xy
GxxGyy
(2)
Where: r2
xy =real cospectrum and q2
xy =imaginary quadspectra
Gxx(f)and Gyy(f) =as in Equation (1)
Phase: 159.1549 tan 1(q/r)/fc
Where: rand q=as in Eq.2; fc =center frequency of filter
For a more detailed explanation or discussion of these please
see Otnes and Enochson (1972) and Thatcher et al. (1986). These
concepts have been used and applied commonly. In fact, a search
in Google Scholar for “EEG coherence pairs” revealed more than
14,500 citations. While this approach has been commonly used
in the past, there are certain limitations in its application and
accuracy. First, there is a confound in pairwise coherence mea-
surements, namely the notion of electrode distance. It has been
observed that the further the distance between electrodes the
lower their coherence value will be regardless of their functional
connectivity, with distances as long as at least 5cm. (Nunez,
1994; Nunez and Srivinasan, 2006; Thatcher et al., 2008). Pairwise
coherence measures for nearby electrodes are biased by volume
conduction, to a degree that varies as a function of inter-electrode
distance such that physically closer pairs manifest higher coher-
ence values. While statistical corrections have been offered for
these concerns (Nunez et al., 1997; Barry et al., 2005), multivariate
approaches that may eliminate this problem should be desired.
Other reasons for concern include a vast array of possible
comparisons (171 comparisons in one frequency band), and that
many of these pairs do not correspond to known neuronal path-
ways. Lastly, pairwise coherence estimates are not precise in their
anatomical locations as there is a presumption of a two dimen-
sional and not a 3-dimensional space (Black et al., 2008). It
has further been observed that multivariate strategies to assess
coherence metrics are more accurate and effective than their pair-
wise counterparts (Kus et al., 2004; Barry et al., 2005; Pollonini
et al., 2010). For example, Duffy and Als (2012) used principal
components analysis of coherences (multivariate approach) and
demonstrated the ability to distinguish between children with
autism and neurotypical controls.
MULTIVARIATE APPROACHES TO COHERENCE ANALYSIS
Multivariate, advanced statistics models, have rarely been applied
to the issue of coherence in the autistic brain. With these new
advances in analytic methods it is hoped that we will come closer
to understanding these dynamic phenomena. Hudspeth (1994)
was one of the first to investigate a multifactorial representation
of EEG covariance. He and his students obtained multichannel
EEG data and computed all combinations and similarities and
differences among the waveforms to produce a triangular cor-
relation matrix for each subject. The correlation matrices were
then factored with principal components analysis to obtain three
eigenvectors and the weighting coefficients required to project
each of the waveforms into a 3-dimensional geometric repre-
sentation of the cortical surface of the brain. When processed
in this way, this integration of factored data reduces the redun-
dancy in the EEG waveforms and patterns and correspond to
known neural network pathways. This is the predecessor of Duffy
and Als (2012) with enhanced complexity. The first three prin-
ciple components are summed to create a 3-dimensional rep-
resentation of these multivariate coherences. When EEG data is
represented in this way, the resulting eigenimages reveal similar-
ities and differences across systems in the brain often grouped
together by cortical function or neuronal systems. Deviations
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Coben et al. EEG connectivity in autism
from these expected relationships points to dysfunctional aspects
of coherence. EEG data is gathered based on the classic 10–20
international system/electrode configuration (Jasper, 1958). In
this system of analysis, these points in space are redrawn in
3-dimensional space based on each locations’ multidimensional
relationship with all other locations based on horizontal, sagittal
and coronal views. As such, connectivity patterns are determined
by the inter-relationships among all combinations of inputs and
are thus considered multivariate or multi-source in nature.
A clinical example of this is now presented below in Figure 1.
This is based on an EEG recording performed with a 12 year old
girl diagnosed with autism with her eyes open and fixed on a
spot directly in front of her. Her most prominent clinical feature
included very limited social skills. The EEG data was consistent
with a mu rhythm (Kuhlman, 1978)thatdoesnotsuppressto
movement or observation of social scenes (Oberman et al., 2007)
and is, thus, considered indicative of mirror neuron dysfunc-
tion (Oberman et al., 2005). This system of coherence assessment
was created by Hudspeth (2006) and is contained within the
NeuroRep QEEG Software system. The method of calculation
has been described above as these eigen images can be viewed
as an image in 3-dimensional space representing the functional
proximity or coherence among the various electrodes based on
the 10/20 International EEG recording system (Niedermeyer and
Lopes da Silva, 2004). As such, electrode positions that are closer
in proximity reflect greater hypercoherences and electrodes that
are further apparent are indicative of greater hypocoherences. As
may be seen in Figure 1 this analysis reveals a pattern of mixed
hypo and hypercoherences with prefrontal and parietal-posterior
temporal regions being hyperconnected among themselves and
large regions of hypocoherences across much of the right hemi-
sphere but especially from posterior frontal to posterior temporal
regions.
sLORETA FUNCTIONAL CONNECTIVITY
Standardized low-resolution brain electromagnetic tomography
(sLORETA) is a method of probabilistic source estimation of
EEG signals in standardized brain atlas space utilizing a restricted
inverse solution (Pascual-Marqui et al., 1994, 2002). sLORETA
has been used to examine EEG sources in depression (Pizzagalli
et al., 2003), epilepsy (Zumsteg et al., 2006), and evaluating tem-
poral changes associated with differential task specific default net-
work activity (Cannon and Baldwin, 2012). Recently, sLORETA
and fMRI were shown to localize DMN regions with comple-
mentaryaccuracy(Cannon et al., 2011). Recent statistical and
theoretical advances have led to the use of this technology in the
measurement of source coherences (Pascual-Marqui, 2007).
There has been rigorous discourse over the localization accu-
racy of low-resolution electromagnetic tomography (LORETA)
and its evolution toward standardized low-resolution electro-
magnetic tomography (sLORETA) (Pascual-Marqui et al., 1994;
Pascual-Marqui, 2002). The most important issue at hand for
any EEG localization or functional neuroimaging technique is
the fact that none of these methods localize the “true” source,
rather they model the source with probabilistic techniques. This
includes all methods that utilize statistical/mathematical mod-
eling, including functional magnetic resonance imaging (fMRI)
and magnetoencephalography (MEG) (Knyazev, 2013). Thus,
when using sLORETA in this fashion, we do operate under cer-
tain assumptions/restrictions. First, we are restricted to cortical
gray matter; including the hippocampus and the computations
and source estimations are restricted by geometric constraints.
Additionally, in the most basic sense it would be optimal to
evaluate the source estimates provided by sLORETA to an indi-
vidual’s specific MRI scan, thus we utilize a standardized MRI
from the Montreal Neurological Institute with 6340 5 mm3voxels
and with it the potential error (Collins et al., 1994). In the local-
ization of EEG sources, recent works have shown the sLORETA
and LORETA methods to improve and even outperform other
methodologies in accuracy (Grech et al., 2008; SaeidiAsl and
Ahmad, 2013) with the addition of regularization parameters.
Additionally, standardized LORETA is not a modification of the
original LORETA, rather it does not utilize the Laplacian operator,
instead it utilizes standardized current density.
Importantly, for this particular single case study we extrap-
olated CSD for each frequency range to enter into bivariate
procedures to compute the person correlation coefficient for the
mean total relative current source density for each of the ROIs
FIGURE 1 | NeuroRep Multivariate Connectivity analyses showing eigen
images in the horizontal place across delta, theta, alpha, and beta
frequencies. Observable features include; (1) right hemisphere (temporal)
hypocoherences across all frequency bands, (2) hypercoherences in the alpha
band over prefrontal regions, and (3) right parietal-posterior temporal
hypercohences in the theta and alpha frequency bands.
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Coben et al. EEG connectivity in autism
included in this study. For larger sample sizes, each frequency
domain can be analyzed and the results do not correspond to
issues with excessively high correlations in neuroimaging stud-
iesasreportedinVul et al. (2009), rather it appears that task and
subjective mental activity are important to understanding func-
tional coupling that occurs within and between networks in the
human brain (Cannon and Baldwin, 2012). The basis for using
a correlation procedure is that functional relationships between
groups of neurons within the brain can exist, even if the struc-
tural relationships are unknown. We have evaluated the use of
correlations using two neuroimaging methods (sLORETA/fMRI)
with accurate results in the default network (Cannon et al., 2011,
2012). In any experiment utilizing discrete or distributed sources
of the EEG volume conduction is a formidable concern. In short,
volume conduction decreases as a function of distance from a cur-
rent source at zero phase lag; however, if volume conduction is a
problem in any sense then phase lag differences must be near zero
and remain near zero independent of distance (Kauppinen et al.,
1999; Thatcher et al., unpublished manuscript).
The distributed source localization problem and its solution as
computed by sLORETA can be stated as (Pascual-Marqui, 2002;
Liu et al., 2005)
=KJ +c1(3)
Where is an N×1 vector containing the scalp electric poten-
tials measured from NEelectrodes on the scalp, Jis a 3M×
1 vector representing current sources at Mlocations within the
brain volume, with three orthogonal components per location
and cbeing a common reference. Kistheleadfiledmatrixrepre-
senting the system transfer coefficients from each source to each
measuring point (Pascual-Marqui, 2002). Regularization using
a zero-order Tikhonov-Philips cost function permits a unique
solution to Equation (1) (Hansen, 1994)
min
JKJ2+αJ2(4)
Where αis the regularization parameter using the L-curve
method. The source estimation is then derived as
ˆ
J=T(5)
where
T=KT[KKT+αI]1(6)
Substituting (3) into (5) yields
ˆ
J=TKJ =KT[KKT+αI]1KJ =RJ (7)
where Ris the resolution matrix, defined as
R=KT[KKT +αI]1K(8)
The resolution matrix illustrates a map from the authentic source
activity to the estimated activity, with Rbeing an identity matrix.
Thus, the basic functional concept of sLORETA is to normalize
the estimation using a block-by-block inverse of the resolution
matrix using (8)
ˆ
JT
l(Rll)1ˆ
Jl(9)
where ˆ
Jlis a 3 ×1 vector of the source estimate at the lth voxel and
Rll is a 3 ×3 matrixcontaining the lth diagonal block of the reso-
lution matrix. sLORETA was shown to give the best performance
in terms of localization error and ghost sources, with different
noise levels (Grech et al., 2008).
METHODS
Aregionofinterest(ROI)filewiththeMNIcoordinatesforthe
15 seed points for the center voxel within Brodmann Area (BA)
regions was constructed (see Tab l e 1 ). These ROIs were selected
apriori based on their known involvement in the mirror neuron
system and social perceptual networks. Each of the ROI values
consisted of the mean current source density from each ROI seed
Table 1 | ROIs for this study: in the table from left to right are the x, y, and z MNI coordinates for center voxel, Lobe, structural nomenclature
and Brodmann Area.
X-MNI Y-MNI Z-MNI Lobe Structure Brodmann area
50 20 15 Frontal lobe Inferior frontal gyrus 45
30 25 15 Frontal lobe Inferior frontal gyrus 47
45 35 20 Frontal lobe Middle frontal gyrus 46
25 55 5 Frontal lobe Superior frontal gyrus 10
20 45 20 Frontal lobe Superior frontal gyrus 11
40 5 10 Sub-lobar Insula 13
25 75 10 Occipital lobe Cuneus 30
45 20 30 Temporal lobe Fusiform gyrus 20
545 25 Limbic lobe Posterior cingulate 23
0 20 20 Limbic lobe Anterior cingulate 33
20 10 25 Limbic lobe Parahippocampal gyrus 28
10 50 35 Parietal lobe Precuneus 31
5 30 20 Limbic lobe Anterior cingulate 24
45 55 15 Temporal lobe Fusiform gyrus 37
40 15 30 Temporal lobe Superior temporal gyrus 38
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Coben et al. EEG connectivity in autism
Table 2 | Results for the sLORETA correlation analyses.
Correlations
BA45 BA47 BA46 BA10 BA11 BA13 BA30 BA20 BA23 BA33 BA28 BA31 BA24 BA37 BA38
BA45 Pearson correlation 1 0.584 0.940 0.381 0.358 0.977*0.553 0.922 0.782 0.547 0.712 0.927 0.531 0.802 0.607
Sig. (2-tailed) 0.416 0.060 0.619 0.642 0.023 0.447 0.078 0.218 0.453 0.288 0.073 0.469 0.198 0.393
N4444 44444444444
BA47 Pearson correlation 0.584 1 0.817 0.968*0.967*0.413 0.909 0.728 0.910 0.883 0.976*0.804 0.889 0.739 0.993**
Sig. (2-tailed) 0.416 0.183 0.032 0.033 0.587 0.091 0.272 0.090 0.117 0.024 0.196 0.111 0.261 0.007
N4444 44444444444
BA46 Pearson correlation 0.940 0.817 1 0.670 0.644 0.848 0.723 0.918 0.896 0.783 0.886 0.962*0.773 0.823 0.820
Sig. (2-tailed) 0.060 0.183 0.330 0.356 0.152 0.277 0.082 0.104 0.217 0.114 0.038 0.227 0.177 0.180
N4444 44444444444
BA10 Pearson correlation 0.381 0.968*0.670 1 0.994** 0.185 0.829 0.533 0.781 0.896 0.891 0.630 0.906 0.559 0.941
Sig. (2-tailed) 0.619 0.032 0.330 0.006 0.815 0.171 0.467 0.219 0.104 0.109 0.370 0.094 0.441 0.059
N4444 44444444444
BA11 Pearson correlation 0.358 0.967*0.644 0.994** 1 0.167 0.871 0.546 0.800 0.845 0.898 0.633 0.858 0.597 0.951*
Sig. (2-tailed) 0.642 0.033 0.356 0.006 0.833 0.129 0.454 0.200 0.155 0.102 0.367 0.142 0.403 0.049
N4444 44444444444
BA13 Pearson correlation 0.977*0.413 0.848 0.185 0.167 1 0.434 0.882 0.678 0.361 0.572 0.858 0.342 0.760 0.450
Sig. (2-tailed) 0.023 0.587 0.152 0.815 0.833 0.566 0.118 0.322 0.639 0.428 0.142 0.658 0.240 0.550
N4444 44444444444
BA30 Pearson correlation 0.553 0.909 0.723 0.829 0.871 0.434 1 0.806 0.951*0.609 0.946 0.826 0.619 0.890 0.952*
Sig. (2-tailed) 0.447 0.091 0.277 0.171 0.129 0.566 0.194 0.049 0.391 0.054 0.174 0.381 0.110 0.048
N4444 44444444444
BA20 Pearson correlation 0.922 0.728 0.918 0.533 0.546 0.882 0.806 1 0.937 0.522 0.858 0.989*0.515 0.970*0.779
Sig. (2-tailed) 0.078 0.272 0.082 0.467 0.454 0.118 0.194 0.063 0.478 0.142 0.011 0.485 0.030 0.221
N4444 44444444444
BA23 Pearson correlation 0.782 0.910 0.896 0.781 0.800 0.678 0.951*0.937 1 0.685 0.978*0.958*0.686 0.951*0.946
Sig. (2-tailed) 0.218 0.090 0.104 0.219 0.200 0.322 0.049 0.063 0.315 0.022 0.042 0.314 0.049 0.054
N4444 44444444444
BA33 Pearson correlation 0.547 0.883 0.783 0.896 0.845 0.361 0.609 0.522 0.685 1 0.807 0.642 1000** 0.439 0.824
Sig. (2-tailed) 0.453 0.117 0.217 0.104 0.155 0.639 0.391 0.478 0.315 0.193 0.358 0.000 0.561 0.176
N4444 44444444444
BA28 Pearson correlation 0.712 0.976*0.886 0.891 0.898 0.572 0.946 0.858 0.978*0.807 1 0.908 0.810 0.866 0.990*
Sig. (2-tailed) 0.288 0.024 0.114 0.109 0.102 0.428 0.054 0.142 0.022 0.193 0.092 0.190 0.134 0.010
N4444 44444444444
BA31 Pearson correlation 0.927 0.804 0.962*0.630 0.633 0.858 0.826 0.989*0.958*0.642 0.908 1 0.635 0.946 0.839
Sig. (2-tailed) 0.073 0.196 0.038 0.370 0.367 0.142 0.174 0.011 0.042 0.358 0.092 0.365 0.054 0.161
N4444 44444444444
BA24 Pearson correlation 0.531 0.889 0.773 0.906 0.858 0.342 0.619 0.515 0.686 1000** 0.810 0.635 1 0.437 0.830
Sig. (2-tailed) 0.469 0.111 0.227 0.094 0.142 0.658 0.381 0.485 0.314 0.000 0.190 0.365 0.563 0.170
N4444 44444444444
BA37 Pearson correlation 0.802 0.739 0.823 0.559 0.597 0.760 0.890 0.970*0.951*0.439 0.866 0.946 0.437 1 0.807
Sig. (2-tailed) 0.198 0.261 0.177 0.441 0.403 0.240 0.110 0.030 0.049 0.561 0.134 0.054 0.563 0.193
N4444 44444444444
BA38 Pearson correlation 0.607 0.993** 0.820 0.941 0.951*0.450 0.952*0.779 0.946 0.824 0.990*0.839 0.830 0.807 1
Sig. (2-tailed) 0.393 0.007 0.180 0.059 0.049 0.550 0.048 0.221 0.054 0.176 0.010 0.161 0.170 0.193
N4444 44444444444
*Correlation is significant at the 0.05 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
Frontiers in Human Neuroscience www.frontiersin.org February 2014 | Volume 8 | Article 45 |6
Coben et al. EEG connectivity in autism
and one single voxel (its nearest neighbor) for total voxel size
10 mm. The resulting file produced the average current source
density for each frequency domain across multiple EEG segments
for all subjects for each seed (ROI). The CSD data for each fre-
quency band were organized into Microsoft Excel spreadsheets
and then entered into SPSS 19 for analysis. sLORETA images cor-
responding to the estimated neuronal generators of brain activity
within each given frequency range were calculated (Frei et al.,
2001). This procedure resulted in one 3D sLORETA image for
this single subject for each frequency range. We entered each fre-
quency domain into the analysis for an N of 4 (delta 0.5–4.0 Hz;
theta 4–8 Hz; alpha 8–12 Hz, and beta 12–32 Hz). The sequence of
steps involved in generating the sLoreta source coherence image is
presented in Figure 2.
The findings for this same case as described above are pre-
sented in Figure 3. The most apparent findings from this analysis
seem to be regions that are overconnected with each other and
that these regions often involve close neighbors or regions of close
proximity (see Tabl e 2 ). These include most profoundly regions
of the anterior cingulate that are completely (R=1.0) hyper-
connected to each other and not to any other ROI. ROIs in and
around the right frontal lobe (11, 10, 46, 47) also seem to form
a loop of highly connected activity while their connections to
other regions are quite limited. The fusiform gyrus is highly con-
nected to the posterior cingulate and pre-cuneus, but again not to
other ROIs. What is missing is a link between the fusiform gyrus,
superior temporal gyrus, insula and inferior frontal regions that
forms the social perceptual system (Pelphrey et al., 2004). This
important neuronal system appears to be underconnected in this
case.
EFFECTIVE CONNECTIVITY AS MEASURED BY GRANGER
CAUSALITY
One of the critiques of other coherence methods has been that
they are largely based on the concept of correlation or similarity.
Even sLORETA coherence is still the similarity between sources
of EEG activity. An advanced statistical technique for investi-
gated directed causation that uses multiple autoregressive analyses
is Granger causality and it’s related concepts of partial directed
coherences (Seth, 2010). Granger causality analysis (GCA) is a
method for investigating whether one time series can correctly
forecast another (Bressler and Seth, 2010). Granger causality
(GC) is a data-driven approach based on linear regressive mod-
els and requires only a few basic assumptions about the original
data statistics. Recently in neuroscience applications, GC has been
used to explore causal dependencies between brain regions by
investigating directed information flow or causality in the brain. It
uses the error prediction of autoregressive (AR) or multi-variant
autoregressive (MAR) models to estimate if a brain process is a
Granger-cause of another brain process.
METHODS
To perform such an analysis on this same EEG data stream as
used in the two examples above, we utilized the SIFT (Source
FIGURE 2 | Procedure to examine the associations between the center
voxel within a specified Brodmann Area (BA) and its nearest neighbor
(10mm3). Listed in the figure from to p to bottom are the steps used to
process EEG data and create the correlation maps between regions of
interest (ROIS). In short, EEG data must be processed first with careful
attention given to artifact contamination and its potential influence across all
steps of the sLORETA procedures. The next step is to create the sLORETA
files in order to extract the CSD at specified ROIs. Finally, using any statistical
program the correlations between the ROID, or networks of interest can be
contrasted for functional associations.
Frontiers in Human Neuroscience www.frontiersin.org February 2014 | Volume 8 | Article 45 |7
Coben et al. EEG connectivity in autism
FIGURE 3 | Each of the 15 ROIs for this case study are represented in a
different color. The lines indicate significant correlations between the
colored ROI and other regions. The color of the line is the same as the ROI
in relation to its functional connectivity with other ROIs.
Information Flow Toolbox) toolbox from EEGLAB v.12 (Delorme
et al., 2011). A key aspect of SIFT is that it focuses on esti-
mating and visualizing multivariate effective connectivity in the
source domain rather than between scalp electrode signals. This
should allow us to achieve finer spatial localization of the net-
work components while minimizing the challenging signal pro-
cessing confounds produced by broad volume conduction from
“neural” sources to the scalp electrodes. From our eyes open
resting EEG data we have virtually epoched this stream into
1-s segments. Independent Component Analysis was then used
to extract unique, independent components from the data. To
fit multiple component dipoles and determine their locations
DIPFIT toolbox was then applied. Then by investigating the
dipole locations and the components topographical maps, only
good “neural” components that are related to neural process in
the brain have been included for further processing. These data
were then fit into a MAR model using Vieira-Morf algorithm.
For our data the model and after some trials and errors and
model validation process, the MAR model order has been set to
5. In addition, the frequency band of interest has been selected
from 1 to 30 Hz and the most obvious connectivity measure was
Grager-Geweke Causality (GGC).
These methods of operation are summarized in Figure 4.This
takes the EEG data from sensory to source space via indepen-
dent component analysis and dipole localization. This diminishes
the issue of volume conduction (see Astolfi et al., 2007; Akalin
Acar and Makeig, 2013). Once dipole localization has been per-
formed, these data are subjected to MVAR and Granger Causality
(GC) analysis as presented above. Within a reasonable range of
values, changes in model order may show little effect on the spec-
tral density (and by extension coherence) (e.g., see Florian and
Pfurtscheller, 1995). Our model order has been based on Akaike
Information Criterion (AIC) and Bayesian Information Criterion
(BIC) criteria to maximize model effects. Statistically, the criti-
cal issue for GC is the ratio between the number of independent
observations (i.e., samples) and the model complexity (i.e., num-
ber of parameters). If the number of observations is large relative
to the number of parameters then the model order selection cri-
teria are still valid. If the number of observations is small, then
we might run into problems with AIC and other asymptotic
estimators, but there are corrections for that (corrected akaike
information criterion). In our data set (case epoching), we have
plenty of data available and the ratio of observations [total data
samples within a time window (x trials)] to parameters is >40
suggesting that we have a valid model using AIC (Burnham,
2004).
RESULTS
Our findings for this case are presented in Figure 5. This, again
demonstrates regions of over and under-connectivity. There
appear to be several regions of heightened causality whose major
influence is only toward close neighbors. This includes regions
of the prefrontal cortex, anterior cingulate, and bilateral inferior
parietal lobules. In each instance, these regions are somewhat iso-
lated from each other and other important ICs as well. What
is also clear is that there are long connections throughout the
right hemisphere that are largely under-connected. These span as
far away as the cuneus to the inferior frontal gyrus and include
regions of the temporal lobes and underlying areas such as the
fusiform gyrus and superior temporal gyrus.
COMPARISON OF COHERENCE TECHNIQUES
While it has not been shown, a pairwise coherence analysis of
this case has shown very few significant coherence anomalies. The
ones that are present include frontal hypocoherence and bilateral
occipital-temporal hypocoherences. This is the opposite of what
is shown in the multivariate analyses. All forms of multivariate
analysis shown have suggested a combination of local hyperco-
herence and long distance hypocoherence across right frontal to
posterior temporolimbic regions. This, in this case, clearly shows
a difference between pairwise and multivariate estimates.
Comparing these to know structural connectivity was pos-
sible in this case in the form of MR-DTI analysis within this
same system of concern (mirror neuron system). This suggests
the presence of prefrontal and anterior cingulate hyperconnectiv-
ity and dramatic hypoconnectivity from frontal to temporolimbic
regions. Comparing this to the multivariate analyses is interesting
as there is similarity across all of these. The resemblance of these
measures of functional connectivity to the reality of structural
connectivity in this case is seen in its’ greatest detail in multi-
variate measures that localize to source space (sLoreta, SIFT GC).
As such, one limitation of the first method (Hudspeth NREP) is
that it does not source localize activit prior to generating eigenim-
ages of sensory covariances. GC has certain possible advantages
including measuring the degree, directionality of connectivity,
Frontiers in Human Neuroscience www.frontiersin.org February 2014 | Volume 8 | Article 45 |8
Coben et al. EEG connectivity in autism
FIGURE 4 | SIFT/Granger (GGC) causality sequence of processing.
FIGURE 5 | SIFT/Granger (GGC) causality brain image. Levels of greater connectivity are shown with thicker lines and brighter colors. Direction of causality
is indicated by the key in the upper left hand corner. ICs and their localization are listed as part of Table 3.
reciprocal influences and localization to regions that are deeper
than is possible with sLoreta. It should be recalled that these
observations are based on theory and one a single case study.
Clearly, much more research is needed in this area of study.
DISCUSSION
Neuroimaging technologies and research has shown that autism is
largely a disorder of neuronal connectivity. While advanced work
is being done with fMRI, MRI-DTI, SPECT and other forms of
structural and functional connectivity analyses, the use of EEG
for these purposes is of additional great utility. Cantor et al.
(1986) were the first to examine the utility of pairwise coher-
ence measures for depicting connectivity impairments in autism.
Since that time research has shown a combination of mixed over
and under-connectivity that is at the heart of the primary symp-
toms of this multifaceted disorder. Nevertheless, there is reason
Frontiers in Human Neuroscience www.frontiersin.org February 2014 | Volume 8 | Article 45 |9
Coben et al. EEG connectivity in autism
Table 3 | SIFT/GCC maximal values between ICs.
From
To
12358910151819
1 0.57 0.50 0.59 0.21 0.22 0.89 0.14 0.36 0.12
2 0.36 0.49 1.51 0.26 0.10 0.50 0.15 0.11 0.28
3 0.04 1.09 0.15 0.52 0.1 0.28 0.10 0.24 0.80
5 0.85 1.31 0.39 0.13 0.09 0.34 0.31 0.05 0.84
8 0.61 0.51 0.82 0.2 0.38 0.99 0.29 1.04 0.13
9 0.24 0.29 0.24 0.08 0.28 0.48 1.22 0.29 0.17
10 1.35 0.35 0.46 0.19 0.72 0.19 0.38 0.92 0.48
15 0.30 0.26 0.41 0.11 0.34 1.07 0.87 0.30 0.15
18 0.39 0.08 0.74 0.11 1.18 0.26 1.87 0.17 0.29
19 0.40 0.66 2.08 2.39 0.31 0.19 1.32 0.18 0.72
Independent components included: 1 (Brodmann area (BA) 32; Anterior
Cingulate), 2 (BA 10; Middle Frontal Gyrus), 3 (BA 40; Inferior Parietal Lobule),
5 (BA 10; Middle Frontal Gyrus), 8 (BA 37; Fusiform Gyrus), 9 (BA 19; Lingual
Gyrus), 10 (BA 40; Inferior Parietal Lobule), 15 (BA 22; Superior Temporal Gyrus),
18 (BA 18; Cuneus), and 19 (BA 10; Middle Frontal Gyrus).
to believe that these simplistic pairwise measurements under rep-
resent the true and quite complicated picture of connectivity
anomalies in these persons. We have presented three different
forms of multivariate connectivity analysis with increasing levels
of sophistication. These all seem able to capture the complex-
ity of such cases and certainly moreso than pairwise estimates
have. There does appear to be a value in using measures that
localize the source of EEG activity and judge coherence from
these sources. Further, the promise of using MVAR advanced sta-
tistical methods to judge effective connectivity and causation is
exciting.
Clearly,thereismuchworktobedonetofurtherthescientic
underpinnings of these approaches. Future work should extend
these forms of analysis to greater sample sizes of autistic children
and adults to judge their validity and utility. Comparing findings
from autistics to other diagnostic and typically developing sam-
ples will be crucial. Lastly, the true value of any form of assessment
for autistic children may be in it’s applicability to further treat-
ment outcomes for these children. Coben (2013) has shown that
such metrics may be used to engineer more effective treatment
plans than traditional neurofeedback with impressive outcomes
as a result. It is hoped that advancements with such assessment
techniques will further sharpen such treatment successes and
decrease durations of treatment.
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Conflict of Interest Statement: The authors declare that the research was con-
ducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 17 June 2013; accepted: 20 January 2014; published online: 26 February
2014.
Citation: Coben R, Mohammad-Rezazadeh I and Cannon RL (2014) Using quan-
titative and analytic EEG methods in the understanding of connectivity in autism
spectrum disorders: a theory of mixed over- and under-connectivity. Front. Hum.
Neuro sci. 8:45. doi: 10.3389/fnhum.2014.00045
This article was submitted to the journal Frontiers in Human Neuroscience.
Copyright © 2014 Coben, Mohammad-Rezazadeh and Cannon. This is an open-
access article distributed under the terms of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction in other forums is permitted, provided
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Frontiers in Human Neuroscience www.frontiersin.org February 2014 | Volume 8 | Article 45 |12
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Autism spectrum disorders (ASD) are a group of pervasive developmental disorders impacting communication, social skills, behavioral interests, and daily functioning. With rates rising to as high as 1 in 80 in the Unites States alone, their impact on children, families, and our society is immense. Despite this, treatment for these conditions is poorly understood, and most have limited empirical support. While ASD can be conceptualized as having system-wide effects in the human body, many of the primary symptoms we associate with these children are clearly related to dysfunction of the central nervous system. While certain brain regions have been shown susceptibility, connectivity across regions of the brain appears to be the primary dysfunction leading to symptoms and developmental delays in these children. Any successful treatment should be able to demonstrate the ability to change and improve these primary effects. Neurofeedback is currently being studied as a noninvasive intervention with the potential to do just that. Empirical evidence is emerging, demonstrating this as a potentially effective and safe form of intervention for ASD. There is also preliminary data suggesting that this intervention may facilitate therapeutic enhancements in brain functioning and connectivity and that the results of treatment may endure even after the therapy has ended. Clearly, more research is needed to demonstrate the efficacy of this intervention, mechanisms that underlie these changes, and studies looking at the duration of enduring effects.
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Autistic spectrum disorders (ASD) are a heterogeneous group of pervasive developmental disorders including autistic disorder, Rett’s disorder, childhood disintegrative disorder, pervasive developmental disorder-not otherwise specified (PDD-NOS), and Asperger’s disorder. Children with ASD demonstrate impairment in social interaction, verbal and nonverbal communication, and behaviors or interests (DSM-IV-TR; APA 2000). ASD may be comorbid with sensory integration difficulties, mental retardation, or seizure disorders. Children with ASD may have severe sensitivity to sounds, textures, tastes, and smells. Cognitive deficits are often associated with impaired communication skills (National Institute of Mental Health; NIMH 2006). Repetitive stereotyped behaviors, perseveration, and obsessionality, common in ASD, are associated with executive deficits. Executive dysfunction in inhibitory control and set shifting have been attributed to ASD (Schmitz et al. 2006). Seizure disorders may occur in one out of four children with ASD, frequently beginning in early childhood or adolescence (National Institute of Mental Health; NIMH 2006).
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The ideas underlying the quantitative localization of the sources of the EEG review within the brain along with the current and emerging approaches to the problem. The ideas mentioned consist of distributed and dipolar source models and head models ranging from the spherical to the more realistic based on the boundary and finite elements. The forward and inverse problems in electroencephalography will debate. The inverse problem has non-uniqueness property in nature. More precisely, different combinations of sources can produce similar potential fields occur on the head. In contrast, the forward problem does have a unique solution. The forward problem calculates the potential field at the scalp from known source locations, source strengths and conductivity in the head, and it can be used to solve the inverse problem. In the final part of this paper, we compare the performance of three well-known EEG source localization techniques which applied to the underdetermined (distributed) source localization of the inverse problem. These techniques consist of LORETA, WMN and MN, which comparing by testing localization error.
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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.