The Relative Efﬁcacy of Connectivity Guided and Symptom Based
EEG Biofeedback for Autistic Disorders
Robert Coben ÆThomas E. Myers
Published online: 1 August 2009
!Springer Science+Business Media, LLC 2009
Abstract Autism is a neurodevelopmental disorder
characterized by deﬁcits in communication, social inter-
action, and a limited range of interests with repetitive
stereotypical behavior. Various abnormalities have been
documented in the brains of individuals with autism, both
anatomically and functionally. The connectivity theory of
autism is a recently developed theory of the neurobio-
logical cause of autisic symptoms. Different patterns of
hyper- and hypo-connectivity have been identiﬁed with the
use of quantitative electroencephalogray (QEEG), which
may be amenable to neurofeedback. In this study, we
compared the results of two published controlled studies
examining the efﬁcacy of neurofeedback in the treatment
of autism. Speciﬁcally, we examined whether a symptom
based approach or an assessment/connectivity guided based
approach was more effective. Although both methods
demonstrated signiﬁcant improvement in symptoms of
autism, connectivity guided neurofeedback demonstrated
greater reduction on various subscales of the Autism
Treatment Evaluation Checklist (ATEC). Furthermore,
when individuals were matched for severity of symptoms,
the amount of change per session was signiﬁcantly higher
in the Coben and Padolsky (J Neurother 11:5–23, 2007)
study for all ﬁve measures of the ATEC. Our ﬁndings
suggest that an approach guided by QEEG based connec-
tivity assessment may be more efﬁcacious in the treatment
of autism. This permits the targeting and amelioration of
abnormal connectivity patterns in the brains of people who
Keywords Autism !Quantitative EEG !Neurofeedback !
Autistic spectrum disorders (ASD) are a group of pervasive
developmental disabilities characterized by deﬁcits in
communication, social interaction and restricted repetitive
behavior. The spectrum includes Autistic Disorder, Rett’s
Disorder, Childhood Disintegrative Disorder, Asperger
Disorder, and Pevasive Developmental Disorder Not
Otherwise Speciﬁed (PDD-NOS) (Tidmarsh and Volkmar
2003). The prevalence of these disorders appears to be on
the rise, with studies indicating that about 1 out of 150
children will be diagnosed with an ASD (Center for Dis-
ease Control and Prevention 2006).
Autistic Disorder is characterized by impaired social
interaction, delay or total lack of spoken language and
communication, as well as repetitive stereotyped behaviors,
interests or activities (APA 2000;Diagnostic and Statistical
Manual of Mental Disorders, Fourth Edition Text Revision;
DSM-IV-TR). Asperger’s Disorder is often associated with
high cognitive function, literal pedantic speech, difﬁculty
comprehending implied meaning, problems with ﬂuid
movement, and inappropriate social interaction. PDD-NOS
refers to the category of deﬁcits in language and social skills
that do not meet the criteria for other disorders. In contrast,
Childhood Disintegrative Disorder and Rett’s Disorder are
characterized by intervals of normal early development
followed by loss of previously acquired skills. Although
communication and social skill deﬁcits are common among
these conditions, there remains a substantial degree of vari-
ability in terms of onset and severity of symptomatology
within the Autistic Spectrum of Disorders (Attwood 1998;
R. Coben (&)!T. E. Myers
Neurorehabilitation & Neuropsychological Services, 1035 Park
Blvd., Suite 2B, Massapequa Park, NY 11762, USA
e-mail: email@example.com; firstname.lastname@example.org
Appl Psychophysiol Biofeedback (2010) 35:13–23
Hamilton 2000; McCandless 2005; Sicile-Kira 2004; Siegel
The exact cause of autism is unknown, though many
studies have noted differences in the structure and function
of the brains of individuals with autism. Abnormal brain
morphology in autism was ﬁrst noted when Kanner (1943)
observed an enlargement of the heads of children diagnosed
with autism. These anecdotal ﬁndings were then corrobo-
rated by more controlled studies which showed that mac-
rocephaly was present in approximately 20% of individuals
with autism (Bailey et al. 1993; Courchesne et al. 2003;
Davidovitch et al. 1996) and is supported by neuroimaging
studies (Courchesne et al. 2001; Filipek et al. 1992; Piven
et al. 1996) as well as ﬁndings of increased brain weight
(Bailey et al. 1998; Courchesne et al. 1999). Speciﬁcally,
there may be an increase in temporal, parietal, and occipital
lobe volume, but not the frontal lobe (Piven et al. 1996).
This pattern may be interpreted as either abnormal posterior
brain enlargement, or a frontal lobe abnormality in which
the frontal lobes lag behind the rest of the brain in their
development. While numerous studies suggest there is an
increase in total brain volume in Autism, this anomaly does
not appear to be present at birth. Rather, during the ﬁrst
2 years of life there is overgrowth, followed by a decrease
in the normal growth process (Courchesne et al. 2001,
Courchesne 2004). It has been suggested that the reason for
this abnormal growth process is that there may be dys-
function in the normal pruning process (Frith 2003). In
addition to the early synaptogenesis in childhood, the
pruning process eliminates faulty connections, which are
not strengthened through long term potentiation.
Some studies have found increased cell packing density
and reduced cell size in various brain regions (Kemper and
Bauman 1998), while other studies have shown a decrease
in Purkinje cell density in the cerebellum of autistic cases
(Kemper and Bauman 1998; Bailey et al. 1998). In an
attempt to localize cerebral dysfunction in Autism, various
functional neuroimaging studies have been conducted.
Schmitz et al. (2006) found that individuals with ASD had
signiﬁcantly increased brain activity associated with the
left inferior and orbital frontal gyrus (associated with motor
inhibition), left insula (regulating interference inhibition),
and parietal lobes (required for set shifting). Increased
frontal gray matter density in areas of increased functional
activation was also observed. Increased frontal metabolite
levels have been associated with obsessional behavior in
Asperger Syndrome (Murphy et al. 2002), and have been
reported in the amygdala-hippocampal regions in ASD
(Page et al. 2006). Others have found signiﬁcant bilateral
temporal hypoperfusion in the superior temporal gyrus and
superior temporal sulcus in children with Autism (Boddaert
et al. 2002).
Structural changes linked to Autism have indicated a
signiﬁcant reduction in total grey matter volume, particu-
larly within fronto-striatal and parietal networks, along
with increased cerebral spinal ﬂuid (CSF) volume, and
reduced white matter in the cerebellum, left internal cap-
sule, and fornices (McAlonan et al. 2005). MRI based
morphometric analysis found that overall, whole brain
volume was moderately increased (Herbert et al. 2003).
Factor analysis, however, showed signiﬁcant heterogeneity
of brain differences in autism and demonstrated the difﬁ-
culty in looking for a speciﬁc brain area to be implicated in
the disorder. Rather, it has been suggested that their may be
abnormalities in the pathways, and that pervasive core
processing deﬁcits, impaired complex information pro-
cessing, or weak central coherence in Autism may be
associated with abnormal white matter.
Deﬁcits in cross-modal information processing and
corticocortical connections may be linked to behavior and
communication impairment in Autism (Herbert et al.
2004). Cell minicolumn anomalies of the cerebral cortex
representing connectivity linking afferent, efferent, and
interneuronal connections have been reported in Autism
(Casanova et al. 2002), as well as reduced white matter
concentration (Chung et al. 2004), including regions of the
corpus callosum (Piven et al. 1997). The corpus callosum is
the most robust white matter ﬁber tract in the brain and
connects most of the two cerebral hemispheres. Thus, it
plays a major role in interhemispheric neural connectivity.
Several studies now have found this pathway to be aberrant
in Autism (Alexander et al. 2007; Boger-Megiddo et al.
2006; Chung et al. 2004; Courchesne et al. 1993; Vidal
et al. 2006).
The aforementioned research and multiple brain regions
implicated in Autism provide support for cerebral con-
nectivity deﬁcits in Autism. In the 1980s, Uta Frith sug-
gested that autistic behaviors may be explained by the
individual’s lack of ability to integrate information due to
an obsessive focus on details. She attributed this to a lack
of communication between frontal brain areas which would
typically integrate the information with more posterior
areas (Wickelgren 2005). Since that time, much research
has been conducted in support of this connectivity deﬁcit
Research utilizing fMRI has reported a pattern of un-
derconnectivity in Autism (Just et al. 2007). A decreased
degree of synchronization between frontal and parietal
areas of activation was noted during an executive function
task, suggesting that cortical underconnectivity is associ-
ated with a deﬁcit in the neural and cognitive integration of
information. Others have found anomalies in connectivity
(associated with inter-regional grey matter correlations) of
limbic-striatal social brain systems in Autism (McAlonan
14 Appl Psychophysiol Biofeedback (2010) 35:13–23
et al. 2005). Functional underconnectivity associated with
reduced cortical activation and synchronization during a
sentence comprehension task (Just et al. 2004), and even
during the resting brain state has been found in Autism
(Cherkassky et al. 2006).
Autism has also been classiﬁed as a disorder of arousal-
modulating systems associated with atypically increased
functional connectivity, in addition to areas of undercon-
nectivity. Research utilizing fMRI bold oxygen level
dependent (BOLD) signal during simple visuomotor
coordination has indicated greater thalamocortical func-
tional connectivity in Autism. Excessive connectivity was
noted in the left insula, right postcentral, and middle frontal
regions. Increased thalamocortical functional connectivity
may be associated with excessive synaptic generation and
reduced pruning which may be linked to brain enlargement
in Autism (Mizuno et al. 2006).
Courchesne and Pierce (2005) described a pattern of
over-connectivity (hyperconnectivity) within the frontal
lobe, with long-distance disconnection (hypoconnectivity)
between the frontal lobe and other brain regions associated
with ASD. Reduction of long-distance cortical to cortical
reciprocal activity and coupling disrupts the integration of
information from emotional, language, sensory, and auto-
nomic systems (Courchesne and Pierce 2005).
By dividing cerebral white matter with a white matter
parcellation technique, Herbert et al. (2004) found that the
increase in white matter was in the radiate (outer) zones of
all cerebral lobes and longer myelinating regions. In con-
trast, inner zone white matter volumes showed no difference
compared to a control group. Since deeper myelination
occurs earlier on, the authors interpreted this ﬁnding as
supporting a postnatal disturbance which disrupts primarily
cortico-cortical connections. In a review of neuropatho-
logical ﬁndings in Autism, Herbert (2005) indicated that
neuroinﬂammation is present in Autism and also contributes
to the increased cranial volume. The overall increase in
volume may result in dysfunction of the ability to integrate
information between different parts of the brain. Herbert
further speculated that disconnectivity may result in speciﬁc
dysfunction, not just pervasive, nonspeciﬁc deﬁcits.
Therefore, domains most likely to be affected by the
inﬂammatory response are those which require more coor-
dination and communication between brain areas, such as
language and executive functioning.
The connectivity theory of autism has become an
empirically supported theory describing the neurobiologi-
cal basis of Autism, with evidence suggesting that it is an
overgrowth of white matter during the ﬁrst 2 years of life,
followed by a retardation of growth thereafter which leads
to disordered connectivity (Hughes 2007). Because EEG
measures electrical activity across the brain with high
temporal resolution, it lends itself well to the investigation
of connectivity through EEG coherence measurement.
Computerized EEG analyses have indicated that chil-
dren with Autism have signiﬁcantly greater coherence
between hemispheres in the beta band than typically
developing children. They also have been found to have
higher coherence in the alpha band than normal controls,
and less inter- and intrahemispheric asymmetry than either
children who are developing typically or who have mental
handicaps (Cantor et al. 1986).
Murias et al. (2007) assessed functional connectivity
with EEG coherence during an eyes closed resting state.
Relative to controls, adults with ASD showed long range
alpha band coherence reductions in frontal-occipital and
fronal-parietal areas. The alpha band represents more
globally dominant functions, which are more dependent on
corticocortical and callosal ﬁbers (Nunez 1995; Nunez and
Srinivasan 2006). Adults with ASD also showed increased
coherence at temporal recording sites between 3–6 Hz,
reﬂecting intact locally dominant cortical activity. These
ﬁndings support the hypothesis of a weak connection
between frontal and other areas.
Coben et al. (2008), using quantitative EEG (QEEG),
found that children who were autistic showed decreased
intrahemispheric coherences across short-medium as well
as long inter-electrode distances within delta and theta
bands. In addition, there were reduced interhemispheric
coherences in the alpha band in temporal regions, and
reduced interhemispheric coherences in beta in central,
parietal, and occipital regions. Greater relative theta was
especially prevalent in the right posterior region, while
lower beta was noted across the right hemisphere, espe-
cially over the right frontal region.
At least two critical issues result from the aforemen-
tioned ﬁndings. First, through scientiﬁc investigation, we
must learn how to prevent these problems from taking
place. Second, we must improve the evaluation and treat-
ment of connectivity disturbances after they occur. The
EEG appears to be good candidate for the evaluation of
neural connectivity in Autism, based on coherence analy-
ses. Speciﬁcally, we propose that EEG biofeedback can be
utilized to remedy aberrant coherence patterns.
Although there have been only a few studies investigating
the use of neurofeedback in the treatment of autism, there is
ample evidence documenting the efﬁcacy of neurofeedback
for various other neuropsychological disorders, including
ADHD (Fuchs et al. 2003; Heinrich et al. 2004; Lubar and
Lubar 1984), epilepsy (Lubar et al. 1981; Monderer et al.
2002; Sterman 2000; Sterman and Friar 1972; Walker and
Kozlowski 2005), traumatic brain injury (TBI) (Byers 1995;
Hoffman et al. 1996; Keller 2001; Schoenberger et al. 2001;
Walker et al. 2002), anxiety disorders (Moore 2000), and
Appl Psychophysiol Biofeedback (2010) 35:13–23 15
substance abuse disorders (Trudeau 2005). Furthermore,
neurofeedback (NF) appears to have long lasting effects,
something that pharmacological therapies often lack (Ayers
1995). The majority of these studies have utilized symptom
based neurofeedback protocols, which has been the tradi-
tional form of treatment.
Quantitative electroencephalograph guided neurofeed-
back studies have recently demonstrated efﬁcacy for treating
obsessive-compulsive disorder (Hammond 2003), behav-
ioral difﬁculties found in children who have been abused
and/or neglected (Huang-Storms et al. 2007), post-traumatic
symptoms (Walker et al. 2002) of traumatic brain injury; as
well as learning disabilities (Thornton and Carmody 2005).
These accumulated studies are adding evidence in support of
the efﬁcacy of QEEG guided neurofeedback protocols. We
have been unable to ﬁnd any published studies directly
comparing the efﬁcacy of symptom based neurofeedback
and QEEG guided neurofeedback. Although there has been
some research documenting the efﬁcacy of neurofeedback in
ASD, these two distinct approaches have not been compared
in this population.
Cowan and Markham (1994) conducted the ﬁrst case
study of neurofeedback with Autism. QEEG analysis,
performed on an 8 year old girl diagnosed with high
functioning Autism during an eyes open and at rest state,
indicated an abnormally high amount of alpha (8–10 Hz)
and theta (4–8 Hz) activity predominately in the parietal
and occipital lobes. Based on these results, a neurofeed-
back protocol was designed to suppress the ratio of theta
and alpha (4–10 Hz) to beta (16–20). Following 21 ses-
sions, the child showed increased sustained attention,
decreased autistic behaviors (inappropriate giggling, spin-
ning), and improved socialization based on parent and
teacher reports. Attention improved substantially, as
assessed by the Test of Variables of Attention (TOVA),
and this was maintained at a 2 year follow-up.
Two controlled studies have been published that have
investigated group differences in the efﬁcacy of neuro-
feedback for autistic spectrum disorders. Jarusiewicz (2002)
administered between 20 and 69 sessions of neurofeedback
to a group of 12 autistic children. They were matched for
age, sex, and disorder severity to a control group of autistic
children. Treatment efﬁcacy was determined by scores on
the Autism Treatment Evaluation Checklist (ATEC). Her
neurofeedback protocols were selected based on the indi-
vidual child’s symptoms and were determined by the Oth-
mer Assessment (1997). Initial protocols provided reward
for activity at site C4, referenced to the contralateral ear, in
the 10–13 Hz range. Fifty-four percent of the sessions uti-
lized this protocol. Children with vocalization problems had
an F7 electrode placement with right ear reference. Rewards
were for 15–18 Hz and inhibits were at 2–7 and 22–30 Hz.
If no signs of overstimulation were shown after 5 min,
additional 5 min increments were added, up to a maximum
of 30 min. This protocol accounted for 15% of sessions.
For children who required help with socialization and
communication, a bipolar F3-F4 electrode placement was
utilized with 7–10 and 14.5–17.5 Hz rewards and 2–7 and
22–30 Hz inhibits. This protocol was used 12% of the time
and was discontinued if inappropriate laughing or giggling
were noted in the child. Children with emotional instability
were given a T3–T4 placement, beginning with 9–12 Hz
rewarded and 2–7 and 22–30 Hz inhibited. Protocol fre-
quencies were increased or decreased depending on whe-
ther children were sad, anxious, or hyperactive. Training
sessions were generally given one to three times per week,
with two sessions being the most common.
Neurofeedback resulted in all children showing
improvement, as based on ATEC scores, with signiﬁcant
improvements noted in 8–56%, or an average 26% reduc-
tion of symptoms. Speciﬁcally, improvement was noted in
the areas of sociability (33%), speech/language/commu-
nication (29%), health (26%), and sensory/cognitive
awareness (17%). These results stand in contrast to a 3%
overall reduction in the control group. Furthermore, parents
reported behavioral improvements in socialization, vocal-
ization, anxiety, schoolwork, tantrums, and sleep. Only
minimal changes were noted in the control group.
In contrast to the study by Jarusiewicz, Coben and
Padolsky (2007) utilized assessment guided neurofeedback
on 37 patients over the course of 20 sessions, compared to a
wait-list control group. The training protocol was based on
several measures including ratings scales, neuropsycho-
logical data, several neurobehavioral rating scales, and
primarily QEEG. The focus was on reducing hypercon-
nectivity, principally in posterior-frontal to anterior-tem-
poral regions, and was based on regions of maximal
hyperconnectivity. Hyperconnectivity was chosen as an
early training goal based on our perception of its preva-
lence and priority within our connectivity theory of autism
(Coben et al. 2008). It was also shown to be effective in
previous studies (see Coben and Padolsky 2007, for a
review). For example, Coben (2007) reported a case study
of a boy who was autistic who showed a 45% reduction in
autistic symptoms, improvement on various neuropsycho-
logical measures, and reductions in connectivity in theta,
alpha, and beta bands. This example shows how protocols
are designed based on this connectivity approach. This
protocol remained constant throughout all 20 sessions, and
were conducted twice per week. Eighty-nine percent of the
37 patients had sequential (bipolar) versus unipolar mon-
tages. Ninety-four percent of the sequential (bipolar)
montages included frontal or temporal electrode sites
including F8-F7, Ft8-Ft7, T4-T3, or F7-F8. In one case,
F6-F5 was applied and in the other F4-F3. Reward bands
ranged anywhere from 5–16 Hz. A delta inhibit protocol as
16 Appl Psychophysiol Biofeedback (2010) 35:13–23
low as 1–2 Hz, ranging to as high as 6 Hz, was utilized for
92% of the patients. In 100% of patients, a high beta inhibit
protocol was applied ranging from 18–50 Hz with the
greatest overlap at 18–30 Hz. A third inhibit ranging within
a 7–14 Hz range was utilized for 68% of the patients.
Following neurofeedback, parents reported symptom
improvement in 89% of the experimental group, compared
to the control group in which 83% of subjects remained
unchanged. Neuropsychological improvement was noted in
the areas of attention, visual perceptual functioning, lan-
guage, and executive functioning. We (Coben and Padolsky
2007) found a 40% reduction in core ASD symptoms as
rated by the ATEC total scores, along with decreased hy-
perconnectivity in 76% of the experimental group as
assessed by follow-up QEEG. These results suggest that
decreased hyperconnectivity results in improvement in
treatment outcomes measures in autism.
In this study, we hypothesized that QEEG connectivity
guided neurofeedback, would have greater relative efﬁcacy
when compared to symptom based neurofeedback. Spe-
ciﬁcally, we expect to see greater improvement in symp-
tom severity, over the course of fewer sessions when
comparing these two approaches. Additionally, because
there were differences in both the number of participants
and severity of symptoms of autism between these studies,
we predicted that symptom severity would not impact the
greater efﬁcacy seen with QEEG connectivity guided
Methods of Comparison
In order to investigate whether there are any differences in
the effectiveness of QEEG connectivity guided and
symptom based neurofeedback, we compared the results of
Jarusiewicz’s (2002) study to those of Coben and Padolsky
(2007). Both of these studies utilized neurofeedback with
the methods of the two studies described above. The main
difference between the two studies is that Coben and
Padolsky (2007) utilized a QEEG assessment guided neu-
rofeedback protocol based on abnormal connectivity, while
Jarusiewicz (2002) administered neurofeedback protocols
based on the individual child’s symptoms as determined by
the Othmer Assessment (1997).
Because the sample size of Coben and Padolsky’s study
was larger (n=37) and displayed less severe autistic
symptomotology, separate analyses were conducted with
and without equal sample sizes. To equate the groups in
terms of sample size and symptom severity (as measured by
ATEC scores), 25 children from Coben and Padolsky’s study
with the lowest scores on the ATEC were removed. An
independent groups t-test was conducted to examine group
differences in both pre- and post-treatment ATEC subtest
scores (Speech/Language Communication, Sociability,
Sensory/Cognitive Awareness, Health/Physical/Behavior)
and total scores. The difference in scores (pre-treatment
score minus post-treatment score) and percent change scores
(post-treatment score divided by pre-treatment score) were
also examined between the two groups. A regression analysis
was then performed to determine if age predicted outcome.
Another independent groups t-test was performed to exam-
ine differences in the amount of change that occurred per
session, deﬁned as the amount of change that occurred
pre-post neurofeedback divided by the number of sessions of
As noted in their original papers, both studies showed
signiﬁcant improvement in symptoms of autism as mea-
sured by ATEC scores. When comparing the two study
groups there were no signiﬁcant differences in race or
gender. Data on handedness, IQ and medication were
unavailable for Jarusiewicz’s (2002) group. While Jar-
usiewicz’s group was signiﬁcantly older statistically [t
(22) =-2.743, p=.012], this difference is not believed
to be clinically signiﬁcant (less than a 3 year difference
between groups). When sample sizes were not equated,
signiﬁcant differences were found between the two data
sets in the total score at post-treatment [t(40) =3.028,
p=.003] and percent change (see Table 1) that occurred
between groups [t(df =32.8 with equal variances not
assumed) =-2.122, p=.041]. However, the subjects in
Jarusiewicz’s (2002) study were signiﬁcantly more
impaired at pre-treatment as well [t(41) =2.480,
p=.017]. Therefore, differences between groups were
further examined with equal sample sizes, which permitted
comparisons considering equivalent severities by removing
25 subjects with the most severe symptoms of autism.
On the ATEC, there were no signiﬁcant group differences
in any of the pretreatment scores (see Table 2). Signiﬁcant
differences were found on the Sensory/Cognitive Awareness
scale when comparing post-treatment scores [t(22) =
3.068, p=.006], difference scores [t(22) =-2.249,
p=.035] (see Fig. 1), and percent change scores [t(22) =
-2.442, p=.023] (see Table 3for a listing of all percent
change scores). Although there were no signiﬁcant differ-
ences in post-treatment Health/Physical/Behavior subtest
scores, the percent change score was signiﬁcant [t(22) =
-2.099, p=.047]. Signiﬁcant differences were also found
between groups when comparing both the total ATEC dif-
ference scores [t(22) =-3.032, p=.006] (see Fig. 2) as
well as percent change scores for the Sensory/Cognitive
Awareness [t(22) =-2.442, p=.023], Health/Physical/
Appl Psychophysiol Biofeedback (2010) 35:13–23 17
Behavior [t(22) =-2.099, p=.047], and total scores
[t(22) =-2.853, p=.009] (see Fig. 3). Regression anal-
ysis using age as a predictor was not signiﬁcant for any of the
subscales of the ATEC, indicating the differences in age
between the two groups could not account for the differences
in treatment outcomes.
It is also important to note that Jarusiewicz (2002) used
a signiﬁcantly greater number of sessions [t(22) =3.160,
p=.005] than Coben and Padolsky (2007) to achieve their
outcomes. When the amount of change which occurred per
session was compared, Coben and Padolsky’s (2007) study
demonstrated signiﬁcantly greater change on all subscales
of the ATEC (see Table 4; Fig. 4), including the total score
(see Fig. 5). Speciﬁcally, greater change was noted in the
areas of Speech/Language Communication [t(22) =
-3.092, p=.005], Sociability [t(22) =-2.608, p=.016],
Sensory/Cognitive Awareness [t(11.9) =-2.947, p=
.012], Health/Physical/Behavior [t(22) =-3.471, p=
.002]), and total autistic symptoms [t(22) =-4.471,
p\.001]. We found a threefold improvement per session
(ATEC Total percent change per session; 0.84 vs. 2.31%)
in the QEEG based study as compared to the symptom
based study. Thus, more efﬁcacious results were demon-
strated in fewer treatment sessions.
Table 1 Percent change between pre- and post-treatment scores—all subjects
NMean SD t df Sig. (2-tailed)
Jarusiewicz (2002) 12 34.17 26.10 .312 39 .757
Coben and Padolsky (2007) 29 22.41 128.62
Jarusiewicz (2002) 12 30.33 30.79 -.954 40 .346
Coben and Padolsky (2007) 30 39.90 28.81
Jarusiewicz (2002) 12 16.08 9.28 -3.148 34.48* .003
Coben and Padolsky (2007) 30 40.52 39.23
Jarusiewicz (2002) 12 22.75 19.36 -.912 40 .367
Coben and Padolsky (2007) 30 32.97 36.64
Jarusiewicz (2002) 12 26.17 14.39 -2.122 32.80* .041
Coben and Padolsky (2007) 30 38.83 23.48
* df for equal variances not assumed
Table 2 Pre-treatment ATEC scores
NMean SD t df Sig. (2-tailed)
Jarusiewicz (2002) 12 13.65 5.83 0.78 22 0.446
Coben and Padolsky (2007) 12 11.42 8.08
Jarusiewicz (2002) 12 14.85 6.22 -1.54 22 0.138
Coben and Padolsky (2007) 12 18.92 6.72
Jarusiewicz (2002) 12 17.67 4.02 1.45 22 0.162
Coben and Padolsky (2007) 12 15.08 4.70
Jarusiewicz (2002) 12 18.69 10.82 -1.22 22 0.236
Coben and Padolsky (2007) 12 23.67 9.12
Jarusiewicz (2002) 12 64.86 21.08 -0.63 22 0.537
Coben and Padolsky (2007) 12 69.92 18.29
18 Appl Psychophysiol Biofeedback (2010) 35:13–23
One of the ongoing debates among neurofeedback pro-
viders is whether treatment should be assessment based or
symptom based (Hammond et al. 2004). However, few
empirical studies have been conducted to examine differ-
ences in the efﬁcacy of these approaches. This was the ﬁrst
attempt to compare assessment (QEEG) and symptom
guided neurofeedback protocols in an autistic population,
albeit not a direct comparison.
It is important to note that both studies have provided
evidence suggesting that neurofeedback is an effective form
of intervention for autism. Jarusiewicz (2002) demonstrated
a 26% average reduction in symptoms of autism following
neurofeedback. Coben and Padolsky (2007) demonstrated a
40% reduction in symptoms of autism in addition to
improvement on various neuropsychological and neuro-
physiological measures post-neurofeedback. However,
when comparing the two studies, the assessment guided
neurofeedback resulted in signiﬁcantly lower scores on
measures of Sensory/Cognitive Awareness and Health/
Physical/Behavior, as well as total treatment effectiveness.
The Sensory/Cognitive Awareness scale assesses an indi-
vidual’s responsiveness to their environment, understanding
of explanations and events, and demonstrating imagination
and interest in things. The Health/Physical/Behavior scale
assesses health functioning, such as gastrointestinal issues,
sleep, diet; level of physical activity (i.e., hyperactive,
Fig. 1 Signiﬁcant differences were found between Jarusiewicz
(2002) and Coben and Padolsky (2007) when comparing pre-
treatment scores on the sensory/cognitive awareness scale of the
ATEC with post-treatment scores
Table 3 Percent change between pre- and post-treatment scores—equal sample sizes
NMean SD t df Sig. (2-tailed)
Jarusiewicz (2002) 12 34.17 26.10 -1.86 22 0.076
Coben and Padolsky (2007) 12 56.50 32.25
Jarusiewicz (2002) 12 30.33 30.79 -1.15 22 0.262
Coben and Padolsky (2007) 12 42.83 21.62
Jarusiewicz (2002) 12 16.08 9.28 -2.44 22 0.023
Coben and Padolsky (2007) 12 42.33 36.07
Jarusiewicz (2002) 12 22.75 19.36 -2.10 22 0.047
Coben and Padolsky (2007) 12 41.00 23.07
Jarusiewicz (2002) 12 26.17 14.39 -2.85 22 0.009
Coben and Padolsky (2007) 12 46.25 19.69
Fig. 2 Signiﬁcant differences were found between Jarusiewicz
(2002) and Coben and Padolsky (2007) when comparing pre-
treatment total ATEC scores with post-treatment total ATEC scores
Appl Psychophysiol Biofeedback (2010) 35:13–23 19
lethargic); and overall behavior including anxiety, mood,
repetitive movements and speech, agitation, and sensitivity
to sounds and pain. These ﬁndings could not be accounted
for by differences in age between the two groups.
There was a large disparity in the number of sessions
required to produce the changes observed in these studies.
Whereas Coben and Padolsky (2007) used 20 sessions of
neurofeedback for each subject, Jarusiewicz (2002) used
between 20 and 69 sessions (mean of 36 sessions). Coben
and Padolsky (2007) administered signiﬁcantly fewer ses-
sions, which resulted in signiﬁcantly greater change per
session on all scales of the ATEC. Not only was greater
improvement noted in their group, but it was accomplished
more quickly. We found a threefold improvement per
session (ATEC Total percent change per session; 0.84 vs.
2.31%) in the QEEG based study as compared to the
symptom based study. Thus, greater results were demon-
strated in much fewer treatment sessions. This is particu-
larly important considering that individuals who are
autistic often have difﬁculty sitting through extensive
treatment sessions, and so reducing the number of sessions
needed would be particularly beneﬁcial to this group.
Our reanalysis suggest that neurofeedback guided by a
QEEG assessment may be more efﬁcacious than a
Fig. 3 Signiﬁcant differences were found between Jarusiewicz
(2002) and Coben and Padolsky (2007) when comparing the percent
of change that occurred between pre- and post-treatment scores on the
sensory/cognitive awareness scale, health/physical/behavior scale,
and total ATEC
Table 4 Percent change per session
NMean SD t df Sig. (2-tailed)
Jarusiewicz (2002) 12 1.12 1.03 -3.092 22 0.005
Coben and Padolsky (2007) 12 2.83 1.62
Jarusiewicz (2002) 12 1.01 1.06 -2.608 22 0.016
Coben and Padolsky (2007) 12 2.15 1.08
Jarusiewicz (2002) 12 .55 .37 -2.947 22 0.012
Coben and Padolsky (2007) 12 2.12 1.80
Jarusiewicz (2002) 12 .68 .74 -3.471 22 0.002
Coben and Padolsky (2007) 12 2.05 1.15
Jarusiewicz (2002) 12 .84 .57 -4.471 22 0.000
Coben and Padolsky (2007) 12 2.31 .98
Fig. 4 The amount of change which occurred per session in Coben
and Padolsky (2007) was signiﬁcantly greater than the amount of
change which occurred per session in Jarusiewicz (2002) for all
subscales of the ATEC
20 Appl Psychophysiol Biofeedback (2010) 35:13–23
symptom based approach to neurofeedback. However,
there are different approaches even within QEEG guided
neurofeedback, including both power and coherence
training protocols. The number of electrode locations also
can vary between assessments. However, a full QEEG
assessment allows the clinician to pinpoint the area of
abnormality and train it accordingly. Coben and Padolsky
(2007) produced their results by reducing hyperconnec-
tivity in 76% of the experimental group, which led to
improved treatment outcomes. In fact, this was the ﬁrst
published study to demonstrate the effectiveness of
coherence training for reducing the symptoms of autism.
Recently, evidence has been accumulating in support of
a connectivity theory of autism (Alexander et al. 2007;
Boger-Megiddo et al. 2006; Coben et al. 2008; Coben and
Myers 2009; Chung et al. 2004; Courchesne and Pierce
2005; Courchesne et al. 1993,2005; Just et al. 2007;
Murias et al. 2007; Vidal et al. 2006). Coherence training is
a direct application to address these ﬁndings, which are
backed by numerous empirical studies.
Despite the promising results of our comparison, there
were several limitations in this analysis. Perhaps most
importantly, it did not involve a direct comparison of the
two groups. The studies took place 5 years apart, in dif-
ferent locations, and with different sample sizes. Although
the samples were equated in both number of subjects and
symptom severity, the sample sizes were relatively small.
Although these ﬁndings must be viewed with caution as a
result, our levels of signiﬁcance suggest the group differ-
ences are meaningful.
Future studies should be conducted to directly compare
these two methods of neurofeedback treatment, both in
individuals who are autistic as well as in the treatment of
other psychological/neuropsychological disorders, with
larger sample sizes. In this analysis, our results were based
on coherence training to correct areas of abnormal con-
nectivity. Future studies should investigate whether this
method of treatment is superior to other QEEG assessment
based protocols that may focus more on changing EEG
frequency and amplitude. As a follow up to our study, it
would also be interesting to see if individuals treated by
these different approaches varied with respect to long-term
maintainence of gains following the completion of
The type of coherence training shown to be of value
here (for hyperconnectivity principally in posterior-frontal
to anterior-temporal regions) is based on just one of the
many abnormalities noted on QEEG that may be amelio-
rated by neurofeedback training. Coben and Myers (2009)
outlined seven patterns of abnormal connectivity in autistic
spectrum disorders; some hyper- and some hypo-con-
nected. Theoretically, any individual may present with
between one and seven of these abnormalities. It is likely
that treatment of all abnormalities present would lead to the
highest reduction of symptoms. Other types of abnormali-
ties in Mu rhythm (Bernier et al. 2007), excessive theta
(Coben et al. 2008), and higher theta and beta 1 power
(Murias et al. 2007) among others have also been docu-
mented. No studies have been conducted, to our knowl-
edge, that have addressed all abnormalities in patients.
Furthermore, it would be difﬁcult to parse out the effects of
one protocol from another to demonstrate differential
efﬁcacy. Future research may address if sequential or
simultaneous treatment of EEG abnormalities is more
Ideally, there should be randomized controlled trials
(RCTs) in order to demonstrate the efﬁcacy of neurofeed-
back as a validated treatment (LaVaque et al. 2002). Future
research should be conducted with double blind, placebo
controlled trials for both neurofeedback approaches. The
connectivity guided model should be further investigated,
in particular with individuals diagnosed as autistic.
Alexander, A. L., Lee, J. E., Lazar, M., Boudos, R., DuBray, M. B.,
Oakes, T. R., et al. (2007). Diffusion tensor imaging of the
corpus callosum in autism. NeuroImage, 34, 61–73.
American Psychiatric Association. (2000). Diagnostic and statistical
manual of mental disorders, fourth edition, text revision.
Washington, DC: American Psychiatric Publishing, Inc.
Attwood, T. (1998). Asperger’s syndrome: A guide for parents and
professionals. London, England: Jessica Kingsley Publishers.
Ayers, M. E. (1995). Long-term follow-up of EEG neurofeedback
with absence seizures. Biofeedback and Self-Regulation, 20(3),
Fig. 5 The amount of change in Total ATEC scores per session was
signiﬁcantly greater in Coben and Padolsky (2007) than the amount of
change per session in Jarusiewicz (2002)
Appl Psychophysiol Biofeedback (2010) 35:13–23 21
Bailey, A., Luthert, P., & Bolton, P. (1993). Autism and megalen-
cephaly. Lancet, 341, 1225–1226.
Bailey, A., Luthert, P., Dean, A., Harding, B., Janota, I., Montgomery,
M., et al. (1998). A clinicopathological study of autism. Brain,
Bernier, R., Dawson, G., Webb, S., & Murias, M. (2007). EEG mu
rhythm and imitation impairments in individuals with autism
spectrum disorder. Brain and Cognition, 64(3), 228–237.
Boddaert, N., Chabane, N., Barthelemy, C., Bourgeois, M., Poline, J. B.,
Brunelle, F., et al. (2002). Bitemporal lobe dysfunction in infantile
autism: Positron emission tomography study. Journal of Radiol-
ogy, 83, 1829–1833.
Boger-Megiddo, I., Shaw, D. W., Friedman, S. D., Sparks, B. F., Artru,
A. A., Giedd, J. N., et al. (2006). Corpus callosum morphometrics
in young children with autism spectrum disorder. Journal of
Autism and Developmental Disorders, 36(6), 733–739.
Byers, A. P. (1995). Neurofeedback therapy for a mild head injury.
Journal of Neurotherapy, 1(1), 22–37.
Cantor, D. S., Thatcher, R. W., Hrybyk, M., & Kaye, H. (1986).
Computerized EEG analyses of autistic children. Journal of
Autism and Developmental Disorders, 16(2), 169–187.
Casanova, M. F., Buxhoeveden, D. P., & Brown, C. (2002). Clinical
and macroscopic correlates of minicolumnar pathology in
autism. Journal of Child Neurology, 17(9), 692–695.
Center for Disease Control and Prevention (2006). How common are
autistic spectrum disorders (ASD)?. Retrieved December 1, 2006,
Cherkassky, V. L., Kana, R. K., Keller, T. A., & Just, M. A. (2006).
Functional connectivity in a baseline resting-state network in
autism. NeuroReport, 17(16), 1687–1690.
Chung, M. K., Dalton, K. M., Alexander, A. L., & Davidson, R. J.
(2004). Less white matter concentration in Autism: 2D voxel-
based morphometry. NeuroImage, 23(1), 242–251.
Coben, R. (2007). Connectivity-guided neurofeedback for autistic
spectrum disorder. Biofeedback, 35(4), 131–135.
Coben, R., Clarke, A. R., Hudspeth, W., & Barry, R. J. (2008). EEG
power and coherence in autistic spectrum disorder. Clinical
Neurophysiology, 119(5), 1002–1009.
Coben, R., & Myers, T. E. (2009). Connectivity theory of autism: Use
of connectivity measures in assessing and treating autistic
disorders. Journal of Neurotherapy.
Coben, R., & Padolsky, I. (2007). Assessment-guided neurofeedback
for autistic spectrum disorder. Journal of Neurotherapy, 11(1),
Courchesne, E. (2004). Brain development in autism: Early over-
growth followed by premature arrest of growth. Mental Retar-
dation and Developmental Disabilities Research Reviews, 10,
Courchesne, E., Carper, R., & Akshoomoff, N. (2003). Evidence of
brain overgrowth in the ﬁrst year of life in autism. Journal of the
American Medical Association, 290, 337–344.
Courchesne, E., Karns, C. M., Davis, H. R., Ziccardi, R., Carper, R.
A., Tigue, Z. D., et al. (2001). Unusual brain growth patterns in
early life in patients with autistic disorder: An MRI study.
Neurology, 57, 245–254.
Courchesne, E., Muller, R. A., & Saitoh, O. (1999). Brain weight in
autism: Normal in the majority of cases, megalencephalic in rare
cases. Neurology, 52, 1057–1059.
Courchesne, E., & Pierce, K. (2005). Why the frontal cortex in autism
might be talking only to itself: Local over-connectivity but long-
distance disconnection. Current Opinion in Neurobiology, 15,
Courchesne, E., Press, G. A., & Yeung-Courchesne, R. (1993).
Parietal lobe abnormalities detected with MR in patients with
infantile autism. American Journal of Roentgenology, 160,
Courchesne, E., Redcay, E., Morgan, J. T., & Kennedy, D. P. (2005).
Autism at the beginning: Microstructural and growth abnormal-
ities underlying the cognitive and behavioral phenotype of
autism. Development and Psychopathology, 17, 577–597.
Cowan, J., & Markham, L. (1994). EEG biofeedback for the attention
problems of autism: A case study. Paper presented at the Annual
Meeting of the Association for Applied Psychophysiology and
Biofeedback, Atlanta, GA.
Davidovitch, M., Patterson, B., & Gartside, P. (1996). Head
circumference measurements in children with autism. Journal
of Child Neurology, 11, 389–393.
Filipek, P. A., Richelme, C., Kennedy, D. N., Rademacher, J., Pitcher,
D. A., Zidel, S., et al. (1992). Morphometric analysis of the brain
in developmental language disorders and autism. Annals of
Neurology, 32, 475.
Frith, C. (2003). What do imaging studies tell us about the neural
basis of autism? Novartis Foundation Symposium, 251, 149–166.
Discussion 166–176, 281–197.
Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzelier, J. H., & Kaiser,
J. (2003). Neurofeedback treatment for attention-deﬁcit hyper-
activity disorder in children: A comparison with methylpheni-
date. Applied Psychophysiology and Biofeedback, 28(1), 1–12.
Hamilton, L. (2000). Facing autism: Giving parents reasons for hope
and guidance for help. Colorado Springs, CO: WaterBrook Press.
Hammond, D. C. (2003). QEEG-guided neurofeedback in the
treatment of obsessive compulsive disorder. Journal of Neuro-
therapy, 7(2), 25–52.
Hammond, D. C., Walker, J., Hoffman, D., Lubar, J. F., Trudeau, D.,
Gurnee, R., et al. (2004). Standards for the use of quantitative
electroencephalography (QEEG) in neurofeedback: A position
paper of the International Society for Neuronal Regulation.
Journal of Neurotherapy, 8(1), 5–27.
Heinrich, H., Gevensleben, H., Freisleder, F. J., Moll, G. H., &
Rothenberger, A. (2004). Training of slow cortical potentials in
attention-deﬁcit/hyperactivity disorder: Evidence for positive
behavioral and neurophysiological effects. Biological Psychia-
try, 55, 772–775.
Herbert, M. R. (2005). Large brains in autism: The challenge of
pervasive abnormality. The Neuroscientist, 11(5), 417–440.
Herbert, M. R., Ziegler, D. A., Deutsch, C. K., O’Brien, L. M., Lange,
N., Bakardjiev, A., et al. (2003). Dissociations of cerebral cortex,
subcortical and cerebral white matter volumes in autistic boys.
Brain, 126, 1182–1192.
Herbert, M. R., Ziegler, D. A., Makris, N., Filipek, P. A., Kemper, T.
L., Normandin, J. J., et al. (2004). Localization of white matter
volume increase in autism and developmental language disorder.
Annals of Neurology, 55(4), 530–540.
Hoffman, D. A., Stockdale, S., & Van Egren, L. (1996). EEG
neurofeedback in the treatment of mild traumatic brain injury.
Clinical Encephalography, 27(2), 6.
Huang-Storms, L., Bodenhamer, E., Davis, R., & Dunn, J. (2007).
QEEG-guided neurofeedback for children with histories of abuse
and neglect: Neurodevelopmental rationale and pilot study.
Journal of Neurotherapy, 10(4), 3–16.
Hughes, J. R. (2007). Autism: The ﬁrst ﬁrm ﬁnding=underconnectiv-
ity? Epilepsy & Behavior, 11(1), 20–24.
Jarusiewicz, B. (2002). Efﬁcacy of neurofeedback for children in the
autistic spectrum: A pilot study. Journal of Neurotherapy, 6(4),
Just, M. A., Cherkassky, V. L., Keller, T. A., Kana, R. K., &
Minshew, N. J. (2007). Functional and anatomical cortical
underconnectivity in autism: Evidence from an fMRI study of an
executive function task and corpus callosum morphometry.
Cerebral Cortex, 17(4), 951–961.
Just, M. A., Cherkassky, V. L., Keller, T. A., & Minshew, N. J.
(2004). Cortical activation and synchronization during sentence
22 Appl Psychophysiol Biofeedback (2010) 35:13–23
comprehension in high-functioning autism: Evidence of under-
connectivity. Brain, 127(8), 1811–1821.
Kanner, L. (1943). Autistic disturbances of affective contact. Nervous
Child, 2, 217–307.
Keller, I. (2001). Neurofeedback therapy of attention deﬁcits in
patients with traumatic brain injury. Journal of Neurotherapy,
Kemper, T. L., & Bauman, M. (1998). Neuropathology of infantile
autism. Journal of Neuropathology and Experimental Neurol-
ogy, 57, 645–652.
LaVaque, T. J., Hammond, D. C., Trudeau, D., Monastra, V., Perry,
J., Lehrer, P., et al. (2002). Template for developing guidelines
for the evaluation of the clinical efﬁcacy of psychophysiological
evaluations. Applied Psychophysiology and Biofeedback, 27(4),
Lubar, J. O., & Lubar, J. F. (1984). Electroencephalographic
biofeedback of SMR and beta for treatment of attention deﬁcit
disorders in a clinical setting. Biofeedback and Self Regulation,
Lubar, J. F., Shabsin, H. S., Natelson, S. E., Holder, G. S., Pamplin,
W. E., & Krulikowski, D. I. (1981). EEG operant conditioning in
intractible epileptics. Archives of Neurology, 38, 700–704.
McAlonan, G. M., Cheung, V., Cheung, C., Suckling, J., Lam, G. Y.,
Tai, K. S., et al. (2005). Mapping the brain in autism: A voxel-
based MRI study of volumetric differences and intercorrelations
in autism. Brain, 128(2), 268–276.
McCandless, J. (2005). Children with starving brains: A medical
treatment guide for autism spectrum disorder. Putney, VT:
Mizuno, A., Villalobos, M. E., Davies, M. M., Dahl, B. C., & Muller,
R. A. (2006). Partially enhanced thalamocortical functional
connectivity in autism. Brain Research, 1104(1), 160–174.
Monderer, R. S., Harrison, D. M., & Haut, S. R. (2002). Review:
Neurofeedback and epilepsy. Epilepsy & Behavior, 3, 214–218.
Moore, N. C. (2000). A review of EEG biofeedback treatment of
anxiety disorders. Clinical Electroencephalography, 31(1), 1–6.
Murias, M., Webb, S. J., Greenson, J., & Dawson, G. (2007). Resting
state cortical connectivity reﬂected in EEG Coherence in
individuals with Autism. Biological Psychiatry, 62(3), 270–273.
Murphy, D. G. M., Critchley, H. D., Schmitz, N., McAlonan, G., Van
Amelsvoort, T., Robertson, D., et al. (2002). A Proton magnetic
resonance spectroscopy study of the brain. Archives of General
Psychiatry, 59(10), 885–891.
Nunez, P. L. (1995). Neocortical dynamics and human EEG rhythms.
New York: Oxford University Press.
Nunez, P. L., & Srinivasan, R. (2006). Electric ﬁelds of the brain: The
neurophysics of EEG (2nd ed.). New York: Oxford University
Othmer, S. (1997). Assessment. EEG spectrum biofeedback training
manual. Encino, CA: EEG Spectrum, Inc.
Page, L. A., Daly, E., Schmitz, N., Simmons, A., Toal, F., Deeley, Q.,
et al. (2006). In vivo H-magnetic resonance spectroscopy study
of amygdala-hippocampal and parietal regions in autism.
American Journal of Psychiatry, 163, 2189–2192.
Piven, J., Arndt, S., Bailey, J., & Andreasen, N. (1996). Regional
brain enlargement in autism: A magnetic resonance imaging
study. Journal of the American Academy of Child and Adoles-
cent Psychiatry, 35, 530–536.
Piven, J., Bailey, J., Ranson, B. J., & Arndt, S. (1997). An MRI study
of the corpus callosum in autism. American Journal of Psychi-
atry, 154, 1051–1056.
Schmitz, N., Rubia, K., Daly, E., Smith, A., Williams, S., & Murphy,
D. G. (2006). Neural correlates of executive function in autistic
spectrum disorders. Biological Psychiatry, 59(1), 7–16.
Schoenberger, N. E., Shiﬂett, S. C., Esty, M. L., Ochs, L., & Matheis,
R. J. (2001). Flexyx neurotherapy system in the treatment of
traumatic brain injury: An initial evaluation. Journal of Head
Trauma Rehabilitation, 16(3), 260–274.
Sicile-Kira, C. (2004). Autism spectrum disorders: The complete
guide to understanding autism, asperger’s syndrome, pervasive
developmental disorder, and ASDs. New York: The Berkley
Siegel, B. (1996). The world of the autistic child: Understanding and
treating autistic spectrum disorders. New York: Oxford Univer-
Sterman, M. B. (2000). Basic concepts and clinical ﬁndings in the
treatment of seizure disorders with EEG operant conditioning.
Clinical Electroencephalogry, 31, 45–55.
Sterman, M. B., & Friar, L. (1972). Suppression of seizures in an
epileptic following sensorimotor EEG feedback training. Elec-
troencephalography and Clinical Neurophysiology, 33, 89–95.
Thornton, K. E., & Carmody, D. P. (2005). Electroencephalogram
biofeedback for reading disability and traumatic brain injury.
Child and Adolescent Psychiatric Clinics of North America,
Tidmarsh, L., & Volkmar, F. R. (2003). Diagnosis and epidemiology
of autism spectrum disorders. Canadian Journal of Psychiatry,
Trudeau, D. L. (2005). Applicability of brain wave biofeedback to
substance use disorder in adolescents. Child and Adolescent
Psychiatric Clinics of North America, 14, 125–136.
Vidal, C. N., Nicolson, R., DeVito, T. J., Hayashi, K. M., Geaga, J.
A., Drost, D. J., et al. (2006). Mapping corpus callosum deﬁcits
in autism: an index of aberrant cortical connectivity. Biological
Psychiatry, 60(3), 218–225.
Walker, J. E., & Kozlowski, G. P. (2005). Neurofeedback treatment of
epilepsy. Child and Adolescent Psychiatric Clinics of North
America, 14, 163–176.
Walker, J. E., Norman, C. A., & Weber, R. K. (2002). Impact of
qEEG-guided coherence training for patients with a mild closed
head injury. Journal of Neurotherapy, 6(2), 31–45.
Wickelgren, I. (2005). Autistic brains out of synch? Science, 308,
Appl Psychophysiol Biofeedback (2010) 35:13–23 23