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The Relative Efficacy of Connectivity Guided and Symptom Based EEG Biofeedback for Autistic Disorders

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Autism is a neurodevelopmental disorder characterized by deficits in communication, social interaction, 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 neurobiological 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 connectivity 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 are autistic.
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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
are autistic.
Keywords Autism !Quantitative EEG !Neurofeedback !
Assessment !Efficacy
Introduction
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: robcoben@optonline.net; drcoben@thebrainlabs.com
123
Appl Psychophysiol Biofeedback (2010) 35:13–23
DOI 10.1007/s10484-009-9102-5
Hamilton 2000; McCandless 2005; Sicile-Kira 2004; Siegel
1996).
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
hypothesis.
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
123
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
123
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
123
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
neurofeedback.
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
NF administered.
Results
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
123
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)
Speech/lang/comm
Jarusiewicz (2002) 12 34.17 26.10 .312 39 .757
Coben and Padolsky (2007) 29 22.41 128.62
Sociability
Jarusiewicz (2002) 12 30.33 30.79 -.954 40 .346
Coben and Padolsky (2007) 30 39.90 28.81
Sens/cog awareness
Jarusiewicz (2002) 12 16.08 9.28 -3.148 34.48* .003
Coben and Padolsky (2007) 30 40.52 39.23
Health/phys/behavior
Jarusiewicz (2002) 12 22.75 19.36 -.912 40 .367
Coben and Padolsky (2007) 30 32.97 36.64
Total
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)
Speech/lang/comm
Jarusiewicz (2002) 12 13.65 5.83 0.78 22 0.446
Coben and Padolsky (2007) 12 11.42 8.08
Sociability
Jarusiewicz (2002) 12 14.85 6.22 -1.54 22 0.138
Coben and Padolsky (2007) 12 18.92 6.72
Sens/cog awareness
Jarusiewicz (2002) 12 17.67 4.02 1.45 22 0.162
Coben and Padolsky (2007) 12 15.08 4.70
Health/phys/behavior
Jarusiewicz (2002) 12 18.69 10.82 -1.22 22 0.236
Coben and Padolsky (2007) 12 23.67 9.12
Total
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
123
Discussion
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)
Speech/lang/comm
Jarusiewicz (2002) 12 34.17 26.10 -1.86 22 0.076
Coben and Padolsky (2007) 12 56.50 32.25
Sociability
Jarusiewicz (2002) 12 30.33 30.79 -1.15 22 0.262
Coben and Padolsky (2007) 12 42.83 21.62
Sens/cog awareness
Jarusiewicz (2002) 12 16.08 9.28 -2.44 22 0.023
Coben and Padolsky (2007) 12 42.33 36.07
Health/phys/behavior
Jarusiewicz (2002) 12 22.75 19.36 -2.10 22 0.047
Coben and Padolsky (2007) 12 41.00 23.07
Total
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
123
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)
Speech/lang/comm
Jarusiewicz (2002) 12 1.12 1.03 -3.092 22 0.005
Coben and Padolsky (2007) 12 2.83 1.62
Sociability
Jarusiewicz (2002) 12 1.01 1.06 -2.608 22 0.016
Coben and Padolsky (2007) 12 2.15 1.08
Sens/cog awareness
Jarusiewicz (2002) 12 .55 .37 -2.947 22 0.012
Coben and Padolsky (2007) 12 2.12 1.80
Health/phys/behavior
Jarusiewicz (2002) 12 .68 .74 -3.471 22 0.002
Coben and Padolsky (2007) 12 2.05 1.15
Total
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
123
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
neurofeedback.
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
effective.
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.
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... By modulating or stabilizing the feedback signal, the user learns to regulate a particular cognitive or mental state. NF has several different modes, including that based on EEG [40][41][42] and on based on functional magnetic resonance imaging (fMRI) [43,44]. fMRI-based NF can be used to measure activity in a target region of a participant's brain in what is experienced as real time. ...
... For comparison with previously reported neural activity data, we compared the training effect for the same target words "light" and "right" at the same time (12 sessions) in the NF group only. This was because these target words were used in our previous study [42]. Furthermore, in our previous study, the participants completed 12 sessions per day. ...
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Listening is critical for foreign language learning. Listening difficulties can occur because of an inability to perceive or recognize sounds while listening to speech, whereas successful listening can boost understanding and improve speaking when learning a foreign language. Previous studies in our laboratory revealed that EEG-neurofeedback (NF) using mismatch negativity event-related brain potential successfully induced unconscious learning in terms of auditory discrimination of speech sounds. Here, we conducted a feasibility study with a small participant group (NF group and control group; six participants each) to examine the practical effects of mismatch negativity NF for improving the perception of speech sounds in a foreign language. Native Japanese speakers completed a task in which they learned to perceive and recognize spoken English words containing the consonants "l" or "r". Participants received neurofeedback training while not explicitly attending to auditory stimuli. The results revealed that NF training significantly improved the proportion of correct in discrimination and recognition trials, even though the training time for each word pair was reduced to 20% of the training time reported in our previous study. The learning effect was not affected by training with three pairs of words with different vowels. The current results indicate that NF resulted in long-term learning that persisted for at least 2 months.
... Despite mixed results, many of neurofeedback findings underscores its potential as an effective alternative treatment for ADHD and more recently, for ASD-related symptoms (van Hoogdalem, Feijs, Bramer, Ismail, & van Dongen, 2020). Studies have reported normalization of brain function, as well as significant improvements in areas of executive function and socio-communication in children with only ASD following neurofeedback treatment (Kouijzer, van Schie, de Moor, Gerrits, & Buitelaar, 2010;Coben & Myers, 2010;Datko, Pineda, & Müller, 2018;Kouijzer, de Moor, Gerrits, Buitelaar, & van Schie, 2009;Pineda et al., 2008;Thompson, Thompson, & Reid, 2010), with gains sustained for up to 12 months (Kouijzer et al., 2009). ...
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... Moreover, the QEEG is a basic component of therapeutic methods including QEEG biofeedback. In this method, coaching the patients to influence their EEG frequencies allows the mitigation of the symptoms of some behavioral disturbances including dementia [10], autism spectrum disorders [11] and attention deficit hyperactivity disorder [12]. Thus, accurate QEEG diagnostics may create a solid basis for considering the regular use of EEG biofeedback therapy as an individual treatment form for those patients in whom neurologic COVID-19 sequelae closely resemble the symptoms of the above-mentioned conditions where QEEG neurofeedback has already been successfully used. ...
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Introduction and purpose: The SARS-CoV-2 virus is able to cause abnormalities in the functioning of the nervous system and induce neurological symptoms with the features of encephalopathy, disturbances of consciousness and concentration and a reduced ability to sense taste and smell as well as headaches. One of the methods of detecting these types of changes in COVID-19 patients is an electroencephalogram (EEG) test, which allows information to be obtained about the functioning of the brain as well as diagnosing diseases and predicting their consequences. The aim of the study was to review the latest research on changes in EEG in patients with COVID-19 as a basis for further quantitative electroencephalogram (QEEG) diagnostics and EEG neurofeedback training. Description of the state of knowledge: Based on the available scientific literature using the PubMed database from 2020 and early 2021 regarding changes in the EEG records in patients with COVID-19, 17 publications were included in the analysis. In patients who underwent an EEG test, changes in the frontal area were observed. A few patients were not found to be responsive to external stimuli. Additionally, a previously non-emerging, uncommon pattern in the form of continuous, slightly asymmetric, monomorphic, biphasic and slow delta waves occurred. Conclusion: The results of this analysis clearly indicate that the SARS-CoV-2 virus causes changes in the nervous system that can be manifested and detected in the EEG record. The small number of available articles, the small number of research groups and the lack of control groups suggest the need for further research regarding the short and long term neurological effects of the SARS-CoV-2 virus and the need for unquestionable confirmation that observed changes were caused by the virus per se and did not occur before. The presented studies described non-specific patterns appearing in encephalograms in patients with COVID-19. These observations are the basis for more accurate QEEG diagnostics and EEG neurofeedback training.
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Autism is a pervasive neurodevelopmental disorder of multifactorial causation and phenotypical variation. The nature of the disorder together with the difficulty in planning and implementing highly effective treatment models, have directed the scientific research towards discovering and implementing new intervention or/and therapeutic models, of different type and philosophy. Brain – computer interface systems as intervention tools in autism comprise an approach consistent with the demands of the new era. The dissertation aims at examining applied, non-invasive research protocols of this kind, placing emphasis on the way they were implemented and their effectiveness. The review was conducted on published research in the last decade, concerning ages 4-21.
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Neurofeedback training is a treatment modality of potential use for improving self-regulation skills in autism spectrum disorder (ASD). Multiple studies using neurofeedback to target symptoms of ASD have been reported. These studies differ among themselves in the type of training (e.g., theta-to-beta ratio, coherence, etc.), topography (Cz or Pz), guidance by quantitative EEG (qEEG), and number of sessions (e.g., 20 vs. 30, etc.). In our study, we proposed that prefrontal neurofeedback training would be accompanied by changes in relative power of EEG bands (e.g., 40 Hz-centered gamma band) and ratios of individual bands (e.g., theta-to-beta ratio) and changes in autonomic activity. Outcome measures included EEG, autonomic measures (heart rate, heart rate variability [HRV] indexes, respiration rate, and skin conductance level [SCL]), and behavioral ratings by parents/caregivers. In this pilot feasibility study on 14 children with ASD with comorbid ADHD (~10.28 years SD = 1.93, 3 females), we administered a 24 session-long course of neurofeedback from the AFz site. The protocol used training for wide-band EEG amplitude suppression (“InhibitAll”) with simultaneous upregulation of the index of 40 Hz-centered gamma activity. Quantitative EEG (QEEG) analysis at the prefrontal training site was completed for each session of neurofeedback in order to determine the amplitude of the individual bands (delta, theta, alpha, beta, and gamma), the ratio of the EEG bands of interest (e.g., theta-to-beta ratio [TBR]), and relative power of 40 Hz-centered gamma across neurofeedback sessions. In this study, we analyzed Aberrant Behavior Checklist (ABC), Social Responsiveness Scale (SRS-2), and Achenbach’s ASEBA ratings by caregivers (pre- and posttreatment). We found a significant reduction in Irritability and Hyperactivity subscales of the ABC, decrease of T-score on SRS-2, and decrease in Attention Deficit scores of the ASEBA posttreatment. Successful neurofeedback sessions were featured by the changes in SCL, decreased HR, increased HRV (reflected in decreased LF/HF ratio of HRV and increased RMSSD of HRV), and decreased respiration rate. Profiles of psychophysiological changes during individual sessions and across the whole course of neurofeedback training showed active engagement of participants during training process, resulting in gradual decrease of anxiety markers across the whole course of experimental intervention using prefrontal neurofeedback training. Future research is needed to assess QEEG changes in other topographies using brain mapping, more prolonged courses, and other outcome measures including clinical behavioral evaluations to judge the clinical utility of prefrontal neurofeedback in children with ASD with co-occurring ADHD. The current series support a need to address various factors affecting outcome of neurofeedback-based intervention, specifically the question of length of treatment.
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Neuropathological studies in autism spectrum disorder (ASD) suggest the presence of a neuronal migrational disorder that alters the excitatory–inhibitory bias of the cerebral cortex. More specifically, in ASD, there appears to be widespread loss of parvalbumin (PV)-positive interneurons manifested as abnormalities in gamma oscillations (neural network instabilities), epileptogenesis, and impaired cognitive functions. Transcranial magnetic stimulation (TMS) is one of the first treatment to target this putative core pathological feature of ASD. Studies show that low-frequency TMS over the dorsolateral prefrontal cortex (DLPC) of individuals with ASD decreases the power of gamma activity while improving both executive function skills related to self-monitoring behaviors as well as the ability to apply corrective actions. Studies from our group have also shown that low-frequency TMS in ASD provides a reduction of stimulus-bound behaviors and diminished sympathetic arousal. Results become more significant with an increasing number of sessions and bear synergism when used along with neurofeedback.
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The article provides an overview of scientific works devoted to methods of correcting the development of children with autism spectrum disorders (ASD) based on EEG biofeedback (neurofeedback). According to the World Health Organization, one in 160 children are currently diagnosed with ASD. In 2018, about 0.1 % of the child population in Russia suffered from autism. Moreover, the incidence of the disease is increasing every year. Genetic disorders are the most likely cause of ASD. Dysfunctions of 69 genes are highly likely to cause ASD. Most of these genes are pleiotropic. They affect the proliferation, differentiation and migration of nerve cells, the growth of axons and synaptogenesis, the synthesis of neurotransmitters and the development of receptors for them. Several genes involved in the development of ASD undergo epigenetic modifications under the influence of the environment and pathogens. The key in the onset of ASD is probably a violation of the synaptic pruning process. Pruning is necessary to reduce redundant connections and improve the efficiency of the central nervous system. Based on this, the researchers put forward a hypothesis explaining the symptoms of ASD as a result of a violation of structural and functional brain connectivity. Such disturbances are likely to cause abnormalities in the functioning of the brain mirror system (BMS). Disorders of the synaptic organization of the brain correlate with indicators of cognitive, emotional and behavioral tests, EEG characteristics. The study of phase coherence in several EEG frequency ranges demonstrated the presence of global hypo- and local hyper-connectivity in patients with ASD. The absence of suppression or desynchronization of the mu rhythm may indicate a malfunction of the BMS. The child’s brain is highly plastic. Therefore, early corrective intervention can improve the developmental outcomes of a child with ASD. Modern research demonstrates the possibility of effective application of neurofeedback for the correction of the disease. One of the strategies is the use of neurofeedback trainings to reduce anxiety in children with ASD. Another strategy is aimed at regulating the coherence of EEG signals. Researchers consider the most promising strategy for learning mu rhythm modulation using neurofeedback. This neurofeedback protocol affects the functioning of the BMS. According to the research results, after the neurofeedback trainings, the normalization of the functional cerebral connectivity according to the mu rhythm was established. Further research in this direction can become the basis for the most effective methods of treating ASD.
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La violencia doméstica en sus múltiples manifestaciones constituye un problema de salud pública, de derechos humanos y de género, de relevancia social a nivel local, nacional y mundial. Actualmente, existe cada vez un mayor consenso sobre las consecuencias de este problema por parte de diferentes asociaciones civiles y científicas, organizaciones internacionales y gobiernos de diferentes países (Agencia de los Derechos Fundamentales de la Unión Europea [FRA] 2014; Mitchell Wight, Van Heerden & Rochat, 2016; Morrison, Ellsberg & Both, 2005; Organización Panamericana de la Salud, Organización Mundial de la Salud & Centros de control y Prevención de las Enfermedades de los Estados Unidos, 2014; World Health Organization, 2013).
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Biofeedback is a non-invasive process to electronically monitor normal automatic bodily function to acquire its voluntary control. Traditional medical models place the onus on the physician to “cure” the illness. Biofeedback places responsibility on the patient to gain self-control. Its application as evidence-based practice in neurodevelopmental disorders is a nascent, unexplored, and debated area of study. This chapter outlines the meaning, nature, types, protocols, procedure, practices, challenges, benefits, and limitations in its use. Its history is traced for efficacy vis-à-vis other treatments, and other issues like cost-effectiveness, certification of professionals, gadget-enabled, and computer-assisted variants. Studies have attempted, albeit with methodological limitations, to validate its utility for neurodevelopmental disorders without any definitive or conclusive evidence for or against its use given the inability to replicate results, control or exclude confounding factors, placebo effects, and/or bias. An agenda for prospective research is given.
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Objective: The neural basis for autistic spectrum disorders is unclear, but abnormalities in the development of limbic areas and of glutamate have been suggested. Proton magnetic resonance spectroscopy (¹H-MRS) can be used to measure the concentration of brain metabolites. However, the concentration of glutamate/glutamine in brain regions implicated in autistic spectrum disorders has not yet been examined in vivo. Method: The authors used ¹H-MRS to investigate the neuronal integrity of the amygdala-hippocampal complex and a parietal control region in adults with autistic spectrum disorders and healthy subjects. Results: People with autistic spectrum disorders had a significantly higher concentration of glutamate/glutamine and creatine/phosphocreatine in the amygdala-hippocampal region but not in the parietal region. Conclusions: Abnormalities in glutamate/glutamine may partially underpin the pathophysiology of autistic spectrum disorders, and the authors confirm earlier reports that limbic areas are metabolically aberrant in these disorders.
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Purpose: Childhood autism is a severe developmental disorder that impairs the acquisition of some of the most important skills in human life. Progress in understanding the neural basis of childhood autism requires clear and reliable data indicating specific neuroanatomical or neurophysiological abnormalities. The purpose of the present study was to research localized brain dysfunction in autistic children using functional brain imaging. Patients and methods: Regional cerebral blood flow (rCBF) was measured with positron emission tomography (PET) in 21 primary autistic children and 10 age-matched non autistic children. Results: A statistical parametric analysis of rCBF images revealed significant bilateral temporal hypoperfusion in the associative auditory cortex (superior temporal gyrus) and in the multimodal cortex (superior temporal sulcus) in the autistic group (p<0.001). In addition, temporal hypoperfusion was detected individually in 77% of autistic children. Conclusion: These findings provide robust evidence of well localized functional abnormalities in autistic children located in the superior temporal lobe. Such localized abnormalities were not detected with the low resolution PET camera (14-22). This study suggests that high resolution PET camera combined with statistical parametric mapping is useful to understand developmental disorders.
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There is no clear evidence from imaging studies for specific structural abnormalities in the brains of people with autism. The most robust observation is of greater total brain volume. There is evidence that this greater volume is not present at birth, but appears during the first few years. This brain enlargement might be a marker of abnormal connectivity due to lack of pruning. While abnormalities have often been reported in the cerebellum and the amygdala, these are difficult to interpret since both increases and decreases in the size of these structures have been observed. Another way of identifying the neural basis of autism is to investigate brain systems underlying cognitive functions compromised in this disorder such as face perception and ‘theory of mind’. Autistic people fail to activate the ‘fusiform face area’ during face perception tasks and show weak activation of medial frontal cortex and superior temporal gyrus when performing theory of mind tasks. These problems stem from a lack of integration of sensory processing with cognitive evaluation. I speculate that this problem reflects a failure of top-down modulation of early sensory processing. The problem could result from abnormal connectivity and lack of pruning.
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A neuropathological study of autism was established and brain tissue examined from six mentally handicapped subjects with autism. Clinical and educational records were obtained and standardized diagnostic interviews conducted with the parents of cases not seen before death. Four of the six brains were megalencephalic, and areas of cortical abnormality were identified in four cases. There were also developmental abnormalities of the brainstem, particularly of the inferior olives. Purkinje cell number was reduced in all the adult cases, and this reduction was sometimes accompanied by gliosis. The findings do not support previous claims of localized neurodevelopmental abnormalities. They do point to the likely involvement of the cerebral cortex in autism.
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Background Asperger syndrome (AS; an autistic disorder) is associated with impaired social skills and obsessional/repetitive behavior. Patients with autism have significant abnormalities in the frontal lobe and frontoparietal connectivity. Nobody has examined the relationship between abnormalities in the frontal and parietal lobes and clinical symptoms in people with AS. Methods We used in vivo proton magnetic resonance spectroscopy to examine neuronal integrity of the medial prefrontal and parietal lobes in 14 non–learning-disabled adults with AS and 18 control subjects (of similar sex, age, and IQ). We obtained measures of the prefrontal lobe in 11, the parietal lobe in 13, and both lobes in 10 subjects with AS. We measured concentrations and ratios of N-acetylaspartate (NAA), creatine and phosphocreatine (Cr + PCr), and choline (Cho). Levels of NAA, Cr + PCr, and Cho are indicators of neuronal density and mitochondrial metabolism, phosphate metabolism, and membrane turnover. Frontal metabolite levels were correlated with scores on the Yale-Brown Obsessive Compulsive Scale and the Autism Diagnostic Interview. Results Subjects with AS had a significantly higher prefrontal lobe concentration of NAA (z = –3.1; P= .002), Cr + PCr (z = –2.2; P = .03), and Cho (z = –2.9; P = .003). Increased prefrontal NAA concentration was significantly correlated with obsessional behavior (τ = 0.67; P= .005); increased prefrontal concentration of Cho, with social function (τ= 0.72; P = .02). We found no significant differences in parietal lobe metabolite concentrations. Conclusion Subjects with AS have abnormalities in neuronal integrity of the prefrontal lobe, which is related to severity of clinical symptoms.
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