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Neurofeedback improves executive functioning in children with autism spectrum disorders

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Seven autistic children diagnosed with autism spectrum disorders (ASD) received a neurofeedback treatment that aimed to improve their level of executive control. Neurofeedback successfully reduced children's heightened theta/beta ratio by inhibiting theta activation and enhancing beta activation over sessions. Following treatment, children's executive capacities were found to have improved greatly relative to pre-treatment assessment on a range of executive function tasks. Additional improvements were found in children's social, communicative and typical behavior, relative to a waiting list control group. These findings suggest a basic executive function impairment in ASD that can be alleviated through specific neurofeedback treatment. Possible neural mechanisms that may underlie neurofeedback mediated improvement in executive functioning in autistic children are discussed.
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Neurofeedback improves executive functioning in children
with autism spectrum disorders
Mirjam E.J. Kouijzer
a,b
, Jan M.H. de Moor
a
, Berrie J.L. Gerrits
b
,
Marco Congedo
c
, Hein T. van Schie
a,
*
a
Behavioral Science Institute, Radboud University Nijmegen, Nijmegen, The Netherlands
b
Neurofeedback Nijmegen, Nijmegen, The Netherlands
c
GIPSA-lab, UMR5216 Centre National de la Recherche Scientifique Universite
´Joseph Fourier – Universite
´Pierre
Mende
`s-France – Universite
´Stendhal – Institut Polytechnique de Grenoble, France
Neurofeedback refers to a form of operant conditioning of electrical brain activity, in which
desirable brain activity is rewarded and undesirable brain activity is inhibited. Neurofeedback is
believed to elicit growth and changes at cellular levels of the brain, which in turn support brain
functioning and behavioral cognitive performance (Demos, 2005). In the domain of intervention,
neurofeedback training is useful in treatment of different disorders in adults and children. Positive
effects of neurofeedback in adults have been found for Attention Deficit Hyperactivity Disorder
Research in Autism Spectrum Disorders xxx (2008) xxx–xxx
ARTICLE INFO
Article history:
Received 21 April 2008
Accepted 2 May 2008
Keywords:
Neurofeedback
Autism spectrum disorder
Executive function
Theta/beta ratio
Anterior cingulate cortex
ABSTRACT
Seven autistic children diagnosed with autism spectrum disorders
(ASD) received a neurofeedback treatment that aimed to improve
their level of executive control. Neurofeedback successfully reduced
children’s heightened theta/beta ratio by inhibiting theta activation
and enhancing beta activation over sessions. Following treatment,
children’s executive capacities were found to have improved greatly
relative to pre-treatment assessment on a range of executive
function tasks. Additional improvements were found in children’s
social, communicative and typical behavior, relative to a waiting list
control group. These findings suggest a basic executive function
impairment in ASD that can be alleviated through specific
neurofeedback treatment. Possible neural mechanisms that may
underlie neurofeedback mediated improvement in executive
functioning in autistic children are discussed.
ß2008 Elsevier Ltd All rights reserved.
* Corresponding author at: Department of Social and Cultural Psychology, Behavioral Science Institute (BSI), Radboud
University Nijmegen, P.O. Box 9104, 6500 HE Nijmegen, The Netherlands. Tel: +31 24 36 12575; fax: +31 24 36 12677.
E-mail address: h.vanschie@psych.ru.nl (H.T. van Schie).
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Contents lists available at ScienceDirect
Research in Autism Spectrum
Disorders
Journal homepage: http://ees.elsevier.com/RASD/default.asp
1750-9467/$ – see front matter ß2008 Elsevier Ltd All rights reserved.
doi:10.1016/j.rasd.2008.05.001
Please cite this article in press as: Kouijzer, M.E.J, et al., Neurofeedback improves executive
functioning in children with autism spectrum disorders, Res Autism Spectr Disord (2008),
doi:10.1016/j.rasd.2008.05.001
(ADHD) (Kropotov et al., 2005), traumatic brain injury (Thornton, 2000), epilepsy (Sterman, 2000),
depression (Hammond, 2003), migraine (Kropp, Siniatchkin, & Gerber, 2002), addiction (Trudeau,
2005), anxiety disorders (Moore, 2000), and general cognitive performance (Vernon et al., 2003).
Less is known about the effects of neurofeedback in children. In children, research on the effects of
neurofeedback is mainly carried out in the area of ADHD (Fuchs et al., 2003; Monastra et al., 2005;
Vernon, Frick, & Gruzelier, 2004), but positive effects of neurofeedback have also been found for
children with migraine (Kropp et al., 2002) and learning disorders (Fernandez et al., 2003;Thornton &
Carmody, 2005). ADHD is typically characterized by a heightened ratio between theta (4–8 Hz) and
beta (12–21 Hz) activity in the ongoing EEG during rest. Neurofeedback protocols that have aimed at
inhibiting theta activity while rewarding beta activity have led to successful alleviation of symptoms
associated with ADHD such as deficits in sustained attention, impulsivity, and control over
hyperactive behaviors (reviews in Butnik, 2005;Fox, Tharp, & Fox, 2005).
Several studies suggest that neurofeedback protocols that have been successful for treatment of
ADHD may also be efficacious for treating children with autistic related deficits. Sichel, Fehmi, and
Goldstein (1995) report on Frankie, a 8.5-year-old boy with a mild form of autism and attention
impairments suggesting ADHD. Frankie’s 19-channel QEEG demonstrated theta (4–8 Hz) to beta (13–
21 Hz) ratios of 3.59 (Cz), 3.40 (C3), 3.03 (C4), 3.98 (Pz), 4.07 (P3), 3.63 (P4), and 3.02 (Fz). After 31
neurofeedback sessions aimed at inhibiting theta (4–8 Hz) and rewarding low beta (12–15 Hz), his
mother reported positive changes in all the diagnostic criteria defining autism in DSM-III-R (e.g.
attending and reacting to others, imaginative play, seeking comfort, more talking and eye contact).
QEEG furthermore revealed that theta/beta power ratios had dropped below 3.0 at C3, C4, Fz, Pz, and
P4.
Further support for a relation between theta/beta power and autism was provided by Jarusiewicz
(2002) who conducted a group study investigating effects of neurofeedback in 12 autistic children,
compared with matched controls. The main protocol aimed at inhibiting theta (2–7 Hz) and increasing
sensory motor rhythm (SMR) activity (10–13 Hz) over the right motor area. Results indicated a
substantial decline in autistic behavior (26% as compared to 3% for the controls) as reflected by the
Autism Treatment Evaluation Checklist (ATEC). Parent reports furthermore indicated considerable
improvements on socialization, vocalization, school work, anxiety, tantrums, and sleep, whereas no or
minimal changes were found for the control group.
More recently Scolnick (2005) conducted a neurofeedback study with five children diagnosed with
Asperger disorder, each with unique behavioral problems, i.e. poor social skills, lack of empathy, and
inflexibility, coupled with abnormal high theta/beta ratios varying from 2.19 to 6.89. Each child’s
protocol was determined on the basis of their individual QEEG and consisted of variations on the
theme of rewarding 12–15 Hz in the lower beta range while inhibiting slower 4–10 Hz activity in the
theta band. After 24 sessions of neurofeedback, parents and teachers reported improvements in
behavior, i.e. less anxiety, more flexibility, higher self-esteem, more empathy, improvement in
frustration toleration, increased social interaction, and fewer severe mood changes. Furthermore, in
two of the five children, theta/beta ratios changed into a positive direction.
The above studies suggest that neurofeedback protocols that inhibit theta and reward beta or SMR
may hold particular value for the treatment of autistic children, similar to the treatment of ADHD.
Surprisingly, however, no functional explanations currently exist for these improvements and little is
known about the neural mechanisms involved. In light of the increasing popularity and clinical use of
neurofeedback, however, fundamental explanations become increasingly more relevant. Vice versa,
the efficacy of evolved protocols and practices may help to advance more fundamental insight into
impairments underlying neuropsychological deficits such as autism and ADHD. The aim of the current
study therefore is twofold. On the one hand we wished to contribute to clinical practice by evaluating
the efficacy of neurofeedback for treatment of autism, whereas on the other hand we intended to
further our understanding of the possible (neural) mechanisms supporting treatment effects.
In order to optimize the neurofeedback treatment protocol for children with ASD and its rationale,
further methodological improvement is necessary in the form of controlled studies, larger sample
sizes, a more accurate description of sample characteristics and collection of follow-up data. Another
guiding principle should be the assessment of the clients’ satisfaction with the treatment and
procedure to enhance the social validity of the approach. Social validity refers to the use of evaluative
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functioning in children with autism spectrum disorders, Res Autism Spectr Disord (2008),
doi:10.1016/j.rasd.2008.05.001
feedback from clients to guide program planning and evaluation (Schwartz & Baer, 1991). Social
validity may be evaluated at three levels of treatment: goals, procedures, and outcomes (Wolf, 1978).
In the current study we included the above guidelines and evaluative measures (cf. Heinrich,
Gevensleben, & Strehl, 2007) to further validate the use of neurofeedback treatment of ASD.
In addition to the practical evaluation of neurofeedback treatment of ASD, the current study aimed
to contribute to our understanding of the cognitive and neural mechanisms that underlie
neurofeedback improvements in ASD. We hypothesize that the reason for the efficacy of
neurofeedback protocols that reduce theta and reward beta lies primarily in the enhancement of
activation in the anterior cingulate cortex (ACC). The ACC is one of the main generators of theta
(Meltzer, Negishi, Mayes, & Constable, 2007;Onton, Delorme, & Makeig, 2005;Tsujimoto, Shimazu, &
Isomura, 2006), and is well known for its role in regulating cognitive and emotional processes in the
brain contributing to cognitive control and executive function (review in Bush, Luu, & Posner, 2000).
Neuroimaging studies investigating the neural basis of ADHD and ASD have reported hypo-activation
and functional under-connectivity of the ACC (Bush, Valera, & Seidman, 2005;Cherkassky, Kana,
Keller, & Just, 2006) which could explain why cognitive deficits associated with ADHD and ASD often
seem to fall within the domain of self-regulation and executive function (Barkley, 1997). Furthermore,
combined EEG-fMRI studies have indicated a negative relationship between theta power and BOLD
signal in the ACC (Meltzer et al., 2007), in line with the hypothesis that theta activation in autistic
children is associated with under-activation of the ACC (Murias, Webb, Greenson, & Dawson, 2007).
Following the above reasoning we predict that down-regulation of theta activity should enhance
activation of the ACC and executive control mechanisms of the brain, which should lead to more
efficient behavior of ASD children on tasks requiring executive function. To investigate the
hypothesized relationship between theta and executive function, a group of children from the autism
spectrum were selected for neurofeedback training that reduced theta activity while rewarding low
beta activity, in accordance with the standard ADHD treatment protocol. A waiting-list control group,
also diagnosed with ASD, received neurofeedback training at a later time and served as a baseline to
determine treatment effects of neurofeedback on children’s executive, social and neurophysiological
levels of functioning.
1. Method
1.1. Participants
Fourteen children with ASD (12 males; 2 females) with a mean age of 10.1 years (range 8–12 years)
were recruited by an advertisement in a magazine for parents of ASD children. Inclusion criteria were
an IQ-score of 70 and above and the presence of ASD as diagnosed by a child psychiatrist or health care
psychologist. All participants had the diagnosis pervasive developmental disorder—not otherwise
specified (PDD-NOS). Each diagnosis was confirmed by a clinical psychologist and by results on the
CCC questionnaire. Excluded were children using medication, children with a history of severe brain
injury, and children with co-morbidity such as ADHD and epilepsy. The seven children who applied
first, were assigned to the intervention group. The control group included seven children who were
recruited out of a larger group of children who applied later and were selected to match children of the
intervention group on diagnosis, age, sex, and intelligence scores. Table 1 represents the demographic
characteristics of the intervention and the control group. There were no significant differences
between both groups with respect to the variables sex, mean age, total IQ, verbal IQ, and performal IQ.
Children in the control group were invited for neurofeedback training after finishing the present study.
1.2. Procedure
A non-randomized pre-test–post-test control group design with individual matching was used
with follow-up measurements after 3 months. During a baseline period, all participants were pre-
tested on QEEG and a range of executive functions tasks, and parents completed a communication
checklist. After 40 sessions of neurofeedback, or comparable time interval for the waiting-list control
group, QEEG, executive functions skills, and communicative abilities were re-collected. During follow-
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up, 3 months after ending neurofeedback sessions, again, QEEG, executive functions skills and
communicative abilities were measured together with a questionnaire to estimate behavioral
improvements in children. For the intervention group, the follow-up measurement included a social
validity questionnaire. The research design was authorized by an ethics committee for behavioral
sciences.
An interview was conducted with the parents prior to the neurofeedback treatment to survey the
anamneses of the child, family history, and current problems of the participant. Procedures and
possible side effects were explained to all participants. All participants signed informed consent. Pre-
and post-treatment measures took 2 h for each participant to complete. Tasks for executive
functioning were given to all participants in a fixed order. Questionnaires were filled out by the
parents at home.
1.3. QEEG measurement
Children’s QEEG (quantitative electroencephalogram) was recorded and digitized with a TruScan
32 Acquisition EEG System (Deymed Diagnostic, USA). Data were acquired using a stretchable
electrode cap embedded with 19 sensors at scalp locations Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4,
T5, P3, Pz, P4, T6, O1, and O2, according to the International 10/20 System (Jasper, 1958). A ground
electrode was placed between Fp2 and F8 and two ear clips were used as reference electrodes (A1 and
A2). Impedance was kept below 5 k
V
, with a maximum difference of 1 k
V
between electrodes. Data
were collected for 3 min in an eyes open and an eyes closed condition.
1.4. Neurofeedback training
A portable NeXus-4 amplifier and recording system (Mindmedia, The Netherlands) was used for
neurofeedback training and concurrent data collection. Ag/AgCl disposable snap-on sensors (MedCaT,
The Netherlands) were applied to the patients’ scalp at locations C3 and C4.
Each participant in the intervention group visited a private practice twice a week until 40
sessions were completed. Training was carried out by a state licensed psychotherapist with
extensive training in neurofeedback. During each session a protocol was carried out, which
consisted of a baseline of 3 min (i.e. no feedback), followed by seven 3-min intervals of
neurofeedback. Neurofeedback intervals were separated by 1-min rest intervals, in which the
participant was instructed to sit still and relax, without receiving feedback. Neurofeedback
training followed a standard ADHD training protocol (Heinrich et al., 2007 for review) aimed at
reducing theta activity (4–7 Hz) while increasing activity in the low beta band (12–15 Hz)
1
at C4
(reference at A1). The signal at location C4 was fed back to the patient in visual form. Theta and
beta activity were visualized in separate bar graphs on the computer screen and participants were
instructed to ‘‘try to move down the theta activity below the criterion line on the computer screen
Table 1
Demographic characteristics of the intervention group (IG) and the control group (CG)
Variable Intervention group (n= 7) Control group (n=7) p
Gender (male/female) 6/1 6/1
Mean age (years)
a
9.63 (1.53) 10.64 (1.41) .220
Mean total IQ
b
92.50 (16.05) 93.83 (13.67) .891
Mean verbal IQ
c
97.80 (18.38) 95.40 (18.15) .841
Mean performal IQ
d
99.60 (25.77) 93.40 (9.71) .628
Note. Standard deviations are in parentheses.
a
Age range IG: 8–12, CG: 9–12.
b
Total IQ range IG: 73–111, CG: 82–199.
c
Verbal IQ range IG: 77–119, CG: 78–125.
d
Performal IQ range IG: 73–134, CG: 81–108.
1
Sometimes low beta activity is also referred to as SMR (sensorimotor rhythm) to indicate its assumed rolandic origin.
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functioning in children with autism spectrum disorders, Res Autism Spectr Disord (2008),
doi:10.1016/j.rasd.2008.05.001
and to move up the beta activity above the criterion line, using the feedback to guide you’’. During
intervals when specified amplitude conditions were met, participants were rewarded by the
continuation of a short movie that was selected to fit each child’s individual interest and age. All
movies were presented with audio. When participants failed to maintain power within the
required range, the movie and music would stop playing. Individual criteria were set to allow each
participant to reach the reward.
1.5. Executive function tasks
According to Smidts (2003), executive functions are typically divided into four separate
subdomains, each including one or more executive function tests.
1.5.1. Attentional control
Attentional control encompasses selective attention, visual as well as auditory, and response
inhibition. Visual selective attention was measured by the Continuous Performance Test (CPT), a
subtest of the neurocognitive test battery CNS Vital Signs (CNSVS). In the CPT, the participant has to
respond to one particular character on the computer screen while ignoring other characters during
5 min. The score for visual selective attention is based on the amount of errors of the CPT (range 0–
200). Selective attention for auditory stimuli was measured by the Test of Sustained Selected
Attention (TOSSA; Kova
´cs, 2005b). In the TOSSA, participants have to respond to sets of 3 beeps while
ignoring sets of 2 or 4 beeps. Beeps are presented during 8 min with variable speed. The test score
reflects the percentage of good answers, calculated by dividing the number of hits by the total
amount of items, times 100. Response inhibition is divided in a verbal and a motor variant. Verbal
response inhibition was assessed by the Stroop test (Stroop, 1935). In this test, participants have to
read aloud as soon as possible (A) 100 words (green, red, yellow, and blue), (B) the color of 100 colored
rectangles, and (C) the color of the ink of 100 written incongruent color names. The goal in part C is to
pronounce the name of the color of the ink, while ignoring reading the word. The score on this test is
represented by the interferential time (time C minus time B). Motor response inhibition was assessed
with the response inhibition score (RIS; range 0–100) of the TOSSA, based on the number of
commission errors.
1.5.2. Cognitive flexibility
Cognitive flexibility covers verbal memory and visual memory, set-shifting, concept generation,
and feedback utilization. Verbal memory and visual memory were assessed by the Verbal Memory
Test (VBM) and the Visual Memory Test (VIM) of the CNSVS, respectively. In the VBM and the VIM,
participants have to memorize words (n= 15, VBM) and geometric figures (n= 15, VIM) and later
recognize them in a series of distracters (n= 15 for both tests). The sum of correct responses was
calculated to get a final score for verbal memory (maximum = 60) and a final score for visual memory
(maximum = 60). Set-shifting was examined by the Trail Making Test (TMT; Reitan, 1956). In the TMT,
participants have to switch between the numerical mode and the alphabetic mode by connecting 26
numbers and characters in the 1-A-2-B-3-C order. A score on the TMT is comprised of the total time
needed to finish the test, translated into an age related t-score (range 20–75). Concept generation and
feedback utilization were examined by the Milwaukee Card Sorting Test (MCST; Kova
´cs, 2005a), a
computerized version of the Wisconsin Card Sorting Test. The participant had to generate and apply a
non-spoken rule for sorting cards (n= 60), based on feedback (e.g. ‘good’ or ‘fault’). These card sorting
principles can be either color, shape, or number and change after every 10 correct answers. An
indicator for cognitive flexibility is the number of categories (range 0–6) a participant creates with 60
cards.
1.5.3. Goal setting
Goal setting was assessed by the Tower of London (TOL; Kova
´cs, 2005c). Participants had to copy a
construction of blocks and bars by moving three prearranged different colored blocks along three bars
of different lengths. The score on the TOL, is a percentage calculated by dividing the participants’ score
by the maximum score, times 100.
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Please cite this article in press as: Kouijzer, M.E.J, et al., Neurofeedback improves executive
functioning in children with autism spectrum disorders, Res Autism Spectr Disord (2008),
doi:10.1016/j.rasd.2008.05.001
1.5.4. Speed and efficiency
Speed and efficiency was measured by the Symbol Digit Coding (SDC) of the CNSVS. Participants
had to code as many symbols as possible within 2 min, according to a set of eight symbol–digit
pairings that are displayed continuously for reference on screen. A score for speed and efficiency is
calculated by the number of correct responses minus the number of errors on the SDC.
1.6. Questionnaires
1.6.1. Children’s Communication Checklist (CCC-2-NL)
The CCC-2-NL (Geurts, 2007) was used to assess improvement in participant’s language structure,
pragmatics, and social interaction. Language structure includes the subscales speech production,
syntax, semantics, and coherence. The domain of pragmatics consists of the subscales inappropriate
initiation, stereotyped conversation, use of context, and non-verbal communication. The domain of
social interaction includes the subscales social relations and interests. An age-related standard score
was calculated for each subscale and for the composed scales general communication (language
structure and social interaction) and pragmatics.
1.6.2. AUTI-R
An adapted version of the AUTI-R (Berckelaer-Onnes & Hoekman, 1991) was used to study
improvement of children in the intervention group on social interaction, communication, and
restricted, repetitive, and stereotyped patterns of behavior, interests and activities. Eleven items of the
AUTI-R that were considered not relevant for the present study and five items that did not fall into the
categories social interaction, communication or restricted, repetitive, and stereotyped patterns of
behavior, interests and activities, were excluded from the list. The adapted questionnaire contained 33
items, subdivided into the scales Social interaction (n= 10), Communication (n= 8), and Behavior
(n= 15). Items on the questionnaire were rated on a 5-point scale, with 1 point indicating low
progression and 5 points indicating strong progression. Mean scores were calculated for the subscales
Social interaction, Communication, and Behavior, and for the complete questionnaire.
1.6.3. Social validity
Social validity was assessed by a self-constructed anonymous 5-point scale questionnaire with 15
items about the Goals of treatment (n= 4), Treatment procedures (n= 8), and Outcomes (n=3)(Wolf,
1978). All items were scored, with 1 point indicating low satisfaction and 5 points indicating high
satisfaction. Sum scores were calculated for each subscale to evaluate the acceptability of the
neurofeedback treatment. Three open response questions were added to assess whether parents had
any remarks, whether they had suggestions to improve neurofeedback treatment, and whether they
would recommend neurofeedback treatment to others.
1.7. Data analysis
1.7.1. QEEG
Eye blinks and other artifacts were manually removed from the raw EEG data by an independent
EEG specialist and statistician, who was blind to the subject’s classification (i.e. intervention group vs.
control group) and the type of EEG (i.e. pre- vs. post-training). The raw data were processed with fast
Fourier transformation to determine the magnitude of each frequency band in microvolt. Separate
power measures were calculated for delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), low beta (12–
15 Hz), beta 2 (15–18 Hz), beta 3 (18–25 Hz), and high beta (25–30 Hz). EEG data of all individuals
were compared with the Neuroguide (Thatcher et al., 2003) database, which provides reliable
descriptors of normative brain electrical activity (John et al., 1988). Linked ears montages were used.
Data from all 19 electrode sites were used for analysis. The split-half reliability and test–retest
reliability of the artifact free data of all subjects were above .95 (p<.05). Absolute power (the amount
of energy in
m
V
2
), relative power (the percentage of power in a frequency band relative to the total
power contained by all other frequency bands), and coherence were calculated for each participant,
frequency band, and individual electrode lead. All power and coherence values were subsequently
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transformed to Z-scores, reflecting deviancy from the normative database (Hughes & John, 1999). A 2
(Time: time1 vs. time2) 2 (Group: intervention vs. control) mixed MANOVA was performed to look
for treatment effects in the intervention group relative to controls.
1.7.2. Session data
Eye blinks and other artifacts were manually removed from the raw EEG data of 40 sessions,
collected at C3 and C4 during training intervals. The raw data were Fast Fourier Transformed (FFT) to
determine the power of each frequency. Separate power measures were calculated for delta (1.5–
3.5 Hz), theta (4–8 Hz), alpha (8–12 Hz), low beta (12–15 Hz), beta 2 (13–21 Hz), and high beta (22–
30 Hz). Power values of each frequency band were log-transformed. A 2 (Time: first sessions vs. last
sessions) 2 (Location: C3 vs. C4) mixed MANOVA was conducted to compare power during the first
20 sessions with the final 20 sessions. Furthermore, the efficacy of neurofeedback over sessions per
frequency band was estimated for each individual subject by calculating a linear regression line and
Spearman regression coefficient fitting the progression of power values over sessions.
1.7.3. Executive function tasks
Results of a one-sample Kolmogorov–Smirnov test showed that data on each variable did not
deviate significantly from normality. A MANOVA was conducted to test differences in executive
functions for the intervention group and the control group at time1. Neurofeedback related changes in
executive functions were verified by performing a 2 (Time: time1 vs. time2) 2 (Group: intervention
vs. control) mixed MANOVA.
1.7.4. Questionnaires
Results of a one-sample Kolmogorov–Smirnov test showed that data on each variable of the CCC-2
did not deviate significantly from normality. MANOVA was conducted to test for differences on the
CCC-2 between the intervention group and controls at time1. Neurofeedback related changes on the
CCC-2 were verified by performing a 2 (Time: time1 vs. time2) 2 (Group: intervention vs. control)
mixed MANOVA.
In order to assess whether the intervention group decreased in ASD-symptoms more than the
control group, a comparison between scores on the adapted AUTI-R of the intervention group and
controls was made using a MANOVA with between-subjects factor Group.
The social validity of the neurofeedback treatment was evaluated via the sum scores of the
subscales Goals, Procedures, Outcomes, and via open response questions.
2. Results
2.1. Session data
At the individual level, Spearman’s correlation coefficients showed a significant reduction of theta
power (4–7 Hz) over 40 sessions of neurofeedback in five participants at C4 (ps<.05, r=.596 to
.718) and in the same five participants at C3 (ps<.05, r=.496 to .771). Two participants did not
show significant reduction of theta power at C4 (p= .411, r= .035; p= .359, r= .056) and C3 (p= .018,
r= .453; p= .170, r= .135). Results of theta reduction at C3 and C4 for all participants can be found in
Fig. 1.
Low beta power (12–15 Hz) increased significantly over time for five participants at C4 (ps<.05,
r= .218–.410) and for six participants at C3 (ps<.05, r= .253–.529). Two participants did not show
significant increase of low beta power at C4 (p= .311, r= .079; p= .173, r= -.145) and one participant
did not show significant increase at C3 (p= .372, r= .051) (see Fig. 2).
Besides changes in theta and low beta power, changes in delta power (1.5–3.5 Hz) were found as
well. Delta power decreased significantly in five participants at C4 (ps<.05, r=.449 to .555) and in
five participants at C3 (p<.05, r=.291 to .562). No increase in delta power was found in two
participants at C4 (p= .125, r= .177; p= .356, r= .125) and at C3 (p= .263, r= .098; p= .054, r=.243).
Results can be found in Fig. 3. In alpha power (8–12 Hz), beta 2 power (13–21 Hz), and high beta power
(22–30 Hz), no unanimous patterns of change were found.
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Analysis at group level further supported our results. A 2 (Time: first sessions vs. last sessions) 2
(Location: C3 vs. C4) mixed MANOVA showed significant reduction of theta power (4–7 Hz)
(F(1,6) = 11.419, p<.05,
h
= .656) and significant increase of low beta (12–15 Hz) (F(1,6) = 21.922,
p<.01,
h
= .785) at C3 and C4 over 40 sessions of neurofeedback. Besides power changes in theta and
low beta, a significant decrease of delta power (1.5–3.5 Hz) over time was found as well
(F(1,6) = 6.982, p<.05,
h
= .538). For alpha power (8–12 Hz), beta2 power (13–21 Hz), and high
beta power (22–30 Hz), no significant effects of time were found.
Decrease of delta power was significantly correlated with decrease in theta power (r= .667,
p<.01) and with increase in low beta power (r=.695, p<.01). The correlation between decrease in
theta and increase in low beta power was highly significant (r=.811, p<.001).
2.2. QEEG
The absolute and relative power of each frequency band for all 19 channels for the intervention
group and the controls were compared using MANOVA. In order to claim a treatment effect, we need
Fig. 1. Average theta (4–7 Hz) power during neurofeedback sessions recorded over C3 (left graph) and C4 (right graph)
indicating the reduction in theta power over consecutive sessions. Regression lines reflect the slope of theta reduction over time
for each individual patient, with
*
p<.05 and
**
p<.01.
Fig. 2. Average low beta (12–15 Hz) power during neurofeedback sessions recorded over C3 (left graph) and C4 (right graph)
indicating the increase in low beta power over consecutive sessions. Regression lines reflect the slope of beta enhancement over
time for each individual patient, with
*
p<.05 and
**
p<.01.
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functioning in children with autism spectrum disorders, Res Autism Spectr Disord (2008),
doi:10.1016/j.rasd.2008.05.001
the interaction between Time (time1 vs. time2) and Group (intervention vs. control) to be significant.
The mixed MANOVA suggested no significant multivariate interaction between Time and Group in the
target frequency bands, i.e. absolute (F(1,12) = 2.382, p= .149,
h
= .166) or relative theta power
(F(1,12) = .986, p= .340,
h
= .076) and absolute (F(1,12) = .018, p= .897,
h
= .001) or relative low beta
power (F(1,12) = .614, p= .449,
h
= .049). Univariate results of absolute and relative theta and low beta
power in 19 separate electrodes revealed no significant interaction effects either (range of F-
values = .000–3.977, ps>.05). A similar 2 (Time: time1 vs. time2) 2 (Group: intervention vs.
control) MANOVA for the other frequency bands, i.e. delta, alpha, beta2, beta3 and high beta revealed
no significant multivariate effects, neither for absolute nor relative power (range of F-values = .000–
1.820, ps>.05).
For the analysis of coherence, a 2 (Time: time1 vs. time2) 2 (Group: intervention vs. control)
mixed MANOVA was performed. Univariate results revealed a significant reduction of hypo
connectivity in theta power at time2 (F-values up to 17.572, ps<.05), especially between frontal and
central/temporal electrodes. However, since this reduction was found in both the intervention and the
control group, no significant interaction effects were found (range of F-values = .000–2.914, ps>.05).
2.3. Executive function tasks
A MANOVA was conducted to test the hypothesis that participants in the intervention group would
display the same scores as participants in the control group at time1. No statistical significant
differences between intervention and control group were found on tests for executive functioning at
time1 (F(1,12) = 1.066, p= .577,
h
= .842).
To analyze whether children in the intervention group scored significantly higher on tests for
executive functioning at time2 compared to the matched control group, a 2 (Time: time1 vs.
time2) 2 (Group: intervention vs. control) mixed MANOVA was performed. In order to claim a
treatment effect and to control for practice effects, we need the interaction to be significant.
2.3.1. Attentional control
Participant’s capacity for attentional control was tested using separate measures targeting
children’s attentional capacity in the visual and auditory domains and their ability to inhibit verbal
and manual response tendencies. Table 2 reports the behavioral results of all executive function tests
gathered for both groups at time1 and time2. No significant interaction between Time and Group was
found for measures of visual selective attention (F(1,11) = .047, p= .832,
h
= .004). Both groups made
very little errors in detecting a target letter in a continuous stream of distractors, leaving little or no
Fig. 3. Average delta (1.3–3.5 Hz) power during neurofeedback sessions recorded over C3 (left graph) and C4 (right graph)
indicating the reduction in delta power over consecutive sessions. Regression lines reflect the slope of delta reduction over time
for each individual patient, with
*
p<.05 and
**
p<.01.
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functioning in children with autism spectrum disorders, Res Autism Spectr Disord (2008),
doi:10.1016/j.rasd.2008.05.001
room for improvement at time2 (values for visual selective attention in Table 2 represent the amount
of errors found in 200 items). However, a significant Time Group interaction effect was found for
measures of auditory selective attention (F(1,11) = 8.437, p= .014,
h
= .434). Children in the
intervention group showed a considerable improvement in their ability to correctly detect auditory
targets in the TOSSA, from 48% to 62% correct responses after neurofeedback training, as compared to
the control group who showed minimal improvement from 68% to 69% correctly detected targets. In
addition, a significant interaction between Time and Group was found for children’s capacity to inhibit
verbal responses (F(1,11) = 4.890, p= .049,
h
= .308). Interference effects of written names were
strongly reduced from 68 s before to 30 s after neurofeedback training for the intervention group. The
control group also showed a difference between interference effects at time1 and time2 (66 and 50 s
respectively) but this reduction was about half the size of the effect found for the intervention group.
Consistent with the increased ability to inhibit verbal responses, children of the intervention group
were also better able to inhibit impulsive tendencies in responding on the TOSSA, suggesting
improved inhibition capacity after neurofeedback training (78% correctly inhibited before training vs.
90% after neurofeedback training). Only minimal improvements in impulse control were found for the
controls (89% correct inhibitions at time1 followed by 91% correct inhibitions at time2), resulting in a
significant Time Group interaction, F(1,11) = 5.064, p= .046,
h
= .315.
2.3.2. Cognitive flexibility
Children’s cognitive flexibility was investigated using measures of visual and verbal memory, set-
shifting and concept generation. Neurofeedback training did not influence children’s capacity to
memorize and recognize words (F(1,11) = .021, p= .889,
h
= .002) and geometric shapes
(F(1,11) = .004, p= .952,
h
= .000). Both groups showed a minimal non-significant reduction of
performance from time1 to time2 (see Table 2), on verbal memory, F(1,11) = .355, p= .563,
h
= .031,
and visual memory, F(1,11) = .138, p= .717,
h
= .012. However, children’s set-shifting ability as
indexed by the TMT did show a significant Time Group interaction (F(1,11) = 5.602, p= .037,
h
= .337), reflecting improved cognitive flexibility and sequencing after neurofeedback treatment. For
the intervention group t-scores improved from 30 (time1) to 47 (time2), whereas only a small
improvement was found for the control group with t-scores improving from 30 (time1) to 34 (time2).
Also concept generation and use of feedback, as measured by the MCST, were found to improve
significantly for the intervention group as compared to the control group, F(1,11) = 5.081, p= .046,
h
= .316. After neurofeedback, ASD children discovered an average of 5 (out of 6) card sorting rules,
whereas before training they only reached an average of 2.5. In contrast, the performance of the
control group was comparable at time1 (3.5 rules) and time2 (3.8 rules).
Table 2
Means and standard deviations of the intervention group (IG) and the control group (CG) at time 1 and time 2 on tests for
executive functions
Time1 Time2
IG, M(S.D.) CG, M(S.D.) IG, M(S.D.) CG, M(S.D.)
Attentional control
- Visual selective attention 4.33 (2.81) 9.14 (14.44) 4.17 (4.26) 7.29 (8.90)
- Auditory selective attention 47.87 (14.21) 67.79 (25.61) 62.40 (14.18) 68.90 (27.30)
- Inhibition of verbal responses 68.17 (18.87) 65.71 (31.53) 30.00 (12.12) 50.14 (26.59)
- Inhibition of motor responses 78.50 (13.16) 89.84 (11.02) 89.93 (9.20) 91.47 (9.66)
Cognitive flexibility
- Verbal memory 53.33 (3.62) 51.29 (2.63) 52.17 (4.07) 50.57 (6.604)
- Visual memory 46.00 (3.74) 41.00 (5.57) 45.00 (4.34) 40.29 (8.321)
- Shifting 30.00 (15.68) 29.71 (10.50) 47.00 (13.27) 34.00 (13.29)
- Concept generation 2.55 (1.48) 3.50 (1.70) 4.96 (.45) 3.83 (1.42)
Goal setting 55.45 (9.07) 55.84 (18.17) 75.85 (9.17) 57.03 (11.89)
Speed and efficiency 34.33 (7.06) 41.00 (15.52) 41.33 (5.13) 43.86 (10.96)
Note.M: mean, S.D.: standard deviation.
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doi:10.1016/j.rasd.2008.05.001
2.3.3. Goal setting
Analysis of children’s goal setting capacity as assessed by the TOL showed a significant interaction
between Time and Group, F(1,11) = 7.198, p= .021,
h
= .396, reflecting a clear improvement in
complex sequential problems after neurofeedback training, as compared to the control children. At
time1 children from both groups reached an average performance of 55 (range 0–138). However,
whereas children of the control group showed little improvement (57 at time2), children of the
intervention group drastically improved their capacity score to 76 at time2.
2.3.4. Speed and efficiency
Children’s combined score for speed and efficiency on the SDC indicated a stronger improvement
for the intervention group than for the control group (see Table 2), but the required interaction
between Group and Time was not found significant (F(1,11) = .397, p= .542,
h
= .035).
A 2 (Time: time2 vs. follow-up) 2 (Group: intervention vs. control) mixed MANOVA indicated no
significant differences between post-treatment and 3-month follow-up measurements of children’s
executive functioning at follow-up, F(1,11) = .987, p= .602,
h
= .832.
2.4. Questionnaires
2.4.1. CCC-2
The CCC-2 measured parents’ appreciation of their children’s communication skills for different
aspects (subscales) of communication. A MANOVA was conducted in order to test the hypothesis that
participants in the intervention group would display the same scores on the CCC-2 questionnaire as
participants in the control group at time1. No statistically significant differences between intervention
and control group were found on the CCC-2 questionnaire collected at time1, F(1,12) = 54.149,
p= .106,
h
= .998.
To analyze whether children in the intervention group scored significantly higher on the CCC-2 at
time2 compared to the matched control group, a 2 (Time: time1 vs. time2) 2 (Group: intervention
vs. control) mixed MANOVA was performed. Separate analysis of the communication subscales of the
CCC-2 showed a significant Time Group interaction effect for non-verbal communication,
F(1,12) = 5.505, p= .037,
h
= .314, reflecting an improvement in non-verbal communication for the
intervention group, relative to the control group. For none of the other subscales the interaction
between Time and Group was found significant, all ps>.05. In Table 3 the average ratings of children’s
communication skills are reported for sub- and compound-scales of the CCC-2 for the control group
and the intervention group at time1 and time2. Lower values in Table 3 reflect better communication
skills. Analysis of the two compound scales, general communication and pragmatics, revealed a
significant interaction effect between Time and Group for general communication, F(1,12) = 5.379,
Table 3
Test results of the CCC-2 for the intervention group (IG) and the control group (CG) at time1 and time2
Time1 Time2
IG, M(S.D.) CG, M(S.D.) IG, M(S.D.) CG, M(S.D.)
General communication 115.14 (10.45) 115.86 (9.42) 101.29 (12.09) 114.29 (16.45)
Pragmatics 60.57 (7.00) 60.71 (7.25) 54.14 (5.579) 65.86 (20.84)
- Speech production 12.86 (2.54) 12.14 (3.63) 10.86 (2.96) 11.43 (4.08)
- Syntax 12.71 (1.89) 14.43 (1.40) 11.29 (2.69) 14.71 (1.89)
- Semantics 12.29 (2.29) 13.14 (1.57) 12.00 (2.08) 13.43 (1.40)
- Coherence 15.43 (1.81) 15.43 (1.51) 14.28 (1.50) 14.14 (3.58)
- Inappropriate initialization 14.29 (1.89) 13.57 (2.76) 13.86 (1.57) 14.57 (2.57)
- Stereotyped conversation 15.14 (2.27) 15.57 (1.40) 13.57 (1.81) 14.43 (3.64)
- Context use 15.14 (1.77) 16.71 (1.89) 13.71 (1.80) 16.14 (2.54)
- Non-verbal communication 15.86 (2.34) 14.86 (2.85) 13.71 (2.50) 15.57 (2.76)
- Social relations 15.57 (1.90) 14.42 (2.63) 14.57 (2.07) 14.57 (2.44)
- Interests 13.57 (1.90) 14.00 (2.16) 12.14 (3.67) 14.14 (2.04)
Note.M: mean, S.D.: standard deviation.
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p= .039,
h
= .310, but not for pragmatics, F(1,12) = .036, p= .852,
h
= .003. Parents of children in the
intervention regarded their children’s communication skills as more advanced after neurofeedback
training than before, whereas no such difference was found for the control group.
A 2 (Time: time2 vs. follow-up) 2 (Group: intervention vs. control) mixed MANOVA indicated no
significant changes in scores on the CCC-2 3 months after neurofeedback training was ended
(F(1,12) = .253, p= .930,
h
= .752).
2.4.2. AUTI-R
The AUTI-R measured parents’ evaluation of children’s improvements on social interaction,
communication, and typical behavior. Table 4 shows the average improvement for the intervention
and control group for each subscale of the AUTI-R. Following treatment, parents’ ratings suggested
improvements for children in the intervention group on social interaction, communication, and
typical behavior as compared to children in the control group. A MANOVA with between subjects
factor Group was used to analyze the results of the three subscales of the adapted AUTI-R. A significant
increase in desired behavior after neurofeedback training was found for the intervention group in
comparison with the control group. Children’s social interaction ability was valued to be improved
following treatment, as compared to the control group, F(1,12) = 17.775, p= .001,
h
= .618. Children’s
communication ability was assessed to be enhanced in comparison to the assessment of children in
the control group, F(1,12) = 29.054, p= .000,
h
= .725. Furthermore, typical autistic behavior was found
to be attenuated as compared to the assessment of children in the control group, F(1,12) = 7.782,
p= .018,
h
= .414.
2.4.3. Social validity
Social validity of the intervention was assessed using 5-point rating scales (5 = high satisfaction,
1 = low satisfaction). Neurofeedback treatmentwas a socially acceptable treatmentmethod with respect
to its goals, procedures, and outcomes. Parents of children in the intervention group indicated that they
were well informed about thegoals of treatment beforeintervention started (M= 4.67). Parents rated the
treatment as being neither aggravating for their child (M= 4.47), nor for themselves (M=3.34).Viewing
video’s during training was rated not to be aggravating at all for the children(M= 5), as was placement of
electrodes on the scalp (M= 4.83). The requirement of visiting the private practice twice a week for
training (M= 3.17) and for pre- and post-assessment (M= 3.17) was considered the most aggravating
part of the procedure for the parents, althoughthe mean scores on these itemsare still relatively positive,
i.e. in the direction of ‘not aggravating’. Parents indicated to be satisfied with the outcomes of the
treatment with respect to children’s social behavior (M= 3.83), communication skills (M=3.83),and
typical behavior (M= 3.83). All parents would recommend neurofeedback treatment to other parents of
children with ASD. Only two parents had suggestions for improvement of neurofeedback treatment,
which were a real lifeexperience of neurofeedback treatment for the parents themselves and more time
for evaluation during treatment. No parent had any other further remarks in addition to the personal
explanation of their answers on the 5-point scales.
3. Discussion
The present study evaluated the effects of a specific ADHD neurofeedback training protocol for
treatment of autistic children. Reduction of theta power was hypothesized to improve children’s
Table 4
Means and standard deviations of the subscales of the adapted AUTI-R for the intervention group (IG) and the control group (CG)
IG, M(S.D.) CG, M(S.D.)
Social interaction 36.50 (3.51) 30.71 (.92)
Communication 29.00 (1.79) 24.14 (.64)
Typical behavior 48.33 (3.44) 44.14 (1.06)
Total 113.83 (7.17) 99.00 (1.95)
Note.M: mean, S.D.: standard deviation.
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executive capacities by enhancing activation of the ACC, which is one of the main generators of theta
activation over central areas. Consistent with our prediction, children of the intervention group made
large improvements in performance on a range of executive function tasks after neurofeedback
training, whereas no such effects were found for a matched control group. These findings provide
further support for the impairment of executive functions in autism, and reinforce existing
neurobiological views on autism that suggested abnormal functioning of the ACC. Furthermore, our
findings provide further evidence in support of the view that neurofeedback may hold particular value
for treatment of children with ASD which might be comparable with the effects found with ADHD.
At a neurophysiological level, neurofeedback training successfully reduced theta power (4–7 Hz)
and significantly increased low beta power (12–15 Hz) in all but two of seven participants in the
intervention group. Interestingly, and consistent with our hypothesis that neurofeedback protocols
that target children’s theta/beta ratio mainly work because they reduce theta power, attenuation of
theta power was found more reliable than enhancement of beta power over sessions. Children’s
individual Spearman correlation coefficients reflected significant reductions of theta in five
participants showing consistent effects over both hemispheres at C4 (average r= .68) and C3
(average r= .64), and enhancement of beta in five participants at C4 (average r= .30) and C3 (average
r= .38). Furthermore, consistent decreases in delta power (1.5–3.5 Hz) were found for five participants
at C4 (average r= .55) and at C3 (average r= .45). The gradual reduction in delta power probably co-
occurred in conjunction with the reduction in theta power, which is further supported by the strong
correlation between power reductions of both frequencies over time (r= .67).
Considering the consistent suppression of theta and delta frequencies and enhancement of low
beta activation over time across sessions, one could imagine structural changes in QEEG to develop
between pre- and post-test recordings. However, no significant changes were found in the QEEG of the
intervention group as compared with QEEG data of the control group. Our findings are in line with
results of Kropotov et al. (2007) who found no notable changes in QEEG power spectra of children with
ADHD after neurofeedback training, although neurofeedback was found to affect the amplitude of
event-related potential (ERP) components.
Coben and Padolsky (2007) found changes in children’s QEEG coherence after neurofeedback
training reflecting a decrease in cerebral hyper-connectivity in 76% of all children of the intervention
group. QEEG coherence values were only available for the intervention group, not for the control
group. In the present study, changes in connectivity were found for both the intervention and the
control group. These findings suggest a test–retest effect between pre- and post-test EEG assessment
which could, e.g. reflect differences in vigilance or arousal between the two assessments. That is,
young children may be more alert and attentive during their first EEG assessment as compared to the
second time. This different mental state may be responsible for the observed differences in QEEG
coherence between the pre- and post-test in both groups. Another explanation for the absence of
differences in QEEG coherence is the small sample size that was used.
At a cognitive level, neurofeedback training was hypothesized to improve the executive functions
of children with ASD, comparable with the success of the protocol in the treatment of ADHD (Butnik,
2005). Significant improvement in attentional control, cognitive flexibility and goal setting were noted
for children in the intervention group when compared to children in the control group. These results
are important because they reflect a serious cognitive improvement in the intervention group that
cannot be reduced to differences in perceived well-being, e.g. by parents. Instead, these findings
indicate that neurofeedback training was associated with a clear improvement in cognitive
functioning on tasks requiring executive control. Improvements were found for the majority of tasks
taxing executive control, with strong improvements on sustained auditory selective attention (30%
more correct responses), inhibition of verbal responses (55% reduction in response interference time),
inhibition of motor responses (15% reduction of commission errors), set shifting (57% reduction of
time needed to switch between the numerical and alphabetical mode), concept generation (50%
increase in the number of card sorting categories created), and planning ability (37% increase in
performance on the Tower of London task). Symbol digit coding was found improved (20% more
accurate) for the treatment group, but the difference with the improvement of the control group (7%)
was not significant. No noteworthy improvements were found on tasks taxing verbal and visual
memory, and sustained visual attention. Most children showed to be already highly efficient on these
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tasks before the start of the neurofeedback treatment at time1, leaving little room for further
improvement. Coben and Padolsky (2007) evaluated executive functioning of children with ASD after
neurofeedback training using a questionnaire completed by parents and teachers. In agreement with
the present results a significant improvement on measures of executive functions was reported. The
present experimental findings further extend these previous results by showing enhanced
performance on a range of cognitive tasks requiring executive control. Whereas the appraisal of a
child’s level of executive functioning might be influenced by wishful thinking or social expectation,
such factors cannot explain a 40% average increase in cognitive performance. The fact that similar
improvements were found over a range of different executive tasks further strengthens the conclusion
that neurofeedback substantially enhanced the executive capacity of children with ASD. These results
are furthermore in line with recent models that suggest a single genetic factor to underlie most
executive functions (Friedman et al., 2008).
We hypothesized that the elevated theta power that characterizes autistic children is functionally
related to their executive impairment. Electroencephalographic and magnetoencephalographic studies
have localized frontal theta activation to the rostral ACC (Gevins, Smith, & McEvoy, 1997;Ishii et al.,
1999) and studies combining EEG and fMRI have consistently found correlations between theta power
and BOLD signal in rostral ACC (Meltzer et al., 2007;Pizzagalli, Oakes, & Davidson, 2003;Sammer et al.,
2007). Interestingly, ACC activation and theta power appear to be inversely related. High-functioning
autistic individuals show hypoactivation and reduced connectivity of the ACC (Cherkassky et al., 2006;
Kana, Keller,Minshew, & Just, 2007) whereas EEGmeasures consistently indicateelevated levels of theta
power over medial frontal areas in ASD (e.g. Murias et al., 2007). Meltzer et al. (2007) found increasing
working memory load to be associated with enhancements of EEG theta power which correlated
negatively withBOLD signal in a network of areas including the rostral ACC (Meltzer et al., 2007).Similar
findings were reported by Sammer et al. (2007) using mental arithmetic-induced workload and Kana
et al. (2007) usinga response inhibition paradigm.Interestingly, deactivation of the ACC during cognitive
demanding tasks is often found in association with deactivations of other (medial) areas, such as the
precuneus,which together have beenlabeled the default mode network(DMN) reflecting itshigh default
metabolism during rest (Gusnard, Raichle, & Raichle, 2001). Much interest has developed in
understanding the function of the DMN and several interesting views have been formulated which
appear to converge on the idea that the DMN is involved in self-referential processing (Northoff et al.,
2006) and understanding others’ intentions through mental simulation (Uddin et al., 2007). These
findings may have implications for understanding social impairments in ASD. However, for the present
discussion it is first important to note that the rostral ACC is not directly involvedin executing cognitive
control (Rushworth et al., 2004), but that its activation is inversely related to other areas that are
activated duringcognitive tasks, such as the lateral prefrontalcortex (Greicius, Krasnow,Reiss, & Menon,
2003). Following this suggestion, Fox, Snyder, et al. (2005) discovered strong spontaneous
anticorrelations between a ‘‘task-negative’’ DMN and an opposing ‘‘task-positive’’ attentional network,
in a resting state. Kelly et al. (2008) furthermore found differences in individual attentional capacity to
depend on the strength of the negative correlation between the two opposingnetworks, with a reduced
antiphase relation resultingin more variable behavioral performance. In addition, a recent fMRI studyby
Weissman et al. (2006) indicated that a failure to suppress the DMN may result in lapses of attention.
Uddin et al. (in press) yield further support for this view by indicating that the balance between the two
networks is primarily controlled by the DMN.
Importantly, these findings provide a possible mechanism through which we can understand the
relation between theta power, ACC activation, and executive function. The enhancement of theta that
is consistently found during cognitive effortful tasks, such as use of working memory (Jensen & Tesche,
2002), mental arithmetic (Mizuhara, Wang, Kobayashi, & Yamaguchi, 2004), error monitoring (Luu,
Tucker, & Makeig, 2004), and sentence comprehension (Bastiaansen, van Berkum, & Hagoort, 2002),
probably reflects deactivation of the rostral ACC/DMN, to allow activation in (task-positive) areas
supporting the processing of external goals (cf. Fransson, 2005). Consistent with the hypothesis that
the executive problems of autistic individuals may originate from a defective DMN, Kennedy et al.
(2006) recently found that autistic subjects, as compared with controls, did not deactivate their DMN
during a range of cognitive and emotional Stroop tasks. Inability to deactivate or modulate activation
of the DMN might thus impair the engagement of task-positive areas exerting cognitive control.
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So far we have mainly focused on theta and its possible contribution to improvements in executive
control. However, in addition to theta reduction the neurofeedback protocol also operated to enhance
beta activation, which might also have contributed to the success of the treatment. Interestingly,
whereas theta activation is negatively related to activation in medial frontal areas, beta power appears
to be positively related to activation in those same areas, as is indicated by recent EEG-fMRI studies
(Laufs et al., 2003;Mantini, Perrucci, Del Gratta, Romani, & Corbetta, 2007) and intracerebral
recordings studying the neural origins of the beta rhythm (Boc
ˇkova
´, Chla
´dek, Jura
´k, Hala
´mek, & Rektor,
2007). That is, comparable with the effect of theta, enhancing beta should also increase activation in
the DMN. In other words, the effects of reducing theta and at the same time enhancing beta power may
actually work together in parallel to increase activation of hypoactive areas of the DMN in ASD
patients.
2
Interestingly, the hypothesis that ASD is primarily characterized by underactivation of the DMN
may explain both executive dysfunctioning and social deficits that are typical of ASD. As was indicated
earlier, parts of the DMN are known to be involved in self-referential processes and internal models of
the self (reviews in Northoff & Bermpohl, 2004;Northoff et al., 2006). Importantly, the capacity to
mentalize about others’ intentions and their internal states is thought to rely for a large part on our
ability to simulate others’ thoughts and feelings via the self. That is, we can understand what others
might be feeling, thinking, or aiming for, by putting ourselves into their shoes, i.e. by imagining what
we would feel, think or do in their situation (Keysers & Gazzola, 2007). In other words, impairments of
the DMN supporting self-referential thought could well be held responsible for a reduced ability to
represent intentions and mental states of others, which in turn would result in various social
impairments. Consistent with this perspective, several studies have indicated similar activations of
DMN areas in conditions that required subjects to either think about themselves or think about close
others (see review in Mitchell, Macrae, & Banaji, 2006;Moriguchi et al., 2006;Ochsner et al., 2005;
Seger, Stone, & Keenan, 2004;Uddin et al., 2007). Furthermore, studies investigating structural
abnormalities in autistic brains have been identified to overlap areas that are known to support theory
of mind tasks and social cognition (Abell et al., 1999;Barnea-Goraly, Kwon, & Menon, 2004;Haznedar
et al., 2000).
In line with the above suggestion that neurofeedback enhancement of DMN activation may both
reduce ASD executive dysfunctions and at the same time improve children’s social and communicative
abilities, a significant improvement in general communication was found for children in the treatment
group (14%), but not for children in the control group (7%) on the CCC-2. This result was further
supported by the estimated improvement of children in the treatment group on levels of social
interaction (16%), communication (17%), and typical behavior (9%) as measured by the AUTI-R. These
findings are in line with previous studies that reported significant reductions in ASD symptoms (Coben
& Padolsky, 2007;Jarusiewicz, 2002) and improvements in behavior on several social and cognitive
factors (Scolnick, 2005;Sichel et al., 1995) following neurofeedback training inhibiting theta
activation.
Although the present findings are encouraging, studies with improved methodology regarding the
effectiveness of neurofeedback training for children with ASD and other types of ASD are needed. This
study used the same training protocol for each participant, but evidence is now growing for the use of
an individualized protocol based on the individual EEG. We intend to incorporate protocols based on
individualized EEGs in future research. The most important methodological improvement would be to
control for direct, unintentional effects of neurofeedback training, such as providing extra time and
attention to participants in the intervention group twice a week and learning them to handle an
attention-demanding task like neurofeedback (Heinrich et al., 2007). We also expect indirect influence
of neurofeedback training on children in the intervention group via their parents. Parents have brief
talks or conversations with the neurofeedback trainer the minutes before and after neurofeedback
sessions and during evaluations, and they get advice, encouragement, support, and compliments.
These occasions raise expectations of improvement in parents, act upon parents’ answers on behavior
2
As a side-note, however, bear in mind that both theta and beta rhythms are not the sole property of the DMN areas or its
associated function. Both theta and beta rhythms have been found in association with other areas and in support of different
cognitive functions (e.g. Mantini et al., 2007).
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15
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RASD-102; No of Pages 18
Please cite this article in press as: Kouijzer, M.E.J, et al., Neurofeedback improves executive
functioning in children with autism spectrum disorders, Res Autism Spectr Disord (2008),
doi:10.1016/j.rasd.2008.05.001
questionnaires, and change the parents’ approach to their children. A solution for this problem would
be randomized double blind studies with random feedback for controls. However, the use of such a
placebo condition raises ethical questions and therefore does not seem feasible. Instead of placebo
feedback, neurofeedback training could be compared with established interventions like medication
and behavior therapy (Heinrich et al., 2007), like Fuchs et al. (2003) did in ADHD. However, in the case
of ASD it does not seem easy to create such a design. Comparison with medication is not attainable,
since no appropriate medication is available for ASD (Buitelaar & Willemsen-Swinkels, 2000).
Comparison with an intervention like behavior therapy seems almost impossible, since time and
intensity of both the neurofeedback training and the time-consuming and more intensive behavior
therapy should be kept constant (Matson and Smith, 2008).
In conclusion, application of a typical ADHD neurofeedback protocol to a group of ASD children was
found to be highly affective. Neurofeedback treatment resulted in clear improvements in children’s
executive functioning as reflected in a wide range of tasks. These findings provide further evidence for
a basic executive function impairment in ASD and suggest a relationship between enhanced theta/
beta ratio’s and hypoactivation of the ACC as a possible neural origin of this impairment.
Acknowledgement
We thank all the families who participated in the study. We also thank Erwin Hartsuiker
(MindMedia, The Netherlands) for his share in the availability of neurofeedback equipment.
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doi:10.1016/j.rasd.2008.05.001
... The rationale for adopting the typical ADHD NFB protocols as an intervention of choice for ASD neurotherapy is based on the assumption that neurofeedback protocols successfully applied for treatment of ADHD may also be efficacious to the treatment of children with autism. The evidence that some of the symptoms of ASD can be improved using this approach has been reported in the literature (Jarusiewicz 2002;Coben and Padolsky 2007;Coben 2008Coben , 2013Kouijzer et al. 2009aKouijzer et al. , b, 2010. A study conducted by Jarusiewicz (2002) investigating the utility of neurofeedback in autistic children supports the proposition that the theta-to-beta neurofeedback training protocol, which is generally applied to ADHD, can also be of use in autism (Kouijzer et al. 2009b). ...
... According to Arns et al. (2014), evaluation of neurofeedback for ADHD has gone through a long and winding road but still has to travel further in order to cover all grounds related to clinical effectivity and specificity. More details about specific protocols used in ASD can be found in several published studies on neurofeedback training in autism (Coben 2008(Coben , 2013Coben et al. , 2014Datko et al. 2017;Friedrich et al. 2014Friedrich et al. , 2015Kouijzer et al. 2009aKouijzer et al. , b, 2010Linden and Gunkelman 2013;Pineda et al. 2012Pineda et al. , 2014aSokhadze et al. 2014;Thompson and Thompson 2013;Wang et al. 2016;Zivoder et al. 2015). ...
Chapter
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.
... The rationale for adopting the typical ADHD NFB protocols as an intervention of choice for ASD neurotherapy is based on the assumption that neurofeedback protocols successfully applied for treatment of ADHD may also be efficacious to the treatment of children with autism. The evidence that some of the symptoms of ASD can be improved using this approach has been reported in the literature (Jarusiewicz 2002;Coben and Padolsky 2007;Coben 2008Coben , 2013Kouijzer et al. 2009aKouijzer et al. , b, 2010. A study conducted by Jarusiewicz (2002) investigating the utility of neurofeedback in autistic children supports the proposition that the theta-to-beta neurofeedback training protocol, which is generally applied to ADHD, can also be of use in autism (Kouijzer et al. 2009b). ...
... According to Arns et al. (2014), evaluation of neurofeedback for ADHD has gone through a long and winding road but still has to travel further in order to cover all grounds related to clinical effectivity and specificity. More details about specific protocols used in ASD can be found in several published studies on neurofeedback training in autism (Coben 2008(Coben , 2013Coben et al. , 2014Datko et al. 2017;Friedrich et al. 2014Friedrich et al. , 2015Kouijzer et al. 2009aKouijzer et al. , b, 2010Linden and Gunkelman 2013;Pineda et al. 2012Pineda et al. , 2014aSokhadze et al. 2014;Thompson and Thompson 2013;Wang et al. 2016;Zivoder et al. 2015). ...
Chapter
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.
... While bio-and neurofeedback methods have traditionally focused on externally measured biological, or EEG-based, indicators that typically lack spatial specificity, recent technical advances now permit real-time monitoring and control of specific brain regions and networks via feedback that is obtained during fMRI [127][128][129][130][131]. However, even without spatial localization, neurofeedback paradigms may be effectively for specific symptoms in ASD, e.g., potentially improving components of executive function by reducing atypically heightened theta/beta ratios by inhibiting theta activation and enhancing beta activation [132]. ...
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A wide variety of model systems and experimental techniques can provide insight into the structure and function of the human brain in typical development and in neurodevelopmental disorders. Unfortunately, this work, whether based on manipulation of animal models or observational and correlational methods in humans, has a high attrition rate in translating scientific discovery into practicable treatments and therapies for neurodevelopmental disorders. With new computational and neuromodulatory approaches to interrogating brain networks, opportunities exist for “bedside-to bedside-translation” with a potentially shorter path to therapeutic options. Specifically, methods like lesion network mapping can identify brain networks involved in the generation of complex symptomatology, both from acute onset lesion-related symptoms and from focal developmental anomalies. Traditional neuroimaging can examine the generalizability of these findings to idiopathic populations, while non-invasive neuromodulation techniques such as transcranial magnetic stimulation provide the ability to do targeted activation or inhibition of these specific brain regions and networks. In parallel, real-time functional MRI neurofeedback also allow for endogenous neuromodulation of specific targets that may be out of reach for transcranial exogenous methods. Discovery of novel neuroanatomical circuits for transdiagnostic symptoms and neuroimaging-based endophenotypes may now be feasible for neurodevelopmental disorders using data from cohorts with focal brain anomalies. These novel circuits, after validation in large-scale highly characterized research cohorts and tested prospectively using noninvasive neuromodulation and neurofeedback techniques, may represent a new pathway for symptom-based targeted therapy.
... Dassen, & Jansen, 2016;Kouijzer, de Moor, Gerrits, Congedo, & van Schie, 2009) and to change underlying structural brain characteristics in the TPJ and DLPFC (e.g., Jausovec & Jausovec, 2012;Klimecki et al., 2019;Valk et al., 2017). Thus, it is conceivable that behavioral trainings and neuro-modulation techniques that target the brain areas involved in social cognition and strategic reasoning could help to increase the number of punishers, be it conditional or independent, thereby promoting the enforcements of social norms via third-party punishment. ...
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The act of punishing unfair behavior by unaffected observers (i.e., third-party punishment) is a crucial factor in the functioning of human societies. In everyday life, we see different types of individuals who punish. While some individuals initiate costly punishment against an unfair person independently of what other observers do (independent punishers), others condition their punishment engagement on the presence of another person who punishes (conditional punishers). Still others do not want to partake in any sort of punishment (nonpunishers). Although these distinct behavioral types have a divergent impact on human society, the sources of heterogeneity are poorly understood. We present novel laboratory evidence on the existence of these three types. We use anatomical brain characteristics in combination with stated motives to characterize these types. Findings revealed that independent punishers have larger gray matter volume in the right temporo-parietal junction compared to conditional punishers and nonpunishers, an area involved in social cognition. Conditional punishers are characterized by larger gray matter volume in the right dorsolateral prefrontal cortex, a brain area known to be involved in behavioral control and strategic reasoning, compared to independent punishers and nonpunishers. Finally, both independent punishers and nonpunishers are characterized by larger gray matter volume in an area involved in the processing of social and monetary rewards, that is, the bilateral caudate. By using a neural trait approach, we were able to differentiate these three types clearly based on their neural signatures, allowing us to shed light on the underlying psychological mechanisms.
... Different neurological diseases can cause changes in brainwaves (delta, theta, alpha, beta and gamma wave activity) of different areas of the brain, so patients are able to learn how to correct the brain activities in disturbed areas and change them into normal ones through NFB training (Bosl et al., 2018;Cai et al., 2018;Jalali and Sho'ouri, 2021;Lubar, 1991;Sharma and Chopra, 2020). As a result, NFB can be utilized to treat or alleviate symptoms of various diseases such as depression, anxiety, hyperactivity, and autism (Hammond, 2005;Moore, 2000;Lee and Jung, 2017;Vernon et al., 2004aVernon et al., , 2004bKouijzer et al., 2009;Bazanova et al., 2018;Horrell et al., 2010;Van Doren et al., 2019;Wangler et al., 2011). In addition, the EEG signals of individuals who are actually competent in a particular field can be different from those of people who are non-skilled or novice (Vernon, 2005a;Bhattacharya and Petsche, 2002, 2005a, 2001a, 2001b, 2005bWagner, 1975a;Shourie et al., 2013aShourie et al., , 2014Shourie et al., , 2013bShourie et al., , 2011Salazar et al., 1990;Fink et al., 2009;Soltani et al., 2019;Karkare et al., 2009a). ...
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In this observational study the outcomes of an EEG-based infra-low-frequency (ILF) neurofeedback intervention on patients with attention deficit (hyperactivity) disorder (ADHD) are presented. The question is addressed whether this computer-aided treatment, which uses a brain-computer-interface to alleviate the clinical symptoms of mental disorders, is an effective non-pharmaceutical therapy for ADHD in childhood and adolescence. In a period of about 15 weeks 196 ADHD patients were treated with about 30 sessions of ILF neurofeedback in an ambulant setting. Besides regular evaluation of the severity of clinical symptoms, a continuous performance test (CPT) for parameters of attention and impulse control was conducted before and after the neurofeedback treatment. During and after the therapy, the patients did not only experience a substantial reduction in the severity of their ADHD-typical clinical symptoms, but also their performance in a continuous test procedure was significantly improved for all examined parameters of attention and impulse control, like response time, variability of reaction time, omission errors and commission errors. In a post neurofeedback intervention assessment 97% of patients reported improvement in symptoms of inattention, hyperactivity or impulsivity. Only 3% of the patients claimed no noticeable alleviation of ADHD-related symptoms. These results suggest that ILF neurofeedback is a clinically effective method that can be considered as a treatment option for ADHD and might help reducing or even avoiding psychotropic medication.
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In nearly all studies within the domain of neurofeedback, a threshold has been defined for each training feature in a way that subjects' status can be evaluated during training according to the given value. In this study, a hard boundary-based neurofeedback training (HBNFT) method based on the determination of decision boundary using support vector machine (SVM) classifier was proposed in which subjects' status were clarified considering a decision boundary and they could also be encouraged once entering a target area. In this method, a scoring index (SI) was similarly defined whose value was determined in accordance with subject performance during training. The results revealed that employing a classifier and determining a decision boundary instead of using a threshold could prove more successful in accurately guiding them towards a target area and also meet no needs to choose a basis for determining a threshold. Moreover, it was likely that the proposed method could be more efficient in controlling features and preventing extreme changes compared to those using variable thresholds.
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Presents the introductory editorial for this issue of the publication.
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