Running head: IS EEG-BIOFEEDBACK EFFECTIVE IN AUTISM
Is EEG-biofeedback an Effective Treatment in Autism Spectrum Disorders? A Randomized Controlled Trial
Mirjam E. J. Kouijzer1, Hein T. van Schie1, Berrie J. L. Gerrits2, Jan K. Buitelaar3, & Jan M. H. de Moor1
1
Behavioural Science Institute, Radboud University Nijmegen, the Netherlands, 2 BeterBrein, the Netherlands, 3 Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen, Department of Clinical Neuroscience, the Netherlands
Correspondence to Mirjam Kouijzer, Radboud University Nijmegen, Behavioural Science Institute, P.O. Box 9104, 6500 HE Nijmegen, the Netherlands. Tel.: +31243611167. Email:
[email protected]
EEG-BIOFEEDBACK ASD 2 Abstract EEG-biofeedback has been reported to reduce symptoms of autism spectrum disorders (ASD) in several studies. However, these studies did not control for unspecific effects of EEG-biofeedback and did not distinguish between participants who succeeded in influencing their own EEG activity and participants who did not. To overcome these methodological shortcomings, this study evaluated the effects of EEG-biofeedback in ASD in a randomized pretest-posttest control group design with blinded active comparator and six months follow-up. Thirty-eight participants were randomly allocated to the EEG-biofeedback, skin conductance (SC)-biofeedback or waiting list group. EEG- and SC-biofeedback sessions were similar and participants were blinded to the type of feedback they received. Assessments pre-treatment, post-treatment, and after six months included parent ratings of symptoms of ASD, executive function tasks, and 19-channel EEG recordings. Teacher ratings of symptoms of ASD were collected pre- and post-treatment. Fiftyfour percent of the participants significantly reduced delta and/or theta power during EEGbiofeedback sessions and were identified as EEG-responders. In these EEG-responders, no reductions of symptoms of ASD were observed by parents or teachers, but they showed significant improvement in cognitive flexibility as compared to participants who responded to SC-biofeedback. EEG-biofeedback seems to be an applicable tool to change EEG activity and has specific effects on cognitive flexibility, but it did not result in reductions in symptoms of ASD. No reliable unspecific effects of EEG-biofeedback were demonstrated. Further large scale clinical trials with EEG-biofeedback in ASD seem to be warranted.
Keywords: EEG-biofeedback; skin conductance; autism spectrum disorders
EEG-BIOFEEDBACK ASD 3 Is EEG-biofeedback an Effective Treatment in Autism Spectrum Disorders? A Randomized Controlled Trial Autism spectrum disorders (ASD) are characterized by qualitative abnormalities in social behavior and communication skills and restricted, repetitive, and stereotyped patterns of behavior, interests, and activities. Currently, no curative treatment is available for individuals with ASD, though many behavioral training programs exist to reduce symptoms of ASD. Behavioral training programs based on applied behavior analysis (ABA) are evidence based treatments that have been shown to be effective in the reduction of the core features of ASD (Peters-Scheffer, Didden, Korzilius, & Sturmey, 2011). However, such behavioral training programs often do not entirely take away the symptoms of ASD, are expensive and demanding to administer, and take years to complete. Medication may also play a role in the management of associated symptoms of ASD, such as irritability, rigidity, hyperactivity, impulsivity, and inattention, but side-effects may compromise therapeutic benefits (King & Bostic, 2006). In the search of alternative treatment options for children with ASD, electroencephalography (EEG)biofeedback emerged as a promising option for reducing symptoms of ASD. The rationale of applying EEG-biofeedback to children with ASD is based on findings from EEG studies. These studies have revealed abnormal patterns of EEG activity in individuals with ASD as compared to normal controls, such as increased delta and theta power (Chan, Sze, & Cheung, 2007; Murias, Webb, Greenson, & Dawson, 2007; Pop-Jordanova, Zorcec, Demerdzieva, & Gucev, 2010), decreased alpha power (Chan et al., 2007; Murias et al., 2007), and increased beta (Murias et al., 2007) and gamma power (Orekhova et al., 2007). Another consistent finding concerns local over-connectivity and long-distance under-connectivity in individuals with ASD (Wass, 2011). Finally, dysfunctions of the mirror neuron system were
EEG-BIOFEEDBACK ASD 4 reflected in the EEGs of children with ASD (Oberman et al., 2005). Using EEG-biofeedback to influence the activation of neural mechanisms that underlie deviating EEG activity was proposed to result in reductions of symptoms of ASD (Demos, 2005; Hammond, 2006; Sterman & Egner, 2006). Previous studies that investigated the effects of EEG-biofeedback in children with ASD revealed improvement in social interactions and verbal and non-verbal communication skills after EEG-biofeedback (Coben & Padolsky, 2007; Jarusiewicz, 2002; Kouijzer, de Moor, Gerrits, Congedo, & van Schie, 2009b; Kouijzer, van Schie, de Moor, Gerrits, & Buitelaar, 2010; Scolnick, 2005; Sichel, Fehmi, & Goldstein, 1995; Thompson, Thompson, & Reid, 2010). In addition, executive functions improved after EEG-biofeedback (Coben & Padolsky, 2007; Kouijzer et al., 2009b; Kouijzer et al., 2010). Finally, EEG-biofeedback was found to successfully influence several EEG frequency bands, such as theta power and low beta power (Scolnick, 2005; Sichel et al., 1995; Kouijzer et al., 2009b). However, many of the previous studies have methodological shortcomings which make it difficult to draw the conclusion that EEG-biofeedback for individuals with ASD is evidence based. That is, the apparent effects of EEG-biofeedback might have been produced by unspecific effects of EEG-biofeedback such as treatment expectancy, implicit training of attention, and intensive one-to-one contact with the therapist (Heinrich, Gevensleben, & Strehl, 2007). Furthermore, previous studies did not differentiate between participants who succeed in influencing their own EEG activity, i.e. EEG-responders, and participants who do not, i.e. EEGnon responders. However, a difference in outcome can be expected between these two groups of participants. That is, a higher level of improvement should be expected in EEG-responders as compared to EEG-non responders (Lubar, Swartwood, Swartwood, & O’Donnell, 1995).
EEG-BIOFEEDBACK ASD 5 This study aimed to control for unspecific effects of EEG-biofeedback and took into account differences between EEG-responders and EEG-non responders. In order to control for unspecific effects of EEG-biofeedback, the effects of EEG-biofeedback were compared with the effects of skin conductance (SC)-biofeedback and a waiting list group. SC is the amount of electrical current the skin allows to pass after a very small electrical current is applied to the skin (Peek, 2003). SC-biofeedback can be applied in the treatment of hypertensive patients in order to relax patients (McGrady & Linden, 2003). In the present study, EEG- and SC-biofeedback sessions were similar and participants were not aware of the type of biofeedback they received. EEG-biofeedback aimed at reducing absolute power in the frequency band that showed maximal deviations from normality in the participant’s pre-treatment EEG recording; SC-biofeedback aimed at reducing SC. Participants of the waiting list group received no treatment. In accordance with earlier research, we hypothesize that EEG-biofeedback has specific effects to children and adolescents with ASD. That is, we expect EEG-biofeedback to change EEG activity in the direction of normality in a substantial subsample of the participants, i.e. the EEG-responders (cf. Hanslmayr, Sauseng, Doppelmayr, Schabus, & Klimesch, 2005; Kouijzer et al., 2010; Kropotov et al., 2005; Lubar et al., 1995). We expect these EEG-responders to show reduced symptoms of ASD, improved executive functions, and altered 19-channel EEG recordings, as compared to participants who respond to SC-biofeedback. Our second hypothesis is that EEG-biofeedback has unspecific effects to children and adolescents with ASD. That is, EEG- and SC-non responders are expected to improve on symptoms of ASD, executive functions, and 19-channel EEG, as compared to participants of the waiting list group.
Methods
EEG-BIOFEEDBACK ASD 6 Participants A total of 320 school files of a secondary school for special education were screened to select participants for the present study. Inclusion criteria were an age between 12 and 18 years, an IQ-score of 80 or above and the presence of autistic disorder, Asperger disorder or PDD-NOS as clinically diagnosed by a certified child psychiatrist or health care psychologist, according to the DSM-IV-TR criteria (American Psychiatric Association, 2000). Excluded were students with a history of severe brain injury or comorbid diagnoses such as ADHD and epilepsy as diagnosed by a certified child psychiatrist or health care psychologist. Eighty-seven students were selected and invited to participate in the study. Thirty-eight students aged 12 to 18 years voluntarily signed in for participation in the study. These participants were randomly assigned to the EEGbiofeedback, SC-biofeedback or waiting list group. There were no pre-treatment statistical differences between these groups concerning demographic and clinical variables (see Tables 1). The diagnoses of 35 participants were confirmed by the results of the Autism Diagnostic Interview revised (ADI-R; Lord, Rutter, & Le, 1994). All interviews were conducted by a certified clinician. Autistic disorder and Asperger disorder were indicated if participants met the criteria on
all three subscales, i.e. reciprocal social interaction (cut-off ≥ 10), communication (cut-off ≥ 7), and restricted, repetitive, and stereotyped behavior (cut-off ≥3). Definite delays in language development are required for autistic disorder, but not for Asperger disorder. In the case of PDDNOS, the criteria for only two of the three domains must be attained (cf. Verté, Geurts, Roeyers, Oosterlaan, & Sergeant, 2006). Diagnoses of three participants were not in accordance with the ADI-R criteria for ASD, but their scores on the Social Communication Questionnaire met the criteria for ASD (cut-off ≥ 15). Therefore, these participants were not removed from the sample. Eight participants used medication, i.e. Risperdal (n=4), Risperdal and Fluoxetine (n=1),
EEG-BIOFEEDBACK ASD 7 Fluvoxamine (n=1), Dipiperone (n=1), and Enalapril (n=1). The study was approved by the local medical-ethics committee. Written informed consent was obtained from all participants and their parents.
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Measures Symptoms of ASD. The version ‘Current situation’ of the Social Communication Questionnaire (SCQ; Rutter, Bailey, & Lord, 2003; translated into Dutch by Warreyn, Raymaekers, & Roeyers, 2004) is a 40-item questionnaire related to ADI-R criteria for ASD. Response categories are ‘yes’ and ‘no’. The outcome measures are scores for the subscales ‘reciprocal social interactions’ (range 0-15), ‘communication’ (range 0-13), and ‘restricted, repetitive, and stereotyped behavior’ (range 0-8). A cut-off score of 15 or higher was determined to be indicative of a diagnosis of autistic disorder. Psychometric properties of the SCQ have been evaluated in several studies that demonstrated good sensitivity and specificity (Berument, Rutter, Lord, Pickles, & Bailey, 1999; Bolte, Crecelius, & Poustka, 2000). Parent and teacher ratings were collected at two time points: pre- and post-training. Parent ratings were additionally collected at follow-up after six months. The parents’ SCQ total score (range 0-36) constituted the primary outcome measure of this study. Clinical improvement. The ‘improvement scale’ of the Clinical Global Impression (CGI) requires a clinician to assess a client’s overall symptomatic change as compared to baseline (Guy, 1976). The CGI is a 7-point scale ranging from ‘very much improved’ to ‘very much worse’. It
EEG-BIOFEEDBACK ASD 8 has acceptable validity and was demonstrated to be sensitive to change (Berk et al., 2008). The CGI was filled out by the EEG- or SC-biofeedback therapist after the final session. Cognitive flexibility. The Trail Making Test (TMT; Reitan, 1956) was used to measure cognitive flexibility. The TMT has a test-retest reliability of .54 (Echemendia, Lovell, Collins, & Prigatano, 1999). Participants have to locate and connect 26 numbers (part A), 26 characters (part B), and 26 numbers and characters in the 1-A-2-B-3-C – order (part C), as soon as possible and in the right order. The score for cognitive flexibility is represented by the time in seconds that is needed to finish part C minus the time in seconds that is needed to finish part B. Inhibition. The Stroop task (Stroop, 1935) was used to measure inhibition. The Stroop task has a test-retest reliability of .67 (Franzen, Tishelman, Sharp, & Friedman, 1987). Participants have to read aloud 100 words (part A), the color of 100 colored rectangles (part B), and the color of the ink of 100 written incongruent color names (part C) as soon as possible. The ability to inhibit reading aloud the written word in part C is represented by the interferential time, i.e., the time in seconds that is needed to finish part C minus the time in seconds that is needed to finish part B. Planning. The Tower of London (TOL) was used to measure planning skills. The TOL has a test-retest reliability of .66 (Kovács, 2005a). In this task, participants have to copy a construction of blocks and bars by moving three prearranged colored blocks along three bars of different lengths. The score for planning is calculated by dividing the number of correctly solved items by the maximum score of 12, times 100. Attention. The Test of Sustained Selective Attention (TOSSA) was used to measure attention. The TOSSA has a test-retest reliability of .86 (Kovács, 2005b). In this task, participants have to respond to sets of three beeps while ignoring sets of two or four beeps during eight
EEG-BIOFEEDBACK ASD 9 minutes. An attention score was calculated by dividing the number of hits by the total amount of items, times 100. Working memory. The subtest Digit Span was adopted from the Wechsler Intelligence Scale for Children, 3rd version, Dutch version (WISC-III-NL; Kort et al., 2002) to measure working memory. The test-retest reliability of this subtest is .83 (Rowe, 2005). This task requires participants to repeat series of numbers of increasing length in forward and reverse order. The series are verbally presented. The score for working memory is the amount of correctly repeated series (maximum is 30). 19-Channel EEG. A Mitsar EEG 201 System was used for recording and digitizing EEG. Data were acquired using a stretchable electrode cap containing 19 sensors and ground (AFz), according to the International 10/20 System (Jasper, 1958). Two ear clips were used as reference electrodes. Impedance was kept below 5 kΩ. Data were collected for three minutes in rest and task conditions. In rest conditions, participants were instructed to sit still and relax with their eyes open or closed. Task conditions included a 2-back task, self referential tasks, and movement task. In the 2-back task, participants pressed a button when they observed a number on a computer screen that was similar to the number that was shown two numbers before. This task was included to investigate EEG activity during cognitive demanding conditions (Gevins, Smith, McEvoy, & Yu, 1997). In the self referential tasks, participants judged on a five point scale how applicable a series of personality traits were to themselves (part 1) or the Dutch queen (part 2) or they counted how often the letter ‘e’ appeared in each word (part 3; based on Rogers, Kuiper, & Kirker, 1977). This task was included to investigate EEG activity during tasks involving self- and other-referential processing (Gusnard, Akbudak, Shulman, & Raichle, 2001). In the movement task, participants had to open and close their fist while watching their own movement (Oberman
EEG-BIOFEEDBACK ASD 10 et al., 2005). This task was included to investigate EEG activity during movement. Analyzing the EEG data of all conditions with MANOVA revealed no differences between conditions. Consequently, EEG power values were averaged over conditions. Treatment expectancy. A 5-item questionnaire on treatment expectancy was developed by the authors of this study and was based on Borkovec & Nau (1972). Response was given on a 9point scale ranging from ‘completely negative’ to ‘completely positive’. An example of an item is ‘Do you think biofeedback will reduce your autistic symptoms?’. The outcome measure was the mean score on all items (range 1-9). This questionnaire was filled out by participants, parents, and teachers before participants were randomly allocated to one of the research groups.
Procedure A randomized pretest-posttest control group study design with blinded active comparator group and six months follow-up was used to investigate the effects of EEG-biofeedback. The setup of EEG- and SC-biofeedback treatment was similar. The only difference was that in the EEGbiofeedback group, feedback was provided on EEG and in the SC-biofeedback group, feedback was provided on SC. Both EEG- and SC-biofeedback treatment included 40 individual sessions that were provided twice a week and at the school of the participants. Pre-treatment assessment took place about one week before the first session. Post-treatment and follow-up assessments were done about one week after the last session and after six months respectively. Participants, parents, and teachers were blinded for treatment allocation to EEG- or SC-biofeedback groups, but not for the waiting list group. Both treatments were introduced as experimental, but promising treatments for individuals with ASD. Participants of the waiting list group were given the opportunity to have biofeedback treatment after the six months follow-up was completed.
EEG-BIOFEEDBACK ASD 11 A Nexus-4 amplifier and recording system and Ag/AgCl disposable snap-on electrodes were used for EEG- and SC-biofeedback treatment. During EEG- and SC-biofeedback sessions, participants sat in front of a computer screen while EEG and SC were measured concurrently. Each session comprised seven three-minute intervals of EEG- or SC-biofeedback, separated by one-minute rest intervals. The task was to decrease the bar graph on the computer screen. This bar graph represented EEG activity in the EEG-biofeedback group and SC in the SC-biofeedback group. If the bar graph moved below the criterion line, participants were rewarded with a counter and a film clip with sound that was selected to be of interest to the participants. Different film clips and sounds were used in each biofeedback interval. Feedback was provided in 50-80% of the time. SC feedback was derived from electrodes attached to the participants’ index finger of their non-dominant hand. EEG feedback (reference: mastoids, bandwidth: 1-30 Hz, sampling rate: 250 Hz) was derived from an electrode attached to the scalp of the participant. The electrode location and the EEG frequency band were individually defined by comparing each participant’s pre-treatment 19-channel EEG recording to a normative database (i.e., Neuroguide). This database produces the deviation from normality of each single Hertz bin at each electrode site. The electrode location and the EEG frequency band that showed the largest deviation from normality were selected for EEG-biofeedback. The Neuroguide database has a satisfactory level of reliability and construct and content validity (Thatcher, Walker, Biver, North, & Curtin, 2003).
Statistical analyses Eye blinks and other artifacts were manually removed from EEG and SC signals recorded during sessions. In line with our intention to distinguish between responders and non responders to EEG- and SC-biofeedback, we used Spearman correlations to calculate whether participants
EEG-BIOFEEDBACK ASD 12 showed a significant negative correlation between the mean amplitude of the EEG (EEGbiofeedback group) or SC (SC-biofeedback group) signal that was used during the biofeedback sessions and the number of sessions. Participants who showed a negative correlation between the EEG or SC amplitude and the number of sessions were referred to as EEG- or SC-responders; participants who did not show such a correlation were referred to as EEG- or SC-non responders (cf. Hanslmayr et al., 2005; Kouijzer et al., 2010; Lubar et al., 1995). Separate analyses were conducted to investigate specific and unspecific effects of EEGbiofeedback on symptoms of ASD and executive functions. In the analysis of specific effects of EEG-biofeedback, the data of EEG- and SC-responders were compared with a repeated measures MANOVA with within-subjects factor Time (2 levels: pre- and post-treatment) and betweensubjects factor Group (2 levels: EEG- and SC-responders). Follow-up data after six months were analyzed with a repeated measures MANOVA with within-subjects factor Time (3 levels: preand post-treatment and follow-up) and between-subjects factor Group (2 levels: EEG- and SCresponders). In the analysis of unspecific effects of EEG-biofeedback, the data of EEG- and SCnon responders and waiting list controls were compared with a repeated measures MANOVA with within-subjects factor Time (2 levels: pre- and post-treatment) and between-subjects factor Group (3 levels: EEG- and SC-non responders and waiting list controls). Follow-up data after six months were analyzed with a repeated measures MANOVA with within-subjects factor Time (3 levels: pre- and post-treatment and follow-up) and between-subjects factor Group (3 levels: EEGand SC-non responders and waiting list controls). Separate analyses were applied for parent and teacher ratings of symptoms of ASD. Specific and unspecific effects of EEG-biofeedback on clinical improvement of the participants were examined with ANOVA with between-subjects factor Group (2 levels: EEG-
EEG-BIOFEEDBACK ASD 13 and SC-responders) and ANOVA with between-subjects factor Group (3 levels: EEG- and SCnon responders and waiting list controls) respectively. In order to analyze 19-channel EEG data, eye blinks and other artifacts were manually removed from the raw data. Subsequently, the data were processed with fast Fourier transformations and averages were calculated per electrode and frequency band (delta: 1-4 Hz, theta: 4-8 Hz). In order to identify specific effects of EEG-biofeedback, the data of EEG- and SCresponders were compared with a repeated measures MANOVA with within-subjects factors Time (2 levels: pre- and post-treatment), Electrode (3 levels: Fz, Cz, and Pz), and Frequency band (2 levels: delta and theta) and between-subjects factor Group (2 levels: EEG- and SCresponders). Follow-up data after six months were analyzed with a repeated measures MANOVA with within-subjects factors Time (3 levels: pre- and post-treatment and follow-up), Electrode (3 levels: Fz, Cz, and Pz), and Frequency band (2 levels: delta and theta) and between-subjects factor Group (2 levels: EEG- and SC-responders). In order to identify unspecific effects of EEGbiofeedback, the data of EEG- and SC-non responders and waiting list controls were compared with a repeated measures MANOVA with within-subjects factors Time (2 levels: pre- and posttreatment), Electrode (3 levels: Fz, Cz, and Pz), and Frequency band (2 levels: delta and theta) and between-subjects factor Group (3 levels: EEG- and SC-non responders and waiting list controls). Follow-up data after six months were analyzed with a repeated measures MANOVA with within-subjects factors Time (3 levels: pre- and post-treatment and follow-up), Electrode (3 levels: Fz, Cz, and Pz), and Frequency band (2 levels: delta and theta) and between-subjects factor Group (3 levels: EEG- and SC-non responders and waiting list controls). Effects of treatment expectancy were investigated with ANOVA with between-subjects factor Group (2 levels: EEG- and SC-biofeedback) and ANOVA with between-subjects factor
EEG-BIOFEEDBACK ASD 14 Group (3 levels: EEG- and SC-non responders and waiting list controls) respectively. Separate analyses were applied for participant, parent, and teacher ratings of treatment expectancy. All analyses were carried out at the pre-specified two-sided alpha level of .05. An alpha level between .05 and .1 was considered marginally significant.
Results EEG database comparisons Comparing the pre-treatment EEG recording of each participants in the EEG-biofeedback group to a normative database revealed maximal deviations from normality at Cz (n=8) or CFz (n=5). These deviations were found in delta and/or theta power (2-9 Hz), resulting in the following frequency bands to be inhibited during EEG-biofeedback treatment: 2-7 Hz (n=4), 3-7 Hz (n=2), 3-9 Hz (n=1), 4-7 Hz (n=4), 5-7 Hz (n=1), and 5-9 Hz (n=1).
Evaluation of the EEG- and SC-biofeedback treatment Number of sessions. Because of frequent school absence due to illness, tiredness or overstimulation during school days, four participants of the EEG-biofeedback group and five participants of the SC-biofeedback group did not complete the 40 EEG- or SC-biofeedback sessions. In stead, these participants completed 34, 33, 29, and 23 EEG-biofeedback sessions and 38, 35, 32, 31, and 29 SC-biofeedback sessions, respectively. Data of both participants who completed 40 sessions and participants who did not were included in the analyses. Blinding. There were no statistical differences between the EEG- and SC-biofeedback groups regarding the success in blinding. Fifty-eight percent of the participants of both groups thought they had received a combination of EEG- and SC-biofeedback; 33% of the EEG-
EEG-BIOFEEDBACK ASD 15 biofeedback group and 42% of the SC-biofeedback group thought they had received EEGbiofeedback.
Identification of responders and non responders Analyzing mean EEG amplitudes during EEG- and SC-biofeedback sessions indicated that seven participants of the EEG-biofeedback group showed a negative correlation of delta and/or theta power across sessions, r’s -.356 to -.803, p’s
