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Neurofeedback (NF) has gained increasing interest in the treatment of attention-deficit/hyperactivity disorder (ADHD). Given learning principles underlie NF, lasting clinical treatment effects may be expected. This systematic review and meta-analysis addresses the sustainability of neurofeedback and control treatment effects by considering randomized controlled studies that conducted follow-up (FU; 2-12 months) assessments among children with ADHD. PubMed and Scopus databases were searched through November 2017. Within-group and between-group standardized mean differences (SMD) of parent behavior ratings were calculated and analyzed. Ten studies met inclusion criteria (NF: ten studies, N = 256; control: nine studies, N = 250). Within-group NF effects on inattention were of medium effect size (ES) (SMD = 0.64) at post-treatment and increased to a large ES (SMD = 0.80) at FU. Regarding hyperactivity/impulsivity, NF ES were medium at post-treatment (SMD = 0.50) and FU (SMD = 0.61). Non-active control conditions yielded a small significant ES on inattention at post-treatment (SMD = 0.28) but no significant ES at FU. Active treatments (mainly methylphenidate), had large ES for inattention (post: SMD = 1.08; FU: SMD = 1.06) and medium ES for hyperactivity/impulsivity (post: SMD = 0.74; FU: SMD = 0.67). Between-group analyses also revealed an advantage of NF over non-active controls [inattention (post: SMD = 0.38; FU: SMD = 0.57); hyperactivity-impulsivity (post: SMD = 0.25; FU: SMD = 0.39)], and favored active controls for inattention only at pre-post (SMD = - 0.44). Compared to non-active control treatments, NF appears to have more durable treatment effects, for at least 6 months following treatment. More studies are needed for a properly powered comparison of follow-up effects between NF and active treatments and to further control for non-specific effects.
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European Child & Adolescent Psychiatry (2019) 28:293–305
Sustained eects ofneurofeedback inADHD: asystematic review
JessicaVanDoren1 · MartijnArns2,3,4 · HartmutHeinrich1,5 · MadelonA.Vollebregt4,6· UteStrehl7·
Received: 5 October 2017 / Accepted: 5 February 2018 / Published online: 14 February 2018
© The Author(s) 2018. This article is an open access publication
Neurofeedback (NF) has gained increasing interest in the treatment of attention-deficit/hyperactivity disorder (ADHD). Given
learning principles underlie NF, lasting clinical treatment effects may be expected. This systematic review and meta-analysis
addresses the sustainability of neurofeedback and control treatment effects by considering randomized controlled studies
that conducted follow-up (FU; 2–12months) assessments among children with ADHD. PubMed and Scopus databases
were searched through November 2017. Within-group and between-group standardized mean differences (SMD) of parent
behavior ratings were calculated and analyzed. Ten studies met inclusion criteria (NF: ten studies, N=256; control: nine
studies, N=250). Within-group NF effects on inattention were of medium effect size (ES) (SMD=0.64) at post-treatment
and increased to a large ES (SMD=0.80) at FU. Regarding hyperactivity/impulsivity, NF ES were medium at post-treatment
(SMD=0.50) and FU (SMD=0.61). Non-active control conditions yielded a small significant ES on inattention at post-
treatment (SMD=0.28) but no significant ES at FU. Active treatments (mainly methylphenidate), had large ES for inattention
(post: SMD=1.08; FU: SMD=1.06) and medium ES for hyperactivity/impulsivity (post: SMD=0.74; FU: SMD=0.67).
Between-group analyses also revealed an advantage of NF over non-active controls [inattention (post: SMD=0.38; FU:
SMD=0.57); hyperactivity–impulsivity (post: SMD=0.25; FU: SMD=0.39)], and favored active controls for inattention
only at pre-post (SMD=− 0.44). Compared to non-active control treatments, NF appears to have more durable treatment
effects, for at least 6months following treatment. More studies are needed for a properly powered comparison of follow-up
effects between NF and active treatments and to further control for non-specific effects.
Keywords Neurofeedback· EEG biofeedback· ADHD· Meta-analysis· Sustainability· Follow-up
Clinical guidelines for attention-deficit/hyperactivity disor-
der (ADHD) recommend multimodal treatment approaches,
with current evidence suggesting that medication, including
methylphenidate and various amphetamine formulations, in
Electronic supplementary material The online version of this
article (https :// 7-018-1121-4) contains
supplementary material, which is available to authorized users.
* Martijn Arns
1 Department ofChild andAdolescent Mental Health,
University Hospital Erlangen, Erlangen, Germany
2 Department ofExperimental Psychology, Utrecht University,
Utrecht, TheNetherlands
3 neuroCare Group, Munich, Germany
4 Research Institute Brainclinics, Bijleveldsingel 34,
6524ADNijmegen, TheNetherlands
5 kbo-Heckscher-Klinikum, Munich, Germany
6 Department ofCognitive Neuroscience, Donders Institute
forBrain, Cognition andBehaviour, Radboud University
Medical Centre, Nijmegen, TheNetherlands
7 Institute forMedical Psychology, University ofTuebingen,
Tuebingen, Germany
8 Department ofPsychiatry andBiobehavioral Science,
David Geffen School ofMedicine, University ofCalifornia,
LosAngeles, USA
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294 European Child & Adolescent Psychiatry (2019) 28:293–305
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conjunction with psychosocial treatment are most effec-
tive in the short-term [1]. Medication treatments have large
effect size in the acute treatment of ADHD [2] and, when
combined with psychosocial treatments, large effects up to
2years of treatment were observed [3, 4]. Nevertheless, it
is widely accepted that further treatments with long-lasting
effects have to be developed and evaluated.
Over the last decade, an increasing number of studies
investigating non-pharmacological treatments have been
published. Neurofeedback (NF), which aims at improving
self-regulation of brain activity (most often the electroen-
cephalogram, EEG) using a brain–computer interface, has
gained popularity [5]. A promising aspect of neurofeedback
is that it may rely on procedural learning, thereby poten-
tially allowing lasting effects and thus longer clinical benefit
after completion of neurofeedback treatment. In their review,
Arns and Kenemans [6] found that the clinical effects of
neurofeedback were maintained across 6 and 24-month fol-
low-up periods, with a trend for larger symptom decreases
for hyperactivity/impulsivity after 24months than after
6months, albeit only based on two randomized studies at
6months and only one at the 24-month follow-up, thus limit-
ing the generalizability of the findings. A systematic review
and meta-analysis that assess the sustainability of clinical
effects of NF studies is, therefore, desirable.
In recent years, several randomized control studies
(RCTs) and meta-analyses have been published on the effi-
cacy of neurofeedback for children with ADHD, overall
with mixed results and interpretations [711]. Regarding
RCTs published over the last decade, one major issue is
the lack of standardization of neurofeedback protocols and
implementations. Neurofeedback treatments using theta/
beta, slow cortical potential (SCP), or sensorimotor-rhythm
(SMR) protocols have been well studied and can be seen as
‘standard’ neurofeedback treatments (for review and discus-
sion see: Arns etal. [5] and Figure S-2 in Supplementary
Material). These ‘standard’ protocols have been selected
as the primary protocols for NF research based on findings
that children with ADHD have specific deficits in compari-
son with healthy controls: e.g. increased theta/beta ratios
in subgroups of ADHD patients hypothesized to be related
to inattention [12]; decreased contingent negative varia-
tion amplitudes (targeted by SCP training) [13]; addressing
hyperkinetic behavior by means of training sensorimotor
rhythm [14]. Application of these standard neurofeedback
protocols in ADHD have most consistently resulted in clini-
cal benefit in children with ADHD, whereas application of
other neurofeedback protocols has yielded more variable and
mixed results [5, 8]. A second issue with currently available
neurofeedback RCTs is that several studies deviated from
their initial clinical trials register, with samples ranging from
34% [15] to 60% [16, 17] smaller than their preregistered
sample size. This raises the likelihood of over-interpreting
results from these studies, which are insufficiently powered.
This issue is addressed using meta-analyses since effects are
combined across studies, resulting in increased statistical
A third issue concerns the specificity of NF treatment
effects. While NF has been shown to be beneficial for the
treatment of ADHD symptoms, it remains a debate whether
behavioral improvements are the result of specific aspects
of the NF treatment such as the style of training, or active
learning of control over their brain state that is then gen-
eralized to daily life or whether non-specific treatment
effects such as unconditional positive regard of the thera-
pist, positive expectation of change, or repeated practice of
sitting at a computer for increasing lengths of time leads
to behavioral change. To control these non-specific or pla-
cebo effects, double-blind placebo-controlled studies are
often requested—comparable to what is considered as gold
standard in drug research. However, in regard to NF, there
are methodical and ethical issues to consider which have led
to the development of control conditions such as cognitive
training or EMG biofeedback and assessments for placebo
factors via evaluation scales [18, 19]. Though it is known
that placebo effects may last over longer periods [20], it
seems unlikely that they grow larger over time. Hence focus-
ing on longer-term outcomes of NF may also help to clarify
the placebo/specific vs. non-specific issue.
Two recent meta-analyses on the acute efficacy of neu-
rofeedback for children with ADHD published by the Euro-
pean ADHD Guidelines Group (EAGG) have used the inter-
esting concept of most-proximal (e.g. least blinded, often
parent ratings) versus probably blinded measures (most often
teacher ratings), assuming that the probably blinded meas-
ures (i.e., teacher ratings) are less susceptible to expecta-
tion/non-specific effects and, therefore, more valid [8, 11].
However, this approach has limitations. For example, par-
ent–teacher correlations on behavior rating scales are only
modestly correlated (ranging from 0.23 to 0.49) [21, 22],
suggesting different aspects of the disorder may be detected
by different raters or in different settings. Furthermore, in a
large candidate gene study, parent-rated hyperactive–impul-
sive behaviors were significantly associated with candidate
gene pathways whereas teacher ratings were not [23]. Addi-
tionally, teacher-ratings are sensitive to effects of methylphe-
nidate [24], which could possibly skew the interpretation of
studies that randomize ADHD treatments against a methyl-
phenidate control, when primarily relying on teacher reports.
Finally, for investigating long-term effects, it may not be
advisable to rely on teacher ratings, since the child may have
more than one teacher over time, potentially compromising
the reliability of the rating.
A second limitation of the aforementioned meta-analyses
is the use of between-group effect sizes, which is a good
practice for compiling results from studies that used similar
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295European Child & Adolescent Psychiatry (2019) 28:293–305
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designs and control groups; comparing for example, psycho-
stimulants to placebo. However, the utility of this method for
studying results across neurofeedback studies, with various
kinds of control groups (ranging from waiting lists, cogni-
tive training to medication), is more challenging. Therefore,
while between-group effect sizes are useful for controlling
for non-specific effects of treatment, they can miss clinical
effects of neurofeedback that are masked by the active or
semi-active control conditions, thus warranting the use of a
within-group effect-size approach, for example as used by
Arns etal. [7], or separately analyze the results for active vs.
semi-active control groups.
To address the above concerns, we have conducted a
systematic review and meta-analysis of the post-treatment
follow-up period of randomized EEG NF studies among
children and adolescents with ADHD. Methodologically,
we (1) used within-group effect sizes to address the issue
of different control groups; (2) used between-group effect
sizes to control for non-specific effects of treatment; (3)
applied a meta-analytical approach to address the issue of
underpowered studies; and (4) focused on the sustainability
of treatment effects by looking specifically at the follow-up
(FU) period (i.e., pre-FU, post-FU time points), which will
provide information regarding the plausibility of sustainable
NF effects, relative to other treatments, in ADHD.
Study selection
The protocol of this meta-analysis was not preregistered.
A literature search was conducted up to 29th of Novem-
ber 2017 via PubMed and Scopus by author JVD, look-
ing for studies investigating Neurofeedback or EEG Bio-
feedback in ADHD using combinations of the following
keywords: ‘Neurofeedback’, ‘EEG Biofeedback’, ‘Neu-
rotherapy’, ‘SCP’ OR ‘Slow Cortical Potentials’ AND
ADHD’, ‘ADD’, ‘Attention Deficit’ OR ‘Attention Deficit
Hyperactivity Disorder’. Furthermore, prior meta-analyses
and systematic review reference lists were inspected for
potentially missed studies [7, 8, 10, 11]. After exclusion of
duplicate publications, abstracts were screened for inclu-
sion criteria first by author HH and then by a research
assistant to prevent missing studies. Studies that remained
of interest were then screened based on their full text by
JVD. Inclusion criteria were: (1) randomized controlled
EEG neurofeedback trials published in peer-reviewed
journals; (2) primary diagnosis of ADHD; (3) mean child
age<18years old; (4) available data at a follow-up (FU)
time point for 2 to 12months post-treatment; (5) stand-
ardized mean and standard deviations (SD) for all three
assessments (pre, post, and FU) for at least one of the
following domains had to be available: inattention, hyper-
activity, or hyperactivity/impulsivity ratings from a DSM-
IV/5-based rating scale (these values were taken based on
availability with parental ratings taking priority, then self-
ratings and lastly teacher ratings); (6) publication avail-
able in English; (7) total study sample larger than N=10;
8) less than 50% of participants began or stopped taking
medication between post and FU assessments.
In most of the studies, ‘standard’ neurofeedback protocols
[5] were used, i.e., theta/beta and theta/SMR (sensorimotor
rhythm) training (defined as a down-training of theta and
up-training of beta and SMR, respectively) and slow corti-
cal potential training (SCP) training (addressing modulation
of positive and negative SCPs), for details see Table1 and
Supplementary Figure S-2. Exceptions were the studies of
Arnold etal. [25] and Bink etal. [26] which targeted more
EEG frequency bands (theta, alpha, SMR, and beta), and a
sensitivity analysis was conducted to separately assess the
effects for standard protocols. When the means and SDs
from a given study were not available, or it was unclear if
planned follow-up measurements were published, this infor-
mation was requested via email from the authors. If authors
did not respond or did not provide the missing information,
and if there was not sufficient information available based
on the publication, the study was excluded from the meta-
analysis. Studies were additionally screened for duplicate
data based on author, publication year, participant numbers
and trial registration number (if available). When possible
duplicate data was found, the authors were contacted to
clarify whether the data sets were independent.
Data extraction/outcome measures
Data were first extracted by JVD and checked by HH. The
following pre-, post- and FU-assessment measures were
extracted from the included studies:
1. Demographic and clinical data: age (mean and standard
deviation), medication use, ADHD subtype.
2. Experimental procedure: NF method, control method,
feedback electrode, average number of sessions, session
3. Outcome measures:
Symptom domains: Assessed from parent report with
a validated ADHD rating scale (e.g., DSM-IV rating
scale [27], Conners [28], Barkley [29], FBB-HKS [30],
SWAN [31])
(i) Inattention
(ii) Hyperactivity/impulsivity (if no combined
measure was available, the hyperactivity score
was used).
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296 European Child & Adolescent Psychiatry (2019) 28:293–305
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Table 1 Characteristics of included studies
Study or subgroup Year N (FU) Age Treatment FU
total NMedication
Heinrich etal. [36] 2004 13 11.1±1.6 SCP (Cz); 25 sessions of 50min 3 FBB-HKS 6/13 No change
Gevensleben etal. [52] 2010 38 9.9±1.3 SCP+theta (4–8Hz)/beta (13–20Hz); Cz; 36 sessions of 50min 6 FBB-HKS 0/38 No change
Arnold etal. [25] 2013 25 9.0±1.5 Theta/alpha; beta/SMR; Cz; 40 sessions of 45min 2 Conners DSM 7/25 Increased
Li etal. [39] 2013 31 10.8±2.6 MPH (pre)+NF; theta (4–8Hz)/SMR (12–15Hz); electrode NR;40 ses-
sions of 25–30min
6 ADHD RS-IV 31/31 Decreased
Meisel etal. [37] 2013 12 9.5±1.8 Theta (4–7Hz)/beta 15–20Hz); Cz or FCz; 40 session of 30min 2 ADHD RS-IV 2/12 Increased
Steiner etal. [34] 2014 34 8.4±1.1 Theta (4–8Hz)/SMR (12–15Hz); electrode NR; 40 sessions of 45min 6 Conners 27/34 Maintained
Christiansenb2014 18 8.7±1.4 SCP; Cz; 30 sessions of 50min 6 Conners DSM 1/18 Decreased
Bink etal. [26] 2016 41 15.8±3.3 TAU+NF; theta/alpha (4–7, 8–11Hz) , SMR (13–15Hz) , beta/
gamma (22–36Hz) ; Cz; 40 sessions of 30min
19/41 NR
Duric etal. [38] 2017 24 11.3±2.8 Theta (4–7Hz)/beta (16–20Hz); Cz; 30 sessions of 40min 6 Barkley 0/24 No change
Gelade etal. [35] 2017 20 9.8±1.9 Theta (4–8Hz)/beta (13–20Hz); Cz; 30 sessions of 45min 6 SWA N 0/20 No change
Control conditions
Gevensleben etal. [52] 2010 23 9.4±1.1 Attention training; 36 sessions of 50min 6 FBB-HKS 0/23 No change
Arnold etal. [25] 2013 11 8.7±2.1 Sham neurofeedback; 40 sessions of 45min 2 Conners DSM 0/11 No change
Li etal. [39] 2013 29 10.4±2.9 MPH (pre)+non-feedback attention training 40 sessions of 25–30min 6 ADHD RS-IV 29/29 Maintained
Meisel etal. [37] 2013 11 8.9±1.5 MPH (inferior dosage: 1mg/kg/day) 2 ADHD RS-IV 11/11 No change
Steiner etal. [34] CT 2014 34 8.9±1.0 Cognitive training; 40 sessions of 45min 6 Conners 14/34 Increased
Steiner etal. [34] WL 2014 36 8.4±1.1 Wait list 6 Conners 20/36 Increased
Christiansenb2014 21 8.9±1.2 Self-management; 30 sessions of 50min 6 Conners DSM 6/21 Increased
Bink etal. [26] 2016 19 16.2±3.4 TAU 12 ADHD RS-IV
12/19 NR
Duric etal. [38] 2017 28 10.8±2.4 MPH (1mg/kg/day; range: 20 to 60 mg) 6 Barkley 29/29 No change
Gelade etal. [35] MPH 2017 21 9.0±1.2 MPH (5–20mg daily) 6 SWA N 21/21 NR
Gelade etal. [35] PA 2017 17 9.6±1.8 Physical activity training; 28 sessions of 30min 6 SWA N 0/17 No change
For neurofeedback (NF) protocols, frequency bands and feedback electrodes are listed. Parent ratings were considered (except for Bink etal. [26] which used self-reports)
SCP slow cortical potential, SMR sensorimotor rhythm, MPH methylphenidate, PA physical activity, FBB-HKS German ADHD rating scale for parents, ADHD RS-IV ADHD Rating Scale-IV,
Conners Conners questionnaire subscale, Conners DSM DSM subscale within the Conners questionnaire; SWAN strengths and weaknesses of ADHD symptoms and Normal Behavior Scale, NR
not reported
a For more study details see Table S-4
b Unpublished data from Christiansen etal. [40]
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297European Child & Adolescent Psychiatry (2019) 28:293–305
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These measures were used as treatment endpoints at post-
treatment and FU relative to baseline values. Additionally,
the change from post-treatment to FU was assessed to deter-
mine if changes occurred after the treatment had stopped.
A random effects model (due to inherent heterogeneity
between studies) and the inverse variance statistical method
was used to calculate the standardized mean difference
(SMD), 95% confidence intervals, and χ2 statistic using
RevMan version 5.3 [32]. Within-group and between-group
analyses were conducted for the following time points using
the means and standard deviations provided in the papers or
by the authors: (1) pre- and post-treatment; (2) pre-treatment
and follow-up; (3) post-treatment and follow-up. Within-
group analyses used the values as presented in the papers (no
additional calculation was necessary). Between-group means
were calculated by subtracting the mean of the second time
point from the mean of the first time point (ex. pre-treatment
minus post-treatment, pre-treatment minus FU-treatment,
post-treatment minus FU-treatment). The standard devia-
tion of the first point was used for analysis.
Although active treatment effects are not the main focus
of this paper, the control conditions were analyzed as a
whole and assessed using two sub-analyses (non-active and
active control conditions) to provide a frame of reference
for the effects of neurofeedback against different type of
controls. Due to the diversity of the control conditions, we
chose to separate them as ‘active’ (proven to have a clinical
effect in the treatment of ADHD: methylphenidate and self-
management training) and ‘non-active’ (all conditions that
do not classify as active). For a more detailed explanation
of these groupings see Table S-2 in the supplement. See
Tables S-5 and S-6 for values used. When the χ2 statistic of
a sample (Qt) was significant (p<0.05)—indicating that
the variance among effect sizes is greater than expected by
sampling error—studies were assessed for possible heteroge-
neity causes and the resulting studies were omitted from the
meta-analysis, for example based on the type of treatment
(separating active and non-active conditions in the control
groups). Active treatments were defined as medication or
psychotherapy (self-management) that was started system-
atically after pre-treatment assessment. Additionally, a sen-
sitivity analysis was conducted including only studies that
used standard NF protocols (theta/beta, theta/SMR, or SCP).
To assess publication bias, MetaWin version 2.1 [33] was
used to calculate the fail-safe number (Rosenthal’s method:
A total of ten studies met inclusion criteria for at least one
of the parameters and conditions, resulting in ten studies in
the NF arm and nine studies in the control arm (two control
studies had two control groups [34, 35]). See Figure S-1
for the Preferred Reporting Items for Systematic Reviews
and Meta-Analyses (PRISMA) inclusion flow diagram and
Table1 for characteristics of included studies. The PRISMA
checklist is available in the supplementary material (Table
S-1). The supplementary material has a complete list of
inclusions (Table S-4) and exclusions (Table S-3). Included
studies resulted in a total of 506 participants with ADHD
(256 neurofeedback, 250 control). Follow-up time periods
were 2months (K=2), 3months (K=1), 6months (K=6)
and 12months (K=1).
The control group from Heinrich etal. [36] was excluded
because most of the controls began psychotherapy between
post and FU (personal communication with first author). The
2-month FU time point from the Meisel etal. [37] study
was included instead of the 6-month FU because over 50%
of their NF participants began medication between post-
and 6-month FU measurements while only two participants
started taking medication between post and 2-month FU. The
combined NF+MPH arm of Duric etal. [38] was excluded
since the NF treatment was accompanied by another active
treatment, which did not allow differentiation of treatment
effects; this differed from other studies in which NF children
could receive medication if they had been already medicated
prior to study participation, ensuring that the baseline meas-
urements already included medication effects. The supple-
mentary analysis was used from Gelade etal. [35] instead
of the primary analysis to account for participant drop-out
and medications change. For the majority of the studies, data
were available for participants who completed assessments
at all three time points (pre-, post-, FU) with the exception
of Li etal. [39], in which drop-out from baseline to FU were
3.13% (N=1) for the NF group and 9.38% (N=3) for the
control group.
Regarding medication change for included studies, the
number of participants who began or stopped taking med-
ication did not change over time for most of the studies,
with the exception of four [25, 26, 37, 40]. In summary: of
the total NF participants three stopped taking medication
[pre-post (N=2), post-FU (N=1)], while nine began tak-
ing medication [pre-post (N=1), post-FU (N=8)]; for the
control group only one participant stopped taking medica-
tion from post to FU time point. Dosage was allowed to
be changed in five studies (see Table1); however, dosage
change was variable with some groups maintaining (one NF,
one control), decreasing (two NF) or increasing (two NF,
three control) dosage. See Table S-4 for details.
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298 European Child & Adolescent Psychiatry (2019) 28:293–305
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Within‑group analyses
Forest plots and results for inattention are presented in
Fig.1; Bar plots in Figure S-3. The test for heterogeneity
was not significant for NF at pre-post (χ2=9.69, df=9,
n.s.) or pre-FU (χ2=12.37, df= 9, n.s.), while controls
did display significant heterogeneity for both pre-post
(χ2=27.95, df=10, p<0.05) and pre-FU (χ2=29.60,
df=10, p<0.05) when all controls were included. When
considering non-active controls only, heterogeneity was no
longer significant for the pre-post measurement (χ2=6.72,
df=6, n.s.), but remained significant for the pre-FU meas-
urement (χ2=14.02, df=6, p<0.05). When only active
controls were included, pre-post heterogeneity remained
significant (χ2= 10.12, df = 3, p < 0.05) while pre-FU
(χ2=0.48, df=3, n.s.) was not significant. These results
suggest that when heterogeneity is significant for controls,
it will be useful to examine SMDs for active and non-active
controls separately, which will be done for the remainder
of the paper. This reduces the number of studies examined,
however, and caution will be used when interpreting the
results. Heterogeneity was non-significant for all groups for
the post-FU measurement: NF (χ2=2.29, df=9, n.s.); non-
active (χ2=4.05, df=6, n.s.); active (χ2=7.40, df=3, n.s.).
NF yielded a significant medium effect size (SMD=0.64;
95% CI 0.45, 0.82) for the change from the pre- to post-
treatment measurements and a significant large effect size
(SMD=0.80; 95% CI 0.58, 1.01) for the change from pre-
to FU measurement; however, post-treatment to FU was
not significant (SMD=0.14; 95% CI − 0.03, 0.31). For
non-active controls, a small, significant effect for pre-post
(SMD=0.28; 95% CI 0.05, 0.51) was found but the small
effect at pre-FU was no longer significant (SMD=0.29;
95% CI − 0.04, 0.63). When looking at only active controls,
there were large, significant effect sizes at both pre-post (sig-
nificant heterogeneity) (SMD=1.08; 95% CI 0.45, 1.72)
and pre-FU (non-significant heterogeneity) (SMD=1.06;
95% CI 0.73, 1.39). Post-treatment to FU was not significant
for either control group. The fail-safe numbers for NF were:
pre-post (156.0), pre-FU (190.7). For the control conditions,
the fail-safe numbers were: 1. non-active controls: pre-post
(11.2), pre-FU (1.1); 2. active controls: pre-post (13.5), pre-
FU (55.2).
Forest plots and results for hyperactivity/impulsivity are
presented in Fig.2; Bar plots in Figure S-3. The test for
heterogeneity was not significant for any of the hyperactiv-
ity/impulsivity measurements, indicating that the variance
of SMD was not large enough to be attributed to sampling
error only (see Fig.2). For comparability to the inatten-
tion domain, the active and non-active control groups are
reported separately.
For NF, a significant medium effect size (SMD=0.50;
95% CI 0.33, 0.68) was found for the pre-post measurement
and a medium effect size (SMD=0.61; 95% CI 0.43, 0.79)
for the pre-FU measurement; the post-FU measurement was
not significant (SMD=0.11; 95% CI − 0.06, 0.28).
Analysis of non-active control groups indicated that none
of the measurements (pre-post, pre-FU, post-FU) were sig-
nificant. When only active controls were considered, there
were significant medium effect sizes for both pre-post
(SMD=0.74; 95% CI 0.41, 1.06) and pre-FU (SMD=0.67;
95% CI 0.35, 0.99). For both control analyses, post-FU was
not significant. The fail-safe numbers for NF were: pre-post
(107.6), pre-FU (163.4). For the controls, the fail-safe num-
bers were: 1. non-active controls: pre-post (0), pre-FU (0);
2. active controls: pre-post (25), pre-FU (18.4).
Between‑group meta‑analysis
Forest plots and results for inattention and hyperactivity/
impulsivity are presented in Fig.3.
The test for heterogeneity was significant when including all
studies at pre-post (χ2=30.60, df=10, p<0.05) and signif-
icant at pre-FU (χ2=33.27, df=10, p<0.001). Considering
only trials with non-active control conditions, heterogeneity
was not significant for the pre-post measurement (χ2=7.83,
df=6, n.s.) or the pre-FU measurement (χ2=7.48, df=6,
n.s.). For active controls, heterogeneity was not significant
for pre-post (χ2=5.00, df=3, n.s.) or for pre-FU (χ2=7.05,
df=3, n.s.).
When including only studies with non-active con-
trol conditions, a significant small effect size for pre-post
(SMD=0.38; 95% CI 0.14, 0.61) and a medium effect size
for pre-FU (SMD=0.57; 95% CI 0.34, 0.81) were observed
favoring neurofeedback. When only active controls were
included, a pre-post effect size favoring active controls was
significant (SMD=− 0.44; 95% CI − 0.86, − 0.02) but at
pre-FU it was no longer significant. Post-training to FU was
not significant for either analysis.
The test for heterogeneity was not significant for any of the
hyperactivity/impulsivity measurements, indicating that
the variance of SMD was not large enough to be attributed
to sampling error only. The between-group analysis of all
control groups resulted in a significant small effect size
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299European Child & Adolescent Psychiatry (2019) 28:293–305
1 3
favoring NF (SMD=0.32; 95% CI 0.15, 0.49) at pre-FU,
pre-post was not significant. When including only non-
active control groups (done for comparison with inatten-
tion, not due to heterogeneity), results favored NF with a
significant small effect for the pre-post (SMD=0.25; 95%
CI 0.05, 0.45) measurement and a small effect for pre-FU
(SMD=0.39; 95% CI 0.19, 0.59). Including only active
controls resulted in no significant findings for either pre-
post or pre-FU. For both control analyses post-FU was not
significant; however, post-FU did show a trend toward sig-
nificance favoring neurofeedback over all control groups
(p=0.08; SMD=0.15; 95% CI − 0.02, 0.32].
Sensitivity meta‑analysis: ‘standard’ NF training
See Tables S-7 and S-8 for details regarding this analysis.
The tests for heterogeneity as well as effect sizes were simi-
lar to those seen in the within-group and between-group
analyses including all studies, with slightly stronger effect
sizes seen for the sensitivity analysis regarding pre-post and
pre-FU time points (increase in SMD ranging from 0.01 to
0.14), however, with small changes in both directions for
post-FU (change ranging from − 0.02 decrease to 0.02
The tests for heterogeneity as well as effect sizes were simi-
lar to those seen in the within-group and between-group
analyses including all participants (decrease of 0.01 SMD
to increases in SMD up to 0.02).
This meta-analysis investigated the effects of neurofeedback
and control conditions directly after treatment and during a
follow-up period (2–12months post-treatment), in which
Fig. 1 Forest Plot of within-group analysis for inattention param-
eter. Total standardized mean difference (SMD) with 95% confidence
interval, overall effect, and heterogeneity are reported. Due to signifi-
cant heterogeneity in the initial control analysis, additional analyses
examining non-active and active controls separately were included.
Pre-Post refers to the difference in means at pre- and post-measure-
ment, and similarly for pre-FU and post-FU
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300 European Child & Adolescent Psychiatry (2019) 28:293–305
1 3
no additional neurofeedback sessions or booster sessions
were performed. For neurofeedback, a medium SMD for
inattention and hyperactivity/impulsivity were found post-
treatment, which changed to a large SMD for inattention and
remained a medium SMD for hyperactivity/impulsivity at
follow-up (relative to baseline). Non-active control groups
yielded a significant small effect size at pre-post that was
no longer significant at FU for inattention, and there were
no significant effects found for the hyperactivity/impulsivity
domain. Active controls had significant large effect sizes for
inattention and medium effect sizes for hyperactivity at both
pre-post and pre-FU. The between-group analysis was found
to significantly favor NF over non-active control groups for
both inattention and hyperactivity/impulsivity at pre-post
(small effect sizes) and pre-FU (small to medium effect
sizes). Active controls were found to be significantly supe-
rior regarding inattention at pre-post but no longer at follow-
up. In summary, focusing on the pre-treatment to follow-up
results, neurofeedback was superior to non-active control
groups and similarly effective for inattention and hyper-
activity/impulsivity compared to active treatments. These
findings provide evidence that there are sustained clinical
benefits after neurofeedback and active treatments over an
average 6–12month follow-up period, whereas effects of
non-active control groups are no longer significant at FU.
The significant improvement in symptoms at FU for both
inattention and hyperactivity–impulsivity in the neurofeed-
back conditions indicates that NF results in lasting effects for
approximately 6months and potentially up to 1year. Com-
parison of effect-sizes between neurofeedback and active
control groups showed overlapping confidence intervals
(also visualized in Figure S-3), and no significant difference
in the between-group analysis, suggesting NF and active
controls having similar effects in the respective FU period;
however, this finding needs to be viewed with caution due
to the small number of studies in the active controls groups
(K=4). The tendency in the within-group analyses for a
small further improvement in the NF group (inattention:
Fig. 2 Forest Plot of within-group analysis for hyperactivity/
impulsivity parameter. Total standardized mean difference (SMD)
with 95% confidence interval, overall effect, and heterogeneity are
reported. Analysis of the control condition separately for non-active
and active controls was conducted for comparability to the inattention
parameter analysis. Pre-Post refers to the difference in means at pre-
and post-measurement, and similarly for pre-FU and post-FU
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301European Child & Adolescent Psychiatry (2019) 28:293–305
1 3
SMD=0.14; hyperactivity/impulsivity: SMD=0.11) from
post-treatment to FU, albeit not significant, is in line with the
further improvement effects seen after two-year follow-up
[6, 41]. This tendency was non-existent and moves primarily
in the opposite direction (SMD=− 0.1 to 0) for the active
and non-active controls (see Figure S-3). Accordingly, post-
FU results of the between-group analyses were slightly in
favor of NF (SMDs between 0.1 and 0.2, with a statistical
trend for hyperactivity/impulsivity) suggesting that effects
may become significant with further studies available. In any
case, current results do support the sustainability of the clin-
ical benefits of neurofeedback after cessation of treatment.
Regarding the use of medication, we only focused on
follow-up periods of 2–12months, and effects for active
treatments demonstrated sustained clinical benefit for these
periods (in which children were actively taking medication).
This is line with previous studies which demonstrated clini-
cal benefits of psychostimulant medication at 12months [42]
and 24months [3, 4]; however, the clinical benefits of psy-
chostimulant medication (when naturalistically assessed) are
not empirically supported for longer follow-up periods of
2–8years [4345]. Despite this, recent epidemiological stud-
ies have found that continued controlled medication intake
can have positive benefits for patients with ADHD [4648].
However, Swanson etal. [49] reported that at 12–16-year
follow-up of long-term medication use (both consistent and
inconsistent use over this time period) was not associated
with reduced symptom severity, but it was associated with
decreased adult height. These contrasting findings suggest
that while medication may have some long-term benefits,
the adherence of medication intake may be problematic and
long-term medication exposure may be related to potential
Fig. 3 Forest Plot of between-group analysis for inattention and
hyperactivity/impulsivity parameter. Total standardized mean differ-
ence (SMD) with 95% confidence interval, overall effect, and hetero-
geneity are reported. Analysis of the studies separately for non-active
and active controls was conducted for comparability to the inattention
parameter analysis. Pre-Post refers to the difference in means at pre-
and post-measurement, and similarly for pre-FU and post-FU
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302 European Child & Adolescent Psychiatry (2019) 28:293–305
1 3
physical side effects. The follow-up periods used in our
study were probably not long enough to demonstrate the
decreased efficacy of medication as reported in the above
studies [4345], and since these studies were controlled dur-
ing the FU periods and not naturalistic, it is likely that medi-
cation adherence was high. While NF follow-up treatment
effects have not been studied for such long-time intervals,
the short-term clinical effects of NF appear to be sustained
(without continued training) for an average 2–12month FU,
suggesting potential promise of this approach for sustained
clinical benefit in ADHD.
Interestingly, a strong point of our findings is that, despite
past heterogeneity in the application of neurofeedback pro-
tocols [5], most of the studies included in this paper used
‘standard NF protocols’, the exceptions being Arnold etal.
[25] and Bink etal. [26]. Additionally, Arnold etal. [25]
used an ‘entertaining’ NF protocol that may not be compat-
ible with principles of learning theory. However, the use of
uniform protocols by most of the papers is also evidenced by
the absence of significant heterogeneity. Considering only
standard protocols, we found that the results supporting NF
over non-active controls are slightly strengthened when only
including standard NF protocols. This finding supports the
continued use of these protocols for future NF studies. Addi-
tionally, our significant findings are in line with those of
Cortese etal. [8] who demonstrated significant between-
group effects even for ‘probably blinded’ ratings (teacher
ratings) at post-treatment when only standard NF protocols
for total ADHD and inattention symptoms were considered.
When considering placebo effects of NF training, they
may still operate at follow-up but we are not aware of ten-
dencies for further improvements of placebo effects for
other treatments of other disorders. NF treatment is found
to be superior to non-active controls in this analysis, and
the effects of non-active controls were not significant at FU,
neither for inattention nor hyperactivity/impulsivity. These
findings support the idea that NF does indeed have a differ-
ent, specific effect due to its actual training and not simply
due to the non-specific or placebo effects related to the set-
ting, the therapist–patient relationship or expectations. To
verify this more RCT’s using controls that closely mimic
NF training are required.
Possible limitations andopen questions
Results of the current meta-analysis should be interpreted in
line with its limitations.
While some of the studies included here do simultane-
ously use medication and NF (which may influence the
results), the number of participants taking medication did
not change for most studies, (only in two studies the NF
participants began taking medication between pre- and FU
measurement [25, 37]), suggesting that medication changes
are not an explanation for the effects found here. Addition-
ally, while dosage was allowed to be changed in five of these
studies, two NF groups decreased dosage and two increased
dosage while three control groups increased dosage. These
changes should not bias the data in favor of the NF, but
rather suggest that the NF effects may be slightly masked by
the dosage increase seen in the control groups.
When comparing the between-group effect sizes for pre-
to post-treatment between this study (Fig.3) and the latest
EAGG meta-analysis by Cortese etal. [8], a small effect
for inattention (SMD=0.36) and hyperactivity/impulsivity
(SMD=0.26) was found. In the current study, SMD’s are
lower when including all controls (inattention: SMD=0.09;
hyperactivity/impulsivity: SMD=0.16) and nearly identi-
cal when considering only non-active controls (inattention:
SMD=0.38; hyperactivity/impulsivity SMD=0.25). This
indicates that the studies we have included are representa-
tive and not biased towards more effective studies (opposite
file-drawer problem, i.e. higher likelihood that positive stud-
ies are more often published). But, while we attempted to
address the file drawer problem by assessing the fail-safe
numbers for our analysis, a potential reporting bias cannot be
definitively excluded. However, our finding of much larger
fail-safe numbers for NF (generally≥100) than for control
conditions (active<56, non-active<12) do suggest that
the NF condition results are probably not influenced by a
reporting bias.
Inherently when investigating FU periods, there are addi-
tional limitations involved including completer bias (a bias
is introduced because of the factors that cause a person to be
involved during a FU time-point) and the lack of an intention
to treat analysis (ITT) available for studies using a FU time
point. This type of analysis is considered more conservative
than the per protocol analyses found in the majority of the
papers included here. We chose specifically to not run a risk
of bias assessment because this model does not work well for
NF studies since it relies heavily on blinding, which poses a
problem since most NF studies were not blinded.
As already mentioned, more carefully designed RCTs
with longer follow-up time periods are needed before defi-
nite conclusions can be drawn. However, the meta-analysis
of Cortese etal. [8] on the acute effects of NF was comprised
of studies with a comparable design (“well-controlled”) and
about the same number of participants (ca. 500). Therefore,
this meta-analysis on follow-up effects at the present time
may also allow us to derive the first relevant conclusions
about the lasting effects of NF treatment.
Future research should focus on addressing both post- and
FU-effects of NF and other non-pharmacological treatments
for ADHD. Additionally, based on the current findings of
within-group effects, placebo or non-specific treatment
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303European Child & Adolescent Psychiatry (2019) 28:293–305
1 3
effects in NF cannot be ruled out and better controls for these
effects should continue to be investigated. Finally, it should
be noted that the specificity of neurofeedback effects cannot
only be derived from RCTs and a meta-analysis of RCTs
investigating behavioral outcome. Associations between the
behavioral and neurophysiological level (e.g., neuroregula-
tion skills) have already been documented with respect to the
post-treatment outcome [17, 50, 51], but are largely missing
from the literature that has conducted FU measurements.
These parameters provide an additional method to evaluate
treatment effects and should be included in future research
of long-term follow-ups.
Our meta-analytic results of NF treatment follow-up sug-
gest that there are sustained symptom reductions over time
in comparison with non-active control conditions. The
improvements seen here are comparable to active treat-
ments (including methylphenidate) at a short-term FU of
2–12months. As such, NF can be considered a non-phar-
macological treatment option for ADHD with evidence of
treatment effects that are sustained when treatment is com-
pleted and withdrawn. Future research should focus on the
comparison of standardized NF treatments with standard-
ized control treatments, controlling for unspecific effects
and changes in additional treatments (medication). Given
the need for additional treatments for ADHD with long-term
outcomes, clinical trials of NF should aim for primary out-
come measures that compare pre-treatment with systematic
long-term follow-up behavioral ratings, to address sustain-
ability of effects.
Acknowledgements The authors recognize Dr. Nezla Duric, Dr. Hanna
Christiansen, Dr. Naomi Steiner and Dr. L. Eugene Arnold for provid-
ing additional information that made inclusion of their data in this
meta-analysis possible.
Funding No funding was provided for this project.
Compliance with ethical standards
Conflict of interest MA reports options from Brain Resource (Sydney,
Australia); is director and owner of Research Institute Brainclinics, a
minority shareholder in neuroCare Group (Munich, Germany), and a
co-inventor on 4 patent applications (A61B5/0402; US2007/0299323,
A1; WO2010/139361 A1; WO2017/099603 A1) related to EEG,
neuromodulation and psychophysiology, but does not own these nor
receives any proceeds related to these patents; Research Institute
Brainclinics received research funding from Brain Resource (Sydney,
Australia) and neuroCare Group (Munich, Germany), and equipment
support from Deymed, neuroConn and Magventure; however, data
analyses and writing of this manuscript were unconstrained. US has
been paid for public speaking by Novartis, Medice, neuroCare, the
German Society for Biofeedback, and Akademie König und Müller.
All other authors have indicated no potential conflicts of interest rel-
evant to this article to disclose.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution 4.0 International License (http://creat iveco
mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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Supplementary resource (1)

... While the former is rated by most proximal (i.e., parents) and/or possibly blind (e.g., teachers) evaluators, the latter involves computerized or paper neurocognitive tests as an objective assessment of the patient's clinical condition [8]. Based on behavioral rating scales, the majority of previous meta-analytical studies [5,7,[9][10][11][12][13] demonstrated positive treatment effects from the most-proximal evaluators, while the results from possibly blinded evaluators were inconsistent [5,7,[9][10][11][12][13]. In contrast, an objective or computerized performance test may be less susceptible to informant bias [14] despite the lack of tangible evidence to support this proposal. ...
... While the former is rated by most proximal (i.e., parents) and/or possibly blind (e.g., teachers) evaluators, the latter involves computerized or paper neurocognitive tests as an objective assessment of the patient's clinical condition [8]. Based on behavioral rating scales, the majority of previous meta-analytical studies [5,7,[9][10][11][12][13] demonstrated positive treatment effects from the most-proximal evaluators, while the results from possibly blinded evaluators were inconsistent [5,7,[9][10][11][12][13]. In contrast, an objective or computerized performance test may be less susceptible to informant bias [14] despite the lack of tangible evidence to support this proposal. ...
... In contrast, the inclusion of more trials (n = 14) in the current meta-analysis enabled the assessment of the therapeutic impacts of EEG-NF on different components of attentional performance as well as the identification of important factors that could influence treatment outcomes through subgroup analysis and meta-regression. Most of the previous seven meta-analyses of the efficacy of surface EEG-NF in patients with ADHD evaluated treatment outcomes mainly based on behavioral rating with the inclusion of laboratory measures only in one study [5,7,[9][10][11][12][13]. All studies consistently found that surface EEG-NF was effective for improving inattention in ADHD patients when assessed by mostproximal evaluators, mostly parents, while the results from possibly blind evaluators (i.e., teachers) remained inconsistent [5,7,[9][10][11][12][13]. ...
Full-text available
Background The efficacy of surface electroencephalographic neurofeedback (EEG-NF) for improving attentional performance assessed by laboratory measures in patients with attention-deficit/hyperactivity disorder (ADHD) remains unclear. Methods Following the PRISMA guidelines, the PubMed, Embase, ClinicalKey, Cochrane CENTRAL, ScienceDirect, Web of Science, and databases were systematically searched for randomized controlled trials on the efficacy of surface EEG-NF against ADHD focusing on attentional performance evaluated by laboratory measures from inception to January 2022. Results Fourteen eligible studies were analyzed. Of the 718 participants involved, 429 diagnosed with ADHD received EEG-NF treatment. Significant improvement in attentional performance in ADHD subjects receiving EEG-NF was noted compared to their comparators (p < 0.01). Besides, there was a significant EEG-NF-associated beneficial effect on sustained attention (Hedges’ g = 0.32, p < 0.01), whereas the impact on selective attention (p = 0.57) and working memory (p = 0.59) was limited. Moreover, protocol including beta wave enhancement was superior to that only focusing on reducing theta/beta ratio or modulation of slow cortical potential. Subgroup analyses showed that three sessions per week of EEG-NF produced the best effect, while the efficacy of surface EEG-NF was much poorer (Hedges’ g = 0.05) when only studies that blinded their participants from knowledge of treatment allocation were included. No significant difference was noted in the improvement of attentional performance 6–12 months after EEG-NF intervention (n = 3, p = 0.42). Conclusions Our results demonstrated the satisfactory effectiveness of surface EEG-NF for improving sustained attention, especially when beta wave enhancement was included, despite its failure to sustain a long-term effect. Further large-scale trials are warranted to support our findings.
... Yet, there is limited empirical evidence to support their long-term efficacy, in part due to the challenges of conducting such studies [6][7][8]. Promising alternative treatments, such as dietary interventions, cognitive training (including programmes such as Cogmed) and neurofeedback therapy are still in need of high-quality evidence to support their efficacy, while omega-3 fish oil supplementation has seen waning evidence of effect with better quality trials [9][10][11][12][13][14]. Very often, clinical treatment for ADHD does not involve one single intervention, but a multi-modal approach to address the multiple areas of needs. ...
... Neurofeedback therapy, which trains a person to modify their own EEG waves and thereby improve their ADHD symptoms, has been explored extensively among the children and adolescent population. Clinical trials reported sustained improvements in inattentive symptoms, while improvements in hyperactive/impulsive symptoms yielded mixed results [9,20]. The therapy generally follows a protocol to modify specific EEG parameters with standard neurofeedback training protocols being theta/beta, sensorimotor rhythm and slow cortical potential [21]. ...
Full-text available
Background Attention deficit hyperactivity disorder (ADHD) is a prevalent child neurodevelopmental disorder that is treated in clinics and in schools. Previous trials suggested that our brain–computer interface (BCI)-based attention training program could improve ADHD symptoms. We have since developed a tablet version of the training program which can be paired with wireless EEG headsets. In this trial, we investigated the feasibility of delivering this tablet-based BCI intervention at home. Methods Twenty children diagnosed with ADHD, who did not receive any medication for the preceding month, were randomised to receive the 8-week tablet-based BCI intervention either in the clinic or at home. Those in the home intervention group received instructions before commencing the program and got reminders if they were lagging on the training sessions. The ADHD Rating Scale was completed by a blinded clinician at baseline and at week 8. Adverse events were monitored during any contact with the child throughout the trial and at week 8. Results Children in both groups could complete the tablet-based intervention easily on their own with minimal support from the clinic therapist or their parents (at home). The intervention was safe with few reported adverse effects. Clinician-rated inattentive symptoms on the ADHD-Rating Scale reduced by 3.2 (SD 6.20) and 3.9 (SD 5.08) for the home-based and clinic-based groups respectively, suggesting that home-based intervention was comparable to clinic-based intervention. Conclusions This trial demonstrated that the tablet version of our BCI-based attention training program can be safely delivered to children in the comfort of their own home. Trial registration This trial is registered at as NCT01344044
... This is a noninvasive intervention in which self-regulation of brain activity is sought through operant conditioning (Strehl, 2014). The therapeutic value of NFT has been explored in multiple populations, including people diagnosed with attention-deficit/hyperactivity disorder (Arns et al., 2014;Van Doren et al., 2019;Wangler et al., 2011), epilepsy (Schoenberg & David, 2014), and autism (Thompson et al., 2010). Yet, questions remain over whether NFT is an effective treatment in some areas of psychiatry and well-designed studies are still required to assess this (Begemann et al., 2016). ...
... We also observed a time course effect in several clinical measures, where the pain and function continued to improve over time. These findings have also been observed previously 69,70 , where clinical symptoms and neurophysiological variables neither regressed to baseline nor remained stable but continued to improve for weeks following NF training. While this might be due to practice effects of learning to control neural activity or reflect slow consolidation processes 71,72 , other mechanistic speculations such as self-reinforcement of brain over time to strengthen the correlational structure of network brain activity have also been proposed 69 and need further research. ...
Full-text available
Chronic low back pain (CLBP) is a disabling condition worldwide. In CLBP, neuroimaging studies demonstrate abnormal activities in cortical areas responsible for pain modulation, emotional, and sensory components of pain experience [i.e., pregenual and dorsal anterior cingulate cortex (pgACC, dACC), and somatosensory cortex (SSC), respectively]. This pilot study, conducted in a university setting, evaluated the feasibility, safety, and acceptability of a novel electroencephalography-based infraslow-neurofeedback (EEG ISF-NF) technique for retraining activities in pgACC, dACC and SSC and explored its effects on pain and disability. Participants with CLBP (n = 60), recruited between July’20 to March’21, received 12 sessions of either: ISF-NF targeting pgACC, dACC + SSC, a ratio of pgACC*2/dACC + SSC, or Placebo-NF. Descriptive statistics demonstrated that ISF-NF training is feasible [recruitment rate (7 participants/month), dropouts (25%; 20–27%), and adherence (80%; 73–88%)], safe (no adverse events reported), and was moderate to highly acceptable [Mean ± SD: 7.8 ± 2.0 (pgACC), 7.5 ± 2.7 (dACC + SCC), 8.2 ± 1.9 (Ratio), and 7.7 ± 1.5 (Placebo)]. ISF-NF targeting pgACC demonstrated the most favourable clinical outcomes, with a higher proportion of participants exhibiting a clinically meaningful reduction in pain severity [53%; MD (95% CI): − 1.9 (− 2.7, − 1.0)], interference [80%; MD (95% CI): − 2.3 (− 3.5, − 1.2)], and disability [73%; MD (95% CI): − 4.5 (− 6.1, − 2.9)] at 1-month follow-up. ISF-NF training is a feasible, safe, and an acceptable treatment approach for CLBP.
... In 2016, 1 out of 10 children with ADHD have received neurofeedback (Danielson et al., 2017). The research on the efficacy of neurofeedback for ADHD has varied in its scientific rigor (Arns et al., 2014) and has mixed results from previous studies and meta-analyses (Cortese et al., 2018;Van Doren et al., 2019). The International Collaborative ADHD Neurofeedback (ICAN) Study was a 2-site, double-blind, randomized clinical trial (Clinical Trials Registration:, ...
Full-text available
We examined psychiatric comorbidities moderation of a 2-site double-blind randomized clinical trial of theta/beta-ratio (TBR) neurofeedback (NF) for attention deficit hyperactivity disorder (ADHD). Seven-to-ten-year-olds with ADHD received either NF (n = 84) or Control (n = 58) for 38 treatments. Outcome was change in parent-/teacher-rated inattention from baseline to end-of-treatment (acute effect), and 13-month-follow-up. Seventy percent had at least one comorbidity: oppositional defiant disorder (ODD) (50%), specific phobias (27%), generalized anxiety (23%), separation anxiety (16%). Comorbidities were grouped into anxiety alone (20%), ODD alone (23%), neither (30%), or both (27%). Comorbidity (p = 0.043) moderated acute effect; those with anxiety-alone responded better to Control than to TBR NF (d = − 0.79, CI − 1.55– − 0.04), and the other groups showed a slightly better response to TBR NF than to Control (d = 0.22 ~ 0.31, CI − 0.3–0.98). At 13-months, ODD-alone group responded better to NF than Control (d = 0.74, CI 0.05–1.43). TBR NF is not indicated for ADHD with comorbid anxiety but may benefit ADHD with ODD. Clinical Trials Identifier: NCT02251743, date of registration: 09/17/2014
Full-text available
In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.
The need for engaging treatment approaches in mental health care has led to the developing of new applications based on serious game approaches. These approaches have facilitated the development of promising tools for dealing with attention deficit hyperactivity disorder. This paper presents a novel system called CogniDron-EEG. This system is based on a brain-computer interface for flying indoor a drone for cognitive training purposes. We conducted a controlled trial with ten healthy children aged 7-14 to test the functional suitability and usability of the CogniDron-EEG system. Also, this study allowed us to evaluate the preference between our system and another system based on video games. Therefore, participant subjects used our CogniDron-EEG system and a system called Nexus to identify the users’ preferences concerning these two systems. The findings suggest that participants were satisfied with the CogniDron-EEG and provide the basis for further development and research on the CogniDron-EEG system. Therefore, the proposed system in this paper opens a new branch of research on drones to study their advantages and disadvantages of using them for cognitive training purposes. Additionally, implications for developing human–robot interaction and serious games in the mental health context are discussed.
Full-text available
To assess the long-term effects of neurofeedback (NFB) in children with attention deficit hyperactivity disorder (ADHD), we compared behavioral and neurocognitive outcomes at a 6-month naturalistic follow-up of a randomized controlled trial on NFB, methylphenidate (MPH), and physical activity (PA). Ninety-two children with a DSM-IV-TR ADHD diagnosis, aged 7–13, receiving NFB (n = 33), MPH (n = 28), or PA (n = 31), were re-assessed 6-months after the interventions. NFB comprised theta/beta training on the vertex (cortical zero). PA comprised moderate to vigorous intensity exercises. Outcome measures included parent and teacher behavioral reports, and neurocognitive measures (auditory oddball, stop-signal, and visual spatial working memory tasks). At follow-up, longitudinal hierarchical multilevel model analyses revealed no significant group differences for parent reports and neurocognitive measures (p = .058–.997), except for improved inhibition in MPH compared to NFB (p = .040) and faster response speed in NFB compared to PA (p = .012) during the stop-signal task. These effects, however, disappeared after controlling for medication use at follow-up. Interestingly, teacher reports showed less inattention and hyperactivity/impulsivity at follow-up for NFB than PA (p = .004–.010), even after controlling for medication use (p = .013–.036). Our findings indicate that the superior results previously found for parent reports and neurocognitive outcome measures obtained with MPH compared to NFB and PA post intervention became smaller or non-significant at follow-up. Teacher reports suggested superior effects of NFB over PA; however, some children had different teachers at follow-up. Therefore, this finding should be interpreted with caution. Clinical trial registration Train your brain and exercise your heart? Advancing the treatment for Attention Deficit Hyperactivity Disorder (ADHD), Ref. no. NCT01363544,
Full-text available
Placebos are sham medical treatments. Nonetheless, they can have substantial effects on clinical outcomes. Placebos depend on a person's psychological and brain responses to the treatment context, which influence appraisals of future well-being. Appraisals are flexible cognitive evaluations of the personal meaning of events and situations that can directly impact symptoms and physiology. They also shape associative learning processes by guiding what is learned from experience. Appraisals are supported by a core network of brain regions associated with the default mode network involved in self-generated emotion, self-evaluation, thinking about the future, social cognition, and valuation of rewards and punishment. Placebo treatments for acute pain and a range of clinical conditions engage this same network of regions, suggesting that placebos affect behavior and physiology by engaging this brain system to change how a person evaluates their future well-being and the personal significance of their symptoms. Expected final online publication date for the Annual Review of Clinical Psychology Volume 13 is May 7, 2017. Please see for revised estimates.
Full-text available
Background: Different treatment approaches aimed at reducing attention-deficit/hyperactivity disorder (ADHD) core symptoms are available. However, factors such as intolerance, side-effects, lack of efficacy, high new technology costs, and placebo effect have spurred on an increasing interest in alternative or complementary treatment. Aim: The aim of this study is to explore efficacy of multimodal treatment consisting of standard stimulant medication (methylphenidate) and neurofeedback (NF) in combination, and to compare it with the single treatment in 6-month follow-up in ADHD children and adolescents. Methods: This randomized controlled trial with 6-month follow-up comprised three treatment arms: multimodal treatment (NF + MED), MED alone, and NF alone. A total of 130 ADHD children/adolescents participated, and 62% completed the study. ADHD core symptoms were recorded pre-/post-treatment, using parents’ and teachers’ forms taken from Barkley’s Defiant Children: A Clinician’s Manual for Assessment and Parent Training, and a self-report questionnaire. Results: Significant ADHD core symptom improvements were reported 6 months after treatment completion by parents, teachers, and participants in all three groups, with marked improvement in inattention in all groups. However, no significant improvements in hyperactivity or academic performance were reported by teachers or self-reported by children/adolescents, respectively, in the three groups. Changes obtained with multimodal treatment at 6-month follow-up were comparable to those with single medication treatment, as reported by all participants. Conclusions: Multimodal treatment using combined stimulant medication and NF showed 6-month efficacy in ADHD treatment. More research is needed to explore whether multimodal treatment is suitable for ADHD children and adolescents who showed a poor response to single medication treatment, and for those who want to reduce the use of stimulant medication.
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Background: The Multimodal Treatment Study (MTA) began as a 14-month randomized clinical trial of behavioral and pharmacological treatments of 579 children (7-10 years of age) diagnosed with attention-deficit/hyperactivity disorder (ADHD)-combined type. It transitioned into an observational long-term follow-up of 515 cases consented for continuation and 289 classmates (258 without ADHD) added as a local normative comparison group (LNCG), with assessments 2-16 years after baseline. Methods: Primary (symptom severity) and secondary (adult height) outcomes in adulthood were specified. Treatment was monitored to age 18, and naturalistic subgroups were formed based on three patterns of long-term use of stimulant medication (Consistent, Inconsistent, and Negligible). For the follow-up, hypothesis-generating analyses were performed on outcomes in early adulthood (at 25 years of age). Planned comparisons were used to estimate ADHD-LNCG differences reflecting persistence of symptoms and naturalistic subgroup differences reflecting benefit (symptom reduction) and cost (height suppression) associated with extended use of medication. Results: For ratings of symptom severity, the ADHD-LNCG comparison was statistically significant for the parent/self-report average (0.51 ± 0.04, p < .0001, d = 1.11), documenting symptom persistence, and for the parent/self-report difference (0.21 ± 0.04, p < .0001, d = .60), documenting source discrepancy, but the comparisons of naturalistic subgroups reflecting medication effects were not significant. For adult height, the ADHD group was 1.29 ± 0.55 cm shorter than the LNCG (p < .01, d = .21), and the comparisons of the naturalistic subgroups were significant: the treated group with the Consistent or Inconsistent pattern was 2.55 ± 0.73 cm shorter than the subgroup with the Negligible pattern (p < .0005, d = .42), and within the treated group, the subgroup with the Consistent pattern was 2.36 ± 1.13 cm shorter than the subgroup with the Inconsistent pattern (p < .04, d = .38). Conclusions: In the MTA follow-up into adulthood, the ADHD group showed symptom persistence compared to local norms from the LNCG. Within naturalistic subgroups of ADHD cases, extended use of medication was associated with suppression of adult height but not with reduction of symptom severity.
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Background: Estimates of the effectiveness of neurofeedback as a treatment for attention-deficit hyperactivity disorder (ADHD) are mixed. Aims: To investigate the long-term additional effects of neurofeedback (NFB) compared with treatment as usual (TAU) for adolescents with ADHD. Method: Using a multicentre parallel-randomised controlled trial design, 60 adolescents with a DSM-IV-TR diagnosis of ADHD receiving NFB+TAU (n=41) or TAU (n=19) were followed up. Neurofeedback treatment consisted of approximately 37 sessions of theta/sensorimotor rhythm (SMR)-training on the vertex (Cz). Outcome measures included behavioural self-reports and neurocognitive measures. Allocation to the conditions was unmasked. Results: At 1-year follow-up, inattention as reported by adolescents was decreased (range ηp(2)=0.23-0.36, P<0.01) and performance on neurocognitive tasks was faster (range ηp(2)=0.20-0.67, P<0.005) irrespective of treatment group. Conclusions: Overall, NFB+TAU was as effective as TAU. Given the absence of robust additional effects of neurofeedback in the current study, results do not support the use of theta/SMR neurofeedback as a treatment for adolescents with ADHD and comorbid disorders in clinical practice. Declaration of interest: None. Copyright and usage: © The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.
Importance Individuals with attention-deficit/hyperactivity disorder (ADHD) are at greater risk for academic problems. Pharmacologic treatment is effective in reducing the core symptoms of ADHD, but it is unclear whether it helps to improve academic outcomes. Objective To investigate the association between the use of ADHD medication and performance on higher education entrance tests in individuals with ADHD. Design, Setting, and Participants This cohort study observed 61 640 individuals with a diagnosis of ADHD from January 1, 2006, to December 31, 2013. Records of their pharmacologic treatment were extracted from Swedish national registers along with data from the Swedish Scholastic Aptitude Test. Using a within-patient design, test scores when patients were taking medication for ADHD were compared with scores when they were not taking such medication. Data analysis was performed from November 24, 2015, to November 4, 2016. Exposures Periods with and without ADHD medication use. Main Outcomes and Measures Scores from the higher education entrance examination (score range, 1-200 points). Results Among 930 individuals (493 males and 437 females; mean [SD] age, 22.2 [3.2] years) who had taken multiple entrance tests (n = 2524) and used ADHD medications intermittently, the test scores were a mean of 4.80 points higher (95% CI, 2.26-7.34; P < .001) during periods they were taking medication vs nonmedicated periods, after adjusting for age and practice effects. Similar associations between ADHD medication use and test scores were detected in sensitivity analyses. Conclusions and Relevance Individuals with ADHD had higher scores on the higher education entrance tests during periods they were taking ADHD medication vs nonmedicated periods. These findings suggest that ADHD medications may help ameliorate educationally relevant outcomes in individuals with ADHD.
Importance: Motor vehicle crashes (MVCs) are a major public health problem. Research has demonstrated that individuals with attention-deficit/hyperactivity disorder (ADHD) are more likely to experience MVCs, but the effect of ADHD medication treatment on the risk of MVCs remains unclear. Objective: To explore associations between ADHD medication use and risk of MVCs in a large cohort of patients with ADHD. Design, setting, and participants: For this study, a US national cohort of patients with ADHD (n = 2 319 450) was identified from commercial health insurance claims between January 1, 2005, and December 31, 2014, and followed up for emergency department visits for MVCs. The study used within-individual analyses to compare the risk of MVCs during months in which patients received ADHD medication with the risk of MVCs during months in which they did not receive ADHD medication. Exposures: Dispensed prescription of ADHD medications. Main outcomes and measures: Emergency department visits for MVCs. Results: Among 2 319 450 patients identified with ADHD, the mean (SD) age was 32.5 (12.8) years, and 51.7% were female. In the within-individual analyses, male patients with ADHD had a 38% (odds ratio, 0.62; 95% CI, 0.56-0.67) lower risk of MVCs in months when receiving ADHD medication compared with months when not receiving medication, and female patients had a 42% (odds ratio, 0.58; 95% CI, 0.53-0.62) lower risk of MVCs in months when receiving ADHD medication. Similar reductions were found across all age groups, across multiple sensitivity analyses, and when considering the long-term association between ADHD medication use and MVCs. Estimates of the population-attributable fraction suggested that up to 22.1% of the MVCs in patients with ADHD could have been avoided if they had received medication during the entire follow-up. Conclusions and relevance: Among patients with ADHD, rates of MVCs were lower during periods when they received ADHD medication. Considering the high prevalence of ADHD and its association with MVCs, these findings warrant attention to this prevalent and preventable cause of mortality and morbidity.
Objective: We performed meta-analyses of randomized controlled trials to examine the effects of neurofeedback on attention-deficit/hyperactivity disorder (ADHD) symptoms and neuropsychological deficits in children and adolescents with ADHD. Method: We searched PubMed, Ovid, Web of Science, ERIC, and CINAHAL through August 30, 2015. Random-effects models were employed. Studies were evaluated with the Cochrane Risk of Bias tool. Results: We included 13 trials (520 participants with ADHD). Significant effects were found on ADHD symptoms rated by assessors most proximal to the treatment setting, that is, the least blinded outcome measure (standardized mean difference [SMD]: ADHD total symptoms = 0.35, 95% CI = 0.11-0.59; inattention = 0.36, 95% CI = 0.09-0.63; hyperactivity/impulsivity = 0.26, 95% CI = 0.08-0.43). Effects were not significant when probably blinded ratings were the outcome or in trials with active/sham controls. Results were similar when only frequency band training trials, the most common neurofeedback approach, were analyzed separately. Effects on laboratory measures of inhibition (SMD = 0.30, 95% CI = -0.10 to 0.70) and attention (SMD = 0.13, 95% CI = -0.09 to 0.36) were not significant. Only 4 studies directly assessed whether learning occurred after neurofeedback training. The risk of bias was unclear for many Cochrane Risk of Bias domains in most studies. Conclusion: Evidence from well-controlled trials with probably blinded outcomes currently fails to support neurofeedback as an effective treatment for ADHD. Future efforts should focus on implementing standard neurofeedback protocols, ensuring learning, and optimizing clinically relevant transfer.