prefrontal neuromodulation using rtms improves error monitoring and correction function in autism
TRANSCRIPT
Prefrontal Neuromodulation Using rTMS Improves ErrorMonitoring and Correction Function in Autism
Estate M. Sokhadze • Joshua M. Baruth •
Lonnie Sears • Guela E. Sokhadze •
Ayman S. El-Baz • Manuel F. Casanova
Published online: 7 February 2012
� Springer Science+Business Media, LLC 2012
Abstract One important executive function known to be
compromised in autism spectrum disorder (ASD) is related
to response error monitoring and post-error response cor-
rection. Several reports indicate that children with ASD
show reduced error processing and deficient behavioral
correction after an error is committed. Error sensitivity can
be readily examined by measuring event-related potentials
(ERP) associated with responses to errors, the fronto-cen-
tral error-related negativity (ERN), and the error-related
positivity (Pe). The goal of our study was to investigate
whether reaction time (RT), error rate, post-error RT
change, ERN, and Pe will show positive changes following
12-week long slow frequency repetitive TMS (rTMS) over
dorsolateral prefrontal cortex (DLPFC) in high functioning
children with ASD. We hypothesized that 12 sessions of
1 Hz rTMS bilaterally applied over the DLPFC will result
in improvements reflected in both behavioral and ERP
measures. Participants were randomly assigned to either
active rTMS treatment or wait-list (WTL) groups. Baseline
and post-TMS/or WTL EEG was collected using 128
channel EEG system. The task involved the recognition of
a specific illusory shape, in this case a square or triangle,
created by three or four inducer disks. ERN in TMS
treatment group became significantly more negative. The
number of omission errors decreased post-TMS. The RT
did not change, but post-error RT became slower. There
were no changes in RT, error rate, post-error RT slowing,
nor in ERN/Pe measures in the wait-list group. Our results
show significant post-TMS differences in the response-
locked ERP such as ERN, as well as behavioral response
monitoring measures indicative of improved error moni-
toring and correction function. The ERN and Pe, along
with behavioral performance measures, can be used as
functional outcome measures to assess the effectiveness of
neuromodulation (e.g., rTMS) in children with autism and
thus may have important practical implications.
Keywords Autism � Error monitoring �Event-related potential � Reaction time � TMS
Introduction
Autism spectrum disorder (ASD) is characterized by severe
disturbances in reciprocal social relations, varying degrees
of language and communication difficulties, and behavioral
patterns which are restricted, repetitive, and stereotyped
(American Psychiatric Association 2000). One of the
neurocognitive models of ASD outlines deficits in execu-
tive functioning skills such as problem-solving, flexible
set-shifting, and forward planning of goal-directed behav-
iors (Ozonoff 1997). These deficits have been related to
specific frontal structures, including the dorsolateral pre-
frontal and midfrontal cortices and associated neural cir-
cuitries (Bishop 1993). Executive deficit hypothesis of
E. M. Sokhadze (&) � J. M. Baruth � M. F. Casanova
Department of Psychiatry and Behavioral Sciences, University
of Louisville School of Medicine, Louisville, KY 40202, USA
e-mail: [email protected]
L. Sears
Department of Pediatrics, University of Louisville School
of Medicine, Louisville, KY, USA
G. E. Sokhadze
Department of Psychology and Brain Sciences,
University of Louisville, Louisville, KY, USA
A. S. El-Baz
Department of Bioengineering, University of Louisville,
Louisville, KY, USA
123
Appl Psychophysiol Biofeedback (2012) 37:91–102
DOI 10.1007/s10484-012-9182-5
autism (Hill 2004) emphasizes that many of the everyday
behaviors of autistic individuals, such as preservative
responding, repetitive behavior, poor imitation skills, and
joint attention impairments may involve an inability to
monitor ongoing behaviors consistently and accurately
(Mundy 2003; Mundy and Neal 2001). Self-monitoring
impairments in ASD have been reported earlier (Russell
1997; Russell and Jarrold 1998). Several recent reports
(Bogte et al. 2007; Henderson et al. 2006; Sokhadze et al.
2010b; Thakkar et al. 2008; Vlamings et al. 2008) indicate
that children and adult patients with ASD show reduced
error processing and deficient behavioral correction after
an error has been committed. This finding could be
explained as a reflection of ASD patients’ lower sensitivity
to behavioral errors and/or reduced behavior correction
ability.
Performance on behavioral tasks is monitored by a frontal
brain system that is responsive to errors (Falkenstein et al.
2000; Gehring and Knight 2000; Gehring et al. 1993; Luu
et al. 2000, 2003). Evidence from functional magnetic res-
onance imaging (fMRI), electroencephalographic (EEG),
and event-related potential (ERP) studies outline that error
monitoring is a function of the dorsolateral prefrontal cortex
(DLPFC) and the medial frontal cortex (MFC), including
the supplementary eye fields, rostral cingulate motor area,
and dorsal anterior cingulate cortex (ACC) (reviewed in
Ridderinkhof et al. 2004). A series of neuropathological
studies in autism suggest the presence of significant mini-
columnar abnormalities in brain regions related to error
monitoring, specifically in the DLPFC, the MFC, and the
ACC (Casanova 2005,2007; Casanova et al. 2006a, b, 2003).
Error sensitivity in psychophysiological studies is usu-
ally examined by measuring response-locked ERP com-
ponents associated with responses to errors. Two specific
components relevant in this context are the error-related
negativity (ERN, more rarely referred to as Ne), and the
error-related positivity (Pe). The ERN is a response-locked
negative ERP deflection, emerging between 20 and 150 ms
after the onset of an incorrect behavioral response,such as a
commission error. This negative wave is followed by a
positive wave referred to as the Pe potential. Although
there is discussion about the exact meaning of the Pe (Luu
et al. 2003; Mathalon et al. 2003; Overbeek et al. 2005),
most studies indicate that the Pe is related to the conscious
recognition of the error (Nieuwenhuis et al. 2001), or the
attribution of motivational significance to the committed
error (Falkenstein et al. 2000). This suggests that the ERN
reflects an initial automatic brain response as a result of an
error, and the Pe possibly indicates the conscious reflection
and comprehension of the error (Overbeek et al. 2005). The
magnitude of the ERN is associated with behavioral evi-
dence of self-monitoring (i.e., self-correction and post-
error slowing responses), and therefore is interpreted as a
biomarker of error processing (Yeung et al. 2004). Dipole
modeling has localized ERN sources to the caudal ACC,
while Pe has been localized to the more rostral ACC
division (Bush et al. 2000; Gehring and Knight 2000;
Herrmann et al. 2004; Luu et al. 2000; Sokhadze et al.
2010b; Taylor et al. 2007; Van Veen and Carter 2002).
ERN and Pe are generally accepted as neural indices of
response-monitoring processes in psychophysiological
research and clinical neurophysiology.
The connection between ACC dysfunction and autism
has been empirically demonstrated in several ERN-based
studies. Children with high functioning autism displayed
longer ERN latencies, but did not differ in amplitude of the
ERN relative to children in the control group in the Eriksen
flanker task. (Henderson et al. 2006). There is other evi-
dence of abnormal response monitoring in autism, such as
reduced error self-correction (Russell and Jarrold 1998),
and reduced post-error slowing (Bogte et al. 2007). Since
the evaluation of ongoing behavior and its consequences is
necessary to determine whether or not current behavior
adjustment strategies should be maintained, abnormal
response monitoring and deficient adaptive correction may
contribute to the behavioral inflexibility and rigidity asso-
ciated with ASD.
According to the error monitoring and correction model
by Gehring and Knight (2000), the ACC monitors for
errors and response conflicts, but depends on the DLPFC
for processing necessary to implement error correction or
conflict resolution. The prefrontal cortex maintains action
representation necessary for decision-making, because
without such decision the ACC is unable to distinguish
correct from incorrect responses in a context of the task at
hand. The model suggests that a system other than the ACC
or the DLPFC (e.g., basal ganglia) implements corrective
action, but it operates under the strong modulatory pre-
frontal influences. The neuroanatomical basis of executive
function still appears to be the DLPFC rather than the
ACC. A coordinated activation of the PFC and the ACC
serve diverse executive functions including error monitor-
ing and post-error adjustment control.
In our prior study (Sokhadze et al. 2010b), we examined
the possibility that children with ASD exhibit a deficiency
in the processing of errors, reflected by a reduction and
delays in the ERN and Pe response-locked brain potentials.
As expected, our results showed that ASD patients had
higher rates of error for novel distractors in of the visual
oddball task. In addition, neurodevelopmentally normal
subjects showed slower reaction time (RT) and decreased
error rates after commission of an error. These results have
been interpreted as changes in the speed–accuracy strategy
of the subjects, possibly due to error-induced control
processes and concomitant corrective adjustments. The
patients with ASD had an opposite response, showing
92 Appl Psychophysiol Biofeedback (2012) 37:91–102
123
faster post-error RTs. Furthermore, we found lower ERN
amplitudes, as well as prolonged Pe in ASD, compared to
typical controls. The reduced ERN and altered Pe, along
with a lack of post-error RT slowing, was interpreted as an
insensitivity to detect and monitor response errors and
reduced ability of execute corrective actions in subjects
with autism (Sokhadze et al. 2010b). These results were
indicative of reduced error awareness, and a failure in
adjustment in ASD when dealing with situations where
erroneous responses may occur.
In the last two decades, transcranial magnetic stimula-
tion (TMS) has been used increasingly as an experimental
investigation tool to explore the mechanisms and conse-
quences of cortical plasticity in the human cortex (George
et al. 2003; Wassermann and Lisanby 2001). Repetitive
TMS (rTMS) offers a noninvasive method for altering
excitability of the brain. This method uses an electromag-
net placed on the scalp that generates magnetic field pulses
of very short duration (100–300 ms) approximately 1.5–2.2
T in strength. Magnetic fields pass largely undistorted
through the scalp and skull (George et al. 1999). By con-
vention, rTMS in 0.3–1 Hz frequency range is referred to
as ‘‘slow,’’ whereas ‘‘fast’’ rTMS refers to stimulation
greater than 1 Hz. Hoffman and Cavus (2002) in their
review of slow rTMS studies proposed long-term depres-
sion and long-term depotentiation as potential models for
understanding the mechanism of slow rTMS. Neocortical
long-term depression and changes in the cortical excit-
ability induced by slow rTMS appear to accumulate in an
additive fashion as the number of stimulations is increased
over many days. Several reviews concluded that rTMS can
be generally considered safe for use in pediatric popula-
tions, as no significant adverse effects or seizures have
been reported (Garvey and Mall 2008; Quintana 2005). The
lasting effects of rTMS offer new possibilities to study
dynamic aspects of the pathophysiology of a variety of
diseases and may have therapeutic potential in some psy-
chiatric disorders, and specifically ASD.
Within the context of ASD, rTMS may have unique
potential applications as a treatment modality. It has been
suggested that a wide range of deficits in autism might be
understood by disrupted information integration in the
brain, and more specifically, high local connectivity at the
expense of deficiencies in long-range connectivity (Just
et al. 2004; Rippon et al. 2007), and an increase in the ratio
of cortical excitation to cortical inhibition (Rubenstein and
Merzenich 2003). Locally overconnected neural networks
may explain the superior ability of autistic children in
isolated tasks (e.g., visual discrimination), while, at the
same time, deficiencies in long-range connectivity may
explain other features of the disorder (e.g., behavioral
stereotypy). Higher-than-normal cortical noise and an
increase in the ratio of cortical excitation to inhibition may
be a reason of the strong aversive reactions to auditory,
tactile, and visual stimuli frequently recorded in autistic
individuals as well as a higher incidence of epilepsy
(Gillberg and Billstedt 2000).
One possible explanation for higher-than-normal corti-
cal noise and abnormal neural connectivity in ASD is the
recent finding of minicolumnar abnormalities. Minicol-
umns are considered the basic anatomical and physiologi-
cal unit of the cerebral cortex (Mountcastle 2003), and
contain pyramidal cells that extend the cortical width sur-
rounded by a neuropil space consisting of several species
of GABAergic, inhibitory interneurons (i.e. double-bou-
quet, basket, and chandelier cells) (Casanova 2007;
Casanova et al. 2002a, b)). Double-bouquet cells in the
peripheral neuropil space of minicolumns provide a ‘‘ver-
tical stream of negative inhibition’’ (Mountcastle 2003)
surrounding the minicolumnar core. Our preliminary
studies indicate that minicolumns are reduced in size and
increased in number in the autistic brain, especially the
prefrontal cortex (Casanova et al. 2002a, b, 2006a). More
specifically, minicolumns in the brains of autistic patients
are narrower and contain less peripheral, neuropil space
(Casanova et al. 2006a; Casanova 2005). The lack of a
‘buffer zone’ normally afforded by lateral inhibition and
appropriate neuropil space may adversely affect the func-
tional distinctiveness of minicolumnar activation, and
could result in isolated islands of coordinated excitatory
activity (i.e., possible seizure foci). In addition, the effect
of loss of surround inhibition may result in an increase in
the ratio of cortical excitation to inhibition and signal/
sensory amplification which may impair executive func-
tioning in patients with ASD. In terms of error monitoring,
dysfunctions of DLPFC and ACC connectivity may result
in poor processing of response errors and diminished
ability to adjust behavioral outcome during performance on
a speeded reaction time task.
Recently, the biophysical mechanisms underlying TMS
were extensively reviewed (see Wagner et al. 2009). The
review specifically addressed issues such as the location of
the TMS (i.e., area of coil application), intensity and fre-
quency of magnetic pulses. In our study, low frequency
rTMS was applied over the dorsal prefrontal cortex. We
hypothesize that contrary to other inhibitory cells (i.e.,
basket and chandelier), whose projections keep no constant
relation to the surface of the cortex, the geometrically exact
orientation of double-bouquet cells and their location at the
periphery of the minicolumn (inhibitory surround) makes
them the appropriate candidate for induction by a magnetic
field applied parallel to the prefrontal cortex. Over a
course of treatment, ‘slow’ rTMS may restore the balance
between cortical excitation and cortical inhibition and lead
to improved long-range cortical connectivity. Enhanced
functional connectivity between the DLPFC and the ACC
Appl Psychophysiol Biofeedback (2012) 37:91–102 93
123
following slow rTMS treatment may result in observed
facilitated error detection, monitoring and correction in
individuals with ASD.
We propose that low frequency rTMS over the DLPFC
may improve executive functioning in children with aut-
ism. The overall aim of this study was to investigate
behavioral responses and ERP indices of performance
monitoring in children with autism enrolled either in rTMS
or wait-list group. The goal of our study was to investigate
whether RT, error rate, post-error RT change, ERN, and Pe
will show positive changes following 12-week long slow
frequency rTMS over DLPFC in 40 high functioning
children with ASD diagnosis. We hypothesized that 12
sessions of 1 Hz rTMS bilaterally applied over the DLPFC
will result in improvements reflected in both behavioral
performance measures and such error-related potentials as
ERN and Pe. We predicted faster RT, lower error rate,
shorter latencies and higher amplitudes of ERN and Pe
measures in the rTMS treatment group as compared to the
wait-list group of children with autism.
Methods
Participants
Participants with autism spectrum disorder were recruited
through the University of Louisville Weisskopf Child
Evaluation Center (WCEC). Study enrollment eligibility
age range was 9–21 years. Diagnosis was made according
to the Diagnostic and Statistical Manual of Mental Disor-
ders (DSM-IV-TR) (APA 2000), and was further ascer-
tained with the Autism Diagnostic Interview–Revised
(ADI-R) (LeCouteur et al. 2003), and a medical evaluation
by a developmental pediatrician. Thirty six participants had
autistic disorder diagnosis, while 4 had Asperger disorder
diagnosis. Participants with a history of seizure disorder,
significant hearing or visual impairment, a brain abnor-
mality conclusive from imaging studies, or an identified
genetic disorder were excluded.
The mean age of 20 participants enrolled in the rTMS
treatment group (TMS group) was 13.5± (standard devia-
tion) 2.5 years (range 10–19 years, 16 males, 4 females),
while the mean age of 20 participants assigned to wait-list
group (WTL group) was 14.1 ± 2.4 years (11–21 years,
16 males, 4 females). The mean full scale IQ scores
assessed using WISC-IV (Wechsler 2003) or WASI
(Wechsler 2004) was 90.8 ± 15.2. IQ difference between
groups was not statistically significant. The approximate
household incomes and parents education level used to
assess socioeconomic status of children’s families also did
not show any statistically significant group differences. All
participants except 4 in the TMS group and 3 in the WTL
group were right-handed (assessed using Edinburgh hand-
edness inventory, Oldfield 1971).
Participating subjects and their parents (or legal
guardians) were provided with full information about the
study including the purpose, requirements, responsibili-
ties, reimbursement, risks, benefits, alternatives, and role
of the local Institutional Review Board (IRB). All
questions were answered before consent signature was
requested. Subjects’ families were reimbursed at the rate
of $25 per visit (total $50) for their time and cost of
parking.
ERP Data Acquisition, and Signal Processing
EEG Recording
Electroencephalograpic (EEG) data were acquired with
a 128 channel Electrical Geodesics Inc. (EGI) system
(v. 200) consisting of Geodesic Sensor Net electrodes
(silver/silver chloride electrodes in a sponge soaked in
KCl), Net Amps, and Net Station software (Electrical
Geodesics Inc., Eugene, OR). EEG data were sampled at
500 Hz and 0.1–200 Hz analog filtered. Impedances were
kept under 50 KX, in accordance with Net Station’s sug-
gested settings.
Response-locked EEG data are segmented off-line into
1,000 ms epochs spanning 500 ms pre-stimulus to 500 ms
post-stimulus around the critical event (commission error).
Data are digitally screened for artifacts (eye blinks,
movements), and contaminated trials are removed using
artifact rejection tools. The Net Station Waveform Tools’
Artifact Detection module in ‘‘off-line’’ mode marks EEG
channel ‘‘bad’’ if fast average amplitude exceeds 200 lV,
differential average amplitude exceeds 100 lV, or if
channel has zero variance. Segments are marked ‘‘bad’’ if
contain more than 10 bad channels, if eye blink or eye
movement are detected ([70 lV). Additional screening
and removal of artifacts related to eye-blinks, lateral and
vertical eye movements were done using visual inspection
of EEG recording and 2 channels of electrooculaogram
(EOG, EGI channels 126 and 127) by experienced elec-
troencephalographist. After detection of bad channels, the
Net Station’s ‘‘Bad channel replacement’’ function is used
for the replacement of data in bad channels with data
interpolated from the remaining good channels (or seg-
ments) using spherical splines (Luu et al. 2001; Srinivasan
et al. 1998). Remaining data are digitally filtered using
60 Hz Notch and 0.3–20 Hz bandpass filters to remove
residual noise and are then segmented by condition and
averaged to create ERPs. Averaged ERP data are baseline
corrected (500 ms) and re-referenced into an average
reference frame.
94 Appl Psychophysiol Biofeedback (2012) 37:91–102
123
ERP Procedure and Materials
This test represents a modification of a three category
oddball task with Kanizsa illusory figures (Kanizsa 1976).
For a detailed description of this task, see Fig. 1. Subjects
were instructed to remain as still as possible, to refrain
from blinking, and to keep their gaze on the fixation cross
in the middle of the screen. The stimuli were white figures
displayed on a black background. Subjects were instructed
to press the first button on a 5-button keypad with their
right index finger when a target appeared, and to ignore
non-target Kanizsa and standard stimuli. Stimulus presen-
tation and behavioral response data collection was con-
trolled by E-Prime software (Psychology Software Tools,
PA). Each subject participated in at least one session of
EEG net conditioning and familiarization before the first
test. The TMS group completed the Kanizsa illusory figure
test (240 trials per test) before TMS and after TMS. The
waiting-list control group was administered the test with
the same interval (8–10 weeks). Visual stimuli were pre-
sented on a 1500 display and behavioral responses were
collected using a keypad. Behavioral response measures
were mean reaction time (RT in ms) and response accuracy
(percent of correct responses). Number and percent of
commission and omission errors was calculated for each
participant. Besides RT to correct responses, we calculated
RT of correct response in the first post-error trial to eval-
uate normative post-error RT slowing function.
TMS Procedure
A trained electrophysiologist delivered rTMS stimulation
over the cortical area controlling the contralateral FDI
using a Magstim Rapid instrument (Magstim Corporation,
Sheffield, England) to detect resting motor threshold (MT).
The MT was determined for each hemisphere in all indi-
viduals by gradually increasing the output of the machine
by 5% until a 50 mV deflection or a visible twitch in the
First Dorsal Interosseous (FDI) muscle was identified in 2
out of 3 trials. Electromyographic (EMG) responses were
monitored continuously from the hand contralateral to
stimulated hemisphere using a C-2 J&J physiological
monitor and software (J&J Engineering, Inc., Poulsbo,
WA). Subjects were familiarized with the laboratory and
procedure before the first TMS session.
The TMS treatment course was administered once per
week for 12 weeks (a total of twelve 1 Hz rTMS treat-
ments); the first six treatments were over the left DLPFC
while the remaining six were over the right DLPFC. This is
a sequence and session frequency that we already suc-
cessfully used in our previous studies (see Baruth et al.
2010a, b, c; Sokhadze et al. 2009b, 2010a). The site for
stimulation was found by placing the coil 5 cm anterior,
and in a parasagital plane, to the site of maximal FDI
stimulation. The figure-eight coil, with a 70-mm wing
diameter was kept flat over the scalp. Subjects were
wearing a swimming cap to outline the TMS coil position
and aid in its placement for each session. Stimulation was
done at 1 Hz and 90% MT, with a total of 150 pulses/day
(fifteen 10 s trains with a 20–30 s interval between the
trains). Based on review of TMS literature, we have con-
cluded that at least 100 pulses/per session should be
administered per session (see Helmich et al. 2006 for
review). We selected 1 Hz as the stimulation frequency as
studies have shown that low-frequency rTMS (B1 Hz)
increases inhibition of stimulated cortex (e.g., Maeda et al.
2000). Furthermore, the risk of seizures was minimized at
this low rTMS frequency. Selection of 90% of the MT was
based on the experience of numerous publications where
rTMS was used for the stimulation of DLPFC in different
psychiatric and neurological conditions (for reviews see
Daskalakis et al. 2002; Gershon et al. 2003; Greenberg
2007; Holtzheimer et al. 2001; Loo and Mitchell 2005;
Rosenberg et al. 2002; Wassermann and Lisanby 2001).
We also wanted to keep the stimulation power below MT
as an extra safety precaution against the increased risk of
seizure within this study population.
Event-Related Potentials (ERP)
ERP dependent measures were the adaptive mean ampli-
tude and latency of the ERN (40–150 ms post-stimulus)
and Pe (100–300 ms). The frontal and fronto-central ROI
for both ERN and Pe components included following 5
EGI channels: FCz, EGI channels 7 and 13 (left hemi-
sphere, between FCz and FC3 and C1), and EGI channels
113 and 107 (right hemisphere, between FCz and FC2 and
C2). Detection of adaptive mean values for ERN/Pe
Fig. 1 In this experiment we used Kanizsa and non-Kanizsa figures
as stimulus material. In particular, the stimulus types are Kanizsa
square (target), Kanizsa triangle, non-Kanizsa square, and non-
Kanizsa triangle. The non-target Kanizsa triangle is introduced to
differentiate processing of Kanizsa figures and targets. The stimuli
consist of either three or four inducer disks which are considered the
shape feature, and they either constitute an illusory figure (square,
triangle) or not (collinearity feature)
Appl Psychophysiol Biofeedback (2012) 37:91–102 95
123
amplitude and relevant latencies was performed using Net
Station statistic export tools and ascertained by a custom-
made Matlab application for ERP peak minimum and
maximum detection within a selected time window
(Clemans et al. 2011a). This program proved to be effec-
tive in correct detection of ERP peaks and showed that
EGI NetStation adaptive mean amplitude algorithm (e.g.,
maximum of positive peak showing at least 3 preceding
ascending points followed by 3 descending points) provide
correct peak amplitude and latency values. Furthermore,
validation of correct peaks detection was confirmed using
another custom Matlab program, this time with application
of wavelet transformation (Clemans et al. 2011b). This
study focused only on response-locked ERPs, while stim-
ulus-locked ERPs during correct responses to target and
non-target stimuli at various topographies were analyzed
separately and will be reported elsewhere as a separate
publication.
Statistical Data Analysis
Statistical analyses were performed on the subject-aver-
aged behavioral and ERP data. The primary analysis model
was the repeated measures ANOVA, with the following
dependent variables: Reaction time (RT in ms), accuracy
(percent of correct responses), commission error rate
(percent of pressing key to non-target stimuli), omission
error rate (percent of missed target stimuli), post-error RT,
(i.e., difference between post-error RT and preceding cor-
rect response RT), and amplitudes (in lV), changes of
amplitude (post-minus-pre in lV), latencies (in ms), and
changes in latencies (post-minus-pre in ms) of ERN and Pe
components at the ROI. The between subject factors were
Group (TMS, WTL). Repeated within group factor was
Time (pre, post [TMS/or waiting period]). A priori
hypotheses were tested with the Student’s t tests for 2
groups with equal variance. In all ANOVAs, Greenhouse-
Geisser corrected p values were employed where appro-
priate. Partial Eta Squared (g2) and observed power
computed using alpha = 0.05 were calculated and reported
(Murphy and Myors 2003).
Results
Behavioral Responses
Comparison of RT to targets at pre and post tests showed
Time (pre-, post) X Group (TMS, WTL) interaction, F(1,
38) = 4.52, p = 0.039, g2 = 0.10, observed power com-
puted using alpha = 0.05 was 0.55. However, the paired-
sample t test (Table 1) showed that post-TMS RT time
decrease in the treatment group was not significant. Anal-
ysis of commission and omission errors using repeated
measure ANOVA (Time X Group) yielded a statistically
significant between-group difference in the omission error
rate, F(1, 38) = 5.01, p = 0.03. Furthermore, paired
samples test (Table 1) showed significant decrease of
omission error rates post-TMS (-33.0 ± 52.7%, t(19) =
2.26, p = 0.034), but not post waiting period (-15.8 ±
66.8%, t(19) = 0.12, p = 0.342). There were no between
group differences in commission error rate (see Table 1).
ANOVA showed that differences between TMS and WTL
on post-error RT changes were significant, i.e., Time X
Group interaction, F(1, 38) = 7.35, p = 0.009. The TMS
group showed post-error slowing with significant positive
change in post-error RT calculated as post TMS post-error
RT change minus baseline post-error RT change (37.33 ±
49.47 ms, t(19) = 2.87, p = 0.009). Histogram of RT
changes frequency distribution at pre and post assessments
is presented in Fig. 2. Most of post-error RT changes in the
TMS group were positive as it is shown on the Fig. 2. At
the baseline both groups demonstrate faster post-error RTs
compared to correct response RTs. TMS group presented
slowing of post-error RTs with a positive peak of distri-
bution curve, whereas the wait-list group do not show any
changes from the baseline and the peak of distribution
curve remained negative.
Table 1 Reaction time, post-error RT changes, and response accuracy measures in the Kanizsa illusory figure visual oddball task
Behavioral measure TMS group Wait-list group
Baseline Post df p Baseline Second test df p
Reaction time (RT, ms) 492 (118) 466 (111) 0.94 0.355 487 (133) 490 (89) 0.18 0.845
Post-error RT change (ms) -22.3 (38.2) 10.6 (45.4) -2.84 0.009** -46.5 (48.3) -30.6 (38.8) -0.97 0.342
Commission errors (%) 9.4 (15.0) 4.5 (7.4) 1.66 0.109 13.5 (19.4) 6.4 (12.7) 1.69 0.107
Omission errors (%) 3.5 (5.8) 0.9 (1.1) 2.26 0.034* 2.2 (3.5) 2.3 (3.5) -0.12 0.903
Mean values with standard deviations (SD). N = 20 in each group. Paired sample t test results with degrees of freedom (df) and p values are
presented for all post versus baseline comparisons for each group (TMS and WTL)
* p \ 0.05, ** p \ 0.01
96 Appl Psychophysiol Biofeedback (2012) 37:91–102
123
Response-Locked Event-Related Potentials
Comparison of ERN on repeated test (within group factor
being pre and post) revealed significant between group
(TMS vs. WTL) differences in both amplitude and latency.
Amplitude of ERN analysis using repeated measures
ANOVA yielded significant Time X Group interaction,
F(1, 39) = 4.20, p = 0.049, g2 = 0.14, observed power =
0.51). T test of the ERN amplitude changes in the TMS
group showed significant increase of negativity (-4.74 ±
7.83 lV, t(19) = 2.74, p \ 0.041), while in the WTL
group ERN did not show any changes (0.74 ± 7.53 lV,
t(19) = 1.22, p = 0.245).
Latency of ERN also showed significant Time X Group
effect (F(1, 38) = 9.17, g2 = 0.25, p = 0.004, observed
power = 0.85). T test showed that the latency significantly
shortened in the TMS group (-44.0 ± 43.7 ms, t(19) =
4.72, p \ 0.001), while practically did not change in the
WTL group (3.92 ± 46.4 ms, t(19) = 0.39, p = 0.703).
Changes in amplitude and latency characteristics of Pe
wave were not significant. Neither group showed statisti-
cally significant changes in Pe amplitude and there were no
differences between groups (Fig. 3). Figure 5 illustrates the
shapes of ERN and Pe waveforms in both groups at base-
line and at the second test (post-TMS or post wait period).
Screenshot of individual fronto-central EEG sites pre and
post-TMS are presented at Fig. 6.
Discussion
Our results show that low frequency rTMS over the
DLPFC in children with autism resulted in enhanced
behavioral performance in a visual attention task, as
expressed in lower omission error rates, increased ERN
amplitude, and shortened latency of the ERN (Figs. 3, 4, 5,
6). It is important to emphasize that children in the TMS
treatment group, as compared to the wait-list group,
Fig. 2 Histogram of
distribution of individual post-
error RTs in TMS and wait-list
groups of children with autism.
At the baseline both groups
demonstrate faster post-error
RTs compared to correct
response RTs. TMS group
shows slowing of post-error RTs
with a positive peak of
distribution curve, whereas the
wait-list group do not show any
changes from the baseline and
the peak of distribution curve
remains negative
Appl Psychophysiol Biofeedback (2012) 37:91–102 97
123
showed post-error slowing of reaction time (Fig. 2), which
is considered as a normative marker of a post-error cor-
rective function. This is an interesting finding considering
that autistic children consistently demonstrated post-error
speeding of motor responses in our previous study where
children with ASD were compared with typically devel-
oping children in a visual oddball task with novel distrac-
tors (Sokhadze et al. 2010b).
The process of performance monitoring is an essential
prerequisite for adaptively altering behavioral responses
and making decisions for correction of behavior according
to task demands. Executive function of behavioral perfor-
mance monitoring comprises error detection and response
conflict monitoring, functions that can be measured by their
electrophysiological correlates (i.e., event-related poten-
tials). An erroneous response in healthy adults and children
(Arbel and Donchin 2009, 2011) is associated with a
fronto-central ERN component (Gehring et al. 1993). The
specific functional significance of the ERN may reflect
mismatch (Mars et al. 2005) or response conflict between
erroneous and correct responses (Carter et al. 1998; van
Veen and Carter 2002).
Fig. 3 Changes of ERN (dERN) and Pe (dPe) amplitudes from
baseline levels in TMS and WTL groups at the second test. Bars are
means with standard errors (N = 20 per group). Only post-TMS
changes of the ERN were statistically significant (* p \ 0.05)
Fig. 4 Latency of the fronto-central ERN in TMS and WTL groups
showing significant decrease only in the TMS group at post-treatment
test
Fig. 5 Error-related Negativity (ERN) and Positivity (Pe) from the
fronto-central midline EEG sites. Grandaverage waveforms across 5
sites (N = 20 per group) show more negative amplitude and shorter
latency of the ERN in the TMS group as compared to the WTL group
Fig. 6 Screenshot of error-related potentials (ERN/Pe) at 4 fronto-
central EEG sites of recording in the TMS group at baseline and after
the rTMS treatment course. The ERN is more negative and has
sharper onset of the negative peak. The Pe does not show any visible
differences post-TMS
98 Appl Psychophysiol Biofeedback (2012) 37:91–102
123
Contrary to our expectations, the Pe component did not
show any significant post-TMS changes. This electrocor-
tical response-locked component, associated with error
monitoring occurs after the ERN, has a more posterior
distribution and is manifested as a positivity elicited after
the ERN (Falkenstein et al. 2000). Far less research has
addressed the function of the Pe. It is elicited, unlike the
ERN, only after full errors of which the subject is aware,
which suggests that it represents conscious error-recogni-
tion processes (Nieuwenhuis et al. 2001; Overbeek et al.
2005). It is difficult to interpret why rTMS exerted effects
on error detection process (ERN), but not on more con-
scious error-recognition process in this study, Our prior
findings in studies using rTMS in children with autism
(Baruth et al. 2010c; Sokhadze et al. 2009a, b, 2010a)
specifically outlined that most of stimulus-locked EEG
changes (both ERP and evoked EEG gamma) occurred at
the early stages of signal processing (i.e., around
100–200 ms post-stimulus), and enhanced differentiation
of target from non-target cues. Resulting post-TMS facil-
itation of target recognition and more effective early sup-
pression of non-target distracters leads to less pronounced
carry over of non-target over-processing so typical for
autism. As result we found more positive TMS effects in
the early ERP components rather than in the late cognitive
ERPs and concluded that majority of magnetic stimulation
effects were due to enhanced inhibition of irrelevant
stimuli and less effortful discrimination of target and non-
target cues during performance on a task.
Another possible explanation of the lack of rTMS effects
on Pe component might be sought in methodology, as it is
known that this positive component has more posterior peak
distribution topography and probably was not sufficiently
well captured at the fronto-central ROI used in our study.
Another critical methodological issue to be considered in
our failure to show rTMS effects on Pe in ASD is the large
variance of number of erroneous trials on which Pe analysis
is based. Usually, this measure depends on the number of
errors made and amount of artifacts (Franken et al. 2007).
Although there was no difference observed between groups
at the baseline test in a number of errors, TMS group had
less errors committed post-treatment, hence the number of
errors and variance of the Pe was higher than that in the
WTL group. It is possible that the amplitude and latency of
the Pe wave were affected more by the reduced number of
segments analyzed on the second test in the TMS group.
Our future studies will extend the number of trials in the test
and will consider more traditional forced choice speeded
reaction time tasks such as, Eriksens flanker task (Eriksen
and Eriksen 1974) to have more commission errors and
more reliable Pe wave.
It must be noted though, that our prior study (Sokhadze
et al. 2010b) did show Pe differences between ASD and
typical children on the same visual oddball task, and these
differences were expressed in a significantly prolonged
latency of the Pe in autism. In a recent study (Hewig et al.
2011) are discussed the relationships between conscious
awareness and the ERN and Pe in cognitive tasks, and a
phenomenon of dissociation of Pe and ERN effects
dependent on recognition of an error. The generation of Pe
process might be also affected by the absence of feedback
about the accuracy of the response that lowered awareness
of committed error. Dissociation of Pe and ERN effects of
rTMS definitely is an interesting finding that warrants more
research on this topic.
Overall, our preliminary results (Sokhadze et al. 2009b,
2010a; Baruth et al. 2010a, b, 2011) show promising results
for TMS as a treatment modality targeting core symptoms
of ASD. Current finding of post-TMS improvement in
certain executive functions such as error monitoring adds
new angle to understanding of neuropathological mecha-
nisms of ASD symptoms. We already showed that treat-
ment with ‘slow’ rTMS decreased excess gamma activity
to distractors and amplified ERP responses to target cues in
ASD patients during visual tasks and improved the signal
differentiation between processing relevant and irrelevant
stimuli (Sokhadze et al. 2009b, 2010a). Additionally, rTMS
dramatically improved the coordinated activity or coher-
ence between different regions of the brain (e.g., frontal
and parietal cortices) and significantly improved repetitive
and restricted behavior patterns associated with ASD
assessed using clinical evaluation instruments.
Our results suggest that low-frequency rTMS may
improve the inhibitory tone, and decrease the ratio of
cortical excitation to inhibition in ASD, This may lead to
improved long-range connectivity within prefrontal and
midfrontal mesial cortical structures.
Several limitations of the study should be mentioned. We
had to take into consideration is that almost up to 75% of
individuals with autism have predisposition to seizures and
experienced at least one epileptic episode in their lifespan. We
had to be even more careful (with regards to intensity, fre-
quency, session pace and other parameters of rTMS) con-
sidering vulnerability of our pediatric population. It is often
reported in rTMS studies that effects of magnetic stimulation
usually do not wash out in approximately 1 week. We believe
that switching from more frequent (2–3 sessions per week as
we initially did in our earlier studies, Sokhadze et al. 2009) to
more rare (once per week session) significantly improved our
protocol because we started seeing better clinical outcomes. It
is possible that length of treatment course rather than intensity
(i.e., short term intense course) is one of the major keys of
behavioral and EEG/ERP improvements that we observe in
our later rTMS trials in autism (Baruth et al. 2010b, 2011;
Sokhadze et al. 2010a). We do acknowledge that the power
and schedule of our rTMS is relatively lower than those used
Appl Psychophysiol Biofeedback (2012) 37:91–102 99
123
by other neuromodulation groups, though it must be empa-
thized that they were treating other psychopathologies
(depression, schizophrenia) or neural disorders (e.g., Parkin-
son disease) and mostly in adult population. Another limita-
tion of our study is the fact that we used a waiting-list group
control rather than a randomized clinical trial design with a
‘‘placebo’’ group being a group of children with ASD
undergoing a sham TMS. We do have a sham TMS coil,
however, considering that the preliminary stage of our
research decided instead to start with a wait-list group design
before moving to a RCT design.
Even though our study was limited to assessment of only
behavioral (reaction time and accuracy) and electrocortical
(ERN/Pe) responses without reporting formal clinical
evaluations, it is still possible to suggest that TMS has the
potential to become an important therapeutic tool in ASD
treatment, and may play an important role in improving the
quality of life of many children with the disorder. TMS
provides a non-invasive method of induction of focal cur-
rents in the brain, as well as transient modulation of the
function of the targeted cortex areas. Despite the fact that
TMS is now widely used as a diagnostic and therapeutic
tool in adults, its application to date has been limited in
children. Nonetheless, TMS offers unique opportunities to
gain insights into the neurophysiology of a child’s brain. In
our future studies we plan to use transcranial direct currect
stimulation (tDCS) as a technique that might have similar
behavioral and electrocortical effects in ASD. The ERN
and Pe along with behavioral performance measures can be
used as functional outcome measures to assess the effec-
tiveness of neurotherapy (e.g., rTMS, tDCS, or neuro-
feedback) in children with autism and thus may have
important practical implications.
Acknowledgments Funding for this work was provided by the
National Institutes for Health grant R01 MH86784 to Manuel
Casanova.
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