prefrontal neuromodulation using rtms improves error monitoring and correction function in autism

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.


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)


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


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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


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


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


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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