Cerebral Cortex June 2014;24:1502–1517
doi:10.1093/cercor/bht002
Advance Access publication January 24, 2013
Involvement of Human Internal Globus Pallidus in the Early Modulation of Cortical
Error-Related Activity
María Herrojo Ruiz1, Julius Huebl1, Thomas Schönecker1, Andreas Kupsch1, Kielan Yarrow2, Joachim K. Krauss3,
Gerd-Helge Schneider4 and Andrea A. Kühn1
1
Department of Neurology, Charité-University Medicine Berlin, Berlin, Germany 2Department of Psychology, City University
London, London, UK 3Department of Neurosurgery, Medical University Hanover, Hanover, Germany and 4Department of
Neurosurgery, Charité-University Medicine Berlin, Berlin, Germany
Address correspondence to Prof Dr Andrea A. Kühn, Department of Neurology, Charité-University Medicine Berlin, Campus Virchow, Berlin,
Germany. Email: [email protected]
The detection and assessment of errors are a prerequisite to adapt
behavior and improve future performance. Error monitoring is afforded by the interplay between cortical and subcortical neural
systems. Ample evidence has pointed to a specific cortical errorrelated evoked potential, the error-related negativity (ERN), during
the detection and evaluation of response errors. Recent models of
reinforcement learning implicate the basal ganglia (BG) in early error
detection following the learning of stimulus–response associations
and in the modulation of the cortical ERN. To investigate the influence of the human BG motor output activity on the cortical ERN
during response errors, we recorded local field potentials from the
sensorimotor area of the internal globus pallidus and scalp electroencephalogram representing activity from the posterior medial
frontal cortex in patients with idiopathic dystonia (hands not
affected) during a flanker task. In error trials, a specific pallidal
error-related potential arose 60 ms prior to the cortical ERN. The
error-related changes in pallidal activity—characterized by theta
oscillations—were predictive of the cortical error-related activity as
assessed by Granger causality analysis. Our findings show an early
modulation of error-related activity in the human pallidum,
suggesting that pallidal output influences the cortex at an early
stage of error detection.
Keywords: basal ganglia, error monitoring, error-related negativity, Granger
causality, response errors
Introduction
Motor tasks, such as typing on a keyboard or playing piano,
are prone to errors. Error detection and adaptive learning,
which lead to the avoidance of future errors, are essential for
successful motor performance. These error-monitoring mechanisms are implemented at different levels of the central
nervous system, including cortical and subcortical areas. The
focus of the present study is based on the investigation of
cortico-basal ganglia (BG) interactions in processing response
errors.
The error-related negativity (ERN) is a specific event-related
cortical potential (ERP) peaking at 50–100 ms after erroneous
outcomes that can be used to study the function of the errormonitoring system (Falkenstein et al. 1990; Gehring et al.
1993; Taylor et al. 2007; Ullsperger et al. 2007). An ERN is generally associated with error commission and interpreted as an
index of (1) the mismatch between an expected and upcoming
response (Gehring et al. 1993; Falkenstein et al. 1995) or (2)
the degree of conflict between competing erroneous and
correct responses (Botvinik et al. 1999; Coles et al. 2001).
A multitude of neurophysiological, functional imaging,
© The Author 2013. Published by Oxford University Press. All rights reserved.
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and computational modeling studies implicate the posterior
medial frontal cortex ( pMFC) and dorsal anterior cingulate
cortex in the detection and evaluation of response errors and
generation of the ERN/error-related activity (Ridderinkhof
et al. 2004; Debener et al. 2005; Taylor et al. 2007; Ullsperger
et al. 2007; Agam et al. 2011; Silvetti et al. 2011). However,
more recent magnetoencephalography (MEG) and functional
magnetic resonance imaging evidence from response-time
conflict tasks point to a broader network of areas modulating
the ERN. These areas include the supplementary motor area,
insula, inferior and superior frontal gyrus, parietal lobe, precuneus, and thalamus (Kiehl et al. 2000; Hester et al. 2004, 2009;
Holroyd et al. 2004; Doñamayor et al. 2011). The findings of
abnormal ERN modulation and/or behavioral performance
during error monitoring in patients with Parkinson’s disease
(Falkenstein et al. 2001; Stemmer et al. 2007; Willemssen et al.
2009), Huntington’s disease (Beste et al. 2006, 2008), Tourette
syndrome (Johannes et al. 2002), or focal lesions of the BG
(Ullsperger and von Cramon 2006) and the thalamus (Seifert
et al. 2011) all point to the BG as an important subcortical relay
station in monitoring and adjusting motor performance.
These observations are in line with the notion of reinforcement learning (RL; Holroyd and Coles 2002; Cockburn and
Frank 2011) where the mesencephalic dopaminergic system
encodes reward prediction errors that teach the BG via temporal difference (TD) learning to link cues or actions to their
estimated “future” outcome and reward (Sutton 1988; Schultz
et al. 1997; Sutton and Barto 1998; Schultz 2002). As a consequence, BG may be able to predict upcoming nonrewarded
actions, thereby influencing the ERN. To test this prediction,
we investigated whether error-related activity can be directly
recorded from the human pallidum and evaluated the timing
and directionality of cortico-BG interactions during the processing of response errors arising from laterality errors in a
reaction time task with automatic stimulus-response mapping.
BG activity may also influence the forward control of movements in order to maximize the ratio between cost and
benefit based on the predicted costs (energy cost and error/
accuracy cost) and rewards (Shadmehr and Krakauer 2008).
Within this framework, so far formulated and tested in reaching movements (Mazzoni et al. 2007; Shadmehr and Krakauer
2008), prediction of costs and “implicit” rewards associated
with upcoming actions by the BG would enable these nuclei
to influence the cortical error-monitoring processes in
advance of future outcomes. Thus, early motor BG activity
predictive of upcoming errors would support that an implicit
motivational circuit might operate within the motor
frontal-BG-thalamocortical loop.
Animal data have shown a specific error-related single-cell
neural activity in different nuclei of the BG such as the
primate dorsal pallidum (GPi and external globus pallidus
[GPe]) or rodent subthalamic nucleus (STN) during error commission, yet in tasks quite different than those probed in
humans (Arkadir et al. 2004; Hong and Hikosaka 2008;
Lardeux et al. 2009; Hong et al. 2011). It is noteworthy that
the seminal works of Hikosaka (e.g. Hikosaka et al. 2008)
emphasize that error- and reward-modulated activity can be
recorded in the primate dorsal pallidum, part of the “sensorimotor” cortico-BG-thalamocortical loop.
Direct recordings of neuronal activity from the human BG
are available in patients undergoing deep brain stimulation
(DBS) for movement disorders. Up to now, invasive recordings exploring the generation of the ERN in humans have
been limited to a single case in a patient with therapy resistant
alcohol dependency and a single case in a patient with obsessive–compulsive disorder, where this ERP component was demonstrated in recordings from the nucleus accumbens (NAcc;
Münte et al. 2008; Heinze et al. 2009). Interestingly, the errorrelated local field potential (LFP) activity in the NAcc precedes
the cortical ERN by approximately 40 ms. The aim of the
present study was to investigate the role of the human sensorimotor area of the globus pallidus internus (GPi)—the main
BG motor output station—in error detection and modulation
of the cortical ERN during the performance of a flanker task
(Kopp et al. 1996). We selected this task to compare our
results with those from previous studies using the flanker
task, which suggested an influence of the BG on the cortical
ERN (Ullsperger and von Cramon 2006; Münte et al. 2008;
Seifert et al. 2011). A special emphasis was set on the assessment of the directionality in the dynamic interactions between
the cortical and subcortical/pallidal activity during errormonitoring processes. To this end, we used the index of
partial directed coherence (PDC), which was introduced for a
frequency domain analysis of linear Granger causality to test
for “direct” directed statistical temporal influence among
recorded signals (Baccala and Sameshima 2001). Here, the
concept of Granger causality (Granger 1969) refers to the
determination of causal influences estimated as statistical
temporal predictability among signals.
Direct recordings were performed in the GPi in 9 patients undergoing DBS for severe cervical or segmental dystonia. Importantly, dystonia did not involve the hands in any of the patients;
thus all patients had clinically normal hand motor function.
the most ventral and contact 3 was the most dorsal contact. Target coordinates were based on the direct visualization of the GPi in the individual stereotactic T2-weighted magnetic resonance imaging (MRI) or
based on the intercommissural line as determined by stereotactic computed tomography (CT). The intended coordinates at the tip of electrode 0 were 17.4–22 mm lateral from the midline, 2–4 mm in front of
the midcommissural point, and 2–4 mm below the midcommissural
point as determined by MRI adjusted to the individual patient’s
anatomy. Correct placement of the DBS electrode was confirmed by
intraoperative microelectrode recordings and location of typical side
effects evoked by direct macrostimulation in all patients. Moreover,
the localization of DBS electrodes was verified by postoperative MRI
(cases #1–6, 8) or stereotactic CT (cases #7, 9) using automated normalization and contact localization in standard Montreal Neurological
Institute (MNI) stereotactic space coordinates (accuracy of contact
localization is 1–2 mm; see details in Schönecker et al. 2009) in all
patients (Fig. 1). The evaluation of the clinical efficacy of chronic DBS
on motor symptoms (31% mean improvement on Toronto Western
Spasmodic Torticollis Rating Scale [TWSTRS, cases #1–4, 6, 9] or
Burke–Fahn–Marsden Dystonia Rating Scale [BFMDRS, cases #5, 8] at
least 3 months after DBS surgery) further supported correct placement of DBS electrodes within the motor area of GPi.
We additionally recruited 10 healthy participants matched in age
(52 [6] years) to the patient group in order to have reference values in
terms of performance and ERN data with the parameter settings of
the flanker task in the current study.
Materials and Methods
Recording
Recordings were made 2–4 days postoperatively, while electrodes
were externalized and before implantation of the pulse generator. LFP
activity was recorded from GPi bipolarly from all 4 adjacent contact
pairs along the macroelectrode in all patients. The 4 contacts on each
macroelectrode in left and right GPi (contacts 0, 1, 2, and 3) corresponded to bipolar recordings from contact pairs 01, 12, and 23.
Signals were bandpass filtered between 0.5 and 250 Hz, amplified
50 000-fold using a Digitimer D360 (Digitimer Ltd., Welwyn Garden
City, Hertfordshire, UK), and sampled at 1 kHz through a 1401 A-D
converter (Cambridge Electronic Design, Cambridge, UK) onto a
computer using the Spike 2 software (Cambridge Electronic Design).
Simultaneously, continuous scalp electroencephalogram (EEG) was
recorded from frontocentral, central and parietal electrodes at the
midline (FCz, Cz and Pz, International 10-20 System) and referenced
to linked earlobes. The selection of these EEG electrode positions was
based on the involvement of the pMFC in the generation of the
Patients, Surgery, and Electrode Localization
Nine patients with idiopathic dystonia (49 [standard error of the
mean, SEM 4] years old) participated in this study. All patients were
right-handed. Six of the 9 patients suffered from cervical dystonia
(CD) 1 patient had Meige’s syndrome (oro-facial dystonia), and 2 suffered from segmental dystonia (SD), which did not affect the limbs.
Clinical details are presented in Table 1. Patients gave informed
consent prior to participation in the study, which was approved by
the local Ethics Committee. DBS electrodes were targeted in all
patients bilaterally in the posteroventral lateral “motor” portion of
GPi. The macroelectrode used was model 3389 ( patients #1–6 and 8:
Berlin DBS Center) and model 3387 (Medtronic Neurological Division, MN, USA; patients #7, 9: Hanover DBS Center) with 4 platinum–iridium cylindrical surfaces (1.27 mm diameter and 1.5 mm
length) and a contact-to-contact separation of 0.5 mm. Contact 0 was
Paradigm
The paradigm was a modification of the speeded flanker task (Kopp
et al. 1996). Flanker stimuli consisted of vertical arrays of 4 horizontal
arrowheads, whose onset was followed after 70 ms by the onset of a
central arrowhead, the target stimulus (Fig. 2). The target arrows
pointed either in the same direction as the flankers (congruent trials)
or in the opposite direction (incongruent trials; 50% rate each). Flankers and targets vanished 20 ms after target onset. Patients were instructed to respond with maximal accuracy and speed to the target
arrow by pressing down a button with the thumb of the corresponding hand (left/right). Importantly, patients were holding in each hand
one device with a button, and therefore, the button press in the
correct direction required only a small movement of the thumb. No
instructions regarding the correction of erroneous responses were
provided. Following their response, a fixation cross was presented at
the center of the screen during an interstimuli interval randomized
between 2500 and 3500 ms. Participants had to fixate the central
cross to avoid eye movements.
Five blocks of 64 trials each were performed. Within each block,
trials were selected at random and without replacement to yield 16
trials from each of 4 categories (the combination of left/right targets
with congruent/incongruent flankers). Adaptive behavioral feedback
was given every 20 trials with regard to accuracy or speed. The types
of trials available for analysis were congruent correct, congruent erroneous, incongruent correct, and incongruent erroneous.
Cerebral Cortex June 2014, V 24 N 6 1503
Table 1
Patients’ demographics
Patient
no. (sex)
Diagnosis
Age Onset age
Preoperative medication
Preoperative clinical
scores (TWSTRS
severity subscore)
Postoperative clinical
scores (TWSTRS
severity subscore)
1 (M)
Cervical dystonia
33
31
None
23
14
2 (M)
Cervical dystonia (Torticollis)
45
44
One year preop trihexyphenidyl 24
2 mg
3 (M)
Cervical dystonia
44
42
None
14
4 (M)
Cervical dystonia
33
19
None
17
5 (M)
Segmental dystonia, primarily cervical
(ante-lateroflexion of the upper body)
57
12
Trimipramin 100 mg,
Tetrazepam 50 mg
n.a.
6 (F)
Cervical dystonia
51
3
7 (M)
Blepharospasm-oromandibular dystonia
76
1
8 (F)
Segmental dystonia (Torticollis with
orolinguar dystonia)
48
9 (M)
Cervical dystonia
58
23
Amitryptiline 25 mg
n.a.
15
Four months before:
Tetrabenazin 25 mg, Pimozid
1 mg Biperiden (dosage N/At)
25 (BFMDRS
movement score
12/120)
37
Clonazepam
2 × 1 mg
27
Contact pairs
selected for LFP
analysis (PDC
analysis)
R: 01, 12, 23
L: 01, 12
(R: 12
L: 01)
21
R: 12
L: 12, 23
(R: 12
L: 12)
12
R: 12, 23
L: 12, 23
(R: 23
L: 12)
14
R: 01, 12, 23
L: 12, 23
(R: 12
L: 23)
(BFMDRS movement
R: 01, 23
score) 14/120
L: 01, 23
(R: 01
L: 01)
9
R: 23
L: 01, 12, 23
(R: 23
L: 01)
n.a.
R: 01, 12
−
(R: 12
−)
15 (BFMDRS movement R: 01, 12, 23
score 5.5/120)
L: 01, 12, 23
(R: 01
L: 01)
20
R: 01, 12
L: 01
(R:12
L:01)
Stimulation settings
R: 1−, 2−; 90 μs,
130 Hz, 2.3 V
L: 1−, 2−; 90 μs,
130 Hz, 1.5 V
R: 0−; 90 μs,130 Hz,
2.3 V
L: 1−; 90 μs, 130
Hz, 2 V
R: 0−; 60 μs, 60 Hz,
3.8 V
L: 1−; 60 μs, 60 Hz,
5V
R: 1−; 60 μs, 160
Hz, 3.3 V
L: 2−; 60 μs, 160
Hz, 2.5 V
R: 0−; 60 μs, 150
Hz, 3.5 V
L: 1−, 3−; 60 μs,
150 Hz, 4 V
R: 0−; 90 μs, 140
Hz, 3 V
L: 1−; 90 μs, 140
Hz, 3.5 V
n.a.
R: 1−, 2−; 90 μs,
130 Hz, 3.7 V
L: 2−, 3−; 90 μs,
130 Hz, 3.7 V
R: 1−, 2+; 180 μs,
130 Hz, 3.5 V
L: 1−, 2+; 180 μs,
130 Hz, 3.5 V
Note: Individual patient’s data including presurgical medication. Motor scores provided are Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS I severity scale, maximum 35). In the case of
SD, additional Burke–Fahn–Marsden Dystonia Rating Scale (BFMDRS, movement scale; maximum 120) is provided. Postoperative scores were assessed at least 3 months after electrode implantation
(8 months on average). Average improvement in TWSTRS scores of 31%.
cortical ERN and the posterior so-called error positivity (Pe), which
often follows the ERN 200–450 ms after incorrect responses (Pe; van
Veen and Carter 2002; Ullsperger et al. 2007). In addition, transverse
electrooculogram (EOG) was recorded to monitor for vertical and
horizontal eye movements. EEG signals were also bandpass filtered at
0.5–250 Hz, amplified 25 000-fold, and sampled at 1 kHz.
Analysis
Behavioral Data
For each subject, reaction times (RT) exceeding 2 times the standard
deviation around their mean value for each response-type distribution
(congruent and incongruent: Error and correct) were discarded from
the analysis, leaving an average of 300 trials per subject. Data in
Table 2 are provided as sample mean and SEM.
Preprocessing of EEG and LFP Data
We used the EEGLAB Matlab Toolbox (Delorme and Makeig 2004) for
visualization and filtering purposes. Stimulus-locked and responselocked epochs were selected in a window spanning [−1000, 3000] ms
around target onset and [−1000, 1000] ms around response onset,
respectively. The notation [a, b] will be used to indicate a temporal
window spanning from the first (a) to the second (b) time point with
respect to either stimulus or response.
As assessed by visual inspection of the EEG, LFP, and EOG signals,
data epochs containing artifacts due to eye movements, eye blinks,
1504 Error Detection in GPi
•
Herrojo Ruiz et al.
and/or muscular activity were removed from further analysis. The
mean number (SEM) of remaining trials per subject averaged across
EEG and LFP recordings for the different response types was: 140
([10] congruent correct), 90 ([20] incongruent correct), 30 ([20] incongruent error), and 10 ([6] congruent error).
An important issue in the analysis of the electrical potentials from
bipolar recordings is that the term “polarity” cannot be defined due to
the measurement of the “differential” electrical potential from 2 local
sources. Notwithstanding this issue, in analogy to the description of
cortical ERP as the modulation of positive and negative amplitudes,
we use the terms “negative” and “positive” polarity by convention to
describe LFP–ERP waveforms. Importantly, however, this convention
aims exclusively at a simpler description of the LFP–ERP data, and no
true assignment of polarity is implied. Similarly, the polarity of the
LFP–ERP modulations depends on the precise positioning of the
bipolar contact pairs with respect to the generator of the activity. To
normalize the polarity of the LFP–ERPs in all bipolar contacts in all
patients, we proceeded as follows: (1) First, we averaged all bipolar
“stimulus-locked” LFP–ERP waveforms across the left and right GPi
and across patients for incongruent correct trials. Correct event types
had the largest number of trials; therefore, we obtained a smooth
stimulus-locked grand-average ERP waveform (Supplementary
Fig. 1A) that (2) was used as a template against which we could
compare a reference single-contact pair stimulus-locked LFP–ERP per
GPi side (typically R01 and L01) in each patient. (3) Finally, we assessed whether there was a phase reversal between the stimuluslocked waveforms recorded from neighbor contact pairs in each GPi
Figure 1. Normalized localization in the standard MNI stereotactic space coordinates of selected contact pairs (depicting center of contact pair). (A) Localizations of the center
(+) of the bipolar pair of 31 contacts (7 Berlin patients, #1–6 and 8; for the Hannover patients, #7 and 9, stereotactic CT and no space coordinates were available) selected for
LFP analysis are shown on corresponding horizontal slices (Z) of the MNI standard brain template (dimensions in mm: x: medio-lateral, y: antero-posterior, z: dorso-ventral
direction). (B) Localizations depicted on corresponding coronal slices (Y). Put, putamen; GPe, globus pallidum ext.; GPi, globus pallidum int. Note that, in all selected contact pairs,
at least one single contact was located within the GPi. Center locations at the GPi–GPe interface originate in contact pairs with one contact inside GPi and another outside of it.
In relation to the center point of the anterior commissure, the horizontal level z = −3 mm is located approximately 1.3 mm more dorsal and the level z = −4.75 mm is located
0.45 mm more ventral. Mean nuclear boundaries of the globus pallidus are outlined (black) based on the Harvard-Oxford subcortical probabilistic structural atlas (N = 20 patients).
Figure 2. Time course of experimental paradigm. Flanker distractors presented around the fixation point were followed after 70 ms by a central target arrowhead. All stimuli
vanished 20 ms after target onset. Adaptive behavioral feedback was given every 20 trials with regard to accuracy or speed. Finally, a variable fixation time between 2500 and
3500 ms preceded the next trial.
side (see e.g. in Supplementary Fig. 1B). To this end, we low-pass
filtered at 20 Hz the waveform epochs (in analogy to ERP analysis,
see next sections) and applied the Hilbert transform to obtain
the phase values wik ðt; f Þ for each bipolar recording i, at time
point t, and trial number k. The index of pairwise phase coupling at
time t between each pair of neighbor channels i and j can be
assessed
Xwith the formula (e.g. Pikovsky et al. 2001):
n
ij ¼ 1
exp½iðw jk wik Þ where n is the number of trials. AcR
n
k¼1
cording to that expression, which refers to the norm of the vectorial
sum on the unit circle within the 1-dimensional complex space C 1,
ij index is “strictly positive” and approaches 1 (0) for the strict
the R
(no) phase relationship between the considered electrode pair across
the epochs. Here, however, we wanted to detect phase reversals,
which would arise from phase differences within the range [π/2, 3π/2]
ij ¼ 1 at stable
and should lead to a maximum negative value R
phase differences of π for each trial k (Supplementary Fig. 1C). Conse ij
quently, we assigned negative values to the vectorial sum for R
1 Xn
when the real part of the resultant vector
exp½iðw jk wik Þ
k¼1
n
was negative (negative cosine of the angle of resultant vector, angle
within [π/2, 3π/2]). Finally, the index of pairwise phase coupling was
Cerebral Cortex June 2014, V 24 N 6 1505
Table 2
Performance data in the 9 patients and 9 healthy participants are given as mean and SEM
Patient group
Incongruent error
Incongruent correct
Congruent error
Congruent correct
Control group
RT in ms
Number trials
Events percentage
RT in ms
Number trials
Events percentage
350 (20)
450 (30)
380 (60)
390 (20)
30 (20)
100 (20)
10 (6)
140 (10)
20 (10)
80 (10)
6 (4)
94 (4)
330 (30)
460 (20)
430 (20)
390 (20)
8 (3)
120 (2)
1.6 (0.7)
127 (3)
7 (4)
93 (3)
1.5 (0.7)
98.5 (0.7)
averaged across time points within the interval [0, 600] ms poststimulus, reflecting the largest amplitude modulations (Supplementary
ij was computed as
Fig. 1A). The significance level for value c ¼ R
2
enc , according to the asymptotic formula given by Strutt (1905; see
similar approach in Rodriguez-Oroz et al. 2011).
Following this procedure, 30% of the bipolar LFPs used for the
analysis (R01 in patient #1, R12, L12, L23 in patient #2, R01, R23, L01,
L23 in #5, and L01, L12 in #9; none of the contacts from these bipolar
pairs was in the GPe) were flipped. Note that the stimulus-locked
correct LFP epochs showed a prominent negative deflection in the
grand-average waveform (e.g. see Fig. 3A), and this convention was
kept throughout the paper. Importantly, the “response-locked” LFP–
ERPs were not assessed during the flipping of the waveforms. Consequently, the polarity reversal procedure did not aim at maximizing
response-related differences. Note that the polarity convention does
not affect the indexes of spectral power or PDC (see next sections).
All bipolar contact pairs per electrode which had at least one contact
within the GPi based on the postoperative electrode localization (see
patients and surgery) were used for further LFP analysis (N = 36; 17
GPi, 9 patients). Left GPi in patient #7 was not included due to artifacts.
In this study, the terms “contralateral” and “ipsilateral” are used for
GPi in relation to the overt button press (i.e. hand moved). For instance, during responses with the right hand—irrespective of whether
the response is correct or erroneous—the contralateral GPi refers to
the left GPi.
Event-Related Potentials
Stimulus- and response-locked epochs were averaged separately for
erroneous and correct trials, and in congruent and incongruent trials
separately, following previous subtraction of baseline activity (300–
100 ms prior to the flanker onset). The analysis focused primarily on
the incongruent epochs, because of an insufficient number of error
trials in the congruent condition. Unless otherwise stated, these trials
will be hereafter termed erroneous (E) and correct (C). Exclusively for
the standard ERP analysis, data epochs were additionally low-pass filtered using a finite impulse response at a cutoff frequency of 20 Hz.
GPi activity during movement onset was observed in the responselocked representation, whereas GPi activity during motor preparation
was evident in the stimulus-locked representation.
Directionality Analysis Between GPi and pMFC Activity
We were particularly interested in analyzing the frequency-specific directionality of the interactions between pallidal and cortical activity
during an early detection of errors in incongruent trials. To this end,
the PDC was used (Baccala and Sameshima 2001), because it is a sensitive and reliable Granger causality-based method to assess direct
statistical temporal influence between recorded signals in the frequency domain. Specifically, the main focus of this analysis was to
investigate whether the “subcortical-to-cortical” direction of predictability was enhanced in incongruent erroneous trials when compared
with correct trials. This outcome would imply that pallidal LFP activity
influences cortical EEG activity more prominently in error trials. In
addition, despite an expected pattern of bidirectional coupling
between GPi and pMFC, we tested whether the cortical → GPi and
GPi → cortical directions of influence were differentiated.
In the following, the procedure to compute the PDC in our study is
outlined (for more details see Supplementary Material). The PDC is
computed from the coefficients of the multivariate autoregressive
(MVAR) modeling transformed to the frequency domain. The order of
1506 Error Detection in GPi
•
Herrojo Ruiz et al.
Figure 3. Time course of the GPi activity during movement preparation. (A)
Stimulus-locked ERP waveforms in the contralateral GPi relative to the button press.
Waveforms to correct and erroneous responses are denoted by gray and black lines,
respectively. (B) Same in the ipsilateral GPi. F and T denote onset of the flanker and
target stimuli, respectively. Rcorr and Rerr denote the button press in correct and
erroneous responses. Note that the time of response for correct and error trials
differed by approximately 100 ms. Moreover, the SEM of the RT was 30 ms in
correct trials and 20 ms in error trials.
the MVAR was estimated with the Bayesian information criterion
(Schwarz 1978). In each patient, the MVAR model was fitted on multiple trials to increase the reliability of the model parameters (Ding
et al. 2000)—after appropriate normalization in order to achieve local
stationarity in the single trials (Ding et al. 2000)—and solved in the 4to 100-Hz frequency range for a set of 5 time series: FCz, Cz, Pz, one
contact pair in the contralateral, and one in the ipsilateral GPi. In each
patient, the single contact pair per GPi side was selected based on a
correct localization and a prominent response-locked LFP–ERP modulation in error and correct trials (Table 2). The signals were downsampled to 250 Hz to improve frequency resolution, while not
interfering with the time scale of interactions of interest (Schlögl and
Supp 2006; Florin et al. 2010). At a sampling rate of 250 Hz, the time
interval between consecutive points in the time series is 4 ms, which
was acceptable since we did not expect an interaction between cortical and pallidal oscillatory activity at a shorter time scale due to the
inherent delay in transmission times by the neural pathways (∼8 ms
from M1 to GPi through the faster cortico-subthalamo-pallidal
pathway in monkeys, see Nambu et al. 2000). The data for each
channel consisted of the concatenation of all response-locked epochs
corresponding to each response type (erroneous and correct) within
[−300, 100] ms with respect to the button press. This time interval
was selected based on the temporal modulation of early pallidal errorrelated potentials and cortical ERN (see Results). The PDC index was
calculated for erroneous and correct trials separately from the fitted
MVAR coefficients. MVAR and PDC computations were performed
with the Biosig toolbox (http://biosig.sf.net/) and additional custommade scripts for MATLAB®. Finally, for statistical analyses, a first
single-subject assessment of the significant PDC values in each condition was performed. This analysis aimed at revealing the significant
PDC values at the single-subject level, which were later used in the
statistical group analysis. The statistical significance of single-subject
PDC values was assessed by computing the 95% confidence level
based on the estimated standard error of the Jackknife distribution
(e.g. Efron 1992; Schlögl and Supp 2006). Next, at the group level,
statistical differences in the PDC index across subjects were estimated
by means of nonparametric permutation tests (see Statistics).
Time-Frequency Analysis of LFP Signals
We investigated changes in response-locked spectral power in the GPi
focusing on the theta (4–8 Hz) and gamma (30–100 Hz) frequency
ranges motivated by: (1) The general increase in theta band power
observed over the pMFC during and following errors, a phenomenon
which is at the basis of the generation of the cortical ERN (Luu et al.
2004; Trujillo and Allen 2007; Marco-Pallarés et al. 2008; Cavanagh
et al. 2009). (2) Recent data pointing to a facilitation of movement
gain via GPi gamma band oscillations (Brücke et al. 2012). To this
end, we used the wavelet energy, which was computed as the average
across epochs of the squared norm of the response-locked complex
wavelet transform in [−1000, 1000] ms around the button press. After
subtracting the prestimulus baseline level (from 300 to 100 ms prior
to the flanker onset), we normalized the wavelet energy with the standard deviation of that prestimulus baseline period and expressed it as
a percentage of power change (see details in Trujillo et al. 2005). The
normalization procedure reduced the effects of intersubject and interelectrode variability (see Supplementary Material for more details).
The Morlet wavelet transform was computed as follows: Raw
stimulus-locked LFP epochs were extracted in the interval [−1000,
3000] ms and then convolved with the complex Morlet wavelet to
obtain time-frequency (TF) phases wik ðt; f Þ, at an electrode i and epoch
k, and amplitudes Aik ðt; f Þ ¼ jWxik ðt; f Þj of the wavelet-transformed
LFP signal x(t). The constant η characterizes the family of wavelet functions in use and defines the constant relation between the center frequency and the bandwidth h ¼ f =sf . We selected a value η = 7, which
provides a good compromise between high-frequency resolution at
low frequencies and high temporal resolution at high frequencies
(Mallat 1999). The frequency domain was sampled from 4 to 100 Hz
with a 1-Hz interval between each frequency.
Statistics
The assessment of statistical differences between conditions was performed with the use of a nonparametric pairwise permutation test
(Good 2005) across subjects, with the difference in sample means as
test statistic. In this procedure, we first created the joint distribution of
samples from both conditions (e.g. for N = 9 subjects per condition,
the joint distribution has 2N = 18 samples) and then rearranged randomly the sample indexes prior to constructing replications of the 2
samples of size N with the first and second half of the rearranged
joint distribution. We performed n = 5000 rearrangements, drawn at
random from the complete permutation distribution (Monte Carlo Permutation Test; see Supplementary Material). The P-values were then
obtained as the frequencies that the replications of the test statistic
had a larger absolute value than the experimental difference.
We primarily investigated differences between erroneous and
correct incongruent trials to evaluate the time course of pallidal
activity during the processing and resolution of the conflict between
coactivated responses. To this aim, 2-factorial tests with factors Laterality (2 levels: Ipsilateral GPi and contralateral GPi) and Response
type (2 levels: Incongruent error [E] and incongruent correct [C]) were
computed by means of 5000 synchronized rearrangements (Good
2005; Basso et al. 2007). Furthermore, statistical differences between
ipsilateral and contralateral GPis were estimated by means of paired
permutation tests.
In addition, despite the limitations of the small number of congruent erroneous trials, we analyzed differences between the ERPs in erroneous and correct congruent trials to detect modulations induced
by errors when no explicit conflict is present. Moreover, to additionally disentangle the roles of conflict and correctness in the modulations of pallidal ERP activity, we performed a 2-factorial analysis
with factors Conflict (congruent/incongruent trials) and Response
type (error/correct) on the LFP–ERPs. This test was complemented by
unifactorial tests performed via the permutation test separately on
correct and erroneous trials by comparing congruent and incongruent
stimuli conditions.
In the case of grand-average LFP–ERP analysis, the permutation test
was evaluated across subjects, after averaging data from the left and
right GPi. Similarly, LFP–ERP and TF analyses in contralateral or ipsilateral GPis were performed across subjects. Specifically, 9 patients
were equivalent to 9 contralateral GPis after averaging epochs coming
from both the left (when contralateral to the response: i.e. right-hand
response) and right GPis (when contralateral: i.e. left-hand response).
In patient #7 (Table 1), only epochs from the “right” contralateral GPi
were used. Similarly, 9 patients were equivalent to 9 ipsilateral GPis.
The statistical analyses of grand-average ERP waveforms focused on
the temporal windows [−300, 400] ms for response-locked waveforms
and [−200, 600] ms for stimulus-locked potentials. Statistical differences in response-locked spectral power were analyzed between −400
and 500 ms around the button press (broader time window due to the
spread of wavelet functions) and in the 4- to 100-Hz frequency range.
Within these time and frequency ranges of interest, statistical tests
were assessed at each point. Differences were considered significant if
P < 0.05. Multiple comparisons arising from TF analysis or temporal
spread in ERP waveforms were corrected by controlling the false discovery rate (FDR) at level q = 0.05 by means of an adaptive 2-stage
linear step-up procedure (Benjamini et al. 2006). The corrected
threshold P-value obtained from this procedure, pth, was used to reject
all null hypotheses fulfilling the condition: P-value < pth. Throughout
the paper, pth is given when multiple comparisons are performed.
Results
Behavioural data for patients and healthy age-matched controls are provided in Table 2 as mean and SEM. Patients made
about 20% (10) errors in the incongruent condition, and 10%
(6) errors in congruent trials. In accordance with the extent of
response conflict, the RT of correct responses in incongruent
trials was significantly longer than in congruent trials
(P = 0.0130). In addition, incongruent erroneous responses
were faster than correct responses (P = 0.0234). Patients made
few corrections following incongruent errors (2 [3] trials) or
congruent errors (1 [1] trials), except patient #7, who corrected 28 congruent errors and 29 incongruent errors (∼40%).
Reaction times and percentage of error corrections were
similar between the patient group and healthy age-matched
controls (no significant differences, see details in Supplementary Results), suggesting that the low error-correction rate in
patients was not disease related. Note that the small percentage of corrections in the patient and control groups might be
due to the speed–accuracy feedback being provided every 20
trials: No explicit trial-by-trial speed–accuracy pressure was
exerted on the participants, which might have consequently
led to the smaller coactivation of conflicting responses, such
that only one response reached activation threshold.
Cerebral Cortex June 2014, V 24 N 6 1507
Importantly, healthy participants did commit significantly
fewer errors when compared with the patients (congruent
errors: P = 0.0350, incongruent trials: P = 0.0164, permutation
test).
Finally, there was a significant posterror slowing in incongruent trials, reflected in the larger RT in correct trials following errors when compared with the RT in correct trials
following correct responses ( posterror slowing = 20 ms;
P = 0.02). This finding resembles the RT slowing subsequent
to errors in our control group (30 ms, n.s. between-group
differences) and in previous studies (Fiehler et al. 2005;
Münte et al. 2008).
Error-Related Cortical and Pallidal ERP Activity
For the sake of clarity, we report first the main findings from
our analysis focusing on incongruent trials, termed erroneous
(E) and correct (C) responses. At the end of this section,
however, we outline the outcomes of the comparison
between congruent and incongruent trials, which are
expanded in Supplementary Material.
Stimulus-Locked Representation
During motor preparation following target onset, the
stimulus-locked pallidal ERPs (Fig. 3) elicited an amplitude
modulation of negative polarity (see polarity notation by convention in Materials and Methods) around 100–400 ms, which
was observed in both GPi sides and in both response types. A
2-factorial analysis with factors Laterality and Response type
demonstrated significant effects for Response type in [150,
190] ms, due to a larger amplitude increase in correct trials,
and for Laterality in [350, 450] ms, due to more enhanced amplitude change in the ipsilateral GPi (P < pth = 0.01, control of
FDR). The Response-type effect resembles the cortical N200,
a negative stimulus-locked fronto-central ERP component
with larger amplitude in incongruent trials leading to correct
responses (Kopp et al. 1996). In addition, we found a significant interaction effect (P < pth = 0.01) in [150, 190] ms (larger
correct-error difference in the ipsilateral GPi, post hoc analysis, P < pth = 0.008) and in the broad interval [400, 600] ms
(larger correct-error difference in the ipsilateral GPi against
larger error-correct difference in the contralateral GPi, post
hoc analysis, P < pth = 0.004). These results related to the lateralization of the ERP to the ipsilateral GPi, and more prominently in correct trials, might imply “inhibitory mechanisms”
of the corresponding response side (flanker direction is suppressed in correct trials, whereas target direction is suppressed in error trials).
Response-Locked Representation
We found similar peak-to-peak cortical ERN amplitude in
patients and control subjects (Supplementary Results). In
patients, the grand-average response-locked cortical ERP for
erroneous trials revealed a negative amplitude deflection with
a peak latency of 60 ms at FCz and Cz, corresponding to the
ERN (representative electrode position Cz is shown in
Fig. 4A), which was not present in correct trials. A significant
negative difference between error and correct trials was obtained at FCz and Cz in [40, 80] ms (P < pth = 0.01, after
control of FDR, Fig. 4A). This effect was followed by a significant positive difference at Cz and Pz within 150–185 ms
(P < pth = 0.01, Fig. 4A), due to the positive deflection in error
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trials resembling the earlier component of Pe (see van Veen
and Carter 2002 for earlier Pe at Cz).
Similarly, the grand-average response-locked pallidal ERP
in erroneous trials showed an error-related negative amplitude
deflection at response onset that was clearly distinct from the
ERP in correct trials (Fig. 4B). Raw data and single-subject
ERPs in one representative patient are presented in Supplementary Figure 3. It is noteworthy that there were no significant differences between the number of trials with the
right- and left-hand movements for any event type (P = 0.65
for errors and P = 0.41 for correct trials).
Permutation tests revealed a significant difference between
pallidal error and correct waveforms in [−20, 140] ms
(P < pth = 0.015). This effect was due to prominent differences
between potentials of different polarity and large amplitude
emerging in erroneous and correct trials. These potentials
peaked at response onset (0 ms) in erroneous trials and at
70 ms in correct trials, leading to peak latency at 70 ms in the
difference waveform erroneous minus correct (Supplementary
Fig. 2). Importantly, the peak latency (at 0 ms) of the pallidal
ERP preceded the cortical “error” waveform by approximately
60 ms. An additional significant difference was obtained
earlier in [−260, −150] ms (P < pth = 0.015) and was due to a
negative amplitude increase in correct trials, not present in
erroneous trials.
A 2-factorial analysis demonstrated the significant main
effects for factors Laterality and Response type in the aforementioned time intervals (P < pth = 0.01; synchronized
rearrangements). These main effects reflected: (1) A robust
difference between erroneous and correct trials, and (2) a
different ERP modulation between the ipsilateral and contralateral GPis. A significant interaction was obtained in [−30,
100] ms (P < pth = 0.01). Additional post hoc analyses revealed
that the negative ERP component around the button press in
erroneous trials was significantly larger in the ipsilateral GPi
(erroneous: Contra vs. ipsi GPi, ERP in [−20, 140] ms;
P < pth = 0.011; Fig. 4C), whereas the positive component in
correct trials had a significantly larger amplitude in the contralateral GPi (correct: Contra vs. ipsi GPi, ERP in [−20, 140]
ms; P < pth = 0.020). Note also that, in the stimulus-locked
representation (Fig. 3B), a second negative deflection
emerges in error trials around the time of the erroneous
response at approximately 350 ms. Importantly, in the post
hoc analysis, we found no significant differences around
−200 ms between ipsilateral and contralateral GPis, neither in
correct, nor in error, trials.
Thus, we obtained a specific pallidal ERP modulation
around the onset of the erroneous response, peaking at
button press, and which was more prominent in the GPi ipsilateral to the wrong overt response. The nature of the effect
around −200 ms remains unclear, but see below (congruent
trials).
To test the possibility that the differences between ERPs in
both conditions arose from shorter RTs in erroneous trials, we
classified correct trials in each patient into 3 RT intervals
based on an equal number of trials (mean 30 [SEM 10] in each
RT bin). The resulting bins had mean RT values at 353 ms
(short RT), 430 ms (medium RT), and 548 ms (long RT), with
the short RT bin being more closely matched to reaction times
in erroneous trials (Table 2). As observed in Figure 4D, the
ERP profile in correct trials was invariant with respect to
RT modulation (Fig. 4D). An additional control test was
Figure 4. Time course of the ERPs to correct (C) and erroneous (E) responses in the incongruent condition. (A) Cortical response-locked ERPs at the electrode position Cz for E
(black), C (magenta) trials revealed the significant components ERN and Pe (gray area: P < 0.05, controlled for FDR) (B) Response-locked GPi ERP waveforms averaged across 9
subjects (17 GPi) demonstrated a prominent significant difference between erroneous and correct trials prior to and around response onset (P < 0.05, controlled for FDR). (C)
Response-locked GPi ERP waveforms separately for the ipsilateral (left panel) and contralateral (right panel) GPi. Note that the terms contralateral and ipsilateral are used for GPi
in relation to the overt button press (i.e. hand moved). (D) To rule out the possibility that differences in GPi LFP responses were due to differences in reaction times between
error and correct trials, correct trials were classified into 3 RT intervals with an equal number of epochs, thus leading to the mean RT values in each interval of 353 ms (short
RT), 430 ms (medium), and 548 ms (long RT). No differences in the ERP profile of correct trials with respect to RT modulation were observed. (E) Directionality analysis in
incongruent trials. Grand-average significant PDC estimates after subtraction of the single-subject confidence threshold (P < 0.05, assessed with the estimated standard error of
the Jackknife distribution) are presented for the directions GPi to posterior median frontal cortex ( pMFC: FCz, Cz; left panel), ipsilateral GPi to pMFC (middle panel), and
contralateral GPi to pMFC (right panel). Significant group-level differences between erroneous and correct trials are denoted by the gray areas and were computed at alpha level
0.05 and controlled for FDR.
Cerebral Cortex June 2014, V 24 N 6 1509
performed aiming to disentangle the influence of proximal
and distal errors on the early error-related LFP negative deflection. For that purpose, each single-subject distribution of
differences between indices of consecutive error trials was
binned into 2 samples (i.e. close indices: Proximal errors and
distant indices: Distal errors) separated by the median value.
If the error-related LFP deflection is affected by the degree of
expectancy in error occurrence, the waveforms to both proximal and distal errors should differ. The results demonstrated
that ERP-LFP waveforms in erroneous trials were identical in
both proximal and distal errors (details in Supplementary
Material and Supplementary Fig. 4). Thus, the frequency of
error occurrence in the recent performance history did not
modulate the early negative erroneous LFP deflection.
Finally, we present the results of the analysis of congruent trials, which can provide further understanding of the
early modulations of pallidal ERP activity when there is no
explicit conflict as a potential confounding factor. In
addition, the RT in erroneous and correct congruent trials
was similar (P = 0. 811). We found significant differences
between erroneous and correct congruent trials in time
windows similar to those reflecting the effects in incongruent trials: [−200, −170] ms and [−10, 60] ms (P < pth = 0.01,
permutation test); the earlier effect was due to larger negative amplitude modulation in correct trials, whereas the
later effect reflected a prominent negative deflection at
button press exclusively in erroneous trials (Supplementary
Fig. 5). There were no laterality effects in congruent correct
trials (n.s. differences; this analysis was not available for
errors due to the small number of trials). A 2-factorial statistical assessment with factors Conflict and Response type
of the stimulus-locked ERPs revealed a significant interaction within [340, 420] and [470, 510] ms (P < pth = 0.012),
related to the significant stimulus-locked ERP differences in
these range between “correct” trials with and without conflict (within 380–510 ms, post hoc analysis, P < pth = 0.0078,
permutation test, Supplementary Fig. 6A), not present in
error trials. The analysis of response-locked ERPs demonstrated a main effect for Response type at [−300, −60] ms
and [−20,140] ms (P < pth = 0.0234) and an interaction
effect at [−10, 140] ms (P < pth = 0.0234). The significant
interaction effect reflected different modulations around the
button press between trials with or without conflict “exclusively” in correct responses ( post hoc permutation test,
P < pth = 0.01; Supplementary Fig. 6B, left), and not present
in erroneous responses (n.s. differences). The responselocked differences between correct congruent and incongruent trials did not originate in the difference response
latency of these types of responses (Supplementary Fig. 6B,
right). In summary, whether trials had a explicit response
conflict or not led to significantly different ERP patterns exclusively in correct trials (but not in error trials), at the
time around button press. No difference was found in the
premovement period. Erroneous responses lead to markedly
similar response-locked ERP profiles independently of the
degree of conflict (Supplementary Fig. 7, n.s. differences).
These outcomes support that our main finding of errorrelated negative deflection peaking at the button press
might be primarily reflecting an early error-detection signal
as a result of monitoring the unfolding of response inhibition patterns, even in the absence of explicit conflict.
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Directionality Analysis Between Error-Related GPi
and pMFC Activity
Single-subject directionality analysis between response-locked
EEG and LFP signals in incongruent trials was performed by
means of the PDC (Baccala and Sameshima 2001). Note that
this index assesses in a frequency-specific manner how
activity from one recorded source helps in predicting the temporal evolution of activity in a different source, which is interpreted as a temporal influence. The PDC analysis focused
here on the range 4–100 Hz and in [−300, 100] ms, as a time
window of interest spanning the modulations in the GPi ERPs
by erroneous/correct responses and the emergence of the cortical ERN. The single-patient MVAR order parameter ranged
from 2 to 7. PDC values in significant frequency bins were
pooled together among contralateral and ipsilateral GPis for
directions (1) GPi to pMFC (FCz and Cz) and (2) for the opposite direction. We observed a significant “bidirectional”
statistical influence between GPi and pMFC in all patients at
4–100 Hz in both error trials (P < 0.05, based on the standard
error of the Jackknife distribution) and correct trials
(P < 0.05). Group analysis of PDC values demonstrated no significant differences between the 2 directions of influence in
either erroneous or correct trials ( permutation test, P > 0.05).
However, when considering differences between erroneous
and correct trials, rather than between the 2 directions of
influence, a significant difference emerged for the PDC at
40–65 Hz, due to larger temporal predictability in error trials,
but exclusively when considering the influence of GPi on the
pMFC (P < pth = 0.02; Fig. 4E, left panel). For the reverse direction of influence, from pMFC to GPi, there were no significant differences between erroneous and correct trials
(P > 0.05). Accordingly, for the direction GPi to pMFC, we investigated further the PDC index by means of a 2-factorial
analysis with factors Laterality and Response type. This analysis pointed to a significant interaction in the 4- to 65-Hz band
(P < pth = 0.0236; synchronized rearrangements; no main
effects). The interaction was the result of larger and significant
difference in the PDC index between error and correct trials
when considering the ipsilateral GPi as a source of influence
(P < pth = 0.02; 10–65 Hz; post hoc permutation test, Fig. 4E,
middle panel; no significant difference for contralateral GPi).
Modulation of Spectral Power in GPi
We investigated whether the index of spectral power in different frequency ranges dissociated between error-related and
movement-related processes. A general pattern of enhanced
theta-band and decreased beta-band spectral power prior to
and following response onset was observed (Fig. 5A–D). In
addition, there was a prominent increase in gamma spectral
power at button press, more salient in correct trials and in the
contralateral GPi (Fig. 5C). The 2-factorial analysis confirmed
a significant main effect for factors Laterality in [−50, 80] ms
and 50–100 Hz (gamma band, P < pth = 10−5) and Response
type within [−185, −120] ms at 24–30 Hz (beta range) and in
[−250, −200] at 40–60 Hz (gamma range; P < pth = 10−5). The
Laterality effect is consistent with the previous findings of
larger gamma oscillations over the contralateral GPi (Brücke
et al. 2008, 2012). Additionally, significant interaction effects
P < pth = 0.0026) were found: 1) in the theta (4–7 Hz) band
prior to the response from −300 to −100 ms, and at 70–90 Hz
in the gamma band (2) from −270 to −250 ms, and (3) between
Figure 5. Grand-average response-locked normalized spectral power within 4–100 Hz in the GPi during the incongruent condition. (A) Time-frequency representation (TFR) of the
spectral power in error trials in the contralateral GPi. (B) Same in the ipsilateral GPi. TFR in correct trials in the contralateral (C) and ipsilateral GPis (D). TFR of the error-correct
difference in oscillatory activity in the ipsilateral (E) and contralateral (F) GPis. Significant error-correct differences in panels E and F, provided by the post hoc permutation test,
are denoted by the black contour (P < pth = 0.005, controlled for FDR). The index of normalized spectral power indicates difference values between wavelet energy and
prestimulus baseline wavelet energy relative to the standard deviation of the baseline. Note that, exclusively in the ipsilateral GPi, the [−200, 0] ms pre-error theta-band spectral
power was significantly higher than prior to correct responses.
−50 and 50 ms around the response onset. No interaction
effects were found in the alpha or beta frequency ranges.
Post hoc unifactorial analyses were performed to test for
differences between error and correct trials in the contralateral
and ipsilateral GPis separately (Fig. 5E,F). Erroneous
responses were associated with significantly larger theta oscillations in the ipsilateral GPi from −200 to 50 ms
(P < pth = 0.005, Fig. 5F), an effect that resembles the cortical
error-related theta increase typically observed in the postresponse interval (Luu et al. 2004; Trujillo and Allen 2007;
Marco-Pallarés et al. 2008; Cavanagh et al. 2009). A similar
significant enhancement of theta-band spectral power in erroneous trials was found at the cortical level (Supplementary
Fig. 8).
Motor output at the button press led to larger gamma amplitude oscillations in correct trials than in error trials at [−20,
20] ms, exclusively in the contralateral GPi (P < pth = 0.005,
Fig. 5E). Finally, additional significant differences were
obtained bilaterally in the GPi in the beta and gamma bands
within [−125, 150] and [−215, −290] ms, respectively
(P < pth = 0.005, Fig. 5E,F), due to a more prominent decrease
of activity prior to correct responses.
Discussion
This is the first account in humans of prominent and specific
modulation of pallidal LFP activity prior to erroneous
responses in a flanker task, preceding the cortical ERN by
60 ms. This finding was complemented by directionality
analysis between cortical and pallidal activity with PDC,
showing pallidal influence on cortical activity preferentially in
error trials. Moreover, early theta-band spectral power increased in the ipsilateral GPi with erroneous responses, in
line with the larger negative GPi ERP component that
emerged ipsilateral to overt errors. These data resemble the
general increase in theta power over the pMFC during and
Cerebral Cortex June 2014, V 24 N 6 1511
following errors, a phenomenon that is at the basis of the generation of the cortical ERN (Luu et al. 2004; Trujillo and Allen
2007; Marco-Pallarés et al. 2008; Cavanagh et al. 2009). The
implication of our findings is that the human GPi is involved
in the initial stages of error detection and may influence the
generation of the cortical ERN.
The oscillatory pallidal error-related modulations were
distinct from activity patterns during (1) movement preparation and (2) movement execution. (1) The preparatory
movement-related activity was reflected in bilateral decreased
beta and gamma spectral power effects, which were more
salient in correct trials. Previous findings in several BG structures have supported a general role of bilateral reduced betaband spectral power in preparation and during execution of
movements (Alegre et al. 2004; Kühn et al. 2004; Brücke et al.
2008). Our data point to a facilitation of movement preparation in correct trials indexed by the larger beta spectral
power decrease. (2) Activity at movement onset was reflected
in the increased gamma spectral power lateralized to the contralateral GPi, which was larger in correct trials. Lateralized
movement-related gamma spectral power is an important
feature of physiological BG function (Alegre et al. 2004;
Kempf et al. 2007; Brücke et al. 2008; Liu et al. 2008) and
cortical activity (Crone et al. 1998; Cheyne et al. 2008; Huo
et al. 2010; Muthukumaraswamy 2010). More recently, pallidal gamma spectral power has been proposed to index BG
influence on motor gain (Brücke et al. 2012). Accordingly,
our findings of reduced gamma spectral power at overt erroneous responses imply that GPi activity might have facilitated
a movement of reduced motor gain, in line with reduced
force observed during overt erroneous responses in previous
studies (Gehring et al. 1993).
Note that the specific error-related findings (ERP, PDC, and
theta oscillations) indicate that activity in the ipsilateral GPi
reflects to a larger extent the differences between error and
correct trials. This is an interesting aspect, which is better understood with the findings of the stimulus-locked ERP analysis: The larger amplitude modulation at [150,190] ms in
correct trials relative to error trials (significant main effect
Response type) resembles the cortical N200, which is larger
in incongruent correct trials and has been linked to the inhibition of competing responses (Kopp et al. 1996; Coles et al.
2001). This early putative inhibition-related effect approximately 200 ms after stimuli presentation might correspond to
the significant differences observed within [−260, −150] ms in
the response-locked representation. Moreover, also in the
stimulus-locked representation, the interaction effect at
[150,190] ms demonstrated larger correct-error modulation in
the ipsilateral GPi, indicating that, at an early stage during
motor preparation, there might be larger inhibition in the ipsilateral GPi in correct trials. In error trials, this early inhibitory
process is weaker and is lateralized to the side ipsilateral to
the “erroneous” response. These outcomes were paralleled by
the later significant main effect of Laterality in the stimuluslocked ERP between 350 and 450 ms, reflecting also larger
activation in the ipsilateral GPi. In summary, stimulus-locked
pallidal ERP analysis emphasized the lateralization of the
activity to the ipsilateral GPi.
To further understand the nature of the early pallidal ERP
modulations, we performed analysis of congruent trials. The
outcomes showed that, even in the absence of potential confounding factors, such as conflict and reaction time, there is a
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larger negative ERP modulation 200 prior to the button press
in correct, but not in error, trials. Therefore, we hypothesize
that the pallidal activity modulation emerging 200 ms prior to
the button press might be related to an enhanced inhibitory
level necessary for the correct response to be selected. This
interpretation is, however, limited by the current experimental paradigm (see below), in which a potential overlap of
several processes, such as general inhibition (“break”) of all
responses, specific lateralized inhibition, response selection,
and reward prediction, could modulate pallidal population
activity.
Following on from this, interestingly, around the time of
the erroneous response onset, a second negative deflection
was observed in error trials, which was more enhanced in the
ipsilateral GPi. This error-related signal was similar in amplitude and latency both in congruent and incongruent trials,
which suggests that it was not affected by explicit conflict
between competing responses or by the timing of the
response. We speculate that, in the response-locked representation, the error-related modulations at button press might
reflect the outcome of a BG mechanism monitoring the preceding ongoing pallidal activity influencing response selection, but which might be independent of explicit response
conflict. This monitoring mechanism might trigger an errordetection process in association with the incorrectly suppressed response side. An early initiation of a corrective movement (Rodríguez-Fornells et al. 2002) is unlikely to account
for the larger ipsilateral error-related modulations since participants were not encouraged to correct wrong responses
and, therefore, did not correct in the vast majority of the trials.
Studies using directed transfer function, PDC, or partial coherence (PC) as measures of directionality between cortex
and BG activity have primarily focused on Parkinson’s disease
patients, and the outcomes have revealed a variable pattern of
directionality between cortex and STN depending on the frequency range (Williams et al. 2002; Fogelson et al. 2006; Lalo
et al. 2008; Litvak et al. 2011). It is noteworthy that the patterns of bidirectional influences reported in these studies
were not necessarily accompanied by changes in LFP, MEG,
or EEG spectral power in the same frequency ranges. Consistent with this, here the PDC index between GPi and cortex
was larger in error trials in the gamma frequency range
(40–65 Hz), whereas the broad error-related changes in spectral power were localized to the theta frequency range. Moreover, since during dystonic movements the GPi drives muscle
EMG activity in the theta frequency range (Sharott et al.
2008), the net PDC findings in the gamma band cannot be
accounted for by the abnormally enhanced low frequency BG
activity in dystonia.
What could the role of the human sensorimotor GPi be
during this early stage of error monitoring?
Here, 2 frameworks can be useful to understand the outcomes of our study. The computational RL model of Holroyd
and Coles (2002) proposes that error signals generated in the
BG are modulated by action-related rewards encoded by the
mesencephalic dopamine system prior to their projection to
the anterior cingulate cortex (ACC) in the pMFC. An ACC acts
as a “control filter” and chooses an appropriate motor
response. In this view, the ERN reflects mismatch between
the expected goal and predicted upcoming action (Gehring
et al. 1993; Falkenstein et al. 1995). The RL model posits that,
following learning of stimulus–response associations and
their corresponding reinforcements in the striatum, reward
prediction error signals propagate back from reward delivery
to preceding actions and cues via TD signals (Sutton 1988;
Sutton and Barto 1998; McClure et al. 2003). As a consequence, predictive signals linked in the BG to actions can be
computed in advance of the future reward.
An expansion of the RL framework based on recent neurophysiological data from the primate sensorimotor GPi and
lateral habernula (LHb) neurons has been put forward by Hikosaka and colleagues (Hong and Hikosaka 2008; Hikosaka
et al. 2008; Hong et al. 2011). Here, it has been proposed that
the sensorimotor GPi has 2 functionally distinct outputs, one
involved in motor execution and the other involved in reward
evaluation. Overall, these studies highlight the plausibility of
recording error-related or negative reward prediction signals
in the sensorimotor GPi, at an early stage of movement selection. Recently, Cockburn and Frank (2011) have linked the RL
model by Holroyd and Coles (2002) and the conflict monitoring model in the ACC (Botvinick et al. 2001), in an explicit
attempt to account for the feedback ERN ( peaking 200 ms
after negative feedback; Cohen and Ranganath 2007). In their
model, learning of the “explicit” reward contingencies by the
ACC eventually leads to an independence from the BG gating
mechanisms.
This aspect leads us to the second framework to interpret
our data: The contributions of the BG to the optimization of
behavior and therefore to performance monitoring can be
conceptualized within the optimal feedback control framework (Todorov and Jordan 2002). Within this computational
framework, some studies have emphasized a possible role of
the BG in optimal motor control by estimating the expected
costs (energy and accuracy cost) of the motor commands and
the expected implicit rewards of the predicted sensory states
(Shadmehr and Krakauer 2008). Empirical data on the control
of reaching movements have supported this view and have
stressed that an implicit motivational circuit might operate
within the motor frontal-BG-thalamocortical loop (Mazzoni
et al. 2007; Niv et al. 2007). It remains yet unclear how cost
functions can be implemented for motor tasks characterized
by “discrete” movements such as thumb presses in the flanker
task.
An additional important consideration is that an implicit
motivational circuit within the BG motor loop might drive
automatic or spontaneous behavior (Mazzoni et al. 2007; Niv
et al. 2007). Here, we used a flanker task that is characterized
by an automatic “overlearned” stimulus–response association,
and performance was motivated by implicit (i.e. without
awareness) reward. Therefore, predictive signals of an upcoming error are likely to be more relevant in our task than
reward prediction error signals following outcomes. The
former may contribute to the ERN modulation, whereas the
later play a major role in learning stimulus–response associations (Pasupathy and Miller 2005) and modulating the cortical feedback ERN (Cohen and Ranganath 2007). Moreover,
the frequency of error occurrence in the recent performance
history did not modulate the early negative error-related LFP
deflection ( proximal = distal errors), which supports the
interpretation that the reported pallidal ERPs in erroneous
trials reflect automatic predictive processes in the BG and are
not affected by the degree of expectancy of error occurrence.
It must be clarified, however, that the time course of the
pallidal–cortical interactions during error-detection reported
here is probably specific to response errors arising in the
context of automatic stimulus-response mappings. This may
account for the differences with the existing literature on
error processing, particularly with studies focusing on online
error correction during arm reaching or trajectory movements.
In that context, some studies highlight the role of the posterior parietal cortex and cerebellum (Desmurgert and
Grafton 2000; Desmurget et al. 2001), whereas others imply a
contribution of the BG (Houk et al. 2007; Tunik et al. 2009)
or contradict it (Desmurget et al. 2004). It might be that the
BG contributions to these processes are intertwined with the
modulation of force grip (Grafton and Tunik 2011). Together,
the alternative hypotheses for the involvement of the different
cortical and subcortical structures in error monitoring
demand further elucidation in future research. In this line, a
limitation of our study is that, during the flanker task, several
processes might overlap in the recordings of the LFP activity
in the GPi, therefore leading to potential confounding factors
in the interpretation of the activity 200 prior to the button
press. These processes might include: General inhibition of
all responses, specific lateralized inhibition, response selection, and reward prediction. Future work should complement
our novel findings by investigating the influence of pallidal
activity on the cortical ERN with new paradigms designed to
exclude the inhibition of competing responses as a potential
confounding factor. Another possibility would be to use more
experimental blocks in the flanker task to increase the
number of congruent erroneous epochs and to focus the
analysis exclusively on congruent trials.
Up to now, empirical evidence in support for the BG contribution to the human cortical ERN comes predominantly from
studies in patients with BG dysfunction or lesions (Falkenstein
et al. 2001; Johannes et al. 2002; Frank et al. 2004; Beste et al.
2006, 2008; Ullsperger and von Cramon 2006; Willemssen
et al. 2009; Seifert et al. 2011). In particular, focal lesions in
the GPe, GPi, ventral anterior, and ventral lateral anterior thalamic nuclei, the two latter being termination sites of pallidal
efferents, lead to a reduction or even absence of the cortical
ERN (Ullsperger and von Cramon 2006; Seifert et al. 2011)
and corresponding behavioral consequences (Seifert et al.
2011). Invasive recordings from the NAcc during performance
of a flanker task have revealed error-related modulation of
LFP activity, which precedes the midline ERN by 40 ms
(Münte et al. 2008; Heinze et al. 2009). Here, it was suggested
that the NAcc integrates contextual information from motor,
limbic, and cognitive areas (Mogenson et al. 1980) by weighting the error signals that are sent from different subcortical
and cortical areas to guide motivated behavior (Münte et al.
2008). Alpha–gamma band cross-frequency coupling in the
NAcc might be a neural mechanism facilitating the gating of
information from the limbic system to the BG for rewardguided learning (Cohen et al. 2009a, 2009b). Following NAcc
DBS, functioning of the performance monitoring system can
be restored (Kuhn et al. 2011). Our GPi findings converged
with the NAcc data on error monitoring (Münte et al. 2008;
Heinze et al. 2009) in that the subcortical error-related modulation preceded the cortical ERN by 60 ms. Therefore, it seems
that erroneous responses in the flanker task modulate LFP
activity in subcortical structures earlier than pMFC activity.
This is in parallel to the observation that thalamic LFP activity
precedes cortical ERPs during target detection in an oddball
paradigm (Klostermann et al. 2006).
Cerebral Cortex June 2014, V 24 N 6 1513
It has to be considered that our findings were collected in
patients, and motor performance might therefore be modulated by the underlying disease. However, we examined
patients with cervical or segmental dystonia, who preserved
normal hand motor function. The similar reaction times in
patients and healthy participants suggested no major abnormality in motor output in our patients. That said, we need to
consider that patients committed significantly more errors
which, due to the implicit speed-accuracy trade off in the
flanker task, reveals that, at a similar speed level, task performance was affected in the patient group at the level of
“response selection”. Due to the reduced inhibition demonstrated at several levels of the central nervous system in
patients with dystonia (e.g. Hallet 1998), it remains unclear
whether the larger error rates in the patient group reflect: 1)
A deficiency in the correct inhibition of conflicting responses,
or (2) the less attentive postoperative state of the patients.
However, the similar posterror slowing data, ERN
peak-to-peak values, and number of corrected errors in both
experimental groups imply that “error-monitoring” processes
were engaged similarly in the task in the patient and control
groups.
Animal data has not so far assessed the cortical–subcortical
interactions during error monitoring. Evidence for a cortical
ERN-like component can be found in macaques (Godlove
et al. 2011), whereas specific error-related modulation of
spiking activity in BG structures has been found in the GPe,
STN, GPi, NAcc (Arkadir et al. 2004; Taha et al. 2007; Hong
and Hikosaka 2008; Lardeux et al. 2009; Hong et al. 2011). A
direct link to human BG data in relation to the ERN is yet
unclear, as animal and human error-related data have typically
been studied separately using different tasks. Of special relevance, here are the studies of Hikosaka and colleagues
because of their emphasis on the modulation of activity in the
primate dorsal pallidum—corresponding to the sensorimotor
loop—by negative and positive reward predictions (Hikosaka
et al. 2008). The current findings are likely to be associated
with the posteroventral lateral GPi within the sensorimotor
cortico-striatal-thalamocortical circuit, which was also the
target area of pallidal DBS in our patients. Here, it has to be
considered that only contact pairs that had at least one contact
placed in the GPi according to postoperative imaging were selected for LFP analysis. Clinical efficacy of DBS further supports correct placement of the electrode in the sensorimotor
GPi, although contact localization cannot be exclusively
related to the sensorimotor part of the GPi. Parallel information processing has been postulated along the segregated
(cognitive, limbic, and motor) cortical-BG-thalamocortical
loops (Albin et al. 1989; DeLong 1990), which, however, converge to some extent, thus enabling the integration of information processing (Draganski et al. 2008; Turner and
Desmurget 2010). Evidence for the parallel processing of
error signals in the limbic and motor loops can be found in
the LHb-projecting neurons located in the border of the GPi
in monkeys (Hong and Hikosaka 2008). Note, however, that
LFP recordings in the human pallidum do most likely not exclusively reflect activity paralleled by the border cells in the
monkey.
Finally, the time course of the error-related pallidal and cortical activity modulations documented in the present study
would suggest that pallidal high-frequency stimulation—due
to interference with physiological activity within the cortico1514 Error Detection in GPi
•
Herrojo Ruiz et al.
striatal-thalamic loop—may alter the integrity of the errormonitoring system at the BG level leading to behavioral
consequences. Specifically, we would predict that stimulationinduced disruption of the output activity from the BG interferes with the neural signature of early error detection
observed in the sensorimotor GPi and its projection to the
cortex, possibly leading to larger error rates and reduced ERN
amplitudes, rather than changes in reaction time. Such investigations could complement preliminary results regarding the
effect of focal lesions in the GPi on performance monitoring
during a flanker task (1 patient, Ullsperger and von Cramon
2006) and should be tested in future studies.
In summary, our investigations into the role of the sensorimotor GPi in error processing revealed error-related pallidal
activity that precedes the cortical ERN and drives cortical
activity in the gamma frequency range, suggesting an early
modulation of error-related activity in the human GPi. In
addition, similarly to the studies emphasizing theta oscillations as the main contributors to spectral content of the cortical ERN, we found that pallidal theta oscillations
characterize the early error-related oscillatory modulations.
Finally, stimulus-locked ERP modulations during motor preparation highlighted the general role of the ipsilateral GPi in
the initial inhibition of conflicting responses, which was
weaker and lateralized to the wrong side in error trials. Consequently, we propose that the prominent influence of pallidal
error-related activity on the cortical ERN results from monitoring the earlier pallidal inhibition processes necessary for adequate response selection. But future work should identify in a
more mechanistic way the early processes that lead to the
putative pallidal error-detection signal.
Supplementary Material
Supplementary material can be found at: http://www.cercor.oxfordjournals.org/
Funding
This work was supported by the German Research Association (DFG) through project HE 6103/1-1 to M.H.R. and
project KFO 247 to A.A.K., A.K., J.H., and G.H.S.
Notes
We thank Carsten Allefeld and Felix Blankenburg for their comments
on the first version of this manuscript, Markus Ullsperger for advice
with the flanker task in a previous study and suggestions, and Vadim
Nikulin for fruitful discussions. Conflict of Interest: J.K.K. is a consultant to Medtronic.
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