EEG responses to TMS are sensitive to changes in the perturbation parameters and repeatable over time.

Abstract

High-density electroencephalography (hd-EEG) combined with transcranial magnetic stimulation (TMS) provides a direct and non-invasive measure of cortical excitability and connectivity in humans and may be employed to track over time pathological alterations, plastic changes and therapy-induced modifications in cortical circuits. However, the diagnostic/monitoring applications of this technique would be limited to the extent that TMS-evoked potentials are either stereotypical (non-sensitive) or random (non-repeatable) responses. Here, we used controlled changes in the stimulation parameters (site, intensity, and angle of stimulation) and repeated longitudinal measurements (same day and one week apart) to evaluate the sensitivity and repeatability of TMS/hd-EEG potentials.
In 10 volunteers, we performed 92 single-subject comparisons to evaluate the similarities/differences between pairs of TMS-evoked potentials recorded in the same/different stimulation conditions. For each pairwise comparison, we used non-parametric statistics to calculate a Divergence Index (DI), i.e., the percentage of samples that differed significantly, considering all scalp locations and the entire post-stimulus period. A receiver operating characteristic analysis showed that it was possible to find an optimal DI threshold of 1.67%, yielding 96.7% overall accuracy of TMS/hd-EEG in detecting whether a change in the perturbation parameters occurred or not.
These results demonstrate that the EEG responses to TMS essentially reflect deterministic properties of the stimulated neuronal circuits as opposed to stereotypical responses or uncontrolled variability. To the extent that TMS-evoked potentials are sensitive to changes and repeatable over time, they may be employed to detect longitudinal changes in the state of cortical circuits.

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EEG Responses to TMS Are Sensitive to Changes in the
Perturbation Parameters and Repeatable over Time
Silvia Casarotto, Leonor J. Romero Lauro, Valentina Bellina, Adenauer G. Casali, Mario Rosanova,
Andrea Pigorini, Stefano Defendi, Maurizio Mariotti, Marcello Massimini*
Department of Clinical Sciences ‘‘L. Sacco’’, Universita ` degli Studi di Milano, Milan, Italy
Abstract
Background: High-density electroencephalography (hd-EEG) combined with transcranial magnetic stimulation (TMS)
provides a direct and non-invasive measure of cortical excitability and connectivity in humans and may be employed to
track over time pathological alterations, plastic changes and therapy-induced modifications in cortical circuits. However, the
diagnostic/monitoring applications of this technique would be limited to the extent that TMS-evoked potentials are either
stereotypical (non-sensitive) or random (non-repeatable) responses. Here, we used controlled changes in the stimulation
parameters (site, intensity, and angle of stimulation) and repeated longitudinal measurements (same day and one week
apart) to evaluate the sensitivity and repeatability of TMS/hd-EEG potentials.
Methodology/Principal Findings: In 10 volunteers, we performed 92 single-subject comparisons to evaluate the
similarities/differences between pairs of TMS-evoked potentials recorded in the same/different stimulation conditions. For
each pairwise comparison, we used non-parametric statistics to calculate a Divergence Index (DI), i.e., the percentage of
samples that differed significantly, considering all scalp locations and the entire post-stimulus period. A receiver operating
characteristic analysis showed that it was possible to find an optimal DI threshold of 1.67%, yielding 96.7% overall accuracy
of TMS/hd-EEG in detecting whether a change in the perturbation parameters occurred or not.
Conclusions/Significance: These results demonstrate that the EEG responses to TMS essentially reflect deterministic
properties of the stimulated neuronal circuits as opposed to stereotypical responses or uncontrolled variability. To the
extent that TMS-evoked potentials are sensitive to changes and repeatable over time, they may be employed to detect
longitudinal changes in the state of cortical circuits.
Citation: Casarotto S, Romero Lauro LJ, Bellina V, Casali AG, Rosanova M, et al. (2010) EEG Responses to TMS Are Sensitive to Changes in the Perturbation
Parameters and Repeatable over Time. PLoS ONE 5(4): e10281. doi:10.1371/journal.pone.0010281
Editor: Pedro Antonio Valdes-Sosa, Cuban Neuroscience Center, Cuba
Received February 12, 2010; Accepted March 30, 2010; Published April 22, 2010
Copyright: ? 2010 Casarotto et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by European Grant Strep LSHM-CT-2005-51818, by PRIN 2006 (Progetti di Ricerca di Interesse Nazionale - National Interest
Research Projects) supported by the Italian Ministry of Research and University, and by European Grant Strep ICT- 2007-224328 ‘‘Predict AD’’ (to M. Massimini). The
funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: marcello.massimini@unimi.it
Introduction
Several studies have suggested that the combination of high-
density electroencephalography (hd-EEG) and transcranial mag-
netic stimulation (TMS) [1,2] may be employed to directly and
non-invasively gauge cortical excitability and connectivity in
humans [3–16]. Global and/or local changes in the excitability
and connectivity patterns of cortical circuits underlie most
neuropsychiatric conditions and their treatment. Thus, at least
in principle, TMS/hd-EEG may be employed at the patient’s
bedside to track over time pathological alterations, plastic changes
and therapy-induced modifications in cortical circuits.
A first step to evaluate the potential of TMS/hd-EEG as a
diagnostic and monitoring tool involves defining the sensitivity and
repeatability of this technique. In other words, before employing
TMS/hd-EEG at the patient’s bedside, one would like to assess to
what extent TMS-evoked potentials reflect particular electrophys-
iological properties of the stimulated cortical circuits rather than a
stereotypical brain’s reaction, or uncontrollable variability. Indeed,
TMS-evoked potentials would have limited diagnostic/monitoring
application if they were found not to change when different
neuronal subsets are stimulated, or if they were found to vary
randomly when stimulation parameters are kept constant. Ideally,
TMS-evoked potentials, recorded across different sessions in a
healthy brain, should always change significantly if stimulation
parameters are varied (100% sensitivity) and should not change if
stimulation parameters are kept constant (100% repeatability).
Separate experimental evidences have suggested that TMS-
evoked potentials have a certain degree of sensitivity to changes in
stimulation parameters, such as location [10,12,17,18], intensity
[13,19] and direction of the induced current with respect to the
cortical surface [15]. Moreover, a few works have demonstrated
that TMS-evoked potentials can also detect changes in the state of
cortical circuits, such as the ones induced by alcohol intake
[9,11,20], by falling asleep [16,21] and by induction of cortical
potentiation with repetitive TMS [22]. To the best of our
knowledge, repeatability has been, so far, evaluated only in one
work by Lioumis et al. [23]. This work suggested that, at least at the
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group level, the amplitude and latency of selected components of
TMS-evoked potentials tend to be stable over time when
stimulation parameters are constant.
Altogether, the evidence reported above, although not system-
atic, suggest that TMS/hd-EEG is a reliable technique. The aim
of the present work is to perform a statistical joint evaluation of the
sensitivity and repeatability of TMS/hd-EEG measures. Overall,
we recorded 100 TMS/hd-EEG sessions in 10 healthy volunteers
and we systematically performed 92 pairwise comparisons at the
single-subject level between the TMS-evoked potentials obtained
either using different stimulation parameters, namely site,
intensity, and angle of stimulation (change comparison - C), or
keeping stimulation parameters constant over time (no-change
comparison - NC). For each comparison, we applied non-
parametric statistics to compute a Divergence Index (DI), i.e. the
percentage of spatial-temporal samples that differed significantly
between two sessions of TMS-evoked potentials. At this point,
considering each DI as a threshold, we performed receiver
operating characteristic (ROC) analysis and we computed the true
positive rate (the fraction of C comparisons with DI . threshold)
and the true negative rate (the fraction of NC comparisons with a
DI , threshold).
ROC analysis showed that the overall accuracy of TMS/hd-
EEG in disclosing changes in the stimulation parameters was
96.7%, with 95% sensitivity and 100% specificity (i.e. repeatabil-
ity) at a DI threshold of 1.67%. These results demonstrate that
TMS-evoked potentials are sensitive (non-stereotypical) and
repeatable (non-random) responses and that they reflect, deter-
ministically, particular properties of the stimulated set of cortical
neurons. To the extent that TMS-evoked potentials are sensitive
and repeatable for changes in the stimulation parameters, they
may also be accurate in detecting longitudinal changes in the state
of cortical circuits.
Materials and Methods
Participants
Ten right handed healthy volunteers (7 males, 3 females, mean
age 26.9 years) were enrolled into the study after a neurological
screening to exclude potential adverse effects of TMS. Subjects
with medical history of seizures, convulsions, loss of consciousness
and traumatic brain injury, carriers of intracranial metallic objects
and/or of cardiac pace-makers were excluded from the study. The
entire experimental procedure was approved by the Local Ethical
Committee of the Hospital ‘‘L. Sacco’’ and each volunteer gave
written informed consent to participation.
TMS targeting
Structural magnetic resonance images (MRI) were recorded
from all subjects at 1 mm3spatial resolution (1T Philips scanner).
Three TMS targets were identified on individual MRIs in the left
occipital lobe (Brodmann’s area - BA19), the left parietal lobe
(BA7) and the left frontal lobe (BA6). Precision and reproducibility
of stimulation were achieved by using a Navigated Brain
Stimulation (NBS) system (Nexstim Ltd., Helsinki, Finland), that
employs a 3D infrared Tracking Position Sensor Unit to map the
positions of TMS coil and subject’s head within the reference
space of individual structural MRI. In addition, the NBS system
computes on-line the distribution and intensity (V/m) of the
intracranial induced electric field using a locally best-fitting
spherical model of the subjects’ head and brain and taking into
account the exact shape, 3D position and orientation of the TMS
coil. Stimulation intensity, expressed as a percentage of the
maximal output of the stimulator, was kept between 40–75% for
all subjects, corresponding to an electric field between 110–
120 V/m on the cortical surface. In each area, the TMS hot spot
(i.e. location of the maximum electric field induced by TMS on the
cortical surface) was always kept on the convexity of the gyrus,
about 1 cm lateral to the midline. These medial stimulation sites
were selected because they are easily accessible and far from major
head or facial muscles whose unwanted activation may affect EEG
recordings. The reproducibility of the stimulation coordinates
across sessions was guaranteed by a virtual aiming device that
indicated in real-time any deviation from the desired target greater
than 3 mm. The TMS stimulator consisted of a Focal Bipulse 8-
Coil (mean/outer winding diameter ca. 50/70 mm, biphasic pulse
shape, pulse length ca. 280 ms, focal area of the stimulation hot
spot 0.68 cm2) driven by a Mobile Stimulator Unit (Eximia TMS
Stimulator, Nexstim Ltd., Helsinki, Finland). The coil was always
placed tangentially to the scalp, in order to optimize transmission
of the magnetic field to the cortical surface. TMS pulses were
delivered at an inter-stimulus interval randomly jittered between
700–900 ms (equivalent to ca. 1.1–1.4 Hz).
EEG recording
Continuous hd-EEG was recorded using a 60-channel TMS-
compatible amplifier (Nexstim Ltd., Helsinki, Finland). This
equipment ensured artefact-free EEG recordings starting from
8 ms after the TMS pulse [1]. Impedance at all electrodes was kept
below 5 kV. EEG signals were band-pass filtered between 0.1–
500 Hz and sampled at 1,450 Hz with 16 bit resolution. Vertical
electrooculogram (EOG) was recorded by two extra sensors. A
total of about 200 trials were collected for each TMS/hd-EEG
session. Contamination of TMS-evoked potentials by auditory
responses to the clicks produced by the TMS coil’s discharge was
prevented by masking noise and by placing a thin layer of foam
between coil and scalp. After each session, electrodes’ position was
digitized using a 3D Infrared Tracking Position Sensor Unit (for
more details about the EEG recording procedures see [16,21,24]).
General experimental design
The experimental protocol consisted of two main arms, aimed
at evaluating the sensitivity and the repeatability, respectively, of
TMS-evoked potentials. In order to test for sensitivity, different,
randomly ordered, TMS sessions were performed in the same day
(day1) varying only one stimulation parameter at a time (either
site, or intensity, or angle of the TMS-induced current). TMS-
evoked potentials were considered sensitive to the extent that they
changed when stimulation parameters were changed. In order to
evaluate repeatability, a subset of TMS sessions was repeated later
in the same day (day1) as well as one week afterward (day8),
without changing any stimulation parameter. TMS-evoked
potentials were considered repeatable to the extent that they did
not change over time when stimulation parameters were kept
constant. In order to quantify sensitivity, single-subject pairwise
comparisons were performed between TMS-evoked potentials
obtained with different stimulation parameters. We called these
comparisons ‘‘change comparisons’’ (C). To quantify repeatability,
we performed single-subject pairwise comparisons between TMS-
evoked potentials with identical stimulation parameters. We called
these comparisons ‘‘no change comparisons’’ (NC). As described
below, each pairwise comparison involved applying a non-
parametric test based on random permutations between the
single-trial TMS-evoked potentials of the two sessions.
Change comparison (sensitivity)
Stimulation site.
between TMS-evoked responses to perturbation of BA6, BA7, and
Pairwise comparisons were carried out
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BA19 for each subject separately. Using the NBS, stimulation
intensity (I%, expressed as a percentage of the maximum
stimulator’s output) was always adjusted in each subject and in
each area in order to compensate for local differences in scalp-to-
cortex distance and to generate a comparable electric field
between 110–120 V/m. The NBS was also used to keep the
angle of stimulation parallel to the cortex midline (0u angle). In this
case, the total number of pairwise comparisons was 22 instead of
30, because in 4 out of 10 subjects cortical responses to
perturbation of one area (BA6 in 3 subjects and BA19 in 1
subject) were corrupted by artefacts and therefore excluded from
the analysis.
Stimulation intensity.
Single-subject pairwise comparisons
were carried out between TMS-evoked responses obtained at I%
and I%+10%. Using the NBS, the stimulation target, as well as the
angle of the induced currents were kept unvaried. A total of 20
comparisons were performed, involving BA6 in 4 subjects, BA7 in
7 subjects, and BA19 in 9 subjects.
Stimulation angle.
Pairwise comparisons were carried out
between TMS-evoked responses obtained with a stimulation angle
of 0u and responses obtained after clockwise rotating the angle by
45u and 90u. Intensity and site of stimulation were kept constant.
Overall, 20 comparisons were evaluated, involving BA6 in 2
subjects, BA7 in 4 subject, and BA19 in 4 subjects (10 comparisons
0u vs. 45u and 10 comparisons 0u vs. 90u).
No change comparison (repeatability)
Same day.
For each subject, the first TMS session (intensity
I%, direction 0u, stimulation site either BA6, or BA7, or BA19)
was compared with an identical session repeated on day1, at the
end of the experiment, without changing any stimulation
parameter (10 comparisons). This procedure allowed to control
for possible plasticity-related modifications induced by repeated
TMS sessions.
One week apart.
A subset of the TMS/hd-EEG sessions
recorded on day1 (intensity I% and direction 0u) was compared
with identical sessions accurately replicated on day8, namely
stimulation of BA6 in 4 subjects, of BA7 in 7 subjects and of BA19
in 9 subjects. In these cases, we carefully controlled that not only
the stimulation parameters, but also other environmental and
subjective conditions (such as daytime, room brightness, subject’s
vigilance level) were exactly the same. To further reduce sources of
measurement variability, electrodes digitization was used to ensure
that the relative position between the EEG cap and subject’s head
did not differ across the two sessions.
Data Analysis
EEG pre-processing.
MATLABH (2006a, The MathWorks, Natick, MA). Visual
inspection of single-trial recordings was performed by a trained
experimenter after automatic rejection of trials with EOG
.70 mV and/or with absolute power of EEG channel F8 in the
fast beta range (.25 Hz) exceeding 0.9 mV2/Hz [25], most likely
contaminated by ocular and/or muscular activity. TMS-evoked
potentials were computed by averaging a minimum of 150 selected
artefact-free single trials, in order to obtain a good signal-to-noise
ratio. Subsequently, channels residually affected by large artefacts
or with poor signal-to-noise ratio were excluded from further
analysis. Finally, the average responses were band-pass filtered (2–
80 Hz), downsampled to 725 Hz, and re-referenced to the
common average reference.
Statistical analysis.
We implemented a non-parametric
permutation-based statistical procedure to perform pairwise
comparisons between TMS-evoked potentials, and to synthesize
Data analysis was carried out using
their degree of diversity in a single value (divergence index - DI),
corrected for multiple hypothesis testing.
At first, a Wilcoxon rank-sum test was applied to check that the
baselines (250 ms pre-stimulus) of the single trials, contributing to
the two TMS-evoked potentials to be compared, had the same
distribution. In case of a negative result, the most deviated trials
were removed and the test was repeated until the baseline
distributions of the two groups of trials were statistically equivalent
(P.0.05). The percentage of rejected trials was always less than
5%. At this point, we could test the null hypothesis that two sets of
TMS-evoked potentials (each one composed by 60 EEG channels
by 182 time points, corresponding to 250 ms post-stimulus
sampled at 725 Hz) are equivalent. If this is the case, ‘‘mixing’’
together, in any random combination, the single trials collected
during the two TMS/hd-EEG sessions should always result in
statistically equivalent TMS-evoked potentials. Otherwise, the null
hypothesis can be rejected. Thus, for each comparison, 1000
‘‘mixed’’ TMS-evoked potentials were obtained by randomly
mixing and averaging 1000 times the single trials collected in two
different sessions (Fig. 1A,B). The set of 1000 values at each post-
stimulus time sample represented the instantaneous empirical null
probabilistic distribution of the voltage of the TMS-evoked
potentials. In order to correct for multiple comparisons in time,
we computed a single distribution for the whole time interval as
follows: i) all instantaneous distributions were centralized around
zero, by shifting them by an amount d(t) (Fig. 1C); ii) for each
centralized distribution, we computed the maximum absolute
value (Fig. 1D); iii) the one-tail (1-a)100thpercentile of the
distribution of the maximum absolute values was used to estimate
a significance threshold G for the whole time window of interest
(Fig. 1D); iv) two boundaries were computed as (+G+d(t)) and
(–G+d(t)). The temporal profile of these boundaries is modulated
by d(t), since G is a fixed threshold. The null hypothesis of
equivalence between two TMS-evoked responses at each time
sample t was rejected with probability of false positives a corrected
for multiple comparisons when at least one of the two original
potentials at that time sample lay beyond the significance
boundaries (Fig. 1E). Finally, for each comparison the DI was
defined as the percentage of significantly different time samples in
the first 250 ms post-stimulus in all 60 EEG channels out of the
total number of spatial-temporal samples. In this way, the DI was
systematically calculated at the sensor level for all pairwise
comparisons (n=92). As a proof of concept, the same statistical
procedure was also carried out at the source level on the time
series of regional cortical currents in one subject (see Source modeling
paragraph below). Cortical meshes were automatically parcellated
into subregions (Automated Anatomical Lobules classification)
using the masks provided by WFUPickAltas tool (freely available
at: http://www.ansir.wfubmc.edu; [24,26,27]).
Sourcemodeling.
The Statistical Parametric Mapping
software package (SPM5, freely available at http://www.fil.ion.
bpmf.ac.uk/spm) was used to warp individual MRIs to the
Montreal Neurological Institute atlas and to compute cortical
meshes (7204 vertices each). EEG sensors and individual meshes
were co-registered by rigid rotations and translations of anatomical
landmarks (nasion, left and right tragus). Conductive head volume
was modeled according to the 3-spheres BERG method [28] as
implemented in the Brainstorm software package (freely available
athttp://neuroimage.usc.edu/brainstorm).
solution was computed on each single trial by applying the
empirical Bayesian approach [24,29–33].
Receiver Operating Characteristic (ROC) analysis.
statistical analysis described above, yielded 92 DIs, resulting from
62 C comparisons (stimulation site, intensity, angle) and from 30
Finally,inverse
The
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NC comparisons (same day, one week apart). ROC analysis was
applied to evaluate the overall ability of TMS-evoked potentials in
disclosing well-controlled modifications of stimulation parameters
against measurement variability/error. Briefly, each measured DI
was set as threshold to decide whether a change occurred (.
threshold), or not (, threshold). Thus, the true positive rate
(sensitivity%)and thetrue
repeatability%) was computed for all 92 DI thresholds. Then,
we plotted the ROC curve as sensitivity% vs 100-specificity%
using a Matlab script (freely available at http://www.mathworks.
com/matlabcentral/fileexchange/19950; Cardillo G., 2008: ROC
curve: compute a Receiver Operating Characteristics curve). The optimal DI
threshold was set in correspondence to the maximum of the
Younden index [34], computed as [sensitivity + specificity - 1].
The percentage of correct classifications across all pairwise
comparisons was measured to quantify accuracy of TMS/hd-
negative rate(specificity,or
EEG, while the area under the ROC curve yielded the probability
of ranking the DI of a randomly chosen C comparison higher than
the DI of a randomly chosen NC comparison.
Results
Starting from 100 TMS/hd-EEG sessions recorded in 10
healthy subjects, we performed 62 C comparisons (22 for changes
in stimulation site, 20 in stimulation intensity and 20 in stimulation
angle) and 30 NC comparisons (10 same-day and 20 one-week-
apart recordings with the same stimulation parameters).
Results of a representative subject are reported in Fig. 2. Here,
one particular TMS/hd-EEG session (stimulation of BA19 at I%
intensity and 0u angle on day1) is taken as a reference (blue) and
compared with four other sessions (red), where stimulation
parameters are varied one at a time. Specifically, the site (BA19
Figure 1. Non-parametric statistical procedure to perform single-subject pairwise comparisons between TMS-evoked potentials.
Single-trial recordings from two different conditions (blue and red lines) were randomly mixed 1000 times (A) and averaged (B). Instantaneous
distributions of averaged voltages were computed and centralized around zero by keeping record of the displacement d(t) (C). The distribution of
maximum absolute values of each centralized distribution was computed and used to define a significance threshold G as the (1-a)100thpercentile
(D). Significance boundaries (gray dotted lines) were computed as (6G + d(t)) and used to define the significantly different time samples (red stars)
between conditions at a specific channel (E).
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Figure 2. Results of pairwise comparisons between TMS-evoked potentials of a representative subject at the sensor (A,B) and at the
source level (C,D). Brain responses to stimulation of BA19 at I% intensity and 0u angle on day1 (blue traces) are compared with brain responses
recorded during four different sessions (red traces), during which stimulation parameters were varied one at a time, namely stimulation site (BA6),
intensity (I%+10%), angle (45u) and day (day8), resulting in 3 C comparisons and one NC comparison. For each comparison, superimposition of pairs of
TMS-evoked potentials in all sensors is displayed in (A), while enlarged view of P1 channel is shown in (B), together with significance boundaries (dotted
gray traces) and significantly different samples (red stars). Pairs of TMS-evoked cortical currents are shown in (C) as current density maps and in (D) as
temporal profile of current density integrated over the left frontal and left occipital lobules, together with significantly different samples (red stars).
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vs. BA6), the intensity (I% vs. I%+10%), the angle (0 vs. 45u) and
the day (day1 vs. day8) of stimulation were varied, resulting in
three C comparisons and one NC comparison. For each
comparison, Fig. 2 displays the superimposition of TMS-evoked
potentials at the sensor level (A,B) as well as the cortical current
density maps (C) and the temporal profile of current density
integrated over the left frontal and occipital lobules (D). This
representation provides a qualitative description of the overall
degree of diversity between different conditions. While TMS-
evoked scalp potentials and cortical currents tended to overlap in
the NC comparison, they were clearly characterized by divergent
spatial-temporal patterns in all the C comparisons, suggesting that
the spatial-temporal characteristics of the brain response to a
direct perturbation markedly depended on each and every
stimulation parameter, e.g. site, intensity and angle.
Fig. 3A summarizes the general results obtained from all
subjects: each coloured dot represents the DI computed for a
specific pairwise comparison. In particular, DIs of the C
comparisons for changes in the stimulation site, intensity and
angle are represented by cyan, black and green dots, respectively;
DIs of NC comparisons are depicted in yellow for same-day
sessions and in red for one-week-apart sessions.
C comparisons resulted in the largest DI values
Generally, comparing the brain responses evoked by TMS
pulses delivered over different stimulation sites revealed obvious
differences in the space distribution and time course of voltages
and currents (Fig. 2, first row). The average DI of all 22 C
comparisons between different stimulation sites was 11.4565.7%
(range 3–19.7%). Inspecting single DI values (Fig. 3A) also showed
that comparisons between distant areas (i.e. BA6 vs BA19, large
cyan dots) was associated with the highest DI values (18.461.2%),
while pairwise comparisons between nearby sites (i.e. BA6 vs. BA7
and BA7 vs. BA19, small cyan dots) resulted in lower DIs
(8.864.3%).
Varying stimulation intensity (Fig. 2, second row) resulted in
amplitude and latency changes of the main TMS-evoked
components, while the general topographical distribution of
voltages and currents tended to be preserved. The average DI
across the 20 C comparisons between different intensities of
stimulation (Fig. 3A, black dots) was 10.8866% (range 2.31–
22.2%).
When changing the angle of the TMS-induced current, the
morphology of cortical responses varied in a rather unpredictable
way, on a single-case basis (Fig. 2, third row). The average DI
value was 3.9263% (range 0.7–13.8%), with no systematic
difference between 0u vs. 45u and 0u vs. 90u pairwise comparisons.
In 4 out of 20 angle comparisons, DI values were smaller (Fig. 3A,
green dots) than the largest DI obtained comparing experimental
sessions with identical stimulation parameters (Fig. 3A, yellow and
orange dots).
NC comparisons results in lowest DI values
When TMS was applied with identical stimulation parameters
at different times, the morphology and the spatial-temporal
dynamics of TMS-evoked potentials were largely preserved
(Fig. 2, fourth row). The average DI was 0.2860.4% (range 0–
1.2%) when comparing same-day recordings and 0.4360.4%
Figure 3. Divergence Index of all pairwise comparisons between TMS-evoked potentials. Single DI values computed over the entire post-
stimulus period (250 ms) are shown in (A) with the following color-coding: DIs of the C comparisons for changes in the stimulation site, intensity and
angle are represented by cyan, black and green dots, respectively, while DIs of NC comparisons are depicted in yellow for same-day sessions and in
red for one-week-apart sessions. DI values computed over different temporal windows of interest (0–60 ms, 60–120 ms, 120–250 ms) are reported in
(B) with the same color-coding, except for NC comparisons that are summed together and plotted in orange.
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(range 0–1.67%) for pairwise comparisons between one-week-
apart sessions. The single DI values for all NC comparisons
(Fig. 3A, yellow and orange dots) were always below 1.67%
Relative differences in the DI value are preserved across
latencies
In order to understand whether the observed differences
between TMS-evoked potentials were preserved over the entire
post-stimulus period, rather than limited to a specific latency, we
computed the DI over three subsequent post-stimulus intervals,
namely 0–60 ms, 60–120 ms and 120–250 ms (Fig. 3B). For each
type of pairwise comparison, the average DI computed at early
latencies (0–60 ms) was significantly higher (P,0.05) as compared
to the one computed for late latencies (120–250 ms), although the
relative differences among types of comparison were preserved
across all time intervals (P,0.01).
ROC Analysis
Results of ROC analysis showed that the optimal DI threshold
according to the Younden index was 1.67% and yielded a 95.1%
sensitivity and 100% specificity (repeatability), corresponding to an
overall accuracy of 96.7%. The efficacy of DI in reliably
quantifying the pairwise differences between TMS-evoked poten-
tials was 99.1%, as measured by the area under the curve (Fig. 4).
Discussion
How accurately TMS-evoked potentials can detect actual
changes in cortical responsiveness? If the EEG response to TMS
tends to be stereotypical, actual changes in cortical responsiveness
may go undetected (low sensitivity). On the other hand, if TMS/
hd-EEG measurements tend to be too variable and noisy, changes
in cortical responsiveness may be overestimated (low repeatability).
Defining sensitivity and repeatability concerns the interpretation
of the responses to any kind of stimulation: however, this task is
particularly relevant when interpreting the EEG responses
triggered by TMS, a technique that activates the brain in a way
that is non-ecological and that is difficult to control.
Unlike sensory stimulation, TMS activates simultaneously a
rather large cortical volume containing both inhibitory and
excitatory fibers, possibly belonging to different functional
subsystems. Thus, it is possible that different TMS perturbations
may result in EEG responses that engage many different circuits
and that are largely overlapping. In addition, TMS not only
perturbs cortical neurons directly but may also activate the brain
indirectly, due to the stimulation of scalp nerves and to the click
sound associated with the coil’s discharge over the subject’s head.
For this reason, it is also conceivable that differences in the brain’s
reaction may be partially obliterated by an invariant event-related
potential triggered by unwanted somatosensory or/and acoustic
stimulations. Altogether, these factors may significantly hamper
the sensitivity of TMS-evoked potentials. On the other hand, due
to the complexity of the technique, TMS-evoked potentials may
also lack repeatability, by showing accidental changes related to
stimulation and/or recording errors. Indeed, stimulating directly
the cortical surface involves the control of several factors, since a
large number of cortical locations can be arbitrarily selected and
perturbed, each one with several stimulation parameters (e.g.
intensity, pulse waveform, and orientation of the magnetic field).
Thus, a lack of precise control of these parameters across
subsequent TMS/hd-EEG sessions, may result in large measure-
ment errors and in an apparent modulation of cortical
responsiveness. Similarly, other factors, such as EEG sensors
positioning, coil temperature, calibration of amplifiers, etc., may, if
not adequately controlled for, affect the repeatability of TMS-
evoked potentials.
In this work, we used controlled changes in the stimulation
parameters (site, intensity, and angle of the induced electric field)
and repeated longitudinal measurements (same day and one week
apart) in order to jointly evaluate the sensitivity and repeatability
of TMS/hd-EEG. In order to synthetically quantify the extent to
which pairs of TMS-evoked potentials, recorded in various
conditions, differed in their overall spatial-temporal pattern, we
employed non-parametric statistics to calculate a Divergence
Index (DI). ROC analysis showed that an optimal DI threshold of
1.67%, yielded a 96.7% accuracy (95.1% sensitivity and 100%
specificity) of TMS-evoked potentials in detecting whether a
change in stimulation parameters occurred, or not. The finding
that TMS-evoked potentials, rather than being stereotypical or
noisy responses, reflect to a large extent deterministic properties of
the stimulated cortical circuits has different implications, as
discussed below.
Sensitivity of TMS-evoked potentials and the
differentiation of cortical circuits
Integration and differentiation (or functional specialization)
within regions are fundamental organizing principles of thalamo-
cortical networks [35,36]. While integration refers to the ability of
the elements of a system to interact with each other, differentiation
may be defined as the system’s ability to react in different ways to
different perturbations. TMS/hd-EEG, by directly exploring
causal interactions (effective connectivity) among cortical areas,
may provide a dependable evaluation of thalamocortical integra-
tion [16,37,38]. On the other hand, to the extent that TMS-
evoked potentials are sensitive to changes in perturbation
Figure 4. ROC analysis applied to the DI. ROC curve is depicted
as a solid black line, interspersed by blank dots representing
the values of sensitivity and specificity (repeatability) associ-
ated to single DI values. The optimal DI value of 1.67% computed
according to the Younden index [34] is shown as a black dot. Dashed
line represents the ROC curve of a random classifier. Gray-shaded
region indicates the area under the curve.
doi:10.1371/journal.pone.0010281.g004
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parameters, they may also gauge the degree of differentiation
within thalamocortical networks. The present work shows that, at
least on a coarse grain, different cortical perturbations result in a
degree of response differentiation that is consistently higher
compared to random test-retest variability (Fig. 3 and 4).
In the present experiments, changing stimulation parameters
almost invariably resulted in higher DIs compared to the no-
change conditions (Fig. 3A). Importantly, this finding was not
limited to the early latencies, and indeed DI values for the C
conditions were significantly larger until 250 ms post-stimulus
(Fig. 3B). This evidence demonstrates that the EEG response to
TMS is primarily due to direct cortical stimulation and to the
ensuing reverberation of activity in a specific network of connected
elements. Moreover, if TMS-evoked potentials were heavily
contaminated by somatosensory, or auditory event-related poten-
tials, the EEG responses generated when stimulating the head in
two sites located a few centimetres away, rotating the stimulation
angle, or increasing slightly the intensity of stimulation would have
a very similar morphology. Thus, our finding suggests that the
collateral stimulation of peripheral nerves by TMS plays a little
role in the generation of TMS-evoked potentials.
Changing the site of stimulation resulted in very different
responses and in high DI values that were even higher when the
responses triggered in areas located far away (area 19 vs. area 6)
where compared (Fig. 3A, large cyan dots). This variability in the
cortical response reflects specific properties of the stimulated
circuits and may be ascribed to local differences in cortical
excitability [17], to differences in the frequency tuning of
corticothalamic modules [18,39] and to differences in the pattern
of cortico-cortical connectivity [4,12,24].
Rotating the coil in the same area produced smaller
modifications of TMS-evoked potentials as compared to changes
of stimulation site. Indeed, 4 out of the 20 pairwise comparisons
between recording sessions with different angles resulted in a DI
,1.67%, i.e. the optimal threshold that maximized sensitivity and
specificity (repeatability) of TMS-evoked potentials in the ROC
analysis. Previous work [40] showed that by stepwise rotating the
coil relative to left motor cortex, the largest muscle responses were
obtained when the coil was 50u to the parasagittal plane, with the
induced current in axis with the main direction of the axons in the
motor ‘‘hand knob’’. In addition, TMS coil orientation has been
shown to affect the motor threshold [41], the degree of selectivity
when stimulating different peripheral muscles [42] and even
cognitive functions [43]. However, while the main orientation of
motor cortex axons is quite predictable across subjects, little is
known, a priori, about the main orientation of fibers in other brain
areas, such as the ones stimulated in this study (BA6, BA7 and
BA19). Thus, it is likely that the 4 pairwise comparisons between
different stimulation angles resulting in low DI values, may be due
to a virtually negligible variation of the induced current direction
as compared to cortical axons. In fact, only by integrating TMS/
hd-EEG with high resolution structural imaging techniques, such
as diffusion tensor imaging (DTI), one may be able to control, in
any cortical area, the coil’s orientation with respect to the main
direction of axons.
Certainly, in order to evaluate the fine grain of cortical
differentiation with TMS/hd-EEG, one should design ad hoc
experiments where stimulation parameters are varied in a
systematic way (i.e. by moving/rotating the coil several times by
a constant step). Meanwhile, it would be interesting to test whether
the DI resulting from two different perturbations decreases in
physiological (sleep, anesthesia) and pathological (coma, epilepsy)
conditions, where the capacity for integration and differentiation
in thalamocortical circuits is thought to be altered [35,37,44,45].
Repeatability of TMS-evoked potentials and longitudinal
changes in cortical circuits
TMS-evoked potentials recorded on the same day, with the
same stimulation parameters (Fig. 3, yellow dots) were very stable
(mean DI 0.28%), despite the fact that, between the two
measurements, several (from 5 to 8) other sessions of repetitive
TMS were carried out. It is well known that repetitive TMS pulses
delivered at low (,1 Hz) and high (.5 Hz) stimulation frequency
can respectively induce a reduction [46] and an increase [22,47] of
brain excitability [48]. Throughout the experiment, we used a
stimulation frequency jittering randomly between 1.1–1.4 Hz.
Our results indicate that this particular stimulation rate does not
induce significant brain reorganization/plasticity processes and
may be used to probe repeatedly the excitability of cortical circuits
without significant interference.
The mean DI between TMS-evoked responses recorded during
identical experimental sessions performed one week apart (0.43%)
was slightly higher than mean DI between same-day sessions
(0.28%). This minor discrepancy might be ascribed to different
factors: i) small co-registration errors, that clearly reduce the
reproducibility of navigation; ii) small errors in re-positioning the
EEG cap; iii) unavoidable and unpredictable biological variability
due to changes in brain excitability that likely takes place on the
time-scale of several days. Nevertheless, this comparison clearly
demonstrated that TMS-evoked potentials, besides being sensitive
to changes, are also very stable over time. Thus, as suggested by
the ROC curve in Fig. 4, repeating after one week a given
perturbation and observing a DI .1.67% would strongly indicate
that, in the mean time, some change occurred in the brain circuits.
In principle, identifying a cut-off level above which one can decide
whether a change in brain responsiveness occurred, or not, is
crucial if one wants to use TMS/hd-EEG to track over time
pathological alterations, plastic changes and therapy-induced
modifications in cortical circuits. The DI can be automatically
computed at the sensor as well as at the source level (Fig. 2) on a
large matrix of spatial-temporal data, without having to select
particular peaks, or components. By design, the DI tends to better
capture changes in amplitude than changes in shape, since two
responses with identical shape but with different amplitude will be
maximally different. This feature may represent a limitation or an
advantage, depending on the case (see below). In the present
experiments, increasing TMS stimulation intensity by 10%
produced changes that were reliably detected by the DI. This
finding suggests that calculating the DI on repeated TMS/hd-
EEG sessions may be effective in revealing rather fine modifica-
tions in cortical excitability (indexed by the response’s amplitude)
due to pathological alterations, i.e. stroke, epilepsy and depression
[49–52] or therapeutic interventions, i.e. electroconvulsive thera-
py, rTMS, neurorehabilitation or drug administration [51,52].
Limitations of the study
The DI values computed in the C conditions allowed detecting
changes in the stimulation parameters with excellent accuracy:
however their absolute values spread over a wide range (Fig. 3A).
This dispersion may be due to a differential susceptibility of
different cortical sites to changes in stimulation parameters. For
example, if delivering TMS pulses on a specific cortical region at
I% intensity activates locally most of the axons, increasing the
stimulation intensity by 10% in this particular area would not
dramatically change the electrical response and would result in a
low DI. Similarly, as already discussed above, stimulating at
different angles would not make much difference if the fibers in the
target area are oriented in all directions (anisotropic arrangement).
The contribution of these factors to the morphology of TMS-
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evoked potentials cannot be easily predicted, even if some insights
may be provided by the integration of high-resolution structural
neuroimaging (such as DTI). At any rate, a systematic under-
standing may only be achieved by varying parametrically the
perturbation parameters, e.g. stimulating at several locations
uniformly distributed on the scalp, delivering TMS pulses at
progressively higher intensities from threshold to saturation,
rotating gradually the angle of the induced current to span the
whole circle. Such an extensive mapping of cortical electrophys-
iology was clearly beyond the scope of the present work, which was
primarily aimed at evaluating, technically, the general level of
accuracy of TMS/hd-EEG. Therefore, here, we tested only a
limited set of all possible stimulation parameters, a task that still
required considerable experimental and methodological efforts,
e.g. recording and analyzing 100 TMS/hd-EEG sessions and
performing 92 pairwise comparisons between TMS-evoked
potentials.
Various classifiers, describing differences between evoked
potentials, could have been used to build the ROC curve. In
this work, we implemented the DI because it allows to quantify the
differences between TMS-evoked potentials starting directly from
the entire matrix of spatial-temporal data, without requiring a
priori information, and to synthesize them into a single number.
However, the ability of the DI in detecting modifications of TMS-
evoked potentials due to physiological and/or pathological
alterations in cortical circuits should be carefully evaluated.
Indeed, the DI is rather conservative (it corrects for multiple
comparisons in time) and explores the entire post-stimulus period,
at all sensors. Thus, since several channels and latency ranges are
often not involved by a TMS-evoked potential, one should
generally expect low absolute DI values. In fact, we found that
maximum DI values were around 20% (Fig. 3). When testing more
specific hypotheses, other methods of classification (e.g., restricting
the DI in space and time, template-matching, Mahalanobis
distance) could be chosen. Clearly, the aim of the present study
was not to develop an optimal classifier, but to use the most
general one in order evaluate the accuracy of TMS/hd-EEG with
a data-driven approach.
Acknowledgments
We thank Giulio Tononi, Matteo Fecchio, and Paola Canali for their help
and comments.
Author Contributions
Conceived and designed the experiments: SC VB MR MM MM.
Performed the experiments: SC VB AGC MR SD MM. Analyzed the
data: SC LJRL AGC MR. Wrote the paper: SC LJRL AP MM.
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