null  null
State-Dependent Variability of Neuronal Responses
to Transcranial Magnetic Stimulation
of the Visual Cortex
Brian N. Pasley,1,2,3 Elena A. Allen,1,2,3 and Ralph D. Freeman1,2,*
Wills Neuroscience Institute
of Optometry
University of California, Berkeley, Berkeley, CA 94720, USA
3These authors contributed equally to this work
*Correspondence: [email protected]
DOI 10.1016/j.neuron.2009.03.012
Electrical brain stimulation is a promising tool for both
experimental and clinical applications. However, the
effects of stimulation on neuronal activity are highly
variable and poorly understood. To investigate the
basis of this variability, we performed extracellular
recordings in the visual cortex following application
of transcranial magnetic stimulation (TMS). Our
measurements of spiking and local field potential
activity exhibit two types of response patterns which
are characterized by the presence or absence of
spontaneous discharge following stimulation. This
variability can be partially explained by state-dependent effects, in which higher pre-TMS activity predicts
larger post-TMS responses. These results reveal the
possibility that variability in the neural response to
TMS can be exploited to optimize the effects of stimulation. It is conceivable that this feature could be
utilized in real time during the treatment of clinical
There is an extensive history of attempts to alter brain function
using external electrical stimulation (Fritsch and Hitzig, 1870;
Kringelbach et al., 2007). A primary focus of this work has
been to establish neural modifications that relieve specific clinical disorders. Conditions such as Parkinson’s disease, epilepsy,
or depression, which often appear resistant to pharmacological
intervention, have shown major improvement after treatment
with invasive electrical stimulation techniques (Kringelbach
et al., 2007). The success of these invasive interventions has
generated interest in the use of transcranial magnetic stimulation
(TMS), a comparatively noninvasive technique (Barker et al.,
1985). However, the effectiveness of TMS in therapeutic applications is not clear, and this emphasizes the need for a basic
understanding of TMS mechanisms (Burt et al., 2002; Couturier,
2005; Fregni et al., 2005; George et al., 1996; Gross et al., 2007;
Martin et al., 2003).
The major challenge facing the therapeutic use of TMS, or
any brain stimulation technique, is the difficulty in predicting
how underlying neural circuits will be altered by the application
of electrical fields. This problem is inherently complex, as the
cumulative effect of stimulation depends on numerous factors.
These may include: the structure of the targeted neural circuit,
the profile of neural activity during application, the responses
of different cell classes (e.g., excitatory versus inhibitory; projecting versus local neurons), the resulting biochemical or structural
modifications of synaptic connections, and the possible alterations of neuromodulatory inputs. Combined with these biological factors are also a number of flexible stimulation parameters,
such as duration, frequency, intensity, and electric field orientation. Each of these variables has been found previously to alter
the outcome of TMS application (Berardelli et al., 1998; Chen
et al., 1997; Pascual-Leone et al., 1998). Given the dependence
of the effects of TMS on physiological state, brain region, and
stimulation paradigms, it is difficult to identify general principles
by which brain stimulation affects neural function.
It is not surprising, therefore, that the literature in this field
contains some contradictory and potentially confusing findings.
For example, identical stimulation parameters can result in
neuronal activation, suppression, or both, depending on the brain
region (Paus, 2005). In addition, substantial intersubject variation
has been noted both within healthy populations (Cahn et al.,
2003) and with respect to patient populations (Brighina et al.,
2002). Furthermore, even within the same individual, the effects
of TMS appear to depend on the initial cortical activation state
(for a review, see Silvanto and Muggleton, 2008). In these latter
experiments, TMS produces different perceptual or behavioral
outcomes that may depend on the excitability levels of specific
neuronal populations (Silvanto and Muggleton, 2008). The
apparent subtlety and complexity of the physiological effects of
TMS necessitate empirical investigation in order to understand
the stimulation-induced neural activity patterns.
The shortage of available neural data describing the effects of
TMS (e.g., see Allen et al., 2007; de Labra et al., 2007; Moliadze
et al., 2003, 2005), coupled with a potentially broad use of TMS,
motivates the investigation we describe here. We have conducted neurophysiological recordings of spiking activity and
local field potentials (LFPs) in the visual cortex of anesthetized
cats before, during, and after TMS application. A well-controlled
Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc. 291
State-Dependent Neural Effects of TMS
study of TMS in an appropriate animal model is a necessary first
step toward a basic understanding. In a previous report, we
described primary neural responses to short TMS pulse trains
and their relation to hemodynamic signals (Allen et al., 2007). In
the current study, we undertake an extensive analysis to provide
insight into the effects of TMS on single-neuron and population
activity. We describe the variability of responses to TMS and
find evidence for two qualitatively different response patterns
which are characterized by the presence or lack of spontaneous
discharge following stimulation. A portion of this variability can
be explained by state-dependent effects, in which the postTMS response depends on pre-TMS activity levels.
We recorded single-unit and LFP responses at 47 sites in the
primary visual cortex of the anesthetized cat (n = 5 animals). Single
units were classified as simple (n = 17) or complex (n = 30), using
the ratio of the first harmonic to the average firing rate (Skottun
et al., 1991). Recordings were made with either posterior or
superior positioning of a figure-eight TMS coil (Figure 1A). We
find no significant differences in the neural responses to TMS
between electrode-coil configurations of simple and complex
cell classes (rank-sum test, p > 0.2 for all comparisons), and
therefore the data are pooled for all analyses.
Experimental Paradigm
Each trial in our experimental paradigm (Figure 1B) consisted of
a baseline period (40 s), application of a short TMS pulse train,
and a post-TMS recovery period lasting from 5 to 15 min. TMS
stimulation parameters were varied in frequency (1–8 Hz) and
duration (1–4 s) on separate trials, with constant intensity at
100% stimulator output. Throughout each trial, a visual stimulus
optimized to drive the cell was presented repeatedly for 2 s at 8 s
As reported previously (Allen et al., 2007), we observe two
primary effects of TMS. These include a transient elevation of
spontaneous activity immediately following TMS, and a prolonged reduction in visually evoked activity that lasts for several
minutes (Figure 1C). These different response components
are seen clearly when the activity levels during and between
presentations of visual stimuli are separated into evoked and
spontaneous firing rates, respectively (Figure 1D). Additional
experiments without interleaved visual stimuli showed comparable effects of TMS on spontaneous activity (see Figure S1 available online).
Response Variability
We analyzed the trial-by-trial variability of two TMS response
components. The ‘‘spontaneous component’’ reflects the
response to TMS itself. The ‘‘evoked component’’ reflects the
effect of TMS on stimulus processing. Although the effects of
TMS on these components are generally robust, we have
observed considerable variability across both cells and trials.
Figure 2 shows peri-stimulus time histograms (PSTHs) for four
representative cells (A–D), each tested in two separate trials.
These data represent the full range of response patterns we
have observed and suggest an interesting distinction between
292 Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc.
Figure 1. TMS Coil Position and Experimental Paradigm
(A) Illustration of the two coil-electrode configurations. At 28 sites in 3 cats, the
coil was positioned posterior to the visual cortex and angled toward the horizontal plane (left). Penetrations were made with a carbon fiber electrode at an
angle of P45, M0. At 19 recording sites in 2 cats, the coil was positioned
obliquely near the transverse plane, superior to the visual cortex (right). Penetrations were made with a dual tungsten array (interelectrode spacing of
400 mm) at an angle of A45, M0. For both configurations, the midpoint of the
coil was centered on the primary visual cortex craniotomy and was located
between 1 and 2 cm from the skull. No significant differences between the
neural responses to TMS were found for the different electrode-coil configurations (rank-sum test, p > 0.2), and thus these data were pooled in all analyses.
(B) Timeline of a single trial. A visual stimulus (high-contrast drifting grating)
was presented repeatedly for 2 s with an interstimulus interval of 8 s. After
a baseline period (40 s), a short TMS pulse train (1–4 s, 2–8 Hz, 100% stimulator intensity) was applied during an interstimulus interval. Single-unit and
LFP data were collected during response recovery (typically 5–15 min).
(C) Peri-stimulus time histogram (PSTH) of spiking activity during a sample
trial. Downward arrow at time zero denotes the application of a 4 Hz, 2 s
TMS pulse train. In this and all subsequent PSTHs the bin size is 0.5 s.
(D) Firing rate for the same trial as shown in (C), with activity separated into
spontaneous and evoked components. The evoked response (dotted line)
represents average activity during stimulus presentations, while the spontaneous component (solid line) indicates activity that occurred between stimuli.
TMS response components: variability across trials appears
greater for spontaneous compared to visually evoked responses.
To quantify the variability of response components, we examined the relative standard deviation (RSD) of changes in spontaneous and evoked spiking activity in the first minute following
TMS. This variability measure is similar to the Fano factor
State-Dependent Neural Effects of TMS
Figure 3. Trend in Spontaneous Response to TMS over Time
Figure 2. Examples of Variability in TMS Responses
PSTHs of two sample trials with identical TMS parameters for four different
cells. Downward solid arrows denote application of the TMS pulse train.
Open arrows signify substantial spontaneous discharge following TMS. The
stimulation parameters used in each example are as follows: (A) 4 Hz, 2 s;
(B) 8 Hz, 4 s; (C) 4 Hz, 4 s; and (D) 4 Hz, 2 s. Evoked response components
within single cells are more similar than those between cells. For example,
some neurons reliably show moderate (D) or strong (B) reduction of evoked
spiking following a TMS pulse train, whereas others consistently exhibit little
alteration in stimulus-evoked activity (C). In contrast, spontaneous responses
are extremely variable across identical trials within the same cell. In many
instances (B–D), neurons display substantial spontaneous discharge on one
trial but a complete absence of spontaneous firing on another.
(Stevens and Zador, 1998), and accounts for differences in
response amplitude by normalization of the standard deviation
by the mean response (see Experimental Procedures). The
RSD was calculated over trials with identical stimulation parameters at a given site (n = 23 sets of trials). Trial-to-trial variability in
the spontaneous response (median RSD = 1.71) is significantly
greater than that of the evoked (median RSD = 0.62, Wilcoxon
signed-rank test paired by trial, p < 0.0005). We also compared
the median RSD for trials within cells to the median RSD for
equivalent trials across cells (see Experimental Procedures).
For the evoked response, trial variability is significantly greater
(A) PSTHs of seven consecutive trials from a single cell. A 4 Hz, 2 s TMS pulse
train (downward arrow) was applied in each trial. PSTHs are truncated at 2 min
to highlight spontaneous activity in the first 60 s following TMS (shaded area).
Colors in (A) and (B) represent trial number.
(B) Scatterplot of trial number versus the change in spontaneous firing rate
(DRs) for the set of trials shown in (A). DRs is calculated as the difference
between the average spike rate in the first minute following TMS and the
average value during the baseline period. The dashed line indicates the
least-squares fit to the data.
(C) Scatterplot of normalized trial number versus normalized DRs for 23 sets of
data (n = 112 total trials). For each set of data, the values for DRs and trial
number were transformed into their respective ranks and then normalized by
subtracting the mean rank. Symbols of different sizes are used to indicate
the number of the trials at the same rank coordinates. Trial number and the
spontaneous response exhibit a weak negative correlation (r = 0.26, p <
0.01, t test). No relationship is found between trial number and the evoked
response (r = 0.07, p = 0.46, t test).
across cells than within the same cell (permutation test, p <
0.01). The same is not true for the spontaneous response
component (permutation test, p = 0.51). These results indicate
not only greater trial-to-trial variability in spontaneous activity
but also a lack of evidence for a characteristic spontaneous
response to TMS that could distinguish one cell from another.
Differences between spontaneous and evoked components
are further evident when we examine trends in TMS responses
over time. Throughout experiments, we observed that cells
appeared more likely to exhibit spontaneous discharge on earlier
trials. An example of this trend is shown in Figure 3A, which
displays the PSTHs of seven consecutive trials from a single
unit. Pronounced spontaneous spiking is evident in trials 1–4,
but is considerably reduced or absent in trials 5–7 (Figures 3A
and 3B). Analyzing all trials (grouped by cell and stimulation
parameters), we find a weak, though significant, negative correlation between trial order and the magnitude of post-TMS
Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc. 293
State-Dependent Neural Effects of TMS
spontaneous spiking (Figure 3C; r = 0.26, p < 0.01, t test). No
similar relationship is found for evoked responses (r = 0.07, p =
0.46). A significant difference between spontaneous and evoked
response trends (p < 0.01, one-tailed z test after Fisher’s transformation) argues against a simple decrease in TMS efficacy over
time. Instead, these results suggest the presence of long-term or
cumulative effects of TMS, which appear unique to spontaneous
responses. The source of this long-term effect remains to be
determined, but there is a suggestion of a sensitivity of the spontaneous response to baseline network properties (see below).
Bursting versus Nonbursting Response Patterns
The observation of seemingly all-or-none spontaneous
responses motivated the division of trials into two qualitatively
different groups, which we characterized as bursting (B) or nonbursting (NB). Trials in which the spontaneous firing rate in the
first minute exceeded the baseline rate by two or more standard
deviations were classified as B (n = 60/161). Trials showing
a decrease or no change were classified as NB (n = 56/161).
The remaining 45 trials exhibited an intermediate response (i.e.,
an increase of less than two standard deviations) and were not
included in either group. Both B and NB trial types are observed
in all animals and at virtually every recording site (100% when
considering sites with at least four trials). There are no significant
differences with regard to the proportion of trials at specific stimulation frequencies or durations (c2 test, p = 0.83 and p = 0.77,
respectively). Additionally, simple and complex cell classes
exhibit similar proportions of B and NB trials (c2 test, p = 0.71).
Thus, the division of trials reflects the presence of distinct
response patterns across trials, rather than across stimulation
parameters or cells.
To characterize the different responses of B and NB trials, we
first examined the distributions of interspike intervals (ISIs) in
each group. Figure 4 displays the logarithmic ISI histograms of
spontaneous spikes for B (left) and NB (right) trials. The histograms
of both response types are bimodal, with distinct peaks at short
and long ISIs, a pattern frequently observed for cortical neurons
(e.g., Reich et al., 2000). Prior to TMS (Figure 4A, top), the ISI peaks
of B and NB trials are similarly located at roughly 3 and 200 ms
(determined by fitting a mixture of Gaussians). Following TMS
(Figure 4A, middle), the short ISI peak is unchanged for both trial
types. ISIs of this length may reflect the small refractory period
between action potentials (Izhikevich, 2006), suggesting that
TMS does not alter this intrinsic cellular property. In contrast,
TMS produces a substantial leftward shift in the long ISI peak of
B trials, whereas the NB ISI distribution remains relatively unaltered. This shift is most prominent in the first 30 s post-TMS and
there is a gradual recovery to baseline over 1–2 min (Figure 4B).
The spontaneous discharge induced by TMS, therefore, appears
to occur primarily at intervals of 20–40 ms, or 25–50 Hz. This
frequency range corresponds to gamma band rhythms and is
believed to involve activation of local sensory microcircuits, rather
than a single cell (Liu and Newsome, 2006; Siegel and Konig,
2003). Interestingly, the disruption of spike intervals appears
limited to spontaneous activity, as the ISI distributions of evoked
spiking were relatively unaffected (see Figure S2).
Differential responses of B and NB groups are also evident in
the average time courses (Figure 5). By definition, B trials exhibit
294 Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc.
Figure 4. Distributions of Interspike Intervals before and after TMS
(A) Log interspike interval (ISI) histograms of B trials (left) and NB trials (right)
were constructed from spontaneous spikes (spikes occurring between
presentation of visual stimuli) in 30 s windows. Each histogram spans from
0.4 ms to 8 s in 90 logarithmically spaced bins. Histograms are displayed for
the 30 s prior to TMS (top), the 30 s immediately following TMS (middle), and
a 30 s window occurring roughly 5 min after TMS. For all time periods, the
histograms exhibit two separate ISI peaks, the locations of which are estimated by fitting a mixture of Gaussians. Superimposed over the histograms
are the best-fit Gaussians for short (dark gray) and long (light gray) ISI peaks.
(B) Locations of ISI peaks at short (squares) and long (circles) intervals for all time
periods. Open symbols designate data for B trials, while filled symbols represent
NB trials. Error bars indicate 95% confidence intervals, as estimated with a bootstrap resampling procedure (n = 1000 resamples) (Efron and Tibshirani, 1994).
a large increase in spontaneous spiking, whereas NB trials show
a small though significant and long-lasting reduction (Figures 5A
and 5B). A similar response pattern for LFP power is evident in
higher-frequency bands (30–150 Hz), where TMS induces an
increase in B trials and a prolonged decrease in NB trials (Figures
5C and 5D). The similarity of LFP and spiking response patterns
may appear trivial given the typically close association of these
signals (Heeger and Ress, 2002). However, it is important to
note that LFPs were classified based on single-unit spiking
recorded at the same site. Because LFPs presumably reflect
the aggregate activity of cells near the electrode tip (Logothetis
et al., 2007; Mitzdorf, 1985), the differences in high-frequency
LFP power suggest that neuronal responses to TMS can be relatively homogeneous within a local area (see also Spatial Correlation and Coherence section).
State-Dependent Neural Effects of TMS
Figure 5. Response Time Courses for Bursting and
Nonbursting Response Patterns
(A) Average time courses of the change in spontaneous
spiking activity from baseline (DRs) for B (open symbols) and
NB trials (filled symbols). Error bars signify ± 1 SEM.
(B) Average changes in DRs for time intervals I, II, and III, as
denoted in (A). Intervals I, II, and III correspond roughly to
the first, third, and fifth minute following TMS, respectively.
Asterisks indicate a significant difference from baseline values
(p < 0.05, sign-rank test, corrected).
(C) Spectrograms showing the change in spontaneous LFP
power (DLs) for B (top) and NB (bottom) trials. At each time
point, DLs is calculated as a log ratio relative to the baseline
spontaneous LFP power. Trials were classified as B or NB
based on the activity of the single unit recorded at the same
site. In these and subsequent spectrograms, data are color
mapped symmetrically around zero such that positive values
appear as warm colors, negative values appear as cool colors,
and zero maps to green.
(D) Average changes in DLs for time intervals I, II, and III as
a function of different frequency bands. LFP bands, notated in
(C), are defined as follows: d (delta; 1–4 Hz), q (theta; 4–8 Hz),
a (alpha; 8–12 Hz), b (beta; 12–20 Hz), g (gamma; 20–80 Hz),
hg (high gamma; 80–150 Hz).
(E–H) Average time courses of changes in evoked spiking
(E and F) and evoked LFP power (G and H), displayed in the
same format as (A)–(D). Note that in (E), spontaneous activity
directly preceding the presentation of a visual stimulus has
been subtracted from the evoked response (see Experimental
Procedures). In (H), a plus sign indicates a significant difference between B and NB responses (high gamma band, p <
0.05, rank-sum test, corrected). This difference likely indicates ‘‘contamination’’ from spontaneous activity. Because
spontaneous LFP activity is present throughout the evoked
response, elevations in this activity result in a smaller evoked
decrease for B trials.
In the lower-frequency LFP bands, B and NB responses are
quite similar. Both groups show strong decreases in power that
persist for longer than 5 min after TMS application (Figure 5D,
bottom rows). The distinction between responses in the low and
high frequencies may be related to the different functional roles
attributed to specific brain rhythms (Belitski et al., 2008; Logothetis, 2008). For example, theta band activity is hypothesized to coor-
dinate activity across distant cortical areas (Canolty
et al., 2006), whereas gamma activity is thought
to represent the synchronous processing of local
neurons (Engel et al., 2001; Liu and Newsome, 2006).
We next examined differences in evoked
responses between B and NB groups. One might
expect the presence or absence of strong spontaneous discharge to affect TMS-induced changes
in stimulus-evoked activity. For example, strong
discharge could fatigue the cells, resulting in
a more pronounced reduction in evoked responses.
Conversely, spontaneous discharge could signify
strong activation of a local neural circuit which
might facilitate evoked activity and produce a
more moderate decrease, or even increase, in
the stimulus-evoked response. The average time
courses of evoked spiking, however, support
neither of these scenarios. As shown in Figure 5E, the singleunit responses of B and NB trials are essentially identical. The
effect of TMS on evoked LFPs is largely similar to that for spikes,
in that both B and NB groups show decreases in power across
nearly all frequencies (Figures 5G and 5H).
The similar time courses of evoked activity for B and NB trials
(Figures 5E–5H) contrast sharply with the dissimilar response
Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc. 295
State-Dependent Neural Effects of TMS
pattern for spontaneous activity (Figures 5A–5D). It therefore
appears that spontaneous and evoked response components
are not inherently interrelated. The lack of correlation between
changes in spontaneous and evoked spiking also supports this
notion (r = 0.042, p > 0.5, t test, n = 161 trials).
Figure 6. Influence of Baseline Variables on Responses to TMS
(A) Distribution of stimulus-evoked responses (Re) during the baseline period
for B (open, n = 60) and NB (filled, n = 56) trials. The average Re of B trials
(mean ± SD: 35 ± 19 spikes/s, open arrow) is slightly greater than that of the
NB trials (28 ± 17 spikes/s, filled arrow), leading to a significant difference
between the distributions (p < 0.05, rank-sum test).
(B) Scatterplot of baseline evoked activity (Re) and post-TMS spontaneous
activity (Rs) for all trials (n = 161). Pre-TMS evoked activity and post-TMS spontaneous activity are significantly correlated (r = 0.30, p < 0.0001, t test). In this
and subsequent panels, ‘‘post-TMS’’ variables are defined as the average
value over the first minute following TMS (i.e., interval I). In addition, displayed
correlations cannot be explained by differences in pre-TMS spontaneous
activity, TMS stimulation parameters, or trial number, as factors potentially
contributing explanatory power have been linearly regressed from both variables using partial correlation (see Experimental Procedures).
(C) Scatterplot of pre-TMS evoked LFP high gamma power relative to spontaneous power (Le/s, hg; see Experimental Procedures) and post-TMS spontaneous spiking (Rs) for trials with single-unit and LFP data (n = 138).
(D) Pearson correlation coefficients between baseline Le/s and post-TMS
spontaneous spiking for all LFP frequency bands. The asterisk indicates
a significant correlation (p < 0.05, t test, corrected). The arrow denotes the
coefficient for the data displayed in (C).
296 Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc.
State-Dependent Effects
Thus far, we have characterized the substantial variability of
TMS-induced neural responses. We now investigate possible
factors that may explain this variability. An intriguing possibility
is that the effect of TMS in some way depends on the initial physiological state of the cortex.
Numerous studies have noted robust differences when
applying TMS during distinct brain states, for example during
different levels of visual stimulation (Silvanto et al., 2007) or
spatial attention (Bestmann et al., 2007). We have examined
whether natural fluctuations in cortical activity could yield similar
results by analyzing post-TMS responses as a function of preTMS activity levels. In these analyses, we use a partial correlation
approach (see Experimental Procedures) which controls for the
possible influence of additional factors. These factors include
the mean amplitude of pre-TMS spontaneous activity, TMS stimulation parameters, and trial number. Therefore, reported correlations are those that remain after these factors have been
linearly regressed from both pre- and post-TMS variables.
One possible metric of cortical activity state is the responsiveness of cells to visual stimulation. We examined the distributions
of pre-TMS evoked spiking responses for B and NB groups
(Figure 6A). Although the distributions are broad and overlap
considerably, trials classified as B are slightly more responsive
to visual stimuli compared to those classified as NB. This
difference is small, but significant (B: 35 ± 19 spikes/s, NB: 28 ±
17 spikes/s, mean ± SD; p < 0.05, Wilcoxon rank-sum test).
A regression analysis including all trials (n = 161) indicates
the same relationship: pre-TMS evoked spiking is positively
correlated with TMS-induced spontaneous spiking (Figure 6B;
r = 0.30, p < 0.0001).
To examine visual responsiveness at the population level, we
performed a similar regression analysis using pre-TMS stimulus-evoked LFPs. As shown in Figure 6C, the magnitude of
(E) Power of baseline spontaneous LFPs as a function of trial type. Here, the
LFP power in each band is relative to the total spectral power (see Experimental Procedures). Trials were classified as B or NB both by spiking activity
(squares) and LFP power (circles). Single and double asterisks denote a significant difference between groups at p < 0.05 and p < 0.0005 criteria, respectively (rank-sum test, corrected).
(F and G) Scatterplots of the relative baseline spontaneous LFP power and the
post-TMS spontaneous LFP power (n = 142 trials). A significant positive correlation is found between baseline high gamma power and post-TMS high
gamma power (F). A significant negative correlation is found between baseline
alpha power and post-TMS beta power (G).
(H) Correlation coefficients between the relative pre-TMS spontaneous power
and the post-TMS spontaneous power for all frequency band combinations.
To improve resolution beyond the six traditional bands (i.e., delta through
high gamma), we divided the full frequency range (1–150 Hz) into 15 logarithmically spaced bins. The (ij)th element in the matrix corresponds to the correlation coefficient between the relative pre-TMS power in the ith frequency bin
and the post-TMS Ls in the jth frequency bin. Elements outlined in black correspond to the data displayed in (F) and (G).
State-Dependent Neural Effects of TMS
pre-TMS evoked high gamma power, relative to the spontaneous power in the same band, is significantly correlated with
post-TMS spontaneous firing rate (r = 0.30, p < 0.0005, t test).
Although a positive correlation is also observed for gamma
band power, the lower-frequency bands instead exhibit negative correlations (Figure 6D). This finding is consistent with
previous studies showing a suppression of low-frequency
power during stimulus presentation and a general anticorrelation of power between lower and higher bands (Fries et al.,
2001; Liu and Newsome, 2006; Niessing et al., 2005). Overall,
these results indicate that strong cortical responsiveness to
visual stimuli increases the likelihood of spontaneous discharge
following TMS.
A second possible metric of cortical activity state is the level of
spontaneous, or ongoing, activity. Theoretically, both the baseline spontaneous spike rate and the baseline spontaneous LFP
power can be used to independently assess cortical activity
state. However, because cortical spontaneous spike rates are
typically low (1.4 ± 1.8 spikes/s in this sample), they are not
well suited for a correlation analysis. Thus, we focus our analysis
on the relative LFP power during the pre-TMS period (see Experimental Procedures). The mean spontaneous LFP power spectra
for B and NB groups are shown in Figure 6E. In this analysis, LFP
trials were classified as NB or B using either post-TMS spontaneous spikes or post-TMS spontaneous LFP power. In both
cases, trials were classified as B if TMS induced an increase of
at least two standard deviations above baseline spontaneous
activity, and as NB if there were a decrease or no change.
Regardless of the classification scheme, B trials are associated
with greater power in the high gamma band of pre-TMS spontaneous LFPs compared to NB trials (p < 0.05 for spikes-classifier,
p < 0.0005 for LFP-classifier, Wilcoxon rank-sum test, corrected). At lower-frequency bands (theta and alpha), B trials
have slightly less power than those classified as NB. Although
this difference is difficult to see on the log scale of Figure 6E, it
is statistically significant in the alpha band (p < 0.05 for LFP-classifier, Wilcoxon rank-sum test, corrected).
To better understand the dependence of post-TMS spontaneous activity on baseline LFP power, we calculated the correlation coefficients between these variables for all pairs of frequency
bands. This analysis results in a correlation matrix, shown in
Figure 6H. Two general features are apparent in this matrix. First,
correlations are positive at high frequencies of baseline LFP
power, but negative for low frequencies. Examples of positive
and negative correlations are shown in Figures 6F and 6G,
respectively. Thus, greater relative power in the gamma and
high gamma bands during the pre-TMS baseline predicts larger
power in post-TMS spontaneous LFPs (e.g., Figure 6F). In
contrast, greater relative baseline power in lower bands (delta
to alpha) predicts smaller post-TMS power (e.g., Figure 6G).
The change in correlation direction, which occurs in the lower
beta band (15 Hz), demonstrates the general anticorrelation
between low- and high-frequency power, as noted previously
(Fries et al., 2001; Liu and Newsome, 2006; Niessing et al.,
2005; Romei et al., 2008).
A second important aspect of the correlation matrix is the
presence of relatively stronger correlations at higher frequencies
of the post-TMS spontaneous LFPs. Thus, pre-TMS sponta-
neous LFP power is more predictive of post-TMS changes in
high-frequency power than those at low frequency. This trend
is not surprising, given that the increased variability associated
with post-TMS spontaneous discharge appears primarily in the
gamma and high gamma bands (Figure 5C). Taken together,
these results suggest the following relationship. Application of
TMS during a high activity state, as assessed with responsiveness to visual stimuli or the ongoing level of activity, is more likely
to result in spontaneous discharge than application of the same
pulse train during a low activity state.
The above results describe relationships of state dependence
between pre-TMS activity and post-TMS spontaneous activity.
We have also performed similar analyses for post-TMS evoked
activity. Changes in evoked activity show opposite trends
compared to spontaneous activity: greater baseline spontaneous power in high-LFP bands (alpha and above) is associated
with lower post-TMS evoked power (i.e., stronger reductions in
the evoked activity). The direction of the association switches
for lower bands of pre-TMS spontaneous LFPs, indicating negative correlations. The respective positive and negative correlations are present across all bands of the post-TMS evoked
LFP power, although correlation coefficients are slightly greater
in the higher bands. However, it should be noted that the magnitudes of these correlations are considerably weaker than those
observed for post-TMS spontaneous activity and do not reach
significance after correction for multiple comparisons.
Spatial Correlation and Coherence
In some experiments (n = 34 trials in 2 animals), we used a dualelectrode array to collect data simultaneously from two cortical
sites spaced roughly 400 mm apart (Figure 7). These data permit
us to ask whether neural activity in different cortical locations
exhibits similar responses to TMS.
In general, responses on the two electrodes are similar
(Figure 7A), although there are differences with regard to response
magnitude, particularly in high-frequency bands (Figure 7B).
Interelectrode correlations consequently demonstrate a strong
dependence on frequency band (Figure 7D). Changes in spontaneous LFPs (Figures 7C and 7D) are significantly correlated at
low frequencies (delta through beta, r > = 0.44, p < 0.05, corrected),
but not at higher frequencies. This trend is consistent with previous
work demonstrating a stronger spatial coherence at lower frequencies (Destexhe et al., 1999). Evoked LFP responses reveal similar
frequency dependence (Figure 7D), although overall correlations
are weaker. This is likely due to the fact that visual stimuli were
only optimized for neurons at one site, and did not reliably elicit
neural responses on both electrodes. Thus, despite the spatially
diffuse electric field produced by the TMS coil (Salinas et al.,
2007), these interelectrode correlations indicate that the spontaneous response component is highly local in nature. Response
homogeneity may be limited to a relatively small area (<400 mm).
The simultaneous two-channel LFP data also allow us to
investigate the effect of TMS on the timing of signals between
different populations of neurons. Fine temporal relationships
between the phases of neural signals have been associated
with attention (Buschman and Miller, 2007; Fries et al., 2001;
Saalmann et al., 2007), plasticity (Holscher et al., 1997; Wespatat
et al., 2004), and memory (Buzsaki and Draguhn, 2004), and are
Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc. 297
State-Dependent Neural Effects of TMS
Figure 8. Effect of TMS on Spatial Coherence
(A) Average levels of interelectrode LFP coherence (Cxy) during the pre-TMS
baseline period for spontaneous (solid) and evoked (dotted) activity (n = 34
trials). Error bars signify ± 1 SEM. Asterisks indicate significantly greater coherence during evoked activity (sign-rank test, p < 0.05, corrected).
(B) Spectrograms displaying the change in interelectrode coherence (DCxy)
for spontaneous (top) and evoked (bottom) LFPs. DCxy is expressed as
a percent change from baseline.
(C) Average DCxy for different time intervals and frequency bands. Significant
changes in spontaneous (left) and evoked (right) coherence are denoted with
asterisks (p < 0.05, sign-rank test, corrected).
Figure 7. Correlations between TMS Responses on Different
(A) Sample trace showing 8 s of spontaneous LFPs recorded from two different
electrodes placed approximately 400 mm apart in area 17. Channel 1 denotes
the electrode at which single-unit activity is isolated.
(B) Example spectrograms from three different TMS trials showing changes in
spontaneous LFP power (DLs) on channel 1 (left) and channel 2 (right). The TMS
parameters used in each trial are as follows: sb331x1424, 8 Hz, 4 s;
sb283x0701, 4 Hz, 4 s; and sb331x1003, 8 Hz, 4 s.
(C) The changes in spontaneous theta band power (DLs, q) on channels 1 and 2
are significantly correlated (n = 34, p < 0.0001, t test). Here, DLs, q is calculated
as the change in theta band power between the first minute post-TMS (interval
I) and the pre-TMS baseline period.
(D) Pearson correlation coefficients for DLs between channels 1 and 2 over all
frequency bands. Asterisks indicate significant correlations (p < 0.05, t test,
corrected). The arrow denotes the correlation coefficient for the data shown
in (C). Note that possible confounds of these correlations (i.e., stimulation
parameters and trial number) have been removed through partial correlation
(see Experimental Procedures).
often interpreted as indicators of functional ‘‘connectivity’’
between locations (Bruns, 2004; Lachaux et al., 1999; Pereda
et al., 2005). Here we evaluated interelectrode phase synchrony
using a common measure of spectral coherence. Because
coherence is sensitive to both amplitude and phase relationships, we performed an additional interelectrode analysis exam-
298 Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc.
ining only phase-locking values (see Experimental Procedures).
The results for these analyses are qualitatively similar, and we
therefore describe results only for coherence.
Figure 8A shows the baseline interelectrode coherence prior to
TMS. The trend of coherence over different frequency bands and
the significant elevation of high-frequency coherence during
evoked responses (p < 0.005, corrected) are consistent with
findings from previous studies (e.g., Henrie and Shapley, 2005).
For TMS-induced responses, spontaneous LFPs (Figure 8B,
top) at lower frequencies (8–20 Hz) exhibit a strong decrease
in coherence that slowly decays (Figure 8C, left). At high frequencies (>80 Hz), we observe instead a slight increase in coherence
(Figure 8C, left). Changes in evoked coherence (Figure 8B,
bottom) are very similar, although evoked activity shows a more
pronounced increase in high gamma coherence that persists
for several minutes after TMS (Figure 8C, right).
We note that the effects of TMS on interelectrode LFP-LFP
spectral coherence and phase locking are similar to those found
in our previous report on spike-LFP synchrony (Allen et al., 2007).
The prior analysis examined the relationship between spike
times and phases of the LFP oscillations recorded at the same
electrode. Despite different types of data and methodology,
both analyses indicate that TMS induces desynchronization
and hypersynchronization at lower and higher frequencies,
State-Dependent Neural Effects of TMS
respectively. These results demonstrate the capacity of TMS to
alter signal timing between neural populations, and suggest
that TMS may exert strong effects on functional processes that
depend on spike timing or phase locking.
Our current study has evaluated the variability in neuronal
responses following application of short TMS pulse trains during
the resting state. We find evidence for two divergent response
patterns, defined by the presence or absence of burst firing after
stimulation. Importantly, this effect is shown to be state dependent: higher pre-TMS activity predicts greater post-TMS activity.
Variability in the response to electrical stimulation is a wellknown phenomenon, observed both behaviorally (Ridding and
Rothwell, 2007) and neurophysiologically (Kringelbach et al.,
2007). In our data, variability is principally seen on a trial-to-trial
basis in the degree of spontaneous burst firing. The effect of TMS
on spontaneous activity is the focus of a considerable amount of
TMS literature (e.g., Bestmann et al., 2008; Brighina et al., 2004;
Hallett, 2007; Ridding and Rothwell, 2007; Romei et al., 2008;
Sauseng et al., 2009; Silvanto et al., 2007; Van Der Werf et al.,
2006). For example, TMS studies of phosphene or muscle twitch
thresholds are frequently used to assess cortical excitability
(Bestmann et al., 2007; Brighina et al., 2002; Hallett, 2007; Huang
et al., 2005; Ridding and Rothwell, 2007; Stewart et al., 2001).
These overt behavioral responses are thought to be analogs of
TMS-induced spontaneous bursting. Stimulation-induced overt
responses have been linked to direct activation of motor or
sensory circuits (Tehovnik et al., 2006) and even single neurons
(Houweling and Brecht, 2008; Huber et al., 2008). A hallmark of
these threshold studies is the substantial trial-to-trial variability,
in which overt responses are observed in some trials but not
others. Our neurophysiological findings provide a close parallel
to the robust variability noted in these behavioral studies.
An additional important feature of threshold studies is that preexisting activity levels can modulate the stimulation intensity
required to evoke an overt response. For example, motor or
phosphene thresholds have been shown to be modulated by
spatial attention (Bestmann et al., 2007), motor training (Butefisch et al., 2000), drug application (Oliveri and Calvo, 2003;
Ziemann et al., 2002), epilepsy (Theodore, 2003), and migraine
(Ambrosini et al., 2003). Our finding that the post-TMS burst
response depends on pre-TMS activity levels is consistent with
the hypothesis that changes in baseline activity levels underlie
these behavioral modulations. Notably, recent studies have
begun to investigate the cortical topography of such statedependent responses. Using concurrent TMS-fMRI, investigators have demonstrated that distinct activation patterns are
produced depending on the behavioral task to which stimulation
is paired (Ruff et al., 2006; Sack et al., 2007).
In addition, the effect of TMS on spontaneous activity may be
relevant to clinical applications. Clinical disorders are generally
characterized by abnormal activity revealed during an ongoing
state. The logic of TMS clinical treatment is that it causes disruption of ongoing activity of abnormal circuits (Hallett, 2007;
Ridding and Rothwell, 2007). For example, electroconvulsive
shock therapy utilized extensively for depression is thought to
operate by this principle (Lisanby and Belmaker, 2000). Our
finding that TMS disrupts the temporal structure of spatially
remote sites is consistent with the hypothesis that TMS can be
used to progressively alter abnormal neuronal communication.
It is important to consider the circuit and cellular mechanisms
that underlie the spontaneous response and associated statedependent effects. It is likely that TMS application directly induces
activating current in a subset of cortical cells (Moliadze et al.,
2003; Patton and Amassian, 1954). This activation can elicit reverberating excitatory potentials in postsynaptic cells, producing
a persistent bursting response that outlasts the TMS pulse train
(Patton and Amassian, 1954; Terao and Ugawa, 2002). As our
data indicate, the spontaneous bursting response involves neural
recruitment throughout the local microcircuit, and is therefore
subject to the balance of excitatory and inhibitory synaptic
activity. It is feasible that higher baseline excitability leads to
recurrent excitation (i.e., bursting) upon application of the TMS
pulse train, whereas lower baseline excitability signifies a relatively
greater level of inhibition that dampens recurrent excitation and
prevents burst firing. This explanation of state dependence is
consistent with the current results and with those of numerous
threshold studies (Bestmann et al., 2007; Butefisch et al., 2000;
Oliveri and Calvo, 2003; Romei et al., 2008; Ziemann et al., 2002).
In contrast to the state dependence observed for spontaneous
activity, we found little evidence for state-dependent evoked
activity. This may relate to different mechanisms underlying the
spontaneous and evoked responses (see below). Weak evoked
state dependence may also be due to the specifics of our stimulation paradigm. TMS was applied only during intervals of spontaneous activity, and therefore did not target a distinct neural
population. This differs from a paradigm in which stimulation is
applied during different tasks that recruit largely nonoverlapping
neural populations (Silvanto and Muggleton, 2008). Previous
behavioral work has demonstrated robust state-dependent
effects when pairing stimulation to tasks with different profiles
of neural activation (Silvanto and Muggleton, 2008). An improved
understanding of how to exploit state-dependent effects could
have important implications for optimizing stimulation procedures in therapeutic contexts (e.g., see Miller, 2007).
Our results also permit an examination of a widely held
conceptual account of how TMS interferes with neural function.
This interference has been characterized as a ‘‘virtual lesion’’
(Pascual-Leone et al., 2000), in analogy to structural brain lesions
that produce specific functional deficits. The large decrease in
visually evoked activity following TMS supports this view,
although the physiological processes underlying this suppression have yet to be established. One possible mechanism is
long-term hyperpolarization, which may be due to alterations in
extrinsic synaptic input or intrinsic membrane properties. For
example, electrical stimulation has been shown to substantially
elevate levels of extracellular GABA, which suppresses activity
for several minutes (Mantovani et al., 2006). Alternatively, prolonged neuronal suppression might result from disruption of
normally coordinated activity patterns at the circuit level. Our
data and that of others (Jing and Takigawa, 2000; Oliviero
et al., 2003; Strens et al., 2002) demonstrate that this coordination is disrupted by TMS. Specifically, the temporal relationships
of neural signals, as measured by spike-LFP (Allen et al., 2007)
Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc. 299
State-Dependent Neural Effects of TMS
and LFP-LFP phase synchrony (Figure 8), are altered for several
minutes. If signal patterns between neurons are perturbed, one
would expect a detrimental effect on the functions supported
by those cells. Accordingly, when a neural circuit is probed
with a visual stimulus following TMS, we find an immediate and
prolonged reduction of evoked activity.
The convergence of previous behavioral findings and the
current neuronal analyses strongly suggests that variations in
existing activity levels contribute to the variability of TMS
responses. This relationship may explain, in part, the considerable discrepancies between subjects and trials found in many
brain stimulation studies. Furthermore, our results suggest that
the analysis of TMS responses in terms of the preceding activity
may help to elucidate and interpret stimulation-induced response
patterns. The direct monitoring of neural activity using noninvasive techniques, such as EEG (Massimini et al., 2005; Romei
et al., 2008) or hemodynamic-based imaging (Allen et al., 2007;
Bohning et al., 1999; Ruff et al., 2006; Sack et al., 2007), can
empirically guide the effective use of TMS in both clinical and
experimental settings.
Animal Preparation
All animal procedures are in compliance with the National Institutes of Health
Guide for the Care and Use of Laboratory Animals and are approved by the
Animal Care and Use Committee at the University of California Berkeley. Mature
cats (n = 5) are initially anesthetized with isofluorane (3%–4%). Following
placement of venous catheters, isofluorane is discontinued, and anesthesia is
maintained with intravenous infusion of fentanyl citrate (10 mg $ kg1 $ hr1)
and thiopental sodium (initially 6.0 mg $ kg1 $ hr1). Following the placement
of a tracheal cannula, animals are artificially ventilated with a 25% O2/75%
N2O mixture. Respiration rate is adjusted to maintain expired CO2 between
30 and 36 mmHg (generally between 15 and 25 breaths/min). Body temperature
is maintained at 38 C with a closed-loop controlled heating pad (Love Controls,
IN, USA). A craniotomy over area 17 is performed (Horsley-Clarke coordinates
P4, L2; Horsley and Clarke, 1908), and the dura resected. After completion of
surgical procedures, fentanyl citrate infusion is discontinued, and the rate of
thiopental sodium infusion is gradually lowered to a level at which the animal
is stabilized (typically 1.5 mg $ kg1 $ hr1). After stabilization, paralysis is
induced with pancuronium bromide (0.2 mg $ kg1 $ hr1) to prevent eye movements. EEG, ECG, heart rate, temperature, end-tidal CO2, and intratracheal
pressure are monitored continuously throughout the duration of the experiment.
Experimental Paradigm
Visual stimuli (drifting sinusoidal gratings) are presented on a luminance-calibrated CRT monitor (85 Hz refresh rate, mean luminance 45 cd/m2). Preliminary tests are performed on each neuron to identify the stimulus orientation,
spatial frequency, temporal frequency, position, and size to maximize the
neuron’s spike response. During TMS trials, drifting gratings with optimal
parameters are displayed at 50% contrast for 2 s.
TMS is applied to the visual cortex using a Magstim Rapid system (Magstim
Company, Whitland, UK) with a 70 mm figure-eight coil, which is positioned
using a mechanical camera arm (see Figure 1A). Pulse trains are delivered by
series of TTL digital pulses with parametrically varying frequency (1, 4, 8 Hz)
and duration (1, 2, 4 s) at 100% stimulation intensity. At this intensity and range
of distances (1–2 cm distance from the skull and an additional 3 mm between
the skull and the cortical surface), the induced electric field strength is estimated
to be 100–200 V/m (Salinas et al., 2007). To ensure neural recovery between
TMS trials, each subsequent trial is initiated only when the evoked response has
maintained a steady-state value for over 1 min. We include a minimum of 6 min
between TMS applications, with typical intervals of 10–15 min.
300 Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc.
Data Collection
Neural data are recorded using either NaCl-filled barrels from a multibarrel
carbon fiber microelectrode (Kation Scientific, Minneapolis, MN, USA) or
epoxy-coated tungsten microelectrodes (5 MU, A-M Systems, Carlsborg,
WA, USA). Tungsten electrodes are mounted in a dual array, allowing simultaneous recordings from spatially distinct regions (400 mm apart). For both
electrode types, the LFP signal is obtained from the broadband neural trace
by band-pass filtering between 0.7 and 170 Hz, and the data are digitized at
500 Hz. The multiunit signal is obtained from the broadband signal by filtering
between 500 Hz and 8 MHz. Individual single units are discriminated online
based on the temporal shapes of their extracellular potentials, and spike times
are recorded with 0.04 ms precision. Single-unit data are included in the analysis only if the spike waveform remains stable throughout the duration of the
TMS trial. Of the 47 single units in our sample, 45 have less than 0.1% of their
ISIs within a typical refractory period of 1 ms. The other 2 cells exhibit a shorter
(though not unusual; see Gur and Snodderly, 2006) refractory period and have
less than 0.1% of events within 0.7 ms.
Data Analysis
TMS-induced electrical artifacts are removed from all analyses by excluding
a window of data that spans from the first TMS pulse to 100 ms after the last
pulse. Single-unit data are converted into spike rates (R) by dividing the number
of spikes in a time window by the duration of that window. Spontaneous spike
rate, Rs(t), is defined as the raw firing rate during each 8 s interstimulus interval.
Evoked spike rate, Re(t), is defined as the average spike firing during each 2 s
stimulus presentation following subtraction of the raw spontaneous rate that
immediately precedes the stimulus. This subtraction assumes an additive
model of spike generation, although it is important to note that none of our
results were significantly altered by removing this subtraction from the analysis.
The TMS-induced change in spontaneous spike rate, DRs, is defined as Rs(t) –
Rs(tbaseline), where t denotes time and Rs(tbaseline) denotes the average spontaneous firing rate over the pre-TMS baseline period (40 s interval prior to TMS).
The TMS-induced change in evoked spike rate, DRe, is defined analogously.
Raw LFP signals are converted to LFP power (L) by first removing line noise at
60 and 85 Hz (monitor refresh rate), then using multitaper spectral estimation
over 1 s windows and 5 Hz bandwidth (Pesaran et al., 2002; Thomson, 1982).
The spontaneous LFP power, Lrs ðf; tÞ, is defined as the raw power in frequency
band f during each spontaneous time interval. Evoked LFP power, Lre ðf; tÞ, is
analogously defined for each interval of evoked activity. When comparing
absolute values of LFP power, we used log transformations to normalize
the data distributions (Cohen et al., 2003). Thus, Ls ðf; tÞ = logðLrs ðf; tÞÞ and
Le ðf; tÞ = logðLre ðf; tÞÞ. Changes in LFP power can then be computed as the
simple difference in transformed power values, for example, DLs ðfÞ =
Ls ðf; tÞ Ls ðf; tbaseline Þ, which is mathematically equivalent to the log ratio of
the raw power values:
DLs ðfÞ = log
Lrs ðf; tÞ
Lrs ðf; tbaseline Þ
Similarly, the stimulus-evoked elevation in LFP power relative to the spontaneous activity immediately preceding stimulus (Figures 6C and 6D) can be
defined as
L ðf; tbaseline Þ
Le=s ðfÞ = log er
Ls ðf; tbaseline Þ
or Le=s ðfÞ = Le ðf; tbaseline Þ Ls ðf; tbaseline Þ. To effectively compare pre-TMS
spontaneous LFPs from different sites (Figures 6E–6H), the spectral power
of each trial is normalized by the area under the entire spectrum (Liu and
Newsome, 2006). Thus, ‘‘relative pre-TMS Ls,’’ calculated as
Lr ðf; tbaseline Þ
ðfÞ = P s r
Ls ðf; tbaseline Þ
refers to the relative power in each frequency band.
To compare the variability of spontaneous and evoked responses, we
compute the relative standard deviation (RSD) of each component for a given
set of trials. Equivalent results were obtained using the Fano factor, which is
State-Dependent Neural Effects of TMS
a standard measure of neuronal variability that accounts for differences in
response amplitudes (Stevens and Zador, 1998). These measures are mathematically equivalent up to a square factor: RSD normalizes the standard
deviation by the mean, whereas the Fano factor normalizes the square of
the standard deviation. A set of trials is defined as three or more trials run under
identical conditions (i.e., same site and stimulation parameters). Note that
the same sets of trials (n = 23) are also used in the rank-correlation analysis
(see Figure 3). Variability in response components is further evaluated by
comparing trials recorded at a single cell to those recorded from different cells.
This is achieved using a permutation test, resampling the population to form
equivalent sets of trials with identical stimulation parameters but different
sites. Significance is assessed by comparing the median RSD of the original
sets of trials to the distribution of median RSDs from the resampled sets of
trials (n = 10,000 resamples) (Manly, 1991).
For correlation analyses including all trials (Figures 6 and 7), partial correlation
is used to control for the possible influence of additional variables (Cohen et al.,
2003). Pre- and post-TMS variables of interest are first regressed on confound
factors that include stimulation parameters and trial number. In state-dependency analyses (Figure 6), the pre-TMS spontaneous activity (spike rate or
LFP power, where appropriate) is included as an additional regressor. Correlation is then performed on the residuals. These residuals have the same units as
the original variables, but have been linearly transformed. Thus, the pre- and
post-TMS spike rate residuals can take on negative values (see Figure 6C).
This partial correlation approach ensures that any observed relationship cannot
be due to linear associations between additional variables.
For synchrony analyses, LFP-LFP synchrony between recording sites is
evaluated using the coherence statistic (Mitra and Pesaran, 1999):
S ðfÞ xy
Cxy ðfÞ = pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi;
Sx ðfÞSy ðfÞ
where Cxy is the coherence ranging from 0 to 1, f is frequency, Sx(f) and Sy(f) are
the spectra of the signals recorded from the two sites, and Sxy(f) is the crossspectrum. Because coherence is a biased statistic which varies with sample
size (Jarvis and Mitra, 2001), interelectrode coherence was always calculated
over equivalent time windows (2 s duration). Because coherence is sensitive
to both amplitude and phase coupling, we also computed a phase-locking value
that is insensitive to amplitude changes (Lachaux et al., 1999; Pereda et al.,
2005). The LFP signal was filtered in 5 Hz bands and the instantaneous phase
at each time point was extracted via the Hilbert transform (Lachaux et al.,
1999; Pereda
et al., 2005). The phase-locking value was computed as
PLVðfÞ = jhei4ðtÞ ij, where f is frequency, 4ðtÞ is the difference between the
phases at each electrode and at each time t, and h,i denotes the average over
time (Lachaux et al., 1999; Pereda et al., 2005). The two synchrony measures
were qualitatively similar and therefore results are reported for coherence only.
Supplemental data include two figures and can be found with this article online
We thank R. Bartholomew, N. Lines, A. Koukarine, and L. Gibson for assistance in developing the electrophysiological apparatus and data acquisition
software. This work was supported by research and CORE grants from the
National Eye Institute (EY01175 and EY03176, respectively) and by NSF graduate research fellowship 2003014861.
Accepted: March 6, 2009
Published: April 29, 2009
Ambrosini, A., de Noordhout, A.M., Sandor, P.S., and Schoenen, J. (2003).
Electrophysiological studies in migraine: a comprehensive review of their
interest and limitations. Cephalalgia 23 (Suppl 1), 13–31.
Barker, A.T., Jalinous, R., and Freeston, I.L. (1985). Non-invasive magnetic
stimulation of human motor cortex. Lancet 1, 1106–1107.
Belitski, A., Gretton, A., Magri, C., Murayama, Y., Montemurro, M.A.,
Logothetis, N.K., and Panzeri, S. (2008). Low-frequency local field potentials
and spikes in primary visual cortex convey independent visual information.
J. Neurosci. 28, 5696–5709.
Berardelli, A., Inghilleri, M., Rothwell, J.C., Romeo, S., Curra, A., Gilio, F.,
Modugno, N., and Manfredi, M. (1998). Facilitation of muscle evoked responses
after repetitive cortical stimulation in man. Exp. Brain Res. 122, 79–84.
Bestmann, S., Ruff, C.C., Blakemore, C., Driver, J., and Thilo, K.V. (2007).
Spatial attention changes excitability of human visual cortex to direct stimulation. Curr. Biol. 17, 134–139.
Bestmann, S., Swayne, O., Blankenburg, F., Ruff, C.C., Haggard, P.,
Weiskopf, N., Josephs, O., Driver, J., Rothwell, J.C., and Ward, N.S. (2008).
Dorsal premotor cortex exerts state-dependent causal influences on activity
in contralateral primary motor and dorsal premotor cortex. Cereb. Cortex 18,
Bohning, D.E., Shastri, A., McConnell, K.A., Nahas, Z., Lorberbaum, J.P.,
Roberts, D.R., Teneback, C., Vincent, D.J., and George, M.S. (1999). A
combined TMS/fMRI study of intensity-dependent TMS over motor cortex.
Biol. Psychiatry 45, 385–394.
Brighina, F., Piazza, A., Daniele, O., and Fierro, B. (2002). Modulation of visual
cortical excitability in migraine with aura: effects of 1 Hz repetitive transcranial
magnetic stimulation. Exp. Brain Res. 145, 177–181.
Brighina, F., Piazza, A., Vitello, G., Aloisio, A., Palermo, A., Daniele, O., and
Fierro, B. (2004). rTMS of the prefrontal cortex in the treatment of chronic
migraine: a pilot study. J. Neurol. Sci. 227, 67–71.
Bruns, A. (2004). Fourier-, Hilbert- and wavelet-based signal analysis: are
they really different approaches? J. Neurosci. Methods 137, 321–332.
Burt, T., Lisanby, S.H., and Sackeim, H.A. (2002). Neuropsychiatric applications of transcranial magnetic stimulation: a meta analysis. Int. J. Neuropsychopharmacol. 5, 73–103.
Buschman, T.J., and Miller, E.K. (2007). Top-down versus bottom-up control
of attention in the prefrontal and posterior parietal cortices. Science 315,
Butefisch, C.M., Davis, B.C., Wise, S.P., Sawaki, L., Kopylev, L., Classen, J.,
and Cohen, L.G. (2000). Mechanisms of use-dependent plasticity in the human
motor cortex. Proc. Natl. Acad. Sci. USA 97, 3661–3665.
Buzsaki, G., and Draguhn, A. (2004). Neuronal oscillations in cortical networks.
Science 304, 1926–1929.
Cahn, S.D., Herzog, A.G., and Pascual-Leone, A. (2003). Paired-pulse transcranial magnetic stimulation: effects of hemispheric laterality, gender, and
handedness in normal controls. J. Clin. Neurophysiol. 20, 371–374.
Canolty, R.T., Edwards, E., Dalal, S.S., Soltani, M., Nagarajan, S.S., Kirsch, H.E.,
Berger, M.S., Barbaro, N.M., and Knight, R.T. (2006). High gamma power
is phase-locked to theta oscillations in human neocortex. Science 313,
Chen, R., Classen, J., Gerloff, C., Celnik, P., Wassermann, E.M., Hallett, M.,
and Cohen, L.G. (1997). Depression of motor cortex excitability by lowfrequency transcranial magnetic stimulation. Neurology 48, 1398–1403.
Cohen, J., Cohen, P., West, S.G., and Aiken, L.S. (2003). Applied Multiple
Regression/Correlation Analysis for the Behavioral Sciences (Mahwah, NJ:
Lawrence Erlbaum).
Couturier, J.L. (2005). Efficacy of rapid-rate repetitive transcranial magnetic
stimulation in the treatment of depression: a systematic review and meta-analysis. J. Psychiatry Neurosci. 30, 83–90.
Allen, E.A., Pasley, B.N., Duong, T., and Freeman, R.D. (2007). Transcranial
magnetic stimulation elicits coupled neural and hemodynamic consequences.
Science 317, 1918–1921.
de Labra, C., Rivadulla, C., Grieve, K., Marino, J., Espinosa, N., and Cudeiro, J.
(2007). Changes in visual responses in the feline dLGN: selective thalamic
suppression induced by transcranial magnetic stimulation of V1. Cereb.
Cortex 17, 1376–1385.
Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc. 301
State-Dependent Neural Effects of TMS
Destexhe, A., Contreras, D., and Steriade, M. (1999). Spatiotemporal analysis
of local field potentials and unit discharges in cat cerebral cortex during natural
wake and sleep states. J. Neurosci. 19, 4595–4608.
Logothetis, N.K., Kayser, C., and Oeltermann, A. (2007). In vivo measurement
of cortical impedance spectrum in monkeys: implications for signal propagation. Neuron 55, 809–823.
Efron, B., and Tibshirani, R. (1994). An Introduction to the Bootstrap (New York:
Chapman & Hall).
Manly, C.F.J. (1991). Randomization and Monte-Carlo Methods in Biology
(New York: Chapman & Hall).
Engel, A.K., Fries, P., and Singer, W. (2001). Dynamic predictions: oscillations
and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704–716.
Mantovani, M., Van Velthoven, V., Fuellgraf, H., Feuerstein, T.J., and Moser, A.
(2006). Neuronal electrical high frequency stimulation enhances GABA outflow
from human neocortical slices. Neurochem. Int. 49, 347–350.
Fregni, F., Simon, D.K., Wu, A., and Pascual-Leone, A. (2005). Non-invasive
brain stimulation for Parkinson’s disease: a systematic review and meta-analysis of the literature. J. Neurol. Neurosurg. Psychiatry 76, 1614–1623.
Fries, P., Reynolds, J.H., Rorie, A.E., and Desimone, R. (2001). Modulation of
oscillatory neuronal synchronization by selective visual attention. Science 291,
Fritsch, G., and Hitzig, E. (1870). Ueber die elektrishe Erregarkeit des Grosshirns (Springfield, IL: Charles C. Thomas).
George, M.S., Wassermann, E.M., and Post, R.M. (1996). Transcranial
magnetic stimulation: a neuropsychiatric tool for the 21st century. J. Neuropsychiatry Clin. Neurosci. 8, 373–382.
Gross, M., Nakamura, L., Pascual-Leone, A., and Fregni, F. (2007). Has repetitive transcranial magnetic stimulation (rTMS) treatment for depression
improved? A systematic review and meta-analysis comparing the recent vs.
the earlier rTMS studies. Acta Psychiatr. Scand. 116, 165–173.
Martin, J.L., Barbanoj, M.J., Schlaepfer, T.E., Thompson, E., Perez, V., and
Kulisevsky, J. (2003). Repetitive transcranial magnetic stimulation for the treatment of depression. Systematic review and meta-analysis. Br. J. Psychiatry
182, 480–491.
Massimini, M., Ferrarelli, F., Huber, R., Esser, S.K., Singh, H., and Tononi, G.
(2005). Breakdown of cortical effective connectivity during sleep. Science
309, 2228–2232.
Miller, G. (2007). Neuroscience. Uncovering the magic in magnetic brain stimulation. Science 317, 1846.
Mitra, P.P., and Pesaran, B. (1999). Analysis of dynamic brain imaging data.
Biophys. J. 76, 691–708.
Mitzdorf, U. (1985). Current source-density method and application in cat
cerebral cortex: investigation of evoked potentials and EEG phenomena.
Physiol. Rev. 65, 37–100.
Gur, M., and Snodderly, D.M. (2006). High response reliability of neurons
in primary visual cortex (V1) of alert, trained monkeys. Cereb. Cortex 16,
Moliadze, V., Zhao, Y., Eysel, U., and Funke, K. (2003). Effect of transcranial
magnetic stimulation on single-unit activity in the cat primary visual cortex.
J. Physiol. 553, 665–679.
Hallett, M. (2007). Transcranial magnetic stimulation: a primer. Neuron 55,
Moliadze, V., Giannikopoulos, D., Eysel, U.T., and Funke, K. (2005). Paired-pulse
transcranial magnetic stimulation protocol applied to visual cortex of anaesthetized cat: effects on visually evoked single-unit activity. J. Physiol. 566, 955–965.
Heeger, D.J., and Ress, D. (2002). What does fMRI tell us about neuronal
activity? Nat. Rev. Neurosci. 3, 142–151.
Henrie, J.A., and Shapley, R. (2005). LFP power spectra in V1 cortex: the
graded effect of stimulus contrast. J. Neurophysiol. 94, 479–490.
Holscher, C., Anwyl, R., and Rowan, M.J. (1997). Stimulation on the positive
phase of hippocampal theta rhythm induces long-term potentiation that can
be depotentiated by stimulation on the negative phase in area CA1 in vivo.
J. Neurosci. 17, 6470–6477.
Horsley, V., and Clarke, R. (1908). The structure and functions of the cerebellum examined by a new method. Brain 31, 45–124.
Houweling, A.R., and Brecht, M. (2008). Behavioural report of single neuron
stimulation in somatosensory cortex. Nature 451, 65–68.
Huang, Y.Z., Edwards, M.J., Rounis, E., Bhatia, K.P., and Rothwell, J.C.
(2005). Theta burst stimulation of the human motor cortex. Neuron 45,
Huber, D., Petreanu, L., Ghitani, N., Ranade, S., Hromadka, T., Mainen, Z., and
Svoboda, K. (2008). Sparse optical microstimulation in barrel cortex drives
learned behaviour in freely moving mice. Nature 451, 61–64.
Izhikevich, E.M. (2006). Bursting. Scholarpedia 1, 1300.
Jarvis, M.R., and Mitra, P.P. (2001). Sampling properties of the spectrum and
coherency of sequences of action potentials. Neural Comput. 13, 717–749.
Jing, H., and Takigawa, M. (2000). Observation of EEG coherence after repetitive transcranial magnetic stimulation. Clin. Neurophysiol. 111, 1620–1631.
Kringelbach, M.L., Jenkinson, N., Owen, S.L., and Aziz, T.Z. (2007). Translational principles of deep brain stimulation. Nat. Rev. Neurosci. 8, 623–635.
Lachaux, J.P., Rodriguez, E., Martinerie, J., and Varela, F.J. (1999). Measuring
phase synchrony in brain signals. Hum. Brain Mapp. 8, 194–208.
Lisanby, S.H., and Belmaker, R.H. (2000). Animal models of the mechanisms of
action of repetitive transcranial magnetic stimulation (RTMS): comparisons
with electroconvulsive shock (ECS). Depress. Anxiety 12, 178–187.
Niessing, J., Ebisch, B., Schmidt, K.E., Niessing, M., Singer, W., and Galuske,
R.A. (2005). Hemodynamic signals correlate tightly with synchronized gamma
oscillations. Science 309, 948–951.
Oliveri, M., and Calvo, G. (2003). Increased visual cortical excitability in ecstasy
users: a transcranial magnetic stimulation study. J. Neurol. Neurosurg. Psychiatry 74, 1136–1138.
Oliviero, A., Strens, L.H., Di Lazzaro, V., Tonali, P.A., and Brown, P. (2003).
Persistent effects of high frequency repetitive TMS on the coupling between
motor areas in the human. Exp. Brain Res. 149, 107–113.
Pascual-Leone, A., Tormos, J.M., Keenan, J., Tarazona, F., Canete, C., and
Catala, M.D. (1998). Study and modulation of human cortical excitability with
transcranial magnetic stimulation. J. Clin. Neurophysiol. 15, 333–343.
Pascual-Leone, A., Walsh, V., and Rothwell, J. (2000). Transcranial magnetic
stimulation in cognitive neuroscience—virtual lesion, chronometry, and functional connectivity. Curr. Opin. Neurobiol. 10, 232–237.
Patton, H.D., and Amassian, V.E. (1954). Single- and multiple-unit analysis of
cortical stage of pyramidal tract activation. J. Neurophysiol. 17, 345–363.
Paus, T. (2005). Inferring causality in brain images: a perturbation approach.
Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 1109–1114.
Pereda, E., Quiroga, R.Q., and Bhattacharya, J. (2005). Nonlinear multivariate
analysis of neurophysiological signals. Prog. Neurobiol. 77, 1–37.
Pesaran, B., Pezaris, J.S., Sahani, M., Mitra, P.P., and Andersen, R.A. (2002).
Temporal structure in neuronal activity during working memory in macaque
parietal cortex. Nat. Neurosci. 5, 805–811.
Reich, D.S., Mechler, F., Purpura, K.P., and Victor, J.D. (2000). Interspike
intervals, receptive fields, and information encoding in primary visual cortex.
J. Neurosci. 20, 1964–1974.
Ridding, M.C., and Rothwell, J.C. (2007). Is there a future for therapeutic use of
transcranial magnetic stimulation? Nat. Rev. Neurosci. 8, 559–567.
Liu, J., and Newsome, W.T. (2006). Local field potential in cortical area MT:
stimulus tuning and behavioral correlations. J. Neurosci. 26, 7779–7790.
Romei, V., Brodbeck, V., Michel, C., Amedi, A., Pascual-Leone, A., and Thut, G.
(2008). Spontaneous fluctuations in posterior alpha-band EEG activity reflect
variability in excitability of human visual areas. Cereb. Cortex 18, 2010–2018.
Logothetis, N.K. (2008). What we can do and what we cannot do with fMRI.
Nature 453, 869–878.
Ruff, C.C., Blankenburg, F., Bjoertomt, O., Bestmann, S., Freeman, E.,
Haynes, J.D., Rees, G., Josephs, O., Deichmann, R., and Driver, J. (2006).
302 Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc.
State-Dependent Neural Effects of TMS
Concurrent TMS-fMRI and psychophysics reveal frontal influences on human
retinotopic visual cortex. Curr. Biol. 16, 1479–1488.
Stevens, C.F., and Zador, A.M. (1998). Input synchrony and the irregular firing
of cortical neurons. Nat. Neurosci. 1, 210–217.
Saalmann, Y.B., Pigarev, I.N., and Vidyasagar, T.R. (2007). Neural mechanisms of visual attention: how top-down feedback highlights relevant locations. Science 316, 1612–1615.
Stewart, L.M., Walsh, V., and Rothwell, J.C. (2001). Motor and phosphene
thresholds: a transcranial magnetic stimulation correlation study. Neuropsychologia 39, 415–419.
Sack, A.T., Kohler, A., Bestmann, S., Linden, D.E., Dechent, P., Goebel, R.,
and Baudewig, J. (2007). Imaging the brain activity changes underlying
impaired visuospatial judgments: simultaneous fMRI, TMS, and behavioral
studies. Cereb. Cortex 17, 2841–2852.
Strens, L.H., Oliviero, A., Bloem, B.R., Gerschlager, W., Rothwell, J.C., and
Brown, P. (2002). The effects of subthreshold 1 Hz repetitive TMS on
cortico-cortical and interhemispheric coherence. Clin. Neurophysiol. 113,
Salinas, F.S., Lancaster, J.L., and Fox, P.T. (2007). Detailed 3D models of the
induced electric field of transcranial magnetic stimulation coils. Phys. Med.
Biol. 52, 2879–2892.
Tehovnik, E.J., Tolias, A.S., Sultan, F., Slocum, W.M., and Logothetis, N.K.
(2006). Direct and indirect activation of cortical neurons by electrical microstimulation. J. Neurophysiol. 96, 512–521.
Sauseng, P., Klimesch, W., Gerloff, C., and Hummel, F.C. (2009). Spontaneous
locally restricted EEG alpha activity determines cortical excitability in the
motor cortex. Neuropsychologia 47, 284–288.
Terao, Y., and Ugawa, Y. (2002). Basic mechanisms of TMS. J. Clin. Neurophysiol. 19, 322–343.
Theodore, W.H. (2003). Transcranial magnetic stimulation in epilepsy. Epilepsy
Curr. 3, 191–197.
Siegel, M., and Konig, P. (2003). A functional gamma-band defined by stimulus-dependent synchronization in area 18 of awake behaving cats. J. Neurosci. 23, 4251–4260.
Thomson, D.J. (1982). Spectrum estimation and harmonic analysis. Proc. IEEE
70, 1055–1096.
Silvanto, J., and Muggleton, N.G. (2008). New light through old windows:
moving beyond the ‘‘virtual lesion’’ approach to transcranial magnetic stimulation. Neuroimage 39, 549–552.
Van Der Werf, Y.D., Sadikot, A.F., Strafella, A.P., and Paus, T. (2006). The
neural response to transcranial magnetic stimulation of the human motor
cortex. II. Thalamocortical contributions. Exp. Brain Res. 175, 246–255.
Silvanto, J., Muggleton, N.G., Cowey, A., and Walsh, V. (2007). Neural adaptation reveals state-dependent effects of transcranial magnetic stimulation. Eur.
J. Neurosci. 25, 1874–1881.
Wespatat, V., Tennigkeit, F., and Singer, W. (2004). Phase sensitivity of
synaptic modifications in oscillating cells of rat visual cortex. J. Neurosci. 24,
Skottun, B.C., De Valois, R.L., Grosof, D.H., Movshon, J.A., Albrecht, D.G.,
and Bonds, A.B. (1991). Classifying simple and complex cells on the basis of
response modulation. Vision Res. 31, 1079–1086.
Ziemann, U., Tam, A., Butefisch, C., and Cohen, L.G. (2002). Dual modulating
effects of amphetamine on neuronal excitability and stimulation-induced
plasticity in human motor cortex. Clin. Neurophysiol. 113, 1308–1315.
Neuron 62, 291–303, April 30, 2009 ª2009 Elsevier Inc. 303
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF