Low frequency transcranial electrical stimulation does not entrain sleep rhythms measured by human intracranial recordings

Abstract

Transcranial electrical stimulation has widespread clinical and research applications, yet its effect on ongoing neural activity in humans is not well established. Previous reports argue that transcranial alternating current stimulation (tACS) can entrain and enhance neural rhythms related to memory, but the evidence from non-invasive recordings has remained inconclusive. Here, we measure endogenous spindle and theta activity intracranially in humans during low-frequency tACS and find no stable entrainment of spindle power during non-REM sleep, nor of theta power during resting wakefulness. As positive controls, we find robust entrainment of spindle activity to endogenous slow-wave activity in 66% of electrodes as well as entrainment to rhythmic noise-burst acoustic stimulation in 14% of electrodes. We conclude that low-frequency tACS at common stimulation intensities neither acutely modulates spindle activity during sleep nor theta activity during waking rest, likely because of the attenuated electrical fields reaching the cortical surface.

Full text

ARTICLE
Low frequency transcranial electrical stimulation
does not entrain sleep rhythms measured by
human intracranial recordings
Belen Lafon 1, Simon Henin 2,3, Yu Huang 1, Daniel Friedman 2,3, Lucia Melloni 2,3,4,
Thomas Thesen 3,5, Werner Doyle2,6, György Buzsáki 3,7, Orrin Devinsky 2,3, Lucas C. Parra 1&
Anli A. Liu 2,3
Transcranial electrical stimulation has widespread clinical and research applications, yet its
effect on ongoing neural activity in humans is not well established. Previous reports argue
that transcranial alternating current stimulation (tACS) can entrain and enhance neural
rhythms related to memory, but the evidence from non-invasive recordings has remained
inconclusive. Here, we measure endogenous spindle and theta activity intracranially in
humans during low-frequency tACS and find no stable entrainment of spindle power during
non-REM sleep, nor of theta power during resting wakefulness. As positive controls, we find
robust entrainment of spindle activity to endogenous slow-wave activity in 66% of electrodes
as well as entrainment to rhythmic noise-burst acoustic stimulation in 14% of electrodes. We
conclude that low-frequency tACS at common stimulation intensities neither acutely mod-
ulates spindle activity during sleep nor theta activity during waking rest, likely because of the
attenuated electrical fields reaching the cortical surface.
Corrected: Author correction
DOI: 10.1038/s41467-017-01045-x OPEN
1Department of Biomedical Engineering, City College of New York, 160 Convent Ave, New York, NY 10031, USA. 2New York University Comprehensive
Epilepsy Center, 223 East 34th Street, New York, NY 10016, USA. 3Department of Neurology, New York University School of Medicine, 240 East 38th St,
20th Floor, New York, NY 10016, USA. 4Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Gruneburgweg 14, 60322 Frankfurt am
Main, Germany. 5Department of Physiology and Neuroscience, St. George’s University, St. George’s, Grenada. 6Department of Neurosurgery NYU School of
Medicine, 530 1st Avenue, Suite 7W, New York, NY 10016, USA. 7New York University Neuroscience Institute, 450 East 29th St, New York, NY 10016, USA.
Belen Lafon and Simon Henin contributed equally to this work. Lucas C. Parra and Anli Liu jointly supervised this work. Correspondence and requests for
materials should be addressed to A.Liu. (email: anli.liu@nyumc.org)
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The therapeutic potential of transcranial electrical stimula-
tion (TES) has been examined in over 70 diverse condi-
tions, including major depression, epilepsy, pain, stroke
rehabilitation, Parkinson’s disease, and tinnitus1. TES is motivated
by the well-established biophysical observation that externally
applied electric fields can affect neuronal excitability2–4. However,
behavioral effects in human are often weak and difficult to repli-
cate, which underscores a lack of basic mechanistic understanding
of how applied electrical fields interact with brain activity5–9.
Here, we focus on transcranial alternating current stimulation
(tACS)10, which has potential to affect neural oscillations relevant
to normal cognition and neurological disease11. Specific oscilla-
tion frequencies characterize different arousal states and cognitive
processes, and oscillations coordinate activity between local and
distant brain regions12,13. For example, in non-REM (NREM)
sleep, neocortical slow-wave activity coordinates thalamocortical
sleep spindles14,15. It has been argued that low-frequency tACS,
applied during NREM in healthy human subjects, can boost
associative memory, presumably by entraining these nested
rhythms16–21. Furthermore, low-frequency tACS applied during
waking rest has resulted in widespread increases in theta activity,
associated with improved encoding22. It has been proposed that
tACS applied at the dominant network oscillations may entrain
these endogenous rhythms in the gamma23–25, beta26, alpha27,28,
theta29, and slow frequencies16,30,31.
In this study, we tested a widely cited protocol16, which showed
that declarative memory performance could be enhanced by
applying low-frequency tACS during NREM sleep. This work has
since inspired a number of replication studies, which have shown
either positive results18,19,21 or no effects5,7,32,33. To validate the
physiological underpinnings of this promising memory effect, we
applied low-frequency tACS during sleep while measuring neural
responses intracranially in surgical epilepsy patients. We test, in
particular, the widely held belief that brain rhythms are entrained
by tACS applied at the frequency that matches the endogenous
rhythm. In the case of NREM sleep this is particularly advanta-
geous as one can measure spindle and gamma activity, which is
strongly coupled to the slow wave, yet is uncontaminated by the
low-frequency stimulation artifact34,35.
Thirteen patients undergoing invasive monitoring for epilepsy
surgery were tested with sinusoidal tACS (0.75 and 1 Hz) during a
period of NREM sleep and/or waking rest. The primary outcome
measure during sleep is phase-amplitude coupling (PAC), which
captures the modulation of spindle amplitude (at 10 and 14 Hz)
with the phase of tACS. This measure is motivated by the relevance
of spindles to sleep-dependent cognitive processes, the strong
entrainment of spindles to native slow oscillations36,andthepre-
vious reports of tACS enhancement of endogenous slow-wave and
spindle activity16,17,20,37,38. Our primary outcome measure during
wakefulness is PAC of theta frequency (7 Hz) amplitude to tACS
phase, as theta power has previously been implicated in tACS22.
Secondary outcome measures include gamma (70–110 Hz) mod-
ulation during NREM sleep39 and alpha (10 Hz) and gamma
modulation during waking rest. We also tested on an additional
subject the configuration of Marshall et al.16 with identical electrode
location, charge density, waveform, frequency, and intensity.
Finally, we measured the intensity of induced electric fields at the
cortical surface, and leveraged validated computational models to
estimate field strengths across the brain40.Wedonotfind an acute
effect of the applied tACS on brain rhythms and attribute the
outcome to the weak-induced fields.
As a positive control, we confirm that spindle power is
modulated with the endogenous slow-wave rhythm during sleep
in the same subjects across a majority of electrodes. Additionally,
we find that acoustic stimulation with brief noise bursts reliably
evokes slow-wave and related spindle activity comparable to
effects found in healthy subjects using scalp electro-
encephalography (EEG)41. The null findings on entrainment
together with these positive controls rule out the hypothesis that
low-frequency tACS applied at conventional current intensities
can acutely entrain slow-wave, spindle, gamma, or theta activity.
We conclude that previous reports of behavioral effects of slow
oscillating tACS applied during NREM sleep and waking rest on
memory consolidation are not the result of direct neuronal
entrainment. We discuss alternative mechanisms and propose
new directions for research.
Results
Intracranial measurements of induced electric fields.We
applied low-frequency sinusoidal tACS (0.75, 1 Hz), on 13
patients with medication-refractory epilepsy with implanted
S1
LH
S7
RH
S13
RH S14
RH
S8
RH S9
RH
A2
RH
Grid Strip Depth
A3
LH A4
LH
S2
LH S3
LH
S10
LH
A1
LH
S5
LH S6
LH
S4
RH
S11
RH S12
RH
Fig. 1 Electrode coverage for 18 patients who underwent invasive monitoring for epilepsy surgery and received either tACS or acoustic stimulation. Thirteen
patients had tACS applied at a 1 or 0.75 Hz frequency, at stimulation intensities ranging from 0.5 to 2 mA, during waking rest (S1–S6) and daytime NREM
sleep (S7–S13), one patient had trapezoidal tACS (S14), and four subjects received 0.75 and 1 Hz acoustic stimulation (A1–A4). Electrode placement varied
by clinical indication, and consisted of a combination of strips, grids, and depth electrodes. Seven subjects had bilateral coverage (S1, S2, S3, S6, S8, S10,
S13, S14). A total of 2156 electrodes total were utilized (1700 tACS; 113 Trapezoidal tACS; 343 acoustic), or an average of 120 electrodes per subject.
Further demographic and clinical characteristics, electrode coverage, and stimulation protocols are summarized in Supplementary Table 1
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subdural and depth electrodes (Fig. 1; Methods; Supplementary
Table 1). Overall, we recorded and analyzed signals from 1700
electrodes (mean 131 electrodes per subject) from the 13 patients
receiving TES using this protocol. The positions of the recording
electrodes sites in each patient are shown in Fig. 1. For stimula-
tion, two scalp electrodes (2 cm × 2 cm rubber electrodes) were
placed over the frontopolar and occipital poles, accessible under
the surgical bandage. The aim was to generate the strongest
possible fields in the brain by keeping electrodes at the furthest
distance on the skull42, while remaining apart from temporal
craniotomies so as to replicate the current flow of normal head
anatomy43. We confirmed that the craniotomy defect had only
minor effects on field intensities as models of current flow with
and without the defect yield comparable field intensities (0.05 ±
0.02 V/m and 0.04 ±0.02 V/m, respectively, mean ±Std across
subjects). In one patient (S10), the stimulation electrodes were
placed at three additional locations to examine the effects of
electrode placement on extracerebral and intracerebral current
spread. For three subjects (subjects S11–S13), frontal electrodes
were offset to the left or right (Fp1 or Fp2) so that intracranial
electrode coverage, which was largely constrained to one hemi-
sphere, covered areas of maximal electric fields and thereby
optimize recording of potential entrainment effects. Maximal
stimulation intensity of tACS was adjusted for each subject based
on amplifier saturation and patient sensation. Intensity ranged
from 0.5 to 2.5 mA (current density 0.125–0.625 mA mA/cm2),
which is stronger than in previous reports5,7,16,18,19,32.
Since the electrical field, or the voltage gradient (V/m), induced
in cortex critically determines neuronal polarization44 and thus
neuronal activity4,45, we measured the local gradients in
electrodes across all subdural and depth electrodes. We calculated
the electric fields in the direction of neighboring electrodes
(i.e., the projected field; Fig. 2a) scaled to correspond to 1 mA of
stimulation (voltages scale linearly with applied currents up to
saturation levels of the amplifiers40). The median projected field
measured across all electrode locations and subjects is 0.02 V/m.
At the highest current intensity tested (2.5 mA), peak intensity
reaches only 0.16 V/m across recording electrodes (S10).
To assess the induced electric fields over brain areas not
sampled by electrode recordings and in the direction of maximal
field intensity, we used previously established modeling techni-
ques40. Briefly, computational models were individualized for
each patient based on their magnetic resonance imaging (MRI)
images and calibrated with the measured electric fields in each
patient. The models were then used to predict field intensities
throughout the brain. For one patient (subject S13), electric field
distribution for four different stimulation electrode configura-
tions were estimated. The simulated electric fields predict the
measured fields well (r=0.81 ±0.12, mean and standard
deviation across N=10 subjects modeled, all p<10−5). The
modeling results show larger fields for some locations of the brain
as compared to the recorded fields (Fig. 2b, c), as expected given
the limited electrode coverage. Maximal field magnitudes at
hippocampal depth electrodes have a median of 0.05 mV/mm
and a maximum of 0.11 V/m, for 2 mA stimulation intensity
(N=8 subjects). Median value across all the brains and electrode
locations is 0.08 V/m (Fig. 2).
Spindles entrain to the phase of endogenous slow waves. Of the
13 subjects with tACS, 7 were stimulated with several 5 or 10-min
blocks at 0.75 and 1 Hz during either daytime or nocturnal
NREM sleep (Supplementary Table 1). Four subjects were
stimulated during daytime NREM sleep; three were stimulated
during nocturnal NREM sleep; and six were stimulated during
waking rest. Duration and conditions of stimulation for each
subject varied based on clinical constraints and the total time in
NREM sleep (sleep stages N2 and N3, American Academy of
Sleep Medicine 2012 convention46).
Sleep data from the seven subjects (subjects S7–S13) were
analyzed over two separate nights without stimulation. We first
visually identified periods of NREM sleep in the intracranial
recordings. The average duration of the NREM recorded was
30.0 ±10.5 min. Slow-wave activity spans broad frequency
spectrum between 0.5 and 4 Hz with no clear peak in the
frequency spectrum within these three octaves (Supplementary
Fig. 6and Supplementary Note 1) consistent with scalp EEG
recordings in normal subjects47–49. Therefore, we analyzed
activity and stimulated at both 0.75 and 1 Hz, values previously
used in the literature16,22,31. As a primary outcome measure, we
tested the modulation of spindle amplitude (for fast spindles: 14
Hz bandpass filter with 7 Hz bandwidth; for slow spindles: 10 Hz
bandpass with 5 Hz bandwidth) by the phase of the endogenous
slow oscillations (1 Hz bandpass filter with 1 Hz bandwidth).
PAC was measured with the modulation index for individual
electrodes and statistical significance was established using
surrogate data with randomized phase (see “Methods”),
here and in the remainder of the text. Recording sites that
0.4
abc
Field proj. (recordings)
Subject Subject Subject
Field mag. (models, at electrodes) Field mag. (models, entire brain)
123
456789
10A
10B
10C
10D
11
12
13
123
456789
10A
10B
10C
10D
11
12
13
123456789
10A
10B
10C
10D
11
12
13
0.3
0.2
V/m
0.1
0
0.4
0.3
0.2
V/m
0.1
0
0.4
0.3
0.2
V/m
0.1
0
Fig. 2 Measured and estimated electric field magnitudes. aField projections calculated as the difference in recorded voltages between neighboring
electrodes divided by electrode distance for each subject (with four montage orientations shown for S10), bfield magnitudes at electrode locations
predicted by calibrated current-flow models, and cmodel-predicted field magnitude across the entire brain. Red lines indicate the medians, and boxes span
from 5 to 95% of the data, with whiskers extending to the minima and maxima. All values shown here correspond to the maximal current intensity applied
for each subject during stimulation (S1: 1 mA; S2: 0.75 mA; S3: 1 mA; S4: 1 mA; S5: 1 mA; S6: 1 mA; S7: 1.5 mA; S8: 2 mA; S9: 1.5 mA; S10A: 0.3 mA; S10B: 1
mA; S10C: 1 mA; S10D: 0.3 mA; S11: 0.3 mA; S12: 1 mA; S13: 1 mA). The difference in magnitude across subjects is primarily due to these varying stimulation
intensities
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exhibited PAC are widespread over the cortex, representing
most recorded regions (Fig. 3c). Of the 753 electrodes tested
across subjects, 497 exhibit significant PAC (electrodes with
q<0.05 after FDR correction, electrode count summarized in
Table 1and Fig. 4b). Similar results are obtained with a 0.75 Hz
bandpass filter (509 of 753 electrodes, p=0.55, Fisher exact
ratio test, Supplementary Note 2). Endogenous spindle
power amplitude is highly modulated by the phase of endogenous
slow waves (example electrodes with corresponding p-values in
Fig. 4a). Seventy-seven percent of the channels showing
PAC during night 1 also demonstrate PAC during night 2
(Figs. 3a, 4b, Supplementary Table 2, and Supplementary Fig. 6
for 0.75 Hz bandpass). The differences from one night
to the other may be due to the differing depth of NREM sleep
in the 30-min window tested, in addition to inter-individual
differences.
We also computed the preferred phase of the spindle power
within the endogenous slow-wave cycle for both nights and find
that it is stable across nights (angle difference averaged across
electrodes: −9.7°±13.6°mean and Std across subjects, Fig. 3a,
and Supplementary Fig. 7). 75 ±16% (mean across all subjects) of
the entrained electrodes have a preferred phase closer to 0°, but
25 ±16% align closer to 180°. Slow waves reflect changes from
cortical up and down states, with high and low neuronal firing
activity, respectively49. For most electrodes, 0° corresponds to the
upstate of the slow oscillation (Fig. 3b), associated with increased
broadband gamma activity (70–110 Hz), which is a measure of
neuronal firing35 (Fig. 3a). Electrodes with 180° reflect a sign
reversal of slow waves often observed in intracranial recordings35.
As a secondary outcome measure, we computed the same PAC
for gamma activity and found 533 of 753 electrodes entrained to
the phase of the slow oscillation (Fig. 3a). When we calculated the
difference of their preferred phase (Fig. 3a, Supplementary
Table 2), we find that spindle and gamma activity cluster around
a phase difference of 0° and coincide with the upstate of the slow
oscillation.
S7
Spindle
electrodes
Gamma
electrodes
Gamma–Spindle
electrodes
40
a
bc
100 Night 1
Night 2
50
0
40
30
20
20
10
0
0
40
20
0
–180 0 180
Phase (deg) Phase (deg)
S7-G06
S8-RIP08
0.2mV
1 s
Superior Rostral Right hemisphere
Phase (deg) Phase (deg) Phase (deg) Phase (deg) Phase (deg)
–180 0 180 –180 0 180 –180 0 180 –180 0 180 –180 0 180 –180 0 180
S8 S9 S10 S11 S12 S13
Fig. 3 PAC of fast spindle and gamma activity to the phase of endogenous slow oscillation. aHistogram of the mean preferred phase (of 1 Hz bandpassed
signal) across electrodes with significant PAC for two different nights (Night 1, green; Night 2, orange). Each histogram corresponds to a different subject.
0° corresponds to the positive phase of the raw iEEG traces, +/−180° corresponds to the negative phase. The upstate is characterized by increased fast
spindle (14 Hz) and increased gamma (70–110 Hz) activity. Electrodes with increased spindle/gamma activity in the positive phase (0°) are mostly cortical,
and those with increase in the negative phase (180°) are mostly depth electrodes, which is consistent with previous reports35,49.bExamples with spindle
activity occurring with the positive phase of the slow-wave cycle as seen in the raw iEEG traces for two representative cortical electrodes (subject S7,
electrode G06l; subject S8, electrodes RIP08). cLocations of cortical electrodes with significantly PAC of fast spindle activity for S8. Night 1 (top row,
green) and night 2 (bottom row, orange) show widespread and consistent entrainment across nights. Left to right: top view, frontal view, right view. Black
dots indicate the locations of the subdural grid electrodes
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In addition to measuring PAC, we implemented a spindle
detection algorithm50 and computed their preferred phase within
the slow-wave cycle. We obtain similar results with this spindle
detection algorithm albeit with a smaller number of significantly
entrained electrodes (187 instead of 497 of 753 electrodes;
Supplementary Table 2).
Low-frequency tACS does not entrain sleep spindles. In four
patients during an afternoon nap, and three patients during
night-time sleep, we applied several 5–10-min blocks of con-
tinuous stimulation during NREM sleep (Stages 2 and 3). We
tested tACS at 0.75 and 1 Hz consistent with previous
studies16,17. We reasoned that if the applied low-frequency tACS
entrains endogenous slow-wave activity, then spindle power,
which is coupled to slow-wave activity, should occur in phase
with the applied fields. Because slow-wave activity cannot be
directly assessed during tACS due to the stimulation artifact,
spindle power (14, 10 Hz) was used as an index of the slow-wave
rhythm. We used identical PAC processing and statistical tests as
with endogenous sleep (modulation index r, assessed for sig-
nificance with phase-randomized surrogate data), with the
exception that phase could now be inferred exactly from the
stimulation artifact, readily observed in the raw recordings
(Supplementary Fig. 4). In subject S8, we find significant PAC of
fast spindle power (14 Hz band) in one 1 of 116 electrodes after
correcting for multiple comparisons across electrodes (FDR, q<
0.05; Fig. 4b, for this single electrode N=292 cycles,
p=0.0003 prior for FDR correction). However, PAC in this
electrode does not persist in following stimulation blocks sepa-
rated by only 5-min intervals. The strength of evidence for PAC is
compared across blocks in Supplementary Fig. 8. Other subjects
(S7, S9, S10) do not demonstrate significant fast spindle
entrainment (at q<0.05) during any stimulation blocks.
Figure 4b shows a summary of all electrodes tested at 1 Hz. For
slow spindle power (10 Hz), only 1 of 98 electrodes demonstrates
significant entrainment after correcting for multiple comparisons
(FDR, q<0.05, r=7.48, N=216 cycles, p=0.0005 prior to FDR
correction), but this was not maintained across stimulation blocks
(Table 1).
To further increase the power of the statistical test we
combined the data across tACS blocks. The total stimulation
time tested after concatenating all blocks was 30 min (subject S7),
30 min (subject S8), 20 min (subject S9, 1 Hz), 10 min (subject S9,
0.75 Hz), 10 min (subject S10), 20 min (subject S11), 7.5 min
(subject S12), 20 min (subject S13, 1 Hz), and 20 min (subject S13,
0.75 Hz). After concatenating all blocks and FDR correction, we
find no electrodes demonstrating significant spindle entrainment
at 14 or 10 Hz.
In three subjects (S9, S11, S13) we also tested entrainment of
spindle power during tACS at 0.75 Hz as this frequency is a
commonly used parameter in previously published studies16,17.
Figure 4b shows a summary of all electrodes tested at 0.75 and 1
Hz. Only one electrode (out of 93 total electrodes) in one of the
three subjects (S9) tested demonstrates fast spindle entrainment
with 0.75 Hz tACS comparisons (FDR, q<0.05, r=1.18, N=216
cycles, p=0.0001 prior to FRD correction), and only in one out of
two stimulation blocks. In summary, no stable entrainment of
spindle activity to the applied electric fields was detected for low-
frequency tACS applied for intensities of up to 2.5 mA during
NREM sleep.
Low-frequency tACS does not alter spindle-gamma alignment.
Unlike applied stimulation waveforms, which remain steady
during application, the endogenous slow waves remain periodic
for at most two cycles (Supplementary Fig. 5), and are more often
isolated events. This lack of temporal coherence is the source of
the broadness of the slow-wave activity spectrum and lack of a
well-defined peak in most electrodes (Supplementary Fig. 6).
Because the endogenous slow-wave oscillation is not reliably
discerned during stimulation due to the overwhelming stimula-
tion artifact, we used gamma activity to infer the phase of the
endogenous slow-wave rhythm. It is possible that tACS did not
entrain spindle activity, but nonetheless interferes with the slow-
wave rhythm, which is thought to coordinate activity in higher
frequency bands. We therefore tested whether low-frequency
tACS disrupts the temporal alignment of gamma and spindle
activity, as measured by the cross-correlation of the instantaneous
amplitude of these two rhythms (Fig. 5). As expected from the
Table 1 Summary of PAC results across all subjects and conditions tested
Stimulation
(subject ID)
Frequency Arousal
state
Subjects Total #
electr.
# electr.
tested
Freq. band
tested
# electr.
entrained
Consistent across
blocks (%)
None (S7–S13) 1 Hz
endogenous
NREM 7 860 753 14 Hz (fast
spindle)
497 77
None (S7–S13) 1 Hz
endogenous
NREM 7 860 753 10 Hz (slow
spindle)
361 57
tACS (S7–S13) 1 Hz (.75 Hz) NREM 7 860 598 14 Hz (fast
spindle)
2 (1) 0
tACS (S7–S13) 1 Hz (.75 Hz) NREM 7 860 598 10 Hz (slow
spindle)
10
tACS (S1–S6) 1 Hz Wake 5 716 584 7 Hz (theta) 0 0
tACS (S1–S6) 1 Hz Wake 5 714 578 90 Hz (high
gamma)
10
tACS (S1–S6) 1 Hz Wake 6 840 695 10 Hz (alpha) 8 0
Acoustic (A1–A4) 1 Hz (0.75
Hz)
NREM 4 448 343 14 Hz (fast
spindle)
20 (51) 4
Acoustic (A1–A4) 1 Hz (0.75
Hz)
NREM 4 448 343 10 Hz (slow
spindle)
16 (33) 4
Trapezoidal (S14) 0.75 Hz NREM 1 126 113 1 Hz (slow-
wave)
00
Columns indicate (from left to right): the type of stimulation, if any, along with the subject ID; the frequency of stimulation or analysis that provided the phase for the PAC measure; the arousal state of
the subject; number of subjects tested; total number of electrodes tested across all subjects; frequency band that provided the amplitude for the PAC measure; total number of electrodes that showed
significant entrainment in at least one block of stimulation/night; fraction of electrodes that entrained consistently across two or more blocks/nights of all electrodes tested
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alignment of the two rhythms to the 0° phase or cortical upstate
during endogenous sleep (Fig. 3a), the cross-correlation of the
gamma and spindle power is maximal at zero-time delay (the
median across electrodes and subjects S7–S13 ±the interquartile
range is 0.0020 ±0.16 s) for endogenous NREM sleep (Fig. 5,
Nights 1 and 2). During stimulation, the temporal relationship of
spindle with gamma activity (with a time lag of zero. Night 1:
−0.0156 ±0.020 s; Night 2: −0.0078 ±0.023 s) is maintained
(Fig. 5). This suggests that tACS does not disrupt the temporal
coordination of spindle and gamma activity.
Low-frequency trapezoidal tACS does not entrain slow waves.
To further test our null findings, we performed a close replication
of the original study in which tACS was found to enhance slow-
wave activity16. This was performed in one additional surgical
patient (subject S14), during night-time sleep. We used the same
montage, electrodes, waveform, and (lower) stimulation intensity
of that earlier study16 (bilateral ring electrodes on F3/F4 and
mastoids with 0.75 Hz on/off trapezoid at 0.52 mA) and stimu-
lated during NREM sleep periods. This electrode placement was
facilitated in this one subject by a custom surgical bandage. The
trapezoidal waveform does not permit analysis during stimulation
due to the broad band artifacts they generate (for this reason we
used sinusoidal stimulation for the majority of the experiments).
Thus, here we analyzed whether the slow-wave activity immedi-
ately after stimulation remained time-aligned across trials fol-
lowing previous methods16,37. To achieve comparable statistical
power to these earlier studies in a single subject we stimulated in
80 short blocks (8 cycles, or 10 s duration each; see also discussion
on statistical power in Supplementary Note 3). After correcting
for multiple comparisons (FDR, q<0.05), none of the 113 elec-
trodes exhibit phase-locked slow-wave oscillations (0.5–1.5 Hz) in
the subsequent stimulation-free intervals (using Rayleigh test for
non-uniformity, see “Methods”; uncorrected p-values reported in
Supplementary Fig. 12). Remarkably, subsequent clinical evalua-
tion did not reveal any electrical after-discharge or interictal
abnormalities; the patient was diagnosed as non-epileptic.
Low-frequency tACS does not modulate theta during wake-
fulness. During waking rest, we also tested whether the applied
field modulates theta activity (7 Hz bandpass, 3.5 Hz bandwidth)
as a primary outcome measure, and alpha (10 Hz bandpass, 5 Hz
bandwidth) or gamma activity (90 Hz bandpass, 40 Hz band-
width) as exploratory outcome measures. We applied 1 Hz tACS
to six patients during wakeful rest. After correcting for multiple
comparisons (FDR, q<0.05), none of the 584 electrodes tested
for subjects S1–S4 and S6 exhibit significant PAC. Subject S5 was
not analyzed for theta entrainment due to the presence of
S7
p<0.0001
p=0.0105
p=0.1506 p=0.0100 p=0.8540 p=0.0474 p=0.2342 p=0.4517 p=0.9433 p=0.0005 p=0.0007 p=0.0006 p<0.0001
p=0.0002 p=0.0003 p=0.0006 p=0.0006 p=0.0046 p<0.0001 p<0.0001 p<0.0001 p<0.0001p<0.0001
0.2
a
Night 1
Night 1 Night 2 tACS Acoustic
Stimulation Night 2
Spindle amp. (a.u)
0.1
0
0.2
0.1
0
0.2
0.1
0
–180 0 180
S8
p=0.0002
–180 0 180
S9
p<0.0001
–180 0 180
S10
p<0.0001
–180 0 180
S11
p<0.0001
–180 0 180
S12
p<0.0001
–180 0 180
S13
p=0.0027
–180 0180
A1
p=0.0084
–180 0180
A2
p<0.0001
–180 0 180
A3
p=0.0052
–180 0 180
A4
b
p=0.0034
–180 0180
–180 0 180 –180 0 180 –180 0 180 –180 0 180 –180 0 180 –180 0 180 –180 0 180 –180 0 180 –180 0 180 –180 0 180 –180 0 180
–180 0180 –180 0180 –180 0180 –180 0180 –180 0180 –180 0180 –180 0 180 –180 0 180 –180 0180 –180 0180 –180 0 180
Phase (deg) Phase (deg) Phase (deg) Phase (deg) Phase (deg) Phase (deg) Phase (deg) Phase (deg) Phase (deg) Phase (deg) Phase (deg)
Night Night Night Night Night Night Night Night Night Night TAC S
(1 Hz)
TAC S
(1 Hz)
TAC S
(1 Hz | 0.75 Hz)
TAC S
(1 Hz | 0.75 Hz)
AS
(0.75|1 Hz)
AS
(0.75|1 Hz)
AS
(0.75|1 Hz)
AS
(0.75|1 Hz)
TAC S
(1 Hz)
TAC S
(0.75 Hz)
TAC S
(1 Hz)
Electrodes (#)
150
100
50
0
S7 S8 S9 S10 S11 S12 S13 A1 A2 A3 A4
Night
Fig. 4 Modulation of fast spindle power with phase of the slow oscillation. Different conditions are indicated in color: endogenous sleep (green, orange),
tACS (black), acoustic stimulation (AS, red). aFast spindle power (14 Hz band) relative to the phase of slow oscillation shown in one representative
electrode for each subject. Here, 0° phase corresponds to the physiological upstate of slow-wave activity. For endogenous slow-wave oscillation sleep
periods (green and orange) phase is determined from 1 Hz bandpassed signal. For tACS (black traces) and acoustic stimulation (AS, red traces) blocks
during nap (S7–S10, A2–A3) and during night-time sleep (S11–S13, A1, A4), phase is determined from the stimulation artifact or acoustic trigger pulses in
the recordings, where 0° corresponds to the peak in anodal stimulation (e.g., peak positive stimulation relative to frontal/anodal electrode) for tACS, or the
time of sound delivery in the case of AS. Note the modulation of spindle power with the phase of the endogenous slow oscillation in each subject during
night-time sleep and during acoustic stimulation, and the lack of consistent modulation with the phase of tACS. Both nights have similar preferred phase.
Each column represents the same electrode per subject. bFraction of recordings sites with significantly entrained spindle activity during endogenous sleep,
tACS, and acoustic stimulation. Each bar represents a block of data analyzed. Bar height indicates the number of electrodes available, with a fractionof
electrodes discarded due to poor data quality or excessive interictal activity (white), a fraction of electrodes with non-significant entrainment (gray), and a
fraction of electrodes with significant spindle entrainment (night 1, green; night 2, orange; tACS, black; AS, red) after correction for multiple comparisons.
Each bar during tACS/AS represents a stimulation block of 5 min. During tACS stimulation only two recording sites (in S8, first block, red bar; and S09,
second block of 0.75 Hz, red bar) showed significant spindle entrainment, although this was not a stable finding across stimulation blocks. With acoustic
stimulation, many more electrodes were found to be reliably entrained across both 0.75 and 1 Hz stimulation rates
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significant environmental artifact. For alpha activity, significant
PAC is present in 1 of 122 electrodes tested in subject 1 (FDR,
q<0.05, N=594 cycles, p=0.0004 uncorrected), 2/78 electrodes
in subject S4 (FDR, q<0.05, N=594 cycles, uncorrected
p-values =0.007, 0.003), and 5/155 electrodes in subject S6 (FDR,
q<0.05, N=594 cycles, p=0.0004, 0.0001, 0.0015, 0.0018,
0.0001, uncorrected). None of the 340 electrodes recorded for
subjects S2, S3, and S5 demonstrate significant modulation of
alpha activity with tACS phase. Significant coupling between
stimulation phase and gamma activity is detected in 1/155 (FDR,
q<0.05, N=592 cycles, p=0.0001 uncorrected) recording sites
for subject 6, but not in any of the 423 electrodes tested for
subjects S1, S2, S4, and S5. Subject S3 was not analyzed for
gamma power entrainment due to the presence of significant 60
Hz artifact. However, none of the recording sites with significant
entrainment effects for theta, alpha, or gamma activity in one
stimulation block show consistent effects across repeated stimu-
lation blocks. We conclude that 1 Hz tACS does not entrain theta,
alpha, or gamma activity during waking rest.
Pulsed auditory stimulation induces slow-wave/spindle
entrainment. It could be argued that slow-wave activity cannot
be reliably entrained in epilepsy patients. To directly address this
possibility, we stimulated an additional four patients (subjects
A1–A4) with low-frequency acoustic stimulation (50 ms, pink-
noise bursts repeated at 0.75 and 1 Hz) delivered through ear-
phones during nocturnal NREM sleep. Using the same analysis as
described earlier (modulation index, assessed for significance with
phase-randomized surrogate data), we find a number of electro-
des that showed significant PAC entrainment during both 0.75
and 1 Hz auditory stimulation after FDR correction (q<0.05; p-
values prior to FDR correction are shown in Supplementary
Fig. 10, Fig. 4; number of electrodes entrained for patient at 0.75
Hz, at 1 Hz, and consistently entrained across stimulation blocks
as follows A1: 38, 11, 11; A2: 7, 1, 1; A3: 0, 2, 0; A4: 6, 6, 1). The
lower numbers in A3 may be due to the lower sound volume used
for sleep comfort. In total, we found 51/343 (14.8%) entrained
electrodes during 0.75 Hz, and 20/343 (5.8%) entrained electrodes
during 1 Hz acoustic stimulation, when pooled across all subjects.
In addition, time-locked analysis of the individual electrode
waveforms relative to stimulation onset reveals robust slow-wave
events and an increase in spindle power during the slow-wave
upstate, when compared to the sham stimulation delivered during
a baseline period of sleep (χ2(99) =200.23, p<0.0001, Fig. 6).
These findings suggest that low-frequency auditory stimulation
delivered during NREM can reliably entrain slow-spindle activity
in epilepsy patients.
Finally, to exclude the possibility that we missed a genuine effect,
we performed a number of additional post hoc analyses with less
conservative thresholds, and limiting analysis to electrodes with
highest field intensity, or those with marked spindle activity.
Additionally, we tested for changes in spindle, slow-wave and theta
power before and after stimulation. These analyses are detailed in
Supplementary Note 4, and show no consistent effects.
Discussion
We set out to test the hypothesis that low-frequency tACS can
acutely modulate endogenous slow-wave rhythms during NREM
sleep or theta activity during wakefulness. We find that low-
frequency tACS, at 1 or 0.75 Hz, applied during NREM sleep at
common stimulation intensities does not reliably entrain spindle
oscillations during NREM sleep. This contrasts with spindle
activity that is strongly entrained by endogenous slow oscillations
in almost two-thirds of the electrodes in depth and cortical sur-
face electrodes recorded during two nights of sleep. Gamma
activity also reliably entrains to the upstate of slow-wave activity,
at a similar phase as spindles. Furthermore, time alignment
between the spindle and gamma rhythms is not altered by tACS,
suggesting that co-occurrence of these rhythms to the cortical
upstate was not disrupted by stimulation51. Despite medications,
sleep physiology (i.e., coupling of spindle to slow activity during
NREM sleep) in this patient population resembles that of healthy
subjects49. In contrast, low-frequency auditory stimulation during
NREM sleep evokes reliable slow-spindle events as seen in healthy
subjects41,52. Furthermore, low-frequency tACS during waking
rest does not modulate theta, alpha, or gamma frequency activity
with the phase of stimulation. Thus, low-frequency sinusoidal
tACS does not entrain dominant rhythms during NREM sleep or
waking rest.
S7
20 20
40
60
80
20
40
60
80
20
40
60
80
20
40
60
80
20
40
10
20
20 20 0.2
0
–0.2
Normalized
correlation
40
60
80
20
40
60
80
20
40
60
80
30
40
10
20
30
40
10
20
30
40
40
60
20
40
60
20
40
60
60
20
40
60
20
40
60
100
20
40
60
80
100
20
40
60
80
100
Night 1
electrodes
40
60
20
Night 2
electrodes
40
60
20
TAC S
electrodes
40
60
–1 0 1 –1 0 1 –1 0 1 –1 01–1 01–1 0 1 –1 01
–1 01–1 0 1 –1 01–1 01–1 0 1 –1 01–1 0 1
–1 0
Time lag (s) Time lag (s) Time lag (s) Time lag (s) Time lag (s) Time lag (s) Time lag (s)
1–1 0 1 –1 01–1 01–1 0 1 –1 01–1 0 1
S8 S9 S10 S11 S12 S13
Fig. 5 Cross-correlation between the amplitudes of spindle and gamma oscillations. Panel shows cross correlation for all electrodes for seven subjects
(S7–S13) during two nights of sleep without stimulation and one with tACS. False color indicates correlation values. Vertical axis indicates electrode
number and horizontal axis indicates time lag between the two rhythms. The peak at zero time-lag indicates that spindle and gamma occur at the same
time, negative lag indicates that spindle activity precedes gamma activity
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It is tacitly assumed that tACS can entrain ongoing brain
rhythms to boost memory performance16,18,19. However, pre-
vious studies have not directly measured entrainment of these
latter rhythms. The present study is the first human experiment
to assess the direct physiological effects of tACS by measuring
cortical oscillations via invasive EEG. Here, we demonstrate that
tACS does not necessarily induce coupled slow-spindle rhythms
in human sleep. This specific result is important because of the
exciting but mixed results reported with tACS and sleep-
dependent memory enhancement16,18,19, which highlights the
lack of mechanistic understanding of this technique.
The strengths of our study depend on the simultaneous mea-
surement of brain activity directly from the cortical surface
during stimulation. Intracranial EEG (iEEG) provides superior
signal quality compared to scalp EEG, allowing the direct mea-
surement of induced electrical fields and their acute effect on
mesoscopic brain activity. We collected a large data set with
18 subjects, 2156 electrodes (mean 120 electrodes per subject),
and 900–3600 trials (oscillation cycles) per subject. Furthermore,
to ensure that our null findings are not due to limitations in
analytical technique, we included two positive control conditions,
including endogenous sleep and acoustic stimulation, which
demonstrate that slow-spindle rhythms can be entrained in these
surgical patients and that the analytical methods can detect such
entrainment.
Previous estimates of entrainment of slow-wave activity with
applied slow tACS relied on averaging the periods immediately
following stimulation, which gives a much smaller number of
trials. In other words, measurement during stimulation with iEEG
permits more sensitive detection of PAC as compared to mea-
surement after stimulation with scalp EEG. Finally, we applied
stronger stimulation intensities compared to previously published
reports16,17. In summary, our experimental and analytical
approach permits detection of small changes in modulation,
which we do not find.
Previously proposed explanations for how TES may affect
neuronal activity rely mainly on in vitro slice preparations and
in vivo experiments in rodents53. In vitro studies demonstrate
that a weak uniform (DC) electric field can alter the resting
transmembrane potential, which increases or decreases average
firing rates of the affected neurons23,44. In vitro studies using AC
stimulation demonstrate single neuron entrainment by mod-
ulating the rate and timing of neural firing23,30,54. It is assumed
that small changes in the firing rate and coherence of the affected
neuronal populations may be amplified by network-level
activity23,54. However, the weak effects of the applied field also
compete with the endogenous neuronal drive, which controls the
instantaneous phase and amplitude of the rhythmic activity. For
stimulation to entrain neural activity that does not have a steady
rhythmic behavior, such as slow-wave activity, one would need
fairly strong effects that reset phase in a single cycle. This may
explain why several-fold stronger currents are needed to entrain
some network oscillations compared to the lower intensities
required to alter firing rates of several neurons30,31.
300
a
b
200
Number of slow-wave down-states
Frequency (Hz)
100
000.1 0.2 0.3 0.4 0.5
Latency from audio stimulus onset (s)
0.6 0.7 0.8
Stimulus onset
0.2 s
20 µV
0.9 1
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
00.1 0.2 0.3 0.4 0.5
Time (s)
0.6 0.7 0.8 0.9 1
25
20
15
10
5
Fig. 6 Slow-wave entrainment to 1 Hz acoustic stimulation. aNumber of detected slow-wave oscillations relative to stimulus onset, summed over the 99
electrodes analyzed, in a 5-min interval for subject A4 during stimulation (red) and an equivalent baseline sleep period (black). Inset shows the averaged
evoked response in both conditions in one representative electrode (depth electrode DPMT 03), time-locked to stimulus onset (note the positive peak a
cortical down-state in this depth electrode, which is opposite from the majority of cortical electrodes shown as examples in Fig. 3b). bTFR of time-locked
epochs (e.g., relative to stimulus onset) in one representative electrode (DPMT 03), showing an increase in slow spindle power (8–12 Hz) during slow-
wave “down-states”consistent with previous literature80 (0.4–0.8 s RE: stimulation onset) and fast spindle power (12–18 Hz) during “up-states”(0.15–0.4
s, RE: stimulation onset) as found for endogenous slow-wave oscillations (see Figs. 3,4). TFR is computed relative to baseline sleep period with sham
stimulation. Solid innermost curve represents significant increases relative to baseline after FDR correction (one-tailed t-test, p<0.05)
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Our null findings likely result from the weak-induced electrical
fields. At the highest current intensity applied (2.5 mA), peak
intensities reach only 0.16 V/m across recording electrodes.
Although the recording electrodes may not capture the maximal
electric fields induced by a given stimulation montage, our
model-based extrapolations suggest that peak fields do not exceed
0.43 V/m intracranially when stimulating at 2 mA40, which is
comparable to a recent report of 0.5 V/m as maximal intensities55.
Thus, field magnitudes may be an order of magnitude smaller
than required to entrain slow-wave rhythms, which may explain
why experimental attempts to entrain sleep rhythms for memory
enhancement have been inconsistent5,7. To place these weak-
induced fields into perspective, synchronous neuronal activity
under physiological conditions can generate approximately 1–2
V/m gradient across the CA1 pyramidal layer during theta
oscillations in the hippocampus56 and during slow oscillations in
the neocortex30. Under ideal circumstances when the electric
fields are applied in a phase-locked manner coupling directly with
the cellular membrane in vitro, 0.25 V/m can bias a single neu-
ron’s spike threshold to induce cumulative effects and entrain
network oscillations23,57. In behaving rats, hippocampal firing
rate, but not local field potential oscillations, was modulated at a
threshold of 1 V/m31. In other scenarios, 5 V/m or more may be
needed to entrain network oscillations30,53.
The weak electric fields measured in our study suggest that
higher intensity stimulation may be required to instantaneously
affect network-level neuronal activity. The practical implication
of our findings is that new methods of neuromodulation, which
increase the amount of current reaching the brain, are needed to
advance the field. However, transcranial stimulation intensity is
limited by clinical considerations such as sensory perception.
These constraints are especially relevant for sleep studies (one of
our subjects was wakened by >2 mA stimulation and we had to
decrease the intensity), subject blinding58, and patient safety59.
Another possible explanation for our null findings is the
observation that endogenous slow-wave activity does not have a
steady rhythm, but rather spans wide frequency range (0.5–4 Hz).
In human sleep, slow waves appear to be unitary events, and a
regular rhythm is preserved for at most two cycles. Thus, the
continuous application of a weak, low-frequency stimulation
waveform aligns imprecisely to these irregular events. In order for
spindle activity to align to the stimulation phase, the effect of
stimulation would have to be nearly instantaneous. This would
require pacing of the endogenous rhythm on a nearly cycle-by-
cycle basis. By contrast, the most sensitive effects of stimulation23
(at weak fields of 0.2 V/m) occur when the endogenous rhythm is
narrow-band such that stimulation effects can accumulate over
many cycles, leading to resonant phenomena. Our results there-
fore do not rule out more sensitive resonant effects of tACS for
more rhythmic narrow-band activity such as alpha and spindle
oscillations38.
Finally, these null effects may result from the anatomical dif-
ferences seen when scaling from in vitro to rodent to human
studies. Due to complex cortical folding in humans, portions of
the network that are differently oriented relative to the induced
potential field may demonstrate both excitatory and inhibitory
behavior.
We acknowledge the difference in stimulating electrode pla-
cement between our protocol and previously published protocols,
yet do not conclude that the null effect is because of this
experimental difference. Because of the clinical limitations of the
surgical bandage, we were able to place electrodes in the fron-
topolar and occipital regions, directly below the surgical dressing.
The advantage of maximizing electrode distance as in our pro-
tocol is to decrease the amount of current shunted by the scalp
(compared to electrode placement in the bilateral frontal regions
(F3/F4) and mastoids as prior). This generally should lead to
stronger electric fields in the brain as compared to the
F3/F4 montage42, as supported by our recently validated current
flow models40.
The positive control of acoustic stimulation demonstrates that
slow-wave and spindle activity can be modulated during NREM
sleep in our patients. Acoustic stimulation has previously been
shown to enhance sleep oscillations in healthy subjects52 with an
increase in sleep-dependent memory consolidation41. Using a
low-frequency acoustic analog of tACS during NREM, we observe
sleep spindles are modulated by the rhythmic stimulation in
8–14.3% of the electrodes analyzed. Additionally, the timing of
spindle activity coincides with the upstate for both acoustic sti-
mulation and endogenous slow waves. Together this suggests that
endogenous slow wave may have been entrained by the rhythmic
stimulation. Regardless of the mechanism (entrained vs. induced
spindle), this indicates that modulation of sleep oscillations is
possible, and that the analytic techniques used to assess
entrainment are sensitive to the PAC of slow-wave/spindle events.
There are, however, limitations of our study. First, because the
recording electrodes are placed according to clinical indication,
they do not necessarily capture the maximal electric fields
induced by a given stimulation montage. Despite broad coverage
of the cortical surface, peak electric fields may occur outside the
recorded areas (but should not exceed 0.43 V/m for 2 mA).
Nonetheless, spindle entrainment during endogenous sleep and
acoustic stimulation show the widespread nature of these cortical
rhythms. Even if our electrodes did not capture the maximal
electric fields, we would have expected to see a small number of
electrodes entrained from tACS. Second, while we did examine
changes in power before and after stimulation, the experiments
were not optimally designed for this purpose, and thus fluctua-
tions that were found cannot be attributed to tACS, but are likely
the result for natural fluctuations of power within and across
nights of sleep. Third, we only considered low-frequency stimu-
lation. Given the dependence of oscillatory stimulation of the
specific of network rhythms53, these findings may not generalize
to other tACS frequencies. In summary, as with all null results,
one cannot rule out effects outside the parameters tested here. In
particular, it is possible that there were lasting effects on spindle
power, which we did not resolve here because of their natural
fluctuations during sleep on the time scales of minutes. Such
changes were reported in past studies16,21,60 by averaging over
many subjects and may have resulted from the net-DC currents
used there, which we did not test here.
Patients with refractory focal-onset epilepsy undergoing sur-
gical evaluation represent a unique opportunity to record directly
from the cortical surface during non-invasive stimulation. While
aberrant local networks involved in seizure propagation are
observed, other cortical and subcortical functions are often con-
sidered normal. The potential effects of antiepileptic medications
may decrease overall excitability, although some patients were
tapered off all medications during stimulation. Widespread and
strong spindle entrainment by native slow oscillations during
sleep demonstrates that our methods detected physiologically
meaningful changes despite medication use, similar to what has
been reported previously regarding sleep rhythms recorded in
epilepsy surgical patients61. We demonstrate that entrainment of
spindle activity in epilepsy patients is possible with acoustic sti-
mulation. Finally, an exact replication experiment of previously
published protocols, delivered to a healthy brain, confirmed the
absence of acute entrainment of slow waves and further
strengthens the generalizability of our results.
We emphasize that our null results for tACS do not contradict
the reported behavioral effects. While positive behavioral results
have been found in rodents20, a meta-analysis on memory effects
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reports mixed results in humans33. There may be other means by
which non-invasive stimulation affects brain activity in multiple
indirect ways, including activation of afferent nerves58,62, retina
and the vestibular apparatus63, astrocytes and perivascular
elements64,65, glial activation65, synaptic plasticity66, as well as
through placebo effects67, which merit further exploration.
Our findings suggest that investigation of novel methods of
stimulation delivery are needed. This may include methods that
induce stronger electric fields at the cortical surface (while
minding patient safety and sensation), or may be based on
acoustic and other modes of sensory stimulation. Furthermore,
applying current at the optimal phase of endogenous rhythms in
a closed-loop manner may be more effective68. However, such
responsive methods can be therapeutic only if the impaired net-
work pattern is identified and continually monitored69,70.
In some medically justified cases, chronic closed-loop feedback
stimulation may be beneficial. Electrode plates may be placed
directly onto the skull or on the cortical surface to bypass the
skin, as illustrated by an FDA-approved therapeutic device, used
to detect and disrupt electrographic seizures71. Further progress
requires the investigation of novel electrode arrangements and
stimulation delivery to produce meaningful and reproducible
physiological and behavioral effects.
At a minimum, the present data suggests that tACS at stimu-
lation intensities of up to 2.5 mA does not entrain slow-wave
activity during NREM sleep. More generally, our result challenges
the common assumption that tACS entrains and enhance endo-
genous rhythms. Thus, future studies will now have the burden of
proof when such claims are made.
Methods
Human subjects. This study was performed in epilepsy patients undergoing sur-
gical evaluation with iEEG monitoring at New York University Medical Center
(NYUMC). The protocol was approved by the NYUMC Institutional Review Board
and the Clinical Trials Registration number was NCT02263274 (www.clinicaltrials.
gov). Subjects were eligible according to pre-established criteria, including: (1) age
over 18 years old; (2) undergoing invasive monitoring for seizure localization for
epilepsy surgery; and (3) ability to provide informed consent or have a legal
guardian who could consent. Exclusion criteria included (1) significant cognitive
impairment (IQ <70), (2) facial or forehead skin breakdown that would interfere
with surface electrode placement, (3) contraindication to MRI, (4) known adhesive
allergy, (5) space occupying lesion, and (6) subjects who had an electrographic
seizure for 1 h prior to the stimu lation procedure. All patients (n=17) or their
caregivers provided informed consent. Subjects were enrolled between December
2013 and May 2017. A table listing subject characteristics is included in Supple-
mentary Table 1.
Sleep staging. Stimulation was performed after patients had entered at least 5 min
of continuous NREM sleep, during a daytime nap or nocturnal sleep. Initial sleep
staging was performed by visual online analysis, for the presence of a slow-wave
and spindle activity as detected in the real-time iEEG seen at the bedside by a
physician board-certified in clinical neurophysiology, as well as by direct clinical
observation. As part of standard practice at NYU, an extradural lead is customarily
placed near the vertex of the craniotomy to aid with spindle detection. When the
patient aroused or drifted into a lighter stage of sleep, stimulation was stopped. The
iEEG segments were later confirmed by a second board-certified physician to be
consistent with N2 and N3 sleep. During offline processing, we selected segments
of NREM sleep for analysis by comparing raw spectrograms of sleep (after artifact
subtraction), to demonstrate that the depth of NREM sleep is similar across testing
conditions (Supplementary Fig. 6).
iEEG recordings. iEEG was recorded from implanted subdural platinum-iridium
electrodes embedded in silastic sheets (2.3 mm diameter contacts, 10 mm
center–center spacing, Ad-Tech Medical Instrument, Racine, WI) or depth elec-
trodes (1.1 mm diameter, 5–10 mm center–center spacing). The decision to
implant, placement or recording electrodes, and the duration of invasive mon-
itoring were determined solely on clinical grounds and without reference to this
study. Electrodes were arranged as grid arrays (8 ×8 contacts, 10 or 5 mm center-
to-center spacing), linear strips (1 × 4 to 12 contacts) , or depth electrodes (1 × 8 or
12 contacts), or some combination thereof. Subdural electrodes covered extensive
portions of lateral and medial frontal, parietal, occipital, and temporal cortex of the
left and/or right hemisphere.
Within 24 h after surgical implantation of electrodes, patients underwent a
post-operative brain MRI to confirm subdural electrode placement. Electrode were
localized and mapped onto the pre-implant and post-implant MRI using geometric
models of the electrode strips/girds and the cortical surface72.
Here, we present an efficient method to accurately localize intracranial electrode
arrays based on pre-implantation and post-implantation MR images that
incorporates array geometry and the individual’s cortical surface.
Clinical (macroelectrode) recording equipment. Recordings from grid, strip, and
depth electrode arrays were made using a NicoletOne C64 clinical amplifier (Natus
Neurologics, Middleton, WI), bandpass filtered from 0.16–250 Hz and digitized at
512 Hz. ECoG signals were referenced to a two-contact subdural strip facing
toward the skull near the craniotomy site. A similar two-contact strip screwed to
the skull was used for the instrument ground.
NeuroConn DC stimulator. The DC-STIMULATOR PLUS (NeuroConn, Ger-
many) is a CE-certified medical device for conducting noninvasive TES in humans.
The stimulator is a micro-processor-controlled constant current source, which
continuously monitors electrode impedance, and detect insufficient contact with
the skin. The device is battery powered, and therefore electrically isolated from the
clinical recording electrodes and equipment.
Low-frequency tACS. We performed 0.75 and 1 Hz sinusoidal tACS on 13 epi-
leptic patients with implanted subdural and depth electrodes. Seven subjects were
stimulated during NREM sleep (four daytime nap; three nocturnal sleep) and six
subjects were stimulated during waking rest, eyes closed. Patients were over 18
years old and fluent in English. Subjects were excluded if they had frequent (>2)
electroclinical seizures in the 24 h preceding stimulation . Patient characteristics and
electrode coverage are summarized in Supplementary Table 1and Fig. 1.
All subjects tolerated scalp stimulation. All subjects who were stimulated during
night-time sleep (N=3, subjects 11–13) and most subjects during an afternoon nap
(N=4, subjects 7–10) were able to sleep through trials at stimulation intensities
between 0.5 and 2 mA. One subject (subject 13) woke from sleep and reported an
itching sensation during one stimu lation block with 2.5 mA current intensity. For
the tACS experiments, we recorded and analyzed from 1700 electrodes without
artifacts (mean of 131 electrodes per subject, example electrode in Fig. 3b). There
were no complications from stimulation, and no induced electrographic seizures.
One patient (subject 7) had a typical electroclinical seizure during stimulation.
Because this patient had frequent spontaneous seizures, it was determined by the
patient’s epileptologist that stimulation was unlikely to have caused the seizure.
Furthermore, we enrolled one subject (S14) who had a bilateral subdural strip
and depth survey, to perform a precise replication experiment of prior protocols.
This patient had multiple target clinical events captured, which were non-epileptic
in nature. He did not have any interictal or ictal activity captured during 1 week of
monitoring, even while medications were being withdrawn. In other words, the
patient did not demonstrate any epilepsy-related pathophysiology.
We reviewed the hour of iEEG recording prior to stimulation to exclude recent
seizures. We performed a pre-stimulation clinical assessment (including
assessment of the stimulation skin site and neurologic examination). A physician
(AL) was present at the bedside during the entire procedure to monitor for safety.
The patient’s iEEG recording was monitored in real time at the bedside during
stimulation for seizures.
For patients S1–S13, two stimulating electrodes were placed medially over the
frontal and occipital poles (2 cm × 2 cm rubber electrodes) for patients S1–S10. In
patients S10–S13 the frontal electrode was offset from midl ine by 3 cm (S10 and
S13 left frontal; S11 and S12 right frontal) to minimize distance from stimulating
electrodes to recording electrodes. In one patient S10, the stimulation electrodes
were placed at three additional locations to examine the effects of electrode
placement on extracerebral and intracerebral current spread. Subjects were covered
with a nickel-cadmium shroud to reduce environmental artifact during recording,
and other sources of environmental noise (60 Hz) were minimized in the patient
area. The stimulation protocol used the NeuroConn DC Stimulator Plus
(NeuroConn, Germany), with a biphasic sinusoi dal waveform at 0.75 and 1 Hz, at
variable intensities between 0.3 and 2 mA, for 10 s (cycles) to determine the peak
intensity at which amplifier saturation occurred. Thereafter, subjects were
stimulated with TES at 0.75 and 1 Hz, at variable intensities up to the peak
intensity, for a duration between 5 and 10 min (10 min for A1–A6 and 5 min for
S1–S4). Up to four blocks of stimulation were applied, until subjects woke up. The
more than ten-fold increase of the subdurally recorded iEEG amplitude, compared
to the EEG signal73, allowed for simultaneous recording and stimulation (up to
saturation levels of the amplifiers). Stimulation was immediately stopped in the
event of an electrographic seizure (S7). A repeat clinical assessment (including
assessment of stimulation skin site and neurologic examination) was performed
after stimulation.
For S14, who was enrolled to perform a replication experiment of prior
protocols, we selected a surgical patient who had a bilateral strip and depth survey.
There were two windows that were cut into the patient’s surgical bandage to allow
electrode placement at the F3/F4 positions. Stimulation electrodes (8 mm Ag/Cl
ring type) were applied bilaterally, with anodes at F3/F4 and cathodes on each
mastoid. To test for acute effects, we utilized a stimulation protocol using
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trapezoidal waveform (0.33 s ramp up/0.33 s steady state/0.33 s ramp down/0.33 s
zero current), 0.75 Hz, 0–0.26 mA, for 8 cycles on/8 cycles off (10.66 s ON/10.66 s
OFF), for 80 cycles. Stimulation was started after the first 5 min of NREM sleep.
Low-frequency noise-burst auditory stimulation. Auditory stimulation consisted
of 50 ms pink-noise bursts (1/f spectrum) repeated at a rate of 1 of 0.75 Hz to four
patients (subjects A1–A4). Sound was delivered via flat-profile headphones (Bed-
phones, Millwood, NY), which were placed on the patient’s ears. Placement of the
headphones required access under the surgical bandage and was administered by
an epilepsy physician (AL), with patient verification of correct positioning.
Acoustic pulses (50 ms, pink noise, 5 ms on/off ramps) were digitally generated and
delivered via a laptop placed at the bedside. The sound level of the stimulation was
manually adjusted for each patient to maximize comfort (e.g., ability to sleep with
sound playing in the background). The volume was recorded and estimates of the
sound level presented to each subject were assessed via an ear simulator (KEMAR
Head and Torso simulator, Knowles Research, coupled to a B&K type 3134
Pressure microphone and B&K type 2230 Sound Level Meter, Bruel & Kjaer,
Denmark). Resultant peak sound pressure level estimates for each subject are 72 dB
SPL (subject A1), 68 dB SPL (subject A2), 46 dB SPL (subject A3), 70 dB SPL
(subject A4).
Similar to the procedure used during tACS stimulation, acoustic stimulation
was presented at repetition rates of 0.75 and 1 Hz, in blocks of 5 min during NREM
sleep. For each subject, we collected a block at each stimulation rate during NREM
sleep, which was visually confirmed offline. The order of the presentation blocks
was randomized across subjects. An awake control condition was performed for
each patient, to verify the presence of acoustic ERPs. Additional time-locked TTL
triggers were generated for each stimulus presentation and recorded by the EEG
amplifier’s DC input to aid in offline analysis.
iEEG data preprocessing. All electrodes were inspected for signal quality by
plotting spectrogram, raw voltage, and the power spectrum. We recorded from a
total of 1700 electrodes for this analysis. Electrodes were discarded based on high
60 Hz noise (likely due to poor contact impedance), amplifier saturation (clipping),
or poor removal of the tACS artifact (due to non-stationarity of stimulation
artefact, typically resulting from patient movement). Example of artefact-free
recording electrodes are shown in Supplementary Figs. 2and 3. The electrodes
remaining for each subject during tACS were: 122/126 (S1), 112/126 (S2), 117/126
(S3), 78/84 (S4), 111/124 (S5), 155/254 (S6), 80/128 (S7), 116/122 (S8), 89/100 (S9,
1 Hz tACS), 93/100 (S9, 0.75 Hz), 83/124 (S10), 49/98 (S11), 79/102 (S12), 88/188
(S13, 1 Hz tACS), 93/188 (S13, 0.75 Hz tACS). During endogenous sleep the
electrodes remaining for each subject during night 1 were: 123/126 (S7), 103/122
(S8), 96/100 (S9), 91/124 (S10), 70/98 (S11), 94/102 (S12), 170/188 (S13). During
night 2: 121/126 (S7), 113/122 (S8), 89/100 (S9), 96/124 (S10), 82/98 (S11), 93/102
(S12), 164/188 (S13).
Measurement and modeling of electric fields. During tACS, the current alter-
nates in directionality between two stimulating electrodes. This alternation results
in a sinusoidal signal that can be used to determine the magnitude of the stimu-
lation voltages. Magnitude was estimated by fitting a sinusoid to the signal at each
electrode location and estimating the magnitude of the fitted signal. The output of
this processing was plotted and manually inspected electrode by electrode.
The measured voltage in each location is then used to derive the projected
electric field, by subtracting potential values between adjacent electrode pairs and
dividing by their distance, resulting in V/m. The adjacent electrode was defined as
the closest electrode within a 10 mm vicinity for cortical electrodes on the same
grid array and linear strip, and 5 mm vicinity for depth electrodes on the same
strip, to reflect the different inter-electrode distance. It is important to realize that
this only captures a fraction of the field magnitude at any given location as the field
orientation may not be parallel to the direction of two neighboring electrodes. The
distant stimulating electrodes were expected to generate the strongest field
intensities on the cortical surface directly under the scalp electrodes40. However,
recording electrodes lay predominantly orthogonal to field direction (parallel to the
cortical surface). Thus, the measured field projections will not capture maximal
intensities, except in the rare circumstance that a depth array is precisely
underneath one of the stimulating electrode and oriented toward a second, distant
stimulating electrode.
The computational models were built following our previous work74. Briefly,
the MRI for each patient was automatically segmented by the New Segment
toolbox75 in Statistical Parametric Mapping 8 (Wellcome Trust Centre for
Neuroimaging, London, UK) in Matlab (R2013a, MathWorks, Natick, MA).
Segmentation errors were corrected first by a customized Matlab script74 and then
by hand in ScanIP software (v4.2, Simpleware Ltd., Exeter, UK). The field of view
of the clinical MRI scans was extended down to the neck by co-registering a
standard head74, and pasting the lower portion of the standard head to the model.
The 2 × 2 cm stimulation electrodes were positioned on the model using CAD
software. For each patient, a finite element model was generated from the
segmentation data and then the electric potential distribution was computed
assuming 1 mA current through the stimulation electrodes. Tissue conductiv ities
were adjusted to minimize the mean-square difference between predicted and
measured potentials. With these calibrated models, we then computed electric
fields throughout the brain. Electric potentials of model and measurements
corresponded closely, with correlation values of r=0.95 ±0.04 (mean ±Std across
patients, N=1545 electrodes across 10 subjects). Electric field is the spatial
derivative of these potentials. They are estimated as the difference in electric
potential between neighboring electrodes, divided by the distance. This is the
electric field projected on the orientation of the electrode pair40.
Phase-amplitude coupling. PAC measures the degree to which the amplitude of a
high-frequency oscillations, A
HF
(t), is aligned with the phase of a lower frequency,
ϕ
HF
(t). We were interested in the interaction between the amplitude of spindle
activity band at 14 Hz with the phase of endogenous slow oscillations at 1 Hz (or
0.75 Hz) during sleep as well as entrainment to the applied stimulation (tACS and
acoustic). This section refers to 14 Hz activity, but the identical analysis was done
for power amplitudes in the theta, alpha, and gamma bands (see next paragraph).
To measure entrainment of spindle oscillations during endogenous sleep, we
compared spindle power against the phase of the endogenous slow oscillation
activity (ϕ
LFE
(t), low-frequency endogenous). To measure entrainment to tACS, we
used the phase of the electrical stimulation artifact (ϕ
LFS
(t),
low-frequency stimulation; Supplementary Fig. 1). To obtain the phase during
acoustic stimulation we used the delay from the onset of each noise burst. To
obtain the phase during tACS and remove the stimulation artifact, we first modeled
the 1 Hz artifact as a linear superposition of sines and cosines at multiples of a base
frequency (harmonics up to 40 Hz) by fitting the amplitude of each sine/cosine and
the base frequency. An example of this fitting procedure is shown in Supple-
mentary Fig. 4with the top row indicating the raw signals and the bottom row
showing the signals after the fitted harmonic artifact has been subtracted. The
resulting harmonic fit captures the 1 Hz stimulation artifact including any har-
monic distortion that may have resulted from amplifier nonlinearities. We calcu-
lated the stimulation phase from the harmonic fit (Supplementary Fig. 1D). To
obtain the phase during endogenous sleep, first we applied a complex-valued
Morlet wavelet filter centered at 1 Hz (or 0.75 Hz) with a bandwidth of 1 Hz (or
0.75 Hz; in humans, the center frequency of slow-wave activity is often assumed to
be <1 Hz)48. The instantaneous phase can be directly extracted from the complex-
valued filtered signal. Peak and trough of the slow oscillation are indicated by 0°
and 180°, respectively, which represent the cortical upstate and downstate as dis-
cussed in the main text.
To obtain the instantaneous amplitude of the high-frequency rhythm during
tACS we subtract the harmonic fit (Supplementary Fig. 4) and filtered the residual
iEEG signal with a complex-valued Morlet bandpass for spindle activity (fc =14
Hz, bandwidth =7 Hz), for alpha activity (fc =10 Hz, 5 Hz bandwidth) and for
gamma activity (fc =90 Hz, bandwid th =40 Hz, Supplementary Fig. 1C). The
instantaneous HF power was obtained by taking the absolute value of the filtered
signal (Supplementary Fig. 1E). Patient movement and other artifacts resulted in
outliers in the HF amplitude estimates. We removed these by removing cycles with
excessive power as follows. For each electrode and each LF cycle we compute the
mean power of the HF band (square amplitude averaged over one LF cycle). Cycles
are removed as outliers if their mean power exceeds two times the interquartile
range across all cycles in an electrode. To obtain the amplitude of the high-
frequency rhythm during endogenous sleep and auditory stimulation we used the
same procedures starting with the raw iEEG signal (no harmonic fit is needed).
Outlier rejection was done as before based on the mean power in the high-
frequency band. In addition, for endogenous slow wave we excluded cycles for
which the slow oscillation amplitude was below 50 µV. For the analysis of the
acoustic stimulation we used the same procedures as with the endogenous sleep
except that the phase was defined based on the time since the onset of the noise
burst, and cycle duration determined from the average inter-stimulus interval (TTL
pulse) for each subject.
PAC is measured here using the modulation index r, which is defined as the
absolute value of the time average, r=|<z(t)>|, of the complex-valued quantity,
zðtÞ¼AHFðtÞeiϕLF ðtÞ(Supplementary Fig. 1F). Time average <z(t)>is computed
over cycles and time within a cycle. If z(t) does not have a radially symmetric
distribution this will cause the time average r(modulation index) to be different
from zero. This can be a result of (1) the amplitude A
HF
(t) is consistently higher at
a certain phase, or (2) ϕ
LF
(t) is not uniformly distributed in time. The phase is
uniformly distributed in the case of tACS and acoustic stimulation. However,
during endogenous sleep, phase is extracted from the slow oscillations, which is not
sinusoidal resulting in non-uniform phase distributions. Consequently, we applied
a histogram equalization to the phase distribution, ensuring that non-zero
modulation index is only a result of modulated amplitude coupling, which we
confirmed by testing for significance using surrogate data with constant HF
amplitude.
Randomized surrogate data to estimate statistical significance of PAC. Sig-
nificance was determined by randomizing the phase of each LF cycle and thus
creating the distribution of the modulation index r, under the null hypothesis of no
PAC. Phase randomization makes no assumptions on the distribution of the data,
except that the phase is uniform, which has been addressed in the paragraph above.
For each slow oscillation cycle the low-frequency phase, ϕ
LF
(t), was incremented by
a random value uniformly distributed between 0° and 180°. The phase was shifted
by the same random phase for all electrodes but independently for different cycles.
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The randomizing procedure was repeated 10,000 times and the p-value was
measured as the rate of a random phase having a modulation index higher than the
original data. The minimum numerical p-value possible, given the number of
randomizations, was 10−4(1/number of shuffles). All the p-values were corrected
for multiple comparisons across electrodes using FDR correction76 with q<0.05.
No correction was performed across segments (as we use segments to determine
how reliable a potential entrainment is over longer periods of time) or frequency
bands (as these were planned comparison). Uncorrected p-values for electrodes
that had p<0.05 in at least one stimulation block are shown in Supplementary
Figs. 7–9for tACS, endogenous slow-wave (no stimulation), and acoustic stimu-
lation, respectively.
Note that we examine the acute effects of modulation within a single cycle, thus
permitting many single “trials”(N=300 cycles for 1 Hz and N=225 for 0.75 Hz
within each 5-min block, repeated 2–6 times for each stimulation intensity; see
number of blocks tested for each subject in Supplementary Table 1). This large
number of trials permits, in principle, detection of small changes in power within a
cycle, on the order of a few percent (e.g., assuming independent noise: sqrt(1/300)
=5.6%). See Supplementary Note 3for an extended discussion on statistical power.
To aid comparison with endogenous sleep and tACS stimulation condition, we
analyzed entrainment for different sleep duration segments. The longer the
recordings (approximately 20 min), the more accurate the mean vector strength
technique is at measuring entrainment (approximately 50% of electrodes
entrained). For shorter durations of endogenous sleep (5 min of iEEG) comparable
to the duration of tACS blocks, there is still modulation of spindle activity but in
only 10% of the electrodes.
Spindle event detection. Spindle detection follows existing methods50. Briefly, the
signal is bandpass filtered in the spindle band as above, and instantaneous power is
obtained as the square amplitude of the filtered signal. For each channel a detection
threshold is defined as six times the median of the instantaneous power. For
segments that cross this threshold a lower threshold (1 standard deviation of the
power) is applied to detect onset and offset of the spindle event. Only spindles
whose duration was between 0.2 and 2 s were considered for further analysis50,77.
To avoid false positive detections due to patient movements or high-frequency
artifacts spindle events that coincide with increases in broadband power were
discarded. Events exhibiting broadband power increases (p>0.10, comparing the
maximum broadband power in that event vs. the distribution of broadband power
during the whole recording) were excluded.
Slow-wave detection during auditory stimulation. Additional analyses were
performed on the acoustic stimulation data to assess the physiological origin of the
PAC entrainment. First, a slow-wave detection algorithm was applied during sti-
mulation segments to assess whether underlying slow-wave activity was altered
during stimulation. In contrast to tACS, this is possible for auditory stimulation as
there are no electric stimulation artefacts in the iEEG signal. Following previous
literature61,78, slow-wave detection consisted of bandpass filtering the waveform
(fc =0.75 Hz, bandwidth =0.75 Hz) and using a zero-crossing algorithm to identify
events where two subsequent negative zero-crossing (e.g., from positive to negative)
were within the range of 0.5–2s (2–0.5 Hz, bandwidth =1.5 Hz). Subsequently,
once a slow-wave event was identified, the down-state was identified as the
minimum voltage within this event. Second, time-frequency response (TFR)
functions were calculated to assess whether stimulation trials consisted of phy-
siological sleep spindles. TFRs were computed using a 6-cycle Morlet wavelets
between 5 and 25 Hz and averaging the resulting spectrograms (locked to stimu-
lation onset) across all noise bursts (Fig. 6). Significance between conditions (sleep
vs. baseline) was assessed via paired sample t-test (one-tailed, p<0.05) compared
across all stimulation trials (n=300).
Analysis of changes in power before and after stimulation. Qualitative com-
parison between the NREM during the two nights without stimulation and the
stimulation period show no evident changes in the power spectrum (Supplemen-
tary Fig. 6). To determine if there were significant changes in power at the different
frequency bands before vs. after stimulation we used the Chronux toolbox79 (http://
chronux.org/; version 2.12). Briefly, for each electrode, 30 s immediately after each
5-min stimulation block were compared against 30 s preceding the first block.
Differences in power in the slow oscillation (0.5–1 Hz) and spindle bands (10 and
14 Hz) are shown in Supplementary Fig. 12. Statistical significance of power
changes is computed with the Chronux toolbox for the fast spindle band and slow
oscillation band and FDR corrected (q<0.05).
Entrainment with electric stimulation assessed after stimulation block.In
subject S14 we collected enough stimulation blocks to evaluate slow-wave
entrainment in the intervals immediately following tACS. For this, slow-wave
entrainment was tested following previous reports37. Briefly, the 10-s stimulation-
free interval immediately after stimulation was fit with a sinusoid (0.5–1.5 Hz) and
phase coherence was calculated across all trials. Statistical significance was calcu-
lated using a circular test statistic (Rayleigh test for non-uniformity). In addition, to
test for after-effects, we utilized the same trapezoidal waveform, frequency, and
intensity in five separate 5-min blocks during NREM sleep (5 min ON/5 min OFF).
We compared these sessions against two control periods of endogenous NREM
sleep for spindle (10, 14 Hz) entrainment. The power in the stimulation-free
intervals was calculated for slow oscillations (0.5–1 Hz), slow spindle activity (8–12
Hz), and fast spindle activity (12–15 Hz). This quantity was then compared to the
power during a different night, when the subject was in an equal sleep state. Slow
oscillations and slow spindle activity were averaged across locations close to Fz.
And fast spindle activity was determined across parietal locations.
Code availability. The code used to generate the main findings of the current study
are available from the corresponding author upon reasonable request.
Data availability. The data sets generated during and/or analyzed during the
current study are available from the corresponding author upon reasonable request.
Received: 3 June 2017 Accepted: 15 August 2017
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Acknowledgements
This work was supported by R01 MH107396-01, R44 NS092144, R01 MH-092926,
NS095123, R41 NS076123, NYU Program Project Grant Development Initiative
(PPG-DI), NYU Clinical and Translational Science Institute (CTSI), NYU Finding a
Cure for Epilepsy (FACES), and the Zimin Foundation. The authors would also like to
thank Xiuyuan (Hugh) Wang for his work on the electrode reconstruction, as well as
Preet Minhas for her early work in data collection and patient coordination.
Author contributions
Conception and design: G.B., O.D., A.A.L., D.F., L.C.P.; institutional review board
approval: A.A.L., T.T., L.M.; acquisition of data: A.A.L., B.L., W.D., D.F., S.H.; analysis
and interpretation of data: B.L., Y.H., L.C.P., A.A.L., D.F., G.B., O.D., S.H.; figure pre-
paration: B.L., Y.H., L.C.P., S.H.; drafting the manuscript: A.A.L., L.P., G.B., O.D.; critical
revisions: all authors.
Additional information
Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467-
017-01045-x.
Competing interests: L.P. has shares in Soterix Medical Devices. The remaining authors
declare no competing financial interests.
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ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-01045-x
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