Motor System Interactions in the Beta Band Decrease - Recherche en IA spécialisée au MIT

Motor System Interactions in the Beta Band Decrease
during Loss of Consciousness

Nicole C. Swann1, Coralie de Hemptinne1, Ryan B. Maher2, Catherine A. Stapleton3,
Lingzhong Meng1, Adrian W. Gelb1, and Philip A. Starr1

Abstrait

■ Communication between brain areas and how they are influ-
enced by changes in consciousness are not fully understood.
One hypothesis is that brain areas communicate via oscillatory
processes, utilizing network-specific frequency bands, that can
be measured with metrics that reflect between-region inter-
actions, such as coherence and phase amplitude coupling
(PAC). To evaluate this hypothesis and understand how these
interactions are modulated by state changes, we analyzed electro-
physiological recordings in humans at different nodes of one
well-studied brain network: the basal ganglia–thalamocortical
loops of the motor system during loss of consciousness induced

by anesthesia. We recorded simultaneous electrocorticography
over primary motor cortex (M1) with local field potentials from
subcortical motor regions (either basal ganglia or thalamus) dans
15 movement disorder patients during anesthesia (propofol) induc-
tion as a part of their surgery for deep brain stimulation. Nous
observed reduced coherence and PAC between M1 and the sub-
cortical nuclei, which was specific to the beta band (∼18–24 Hz).
The fact that this pattern occurs selectively in beta underscores
the importance of this frequency band in the motor system and sup-
ports the idea that oscillatory interactions at specific frequencies are
related to the capacity for normal brain function and behavior. ■

INTRODUCTION

How do functionally related brain areas communicate
over long distances? One hypothesis is based on oscilla-
tory synchronization in specific frequency bands, usually
lower than ∼50 Hz (Siegel, Donner, & Ange, 2012).
Coherence is a simple measure of oscillatory synchroni-
zation that may support long-distance neural interactions.
It reflects interactions between brain areas related to the
consistency of the phases and amplitudes of their neu-
ronal signals (Fries, 2005). On the other hand, higher-
frequency, broadband activity (“broadband gamma”,
70–250 Hz) seems to reflect local neural activity (Manning,
Jacobs, Frit, & Kahana, 2009; Miller et al., 2007). Canolty
and colleagues demonstrated that, in some instances,
broadband gamma amplitude is modulated by the phase
of lower-frequency oscillatory rhythms (Canolty et al.,
2006). This phase amplitude coupling (PAC) provides a
mechanism that could explain how low-frequency oscil-
latory changes, which are well suited to coordinate activity
over long distances, can influence local neuronal activity
(Canolty & Knight, 2010; Fries, 2005). When PAC is calcu-
lated from the phase and amplitude of signals recorded
from two different brain areas, it may also reflect inter-
actions between brain regions.

1University of California San Francisco, 2Fidere Anesthesia
Consultants, Mountain View, Californie, 3Alta Bates Summit Medical
Centre, Berkeley, Californie

Transitions in consciousness afford an opportunity to
evaluate how these basic measurements of brain inter-
actions change in the context of profound behavior/state
changes. Although the underlying causal mechanisms of
consciousness remain unclear, there is converging evi-
dence that transitions of consciousness are characterized
by a change of network dynamics wherein local brain net-
works become more isolated from one another, disrupt-
ing the brain’s ability to integrate information (Lewis
et coll., 2012; Alkire, Hudetz, & Tononi, 2008). Because
all but the most basic forms of behavior are only possible
in the conscious state, we hypothesized that the most
functionally important network interactions are only pres-
ent in the conscious state. To better understand how
those interactions might mediate behavior and, peut-être,
relate to communication in brain networks, we sought to
study how simple metrics of brain interactions (coherence
and PAC) in the motor system change during anesthesia-
induced transitions in consciousness.

To address this goal, we combined electrocortico-
graphy (ECoG) and subcortical local field potential
(LFP) recordings in movement disorder patients under-
going surgical implantation of deep brain stimulation
(DBS) leads (de Hemptinne et al., 2013, 2015). Ce
approach provides a unique opportunity to acquire field
potential data from functionally related cortical and sub-
cortical structures (primary motor cortex [M1] and motor
territories of the basal ganglia and thalamus). Because
DBS targets vary across individuals/diagnoses, we have

Journal des neurosciences cognitives 28:1, pp. 84–95
est ce que je:10.1162/jocn_a_00884

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the opportunity to sample from a number of different sub-
cortical nodes in the basal ganglia–thalamocortical (BGTC)
loop (although this does increase variability in our sample).
The BGTC circuit is particularly well suited to address ques-
tions concerning network interactions, because structural
connectivity in the motor loop is relatively well defined,
with connections from cortex to basal ganglia to thalamus
and then back to cortex (Alexander, DeLong, & Strick,
1986). En plus, the electrophysiological signatures in
the motor system have been studied extensively. The beta
frequency range (13–30 Hz) dominates and is dynamically
modulated during movement (Brovelli et al., 2004; Kuhn
et coll., 2004; Cassidy et al., 2002; Vieille femme, Miglioretti, Gordon,
Sieracki, et coll., 1998; Sanes & Donoghue, 1993; Murthy &
Fetz, 1992; Pfurtscheller, 1981). We hypothesized that loss
of consciousness would be characterized by reduced inter-
actions between cortical and subcortical regions, measured
with coherence and inter-region PAC, and that this would
be particularly prominent in the beta frequency band.

MÉTHODES

Patients

Fifteen patients (4 women/11 men, average age = 62 années)
were recruited from the Surgical Movement Disorders
Clinic at the University of California, San Francisco. Patients
were scheduled to undergo surgery to implant a perma-
nent subcortical DBS lead to treat their movement dis-
ordres. All patients were simultaneously participating in
a study of the contribution of cortical oscillatory activity
to movement disorders pathophysiology, using ECoG
from a subdural strip array inserted through the standard
frontal burr hole used for DBS insertion, and temporarily
placed over the primary motor cortex (de Hemptinne
et coll., 2013; Shimamoto et al., 2013; Crowell et al.,
2012). Our standard surgical procedure requires that
patients be awake for microelectrode mapping and DBS
lead insertion, followed by anesthesia induction (pour
wound closure). This procedure provides the opportunity
for brain recording during anesthesia-induced loss of con-
sciousness, avoiding the need to administer anesthetics
solely for research purposes.

The goal of this study was to examine interactions be-
tween nodes of the motor system during loss of con-
sciousness, rather than focusing on a specific movement

disorder. Donc, we included patients with varying
diagnoses and varying subcortical targets within the
motor network to enable the identification of network
motifs common to changes in consciousness independent
of disease state. Twelve of the patients had Parkinson
maladie (PD), two had essential tremor (ET), et un
had primary dystonia. Four PD patients had DBS leads
placed into the globus pallidus interna (GPi), alors que
the other eight had subthalamic nucleus (STN) leads,
with target choice dictated by clinical criteria (Follett
et coll., 2010). The DBS target for ET patients was the ventro-
lateral thalamus. The target for the dystonia patient was
GPi. The study methodologies were approved by the insti-
tutional ethics committee and are in agreement with the
Declaration of Helsinki. All patients provided written
informed consent to participate in the study, and the use
of a temporary cortical ECoG array placed for research
purposes was an explicit part of the consent discussion.

ECoG Strip Placement

ECoG was recorded from one hemisphere during the
surgery. For patients receiving unilateral DBS, the ECoG
was placed ipsilateral to the side of the DBS. For patients
receiving bilateral surgery, the side of the ECoG was
determined based on the clearest anatomic demarcation
of the central sulcus on preoperative MRI. Eleven patients
had left side recordings, whereas four had right.

The data from the dystonia patient and five PD patients
were collected with a six-electrode ECoG strip (1 cm spac-
ing between electrodes; see Figure 1A), and the other
seven PD patients and both ET patients were recorded
with a 28-electrode ECoG strip (4 mm spacing between
électrodes; see Figure 1B). The type of ECoG strip used
depended on the parent study of movement disorders
physiology, because during this study period, we transi-
tioned from the lower spatial resolution recording to a
higher-resolution technique. Each strip was placed with
at least one electrode covering M1. The intended target
location for the center of the ECoG strip was the arm area
of M1, 3 cm from the midline and slightly medial to the hand
knob ( Yousry et al., 1997). Adequate localization of the
ECoG strip was confirmed using either preoperative MRI
merged to an intraoperative CT (13 participants) or using
intraoperative lateral fluoroscopy with a radio-opaque

Chiffre 1. 3D brain
reconstructions of each
participant’s MRI with the
low-resolution (UN) et
high-resolution (B) ECoG strips
and the subcortical DBS electrode,
positioned in STN (indicated
with red arrows) (C). Electrode
locations (shown in red) sont
visualized by merging an
intraoperative CT with a
preoperative MRI.

Swann et al.

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marker placed on the skin indicating the intended target
in M1 that was visible relative to the electrode strip (2 par-
ticipants), as previously described (de Hemptinne et al.,
2013, 2015; Shimamoto et al., 2013; Crowell et al., 2012).
In all participants, functional localization was also exam-
ined using somatosensory evoked potentials (SSEPs, fre-
quency = 2 Hz, pulse width = 200 μs, pulse train length =
160, amplitude = 25–40 mAmp), as has been reported
(de Hemptinne et al., 2013; Shimamoto et al., 2013; Crowell
et coll., 2012). Note that for the 28-electrode strip, là
were two rows of 14 électrodes (see Figure 1B), so it
was expected that two electrodes localized immediately
anterior to the central sulcus would show a phase reversal
relative to the postcentral sulcus electrodes.

Tableau 1. MOAA/S Responsiveness Scale

State

Responsiveness

Responds readily to name spoken in normal tone

Lethargic response to name spoken in normal tone

Responds only after name is called loudly

and/or repeatedly

Responds only after mild prodding or shaking

Responds only after painful trapezius squeeze

No response after painful trapezius squeeze

Rating criteria for each state.

DBS Electrode Implantation

ECoG and LFP Recordings

Anatomic targeting of the desired subcortical structure
was performed as previously described (Starr et al.,
2002, 2006; Papavassiliou et al., 2004). For the GPi and
STN targets, the proper location was verified by elic-
iting movement-related single-cell discharge patterns
(Starr et al., 2002). For all regions, the correct placement
was verified by test stimulation, as well as intraoperative
fluoroscopy (all patients), intraoperative CT (13 patients;
Shahlaie, Larson, & Starr, 2011), and postoperative MRI
(all patients).

Consciousness Assessment and
Anesthesia Parameters

Continuous recordings were taken after DBS placement,
but prior to burr hole closure and DBS pulse generator
placement, while the patients were slowly anesthetized
with propofol. Data from PD patients were recorded
after at least 12 hr off antiparkinsonian medications.
Consciousness was assessed every 3 min using the Mod-
ified Observers Assessment of Alertness/Sedation Scale
(MOAA/S; see Table 1; Chernik et al., 1990). Ratings were
recorded by an anesthesiologist (AG, LM). When the
clock started for the assessment session (time zero), un
button was pressed, which generated a voltage deflection
in an auxiliary channel digitized with the electrophysiology
data. This synchronized the times noted by the anesthe-
siologist with the electrophysiology data.

Anesthesia induction with propofol was performed
slowly with the goal of achieving an estimated plasma
level (Marsh Model) de 4 mcg/ml at approximately 15 min
(Smith et al., 1994). Induction ended when all patients no
longer responded to painful stimulation according to
MOAA/S scale (see Table 1). Propofol sedation had also
been used during the drilling of the burr holes prior to
intracranial recording but was stopped for subcortical
mapping and DBS lead testing, at least 60 min prior to
initiating the recordings analyzed here. This is ample time
for the effects of propofol to wear off (Raz, Eimerl, Zaidel,
Bergman, & Israel, 2010; Fechner et al., 2004).

ECoG recordings were performed using either the Alpha
Omega Microguide Pro, Alpha Omega, Inc. (Alpharetta, GA;
for the six-electrode strip; Figure 1A) or the Tucker Davis
Technologies Recording System (for the 28 électrode
strip; Figure 1B). For the Alpha Omega system, data
were sampled at 3000 Hz. Data from each of the five
more posterior electrodes were referenced to the most
anterior electrode. A needle electrode in the scalp served
as the ground. Data were band-pass filtered at 1–500 Hz.
For the Tucker Davis System, data were sampled at
3051 Hz. All electrodes were referenced to a scalp needle
electrode that also served as the ground. Data were low-
pass filtered at 1500 Hz. Because there was no high-pass
filter applied during data acquisition, the mean of each
electrode was subtracted during offline preprocessing
to detrend the data. See Figure 2A for an example of the
raw ECoG data.

LFP recordings were from the subcortical target ipsi-
lateral to ECoG placement were recorded in a bipolar
configuration from the middle two cylindrical contacts
(1.5 mm height, 1.2 mm diameter) of a quadripolar lead
(Medtronic model 3389, 0.5 mm between contacts, eight
patients or Medtronic model 3387, 1.5 mm between con-
tacts, seven patients), with Electrode 1 as the active and
Electrode 2 as the reference. The guide tube for the DBS
lead served as ground. For the six patients for whom the
six-electrode cortical strip was used, the LFP recordings
were done with the Alpha Omega system using the same
Alpha Omega parameters described above for ECoG. Pour
all the PD patients who were tested with the 28-electrode
strip, LFP data were still collected using the hardware
from the Alpha Omega system (which was necessarily
present as it is FDA-approved for clinical microelectrode
recordings for mapping); cependant, the analog signal was
streamed to the Tucker Davis system and digitized there
à 25,000 Hz sampling rate and then subsequently down-
sampled. For the sessions with ET patients, the Alpha
Omega system was not present in the room, as micro-
electrode recordings were not used for ET. Ainsi, LFPs were
recorded in the same manner as the ECoG electrodes (data

Journal des neurosciences cognitives

Volume 28, Nombre 1

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Chiffre 2. Example data
from individual participant 6.
One second of raw M1 ECoG
(UN) data and raw LFP data
(from STN) (B). Log PSD
from M1 (C) and STN (D).
The gray bars indicate 60-Hz
noise and the harmonics.
(E) Coherence between
M1 and STN. (F) Histogram
of mean M1 broadband
gamma amplitude (70–150 Hz)
binned by subcortical beta
phase (18–20 Hz). Ici,
modulation is apparent.
A flat distribution would
suggest no PAC.

from Electrodes 1 et 2 of the DBS electrode were both re-
corded with the needle electrode as the reference and
ground) and then subsequently referenced in a bipolar con-
figuration offline. See Figure 2B for an example of the raw
LFP data.

Electrophysiological Preprocessing

All data were initially down-sampled to 1000 Hz, and line
frequency noise (à 60 Hz, 120 Hz, et 180 Hz) was re-
moved using a third-order Butterworth notch filter,
which spanned a 4-Hz range centered at each frequency
(c'est à dire., 58–62 Hz for 60 Hz). Data were then re-referenced.
For the six-electrode strip, a bipolar montage was used
such that each electrode was referenced to the anterior
électrode (C1–C2, C2–C3, etc.). For the 28-electrode
strip a common average reference was used including
all ECoG electrodes (excluding any with obvious, con-
tinuous noise). Each reference strategy is best suited
for the given electrode configuration: An average refer-
ence montage is not appropriate when there are only
five active electrodes to contribute to the reference, et

a bipolar reference is less optimal for the 28 électrode
strip with small contacts and very close spacing. To deter-
mine if the different referencing schemes influenced our
résultats, we re-ran our analyses using an average reference
scheme for all participants, and the results were similar;
thus, this difference in analysis is unlikely to have strongly
influenced our findings. Subcortical LFP electrodes were
processed in the same way as above (down-sampled and
filtered). All raw data were manually inspected and
periods of artifact (par exemple., electrical artifact caused by medical
equipment or movement) were noted and excluded from
analyse.

During data recording, the anesthesiologist performed
ratings every 3 min to assess the patient’s level of con-
sciousness (see Table 1). Data were assigned a conscious-
ness rating according to the assessment at the end of
each 3-min time segment. Par exemple, if after 3 min
the anesthesiologist rated the patient’s alertness as a
5 and at 6 min rated it as a 4, the segment of the electro-
physiology data corresponding to 0–3 min would be
marked as “State 5” and times 3–6 min would be marked
as “State 4.” Not all patients were observed at each state.

Swann et al.

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The longest artifact-free period of each state was used
for analysis. En moyenne, this was 218.1 sec and was
always greater than 31 sec. Because both coherence and
PAC can vary over time even without an overt change in
behavioral state, we included the longest data segments
possible for each participant and each level of conscious-
ness. Cependant, because coherence and PAC values can
also be influenced by differences in the length of data
analyzed, we conducted a control analysis to verify that
differences in data length were not systematically driving
our observed differences. In this analysis, for each par-
ticipant, data of the same length from each state were
used for comparison.

Pour chaque patient, the cortical electrode(s) that most
closely corresponded to M1 were selected based on
both the anatomical localization and SSEP waveforms.
This was done prior to examining the data from the
anesthesia induction file. For the 28-electrode strip, là
were two rows of 14 électrodes, so we selected one
M1 electrode from each row that met these same criteria.
Dans ce cas, pouvoir, coherence, and PAC (described be-
faible) were calculated separately for each of these elec-
trodes and then averaged for each electrode pair. Pour
the six-electrode strip, the electrode selection procedure
described above typically led to the selection of one elec-
trode, which clearly lay over M1 (at the border of the cen-
tral sulcus and the precentral gyrus), that also showed a
clear SSEP phase reversal. For the 28-electrode strip,
where the electrodes were smaller and spaced closer to-
gether, definitive selection of the two optimal M1 elec-
trodes was less clear (regardless of reference scheme
used). To test whether ambiguous selection of the M1
electrode may have influenced our results, we conducted
a test wherein all electrodes on each strip were included in
the analysis. (Dans ce cas, signal processing calculations
described below were calculated for each electrode
separately, and then the final results were averaged,
such that each participant contributed only one data point
to the group statistics.) Although this method clearly
provides less spatial specificity, results were similar even
when all electrodes were included. Ainsi, ambiguity in
the selection of the optimal M1 electrode did not distort
our results. The fact that results were similar for the M1
électrode, and the entire strip, most likely reflects the fact
that the strip was placed to span precentral and postcentral
gyri, which generate similar oscillatory signatures (Vieille femme,
Miglioretti, Gordon, Sieracki, et coll., 1998).

Electrophysiological Signal Processing

All analyses used a combination of custom Matlab scripts
and EEGLAB functions (Delorme & Makeig, 2004). Nous
implemented two main types of analyses. D'abord, we ana-
lyzed the activity of each area (M1 and the subcortical
region) separately. We calculated power spectral density
(PSD) and PAC (Tort et al., 2008; Canolty et al., 2006)
within each region. Analyses for each patient, state, et

region (M1 and subcortical) were calculated separately
in Matlab.

PSD analysis used the Welch method ( pwelch function
in Matlab with a 512-msec window, 256 msec of overlap;
see Figure 2C and D for an example of PSD in one par-
ticipant). Statistics were then calculated based on the log
PSD values. Cependant, results were similar if non-log-
transformed values were used.

PAC was calculated using the Kullback–Leibler-based
modulation index method, which has been previously
described (de Hemptinne et al., 2013; Tort et al., 2008).
In brief, the M1 ECoG and subcortical LFP signal were
filtered separately using a two-way FIR1 filter (eegfilt with
‘fir1’ parameters). Low-frequency signals were filtered
individually at frequencies ranging from 2 à 50 Hz, avec
a 2-Hz bandwidth, and the phase was extracted from this
signal using a Hilbert transform. De la même manière, the amplitude
of the high-frequency broadband gamma signal was
extracted by taking the Hilbert transform of the band-
pass filtered data (70–150 Hz). Then the distribution of
the instantaneous amplitude envelope was computed
for every 20° interval of the instantaneous phase (voir
Figure 2F). The coupling (modulation index) entre
the phase of each low-frequency rhythm and the high-
frequency amplitude was then determined by computing
the entropy values of this distribution and normalizing
by the maximum entropy value (Tort et al., 2008).

Deuxième, we analyzed subcortical–cortical interactions by
calculating coherence and cross-structure PAC. Analyses
were performed for each participant and state separately,
using signals from both the M1 ECoG and the subcortical
LFP. We calculated coherence for frequencies ranging
depuis 2 à 50 Hz, with a 2-Hz bandwidth, by filtering both
the M1 ECoG and subcortical LFP using the same two-way
FIR1 filter used to calculate PAC. Complex signals were
then obtained for each filtered signal by taking the Hilbert
transform. Coherence between the ECoG electrode(s)
and the subcortical LFP was calculated using the cor-
responding autospectra (Wxx and Wyy) and cross-spectra
(Wxy) of the filtered complex signals.

Coh fð Þ ¼

(cid:1)
(cid:1)

Wxy fð Þ
√Wxx fð Þ√Wyy fð Þ

Here x and y refer to data from the two regions (c'est à dire., cor-
tical and subcortical). Wxy was calculated by taking the
sum over time of the complex signal of x multiplied by
the conjugate of the complex signal of y. Wxx and Wyy
were calculated as the sum over time of the amplitude
of each (x and y) complex signal. See Figure 2E for an
example of coherence at all frequencies in one patient.

Between-region PAC was calculated in the same way as
within-region PAC described above, except that the low-
frequency phase component and the high-frequency
amplitude component were derived from signals from dif-
ferent brain regions (M1 ECoG and subcortical LFP). Nous
examined PAC using both the phase of the subcortical

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Volume 28, Nombre 1

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LFP and amplitude of M1 ECoG and the opposite configu-
ration. See Figure 2F for an example of PAC in one patient.

Statistical Analysis of Data Grouped across Patients

For statistical comparisons of the change across patients
during anesthesia induction, a nonparametric paired
sign-rank test was computed comparing, for each patient,
the metric of interest (pouvoir, within region PAC, coher-
ence, or between region PAC) for data corresponding to
the state during which the patient was most awake (State
5, or closest to 5) to the data corresponding to the state
during which the patient was most asleep (State 0, ou
closest to 0). This was done separately for each fre-
quency. Results were then corrected for multiple compar-
isons (for all frequencies examined) using a false discovery
rate (FDR) correction.

RÉSULTATS

Propofol and Monitoring Results

Each patient reached an unconscious state (c'est à dire., MOAA/S
value = 0 ou 1). This was achieved on average in 22.5 min
from the start of the file recording (SD = 8 min). Pour
each patient, a minimum of three anesthetic states were
observed.

56 μV for the most awake state and 64 μV for the most
asleep state ( p < .04, with a paired, sign-rank test). The subcortical LFP root mean square values were on average 8 μV (not significantly different for awake vs. asleep, p >
.15; Chiffre 2).

During induction, the subcortical LFP was charac-
terized by an increase in low-frequency power (4–6 Hz,
p < .05, FDR-corrected) and a decrease in higher-frequency (broadband) activity (all frequencies 20–250 Hz, p < .05, FDR-corrected; Figure 3). In the cortex, a similar pattern was observed for the low-frequency power increase (2–6 Hz, p < .05, FDR-corrected) and high-frequency decreases (144–250 Hz, but not all frequencies in this range were significant, p < .05). However, the decrease in high- frequency power in the cortex was not significant when correcting for multiple comparisons (Figure 4). Although the cortical high-frequency effect was weak, previous ECoG work has shown similar cortical changes in these frequency ranges during propofol induction (Verdonck, Reed, Hall, Gotman, & Plourde, 2014; Breshears et al., 2010). Thus, although our result was not statistically robust, it trends in the direction reported by others. Of note, M1 beta power did not change with induction of anesthesia. Although subcortical beta power did de- crease during induction, this was a nonspecific effect, because all frequencies above 20 Hz were reduced. High-frequency Spectral Power Decreases and Low-frequency Power Increases in the Cortex and Subcortical Nuclei The signal amplitudes were typical for ECoG and LFP data, with an average root mean square value for M1 of Coherence between Cortex and Subcortical Nuclei Decreases during Induction Across patients, there was a spectrally specific decrease in coherence in the beta band (18–24 Hz) during propofol- induced loss of consciousness ( p < .05, FDR-corrected; D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / e j d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 8 8 / 4 1 1 / 9 8 5 4 0 / 2 1 6 7 1 8 o 4 c 5 n 3 _ 0 a / _ j 0 o 0 c 8 n 8 _ 4 a p _ d 0 0 b 8 y 8 g 4 u . e p s d t o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j f / t . u s e r o n 1 7 M a y 2 0 2 1 Figure 3. Subcortical power changes. (A) Percent power change for all frequencies and participants. Percent change was calculated as: ((asleep − awake) / awake) * 100. Significant differences are indicated with one ( p < .05, FDR-corrected) or two asterisks ( p < .01, FDR-corrected). Participants are grouped by region of LFP recordings, indicated with labels (i.e., GPi, STN, and ventrolateral thalamus [VLT]) and separated by black bars. Participant 1 was the dystonia patient, Participants 2–5 and 6–13 were PD patients, and Participants 14–15 were ET patients. To be consistent with subsequent figures, only frequencies below 50 Hz are shown, although higher frequencies were analyzed and described in C. (B) Individual participant log power for each state averaged across the frequencies with a significant increase in power (4–6 Hz). Participant order is the same as in A, with Participants 1–4 making up the first row. (C) Same as B, but showing average log power for all frequencies with a significant decrease during induction (20–250 Hz). Swann et al. 89 Figure 4. M1 power changes. (A) Same as Figure 3A, except M1 power changes. To be consistent with subsequent figures, only frequencies up to 50 Hz are shown, although higher frequencies were analyzed and described in C. The asterisk indicates significant frequencies ( p < .05, FDR-corrected), and the line without an asterisk indicates significance at p < .05, uncorrected. Participants 1, 2, 3, 6, 7, and 8 were recorded with the low-resolution strip. The others were recorded with the high-resolution strip. (B and C) Same as Figure 3B and C except showing M1 power changes for frequencies with a significant increase (B, 2–6 Hz, p < .05, FDR-corrected) and (C) significant decrease (>144 Hz, p < .05, uncorrected). Figure 5). This occurred in the setting of no significant change in cortical beta power and a nonspecific decrease in power in all frequencies above 20 Hz in the subcortical LFP. The control analysis for which the same recording durations were used for both the “awake” and “asleep” epochs produced similar results (significant decrease in coherence for 20–24 Hz, p < .05 FDR-corrected). Thus, variability in data length is not driving the observed effect. We also examined the change in the phase of the beta coherence, and although some patients showed a change, it was not consistent across patients (data not shown). Coherence patterns at other frequencies were variable across patients. Phase Amplitude Coupling between Cortex and Subcortical Nuclei Decreases during Induction We calculated PAC in two ways. First, we tested the within-region PAC for M1 and the subcortical target separately and did not observe any significant effects associated with anesthesia induction when correcting for multiple comparisons. We then calculated inter-region PAC taking the phase from the subcortical target and the amplitude from the M1 ECoG and vice versa. With phase from the subcorti- cal target and amplitude from the M1 ECoG, there was a significant decrease in PAC during induction, again in the beta band (18–20 Hz, p < .05, FDR-corrected; see Figure 6). The control analysis for which the same re- cording durations were analyzed for both the “awake” and “asleep” epochs produced similar, although slightly weaker results (significant decrease in inter-region PAC for 18–20 Hz, p < .05, uncorrected). Thus variability in data length is unlikely to be driving the observed effect. Results in other frequency ranges were variable across participants. Calculating the PAC using the cortical phase and subcortical amplitude revealed no significant results associated with anesthesia induction. D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / e j d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 8 8 / 4 1 1 / 9 8 5 4 0 / 2 1 6 7 1 8 o 4 c 5 n 3 _ 0 a / _ j 0 o 0 c 8 n 8 _ 4 a p _ d 0 0 b 8 y 8 g 4 u . e p s d t o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j f / t . Figure 5. M1 and subcortical coherence changes. (A) Same as Figure 3A, except M1 and subcortical coherence changes. Significant differences are indicated with one ( p < .05, FDR-corrected) or two asterisks ( p < .01, FDR-corrected). (B) Individual participant coherence shown for each state for 18–24 Hz. Participant order is the same as in Figure 3B. u s e r o n 1 7 M a y 2 0 2 1 90 Journal of Cognitive Neuroscience Volume 28, Number 1 Figure 6. M1 and subcortical PAC changes. PAC is calculated using subcortical phase and broadband M1 amplitude (70–150 Hz). (A) Same as Figure 3A, except PAC changes. Significant differences are indicated with one ( p < .05, FDR-corrected) or two asterisks ( p < .01, FDR-corrected). (B) Individual participant PAC averaged between 18 and 20 Hz. Participant order is the same as in Figure 3B. D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / e j d o u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 8 8 / 4 1 1 / 9 8 5 4 0 / 2 1 6 7 1 8 o 4 c 5 n 3 _ 0 a / _ j 0 o 0 c 8 n 8 _ 4 a p _ d 0 0 b 8 y 8 g 4 u . e p s d t o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 i 2 3 e s / j . t f / u s e r o n 1 7 M a y 2 0 2 1 DISCUSSION To evaluate changes in interactions between connected brain regions of the motor network associated with loss of consciousness, we collected multisite field potential recordings from structures in the motor network in 15 par- ticipants undergoing DBS surgery and examined changes in oscillatory activity. We found a decrease in interactions, measured with coherence and inter-region PAC, between brain regions in BGTC loops during loss of consciousness, specific to the beta band. Importantly, the beta frequency band was the only band to have a consistent change in between-structure coherence and PAC across participants, and this was not the case for cortical or subcortical spectral power. We also observed a decrease in broadband gamma activity in the subcortical nuclei and a similar, although weaker, decrease in cortex. This was accompanied by an increase in low-frequency power in both cortex and sub- cortical nuclei. Throughout the brain, cortical structures communicate with functionally homologous subcortical modulators, and this communication is thought to be mediated in part by synchronization in oscillatory activity. The motor system represents one such pair, with well-characterized anatomic connections from cortical motor areas to basal ganglia and thalamus (Alexander et al., 1986), and robust oscillatory activity that is predominant in the alpha or beta range (van Wijk, Beek, & Daffertshofer, 2012; Crone, Miglioretti, Gordon, Sieracki, et al., 1998; Pfurtscheller, 1981). Our focus on the motor system was motivated by the fact that it can be ethically studied using invasive electrophysiological methods during DBS surgery. We expect that the motor system is a reasonable model for other brain networks and that some of the observed patterns may be generally applicable, with network- dependent variation in the particular frequency that is most strongly influenced. Changes in coherence and PAC, reflecting interactions between brain regions, have been shown to vary with changes in behavior in a number of different brain networks (Siegel et al., 2012; Canolty & Knight, 2010). Furthermore, some of the cortical patterns we observe in the motor system are similar to those ob- served in another ECoG study, which examined more widespread regions of cortex (Breshears et al., 2010). However, because we focused specifically on the motor system, we cannot be sure of the generalizability of our results. Support for the Importance of Synchronized Beta Activity in the Motor System Converging evidence suggests that one consequence of loss of consciousness is functional disconnection be- tween distributed brain regions (Sarasso et al., 2014; Lewis et al., 2012; Alkire et al., 2008; Laureys, 2005; Engel & Singer, 2001). For cortical and subcortical interactions, in particular, this hypothesis is supported by functional imag- ing studies of the recovery of awareness following trau- matic unconsciousness (Laureys & Schiff, 2012; Goldfine & Schiff, 2011) and by the finding that cortical–subcortical structural connectivity correlates with levels of awareness in patients with severe head trauma (Zheng, Reggente, Lutkenhoff, Owen, & Monti, 2014; Fernandez-Espejo et al., 2011). Our results support this hypothesis because two measures that reflect interactions between brain regions, coherence and cross-structure PAC, both decrease during loss of consciousness. These measures are not fully inde- pendent, because changes in coherence are likely to affect cross-structure PAC. Communication in different brain networks may occur through synchronization at particular frequencies, with the specific frequency varying, depending on the brain network (Siegel et al., 2012; Fries, 2005). The motor net- work is dominated by activity in the alpha–beta range (Miller et al., 2012; Yanagisawa et al., 2012; Brovelli et al., 2004; Kuhn et al., 2004; Cassidy et al., 2002; Crone, Miglioretti, Gordon, Sieracki, et al., 1998; Sanes & Donoghue, 1993; Murthy & Fetz, 1992; Pfurtscheller, 1981). Thus, the re- duction in beta coherence seen in this study would be Swann et al. 91 expected to impair communication between the motor cortex and its subcortical modulators, consistent with the “communication through coherence” hypothesis (Fries, 2005). PAC provides a mechanism to explain how co- herence might influence local neural activity (Canolty & Knight, 2010; Canolty et al., 2006). Thus, our observed coherence and PAC decreases are consistent with a general reduction in motor network connectivity and, perhaps, communication, which can be detected with both metrics. We predict that a similar phenomenon might be observed in a different frequency range if a different brain network (with a different predominant frequency range) were studied. Opposing Patterns of Functional Connectivity in Consciousness Some studies, similar to our own, show a decrease in synchrony between distributed areas during loss of consciousness (Chennu et al., 2014; Sarasso et al., 2014; Bonhomme, Boveroux, Brichant, Laureys, & Boly, 2012; Massimini, Ferrarelli, Sarasso, & Tononi, 2012; Laureys et al., 1999), whereas others showed an increase (Mukamel et al., 2014; Breshears et al., 2010; Ching, Cimenser, Purdon, Brown, & Kopell, 2010; Arthuis et al., 2009; Feshchenko, Veselis, & Reinsel, 2004). One explanation for these seemingly contradictory findings may lie in the distinction between “healthy” synchrony, necessary to coordinate communication between separated brain areas (Siegel et al., 2012; Fries, 2005), and abnormal oscillatory patterns, which may break up normally synchronized activ- ity or constrain neural activity in an inflexible pattern. An- esthesia may cause some populations of neurons to fire in an oscillatory, inflexible, way, which then precludes pat- terns of synchronization necessary for conscious behavior. Indeed, there have been studies that observe both patterns during loss of consciousness, that is, both an increase in oscillatory activity in certain frequency ranges or brain net- works, coincident with a decrease in oscillatory activity in others (Purdon et al., 2013; Lewis et al., 2012). Alternative Interpretations of the Coherence and PAC Results We have observed a decrease in coherence and PAC dur- ing anesthesia induction that is specific to the beta band and not accompanied by a change in beta power in cortex (and only a general decrease in all frequencies >20 Hz in subcortical areas). We interpret this as a reflec-
tion of a reduction in communication throughout the
motor system, which occurs specifically in beta. Comment-
jamais, alternative interpretations are possible. Pour dans-
position, the power decrease in the subcortical regions
may make the subcortical phase estimate of beta less
reliable, reducing coherence. We cannot rule out this
interpretation, cependant, if this were the only factor driv-
ing the effect, we might expect a more broadband coher-

ence change, because all frequencies above 20 Hz
decreased in the subcortical areas. En outre, this inter-
pretation is not necessarily mutually exclusive with the
hypothesis that communication at specific frequencies is
decreased, because a decrease in communication could
also be driven by a decrease in power at the dominant
frequency for a particular structure within a brain network.
Another interpretation is that movement could have
influenced the recordings, because beta is modulated
by movement (Vieille femme, Miglioretti, Gordon, Sieracki,
et coll., 1998; Pfurtscheller, 1981). Although all our patients
were at rest during the recordings, perhaps subtle, unde-
tectable movements were present in the early portions of
the recording that decreased over time with anesthesia
induction. Cependant, this interpretation would predict
changes opposite to those observed here (c'est à dire., an in-
crease in coherence with anesthesia induction), because
movement has been associated with a decrease in coher-
ence in the alpha/beta range between cortical and sub-
cortical motor structures (Alegre et al., 2010; Lalo et al.,
2008; Cassidy et al., 2002).

Changes in Power in Specific Brain Regions May
Reflect Reduced Neuronal Activity or Relate
to Disconnection between Brain Regions

The propofol-induced reduction in cortical broadband
activity that we observed was weak but is in general agree-
ment with previous human studies examining ECoG dur-
ing anesthesia induction ( Verdonck et al., 2014; Breshears
et coll., 2010). The subcortical broadband gamma reduc-
tion confirms similar findings in rodent studies (Reed,
Plourde, Tobin, & Chapman, 2013) and in one small series
of thalamic recordings in humans ( Verdonck et al., 2014).
Broadband gamma power in cortex is thought to be an
index of local neural activity and a surrogate for neural
spiking (Manning et al., 2009; Miller et al., 2007). Le
interpretation of broadband gamma in subcortical struc-
tures is less clear but may similarly represent the sum of
local neuronal spiking. En effet, event-related broadband
gamma changes similar to those reported in cortex have
been observed in subcortical structures (Hamame, Alario,
Llorens, Liegeois-Chauvel, & Trebuchon-Da Fonseca,
2014; Ray et al., 2012).

Gamma power decreases are often associated with low-
frequency power increases (Vieille femme, Miglioretti, Gordon, &
Lesser, 1998; Vieille femme, Miglioretti, Gordon, Sieracki, et coll.,
1998). The low-frequency power increases (2–6 Hz) que
we observe in both cortex and subcortex may relate to
this same process. Donc, one interpretation is that
the changes in power observed during loss of conscious-
ness relate to decreased neural activity in regions that play
key roles in generating behavior.

An alternative explanation is that the low-frequency (2–
6 Hz) power changes in both cortex and subcortex have
a different etiology than the high-frequency power changes.
Several studies have reported low-frequency power

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increases in cortex during anesthesia induction (Verdonck
et coll., 2014; Purdon et al., 2013; Lewis et al., 2012; Breshears
et coll., 2010). In some cases, this emerging low-frequency
activity is asynchronous across cortex and has been inter-
preted as a mechanism whereby different brain networks
become disconnected from one another (Purdon et al.,
2013; Lewis et al., 2012). In many of these studies, le
low-frequency activity is in a slightly lower-frequency
range (<2 Hz) than is observed here; nevertheless, other studies have shown these changes over a broader frequency range ( Verdonck et al., 2014), and so it is possible that similar mechanism involved. Limitations There are several limitations to our study. Although our recording methods high temporal resolution, the precision with which the state of consciousness is assessed, using standard anesthesia scales, less tem- porally precise. This may limited we could detect. All participants were movement dis- order patients who necessarily abnormal motor networks, subcortical recording sites limited to clinically indicated targets for ethical reasons. We sought minimize this problem by including patients with different diagnoses, presumably differ- ent pathophysiologies, focusing on patterns that were consistent across patients. Nevertheless, an alter- native interpretation activity associated with induction reflect cessation symptoms, which occurs anesthesia. Additionally, inclusion dif- ferent diagnoses further increased heterogeneity of our sample, decreased sensitivity for finding effects makes more com- plex. Likewise, although examination multiple sub- cortical allowed us focus motifs common to nodes BGTC motor loop, also in- creased potential signal variability. Thus, we may be insensitive present most strongly in only certain regions. Finally, the use ECoG strip arrays can inserted via burr hole spatial sampling. Our results not generalize entire cortex (although there simi- larities between data recorded greater spatial sampling; see Breshears 2010). Investigation of other, distributed brain networks was precluded by constraints working human patients in operating room. Conclusion Using method simultaneously collect high resolution from human cortex nuclei, we narrow- band reduction system interactions beta band during loss consciousness. These support communication throughout involves synchronous oscillatory frequency-specific breaks down when organized recruitment network for behavior impossible (i.e., unconsciousness). These shed light possible general mechanisms of neural communication brain. Acknowledgments We would like thank Dr. Jill Ostrem help patient recruitment, Oana Maties assess- ments, Bradley Voytek helpful comments the manuscript. We all who participated work supported the National Institutes Health (grant R01NS069779 P. A. S.). Reprint requests should sent Nicole C. Swann, Health Sciences East, Rm #823, 513 Parnassus Avenue, San Francisco, CA 94143, or e-mail: Nicole.Swann@ucsf.edu. REFERENCES Alegre, M., Rodriguez-Oroz, M. C., Valencia, Perez-Alcazar, M., Guridi, J., Iriarte, al. (2010). Changes subthalamic activity observation Parkinson’s disease: Is mirror mirrored basal ganglia? Clinical Neurophysiology, 121, 414–425. Alexander, G. E., DeLong, R., & Strick, L. (1986). Parallel organization functionally segregated circuits linking basal ganglia cortex. Annual Review Neuroscience, 9, 357–381. Alkire, T., Hudetz, G., Tononi, (2008). Consciousness and Science, 322, 876–880. Arthuis, Valton, L., Regis, Chauvel, P., Wendling, F., Naccache, (2009). Impaired during temporal lobe seizures related long-distance cortical-subcortical synchronization. Brain, 132, 2091–2101. Bonhomme, V., Boveroux, Brichant, J. F., Laureys, S., Boly, M. (2012). Neural correlates general anesthesia functional magnetic resonance imaging (fMRI). Archives Italiennes de Biologie, 150, 155–163. Breshears, D., Roland, Sharma, Gaona, M., Freudenburg, Z. Tempelhoff, Stable and dynamic cortical electrophysiology and emergence propofol Proceedings Academy Sciences, U.S.A., 107, 21170–21175. Brovelli, A., Ding, Ledberg, Chen, Y., Nakamura, & Bressler, S. (2004). Beta oscillations large-scale sensorimotor network: Directional influences revealed Granger causality. National Academy 101, 9849–9854. Canolty, R. Edwards, Dalal, Soltani, Nagarajan, S. Kirsch, H. (2006). High gamma power is phase-locked theta neocortex. Science, 313, 1626–1628. Canolty, Knight, T. The role of cross-frequency coupling. Trends Cognitive Sciences, 14, 506–515. Cassidy, Mazzone, Oliviero, Insola, Tonali, P., Di Lazzaro, (2002). Movement-related in synchronization ganglia. 125, 1235–1246. Chennu, Finoia, Kamau, Allanson, Williams, B., Monti, (2014). Spectral signatures reorganised brain disorders consciousness. PLoS Computational Biology, 10, e1003887. Swann al. 93 D o w n l o a d e d f r o m l l >
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