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Anticipatory human subthalamic area beta-band power
responses to dissociable tastes correlate with weight gain
Bina Kakusaa,
Yuhao Huanga,
Daniel A.N. Barbosaa,
Austin Fenga,
Sandra Gattasa,
Rajat Shivacharana,
Eric B. Leea,
Fiene M. Kuijpera,
Sabir Salujaa,
Jonathon J. Parkera,
Kai J. Millerc,
Corey Kellerb,
Cara Bohonb,
Casey H. Halperna,*
aDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
bDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine,
Stanford, CA, 94305, USA
cDepartment of Neurosurgery, Mayo Clinic, Rochester, MN, 55905, USA
Abstract
The availability of enticing sweet, fatty tastes is prevalent in the modern diet and contribute to
overeating and obesity. In animal models, the subthalamic area plays a role in mediating appetitive
and consummatory feeding behaviors, however, its role in human feeding is unknown. We used
intraoperative, subthalamic field potential recordings while participants (
n
= 5) engaged in a task
designed to provoke responses of taste anticipation and receipt. Decreased subthalamic beta-band
(15–30 Hz) power responses were observed for both sweet-fat and neutral tastes. Anticipatory
This is an open access article under the CC BY NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
*Corresponding author at: Stanford University School of Medicine, 453 Quarry Road, Neurosurgery, MC: 5327, Palo Alto, CA 94304,
USA. chalpern@stanford.edu (C.H. Halpern).
Author contribution
CH, CB, BK, and YH made substantial contributions to the conceptualization and methodology of the work. BK, YH, AF, RS, EBL,
FMK, DANB, SG, SS, KJM, and CK performed data curation and formal analysis. All authors contributed to writing the original draft
and provided substantially to manuscript review and editing. All authors have approved and agree to be personally accountable for the
submitted version.
Supplementary data to this article can be found online at https://doi.org/10.1016/j.nbd.2021.105348.
Declaration of Competing Interest
None.
HHS Public Access
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responses to taste-neutral cues started with an immediate decrease in beta-band power from
baseline followed by an early beta-band rebound above baseline. On the contrary, anticipatory
responses to sweet-fat were characterized by a greater and sustained decrease in beta-band power.
These activity patterns were topographically specific to the subthalamic nucleus and substantia
nigra. Further, a neural network trained on this beta-band power signal accurately predicted
(AUC ≥ 74%) single trials corresponding to either taste. Finally, the magnitude of the beta-band
rebound for a neutral taste was associated with increased body mass index after starting deep brain
stimulation therapy. We provide preliminary evidence of discriminatory taste encoding within the
subthalamic area associated with control mechanisms that mediate appetitive and consummatory
behaviors.
Keywords
Deep brain stimulation; Feeding behaviour; Electrophysiology; Subthalamic area; Artificial neural
network; Beta-band power; Body-mass index; Parkinson’s disease
1. Introduction
The subthalamic area, including the subthalamic nucleus (STN), substantia nigra (SN), and
zona incerta (ZI), is classically considered part of the basal ganglia involved in motor
processing including locomotion, reaching, and orofacial movement (Nambu et al., 1996;
Quinn et al., 2015; Baunez et al., 2002). Moreover, the subthalamic area is well-positioned
to integrate and regulate not only motor but also motivation and cognitive processing needed
for coordinated responses to incentives, and even food to maintain energy homeostasis
(Lardeux et al., 2009; Espinosa-Parrilla et al., 2013; Weintraub and Zaghloul, 2013; Stice
et al., 2008a). How this region mediates appetitive and consummatory feeding behavior in
humans remains unknown, but animal studies have suggested topographically heterogenous
roles in feeding within the subthalamic area (Baunez et al., 2002; Lardeux et al., 2009;
Zhang and van den Pol, 2017). Specifically, STN neuronal firing rates responded to food-
related cues and corresponded to food preference (Lardeux et al., 2009; Espinosa-Parrilla et
al., 2013). Further, ZI GABAergic neurons were reported to exhibit increased activity with
food deprivation and administration of ghrelin, and driving these neurons increased sweet,
high-fat (sweet-fat) food interaction and consumption and increased weight gain (Zhang and
van den Pol, 2017).
While electrophysiological data from humans are lacking, there have been variable reports
of increased body weight and consummatory feeding behaviors in a subset of patients
following subthalamic area deep brain stimulation (DBS) (Serranova et al., 2011; Serranova
et al., 2013; Aiello et al., 2017). While these effects are likely to be multifactorial, they
suggest, in line with preclinical findings, that a physiologic control signal exists in the
subthalamic area that is disrupted by DBS (and lesions in rodents) and may explain the
observed variability in weight gain (Baunez et al., 2002; Baunez et al., 2005; Uslaner et
al., 2008). Examining this focal area in awake feeding human subjects is made possible by
collecting electrophysiological recording data during DBS surgery (Zaghloul et al., 2009).
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The sweet-fat macronutrient combination has been reported to provoke feeding beyond
homeostatic needs across species (Zhang and van den Pol, 2017; Drewnowski, 1997).
We hypothesized that responses to a sweet-fat solution exist in topographically-specific
regions of the subthalamic area and used a validated taste-incentive task paradigm to isolate
anticipation and receipt phases of feeding (Stice et al., 2008b). Further, we hypothesized
that the characteristics of these responses may correlate with subject-specific behavioral
outcomes represented by postoperative body mass index (BMI) (Balestrino et al., 2017;
Foubert- Samier et al., 2012). To our knowledge, this is the first study to identify
discriminatory and topographically organized taste encoding within the human subthalamic
area.
2. Methods and materials
2.1. Intraoperative cohort
We recruited patients with Parkinson’s disease (PD) undergoing clinically indicated DBS
surgery. The study was approved by the Stanford University Institutional Review Board
(IRB#33146) and all participants provided written informed consent.
2.2. Lead implantation
Participants were instructed to stop long-acting and short-acting dopaminergic medications
over 24 and 12 h prior to surgery, respectively. On the day of surgery, we carried out
computer-assisted, image-guided stereotactic placement of the DBS electrodes into the
subthalamic nucleus via a frameless approach described previously (Quinn et al., 2015).
Pre-operatively, stereotactic coordinates for STN were used (12 mm lateral, 3 mm posterior
and 4 mm inferior to the midpoint of the AC–PC line) and optimized with direct targeting
using a T2-sequence (Benabid et al., 1996; Hamid et al., 2005). Conscious sedation during
operative opening (including trephination) and closing was achieved using dexmedetomidine
and propofol to minimize neurocognitive effects. All agents were turned off at least 20
min prior to microelectrode recordings to ensure the patient was appropriately conscious
during the single pass of microelectrode recordings (Neuroprobe Sonus-Shielded Tungsten
STR-009080; Alpha Omega, Inc.) and sensorimotor testing that preceded DBS lead
implantation and task-based recordings. Once identified, the microelectrode was replaced
by an 8-contact DBS lead (Boston Scientific DB-2201,
N
= 4, or DB-2202,
N
= 1,
Marlborough, MA; USA). After implantation of the left hemispheric lead, field potentials
were recorded from the left hemisphere as the participant completed the task described
below. Following task completion, intra-operative test stimulation was used to confirm
therapeutic response at target, and the leads was permanently fixed.
2.3. Sweet-fat incentive task paradigm
Participants completed a task that elicits neural responses during anticipatory and receipt
of a sweet-fat solution (Stice et al., 2008b; Bohon and Stice, 2011; Bohon, 2017; Stice
et al., 2010). Prior to each session, a sweet-fat solution (McDonalds chocolate milkshake)
and taste-neutral solution were prepared and stored in a refrigerator as described previously
(Bohon and Stice, 2011).
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This task lasted approximately 13 min and was presented on an LCD monitor. The task
consisted of 80 trials divided, with each divided into 5 screens (Fig. 1A top). For each
trial, participants were instructed to fixate to a point on the screen (2 s), cued (1 s) with
an image of a sweet-fat (glass of chocolate milkshake; 50% of trials) or a taste-neutral
(glass of water), and instructed to again fixate to a point on the screen as they anticipated
receipt of the cued solution (2 s). The order of stimulus presentation was randomized
across participants. Solutions were loaded into respective, 60 ml syringes and attached to a
MATLAB-programmable dual syringe pump (Braintree Scientific BS-8000 Dual) to ensure
consistent volume, rate, and timing of taste delivery. Syringes were connected via Tygon
tubing to a custom manifold fitted into the participants’ mouths to provide solution delivery
to a consistent segment of the tongue (Stice et al., 2008b). To maintain attention, participants
were directed to press a keypad button using their dominant hand, immediately after cue
presentation (Stice et al., 2008b). Next, a “Delivering taste” message (2 s) followed by
a fixation screen (1 s) appeared, during which a 0.5 cc solution of the cued taste was
administered to the participant. Finally, a “Swallow” message (2 s + 0.5–1 s jitter) directed
the participant to swallow and prepare for the next trial. Subjective ratings of solution
palatability using a 10-point Likert scale and solution preference (sweet-fat vs taste-neutral)
were collected following a practice session immediately prior to surgery.
2.4. DBS lead localization and subcortical atlas
Pre-operative T1-weighted MRI and post-operative CT images obtained after DBS lead
placement were linearly registered using Advanced Normalization Tools (ANTs). DBS
contacts were then reconstructed in each participant’s native space according to electrode
artifacts from the co-registered post-operative CT images. ANTs was used to perform a two-
step linear and non-linear registration between each participant’s native T1 and the standard
MNI152 space. The non-linear registration was optimized for the subcortical regions for
improved registration accuracy. The optimized local registration was achieved by using a
dilated subcortical mask to constrain the cost function. Regions of interest (ROIs) were
extracted from the MNI-defined DBS Intrinsic Template Atlas (DISTAL), which contains
101 thalamic and extra-thalamic nuclei including the STN, the SN, and the ZI (Chakravarty
et al., 2006; Ewert et al., 2017). The ROIs were registered onto the participant’s native brain
T1-space and overlaid to the reconstructed DBS contacts.
For visualization purposes, images were loaded into MATLAB 2016b (MathWorks, Inc.,
Natick, MA; USA). Using the Lead-DBS toolbox (v2), these imaging protocols were
normalized into standard MNI152 space (point X = 0, Y = 0, Z = 0 at the anterior
commissure), co-registered, and the results of which were confirmed manually (Horn et
al., 2019). DBS contacts were automatically pre-reconstructed using the phantom-validated
and fully-automated PaCER method and results were manually confirmed in 3DSlicer (v4)
(Husch et al., 2018; Fedorov et al., 2012). The reconstructed electrode trajectory was merged
with ROIs as defined in DISTAL.
2.5. DBS channel data acquisition and field potential processing
DBS recordings (1375 Hz sampling rate) were amplified and stored for offline analysis
using a NeuroOmega recording system (AlphaOmega, Alpharetta, GA; USA). Data were
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analyzed in MATLAB using the FieldTrip toolbox (v2.3) (Oostenveld et al., 2011). Line
noise was removed using a zero-phase, band-stop filter at 60 Hz harmonics. The field
potential signal was extracted using a 300 Hz, 6th order, two-pass, zero-phase, Butterworth
IIR low-pass filter.
Re
-referencing was carried out using a Laplacian scheme on sequential
channels to maximize the local population-level signal and to keep all contacts as sources
(Li et al., 2018; Hindriks et al., 2016). In the participant with a segmented lead (“1–3–3–
1” scheme), both segmented rings were referenced against the adjacent unsegmented ring
and the corresponding segment of the adjacent segmented ring. Both unsegmented rings
were referenced against the average of all segments in the adjacent segmented ring. The
data were then epoched to task cue onset from − 2 to 6 s (s) with onsets of cue and
solution delivery at 0 s and 3 s; respectively. Time-frequency (TF) analysis was carried out
with a multi-taper convolution method using discrete prolate spheroidal sequences as tapers
with power extracted by squaring the magnitude of the complex Fourier-spectra. The TF
spectrogram was log transformed, and z-scored against the pre-cue baseline (− 0.6 to − 0.1
s) within each channel. For frequency-band-specific power analyses, the TF spectrograms
were averaged in the frequency domain by division into 6 discrete frequency-bands: delta
(1–4 Hz), theta (5–7 Hz), alpha (8–14 Hz), beta (15–30 Hz), low gamma (35–70 Hz), and
high gamma (75–150 Hz). To determine frequency bands of interest, the power signal in
each frequency-band was averaged during baseline, anticipatory, and receipt time periods
for both tastes. Responsive frequency bands were defined as those in which the magnitude
of power during anticipation or receipt differed from baseline power. Significance was
determined using one-way ANOVA with
p
-values corrected using the false discovery rate
method (FDR-adjusted) at an alpha threshold of 0.05.
2.6. Definition of electrode responsivity as an electrode inclusion criteria
A channel was classified as cue- or receipt- responsive if band power significantly differed
for either taste during anticipation (0 to 3 s) or receipt periods (3 to 6 s), respectively,
compared to baseline (−0.1 to −0.6 s). A channel was classified as cue- or receipt-specific if
band power significantly differed between tastes during anticipation or receipt, respectively.
All 6 frequency-band ranges were examined. Statistical significance was calculated using
a cluster-based permutation approach (Maris and Oostenveld, 2007). Briefly, channel-time
cluster significance was quantified by the maximum sum of an independent-sample
t
-test
value with a cluster alpha-threshold of 0.05. The significance probability was then calculated
using a Monte-Carlo estimate (resampling statistic) with 500 random permutations using
the cluster-based statistic and a critical alpha threshold of 0.05. Observations for unpaired
permutation testing were taken from trials at each channel of interest per participant,
independently.
2.7. Neural network modeling
TF spectrograms were used to train and test an artificial neural network (ANN) model
with a single-hidden layer designed in JMP Pro v14 (SAS Institute Inc., Cary, NC;
USA). The model was designed with nodes using both hyperbolic tangent (TanH) and
Gaussian activation functions. The number of nodes under each function was selected from
preliminary analysis evaluating the minimum root-mean-square error of models with 1 to
20 TanH and 1 to 10 Gaussian function nodes (Spuler et al., 2015). Model observations
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were generated from the trials of cue- and receipt-responsive channels across participants.
The features fed into the model consisted of power in responsive frequency-bands and
of time, with anticipatory and receipt periods divided into 6, 500 ms time windows.
ANN classification was performed separately for anticipation and for receipt. Observations
were obtained from trials at each channel per participant and semi-randomly divided into
independent training (70%), validation (20%), and testing (10%) subsets. Semi-random
partitioning of observations was performed to ensure that trials within each participant
were evenly divided between the 3 subsets. To minimize overfitting, the validation set
was used to apply an optimal penalty on model parameters using the absolute penalty
method (Tibshirani, 1996). In addition, after training was complete, we performed a final,
independent assessment of the model’s performance with observations not used in training
or validation (i.e. the testing subset). Model performance was assessed using receiver
operant curve analysis to calculate the area under the curve (AUC) as an outcome measure in
addition to the overall accuracy, and the true and false positive rates. Statistical significance
was determined by permutation testing with a critical alpha threshold of 0.05. Features
were assessed for their contribution to the model’s total effect using JMPs ‘Assess Variable
Importance’ function within the prediction profiler toolbox (Saltelli, 2002). Briefly, feature
importance is measured by varying feature value sampling with the Monte Carlo method to
estimate impact on response variability of the feature alone and in combination with other
features (total effect). Features with the largest contribution to the effect size were defined as
having a total effect above the 95% confidence interval (95% CI) of the mean distribution of
all sampled effects across features and permutations.
2.8. Body-mass index and band power association
In all 5 participants, available BMI measures were collected from pre- and post-operative
clinic visits. Using BMI measures, participants were stratified into a BMI gain (vs no BMI
gain) group if the average post-operative BMI was significantly greater than pre-operative
BMI. Statistical significance was determined by paired t-testing. Then using band power
measures sampled from all 40 trials per condition of all 8 channels per participant, we
examined if participants could independently be stratified into the same two groups. To
quantify the normalized band power distributions during the time segments of interest,
mean values and 95% CIs were derived utilizing bootstrapping (random sampling with
replacement, using 1000 repetitions). Statistical significance was classified as average power
above the 95% CI.
2.9. Availability of materials and data
The data that support the findings of this study are available from the corresponding author,
upon reasonable request.
3. Results
Five participants (average age of 67.6 years, 2 females, all right-handed) undergoing STN
DBS surgery performed the sweet-fat incentive task (Table 1). All participants underwent
DBS implantation with task-related electrophysiological recordings from the subthalamic
area. All participants preferred the sweet-fat over the taste-neutral solution with the average
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rating for the sweet-fat solution being 7.4 (range 5 to 10) on a 10-point Likert rating scale.
The median preoperative BMI was 29.9 kg/m2 (range: 15.3 to 36.8 kg/m2).
3.1. Beta-band power characterizes subthalamic area responses to sweet-fat vs taste-
neutral tastes
Compared to other measured frequency bands, beta-band (15–30 Hz) power predominated
in the subthalamic area (N = 40 channels in 5 participants; Fig. 1A, FDR-adjusted
p
<
0.05; Fig. 1B top inset; one-way ANOVA, FDR-adjusted
p <
0.05) and on average,
decreased significantly in response to both tastes during anticipation and receipt (Fig.
1B–C, one-way ANOVA, FDR-adjusted
p <
0.05). Of the 40 channels, 17 (42.5%)
demonstrated cue-responsive and 14 (35%) demonstrated cue-specific beta-band power
changes in4 (80%) participants during anticipation (Fig. S1A; Fig. 2A). Of the 40 channels,
7 (17.5%) demonstrated receipt-responsive and 4 (10%) demonstrated receipt-specific beta-
band changes in 3 (60%) participants. Thus, further analysis was focused on the beta-band
range.
Cluster-based permutation testing among cue-responsive channels (
N
= 17 channels in 4
participants) revealed decreased beta-band power0.5 to1s after both taste cues, however,
sweet-fat cues elicited a greater magnitude response (Fig. 2B top; cluster-based permutation,
p <
0.05). To further characterize the signal’s temporal dynamics, beta-band power was
divided and averaged into 500 ms time windows (from 0.5 to 1 s – i.e. T1 – up to 5.5
to 6 s – i.e. T12 after cue; Fig. 2B bottom). Following the initial decrease, beta-band
power increased earlier for taste-neutral cues (beta-band rebound) 1.5 s after cue (T4) while
remaining decreased for sweet-fat cues throughout anticipation (cluster-based permutation
testing,
p <
0.05). During receipt, decreased beta-band power was taste-neutral specific
and occurred within 1 safter solution delivery (cluster-based permutation testing, p
<
0.05).
Beta-band rebounds were observed 1 s after delivery of both tastes.
Participant button-press reactions times to cue onset did not significantly differ between
sweet-fat (median: 0.73 s, interquartile range: 0.37 s) and taste-neutral conditions (0.71
s, interquartile range: 0.58 s; pooled
t
-test,
p
= 0.55). With trials time-locked to the button-
press (instead of taste cue) onset, similar patterns of beta-band power decrease and rebound
were observed (Fig. S2A–B; cluster-based permutation testing,
p <
0.05). However, the
overall magnitude of the beta-band power decrease during the first second was greater
when trials were time-locked to the taste cue vs the button press events. Further, during the
beta-band rebound period, rebound magnitude was significant greater for the neutral taste,
and beta-band power was significantly more suppressed for the sweet-fat taste when trials
were time-locked to the taste cue vs the button press event (Fig. S2C–D; FDR-adjusted
p <
0.05).
3.2. The STN, SN, and ZI exhibit topographically heterogenous beta-band responses
During both anticipation and receipt, most responsive channels observed were situated in
the ventroposterior aspect of the recorded subthalamic area corresponding to the STN (Fig.
S1B–C; one-way ANOVA, FDR-adjusted
p <
0.05). During anticipation in the STN (
N
=
10 channels), an early, non-discriminatory, beta-band power decrease was observed (Fig. 2C
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top; T2: 0.5 to 1 s; cluster-based permutation,
p <
0.05), and followed by a taste-neutral
specific beta-band rebound (Fig. 2C top; T4: 1.5–2 s; cluster-based permutation,
p <
0.05).
A similar pattern of responses was observed in the SN (
N
= 3 channels; Fig. 2C middle;
cluster-based permutation,
p <
0.05). During receipt, the STN and SN were characterized
an early (T8: 3.5 to 4 s) and late (T11-T12: 5 to 6 s) taste-neutral-specific beta-band power
decreases (cluster-based permutation,
p <
0.05).
The changes in beta-band power in the ZI (
N
= 4 channels) differed from those in the STN
and SN (Fig. 2C bottom). First, while the STN and SN exhibited exclusively decreased
beta-band power during anticipation, the ZI displayed both increased (
N
= 2) and decreased
(N = 2) power responses. The decreased power responses were specific to sweet- fat cues
(cluster-based permutation,
p <
0.05) and situated more dorso-laterally in the ZI (Fig. S1D,
one-way ANOVA, FDR-adjusted
p <
0.05). The increased beta-band power cue-response
changes were non-discriminatory. No receipt-responsive or –specific changes were measured
in either region of the recorded ZI.
3.3. Trial-level subthalamic activity predicts anticipated and received tastes
To further characterize the observed patterns of beta-band activation, we utilized ANN for
non-linear predictive modeling (Fig. 3A). Observations were selected from trials of the
15 cue-responsive channels characterized by decreased beta-power in the STN, SN, and
ZI, excluding the 2 channels with increased beta-power responses. The ANN classified
sweet-fat vs taste-neutral trials with an AUC = 0.75 (confidence interval–CI: 0.74–0.75)
during anticipatory (Fig. 3B left) and an AUC = 0.73 (95% CI: 0.72–0.74) during receipt
periods (Fig. 4C left). During anticipation, the overall accuracy was 69% with false-positive
rates of 28% and 34% for sweet-fat and taste-neutral solutions, respectively. During receipt,
the overall accuracy was 67% with false-positive rates of 32% and 33% for sweet-fat and
taste-neutral solutions, respectively. Of the features used during anticipation, the beta-band
rebound period (T4: 1.5 to 2 s after cue-onset) had the largest contribution to the observed
total effect (
p <
0.05; Fig. 3B right). During receipt, the early (T8: 3.5 to 4 s) and late
(T12: 4.5 to 5 s) taste-neutral -specific response periods had the largest contributions to the
observed total effect (
p <
0.05; Fig. 4C right).
3.4. Increased post-operative BMI is associated with beta-band responses
Two participants (P1 and P5) had significantly increased BMI in the months post-operatively
compared to a pre-operative BMI baseline (BMI gain group; Fig. 4A–B: FDR-adjusted
p
-value
<
0.05 on one-way ANOVA; Fig. 4B: R2 = 0.94, F(1,7) = 114,
p <
0.0001). The
remaining 3 participants (P2–4) had no statistically significant change in BMI several
months post-operatively (no BMI gain; R2 = 0.01, F(1,10) = 0.11,
p
= 0.75). Averaged
beta-band power from all channels (
N
= 40 channels in 5 participants) revealed significantly
larger response magnitudes for neutral tastes during all the 3 discriminatory time periods
(T4, T8, and T12) in the BMI gain vs no BMI gain group (Fig. 4 C, p
<
0.05 bootstrap test).
Beta-band responses for sweet-fat during these time periods did not stratify participants into
these groups (Fig. 4D). Both participants that gained weight had chronically-active contacts
which were task responsive (Fig. 2A).
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4. Discussion
Our everyday environments are filled with sensory cues that modulate neural activity and
influence behavior. The subthalamic area is a major node in this process that is thought to
mediate inhibitory control, incentive salience, and goal-oriented behavior (Serranova et al.,
2011; Frank, 2006). To our knowledge, we demonstrate, for the first time using intracranial
electrophysiological recordings, that the human subthalamic area responds to taste incentives
during periods of anticipation and receipt. Specifically, beta-band power changes in the
STN, SN, and ZI, respond to and can accurately discriminate (AUC ≥ 74%) tastes during
both anticipatory and receipt. Finally, subthalamic beta-band power responses to neutral
taste can stratify individuals that gain vs do not gain BMI following DBS. Together, these
preliminary findings provide experimental and clinical evidence that the subthalamic area is
not only involved in motor planning but also in taste anticipation and receipt.
In this study, beta-band power was prominent, compared to a wide spectrum of frequency-
band ranges within the subthalamic area (Kuhn et al., 2004; Levy et al., 2000). This aligns
with prior literature associating subthalamic beta-band power with motor processing and
control (Jenkinson and Brown, 2011; Williams et al., 2005; Wingeier et al., 2006). Further,
these studies demonstrate that beta-band rebounds (i.e. switch from decreased to increased
power) may function to dynamically adapt motor performance and behavior in the context
of imagined, observed, or performed movements (Kuhn et al., 2004; Marceglia et al., 2009;
Kuhn et al., 2006; Torrecillos et al., 2018). In go/no-go task paradigms, movement during
‘go’ trials (i.e. favored movement initiation) was characterized by a greater decrease in
beta-band power compared to ‘no-go’ trials (i.e. suppressed movement initiation) where
beta-band rebounds interrupted the decreased response (Kuhn et al., 2004; Marmor et al.,
2020; Alegre et al., 2013). In the few studies describing subthalamic beta-band responses
to reward, greater beta-band power decreases have been associated with increased size of
monetary rewards, and with increased magnitude of received reinforcements (Duprez et al.,
2019; Schroll et al., 2018). With regard to taste-motivated activity in the human subthalamic
area, prior studies have been limited by functional MRI which offers poor temporal
resolution that does not match up to behavioral timescales, provides indirect measures of
local neuronal population activity, and has a low specificity for small subcortical nuclei g
(Branch et al., 2013; Thanarajah et al., 2019; de Hollander et al., 2017). Therefore, our
findings, in conjunction with the previously described reward studies, implicate decreased
beta-band power with motivational reinforcement for sweet-fat tastes and the beta-band
rebound response with inhibitory control toward the less preferred neutral taste for the
first time. However, the results of this study are preliminary and further research with a
broader range of foods and dietary tastes is needed to explore the inhibitory effects of these
responses across the subthalamic area.
The differences in anticipatory responses to sweet-fat and neutral taste were related to
taste-preference for the sweet-fat solution and not to the button-press action requested upon
cue presentation. The button press in this task was used to maintain participant attention,
it was used for both sweet-fat and taste-neutral solutions, was not coded to change receipt
outcome, and there were no reaction time differences between the two stimuli. This simple
motor action has been shown to drive robust topographically-dependent beta-band power
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fluctuations in the subthalamic area (Torrecillos et al., 2018; Priori et al., 2013). However,
if the observed beta-band response reported here was due solely to motor activity, we would
expect similar changes between sweet-fat and taste-neutral conditions. Nonetheless, the
influence of the button press on beta-band activity cannot be excluded. Reassuringly, Buot
et al. (2012) found that they could elicit higher magnitude, event-related potentials in the
STN to unpleasant vs neutral whether a motor or passive response was required following
the image, but the magnitude was larger when paired with a motor response (Baunez et al.,
2005). Further, subthalamic beta-band power responses to rewards have been demonstrated
in contexts where no motor action was required (Oswal et al., 2012; Al-Ozzi et al., 2020).
Here, we demonstrate that when trials are time-locked to the taste cue vs the button press,
the magnitude of the beta-band power decrease and beta-band rebound are significantly
greater for the former, further suggesting role of anticipatory taste processing.
Compared to prior electrophysiologic studies, the task paradigm in this study allowed for,
the food-taste component of receipt to be better separated from the motor (i.e. swallowing)
phase (Espinosa-Parrilla et al., 2013). After solutions were delivered onto the participant’s
tongue, participants were instructed to refrain from swallowing until prompted 3 s later.
During receipt, decreased power responses specific to the neutral taste were observed early
in the STN and later in the SN. Prior PET imaging studies have similarly reported on a
temporal dissociation between these regions with dopamine release within 5 min, and 15–
20 min after sweet-fat (vs “tasteless solution”) intake in the STN and SN, respectively…
(Thanarajah et al., 2019) Unfortunately, BOLD changes were not reported or characterized
in these regions likely due to the previously mentioned limitations of this modality. The
temporally shifted responses between these two physiologically inter-dependent regions
may relate to their connectivity within the basal ganglia system circuitry, however, further
investigation teasing out these components is needed.
Finally, subthalamic beta-band activity was associated with having increased (vs not
increased) BMI after starting DBS therapy. Here, increased BMI was observed only among
participants with greater beta-band power magnitude during important discriminatory time
periods, namely an exaggerated anticipatory beta-band rebound response to the neutral taste.
In prior literature, weight gain following subthalamic DBS for PD has been extensively
reported (Foubert-Samier et al., 2012; Mills et al., 2012; Locke et al., 2011). One proposed
mechanism is that participants have both decreased energy demands and increased ability to
feed due to a reduction in resting tremor and rigidity (Balestrino et al., 2017; Foubert-Samier
et al., 2012; Sauleau et al., 2016). However, these findings are inconsistent across studies
(Locke et al., 2011). Other studies suggest a primarily metabolic mechanism for weight gain,
with decreased lipid and increased glucose oxidation, measured by indirect calorimetry,
following STN-DBS-vs levodopa-treated PD patients (Perlemoine et al., 2005).While the
present preliminary work is unable to substantiate a causal relationship, we propose two
possible mechanisms. First, the beta-band rebound shown to underlie inhibitory control
may be disrupted by DBS, leading to increased likelihood of weight gain. Prior literature
has highlighted the multimodal effects of DBS both as an inhibitor (i.e. functional lesion)
and exciter of local and distant neural activity (Xiao et al., 2018; McIntyre et al., 2004).
Participants who require heightened beta-band rebound responses to avoid less-preferred
foods may be more susceptible to weight gain following disruptive DBS. A second
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possibility is that patients gained weight through a pre-existing process, not captured in
our study (lifestyle, eating habits, etc.) that was perhaps facilitated by DBS. Prior animal
studies have reported that STN lesions differentially modulate appetitive behavior in mice
with preexisting binge-drinking (i.e. high preference for alcohol over water) vs those
without this preexisting behavior (i.e. no high preference for alcohol over water) (Lardeux
and Baunez, 2008). Following STN lesioning, mice with pre-existing alcohol preference
had further increased place preference for alcohol-paired environments. However, mice
without pre-existing alcohol preferences had increased place preference for water-paired
environments following STN lesions. Here, the differential beta-band power patterns may
serve as a marker for patients with a pre-existing preference for increased feeding that
was facilitated by STN DBS, as a functional lesion, and resulted in increased weight gain.
Of note, the two patients with increased BMI after DBS were both female and had the
lowest pre-operative baseline BMIs. However, a review of 38 studies examining predictors
of BMI change following STN DBS for PD reported no effect of gender or pre-operative
BMI (Steinhardt et al., 2020). Our preliminary results pave the way for additional studies to
further evaluate these mechanisms as predictors of post-operative weight gain.
Our study provides preliminary evidence that the subthalamic area beta-band power plays a
role in taste processing. Nonetheless, it should be noted that the sample size was limited in
large part due to the unique study design of delivering actual liquid
per oral
intraoperatively.
However, given multi-channel recording, each participant provided multiple data points
for analysis. Moreover, given the inherent limitation of the recording apparatus used for
the study, field potentials were only collected unilaterally in the participant’s dominant
hemisphere to standardize across the extended case series and to optimize evaluation of
any possible reaction time differences (Kerr et al., 1963). We cannot exclude the influence
of expected and actual putative differences between the two solutions or ocular saccades
on reported electrophysiologic difference. However, these differences are not expected to
contribute heavily during the anticipatory phase prior to taste delivery or receipt phase prior
to explicit swallow instructions. It is also unclear if our findings are generalizable to subjects
without PD. Nevertheless, the finding of a potential control signal within the subthalamic
area that when disrupted by DBS leads to weight gain is a conceptual advance that demands
further translational study.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgements
We thank the patients for their collaboration.
This study was supported by the Stanford Neurosurgery start-up funds including support from the John A. Blume
Foundation and the William Randolph Hearst Foundation, USA; the Brain and Behavior Research Foundation,
USA; and the Stanford School of Medicine Medical Scholars Program. We thank Dr. Thomas Prieto (Stanford
University School of Medicine) for contributions in methodology, and data curation. Finally, we thank all our
patients for their collaboration.
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Fig. 1.
Baseline beta-band power and beta-band responses to the sweet-fat incentive task
predominate over power changes in other frequency-band ranges (A top) Schematic of
a single trial in the sweet-fat incentive task. After fixation (baseline), participants were
cued with an image of a sweet-fat solution (chocolate milkshake; 50% of trials) or a
taste-neutral solution (water; anticipatory phase), and subsequently received a taste of the
cued solution (receipt phase) for a total of 80 trials. (A bottom) Time-frequency (TF)
spectrogram from the all 40 recorded channels showing a focus of time-locked activity
changes in the beta-band (15–30 Hz) frequency range. Onset times for the cue and solution
delivery (soln delivery) marked by vertical dashed lines. (B) Frequency-band power during
anticipation (top) and receipt (bottom) of sweet-fat and taste-neutral solutions and during
the pre-stimulus baseline. Frequency bands analyzed along x-axis include delta (δ), theta
(θ), alpha (α), beta (β), low gamma (γlow), and high gamma (γhigh). (A inset) Comparison
of baseline power at each frequency band. At baseline, beta-band power was prominent in
the subthalamic area (FDR-adjusted
p <
0.05). Further, during both anticipation and receipt,
changes in frequency-band power are significant only in the beta-band frequency range in
response to both sweet-fat and taste-neutral stimuli. *FDR-adjusted p
<
0.05 on one-way
ANOVA.
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Fig. 2.
The sweet-fat incentive task elicits beta-band power activity that characterizes
topographically heterogenous responses to sweet-fat and taste-neutral stimuli in the
subthalamic area. (A) Reconstructed DBS lead trajectories of all participants (colored lines)
within the subthalamic area – substantia nigra (SN), subthalamic nucleus (STN), zona
incerta (ZI) – showing anticipation-(top) and receipt- (bottom) specific (black outline),
responsive (green filled spheres), and nonresponsive (grey spheres) contacts. (Inset top)
Representative image of localization and medial view orientation of depicted reconstruction
within the left hemisphere. Onset times for the cue and solution delivery (soln delivery)
marked by vertical dashed lines. (B) Beta-band power tracing (top) from all responsive
channels demonstrating decreased beta-power to both solutions in 0 to 1 s after cue, with
greater decreased magnitude for sweet-fat over taste-neutral cues (cluster-based permutation
testing,
p <
0.05). During the subsequent 1 to 3 s after cue, beta-band is discriminatory
as it remains decreased for sweet-fat, but not taste-neutral cues (cluster-based permutation
testing, p
<
0.05). During receipt, significant discriminatory signal changes are observed
between 5 and 6 s after cue (cluster-based permutation testing,
p <
0.05). (B bottom) Beta-
band responses to sweet-fat (left) and taste-neutral (right) stimuli averaged over 500 ms time
windows. Anticipation of sweet-fat stimuli is characterized by a sustained decreased power
response while the taste-neutral anticipatory response is shortened by a beta-band rebound
(i.e. switch from decreased to increased power). (C) Beta-band power tracings averaged
across responsive channels within 3 subregions of the subthalamic area. During anticipation,
the STN (top) and the SN (middle) were characterized by an early decrease in beta-band
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power to both solution cues that then became discriminatory (cluster-based permutation
testing,
p <
0.05). In the ZI (bottom), both decreased (Type 1, left) and increased (Type
2, right) power responses were observed during anticipation. However, decreased power
responses were specific for sweet-fat and not taste-neutral cues. During receipt, the STN had
early responses followed by those in the SN. No receipt responses were captured in the ZI.
*
p <
0.05 on cluster-based permutation testing.
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Fig. 3.
Subthalamic area beta-band response is sufficient in classifying trials belonging to sweet-fat
vs taste-neutral conditions. (A) Schematic of the artificial neural network (ANN) design.
Observations were formed from each trial of the 15 and 7 channels across participants
(P1, P4, P5) with decreased beta-band responses with decreased beta-band power responses
during anticipation and receipt, respectively. The 2 channels in P2 with increased beta-band
anticipatory responses were excluded. The 6 model features consisted of beta-band power
divided into 6, 500 ms time segments (T1-T6) for both anticipation and receipt. (B left)
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Performance of the trained ANN in classifying sweet-fat vs taste-neutral trials (in testing
subset independent from data used to train the classifier) during the anticipatory phase.
Classifier performance had an overall accuracy of 69% (95% CI: 68–70%) with overall
true-positive rates (TPR) of 66% and 72% and false-positive rates 28% and 34% for sweet-
fat and taste-neutral stimuli. (B right) Feature importance in model performance during
anticipation as measured by main total effect (i.e. relative contribution of each factor alone
and in combination). Beta-band responses during 1.5 to 2 s (T4) after cue had the largest
impact on model performance (
p <
0.05). (C left) Performance of the trained ANN in
classifying sweet-fat vs taste-neutral trials (in testing subset independent from data used to
train the classifier) during the receipt phase. Classifier performance had an overall accuracy
of 67% (95% CI: 66–78%) with overall TPR of 67% and 68% and false-positive rates
32% and 33% for sweet-fat and taste-neutral stimuli. (C right) Feature importance in model
performance during receipt as measured by main total effect (i.e. relative contribution of
each factor alone and in combination). Beta-band responses during soln delivery at 3.5 to 4 s
(T8) and 6.5 to 6 s (T12) had the largest impact on model performance (
p <
0.05). *p
<
0.05
using the 95% confidence interval (CI) of calculated means across time-segments with 100
permutations.
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Fig. 4.
Subthalamic area beta-band rebound to taste-neutral food is associated with weight gain.
(A) Averaged pre- and post-operative BMIs for each subject. Measures were taken from 3
distinct pre-operative clinic visits within 6 months of implantation, and variable number of
at least 2 clinic visits to 15-months post-operatively. P1 and P5 had significant BMI gain
post-operatively (BMI gain group; FDR-adjusted p-value
<
0.05). (B) Overall BMI change
from day of DBS implantation. In P1 and P5, there was a significant trend toward increased
BMI (R2 = 0.94, F(1,7) = 114,
p <
0.0001, BMI Gain group). The remaining participants
did not show a consistent post-operative BMI trend (R2 = 0.01, F(1,10) = 0.11, p = 0.75,
No BMI Gain group). (C) Beta-band responses to taste-neutral stimuli during anticipatory
(T4: 1.5 to 2 s) and receipt periods (T2: 3.5 to 4 s and T6: 5.5 to 6 s) that discriminated
solutions averaged across all 8 contacts in each participant (P1- P5). Beta-band response
to taste-neutral stimuli, in each period, was significantly different between participants that
had an increase vs no increase in BMI (
p <
0.05). (D) Beta-band responses to sweet-fat
stimuli during anticipatory and receipt periods that discriminated solutions average across
contacts in each participant (P1-P5). No beta-band response threshold to sweet-fat stimuli
discriminated between participants that had an increase or no increase in body-mass index
Kakusa et al. Page 21
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(BMI). *p
<
0.05 using the 95% confidence interval (CI) of calculated means across time-
segments with 100 permutations.
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Kakusa et al. Page 23
Table 1
Intraoperative cohort participant characteristics.
ID Age (years) Sex Disease duration (years) Clinical phenotype Pre-operative UPDRS Sweet-fat rating Preferred sweet-fat Preoperative BMI
P1 61 F 8 AR 31 7/10 Yes 28.2
P2 71 M 5 TR 35 6/10 Yes 29.3
P3 66 M 14 AR 40 10/10 Yes 36.8
P4 75 M 13 TR 66 9/10 Yes 34.9
P5 65 F 8 TR 26 5/10 Yes 15.5
All participants were right-hand dominant. F = female, M = male, AR = akinetic-rigid, TR = tremor-dominant.
Neurobiol Dis
. Author manuscript; available in PMC 2022 June 20.
