NeuroImage: Clinical 32 (2021) 102846
Available online 4 October 2021
2213-1582/© 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license
Connectivity correlates to predict essential tremor deep brain stimulation
outcome: Evidence for a common treatment pathway
Erik H. Middlebrooks
, Lela Okromelidze
, Joshua K. Wong
, Robert S. Eisinger
Mathew R. Burns
, Ayushi Jain
, Hsin-Pin Lin
, Jun Yu
, Enrico Opri
, Andreas Horn
, Kelly D. Foote
, Michael S. Okun
, Alfredo Qui˜
, Ryan J. Uitti
Sanjeet S. Grewal
, Takashi Tsuboi
Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA
Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
Department of Neurology, Emory University, Atlanta, GA, USA
Movement Disorder and Neuromodulation Unit, Department of Neurology with Experimental Neurology, Charit´
e – Universit¨
atsmedizin Berlin, Corporate Member of
at Berlin and Humboldt-Universit¨
at zu Berlin, Berlin, Germany
Department of Neurosurgery, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Deep brain stimulation
Background and purpose: Deep brain stimulation (DBS) is the most common surgical treatment for essential tremor
(ET), yet there is variation in outcome and stimulation targets. This study seeks to consolidate proposed stim-
ulation “sweet spots,” as well as assess the value of structural connectivity in predicting treatment outcomes.
Materials and methods: Ninety-seven ET individuals with unilateral thalamic DBS were retrospectively included.
Using normative brain connectomes, structural connectivity measures were correlated with the percentage
improvement in contralateral tremor, based on the Fahn-Tolosa-Marin tremor rating scale (TRS), after parameter
optimization (range 3.1–12.9 months) using a leave-one-out cross-validation in 83 individuals. The predictive
feature map was used for cross-validation in a separate cohort of 14 ET individuals treated at another center.
Lastly, estimated volumes of tissue activated (VTA) were used to assess a treatment “sweet spot,” which was
compared to seven previously reported stimulation sweet spots and their relationship to the tract identied by
the predictive feature map.
Results: In the training cohort, structural connectivity between the VTA and dentato-rubro-thalamic tract (DRTT)
correlated with contralateral tremor improvement (R =0.41; p <0.0001). The same connectivity prole pre-
dicted outcomes in a separate validation cohort (R =0.59; p =0.028). The predictive feature map represented
the anatomical course of the DRTT, and all seven analyzed sweet spots overlapped the predictive tract (DRTT).
Conclusions: Our results strongly support the possibility that structural connectivity is a predictor of contralateral
tremor improvement in ET DBS. The results suggest the future potential for a patient-specic functionally based
surgical target. Finally, the results showed convergence in “sweet spots” suggesting the importance of the DRTT
to the outcome.
Abbreviations: COG, center-of-gravity; DBS, deep brain stimulation; DRTT, dentato-rubro-thalamic tract; dDRTT, decussating portion of the DRTT; ET, essential
tremor; FEM, nite element method; FWE, family-wise error; MPRAGE, magnetization-prepared rapid gradient-echo; ndDRTT, non-decussating portion of the DRTT;
PSA, posterior subthalamic area; TRS, Fahn-Tolosa-Marin tremor rating scale; VIM, ventral intermediate nucleus; VOp, ventralis oralis posterior nucleus; VTA,
volume of tissue activated.
* Corresponding author at: Departments of Radiology and Neurosurgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224, USA.
E-mail address: firstname.lastname@example.org (E.H. Middlebrooks).
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/ynicl
Received 6 June 2021; Received in revised form 14 August 2021; Accepted 27 September 2021
NeuroImage: Clinical 32 (2021) 102846
Essential tremor (ET) is one of the most common movement disor-
ders worldwide, with an estimated prevalence of 0.9% (Louis and Fer-
reira, 2010). Almost half of individuals with ET will fail pharmacological
therapy and require alternative treatments (Thanvi et al., 2006; Louis
et al., 2010). Deep brain stimulation (DBS) is well-established as the
most common surgical treatment for ET. Despite widespread use, there
continues to be variation in targeting approaches, as well as a failure to
converge on a single therapeutic target or pathway (Okun et al., 2005).
Thalamic DBS for ET has traditionally targeted the ventral interme-
diate nucleus (VIM) region of the thalamus. More recently, there has
been increasing interest in the posterior subthalamic area (PSA),
including the caudal zona incerta, but superiority of one target over
others has yet to be unequivocally proven (Eisinger et al., 2018; Fyta-
goridis et al., 2012; Holslag et al., 2018; Sandvik et al., 2012). Moreover,
recent studies have also postulated the existence of stimulation “sweet
spots” more anteriorly in the region of the ventralis oralis posterior
nucleus (VOp) or along the VIM/VOp border (Middlebrooks et al., 2018;
Middlebrooks et al., 2018; Elias et al., 2021; Kim et al., 2018; Pouratian
et al., 2011; Tsuboi et al., 2021). The heterogeneity between these tar-
geting sweet spots has left gaps in our understanding of a potential ideal
Brain connectivity, as assessed by MRI, has been increasingly
explored to understand and to predict DBS outcomes in ET. These studies
have collectively observed that stimulation in the cerebello-thalamo-
cortical motor network may be responsible for improved tremor con-
trol (Middlebrooks et al., 2018; Middlebrooks et al., 2018; Tsuboi et al.,
2021; Coenen et al., 2011; Coenen et al., 2020; Coenen et al., 2017;
Coenen et al., 2011; Al-Fatly et al., 2019; Akram et al., 2018; Fenoy and
Schiess, 2017; Fenoy and Schiess, 2018; Anthofer et al., 2017). A critical
component of this network has been historically referred to as the
dentato-rubro-thalamic tract (DRTT). The traditional description of this
tract is one that connects the dentate nucleus with the contralateral
thalamus and motor cortex; however, these bers do not synapse within
the red nucleus despite their name (Middlebrooks et al., 2020). While
these decussating bers (dDRTT) constitute the majority of the DRTT,
the existence of a smaller non-decussating portion (ndDRTT) has been
recently shown by MRI diffusion tractography and human histological
studies (Tsuboi et al., 2021; Middlebrooks et al., 2020; Meola et al.,
2016; Petersen et al., 2018; Tacyildiz et al., 2021). These decussating
and non-decussating bers have been shown to possess a distinct spatial
gradient within the thalamus, with the dDRTT bers in general situated
more anteriorly (Tsuboi et al., 2021; Middlebrooks et al., 2020; Petersen
et al., 2018).
Converging evidence highlights the potential of connectivity-based
targeting and programming for ET DBS. However, previous studies
have been limited by small sample size or the use of bilateral electrodes.
The inclusion of bilateral DBS electrodes has the potential to confound
the observed tremor improvement due to the potential for unpredictable
ipsilateral effects or unequal tract activation between the hemispheres
(Noecker et al., 2021). We aimed to show that structural connectivity
could be predictive of ET DBS outcome through modulation of the
cerebello-thalamo-cortical motor network in a large cohort of unilateral
ET DBS individuals. The predictive connectivity ngerprints from this
cohort were then used for validation in a second cohort drawn from
another institution. Additionally, we assessed the stimulation “sweet
spot” and compared it to existing reported “sweet spots.” We sought to
either conrm or deny the potential existence of a common tract uni-
fying the “sweet spots” reported in the literature.
2.1. Study design
This multicenter, retrospective cohort study was approved by the
Institutional Review Boards of the University of Florida and Mayo Clinic
Florida. The inclusion criteria of the present study were (1) diagnosis of
essential tremor dened by the Movement Disorders Society (isolated
tremor syndrome of bilateral upper limb action tremor with or without
tremor in other body regions) (Bhatia et al., 2018); (2) unilateral
thalamic DBS implantation, (3) preoperative clinical evaluation using
the Fahn-Tolosa-Marin tremor rating scale (TRS), (4) postoperative TRS
after DBS programming optimization, (5) preoperative brain MRI
including a high-resolution, T1-weighted magnetization-prepared rapid
gradient-echo (MPRAGE) sequence and high-resolution postoperative
CT, (6) absence of other brain surgeries, and (7) absence of secondary
etiologies for tremor or neurodegenerative diseases. We excluded in-
dividuals with tremor syndromes with additional features of parkin-
sonism, ataxia, myoclonus, or questionable dystonia (i.e., essential
tremor plus). We identied 83 ET individuals in the training cohort from
the University of Florida and 14 in the validation cohort from Mayo
Clinic Florida meeting the inclusion criteria (total of 97 individuals
2.2. Perioperative procedures and assessments
As the standard of care at the University of Florida and Mayo Clinic
Florida, all the patients underwent unilateral DBS implantation, and
staged implantation of the other side was considered later. In this study,
tremor outcomes were assessed using the scores when the patients were
treated only with unilateral DBS. Imaging protocols have been previ-
ously described (Middlebrooks et al., 2018), and are also summarized in
Supplemental Methods. Perioperative procedures and assessments for
ET individuals from the University of Florida cohort have also been
previously described (Tsuboi et al., 2021). Briey, DBS leads were
implanted in the University of Florida cohort under local anesthesia with
intraoperative microelectrode recordings and macrostimulation testing.
Using in-house software (Morishita et al., 2010), we aimed to place the
electrode at the VIM/VOp border with the most ventral contact deep to
the thalamus and the dorsal contacts in the posterior aspect of the VOp.
Individuals recruited from the Mayo Clinic cohort were selected from
the Mayo Clinic Movement Disorders Neurology Clinic after decision to
undergo unilateral VIM DBS for ET. After application of a stereotactic
headframe, a stereotactic CT was performed. The CT images were cor-
egistered to the preoperative MRI on the surgical planning workstation.
Using Guiot’s relationships to target the Vim, an initial target was
planned at the level of the anterior commissure-posterior commissure
(AC-PC) and one-fourth the AC–PC distance anterior to the PC. A lateral
coordinate equal to the sum of one-half the width of the third ventricle
plus 11.5 mm was initially selected. The electrode was advanced
through a burr hole with the patient in a semisitting position with 30-de-
gree head elevation. Macrostimulation was performed to assess tremor
improvement and thresholds for stimulation of the internal capsule,
paresthesias, speech disturbances, or other adverse effects. If tremor
control was adequate without adverse effects, no further adjustments
were made. If the result was unsatisfactory, the electrode was reposi-
tioned according to the stimulation effect obtained. Implants included
the model 3387 lead and pulse generator (Activa PC/SC or Soletra;
Medtronic Inc, Minneapolis, MN, USA) or an 8-contact lead or direc-
tional lead and pulse generator (Vercise or Vercise Cartesia; Boston
Scientic Corp, Marlborough, MA, USA). Approximately 3 months after
surgery, high-resolution CT was obtained using a dual-energy protocol
(80 kV and 150 kV) with an in-plane resolution of 0.5 ×0.5 mm and slice
thickness of 0.4 mm.
For both cohorts, monthly visits were scheduled to optimize stimu-
lation parameters. Optimization was typically achieved within 6 months
of initial programming. An itemized TRS score was assessed by a skilled
examiner prior to surgery and after programming optimization using the
optimized programming settings. The contralateral tremor score was
calculated from the lateralized TRS motor scores (items 5, 6, 8, 9, and
11–14) on the body side contralateral to the DBS. Percentage
E.H. Middlebrooks et al.
NeuroImage: Clinical 32 (2021) 102846
improvement in contralateral tremor score from preoperative baseline
to optimized postoperative assessment was the primary outcome
2.3. Image processing
A forked version of the Lead-DBS software package (http://www.lea
d-dbs.org) (Horn et al., 2019) was used for electrode localization and
estimation of volumes of tissue activated (VTA). Lead-DBS was modied
to integrate functionality for unilateral electrodes, and the code used is
freely available (https://github.com/oprienrico/leaddbs_dev/tree/de
v_patched). Modications have now been integrated to the main
branch and are available from Lead-DBS v2.5 onwards.
The high-resolution postoperative CT images were coregistered to
preoperative MPRAGE images using a two-stage linear registration in
Advanced Normalization Tools (http://stnava.github.io/ANTs/)
(Avants et al., 2008). The images were then normalized into
MNI_ICBM_2009b_NLIN_ASYM space—based on the MPRAGE image-
s—with the SyN registration method in Advanced Normalization Tools
(Avants et al., 2008; Fonov et al., 2011). A ve-stage nonlinear trans-
form was applied: two linear (rigid and afne) registrations, whole-brain
nonlinear SyN-registration, and two nonlinear SyN-registrations with a
focus on subcortical nuclei (Sch¨
onecker et al., 2009). A subsequent
afne transform that was restricted to subcortical regions of interest was
performed to ensure accurate subcortical registration (Horn et al.,
2019). Electrodes were localized using an automated and phantom-
validated approach implemented in Precise and Convenient Electrode
Reconstruction for Deep Brain Stimulation (PaCER) (Husch et al., 2018)
and, after manual adjustment, visually inspected for accuracy.
Using a nite element method (FEM)-based model in Lead-DBS
(Horn et al., 2019), a VTA was estimated for each patient’s optimized
programming settings. The E-eld was estimated on a tetrahedral mesh
that includes two tissue compartments (gray and white matter), insu-
lating components, and electrode contacts. Conductivity values were
adapted for the range of frequencies used in this cohort: 0.092 S/m and
0.06 S/m for gray and white matter conductivity, respectively. A
modied FieldTrip-SimBio pipeline, implemented in Lead-DBS, was
used to estimate the E-eld distribution with VTA shape based on a
typical threshold of >0.2 V/mm (Astrom et al., 2015; Vorwerk et al.,
2018). The right hemisphere VTAs were nonlinearly ipped to the left
2.4. VTA analysis
Stimulation sweet spot was assessed for percentage improvement in
contralateral tremor score using modications of methods in Dembek
et al. (2017). The binary left hemisphere and mirrored right hemisphere
VTAs were multiplied by the subject’s percentage improvement to
create a weighted improvement mask. The weighted improvement mask
was then averaged to generate an improvement heat map. Next, a mask
for statistical signicance was created using the masked weighted VTAs
in a voxel-wise, two-sided non-parametric permutation test using
10,000 permutations. Due to the large number of voxels in regions well
beyond the area of stimulation, p values from statistical tests can be
artifactually improved by the excessive directions of freedom. To ac-
count for this, all zero voxels and those voxels with VTA overlap in less
than 15% of subjects were excluded from analysis. The signicance
mask was generated for only those voxels with FWE-corrected p <0.05
and applied to the average improvement heat map. The sweet spot was
determined by assessing the cluster center-of-gravity (COG) for the
resulting heat map.
2.5. Structural connectivity processing
The left hemisphere and mirrored right-hemisphere VTAs were used
as seeds for structural connectivity assessment. A normal control dataset
of 124 healthy subjects in the Human Connectome Project (htt
ps://www.humanconnectome.org) (Setsompop et al., 2013) was uti-
lized, as detailed in Tsuboi et al. (2021) For each VTA, probabilistic
tractography was performed using “probtrackx2_gpu” from the FMRIB
Software Library v6.0.3 (http://fsl.fmrib.ox.ac.uk) in each of the 124
subjects with 20,000 samples, curvature threshold of 0.2, modied Euler
streamlining, and step length of 0.5 mm. A region-of-avoidance included
the hemisphere contralateral to the VTA and corpus callosum. The
resultant probability paths were averaged for all 124 control subjects
giving an averaged probability map for each subject’s VTA.
2.6. Structural connectivity analysis – training dataset
Next, to assess whether the probability distribution was predictive of
improvement in the 83-patient training cohort, a leave-one-out cross
validation was performed using the averaged probability map for each
subject. We treated this probability distribution analogously as con-
nectivity ngerprints seeding from DBS stimulation sites. Group R-maps
(voxel-wise correlations of ngerprint values with clinical improvement
values) were generated with all individuals except one, which was
withheld for validation. The structural connectivity ngerprint for the
left-out patient was then again used to measure spatial similarity with
the R-map generated from the remainder of the cohort (using spatial
correlations). Pearson correlation was performed using the similarity
index versus measured clinical improvement and p <0.05 was consid-
ered statistically signicant.
2.7. Structural connectivity analysis – validation dataset
To assess generalizability of the predictions, the structural connec-
tivity ngerprint for each patient in the 14-patient validation dataset
was used to measure spatial similarity with the R-map generated from
the complete 83-patient training cohort (same process as in the cross-
validation step). Pearson correlation was performed using the similar-
ity index versus measured clinical improvement and p <0.05 was
considered statistically signicant.
2.8. Comparison to previous sweet spots
To assess spatial location of the VTA sweet spot for contralateral
tremor improvement in the current cohort, as well as comparison of
previously reported targets (Elias et al., 2021; Tsuboi et al., 2021; Al-
Fatly et al., 2019; Akram et al., 2018; Papavassiliou et al., 2004; Mid-
dlebrooks et al., 2021; Kübler et al., 2021), each of the previously re-
ported MNI sweet spots (Table 2) were plotted in relation to the
predictive tract ngerprint. The R-map derived from the training cohort
was thresholded at R >0.1 and the distance from each coordinate to the
nearest predictive voxel was calculated as a 3D Euclidean distance.
2.9. Statistical analysis
Subject demographics, baseline scores, postoperative improvement,
and DBS parameters were expressed as mean and SD. Comparison be-
tween the training and validation cohorts was performed using a
nonparametric Mann-Whitney U test.
2.10. Data availability
Data are available upon specic request pending a formal data
sharing agreement and approval from the authors’ and requesting re-
searcher’s local ethics committees.
E.H. Middlebrooks et al.
NeuroImage: Clinical 32 (2021) 102846
3.1. Clinical outcomes
Demographic and clinical information are summarized in Table 1.
All individuals underwent unilateral thalamic DBS with a mean follow-
up period of 6.8 ±1.5 months (range 3.1–12.9 months). There was no
signicant difference in the age at surgery, sex, or age of onset between
the cohorts (p >0.05); however, the disease duration was greater in the
training cohort (28.4 vs. 18.4 years; p =0.04). Total TRS score at
baseline was greater in the training cohort compared to the validation
cohort (51.3% vs. 42.8%; p =0.003), but contralateral TRS tremor score
was not signicantly different (16.3% vs. 16.1%; p =0.97). Likewise,
there was a similar observed improvement in contralateral TRS score
after surgery between the training and validation cohort (71.4% vs.
69.1%; p =0.72).
3.2. VTA analysis
The electrode contact positions relative to VIM and VOp from the
DBS Intrinsic Atlas (DISTAL) (Ewert et al., 2018) are shown for the
training cohort in Fig. 1A and the validation cohort in Fig. 1B. The active
contact positions for the training cohort color-coded by contralateral
tremor improvement are shown relative to VIM and VOp (Fig. 1C) and to
dDRTT (Fig. 1D). The masked weighted VTA heat maps for contralateral
tremor improvement are shown in Fig. 2. The cluster peak COG for
contralateral tremor improvement was along the ventral VIM/VOp
border (MNI = − 15.5/−15.5/0.5) in the training cohort and was more
medial and superior in the validation cohort (MNI = − 13.5/−15.5/2).
3.3. Structural connectivity analysis
The mean structural connectivity for the training cohort is shown in
Fig. 3A and shows greatest connectivity to the primary motor, sensory,
supplementary motor, and premotor cortices. A similar pattern of mean
connectivity is seen in the validation cohort (Fig. 3B).
In the training cohort, a leave-one-out cross validation shows that
connectivity ngerprint is predictive of contralateral tremor improve-
ment within the cohort (r =0.41; p <0.0001). The group R-map (Fig. 3D-3F) shows that the voxels most predictive of contralateral
tremor improvement correspond to the DRTT. In cross-validation with
the separate validation cohort (Fig. 3G), the connectivity ngerprint
from the training cohort was predictive of tremor improvement (r =
0.59; p =0.025).
3.4. Comparison to previous sweet spots
Comparison of the current stimulation sweet spot with multiple
existing published sweet spots (Table 2) showed a mean distance of 0 ±
0 mm, meaning that every reported coordinate overlapped with the
predictive tract derived from the training cohort (Fig. 4A & 4B).
Our study revealed that a structural connectivity ngerprint was an
independent predictor of contralateral tremor improvement within our
training cohort, as well as predictive of improvement in a separate in-
dependent cohort. Further, we showed that the heterogeneity in recently
reported stimulation “sweet spots” can potentially be explained by their
distance to a common pathway, the dentato-rubro-thalamic tract.
The strengths of our study included the use of unilateral electrodes,
which minimized potential confounds from ipsilateral microlesion ef-
fects from a second DBS electrode on the measurement of contralateral
tremor change. Unilateral implants also facilitated the evaluation of
pure ipsilateral change in tremor without similar confounds. It is
possible that a higher incidence of stimulation-induced side effects in
Baseline patient characteristics and DBS outcomes.
n =83 n =14
Age at DBS (years) 68.2 ±9.9 69.1 ±8.4 0.81
Disease duration before DBS (years) 28.4 ±17.7 18.4 ±15.1 0.04
Age at onset (years) 39.8 ±20.7 50.8 ±13.6 0.07
Sex (male, %) 65.1% 42.9% 0.14
TRS total score at baseline 51.3 ±14.8 42.8 ±10.2 0.003
Contralateral TRS tremor score at
16.3 ±4.7 16.1 ±4.4 0.97
TRS total score improvement after
54.7 ±21.2 –
Contralateral TRS tremor score
improvement after DBS (%)
71.4 ±22.2 69.1 ±24.1 0.72
Follow-up period after DBS (months) 6.8 ±1.5 7.1 ±1.9
Monopolar / Bipolar stimulation 54 / 29 2 / 12
Stimulation voltage (V) 2.5 ±0.8 3.1 ±1.0 0.01
Stimulation pulse width (
s) 97.2 ±23.4 74.3 ±13.4 <0.0001
Stimulation frequency (Hz) 149.1 ±
149.6 ±21.2 0.29
Data are presented as mean ±SD unless otherwise indicated. DBS =deep brain
stimulation; TRS =Fahn-Tolosa-Marin Tremor Rating Scale.
* Contralateral TRS motor scores indicate lateralized scores contralateral to DBS
implantations. Items 5, 6, 8, 9, and 11–14.
Total TRS Score not available for validation cohort due to lack of TRS Part C on
Fig. 1. Sagittal image showing the relationship of electrodes in the training
cohort (A) and validation cohort (B) relative to the ventral intermediate nucleus
(VIM) and ventralis oralis posterior (VOp) nucleus from the DISTAL atlas
(Ewert et al., 2018). (C) Active contacts weighted by percentage improvement
in contralateral tremor relative to VIM and VOp. (D) Active contacts weighted
by percentage improvement in contralateral tremor relative to the decussating
portion of the dentato-rubro-thalamic tract (dDRTT). Background brain tem-
plate provided by Edlow et al. (2019).
E.H. Middlebrooks et al.
NeuroImage: Clinical 32 (2021) 102846
bilateral implants could inuence the choice of stimulation parameters
and this could have introduced bias into our study. Despite this limita-
tion, we report the largest ET cohort, to our knowledge (N =97), to
undergo both connectivity and VTA analysis. Our results add a new
dimension to the several prior small studies and bilateral implant co-
horts (Middlebrooks et al., 2018; Middlebrooks et al., 2018; Coenen
et al., 2011; Coenen et al., 2020; Coenen et al., 2017; Coenen et al.,
2014; Coenen et al., 2011; Al-Fatly et al., 2019; Akram et al., 2018;
Fenoy and Schiess, 2017; Fenoy and Schiess, 2018; Anthofer et al.,
Our results show that contralateral TRS motor improvement was
predicted by structural connectivity to the DRTT. The predictive maps
revealed were consistent with those described by Al-Fatly et al. (Al-Fatly
et al., 2019) The DRTT has traditionally been described as efferent
cerebellar bers extending from the dentate nucleus through the
ipsilateral superior cerebellar peduncle before decussating in the
midbrain to reach the contralateral VIM and VOp, and ending in the
contralateral primary motor cortex (Petersen et al., 2018; Gallay et al.,
2008). Subsequently, a smaller portion of the DRTT consisting of bers
extending from the dentate nucleus to the ipsilateral thalamus and
motor cortex without decussating, the ndDRTT, were shown in animal
and human histological studies (Tacyildiz et al., 2021; Flood and Jansen,
1966; Wiesendanger and Wiesendanger, 1985). These ndings have
been supported by more recent exploration using MRI tractography and
brain microdissection in human (Middlebrooks et al., 2020; Meola et al.,
2016; Petersen et al., 2018; Tacyildiz et al., 2021). These ndDRTT bers
make up a minority of the DRTT (<25% of tracts) (Meola et al., 2016)
and their role in tremor has not been well established. Converging evi-
dence from functional and anatomical studies shows a lateral and
posteromedial motor region of the dentate nucleus, which contributes a
Fig. 2. Statistically signicant average improvement heat map for percentage improvement in contralateral tremor score (A, axial; B, coronal; and C, sagittal views)
relative to the ventral intermediate nucleus (VIM; green) and ventralis oralis posterior (VOp; blue) from the DISTAL atlas (Ewert et al., 2018). Crosshairs show the
cluster center of gravity indicating the point of greatest improvement. (For interpretation of the references to color in this gure legend, the reader is referred to the
web version of this article.)
Fig. 3. Results of structural connectivity analysis for the training cohort (A) and validation cohort (B). (C) Scatterplot illustrates the correlation between the
empirical improvement in contralateral tremor compared to similarity to the predictive R-map for each subject from the leave-one-out cross-validation (r =0.41; p <
0.0001). Sagittal (D), axial (E), and coronal (F) images show the tract most correlated with contralateral tremor improvement with greatest correlation seen with the
DRTT. (G) Scatterplot shows cross-validation results for the validation cohort from a second institution based on the connectome ngerprints from the training cohort
showing that the training cohort is predictive of outcomes in the second cohort (r =0.59; p =0.025).
E.H. Middlebrooks et al.
NeuroImage: Clinical 32 (2021) 102846
majority of bers to the dDRTT, with a smaller fraction to the ndDRTT,
potentially explaining reported ipsilateral motor effects. Meanwhile, the
ndDRTT constitutes a large fraction of anteriomedial dentate nucleus
bers, thought to contribute to nonmotor functions (Tacyildiz et al.,
2021; Küper et al., 2012; Ellerman et al., 1994; Middleton and Strick,
2000; Middleton and Strick, 1998). Nevertheless, the ndDRTT has been
used as a biomarker in several DBS studies, possibly due to challenges in
reconstructing crossing bers of the dDRTT using tractography (Coenen
et al., 2016; Sammartino et al., 2016; Coenen et al., 2011; Coenen et al.,
2020; Coenen et al., 2017; Coenen et al., 2014; Coenen et al., 2011). This
limitation is an important consideration given the variability in the
spatial location of these two components. While they are more coher-
ently organized within the ventral thalamic region, the tracts have a
more distinct spatial separation in the PSA (Middlebrooks et al., 2020;
Petersen et al., 2018). This anatomical distribution provides a critical
point for evaluating discrepancies in reporting stimulation “sweet spots”
for ET. Unfortunately, distinction between the effects of these tracts
from our cohort cannot be completely assessed due to the large amount
of overlap of both tracts in the ventral thalamic region, as well as
preferential tracking of ndDRTT bers by tractography algorithms.
Future studies will be needed to better understand the role of these two
different components of the DRTT.
Recently, connectivity and VTA studies have questioned the tradi-
tional mantra of VIM stimulation for tremor control (Middlebrooks
et al., 2018; Kim et al., 2018; Pouratian et al., 2011) despite criticism
(Akram et al., 2019). Such critiques were based on the traditional
concept of nuclear effects rather than the underlying tractographic
anatomy. Nevertheless, two of the largest cohorts of ET individuals
undergoing VTA sweet spot analysis, the current study and Elias et al.
(2021) have both revealed more anterior stimulations along the VIM/
VOp border and more anterior stimulations in VOp. These ndings are in
contradiction to the more posterior location of sweet spots in the more
ventral or posterior subthalamic region, which are more closely related
to the location of VIM. Our results can potentially reconcile these
Summary of studies reporting stimulation “sweet spots.”
Reference Study Type Electrode Side
MNI Sweet Spot
Elias et al.
Unilateral 39 16.8 −17.3 / −13.9 /
Total TRS 57.2 42.8%
Tsuboi et al.
Unilateral 20 6.6 −15 / −17 / 1 Total TRS*, TRS Motor
TRS Motor Score
Al-Fatly et al.
Bilateral 36 12 −16 / −20 / −2 Total TRS, Head
Contralateral UE Score
et al. (2021)
Unilateral 6 3 −15 / −18.5 /
Total TRS 34.3 64.5%
et al. (2004)
37 26 −14.5 / −17.7 /
Limited TRS of
**Akram et al.
Unilateral 5 23.6 −12.5 / −16 /
Total TRS 81.6 34.0%
***Kübler et al.
Bilateral 30 14 −12 / −19.5 /
TRS Parts A & B, TRS of
MNI =Montreal Neurological Institute template space; TRS =Fahn-Tolosa-Marin Tremor Rating Scale; UE =upper extremity.
* Unpublished data.
** Coordinates approximated from image gures.
*** Point of maximum tremor improvement.
Fig. 4. Relationship of the predictive tract derived from the training cohort (threshold R >0.1) to the current study sweet spot for contralateral tremor improvement
and previously reported tremor sweet spots (Elias et al., 2021; Tsuboi et al., 2021; Al-Fatly et al., 2019; Akram et al., 2018; Papavassiliou et al., 2004; Middlebrooks
et al., 2021; Kübler et al., 2021). (A) Sagittal and (B) coronal views show overlap of all targets with the predictive tract, which represents the DRTT. Background
provided by Edlow et al. (2019).
E.H. Middlebrooks et al.
NeuroImage: Clinical 32 (2021) 102846
variations in reported targeting by revealing a common underlying
pathway, the DRTT, along the course of both PSA and ventral thalamic
stimulation targets. There was reproducibility of tremor control at
various locations along the DRTT (e.g., PSA, VIM/VOp), and may
potentially explain observations that more posterior VIM stimulation
can lead to tolerance (Sandoe et al., 2018). In addition, our results
support the potential of individualized targeting of DRTT, which has
been recently shown as feasible and effective in clinical practice (Mid-
dlebrooks et al., 2021).
Several limitations of the present study should be considered. First,
clinical assessments were limited to the retrospective analysis of patient
records. While metrics used for the study were meticulously documented
by experienced examiners, other information regarding side effects or
other outcomes may have been less consistent. Along the same lines, the
mean and median duration of follow up was slightly more than 6
months, which limits assessment of factors affecting long-term DBS
benet. Second, the use of normative connectomes poses a potential
limitation. While there may be pathological alterations in the repre-
sented networks in the setting of ET, the diffusion metrics are primarily
used in this study from an anatomic perspective only. The anatomic
connections from such normative connectomes compared to patient-
specic cohorts has been previously shown as a reliable metric (Wang
et al., 2021). Prior studies have also supported the use of normative
connectomes by their ability to predict both treatment effects and side
effects from DBS (Tsuboi et al., 2021; Al-Fatly et al., 2019; Tsuboi et al.,
2021; Horn et al., 2017; Baldermann et al., 2019). There were many
inherent limitations of tractography that have been previously well
described, such as difculty with modeling crossing bers and reliability
of tracking through regions with lower fractional anisotropy (e.g.
thalamic gray matter). We used a more computationally intensive
approach of probabilistic tractography, which is more favorable in
identifying such plausible tracts, at the risk of increased false bers.
Third, there are inherent limitations from lead localization, co-
registration, and normalization processes, as well as the inability to
directly visualize most thalamic nuclei on MPRAGE images resulting in a
reliance on atlases. Fourth, the presence of connectivity between two
regions does not ensure a stimulation effect occurs with specic stimu-
lation parameters (e.g., frequency, pulse width, etc.) (Middlebrooks
et al., 2020). Whether all regions connected to a VTA are affected by the
chosen stimulation parameters remains speculative. Fifth, the earlier
studies analyzed the “sweet spots” for tremor improvement using
different methodologies. Therefore, the meta-analysis of the current and
earlier studies should be interpreted carefully. Finally, we only included
individuals with ET, which limits the extrapolation of our ndings to
other tremor syndromes.
Using a large cohort of individuals with unilateral thalamic DBS, we
have shown the potential value of structural connectivity in predicting
ET outcomes. Additionally, our results reveal compelling evidence for a
common tract, the DRTT as the unifying “sweet spot.” We suggest that
the provision of a patient-specic network target for direct surgical
targeting and device programming has the potential to improve ET DBS
CRediT authorship contribution statement
Erik H. Middlebrooks: Conceptualization, Methodology, Software,
Formal analysis, Writing - original draft. Lela Okromelidze: Concep-
tualization, Methodology, Software, Data curation, Writing - review &
editing. Joshua K. Wong: Conceptualization, Methodology, Investiga-
tion, Data curation, Writing - review & editing. Robert S. Eisinger:
Conceptualization, Methodology, Investigation, Data curation, Writing -
review & editing. Mathew R. Burns: Investigation, Data curation,
Writing - review & editing. Ayushi Jain: Data curation, Writing - review
& editing. Hsin-Pin Lin: Investigation, Data curation, Writing - review
& editing. Jun Yu: Investigation, Data curation, Writing - review &
editing. Enrico Opri: Conceptualization, Methodology, Software,
Formal analysis, Writing - review & editing. Andreas Horn: Concep-
tualization, Methodology, Software, Formal analysis, Writing - review &
editing. Lukas L. Goede: Conceptualization, Methodology, Software,
Formal analysis, Writing - review & editing. Kelly D. Foote: Investiga-
tion, Writing - review & editing, Supervision. Michael S. Okun:
Conceptualization, Investigation, Writing - review & editing, Supervi-
sion. Alfredo Qui˜
nones-Hinojosa: Writing - review & editing, Super-
vision. Ryan J. Uitti: Conceptualization, Writing - review & editing,
Supervision. Sanjeet S. Grewal: Conceptualization, Methodology,
Writing - review & editing, Supervision. Takashi Tsuboi: Conceptuali-
zation, Methodology, Formal analysis.
Declaration of Competing Interest
Dr. Middlebrooks has received research support from Varian Medical
Systems, Inc. and Boston Scientic Corp. He has also received institu-
tional research support from Mayo Clinic and as a Site PI, Co-I, and
consultant on NIH supported grants unrelated to the current study. He is
also a consultant for Boston Scientic Corp.
Dr. Burns receives salary support from the Parkinson’s Foundation.
Dr. Horn was supported by the German Research Foundation
(Deutsche Forschungsgemeinschaft, Emmy Noether Stipend 410169619
and 424778381 – TRR 295) as well as Deutsches Zentrum für Luft- und
Raumfahrt (DynaSti grant within the EU Joint Programme Neurode-
generative Disease Research, JPND). A.H. is participant in the BIH-
e Clinician Scientist Program funded by the Charit´
atsmedizin Berlin and the Berlin Institute of Health.
Dr. Foote has served as a consultant for Medtronic and Boston Sci-
entic and has received honoraria for these services. He has received
research support from Medtronic, Boston Scientic, Abbott/St. Jude,
and Functional Neuromodulation. He has received fellowship support
Dr. Okun serves as a consultant for the National Parkinson Founda-
tion, and has received research grants from NIH, NPF, the Michael J. Fox
Foundation, the Parkinson Alliance, Smallwood Foundation, the
Bachmann-Strauss Foundation, the Tourette Syndrome Association, and
the UF Foundation. Dr. Okun’s DBS research is supported by: R01
NR014852 and R01NS096008. Dr. Okun has previously received hon-
oraria, but in the past >60 months has received no support from in-
dustry. Dr. Okun has received royalties for publications with Demos,
Manson, Amazon, Smashwords, Books4Patients, and Cambridge
(movement disorders books). Dr. Okun is an associate editor for New
England Journal of Medicine Journal Watch Neurology. Dr. Okun has
participated in CME and educational activities on movement disorders
(in the last 36) months sponsored by PeerView, Prime, QuantiaMD,
WebMD, Medicus, MedNet, Henry Stewart, and by Vanderbilt Univer-
sity. The institution and not Dr. Okun receives grants from Medtronic,
Abbvie, Allergan, and ANS/St. Jude, and the PI has no nancial interest
in these grants. Dr. Okun has participated as a site PI and/or co-I for
several NIH, foundation, and industry sponsored trials over the years but
has not received honoraria.
nones-Hinojosa is supported by the Mayo Clinic Professorship
and a Clinician Investigator award, and Florida State Department of
Health Research Grant, and the Mayo Clinic Graduate School, as well as
the NIH (R43CA221490, R01CA200399, R01CA195503, and
Dr. Grewal is a consultant for Boston Scientic Corp. and Medtronic,
E.H. Middlebrooks et al.
NeuroImage: Clinical 32 (2021) 102846
We would like to thank Harith Akram, M.D. for his contribution of
prior study data. Mayo Clinic Florida data [in part] were previously
collected from study funded by Mayo Clinic Transform the Practice
Award. Additional data were provided [in part] by the Human Con-
nectome Project, WU-Minn Consortium (Principal Investigators: David
Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH
Institutes and Centers that support the NIH Blueprint for Neuroscience
Research; and by the McDonnell Center for Systems Neuroscience at
Washington University. We acknowledge the Parkinson’s Foundation
Center of Excellence at the University of Florida and the UF INFORM
This research did not receive any specic grant from funding
agencies in the public, commercial, or not-for-prot sectors.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
Akram, H., Dayal, V., Mahlknecht, P., Georgiev, D., Hyam, J., Foltynie, T., Limousin, P.,
De Vita, E., Jahanshahi, M., Ashburner, J., Behrens, T., Hariz, M., Zrinzo, L., 2018.
Connectivity derived thalamic segmentation in deep brain stimulation for tremor.
Neuroimage Clin. 18, 130–142.
Akram, H., Hariz, M., Zrinzo, L., 2019. Connectivity derived thalamic segmentation:
separating myth from reality. Neuroimage Clin. 22, 101758. https://doi.org/
Al-Fatly B, Ewert S, Kubler D, et al. Connectivity prole of thalamic deep brain
stimulation to effectively treat essential tremor. Brain 2019;142:3086–3098.
Anthofer, J., Steib, K., Lange, M., Rothenfusser, E., Fellner, C., Brawanski, A., Schlaier, J.,
2017. Distance between active electrode contacts and dentatorubrothalamic tract in
patients with habituation of stimulation effect of deep brain stimulation in essential
tremor. J. Neurol. Surg. A Cent. Eur. Neurosurg. 78 (04), 350–357.
Astrom, M., Diczfalusy, E., Martens, H., Wardell, K., 2015. Relationship between neural
activation and electric eld distribution during deep brain stimulation. IEEE Trans.
Biomed. Eng. 62 (2), 664–672.
Avants, B., Epstein, C., Grossman, M., Gee, J., 2008. Symmetric diffeomorphic image
registration with cross-correlation: evaluating automated labeling of elderly and
neurodegenerative brain. Med. Image Anal. 12 (1), 26–41.
Baldermann, J.C., Melzer, C., Zapf, A., Kohl, S., Timmermann, L., Tittgemeyer, M.,
Huys, D., Visser-Vandewalle, V., Kühn, A.A., Horn, A., Kuhn, J., 2019. Connectivity
prole predictive of effective deep brain stimulation in obsessive-compulsive
disorder. Biol. Psychiatry 85 (9), 735–743.
Bhatia, K.P., Bain, P., Bajaj, N., Elble, R.J., Hallett, M., Louis, E.D., Raethjen, J.,
Stamelou, M., Testa, C.M., Deuschl, G., 2018. Consensus Statement on the
classication of tremors. from the task force on tremor of the International Parkinson
and Movement Disorder Society. Mov. Disord. 33 (1), 75–87.
Coenen, V.A., Allert, N., M¨
adler, B., 2011. A role of diffusion tensor imaging ber
tracking in deep brain stimulation surgery: DBS of the dentato-rubro-thalamic tract
(drt) for the treatment of therapy-refractory tremor. Acta Neurochir. (Wien) 153 (8),
Coenen, V.A., Madler, B., Schiffbauer, H., et al., 2011. Individual ber anatomy of the
subthalamic region revealed with diffusion tensor imaging: a concept to identify the
deep brain stimulation target for tremor suppression. Neurosurgery;68:1069-1075;
Coenen, V.A., Allert, N., Paus, S., et al., 2014. Modulation of the cerebello-thalamo-
cortical network in thalamic deep brain stimulation for tremor: a diffusion tensor
imaging study. Neurosurgery ;75:657-669; discussion 669-670.
Coenen, V.A., Rijntjes, M., Prokop, T., Piroth, T., Amtage, F., Urbach, H., Reinacher, P.C.,
2016. One-pass deep brain stimulation of dentato-rubro-thalamic tract and
subthalamic nucleus for tremor-dominant or equivalent type Parkinson’s disease.
Acta Neurochir (Wien) 158 (4), 773–781.
Coenen, V.A., Varkuti, B., Parpaley, Y., Skodda, S., Prokop, T., Urbach, H., Li, M.,
Reinacher, P.C., 2017. Postoperative neuroimaging analysis of DRT deep brain
stimulation revision surgery for complicated essential tremor. Acta Neurochir (Wien)
159 (5), 779–787.
Coenen, V.A., Sajonz, B., Prokop, T., Reisert, M., Piroth, T., Urbach, H., Jenkner, C.,
Reinacher, P.C., 2020. The dentato-rubro-thalamic tract as the potential common
deep brain stimulation target for tremor of various origin: an observational case
series. Acta Neurochir (Wien) 162 (5), 1053–1066.
Dembek, T.A., Barbe, M.T., Åstr¨
om, M., Hoevels, M., Visser-Vandewalle, V., Fink, G.R.,
Timmermann, L., 2017. Probabilistic mapping of deep brain stimulation effects in
essential tremor. Neuroimage Clin 13, 164–173.
Edlow, B.L., Mareyam, A., Horn, A., Polimeni, J.R., Witzel, T., Tisdall, M.D.,
Augustinack, J.C., Stockmann, J.P., Diamond, B.R., Stevens, A., Tirrell, L.S.,
Folkerth, R.D., Wald, L.L., Fischl, B., van der Kouwe, A., 2019. 7 Tesla MRI of the ex
vivo human brain at 100 micron resolution. Sci. Data 6 (1).
Eisinger, R.S., Wong, J., Almeida, L., Ramirez-Zamora, A., Cagle, J.N., Giugni, J.C.,
Ahmed, B., Bona, A.R., Monari, E., Wagle Shukla, A., Hess, C.W., Hilliard, J.D.,
Foote, K.D., Gunduz, A., Okun, M.S., Martinez-Ramirez, D., 2018. Ventral
intermediate nucleus versus zona incerta region deep brain stimulation in essential
tremor. Mov Disord Clin Pract 5 (1), 75–82.
Elias, G.J.B., Boutet, A., Joel, S.E., et al., 2021. Probabilistic mapping of deep brain
stimulation: insights from 15 years of therapy. Ann. Neurol. 89, 426–443.
Ellerman, J.M., Flament, D., Kim, S.-G., Fu, Q.-G., Merkle, H., Ebner, T.J., Ugurbil, K.,
1994. Spatial patterns of functional activation of the cerebellum investigated using
high eld (4 T) MRI. NMR Biomed. 7 (1-2), 63–68.
Ewert, S., Plettig, P., Li, N., Chakravarty, M.M., Collins, D.L., Herrington, T.M., Kühn, A.
A., Horn, A., 2018. Toward dening deep brain stimulation targets in MNI space: a
subcortical atlas based on multimodal MRI, histology and structural connectivity.
NeuroImage 170, 271–282.
Fenoy, A.J., Schiess, M.C., 2017. Deep brain stimulation of the dentato-rubro-thalamic
tract: outcomes of direct targeting for tremor. Neuromodul.: J. Int. Neuromodul, Soc.
20 (5), 429–436.
Fenoy, A.J., Schiess, M.C., 2018. Comparison of tractography-assisted to atlas-based
targeting for deep brain stimulation in essential tremor. Mov. Disord. 33 (12),
Flood, S., Jansen, J., 1966. The efferent bres of the cerebellar nuclei and their
distribution on the cerebellar peduncles in the cat. Acta Anat. (Basel) 63 (2),
Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L., 2011.
Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54 (1),
Fytagoridis, A., Sandvik, U., Åstr¨
om, M., Bergenheim, T., Blomstedt, P., 2012. Long term
follow-up of deep brain stimulation of the caudal zona incerta for essential tremor.
J. Neurol. Neurosurg. Psychiatry 83 (3), 258–262.
Gallay, M.N., Jeanmonod, D., Liu, J., Morel, A., 2008. Human pallidothalamic and
cerebellothalamic tracts: anatomical basis for functional stereotactic neurosurgery.
Brain Struct. Funct. 212 (6), 443–463.
Holslag, J.A.H., Neef, N., Beudel, M., Drost, G., Oterdoom, D.L.M., Kremer, N.I., van
Laar, T., van Dijk, J.M.C., 2018. Deep brain stimulation for essential tremor: a
comparison of targets. World Neurosurg. 110, e580–e584.
Horn, A., Reich, M., Vorwerk, J., Li, N., Wenzel, G., Fang, Q., Schmitz-Hübsch, T.,
Nickl, R., Kupsch, A., Volkmann, J., Kühn, A.A., Fox, M.D., 2017. Connectivity
predicts deep brain stimulation outcome in Parkinson disease. Ann. Neurol. 82 (1),
Horn, A., Li, N., Dembek, T.A., Kappel, A., Boulay, C., Ewert, S., Tietze, A., Husch, A.,
Perera, T., Neumann, W.-J., Reisert, M., Si, H., Oostenveld, R., Rorden, C., Yeh, F.-C.,
Fang, Q., Herrington, T.M., Vorwerk, J., Kühn, A.A., 2019. Lead-DBS v2: towards a
comprehensive pipeline for deep brain stimulation imaging. NeuroImage 184,
Husch, A., Petersen, M.V., Gemmar, P., Goncalves, J., Hertel, F., 2018. PaCER – a fully
automated method for electrode trajectory and contact reconstruction in deep brain
stimulation. Neuroimage Clin 17, 80–89.
Kim, W., Sharim, J., Tenn, S., et al., 2018. Diffusion tractography imaging-guided
frameless linear accelerator stereotactic radiosurgical thalamotomy for tremor: case
report. J. Neurosurg. 128, 215–221.
Kübler, D., Kroneberg, D., Al-Fatly, B., Schneider, G.-H., Ewert, S., van Riesen, C.,
Gruber, D., Ebersbach, G., Kühn, A.A., 2021. Determining an efcient deep brain
stimulation target in essential tremor – cohort study and review of the literature.
Parkinsonism Relat. Disord. 89, 54–62.
Küper, M., Thürling, M., Stefanescu, R., Maderwald, S., Roths, J., Elles, H.G., Ladd, M.E.,
Diedrichsen, J., Timmann, D., 2012. Evidence for a motor somatotopy in the
cerebellar dentate nucleus–an FMRI study in humans. Hum. Brain Mapp. 33 (11),
Louis, E.D., Ferreira, J.J., 2010. How common is the most common adult movement
disorder? Update on the worldwide prevalence of essential tremor. Mov. Disord. 25
Louis, E.D., Rios, E., Henchcliffe, C., 2010. How are we doing with the treatment of
essential tremor (ET)?: Persistence of patients with ET on medication: data from 528
patients in three settings. Eur. J. Neurol. 17, 882–884.
Meola, A., Comert, A., Yeh, F.-C., Sivakanthan, S., Fernandez-Miranda, J.C., 2016. The
nondecussating pathway of the dentatorubrothalamic tract in humans: human
connectome-based tractographic study and microdissection validation. J. Neurosurg.
124 (5), 1406–1412.
Middlebrooks, E.H., Okromelidze, L., Carter, R.E., et al., 2021. Directed stimulation of
the dentato-rubro-thalamic tract for deep brain stimulation in essential tremor: a
blinded clinical trial. Neuroradiol. J., 19714009211036689.
Middlebrooks, E.H., Holanda, V.M., Tuna, I.S., Deshpande, H.D., Bredel, M., Almeida, L.,
Walker, H.C., Guthrie, B.L., Foote, K.D., Okun, M.S., 2018. A method for pre-
operative single-subject thalamic segmentation based on probabilistic tractography
for essential tremor deep brain stimulation. Neuroradiology 60 (3), 303–309.
Middlebrooks, E.H., Tuna, I.S., Almeida, L., Grewal, S.S., Wong, J., Heckman, M.G.,
Lesser, E.R., Bredel, M., Foote, K.D., Okun, M.S., Holanda, V.M., 2018. Structural
connectivity-based segmentation of the thalamus and prediction of tremor
E.H. Middlebrooks et al.
NeuroImage: Clinical 32 (2021) 102846
improvement following thalamic deep brain stimulation of the ventral intermediate
nucleus. Neuroimage Clin. 20, 1266–1273.
Middlebrooks, E.H., Domingo, R.A., Vivas-Buitrago, T., Okromelidze, L., Tsuboi, T.,
Wong, J.K., Eisinger, R.S., Almeida, L., Burns, M.R., Horn, A., Uitti, R.J., Wharen, R.
E., Holanda, V.M., Grewal, S.S., 2020. Neuroimaging advances in deep brain
stimulation: review of indications, anatomy, and brain connectomics. AJNR Am. J.
Neuroradiol. 41 (9), 1558–1568.
Middlebrooks, E.H., Lin, C., Okromelidze, L., Lu, C.-Q., Tatum, W.O., Wharen, R.E.,
Grewal, S.S., 2020. Functional activation patterns of deep brain stimulation of the
anterior nucleus of the thalamus. World Neurosurg 136, 357–363.e2.
Middleton, F.A., Strick, P.L., 1998. Cerebellar output: motor and cognitive channels.
Trends Cogn. Sci. 2 (9), 348–354.
Middleton, F.A., Strick, P.L., 2000. Basal ganglia and cerebellar loops: motor and
cognitive circuits. Brain Res. Brain Res. Rev. 31, 236–250.
Morishita, T., Foote, K.D., Burdick, A.P., Katayama, Y., Yamamoto, T., Frucht, S.J.,
Okun, M.S., 2010. Identication and management of deep brain stimulation intra-
and postoperative urgencies and emergencies. Parkinsonism Relat. Disord. 16 (3),
Noecker, A.M., Frankemolle-Gilbert, A.M., Howell, B., et al., 2021. StimVision v2:
examples and applications in subthalamic deep brain stimulation for Parkinson’s
disease. Neuromodul.: J. Int. Neuromodul. Soc. 24, 248–258.
Okun, M.S., Tagliati, M., Pourfar, M., Fernandez, H.H., Rodriguez, R.L., Alterman, R.L.,
Foote, K.D., 2005. Management of referred deep brain stimulation failures: a
retrospective analysis from 2 movement disorders centers. Arch. Neurol. 62 (8),
Papavassiliou, E., Rau, G., Heath, S., et al., 2004. Thalamic deep brain stimulation for
essential tremor: relation of lead location to outcome. Neurosurgery 54:1120–1129;
Petersen, K.J., Reid, J.A., Chakravorti, S., Juttukonda, M.R., Franco, G., Trujillo, P.,
Stark, A.J., Dawant, B.M., Donahue, M.J., Claassen, D.O., 2018. Structural and
functional connectivity of the nondecussating dentato-rubro-thalamic tract.
NeuroImage 176, 364–371.
Pouratian, N., Zheng, Z., Bari, A.A., Behnke, E., Elias, W.J., DeSalles, A.A.F., 2011. Multi-
institutional evaluation of deep brain stimulation targeting using probabilistic
connectivity-based thalamic segmentation. J. Neurosurg. 115 (5), 995–1004.
Sammartino, F., Krishna, V., King, N.K.K., Lozano, A.M., Schwartz, M.L., Huang, Y.,
Hodaie, M., 2016. Tractography-based ventral intermediate nucleus targeting: novel
methodology and intraoperative validation. Mov. Disord. 31 (8), 1217–1225.
Sandoe, C., Krishna, V., Basha, D., Sammartino, F., Tatsch, J., Picillo, M., di Biase, L.,
Poon, Y.-Y., Hamani, C., Reddy, D., Munhoz, R.P., Lozano, A.M., Hutchison, W.D.,
Fasano, A., 2018. Predictors of deep brain stimulation outcome in tremor patients.
Brain Stimul. 11 (3), 592–599.
Sandvik, U., Koskinen, L.O., Lundquist, A., et al., 2020. Thalamic and subthalamic deep
brain stimulation for essential tremor: where is the optimal target? Neurosurgery;70:
840-845; discussion 845-846.
onecker, T., Kupsch, A., Kühn, A.A., Schneider, G.-H., Hoffmann, K.-T., 2009.
Automated optimization of subcortical cerebral MR imaging-atlas coregistration for
improved postoperative electrode localization in deep brain stimulation. AJNR Am.
J. Neuroradiol. 30 (10), 1914–1921.
Setsompop, K., Kimmlingen, R., Eberlein, E., Witzel, T., Cohen-Adad, J., McNab, J.A.,
Keil, B., Tisdall, M.D., Hoecht, P., Dietz, P., Cauley, S.F., Tountcheva, V., Matschl, V.,
Lenz, V.H., Heberlein, K., Potthast, A., Thein, H., Van Horn, J., Toga, A., Schmitt, F.,
Lehne, D., Rosen, B.R., Wedeen, V., Wald, L.L., 2013. Pushing the limits of in vivo
diffusion MRI for the Human Connectome Project. NeuroImage 80, 220–233.
Tacyildiz, A.E., Bilgin, B., Gungor, A., Ucer, M., Karadag, A., Tanriover, N., 2021.
Dentate nucleus: connectivity-based anatomic parcellation based on superior
cerebellar peduncle projections. World Neurosurg. 152, e408–e428.
Thanvi, B., Lo, N., Robinson, T., 2006. Essential tremor-the most common movement
disorder in older people. Age Ageing;35:344-349.
Tsuboi, T., Charbel, M., Peterside, D.T., Rana, M., Elkouzi, A., Deeb, W., Ramirez-
Zamora, A., Lemos Melo Lobo Joli Lopes, J., Almeida, L., Zeilman, P.R., Eisinger, R.
S., Foote, K.D., Okromelidze, L., Grewal, S.S., Okun, M.S., Middlebrooks, E.H., 2021.
Pallidal connectivity proling of stimulation-induced dyskinesia in Parkinson’s
disease. Mov. Disord. 36 (2), 380–388.
Tsuboi, T., Wong, J.K., Eisinger, R.S., et al., 2021. Comparative connectivity correlates of
dystonic and essential tremor deep brain stimulation. Brain 2021;144:1774–1786.
Vorwerk, J., Oostenveld, R., Piastra, M.C., Magyari, L., Wolters, C.H., 2018. The
FieldTrip-SimBio pipeline for EEG forward solutions. Biomed. Eng. Online 17 (1).
Wang, Q., Akram, H., Muthuraman, M., et al., 2021. Normative vs. patient-specic brain
connectivity in deep brain stimulation. NeuroImage1;224:117307.
Wiesendanger, R., Wiesendanger, M., 1985. Cerebello-cortical linkage in the monkey as
revealed by transcellular labeling with the lectin wheat germ agglutinin conjugated
to the marker horseradish peroxidase. Exp. Brain Res. 59, 105–117.
E.H. Middlebrooks et al.