Network Substrates of Centromedian Nucleus Deep Brain Stimulation in Generalized Pharmacoresistant Epilepsy

Abstract

Deep brain stimulation (DBS), specifically thalamic DBS, has achieved promising results to reduce seizure severity and frequency in pharmacoresistant epilepsies, thereby establishing it for clinical use. The mechanisms of action are, however, still unknown. We evidenced the brain networks directly modulated by centromedian (CM) nucleus-DBS and responsible for clinical outcomes in a cohort of patients uniquely diagnosed with generalized pharmacoresistant epilepsy. Preoperative imaging and long-term (2–11 years) clinical data from ten generalized pharmacoresistant epilepsy patients (mean age at surgery = 30.8 ± 5.9 years, 4 female) were evaluated. Volume of tissue activated (VTA) was included as seeds to reconstruct the targeted network to thalamic DBS from diffusion and functional imaging data. CM-DBS clinical outcome improvement (> 50%) appeared in 80% of patients and was tightly related to VTAs interconnected with a reticular system network encompassing sensorimotor and supplementary motor cortices, together with cerebellum/brainstem. Despite methodological differences, both structural and functional connectomes revealed the same targeted network. Our results demonstrate that CM-DBS outcome in generalized pharmacoresistant epilepsy is highly dependent on the individual connectivity profile, involving the cerebello-thalamo-cortical circuits. The proposed framework could be implemented in future studies to refine stereotactic implantation or the parameters for individualized neuromodulation.

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Neurotherapeutics
https://doi.org/10.1007/s13311-021-01057-y
ORIGINAL ARTICLE
Network Substrates ofCentromedian Nucleus Deep Brain Stimulation
inGeneralized Pharmacoresistant Epilepsy
CristinaV.TorresDiaz1· GabrielGonzález‑Escamilla2 · DumitruCiolac2,3,4· MartaNavasGarcía1·
PalomaPulidoRivas1· RafaelG.Sola1· AntonioBarbosa5· JesúsPastor6· LorenaVega‑Zelaya6· SergiuGroppa2
Accepted: 5 April 2021
© The Author(s) 2021
Abstract
Deep brain stimulation (DBS), specifically thalamic DBS, has achieved promising results to reduce seizure severity and
frequency in pharmacoresistant epilepsies, thereby establishing it for clinical use. The mechanisms of action are, however,
still unknown. We evidenced the brain networks directly modulated by centromedian (CM) nucleus-DBS and responsible
for clinical outcomes in a cohort of patients uniquely diagnosed with generalized pharmacoresistant epilepsy. Preoperative
imaging and long-term (2–11years) clinical data from ten generalized pharmacoresistant epilepsy patients (mean age at
surgery=30.8±5.9years, 4 female) were evaluated. Volume of tissue activated (VTA) was included as seeds to reconstruct
the targeted network to thalamic DBS from diffusion and functional imaging data. CM-DBS clinical outcome improve-
ment (>50%) appeared in 80% of patients and was tightly related to VTAs interconnected with a reticular system network
encompassing sensorimotor and supplementary motor cortices, together with cerebellum/brainstem. Despite methodological
differences, both structural and functional connectomes revealed the same targeted network. Our results demonstrate that
CM-DBS outcome in generalized pharmacoresistant epilepsy is highly dependent on the individual connectivity profile,
involving the cerebello-thalamo-cortical circuits. The proposed framework could be implemented in future studies to refine
stereotactic implantation or the parameters for individualized neuromodulation.
Keywords Deep brain stimulation· Brain networks· Centromedian nucleus· Neuromodulation· Generalized epilepsy
Introduction
Epilepsy is a very common chronic neurological disorder
characterized by spontaneous recurrent seizures, presenting
a high prevalence and leading to an enormous psychosocial
burden for patients, families, caregivers, and health systems
[1]. Approximately 30% of epilepsy patients will not have
adequate seizure control with pharmacotherapy alone [1]
and long periods of incomplete seizure control have consid-
erable consequences leading to disease worsening, cognitive
and mental symptoms, and a massive decline in quality of
life.
Recent work brought first important hints for the mecha-
nisms underlying generalized pharmacoresistant epilepsy,
showing widespread reduced structural integrity, within
the frontal, sensorimotor, and parietal cortices, as well as
the anterior cingulate [2, 3], which accelerates in patients
with poorly controlled seizures [2]. EEG-fMRI studies have
shown that during generalized seizures, a characteristic pat-
tern of subcortical (medio-dorsal thalamic and striatum)
Cristina V. Torres Diaz and Gabriel González-Escamilla
contributed equally to this work.
* Gabriel González-Escamilla
ggonzale@uni-mainz.de
1 Department ofNeurosurgery, University Hospital La
Princesa, Madrid, Spain
2 Movement Disorders andNeurostimulation, Department
ofNeurology, Focus Program Translational Neuroscience
(FTN), University Medical Center oftheJohannes Gutenberg
University Mainz, Rhine Main Neuroscience Network
(rmn2), Mainz, Germany
3 Laboratory ofNeurobiology andMedical Genetics, Nicolae
Testemitanu, State University ofMedicine andPharmacy,
Chisinau, RepublicofMoldova
4 Department ofNeurology, Institute ofEmergency Medicine,
Chisinau, RepublicofMoldova
5 Department ofNeuroradiology, University Hospital La
Princesa, Madrid, Spain
6 Department ofClinical, Neurophysiology University Hospital
La Princesa, Madrid, Spain

C.V.Torres Diaz et al.
1 3
activation and cortical deactivation occurs [4]. Particularly,
the activation of the cortico-reticular (centromedian [CM]
nucleus of the thalamus and parafascicular [Pf]) nuclei of the
thalamus precede the activation of the anterior nucleus, sug-
gesting that the CM-Pf complex as driving the generation,
or early propagation of generalized seizures, while anterior
nucleus activity supports its maintenance [4]. For other
structures, such as the cerebellum and brainstem, despite
the available evidence, their particular role is less clear. A
potential antiseizure modulatory effect of the cerebellum can
be postulated as reduced cerebellar functional connectivity
is related to pharmacoresistance [5]. These findings serve to
postulate a network state associated with the pathophysiol-
ogy of generalized pharmacoresistant epilepsy. However, its
specific attribution for the therapeutic interventions in gener-
alized pharmacoresistant epilepsy remains to be elucidated.
Deep brain stimulation (DBS) of the CM has been
recently introduced as a safe and promising therapy in
patients with pharmacoresistant epilepsy [6]. The efficacy of
CM-DBS may, however, depend on the epilepsy syndrome,
i.e., possibly being more effective in patients with general-
ized than focal epilepsy [6]. First insights into CM-DBS
efficiency and way of action have been obtained from studies
in patients with mixed seizure types [7] or Lennox-Gastaut
syndrome [8]. Thus, a conceptual framework of CM-DBS
in pharmacoresistant epilepsies and mainly in generalized
forms is still lacking. Moreover, stratification algorithms to
identify optimal candidates for CM-DBS are still pending.
We postulate that the evaluation of the connectivity profiles
of CM-DBS will unmask a robust neuroanatomical substrate
common for all patients. Such substrate can be identified
from associations between individual seizure reductions and
the connectivity profile of the targeted network. Increasing
evidence shows that stimulation of white matter tracts is,
at least in part, responsible for the therapeutic effects of
DBS in network disorders [9, 10]. Connectivity, thus, can
be used for the target definition in stereotactic implantation
or identification of surgery candidate patients. For this rea-
son, we use structural and functional connectivity as a main
tool for delineating the networks directly associated with
CM-DBS clinical outcomes in patients with generalized
pharmacoresistant epilepsy. This strategy will further not
only reduce the heterogeneity across patients and studies
[11, 12] but also elucidate the physiological and mechanistic
substrates of CM-DBS. If available, this information could
be used to yield optimal clinical efficacy for this therapy
and translation into clinical practice through adjustment of
stimulation parameters.
Material andMethods
Patients
We conducted a retrospective study of 10 patients with
generalized pharmacoresistant epilepsy (mean age at sur-
gery=30.8±5.9years, 4 female), defined according to
the International League Against Epilepsy (ILAE) guide-
lines [13], who have undergone CM-DBS at our institution
between 2008 and 2019. Day-to-day functioning was evalu-
ated through the Global Assessment of Functioning scale
(GAF) [9], and quality of life (QoL) was evaluated through
the Diagnostic and Statistical Manual of Mental Disorders
(DSM-IV) [9, 14].
A multidisciplinary team, consisting of neurosurgeons,
epileptologists, psychiatrists, neurophysiologists, radiolo-
gists, and neuropsychologists, determined the indications for
the DBS. Table1 depicts the specific inclusion and exclusion
criteria for the study. In eight patients, before the DBS, vagal
stimulation implantation was performed with short-lasting
beneficial response. Each patient’s family completed the
diary of seizures. In all cases, there was a co-existing devel-
opmental delay. Prior to surgery, patients were evaluated
through medical history, physical examination, magnetic res-
onance imaging (MRI), 99mTc-HmPAO single-photon emis-
sion tomography (SPECT), 19 electrodes scalp electroen-
cephalography (EEG) (Cadwell®, Kennewick, WA, USA),
and video-EEG (VEEG, XLTEK®, Oakville, ON, Canada).
EEG electrodes were placed according to the 10–20 system,
including supplementary electrodes at bilateral basal tem-
poral lobes. VEEG was used to determine the seizure type,
frequency, and electro-clinical features.
Table 1 Inclusion and exclusion criteria
Inclusion criteria Exclusion criteria
• Age > 18years.
• A clear diagnosis of epilepsy (confirmed by surface or intracranial
VEEG).
• Patients were not candidates for resective surgery.
• Seizure frequency greater than 10/month.
• Stable doses of antiepileptic drugs in the last 6months.
• The family was able to fill out the diary of seizures.
• No structural abnormalities in MRI that could impact centrome-
dian nucleus targeting or its connections.
• Concomitant neurological or psychiatric disorders (although epilep-
tic encephalopathy is not excluded).
• A history of poor adherence to treatment.
• Temporal lobe epilepsy.

Network Substrates ofCentromedian Nucleus Deep Brain Stimulation inGeneralized…
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Surgical Procedure
DBS implantation into the CM was performed under gen-
eral anesthesia with propofol and isoflurane using an MRI-
guided stereotactic protocol, previously described elsewhere
[15]. In brief, a stereotactic frame (Leksell Frame G, Elekta,
Stockholm, Sweden) was placed and used to determine the
target coordinates (X=9, Y=−9, Z=0). After stereotactic
frame positioning, correct targeting was evaluated according
to the presence of a specific thalamic response to somato-
sensory evoked potentials (delta waves) at the level of the
cerebral cortex, induced by electrical stimulation at 6Hz
(monophasic pulse width 100µs, amplitudes between 1 and
3mA). Cortical responses were assessed by intraoperative
microelectrode recordings (MER) using five microelec-
trodes implanted through bilateral frontal burr holes in a
transparenchymal extraventricular trajectory and scalp EEG
[16]. Following MER target verification, each patient was
implanted with two quadripolar DBS electrodes (model:
Medtronic 3389, Medtronic, Minneapolis, MN, USA, or
Abbott 6149, St. Jude Medical Inc., Saint Paul, MN, USA)
connected to pulse generators (Kinetra, Medtronic, Inc.,
Minneapolis, MN, U.S.A., and Libra PC, Abbott) placed
in the subclavicular area. Final electrode positioning was
revised with intraoperative radioscopy and with genera-
tion of delta waves on EEG at macrostimulation. No modi-
fications in medical therapy were allowed during the first
12months after CM-DBS. Postoperative whole-brain MRI
was performed in all patients to verify the electrodes’ posi-
tion and rule out surgery complications.
CM‑DBS Configuration
After CM-DBS implantation, patients were subsequently
monitored by VEEG during 3–5days. Optimal stimulation
parameters and DBS active contacts were selected accord-
ing to recorded cortical delta waves (6Hz) generation [17].
CM-DBS was activated at 60Hz and 90 μs, and up to 5V,
depending on the initially recorded responses. DBS was
activated 3 months after DBS implantation in all patients;
however, 60% of patients remained uninformed about this
until the 6-month visit. Patients were clinically evaluated
at three months before DBS activation, to inform about the
confusion factor of the electrode insertion effect (“micro-
lesion” or “honeymoon” effects of implantation) on the
6-month visit onwards. Between DBS activation and the
6-month visit, stimulators were individually adapted accord-
ing to EEG improvement. Postoperative seizure frequency
was assessed by seizure diaries and by VEEG during the
first postoperative week and every 6months over 2years.
The average follow-up time after CM-DBS was 92.4months
(42–129months).
MRI Acquisition
Whole-brain imaging data were acquired on a 1.5-T MRI
scanner (General Electric Healthcare, Waukesha, WI).
Pre-operative diffusion data were acquired using a single-
shot echo-planar imaging pulse sequence with follow-
ing parameters: repetition time (TR)=11s; field of view
(FoV)=280×280mm; matrix size=128×128; slice
thickness=3mm; voxel size 1×1mm; 25 gradient direc-
tions and a b-value of 1000s/mm2, and an additional volume
without diffusion weighting (b=0s/mm2). A T2-weighted
sequence was acquired using a 3D magnetization-prepared
cube fast spin gradient echo (FoV=25.6mm, slice thick-
ness=1mm, TR=2500, echo train length=100, band-
width=62.5 and matrix size=256 × 256). Pre- and post-
operative T1-weighted images were acquired using a 3D
magnetization-prepared rapid gradient-echo (MPRAGE)
sequence with the following parameters: voxel size of
1× 1; slice thickness= 1mm, FoV=25.6mm; matrix
size=256 × 256; TR= 8300; echo train length= 3100;
bandwidth=31.25.
DBS Electrode Reconstruction andLocalization
Image processing and electrode localization were carried out
by using the Lead-DBS toolbox (v.2.3; https:// www. lead-
dbs. org/) with default parameters [18]. Briefly, preopera-
tive and postoperative MRI scans were co-registered using
a linear transform in SPM12 (http:// www. fil. ion. ucl. ac. uk/
spm/ softw are/). Pre- and post-operative images were then
normalized into MNI space (2009b non-linear asymmet-
ric) using a fast diffeomorphic image registration algorithm
(DARTEL) as implemented in SPM12 [19]. Brain shifts in
postoperative acquisitions were corrected by applying the
“subcortical refine” setting as implemented in the Lead-DBS
[18]. Finally, DBS electrodes were manually localized based
on the post-operative acquisitions by using the “display”
tool in SPM12. All steps were visually inspected to ensure
data quality. To graphically illustrate the electrode locations,
two-dimensional slices were plotted using the 7-T 100-μm
ex vivo human brain MRI [20] template as a background
image and the thalamic nuclei boundaries as delineated in
the THOMAS atlas [21] as reference.
Estimation ofVTA
Stimulation parameters, i.e., active contacts and amplitudes,
of each individual patient were applied to calculate VTAs,
representing a rough approximation of the surrounding
tissue modulated by DBS, using a finite element method
(FEM) approach [18]. Anisotropic conductivity values for
gray (σ=0.33S/m) and white matter (σ=0.14S/m) were

C.V.Torres Diaz et al.
1 3
chosen. The electric field threshold was set to e=0.2V/mm,
which approximates previous VTA radius estimates [18].
Diffusion Imaging Pre‑processing andTractography
Diffusion MRI data underwent correction of eddy current
distortions and subject movement, followed by registration
to the corresponding T1 image using the normalized mutual
information algorithm implemented in SPM12. Then, deter-
ministic tractography was performed using the generalized
Q-sampling imaging method from the DSI studio (http:// dsi-
studio. labso lver. org) using the default parameter sets imple-
mented in the Lead-Connectome (www. lead- conne ctome.
org). The resulting whole-brain set of 200.000 fiber tracts in
the patient space were transformed into MNI (ICBM 2009b
Nonlinear Asymmetric) space and merged into one whole-
brain connectome [18].
Resting‑State Functional Imaging
Normative resting-state fMRI was obtained from 1000
healthy subjects using a 3-T Tim Trio MRIscanner (Siemens
Healthcare, Erlangen, Germany) with a 12-channel receive
onlycoil, as part of the publicly available Brain Genomics
Superstruct Project (GSP) [22]. fMRI data were acquired
at 3-mm isotropic resolution with TR=3000ms and 124
frames. fMRI data pre-processing included: (1) removal
of the first five frames, (2) motion correction using rigid
body translation and rotation, (3) slice timing correction,
(4) alignment with structural image, (5) normalization to
MNI space using the deformation matrices obtained dur-
ing MRI preprocessing using the CAT12 toolbox (Struc-
tural Brain Mapping group, Jena University Hospital, Jena,
Germany), (6) smoothing by a 6mm full-width half-max-
imum (FWHM) kernel, (7) nuisance covariate regression
(including six motion correction parameters, and averaged
WM and CSF signals), and (8) band-pass filtering (between
0.01 and 0.08Hz). WM and CSF masks were obtained
from segmentation of the anatomical T1 image, followed
by binarization of the probabilistic tissue maps at a threshold
of 0.9 and 0.7, respectively. All preprocessing steps were
carried out following recommended guidelines [23] in
SPM12.
Connectivity Analysis
VTAs were used as seeds to estimate diffusion- and fMRI-
based connectivity to other brain areas. For diffusion imag-
ing, only fibers that traversed through the VTA and ter-
minated in distinct brain regions defined according to the
Harvard–Oxford atlas [24] were selected. Next, we used a
method referred to as “discriminative fibertract analysis”
[25] to select the fibers that are strongly discriminative for
better clinical outcomes across patients [26]. Briefly, for
each fiber connecting the VTAs with the rest of the brain,
the algorithm searches whether the fiber passes close to an
active contact of patients with optimal seizure improvement
and is far from contacts of patients with poor improvement.
This search results in a “statistical” score assigned to the
fiber (see statistical analyses for details on scores). High
scores mean that a particular fiber has a strong discrimina-
tive value for the clinical outcome.
For functional connectivity, the time series sampled from
VTA voxels were spatially correlated (Pearson’s product-
moment correlation) with the time series from every other
voxel in the brain for each of the 1000 normative images.
Then the individual correlation maps were z-transformed
using Fisher’s transformation and used to compute a whole-
brain connectivity t-map.
Statistical Analysis
Statistical comparisons between preoperative and postop-
erative clinical variables were conducted under the general
linear model (GLM) by firstly fitting a repeated measures
ANOVAs (rm-ANOVA) with 95% confidence (p<0.05),
followed by pairwise post hoccomparisons by means of
Tukey–Kramer significance difference criterion. Associa-
tions between volume intersections (between each patient’s
bilateral contact coordinates/VTAs and the bilateral CM)
and clinical outcomes were evaluated by setting linear
regression analysis (two-sided) under the general linear
model. Here, r coefficients are presented as indicators of
the effect sizes. Streamline scoring during the fibertract dis-
criminative analysis was effectuated by conducting mass-
univariate two-sided tests, comparing improvement values of
connected VTAs against those of unconnected VTAs. Thus,
in this step, each fiber receives a discriminative value in
form of a t-score that can be positive (indicating fibers pre-
dominantly connected to VTAs that are associated with bet-
ter treatment response) or negative (indicating the opposite).
Based on this t-score, only the top 10% of all discriminative
fibers were kept [26]. All statistical analyses were conducted
in Matlab (R2017b, The MathWorks®).
Results
Patient Evaluation
Table2 summarizes baseline characteristics of the included
patients with generalized pharmacoresistant epilepsy. All
patients had generalized epileptiform discharges on EEG
recordings. Among other seizure types, the majority of
patients presented generalized tonic–clonic seizures. No
patient had post-surgical complications, nor paresthesia.

Network Substrates ofCentromedian Nucleus Deep Brain Stimulation inGeneralized…
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After CM-DBS eight of the ten patients (80%) presented a
decrease in seizure frequency of at least 50% (Fig.1a). Com-
pared to baseline and to the pre-DBS activation period (three
months), seizure frequency significantly improved in time
(mean % improvement, 3 months=25%, 6 months=45%,
12months=52%, 18months=52%, 24months=56%,
last follow-up=51%). rmANOVA across all time points
(p=4.8e−11, F(6,48) = 18.63) (Fig.1a). The last fol-
low-up varied in time among the subjects (>3 and up to
10.8years). Although, the clinical outcomes were compa-
rable between the 24months follow-up and the last follow-
up (all p>0.05), in order to avoid confounding effects of
variable therapy duration at last follow-up, improvement
at the 24-month follow-up was used for the interpretation
of long-term time effects of CM-DBS. Significant clinical
improvement at 24months was evidenced compared to base-
line (post hoc p=0.002) and 3-month (post hoc p=0.048)
data. Post hoc analyses showed no further differences across
time points. These findings suggest that seizure improve-
ment in all clinical assessments after 6months was achieved
in comparison to the first three months after the DBS sur-
gery, thus excluding any electrode insertion effect (“micro-
lesion” or “honeymoon” effects of implantation) on further
follow-up assessments.
Patients’ evaluation further evidenced similar effects
after CM-DBS in the day-to-day functioning (Fig.1b)
Table 2 Patient demographic and clinical baseline characteristics
VEEG= video-electroencephalogram; TCG = tonic–clonic generalized seizures; SB = seizure burden (sec/day); QoL = quality of life assessed
through by the Diagnostic and Statistical Manual of Mental Disorders Z(DSM-IV); n/a = not available
Age at surgery Daily seizure
frequency
Dominant
seizure type
(VEEG)
Further seizure
types
SB (baseline) SB (DBS-
OFF)
SB (DBS-ON) QoL-DSM-IV Last follow-
up (months)
30 yrs 2–30 Myoclonic Abscence,
atonic
15 10 2 20 88
35 yrs 4 TCG n/a 265 45 15 40 124
21 yrs 1 Tonic Abscence 180 80 32 30 71
39 yrs 1 TCG n/a 7200 7200 7200 30 123
39 yrs 1–20 Spasms Myoclonic,
tonic
1000 2000 40 70 6
30 yrs 10–20 Atonic Abscence 75 54 33 50 129
27 yrs 4–10 (clus-
tered, weekly
mean)
TCG n/a 60 1.5 30 20 52
25 yrs 1 TCG n/a 90 130 12 40 124
28 yrs 2–21 TCG n/a 584 584 340 50 42
34 yrs 1–4 Atonic Abscence,
myclonic
240 120 60 40 79
Fig. 1 Long-term clinical improvement after CM-DBS. (a) Seizure
frequency improvement presented as percentage in comparison to
baseline (BL). (b) Day-to-day functioning measured through the
Global Assessment of Functioning (GAF). (c) Quality of life meas-
ured by the Diagnostic and Statistical Manual of Mental Disorders
(DSM-IV). All graphs depict an overall improvement for the patients
after CM-DBS. The minimum follow-up (FU) for all patients was
24months, whereas the last FU ranged from 42 to 129months

C.V.Torres Diaz et al.
1 3
and quality of life (Fig.1c), evaluated through the GAF
and DSM-IV, respectively. Significant improvement
across all follow-ups was observed for GAF (rmANOVA
p=3.9e−31, F(6,48)=179.57), and for QoL (p=1.5e−20,
F(6,48)=59.48). Post hoc evaluation between 24-month
follow-up with the baseline was significant for both GAF
(p=1.54e−06) and QoL (p=7.8e−5). Post hoc evaluation
between the 24-month follow-up with the 3-month follow-
up was also significant (GAF p=0.022; QoL p=0.042).
Accompanying the findings on DSM-IV, improvement in
quality of life ofpatients was reflected as (i) less interference
in daily activities due to seizures; (ii) fewer hospital admis-
sions due to status epilepticus, aspiration pneumonia, and
hyperthermia; (iii) less time in post-critical period; and (iv)
cognitive improvement in three patients, so they were more
interactive with their families and independent to perform
their daily activities. Indeed, one of the patients was able to
go by bus to school, whereas that was considered impossi-
ble in the preoperative period, thus supporting the observed
clinical improvement.
Electrode Localization andClinical Outcome
Schematic depiction of implanted DBS electrodes, includ-
ing lead width, contact length, and intercontact distance is
presented in Fig.2a. In brief, each lead has four stimula-
tion contacts (C0, C1, C2, and C3) spaced 0.5mm apart.
Electrode localization confirmed accurate placement of the
electrode leads within the target region in the thalamus (CM
and Pf) in all patients (Fig.2 b and c). However, there was
some observable heterogeneity across individuals, mostly
in the left hemisphere. Such heterogeneity is not surprising
Fig. 2 CM-DBS localization overview. (a) Schematic representation
of DBS electrodes. (b) and (c) Frontal and superior 3D view repre-
sentations of the DBS contact locations. The THOMAS atlas [21]
was used as reference for delimitation of the thalamic centromedian
(CM, red) and parafascicular (Pf, yellow) nuclei. A 7T 100-μm T1
MRI scan of an ex vivo human brain [20] is used as the background
image for 2D slices. (d) Association between the volumes of tis-
sue activated (VTA) and the CM location with seizure frequency
improvement (%). (e) Association between VTA and Pf location with
seizure frequency improvement (%). (f) Association between VTA
and combined CM/Pf location with seizure frequency improvement
(%)

Network Substrates ofCentromedian Nucleus Deep Brain Stimulation inGeneralized…
1 3
as it clearly follows the overall variability in the clinical
DBS outcomes. Particularly, the optimal DBS outcome
was associated with shorter distances between VTA and
CM (r=0.859, p=0.0015; Fig.2d) and to a lesser extent
between VTA to Pf (r=0.809, p=0.0046; Fig.2e). The
distances to combined CM-Pf also correlated to seizure
improvement (r=0.85, p=0.0018; Fig.2f).
Connectivity Analysis andClinical Outcomes
Analysis of the structural connectome revealed a high num-
ber of fibers connecting the VTA to the brainstem/cerebel-
lum (mean count across subjects ± SD=1.35e−7±2.2e−7),
postcentral cortex (8.29e−8±1.3e−9), precentral cortex
(1.47e−7±2.5e−7), supplementary motor area (SMA;
3e−7±3.7e−7), middle frontal gyrus (2.64e−8±6.6e−8),
and superior frontal cortex (9.41e−8±1.7e−8). CM-DBS-
related seizure frequency improvement was associated with
the number of connecting fibers to the brainstem/cerebel-
lum (r=0.684, p=0.015), postcentral cortex (r=0.665,
p=0.018), precentral cortex (r=0.686, p=0.0.014), sup-
plementary motor area (r=0. 637, p=0.024), middle fron-
tal gyrus (r=0.0.71, p=0.011), and superior frontal cortex
(r=0.825, p=0.0017) (Fig.3). Of notice, repetition of
the analysis without the patient not responding to CM-DBS
did not change the results (see Supplementary file 11 Fig.1). These
results indicate that a particular proportion of these fiber
projections and not all connected fibers (Fig.4a) are respon-
sible for CM-DBS clinical outcome. In order to test this
hypothesis, the discriminative fiber analysis was conducted,
evidencing that among connected fibers those projecting
from the VTA to the brainstem and traversing to the cerebel-
lum, together with the fibers connecting to the sensorimotor
and supplementary motor cortices are tightly associated with
optimal CM-DBS outcome (Fig.4b). These discriminative
fiber tracts overlap with the ascending reticular activating
system (ARAS [27]; Fig.4c), particularly corresponding
greatly to the spinothalamic tract (STT), and as well to the
superior cerebellar peduncle anterior spinocerebellar tract
(SCPSC) and the lateral lemniscus (LL) (Fig.4d).
For functional connectivity, positive connectivity was
found with the cerebellum and brainstem. Cortically, con-
nectivity was detectable with the sensorimotor cortex, SMA,
middle frontal cortex, medial temporal cortex, and anterior
cingulate. Subcortically, positive connectivity was found
with the thalamus, extending to the striatum (globus pal-
lidus, putamen, and caudate) and subthalamic nucleus. No
areas of negative connectivity were found (Fig.5). Thus, the
Fig. 3 Association between CM-DBS and clinical outcome. Regres-
sion plots for the association between the seizure frequency improve-
ment and the CM-DBS-modulated number of fibers with the (a)
brainstem, (b) postcentral (sensory) cortex, (c) precentral (motor)
cortex, (d) supplementary motor area (SMA), (e) middle frontal
gyrus, and (f) superior frontal cortex. All associations were con-
ducted using independent general lineal models. Blue shaded areas
represent 95% confidence intervals

C.V.Torres Diaz et al.
1 3
patterns of functional connectivity are markedly similar to
the structural connectivity results.
Additionally, associations between CM-DBS stimula-
tion intensity and seizure frequency improvement (r=0.78,
p= 0.012) and between stimulation intensity and VTAs
(r=0.85, p=0.0037) were further attested.
Discussion
Our results show that the connectivity patterns of the CM-
DBS-modulated fiber tracts in generalized pharmacoresist-
ant epilepsy are responsible for the reduction of seizure
frequency and, hence, improved clinical outcomes in these
patients. Seizure reduction was associated with the patients’
specific local CM tissue responses (individual VTAs);
patients with suboptimal clinical outcomes had greater dis-
tances between the DBS electrode locations and the CM.
In addition, the combination of diffusion tractography and
functional connectome imaging analysis demonstrated that
CM-DBS modulated a well-delineated network, mainly
composed of the sensorimotor and supplementary motor
cortices, brainstem, and cerebellar regions. These results
suggest a modulation of the reticular system to optimally
suppress seizures in patients with generalized pharmacore-
sistant epilepsy. Our results highlight the importance of
implementing diffusion MRI in assisting the surgical tar-
geting for DBS in pharmacoresistant epilepsy.
Fig. 4 Delineation of CM-DBS structural network connectivity.
(a) All fibers connected to the volumes of tissue activated (VTAs)
across patients. (b) Discriminative fibers associated with clinical
seizure improvement. The top 10% predictive fibers are displayed.
Fibers in white to red scale represent the t-values for the positive
association between selected fibers and seizure frequency improve-
ment. Fibers with the strongest discriminative value cross from the
VTAs in the centromedian nucleus (CM) to the brainstem and cer-
ebellum. On the cortical side, these fibers project to the sensorimotor
and supplementary motor cortices. Among cerebellar/brainstem fib-
ers, projections occur in a similar fashion as the ascending reticular
activating system (ARAS) as depicted in the Brainstem Connectome
Atlas [27] (c), with the highest overlap with spinothalamic (STT), fol-
lowed by the lateral lemniscus (LL) and superior cerebellar pedun-
cle spinocerebellar (SCPSC) tracts (d). A 7T 100-μm T1 MRI scan
of an ex vivo human brain [20] is used as background image for 2D
slices. CST corticospinal tract, FPT fronto-pontine tract, ICPMC infe-
rior cerebellar peduncle medulla oblongata cerebellar, ICPPVC infe-
rior cerebellar peduncle vestibulocerebellar, ML medial lemniscus,
POTPT parieto-occipito-temporo-pontine tract, SCPCR superior cer-
ebellar peduncle cerebellorubral, SCPCT superior cerebellar peduncle
cerebellothalamic, MCP middle cerebellar peduncle

Network Substrates ofCentromedian Nucleus Deep Brain Stimulation inGeneralized…
1 3
The CM is not only important for arousal and mainte-
nance of consciousness but is also vital for sensorimotor
coordination and the regulation of cardiac, respiratory,
muscle, and reflex activities [28], making it a valuable
DBS target for epilepsy treatment. Nonetheless, hetero-
geneity in target region and coordinates and parameters
of stimulation [8, 11, 12, 29] have been used to explain
the inconsistency in DBS outcomes. However, differences
in DBS efficacy can be attributed to the heterogeneity in
patient selection criteria and inclusion of different epilep-
tic syndromes, further limiting results’ interpretation and
agreement among the studies. Accompanying the sugges-
tion that CM-DBS may be more effective in patients with
generalized epilepsy [6] in comparison to other epilepsy
syndromes, the introduction of diffusion and functional
brain imaging approaches has shown to facilitate the study
of neurophysiological characteristics of CM-DBS in other
epileptic syndromes [7, 8]. Interestingly, to our knowl-
edge, the current study is the first to assess both structural
and functional connectivity of CM-DBS in a cohort of
patients uniquely suffering from generalized pharmacore-
sistant epilepsy, representing a clear step forward in the
understanding of CM-DBS mechanisms.
In our study, CM-DBS modulated two main fiber tracts
within the reticular formation, including the brainstem-
thalamo-cortical projections (ARAS) and the descending
pathways to the spinal cord via the reticulospinal tracts
(brainstem and superior cerebellar peduncle projections).
The CM, particularly its lateral part, has reciprocal connec-
tions with the motor and primary somatosensory cortices
[28]. CM also receives cholinergic and non-cholinergic (i.e.,
serotonergic and noradrenergic) inputs from the descending
pathways. Within the descending fibers, activation of inhibi-
tory Purkinje cells, likely results in the suppression of excit-
atory cerebellar output to the thalamus and thalamocortical
projections, resulting in overall decreased cortical excitabil-
ity[30]. Such intricated structural architecture is consistent
with our structural and functional connectivity findings, sup-
porting the key role of main efferent and afferent centrome-
dian connections in the efficacy of CM-DBS [28], disrupting
aberrant network synchrony in the reticulo-thalamo-cortical
circuits [7], and interrupting or decreasing the risk of sei-
zure activity [31], likely by inducing desynchronization and
inhibition of electrical conduction through the evidenced
pathways. Thus, the efficacy of DBS in epilepsy patients
may be dependent on the specific cerebello-cortico-thalamic
Fig. 5 The CM-DBS-targeted
functional network connectivity.
Functional connectivity CM-
DBS showed a very symmetric
pattern across cerebral hemi-
spheres that closely reproduced
the structural connectivity
pattern. The red to yellow color
bar depicts the intensity of the
connectivity from VTA to the
rest of the brain. Specifically,
the strongest connectivity
appeared in the surrounding
thalamic nuclei, followed by the
brainstem and spreading to the
cerebellum. Cortically, strong
connectivity was detected in
the anterior cingulate cortex,
extending to the supplementary
motor areas, the precentral and
postcentral gyri, middle frontal
cortex, and insula. Finally,
connectivity was also depicted
in the medial temporal and
occipital cortices

C.V.Torres Diaz et al.
1 3
connectivity profiles of distinct thalamic subdivisions [32]
and network modulation of brain states [33, 34].
The high number of projections from CM to the senso-
rimotor areas [28] can help explain why the representation
of generalized seizures is seen in these areas in functional
studies [35]. Accordingly, associations between white matter
fiber connections from CM-DBS active contacts and EEG
activation in the frontal, left temporal, and right anterior
temporal areas and CM-DBS outcomes has been reported
in patients with pharmacoresistant epilepsy [7]. Unfortu-
nately, in the mentioned study more multifocal (n=7) than
generalized (n=3) epilepsy patients were included, whereas
significant activation during CM-DBS across the whole-cor-
tex was detected. Further, the authors considered only fiber
projections from CM to cortical sites, and electrode contacts
were activated according to the seizure frequency [7]. In
contrast, in our study, all patients presented a generalized
seizure type, the choice of active electrode contacts, and
stimulation parameters were based not only on the reduc-
tion of seizure frequency but also on the induction of delta
and theta waves, characteristic of evolving rhythmic activity
of seizures [12]. Moreover, our tractography approach was
conducted on a whole-brain basis and combined with a dis-
criminative fibertract analysis method [25], altogether lead-
ing to a detailed detection of fiber tracts of long-term CM-
DBS outcome, involving the reticular system network. Thus,
beyond patient selection, differences in DBS contact activa-
tion algorithm and whole-brain analysis could explain the
different patterns of CM network connectivity and patients’
clinical responses to CM-DBS in our study.
A recent CM-DBS study, including 16 patients diagnosed
with Lennox-Gastaut syndrome, used resting-state fMRI to
model connectivity from electrode locations, resulting in
a network composed of sensorimotor/premotor and limbic
(cingulate, parahippocampal, insular) cortices, brainstem,
cerebellum, and striatum (caudate, putamen) [8]. Notewor-
thy, the authors based their conclusions on existing indirect
connections between CM and frontal cortices through the
striatum [8], whereas the direct and reciprocal connections
to premotor and sensorimotor cortices and to the cerebel-
lum through the brainstem [28] were neglected, limiting
the extent of their conclusions. While it is true that frontal
regions are involved in the modulation of cortical processing
during attention-demanding tasks [28], in Warren etal. [8],
the utility of physiological recordings (e.g., intraoperative
microelectrode recordings) and fMRI data is constrained
by its application on anaesthetized patients. Importantly,
even when we found fibers connecting to regions within the
basal ganglia, including the striatum (see Supplementary
file 11 Fig.2), which act as intermediate regions connecting the
CM with the prefrontal cortex [28], these fibers were not
highly discriminative for the optimal outcome to DBS in our
patients (seeSupplementary file 11Fig.3). This goes well
with the lack of functional connectivity to frontal areas in our
analyses using the normative data. In this context, prospective
studies are needed using subject-specific functional imaging
to better understand this phenomena, also accounting for the
possible interaction between electrode locations and func-
tional connectivity for the optimal CM-DBS outcomes.
A previously described pattern of thalamic activation
during seizures, namely, the earlier activation of CM/Pf fol-
lowed by the anterior nucleus, suggests that the brainstem
reticular formation could drive the generalized seizures [4].
Thus, the mechanistic effect of CM-DBS on seizure reduc-
tion in generalized epilepsy could rely on the recruitment of
physiological circuits of the CM and interruption of seizure
activity propagation along the cerebello-thalamo-cortical
pathways. Consistently, the sensorimotor and premotor
regions entrain long-range synchronization of ictal activity
within the thalamocortical networks in generalized epilepsy
[36]. Although the role of connectivity between the CM and
brainstem/cerebellum in the antiepileptic effect of CM-DBS
still needs deeper evaluation, an indirect activation of motor
cortical and hippocampal regions through superomedial cer-
ebellar cortex seems possible [37]. Therefore, DBS efficacy
in pharmacoresistant patients may rely on the integrity of
both the cerebellum and the superior cerebellar peduncle
[38]. This, in turn, may explain the variability in the efficacy
of suppressing generalized seizures when stimulating the
cerebellar nuclei [38].
Despite the CM-pf complex is considered the major
source of direct input to the striatum [28], our functional
connectivity analysis depicted that direct connections to the
brainstem/cerebellum and sensorimotor cortices were dis-
criminative for the seizure improvement. In our patients,
only few fibers were seen connecting to the subthalamic
nucleus (supplementary Fig.3); this, however, does not
deny the involvement of the striatum in generalized seizures.
On the contrary, it suggests that it might have a differential
involvement for aberrant activity propagation and control
across neurological conditions, including Lennox-Gastaut
syndrome or patients with absence seizures [39], patients
with several comorbid psychiatric conditions (e.g., Tourette
syndrome) [40, 41], or in patients with specific gene muta-
tions (STXBP1 and SCN2A) as evidenced in animal models
of such conditions [42].
Study Limitations
While our study provides valuable information for the
detection of optimal targets for stimulation and the involved
network, it does not go without limitations. First, the small
sample size limits the generalizability of the study. Still, pre-
vious stimulation studies have not only used similar sample
sizes but also combined epilepsy or seizure types [7, 8, 43].
The relative low incidence and prevalence of generalized

Network Substrates ofCentromedian Nucleus Deep Brain Stimulation inGeneralized…
1 3
pharmacoresistant epilepsy [1] and the used strict inclu-
sion criteria make it difficult to increase the sample size
in the current study, but it, indeed, turns the current study
population into a unique opportunity to study the efficacy of
CM-DBS. In light of this, the results may be considered pre-
liminary, however, given the exhaustive and detailed inclu-
sion criteria of the patients, together with the high overlap
between the structural and functional connectivity, the latter
coming from a normative cohort (n=1000), the results are
expected to have good replicability in an independent, and
possibly larger, dataset.
Second, the co-registration technique of preoperative and
postoperative patient images could be an additional source of
methodological errors. To minimize methodological effects,
also related to MRI acquisitions, we used the procedures
implemented in a recently established advanced computa-
tional framework (Lead-DBS toolbox), including brain shift
correction, multispectral normalization, subcortical refine-
ment, and visual confirmation of the correct electrode place-
ment. Finally, by using diffusion tractography, it is possible
that we are reconstructing a high proportion of false-positive
connections, hence, limiting the in vivo characterization of
CM-DBS. However, the applied tractography method has
been shown to achieve the greatest valid fiber connections
among tractography algorithms [44]. Moreover, functional
connectivity analysis, using independent normative data of
1000 young individuals, depicted a very similar network
pattern. The congruency between structural and functional
results strongly suggests that the identified network could
play a key role in the efficacy of CM-DBS.
Since the current study only included patients diagnosed
with generalized epilepsy, we cannot ensure that the modu-
lated network is specific to generalized seizures. Nonethe-
less, the results evidence that the recruitment of the specific
CM-driven circuits mediates the anti-seizure effect and long-
term clinical outcomes of CM-DBS.
Even when no MRI evidence of anomalies affecting
CM-targeting exist, it is notorious that we cannot disregard
impact of structural anomalies on the connectivity profiles.
However, given the evidenced correspondence between nor-
mative and individual connectivity [45] and the high over-
lap between our structural and functional connectivity, we
can assume that the resulting networks to CM-DBS are not
greatly affected by structural anomalies.
There are additional concerns regarding surgical planning
and procedure. The DBS implantation procedure has not been
greatly improved in the last 20years [46]. This is besides
technological advancement in brain imaging techniques,
introduction of network reconstructions, and improvement
in target definition with probabilistic tractography [34], none
of which has yet reached the clinical routine. In our case,
the implantation was guided by consensus-coordinates and
electrophysiology, which has been proved highly reliable
among studies [34]. Finally, while it has been largely dif-
ficult to visualize the CM nucleus or CM-Pf complex using
standard MRI acquisitions, recent developments, including
the use of ultra-high MRI atlasing and advanced pipelines
[18, 21], currently allow for its examination in any dataset.
Conclusion
Bilateral CM-DBS delivers significant long-term improve-
ment in seizure frequency and quality of life in generalized
pharmacoresistant epilepsy. In these patients, DBS efficacy
relies on the connectivity of the CM to the brainstem and
cerebellum, as well as to the sensorimotor and premotor cor-
tices. Detailed knowledge of the disease-specific and CM-
DBS-modulated networks may be an independent predictor
of epilepsy patients who may benefit from DBS therapy. Fur-
ther, an improved targeting within the described networks
may enable the optimization of the neuromodulatory effects
of CM-DBS in epilepsy patients, opening up possibilities
to reduce stimulation-associated side effects or the num-
ber of non-responders. Our results evidence that a detailed
study of the brain network characteristics will enhance the
selection of optimal targets for stimulation among epilepsy
syndromes.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s13311- 021- 01057-y.
Required Author Forms Disclosure forms provided by the authors are
available with the online version of this article.
Author Contribution G.G-E., C.V.T., and S.G. conceptualized the
study. C.V.T., M.N.G., M.N.G., P.P.R., R.G.S., A.B., and L.V-Z. per-
formed patient recruitment, clinical assessment, and data acquisition.
G.G-E. and C.V.T. wrote the paper. G.G-E. performed data analyses,
literature search, and artwork production. D.C. and J.P. revised the
manuscript.
Funding Open Access funding enabled and organized by Pro-
jekt DEAL. This study was funded by the Ministry of Health FIS
(PI17/02193) and the Regional European Fonds of Development.
Data Availability Patient data, including MRI and DBS-MRI, used in
this study are not publicly available due to data privacy regulations
but are available for sharing with qualified investigators on reasonable
request.
Code Availability All codes used to analyze the dataset is openly avail-
able within Lead-DBS and Lead-DBS-Connectome software (https://
github. com/ leadd bs/ leadd bs).
Declarations
Ethics Approval This study was approved by the Clinical Neurophysiol-
ogy University Hospital La Princesa, Madrid (Spain), research ethics
committee (approval 7-04-20, acta CEIm 07/20, registro 4064). Writ-

C.V.Torres Diaz et al.
1 3
ten informed consent was obtained for all patients or from family and
caregivers.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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