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Frontal Lobe Connectivity and Network Community Characteristics are Associated with the Outcome of Subthalamic Nucleus Deep Brain Stimulation in Patients with Parkinson’s Disease


Abstract and Figures

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is nowadays an evidence-based state of the art therapy option for motor and non-motor symptoms in patients with Parkinson's disease (PD). However, the exact anatomical regions of the cerebral network that are targeted by STN-DBS have not been precisely described and no definitive pre-intervention predictors of the clinical response exist. In this study, we test the hypothesis that the clinical effectiveness of STN-DBS depends on the connectivity profile of the targeted brain networks. Therefore, we used diffusion-weighted imaging (DWI) and probabilistic tractography to reconstruct the anatomical networks and the graph theoretical framework to quantify the connectivity profile. DWI was obtained pre-operatively from 15 PD patients who underwent DBS (mean age = 67.87 ± 7.88, 11 males, H&Y score = 3.5 ± 0.8) using a 3T MRI scanner (Philips Achieva). The pre-operative connectivity properties of a network encompassing frontal, prefrontal cortex and cingulate gyrus were directly linked to the postoperative clinical outcome. Eccentricity as a topological-characteristic of the network defining how cerebral regions are embedded in relation to distant sites correlated inversely with the applied voltage at the active electrode for optimal clinical response. We found that network topology and pre-operative connectivity patterns have direct influence on the clinical response to DBS and may serve as important and independent predictors of the postoperative clinical outcome.
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Brain Topogr
DOI 10.1007/s10548-017-0597-4
Frontal Lobe Connectivity andNetwork Community
Characteristics areAssociated withtheOutcome ofSubthalamic
Nucleus Deep Brain Stimulation inPatients withParkinson’s
NabinKoirala1· VinzenzFleischer1· MartinGlaser2· KirstenE.Zeuner3·
GüntherDeuschl3· JensVolkmann4· MuthuramanMuthuraman1· SergiuGroppa1
Received: 2 May 2017 / Accepted: 26 September 2017
© Springer Science+Business Media, LLC 2017
distant sites correlated inversely with the applied voltage at
the active electrode for optimal clinical response. We found
that network topology and pre-operative connectivity pat-
terns have direct influence on the clinical response to DBS
and may serve as important and independent predictors of
the postoperative clinical outcome.
Keywords Parkinson’s disease· Deep brain stimulation·
Structural connectivity· Community structures· Network
AAL Automated anatomical labeling
AUC Area under the curve
BCT Brain connectivity toolbox
COG Center of gravity
DBS Deep brain stimulation
DWI Diffusion-weighted imaging
FWHM Full width at half maximum
H & Y Hoehn and Yahr
MED OFF/ON Medication off/on
MPRAGE Magnetization-prepared rapid
ROC Receiver operating characteristic
ROI Region of interest
SMA Supplementary motor area
STN Subthalamic nucleus
UPDRS Unified Parkinson’s disease rating scale
VTA Volume of tissue activation
Parkinson’s disease is one of the most common neurode-
generative diseases with no permanent cure. Deep brain
Abstract Deep brain stimulation (DBS) of the subthalamic
nucleus (STN) is nowadays an evidence-based state of the
art therapy option for motor and non-motor symptoms in
patients with Parkinson’s disease (PD). However, the exact
anatomical regions of the cerebral network that are targeted
by STN–DBS have not been precisely described and no
definitive pre-intervention predictors of the clinical response
exist. In this study, we test the hypothesis that the clini-
cal effectiveness of STN–DBS depends on the connectivity
profile of the targeted brain networks. Therefore, we used
diffusion-weighted imaging (DWI) and probabilistic tractog-
raphy to reconstruct the anatomical networks and the graph
theoretical framework to quantify the connectivity profile.
DWI was obtained pre-operatively from 15 PD patients who
underwent DBS (mean age = 67.87 ± 7.88, 11 males, H&Y
score = 3.5 ± 0.8) using a 3T MRI scanner (Philips Achieva).
The pre-operative connectivity properties of a network
encompassing frontal, prefrontal cortex and cingulate gyrus
were directly linked to the postoperative clinical outcome.
Eccentricity as a topological-characteristic of the network
defining how cerebral regions are embedded in relation to
Muthuraman Muthuraman and Sergiu Groppa have contributed
* Sergiu Groppa
1 Department ofNeurology, Johannes Gutenberg University,
55131Mainz, Germany
2 Department ofNeurosurgery, Johannes Gutenberg University,
55131Mainz, Germany
3 Department ofNeurology, University ofKiel, 24105Kiel,
4 Department ofNeurology, University ofWürzburg,
97080Würzburg, Germany
Brain Topogr
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stimulation of the subthalamic nucleus (STN–DBS) is cur-
rently a standard evidence-based treatment for PD patients
that substantially improves the motor and non-motor symp-
toms (Weaver etal. 2012; Odekerken etal. 2013; Klingel-
hoefer etal. 2014). Despite the clear clinical benefits, the
STN–DBS mechanisms remain unclear (Udupa and Chen
2015). Furthermore, the STN target and the activity within
it do not have an unequivocal justification for the clinical
response. Earlier studies presented evidence that the stimula-
tion might modulate the neuronal activity within the STN,
while recent studies showed that the DBS rather targets the
fibers entering, exiting or passing the stimulation region and
not just the STN itself (McIntyre and Hahn 2010; Nambu
and Chiken 2015). Moreover, the modulation of the patho-
logical oscillations in distinct brain networks might be a
critical feature of the DBS-induced clinical response (Stein
and Bar-Gad 2013; Brittain and Brown 2014). Studies on
primates and recent studies on humans attested the existence
of the so-called hyperdirect cortical STN projections which
might be of special importance for the effects of STN–DBS
(Brunenberg etal. 2012a; Haynes and Haber 2013).
Apart from the therapeutic benefit, DBS can cause severe
adverse effects. These effects might be long-term and com-
plex like the cognitive decline and neuro-psychiatric symp-
toms which occur with STN–DBS (Fukaya and Yamamoto
2015). Hence, the understanding of the local and systemic
interactions, together with an exact description of the tar-
geted cerebral network would markedly advance our thera-
peutic strategies and improve the efficacy and reliability and
reduce the side effects of actual DBS protocols by refin-
ing the patient selection and developing improved targeting
We hypothesize that the preoperative connectivity pattern
of the interconnected regions is closely related to the clini-
cal effectiveness of STN–DBS. Therefore, we use diffusion-
weighted imaging (DWI) and probabilistic tractography to
reconstruct the anatomical networks and the graph theoreti-
cal framework to quantify their connectivity profile. Net-
work topology properties defined within graph theoretical
analyses have become important measurable characteristics
that explain healthy brain dynamics and help to understand
and model brain disorders (Bullmore and Sporns 2009). The
study of network characteristics in PD using graph theory
in structural MRI has revealed specific global and local
functional network topological changes characterized by a
decrease in clustering and path length between the nodes in
comparison to healthy controls (Olde Dubbelink etal. 2014).
Additionally, structural network analysis using cortical and
subcortical anatomical measurements has shown larger
characteristic path length and reduced global efficiency in
addition to a lower regional efficiency in frontal and parietal
regions for mild cognitive impaired PD patients compared
to healthy controls. This provides support for the role of
aberrant network topology in motor and cognitive impair-
ment in patients with early PD (Pereira etal. 2015). In this
study, we link brain network properties with the postopera-
tive clinical outcome to test the hypothesis that the clinical
response to STN–DBS depends on the connectivity pattern
of the interconnected brain regions and stimulation site.
Subjects andData Acquisitions
In this study, fifteen patients (4 females and 11 males) with
idiopathic PD without dementia and receiving DBS treat-
ment were selected with a mean age of 67.87 ± 7.88years.
For all patients, a high resolution T1-image of the brain
using MPRAGE sequence (TR = 7.7ms, TE = 3.6ms, flip
angle = 8°, 160 slices) was obtained with a 3T MR-Scan-
ner (Philips Achieva) using an 8-channel SENSE head
coil before the DBS surgery. DWI of the whole brain at
2mm isometric voxel resolution covering a field of view of
224 × 224mm was obtained. We recorded three acquisitions
of DWI sequences encompassing 32 gradient directions and
five b0 (no diffusion weighting) images for each acquisi-
tion (b value = 1000s/mm2, TE = 59 ms, TR = 11,855ms,
fat saturation “on”, 60 contiguous slices). The total acquisi-
tion time for the whole protocol was 35min which included
24min (3 × 8min) for DWI sequences. On the first postop-
erative day a further MRI acquisition was performed on a
1.5T scanner (Philips Achieva, Philips Medical Systems,
Best, The Netherlands) with a protocol consisting of a
T1-weighted structural image of the whole brain using a
standard MPRAGE sequence (TR = 10.7ms, TE = 1.96ms,
flip angle = 8°).
Stimulation Parameters
The surgical procedure is previously explained in detail
elsewhere (Groppa etal. 2014). All patients were implanted
with bilateral STN electrodes (model 3389 DBS, Medtronic)
and pulse generators (Activa® PC, biphase stimulation)
with a pulse setting of 60µs at 130Hz. DBS electrode volt-
age is the stimulation intensity at the active electrode as
expressed in Volts for the optimal clinical response. The
voltage was adjusted for each individual patient while the
remaining stimulation parameters (pulse width and stimu-
lation frequency) were left unchanged. The stimulation
adjustment was performed by clinicians who were blinded
to the hypothesis and goals of this study, and values from the
follow-up at 3months after the implantation were included
in the analysis. We choose this time point since no more
impedance changes occur at this post-operative period and
voltage values remain constant. The medical treatment was
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individualized after DBS according to the clinical indication.
The study protocol used was approved by the local ethics
committee and all patients signed a written consent regard-
ing the procedure. The clinical details are shown in Table1.
Data Analysis andNetwork Reconstruction
The obtained images were preprocessed using inbuilt func-
tionality in FSL described in detail elsewhere (Jenkinson
and Smith 2001; Jenkinson etal. 2002; Johansen-Berg
etal. 2004; Behrens etal. 2007). In brief, susceptibility and
motion artefacts correction and diffusion tensor modelling
were performed using the diffusion toolbox (topup and FDT,
part of FSL). Crossing fibers distribution was estimated
using BEDPOSTX (implemented in FSL) and the probabil-
ity of major (f1) and secondary (f2) fiber directions were cal-
culated. All images were aligned and affine-transformed into
the stereotactic space MNI-152. A multi-fiber model was
fit to the diffusion data at each voxel, allowing for tracing
of fibers through regions of crossing or complexity. Here,
we drew 5000 streamline samples from each seed voxel to
form an estimate of the probability distribution of connec-
tions from each seed voxel. When these streamlines reach
a voxel in which more than one direction is estimated, they
follow the direction that is closest to the direction at which
the streamline arrives. Tracts generated are volumes wherein
values at each voxel represent the number of samples (or
streamlines) that passed through that voxel. Here each tract
from every seed mask in the atlas is repeatedly sampled and
only those tracts which passed through at least one other
seed mask were retained. The obtained streamlines were
then used to build the connectivity matrix. For the elimina-
tion of spurious connections, tractography in individual sub-
jects was thresholded to include only voxels through which
at least 10 percent of all streamline samples had passed.
A connectivity matrix was obtained using the seed masks
for 116 regions of interest (ROI) defined by the Automated
Anatomical Labeling atlas (Tzourio-Mazoyer etal. 2002)
for each subject (Fig.1). The links or the entries in the con-
nectivity matrix represent the ratio of number of samples
(or streamlines) that passes through ROI (j) to all generated
streamlines from ROI (i). This weighted connectivity index
between ROIs in the matrix was then analyzed using various
network measures (both global and local) obtained via Brain
Connectivity Toolbox (Rubinov and Sporns 2010) (https://
Community Analysis
In the reconstructed network we aimed to detect intercon-
nected regions forming functionally relevant entities such
as modules. Modules are groups of nodes that have more
connections within themselves than expected in a randomly
sampled group of nodes (Meunier etal. 2009). Module
detection performs a partitioning of the brain into entities
with higher within, than between module correlations (Gir-
van and Newman 2002; Newman 2006). The modules were
identified using the Louvain modularity algorithm as imple-
mented in BCT (Blondel etal. 2008) for each individual sub-
ject connectivity matrix. We performed 5000 iterations with
the Louvain algorithm and the assignment of each region to
a particular module was based on the maximum number of
times/iterations a region was assigned to a module (Ritchey
etal. 2014).
Random Network Formation andComparison
The network measures (see below) were computed in the
obtained modules. To depict neurobiologically meaningful
network properties we compared the values obtained from
each module with values from generated random networks.
The random network was formed by weight reshuffling tech-
nique (Opsahl etal. 2008) for each individual subject. The
procedure reshuffles the weights globally in the network but
maintains the topology of the observed network (also for
each module) and permits a distinct delineation to the devel-
opment of neurodegeneration that leads to a loss of distinct
topology patterns in patients.
Network Measures
In this study we assessed three network measures: charac-
teristic path length, eccentricity and global efficiency. For
a given network, these measures can characterize the effi-
ciency of information transfer at different levels and reveal
the importance of distinct regions within the module.
Characteristic path length is the average shortest dis-
tance between the regions in the network and is a measure
of global integration (Stam etal. 2009). Global efficiency
has been introduced as a network integration parameter to
Table 1 Clinical detail. Clinical parameters assessed before and after
the DBS surgery
Here SD is the standard deviation
Parkinson’s disease patients (n = 15) mean ± SD
Sex (male/female) 11/4
Age (years) 67.8 ± 7.8
Disease duration (years) 13.6 ± 6.5
Preoperative Hoehn–Yahr stage (Med OFF) 3.8 ± 0.8
Preoperative Hoehn–Yahr stage (Med ON) 2.7 ± 0.5
Preoperative UPDRS III (Med OFF) 34.5 ± 8.4
Preoperative UPDRS III (Med ON) 17.4 ± 9.0
Preoperative dose of levodopa or equivalent (mg/day) 827 ± 397.0
Postoperative dose of levodopa or equivalent (mg/day) 335 ± 202.6
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describe information flow over the entire network and is
computed as the average inverse shortest path length. Even
though global efficiency and characteristic path length are
very similar measures, here we selected both as charac-
teristic path length could possibly be more influenced by
disconnected or remote nodes, while global efficiency is
more robust to such extremes (Latora and Marchiori 2001).
Eccentricity is defined as the maximal shortest path length
between any two regions in the network and is a measure of
relative nodal importance (Sporns 2003).
Predicting Anatomical Regions intheNetworks
To establish the role of network modules and distinct ana-
tomical regions within the module, we designed a receiver
operating characteristic (ROC) curve-based analysis. There-
fore, we tested how the connectivity profile of each region
is ranked in comparison to others by comparing the areas
under the curve (AUC). The ROC curves were obtained
using the eccentricity values of the analyzed module and
compared against values from the same module obtained
from the random network. Moreover, for the selection of
these involved modules, only those modules with an AUC
above the threshold of no discrimination (i.e. greater than
0.5), and having a significantly (one sided t-test, p < 0.05)
higher network measures value than in the generated random
networks were considered. This approach ascertained the
modules and regions within the module, for each network
measure, with the strongest association to the outcome vari-
able (i.e. improvement in the motor UPDRS score or DBS
voltage) and having preserved network topology that dif-
fers from random networks. Furthermore in this study, we
concatenated the side-specific and axial values of UPDRSIII
value to obtain a complete evaluation of the clinical out-
come. Since the significant areas were not side specific and
we aimed to develop universal measures that can be imple-
mented in the clinical practice without a priori bias of handi-
ness and laterality of the symptoms, we opted to perform
whole brain analysis.
Determination ofContact‑Specific Masks
Electrode positions and electrode trajectory were determined
using post-operation T1 images. The detailed procedure is
explained elsewhere (Witt etal. 2013). Briefly, the lead
was mathematically modelled by a straight line along the
Fig. 1 Overview of methods. a Preoperative DWI obtained from 15
Parkinson’s patients. b Probabilistic tractography was used to obtain
the connectivity matrix using 116 ROIs as depicted in c. d Illus-
tration of DBS electrodes in STN which was performed in all 15
patients. e The connectivity matrix with the probability from a seed
region to another region. f The random network connectivity matrix
obtained by weight reshuffling of the obtained matrix. g Subdivision
of the whole brain into various modules using the Louvain algorithm
(gamma = 2) represented by different colors. h Visualization of the
network comparison performed between the random network and the
obtained network indicating various network analyses performed
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electrode trajectory. Based on the optimized lead position
the T1 intensity profile was extracted along the trajectory
and the electrode contact positions were then determined
by observing the intensity dip apparent in the extracted
intensity profile. Geometrically determined electrode con-
tact positions were used to create spatially Gaussian weight-
ing masks (Fig.2). Gaussian weights were determined by
specifying the following two standard deviations: (i) along
the lead to model contact dimensions, known from manu-
facturer’s annotations; (ii) from the two other orthogonal
directions to model the stimulation depth. We restricted our
analysis to one different mask extension: a Gaussian shape
with 2 standard deviations along the lead and 2 standard
deviations in depth (corresponds to an isometric mask with
4.7mm full width at half maximum (FWHM), correspond-
ing to a radius of ca. 2.35mm). The multivariate Gaussians
were centered at the exact contact positions. These volumes
were selected considering existing literature that attest that
neural elements up to a distance of 2mm from the active
contact might be reached by the studied DBS stimulation
settings (Ranck 1975). The target coordinates for STN were
defined relative to the midpoint of the anterior and posterior
commissure [mid AC PC] on the T1 MPRAGE images used
for stereotactic planning. To assess if the position of the
electrodes in each patient has an effect on voltage applied or
the post-operative UPDRS, we computed the geodesic dis-
tance of each calculated center of gravity (COG) of the gen-
erated electrode volume of tissue activation (VTA) masks
from the obtained STN position and stereotactic target and
hence correlated it with the DBS parameters (collective
UPDRSIII values and DBS voltage).
Statistical Analysis
The significant difference between the values obtained
for each module in the analyzed network and the random
network generated was assessed by performing a t-test
(p < 0.05). The correlations between the obtained network
measures and DBS clinical parameters were obtained using
Pearson’s correlation coefficient. To ascertain the signifi-
cance of the correlation obtained, further leave-one-out
analysis was performed. Finally, ROC curve analysis was
performed to compare the connectivity profile of the mod-
ules with strongest association to the clinical outcome. All
of the statistical analyses were performed using MATLAB
(ver. 2013, Mathworks, Inc.).
Clinical Assessment
We included 15 patients with PD who were selected for
STN–DBS therapy. The UPDRSIII scores improved after
DBS–STN in both MED OFF [Before: 34.46 ± 8.35, After:
13.86 ± 5.99 (t = 7.75, p < 0.001)] and MED ON state
[Before: 17.4 ± 9.03, After: 11.54 ± 5.77 (t = 2.09, p < 0.05)].
The H & Y scale scores improved after DBS–STN in MED
OFF state from 3.8 ± 0.78 to 2.7 ± 0.48 (t = 3.76, p < 0.01),
and in MED ON state from 2.75 ± 0.54 to 1.9 ± 0.51
(t = 3.59, p < 0.01).
Community Detection
Community detection using the Louvain algorithm
depicted 10 modules encompassing the regions shown in
Fig.3. The ROC curve analysis indicated that 6 of these
modules had an AUC significantly above the threshold
of no-discrimination (Table2). Significant differences in
the analyzed network measures in comparison with the
generated random networks were attested in two mod-
ules: Left-frontal and Central, signifying cerebral regions
with preserved anatomical architecture. Left-frontal com-
prises primarily the prefrontal cortical regions of the left
Fig. 2 DBS electrode
VTA masks. The Gauss-
ian weighted masks overlaid
in MNI152-1mm standard
template for all patients. ‘L’ and
R’ represent the left and right
hemisphere respectively
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hemisphere (7 regions of AAL atlas) while module Cen-
tral chiefly includes the frontal lobe and premotor corti-
cal regions comprising 13 regions of the AAL atlas (see
Table2 for detail).
Network Properties andPostoperative Outcome
In the depicted modules, the network parameters eccentric-
ity, characteristic path length and global efficiency showed
Fig. 3 Modules visualization.
The whole brain subdivided
into various modules using the
Louvain algorithm (gamma = 2)
represented by different colors.
Anatomical annotations are
presented in Table2
Table 2 Modules and ROIs.
Regions of Interest (ROIs) from
AAL atlas assigned to the 10
modules obtained using the
Louvain algorithm as visualized
in Fig.2
1. Left frontal 4. Central Putamen_R
Precentral_L Supp_Motor_Area_L Pallidum_R
Frontal_Sup_L Supp_Motor_Area_R Thalamus_R
Frontal_Mid_L Cingulum_Mid_L Heschl_L
Frontal_Inf_Oper_L Cingulum_Mid_R Temporal_Sup_L
Frontal_Inf_Tri_L Cingulum_Post_L Temporal_Mid_L
Frontal_Sup_Medial_L Cingulum_Post_R 8. Left parieto-temporal
Postcentral_L Calcarine_L Hippocampus_L
2. Right frontal Cuneus_L ParaHippocampal_L
Precentral_R Occipital_Sup_L Amygdala_L
Frontal_Sup_R Precuneus_L Lingual_L
Frontal_Mid_R Precuneus_R Occipital_Inf_L
Frontal_Inf_Oper_R Paracentral_Lobule_L Occipital_Inf_R
Frontal_Inf_Tri_R Paracentral_Lobule_R Fusiform_L
Frontal_Sup_Medial_R 5. Occipital Postcentral_R
3.Frontal-subcortical Calcarine_R Parietal_Sup_R
Frontal_Sup_Orb_L Cuneus_R Parietal_Inf_R
Frontal_Mid_Orb_L Occipital_Sup_R Angular_R
Frontal_Inf_Orb_L Occipital_Mid_R Heschl_R
Rolandic_Oper_L 6. Frontoorbital Temporal_Sup_R
Olfactory_L Frontal_Sup_Orb_R Temporal_Pole_Sup_L
Olfactory_R Frontal_Mid_Orb_R Temporal_Mid_R
Frontal_Mid_Orb_L Frontal_Inf_Orb_R Temporal_Pole_Mid_L
Frontal_Mid_Orb_R 7. Right parieto-temporal Temporal_Inf_L
Rectus_L Rolandic_Oper_R 9. Temporal
Rectus_R Insula_R Temporal_Inf_R
Insula_L Occipital_Mid_L Hippocampus_R
Cingulum_Ant_L Parietal_Sup_L ParaHippocampal_R
Cingulum_Ant_R Parietal_Inf_L Amygdala_R
Caudate_L SupraMarginal_L Lingual_R
Putamen_L SupraMarginal_R Fusiform_R
Pallidum_L Angular_L Temporal_Pole_Sup_R
Thalamus_L Caudate_R Temporal_Pole_Mid_R
10. Cerebellum
Vermis and Cerebellum regions in module
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an association with both the postoperative stimulation
parameter (DBS voltage) for an optimal clinical response
and the motor score (UPDRSIII). The computation of net-
work metrics over the entire network did not predict the
clinical outcome after DBS. In the modules with significant
differences compared to the random network (Left-frontal
and Central), eccentricity showed a significant interdepend-
ency with the DBS voltage (Left-frontal: r = − 0.36, p < 0.05;
Central: r = 0.39, p < 0.05). Eccentricity values from the
Left-frontal and Central modules also correlated signifi-
cantly with the postoperative motor outcome as defined from
UPDRSIII (module Left-frontal: r = 0.47; Central: r = 0.45,
both p < 0.05) affirming the impact on the clinical state.
To determine the role of distinct anatomical regions
within a module, we compared the AUC from the ROC
analysis of eccentricity values from different areas within the
module. In the module Left-frontal, we determined that the
connectivity pattern from the medial superior frontal gyrus,
primarily involved in executive functions, the dorsolateral
superior frontal gyrus, and the opercular part of the inferior
frontal gyrus could be used as classifiers to assess predictive
model accuracy (Fig.4).
In the module Central, the eccentricity in the frontal
cortex and regions of the limbic network represented ana-
tomical areas within the module with the best accuracy for
effect delineation. In this module, the eccentricity values
from the SMA, posterior cingulate gyrus, median cingu-
late and paracingulate gyrus were predictors for the opti-
mal modeling accuracy (Fig.5).
The correlation between the position of the electrode
VTA mask and voltage or postoperative UPDRSIII was
not significant (p > 0.1). Furthermore, the permutation test
(randomise tool in FSL) performed using the VTA masks
and voltage and postoperative UPDRSIII also did not yield
any significant (i.e. p < 0.05, corrected) clusters.
The proposed brain connectivity analysis and commu-
nity detection methods revealed a significant association
between the level of modular topology and connectiv-
ity in the frontal cortex and postoperative outcome after
STN–DBS in PD patients. Patients with a more efficient
(less degraded) network topology in the frontal cortex
(with higher eccentricity in comparison with random
network) would need less voltage to achieve the optimal
motor response. MRI-derived connectivity measures might
serve as important apparative and examiner independent
predictors for the clinical DBS-outcome and might be used
to optimize patient selection.
Fig. 4 Networks comparison in module ‘Left frontal’. Figure in
upper left shows the brain mesh and the included brain regions
(spheres). Yellow lines indicate the weighted connectivity between
them. Upper right: The histogram shows the difference in the net-
work parameters mean with standard deviation from eccentricity (E),
characteristic path length (λ) and global efficiency (G) of PD in com-
parison to random networks. The asterisk represents the statistically
significant difference observed (p < 0.05). Lower left: ROC curves
obtained from the regions within the module. The sensitivity–speci-
ficity plot was obtained from the analysis between the network meas-
ures from PD and random network. The highlighted lines show the
regions in this module with the highest AUC. The plot on the lower
right shows the correlation between eccentricity and DBS voltage in
this module
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Network Structure Detection inPatients forSTN–DBS
Brain functionality can be characterized by local interactions
and a global integration in modules for specific brain func-
tions (Park and Friston 2013). The modular and hierarchi-
cal organization to support the effectivity of brain functions
has already been established non-invasively in humans as
derived from structural and functional MRI network analyses
(Sporns and Zwi 2004; van den Heuvel and Sporns 2011).
It is obvious from the complex layered, modular and hierar-
chical cerebral organization that high frequency stimulation
does not only affect the STN itself, but also distant areas.
Furthermore, the complex interaction with the PD pathology
is not only focused on substantia nigra or STN regions but
encompasses an altered connectivity between interconnected
regions comprising prefrontal, frontal and limbic regions.
To improve actual and future therapeutic strategies an exact
understanding of the complex interplay of PD pathology
and local and global effects of actual therapy strategies i.e.
STN–DBS is needed (Canu etal. 2015). Hence, the pro-
posed community and modular structures analysis has the
highly promising possibility of the exact characterisation of
the interconnected networks that are reached by STN–DBS.
The revealed modules encompass prefrontal, frontal
(SMA) and limbic cortex (cingulate and paracingulate
cortex). The described network of prefrontal and fron-
tal regions is mainly involved in executive functions and
is impaired early in the disease course in PD patients
(Nagano-Saito etal. 2005; Amboni etal. 2008; Koshimori
etal. 2015). Direct connections of STN to these regions
have been described (Brunenberg etal. 2012b). Two recent
studies have shown the importance of these connections
for STN–DBS efficiency, in Parkinsonian rats (Li etal.
2012) showed that STN–DBS activates the layer V neu-
rons in the motor cortex, which contributes to the disrup-
tion of abnormal neural activities, while (Gradinaru etal.
2009) demonstrated a direct and therapeutically beneficial
activation of motor and premotor areas with STN stimula-
tion using optogenetics and solid-state optics. In humans,
only indirect intimations regarding the distributed effects
of DBS exist. Grey matter cortical thickness analysis on
patients with PD has shown widespread cortical thinning
in frontal and premotor regions in comparison to healthy
controls (Pereira etal. 2012). In our previous study (Muth-
uraman etal. 2017), we found that the cortical integrity
is affected mainly in frontal lobe (paracentral area and
superior frontal region) and is correlated with DBS param-
eters and clinical outcome. Furthermore, a recent study has
shown that the modulation of white matter tracts directed
to the superior frontal gyrus and the thalamus is associated
Fig. 5 Networks comparison in module ‘Central’. Figure in upper
left shows the brain mesh an the included brain regions (spheres).
Yellow lines indicate the weighted connectivity between them. Upper
right: The histogram shows the difference in the network parameters
mean with standard deviation from eccentricity (E), characteristic
path length (λ) and global efficiency (G) of PD in comparison to ran-
dom networks. The asterisk represents the statistically significant dif-
ference observed (p < 0.05). Lower left: ROC curves obtained from
the regions within the module. The sensitivity–specificity plot was
obtained from the analysis between the network measures from PD
and random network. The highlighted lines show the regions in this
module with the highest AUC. The plot on the lower right shows the
correlation between eccentricity and DBS voltage in this module
Brain Topogr
1 3
with favorable clinical outcomes and may contribute to the
therapeutic effects of STN–DBS (Vanegas-Arroyave etal.
2016). Our data demonstrates the role of the connectivity
pattern in the frontal and central regions for the STN–DBS
clinical response in patients with PD and presents a first
and direct translational proof of the description of the tar-
geted networks from animal models.
Anatomical Regions ofRelevance forSTN–DBS
Eccentricity is computed as the maximal shortest distance
between two nodes (in this case two brain regions of inter-
est). It is a measure which shows how reachable a brain
region is from others in a specific module (Pavlopoulos
etal. 2011). A module with higher eccentricity values
has an architecture that permits a more efficient informa-
tion transfer to other regions; in this way, regions exert
a stronger influence over other ones and interact more
efficiently. In this study, we demonstrate that eccentricity
values in modules encompassing prefrontal, frontal and
limbic cortex are negatively correlated with the DBS-
electrode voltage. The location of the electrode contact
in STN has been shown in previous studies to be directly
associated with the improvements in the cardinal symp-
toms (tremor, rigidity etc.); with the best clinical outcome
achieved by stimulation in the dorsolateral motor part of
the STN (Voges etal. 2002; Herzog etal. 2004). In our
previous study, we also showed that the precise targeting
of the lateral region of the STN is essential for achiev-
ing sufficient stimulation efficacy (Wodarg etal. 2012).
Although it is possible to identify the STN using MRI
in image-guided surgery, it is still not feasible with the
post-operative low resolution MRI or T1—MPRAGE or
DWI sequences used in this study. The placement of the
lead and choice of active electrode in the STN holds sig-
nificant importance for the postoperative outcome. These
could be tested with available probabilistic STN atlases
however this would also be an approximation to the entire
STN region and not only the focused sensori-motor part.
Therefore we opted to calculate the COG of the registered
VTA in order to confirm only minimal differences in the
location of the active electrodes. In our study, the location
of the active contacts for DBS stimulation for the patient
group was not linked to the collective UPDRSIII values or
the applied voltage for the optimal postoperative outcome.
This could be due to the minimal variation of the location
of the active contacts between the subjects. Even though
the computation of the VTA is the gold standard for the
computation of the targeted neural tissue in DBS studies
(McIntyre etal. 2004; Butson etal. 2007), with image
processing and registration errors, further interpolations
of absolute distances occur which is one limitation of the
method and hence this study.
Predictive Effects ofNetwork Degeneration
andComparison toRandom Architecture
The human brain exhibits small-world topology, a favora-
ble property for efficient information transfer (Stam 2004;
Achard and Bullmore 2007). In addition, this topology is
associated with improved cognitive and motor performance
(Micheloyannis etal. 2006; van den Heuvel etal. 2009).
Neurodegenerative disorders cause disturbances in the opti-
mal organization of brain function and increase randomness
in the network (van Straaten and Stam 2013). Functional net-
works in Alzheimer’s disease lose their normal small-world
structure and regress towards a more random architecture
(Stam etal. 2007). The loss of overall functional connec-
tivity and small-world properties with increased random-
ness of the network was also shown for individuals with
schizophrenia (Liu etal. 2008; Yu etal. 2011). A reduced
network efficiency has been shown in the functional network
in PD patients by the aid of functional MRI (Skidmore etal.
2011). In our study, we observed that in modules encom-
passing frontal and central cortical areas, the eccentricity
values in patients with PD were higher than that of the ran-
dom network indicating the existence of small-world and
hierarchical topology in those modules. The network reor-
ganization with neurodegeneration toward architecture with
less small-world properties and increased randomness moti-
vated us to depict modules with highly organized topology
in comparison to random networks. Hence, we observed that
the effectiveness of STN–DBS is only depicted in modules
where the network topology is preserved and hierarchical
characteristics exist.
The performed network analysis revealed that connectivity
properties of a network in the frontal and central regions are
closely linked to the postoperative clinical outcome follow-
ing STN–DBS. Eccentricity as a topological-characteristic
of the network defining how cerebral regions are embedded
in relation to distant sites shows a clear association between
structural architecture and clinical outcome to functional
DBS neuromodulation in PD. The implementation of con-
nectivity profile analysis into the clinical setting might be an
important tool to help define and improve the postoperative
Acknowledgements This work was supported by the German
Research Foundation (DFG; CRC-TR-128).
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... Specifically, the authors showed that the postoperative outcome of STN-DBS is strongly associated with active stimulation of contacts connected to the primary cortex and supplementary motor area and that can be individually defined. In addition, both DTI profile and network-based connectivity have served as a preoperative predictor for postoperative outcome (Koirala et al., 2018;Gonzalez-Escamilla et al., 2022) or a targeted indicator to trace the therapeutic effect (Strotzer et al., 2019;Huang et al., 2022). ...
... Moreover, identifying the network topology and connectivity using DTI and probabilistic tractography before traditional DBS surgery directly influences PD patients' response to DBS and may serve as significant predictors of the DBS clinical outcome (Koirala et al., 2018). Finally, structural changes, detected by DTI, are not only linked to motor but also cognitive symptoms. ...
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Parkinson’s disease (PD) is the second most common age-related neurodegenerative disease with cardinal motor symptoms. In addition to motor symptoms, PD is a heterogeneous disease accompanied by many non-motor symptoms that dominate the clinical manifestations in different stages or subtypes of PD, such as cognitive impairments. The heterogeneity of PD suggests widespread brain structural changes, and axonal involvement appears to be critical to the pathophysiology of PD. As α-synuclein pathology has been suggested to cause axonal changes followed by neuronal degeneration, diffusion tensor imaging (DTI) as an in vivo imaging technique emerges to characterize early detectable white matter changes due to PD. Here, we reviewed the past 5-year literature to show how DTI has helped identify axonal abnormalities at different PD stages or in different PD subtypes and atypical parkinsonism. We also showed the recent clinical utilities of DTI tractography in interventional treatments such as deep brain stimulation (DBS). Mounting evidence supported by multisite DTI data suggests that DTI along with the advanced analytic methods, can delineate dynamic pathophysiological processes from the early to late PD stages and differentiate distinct structural networks affected in PD and other parkinsonism syndromes. It indicates that DTI, along with recent advanced analytic methods, can assist future interventional studies in optimizing treatments for PD patients with different clinical conditions and risk profiles.
... The final alliance of each region of interest (ROI) to a particular community was based on the maximum number of times-by-iteration the region was assigned to a community. 55,56 During this process, the resolution parameter (γ) was varied (1 to 2.5, in steps of 0.05) to identify a stable and topologically relevant distribution of ROIs in each module. Multi-layer modularity maximization depends upon two free parameters, namely the structural resolution parameter, γ, which determines the size of communities: smaller or larger values of γ result in 57,58 ). ...
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... (2) Anterior cingulate gyrus, an important cortical component of the motor circuit responsible for various movement disorders 23,54 . Probabilistic tractography studies showed that the cingulate gyrus was composed of the anatomical network with the frontal and prefrontal cortex, whose dysfunction may alter the connectivity between the STN and frontal lobe 55 . ...
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While the efficacy of deep brain stimulation (DBS) is well-established in Parkinson's Disease (PD), the benefit of DBS varies across patients. Using imaging features for outcome prediction offers potential in improving effectiveness, whereas the value of presurgical brain morphometry, derived from the routinely used imaging modality in surgical planning, remains under-explored. This review provides a comprehensive investigation of links between DBS outcomes and brain morphometry features in PD. We systematically searched PubMed and Embase databases and retrieved 793 articles, of which 25 met inclusion criteria and were reviewed in detail. A majority of studies (24/25), including 1253 of 1316 patients, focused on the outcome of DBS targeting the subthalamic nucleus (STN), while five studies included 57 patients receiving globus pallidus internus (GPi) DBS. Accumulated evidence showed that the atrophy of motor cortex and thalamus were associated with poor motor improvement, other structures such as the lateral-occipital cortex and anterior cingulate were also reported to correlated with motor outcome. Regarding non-motor outcomes, decreased volume of the hippocampus was reported to correlate with poor cognitive outcomes. Structures such as the thalamus, nucleus accumbens, and nucleus of basalis of Meynert were also reported to correlate with cognitive functions. Caudal middle frontal cortex was reported to have an impact on postsurgical psychiatric changes. Collectively, the findings of this review emphasize the utility of brain morphometry in outcome prediction of DBS for PD. Future efforts are needed to validate the findings and demonstrate the feasibility of brain morphometry in larger cohorts.
... There exists an additional pathway that plays a significant role in oscillating between direct and indirect pathways and is critical to this dynamic balance between these pathways and behavioral flexibility. This is termed the hyperdirect pathway and it originates from the right cerebral hemisphere alone (Koirala et al., 2018;Chen et al., 2020). There are two regions of the right hemisphere that are the points of origin of the hyperdirect pathway which specifically activates the indirect pathway at the caudate and putamen and specifically connects to the subthalamic nucleus of Luys, the main source of the indirect pathway's effect (Chen et al., 2020;Temiz et al., 2020). ...
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... Moreover, in PD patients, cortical atrophy in the frontal cortex and the other regions used in the current study is accelerated already in the early stages of disease [25], and is also associated with the clinical status of motor symptoms [63], PD degeneration [64,65], disease progression [66], nonmotor symptoms such as cognitive impairment [67], and DBS outcomes [51]. Concordantly, it was more recently shown that the clinical effectiveness of STN-DBS is strongly associated with the WM network connectivity profile of the frontal, prefrontal, and cingulate cortices [68]. Our data take these findings a step further, into a new framework that stresses not only the role of the microstructural integrity WM pathways but also the anatomical integrity of the regions belonging to the motor network for optimal improvement after STN-DBS. ...
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IntroductionDeep brain stimulation of the subthalamic nucleus (STN-DBS) is an established therapy for Parkinson's disease (PD). However, a more detailed characterization of the targeted network and its grey matter (GM) terminals that drive the clinical outcome is needed. In this direction, the use of MRI after DBS surgery is now possible due to recent advances in hardware, opening a window for the clarification of the association between the affected tissue, including white matter fiber pathways and modulated GM regions, and the DBS-related clinical outcome. Therefore, we present a computational framework for reconstruction of targeted networks on postoperative MRI.Methods We used a combination of preoperative whole-brain T1-weighted (T1w) and diffusion-weighted MRI data for morphometric integrity assessment and postoperative T1w MRI for electrode reconstruction and network reconstruction in 15 idiopathic PD patients. Within this framework, we made use of DBS lead artifact intensity profiles on postoperative MRI to determine DBS locations used as seeds for probabilistic tractography to cortical and subcortical targets within the motor circuitry. Lastly, we evaluated the relationship between brain microstructural characteristics of DBS-targeted brain network terminals and postoperative clinical outcomes.ResultsThe proposed framework showed robust performance for identifying the DBS electrode positions. Connectivity profiles between the primary motor cortex (M1), supplementary motor area (SMA), and DBS locations were strongly associated with the stimulation intensity needed for the optimal clinical outcome. Local diffusion properties of the modulated pathways were related to DBS outcomes. STN-DBS motor symptom improvement was highly associated with cortical thickness in the middle frontal and superior frontal cortices, but not with subcortical volumetry.Conclusion These data suggest that STN-DBS outcomes largely rely on the modulatory interference from cortical areas, particularly M1 and SMA, to DBS locations.
... In addition, the distribution of crossing fibers was estimated using BEDPOSTX (T. Behrens, Berg, Jbabdi, Rushworth, & Woolrich, 2007; T. E. Behrens et al., 2003), and the probability of major (f1) and secondary (f2) fiber directions was calculated (Koirala et al., 2017;Koirala et al., 2019). The obtained crossing fiber modeled diffusion data were further processed using the automatic tractography scheme using XTRACT toolbox in FSL (Warrington et al., 2020). ...
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Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large-scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5–17 years of age) from six different centers. Six data quality metrics—contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)—and four diffusion measures—fractional anisotropy, mean diffusivity, tract density, and length—were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between-site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group-wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.
... Difficulties in the evaluation of dysexecutive disorders may stem from the difficulty in defining frontal regional specialisation of function (Burgess 1997;Duncan and Owen 2000), although some functional fractionation of frontal regions seems possible (Stuss 2011a). More generally, they result from the functional consequences of connectivity (Koirala et al. 2018). and the inherent complexity of function emerging from collective non-local brain activity (Freeman and Kozma 2000;Pillai and Jirsa 2017;Papo 2019). ...
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Existing neuropsychological tests of executive function often manifest a difficulty pinpointing cognitive deficits when these are intermittent and come in the form of omissions. We discuss the hypothesis that two partially interrelated reasons for this failure stem from relative inability of neuropsychological tests to explore the cognitive space and to explicitly take into account strategic and opportunistic resource allocation decisions, and to address the temporal aspects of both behaviour and task-related brain function in data analysis. Criteria for tasks suitable for neuropsychological assessment of executive function, as well as appropriate ways to analyse and interpret observed behavioural data are suggested. It is proposed that experimental tasks should be devised which emphasize typical rather than optimal performance, and that analyses should quantify path-dependent fluctuations in performance levels rather than averaged behaviour. Some implications for experimental neuropsychology are illustrated for the case of planning and problem-solving abilities and with particular reference to cognitive impairment in closed-head injury.
... ( Bastiani et al., 2019 ). These data were then processed using inbuilt functionality and different toolboxes in FSL described in detail elsewhere ( Behrens et al., 2007 ;Jenkinson and Smith, 2001 ;Koirala et al., 2018 ). In brief, the data was preprocessed for artefact correction (susceptibility, eddy currents and head movements) and individual masks were generated for each brain using the Brain Extraction Toolkit (BET) to isolate the brain from the skull. ...
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Background Studies exploring neuroanatomic correlates of reading have associated white matter tissue properties with reading disability and related componential skills (e.g., phonological and single-word reading skills). Mean diffusivity (MD) and fractional anisotropy (FA) are widely used surrogate measures of tissue microstructure with high sensitivity; however, they lack specificity for individual microstructural features. Here we investigated neurite features with higher specificity in order to explore the underlying microstructural architecture. Methods Diffusion weighted images (DWI) and a battery of behavioral and neuropsychological assessments were obtained from 412 children (6 – 16 years of age). Neurite indices influenced by orientation and density were attained from 23 major white matter tracts. Partial correlations were calculated between neurite indices and indicators of phonological processing and single-word reading skills using age, sex, and image quality metrics as covariates. In addition, mediation analysis was performed using structural equation modeling (SEM) to evaluate the indirect effect of phonological processing on reading skills. Results We observed that orientation dispersion index (ODI) and neurite density index (NDI) were negatively correlated with single-word reading and phonological processing skills in several tracts previously shown to have structural correlates with reading efficiency. We also observed a significant and substantial effect in which phonological processing mediated the relationship between neurite indices and reading skills in most tracts. Conclusions In sum, we established that better reading and phonological processing skills are associated with greater tract coherence (lower ODI) and lower neurite density (lower NDI). We interpret these findings as evidence that reading is associated with neural architecture and its efficiency.
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Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
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While deep brain stimulation of the subthalamic nucleus (STN-DBS) has evolved to an evidence-based standard treatment for Parkinson’s disease (PD), the targeted cerebral networks are poorly described and no objective predictors for the postoperative clinical response exist. To elucidate the systemic mechanisms of DBS, we analysed cerebral grey matter properties using cortical thickness measurements and addressed the dependence of structural integrity on clinical outcome. Thirty one patients with idiopathic PD without dementia (23 males, age: 63.4±9.3, Hoehn and Yahr: 3.5 ± 0.8) were selected for DBS treatment. The patients underwent whole-brain preoperative T1 MR-Imaging at 3 T. Grey matter integrity was assessed by cortical thickness measurements with FreeSurfer. The clinical motor outcome markedly improved after STN-DBS in comparison to the preoperative condition. The cortical thickness of the frontal lobe (paracentral area and superior frontal region) predicted the clinical improvement after STN-DBS. Moreover, in patients with cortical atrophy of these areas a higher stimulation voltage was needed for an optimal clinical response. Our data suggest that the effects of STN-DBS in PD directly depend on frontal lobe grey matter integrity. Cortical atrophy of this region might represent a distinct predictor of a poor motor outcome after STN-DBS in PD patients.
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View largeDownload slide White matter structures near the subthalamic nucleus (STN) have been implicated in the therapeutic benefits of DBS in Parkinson’s disease. Vanegas-Arroyave et al . evaluate the connectivity patterns of effective DBS contacts, and suggest that modulation of pathways involving the superior frontal gyrus and the thalamus contributes to the benefits observed with STN DBS. View largeDownload slide White matter structures near the subthalamic nucleus (STN) have been implicated in the therapeutic benefits of DBS in Parkinson’s disease. Vanegas-Arroyave et al . evaluate the connectivity patterns of effective DBS contacts, and suggest that modulation of pathways involving the superior frontal gyrus and the thalamus contributes to the benefits observed with STN DBS.
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The aim of this study was to assess whether mild cognitive impairment (MCI) is associated with disruption in large-scale structural networks in newly diagnosed, drug-naïve patients with Parkinson's disease (PD). Graph theoretical analyses were applied to 3T MRI data from 123 PD patients and 56 controls from the Parkinson's progression markers initiative (PPMI). Thirty-three patients were classified as having Parkinson's disease with mild cognitive impairment (PD-MCI) using the Movement Disorders Society Task Force criteria, while the remaining 90 PD patients were classified as cognitively normal (PD-CN). Global measures (clustering coefficient, characteristic path length, global efficiency, small-worldness) and regional measures (regional clustering coefficient, regional efficiency, hubs) were assessed in the structural networks that were constructed based on cortical thickness and subcortical volume data. PD-MCI patients showed a marked reduction in the average correlation strength between cortical and subcortical regions compared with controls. These patients had a larger characteristic path length and reduced global efficiency in addition to a lower regional efficiency in frontal and parietal regions compared with PD-CN patients and controls. A reorganization of the highly connected regions in the network was observed in both groups of patients. This study shows that the earliest stages of cognitive decline in PD are associated with a disruption in the large-scale coordination of the brain network and with a decrease of the efficiency of parallel information processing. These changes are likely to signal further cognitive decline and provide support to the role of aberrant network topology in cognitive impairment in patients with early PD. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
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To date, deep brain stimulation (DBS) has already been performed on more than 120,000 patients worldwide and in more than 7,000 patients in Japan. However, fundamental understanding of DBS effects on the pathological neural circuitry remains insufficient. Recent studies have specifically shown the importance of cortico-striato-thalamo-cortical (CSTC) loops, which were identified as functionally and anatomically discrete units. Three main circuits exist in the CSTC loops, namely, the motor, associative, and limbic circuits. From these theoretical backgrounds, it is determined that DBS sometimes influences not only motor functions but also the cognitive and affective functions of Parkinson's disease (PD) patients. The main targets of DBS for PD are subthalamic nucleus (STN) and globus pallidus interna (GPi). Ventralis intermedius (Vim)-DBS was found to be effective in improving tremor. However, Vim-DBS cannot sufficiently improve akinesia and rigidity. Therefore, Vim-DBS is seldom carried out for the treatment of PD. In this article, we review the present state of DBS, mainly STN-DBS and GPi-DBS, for PD. In the first part of the article, appropriate indications and practical effects established in previous studies are discussed. The findings of previous investigations on the complications caused by the surgical procedure and on the adverse events induced by DBS itself are reviewed. In the second part, we discuss target selection (GPi vs. STN) and the effect of DBS on nonmotor symptoms. In the final part, as issues that should be resolved, the suitable timing of surgery, symptoms unresponsive to DBS such as on-period axial symptoms, and the related postoperative programing of stimulation parameters, are discussed.
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The current study investigates both gray and white matter changes in non-demented Parkinson's disease (PD) patients with varying degrees of mild cognitive deficits and elucidates the relationships between the structural changes and clinical sequelae of PD. Twenty-six PD patients and 15 healthy controls (HCs) were enrolled in the study. Participants underwent T1-weighted and diffusion tensor imaging (DTI) scans. Their cognition was assessed using a neuropsychological battery. Compared with HCs, PD patients showed significant cortical thinning in sensorimotor (left pre- and postcentral gyri) and cognitive (left dorsolateral superior frontal gyrus [DLSFG]) regions. The DLSFG cortical thinning correlated with executive and global cognitive impairment in PD patients. PD patients showed white matter abnormalities as well, primarily in bilateral frontal and temporal regions, which also correlated with executive and global cognitive impairment. These results seem to suggest that both gray and white matter changes in the frontal regions may constitute an early pathological substrate of cognitive impairment of PD providing a sensitive biomarker for brain changes in PD.
Applying high-frequency stimulation to deep brain structures, known as deep brain stimulation (DBS), has now been recognized as an effective therapeutic option for patients with a wide range of neurological and psychiatric disorders. DBS targeting the basal ganglia-thalamo-cortical loop, especially the internal segment of the globus pallidus, subthalamic nucleus, and thalamus, has been widely employed as a successful surgical therapy for movement disorders, such as Parkinson’s disease, dystonia, and tremor. However, the exact mechanism underling the beneficial effects of DBS remains to be clarified and is still under debate: Does DBS inhibit or excite local neuronal elements? In this chapter, we will discuss the physiological mechanism of DBS and propose an alternative view: Dbs dissociates input and output signals, resulting in the disruption of abnormal information flow through the stimulation site.
Objective: To use a multimodal approach to assess brain structural pathways and resting state (RS) functional connectivity abnormalities in patients with Parkinson's disease and freezing of gait (PD-FoG). Methods: T1-weighted, diffusion tensor (DT) MRI and RS functional MRI (fMRI) were obtained from 22 PD-FoG patients and 35 controls on a 3.0 T MR scanner. Patients underwent clinical, motor, and neuropsychological evaluations. Gray matter (GM) volumes and white matter (WM) damage were assessed using voxel based morphometry and tract-based spatial statistics, respectively. The pedunculopontine tract (PPT) was studied using tractography. RS fMRI data were analyzed using a model free approach investigating the main sensorimotor and cognitive brain networks. Multiple regression models were performed to assess the relationships between structural, functional, and clinical/cognitive variables. Analysis of GM and WM structural abnormalities was replicated in an independent sample including 28 PD-FoG patients, 25 PD patients without FoG, and 30 healthy controls who performed MRI scans on a 1.5 T scanner. Results: Compared with controls, no GM atrophy was found in PD-FoG cases. PD-FoG patients showed WM damage of the PPT, corpus callosum, corticospinal tract, cingulum, superior longitudinal fasciculus, and WM underneath the primary motor, premotor, prefrontal, orbitofrontal, and inferior parietal cortices, bilaterally. In PD-FoG, right PTT damage was associated with a greater disease severity. Analysis on the independent PD sample showed similar findings in PD-FoG patients relative to controls as well as WM damage of the genu and body of the corpus callosum and right parietal WM in PD-FoG relative to PD no-FoG patients. RS fMRI analysis showed that PD-FoG is associated with a decreased functional connectivity of the primary motor cortex and supplementary motor area bilaterally in the sensorimotor network, frontoparietal regions in the default mode network, and occipital cortex in the visual associative network. Conclusions: This study suggests that FoG in PD can be the result of a poor structural and functional integration between motor and extramotor (cognitive) neural systems. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
Deep brain stimulation (DBS) has been used as a treatment of movement disorders such as Parkinson's disease, dystonia, and essential tremor for over twenty years, and is a promising treatment for depression and epilepsy. However, the exact mechanisms of action of DBS are still uncertain, although different theories have emerged. This review summarizes the current understanding in this field. Different modalities used to investigate DBS such as electrophysiological, imaging and biochemical studies have revealed different mechanisms of DBS. The mechanisms may also be different depending on the structure targeted, the disease condition or the animal model employed. DBS may inhibit the target neuronal networks but activate the efferent axons. It may suppress pathological rhythms or impose new rhythms associated with beneficial effects, and involves neuronal networks with widespread connections. Different neurotransmitter systems such as dopamine and GABA upregulation are involved in the effects of DBS. There are also technical advances to prolong the battery life and specific targeting based on new electrode designs with multiple contacts which have the ability to steer the current towards a specific direction. There is ongoing work in closed loop or adaptive DBS using neural oscillations to provide the feedback signals. These oscillations need to be better characterized in a wide variety of clinical settings in future studies. Individualization of DBS parameters based on neural oscillations may optimize the clinical benefits of DBS. Copyright © 2015. Published by Elsevier Ltd.