![Localization of DBS electrodes within the STN. A: DBS electrodes localizations from thirteen participants in relation to the STN. Electrodes have been reconstructed in MNI space. Red contacts represent clinically active contacts. B: Spatial distribution of active contacts in relation to different STN segments (from DISTAL atlas). All 26 active contacts (red spheres) are projected cumulatively onto the left hemisphere (right contacts have been flipped). Subcortical structures including STN segments were defined from DISTAL subcortical atlas implemented in Lead-DBS [68]. Backdrop is from the high resolution 100 micron postmortem MRI in Edlow et al. [72]. Please note that due to missing post-operative MRI the data from participant no. 8 did not enter the localization analysis. GPe: globus pallidus externus, GPi: globus pallidus internus, RN: red nucleus, STN: subthalamic nucleus.](https://www.researchgate.net/profile/Felicitas-Ehlen/publication/348003499/figure/fig1/AS:974469812195333@1609343116814/Localization-of-DBS-electrodes-within-the-STN-A-DBS-electrodes-localizations-from_Q320.jpg)
Content uploaded by Felicitas Ehlen
Author content
All content in this area was uploaded by Felicitas Ehlen on Dec 30, 2020
Content may be subject to copyright.
RESEARCH ARTICLE
Impact of deep brain stimulation of the
subthalamic nucleus on natural language in
patients with Parkinson’s disease
Felicitas EhlenID
1,2
*, Bassam Al-FatlyID
1
, Andrea A. Ku¨hn
1,3,4,5,6
, Fabian Klostermann
1,3
1Department of Neurology, Humboldt-Universita
¨t zu Berlin and Berlin Institute of Health, Charite
´-
Universita
¨tsmedizin Berlin, Berlin, Germany, 2Department of Psychiatry and Psychotherapy, Ju¨disches
Krankenhaus Berlin, Berlin, Germany, 3Berlin School of Mind and Brain, Humboldt-Universita
¨t zu Berlin,
Berlin, Germany, 4Neurocure Cluster of Excellence, Humboldt-Universita
¨t zu Berlin and Berlin Institute of
Health, Charite
´-Universita
¨tsmedizin Berlin, Berlin, Germany, 5Bernstein Center for Computational
Neuroscience, Humboldt-Universita
¨t zu Berlin and Berlin Institute of Health, Charite
´—Universita
¨tsmedizin
Berlin, Berlin, Germany, 6Deutsches Zentrum fu¨r Neurodegenerative Erkrankungen, Berlin, Germany
*felicitas.ehlen@charite.de
Abstract
Background
In addition to the typical motor symptoms, a majority of patients suffering from Parkinson’s
disease experience language impairments. Deep Brain Stimulation of the subthalamic
nucleus robustly reduces motor dysfunction, but its impact on language skills remains
ambiguous.
Method
To elucidate the impact of subthalamic deep brain stimulation on natural language produc-
tion, we systematically analyzed language samples from fourteen individuals (three female /
eleven male, average age 66.43 ±7.53 years) with Parkinson’s disease in the active (ON)
versus inactive (OFF) stimulation condition. Significant ON-OFF differences were consid-
ered as stimulation effects. To localize their neuroanatomical origin within the subthalamic
nucleus, they were correlated with the volume of tissue activated by therapeutic stimulation.
Results
Word and clause production speed increased significantly under active stimulation. These
enhancements correlated with the volume of tissue activated within the associative part of
the subthalamic nucleus, but not with that within the dorsolateral motor part, which again
correlated with motor improvement. Language error rates were lower in the ON vs. OFF
condition, but did not correlate with electrode localization. No significant changes in further
semantic or syntactic language features were detected in the current study.
Conclusion
The findings point towards a facilitation of executive language functions occurring rather
independently from motor improvement. Given the presumed origin of this stimulation effect
PLOS ONE
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 1 / 19
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Ehlen F, Al-Fatly B, Ku¨hn AA, Klostermann
F (2020) Impact of deep brain stimulation of the
subthalamic nucleus on natural language in
patients with Parkinson’s disease. PLoS ONE
15(12): e0244148. https://doi.org/10.1371/journal.
pone.0244148
Editor: Allan Siegel, University of Medicine &
Dentistry of NJ - New Jersey Medical School,
UNITED STATES
Received: September 12, 2020
Accepted: December 3, 2020
Published: December 29, 2020
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0244148
Copyright: ©2020 Ehlen et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Our data are stored in
the public repository G-Node GIN (doi: 10.12751/g-
node.mb83iq).
within the associative part of the subthalamic nucleus, this could be due to co-stimulation of
the prefrontal-subthalamic circuit.
Introduction
Apart from disabling motor symptoms, Parkinson’s disease (PD) can lead to wide-ranging
non-motor symptoms [1]. These involve cognitive processes [2] which typically also affect
functions relevant to language such as memory, set shifting, flexibility, planning, and the inte-
gration of semantic networks [3–5]. The majority of PD patients thus develop language symp-
toms including impaired fluency [6–9] (for reviews see [10]) with an increase in speech
hesitations [11], and slower speech initiation [12]. On the syntax level, symptoms may com-
prise global impairments in sentence generation [13,14] (cf. [12]), decreased syntactic com-
plexity [11] (cf. [12]), and a specific decline in verbs [15–17]. Moreover, an abnormally low
informational content [14,18] and impaired pragmatic language production have been
described [18,19]. Finally, comprehension deficits regarding complex sentences (e.g., [12,20–
25]) and motor speech dysfunction [12,26] can add to disease-related communication
problems.
Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) is a well-established thera-
peutic option for patients suffering from complex PD motor symptoms despite optimized
drug treatment [27] (for a review see [28]). One proposed mechanism of action is a modula-
tion of excessive beta frequency synchronization within the cortico-basal circuits [29–31]. The
STN is embedded in the inhibitory “indirect” loop [32], while also being monosynaptically
connected with frontal motor and prefrontal cognitive areas [33,34] via the excitatory “hyper-
direct” pathway [35]. In this central position, the nucleus is expected to enable a protraction of
premature answers [34,36] and a modulation [37] of inputs from cortical projection regions.
Further, cross-modal signal integration is a postulated STN function, given marked overlap at
the border zones [34,38,39] of its dorsolateral motor, ventromedial associative, and rostral
limbic sections [32]. Cognitive STN functions, including language processing, have accord-
ingly been suggested to mainly involve inhibitory response control [5,17,33,40–42], response
selection [4,43], and action sequencing [44]. However, although STN DBS applied to the dor-
solateral section exerts strong prokinetic motor effects, its impacts on cognition are ambiguous
with the majority of studies suggesting a DBS-related acceleration of cognitive speed [36,40,
41,45–47], at the expense of premature responses leading to reduced accuracy [40,41,45,48,
49].
Specifically regarding effects of STN DBS on natural language production, only a few stud-
ies have been conducted and delivered equivocal results: [50–54] whereas reduced hesitations,
paraphasias, and errors together with improved lexical retrieval were interpreted as a func-
tional recovery [55], other studies showed vastly unaltered language functions [53,56] or com-
promised grammatical capacities [52].
Since this heterogeneity could relate to differences between language tasks as well as the
impact of variable stimulation fields [57], the current study aims to analyze a comprehensive
range of linguistic parameters in spontaneous language samples from persons with PD with
respect to the volume of tissue activated (VAT) by STN DBS. Under the premise of mainly par-
allel effects on motor and language functions, we hypothesized that STN DBS should primarily
counteract excessive inhibition, resulting in enhanced fluency, sentence generation, and infor-
mative content. At the same time, an increase in language errors could be expected to result
from DBS-induced disinhibition.
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 2 / 19
Funding: This study was supported by the German
Research Foundation (Kl-1276/5 in Clinical
Research Group 247) and the Open Access
Publication Fund of Charite
´– Universita¨tsmedizin
Berlin. Author BA has received a Doctoral Research
Grant from the German Academic Exchange
Service - DAAD. AAK has received honoraria for
advisory activities from Medtronic, Boston
Scientific, Abbott, Ipsen and Teva. FK has received
honoraria for advisory activities from CSL Behring
and Theranexus. The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing interests: The authors have read the
journal’s policy and have the following competing
interests: AAK has received honoraria for advisory
activities from Medtronic, Boston Scientific, Abbott,
Ipsen and Teva. FK has received honoraria for
advisory activities from CSL Behring and
Theranexus. This does not alter our adherence to
PLOS ONE policies on sharing data and materials.
There are no patents, products in development or
marketed products associated with this research to
declare.
Participants and methods
Participants
Fourteen individuals (three female, average age 66.43 ±7.53 years, average disease duration
13.79 ±4.63 years, average DBS treatment duration of 3.14 ±1.95 years) treated with bilateral,
constant voltage-driven STN DBS participated in the study. All participants met the UK Brain
Bank Criteria for PD and were right-handed native German-speakers. The individual STN
DBS settings had been optimized with respect to clinical parameters during a one week stay on
our specialized ward for DBS patients following the implantation and during regular (i.e. quar-
terly) consultations in our specialized outpatients’ department. Compatible with other clinical
cohorts (e.g., [58]), most of our participants were treated with monopolar stimulation as it
requires lower stimulation intensity. Bipolar stimulation had been chosen if an individual
developed adverse effects arising from the comparably large volume of tissue activated by
monopolar stimulation [59]. An overview of individual demographic, disease and DBS-related
data is provided in Table 1.
Exclusion criteria were a previous or current history of brain disease other than PD, includ-
ing all psychiatric disorders such as depression, psychosis, or apathy as well as dementia
(assessed by the Parkinson Neuropsychometric Dementia Assessment; PANDA [60]) or unin-
telligible speech irrespective of the stimulation condition.
To obtain data for ON-OFF comparisons, all participants were examined in two separate
sessions, i.e. in the STN DBS ON and STN DBS OFF condition at an interval of two months
and systematically alternating order. Examinations in the ON condition were carried out
under therapeutic stimulation parameters that had been stable for at least two months prior to
the assessment. For the OFF condition, stimulation was switched off at least thirty minutes
before the examination. The individually optimized antiparkinsonian medication remained
stable and the timing of the assessments was planned individually to ensure the best clinical
“medication-ON” state (starting time at about 11 a.m. for most participants; the last medica-
tion intake was protocolled.
All participants were recruited from the outpatient clinic for movement disorders of the
Charite
´University Hospital Berlin. They gave written informed consent to the study protocol
approved by the ethics committee of the Charite
´–Universita¨tsmedizin Berlin (protocol num-
ber EA2/047/10). The research was conducted in accordance with current guidelines and the
Declaration of Helsinki.
STN DBS implantation
Implantation of tetrapolar, cylindrical DBS electrodes (Medtronics, model 3387; contact
height: 1.5 mm, diameter: 1.27 mm) into the STN had been performed by stereotactic surgery
based on preoperative MRIs using atlas coordinates, intraoperative microelectrode recordings,
and macroelectrode stimulation. Correct localizations had been confirmed by post-operative
MRIs. All operations had been carried out by the same neurosurgical team at the Charite
´Uni-
versity Hospital.
Electrode localization and calculation of volume of tissue activated
Electrode localization and calculation of VAT was performed in all participants except case 8
due to missing postoperative imaging. DBS leads were localized with Lead-DBS open access
MATLAB software (www.lead-dbs.org, version 2 [61]). To estimate of the VAT, Lead-DBS
incorporates pre-existing models (first proposed by [62–65]) as well as tractography algo-
rithms in order to transition to the global volume of modulation [61]. The localization process
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 3 / 19
has been described in detail in earlier studies [65,66] and shall briefly be outlined: postopera-
tive images (CT or MRI) were co-registered to the preoperative MRI using either Advanced
Normalization Tools (ANTs; http://stnava.github.io/ANTs/ [67]) for postoperative CT scans
or the Statistical Parametric Mapping (SPM12) toolbox (http://www.fil.ion.ucl.ac.uk/spm/
software/spm12/) for postoperative MRI scans. Brain-shift correction was implemented in a
further step to adjust for any bias caused by possible operation-related pneumocephalus.
ANTs normalization pipeline was applied to warp preoperative and postoperative acquisitions
to MNI (ICBM 2009b NLIN asymmetric) space. DBS electrodes were automatically recon-
structed in MNI space, followed by manual refinement if needed (electrode localization in the
xyz-space is provided in the S1 Table). Next, the VAT was modelled around each clinically
Table 1. Overview of demographic, disease and DBS-related data.
A
Pt. Age (ys) Sex Body Side of
Onset
School (ys) Hoehn &Yahr Disease Dur. (ys) DBS Dur. (ys)
1 55 M Right 10 2.5 10 2
2 62 M Right 10 2 15 6
3 61 F Left 10 3 18 2
4 67 M right 10 3 16 5
5 77 M right 12 4 19 2
6 75 M right 11 3 15 4
7 72 M left 12 2 15 3
8 55 F right 10 2 15 7
9 62 F right 8 3 22 8
10 70 M right 10 2 11 4
11 74 M right 8 2.5 14 2
12 73 M right 8 2 4 0.5
13 58 M left 13 2 10 4
14 69 M left 10 2 9 0.5
B
Pt. Voltage (V) Impulse Width (μs) Frequency (Hz) Impedance (O) Polarity
left right Left right left right Left right Left right
1 3.4 2.9 90 60 90 90 818 1145 Bi Mo
2 2.6 3.8 90 60 90 90 1035 1010 Bi Mo
3 2.2 3 60 90 90 90 604 594 Mo Mo
4 3.9 2.4 90 90 160 160 812 962 Mo Mo
5 3.7 3.8 60 60 130 130 500 956 Mo Mo
6 3.8 3.8 60 60 90 90 500 459 Mo Mo
7 2.9 2.9 60 60 130 130 766 766 Mo Mo
8 3.5 4.4 60 60 80 80 459 283 Mo Mo
9 1.7 1.3 60 60 130 130 577 441 Mo Mo
10 2.7 2.7 60 60 130 130 712 712 Mo Mo
11 3.5 2.1 60 60 130 130 848 523 Bi Mo
12 2.2 1 60 60 130 130 1083 2365 Mo Mo
13 2.1 2 60 60 130 130 550 523 Mo Mo
14 0.5 0.5 60 60 130 130 2457 2084 Mo Mo
The table provides an overview of the participants included in the language analysis (n = 14) regarding demographic, disease, and DBS related data; bi: bipolar; dur.:
duration; f: female; H&Y: Hoehn & Yahr Stage (DBS ON); m: male; mo: monopolar; Pt.: participant code
https://doi.org/10.1371/journal.pone.0244148.t001
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 4 / 19
used active contact. Therefore, each participant’s clinical stimulation parameters were used to
calculate a volume conductor model of the DBS electrode and surrounding tissue (in analogy
to the procedure described in [65]). For this purpose, a tetrahedral volume mesh based on the
surface meshes of DBS electrodes and subcortical nuclei was generated using the Iso2Mesh
toolbox included in Lead-DBS. Regions that were neither filled with conductive/insulating
electrode material nor with gray matter were assigned to white matter. Gray matter nuclei
were defined by the DISTAL atlas [68]. Conductivities of 0.14 S/m and 0.33 S/m were assigned
to the white and grey matter, respectively (cf. [69]). We used values of 108 S/m for the plati-
num/iridium contacts and 10–16 S/m for the insulated parts of the electrodes. A simulation of
the potential distribution resulting from the DBS followed based on the volume conductor
model. As boundary condition we used the voltage applied to the active electrode contacts. For
monopolar DBS, the surface of the volume network represented the anode. By derivation of a
finite element method solution (toolbox simbio/FieldTrip incorporated in lead dbs software),
the gradient of the potential distribution was determined, resulting in a piecewise continuous
gradient. The extent and shape of the activated tissue volume were defined by setting the gradi-
ent as a threshold value above the frequently used value of 0.2 V/mm (cf. [38,65,70,71]).
Next, each participant’s bilateral VATs were overlapped with the motor and associative area of
the STN as provided by the DISTAL atlas [68] (see Fig 1). For each STN segment, the size of
the overlap cluster was normalized by taking its ratio to total VAT size. To assess whether the
results are dependent on the 0.2 V/mm threshold, we performed a control analysis that did not
include a heuristic/arbitrary threshold, but instead used the full electric field (see S1 Fig).
Motor and neurocognitive assessment
We used the motor section (i.e., part III) of the Unified Parkinson’s Disease Rating Scale
(UPDRS; minimum score = 0; maximum score = 108; higher scores indicate stronger symptom
severity) to assess motor symptom severity and the PANDA [60] (minimum score = 0; maxi-
mum score = 30; higher scores indicate stronger symptom severity) to evaluate cognitive func-
tions such as working memory, executive functions, and verbal fluency. As a part of the
UPDRS motor score, speech intelligibility was scored on the given scale (i.e., 0, 1, 2, 3, or 4
points indicate no, slight, mild, moderate, or severe speech problems, respectively [73]).
Fig 1. Localization of DBS electrodes within the STN. A: DBS electrodes localizations from thirteen participants in relation to the
STN. Electrodes have been reconstructed in MNI space. Red contacts represent clinically active contacts. B: Spatial distribution of active
contacts in relation to different STN segments (from DISTAL atlas). All 26 active contacts (red spheres) are projected cumulatively onto
the left hemisphere (right contacts have been flipped). Subcortical structures including STN segments were defined from DISTAL
subcortical atlas implemented in Lead-DBS [68]. Backdrop is from the high resolution 100 micron postmortem MRI in Edlow et al. [72].
Please note that due to missing post-operative MRI the data from participant no. 8 did not enter the localization analysis. GPe: globus
pallidus externus, GPi: globus pallidus internus, RN: red nucleus, STN: subthalamic nucleus.
https://doi.org/10.1371/journal.pone.0244148.g001
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 5 / 19
Linguistic analysis
Spontaneous language samples. Participants were comfortably seated in a sound-proof
chamber. Semi-structured interviews were carried out by an interviewer trained in psychologi-
cal interviewing. To obtain spontaneous language samples of at least 60 seconds, one out of six
predefined open questions was posed in a randomized order per participant and session (i.e.,
related to i. school days, ii. working life, iii. parents, iv. origin, v. vacation, or vi. hobbies; cf.
[54,56,74]). If the answer did not yield a sufficiently long monologue, the interviewer formu-
lated further questions by either relating to the current answer or by choosing a different ques-
tion out of the six above. All interviews were digitally recorded (software: Audacity
1.3.13-beta, microphone: the t.bone MB 88U Dual). For further analysis, a monologue of
about sixty seconds was excerpted from each language sample. The end of the interviewer’s
question was defined as starting point of each monologue.
Transcription. Interview transcriptions were conducted according to the guidelines
developed for the “Aachener Sprachanalyse” [75] using the computer software ‘Praat’ (version
6.0.29).
Linguistic analysis, parameters. With the aim to control for global comparability
between monologues recorded in both conditions, we first assessed monologue duration (in
seconds; excluding interruptions by the interviewer) and the articulation rate (syllables / s,
excluding pauses >200 ms). Next, to explore typical language difficulties described for PD
patients, we analysed i. the word production rate (words / s), ii. the total duration of linguistic
pauses (i.e., pauses corresponding to the grammatical structure), iii. the total duration of non-
linguistic pauses (i.e., pauses interrupting the natural flow of speech), iv. the language error
rate (ratio language errors / total number of words), v. the clause production rate (clauses / s),
and vi. sentence complexity (ratio complex / simple sentences; i.e., sentences including either at
least one subordinate clause or more than one main clause vs. sentences including only one
main clause). To estimate flexibility of word use, we additionally analysed vii. the rate of open
class words (i.e. words conveying semantic information [76] including full verbs, nouns, adjec-
tives, and modal adverbs which altogether can be extended by acquiring new words [77]) and
viii. the type-token ratio (i.e., the proportion of distinct lexemes).
The above parameters have generally been established in similar study designs [51,53,55,
74,75,78–82]. To additionally evaluate semantic and syntactic measures not represented by
the above parameters, we performed an analysis of stylistic devices. A detailed list of all
assessed stylistic devices is provided in the S2 Table. In summary, the production rate of
semantic stylistic devices and syntactic stylistic devices was each expressed as the sum value of
the subcategories of semantic figures (i.e., ‘addition’, ‘omission’, ‘transposition’, ‘permutation’,
‘other’) and syntactic figures (i.e., ‘addition’, ‘omission’, ‘transposition’, ‘other’), respectively, per
second.
Detailed data is publicly available at https://doi.org/10.12751/g-node.mb83iq.
Linguistic analysis, procedure. To obtain the above described parameters, the mono-
logues were subjected to manual linguistic analysis including the assessment of: number of
words; number and duration of linguistic and non-linguistic pauses; number and type of lan-
guage errors (i.e., grammatical, lexical, phonetic, contextual, stylistic, idiomatic, pragmatic,
logic); number and type of word classes (as defined by the German standard dictionary Duden
[83]); number and type of constituents (28 subcategories); morphosyntactic categorizations of
verbs, nouns, pronouns, adjectives/adverbs including word complexity; number and type of
clauses (i.e., 6 subcategories of main clauses; 18 subcategories of subordinate clauses; sentence
equivalents); number of sentences, and number and type of semantic and syntactic stylistic
devices.
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 6 / 19
Statistical analysis
The UPDRS motor score, PANDA score, and the interval between last medication intake and
assessment onset were compared in the ON vs. OFF condition using two-tailed paired sample
t-tests. The UPDRS speech item was compared using the Wilcoxon signed-rank test.
Regarding the linguistic analyses, we first compared monologue duration and articulation
rate in the ON vs. OFF condition using two-tailed paired sample t-tests. Next, the above
described linguistic parameters i. word production rate, ii. the total duration of linguistic
pauses, iii. the total duration of non-linguistic pauses, iv. language error rate, v. clause produc-
tion rate, vi. sentence complexity, vii. rate of open class words, and viii. type-token ratio were
compared in the ON vs. OFF condition using two-tailed paired sample t-tests (except for sen-
tence complexity where the Wilcoxon signed-rank tests was applicable). To determine the false
discovery rate of these eight ON-OFF comparisons, we applied the Benjamini–Hochberg pro-
cedure setting αto 0.05 (i.e., critical value ((i/m)0.05); with i=p-value rank; m= number
of comparisons; [84]).
Production rates of semantic and syntactic stylistic devices were assessed using two separate
repeated measures ANOVAs (each with the within-subjects factor ‘stimulation’ with two lev-
els). The first contained all categories of semantic figures (i.e., ‘addition’, ‘omission’, ‘transposi-
tion’, ‘permutation’, ‘other’; each expressed as sum value of their subcategories per second), the
second contained all categories of syntactic figures (i.e., ‘addition’, ‘omission’, ‘transposition’,
‘other’; each expressed as sum value of their subcategories per second).
To identify relationships between stimulation fields and changes in linguistic parameters,
we calculated ON-OFF difference values for each significantly changed linguistic parameter
and performed linear Spearman correlations with the ratio of the VAT overlap with the motor
and the associative area of the STN. Since electrodes were switched on and off bilaterally rather
than separately, overlap ratios were averaged for left and right hemispheric VATs.
The same significant ON-OFF difference values were furthermore related to ON-OFF dif-
ference values of the UPDRS motor score and the PANDA score using linear regression
analyses.
Effect sizes of ON-OFF differences were estimated using Cohen’s d(>0.2: small; >0.5:
medium; >0.8: large effect size [85]) for t-tests and partial η
2
(>0.10: small; >0.25: medium;
>0.40: large effect size [86]) for pairwise comparisons resulting from the ANOVAs for stylistic
devices. All statistical analyses were performed in IBM SPSS Statistics (version 25).
Results
The UPDRS motor score was significantly lower in the STN DBS ON vs. OFF condition (ON:
19.571 ±7.871 points; OFF: 38.000 ±13.156 points; p<0.001; Cohen’s d: 1.700). The PANDA
score did not change significantly (ON: 23.643 ±3.225 points; OFF: 22.500 ±3.653 points;
p= 0.345; Cohen’s d: -0.332). Speech was generally well intelligible in both stimulation condi-
tions (ON median: 1 point; OFF median: 1 point; p= 0.656; z<0.001). The interval between
the last medication intake and the beginning of the language assessment did not differ signifi-
cantly between the OFF vs. ON condition (ON: 1.64 ±1.434 hours; OFF: 2.286 ±1.369 hours;
p= 0.089; Cohen’s d: 0.459).
With respect to general comparability of the monologues, no significant ON-OFF differ-
ences were found regarding monologue duration (ON: 65.841 ±9.438 s; OFF: 65.376 ±16.913
s; p= 0.922; Cohen’s d: -0.034) or articulation rate (ON: 5.102 ±0.954 syllables / s; OFF:
4.730 ±0.673 syllables / s; p= 0.251; Cohen’s d: -0.450).
As presented in Table 2, statistical analysis of ON-OFF differences indicated a significantly
higher word production rate and clause production rate in the ON vs. OFF condition and a
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 7 / 19
lower language error rate in the DBS ON condition. No significant differences were indicated
regarding the rate of open class words, the type-token ratio,sentence complexity, and the total
duration of linguistic or non-linguistic pauses.
Regarding stylistic devices, ANOVAs showed a generally higher production rate of semantic
stylistic devices in the ON condition, which failed to reach the level of significance (ON:
.249 ±0.078 devices / s; OFF: .158 ±0.077 devices / s; p= 0.061; Cohen’s d: -1.180) and despite
very large standard deviations indicated a significant increase in figures of permutation (ON:
0.053 ±0.045 figures / s; OFF: 0.017 ±0.019 figures / s; p= 0.016 (after Bonferroni correction);
partial η
2
= 0.489). An increase in syntactic stylistic devices did not reach the level of signifi-
cance (ON: 0.196 ±0.096 devices / s; OFF: 0.144 ±0.082; devices / s; p= 0.063; Cohen’s d:
-0.585) with none of the single syntactic figures changing significantly.
Electrode localizations within the STN as illustrated in Fig 1 (collapsed for the left and right
STN) indicated 20 out of a total of 26 electrodes to be located within the dorsolateral motor
area and six within the ventromedial associative area.
The observed increase in the clause production rate in the ON vs. OFF condition was
strongly and significantly positively correlated with VAT overlap with the associative area of
the STN (r= 0.779, p<0.001; see Fig 2A top). This correlation was on a slightly weaker, yet
significant level also present for the word production rate (r= 0.498, p= 0.047; see Fig 2A bot-
tom). Furthermore, improvement in the UPDRS motor score in the ON condition was posi-
tively correlated with the average ratio of VAT overlap with the motor area of the STN
(r= 0.592, p= 0.026; see Fig 2B). On the contrary, no significant correlation was indicated
between the increase in the clause (r= 0.051, p= 0.420) or word (r= -0.152, p= 0.312) produc-
tion rates and the ratio of overlap with the motor area or between the improvement in the
UPDRS motor score and the overlap with the associative area of the STN (r= -0.306,
p= 0.158).
We identified no significant correlation between the observed reduction in the language
error rate in the ON vs. OFF condition with VAT overlap with the associative (r= -0.08,
p= 0.406) or the motor area of the STN (r= 0.400, p= 0.085).
We found no significant relationship between ON-OFF difference values of the UPDRS
motor score and clause (r
2
= 0.121; p= 0.224) or word (r
2
= 0.103; p= 0.264) production rates
or language error rates (r
2
= 0.005; p= 0.815) and no significant relationship between differ-
ence values of the PANDA score and clause (r
2
= 0.030; p= 0.553) or word (r
2
= 0.005;
p= 0.806) production rates or language error rates (r
2
= 0.055; p= 0.419).
Table 2. Linguistic parameters in the STN DBS ON and OFF condition.
DBS ON DBS OFF
Mean SD Mean SD p-value Cohen´s dBH
Word production rate (Words / s) 2.049 0.43 1.727 0.469 0.004-0.718 0.007
Clause production rate (Clauses / s) 0.306 0.09 0.223 0.091 0.007-0.926 0.014
Language error rate (Language errors / total number of words) 0.095 0.05 0.142 0.057 0.0180.897 0.021
Rate of open class words (Open class words / total number of words) 0.404 0.05 0.377 0.076 0.148 -0.434 0.029
Type-token ratio (Distinct lexemes / all lexemes) 0.621 0.05 0.611 0.068 0.546 -0.171 0.043
Sentence complexity (Complex / simple sentences) 0.744 1039 0.583 0.55 0.861 -0.193 0.05
Total duration of non-linguistic pauses (s) 6.518 3.149 10.020 6.221 0.049 0.710 0.025
Total duration of linguistic pauses (s) 16.076 7.942 16.273 9.330 0.946 0.023 0.050
Paired t-tests were applied to investigate ON-OFF differences (except for ‘sentence complexity’ where the Wilcoxon signed-rank test was applied). ON-OFF differences
were considered as significant if the p-value was below the Benjamini-Hochberg critical value (BH); according values are marked with an asterisk.
https://doi.org/10.1371/journal.pone.0244148.t002
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 8 / 19
Fig 2. Correlations between VAT overlap with associative vs. motor STN and language vs. motor changes. A: VAT overlap (light orange area)
with the associative area (green area) of the STN is depicted on the left; scatter diagrams on the right indicate correlations between VAT overlap with
the associative STN and the increase in clause (right, bottom) and word (right, center) production. B: VAT overlap (light orange area) with the motor
area (blue area) of the STN depicted on the left and the scatter diagram on the right indicate a correlation between VAT overlap with the motor STN
and UPDRS improvement.
https://doi.org/10.1371/journal.pone.0244148.g002
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 9 / 19
Discussion
The present study showed a significant increase in clause and word production rates as well as
a decrease in the language error rate with subthalamic DBS in PD patients. Interestingly, we
found a strong positive correlation between the increase in clause production and VAT overlap
with the associative area of the STN. The same but weaker correlation was found for word pro-
duction. Conversely, no significant relationship was found between the two parameters and
the ratio of VAT overlap with the STN motor area. We identified no significant correlation
between language error rates and the VAT overlap with the associative or motor area of the
STN. Despite large effect sizes, changes in the use of stylistic devices and the total duration of
non-linguistic pauses did not reach the level of significance. No significant ON-OFF differ-
ences were detectable for sentence complexity, the use of open class words or diverse lexemes,
or the total duration of linguistic pauses, each showing rather low effect sizes. In the following,
these results shall be discussed in detail.
The main finding of increased clause and word production rates seems compatible with the
idea that the STN accelerates cognitive speed [40,41,45–47] and particularly modulates lan-
guage on the level of response selection [4,43] and inhibitory control [5,17]. The current
results thus appear to reflect a procedural acceleration of functions typically impaired in PD
patients, i.e., sentence production [13,14] and fluency [6–9]. Importantly, this enhancement
seemed vastly unrelated to the DBS-induced motor improvement, which correlated with the
VAT overlap with the dorsolateral STN.
Thus, DBS appears to unfold differential effects on motor and cognitive functions if elec-
trodes are placed in the hypothesized overlap zone between the motor and associative portion
of the STN, as was mostly the case in the present cohort (see Fig 1). This resembles a dissocia-
tion between STN DBS effects on motor vs. non-motor functions described for cognitive
switching [87], executive functions [36], and impulsiveness [88], which was attributed to a rel-
atively ventral electrode position ([87] but cf. [88]) or a specific recruitment of fibers of the
hyperdirect pathway [36,88]. The present results furthermore seem consistent with an earlier
finding of enhanced verbal fluency performance induced by relatively antero-medial STN DBS
in the largely overlapping cohort [89]. They, however, extend this finding to the clinically
more meaningful production of natural language. By argumentum e contrario, it is further of
note that reduced verbal output was reported for tissue stimulation dorsally [90] or laterally
[91] to the actual target area and that stimulation directly within the associative STN portion
led to lexical and syntactic decline in a previous small-sample study [78]. This underscores the
importance of electrode placement within the anterior part of the dorsolateral STN to obtain
motor improvement by stimulation of the motor section, while also exerting acceleratory
effects on language, probably by co-activation of the associative section closely connected to
prefrontal cortical areas. In this framework it is of interest that experimental low frequency
stimulation of the ventral STN at 5 Hz improved response control in a Stroop task [92] and
dorsolateral stimulation at 4 Hz appeared to normalize the response latency in an interval tim-
ing task [33], which deteriorated at 10 Hz stimulation [93]. The authors therefore proposed a
functional coupling between the prefrontal cortex and the STN in the delta/theta frequency
range, which could be involved in cognitive control processes, including cue processing,
response inhibition, working memory recruitment, and attention [33,92]. Having said that,
improved cognitive parameters also indicated a possible activation of fronto-subthalamic con-
nections when stimulated at 10 Hz [94]. Overall, these results are compatible with the idea of a
relevant role of the prefrontal subthalamic signaling pathway, which can be disturbed or com-
pensated by co-stimulation of the associative part of the STN or its connective fibers, depend-
ing on stimulation parameters, localization and the cognitive requirements of the task type.
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 10 / 19
Given the typical increase of commission errors under STN DBS [40,41,45,48,49,88,95–
97], the current finding of reduced language errors may seem surprising. First, it should be
noted that our cumulative parameter comprised diverse linguistic errors that were distributed
heterogeneously among the participants (see S3 Table). Nevertheless, the result suggests differ-
ential effects of STN DBS on accuracy in the context of naturalistic language production com-
pared to specific cognitive tasks: while stimulation appears to have positive effects on language
accuracy if cognitive load is not manipulated, increased error rates in challenging tasks (e.g.,
[36,41,96,97]) were interpreted as interference of STN DBS with inhibitory control functions,
e.g., if increased speed, decision making, or the need for no-go strategies imposed a significant
cognitive stress on the individual. Recent studies have demonstrated a relationship between
corresponding errors and the connectivity strength between subthalamic VAT and prefrontal
areas (including the supplementary motor area, the ventromedial prefrontal cortex, and the
inferior frontal gyrus; [36,88]) Furthermore—consistent with the hypothesis of the physiologi-
cal "stopping" function of STN—seemingly beneficial effects of preoperatively strong frontos-
triatal network connectivity on low impulsiveness and disinhibition could be reversed by STN
DBS [88]. Future studies could further explore DBS effects on language accuracy by employing
tasks to assess the effects of increased cognitive demands on different types of language errors.
Ultimately, we could not identify a correlation between the reduction of language errors
and the VAT overlap with the cognitive or motor part of the STN. This could be due to their
heterogeneity and possibly to a complex involvement of co-stimulated structures, beyond
those investigated here. It seems therefore important to consider further sources of differential
STN DBS effects. Against the background of a relevant density increase of interneurons from
anterodorsolateral to posteroventromedial, Le
´vesque and Parent (2005) proposed their specific
involvement in the processing of associative information such as motivation, anticipation,
planning and integration within the ventromedial STN [98]. Additionally, two different types
of glutamatergic principal neurons prevail in the dorsolateral vs. ventromedial STN, suggesting
differential functions for sensorimotor vs. associative information processing [98]. Thus, the
increase in word and clause production observed here seems to occur along the gradual transi-
tion of cell composition from dorsolateral to ventromedial, so that distinct stimulation effects
on interneurons and different types of principal neurons could be considered. Worth men-
tioning in this context is a tractographic study using local field potential recordings of dorso-
lateral and further ventral STN stimulation sites, which showed gradual connectivity changes
in dorso-ventral direction, e.g., with respect to the additional motor cortex [29]. A recent diffu-
sion tensor imaging tractography study underscored the involvement of white mater tracts
connecting the stimulated STN tissue with the tegmentum, supplementary, premotor and pri-
mary motor cortex as well as the contralateral cerebellar hemisphere [99]. As is known from
neurostimulation models in non-human animals, complex stimulation-related and neuroana-
tomical factors modulate the susceptibility of neighboring cell bodies and passing axons for
co-stimulation [100]. Thus, given the small size of the STN and its close vicinity to various
gray matter areas and white matter tracts [101], unintended co-stimulation particularly of the
hypothalamus, substantia nigra, and the medial forebrain bundle is believed to causes cogni-
tive and behavioral side effects [102,103]. At the same time, more than 50% of VAT outside
the STN was associated with good clinical outcomes, possibly by therapeutic co-stimulation of
axonal tissue confining the STN dorsally, laterally and posteriorly [57]. Regarding language
functions, a recent study in ten individual described a correlation between postoperatively
improved semantic fluency and VAT outside the STN together with improved motor func-
tions and VAT overlap with the motor STN [104]. Against this background, we would wel-
come an extension of the functional neuroanatomical basis of DBS effects on language by
including tractography or functional connectivity measures in future studies.
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 11 / 19
Important to note in this context, although the associative STN is closely connected with corti-
cal regions putatively supporting language production (i.e. monosynaptic connections with the
prefrontal cortex [33,34] and supplementary motor area [44]), the specific involvement of the
STN in the language system is far from conclusive: whereas the above cortical areas [105–107] as
well as thalamic and striatal areas [107,108] have been proposed to interact with the “traditional”
perisylvian language areas, functional network analyses provided no evidence of a corresponding
engagement of the STN [107–109]. Other network studies, however, suggested an indirect
involvement of the STN in language functions by means of word selection, motor planning, initia-
tion, response inhibition, and action monitoring within the cortical-subcortical network [42,110].
In the present study, faster language production was accompanied by a decrease exclusively
in so-called non-linguistic pauses, i.e., hesitations, which typically interrupt the speech flow in
PD patients [11]. Although this result did not reach the corrected level of significance, it points
in the same direction as two previous studies, which reported an improved ratio of speech to
non-speech pauses [55,111] (but cf. [53]) and could thus indicate an amelioration of underly-
ing word access and selection difficulties (cf. [112]). Importantly, the available data, although
limited by the rather small sample size, seem to speak against a relevant effect of the articula-
tion rate (at syllable level), which did not change significantly.
Our study did not indicate specific effects of STN DBS on the use of diverse lexemes or so-
called open class words. Corresponding results could have been interpreted as an amelioration
of reduced informative content described among PD patients [14,18] and DBS-induced
increases in noun and verb use have indeed been reported in an earlier study [56]. However,
with regard to low effect sizes, the results should be interpreted with caution, as they may be
due to the rather small sample size and do not necessarily indicate the absence of stimulation
effects. Similarly, we found no significant DBS-related effects on sentence complexity, which
can be reduced in PD patients [11] (but cf. [12]). However, as indicated by a very small effect
size, also this result may be skewed by the small sample size and may not necessarily indicate
the absence of stimulation effects.
Our findings regarding stylistic devices remained suggestive only: despite considerable
effect sizes, ON-OFF differences did not reach the level of significance and high standard devi-
ations limit the interpretability. However, there was an increase of so-called permutations
including analogies, metaphors, ironies, and exaggerations, which require cognitive transfer
between concrete expressions and their substitutes and could therefore relate to a DBS-
induced thought flexibilization. Future studies investigating longer speech samples and a larger
cohort may specifically tackle this question.
Limitations of the study
The current study aimed to explore effects of therapeutic DBS on language parameters. It did
not include experimental conditions, such as separated DBS testing per hemisphere or low-fre-
quency stimulation, because we considered the testing to be an excessive strain for the partici-
pants and since we were interested in potential effects, as they occur in the clinical real-word
scenario. Additionally, we focused on VAT overlap with STN subregions whose neuroanatom-
ical segregation is known in detail, leaving aside complex effects due to potential co-stimula-
tion of structures adjacent to the STN or through recruitment of the white matter tracts.
Furthermore, by including a pre-operative condition, future studies could assess the possibility
of active stimulation counteracting adverse effects of DBS implantation on language perfor-
mance (cf. [113]). Also an inclusion of cognitive speed measures could be considered to verify
the here proposed relation between cognitive and linguistic speed enhancements. Moreover, a
possible bias towards participants who particularly profited from STN DBS in our cohort
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 12 / 19
cannot be excluded, given an earlier report on language benefits exclusively in patients with a
left hemispheric disease dominance [56].
Conclusion
In conclusion, therapeutic bilateral DBS of the STN accelerated clause and word production
during natural language in PD patients. The magnitude of this increase was associated with the
VAT within the associative STN, rostral to the dorsolateral motor section of the nucleus. The
observed enhancements seem best compatible with the idea of STN DBS releasing executive
procedures from excessive inhibition. Unexpectedly, this effect did not occur at the expense of
language accuracy. Interestingly, corresponding accelerations appeared independent from
prokinetic DBS effects, which correlated with a more prominent stimulation within the STN
motor region. In sum, subtle variations in electrode localization may exert differential effects
on motor and language functions, which warrant future efforts to expand on approaches for
individualized and symptom-specific neuromodulation.
Supporting information
S1 Fig. Correlations between electric field overlap with associative vs. motor STN and lan-
guage vs. motor changes. Spearman correlations between the intersection of the electric field
with each STN subregion (combined for both hemispheres) and changes in UPDRS, word and
clause production rates, and error rate indicated A) a positive correlation between the
improvement in the UPDRS and the electric field overlap with the motor STN, but not with
the associative STN (top left), B) an association between the increase in the word production
rate and the electric field overlap with the associative STN (on the level of trend), but not the
motor STN (bottom left), and C) a positive correlation between the increase in the clause pro-
duction rate and the electric field overlap with the associative STN, but not with the motor
STN (bottom right). D) changes in error rates were neither significantly related to the electric
field overlap with the associative, nor to the motor STN (top right).
(TIF)
S1 Table. Electrode localization in the XYZ-space. Overview of the localization of active elec-
trodes in the xyz-space; due to missing post-operative MRI the data from participant no. 8 did
not enter the localization analysis. Electrode localizations of all 26 active electrodes from the
thirteen participants are depicted in Fig 1. Pt.: participant code.
(DOCX)
S2 Table. Stylistic devices. Overview of all semantic (A) and syntactic (B) stylistic devices ana-
lyzed, including the overall type and subcategories.
(DOCX)
S3 Table. Language errors. Distribution of language error rates (number of errors per total
word count) across categories and participants.
(DOCX)
Acknowledgments
We would like to thank all participants of this study.
Author Contributions
Conceptualization: Felicitas Ehlen, Fabian Klostermann.
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 13 / 19
Data curation: Felicitas Ehlen.
Formal analysis: Felicitas Ehlen, Bassam Al-Fatly.
Funding acquisition: Fabian Klostermann.
Investigation: Felicitas Ehlen.
Methodology: Felicitas Ehlen, Fabian Klostermann.
Project administration: Fabian Klostermann.
Resources: Andrea A. Ku¨hn, Fabian Klostermann.
Supervision: Andrea A. Ku¨hn, Fabian Klostermann.
Validation: Andrea A. Ku¨hn, Fabian Klostermann.
Writing – original draft: Felicitas Ehlen.
Writing – review & editing: Bassam Al-Fatly, Andrea A. Ku¨hn, Fabian Klostermann.
References
1. Pringsheim T, Jette N, Frolkis A, Steeves TDL. The prevalence of Parkinson’s disease: A systematic
review and meta-analysis. Mov Disord. 2014; 29(13):1583–90. https://doi.org/10.1002/mds.25945
PMID: 24976103
2. Weintraub DB, Zaghloul KA. The role of the subthalamic nucleus in cognition. Rev Neurosci. 2013; 24
(2):125–138. https://doi.org/10.1515/revneuro-2012-0075 PMID: 23327862
3. Nadeau SE, Crosson B. Subcortical aphasia. Brain Lang. 1997; 58(3):355–402. https://doi.org/10.
1006/brln.1997.1707 PMID: 9222518
4. Crosson B. Subcortical functions in language: A working model. Brain Lang. 1985; 25(2):257–92.
https://doi.org/10.1016/0093-934x(85)90085-9 PMID: 3904918
5. Barbas H, Garcı
´a-Cabezas MA
´, Zikopoulos B. Frontal-thalamic circuits associated with language.
Brain Lang. 2013; 126(1):49–61. https://doi.org/10.1016/j.bandl.2012.10.001 PMID: 23211411
6. Fine EM, Delis DC, Paul BM, Filoteo JV. Reduced verbal fluency for proper names in nondemented
patients with Parkinson’s disease: A quantitative and qualitative analysis. J Clin Exp Neuropsychol.
2011; 33(2):226–33. https://doi.org/10.1080/13803395.2010.507185 PMID: 20936559
7. Piatt AL, Fields JA, Paolo AM, Koller WC, Tro
¨ster AI. Lexical, Semantic, and Action Verbal Fluency in
Parkinson’s Disease with and without Dementia. J Clin Exp Neuropsychol (Neuropsychology, Dev
Cogn Sect A). 1999; 21(4):435–43.
8. Signorini M, Volpato C. Action fluency in Parkinson’s disease: A follow-up study. Mov Disord. 2006; 21
(4):467–72. https://doi.org/10.1002/mds.20718 PMID: 16250017
9. Obeso I, Casabona E, Bringas ML, A
´lvarez L, Jahanshahi M. Semantic and phonemic verbal fluency
in Parkinson’s disease: Influence of clinical and demographic variables. Behav Neurol. 2012; 25
(2):111–8. https://doi.org/10.3233/BEN-2011-0354 PMID: 22530265
10. Henry JD, Crawford JR. Verbal fluency deficits in Parkinson’s disease: A meta-analysis. J Int Neurop-
sychol Soc. 2004; 10(4):608–22. https://doi.org/10.1017/S1355617704104141 PMID: 15327739
11. Illes J, Metter EJ, Hanson WR, Iritani S. Language production in Parkinson’s disease: Acoustic and lin-
guistic considerations. Brain Lang. 1988; 33(1):146–60. https://doi.org/10.1016/0093-934x(88)90059-
4PMID: 3342316
12. Walsh B, Smith A. Linguistic Complexity, Speech Production, and Comprehension in Parkinson’s Dis-
ease: Behavioral and Physiological Indices. J Speech Lang Hear Res. 2011; 54(3):787–802. https://
doi.org/10.1044/1092-4388(2010/09-0085) PMID: 21060141
13. Troche MS, Altmann LJP. Sentence production in Parkinson disease: Effects of conceptual and task
complexity. Appl Psycholinguist. 2012; 33:225–251.
14. Murray LL. Spoken Language Production in Huntington’s and Parkinson’s Diseases. J Speech Lang
Hear Res. 2000; 43(6):1350–66. https://doi.org/10.1044/jslhr.4306.1350 PMID: 11193957
15. Pe
´ran P, Rascol O, De
´monet JF, Celsis P, Nespoulous JL, Dubois B, et al. Deficit of verb generation in
nondemented patients with Parkinson’s disease. Mov Disord. 2003; 18(2):150–6. https://doi.org/10.
1002/mds.10306 PMID: 12539207
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 14 / 19
16. Bertella L., Albani G., Greco E., Priano L., Mauro A., SM, et al. Noun Verb Dissociation in Parkinson’s
Disease. Elsevier Sci. 2001; 48(2–3):277–80.
17. Castner JE, Chenery HJ, Silburn PA, Coyne TJ, Sinclair F, Smith ER, et al. Effects of subthalamic
deep brain stimulation on noun/verb generation and selection from competing alternatives in Parkin-
son’s disease. J Neurol Neurosurg Psychiatry. 2008; 79(6):700–5. https://doi.org/10.1136/jnnp.2007.
118729 PMID: 17911182
18. Holtgraves T, Fogle K, Marsh L. Pragmatic language production deficits in Parkinson’s disease. Adv
Park Dis [Internet]. 2013; 02(01):31–6. Available from: http://www.scirp.org/journal/doi.aspx?DOI=10.
4236/apd.2013.21006
19. Hall D, Ouyang B, Lonnquist E, Newcombe J. Pragmatic communication is impaired in parkinson dis-
ease. Int J Neurosci. 2011; 121(5):254–6. https://doi.org/10.3109/00207454.2010.550389 PMID:
21348791
20. Lieberman P, Friedman J, Feldman LS. Syntax comprehension deficits in Parkinson’s disease. J Nerv
Ment Dis. 1990; 178(6):360–5. https://doi.org/10.1097/00005053-199006000-00003 PMID: 2348189
21. Angwin AJ, Chenery HJ, Copland DA, Murdoch BE, Silburn PA. Self-paced reading and sentence
comprehension in Parkinson’s disease. J Neurolinguistics. 2006; 19(3):239–52.
22. Grossman M, Carvell S, Gollomp S, Stern MB, Vernon G, Hurtig HI. Sentence comprehension and
praxis deficits in Parkinson’s disease. Neurology. 1991; 41(10):1620–6. https://doi.org/10.1212/wnl.
41.10.1620 PMID: 1922805
23. Grossman M, Cooke A, DeVita C, Lee C, Alsop D, Detre J, et al. Grammatical and resource compo-
nents of sentence processing in Parkinson’s disease: an fMRI study. Neurology. 2003; 60(5):775–81.
https://doi.org/10.1212/01.wnl.0000044398.73241.13 PMID: 12629232
24. Lieberman P, Kako E, Friedman J, Tajchman G, Feldman LS, Jiminez EB. Speech production, syntax
comprehension, and cognitive deficits in Parkinson’s disease. Brain Lang. 1992; 43(2):169–89. https://
doi.org/10.1016/0093-934x(92)90127-z PMID: 1393519
25. Hochstadt J, Nakano H, Lieberman P, Friedman J. The roles of sequencing and verbal working mem-
ory in sentence comprehension deficits in Parkinson’s disease. Brain Lang. 2006; 97(3):97(3):243–
257. https://doi.org/10.1016/j.bandl.2005.10.011 PMID: 16332387
26. Ho AK, Iansek R, Marigliani C, Bradshaw JL, Gates S. Speech Impairment in a Large Sample of
Patients with Parkinson’s Disease. Behav Neurol. 1999; 11(3):131–7. PMID: 22387592
27. Limousin P, Krack P, Pollak P, Benazzouz A, Ardouin C, Hoffmann D, et al. Electrical stimulation of
the subthalamic nucleus in advanced Parkinsonian’s disease. N Engl J Med. 1998; 339(16):1105–11.
https://doi.org/10.1056/NEJM199810153391603 PMID: 9770557
28. Fox SH, Katzenschlager R, Lim SY, Barton B, de Bie RMA, Seppi K, et al. International Parkinson and
movement disorder society evidence-based medicine review: Update on treatments for the motor
symptoms of Parkinson’s disease. Mov Disord. 2018; 33(8):1248–66. https://doi.org/10.1002/mds.
27372 PMID: 29570866
29. Accolla EA, Herrojo Ruiz M, Horn A, Schneider GH, Schmitz-Hu¨bsch T, Draganski B, et al. Brain net-
works modulated by subthalamic nucleus deep brain stimulation. Brain. 2016; 139(Pt 9):2503–15.
https://doi.org/10.1093/brain/aww182 PMID: 27412387
30. McIntyre CC, Hahn PJ. Network perspectives on the mechanisms of deep brain stimulation. Neurobiol
Dis [Internet]. 2010; 38(3):329–37. Available from: https://doi.org/10.1016/j.nbd.2009.09.022 PMID:
19804831
31. Neumann WJ, Turner RS, Blankertz B, Mitchell T, Ku¨hn AA, Richardson RM. Toward Electrophysiol-
ogy-Based Intelligent Adaptive Deep Brain Stimulation for Movement Disorders. Neurotherapeutics.
2019; 16(1):105–18. https://doi.org/10.1007/s13311-018-00705-0 PMID: 30607748
32. Alexander G, Crutcher M, DeLong M. Basal ganglia-thalamocortical circuits: parallel substrates for
motor, oculomotor, “prefrontal” and “limbic” functions. Prog Brain Res. 1989; 85:119–46.
33. Kelley R, Flouty O, Emmons EB, Kim Y, Kingyon J, Wessel JR, et al. A human prefrontal-subthalamic
circuit for cognitive control. Brain. 2018; 141(1):205–16. https://doi.org/10.1093/brain/awx300 PMID:
29190362
34. Haynes WIA, Haber SN. The organization of prefrontal-subthalamic inputs in primates provides an
anatomical substrate for both functional specificity and integration: Implications for basal ganglia mod-
els and deep brain stimulation. J Neurosci. 2013; 13(11):4804–14. https://doi.org/10.1523/
JNEUROSCI.4674-12.2013 PMID: 23486951
35. Nambu A, Tokuno H, Takada M. Functional significance of the cortico-subthalamo-pallidal “hyperdir-
ect” pathway. Neurosci Res. 2002; 43(2):111–7. https://doi.org/10.1016/s0168-0102(02)00027-5
PMID: 12067746
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 15 / 19
36. Neumann WJ, Schroll H, De Almeida Marcelino AL, Horn A, Ewert S, Irmen F, et al. Functional segre-
gation of basal ganglia pathways in Parkinson’s disease. Brain. 2018; 141(9):2655–69. https://doi.org/
10.1093/brain/awy206 PMID: 30084974
37. Rektor I, Bočkova
´M, Chrastina J, Rektorova
´I, Bala
´z
ˇM. The modulatory role of subthalamic nucleus
in cognitive functions—A viewpoint. Clin Neurophysiol. 2015; 126(4):653–8. https://doi.org/10.1016/j.
clinph.2014.10.156 PMID: 25487910
38. Irmen F, Horn A, Meder D, Neumann WJ, Plettig P, Schneider GH, et al. Sensorimotor subthalamic
stimulation restores risk-reward trade-off in Parkinson’s disease. Mov Disord. 2019; 34(3):366–76.
https://doi.org/10.1002/mds.27576 PMID: 30485537
39. Mallet L, Schu¨pbach M, N’Diaye K, Remy P, Bardinet E, Czernecki V, et al. Stimulation of subterri-
tories of the subthalamic nucleus reveals its role in the integration of the emotional and motor aspects
of behavior. Proc Natl Acad Sci U S A. 2007; 104(25):10661–10616. https://doi.org/10.1073/pnas.
0610849104 PMID: 17556546
40. Ballanger B, Van Eimeren T, Moro E, Lozano AM, Hamani C, Boulinguez P, et al. Stimulation of the
subthalamic nucleus and impulsivity: Release your horses. Ann Neurol. 2009; 66(6):817–24. https://
doi.org/10.1002/ana.21795 PMID: 20035509
41. Frank MJ, Samanta J, Moustafa AA, Sherman SJ. Hold your horses: Impulsivity, deep brain stimula-
tion, and medication in Parkinsonism. Science (80-). 2007; 318(5854):1309–12.
42. Manes JL, Parkinson AL, Larson CR, Greenlee JD, Eickhoff SB, Corcos DM, et al. Connectivity of the
subthalamic nucleus and globus pallidus pars interna to regions within the speech network: A meta-
analytic connectivity study. Hum Brain Mapp. 2014; 35(7):3499–516. https://doi.org/10.1002/hbm.
22417 PMID: 25050431
43. Wallesch CW, Papagno C. Subcortical aphasia. In: Rose FC, Whurr R, Wylie MA, editors. Aphasia.
London: Whurr; 1988. p. 256–287.
44. Caligiore D, Helmich RC, Hallett M, Moustafa AA, Timmermann L, Toni I, et al. Parkinson’s disease as
a system-level disorder. npj Park Dis. 2016;16025. https://doi.org/10.1038/npjparkd.2016.25 PMID:
28725705
45. Jahanshahi M, Ardouin CMA, Brown RG, Rothwell JC, Obeso J, Albanese A, et al. The impact of deep
brain stimulation on executive function in Parkinson’s disease. Brain. 2000; 123(Pt 6):1142–54.
https://doi.org/10.1093/brain/123.6.1142 PMID: 10825353
46. Pillon B, Ardouin C, Damier P, Krack P, Houeto JL, Klinger H, et al. Neuropsychological changes
between “off” and “on” STN or GPi stimulation in Parkinson’s disease. Neurology. 2000; 55(3):411-
(3):411–8. https://doi.org/10.1212/wnl.55.3.411 PMID: 10932277
47. Castner JE, Copland DA, Silburn PA, Coyne TJ, Sinclair F, Chenery HJ. Lexical-semantic inhibitory
mechanisms in Parkinson’s disease as a function of subthalamic stimulation. Neuropsychologia.
2007; 45(14):3167–77. https://doi.org/10.1016/j.neuropsychologia.2007.06.019 PMID: 17706256
48. Witt K, Pulkowski U, Herzog J, Lorenz D, Hamel W, Deuschl G, et al. Deep Brain Stimulation of the
Subthalamic Nucleus Improves Cognitive Flexibility but Impairs Response Inhibition in Parkinson Dis-
ease. Arch Neurol. 2004; 61(5):697–700. https://doi.org/10.1001/archneur.61.5.697 PMID: 15148146
49. Coulthard EJ, Bogacz R, Javed S, Mooney LK, Murphy G, Keeley S, et al. Distinct roles of dopamine
and subthalamic nucleus in learning and probabilistic decision making. Brain. 2012; 35(Pt 12):3721–
34.
50. Fenoy AJ, McHenry MA, Schiess MC. Speech changes induced by deep brain stimulation of the sub-
thalamic nucleus in Parkinson disease: involvement of the dentatorubrothalamic tract. J Neurosurg
[Internet]. 2017; 126(6):2017–27. Available from: https://doi.org/10.3171/2016.5.JNS16243 PMID:
27611200
51. Batens K, De Letter M, Raedt R, Duyck W, Vanhoutte S, Van Roost D, et al. Subthalamic nucleus stim-
ulation and spontaneous language production in Parkinson’s disease: A double laterality problem.
Brain Lang [Internet]. 2015; 147:76–84. Available from: https://doi.org/10.1016/j.bandl.2015.06.002
PMID: 26099950
52. Phillips L, Litcofsky KA, Pelster M, Gelfand M, Ullman MT, Charles PD. Subthalamic Nucleus Deep
Brain Stimulation Impacts Language in Early Parkinson ‘ s Disease. 2012; 7(8).
53. Zanini S, Moschella V, Stefani A, Peppe A, Pierantozzi M, Galati S, et al. Grammar improvement fol-
lowing deep brain stimulation of the subthalamic and the pedunculopontine nuclei in advanced Parkin-
son’s disease: A pilot study. Park Relat Disord [Internet]. 2009; 15(8):606–9. Available from: https://
doi.org/10.1016/j.parkreldis.2008.12.003 PMID: 19138876
54. Van Lier S, Batens K, Santens P, Van Roost D, Van Herreweghe M, De Letter M. The influence of sub-
thalamic nucleus stimulation on pragmatic language production in Parkinson’s disease. Acta Neurol
Belg. 2016; 116(2):163–70. https://doi.org/10.1007/s13760-015-0546-5 PMID: 26442686
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 16 / 19
55. Zanini S, Melatini A, Capus L, Gioulis M, Vassallo A, Bava A. Language recovery following subthala-
mic nucleus stimulation in Parkinson’s disease. Neuroreport. 2003; 14(3):511–6. https://doi.org/10.
1097/00001756-200303030-00043 PMID: 12634514
56. Batens K, De Letter M, Raedt R, Duyck W, Vanhoutte S, Van Roost D, et al. The effects of subthalamic
nucleus stimulation on semantic and syntactic performance in spontaneous language production in
people with Parkinson’s disease. J Neurolinguistics. 2014; 32:31–41.
57. Maks CB, Butson CR, Walter BL, Vitek JL, McIntyre CC. Deep brain stimulation activation volumes
and their association with neurophysiological mapping and therapeutic outcomes. J Neurol Neurosurg
Psychiatry. 2009; 80(6):659–66. https://doi.org/10.1136/jnnp.2007.126219 PMID: 18403440
58. Hancu I, Boutet A, Fiveland E, Ranjan M, Prusik J, Dimarzio M, et al. On the (Non-)equivalency of
monopolar and bipolar settings for deep brain stimulation fMRI studies of Parkinson’s disease patients.
J Magn Reson Imaging. 2019; 49:1736–1749. https://doi.org/10.1002/jmri.26321 PMID: 30552842
59. Volkmann J, Moro E, Pahwa R. Basic algorithms for the programming of deep brain stimulation in Par-
kinson’s disease. Mov Disord. 2006; 21(Suppl. 14):S284 –S289. https://doi.org/10.1002/mds.20961
PMID: 16810675
60. Kalbe E, Calabrese P, Kohn N, Hilker R, Riedel O, Wittchen HU, et al. Screening for cognitive deficits
in Parkinson’s disease with the Parkinson neuropsychometric dementia assessment (PANDA) instru-
ment. Park Relat Disord. 2008; 14(2):93–101. https://doi.org/10.1016/j.parkreldis.2007.06.008 PMID:
17707678
61. Horn A, Li N, Dembek TA, Kappel A, Boulay C, Ewert S, et al. Lead-DBS v2: Towards a comprehen-
sive pipeline for deep brain stimulation imaging. Neuroimage. 2019; 184:293–316. https://doi.org/10.
1016/j.neuroimage.2018.08.068 PMID: 30179717
62. Dembek TA, Barbe MT, Åstro
¨m M, Hoevels M, Visser-Vandewalle V, Fink GR, et al. Probabilistic map-
ping of deep brain stimulation effects in essential tremor. NeuroImage Clin. 2017; 13:164–173. https://
doi.org/10.1016/j.nicl.2016.11.019 PMID: 27981031
63. Kuncel AM, Cooper SE, Grill WM. A method to estimate the spatial extent of activation in thalamic
deep brain stimulation. Clin Neurophysiol. 2008; 119:2148–2158. https://doi.org/10.1016/j.clinph.
2008.02.025 PMID: 18632304
64. Ma
¨dler B, Coenen VA. Explaining clinical effects of deep brain stimulation through simplified target-
specific modeling of the volume of activated tissue. Am J Neuroradiol. 2012; 33(6):1072–80. https://
doi.org/10.3174/ajnr.A2906 PMID: 22300931
65. Horn A, Reich M, Vorwerk J, Li N, Wenzel G, Fang Q, et al. Connectivity Predicts deep brain stimula-
tion outcome in Parkinson disease. Ann Neurol. 2017; 82(1):67–78. https://doi.org/10.1002/ana.
24974 PMID: 28586141
66. Al-Fatly B, Ewert S, Ku¨bler D, Kroneberg D, Horn A, Ku¨hn AA. Connectivity profile of thalamic deep
brain stimulation to effectively treat essential tremor. Brain. 2019; 142(10):3086–98. https://doi.org/10.
1093/brain/awz236 PMID: 31377766
67. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with
cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med Image
Anal. 2008; 12(1):26–41. https://doi.org/10.1016/j.media.2007.06.004 PMID: 17659998
68. Ewert S, Plettig P, Li N, Chakravarty MM, Collins DL, Herrington TM, et al. Toward defining deep brain
stimulation targets in MNI space: A subcortical atlas based on multimodal MRI, histology and structural
connectivity. Neuroimage. 2018; 170:271–82. https://doi.org/10.1016/j.neuroimage.2017.05.015
PMID: 28536045
69. Vorwerk J, Cho JH, Rampp S, Hamer H, Kno
¨sche TR, Wolters CH. A guideline for head volume con-
ductor modeling in EEG and MEG. Neuroimage. 2014; 100:590–607. https://doi.org/10.1016/j.
neuroimage.2014.06.040 PMID: 24971512
70. Hemm S, Mennessier G, Vayssiere N, Cif L, El Fertit H, Coubes P. Deep brain stimulation in move-
ment disorders: Stereotactic coregistration of two-dimensional electrical field modeling and magnetic
resonance imaging. J Neurosurg. 2005; 103(6):949–955. https://doi.org/10.3171/jns.2005.103.6.0949
PMID: 16381180
71. Åstro
¨m M, Diczfalusy E, Martens H, Wårdell K. Relationship between neural activation and electric
field distribution during deep brain stimulation. IEEE Trans Biomed Eng. 2015; 62(2):664–72. https://
doi.org/10.1109/TBME.2014.2363494 PMID: 25350910
72. Edlow BL, Mareyam A, Horn A, Polimeni JR, Witzel T, Tisdall MD, et al. 7 Tesla MRI of the ex vivo
human brain at 100 micron resolution. Sci data. 2019;6: 244(1):1–10.
73. Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, et al. Movement Disor-
der Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS):
Scale presentation and clinimetric testing results. Mov Disord. 2008. https://doi.org/10.1002/mds.
22340 PMID: 19025984
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 17 / 19
74. Hussmann K, Grande M, Meffert E, Christoph S, Piefke M, Willmes K, et al. Computer-assisted analy-
sis of spontaneous speech: Quantification of basic parameters in aphasic and unimpaired language.
Clin Linguist Phonetics. 2012; 26(8):661–80. https://doi.org/10.3109/02699206.2012.683672 PMID:
22774927
75. Grande M, Springer L, Huber W. Richtlinien fu¨r die transkription mit dem programm ASPA (Aachener
Sprachanalyse). Spr Stimme Gehor. 2006; 30(4):179–85.
76. Garrett MF. Word and Sentence Perception. In: Held R, Leibowitz H, Teuber HL, editors. Perception,
Vol 8. Berlin Heidelberg: Springer; 1978. p. 611–625.
77. Du¨rscheid C. Syntax: Grundlagen und Theorien. Go
¨ttingen: Vandenhoeck & Ruprecht; 2012.
78. Homer MA, Rubin SS, Horowitz TD, Richter E. Linguistic testing during ON/OFF states of electrical
stimulation in the associative portion of the subthalamic nucleus. Neuromodulation. 2012; 15(3):238–
45. https://doi.org/10.1111/j.1525-1403.2012.00441.x PMID: 22672051
79. Ehlen F, Vonberg I, Ku¨hn AA, Klostermann F. Effects of thalamic deep brain stimulation on spontane-
ous language production. Neuropsychologia. 2016; 89:74–82. https://doi.org/10.1016/j.
neuropsychologia.2016.05.028 PMID: 27267813
80. Holtgraves T, McNamara P, Cappaert K, Durso R. Linguistic correlates of asymmetric motor symptom
severity in Parkinson’s Disease. Brain Cogn. 2010; 72(2):189–96. https://doi.org/10.1016/j.bandc.
2009.08.004 PMID: 19751960
81. Pennebaker JW, Chung CK, Ireland M, Gonzales A, Booth RJ. The Development and Psychometric
Properties of LIWC2007. LIWC2007 Manuel. 2007. p. 1–22.
82. Weston AD, Shriberg LD, Miller JF. Analysis of Language-Speech Samples with Salt and Pepper. J
Speech, Lang Hear Res. 1989; 32(4):755–66.
83. Duden. Duden; http://www.duden.de, last access: 2020-05-22.
84. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to
Multiple Testing. J R Stat Soc Ser B. 1995; 57(1):289–300.
85. Cohen J. The Effect Size Index: d. In: Statistical Power Analysis for the Behavioral Sciences. 2nd ed.
New York: Lawrence Erlbaum Associates; 1988. p. 20–7.
86. Cohen J. The Analysis of Variance and Covariance. In: Statistical Power Analysis for the Behavioral
Sciences. 2nd ed. New York: Lawrence Erlbaum Associates; 1988. p. 273–407.
87. Greenhouse I, Gould S, Houser M, Aron AR. Stimulation of contacts in ventral but not dorsal subthala-
mic nucleus normalizes response switching in Parkinson’s disease. Neuropsychologia. 2013; 51
(7):1302–1309. https://doi.org/10.1016/j.neuropsychologia.2013.03.008 PMID: 23562963
88. Mosley PE, Paliwal S, Robinson K, Coyne T, Silburn P, Tittgemeyer M, et al. The structural connectiv-
ity of subthalamic deep brain stimulation correlates with impulsivity in Parkinson’s disease. Brain.
2020; 143:2235–2254. https://doi.org/10.1093/brain/awaa148 PMID: 32568370
89. Ehlen F, Schoenecker T, Ku¨hn AA, Klostermann F. Differential effects of deep brain stimulation on ver-
bal fluency. Brain Lang. 2014; 134:23–33. https://doi.org/10.1016/j.bandl.2014.04.002 PMID:
24815947
90. Mikos A, Bowers D, Noecker AM, McIntyre CC, Won M, Chaturvedi A, et al. Patient-specific analysis
of the relationship between the volume of tissue activated during DBS and verbal fluency. Neuro-
image. 2011; 54 Suppl 1:238–46. https://doi.org/10.1016/j.neuroimage.2010.03.068 PMID: 20362061
91. York MK, Wilde EA, Simpson R, Jankovic J. Relationship between neuropsychological outcome and
DBS surgical trajectory and electrode location. J Neurol Sci. 2009; 287(1–2):159–71. https://doi.org/
10.1016/j.jns.2009.08.003 PMID: 19767016
92. Scangos KW, Carter CS, Gurkoff G, Zhang L, Shahlaie K. A pilot study of subthalamic theta frequency
deep brain stimulation for cognitive dysfunction in Parkinson’s disease. Brain Stimul. 2018; 11:456–8.
https://doi.org/10.1016/j.brs.2017.11.014 PMID: 29203130
93. Wojtecki L, Elben S, Timmermann L, Reck C, Maarouf M, Jo
¨rgens S, et al. Modulation of human time
processing by subthalamic deep brain stimulation. PLoS One. 2011; 6(9):1–10. https://doi.org/10.
1371/journal.pone.0024589 PMID: 21931767
94. Wojtecki L, Timmermann L, Jo
¨rgens S, Su¨dmeyer M, Maarouf M, Treuer H, et al. Frequency-depen-
dent reciprocal modulation of verbal fluency and motor functions in subthalamic deep brain stimulation.
Arch Neurol. 2006; 63(9):1273–6. https://doi.org/10.1001/archneur.63.9.1273 PMID: 16966504
95. Irmen F, Huebl J, Schroll H, Bru¨cke C, Schneider GH, Hamker FH, et al. Subthalamic nucleus stimula-
tion impairs emotional conflict adaptation in Parkinson’s disease. Soc Cogn Affect Neurosci. 2017; 12
(10):1594–604. https://doi.org/10.1093/scan/nsx090 PMID: 28985419
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 18 / 19
96. Hershey T, Revilla FJ, Wernle A, Gibson PS, Dowling JL, Perlmutter JS. Stimulation of STN impairs
aspects of cognitive control in PD. Neurology. 2004; 62:1110–1114. https://doi.org/10.1212/01.wnl.
0000118202.19098.10 PMID: 15079009
97. Houvenaghel JF, Duprez J, Argaud S, Naudet F, Dondaine T, Robert GH, et al. Influence of subthala-
mic deep-brain stimulation on cognitive action control in incentive context. Neuropsychologia. 2016;
91:519–530. https://doi.org/10.1016/j.neuropsychologia.2016.09.015 PMID: 27664297
98. Le
´vesque JC, Parent A. GABAergic interneurons in human subthalamic nucleus. Mov Disord. 2005;
5:574–584. https://doi.org/10.1002/mds.20374 PMID: 15645534
99. Vassal F, Dilly D, Boutet C, Bertholon F, Charier D, Pommier B. White matter tracts involved by deep
brain stimulation of the subthalamic nucleus in Parkinson’s disease:a connectivity study based on pre-
operative diffusion tensor imaging tractography. Br J Neurosurg. 2020; 34(2):187–195. https://doi.org/
10.1080/02688697.2019.1701630 PMID: 31833430
100. Ranck JB. Which elements are excited in electrical stimulation of mammalian central nervous system:
A review. Brain Res. 1975; 98:417–40. https://doi.org/10.1016/0006-8993(75)90364-9 PMID:
1102064
101. Emmi A, Antonini A, Macchi V, Porzionato A, De Caro R. Anatomy and Connectivity of the Subthala-
mic Nucleus in Humans and Non-human Primates. Front Neuroanat. 2020; 14(Article 13):1–27.
https://doi.org/10.3389/fnana.2020.00001 PMID: 32116572
102. Gu¨ngo
¨r A, Baydın ŞS, Holanda VM, Middlebrooks EH, Isler C, Tugcu B, et al. Microsurgical anatomy
of the subthalamic nucleus: Correlating fiber dissection results with 3-T magnetic resonance imaging
using neuronavigation. J Neurosurg. 2019; 130:716–732.
103. Hershey T, Campbell MC, Videen TO, Lugar HM, Weaver PM, Hartlein J, et al. Mapping Go-No-Go
performance within the subthalamic nucleus region. Brain. 2010; https://doi.org/10.1093/brain/awq256
PMID: 20855421
104. Avecillas-Chasin JM, Alonso-Frech F, Nombela C, Villanueva C, Barcia JA.Stimulation of the Tracto-
graphy-Defined Subthalamic Nucleus Regions Correlates With Clinical Outcomes. Neurosurgery.
2019; 85(2):E294(2):E294–303. https://doi.org/10.1093/neuros/nyy633 PMID: 30690487
105. Klaus J, Schutter DJLG. The Role of Left Dorsolateral Prefrontal Cortex in Language Processing. Neu-
roscience. 2018; 377:197–205. https://doi.org/10.1016/j.neuroscience.2018.03.002 PMID: 29534975
106. Chung GH, Han YM, Jeong SH, Jack CR. Functional heterogeneity of the supplementary motor area.
Am J Neuroradiol. 2005; 26(7):1819–23. PMID: 16091536
107. AbdulSabur NY, Xu Y, Liu S, Chow HM, Baxter M, Carson J, et al. Neural correlates and network con-
nectivity underlying narrative production and comprehension: A combined fMRI and PET study. Cor-
tex. 2014; 57:107–27. https://doi.org/10.1016/j.cortex.2014.01.017 PMID: 24845161
108. Labache L, Joliot M, Saracco J, Jobard G, Hesling I, Zago L, et al. A SENtence Supramodal Areas
AtlaS (SENSAAS) based on multiple task-induced activation mapping and graph analysis of intrinsic
connectivity in 144 healthy right-handers. Brain Struct Funct. 2019; 224(2):859–82. https://doi.org/10.
1007/s00429-018-1810-2 PMID: 30535758
109. Walenski M, Europa E, Caplan D, Thompson CK. Neural networks for sentence comprehension and
production: An ALE-based meta-analysis of neuroimaging studies. Hum Brain Mapp. 2019; 40
(8):2275–304. https://doi.org/10.1002/hbm.24523 PMID: 30689268
110. Amunts K, Catani M. Cytoarchitectonics, Receptorarchitectonics, and Network Topology of Language.
In: Toga AW, editor. Brain Mapping: An Encyclopedic Reference, vol 2. 2015. p. 177–85.
111. Ahn JS, Sidtis DVL, Sidtis JJ. Effects of Deep Brain Stimulation on Pausing During Spontaneous
Speech in Parkinson’s Disease. J Med Speech Lang Pathol [Internet]. 2014; 21(3):179. Available
from: /pmc/articles/PMC4736729/?report = abstract PMID: 26848252
112. Butterworth B. Hesitation and the production of verbal paraphasias and neologisms in jargon aphasia.
Brain Lang. 1979; 8(2):133–61. https://doi.org/10.1016/0093-934x(79)90046-4 PMID: 487066
113. Ehlen F, Krugel LK, Vonberg I, Schoenecker T, Ku¨hn AA, Klostermann F. Intact lexicon running slowly
—Prolonged response latencies in patients with subthalamic DBS and verbal fluency deficits. PLoS
One. 2013; 8(11). https://doi.org/10.1371/journal.pone.0079247 PMID: 24236114
PLOS ONE
STN DBS effects on language
PLOS ONE | https://doi.org/10.1371/journal.pone.0244148 December 29, 2020 19 / 19
