Investigating Stimulation Protocols for Language Mapping by Repetitive Navigated Transcranial Magnetic Stimulation

Sollmann, Nico; Fuss-Ruppenthal, Sophia; Zimmer, Claus; Meyer, Bernhard; Krieg, Sandro M.

doi:10.3389/fnbeh.2018.00197

ORIGINAL RESEARCH article

Front. Behav. Neurosci., 10 September 2018

Sec. Pathological Conditions

Volume 12 - 2018 | https://doi.org/10.3389/fnbeh.2018.00197

Investigating Stimulation Protocols for Language Mapping by Repetitive Navigated Transcranial Magnetic Stimulation

$\r\nNico Sollmann,,&#x;$ Nico Sollmann^1,2,3^†

Sophia Fuss-Ruppenthal²^†

Claus Zimmer^1,3

Bernhard Meyer²

Sandro M. Krieg^2,3*

¹Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
²Department of Neurosurgery, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
³TUM-Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany

Navigated transcranial magnetic stimulation (nTMS) is increasingly applied to map human language functions. However, studies on protocol comparisons are mostly lacking. In this study, 20 healthy volunteers (25.7 ± 3.8 years, 12 females) underwent left-hemispheric language mapping by nTMS, combined with an object-naming task, over a cortical spot with reproducible naming errors within the triangular or opercular part of the inferior frontal gyrus (trIFG, opIFG: anterior stimulation) and the angular gyrus or posterior part of the superior temporal gyrus (anG, pSTG: posterior stimulation), respectively. Various stimulation intensities [80, 100, and 120% of the resting motor threshold (rMT)], frequencies (2, 5, 10, and 20 Hz), and coil orientations (in steps of 45^∘) were evaluated, and the adjustments leading to the highest error rates (ERs), combined with low occurrences of errors due to muscle stimulation, were considered optimal. Regarding anterior stimulation, 100% rMT, 5 Hz, and a coil orientation of 90^∘ or 270^∘ in relation to the respective stimulated gyrus resulted in optimal results. For posterior stimulation, 100% rMT, 10 Hz, and coil orientations of 90^∘ or 270^∘ were considered optimal. Errors due to facial muscle stimulation only played a considerable role during analyses of high-intensity (120% rMT) or high-frequency stimulation (20 Hz). In conclusion, this is one of the first studies to systematically investigate different stimulation protocols for nTMS language mapping, including detailed analyses of the distribution of ERs in relation to various coil orientations considered during neuronavigated stimulation. Mapping with 100% rMT, combined with 5 Hz (anterior stimulation) or 10 Hz (posterior stimulation) and a coil orientation perpendicular to the respective stimulated gyrus can be recommended as optimal adjustments.

Introduction

Transcranial magnetic stimulation (TMS), a non-invasive method that creates a magnetic field inducing a transient electric field capable of modulating neurons in their activity, is available for neuroscientific and clinical use in human subjects since the middle eighties (Barker et al., 1985; Hallett, 2000). Among its multifarious applications, researchers and clinicians use this technique to induce short-lived language or speech disturbances, depending on the site of stimulation and applied stimulation parameters (Pascual-Leone et al., 1991; Epstein, 1998; Devlin and Watkins, 2007). When performed systematically over several areas of the human brain from outside, it is commonly referred to as language mapping.

While first studies on language mapping by TMS were mostly conducted without simultaneous neuronavigation, the combination of TMS with precise electric-field neuronavigation systems further expanded the role of this evolving technique since we are now able to confirm the cortical area to which TMS is applied very accurately (Ruohonen and Ilmoniemi, 1999; Ruohonen and Karhu, 2010). Thus, navigated TMS (nTMS) became a useful tool both for neuroscience and clinical applications because it allows for tracking of the stimulating coil and spatial identification of stimulated cortical regions, which become so-called language-positive sites when an error is elicited during stimulation under task performance (Lioumis et al., 2012; Picht et al., 2013; Tarapore et al., 2013; Hernandez-Pavon et al., 2014; Rosler et al., 2014). Particularly neurosurgery made advantage of nTMS language mapping in recent years, with neurosurgeons considering such language-positive sites during preoperative planning and intraoperative resection guidance in patients suffering from brain lesions in language-eloquent areas, with the aim of reducing surgery-related functional deficits while maximizing the extent of tumor resection (Picht et al., 2013; Sollmann et al., 2015a, 2018).

However, language mapping by nTMS in patients with brain lesions has repeatedly shown to have a comparably high sensitivity and low specificity when compared to intraoperative direct electrical stimulation (DES) in most studies, which is regarded non-optimal (Picht et al., 2013; Krieg et al., 2014; Ille et al., 2015, 2016; Sollmann et al., 2016b). To improve the specificity of nTMS language mapping, recent studies added nTMS-based diffusion tensor imaging fiber tracking (DTI FT) to cortical mapping, changed the picture-to-trigger interval (PTI) of task presentation, or evaluated different error rate (ER) thresholds for errors induced by stimulation (Krieg et al., 2014; Ille et al., 2015, 2016; Sollmann et al., 2016b). Nevertheless, the overall specificity remained largely unsatisfactory for most stimulated regions (Krieg et al., 2014; Ille et al., 2015, 2016; Sollmann et al., 2016b). Additionally, there have been efforts to improve the underlying stimulation protocol partially responsible for the distribution of induced errors, but such approaches have been limited to specific single adjustments (e. g., the stimulation frequency), only enrolled low subject numbers, or were even performed without neuronavigation before the introduction of nTMS (Pascual-Leone et al., 1991; Epstein et al., 1996; Epstein, 1998; Devlin and Watkins, 2007; Hauck et al., 2015; Sollmann et al., 2015b). Thus, evidence for the dependency of nTMS-induced errors, with respect to different categories of errors and their incidences, on stimulation protocols remains poor. Therefore, the present study aims to investigate various nTMS language mapping protocols by systematically evaluating the effect of stimulation intensity, frequency, and coil orientation on object-naming performance in a group of healthy volunteers.

Materials and Methods

Participants and Procedures

The present study was performed in healthy volunteers. Inclusion criteria were German as mother tongue, age of at least 18 years, and right-handedness according to the Edinburgh Handedness Inventory (EHI) (Oldfield, 1971). Subjects with contraindications for magnetic resonance imaging (MRI) at 3 Tesla or language mapping by nTMS (e.g., implanted medical devices such as cardiac pacemaker or deep brain stimulation electrodes), subjects diagnosed with neurological or psychiatric diseases, or pregnant subjects were excluded.

Prior to language mapping, anatomical MRI was performed in all subjects. On a separate day, each volunteer underwent repetitive nTMS (rTMS) to the triangular or opercular part of the inferior frontal gyrus (trIFG, opIFG: anterior stimulation) and the angular gyrus or posterior part of the superior temporal gyrus (anG, pSTG: posterior stimulation) of the left hemisphere (Figures 1A,B). The mappings were conducted with different stimulation intensities [80, 100, and 120% of the individual resting motor threshold (rMT)], stimulation frequencies (2, 5, 10, and 20 Hz), and coil orientations (in steps of 45°) during performance of an object-naming task. The different adjustments were applied in randomized order, and nTMS-induced errors were counted and categorized during post hoc analyses.

FIGURE 1

FIGURE 1. Anterior and posterior stimulation spots. This figure shows a representative example for left-hemispheric anterior stimulation (A) and for posterior stimulation (B). For anterior stimulation, the triangular or opercular part of the inferior frontal gyrus (trIFG, opIFG) were considered, whereas posterior stimulation was carried out within the angular gyrus or posterior part of the superior temporal gyrus (anG, pSTG). Regions for anterior and posterior stimulation were visually identified in each subject on the reconstructed three-dimensional (3-D) head model in the stimulation software. Mapping was then performed in steps of 5 to 10 mm to identify a cortical spot leading to reproducible and clear naming errors in each region, and these spots were then used during later stimulation to evaluate the effect of stimulation intensity, frequency, and coil orientation. The arrows indicate the orientation of the induced electric field, which is perpendicular to the respective stimulated gyrus/closest sulcus for anterior stimulation (A) and parallel with regards to the respective stimulated gyrus/closest sulcus for posterior stimulation (B). A focal figure-8-shaped stimulation coil with an upward handle position was used, which is shown as a miniature model superimposed on the 3-D head model.

Anatomical MRI

Anatomical cranial MRI (without intravenous contrast administration) was performed with a 3-Tesla magnetic resonance scanner (Achieva 3T, Philips Medical Systems, The Netherlands B.V.), and the obtained sequence was used to be able to apply neuronavigation during language mapping. The acquired three-dimensional (3-D), T1-weighted gradient echo sequence had the following scanning parameters: repetition time/echo time: 9/4 ms, 1 mm³ isovoxel covering the whole head, acquisition time: ∼7 min.

Language Mapping by nTMS

nTMS Setup

Subsequent to the upload of the 3-D T1-weighted gradient echo sequence, the respective subject underwent language mapping by rTMS using a Nexstim eXimia NBS system, version 4.3, with a NexSpeech module (Nexstim Plc., Helsinki, Finland). We used a focal, figure-8-shaped stimulation coil with an upward handle position and automatic overheating protection. The used coil applies biphasic pulses (pulse length: 230 μs) and induces an electric field with a maximum field strength of 172 V/m ± 2% (as measured 25 mm below the coil in a spherical conductor model representing the human head). Maximum stimulator output can be approximately 2.5 × rMT of a hand muscle of a healthy adult by default. The stimulation system enables the visualization of all stimulation areas and simultaneous tracking of the coil due to the combination of the stimulation coil with a neuronavigation unit (Polaris Spectra, Waterloo, ON, Canada) (Ruohonen and Ilmoniemi, 1999; Ruohonen and Karhu, 2010; Lioumis et al., 2012; Picht et al., 2013; Tarapore et al., 2013; Hernandez-Pavon et al., 2014; Rosler et al., 2014). Pulse application can be tracked and controlled on each individual’s reconstructed 3-D head model based on the MRI data set (Ruohonen and Ilmoniemi, 1999; Ruohonen and Karhu, 2010). Furthermore, each stimulation spot is saved and can be reassessed during post hoc analyses.

Prior to language mapping by nTMS, the individual rMT was determined. Pregelled surface electrodes (Neuroline 720, Ambu, Ballerup, Denmark) for electromyography recording were placed over the abductor pollicis brevis and abductor digiti minimi muscles, and single stimulation pulses were applied over the left-hemispheric motor cortex at the area of the anatomical hand knob (Rossini et al., 1994; Yousry et al., 1997; Rossini et al., 2015; Sollmann et al., 2017). After identification of the most excitable spot in that area (motor hotspot) with the electric field being oriented perpendicular to the central sulcus during stimulation, the rMT was automatically determined at that spot by using the system’s built-in threshold-hunting procedure, corresponding to the maximum likelihood algorithm (Rossini et al., 1994, 2015; Awiszus, 2003; Sollmann et al., 2017). Percentages of the individual rMT were then used as stimulation intensities during nTMS.

Object-Naming Task and Baseline

We used an object-naming task during baseline assessment and language mapping by nTMS, which was displayed on a screen located approximately 60 cm in front of the volunteers (Lioumis et al., 2012; Picht et al., 2013; Tarapore et al., 2013; Hernandez-Pavon et al., 2014; Rosler et al., 2014). The task consisted of 80 drawn, black and white pictures of common living and non-living items and was similar to a set of objects used in the Snodgrass and Vanderwart pictures (Snodgrass and Vanderwart, 1980). The objects were presented at an inter-picture interval (IPI; interval between the display of two objects) of 3.0 s with a display time (DT; time of object presentation on the screen) of 0.7 s per object.

Two consecutive rounds of baseline assessment (object naming without simultaneous nTMS) were conducted to assess the individual naming skills of each volunteer with respect to the assortment of objects (Lioumis et al., 2012; Hernandez-Pavon et al., 2014). The subjects were instructed to name the displayed objects in German as quickly and precisely as possible. All objects that were misnamed or named with a delay according to the investigator’s evaluation were discarded. The number of correctly named objects was documented after the two rounds of baseline testing. This approach facilitates later post hoc analyses of nTMS-induced errors (Lioumis et al., 2012; Hernandez-Pavon et al., 2014). The baseline assessments were audio- and video-recorded (Lioumis et al., 2012; Hernandez-Pavon et al., 2014).

Mapping Approach

Subsequent to baseline assessments, language mapping by nTMS was performed with the set of remaining, correctly named objects. The IPI and DT were the same as during baseline assessment; thus, the interval between two consecutive stimulation trains was 3.0 s (equal to the IPI), with one stimulation train being applied per every single object presentation. Furthermore, the PTI (onset time; interval between the display of an object and stimulation pulse onset) was set at 0 s, indicating that stimulation started simultaneously with object presentation (Krieg et al., 2014). Pulse application was automatically triggered by the stimulation system with respect to the screening of objects on the screen. The individual set of objects appeared in randomized order until the investigator manually stopped the presentation sequence.

The left-hemispheric trIFG/opIFG (anterior stimulation) and anG/pSTG (posterior stimulation), which were anatomically identified on the reconstructed 3-D head model (Figures 1A,B), were mapped in steps of 5–10 mm to identify a cortical spot for each of both cortical regions where nTMS led to reproducible and clear naming errors during task performance (Sollmann et al., 2015b). This was done with 100% rMT, 5 Hz/5 pulses (duration of one stimulation train with these adjustments: 2.0 s), and anterior-posterior (a-p) coil orientation (coil oriented horizontally with respect to a line between the external acoustic meatus and nasion) as the most common nTMS language mapping settings at present (Lioumis et al., 2012; Picht et al., 2013; Tarapore et al., 2013; Hernandez-Pavon et al., 2014; Rosler et al., 2014; Sollmann et al., 2015b). The optimal stimulation intensity, frequency, and coil orientation were then evaluated at these spots with respect to the following approach:

– stimulation intensity: stimulation with 80, 100, and 120% rMT (5 Hz/5 pulses, a-p coil orientation, duration of one stimulation train: 2.0 s), 10 consecutive stimulation trains per intensity at the optimal spot (cortical stimulation spot with high error reproducibility, combined with low occurrences of errors due to muscle stimulations according to the investigator’s evaluation during stimulation) within the trIFG/opIFG and anG/pSTG (3 × 10 × 2 = 60 stimulation trains to test for optimal stimulation intensity).

– stimulation frequency: stimulation with 2 Hz/4 pulses, 5 Hz/10 pulses, 10 Hz/20 pulses, and 20 Hz/40 pulses (optimal intensity, a-p coil orientation, duration of one stimulation train: 2.0 s), 10 consecutive stimulation trains per frequency at the optimal spot within the trIFG/opIFG and anG/pSTG (4 × 10 × 2 = 80 stimulation trains to test for optimal stimulation frequency).

– coil orientation: stimulation with variations in steps of 45° (optimal intensity, optimal frequency, duration of one stimulation train: 2.0 s), 10 consecutive stimulation trains per orientation at the optimal spot within the trIFG/opIFG and anG/pSTG (8 × 10 × 2 = 160 stimulation trains to test for optimal coil orientation).

During mapping, the optimal stimulation intensity and frequency to conduct further stimulation according to the protocol were determined during the ongoing stimulation procedure (Sollmann et al., 2015b). This was based on counting the amount of naming errors elicited during nTMS per setting, and the adjustment leading to the highest amount of naming errors, combined with low occurrences of errors presumably due to muscle stimulations, was defined as optimal for each subject (Sollmann et al., 2015b). Exact analyses determining optimal adjustments for the whole cohort followed the mappings.

The course of mappings applying the different stimulation parameters was randomized for each subject using cards for each adjustment. Prior to language mapping, the first region to map was determined by drawing a card (2 options/cards, 1 draw). Then, the different parameters for testing stimulation intensity (3 options/cards, 2 draws) and frequency (4 options/cards, 3 draws) were individually determined to define the chronological order in which the various settings were tested. Regarding coil orientation, only the initial orientation in relation to a-p coil orientation was drawn (8 options/cards, 1 draw), and the other orientations were tested clockwise (in steps of 45°) with respect to the initially used orientation. Like for the baseline assessments, the whole procedure was recorded (Lioumis et al., 2012; Hernandez-Pavon et al., 2014). After the mapping procedure, each subject was instructed to rate overall perceived discomfort during nTMS according to a visual analog scale (VAS) (Langley and Sheppeard, 1985).

Video Analyses

Post hoc video analyses, which were performed by the investigator who had already conducted the mappings (SFR; certified speech therapist working in a neurological rehabilitation unit), were based on video and audio recordings acquired during the individual baseline assessments and mapping procedures. These data were streamed and each naming performance was compared with the individual baseline to detect disruption of object naming (Lioumis et al., 2012; Picht et al., 2013; Tarapore et al., 2013; Hernandez-Pavon et al., 2014; Rosler et al., 2014). Similar to previous approaches, the induced errors were categorized as follows (Corina et al., 2010; Lioumis et al., 2012; Krieg et al., 2016):

– no-response error: no language production during stimulation.

– performance error: incorrect pronunciation of the target word or slurring/stuttering.

– phonological paraphasias: substitutions, additions, or omissions of phonemes of the target word.

– semantic paraphasias: substitution of the target word by a semantically related word.

– neologism: creation of a non-existent word instead of the target word.

Hesitations (delays in naming the target word during stimulation) were not considered during analyses because we did not use objective measurements of voice reaction times. Furthermore, errors that presumably occurred due to stimulation-induced facial muscle activity according to video analysis or reporting of the volunteers were not included in one of these categories, but were registered separately. For further analyses, we considered all naming errors without hesitations or muscle stimulations (no-response errors, performance errors, phonological paraphasias, semantic paraphasias, and neologisms together), no responses, performance errors, others (phonological paraphasias, semantic paraphasias, and neologisms together), and errors due to muscle stimulation.

Statistical Analyses

For all statistical analyses, we used GraphPad Prism (version 7.0, GraphPad Software Inc., La Jolla, CA, United States). A p-value of <0.05 indicated statistical significance for all statistical tests.

Errors of the different categories were counted to form absolute and relative frequencies for the different adjustments applied during anterior and posterior stimulation, respectively. Descriptive statistics including mean, standard deviation (SD), median, minimum and maximum values were calculated for subject- and mapping-related characteristics and naming errors of the different categories. Discomfort perceived according to the VAS and the electric field strength during nTMS were compared between anterior and posterior stimulation spots using Wilcoxon matched-pairs signed rank tests.

Furthermore, we calculated ERs by dividing the number of errors induced by nTMS by the number of stimulations, which was done for anterior and posterior stimulation as well as for the different adjustments and error categories, respectively (Ille et al., 2015, 2016; Krieg et al., 2016). We then evaluated which adjustments led to the highest ERs per error category for the stimulation intensity (80% rMT, 100% rMT, or 120% rMT), frequency (2, 5, 10, or 20 Hz), and coil orientation (0°/360°, 45°, 90°, 135°, 180°, 225°, 270°, or 315°). With respect to the coil orientation, we evaluated both the angulations in relation to a-p coil orientation and the angulations with respect to the stimulated gyrus/closest sulcus. The adjustments leading to the highest ERs, thus reflecting high reproducibilities of errors, were defined as optimal (except for muscle stimulation where the opposite was true). The incidences of single adjustments when defined as optimal were counted separately for the testing of stimulation intensity, frequency, and coil orientation with respect to the different error categories (e. g., a subject showed an ER of 15.0% for no responses regarding stimulation with 80% rMT, 20.0% for 100% rMT, and 30.0% for 120% rMT – depending on the ER, 120% rMT was defined as the optimal intensity in this subject and the incidence of 120% rMT as an optimal setting increased by 1 in terms of stimulation intensity for the whole cohort). The total number was translated into a relative frequency, given as a percentage and reflecting the fraction of a certain adjustment that led to the highest ER. Chi-squared tests were applied to assess differences between the fractions of optimal adjustments vs. the fractions of non-optimal adjustments, again separately for the stimulation intensity, frequency, and coil orientation for anterior and posterior stimulation and the different error categories.

For the optimal adjustments, we calculated coefficients of variation (CVs) as measures for dispersion regarding error numbers of all errors without hesitations or muscle stimulations. This was again achieved separately for anterior and posterior stimulation and for all subjects together as well as for female and male subjects, respectively. Additionally, were performed Mann–Whitney tests to compare error numbers of this error category between genders.

Results

Study Cohort

Twenty healthy volunteers (25.7 ± 3.8 years, 12 females and 8 males) took part in this study. Overall, the stimulation procedure was tolerated well by all volunteers and no adverse events were observed. Stimulation within the trIFG/opIFG was significantly more discomfortable when compared to nTMS to the anG/pSTG (p = 0.0001). There was no statistically significant difference in electric field strengths when comparing anterior and posterior stimulation delivered with optimal adjustments (p = 0.4980). Table 1 displays cohort and mapping characteristics.

TABLE 1

TABLE 1. Cohort characteristics.

Anterior Stimulation

We were able to identify a cortical spot with reproducible naming errors during nTMS in all subjects within the trIFG/opIFG at the beginning of language mapping (Figure 1A). Overall, 3,000 nTMS trains were applied to 20 subjects, leading to 295 naming errors of the different categories (9.8%; 14.8 ± 7.7 errors per subject, median: 13.5 errors, range: 5 – 34 naming errors) and 453 errors presumably due to muscle stimulation (15.1%; 22.7 ± 27.7 errors per subject, median: 16.0 errors, range: 0 – 109 errors). Out of the 295 naming errors elicited, 37 were no responses (12.6%), 210 were performance errors (71.2%), 34 were phonological paraphasias (11.5%), 13 were semantic paraphasias (4.4%), and 1 was categorized as neologism (0.3%).

Stimulation Intensity

There was no statistically significant difference between adjustments considering the different error categories except for muscle stimulations (nTMS with 120% rMT led to the highest ER most frequently, p = 0.0066; Figure 2A). For all errors without hesitations or muscle stimulations, no responses, and performance errors, 100% rMT led to the highest ERs most frequently when compared to stimulation with 80% rMT or 120% rMT (Figure 2A).

FIGURE 2

FIGURE 2. Comparison of error incidences for language mapping with different stimulation intensities. An optimal adjustment for the stimulation intensity (given as % of the individual resting motor threshold [rMT]) was defined as the intensity that resulted in the highest error rate (ER; number of errors/number of stimulations) when compared to the other adjustments (except for errors due to muscle stimulations). The incidences of the adjustments leading to the highest ER were assessed for the whole cohort and different error categories, making it possible that more than one adjustment was counted per person. Panel (A) shows how frequent (in %, y-axis) a certain stimulation intensity led to the highest ER when compared to the other adjustments for anterior stimulation when considering different error categories (x-axis), whereas panel (B) shows the results for posterior stimulation. The biggest column represents the optimal adjustment per error category (except for errors due to muscle stimulations where the opposite was true). Statistically significant differences between adjustments are marked by asterisks (^∗p < 0.05).

Stimulation Frequency

Stimulation with 5 Hz led to the highest ER for all errors without hesitations or muscle stimulations and performance errors most frequently, with a statistically significant difference in comparison to the other adjustments (p = 0.0012, p = 0.0081; Figure 3A). Highest ERs due to muscle stimulation occurred most frequently in response to stimulation with 20 Hz (p < 0.0001; Figure 3A).

FIGURE 3

FIGURE 3. Comparison of error incidences for language mapping with different stimulation frequencies. Similar to the evaluation of optimal stimulation intensities, an optimal adjustment for the stimulation frequency (given in Hz) was determined as the frequency that resulted in the highest error rate (ER; number of errors/number of stimulations) when compared to the other adjustments (except for errors due to muscle stimulations). The incidences of the adjustments leading to the highest ER were assessed for the whole cohort and different error categories, making it possible that more than one adjustment was counted per person. Panel (A) shows how frequent (in %, y-axis) a certain stimulation frequency led to the highest ER when compared to the other adjustments for anterior stimulation when considering different error categories (x-axis), whereas panel (B) shows the results for posterior stimulation. The biggest column represents the optimal adjustment per error category (except for errors due to muscle stimulation where the opposite was true). Statistically significant differences between adjustments are indicated by asterisks (^∗p < 0.05).

Coil Orientation

In relation to a-p coil orientation as the most common adjustment in nTMS language mapping, the highest ERs occurred most frequently for stimulation with 0°/360° (equal to a-p coil orientation), 135°, and 180° when considering all errors without hesitations or muscle stimulations (Table 2 and Figure 4A). However, a statistically significant difference between adjustments for different orientations in relation to a-p coil orientation was only observed for performance errors, with 0°/360° in relation to a-p coil orientation being considered optimal (p = 0.0313; Table 2).

FIGURE 4

FIGURE 4. Comparison of error incidences for language mapping with different coil orientations (all errors without hesitations or muscle stimulations). An optimal coil orientation (assessed in steps of 45°) was determined as the orientation that resulted in the highest error rate (ER; number of errors/number of stimulations) when compared to the other adjustments (except for errors due to muscle stimulations). The incidences of the adjustments leading to the highest ER were assessed for the whole cohort and different error categories, making it possible that more than one adjustment was counted per person. Panel (A) shows how frequent (in %) a certain coil orientation led to the highest ER in relation to anterior-posterior (a-p) coil orientation when compared to the other adjustments for anterior stimulation when considering all errors without hesitations or muscle stimulations, whereas panel (B) shows the results for posterior stimulation. Furthermore, panel (C) shows the results in relation to the stimulated gyrus/closest sulcus for anterior stimulation and panel (D) shows the analog results for posterior stimulation. Dark red segments represent the optimal adjustments under these premises. Corresponding numbers and analyses of statistical significance also for other error categories can be found in Tables 2, 3.

TABLE 2

TABLE 2. Distribution of error types for different coil orientations (in relation to anterior-posterior [a-p] coil orientation).

Regarding coil orientations in relation to the respective stimulated gyrus, 90° and 270° led to the highest ERs most frequently for all errors without hesitations or muscle stimulations (Table 3 and Figures 4C, 5A,B). For this category, the comparison between coil orientations was statistically significant (p = 0.0372; Table 3).

TABLE 3

TABLE 3. Distribution of error types for different coil orientations (in relation to the stimulated gyrus).

For all errors without hesitations or muscle stimulations elicited during nTMS with optimal adjustments, a CV of 55.9% was observed for all subjects together (female subjects: 42.9%, male subjects: 68.5%). However, there was no statistically significant difference in error numbers between genders (p = 0.8797).

Posterior Stimulation

We identified a cortical spot with reproducible naming errors during stimulation within the anG/pSTG in all enrolled subjects (Figure 1B). Application of 3,000 nTMS trains in total resulted in 264 naming errors of the different categories (8.8%; 13.2 ± 6.5 errors per subject, median: 11.0 errors, range: 7 – 35 naming errors) and 93 errors presumably due to muscle stimulation (3.1%; 4.7 ± 13.6 errors per subject, median: 0.0 errors, range: 0 – 60 errors). Out of the 264 naming errors elicited, 30 were no responses (11.4%), 182 were performance errors (68.9%), 33 were phonological paraphasias (12.5%), 17 were semantic paraphasias (6.4%), and 2 were classified as neologism (0.8%).