NeuroImage 100 (2014) 219–236

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Optimal timing of pulse onset for language mapping with navigated repetitive transcranial magnetic stimulation Sandro M. Krieg a,1,2, Phiroz E. Tarapore b,⁎,1, Thomas Picht c,3, Noriko Tanigawa d, John Houde e, Nico Sollmann a,2, Bernhard Meyer a,2, Peter Vajkoczy c,3, Mitchel S. Berger b,4, Florian Ringel a,1,2, Srikantan Nagarajan e,1,5 a

Department of Neurosurgery, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 Munich, Germany Department of Neurological Surgery, University of California at San Francisco, 505 Parnassus Ave, Moffitt, San Francisco, CA 94143, USA c Department of Neurosurgery, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Augustenburger Platz 1, 13353 Berlin, Germany d Department of Otolaryngology, Head and Neck Surgery, University of California at San Francisco, 2380 Sutter St., 1st Floor, San Francisco, CA 94115, USA e Department of Radiology and Biomedical Imaging, University of California at San Francisco, 513 Parnassus Avenue, San Francisco, CA 94143-0628, USA b

a r t i c l e

i n f o

Article history: Accepted 6 June 2014 Available online 17 June 2014 Keywords: Preoperative mapping Language Tumor Transcranial magnetic stimulation Awake surgery

a b s t r a c t Object: Within the primary motor cortex, navigated transcranial magnetic stimulation (nTMS) has been shown to yield maps strongly correlated with those generated by direct cortical stimulation (DCS). However, the stimulation parameters for repetitive nTMS (rTMS)-based language mapping are still being refined. For this purpose, the present study compares two rTMS protocols, which differ in the timing of pulse train onset relative to picture presentation onset during object naming. Results were the correlated with DCS language mapping during awake surgery. Methods: Thirty-two patients with left-sided perisylvian tumors were examined by rTMS prior to awake surgery. Twenty patients underwent rTMS pulse trains starting at 300 ms after picture presentation onset (DELAYED TMS), whereas another 12 patients received rTMS pulse trains starting at the picture presentation onset (ONSET TMS). These rTMS results were then evaluated for correlation with intraoperative DCS results as gold standard in terms of differential consistencies in receiver operating characteristics (ROC) statistics. Logistic regression analysis by protocols and brain regions were conducted. Results: Within and around Broca's area, there was no difference in sensitivity (ONSET TMS: 100%, DELAYED TMS: 100%), negative predictive value (NPV) (ONSET TMS: 100%, DELAYED TMS: 100%), and positive predictive value (PPV) (ONSET TMS: 55%, DELAYED TMS: 54%) between the two protocols compared to DCS. However, specificity differed significantly (ONSET TMS: 67%, DELAYED TMS: 28%). In contrast, for posterior language regions, such as supramarginal gyrus, angular gyrus, and posterior superior temporal gyrus, early pulse train onset stimulation showed greater specificity (ONSET TMS: 92%, DELAYED TMS: 20%), NPV (ONSET TMS: 92%, DELAYED TMS: 57%) and PPV (ONSET TMS: 75%, DELAYED TMS: 30%) with comparable sensitivity (ONSET TMS: 75%, DELAYED TMS: 70%). Logistic regression analysis also confirmed the greater fit of the predictions by rTMS that had the pulse train onset coincident with the picture presentation onset when compared to the delayed stimulation. Analyses of differential disruption patterns of mapped cortical regions were further able to distinguish clusters of cortical regions standardly associated with semantic and pre-vocalization phonological networks

Abbreviations: anG, angular gyrus; CPS, cortical parcellation system; danG, dorsomesial region of anG; DCS, direct cortical stimulation; ECoG, electrocorticography; FN, false negative; FP, false positive; fMRI, functional magnetic resonance imaging; IPI, inter picture interval; MEG, magnetoencephalography; nTMS, navigated transcranial magnetic stimulation; NPV, negative predictive value; pMTG, posterior middle temporal gyrus; PPV, positive predictive value; pSTG, posterior superior temporal gyrus; pSTS, posterior superior temporal sulcus; PTI, picture-trigger interval; ROC, receiver operating characteristics; rTMS, repetitive navigated transcranial magnetic stimulation; RMT, resting motor threshold; SMG, supramarginal gyrus; SPT, Sylvian-parietal-temporal; TN, true negative; TP, true positive; TPJ, temporo-parietal-junction; vanG, ventrolateral region of anG. ⁎ Corresponding author at: Department of Neurological Surgery, University of California at San Francisco, 505 Parnassus Ave, Moffitt, San Francisco, CA 94143, USA. Fax: +1 415 353 2889. E-mail addresses: [email protected] (S.M. Krieg), [email protected] (P.E. Tarapore), [email protected] (T. Picht), [email protected] (N. Tanigawa), [email protected] (N. Sollmann), [email protected] (B. Meyer), [email protected] (P. Vajkoczy), [email protected] (M.S. Berger), [email protected] (F. Ringel), [email protected] (S. Nagarajan). 1 These authors contributed equally. 2 Fax: +49 89 4140 4889. 3 Fax: +49 30 450 560 900. 4 Fax: +1 415 353 3910. 5 Fax: +1 415 502 4302.

http://dx.doi.org/10.1016/j.neuroimage.2014.06.016 1053-8119/Published by Elsevier Inc.

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proposed in various models of word production. Repetitive nTMS predictions by both protocols correlate well with DCS outcomes especially in Broca's region, particularly with regard to TMS negative predictions. Conclusions: With this study, we have demonstrated that rTMS stimulation onset coincident with picture presentation onset improves the accuracy of preoperative language maps, particularly within posterior language areas. Moreover, immediate and delayed pulse train onsets may have complementary disruption patterns that could differentially capture cortical regions causally necessary for semantic and pre-vocalization phonological networks. Published by Elsevier Inc.

Introduction Surgical resection of cerebral lesions within the dominant hemisphere harbors the risk of impairing language. This risk is highest within the so-called language-eloquent regions, which include the inferior frontal, superior temporal, supramarginal, and angular gyri. Thus, lesions in these locations are often resected by awake craniotomy with intraoperative language mapping by bipolar direct cortical stimulation (DCS) as has been previously described (Haglund et al., 1994; Ojemann and Whitaker, 1978; Ojemann et al., 1989; Sacko et al., 2011; Sanai et al., 2008). Despite the high reliability of intraoperative DCS mapping, accurate preoperative language mapping would be valuable for a number of reasons. It would allow for more targeted and therefore faster intraoperative mapping. It may also inform patient selection, allowing clinicians to achieve more extensive resections in patients unable to undergo awake surgery. Moreover, it would help clinicians in discussing the risk of postoperative language deficit with their patients (Picht et al., 2013; Tarapore et al., 2013). Magnetoencephalography (MEG) is one modality that has been explored for this purpose, but it has not yet demonstrated accuracy sufficient to be useful for preoperative mapping (Makela et al., 2007; Van Poppel et al., 2012) although it shows greater promise for language dominance predictions (Findlay et al., 2012) and functional connectivity measures (Findlay et al., 2012; Guggisberg et al., 2008; Martino et al., 2011; Tarapore et al., 2012a). Similarly, when compared with intraoperative DCS during awake surgery, preoperative functional magnetic resonance imaging (fMRI) for language function is insufficiently accurate to serve as a basis for surgical decision-making (FitzGerald et al., 1997; Giussani et al., 2010; Roux et al., 2003; Voss et al., 2013; Yetkin et al., 1997). In contrast, mapping by navigated transcranial magnetic stimulation (nTMS) has been shown to correlate well with intraoperative DCS motor mapping (Krieg et al., 2012; Picht et al., 2009; Picht et al., 2011; Tarapore et al., 2012b). With regard to language mapping, repetitive TMS has been used in the past for language mapping by eliciting language disturbance (Epstein, 1998; Epstein et al., 1996; Pascual-Leone et al., 1991; Sparing et al., 2001; Wassermann et al., 1999). Applicability of TMS to language mapping increased when lower-frequency (4–8 Hz) repetitive TMS was found to be tolerable while effectively and reliably causing language disruption when compared to higher-frequency (16–32 Hz) repetitive TMS (Epstein et al., 1996). Moreover, when lower-frequency repetitive TMS is integrated with a frameless stereotactic navigation system, it was made possible to be used to localize cortical language regions (Lioumis et al., 2012) online as the operator moves the coil across the head. Three other studies have demonstrated that preoperative lower-frequency repetitive nTMS (rTMS) predictions had a good correlation with DCS outcomes gathered during awake surgery (Picht et al., 2013; Sollmann et al., 2013). Lower-frequency rTMS therefore shows promise as a method of preoperative language mapping. However, the accuracy of rTMS language mapping varied across cortical language areas and protocols thus far (Picht et al., 2013; Tarapore et al., 2013). As a result, parameters such as stimulation intensity, frequency, and the timing of pulse train onset must be studied and optimized to maximize the sensitivity and specificity of this modality. Crucial to this investigation is the fine-grained temporal and spatial knowledge accumulated by MEG, (MRI-guided)

chronological TMS, and electrocorticography (ECoG). Past studies using these methods have shown that different language regions get activated or disrupted at different time points (Hulten et al., 2009; Salmelin et al., 1994; Vihla et al., 2006; Wheat et al., 2013). These results suggest that preoperative rTMS language mapping across the various cortical language areas may require customization of pulse train parameters to timely disturb each of the language regions along the time course of language processing and production. We hypothesize that, because of these different time points of activation, rTMS-based mapping would require varying pulse train onset times for each region. Thus, the present study compares the accuracy of two rTMS protocols differing mainly in pulse train onset timing for presurgical language mapping: a 5-Hz pulse train starting at either 0 ms post picture presentation onset (Tarapore et al., 2013, ONSET TMS hereafter) or 300 ms post picture presentation onset (Picht et al., 2013, DELAYED TMS hereafter). The ONSET TMS protocol was motivated by the consistency of the pulse train onset timing between the preoperative TMS and the intraoperative DCS, whereas the DELAYED TMS protocol was motivated by the activation time-windows reported in past activation studies. So far, rTMS-mapped areas reported to be lower in consistency with DCS were mid to posterior temporal regions for both the DELAYED TMS protocol (Picht et al., 2013) and the ONSET TMS protocol (Tarapore et al., 2013) and the supramarginal gyrus (SMG) and the angular gyrus (anG) for the DELAYED TMS protocol (Picht et al., 2013). For these regions, findings from both past activation and disruption studies justify the TMS pulse train onset that starts 300 ms after the DCS onset. According to Indefrey and Levelt's spatio-temporal model (2011, 2004), which was built on a meta-analysis of mostly activation studies, the median peak activation latency is estimated to be 300 ms post picture presentation onset in both SMG and anG, and 320 ms and 360 ms post picture presentation onset in posterior superior temporal gyrus (pSTG). Past disruption studies by TMS in various languages (English, Japanese, Dutch, Italian, Hebrew, and German) with healthy adults have provided information about a range of reasonably early or late pulse-train onset timing and the direction of effects (disruptive or facilitatory) on STG, MTG, SMG, and anG (Table 1). In particular, TMS studies that utilized the object-naming task have provided two pieces of temporal information that favor the DELAYED ONSET protocol for these regions. First, unlike the intraoperative DCS and the ONSET TMS protocol, the pulse train onset in the past TMS studies did not coincide with the picture presentation onset; the onset latency varied from − 100 ms to +500 ms relative to the picture presentation onset. Second, regardless of the pulse frequency and the duration, pulse trains that started at + 225 ms to + 500 ms relative to the picture presentation onset were reported to exert intended disruptive effects on naming latency in these regions (Mima, 2003; Schuhmann et al., 2012), whereas pulse trains that started earlier at − 100 ms to + 200 ms and later at or after +500 ms relative to the picture presentation onset were reported to exert facilitatory effects (Acheson et al., 2011) or no disruptive effects (Acheson et al., 2011; Mima, 2003; Schuhmann et al., 2012) on naming latency in these regions. In the clinical context, the role of the presurgical rTMS mapping is not only to narrow down the possible language positive sites for patients who will undergo stressful intraoperative DCS mapping and increase the confidence in DCS positive sites and DCS negative sites (Tarapore et al., 2013), but also to find a set of

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Table 1 Previous studies. Characteristics of previously published repetitive TMS studies. Regions

Pulse frequency (Hz)

Number of Pulses

Pulse onset latency (ms)

Effects

Task

Language

Dependent variable

Acheson et al. (2011) Acheson et al. (2011) Mima (2003)

mMTG, pMTG pSTG SMG/anG

10 10 20

4 4 2

English English Japanese

Reaction times Reaction times Reaction times

pSTG

40

3

Picture naming

Dutch

Reaction times

Schuhmann et al. (2012)

mSTG

40

3

Picture naming

Dutch

Reaction times

Stoeckel et al. (2009) Localizer Stoeckel et al. (2009) Main experiment Sliwinska et al. (2012) Localizer Sliwinska et al. (2012) Main experiment Sliwinska et al. (2012) Main experiment Sliwinska et al. (2012) Main experiment Romero et al. (2006) Harpaz et al. (2009) Hartwigsen et al. (2010) Hartwigsen et al. (2010)

SMG

10

3

Facilitatory No effect Disruptive Not disruptive Disruptive Not disruptive Not disruptive Both disruptive Not disruptive Not disruptive Disruptive

Picture naming Picture naming Pictograph naming

Schuhmann et al. (2012)

−100 −100 250 100 400 Before 400 525 225, 400 150 525 100

Visual rhyme judgment

English

Reaction times

SMG

(Single pulse)

1

180

Facilitatory

English

Reaction times

SMG

10

5

100

Disruptive

Visual homophone & synonym judgments Visual rhyme judgment

English

Reaction times

Correctly localized SMG

25

2

80, 120, 160

All disruptive

Visual homophone judgment,

English

Reaction times

Correctly localized SMG

25

2

80, 120, 160

All not disruptive

Visual synonym judgment

English

Reaction times

Incorrectly localized SMG

25

2

80, 120, 160

All facilitative

Visual synonym judgment

English

Reaction times

SMG (BA40) Wernicke's area (CP5) SMG (mean MNI) anG (mean MNI)

5 10 10 10

8 5 4 4

100 100 100 100

Disruptive Facilitatory Disruptive Not disruptive

Visual phonological judgment Visual semantic resolution Visual phonological judgment Visual semantic judgments

Italian Hebrew German German

Accuracy Reaction times Reaction times Reaction times

possible language positive sites for patients who cannot undergo intraoperative awake DCS mapping (Picht et al., 2013). When considering these two major roles of presurgical TMS mapping, the DELAYED ONSET TMS protocol is well-motivated to map out possible language positive sites in pSTG, mMTG, SMG and anG, though they may be classified as false positives when compared against DCS outcomes in the standard ONSET DCS protocol. However, one caveat is in order. TMS studies that utilized visual phonological and semantic judgment tasks reported differential taskdependent effects of early pulse train onset on SMG, and anG. Regardless of the pulse frequency (5 to 25 Hz) and duration, when the rTMS starting at 100 ms relative to the visual word presentation onset was applied to the predefined stimulation points in SMG, phonological judgments were reliably delayed (Hartwigsen et al., 2010; Romero et al., 2006; Sliwinska et al., 2012; Stoeckel et al., 2009), whereas semantic judgments were facilitated (Harpaz et al., 2009), or were not delayed (Sliwinska et al., 2012). Semantic judgments were not delayed, either, when the rTMS starting at +100 ms relative to the visual word presentation onset was applied to the predefined stimulation points in anG (Hartwigsen et al., 2010). These results suggest that SMG might be processing phonological information as early as 100 ms after the visual word presentation onset. Consequently, the ONSET TMS protocol might work better for SMG mapping but might miss possible language positive sites in anG. The DELAYED TMS protocol could yield mixed results. In addition to temporal specification, Sliwinska et al. (2012) also addressed the importance of fine-grained spatial specification. They reported that unintended facilitatory effects occurred when stimulations were applied to incorrectly identified target points in SMG, which decreased reaction times for phonological judgments. This spatial issue suggests that evaluating the TMS-DCS correlation in the present study would need cortical parcellation system that takes into account functional distinctions in grouping stimulation points. Edwards et al.'s (2010) ECoG study that analyzed high-resolution measure recorded using high-density microelectrode grids offered two relevant findings about the spatio-temporal functional complexity at the borders of STG in object-naming, which might help decide between different cortical parcellation systems that Picht et al. (2013) and Tarapore et al. (2013) used to group TMS and DCS stimulation points. First, for the ECoG

sensors placed in the mid- to posterior superior temporal regions, slight spatial differences resulted in observations of activations in different time windows, suggesting that functionally different processes coexist in these regions and unfold in different time windows. Specifically, though sensors in most of these regions detected activations only in the post-vocalization time-window, indexing auditory processing evoked by one's own naming utterances, there were small enclaves in the posterior temporal regions where sensors detected increased activations induced in the pre-vocalization time-window: posterior middle temporal gyrus (pMTG) and posterior superior temporal sulcus (pSTS), presumably indexing early semantic and phonological code retrieval. Second, there was also an enclave in temporo-parietal-junction (TPJ), also known as area Sylvian-parietal-temporal (SPT), where sensors detected activation unfolding synchronously to activation detected by sensors placed at the superior part of the ventral pre-motor cortex (vPrG or mPrG) in both the pre- and post-vocalization time-windows, presumably indexing internally generated phonological prediction of upcoming voice signals estimated from speech-motor command signals. Similarly, SMG sensors, together with distant tri-IFG and op-IFG sensors, detected increased activity in the pre-vocalization time window of the object-naming trial, whereas activation in adjacent perirolandic regions (precentral and postcentral gyri (PrG, PoG)) started to increase in the pre-vocalization time window and peaked in the post-vocalization time-window. These findings suggest that, in evaluating TMS outcomes against the gold standard DCS in temporal regions and inferior parietal regions (SMG and anG), it would be more appropriate to group stimulation points by using a cortical parcellation system that has functionally-defined anatomical borders as in Picht et al. (2013) than by using a grid-cell system solely defined by geometric distance as in Tarapore et al. (2013). Past multi-modal studies that coupled lower-frequency navigated rTMS and DCS (Picht et al., 2013; Tarapore et al., 2013) argued for the utility of navigated rTMS for preoperative language mapping based on the high correlation observed between rTMS predictions and DCS outcomes in terms of the signal detection theoretic measures, such as sensitivity, specificity, positive and negative predictive values. As reasoned above, the DELAYED TMS protocol might cause less facilitatory effects and more disruptive effects, which could, in turn, increase the

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proportion of false positive TMS predictions than the ONSET TMS protocol would when compared against the DCS that have pulse train onset coincident with the visual stimulus presentation onset. To quantify such relative differences in disruptive effects, the present study goes one step further and tests the utility of each protocol against a specific criterion. Given the fact that DCS outcomes are predominantly negative (Sanai et al., 2008), it would be reasonable to test whether TMS predictions would be statistically significantly more consistent with the DCS outcomes than simply making a totally negative prediction. Specifically, we examine the region-specific predictability of rTMS for language mapping using a logistic regression model for each protocol. In addition to compare the utility of the two protocols head to head, we also integrate the findings from the two protocols. Specifically, though Picht et al. (2013) summarized the first order cortical response pattern (DCS positive/negative; TMS positive/negative) into the second order cortical response patterns (true positive, false positive, true negative, false negative) and showed their distributions in separate images, they did not relate these distributions to the four contexts of evaluation: sensitivity, specificity, positive predictive value, and negative predictive value (e.g., true positive only regions, false negative only regions, mixed-result regions in the sensitivity context). The review of the pulse parameters presented in the earlier section suggests that, by capitalizing on the differential responsiveness of semantic and phonological regions to brain stimulation, DCS mapping along with nTMS mapping across cortical regions using different pulse train onset latencies could distinguish cortical regions participating in semantic and phonological networks. Analysis of structural networks based on disruption response patterns of cortical regions was not attempted so far either in Picht et al. (2013) or in Tarapore et al.'s (2013). Consequently, the causal inferences about anatomical–functional language networks were not fully drawn from the potentially information-rich data collected for these past clinical nTMS language mapping studies. Much less were they integrated into the body of literature of the basic research that has attempted to build models of language production. By performing this analysis, we relate the present clinical study to the existing basic research literature not only with regard to the temporal specificity of the phonological maintenance and self-monitoring stages in SMG and STG proposed in Indefrey and Levelt's spatio-temporal model of single word production (Indefrey, 2011; Indefrey and Levelt, 2004). When the relationship of these regions with the frontal areas is examined, the relevance would also extend to models of language networks for verbal (e.g., phonological) working memory (Friederici, 2012; Jacquemot and Scott, 2006; Makuuchi and Friederici, 2013; Makuuchi et al., 2009), internally generated phonological representation (Bohland et al., 2010; Hickok, 2012; Rauschecker, 2012; Tian and Poeppel, 2013) and semantic representation (Binder et al., 2009; Bonner et al., 2013; Seghier et al., 2010) proposed in fMRI- and lesionbased spatial models of language processing, all of which have integrated Indefrey and Levelt's spatio-temporal model (2011, 2004) to varying degrees. Thus, the present study compares the accuracy of two rTMS protocols differing mainly in pulse train onset timing for presurgical language mapping: a 5-Hz pulse train starting at either 0 ms post picture presentation onset (Tarapore et al., 2013, ONSET TMS hereafter) or 300 ms post picture presentation onset (Picht et al., 2013, DELAYED TMS hereafter). The results of both methods were correlated with intraoperative DCS mapping during awake surgery as the ground truth.

Materials and methods Ethics The experimental protocol was approved by the three local ethical committees (Ethics Committee Registration Number: 2793/10 and 5497/12) in accordance with the declaration of Helsinki. All patients

provided written informed consent for all medical evaluation and treatment. Study design The study was designed as prospective, non-randomized. In keeping the number of TMS stimulation per patient meaningfully minimal, within-subject protocol comparison at each participating department was not an option. Accordingly, the design of the present multi-centric study does not allow an interpretation that differential TMS prediction consistencies with DCS outcomes are solely due to the protocol difference, in the strictest sense. Although performing a multi-level analysis is an option to solve this problem, there is no information available that tracks TMS coil operators and affiliations. However, TMS coil operators at the three participating institutions all received their initial training from the representatives sent from the same system manufacturer. Moreover, the three participating departments are all specialized centers for brain tumor surgery. Therefore, it might be unreasonably stringent to assume that a skill-level difference of coil operators exists between institutions, such that it would account for the observed difference significantly more than the protocol difference would. In other words, the association of the coil operator with the institution as well as the association of the institution with the protocol might not totally confound the hypothesized effects of the protocol difference; the differential results observed in the two groups could still contain information about systematic effects of protocol difference and the cortical distribution of language functions with varying degrees of conservativeness. Patients Thirty-two patients with lesions in left-sided presumably essential language areas were enrolled in three different neurosurgical departments (Technische Universität München, Charité-Universitätsmedizin Berlin, and University of California at San Francisco). All patients underwent preoperative language mapping with rTMS and intraoperative DCS during awake surgery. Patients were included if they were older than 18 years, seizure frequency was lower than once a week, and the level of aphasia was low enough to perform object-naming tasks. Patients with cardiac pacemaker or cochlear implants were excluded. Patients' languages (English vs. German) co-varied with the protocol groups (ONSET TMS vs. DELAYED TMS, respectively). Language-specific effects might appear when the results of the DCS mapping employing the same standard protocol were compared between the two language groups. However, the dependent variables in the present study (e.g., sensitivity, specificity, positive and negative predictive values) were computed by comparing the TMS predictions against DCM outcomes (i.e., true positive, false positive, true negative, false negative) in each of the language groups. Accordingly, effects of language would be controlled for in the process of deriving these higher order dependent variables. Therefore, it would be reasonable to think that the effects observed in the derived dependent variables between the two groups would reflect the effects of the 0 vs. 300 ms pulse onset timing difference. Preoperative language mapping Mapping procedure Cortical language mapping by rTMS was performed with the Nexstim eXimia NBS system 4.3. with a NexSpeech® module (Nexstim Oy, Helsinki, Finland) and a magnetic stimulator with a biphasic Fig. 8 TMS coil with a radius of 50 mm as has been described recently (Lioumis et al., 2012; Picht et al., 2013; Sollmann et al., 2013). In short, resting motor threshold (RMT) was determined for the right abductor pollicis brevis muscle prior to language mapping (Krieg et al., 2012; Picht et al., 2011; Tarapore et al., 2012b). RMT as measure of the individual's motor cortex excitability was then used for rTMS. To engage

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cortical language areas, an object-naming task with 131 black and white line drawing pictures of common objects was used. The pictures were displayed with a fixed inter picture interval (IPI). The stimulation parameters varied between two groups of patients, as outlined in Table 2. The major protocol difference was the picture-trigger interval (PTI), that is, stimulation onset time measured from the generation of the picture on the computer screen to the onset of the pulse train. Both groups consisted of different individual patients. The ONSET TMS group (12 patients) received rTMS pulse train starting at the picture presentation onset, while there was a 300 ms delay in rTMS pulse train onset in the DELAYED TMS group (20 patients). Since in the DELAYED protocol the rTMS only starts 300 ms later, we have to apply more stimuli in the ONSET group to also cover the complete word processing. The whole mapping was video recorded for later analysis (Lioumis et al., 2012). The patient first completed a baseline testing of the object-naming task. The patient had to name the presented objects in his mother tongue without receiving rTMS. All misnamed objects were then discarded from the image set for the subsequent nTMS mapping. During mapping, the patient had to name the presented objects in his mother tongue while rTMS pulses were applied in a time-locked way over the whole left hemisphere. Stimulation points were 10 mm apart from one another. The stimulation coil was randomly moved from one stimulation point to another between two trains of rTMS, while it was placed in a strictly anterior–posterior orientation and held tangential to the scalp (Lioumis et al., 2012). The same sites were not targeted consecutively. Areas of particular importance for resection, e.g., lesion and their adjacent areas, were examined in detail. Video analysis The naming data were analyzed by reviewing video recordings after the actual mapping session blinded to the sites of stimulation. Any impairment of language performance during the object-naming task with rTMS was compared with the baseline recording and the errors were then put into different categories which have been described previously (Corina et al., 2010; Picht et al., 2013): no-response errors, performance errors, neologisms, semantic paraphasias, phonological paraphasias, and circumlocution errors. Intraoperative language mapping We followed the standard operative setup and mapping procedure described in the literature (Picht et al., 2006, 2013; Sanai and Berger, 2008, 2010; Sanai et al., 2008). In short, we used total intravenous anesthesia (TIVA) in all cases by continuous propofol administration, while intraoperative analgesia was achieved by continuous administration of remifentanyl, and thus we strictly avoided the use of volatile anesthetics before and during surgery. For intraoperative mapping, the sites of cortical stimulation were placed 5–10 mm apart. The raster was tighter close to the planned corticectomy. Electrical stimulation (0–10 mA, 50/60 Hz, 4 s duration)

Table 2 Stimulation characteristics. Stimulation characteristics of both groups. The main difference between the two setups is the onset of rTMS pulse train after picture presentation onset.

rTMS onset (ms) Median inter picture interval (s) Median picture presentation time (ms) Median pulse frequency (Hz) Median number of pulses per train Mean resting motor threshold (% stimulator output) Median stimulation intensity (% resting motor threshold) Matrix sentence

0 ms (N = 12)

300 ms (N = 20)

0 2.5 700 5 10 39

300 3 700 5 5 37

90

100

“This is a…”

None

223

Fig. 1. Cortical parcellation system. Anatomical regions of the cortical parcellation system, as described in Corina et al. (2005).

for direct cortical language mapping was performed using a bipolar electrode with 1 mm diameter tips separated by a distance of 5 mm. For DCS the same paradigm was used in both groups with a 0 ms stimulation onset. Triggering of picture presentation and stimulation onset during awake surgery was performed by an audio signal, which allowed the surgeon to place the electrode on the brain immediately with picture presentation. Verification of the induced language impairment was done by a trained neuropsychologist. A direct cortical electroencephalogram was recorded by electrocorticography. A site was considered as language positive if any interruptive effect was induced in at least 2 out of 3 stimulations (Sanai and Berger, 2010). All positive sites were marked and transferred to the navigation system (BrainLAB Vector Vision Cranial or BrainLAB Curve, Brainlab AG, Feldkirchen, Germany) with the navigation pointer or recorded via intraoperative photographs (Sanai et al., 2008).

Repetitive nTMS vs. intraoperative DCS Signal detection theoretic analysis For anatomical allocation of the cortical stimulation sites, we used the cortical parcellation system (CPS), which divides each hemisphere into 37 individual anatomical regions (Fig. 1, Table 3) (Corina et al., 2005). The cortical gyri belonging to these anatomical CPS subregions

Table 3 Cortical parcellation system. The 20 relevant anatomical regions selected from the cortical parcellation system, which are used for this study. Abbrev.

Anatomy

anG aMTG aSMG aSTG mMFG mMTG mPoG mPrG mSTG opIFG orIFG pMFG pMTG pSMG pSTG trIFG polMTG polSTG vPoG vPrG

Angular gyrus Anterior middle temporal gyrus Anterior supramarginal gyrus Anterior superior temporal gyrus Middle middle frontal gyrus Middle middle temporal gyrus Middle post-central gyrus Middle pre-central gyrus Middle superior temporal gyrus Opercular inferior frontal gyrus Orbital inferior frontal gyrus Posterior middle frontal gyrus Posterior middle temporal gyrus Posterior supramarginal gyrus Posterior superior temporal gyrus Triangular inferior frontal gyrus Polar middle temporal gyrus Polar superior temporal gyrus Ventral post-central gyrus Ventral pre-central gyrus

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were identified in a 3D image reconstructed from T1-weighted MRI slices as described in the literature (Corina et al., 2005; Picht et al., 2013). The nTMS prediction data and intraoperative DCS outcome data were then projected on the subregions in the CPS images and compared separately in 4 pairs according to positive or negative provocation of language disturbances: True positive (TP): rTMS positive and intraoperative DCS positive True negative (TN): rTMS negative and intraoperative DCS negative False positive (FP): rTMS positive and intraoperative DCS negative False negative (FN): rTMS negative and intraoperative DCS positive We then computed a set of second- and third-order variables. Specifically, we first derived receiver operating characteristics (ROC) statistics from the frequencies of TP, TN, FP, and FN: sensitivity, specificity, positive predictive values, and negative predictive values. Next, a pair of the third order terms was derived from sensitivity and specificity: true positive fraction (i.e., sensitivity) and false positive fraction (i.e., 1 — specificity). As described in Lalkhen and McCluskey (2008), these values were, then, plotted in a ROC space to facilitate systematic protocol-by-region comparisons and evaluation. Logistic regression analysis The purpose of the logistic regression analysis is to determine to what extent preoperative rTMS predictions were useful in mapping the anterior vs. posterior language regions with the pulse train starting at 0 ms vs. 300 ms post picture presentation onset. To achieve this goal, the forward, log likelihood ratio regression method was employed (Bagley et al., 2001). Because the gold standard DCS outcomes have been shown to be predominantly negative (Sanai et al., 2008), we evaluated to what extent rTMS predictions were useful compared to simply predicting that all the responses would be negative (completely negative prediction). Table 6 provides an overview. Analysis of disruption patterns of cortical regions Mapped cortical regions were classified by their disruption patterns in response to DCS and TMS for two purposes. This analysis of cortical regions' response patterns reflects a standard assumption for classification in biology: individual units can be clustered so that most of the diversity can be captured by identifying the characteristics of clusters rather than by identifying the characteristics of each individual unit (Schneidman et al., 2002). First, for the analysis based on the effects of DCS and stand-alone nTMS, the concept of the most basic strategy used for functional classification of cells (e.g., ON, OFF, both ON and OFF) was applied to classifying cortical regions (Schneidman et al., 2002). In the present context, for the first round of the analysis, each cortical region was classified into one of the three disruption response patterns: negative-only, positive-only, or negative–positive mixed (mixed for short). Next, for the analysis comparing the rTMS effects against the DCS effects, a second-order ternary classification was performed by adding contextual elaborations. For the analysis in the context of positive predictive values (PPV), each cortical region was classified into one of the three PPV patterns: true positive only, false positive only, or true positive and false positive mixed (mixed for short). Cortical regions where outcomes were false positive only would suggest that disruptive effects in these regions were greater by rTMS than by DCS. Such cases could arise less due to chance but more due to the facts that the scalp–cortex distance differs across cortical regions – smaller for IFG and MFG but greater for anG – and the effects of TMS is a function of the scalp–cortex distance (Stokes et al., 2005). Consequently, IFG and MFG are predicted to be more prone to such an outcome mismatch. Moreover, as predicted from the literature review in the earlier section, specifically for the DELAYED TMS protocol, false-positives might be due to the inconsistent pulse onset timing between DCS and rTMS; for certain processing stages, 0 ms DCS onset might have been too early, whereas 300 ms

rTMS onset might have been appropriate. Because the issues with this types of false-positive were not addressed in Picht et al. (2013) or Tarapore et al. (2013) and such overcalling by the DELAYED ONSET protocol could be apparent, relative to the DCS pulse onset latency, the analysis of false positive only regions in the PPV context is motivated to identify the language regions that DCS starting at 0 ms post picture presentation onset might have missed. For the analysis in the context of sensitivity, each cortical region was classified into one of the three sensitivity patterns: true positive only, false negative only, or true positive and false negative mixed (mixed for short). This classification is motivated to identify the cortical regions that the DELAYED TMS was appropriate and those not, which is the main clinical purpose of the present protocol comparison study. In evaluating word-production models, this classification is motivated to distinguish cortical regions that might engage in language processing at earlier stages and those engaged at later stages. For the analysis in the context of specificity, each cortical region was classified into one of the three specificity patterns: true negative only, false positive only, or true negative and false positive mixed (mixed for short). This analysis is motivated to distinguish cortical regions that were more disrupted by the DELAYED TMS protocol than by the ONSEST TMS protocol (e.g., true-negative only for ONSET TMS and mixed or false positive for DELAYED TMS), which could in turn identify the cortical regions that engage in language processing at later and middle stages of word production that the ONSET TMS protocol might have missed. For the analysis in the context of negative predictive values (NPV), each cortical region was classified into one of the three NPV patterns: true negative only, false negative only, or true negative and false negative mixed (mixed for short). Because most of the DCS outcomes are negative (Sanai et al., 2008), if rTMS outcomes in the NPV context do include false negatives in the DELAYED TMS protocol only, it would suggest that the 300 ms rTMS onset might be too late in causing disruption; if false negatives occur in both protocols, the misses might not be due to the pulse onset timing but might be due to properties specific to the cortical regions instead. Results Patients' clinical and demographic characteristics Between April 2011 and May 2012, 32 patients were enrolled. The ONSET TMS group with an rTMS pulse train onset at 0 ms had a mean age of 45 ± 15 while it was 48 ± 13 years in the DELAYED TMS group. All lesions were located within the left hemisphere. Table 4 provides an overview. Performance in preoperative rTMS mapping All 32 patients tolerated preoperative rTMS language mapping of the whole left hemisphere well. Errors during baseline testing for preoperative object-naming ranged from 0.7% to 76.3% of shown pictures. During rTMS mapping, there were 166–683 stimulation sites (median 452.5) on the left hemisphere. Performance in intraoperative DCS mapping Three patients showed a completely negative DCS language mapping. However, the intraoperative language mapping was possible in all patients. The number of naming errors in other patients ranged from 1 to 8. Correlation between preoperative rTMS and intraoperative DCS mapping Both methods were capable of provoking naming errors. The detection of these errors relied on subjective assessment of the patient

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Table 4 Patient characteristics. Patient characteristics including tumor entity and stimulation parameters used in the study. AA = anaplastic astrocytoma WHO°III; GBM = glioblastoma WHO°IV; PA = pilocytic astrocytoma WHO°I; OA = Oligoastrocytoma WHO°II; AOA = anaplastic oligoastrocytoma WHO°III; AGG = anaplastic ganglioglioma WHO°III; GG = ganglioglioma WHO°I; RMT = resting motor threshold; % output = stimulation intensity in percentage maximum stimulator output; % RMT = stimulation intensity in percentage resting motor threshold. − = deteriorated compared to pre-OP; O = unchanged compared to pre-OP; + = improved compared to pre-OP. Patient No

Handedness

Age (years)

Aphasia pre-OP

Monolingual

Entity

Recurrence

Location

ms M F M F M M M F F M M F F F M F F M F F

R R R R R L R L R R R R R R R R R R R R

25 29 62 56 53 43 51 50 51 40 34 63 47 69 51 57 70 35 52 30

N N N N N Y Y Y Y Y N Y Y N N Y N N N N

Y Y Y Y Y N Y Y Y N Y Y Y Y Y N Y Y Y Y

Cavernoma AA GBM AA AA GBM GBM GBM GBM GBM Cavernoma Astrocytoma°II GBM GBM AA GBM AA AA GBM PA °I

N N N Y N N Y N N N N N Y N N Y N N N N

Frontal Parietal Frontal Temporal Temporal Frontal Parietal Parietal Frontal Temporal Frontal Temporal Temporal Temporal Frontal Frontal Temporal Temporal Frontal Temporal

0 ms 1 M 2 F 3 F 4 M 5 F 6 M 7 F 8 M 9 M 10 M 11 M 12 F

R R R R R R R R R R R R

44 37 31 42 56 56 34 61 63 23 29 65

N N N N N N N N N N N N

Y Y N Y N Y N Y Y Y Y Y

AA OA OA GBM AOA GBM AGG GBM GBM AOA GG GBM

Y Y N Y N Y N Y N Y N N

frontal temporal frontal temporal frontal frontal temporal temporal parietal multiple temporal temporal

300 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Sex

Lesion

performance. Intraoperative DCS mapping data were available for 0 to 19 CPS areas (median 5 CPS regions) per patient, whereas rTMS data were available for 1 to 27 CPS regions (median 11 CPS regions) per patient. The data collected by each modality were divided into two protocol types, and then the correlation of data between the two modalities was compared separately for each of the four different classifications (TP/TN/FP/FN) as presented in pairs of CPS brain templates (TP: Fig. 2; TN: Fig. 3; FP: Fig. 4; FN: Fig. 5). As clearly seen in Figs. 4 and 5, false positive regions and false negative regions distributed more widely in the posterior language area (consisting of temporal and inferior parietal regions) for the DELAYED TMS protocol than for the ONSET TMS protocol. We also derived ROC statistics from the frequencies of TP, TN, FP, and FN: sensitivity, specificity, and positive predictive values, and negative predictive values. These four values are provided for all-regions collapsed and separately for anterior and posterior regions (Table 5, Fig. 6). Next, to make an initial systematic protocol-by-region utility comparison as well as to facilitate interpretation, true positive fraction and false positive fraction were derived as described in the method section and were plotted in an ROC space (Fig. 6). When the results of all regions were collapsed (denoted by circles), the two protocols did not differ in true positive fraction along the vertical axis (90% for ONSET TMS (black circle); 89% for DELAYED TMS (dark gray circle)), but differed in false positive fraction along the horizontal axis (21% for ONSET

Diagnosis due to

Parameter

Aphasia post-OP

Stimulation train frequency (Hz/pulses)

RMT (% output)

Mapping intensity (% RMT)

Seizure Seizure Seizure Speech arrest Speech arrest Speech arrest Follow up MRI Aphasia Seizure Aphasia Seizure Aphasia Follow up MRI Speech arrest Speech arrest Seizure Speech arrest Seizure Aphasia Speech arrest

5 Hz/5 5 Hz/5 5 Hz/5 7 Hz/5 7 Hz/5 5 Hz/5 7 Hz/7 5 Hz/5 5 Hz/5 5 Hz/5 5 Hz/5 7 Hz/7 5 Hz/5 5 Hz/5 10 Hz/5 5 Hz/5 7 Hz/5 5 Hz/5 5 Hz/5 10 Hz/5

35 34 48 53 38 58 25 31 28 39 43 36 30 31 36 31 44 25 48 24

100% 100% 100% 89% 100% 80% 100% 100% 100% 121% 121% 122% 100% 100% 100% 100% 100% 105% 55% 110%

O – O – O O O – – O – – O – – + O O O O

Follow up MRI Follow up MRI Seizure Follow up MRI Seizure Follow up MRI Seizure Follow up MRI Falls Follow up MRI Seizure Speech difficulty

5 Hz/10 5 Hz/10 5 Hz/10 5 Hz/10 5 Hz/10 5 Hz/10 5 Hz/10 5 Hz/10 5 Hz/10 5 Hz/10 5 Hz/10 5 Hz/10

28 35 38 39 44 33 47 50 26 31 48 40

110 90 90 90 80 110 100 110 110 110 100 80

O – O O O O O O O – O O

TMS; 74% for DELAYED TMS): the overall DCS-rTMS consistency was higher for the ONSET TMS protocol than for the DELAYED TMS protocol. Eight patients in the DELAYED group suffered from mild aphasia preoperatively, while no patients in the ONSET group had preoperative aphasia. No group comparison could be performed for this reason. Presence of preoperative aphasia had no significant impact on the sensitivity of TMS language mapping (one-way ANOVA, p b 0.755). Thus, there was no difference in correlation to DCS for these patients compared to non-aphasic patients in the DELAYED group.

Anterior language regions Within the classic Broca's area and its surrounding brain regions (aMFG, mMFG, pMFG, orIFG, trIFG, opIFG, mPrG, vPrG), NPV, PPV, and sensitivity were equivalent between protocols. However, specificity was found to be higher for the ONSET TMS group than for the DELAYED TMS group (67 vs. 28%) (Table 5, Fig. 6). To visualize the results, in Fig. 6, upright triangles were used to denote the results of anterior language regions. The two protocols did not differ in true positive fraction along the vertical axis (100% for ONSET TMS (black upright triangle); 100% for DELAYED TMS (dark gray upright triangle)), but differed in false positive fraction along the horizontal axis (33% for ONSET TMS; 72% for DELAYED TMS). The anterior DCS-rTMS consistency was higher for the ONSET TMS protocol than for the DELAYED TMS protocol.

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Fig. 2. True positive. Regions of true positive rTMS mapping: left: ONSET TMS; right: DELAYED TMS.

Posterior language areas Within the posterior language regions, which include the classic Wernicke's area (vPoG, aSMG, pSMG, anG, aSTG, mSTG, pSTG, aMTG, mMTG, pMTG), the two protocols showed practically significant differences. The ONSET TMS group showed a higher rate of NPV, PPV, and specificity). However, sensitivity was again comparable between the two groups (Table 5, Fig. 6). These results suggest that the higher rate of false positive and false negatives in the posterior regions of the DELAYED TMS protocol is the major source of the differential prediction accuracies between the two navigated rTMS protocols. Fig. 6 (inverted triangles) shows this posterior protocol difference in a simpler way. The two protocols did not differ in true positive fraction along the vertical axis (75% for ONSET TMS (black inverted triangle); 71% for DELAYED TMS (dark gray inverted triangle)), but differed in false positive fraction along the horizontal axis (14% for ONSET TMS; 77% for DELAYED TMS): the overall DCS-rTMS consistency was higher for the ONSET TMS protocol than for the DELAYED TMS protocol. Taken together, though the true positive fraction (i.e., sensitivity) was higher for the anterior regions than in the posterior regions for both protocols, the difference between the protocols lay in the false positive fraction. In both anterior and posterior regions, the DELAYED TMS protocol was higher in the false positive fraction, thus, lower in specificity, than the ONSET TMS protocol was. Moreover, this difference was greater in the posterior regions than in the anterior region. Given the comparable sensitivity across regions, the utility of the protocols hinges on specificity. Accordingly, a practical implication is that the ONSET TMS protocol with higher specificity would be a preferred option for preoperative rTMS language mapping if the pulse frequency is held constant across regions.

section. Navigated TMS predictions in the posterior regions using the DELAYED TMS protocol were not more useful than the completely negative prediction (see Table 6). In the remaining three combinations, rTMS mapping played a significant role in enhancing the prediction accuracy beyond the completely negative prediction. More specifically, goodness-of-fit (see Table 6 for the Goodness-of-fit measures) was greatest in mapping the anterior language regions with the ONSET TMS protocol, followed by mapping the posterior language areas with the ONSET TMS protocol, followed by mapping the anterior regions with the DELAYED TMS protocol. There was one data point that may have adversely affected the prediction accuracy: one false negative case out of 19 rTMS-negative predictions in the posterior region with the ONSET TMS protocol, though it was highest in specificity among the three data sets. Without this one false negative case, the posterior ONSET TMS mapping would have been as accurate in sensitivity as the anterior ONSET TMS mapping and the DELAYED TMS mapping when compared against the ONSET DCS. See the decision about TMS prediction utility at the bottom of Table 6. Practical implications Taken together with the ROC descriptive statistics, preoperative language mapping via rTMS combined with the object-naming task would have higher utility when the rTMS pulse train onset starts at 0 ms rather than 300 ms after the picture presentation onset, especially by its higher specificity in both anterior and posterior regions. With this timing parameter setting for rTMS pulse trains, preoperative rTMS language mapping provides spatial information that would help expedite intraoperative DCS language mapping aimed at safe extensive resection based on negative mapping with small and tailored cortical exposures. Patient outcome

Logistic regression analysis for an rTMS utility comparison In addition to describing the rTMS prediction accuracy in signal detection theoretic terms, to be more accurate in comparing the utility of rTMS predictions among the region × protocol combinations, a logistic regression analysis was further performed as described in the method

In 10 out of 32 patients, aphasia was aggravated within the first 7 postoperative days while one patient even improved. Gross total resection was achieved in 18 cases. Only subtotal resection was possible in 2 cases as the tumor infiltrated areas having eloquent language function. There was no mortality over the course of the study.

Fig. 3. True negative. Regions of true negative rTMS mapping: left: ONSET TMS; right: DELAYED TMS.

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Fig. 4. False positive. Regions of false positive rTMS mapping: left: ONSET TMS; right: DELAYED TMS.

TMS-specific disruption patterns First, to identify groups of cortical regions that were either more disrupted or less disrupted by rTMS than by DCS for each protocol (Figs. 7e, f), we summarized disruption patterns by DCS (Figs. 7a, b) and those by rTMS (Figs. 7c, d) by applying the ternary classification (positive only, negative only, mixed) as described in the method section. When the DCS and rTMS results were compared within each protocol (Figs. 7a vs. c; Figs. 7b vs. d), for both protocols, the number of mixed-outcome regions was greater for rTMS mapping than for DCS mapping, which in turn suggests that false positives occurred to both protocols. Regions that rTMS disrupted more than DCS were trIFG, aSTG, and aMTG for the ONSET TMS protocol and mMFG and polMTG for the DELAYED TMS protocol, all of which were frontal–temporal parts of the so-called semantic processing network (Binder et al., 2009; Price, 2012). More importantly, these identified frontal and anterior temporal regions were reported to have smaller scalp–cortex distances than anG (Stokes et al., 2005). Therefore, this overcalling by rTMS might not be random errors but might rather be anatomically systematic rTMS-specific increased disruption. In contrast, there was only one region that rTMS facilitated (or disrupted less) than DCS: mSTG for the ONSET TMS protocol. This region is standardly a part of the phonological/monitoring processing network (Indefrey, 2011). Regarding the facilitatory effect, it is noteworthy that mMTG and anG are both DCS negative-only and TMS negative-only for the ONSET TMS/DCS mapping. This result was consistent with the facilitatory effects of early pulse train onset on these semantic regions pointed out in the literature review section, which in turn suggests a possible weakness of the ONSET TMS/DCS protocol. Disruption patterns governing positive predictive value In this section, we distinguished groups of cortical regions that showed differential disruption patterns in deriving positive predictive values. The three disruption patterns were true-positive only, falsepositive only, and mixed (Figs. 8a, b). The ONSET TMS protocol distinguished three language network components along the posterior– anterior dimension (Fig. 8a). True positive only in aSMG and pSMG,

which standardly form a locus of phonological working memory (Price, 2012), was consistent with the disruptive effect of early pulse onset reported on SMG in the literature review section. Mixed outcomes in a frontal cluster mMFG, opIFG, vPrG and an outpost pSTG, which are associated with articulatory planning, language motor command, and predicting upcoming auditory feedback, formed a network reported in Edwards et al. (2009) cited in the literature section; false-positive only in trIFG, aSTG, and aMTG, which were the identical set of anterior-temporal components of the semantic processing network, was consistent with the rTMS-specific disrupted regions predicted by the scalp–cortex distance account (Stokes et al., 2005) reported in the literature review section. Considering the consistency with the existing literature, the positive sites by the preoperative ONSET TMS mapping might be informative with increasing confidence from anterior to posterior regions for patients who cannot undergo intraoperative DCS. The DELAYED ONSET protocol distinguished some components of semantic processing network (Fig. 8b). There were four regions where rTMS outcomes were false positive only. Among these regions, polMTG and mMFG are components of anterior-temporal semantic processing network, which were identical with one of the sets of rTMS-specific frontal–temporal semantic regions systematically disrupted by the smaller scalp–cortex distance (Stokes et al., 2005). The two other regions, pSMG and anG, are inferior parietal components of semantic processing network (Binder et al., 2009), identified specifically by the DELAYED ONSET protocol. It is noteworthy that one of the regions of interest, anG, was disrupted by the DELAYED ONSET protocol, but not by the ONSET TMS protocol, which, in turn, provides an initial support for the delayed rTMS onset in the posterior regions at least in the basic research context and might be informative about hotspots in the posterior inferior parietal lobe with some caution when patients cannot undergo intraoperative DCS. Disruption patterns governing sensitivity In this section, we distinguished groups of cortical regions that showed differential disruption patterns in deriving sensitivity. The three disruption patterns were true positive only, false negative only, and mixed (Figs. 8c, d). For the ONSET TMS protocol, there were two

Fig. 5. False negative. Regions of false negative rTMS mapping: left: ONSET TMS; right: DELAYED TMS.

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Table 5 Descriptive statistics. Sensitivity, specificity and positive/negative predictive values over all brain regions as well as separated into anterior (aMFG, mMFG, pMFG, orIFG, trIFG, opIFG, mPrG, vPrG) and posterior (vPoG, aSMG, pSMG, anG, aSTG, mSTG, pSTG, aMTG, mMTG, pMTG) brain regions in all patients. All regions

Sensitivity (%) Specificity (%) Positive predictive value (%) Negative predictive value (%)

Anterior language regions

Posterior language areas

0 ms (N = 12)

300 ms (N = 20)

All (N = 32)

0 ms (N = 12)

300 ms (N = 20)

All (N = 32)

0 ms (N = 12)

300 ms (N = 20)

All (N = 32)

90 79 53 97

89 26 39 81

89 41 41 89

100 67 55 100

100 28 54 100

100 39 54 100

75 86 50 95

71 23 27 67

72 41 29 81

separate clusters where rTMS outcomes were true positive only (Fig. 8c): the frontal language-processing core, mMFG, opIFG, and vPrG and the posterior language processing core, aSMG, pSMG, and pSTG. These results suggest that the ONSET TMS positive outcomes would increase the confidence in DCS positive sites in these two core regions. mSTG, bridging these two true-positive-only clusters, stood out as the sole false-negative-only region, which might reflect functional–anatomical complexity of STG (Edwards et al., 2010). The DELAYED TMS protocol, too, provided a clear-cut anatomical– functional distinction by response properties. In this case, the distinction was ternary along the anterior–posterior dimension (Fig. 8d), suggesting the chronological order of the processing stages. True positive only distributed widely over frontal, peri-rolandic, and part of temporal regions would increase the confidence in DCS positive sites in these regions. In particular, the positive only outcomes in the peri-rolandic articulatory regions (Rauschecker, 2012), the polSTG/aSTG and mMTG/ pMTG semantic regions (Binder et al., 2009), and the broader IFG coverage (opIFG and triIFG) were strengths of the DELAYED TMS positive sites. However, the consistency with the DCS results decreased to mixed in aSTG, mSTG, and pSTG, and to false negative only in pSTG and anG, The decrease in consistency might reflect the effects of

the pulse train onset mismatch between TMS and DCS, though it distinguished the phonological cluster (aSMG, mSTG, pSTG) and semantic cluster (pSMG, anG) in the posterior regions operating in slightly different time windows as suggested by the differential false negative outcomes. Disruption patterns governing specificity In this section, we distinguished groups of cortical regions that showed differential disruption patterns in deriving specificity. The three disruption patterns were true negative only, false positive only, and mixed (Figs. 8e, f). The ONSET TMS protocol divided the cortical regions into two groups again, but in this context, into true negative only and true negative and false positive mixed (Fig. 8e). The mixed regions consisted of the two clusters previously identified in the PPV context: the systematically rTMS-sensitive frontal, anterior-temporal cluster (aMTG, aSTG, and TrIFG) and mMFG associated with semantic processing as well as the frontal, posterior temporal long-distance phonological network (opIFG, vPrG, and the pSTG enclave). In the posterior portion, the true negative only distribution consisted of pSMG and anG. This cluster showed three different outcomes in three different contexts so far; true negative only in the current ONSET TMS specificity context, false positive only in the DELAYED TMS PPV context, and false negative in the DELAYED TMS sensitivity context. An emerging understanding is that, from a basic research perspective, anG might not be a silent region; it might participate in word-production; and the 300 ms rTMS onset was more likely to disrupt the pSMG-anG cluster than the 0 ms rTMS onset. The DELAYED TMS protocol again yielded a map of ternary classification: one true negative only region (aMTG), two false positive only regions (aSMG, anG), and 15 other mixed regions distributed widely over the left hemisphere (Fig. 8f). The results suggest that TMS negative outcomes would not increase the confidence in DCS negative outcomes, which is a weakness of the DELAYED TMS protocol. Though it was as a false negative only region, the result suggests again that anG might not be a silent region in word production and that the DELAYED TMS was more likely to disrupt the pSMG-anG cluster than the ONSET TMS would. Disruption patterns governing negative predictive value

Fig. 6. ROC. Relationships between sensitivity and specificity plotted in the ROC space. Specificity showed great disparity between low and high levels by pulse train onset timing, whereas sensitivity varied between moderate and high levels by brain regions. Black circle = ONSET TMS all regions; black upright triangle = ONSET TMS anterior language; black inverted triangle = ONSET TMS posterior language; dark gray circle = DELAYED TMS all regions; dark gray upright triangle = DELAYED TMS anterior language; dark gray inverted triangle = DELAYED TMS posterior language; light gray circle = both protocols all regions; light gray upright triangle = both protocols anterior language; light gray inverted triangle = both protocols posterior language.

In this section, we distinguished groups of cortical regions that showed differential disruption patterns in deriving negative predictive values. The three disruption patterns were true negative only, false negative only, and mixed (Figs. 8g, h). The ONSET TMS protocol divided mapped regions into two groups: true negative only and mixed. Except for the only one mixed region mSTG, all other TMS negative regions were true negative. This special status of mSTG in the sensitivity was replicated here in the NPV context. The special status of aSMG in the specificity context was replicated here in the NPV context as well, such that aSMG is principally the language positive region where negative outcomes are least likely to be observed. For the DELAYED TMS protocol, the distribution of true negative only and mixed regions in the temporal, IFG, and MFG and peri-rolandic

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Table 6 Inferential statistics. Logistic regression analysis with the forward, log likelihood ratio method to compare the region (anterior vs. posterior) x protocol (ONSET TMS vs. DELAYED TMS) rTMS prediction utilities against the completely negative prediction with the DCS outcomes as gold standard. Region

Anterior language regions

Protocol

0 ms

300 ms

0 ms

300 ms

Model 0 (completely negative prediction): overall accuracy (%) Model 0: −2 Log likelihood Model 0: variable not in the model (rTMS)

71% 25.127 p = 0.006

55% 90.949 p = 0.002

84% 21.983 p = 0.009

100% 67% 76% 15.158 R2CS: 0.378 R2N: 0.542 9.969

100% 28% 61% 77.347 R2CS: 0.186 R2N: 0.249 13.603

75% 86% 84% 16.153 R2CS: 0.208 R2N: 0.356 5.830

72% 88.281 p = 0.592 (n.s.) Analysis terminates at Model 0. N.A. (71%) (23%) (36%) N.A. N.A.

p = 0.002

p b 0.001

p = 0.016

N.A.

rTMS predictions should not be removed

rTMS predictions should not be removed

rTMS predictions should not be removed

unlikely to be better than predicting that all responses were negative

Model 1 with rTMS: Sensitivity (%) Specificity (%) Overall accuracy (%) Model 1: −2 Log likelihood Goodness of fit: Cox & Snell R2; Nagelkerke R2 Model if term (rTMS) is removed: Change in −2 Log likelihood Model if term removed: Significance of the change (df = 1) rTMS prediction utility

areas were similar to the distribution observed for the ONSET TMS protocol (i.e., the left half of the true-negative-only loop). Negative outcomes in these regions would be informative when patients cannot

Posterior language areas

N.A.

undergo intraoperative DCS. However, the posterior temporal and inferior parietal regions showed different response patterns (Fig. 8h). There were two separate inferior parietal regions where rTMS outcomes were

Fig. 7. DCS & rTMS stand-alone mapping results (a, b; c, d); rTMS sensitive regions (e, f). DCS and rTMS stand-alone mapping results and rTMS-sensitive regions. DCS outcomes for the ONSET TMS protocol (a) and for the DELAYED TMS protocol (b). Regarding the color coding, pink represents DCS positive regions; blue represents DCS negative regions; and yellow represents regions with mixed results of DCS positive and DCS negative. TMS predictions for the ONSET TMS protocol (c) and for the DELAYED protocol (d). Regarding the color coding, pink represents TMS positive regions; blue represents TMS negative regions; and yellow represents regions with mixed results of TMS positive and TMS negative. DCS-rTMS difference for the ONSET TMS protocol (e) and for the DELAYED TMS protocol (f). Regarding the color coding, red represents regions where rTMS was more disruptive than DCS; dark gray represents rTMS was less disruptive than DCS.

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false negative only: aSMG and anG. The coupling of aSMG and anG was observed in the current NPV context in addition to the previous specificity context. It should be noted that pSMG patterned with anG when aSMG is not patterned with anG in the PPV and sensitivity contexts, whereas pSMG patterned with pSTG and mSTG when aSMG is patterned with anG in the specificity and NPV contexts. These coupling patterns suggest that SMG is not functionally uniform; aSMG and pSMG might have different functions in relation to anG. We will return to this coupling peculiarity of the inferior parietal lobe later in the discussion section.

Language-specific disruption patterns in DCS maps Though a comparison of the maps of raw DCS outcomes between the two protocols is not a main research question of the present study, it might reveal language-specific disruption patterns (Fig. 9). Regions more disrupted in German than in English corresponded to two distinct language networks: one group consisting of anG, mMTG, aSTG, trIFG corresponds to distributed components participating in the semantic network, whereas the other group consisting of pMFG, mPrG, mPoG, and vPoG corresponds to dorsal peri-rolandic components of the

Fig. 8. Clusters distinguished by disruption patterns. Clusters distinguished by ternary classification of disruption patterns. Clusters distinguished by disruption patterns related to positive predictive values for the ONSET TMS protocol (a) and for the DELAYED TMS protocol (b). Regarding the color coding, pink represents true-positive-only regions; orange represents falsepositive-only regions; yellow represents regions with mixed results of true-positive and false-positive. Clusters distinguished by disruption patterns related to sensitivity for the ONSET TMS protocol (c) and for the DELAYED TMS protocol (d). Regarding the color coding, pink represents true-positive-only regions; green represents false-negative-only regions; and yellow represents regions with mixed results of true-positive and false-negative. Clusters distinguished by disruption patterns related to specificity for the ONSET TMS protocol (e) and for the DELAYED TMS protocol (f). Regarding the color coding, blue represents true-negative-only regions; orange represents false-positive-only regions; and yellow represents regions with mixed results of true-negative and false-positive. Clusters distinguished by disruption patterns related to negative predictive values for the ONSET TMS protocol (g) and for the DELAYED TMS protocol (h). Regarding the color coding, blue represents true-negative-only regions; green represents false-positive-only regions; yellow represents regions with mixed results of true-negative and false-negative.

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speech-motor sequence control cluster. Regions more disrupted in English than in German were two separate regions: mMFG that corresponds to a component participating in the semantic network and aSMG that corresponds to a component participating in the phonological network. Candidate language-specific properties that might be associated with the functions of these regions will be mentioned later in the discussion section. Discussion General results The main challenge of tumors within presumably languageeloquent areas of the brain is the unpredictability of the location of essential language sites due to individual heterogeneity and cerebral plasticity (Duffau, 2006; Ius et al., 2011; Robles et al., 2008; Sanai et al., 2008). We have shown in previous studies that rTMS can be informative and helpful as a non-invasive approach to map language eloquent areas preoperatively in comparison to intraoperative DCS (Picht et al., 2013; Sollmann et al., 2013). In the current study using the object-naming task, we found that both protocols resulted in excellent sensitivity and NPV with no false negative results in the anterior regions, specifically, within and around the classic Broca's area (Ojemann and Whitaker, 1978; Ojemann et al., 1989; Penfield and Boldrey, 1937; Robles et al., 2008; Sanai et al., 2008). Furthermore, we were able to confirm that an onset of rTMS pulse train coinciding with the picture presentation onset not only provides mapping results that are equivalent to those by pulse train onset with a 300 ms delay in the anterior language regions, but also affords superior prediction accuracy in posterior language areas with lower rates of false positives and false negatives, notably in specificity. In the past, fMRI was the only widely available non-invasive modality for preoperative assessment of language eloquent regions. However, several studies, including a meta-analysis, concluded that fMRI is not reliable enough to form the basis for surgical decision-making (FitzGerald et al., 1997; Giussani et al., 2010; Roux et al., 2003). Such spatial inconsistency most likely reflects the methodological differences between rTMS and fMRI (Rutten and Ramsey, 2010; Sollmann et al., 2013). DCS and TMS allow a more targeted causal analysis of cortical regions, while fMRI has its strength in the correlational visualization of cortical networks. Critical to the accuracy of rTMS-based mapping is the recent integration of neuronavigation, which allows for unprecedented accuracy in nTMS localization of targeted cortical regions (Hannula et al., 2005; Krieg et al., 2012; Picht et al., 2011; Ruohonen and Karhu, 2010; Saisanen et al., 2008). Thus, we have extended the domain of accurate application from motor mapping shown previously to language mapping, and suggest that rTMS and DCS should be used to complement

Fig. 9. DCS clusters. DCS clusters and regions that might be specific to language difference. Regarding the color-coding, red represents regions more disrupted in German than in English; dark gray represents regions more disrupted in English than in German.

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fMRI results in clinical (Krieg et al., 2012) and experimental research (Wheat et al., 2013). DCS vs. rTMS Concerning language mapping, rTMS follows very similar principles as DCS: repetitive depolarization of axons involved in language processing which induces a temporary “virtual lesion” (Epstein et al., 1996). Several studies already showed the application of TMS for language tasks (Devlin and Watkins, 2007; Epstein et al., 1996; Vigliocco et al., 2011; Wheat et al., 2013). However, technical differences and shortcomings have been extensively discussed in previously published studies (Picht et al., 2013; Sollmann et al., 2013). Therefore, the present study compared the results of two different stimulation protocols in order to provide more detailed information on how preoperative rTMS language mapping can be optimized. We have shown that varying the pulse train onset parameter does not seem to make a difference in mapping the anterior language areas, but does show differential prediction-outcome accuracies in the posterior regions. Pulse train onset starting at 0 ms post picture presentation onset was associated with lower rates of prediction errors (i.e., false positives and false negatives) in mapping both the anterior and posterior regions than pulse train onset starting at 300 ms post picture presentation onset. In evaluating the two protocols, given the comparable sensitivity across regions, the utility of the protocols hinges on specificity. Accordingly, a practical implication is that the ONSET TMS protocol with higher specificity would be a preferred option for preoperative rTMS language mapping if the pulse frequency is held constant across regions, with DCS also starting at the picture presentation onset. Taken together with the signal detection theoretic descriptive statistics, logistic regression analysis suggests that preoperative language mapping via rTMS combined with the object-naming task would have higher utility when the rTMS pulse train onset starts at 0 ms rather than 300 ms after the picture presentation onset, especially by its higher specificity in both anterior and posterior regions. Additionally, our results provide unique information about language processing itself. Because many recent studies (Schuhmann et al., 2009; Wheat et al., 2013) used rTMS to reveal how language processing unfolds temporally and spatially, our results offer a unique opportunity to relate these studies to DCS results. In the present study, DCS and rTMS revealed comparable language maps, suggesting that both modalities interfere with language processes in similar ways. Previous studies on distribution of language function by DCS demonstrated that areas such as Broca's and Wernicke's are parts of a complex and highly patient-specific language network (Briganti et al., 2012; Ojemann et al., 1989). It is not clear to what extent these so-called “essential” language sites are really crucial for human language function (Baum et al., 2012; Briganti et al., 2012). However, with regard to the areas identified by DCS, we know that if more than one of these DCS-positive cortical language sites is removed, a permanent neurological deficit should be expected postoperatively (Duffau, 2006; Robles et al., 2008). Therefore, preoperative rTMS mapping would be more efficient, when a pulse parameter was optimized not only to maximize specificity for extensive resection, but also to minimize the rTMS false positive predictions, which should be followed up by intraoperative DCS-positive confirmation for careful subtotal resection. Regarding rTMS false positive predictions, the present study revealed specificity systematically varied with the pulse train onset times: lower specificity in language mapping using the DELAYED TMS protocol than in language mapping using the ONSET TMS protocol across regions. More important, specificity was lowest in mapping the posterior regions using the DELAYED TMS protocol and highest in mapping the posterior regions using the ONSET TMS protocol. When comparing the results of rTMS and DCS, the occurrence of false positive rTMS predictions might reflect the fact that the examiners can be more responsive to errors in the off-line video analysis for rTMS

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mapping compared to the intraoperative error detection in real time. Alternatively, the CPS regions still exceed the size of the 10 mm error margin of the DCS, and thus some false positive predictions by rTMS may be due to less dense spatial sampling per CPS region by the DCS. However, there is a growing consensus in the literature that DCS and rTMS correlation is stronger in anterior than in posterior language sites (Picht et al., 2013; Sollmann et al., 2013). This finding persists regardless of differential examiner responsiveness in off-line vs. real time error analysis. Given the known differences in the timing of activation of anterior and posterior sites (Indefrey, 2011), it stands to reason that differences in the timing of pulse train onset are the most likely source of posterior discrepancy in these studies.

Though we took a multicenter approach involving 3 different institutions, the number of data points for rare events (i.e., DCS-positive) was still small to perform model validation separately for the anterior and posterior regions following the logistic regression analysis; needless to say, the number of data points for each separate region of the CPS was still small and inadequate for the application of further inferential statistics. In the future studies, enrollment of a much higher number of patients would allow us to present such analysis. However, with the present study we are able to provide evidence for the first time that the timing of rTMS pulse train onset is critical to generating an accurate rTMS-based language map, particularly in posterior language regions. By establishing these ties, the results of this study are unique in the literature up to now.

Activation studies vs. disruption studies

Adverse events

When considering research in the temporal processing of human language, especially picture naming, the two dominant modalities are MEG and TMS (Acheson et al., 2011; Schuhmann et al., 2009; Wheat et al., 2013). In a recent review of the available data on temporal processing of language in the picture naming task, Indefrey (2011) provides a cogent overview including both TMS and MEG data. This profound analysis clearly shows a significantly earlier activation of posterior language areas compared to the frontal areas such as in and around Broca's region. The onset of activation in these areas was between 0 and 800 ms (Acheson et al., 2011; Hulten et al., 2009; Salmelin et al., 1994; Soros et al., 2003; Vihla et al., 2006) with a median value of 190 ms for mMTG, 320 ms for pSTG, 360 ms for pMTG, 371 ms for mSTG, 280 ms for SMG, and 300 ms for anG (Indefrey, 2011). Concerning the anterior language areas, including aMFG, mMFG, pMFG, trIFG, opIFG, orIFG, mPrG, and vPrG, activation was observed after 200 to 800 ms (Hulten et al., 2009; Salmelin et al., 1994; Schuhmann et al., 2009; Vihla et al., 2006; Wheat et al., 2013) with a median value of 500 ms for opIFG and trIFG, 550 ms for vPrG, and 600 ms for the MFG (Indefrey, 2011). However, when we take a closer look on these data, the window of activation is consistently shorter in TMS studies compared to MEG studies in both anterior (opIFG in Schuhmann et al., 2009: 300– 350 ms (Schuhmann et al., 2009); opIFG & vPrG in Wheat et al., 2013: 225–300 ms (Wheat et al., 2013)) and part of the posterior language areas (mMTG in Schuhmann et al.'s, 2012 follow-up study: disruptive effects in the first time window 225 ms and in the second time window 400 ms; in Acheson et al, 2011: facilitatory effects in −100 to 200 ms). These time differences are likely a result of inherent differences in MEG and TMS: the former records developing synchronized activations, while the latter induces an immediate temporary lesion that precedes neural synchronization (Sliwinska et al., 2012). Taken together, applying the rTMS pulse train starting at or later than 300 ms from the task stimulus presentation onset may risk the failure to timely disturb some of the posterior regions that subserve earlier stages of language processing, and consequently the possibility of increased false negative predictions, as was observed in the DELAYED TMS groups (Fig. 5). It should also be noted that, because of the differences in the timing of pulse onset in our study, different numbers of pulses were needed to cover the relevant window of each trial. Specifically, the ONSET group required more pulses than the DELAYED group. This difference in the number of pulses is not sufficient to explain the differences seen between groups, however. As described above, the critical window of pre-vocal language processing is completed within the time-frame of both stimulation paradigms. Thus, while the two paradigms differ in how much of the initial part of the preverbal processing window they cover, they both cover the end of that window, as well as that of vocalization. The differences seen in this study must therefore be attributed to the variable coverage of the pre-verbal window: delayed onset fails to disrupt the early mechanisms of pre-verbalization, thus resulting in increased false-negatives.

In this series, no adverse events were reported which might have been related to rTMS language mapping. This observation again shows that the use of short low-frequency rTMS trains is not only safe in normal subjects but also in patients with an intracranial lesion and consequent seizures (Epstein et al., 1996; Pascual-Leone et al., 1991). Since intraoperative language mapping by DCS provoked seizures in two patients of this series, TMS mapping was safer than DCS at least in the present limited series. Functional networks suggested by response pattern classification Temporal–frontal semantic network and the efficiency of ternary classification When we compared the results of the ternary classification based on paired signal-detection theoretic dependent variables within and between the ONSET TMS protocol and the DELAYED TMS protocol, the mapped cortical regions were distinguished into six anatomofunctionally and chronologically distinct clusters (Fig. 10). The largest cluster contiguously distributed over temporal and frontal areas. The regions forming this largest cluster match the regions participating in a temporal–frontal semantic network (Binder et al., 2009; Price, 2012), operating in the earlier stages of word production, presumably starting at pMTG and mMTG that are associated with semantic retrieval, then, proceeding anteriorly to aMTG and aSTG that are associated with finegrained semantic retrieval and to polMTG and polSTG that are associated with semantic-phonological mapping, and finally reaching trIFG and mMFG that are associated with word selection (Indefrey, 2011; Price,

Fig. 10. Anatomically-deduced functional clusters. Anatomically-deduced functional clusters based on the ternary classification of disruption patterns. Regarding the color coding, pink represents frontal and temporal regions associated with semantic processing; orange represents inferior parietal regions associated with semantic processing; dark blue represents frontal and posterior temporal regions associated with phonologicalarticulatory network; light blue represents inferior parietal regions associated with phonological processing; green represents middle-frontal and peri-rolandic regions associated with motor sequence processing. Yellow represents mid- to posterior superior temporal regions associated with auditory feedback processing.

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2012). Regarding the efficiency of the ternary classification strategy, this largest cluster covered three of the seven areas that are listed in Binder's et al., (2009) meta-analysis of fMRI studies on semantic network: MTG, IFG, and ventromedial prefrontal cortex (corresponding to mMFG). The semantic network is the largest network for which the ternary classification strategy demonstrated its ability to characterize cortical regions efficiently by forming a cluster of cortical regions that showed a common disruption response pattern. Frontal-posterior temporal long-distance phonological speech-motor network Contiguous to this temporal–frontal semantic network, but operative in slightly different time windows was the cluster consisting of opIFG, vPrG and an outpost pSTG. The regions forming this cluster match the regions participating in the phonological speech-motor network in which opIFG is associated with phonological and phonetic encoding, vPrG is associated with speech-related motor planning, and pSTG is associated with receiving an efferent copy of speech-motor plan that is used to predict upcoming auditory feedback. The ternary classification did not leave pSTG as a random stray, but showed that it patterned with opIFG and vPrG, as was seen in Edwards et al.'s (2010) data. This case is a clear demonstration of the ability of the ternary classification strategy to capture long-distance network. Moreover, the ternary classification results that consistently coupled opIFG and vPrG with an outpost pSTG increased the confidence that pSTG might be the receiving end of the efferent copy of the speech motor command that is hypothesized to be translated into presumably phonological representation used to predict the upcoming auditory feedback proposed in various state feedback models of speech motor control (Edwards et al., 2010; Hickok, 2012; Houde and Nagarajan, 2011; Price et al., 2011). Frontal-peri-rolandic motor-sequence network and its domain-specificity The other contiguous but distinguished cluster consisted of pMFG, mPrG, mPoG, and vPoG. The regions forming this cluster match the regions participating in the motor-sequence processing (Bohland et al., 2010; Rauschecker, 2012). This cluster was positive only for the DELAYED TMS protocol but not for the ONSET TMS protocol. Therefore, this cluster might be operative in a slightly later time window following the processing in opIFG/vPrG. Detailed analysis of complex syllable production in relation to speech-motor sequence control could be made possible in future studies by taking advantage of German target words varying greatly in phoneme sequence complexity and keeping track of trial-specific target words. For example, fass (barrel) is a simple C1VC2 sequence involving two different consonants and one vowel, whereas briefumschlag (envelop) is a complex irregular C1C2V1C3–V2C4–C5C6V3C7 sequence involving seven different consonants and three different vowels. This variation would provide a test ground for Bohland et al.'s (2010) GODIVA model of speech-motor sequence control built on phoneme sequence performance data. Though Bohland et al.'s (2010) model of motor sequence is built on syllable sequence performance data, this cluster has not been incorporated into Indefrey and Levelt's spatio-temporal model of word production (Indefrey, 2011; Indefrey and Levelt, 2004). One argument against incorporating this cluster into a language network would raise the question as to whether this cluster works selectively for speech-motor sequence or generally for motor sequence. So far, Price et al.'s (2011) fMRI study including a finger-tapping task showed that the area covering these regions may not be a speech-specific region. To provide evidence for speech-specific function of this cluster, the syllable sequence manipulation in future studies should include phonologically hierarchically combinatorial sequence in addition to simple linear concatenation, as has been established notably in syntactic studies when arguing that a mental operation of interest is language-specific, but not domaingeneral (e.g., Makuuchi and Friederici, 2013). Regarding the clinical relevance, this speech-motor sequence cluster is not yet listed as non-resectable regions in Ius et al.'s (2011) atlas of

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functional resectability of WHO grade II gliomas. However, considering that syllable-sequencing is associated with this cluster, it may be worthwhile to perform not only presurgical motor mapping but also presurgical language mapping for dorsal tumor patients. Verbal working memory network The sequencing aspect of word production also concerns the verbal working memory, which is standardly hypothesized as a relationship between opIFG and SMG. The ternary classification of the present study coupled a frontal cluster (mMFG, opIFG, vPrG) and a posterior cluster (aSMG, pSMG, pSTG) in the ONSET TMS sensitivity context. There are two major linguistic models of verbal working memory. On one hand, Jacquemot and Scott's (2006) dual buffer model of phonological working memory claims that phonological working memory emerges as the interaction between opIFG and SMG (Jacquemot and Scott, 2006). On the other hand, Makuuchi and Friederici's (2013) model of verbal working memory claims that the different types of verbal working memory are localized to different brain regions (Makuuchi and Friederici, 2013): the inferior frontal sulcus is associated with hierarchically combinatorial syntactic working memory whereas intraparietal sulcus is associated with nonhierarchical syntactic working memory and SMG is associated with linear phonological working memory. Detailed analysis of verbal working memory in relation to target words' morphological and phonological structures could be made possible in future studies by taking advantage of the variations in compound words in German. For example, gehstock (stick for walking) and schreibtisch (desk for writing) are target words that require hierarchical combinatorial operations for compounding, whereas taschenlampe (pocket + lamp − N penlight) is a target word derived by linear concatenation. Variations in compounding operation in German and English could be a test ground to tease apart the hypothesized anatomical differences in hierarchically combinatorial vs. linearly phonological working memory in future studies at bordering regions (opIFG and inferior frontal sulcus; SMG and intraparietal sulcus). Distinguishing models by response patterns of SMG and pSTG Referring to SMG as a locus of phonological representation turns this discussion back to the question about the destination of an efferent copy postulated in the state feedback control model of language production. Tian and Poeppel's (2013) MEG study that employed a covert production task hypothesizes that articulation is planned in IFG and the signals for the plan are sent to the inferior parietal lobe, then, to pSTG (Tian and Poeppel, 2013). The anatomical cluster (mMFG, opIFG, vPrG, aSMG, pSMG, pSTG) that emerged in our ONSET TMS sensitivity context is the closest match with the set of cortical regions postulated in their model. However, the most of the regions in this cluster are also found in the verbal working memory network. Because the data available for the present analysis are limited to the anatomical information, whether this cluster is associated with Tian and Poeppel's (2013) model or verbal working memory models cannot be decided (Tian and Poeppel, 2013). However, the ternary classification revealed opIFG/vPrG/pSTG cluster without SMG in two contexts (ONSET TMS PPV and specificity). Consequently, our results of anatomical clustering suggest pSTG's connection with the pre-vocalization speech-motor components and thus our results are more in accord with Edwards et al.'s (2010) ECoG results than with Tian and Poeppel's (2013) MEG results (Edwards et al., 2010; Tian and Poeppel, 2013). Inferior parietal lobe: subdivisions and connections When SMG is examined further in relation to posterior regions, the complexity of the inferior parietal regions emerged. The most striking result is that aSMG and pSMG were never coupled in the DELAYED TMS context. When aSMG was coupled with anG, pSMG was an odd man out. This grouping was observed in the specificity and NPV contexts. In contrast, when pSMG was coupled with anG, aSMG was an odd man out. This grouping was observed in the

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PPV and sensitivity contexts. These results suggest that aSMG and pSMG might be separately associated with distinct functions and thus the inferior parietal lobe, standardly divided into two regions (SMG and anG), might be better characterized as a tripartite area. Indeed, Lee et al. (2007) provided an anatomo-functionally tripartite view of the inferior parietal lobe (Lee et al., 2007): phonological information is processed in aSMG; word meanings are processed in anG; sounds and meanings are bound into a unified representation in pSMG. Regarding phonological processing in aSMG, when we take a closer look at the SMG disruption points depicted in past studies, it turns out that in the large area of SMG, disruption points were mostly located in ventral peri-Sylvian parts of aSMG for the phonological task for healthy adults (Sliwinska et al., 2012; Tarapore et al., 2013) and for the objectnaming task for tumor patients. Though the strategy used for the current navigated rTMS language mapping is like carpet-bombing across regions, as we increase knowledge about common landmarks for language map hotspots in different cortical regions, future navigated rTMS language mapping might be more like current motor mapping that typically probes around a target point that has an empirically known landmark. Improving the mapping strategy in this way could reduced the number of stimulation, which might in turn lead to increased mapping accuracy. anG: connections and subdivisions In the inferior parietal lobe, anG was the most difficult to characterize chronologically. In fact, anG was a region where both DCS and rTMS outcomes were true negative only in the ONSET TMS context whereas it was a region where DCS and rTMS outcomes were mixed with false positives and false negatives without any true positives or true negatives in the DELAYED TMS context. In Binder's et al., (2009) meta-analysis, anG is listed as one of the seven components of the semantic network and aSMG is listed as a distant semantic subsystem (Binder et al., 2009). This coupling of anG and aSMG by the DELAYED TMS protocol matches Bonner et al.'s (2013) connectivity hypothesis that the somatosensory action knowledge represented in aSMG is integrated into semantic representation in anG (Bonner et al., 2013). The false negative only or false positive only results for the DELAYED TMS protocol suggest that this semantic integration might occur before 300 ms post picture presentation onset or at latest by around 300 ms, which is consistent with the time points for SMG and anG estimated in Indefrey (2011), despite the DCS-TMS inconsistency of the DELAYED TMS protocol in the clinical sense (Indefrey, 2011). Another difficulty with narrowing down the anG activity timewindow might be due to its participation in the default network active in the conscious resting state (Binder et al., 2009). Concerning this possible overlap, Seghier et al. (2010) distinguished functional subdivisions in anG (Seghier et al., 2010). A mid-region of anG (manG) overlapped with the default network, showing the activation level decreased during semantic matching, perceptual matching, and word production tasks, whereas a dorsomesial region of anG (danG) increased activity during all the three tasks, while a ventrolateral region of anG (vanG) increased activity only during a semantic matching task. Their results suggest that speech-mapping outcomes of anG should be analyzed separately for the functionally different three subdivisions (manG, danG, and vanG) in future language mapping studies. mSTG: object-naming specific activity time-window Lastly, regarding mSTG, it stood out as the region containing falsenegative outcomes flunked by regions where outcomes were either true negative only or true positive only both in the ONSET TMS protocol and in the DELAYED ONSET protocol. Reasoning based on the findings from Edwards et al.'s (2010) ECoG study on object-naming, only a few spatial contexts in mSTG could be associated with language errors (Edwards et al., 2010). One context is when DCS/rTMS is applied to a

spot in the mSTG/pSTG borders. Because pSTG is coupled with the opIFG and vPrG responsible for speech-motor control, if a language arrest occurs, then the language error is not likely due to chance. Another context is when DCS/rTMS is applied to a spot close to the mid- to posterior supratemporal sulcus and the adjacent MTG. Because these spots are associated with lexical retrieval, if an anomia occurs, then the language error is unlikely due to chance. In Corina et al.'s (2010) DCS object-naming error analysis, mSTG was characterized by performance errors, semantic errors, and phonological errors (Corina et al., 2010). These errors might be weak forms of speech arrest and anomia in this context. In sum, as mentioned earlier in the aSMG section, as we increase knowledge about landmarks for language-map hotspots in given regions of interest, in the current context, STG, mapping during preoperative object-naming task and its accuracy evaluation would be more like landmark-based focused motor mapping for different fingers in the motor cortex and its accuracy evaluation. Basic research implications and outlook All in all, ternary classification of cortical regions based on pairs of true-positive, true-negative, false-positive, false-negative in the PPV, sensitivity, specificity, and NPV contexts was able to capture clusters of cortical regions that match the clusters of regions associated with semantic and phonological networks that have been proposed by various models of word production and also have been confirmed by past fMRI, ECOG and structural MR studies. At a very basic level, the strength of this ternary classification strategy (e.g., Schneidman et al., 2002) seems to lie in its ability to make use of a fuller range of information about response patterns (Schneidman et al., 2002). It demonstrated that not only DCSrTMS consistent outcomes but also DCS-rTMS inconsistent outcomes might occur systematically, so that functional clusters could be formed. This information efficiency allowed the method to classify cortical regions into clusters without wasting information in most contexts and thus led the method to capturing various anatomical–functional clusters proposed for latest models of language production and those established in the standard models of language processing in general. Disruption patterns of cortical regions in response to DCS and TMS turned out to be invaluable resources for basic research that aims to identify causally necessary, anatomical and functional basis of word-production network with high spatial and temporal resolution. Language-mapping data collected by navigated rTMS could add more refined analyses at different levels of linguistic functions (phonological, semantic, etc.) beyond the level of language-positive and languagenegative distinction where the endeavor of functional localization started. The findings from basic research could, in turn, provide finegrained solutions to help improve the techniques for clinical language mapping. Clinical implications and outlook With this timing parameter setting for rTMS pulse trains, preoperative rTMS language mapping provides spatial information that would help expedite intraoperative DCS language mapping aimed at safe extensive resection based on negative mapping with small and tailored cortical exposures. Moreover, there are many reasons why patients are not able to undergo awake surgery: psychological difficulties, severe medical co-morbidity, and sleep apnea, to name just a few. With the data presented in this study, especially the high negative predictive value of preoperative rTMS mapping, we provide an alternative to awake surgery in this subset of patients who might otherwise be offered biopsy only. Given the survival benefit of increasing extent of resection, even an asleep craniotomy with radical subtotal resection would be expected to confer a survival advantage over biopsy alone (Stummer et al., 2008). This being said, awake craniotomy, if possible, is still preferred.

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Despite our promising results about the theoretically-motivated stimulation onset factor that was found to yield high specificity in the posterior regions with the ONSET TMS protocol (86%), the optimization of the used stimulation parameters has to be further investigated. More specifically, determining stimulation parameters and their settings for the following aims would be of focal interests: to increase the PPV in the anterior region, currently at chance level (ONSET TMS: 55%, DELAYED TMS: 54%) without adversely affecting currently highest possible sensitivity (both 100%) and NPV (both 100%), to improve the currently moderate specificity in mapping the anterior regions with the ONSET TMS protocol (67%) and sensitivity in the posterior region (ONSET TMS: 75%; DELAYED TMS: 71%). To achieve these goals, not only stimulation onset but also intensity, frequency, and duration of the rTMS train should be studied in a controlled and systematic manner. The parameters used in this study were based on previous studies not only on navigated (Lioumis et al., 2012; Picht et al., 2013; Sollmann et al., 2013) but also on non-navigated rTMS (Epstein, 1998; Epstein et al., 1996; Wassermann et al., 1999). Nonetheless, these parameters induced language errors in all enrolled patients. As such, they are all worth being empirically tested, regardless of the extent to which they are theoretically motivated. For basic research, data obtained by immediate and delayed pulse train onsets might complement each other because, as the present study demonstrated, the analysis of disruption patterns could capture cortical regions causally necessary for phonological and semantic network operative in slightly different time-windows.

Conclusions With this study, we have demonstrated that rTMS stimulation onset coincident with picture presentation onset improves the accuracy of preoperative language maps, particularly within posterior language areas. In general, when the stimulation timing is appropriately set, rTMS language mapping is a reliable method of obtaining negative response maps of the left hemisphere with a high specificity. For basic research with the stand-alone nTMS, immediate and delayed pulse train onsets might complement each other because the analysis of disruption patterns could capture cortical regions causally necessary for semantic and phonological networks operative in slightly different time-windows.

Conflict of interest This research was supported in part by a grant from the Berlin Cancer Society to TP and by the German Society for Neurosurgery to SMK. The other authors declare that they have no conflict of interest affecting this study. The study was otherwise completely financed by institutional grants from the five departments. The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper. References Acheson, D.J., Hamidi, M., Binder, J.R., Postle, B.R., 2011. A common neural substrate for language production and verbal working memory. J. Cogn. Neurosci. 23, 1358–1367. Bagley, S.C., White, H., Golomb, B.A., 2001. Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. J. Clin. Epidemiol. 54, 979–985. Baum, S.H., Martin, R.C., Hamilton, A.C., Beauchamp, M.S., 2012. Multisensory speech perception without the left superior temporal sulcus. Neuroimage 62, 1825–1832. Binder, J.R., Desai, R.H., Graves, W.W., Conant, L.L., 2009. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb. Cortex 19, 2767–2796. Bohland, J.W., Bullock, D., Guenther, F.H., 2010. Neural representations and mechanisms for the performance of simple speech sequences. J. Cogn. Neurosci. 22, 1504–1529. Bonner, M.F., Peelle, J.E., Cook, P.A., Grossman, M., 2013. Heteromodal conceptual processing in the angular gyrus. NeuroImage 71, 175–186.

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Optimal timing of pulse onset for language mapping with navigated repetitive transcranial magnetic stimulation.

Within the primary motor cortex, navigated transcranial magnetic stimulation (nTMS) has been shown to yield maps strongly correlated with those genera...
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