Oscillatory and Structural Signatures of Language Plasticity in Brain Tumor Patients: a Longitudinal Study

RESEARCH ARTICLE Oscillatory and structural signatures of language plasticity in brain tumor patients: A longitudinal study Lucia Amoruso 1,2 | Shuang Geng 1,3 | Nicola Molinaro 1,2 | Polina Timofeeva 1,3 | Sandra Gisbert-Muñoz 1,3 | Santiago Gil-Robles 4,5 | Iñigo Pomposo 5 | Ileana Quiñones 1 | Manuel Carreiras 1,2,3 1 Basque Center on Cognition, Brain and Language (BCBL), San Sebastian, Spain 2 IKERBASQUE, Basque Foundation for Science, Bilbao, Spain 3 University of the Basque Country, UPV/EHU, Bilbao, Spain 4 Department of Neurosurgery, Hospital Quiron, Madrid, Spain 5 BioCruces Research Institute, Bilbao, Spain Correspondence Lucia Amoruso, Basque Center on Cognition, Brain and Language (BCBL), 20009 San Sebastian, Spain. Email: [email protected] Funding information Basque Government, Grant/Award Number: BERC 2018-2021; Spanish Ministry of Economy and Competitiveness, Grant/Award Number: RTI2018-096216-A-I00 (MEGLIOMA) and RTI2018-093547-B-I00 (LangConn); Spanish State Research Agency, Grant/Award Number: SEV-2015-0490 and IJCI-2017-31373 Abstract Recent evidence suggests that damage to the language network triggers its functional reorganization. Yet, the spectro-temporal fingerprints of this plastic rearrangement and its relation to anatomical changes is less well understood. Here, we combined magnetoencephalographic recordings with a proxy measure of white matter to investigate oscillatory activity supporting language plasticity and its relation to structural reshaping. First, cortical dynamics were acquired in a group of healthy controls during object and action naming. Results showed segregated beta (13–28 Hz) power decreases in left ventral and dorsal pathways, in a time-window associated to lexico-semantic processing (250–500 ms). Six patients with left tumors invading either ventral or dorsal regions performed the same naming task before and 3 months after surgery for tumor resection. When longitudinally comparing patients' responses we found beta compensation mimicking the category-based segregation showed by controls, with ventral and dorsal damage leading to selective compensation for object and action naming, respectively. At the structural level, all patients showed preoperative changes in white matter tracts possibly linked to plasticity triggered by tumor growth. Furthermore, in some patients, structural changes were also evident after surgery and showed associations with longitudinal changes in beta power lateralization toward the contralesional hemisphere. Overall, our findings support the existence of anatomo-functional dependencies in language reorganization and highlight the potential role of oscillatory markers in tracking longitudinal plasticity in brain tumor patients. By doing so, they provide valuable information for mapping preoperative and postoperative neural reshaping and plan surgical strategies to preserve language function and patient's quality of life. KEYWORDS brain rhythms, brain tumors, language, magnetoencephalography, neuroplasticity Received: 11 June 2020 Revised: 12 December 2020 Accepted: 14 December 2020 DOI: 10.1002/hbm.25328 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. Hum Brain Mapp. 2021;42:1777–1793. wileyonlinelibrary.com/journal/hbm 1777 1|INTRODUCTION Neuroplasticity refers to the brain's ability to modify its structure and function throughout the lifespan, allowing the acquisition of new skills (Carreiras et al., 2009; Maguire et al., 2000) but also coping with brain damage and disease (Payne & Lomber, 2001). When considering this latter aspect, evidence from human studies in stroke (Butefisch et al., 2005; Shimizu et al., 2002) and brain tumor patients (Duffau, 2005; Robles, Gatignol, Lehericy, & Duffau, 2008) underscores the existence of different plasticity patterns, including function persistence within the tumor, function redistribution in perilesional areas, ipsilesional activation of more distant areas and recruitment of contralesional homologs. In line with a hodotopical understanding of brain organization (Catani, 2007; De Benedictis & Duffau, 2011; Duffau, Moritz-Gasser, & Mandonnet, 2014), functional reallocation would be possible thanks to the existence of redundant cortico– subcortical parallel networks potentially unmasked by the lesion. It has been suggested (Ius, Angelini, Thiebaut de Schotten, Mandonnet, & Duffau, 2011) that this high potential for reorganization would be almost confined to the cortical level, with subcortical white matter showing limited to null plasticity. Nonetheless, evidence from stroke (Schlaug, Marchina, & Norton, 2009) and epileptic patients following temporal lobectomy (Jeong, Asano, Juhasz, Behen, & Chugani, 2016; Li et al., 2019), suggests that white matter plasticity in the contralesional hemisphere is somehow possible. Brain function and its reshaping in the damaged brain has been classically studied by means of functional magnetic resonance imaging (fMRI). However, hemodynamic responses are slow (one volume every 2 s) and functions as language, which occur on the subsecond timescale, need also to be examined with high-temporal resolution techniques capable of tracking linguistic processing in real-time. Electroand magneto-encephalography (M/EEG) meet this requirement as they can capture neuronal activity and its oscillatory dynamics with millisecond time resolution, offering a new perspective to study brain plasticity (Reid et al., 2016). Oscillations at different frequency-bands and their synchronization are thought to reflect communication within and between regions (Fries, 2005), relevant for behavior and disease (Uhlhaas et al., 2017). Recently, M/EEG studies have been successful in identifying oscillatory markers of brain damage and language recovery, underscoring the involvement of low-frequency activity in functional compensation. For instance, using MEG, Kielar, Deschamps, Jokel, and Meltzer (2016) reported the involvement of contralesional right alpha-beta activity during semantic processing in stroke patients. Similarly, Traut et al. (2019) found that brain tumor patients exhibited a functional shift in beta language lateralization toward the right hemisphere after left tumor resection. Using EEG, Spironelli, Manfredi, and Angrilli (2013) evaluated post-stroke language reorganization and found bilateral patterns of beta activity in ipsilesional frontal areas and contralesional homologs during semantic processing. Piai, Meyer, Dronkers, and Knight (2017) reported alpha–beta power decreases associated to lexico-semantic retrieval in stroke patients with left hemispheric lesions. Interestingly, while controls showed a left lateralized effect, patients exhibited a right lateralized one, which was ultimately predicted by the probability of splenium damage. Here, we tracked oscillatory dynamics subserving language plasticity in brain tumor patients before and 3 months after surgery for tumor resection. We recorded MEG activity while healthy controls and patients overtly named object and action pictures in Spanish. Of note, brain tumors could affect either ventral or dorsal areas within the left hemisphere which are known to play different roles in representing object and action categories. Briefly, previous evidence (Gleichgerrcht et al., 2016; Lubrano, Filleron, Demonet, & Roux, 2014; Vigliocco, Vinson, Druks, Barber, & Cappa, 2011) suggests that the semantic processing of object and action knowledge is underpinned by partially distinct networks preferentially involving inferior-temporal and fronto-parietal nodes, respectively. Thus, we capitalized on this dissociation to evaluate language function in the healthy and the lesioned brain. Specifically, we expected different alpha-beta compensation patterns depending on tumor location and semantic category, with ventral and dorsal lesions mainly compromising object and action processing, respectively. We also expected that functional changes would be related to structural ones. Thus, we calculated a proxy measure of white matter involvement in language-related tracts and assessed if potential preoperative and/or postoperative structural reshaping was associated with functional longitudinal changes. 2|MATERIALS AND METHODS 2.1 |Participants A total of 26 participants took part in this study. Twenty healthy adults (6 men, age mean = 25.04; SD = 3.94) were recruited through the BCBL database and received economical compensation for their participation. Six patients (3 men, age range 24–59; mean = 40; SD = 12.89) with brain tumors in the left hemisphere mainly involving temporal (n= 3), fronto-parietal (n= 2) or parietal regions (n= 1) were recruited at the Cruces Hospital where they received their diagnosis and performed the awake craniotomy for tumor resection (see Figure 1 for lesion profile). One out of 6 patients had cavernous angiomas, while the other 5 exhibited astrocytomas Grade I and II. Individual patient demographics, lesion and clinical characteristics are summarized in Table 1. All participants were right handed as measured by Edinburgh Handedness Inventory (Oldfield, 1971). They all had normal hearing and normal or corrected to normal vision. All patients and controls reported Spanish as their first language and the average naming BEST score (de Bruin, Carreiras, & Duñabeitia, 2017) in Spanish was 63.17/65 for patients and 64.81/65 for controls. It should be noted, however, that patients and controls also reported knowing some Basque (40/65 for patients and 49/65 for controls), as is common in the population of Donostia-San Sebastian. The study protocol was approved by the Ethics Board of the Euskadi Committee and the Ethics and Scientific Committee of the BCBL, following the declaration of Helsinki. All participants gave their written consent prior to the study. 1778 AMORUSO ET AL. 2.2 |Stimuli and task Semantic processing was assessed with a picture naming task. Pictures were selected from a standardized battery developed by NEURE clinic®(https://www.neure.eu/). The task included two separate sets of 30 colored images with line drawings either depicting objects or a person performing an action, respectively. Object and action stimuli were matched as close as possible for different linguistic variables and differences between stimuli were calculated using Student tests for normally distributed variables and Mann–Whitney for non-normally distributed ones. More specifically, stimuli were matched for frequency (Objects: mean = 25.94, SD = 29.19; Actions: mean = 14.79, FIGURE 1 Lesion profile with respect to major dorsal and ventral white matter tracts. Tumors are shown in blue. Probabilistic location of superior longitudinal fasciculus (SLF, I, II), arcuate fasciculus (AF) and inferior fronto-occipital fasciculus (IFOF) are shown in orange. Patients 1, 2 and 3 (on top) exhibit tumors invading the left temporal lobe, however, none of them compromises the IFOF. Patients 4, 5, and 6 (on bottom) exhibit tumors invading fronto-parietal regions affecting, in all cases, the SLF and in cases 5 and 6 also the AF TABLE 1 Demographics and clinical characteristics of patients Patient ID Age (years) Gender Education (years) Handedness Type of tumor Tumor volume (cm 3 ) Extent of resection (%) P1 33 F 21 R Cavernous Angioma 2.53 84.48 P2 59 M 12 R Astrocytoma Grade II 5.8 100 P3 24 M 16 R Astrocytoma Grade I 1.85 100 P4 46 F 14 R Astrocytoma Grade II 0.55 75.83 P5 31 F 21 R Astrocytoma Grade II 16.44 100 P6 47 M 19 R Astrocytoma Grade II 74.25 100 AMORUSO ET AL.1779 SD = 17.06, W= 562, p= .055), word length in terms of number of letters (Objects: mean = 5, SD = 1; Actions: mean = 6, SD =1, t=−1.519, p= .13), and familiarity (Objects: mean = 6.26, SD = 0.5; Actions: mean = 6.17, SD = 0.56, t= 0.68, p= .49). Name agreement was ≥85% in both conditions. In separate blocks, participants were requested to observe the pictures and name them overtly in Spanish. Production of nouns and verbs was requested in the context of short sentences, which is a more ecological form of speech than isolated naming. More specifically, on top of the object-related images we added the text “Esto es…”[“This is…”in Spanish] to force the production of a short sentence that had to agree in number with the target noun (e.g., “This is a bird”,“This is an apple”). Similarly, on top of the action-related pictures, we included the pronouns “El…”or “Ella…”[“He…”or “She…”, in Spanish]. This introductory text was used as a cue for the production of a sentence that started with the given subject and had a finite verb form in third person singular (e.g., “She sings,”“He writes”). Importantly, the use of these pictures led to participants eliciting sentences comprising concrete nouns (i.e., as opposed to abstract concepts like “love”) and dynamic motor actions (i.e., as opposed to verbs referring to static states like “thinking”). We used MatlabR2012B and Cogent Toolbox for picture presentation. Trials started with a fixation cross lasting for 500 ms, followed by the stimulus displayed for 1 s. ISI randomly varied between 2 and 4 s. Each picture was presented 3 times for a total of 90 trials per block. Each block lasted 10 min, and participants were allowed to take a short break between them. 2.3 |Behavioral assessment Vocal responses were recorded and monitored online by the experimenter while participants performed the task. Naming latencies were calculated using the Chronset tool (Roux, Armstrong, & Carreiras, 2017) which enables the automatic detection of speech onset. Responses containing disfluencies or errors were coded as invalid and excluded from MEG analysis. In addition, response latencies shorter than 200 ms and deviating from participant's mean latency by >2.5 SD in each condition (Miozzo, Pulvermuller, & Hauk, 2015) were also removed (in total, 5.9% of the trials were eliminated). Reaction times (RTs) and correct naming responses were compared between groups using nonparametric Welch's t-tests and Wilcoxon signed-rank for preversus post-surgery stages within the patient's group (see Table 2). Furthermore, we also ran CrawfordHowell (1998) frequentist t-tests for single-case analysis using the psycho Package (Makowski, 2018) on RStudio (Version 1.2.5019) to compare each patient to the control group (see Table 3). 2.4 |MEG and MRI acquisition MEG data were acquired in a magnetically shielded room using a 360-channel Elekta-Neuromag system (Helsinki, Finland). Eyemovements were monitored with two pairs of electrodes in a bipolar montage placed on the external chanti of each eye (horizontal EOG) and above and below right eye (vertical EOG). Electrocardiographic (ECG) activity was also recorded with two electrodes, one positioned just below the right clavicle and the other below the left rib bone. MEG signals were continuously recorded at a 1 kHz sampling rate and on-line filtered to 0.1–330 Hz. The head position inside the helmet was continuously monitored using five head position indicator (HPI) coils. The location of each coil relative to the anatomical fiducials (i.e., the nasion, and left and right preauricular points) was defined with a 3D digitizer (FastrakPolhemus, Colchester, VA). Digitalization of the fiducials plus 200 additional points distributed over the participant's scalp were used during subsequent data analysis to spatially align the MEG sensor coordinates to the native T1 high-resolution 3D structural MRI. Structural images were acquired before and 3 months after surgery for each participant with a Siemens 3T MAGNETOM PRISMAfit MR scanner (Siemens, Munich, Germany) in a separate session (i.e., 1 day before the MEG session). T1-weighted MPRAGE anatomical volumes were acquired with the following parameters: echo time = 2.97 ms, repetition time = 2,530 ms, flip angle = 7and field of view = 256 × 256 ×176 mm 3 , number of axial slices = 176, slice thickness = 1 mm, in-plane resolution = 1 mm ×1 mm. The T2-weighted fluid-attenuated inversion recovery (FLAIR) sequence used the following parameters: echo time = 394 ms, repetition time = 5,000 ms, flip angle = 7and field TABLE 2 Behavioral results. Mean (M) and SD of accuracy and reaction time (RT) in each condition for each group, with p-values from Welch's t-tests comparing performance between patients and controls and Wilcoxon signed-rank for preversus post-surgery stages within the patient's group Controls M (SD) Patients (PRE) M (SD) Patients (POST) M (SD) Controls versus patients (PRE) Controls versus patients (POST) Patients PRE versus POST p-value p-value p-value Accuracy (%) Object naming 98.75 (2.06) 98.27 (2.83) 98.27 (2.83) .71 .71 1.0 Action naming 97.5 (3.33) 97.6 (3.63) 95.43 (3.54) .93 .24 .34 Reaction time (ms) Object naming 946.6 (275.5) 963.8 (144.5) 958.8 (219.2) .36 .74 .68 Action naming 1,059.2 (263.7) 1,105.4 (191.3) 1,096.2 (196) .36 .44 .68 1780 AMORUSO ET AL. of view = 256 ×256 ×176 mm 3 , number of axial slices = 192, 1 mm isotropic resolution. 2.5 |MEG data pre-processing Continuous data were initially pre-processed off-line using the temporal extension of the signal space separation method (Taulu and Simola, 2006) implemented in Maxfilter 2.2 (Elekta-Neuromag), which subtracts external magnetic noise from the MEG recordings, corrects for head movements and interpolates bad channels with algorithms implemented in the software. Subsequent analyses were performed using the FieldTrip toolbox version 20170911(Oostenveld, Fries, Maris, & Schoffelen, 2011) in MatlabR2014B. Recordings were downsampled to 500 Hz and segmented into epochs time-locked to stimulus presentation (i.e., picture to be named) from 500 ms before image onset to 1,000 ms after image onset. Data were filtered with a DFT filter to remove line noise. A semiautomatic procedure was then employed to remove epochs with electromyographic artifacts, SQUID jumps and flat signal. A fast independent component analysis (ICA) was used to identify eye movements, blinks and electrocardiographic artifacts (Jung et al., 2000). The datasets of four healthy participants were excluded from the analysis due to excessive blinking and/or muscular artifacts resulting in the loss of a large number of trials (70%). Thus, subsequent analyses were performed on a total of 16 healthy participants. TABLE 3 Comparison of individual patient scores to control group performance during naming. Mean (M), t-values, and p-values from Crawford-Howell t-tests comparing accuracy and reaction time (RT) during object and action naming before and after surgery for tumor resection Pre-surgery Post-surgery Mean t-value p-value Mean t-value p-value Object naming Reaction times (ms) P1 958.76 0.04 .96 1,014.09 0.23 .81 P2 1,198.29 0.88 .38 1,357.76 1.45 .16 P3 868.91 −0.27 .78 789.25 −0.55 .58 P4 818.81 −0.45 .65 762.78 −0.64 .52 P5 1,067.23 0.42 .67 973.62 0.09 .92 P6 870.76 −0.26 .79 855.33 −0.32 .75 Accuracy (%) P1 96.29 −1.16 .26 96.29 −1.16 .26 P2 93.33 −1.21 .24 93.33 −1.21 .24 P3 100 0.58 .56 100 0.58 .56 P4 100 0.58 .56 100 0.58 .56 P5 100 0.58 .56 100 0.58 .56 P6 100 0.58 .56 100 0.58 .56 Action naming Reaction times (ms) P1 1,004.07 −0.20 .84 1,087.4 0.10 .91 P2 1,444.15 1.42 .17 1,467.46 1.5 .15 P3 890.12 −0.62 .54 882.57 −0.65 .52 P4 1,027.25 −0.11 .9 1,022.13 −0.13 .89 P5 1,177.37 0.43 .67 1,071.77 0.04 .96 P6 1,089.62 0.11 .91 1,046.11 −0.04 .96 Accuracy (%) P1 100 0.72 .47 100 0.72 .47 P2 92.6 −1.43 .17 92.6 −1.43 .17 P3 100 0.72 .47 100 0.72 .47 P4 93.33 −1.21 .24 93.33 −1.21 .24 P5 100 0.72 .47 93.33 −1.21 .24 P6 100 0.72 .47 93.33 −1.21 .24 AMORUSO ET AL.1781 2.6 |Sensor level analysis Time-frequency representations (TFR) were calculated from the artifact-free MEG segments for frequencies ranging from 1 to 30 Hz. TRFs were obtained using Hanning tapers and a fixed window length of 500 ms advancing in 10 ms steps, giving rise to a 2 Hz frequency resolution. Power estimates were calculated separately for each orthogonal direction of a gradiometer pair and then combined, resulting in 102 measurement channels. Power was expressed as relative change with respect to a 500 ms pre-stimulus baseline. On average, conditions comprised 42.33 (SD = 2.02) artifactand errorfree trials for patients (no differences in trial number between object and action naming conditions or preand post-surgery sessions, Wilcoxon signed rank, all ps > .11) and 46.22 (SD = 7.22) for healthy controls (no difference between object and action conditions, p= .22). Importantly, no differences in the number of trials between patients and healthy adults were observed for object and action naming either before or after surgery (Welch's t-tests, all ps > .18). 2.7 |Selection of frequency-band and timewindows Previous M/EEG studies indicate that power changes in the alpha and beta frequency-bands (Piai, Roelofs, Rommers, & Maris, 2015) reflect the retrieval of lexical-semantic information. Furthermore, evidence from studies on stroke (Kielar et al., 2016; Piai et al., 2017; Spironelli et al., 2013) and brain tumor patients (Lizarazu et al., 2020; Traut et al., 2019) points to an involvement of alpha and beta activity in functional compensation. Thus, we focused our analysis on lowfrequency activity including alpha (8–12 Hz) and beta (13–28 Hz) oscillations. The time window was primarily chosen based on methodological constraints imposed by our task. Indeed, previous studies show that in overt production tasks artifact-free brain recordings can be measured up to approximately 400 ms post-stimulus presentation (Ganushchak, Christoffels, & Schiller, 2011). Based on this evidence and visual inspection of the onset of speech production in our data, we focused our TFR analysis to the 0–500 ms time window after picture onset. More specifically, we selected two time-windows capturing early (0–200 ms) and late (200–500 ms) picture-naming related processes, including visual recognition, and conceptualization and lexical selection, respectively (Indefrey, 2011; Indefrey & Levelt, 2004; Liljestrom, Kujala, Stevenson, & Salmelin, 2015). 2.8 |Statistical analysis In order to evaluate functional compensation within patients' language network we first explored the spectro-temporal pattern of responses triggered by our picture naming task in a group of healthy controls. More specifically, we calculated TFRs for object and action naming in early and late time-windows relative to picture onset and compared each of them relative to pre-stimulus baseline activity. Then, once main oscillatory patterns triggered by the task were identified in controls, we assessed longitudinal changes in the group of patients. Specifically, we calculated TFRs for object and action naming before tumor resection and contrasted them with those obtained 3 months after the surgery. In all cases, differences in spectral power between conditions at the sensor level were assessed using cluster-based permutation tests (Maris & Oostenveld, 2007). This test controls for multiple comparisons using a cluster-based correction while maintaining sensitivity based on temporal, spatial and frequency dependency of neighboring samples. The permutation p-value was calculated using the Monte Carlo method with 1,000 random permutations. The threshold for significance testing was a p-value below 5% (two-tailed). Please note that the finding of a significant cluster implies that there is a significant difference between conditions. However, the cluster does not provide exact information about the timing and the spatial location of the effect. In other words, no statements about the onset/offset of the effect at the millisecond level or about its spatial extent can be made (Sassenhagen & Draschkow, 2019). While we had clear hypotheses about the frequency-bands potentially involved in the language effects (i.e., alpha-beta), no specific a priori hypotheses about timing and/or location were held. Thus, we averaged over frequency bins (alpha central frequency = 10.13 Hz and beta central frequency = 20.66 Hz;) but considered all sensors (i.e., combined gradiometers) and time-points within early and late time-windows in the analysis. 2.9 |Source localization Participants' high-resolution 3D structural MRIs were segmented using Freesurfer software (Dale & Sereno, 1993). Co-registration between the MEG sensor coordinates and the participant's MRI coordinates was done by manually aligning the digitized head-surface and fiducial points to the outer scalp surface. The forward model was computed using the Boundary Element Method (BEM) implemented in the MNE software suite (Gramfort et al., 2014); RRID:SCR_005972) for three orthogonal tangential current dipoles (one for each spatial dimension) placed on a homogeneous 5-mm grid source space covering the whole brain. For each source, the forward model was then reduced to its two principal components of highest singular value, which closely correspond to sources tangential to the skull. We used both gradiometers and magnetometers in the source estimation, normalizing each sensor signal by its noise variance (500-ms baseline period prior to picture onset). Brain source activity was calculated for each participant using Linearly Constrained Minimum Variance (LCMV) beamformer approach (Van Veen, van Drongelen, Yuchtman, & Suzuki, 1997). A common filter was computed by combining the cross-spectral density (CSD) matrices from the timefrequency window of the significant sensor-level effects and an equally-sized baseline period prior to picture onset. The common filter was then applied separately to each condition to estimate source power. Since we focused our analysis on the local source power, we 1782 AMORUSO ET AL. only used real-valued filter coefficients (Grutzner et al., 2010). To normalize source activity, the neural activity index (NAI) was calculated as a certain ratio between the power in the experimental conditions and the pre-stimulus baseline (Pcond-Pbase./Pbase). For each session (preand post-surgery), the MEG maps were first co-registered with their corresponding individual MRIs and then normalized to the standard MNI to run group level analyses. This was done by applying a non-linear transformation using the spatial-normalization algorithm implemented in SPM8 and it was checked by one of the authors (LA). Group analyses were performed with the location-comparison method described in (Bourguignon, Molinaro, & Wens, 2018). Briefly, this method generates bootstrap group-averaged maps to build a permutation distribution of location difference between local maxima in the two conditions being compared, and test the null hypothesis that this distance is zero. Local maxima is defined as sets of contiguous voxels displaying higher power than all other neighboring voxels. The threshold for statistical testing at p< .05 was computed as the 95-percentile of the permutation distribution. All supra-threshold local MEG peaks were interpreted as indicative of brain regions likely triggering the sensor-level effects. This robust method has shown to deal well with the spectral leakage of the source-projected MEG data which can result from directly contrasting brain maps for different conditions. 2.10 |3D lesion reconstruction Lesions were manually drawn on the native space of participants' T1-weighted MPRAGE image by a trained technician using the MRIcron software (Rorden, Karnath, & Bonilha, 2007) and further supervised by the neurosurgeons in charge of the patients' awake craniotomy (SGR and IPG). The reconstruction was performed also using information from T2 images when lesion boundaries were not clear in the T1 MRI. The lesion was then normalized to the MNI template and alignment between the reconstructed lesion and the lesion in the native space was checked by one of the authors (IQ). A volume of interest (VOI) was created for each patient each time point. From each preand post-surgery 3D reconstruction, the tumor volume (cm 3 ) was calculated. Extent of resection (cm 3 ) was measured on postoperative imaging as: (Volume of (preoperative 3D Tumor Reconstruction \postoperative Resection)*100/preoperative tumor volume). 2.11 |Structural measure of white mater changes For the structural analysis, preoperative and postoperative T1 and T2 images were pre-processed and analyzed using the Voxel-Based Morphometry (VBM) toolbox and the SPM12 software package. Images were corrected for bias-field inhomogeneity; classified into gray, white matter and cerebrospinal fluid; registered to a standard MNI space using high-dimensional DARTEL normalization (Ashburner, 2007) and further smoothed with a 6 mm full width half maximum (FWHM) Gaussian kernel. We used a segmentation approach based on an adaptive maximum, a posterior technique which does not need a priori information about tissue probabilities (Rajapakse, Giedd, & Rapoport, 1997). We further refined this procedure, by accounting for partial volume effects and by applying a hidden Markov random field model which incorporates spatial prior information of the adjacent voxels into the segmentation estimation (Tohka, Zijdenbos, & Evans, 2004). To assess potential differences in white matter involvement we used a region of interest (ROI) approach. ROIs were defined using a probabilistic tractography atlas (Rojkova et al., 2016). The selected tracts were the superior longitudinal fasciculus (SLF I), the arcuate fasciculus (AF, long branch) and the inferior-fronto-occipital fasciculus (IFOF), which constitute key bundles within dorsal and ventral language pathways and their damage is known to affect language processing (Agosta et al., 2013; Almairac, Herbet, Moritz-Gasser, de Champfleur, & Duffau, 2015; Catani & Mesulam, 2008; Mandelli et al., 2014). For each of these ROIs, we extracted preoperative and postoperative mean volumes in left and right hemispheres and corrected it for brain size using the total intracranial volume (TIV). By doing so, we obtained a proxy marker of white matter involvement based on lesion distribution in relation to white-matter probabilistic distribution derived from the tractography atlas (Rojkova et al., 2016). Comparisons between patient's morphometric values and controls were performed using Crawford-Howell t-tests. 2.12 |Correlational analysis between structure and function First, we calculated a Language laterality index (LI) using the following formula: LI = R−LðÞ=R+LðÞ: where “R”and “L”represent power averaged across sensors for naming conditions (object and action pooled together) in right and left hemispheres, respectively; thus yielding positive values for rightlateralized and negative values for left-lateralized language-related activity. Given the left-lateralized pattern of oscillatory beta responses observed in the healthy control group as well as previous studies using beta power for calculating LI in speech production tasks (Traut et al., 2019), we reasoned that beta activity (13–28 Hz) was better suited than alpha to capture a potential shift toward the right hemisphere triggered by tumor presence and/or resection. Thus, the index was calculated for each patient and session (i.e., before and after surgery) only in the beta band. Furthermore, given the common leftlateralized pattern observed in both object and action naming, we combined them into a unique naming condition to reduce dimensionality and obtain a higher signal-to-noise ratio in the data. Please note, that this methodological choice (i.e., focus on beta responses) was further supported by the longitudinal contrast in patients, showing oscillatory effects circumscribed to the beta frequency-band (see below). LI was tested separately in controls and patients with Wilcoxon signed rank tests against zero and between groups using Welch's t-tests. AMORUSO ET AL.1783 In addition, the same index was used to calculate lateralization of white matter tracts in controls and patients. More specifically, in the case of patients, this index was calculated separately in preoperative and postoperative stages and, in each case, individually compared against the control group using Crawford-Howell t-tests. Finally, Pearson correlations between preoperative and postoperative white matter ROIs LI and beta longitudinal changes (post −presurgery beta LI) were run to establish whether potential reshaping at the structural level was associated with functional one. 3|RESULTS 3.1 |Behavioral results Table 2 shows mean accuracy and reaction time values (RT) for healthy controls and patients, as well as contrasts between groups and surgery stages. Overall, no differences in performance (i.e., accuracy and RTs) were observed between groups. These results were further confirmed at the individual patient level with CrawfordHowell t-tests, which supported the absence of significant behavioral effects (see Table 3). In addition, no differences were observed within patients when comparing performance before and after surgery. This finding was well expected, given that patients with slow-growing brain tumors typically exhibit a normal neurological and behavioral exploration, at least when considering relatively easy low-level tasks (DeAngelis, 2001). Furthermore, the maintenance of this behavioral pattern after surgery speaks in favor of successful language compensation. 3.2 |Oscillatory signatures of picture naming in healthy controls Figure 2 shows the time-frequency representations (TFRs), topographical distributions and source localization plots of the naming conditions as compared to baseline in the alpha (8–12 Hz) and beta (13–28 Hz) frequency-bands. Early time-window effects (0–200 ms): Object and action naming showed early alpha power increases as compared to baseline. This effect was highlighted by significant positive clusters (both Monte Carlo ps = .004, two-tailed), over bilateral posterior and left middle sensors, in the case of objects; and bilateral posterior sensors in the case of actions. Source localization of early alpha effects identified the related local maxima in occipito-parietal regions. In addition, similar beta power increases were observed for object and action naming conditions as indicated by significant positive clusters over left posterior and middle sensors (both Monte Carlo ps = .01, two-tailed). FIGURE 2 Oscillatory signatures of speech production in healthy controls. TFR of alpha and beta power in the object (top panel) and action (bottom panel) conditions over time. TFRs are plotted as relative power change compared to the baseline period over representative significant sensors (objects = M1632 + M1633; highlighted in orange; actions = M1722 + M1723; highlighted in blue). Topographic distribution plots show posterior alpha and beta power increases at early stages (0–200 ms), and left-lateralized anterior and posterior beta power decreases at later stages (200–500 ms) 1784 AMORUSO ET AL. Source localization showed local maxima peaking in left angular gyrus and inferior frontal gyrus (IFG) for objects; and in left supramarginal and orbitofrontal areas for actions (Figure 2, right panel). Late time-window effects (200–500 ms): During this period, a significant negative cluster (Monte Carlo p= .01, two-tailed) encompassing bilateral posterior and middle sensors revealed alpha power decreases only for the object naming condition. At the source level, this effect showed local minima in bilateral occipito-temporal regions. Finally, both conditions showed beta power decreases that were underscored by significant negative clusters over bilateral posterior and anterior sensors in the case of objects (Monte Carlo p= .004, two-tailed); and bilateral posterior and left anterior sensors in the case of actions (Monte Carlo p=.01,two-tailed).Source localization of beta effects showed local minima in the left IFG, irrespectively of the naming condition, while object naming additionally recruited the left anterior temporal pole and action naming the left superior parietal and dorsal premotor cortex (Figure 2, left panel). Overall, these findings provide a baseline to interpret potential language reshaping in patients. In brief, they support previous M/EEG studies (Piai et al., 2015, 2017) showing the involvement of alpha-beta oscillations in speech production and align well with evidence indicating the existence of partially non-overlapping networks for the processing of object and action knowledge, showing a diverse contribution of ventral and dorsal nodes of the language network, respectively (Vigliocco et al., 2011). 3.3 |Functional plasticity in brain tumor patients Figure 3 shows TFRs, topographic distributions of the object effect found in patients with ventral temporal lesions (Figure 3a) and of the action effect, found in patients with dorsal fronto-parietal lesions (Figure 3b). Overall, when comparing oscillatory activity across sessions (postvs. pre-surgery for tumor resection) within each group of patients, we found significant differences between sessions in the beta band (13–28 Hz), with power increases after tumor resection. 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