Cognitive Control Network and Language Reorganization in Patients with Brain Tumors.

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Learn more: PMC Disclaimer | PMC Copyright Notice AJNR Am J Neuroradiol . 2025 Jun;46(6):1238–1248. doi: 10.3174/ajnr.A8638 Cognitive Control Network and Language Reorganization in Patients with Brain Tumors Luca Pasquini a, b, ✉ , Antonio Napolitano c , Leonardo Spitoni c , Maurizio Schmid d , Francesco Dellepiane e , Mehrnaz Jenabi b , Kyung Peck f , Andrei Holodny b, g, h, i Author information Article notes Copyright and License information a From the Neuroradiology Service (L.P.), Radiology Department, Yale New Haven Hospital, Yale Medical School, New Haven, Connecticut b Neuroradiology Service (L.P., M.J., A.H.), Radiology Department, Memorial Sloan Kettering Cancer Center, New York, New York c Medical Physics Department (A.N., L.S.), Bambino Gesù Children’s Hospital, Rome, Italy d Department of Engineering (M.S.), University Roma Tre, Rome, Italy e Neuroradiology Service (F.D.), Radiology Department, Bambino Gesù Children’s Hospital, Rome, Italy f Department of Medical Physics (K.P.), Memorial Sloan Kettering Cancer Center, New York, New York g Department of Radiology (A.H.), Weill Medical College of Cornell University, New York, New York h Brain Tumor Center (A.H.), Memorial Sloan Kettering Cancer Center, New York, New York i Department of Neuroscience (A.H.), Weill Cornell Graduate School of the Medical Sciences, New York, New York ✉ Please address correspondence to Luca Pasquini, Neuroradiology Service, Radiology Department, Yale School of Medicine, New Haven, CT 06510; e-mail: lucapasquini3@gmail.com ; @LucaPasquiniMD; @Yale; @YaleRadiology; @YaleMed ✉ Corresponding author. Received 2024 Sep 29; Accepted 2024 Dec 11. © 2025 by American Journal of Neuroradiology PMC Copyright notice PMCID: PMC12152808  PMID: 39730156 Graphical Abstract Open in a new tab The authors investigated the participation of cognitive control network (CCN) in tumor-induced language reorganization. The results showed that the CCN is involved in language reorganization and is associated with improved speech after surgery. Abstract BACKGROUND AND PURPOSE: The interaction between language and other cognitive networks in patients harboring brain tumors is poorly understood. We studied the modification of the cognitive control network (CCN) induced by brain tumors and its participation in language reorganization. We hypothesized that patients with brain tumors and reorganized language would show a modification of the CCN compared with patients who remain left dominant. MATERIALS AND METHODS: Patients were selected with the criteria: newly diagnosed, pathologically-confirmed left-hemispheric tumor; single lesions; right-handedness; task-based and resting-state fMRI; no artifacts. Age-matched healthy controls (HC) were recruited from open-source databases. Language laterality was calculated by using task-based fMRI. We obtained the CCN through ad hoc independent component analysis on resting-state fMRI. Differences in CCN between patients and HC were characterized by cosine similarity (CS) and earth mover’s distance (EMD). Changes related to language reorganization and patients’ speech were assessed with the t test ( P < .05). Results were corrected for multiple comparisons. RESULTS: One hundred forty-two right-handed patients (35 low-grade and 88 high-grade gliomas; 19 metastases) and 184 HC were included. Two independent components of the CCN were obtained. The t test confirmed significant effects of lateralization on the CCN ( P = .004). Modification of CCN was associated with fewer speech deficits 1 week after surgery ( P = .005). CONCLUSIONS: This study provides evidence that modifications of CCN occur in the setting of language reorganization. Patients exhibiting these modifications perform better at speech evaluation after surgery, suggesting a role of cognitive control in compensating for speech deficits when language reorganizes. SUMMARY PREVIOUS LITERATURE: According to the literature, up to 96% of right-handed subjects are left dominant for language. In brain tumors, this percentage decreases substantially, reflecting some degree of reorganization. Because of its intrinsic complexity, language reorganization likely relies on the interplay with supportive functions such as cognitive control. However, the interaction between the language network and other cognitive networks in the setting of language reorganization is unknown. KEY FINDINGS: This study provides evidence that modifications of CCN arise in the setting of language reorganization, and patients harboring these modifications tend to perform better at speech evaluation after surgery. KNOWLEDGE ADVANCEMENT: The involvement of high-level cognitive functions such as cognitive control may be related to the effort of the patient to overcome the deficits caused by the tumor. The CCN may represent a future target for pre- and postsurgical efforts to maintain speech function. Tumors result in language impairment when they directly invade eloquent areas of the brain; however, changes in discontinuous and distant areas can also be seen, 1 - 3 which can be described as changes in the functional connectome (functional connectivity between cortical regions). 4 Such changes in discontinuous and distant areas, while possibly reflecting extra-local cognitive damage due to a tumor, could also reflect an attempt by the brain to overcome cognitive damage by reorganizing the various nodes in the functional connectome. 5 , 6 The language network is capable of reorganizing itself when focal lesions invade the dominant hemisphere. 2 , 7 According to the literature, up to 96% of right-handed subjects are left dominant for language. 8 In brain tumors, this percentage decreases substantially, 3 probably reflecting some degree of reorganization. The existence of language reorganization has been demonstrated by direct cortical stimulation of the peritumoral cortex 9 as well as functional MRI. 2 , 3 , 5 , 6 , 10 , 11 Specifically, there is a shift of language activation from the classical primary language areas (eg, Broca area or Wernicke area) to other cortical regions located in the tumor’s surroundings as well as in the contralateral hemisphere. 2 , 12 Language reorganization is a complex process involving both hemispheres, possibly representing an urgent attempt to compensate for language deficits caused by tumoral invasion. 13 - 16 Prior studies described the possible influence of tumor characteristics such as grade, location, and molecular profile on language reorganization. 3 Changes in metabolic connectivity reflective of functional reorganization have also been reported. 17 With regard to the preoperative planning, the presence of language reorganization may signify that the surgeon can safely remove a brain tumor growing in the dominant cerebral hemisphere for language without causing any speech deficit. This information can be used to tailor surgical approaches and the extent of resection. Cancer patients may, therefore, have a better quality of life, maintaining their ability to communicate (language production) and understand (language comprehension) others. Because of its intrinsic complexity, language reorganization likely relies on the interplay between language and supportive functions such as cognitive control. However, the dynamics of this phenomenon remain elusive. Particularly, the interaction between the language network and other cognitive networks in the setting of language reorganization is unknown. The cognitive control network (CCN) is typically involved in novel or conflict-laden tasks, which require a set of instructions/intentions to be dynamically converted to stimulus-response associations for proper task performance. 18 This widespread network includes brain regions such as the anterior cingulate cortex/presupplementary motor area, dorsolateral prefrontal cortex, inferior frontal junction, anterior insular cortex, dorsal premotor cortex, and posterior parietal cortex. 18 A recent study by Silvestri et al 19 investigated tumor-induced modifications of network synchronization and topography in a small group of patients with gliomas using a metric called cosine similarity (CS). Their results showed changes in resting-state networks (RSNs), mainly in structurally normal regions outside the tumor and in the edema region. 19 Certain networks were more affected than others, but the cause of this difference remains to be determined. 19 Moreover, it remains unknown whether the changes are the effect of tumoral damage to the brain (such as what one may expect in the area of contrast-enhancing tumor or necrosis) or the effect of the brain reorganizing its functions in the setting of adaptive plasticity. 20 In the present work, we expanded the method employed by Silvestri et al 19 to assess changes in the CCN induced by low-grade gliomas (LGGs), high-grade gliomas (HGGs), and brain metastases by measuring the deviation of network synchronization and topology from age-matched controls. Our study aims to fill the gap in the current understanding of the interplay between language reorganization and high-level cognitive functions–with a focus on cognitive control. We investigated the participation of CCN in tumor-induced language reorganization with the following hypotheses: 1) patients with language reorganization would display significant differences from healthy controls (HCs) in terms of the CCN, which may explain the increased demand for interhemispheric communication; and 2) language reorganization would be associated with better speech performance. MATERIALS AND METHODS Patients This study was approved by the institutional review board with a cross-sectional retrospective design, and informed consent was waived. We conducted our research in agreement with the Declaration of Helsinki. The archive of our institution was reviewed from January 2016 to March 2023 to select patients according to the following inclusion criteria: newly diagnosed left-hemispheric tumor, single lesions (patients with multiple lesions such as multiple metastases were excluded from the analysis), right-handedness, pathologic confirmation, availability of language task-based and resting-state fMRI data acquired at the same time point preoperatively, and absence of tumor-related or patient-related artifacts including drop-out from hemorrhagic tumor components and motion. We recruited patients with both resting-state and task-based fMRI because language laterality is better assessed on task-based fMRI, 3 , 21 while network analyses require a resting-state sequence. Handedness was established through the Edinburgh Handedness Inventory. 22 Age-matched healthy subjects were recruited from online open-source databases. 23 - 25 Speech deficits were measured by an attending neurologist before surgery and 1 week postoperatively and retrospectively retrieved from patients’ clinical charts. Examination included evaluation of speech fluency, repetition, naming, comprehension, and reading/writing. 26 MRI Acquisition MRI was performed by using a 3T scanner (Discovery 750 W, GE Healthcare) with 24-channel head coils. Resting-state fMRI was acquired with single-shot gradient-echo EPI (TR/TE = 2500/32 ms, section thickness = 4 mm, matrix = 64 × 64 mm, FOV = 240 mm, acquisition volume = 160, scanning duration = 5 minutes, 42 seconds). fMRI coverage matching anatomic scans, including 2D FLAIR (TR/TE = 10,000/106 ms, TI = 220 ms, matrix 256 × 256), 2D T1 postcontrast (TR/TE = 600/20 ms, matrix 256 × 256), and 3D T1-weighted anatomic images by using a spoiled gradient recalled-echo sequence (TR/TE = 22/4 ms, matrix 256 × 256, section thickness = 1 mm), were acquired as routine clinical scans. During the resting-state fMRI scan, subjects were instructed to relax, fixate on a central cross, and try to clear their mind. During task-based fMRI for language, patients performed the examination with a phonemic fluency task by using a visual system in which they silently generated words that began with a specific letter displayed on the screen. Two letters (10 seconds each) were displayed in each block. The task was presented as a block paradigm of 60 images, comprising 20 seconds of activation and 40 seconds of rest. Real-time monitoring of the patients’ brain activity and head motion was performed with a dedicated software (Brainwave, Medical Numerics). Image Analysis Tumors were manually segmented by a fellowship-trained neuroradiologist using the 3D Slicer segmentation tool 27 ( https://download.slicer.org/ ). Two ROI were labeled: 1) the area of contrast enhancement, segmented on postcontrast 3D T1-weighted images, and 2) the area of edema/nonenhancing tumor, segmented on FLAIR images. Location labels were assigned automatically in our pipeline from the automated anatomic labeling (AAL) atlas 28 ( https://www.gin.cnrs.fr/en/tools/aal/ ) based on ROI 1 for HGG and metastases and ROI 2 for LGG. Seven main locations were studied, merging sub-areas from the AAL atlas: frontal lobe, parietal lobe, temporal lobe, occipital lobe, central area (basal nuclei), insula, and cerebellum. The measure of tumor occupation of a brain structure was computed as follows: % A r e a = 100 ⋅ ( M a s k T u m o r ∩ S t r u c t u r e A A L ) S t r u c t u r e A A L Task-Based fMRI Laterality Analysis Task-based fMRI was processed with Analysis of Functional NeuroImages software 29 ( https://afni.nimh.nih.gov ) to generate correlation maps of the language task. This analysis included head motion correction with 3D rigid-body registration, spatial smoothing by using a Gaussian kernel with full width at half maximum of 4 mm, and removal of linear trend and high-frequency noise. Statistical parametric maps were obtained by applying cross-correlation to the block paradigms. 30 Functional activation maps of the phonemic fluency task were generated with a correlation threshold of r > 0.5 (uncorrected P = 2×10 −11 ). Voxels with a standard deviation exceeding 8% of the mean signal intensity were set to zero to minimize false-positives. We calculated language laterality on fMRI with 6 laterality indexes (-LIs): hemispheric, frontal lobe, Broca (including the opercular and triangular portion of the inferior frontal gyrus), temporal lobe, Wernicke (including the posterior aspect of the superior temporal gyrus), cerebellar. We used multiple LIs as a proxy of language dominance to include all language-related activations, as in previous studies. 3 , 31 A threshold-independent method was employed to minimize intersubject variability, as previously reported. 31 The LI calculation workflow included the following steps: 1) Montreal Neurological Institute (MNI, https://www.lead-dbs.org/about-the-mni-spaces/ ) 152 Coregistration of 3D T1-weighted and functional images through nonlinear registration (Advanced Normalization Tools, https://stnava.github.io/ANTs/ ), 2) parcellation of structural images through to the AAL atlas, 3) calculation of the mean value of the 5% of the most activated voxels on the correlation map, 4) consideration of the number of voxels above 80% of this mean within each hemispheric ROI, and 5) calculation of LI values through the traditional formula (L-R)/(L + R), where L and R represent the number of voxels in the left and right ROI, respectively. The hemispheric ROI excluded the cerebellum and visual activations (bilateral occipital peri-calcarine cortex corresponding to V1 and V2). Patients were defined as atypical dominant (AD) if LI <0.2, left dominant (LD) if LI ≥0.2. 32 As established in the literature, cerebellar language activation was considered AD if left-sided (normal cerebellar cross-activation in right-handed subjects). 33 Resting-State fMRI Analysis The resting-state fMRI data of patients and HCs was preprocessed by using FMRIB Software Library (FSL) 34 ( https://fsl.fmrib.ox.ac.uk/fsl/docs/#/ ) for motion correction (MCFLIRT tool; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MCFLIRT ) and skull-stripping (BET tool; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET ). Spatial smoothing was performed with a 6-mm Gaussian kernel. Using FSL and SPM12 (Statistical Parametric Mapping 12 v. 7771, https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ), fMRI data of each subject was first spatially coregistered to T1 anatomic images (FSL Linear Registration; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT ) and then to the 152-brain MNI space (SPM12 normalization). Tumor segmentations were also coregistered to MNI standard space by using SPM12. A graphical representation of our workflow is reported in Fig 1 . FIG 1. Open in a new tab Schematic representation of the study workflow. Functional preprocessed data were analyzed with the Group ICA (G-ICA) of the fMRI toolbox (GIFT, http://trendscenter.org/software ) to perform a G-ICA and group-information guided ICA (GIG-ICA). Independent component analysis is a method of blind source signal separation, a class of techniques used to reconstruct a signal from a mixture of unknown sources: for fMRI data, ICA allows one to extract or “unmix” unknown source signals that are linearly mixed together, revealing characteristics of the brain functions. 35 G-ICA is a widely applied technique for computing subject-specific independent components (ICs) from multisubject fMRI data. 36 GIG-ICA estimates the subject-specific ICs under the guidance of the group-level ICs. 37 This approach ensures every IC in a patient or an HC has the same physiologic meaning and can thus be compared across subjects. The analysis was applied to all HC data, setting the number of ICs to 20 through the Infomax ICA algorithm. 38 ICA was repeated 10 times with the ICASSO framework ( http://www.cis.hut.fi/jhimberg/icasso ) to boost reliability and stability. 39 The ICASSO framework is a package used in GIFT for analyzing the reliability of estimated independent components of the ICA algorithm: 39 ICASSO runs ICA several times to evaluate each component, returning a stability/quality (Iq) index. These frameworks were run in Matlab (MathWorks Version 9.9, R2020b, https://www.mathworks.com ). ICs were subsequently categorized into their RSNs by using Component Labeler, a tool provided by GIFT that analyzes how closely ICs resemble established RSNs. Each IC was correlated with a given template (in this study, NeuroMark fMRI 1.0 atlas, 40 https://www.nitrc.org/projects/neuromark/ ), and the ICs of the CCN were selected based on maximum correlation values (r). As per NeuroMark labeling, 40 the resulting CCN involved areas of the superior, inferior, middle frontal gyrus and inferior parietal lobules overlaying mainly with the central executive network. 41 It also included the insula, which is part of the salience network. 41 Among all the ICs, only those with a correlation value >0.3 were considered. The template ICs were used as a spatial constraint within the GIG-ICA back-reconstruction step to compute the spatial pattern at the individual level in both patients and HCs. In the end, for each patient and HC, each back-reconstructed IC consisted of a spatial Z score map. Cosine Similarity CS is a measure commonly used to quantify the similarity between 2 vectors (A and B) in a high-dimensional space. 42 It calculates the cosine of the angle θ between the vectors, which reflects their similarity in terms of direction and orientation. C S = cos ( θ ) = A ⋅ B | | A | | ⋅ | | B | | In the context of IC maps, CS can be employed to assess the similarity or overlap between the spatial patterns represented by the maps. A cosine similarity value of 1 indicates that the 2 IC maps are identical, while a value of 0 indicates no similarity or complete dissimilarity. Intermediate values between 0 and 1 represent varying degrees of similarity. This measure is sensitive to both the spatial pattern and the magnitude of the IC. Other methods are available for computing the similarity between brain maps; for example, the Jaccard index and the Dice coefficient are excellent methods to assess the similarity between masks, 43 evaluating the accuracy of an image segmentation. Since we used a whole-brain map instead of masks in our study, CS was preferred over other similarity measures due to its sensitivity to magnitude. For the i-th IC and j-th subject, the CS between the sub template map and the single-subject map is computed as: C o s i n e S i m i l a r i t y ( I C i j ) = S u b t e m p l a t e ( I C i ) ⋅ M a p S u b j e c t j ( I C i ) | | S u b t e m p l a t e ( I C i ) | | ⋅ | | M a p S u b j e c t j ( I C i ) | | where Subtemplate ( IC i ) = vectorization of i-th IC spatial map from a template computed averaging 90% of random HCs single-subject IC maps (a so-called sub-template). Map Subject j ( IC i ) = vectorization of i-th IC spatial map from j-th subject. Both in controls and patients, the CS was computed within a representative area of the map rather than at the whole-brain level. A cutoff threshold of Z score = 1 was applied, and spurious voxels were eliminated, filtering each individual map with the initial HC template. We computed CS between the maps of single HC subjects and sub template maps and derived their histogram distribution. To obtain a CS distribution in HCs with more values, 90% of random HC maps were averaged to compute a so-called sub template, used as guidance to perform a new GIG-ICA on the remaining 10% of HCs. This step was iterated 40 times. For patients, CS was computed between a single patient at a time and all 40 sub templates. In the end, each patient had a distribution of 40 CS, allowing us to compute the earth mover’s distance (EMD) relative to HC distribution (see next section). Earth Mover’s Distance EMD is a method to evaluate the dissimilarity between 2 multidimensional distributions, which can be applied to variable-length representations of distributions and is commonly encountered in mathematics and computer science. 44 A common informal interpretation of the EMD is treating 2 distributions (ie, µ1 and µ2) as 2 distinct ways of arranging some fluid/continuous commodity in a spatial domain Ω. Thus, the EMD is the minimum cost of transforming the arrangement described by µ1 into the arrangement described by µ2. As the distributions get more distanced, the EMD value increases. Though the distance between 2 distributions can be calculated by comparing their averages, as well as with percentiles (usually 95th), these metrics do not consider the shape difference of a distribution. Conversely, EMD is a more robust metric that can capture geometric structure differences between distributions. 44 EMD provides an intuitive interpretation of how distributions differ, particularly in characterizing overlapping and nonoverlapping distributions. 44 Statistical Analyses Language Laterality. Multiple t tests were computed between EMD (of a certain IC) with left or right laterality to investigate EMD changes related to laterality. Results were corrected for multiple comparisons through the harmonic mean method, a statistical technique to adjust the P values from multiple dependent tests and control the family-wise error rate to protect against false-positives. 45 Speech Performance. A t test was performed between EMDs of patients with and without pre- or postoperative speech deficits. For the sake of the analysis, all types of speech deficits were considered as a single variable (0 = no deficit, 1 = speech deficit of any kind). Results were corrected for multiple comparisons through the harmonic mean method. 45 Tumor Type and Location. The effects of tumor type and location on the CCN were characterized through t-tests between EMDs corresponding to patients with a tumor in different AAL structures (considering only patients with area >1%) and having a specific tumor type (HGG versus LGG versus metastases). Results were corrected for multiple comparisons through the harmonic mean method. 45 RESULTS Patients One hundred forty-two right-handed patients with left-hemispheric tumors were included in the study (mean age 51 years, standard deviation 15, 86 men). Patients were consecutive, retrospectively recruited from our archive. We excluded 10 patients because of postprocessing issues (quality assessment of fMRI maps showed excessive artifacts or nondiagnostic quality). Tumors were diagnosed as 35 LGG, 88 HGG, and 19 metastases. Pathology used the most up-to-date categorization at the time of the surgery. Hemispheric LI demonstrated 50/142 AD patients. Frontal LI demonstrated 47/142 AD patients. Broca LI demonstrated 40/142 AD patients. Temporal LI demonstrated 68/142 AD patients. Wernicke LI demonstrated 70/142 AD patients. Cerebellar LI demonstrated 48/142 AD patients. Patient data are summarized in Table 1 . Tumor locations were: 26 temporal, 27 insular, 42 frontal, 5 occipital, 28 parietal, and 14 central. None of the tumors involved the cerebellum. Table 1: Patient characteristics Age (years) 50.95 ± 15.607 Sex (number of subjects) 86 male, 56 female Tumor type Low-grade glioma 35 High-grade glioma 88 Metastasis 19 Lateralization LI_hemispheric Left = 92 Atypical = 50 LI_frontal Left = 95 Atypical = 47 LI_Broca Left = 102 Atypical = 40 LI_temporal Left = 74 Atypical = 68 LI_Wernicke Left = 72 Atypical = 70 LI_cerebellar Right = 94 Atypical = 48 Speech deficits (number of subjects) Preoperative aphasia 32 Postoperative aphasia 38 Open in a new tab The healthy data set was composed of the resting-state fMRI of 184 age-matched subjects, downloaded from 3 different open-source online data sets: The NIMH Healthy Research Volunteer data set (32 subjects, mean age = 52.1 ± 8.8) ( https://openneuro.org/datasets/ds004215/versions/1.0.2 ) 23 ; Cognitive Control Theoretic Mechanisms of Real-Time fMRI-Guided Neuromodulation (32 subjects, mean age = 52.7 ± 7.6) ( https://openneuro.org/datasets/ds003831/versions/1.0.0 ) 25 ; and Neurocognitive Aging Data Release with Behavioral, Structural, and Multi-Echo Functional MRI Measures (120 subjects, mean age = 65.4 ± 6.1) ( https://openneuro.org/datasets/ds003592/versions/1.0.13 ). 24 Demographics for the HC population are detailed in Table 2 . Table 2: Healthy control characteristics Age (years) 60.9 ± 9.3 Sex (number of subjects) 72 male, 108 female Open in a new tab Speech evaluation revealed 33 patients with preoperative deficits (5 anomia, 4 conduction aphasia, 5 transcortical sensory aphasia, 4 Wernicke aphasia, 4 transcortical motor aphasia, 7 Broca aphasia, 2 mixed transcortical aphasia, 2 dysarthria). Thirty-eight patients demonstrated postoperative deficit (7 anomia, 5 conduction aphasia, 7 transcortical sensory aphasia, 3 Wernicke aphasia, 7 transcortical motor aphasia, 4 Broca aphasia, 3 mixed transcortical aphasia, 1 global aphasia, 1 dysarthria). Six patients did not undergo presurgical speech deficit assessment, and 18 lacked postsurgical speech deficit evaluations in their clinical charts. Cosine Similarity and Quantitative Measures in Patients with Tumor and Healthy Controls Three ICs emerged from the GIG-ICA framework: IC-1 ( r = 0.39), IC-2 ( r = 0.43), IC-3 ( r = 0.11). One component (IC-3) was discarded based on low correlation values. The selected ICs are depicted in Fig 2 . IC-1 includes a large portion of the prefrontal cortex, which is involved in personality, behavior, and intellect functions. 41 IC-2 is more left-skewed in HCs and has more contribution from the middle frontal gyrus and inferior frontal lobe of the left hemisphere. These areas play a key role in speech processing and working memory. 41 The calculated CS of patients with tumor and HCs allowed us to observe differences in network synchronization and topography between patients with tumor and HCs. 42 A graphic representation of CS for the CCN’s components is depicted in Supplemental Data. FIG 2. Open in a new tab Independent components (IC 1 in panel A and IC 2 in panel B ) of the cognitive control network were generated by the GIG-ICA algorithm in HCs and were selected to be analyzed in the study. The ICs are overlaid on a standard brain template (Montreal Neurological Institute - MNI). L = left side; R = right side. Language Reorganization Analysis The t test confirmed significant effects of language reorganization on EMD: CCN was related to Wernicke LI (IC-2, P = .0044), with higher EMD in patients with AD. The modification of the CCN was associated with fewer speech deficits 1 week after surgery (IC 1, P = .0057) (Supplemental Data). The analysis of preoperative language deficits did not achieve statistical significance. Boxplots representing the results are shown in Fig 3 . Differences in EMD distribution among the study groups are depicted in Fig 4 . A visual representation of the significant differences in the CCN components is depicted in Figs 5 and 6 . Differential activation maps between AD and LD, as well as patients with and without postoperative aphasia, are reported in Supplemental Data. These maps display increased activation in the right hemisphere—particularly frontal lobe—of AD patients and nonaphasic patients compared with LD and aphasic patients, respectively. FIG 3. Open in a new tab Boxplot representation of the differences in EMD between patients with and without postoperative speech deficits ( A ) and atypical laterality of Wernicke area ( B ). FIG 4. Open in a new tab Distribution of EMD of the CCN independent components ( IC 1 and IC2 ) in healthy subjects ( blue ) and patients ( red ). FIG 5. Open in a new tab Independent components ( IC 1 and IC2 ) of the cognitive control network generated by the GIG-ICA algorithm in patients without aphasia ( A ) and with postoperative speech deficits ( B ). The ICs are overlaid on a standard brain template (Montreal Neurological Institute). L=left side; R =right side. FIG 6. Open in a new tab Independent components ( IC 1 and IC2 ) of the cognitive control network generated by the GIG-ICA algorithm in patients with left language dominance ( A ) and atypical language dominance ( B ). The ICs are overlaid on a standard brain template (Montreal Neurological Institute). L = left side; R = right side. Effect of Tumor Location and Pathology on CCN Tumor grade and location demonstrated some significant effects on the CCN’s components in the t test (Supplemental Data). However, none of the effects persisted when corrected for multiple comparisons through the harmonic mean method. 45 DISCUSSION The present study aimed at quantifying the modification of CCN in patients with brain tumors as compared with HCs. We used CS and EMD from prior studies 19 , 42 , 44 to assess the deviation of the network from a baseline of healthy subjects in patients with left-hemispheric tumors performing language tasks. The main finding of this study is that CCN is involved in language reorganization, regardless of tumor type and location, and is associated with improved speech after surgery. Tumor-induced language reorganization is an area of intensive study due to its direct repercussions on patient care and surgical planning. 2 , 21 It has been demonstrated that the language network of right-handed patients may change laterality from the expected left dominance to right or co-dominance in response to tumor growth. 3 Language reorganization develops over time 5 , 11 and is associated with structural modifications of the cortex. 10 Prior studies demonstrated that right-handed patients with left-hemispheric tumors could recruit the right hemisphere when performing language tasks, shifting the speech function to right-sided language areas homologues, including the right inferior frontal gyrus. 6 In these patients, the lack of speech arrest during cortical stimulation is associated with interhemispheric reorganization of language. 6 Other authors showed that language network reorganization in patients with gliomas is associated with fewer speech deficits, highlighting the potential for compensatory (adaptive) plasticity of language in the setting of brain tumors. 46 Nevertheless, despite extensive research on the topic, it is unknown whether the reorganization of language function in patients with brain tumors requires the participation of additional networks. Elucidating this biology could profoundly impact patient care: rehabilitation techniques and even plasticity-enhancing therapies through neurostimulation 47 could target the supportive networks together with language to promote reorganization. In fact, preliminary evidence from recent studies demonstrated the feasibility of noninvasive prehabilitation in patients with brain tumors to enhance language plasticity before surgery. 48 The role of CCN in language is an active topic of research. Human speech is tightly connected to the cognitive control function, as demonstrated by several studies. According to the conflict monitoring theory, 49 the detection of conflict during information processing triggers a subsequent up-regulation of cognitive control, for instance, by weakening the impact of task-irrelevant information and strengthening those of task-relevant information from trial to trial. During language comprehension, humans often must recover from conflicting interpretations as quickly and accurately as possible, relying on the CCN. 50 The “dual architecture” theory proposes an integrated perspective of the control of language in which speech processing emerges from a division of labor between the language system and the multiple-demand system responsible for cognitive control. 51 Accordingly, the CCN helps conceptualize the neurocognitive architecture of language, integrating complex aspects such as verbal working memory versus active judgments on lexico-semantic information with language production. 51 Based on this evidence, a role for the CCN in language reorganization seems realistic. A few studies investigated the role of CCN in aphasia recovery after cerebral ischemia. Brownsett et al 52 described the correlation between activity in the CCN system, specifically the salience network, with residual language performance in poststroke aphasia. The authors provide direct evidence that domain-general cognitive control is an essential factor contributing to the potential for recovery from aphasic stroke. Another study applied dynamic causal modeling analyses to the connections between language and control brain areas in aphasic patients after stroke. 53 The results showed a higher connectedness between language and control areas in the patients with better recovery, suggesting that engagement in the interconnected language-control network is crucial in the recovery of language. 53 In elderly subjects, the preservation of speech comprehension relies on the up-regulation of the CCN. 54 In brain tumors, the reorganization of the CCN has been associated with protection of executive functions. 55 Our results corroborate these findings. Additionally, the association between higher EMD of the CCN system and lower speech deficits after surgery supports the idea of an active role of this network in adaptive plasticity of language. Similarly, Yuan et al 46 showed that the spontaneous recovery of language function after brain tumor surgery near eloquent cortices corresponded with changes within the language and cognitive control networks. Our results point to a reorganization of the CCN accompanying interhemispheric reorganization of language, with increased recruitment of the right hemisphere, particularly in frontal regions (Supplemental Data). If confirmed by future studies, our findings will prompt the joint targeting of CCN and language networks in speech rehabilitation and plasticity-enhancing techniques. Tumor grade did not show any significant effects on the CCN in our analyses after correction for multiple comparisons. While functional changes reflective of brain plasticity are thought to develop over time in a progressive fashion, 12 studies from basic neuroscience demonstrated that neuroplasticity may develop as soon as 3–6 months after periods of learning/training. 56 Such a timeframe is compatible with most tumor lesions. Prior authors hypothesized that all focal lesions may trigger plasticity through disinhibition. Thanks to the state of “inhibitory redundance” (dominant GABAergic inhibition) of the brain, focal lesions may promote disinhibition by removing an excess of inhibitory neurons. 20 From our result, one may speculate the CCN is a key player in transitioning the state of disinhibition into actual compensatory plasticity demonstrated by speech preservation. The involvement of high-level cognitive functions such as cognitive control may be related to the effort of the patient to overcome the deficits caused by the tumor. This may be the reason why we obtained significant results by comparing patients’ deficits, while no significant difference was seen by comparing tumor types. Though different tumors may exert different effects on cognition, these were probably too nuanced to be captured by our analysis. Future studies should test this hypothesis in larger cohorts. Our results have several possible clinical applications. The evidence of functional plasticity can inform the surgeon about the potential effects of extended resection. This may lead to changes in surgical approach, target extent of resection, as well as decision-making for multi-step surgery. 57 Additionally, our results may influence the fields of rehabilitation and prehabilitation. 48 The CCN may represent a future target for pre- and postsurgical efforts to maintain speech functions. In a similar way, EMD can be used to quickly assess other potential network targets against a benchmark of HCs to identify their contribution to the reorganization of brain functions—language as well as others. Future work should study the relationship between brain reorganization and EMD fluctuations longitudinally by including more subjects and providing more detailed clinical correlation with prospective neuropsychological testing. This study has some limitations. Because of the retrospective design, clinical data were limited to routine neurologic examinations retrieved from patient medical records. For the same reason, a few patients lacked available clinical data because of missing charts and/or outside surgery and lost follow-up. Detailed neuropsychological testing is needed to confirm our findings and further elucidate the role of supportive networks in language reorganization. In a similar way, our patients did not have complete genetic/molecular characterization, possibly creating an inherent bias in the selection of patients, as those with certain tumor characteristics may have different patterns of language reorganization. 3 Changes in laterality in HGG may be influenced by neuro-vascular uncoupling (NVU). 21 Though this phenomenon may affect fMRI analyses in this type of tumor, the significant association between CCN modifications and better speech performance in our results cannot be explained by NVU-driven changes in laterality. We are hopeful that advancements in research will include new methods to correct NVU false-negatives, therefore enabling more accurate analyses for HGG. Phonemic fluency was the only fMRI task that was consistently available in our patients. In our experience, phonemic fluency is the most specific test for language dominance. Nevertheless, laterality assessment on multiple tasks may be more accurate. In our protocol, resting tasks on resting state is acquired after task-based fMRI. DTI and FLAIR/MPRAGE sequences are obtained in between the fMRI acquisitions. Though possible effects of language tasks on a resting state should be mitigated by the delay included in our protocol, some influences cannot be completely excluded. Postsurgical fMRI assessment was not available in our patients. By using CS and EMD, we were able to quantify differences in patients’ brain networks from controls; however, this type of analysis cannot provide details on specific subregions within the network pattern. The metastasis population was small, limiting the interpretation of the results in this specific group. This was partially due to our inclusion criteria, which required the selection of single lesions to minimize confounding effects in the analysis. Finally, we employed an operator-independent and threshold-independent method to assess language laterality based on previous studies. 31 This choice limits the comparability of our results with those obtained by using different methods. CONCLUSIONS This study provides evidence that modifications of CCN arise in the setting of language reorganization, and patients harboring these modifications tend to perform better at speech evaluation after surgery, which may indicate the development of adaptive language plasticity. If confirmed by future studies, including the use of different techniques such as diffusion tensor tractography and metabolic imaging, the identification of “facilitative networks” for language reorganization may help targeted therapies to preserve speech in patients with brain tumors. Supplementary Material ajnr.A8638_preprint_supplement.pdf ajnr.A8638_preprint_supplement.pdf (1.9MB, pdf) ABBREVIATIONS: AAL automated anatomic labeling AD atypical dominant CCN cognitive control network CS cosine similarity EMD earth mover’s distance FSL FMRIB Software Library G-ICA Group ICA GIFT Group ICA of the fMRI Toolbox GIG-ICA group-information guided ICA HC healthy control HGG high-grade glioma IC independent component LD left dominant LGG low-grade glioma LI laterality index MNI Montreal Neurological Institute NVU neuro-vascular uncoupling RSN resting-state network Footnotes Disclosure forms provided by the authors are available with the full text and PDF of this article at www.ajnr.org . This research has been supported by funding from the National Institutes of Health (NIH): grants NIH-NIBIB R01 EB022720 (Holodny, PI), NIH-NCI R21 CA220144 (Holodny and Peck, PIs), NIH-NCI P30 CA008748 (Vickers, PI). References 1. Briganti C, Sestieri C, Mattei PA, et al. 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