Abstract
To date, the structural characteristics that distinguish language-involved from non-involved cortical areas are largely unclear. Particularly in patients suffering from language-eloquent brain tumors, reliable mapping of the cortico-subcortical language network is of high clinical importance to prepare and guide safe tumor resection. To investigate differences in structural characteristics between language-positive and language-negative areas, 20 patients (mean age: 63.2 ± 12.9 years, 16 males) diagnosed with language-eloquent left-hemispheric glioblastoma multiforme (GBM) underwent preoperative language mapping by navigated transcranial magnetic stimulation (nTMS) and nTMS-based diffusion tensor imaging fiber tracking (DTI FT). The number of language-positive and language-negative points as well as the gray matter intensity (GMI), normalized volumes of U-fibers, interhemispheric fibers, and fibers projecting to the cerebellum were assessed and compared between language-positive and language-negative nTMS mappings and set in correlation with aphasia grades. We found significantly lower GMI for language-positive nTMS points (5.7 ± 1.7 versus 7.1 ± 1.6, p = 0.0121). Furthermore, language-positive nTMS points were characterized by an enhanced connectivity profile, i.e., these points showed a significantly higher ratio in volumes for U-fibers (p ≤ 0.0056), interhemispheric fibers (p = 0.0494), and fibers projecting to the cerebellum (p = 0.0094). The number of language-positive nTMS points (R ≥ 0.4854, p ≤ 0.0300) as well as the ratio in volumes for U-fibers (R ≤ −0.4899, p ≤ 0.0283) were significantly associated with aphasia grades, as assessed pre- or postoperatively and during follow-up examinations. In conclusion, this study provides evidence for structural differences on cortical and subcortical levels between language-positive and language-negative areas, as detected by nTMS language mapping. The results may further increase confidence in the technique of nTMS language mapping and nTMS-based tractography in the direct clinical setting. Future studies may confirm our results in larger cohorts and may expand the findings to patients with other tumor entities than GBM.
Highlights
Resection of intracranial glioma aims at a maximum extent of resection, which should ideally be achieved without causing surgery-related functional deficits that could severely reduce the patients’quality of life [1,2,3,4]
We show the mean and standard deviation (SD) for the number (N) of language-positive and language-negative points as mapped by navigated transcranial magnetic stimulation, intensity ratio (IR), ratio of volumes for U-fibers (RUfibers, as derived from tractography using 100%, 75%, 50%, and 25% of the individual fractional anisotropy threshold (FAT)), and ratio of volumes for interhemispheric fibers (Rcross, using tractography with 25% FAT) as well as fibers projecting to the cerebellum (Rcereb, using tractography with 25% FAT)
We show the correlation results between the number of language-positive and language-negative points as mapped by navigated transcranial magnetic stimulation, intensity ratio (IR), ratio of volumes for U-fibers (RUfibers, as derived from tractography using 100%, 75%, 50%, and 25% of the individual fractional anisotropy threshold (FAT)), ratio of volumes for interhemispheric fibers (Rcross, using tractography with 25% FAT) as well as fibers projecting to the cerebellum (Rcereb, using tractography with 25% FAT) and the aphasia grades for the preoperative, postoperative, and follow-up status
Summary
Resection of intracranial glioma aims at a maximum extent of resection, which should ideally be achieved without causing surgery-related functional deficits that could severely reduce the patients’quality of life [1,2,3,4]. The technique of nTMS has lately been used to conduct language mappings in patients suffering from language-eloquent glioma or other entities of brain tumors [13,14,15,16]. It has been combined with diffusion tensor imaging (DTI) derived from preoperative magnetic resonance imaging (MRI) to provide spatially resolved maps that visualize language-related structures [17,18,19,20,21,22,23,24,25].
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