Abstract:The preservation of language function during brain surgery still poses a challenge. No intraoperative methods have been established to monitor the language network reliably. We aimed to establish intraoperative language network monitoring by means of cortico-cortical evoked potentials (CCEPs). Subjects were six patients with tumors located close to the arcuate fasciculus (AF) in the language-dominant left hemisphere. Under general anesthesia, the anterior perisylvian language area (AL) was first defined by the CCEP connectivity patterns between the ventrolateral frontal and temporoparietal area, and also by presurgical neuroimaging findings. We then monitored the integrity of the language network by stimulating AL and by recording CCEPs from the posterior perisylvian language area (PL) consecutively during both general anesthesia and awake condition. High-frequency electrical stimulation (ES) performed during awake craniotomy confirmed language function at AL in all six patients. Despite an amplitude decline (32%) in two patients, CCEP monitoring successfully prevented persistent language impairment. After tumor removal, single-pulse ES was applied to the white matter tract beneath the floor of the removal cavity in five patients, in order to trace its connections into the language cortices. In three patients in whom high-frequency ES of the white matter produced naming impairment, this "eloquent" subcortical site directly connected AL and PL, judging from the latencies and distributions of cortico-and subcortico-cortical evoked potentials. In conclusion, this study provided the direct evidence that AL, PL, and AF constitute the dorsal language network. Intraoperative CCEP monitoring is clinically useful for evaluating the integrity of the language network.

In order to preserve postoperative language function, we recently proposed a new intraoperative method to monitor the integrity of the dorsal language pathway (arcuate fasciculus; AF) using cortico-cortical evoked potentials (CCEPs). Based on further investigations (20 patients, 21 CCEP investigations), including patients who were not suitable for awake surgery (five CCEP investigations) or those without preoperative neuroimaging data (eight CCEP investigations including four with untraceable tractography due to brain edema), we attempted to clarify the clinical impact of this new intraoperative method. We monitored the integrity of AF by stimulating the anterior perisylvian language area (AL) by recording CCEPs from the posterior perisylvian language area (PL) consecutively during both general anesthesia and awake condition. After tumor resection, single-pulse electrical stimuli were also applied to the floor of the removal cavity to record subcortico-cortical evoked potentials (SCEPs) at AL and PL in 12 patients (12 SCEP investigations). We demonstrated that (1) intraoperative dorsal language network monitoring was feasible even when patients were not suitable for awake surgery or without preoperative neuroimaging studies, (2) CCEP is a dynamic marker of functional connectivity or integrity of AF, and CCEP N1 amplitude could even become larger after reduction of brain edema, (3) a 50% CCEP N1 amplitude decline might be a cut-off value to prevent permanent language dysfunction due to impairment of AF, (4) a correspondence (<2.0 ms difference) of N1 onset latencies between CCEP and the sum of SCEPs indicates close proximity of the subcortical stimulus site to AF (<3.0 mm). Hum Brain Mapp 38:1977-1991, 2017. © 2017 Wiley Periodicals, Inc.

Gliomas are the most common intra-axial primary brain tumour; therefore, predicting glioma grade would influence therapeutic strategies. Although several methods based on single or multiple parameters from diagnostic images exist, a definitive method for pre-operatively determining glioma grade remains unknown. We aimed to develop an unsupervised method using multiple parameters from pre-operative diffusion tensor images for obtaining a clustered image that could enable visual grading of gliomas. Fourteen patients with low-grade gliomas and 19 with high-grade gliomas underwent diffusion tensor imaging and three-dimensional T1-weighted magnetic resonance imaging before tumour resection. Seven features including diffusion-weighted imaging, fractional anisotropy, first eigenvalue, second eigenvalue, third eigenvalue, mean diffusivity and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. We developed a two-level clustering approach for a self-organizing map followed by the K-means algorithm to enable unsupervised clustering of a large number of input vectors with the seven features for the whole brain. The vectors were grouped by the self-organizing map as protoclusters, which were classified into the smaller number of clusters by K-means to make a voxel-based diffusion tensor-based clustered image. Furthermore, we also determined if the diffusion tensor-based clustered image was really helpful for predicting pre-operative glioma grade in a supervised manner. The ratio of each class in the diffusion tensor-based clustered images was calculated from the regions of interest manually traced on the diffusion tensor imaging space, and the common logarithmic ratio scales were calculated. We then applied support vector machine as a classifier for distinguishing between low- and high-grade gliomas. Consequently, the sensitivity, specificity, accuracy and area under the curve of receiver operating characteristic curves from the 16-class diffusion tensor-based clustered images that showed the best performance for differentiating high- and low-grade gliomas were 0.848, 0.745, 0.804 and 0.912, respectively. Furthermore, the log-ratio value of each class of the 16-class diffusion tensor-based clustered images was compared between low- and high-grade gliomas, and the log-ratio values of classes 14, 15 and 16 in the high-grade gliomas were significantly higher than those in the low-grade gliomas (p < 0.005, p < 0.001 and p < 0.001, respectively). These classes comprised different patterns of the seven diffusion tensor imaging-based parameters. The results suggest that the multiple diffusion tensor imaging-based parameters from the voxel-based diffusion tensor-based clustered images can help differentiate between low- and high-grade gliomas.

Laughter consists of both motor and emotional aspects. The emotional component, known as mirth, is usually associated with the motor component, namely, bilateral facial movements. Previous electrical cortical stimulation (ES) studies revealed that mirth was associated with the basal temporal cortex, inferior frontal cortex, and medial frontal cortex. Functional neuroimaging implicated a role for the left inferior frontal and bilateral temporal cortices in humor processing. However, the neural origins and pathways linking mirth with facial movements are still unclear. We hereby report two cases with temporal lobe epilepsy undergoing subdural electrode implantation in whom ES of the left basal temporal cortex elicited both mirth and laughter-related facial muscle movements. In one case with normal hippocampus, high-frequency ES consistently caused contralateral facial movement, followed by bilateral facial movements with mirth. In contrast, in another case with hippocampal sclerosis (HS), ES elicited only mirth at low intensity and short duration, and eventually laughter at higher intensity and longer duration. In both cases, the basal temporal language area (BTLA) was located within or adjacent to the cortex where ES produced mirth. In conclusion, the present direct ES study demonstrated that 1) mirth had a close relationship with language function, 2) intact mesial temporal structures were actively engaged in the beginning of facial movements associated with mirth, and 3) these emotion-related facial movements had contralateral dominance.

Transcranial static magnetic stimulation (tSMS) has been focused as a new non-invasive brain stimulation, which can suppress the human cortical excitability just below the magnet. However, the non-regional effects of tSMS via brain network have been rarely studied so far. We investigated whether tSMS over the left primary motor cortex (M1) can facilitate the right M1 in healthy subjects, based on the hypothesis that the functional suppression of M1 can cause the paradoxical functional facilitation of the contralateral M1 via the reduction of interhemispheric inhibition (IHI) between the bilateral M1. This study was double-blind crossover trial. We measured the corticospinal excitability in both M1 and IHI from the left to right M1 by recording motor evoked potentials from first dorsal interosseous muscles using single-pulse and paired-pulse transcranial magnetic stimulation before and after the tSMS intervention for 30 min. We found that the corticospinal excitability of the left M1 decreased, while that of the right M1 increased after tSMS. Moreover, the evaluation of IHI revealed the reduced inhibition from the left to the right M1. Our findings provide new insights on the mechanistic understanding of neuromodulatory effects of tSMS in human.