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Infraslow activities and high-frequency oscillations (HFOs) are observed in seizure-onset zones. However, the relation between them remains unclear. In this study, we investigated phase-amplitude coupling between infraslow phase (0.016-1 Hz) and HFOs' amplitude of focal impaired awareness seizures followed by focal to bilateral tonic-clonic seizures, in a 28-year-old right-handed man with a dysembryoplastic neuroepithelial tumor. We recorded five habitual seizures. After the time of seizure onset, a significant increase in the power of HFOs was observed, and the power was significantly coupled with θ (4-8 Hz) phase. In contrast, coupling of infraslow activities and HFOs surged a few minutes before the seizure-onset time, and ictal HFOs discharged after that. Collectively, our results show that coupling of infraslow activities and HFOs precedes the seizure-onset time. We infer that such coupling may be a potential biomarker for seizure prediction., Competing Interests: None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines., (© 2020 The Authors. Epilepsia Open published by Wiley Periodicals Inc. on behalf of International League Against Epilepsy.)

Objective: To determine whether training with a brain-computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain., Methods: Twelve patients with chronic phantom limb pain of the upper limb due to amputation or brachial plexus root avulsion participated in a randomized single-blinded crossover trial. Patients were trained to move the virtual hand image controlled by the BCI with a real decoder, which was constructed to classify intact hand movements from motor cortical currents, by moving their phantom hands for 3 days ("real training"). Pain was evaluated using a visual analogue scale (VAS) before and after training, and at follow-up for an additional 16 days. As a control, patients engaged in the training with the same hand image controlled by randomly changing values ("random training"). The 2 trainings were randomly assigned to the patients. This trial is registered at UMIN-CTR (UMIN000013608)., Results: VAS at day 4 was significantly reduced from the baseline after real training (mean [SD], 45.3 [24.2]-30.9 [20.6], 1/100 mm; p = 0.009 < 0.025), but not after random training ( p = 0.047 > 0.025). Compared to VAS at day 1, VAS at days 4 and 8 was significantly reduced by 32% and 36%, respectively, after real training and was significantly lower than VAS after random training ( p < 0.01)., Conclusion: Three-day training to move the hand images controlled by BCI significantly reduced pain for 1 week., Classification of Evidence: This study provides Class III evidence that BCI reduces phantom limb pain., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Objective: Brain-computer interfaces (BCIs) using electrocorticographic (ECoG) signals have been developed to restore the communication function of severely paralyzed patients. However, the limited amount of information derived from ECoG signals hinders their clinical applications. We aimed to develop a method to decode ECoG signals using spatiotemporal patterns characterizing movement types to increase the amount of information gained from these signals., Approach: Previous studies have demonstrated that motor information could be decoded using powers of specific frequency bands of the ECoG signals estimated by fast Fourier transform (FFT) or wavelet analysis. However, because FFT is evaluated for each channel, the temporal and spatial patterns among channels are difficult to evaluate. Here, we used dynamic mode decomposition (DMD) to evaluate the spatiotemporal pattern of ECoG signals and evaluated the accuracy of motor decoding with the DMD modes. We used ECoG signals during three types of hand movements, which were recorded from 11 patients implanted with subdural electrodes. From the signals at the time of the movements, the modes and powers were evaluated by DMD and FFT and were decoded using support vector machine. We used the Grassmann kernel to evaluate the distance between modes estimated by DMD (DMD mode). In addition, we decoded the DMD modes, in which the phase components were shuffled, to compare the classification accuracy., Main Results: The decoding accuracy using DMD modes was significantly better than that using FFT powers. The accuracy significantly decreased when the phases of the DMD mode were shuffled. Among the frequency bands, the DMD mode at approximately 100 Hz demonstrated the highest classification accuracy., Significance: DMD successfully captured the spatiotemporal patterns characterizing the movement types and contributed to improving the decoding accuracy. This method can be applied to improve BCIs to help severely paralyzed patients communicate.

We conducted a multicenter, randomized, patient- and assessor-blinded, sham-controlled trial to investigate the efficacy of repetitive transcranial magnetic stimulation (rTMS) of the primary motor cortex (M1) in patients with neuropathic pain (NP). Patients were randomly assigned to receive 5 daily sessions of active or sham rTMS of M1 corresponding to the part of the body experiencing the worst pain (500 pulses per session at 5 Hz). Responders were invited to enroll in an open-label continuous trial involving 4 weekly sessions of active rTMS. The primary outcome was a mean decrease in a visual analogue scale of pain intensity (scaled 0-100 mm) measured daily during the daily sessions in an intention-to-treat population. Secondary outcomes were other pain scores, quality-of-life measures, and depression score. One hundred forty-four patients were assigned to the active or sham stimulation groups. The primary outcome, mean visual analogue scale decreases, was not significantly different (P = 0.58) between the active stimulation group (mean, 8.0) and the sham group (9.2) during the daily sessions. The secondary outcomes were not significantly different between 2 groups. The patients enrolled in the continuous weekly rTMS achieved more pain relief in the active stimulation group compared with the sham (P < 0.01). No serious adverse events were observed. Five daily sessions of rTMS with stimulus conditions used in this trial were ineffective in short-term pain relief in the whole study population with various NP. Long-term administration to the responders should be investigated for the clinical use of rTMS on NP in the future trials.

Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a diagnosis based on conventional radiomic features and patient age. Multisite preoperative MR images of 164 patients with grade II/III glioma were grouped by IDH and TERT promoter (pTERT) mutations as follows: (1) IDH wild type, (2) IDH and pTERT co-mutations, (3) IDH mutant and pTERT wild type. We applied a CNN (AlexNet) to four types of MR sequence and obtained the CNN texture features to classify the groups with a linear support vector machine. The classification was also performed using conventional radiomic features and/or patient age. Using all features, we succeeded in classifying patients with an accuracy of 63.1%, which was significantly higher than the accuracy obtained from using either the radiomic features or patient age alone. In particular, prediction of the pTERT mutation was significantly improved by the CNN texture features. In conclusion, the pretrained CNN texture features capture the information of IDH and TERT genotypes in grade II/III gliomas better than the conventional radiomic features.

The application of deep learning to neuroimaging big data will help develop computer-aided diagnosis of neurological diseases. Pattern recognition using deep learning can extract features of neuroimaging signals unique to various neurological diseases, leading to better diagnoses. In this study, we developed MNet, a novel deep neural network to classify multiple neurological diseases using resting-state magnetoencephalography (MEG) signals. We used the MEG signals of 67 healthy subjects, 26 patients with spinal cord injury, and 140 patients with epilepsy to train and test the network using 10-fold cross-validation. The trained MNet succeeded in classifying the healthy subjects and those with the two neurological diseases with an accuracy of 70.7 ± 10.6%, which significantly exceeded the accuracy of 63.4 ± 12.7% calculated from relative powers of six frequency bands (δ: 1-4 Hz; θ: 4-8 Hz; low-α: 8-10 Hz; high-α: 10-13 Hz; β: 13-30 Hz; low-γ: 30-50 Hz) for each channel using a support vector machine as a classifier (p = 4.2 × 10 -2 ). The specificity of classification for each disease ranged from 86-94%. Our results suggest that this technique would be useful for developing a classifier that will improve neurological diagnoses and allow high specificity in identifying diseases.

Objectives: Intrathecal baclofen (ITB) therapy is an effective treatment for patients with severe spasticity. However, the effect of ITB therapy on respiratory function has not been reported in detail. In this study we quantitatively analyzed the effects of ITB on the respiratory function of patients with spastic tetraplegia., Methods: We retrospectively reviewed 23 patients who were administrated ITB therapy from January 2009 to December 2012. Six of these 23 patients, who had spastic tetraplegia and were able to undergo spirometric testing, were included this study. The spasticity derived from cervical spinal cord injury in four patients and cerebral palsy (CP) in two patients. Patients' Ashworth Scale scores and spirometer measurements obtained before and 1-6 months after the start of ITB therapy were evaluated and compared., Results: Before ITB therapy, %FVC of all six patients was below 80%, and a restrictive respiratory disorder was diagnosed in five patients and a combined respiratory disorder in one patient. Ashworth Scale scores for both the lower and upper extremities improved significantly with ITB therapy. Forced vital capacity (FVC), %FVC, and forced expiratory volume at one sec also improved significantly with ITB therapy., Conclusions: Respiratory disorders were indeed present in our SCI and CP patients with spastic tetraplegia, and the respiratory function of these patients improved with ITB therapy. Our results suggest that ITB therapy is safe and efficacious in patients with spastic tetraplegia and respiratory dysfunction., (© 2016 International Neuromodulation Society.)

Brain signals recorded from the primary motor cortex (M1) are known to serve a significant role in coding the information brain-machine interfaces (BMIs) need to perform real and imagined movements, and also to form several functional networks with motor association areas. However, whether functional networks between M1 and other brain regions, such as these motor association areas, are related to the performance of BMIs is unclear. To examine the relationship between functional connectivity and performance of BMIs, we analyzed the correlation coefficient between performance of neural decoding and functional connectivity over the whole brain using magnetoencephalography. Ten healthy participants were instructed to execute or imagine three simple right upper limb movements. To decode the movement type, we extracted 40 virtual channels in the left M1 via the beam forming approach, and used them as a decoding feature. In addition, seed-based functional connectivities of activities in the alpha band during real and imagined movements were calculated using imaginary coherence. Seed voxels were set as the same virtual channels in M1. After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain. The significant correlations were distributed mainly to motor association areas for both real and imagined movements. These regions largely overlapped with brain regions that had significant connectivity to M1. Our results suggest that use of the strength of functional connectivity between M1 and motor association areas has the potential to improve the performance of BMIs to perform real and imagined movements.

Seeking to apply brain-machine interface technology in neuroprosthetics, a number of methods for predicting trajectory of the elbow and wrist have been proposed and have shown remarkable results. Recently, the prediction of hand trajectory and classification of hand gestures or grasping types have attracted considerable attention. However, trajectory prediction for precise finger motion has remained a challenge. We proposed a method for the prediction of fingertip motions from electrocorticographic signals in human cortex. A patient performed extension/flexion tasks with three fingers. Average Pearson's correlation coefficients and normalized root-mean-square errors between decoded and actual trajectories were 0.83-0.90 and 0.24-0.48, respectively. To confirm generalizability to other users, we applied our method to the BCI Competition IV open data sets. Our method showed that the prediction accuracy of fingertip trajectory could be equivalent to that of other results in the competition., (Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.)

Non-invasive localization of certain brain functions may be mapped on a millimeter level. However, the inter-electrode spacing of common clinical brain surface electrodes still remains around 10 mm, and some electrodes fail to measure cortical activity due to unconformable plain electrode sheets. Here, we present details on development of implantable electrodes for attaining higher quality electrocorticographic signals for use in functional brain mapping and brain-machine interfaces. We produced personalized sheet electrodes after the creation of individualized molds using a 3D-printer. We created arrays to fit the surface curvature of the brain and inside the central sulcus, with inter-electrode distances of 2.5 mm. Rat experiments undertaken indicated no long term toxicity. We were also able to custom design, rapidly manufacture, safely implant and confirm the efficacy of personalized electrodes, including the capability to attain meaningful high gamma-band information in an amyotrophic lateral sclerosis patient. This sheet electrode may contribute to the higher performance of BMI's.

A brain machine interface (BMI) provides the possibility of controlling such external devices as prosthetic arms for patients with severe motor dysfunction using their own brain signals. However, there have been few studies investigating the decoding accuracy for multiclasses of useful unilateral upper limb movements using non-invasive measurements. We investigated the decoding accuracy for classifying three types of unilateral upper limb movements using single-trial magnetoencephalography (MEG) signals. Neuromagnetic activities were recorded in 9 healthy subjects performing 3 types of right upper limb movements: hand grasping, pinching, and elbow flexion. A support vector machine was used to classify the single-trial MEG signals. The movement types were predicted with an average accuracy of 66 ± 10% (chance level: 33.3%) using neuromagnetic activity during a 400-ms interval (-200 ms to 200 ms from movement onsets). To explore the time-dependency of the decoding accuracy, we also examined the time course of decoding accuracy in 50-ms sliding windows from -500 ms to 500 ms. Decoding accuracies significantly increased and peaked once before (50.1 ± 4.9%) and twice after (58.5 ± 7.5% and 64.4 ± 7.6%) movement onsets in all subjects. Significant variability in the decoding features in the first peak was evident in the channels over the parietal area and in the second and third peaks in the channels over the sensorimotor area. Our results indicate that the three types of unilateral upper limb movement can be inferred with high accuracy by detecting differences in movement-related brain activity in the parietal and sensorimotor areas., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Objective: Paralyzed patients may benefit from restoration of movement afforded by prosthetics controlled by electrocorticography (ECoG). Although ECoG shows promising results in human volunteers, it is unclear whether ECoG signals recorded from chronically paralyzed patients provide sufficient motor information, and if they do, whether they can be applied to control a prosthetic., Methods: We recorded ECoG signals from sensorimotor cortices of 12 patients while they executed or attempted to execute 3 to 5 simple hand and elbow movements. Sensorimotor function was severely impaired in 3 patients due to peripheral nervous system lesion or amputation, moderately impaired due to central nervous system lesions sparing the cortex in 4 patients, and normal in 5 patients. Time frequency and decoding analyses were performed with the patients' ECoG signals., Results: In all patients, the high gamma power (80-150 Hz) of the ECoG signals during movements was clearly responsive to movement types and provided the best information for classifying different movement types. The classification performance was significantly better than chance in all patients, although differences between ECoG power modulations during different movement types were significantly less in patients with severely impaired motor function. In the impaired patients, cortical representations tended to overlap each other. Finally, using the classification method in real time, a moderately impaired patient and 3 nonparalyzed patients successfully controlled a prosthetic arm., Interpretation: ECoG signals appear useful for prosthetic arm control and may provide clinically feasible motor restoration for patients with paralysis but no injury of the sensorimotor cortex., (Copyright © 2011 American Neurological Association.)

Patients with Parkinson's disease (PD) reportedly show deficits in sensory processing in addition to motor symptoms. However, little is known about the effects of bilateral deep brain stimulation of the subthalamic nucleus (STN-DBS) on temperature sensation as measured by quantitative sensory testing (QST). This study was designed to quantitatively evaluate the effects of STN-DBS on temperature sensation and pain in PD patients. We conducted a QST study comparing the effects of STN-DBS on cold sense thresholds (CSTs) and warm sense thresholds (WSTs) as well as on cold-induced and heat-induced pain thresholds (CPT and HPT) in 17 PD patients and 14 healthy control subjects. The CSTs and WSTs of patients were significantly smaller during the DBS-on mode when compared with the DBS-off mode (P<.001), whereas the CSTs and WSTs of patients in the DBS-off mode were significantly greater than those of healthy control subjects (P<.02). The CPTs and HPTs in PD patients were significantly larger on the more affected side than on the less affected side (P<.02). Because elevations in thermal sense and pain thresholds of QST are reportedly almost compatible with decreases in sensation, our findings confirm that temperature sensations may be disturbed in PD patients when compared with healthy persons and that STN-DBS can be used to improve temperature sensation in these patients. The mechanisms underlying our findings are not well understood, but improvement in temperature sensation appears to be a sign of modulation of disease-related brain network abnormalities., (Copyright © 2010 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.)

The frequency profiles and time courses of oscillatory changes when reading words are not fully understood, although there have been many reports that oscillatory dynamics reflect local brain function. In order to clarify oscillatory dynamics, we investigated the frequency and spatiotemporal distributions of neuromagnetic activities during silent reading of words in 23 healthy subjects. Individual data were divided into the following frequency bands: theta (5-8 Hz), alpha (8-13 Hz), beta (13-25 Hz), low gamma (25-50 Hz), and high gamma (50-100 Hz), and were analyzed by synthetic aperture magnetometry (SAM). The time window was consecutively moved in steps of 50 ms. Group analysis was performed to delineate common areas of brain activation. A transient power increase in the theta band occurred first in the bilateral occipital cortices, and then rapidly propagated to the left temporo-occipital areas, left inferior and middle frontal gyri, bilateral medial prefrontal cortices, and finally to the left anterior temporal cortices, which possibly reflects a serial cognitive process. This serial propagation of the transient power increase in the theta band was followed by sustained power decreases in the alpha, beta and low gamma bands. These results suggest that the transient power increase in the theta bands may be associated with priming and propagation of local activities, while sustained power decreases in the alpha, beta and low gamma bands reflect parallel neural processes related to silent reading words. Our results showed a relationship between frequency bands of oscillatory changes and locations. This may have implications in the relationship between frequency bands and functions., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Spinal cord stimulation (SCS) is an effective therapy for chronic neuropathic pain. However, the detailed mechanisms underlying its effects are not well understood. Positron emission tomography (PET) with H(2)(15)O was applied to clarify these mechanisms. Nine patients with intractable neuropathic pain in the lower limbs were included in the study. All patients underwent SCS therapy for intractable pain, which was due to failed back surgery syndrome in three patients, complex regional pain syndrome in two, cerebral hemorrhage in two, spinal infarction in one, and spinal cord injury in one. Regional cerebral blood flow (rCBF) was measured by H(2)(15)O PET before and after SCS. The images were analyzed with statistical parametric mapping software (SPM2). SCS reduced pain; visual analog scale values for pain decreased from 76.1+/-25.2 before SCS to 40.6+/-4.5 after SCS (mean+/-SE). Significant rCBF increases were identified after SCS in the thalamus contralateral to the painful limb and in the bilateral parietal association area. The anterior cingulate cortex (ACC) and prefrontal areas were also activated after SCS. These results suggest that SCS modulates supraspinal neuronal activities. The contralateral thalamus and parietal association area would regulate the pain threshold. The ACC and prefrontal areas would control the emotional aspects of intractable pain, resulting in the reduction of neuropathic pain after SCS., (Copyright (c) 2009 Elsevier Inc. All rights reserved.)

Epileptic activities are known to be modulated by cortical excitability, which is altered with normal brain functioning such as movement. However, the relationship between the epileptic activity and movement has not been well studied. Here, we investigated movement-induced modulation of interictal spikes to reveal the relationship between epileptic activity and the movement-induced modulation of cortical activity. Two patients (three cases) with focal cortical dysplasia (FCD) of the pre- and/or post-central gyrus performed voluntary movements of their hands or mouths. During the movement, the interictal spikes of the sensorimotor cortex, which were measured by electrocorticograms (ECoG), were significantly reduced. This reduction strongly correlated with the event-related desynchronization (ERD) of the cortical oscillatory activity at the lower frequency bands (<25 Hz) during movement. The epileptic activity was suggested to be modulated by the movement, which correlates with the ERD of the cortical oscillatory activity., (2009 Elsevier B.V. All rights reserved.)

Electrocorticography of the primary motor cortex (M1) is a promising tool for controlling a brain-computer interface (BCI). Electrocorticograms (ECoG) of the human M1 within the central sulcus (intrasulcal ECoG) have been rarely examined. In order to evaluate the usefulness of intrasulcal ECoG for BCI, we examined patients with subdural electrodes placed temporarily inside the central sulcus and over the sensorimotor cortex (gyral ECoG). Five patients were asked to perform or imagine two or three classes of simple upper limb movements. Univariate statistical analysis of the results revealed that the intrasulcal ECoG on M1 showed significant variability across movement classes. A support vector machine was used for classification of single-trial ECoG signals to infer movement class (neural decoding). The movement classes were predicted with 80-90% accuracy (chance level: 33% or 50%). To reveal the relative importance of anatomical areas for neural decoding, the decoding performance was compared between gyral and intrasulcal ECoGs. The intrasulcal ECoG on the motor bank showed higher performance than the equally-sized gyral ECoG or the intrasulcal ECoG on the sensory bank. Analysis using a short time window revealed that movement class could be decoded even before movement onset. These results suggest the usefulness of intrasulcal ECoG on M1 to infer upper limb movements and present a promising application for a practical BCI system.

Multimodal imaging is one of the necessary steps in the treatment of malignant brain tumors, and use of magnetic resonance imaging (MRI) and positron emission tomography (PET) are the current gold standard technique for the morphological and biological assessment of malignant brain tumors. In addition, fractional anisotropy (FA) obtained from diffusion tensor imaging (DTI) and 11C-methionine PET are useful to determine the tumor border at the tumor and white matter interface. Although there is no question of their value, a universally accepted cut-off value to discriminate normal and abnormal tissue has not been established. In this study we attempted to calculate and determine the cut-off values in FA and 11C-methionine PET that will allow delineation of the tumor border at the tumor and white matter interface by combining these two modalities. We were able to determine individual cut-off values for 11 patients, and then found an average cut-off value in the T/N ratio of 11C-methionine PET of 1.27 and in FA of 0.26, values similar to those previously confirmed by histological study. Moreover, reconstructing images delineating the tumor border was possible combining these two imaging modalities. We propose that the combined analysis of DTI and 11C-methionine PET has the potential to improve tumor border imaging in glioma patients, providing important information for establishing neurosurgical strategies.