Publications by authors named "Takufumi Yanagisawa"

61 Publications

Swallowing-related neural oscillation: an intracranial EEG study.

Ann Clin Transl Neurol 2021 Jun 5;8(6):1224-1238. Epub 2021 May 5.

Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka, 565-0871, Japan.

Objective: Swallowing is a unique movement due to the indispensable orchestration of voluntary and involuntary movements. The transition from voluntary to involuntary swallowing is executed within milliseconds. We hypothesized that the underlying neural mechanism of swallowing would be revealed by high-frequency cortical activities.

Methods: Eight epileptic participants fitted with intracranial electrodes over the orofacial cortex were asked to swallow a water bolus and cortical oscillatory changes, including the high γ band (75-150 Hz) and β band (13-30 Hz), were investigated at the time of mouth opening, water injection, and swallowing.

Results: Increases in high γ power associated with mouth opening were observed in the ventrolateral prefrontal cortex (VLPFC) with water injection in the lateral central sulcus and with swallowing in the region along the Sylvian fissure. Mouth opening induced a decrease in β power, which continued until the completion of swallowing. The high γ burst of activity was focal and specific to swallowing; however, the β activities were extensive and not specific to swallowing. In the interim between voluntary and involuntary swallowing, swallowing-related high γ power achieved its peak, and subsequently, the power decreased.

Interpretation: We demonstrated three distinct activities related to mouth opening, water injection, and swallowing induced at different timings using high γ activities. The peak of high γ power related to swallowing suggests that during voluntary swallowing phases, the cortex is the main driving force for swallowing as opposed to the brain stem.
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http://dx.doi.org/10.1002/acn3.51344DOI Listing
June 2021

Phase-amplitude coupling of ripple activities during seizure evolution with theta phase.

Clin Neurophysiol 2021 Jun 26;132(6):1243-1253. Epub 2021 Mar 26.

Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan; Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita 565-0871, Japan; Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan.

Objective: High-frequency activities (HFAs) and phase-amplitude coupling (PAC) are key neurophysiological biomarkers for studying human epilepsy. We aimed to clarify and visualize how HFAs are modulated by the phase of low-frequency bands during seizures.

Methods: We used intracranial electrodes to record seizures of focal epilepsy (12 focal-to-bilateral tonic-clonic seizures and three focal-aware seizures in seven patients). The synchronization index, representing PAC, was used to analyze the coupling between the amplitude of ripples (80-250 Hz) and the phase of lower frequencies. We created a video in which the intracranial electrode contacts were scaled linearly to the power changes of ripple.

Results: The main low frequency band modulating ictal-ripple activities was the θ band (4-8 Hz), and after completion of ictal-ripple burst, δ (1-4 Hz)-ripple PAC occurred. The ripple power increased simultaneously with rhythmic fluctuations from the seizure onset zone, and spread to other regions.

Conclusions: Ripple activities during seizure evolution were modulated by the θ phase. The PAC phenomenon was visualized as rhythmic fluctuations.

Significance: Ripple power associated with seizure evolution increased and spread with fluctuations. The θ oscillations related to the fluctuations might represent the common neurophysiological processing involved in seizure generation.
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http://dx.doi.org/10.1016/j.clinph.2021.03.007DOI Listing
June 2021

A Swallowing Decoder Based on Deep Transfer Learning: AlexNet Classification of the Intracranial Electrocorticogram.

Int J Neural Syst 2020 Sep 16:2050056. Epub 2020 Sep 16.

Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan.

To realize a brain-machine interface to assist swallowing, neural signal decoding is indispensable. Eight participants with temporal-lobe intracranial electrode implants for epilepsy were asked to swallow during electrocorticogram (ECoG) recording. Raw ECoG signals or certain frequency bands of the ECoG power were converted into images whose vertical axis was electrode number and whose horizontal axis was time in milliseconds, which were used as training data. These data were classified with four labels (Rest, Mouth open, Water injection, and Swallowing). Deep transfer learning was carried out using AlexNet, and power in the high-[Formula: see text] band (75-150[Formula: see text]Hz) was the training set. Accuracy reached 74.01%, sensitivity reached 82.51%, and specificity reached 95.38%. However, using the raw ECoG signals, the accuracy obtained was 76.95%, comparable to that of the high-[Formula: see text] power. We demonstrated that a version of AlexNet pre-trained with visually meaningful images can be used for transfer learning of visually meaningless images made up of ECoG signals. Moreover, we could achieve high decoding accuracy using the raw ECoG signals, allowing us to dispense with the conventional extraction of high-[Formula: see text] power. Thus, the images derived from the raw ECoG signals were equivalent to those derived from the high-[Formula: see text] band for transfer deep learning.
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http://dx.doi.org/10.1142/S0129065720500562DOI Listing
September 2020

Coupling between infraslow activities and high-frequency oscillations precedes seizure onset.

Epilepsia Open 2020 Sep 8;5(3):501-506. Epub 2020 Aug 8.

Department of Neurological Diagnosis and Restoration Graduate School of Medicine Osaka University Suita Japan.

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.
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http://dx.doi.org/10.1002/epi4.12425DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7469835PMC
September 2020

Pain Control by Co-adaptive Learning in a Brain-Machine Interface.

Curr Biol 2020 10 13;30(20):3935-3944.e7. Epub 2020 Aug 13.

Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK; Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto 619-0237, Japan; Center for Information and Neural Networks, National Institute for Information and Communications Technology, Osaka 565-0871, Japan; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK. Electronic address:

Innovation in the field of brain-machine interfacing offers a new approach to managing human pain. In principle, it should be possible to use brain activity to directly control a therapeutic intervention in an interactive, closed-loop manner. But this raises the question as to whether the brain activity changes as a function of this interaction. Here, we used real-time decoded functional MRI responses from the insula cortex as input into a closed-loop control system aimed at reducing pain and looked for co-adaptive neural and behavioral changes. As subjects engaged in active cognitive strategies orientated toward the control system, such as trying to enhance their brain activity, pain encoding in the insula was paradoxically degraded. From a mechanistic perspective, we found that cognitive engagement was accompanied by activation of the endogenous pain modulation system, manifested by the attentional modulation of pain ratings and enhanced pain responses in pregenual anterior cingulate cortex and periaqueductal gray. Further behavioral evidence of endogenous modulation was confirmed in a second experiment using an EEG-based closed-loop system. Overall, the results show that implementing brain-machine control systems for pain induces a parallel set of co-adaptive changes in the brain, and this can interfere with the brain signals and behavior under control. More generally, this illustrates a fundamental challenge of brain decoding applications-that the brain inherently adapts to being decoded, especially as a result of cognitive processes related to learning and cooperation. Understanding the nature of these co-adaptive processes informs strategies to mitigate or exploit them.
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http://dx.doi.org/10.1016/j.cub.2020.07.066DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575198PMC
October 2020

BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial.

Neurology 2020 07 16;95(4):e417-e426. Epub 2020 Jul 16.

From the Institute for Advanced Co-Creation Studies (T.Y.), Osaka University; Departments of Neurosurgery (T.Y., R.F., M.T., K.H., H.K., Y.S.) and Neuromodulation and Neurosurgery (K.H., Y.S.), Osaka University Graduate School of Medicine; Department of Neuroinformatics (T.Y., R.F., Y.K.), ATR Computational Neuroscience Laboratories, Kyoto, Japan; Computational and Biological Learning Laboratory, Department of Engineering (B.S.), University of Cambridge, UK; Center for Information and Neural Networks (B.S.), National Institute for Information and Communications Technology, Osaka; RIKEN Center for Advanced Intelligence Project (O.Y.), Tokyo; Department of Computational Brain Imaging (O.Y.), ATR Neural Information Analysis Laboratories, Kyoto; and Graduate School of Informatics (Y.K.), Kyoto University, Japan.

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; = 0.009 < 0.025), but not after random training ( = 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 ( < 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.
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http://dx.doi.org/10.1212/WNL.0000000000009858DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455320PMC
July 2020

Nationwide survey of 780 Japanese patients with amyotrophic lateral sclerosis: their status and expectations from brain-machine interfaces.

J Neurol 2020 Oct 1;267(10):2932-2940. Epub 2020 Jun 1.

Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, CoMIT, 2-2 Yamadaoka, Suita, Osaka, 913A565-0871, Japan.

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that causes eventual death through respiratory failure unless mechanical ventilation is provided. Brain-machine interfaces (BMIs) may provide brain control supports for communication and motor function. We investigated the interests and expectations of patients with ALS concerning BMIs based on a large-scale anonymous questionnaire survey supported by the Japan Amyotrophic Lateral Sclerosis Association.

Methods: We surveyed 1918 patients with ALS regarding their present status, tracheostomy use, interest in BMIs, and their level of expectation for communication (conversation, emergency alarm, internet, and writing letters) and movement support (postural change, controlling the bed, controlling household appliances, robotic arms, and wheel chairs).

Findings: Seven hundred and eighty participants responded. Fifty-eight percent of the participants underwent tracheostomy. Approximately, 80% of the patients experienced stress or trouble during communication. For all nine supports, > 60% participants expressed expectations regarding BMIs. More than 98% of participants who underwent tracheostomy expected support with conversation and emergency alarms. Participants who did not undergo tracheostomy exhibited significantly greater expectations than participants with tracheostomy did regarding all five movement supports. Seventy-seven percent of participants were interested in BMIs. Participants aged < 60 years had greater interest in both BMIs.

Interpretation: This is the first large-scale survey to reveal the present status of patients with ALS and probe their interests and expectations regarding BMIs. Communication and emergency alarms should be supported by BMIs initially. BMIs should provide wide-ranging and high-performance support that can easily be used by severely disabled elderly patients with ALS.
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http://dx.doi.org/10.1007/s00415-020-09903-3DOI Listing
October 2020

Neural decoding of electrocorticographic signals using dynamic mode decomposition.

J Neural Eng 2020 06 2;17(3):036009. Epub 2020 Jun 2.

Osaka University, Institute for Advanced Co-Creation Studies, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.

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.
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http://dx.doi.org/10.1088/1741-2552/ab8910DOI Listing
June 2020

Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network.

Sci Rep 2019 12 30;9(1):20311. Epub 2019 Dec 30.

Department of Neurosurgery, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.

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.
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http://dx.doi.org/10.1038/s41598-019-56767-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937237PMC
December 2019

Somatosensation Evoked by Cortical Surface Stimulation of the Human Primary Somatosensory Cortex.

Front Neurosci 2019 24;13:1019. Epub 2019 Sep 24.

Department of Developmental Physiology, National Institute for Physiological Sciences, Okazaki, Japan.

Electrical stimulation of the primary somatosensory cortex using intracranial electrodes is crucial for the evocation of artificial somatosensations, typically tactile sensations associated with specific regions of the body, in brain-machine interface (BMI) applications. The qualitative characteristics of these artificially evoked somatosensations has been well documented. As of yet, however, the quantitative aspects of these evoked somatosensations, that is to say the quantitative relationship between intensity of electrical stimulation and perceived intensity of the resultant somatosensation remains obscure. This study aimed to explore this quantitative relationship by surface electrical stimulation of the primary somatosensory cortex in two human participants undergoing electrocorticographic monitoring prior to surgical treatment of intractable epilepsy. Electrocorticogram electrodes on the primary somatosensory cortical surface were stimulated with varying current intensities, and a visual analogue scale was employed to provide a quantitative measure of intensity of the evoked sensations. Evoked sensations included those of the thumb, tongue, and hand. A clear linear relationship between current intensity and perceived intensity of sensation was observed. These findings provide novel insight into the quantitative nature of primary somatosensory cortex electrical stimulation-evoked sensation for development of somatosensory neuroprosthetics for clinical use.
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http://dx.doi.org/10.3389/fnins.2019.01019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6769168PMC
September 2019

A randomized controlled trial of 5 daily sessions and continuous trial of 4 weekly sessions of repetitive transcranial magnetic stimulation for neuropathic pain.

Pain 2020 02;161(2):351-360

Department of Neuromodulation and Neurosurgery, Osaka University Graduate School of Medicine, , Osaka, Japan.

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.
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http://dx.doi.org/10.1097/j.pain.0000000000001712DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6970577PMC
February 2020

The future potential of the Stentrode.

Expert Rev Med Devices 2019 10 9;16(10):841-843. Epub 2019 Oct 9.

Department of Neurosurgery, Osaka University Medical School , Osaka , Japan.

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http://dx.doi.org/10.1080/17434440.2019.1674139DOI Listing
October 2019

Automatic diagnosis of neurological diseases using MEG signals with a deep neural network.

Sci Rep 2019 03 25;9(1):5057. Epub 2019 Mar 25.

Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan.

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). 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.
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http://dx.doi.org/10.1038/s41598-019-41500-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433906PMC
March 2019

Real-Time Neurofeedback to Modulate β-Band Power in the Subthalamic Nucleus in Parkinson's Disease Patients.

eNeuro 2018 Nov-Dec;5(6). Epub 2018 Dec 21.

Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan.

The β-band oscillation in the subthalamic nucleus (STN) is a therapeutic target for Parkinson's disease. Previous studies demonstrated that l-DOPA decreases the β-band (13-30 Hz) oscillations with improvement of motor symptoms. However, it has not been elucidated whether patients with Parkinson's disease are able to control the β-band oscillation voluntarily. Here, we hypothesized that neurofeedback training to control the β-band power in the STN induces plastic changes in the STN of individuals with Parkinson's disease. We recorded the signals from STN deep-brain stimulation electrodes during operations to replace implantable pulse generators in eight human patients (3 male) with bilateral electrodes. Four patients were induced to decrease the β-band power during the feedback training (down-training condition), whereas the other patients were induced to increase (up-training condition). All patients were blinded to their assigned condition. Adjacent contacts that showed the highest β-band power were selected for the feedback. During the 10 min training, patients were shown a circle whose diameter was controlled by the β-band power of the selected contacts. Powers in the β-band during 5 min resting sessions recorded before and after the feedback were compared. In the down-training condition, the β-band power of the selected contacts decreased significantly after feedback in all four patients ( < 0.05). In contrast, the β-band power significantly increased after feedback in two of four patients in the up-training condition. Overall, the patients could voluntarily control the β-band power in STN in the instructed direction ( < 0.05) through neurofeedback.
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http://dx.doi.org/10.1523/ENEURO.0246-18.2018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6325552PMC
March 2019

A Fully Implantable Wireless ECoG 128-Channel Recording Device for Human Brain-Machine Interfaces: W-HERBS.

Front Neurosci 2018 30;12:511. Epub 2018 Jul 30.

Department of Neurosurgery, Osaka University Medical School, Osaka, Japan.

Brain-machine interfaces (BMIs) are promising devices that can be used as neuroprostheses by severely disabled individuals. Brain surface electroencephalograms (electrocorticograms, ECoGs) can provide input signals that can then be decoded to enable communication with others and to control intelligent prostheses and home electronics. However, conventional systems use wired ECoG recordings. Therefore, the development of wireless systems for clinical ECoG BMIs is a major goal in the field. We developed a fully implantable ECoG signal recording device for human ECoG BMI, i.e., a wireless human ECoG-based real-time BMI system (W-HERBS). In this system, three-dimensional (3D) high-density subdural multiple electrodes are fitted to the brain surface and ECoG measurement units record 128-channel (ch) ECoG signals at a sampling rate of 1 kHz. The units transfer data to the data and power management unit implanted subcutaneously in the abdomen through a subcutaneous stretchable spiral cable. The data and power management unit then communicates with a workstation outside the body and wirelessly receives 400 mW of power from an external wireless transmitter. The workstation records and analyzes the received data in the frequency domain and controls external devices based on analyses. We investigated the performance of the proposed system. We were able to use W-HERBS to detect sine waves with a 4.8-μV amplitude and a 60-200-Hz bandwidth from the ECoG BMIs. W-HERBS is the first fully implantable ECoG-based BMI system with more than 100 ch. It is capable of recording 128-ch subdural ECoG signals with sufficient input-referred noise (3 μV) and with an acceptable time delay (250 ms). The system contributes to the clinical application of high-performance BMIs and to experimental brain research.
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http://dx.doi.org/10.3389/fnins.2018.00511DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090147PMC
July 2018

Training in Use of Brain-Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements.

Front Neurosci 2018 11;12:478. Epub 2018 Jul 11.

Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.

Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training. We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open their right hands during MEG recording. Time-averaged MEG signals were then used to train a real decoder to control the robotic arm in real time. Then, subjects were instructed to control the BMI-controlled robotic hand by moving their right hands for 10 min while watching the robot's movement. During this closed-loop session, subjects tried to improve their ability to control the robot. Finally, subjects performed the same offline task to compare cortical activities related to the hand movements. As a control, we used a random decoder trained by the MEG signals with shuffled movement labels. We performed the same experiments with the random decoder as a crossover trial. To evaluate the cortical representation, cortical currents were estimated using a source localization technique. Hand movements were also decoded by a support vector machine using the MEG signals during the offline task. The classification accuracy of the movements was compared among offline tasks. During the BMI training with the real decoder, the subjects succeeded in improving their accuracy in controlling the BMI robotic hand with correct rates of 0.28 ± 0.13 to 0.50 ± 0.11 ( = 0.017, = 8, paired Student's -test). Moreover, the classification accuracy of hand movements during the offline task was significantly increased after BMI training with the real decoder from 62.7 ± 6.5 to 70.0 ± 11.1% ( = 0.022, = 8, = 2.93, paired Student's test), whereas accuracy did not significantly change after BMI training with the random decoder from 63.0 ± 8.8 to 66.4 ± 9.0% ( = 0.225, = 8, = 1.33). BMI training is a useful tool to train the cortical activity necessary for BMI control and to induce some plastic changes in the activity.
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http://dx.doi.org/10.3389/fnins.2018.00478DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6050372PMC
July 2018

MEG-BMI to Control Phantom Limb Pain.

Neurol Med Chir (Tokyo) 2018 Aug 12;58(8):327-333. Epub 2018 Jul 12.

Department of Neurosurgery, Osaka University Graduate School of Medicine.

A brachial plexus root avulsion (BPRA) causes intractable pain in the insensible affected hands. Such pain is partly due to phantom limb pain, which is neuropathic pain occurring after the amputation of a limb and partial or complete deafferentation. Previous studies suggested that the pain was attributable to maladaptive plasticity of the sensorimotor cortex. However, there is little evidence to demonstrate the causal links between the pain and the cortical representation, and how much cortical factors affect the pain. Here, we applied lesioning of the dorsal root entry zone (DREZotomy) and training with a brain-machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. The DREZotomy successfully reduced the shooting pain after BPRA, but a part of the pain remained. The BMI training successfully induced some plastic changes in the sensorimotor representation of the phantom hand movements and helped control the remaining pain. When the patient tried to control the robotic hand by moving their phantom hand through association with the representation of the intact hand, this especially decreased the pain while decreasing the classification accuracy of the phantom hand movements. These results strongly suggested that pain after the BPRA was partly attributable to cortical representation of phantom hand movements and that the BMI training controlled the pain by inducing appropriate cortical reorganization. For the treatment of chronic pain, we need to know how to modulate the cortical representation by novel methods.
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http://dx.doi.org/10.2176/nmc.st.2018-0099DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092605PMC
August 2018

Non-invasive quantification of human swallowing using a simple motion tracking system.

Sci Rep 2018 03 23;8(1):5095. Epub 2018 Mar 23.

Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.

The number of patients with dysphagia is rapidly increasing due to the ageing of the population. Therefore, the importance of objectively assessing swallowing function has received increasing attention. Videofluoroscopy and videoendoscopy are the standard clinical examinations for dysphagia, but these techniques are not suitable for daily use because of their invasiveness. Here, we aimed to develop a novel, non-invasive method for measuring swallowing function using a motion tracking system, the Kinect v2 sensor. Five males and five females with normal swallowing function participated in this study. We defined three mouth-related parameters and two larynx-related parameters and recorded data from 2.5 seconds before to 2.5 seconds after swallowing onset. Changes in mouth-related parameters were observed before swallowing and reached peak values at the time of swallowing. In contrast, larynx-related parameters showed little change before swallowing and reached peak values immediately after swallowing. This simple swallow tracking system (SSTS) successfully quantified the swallowing process from the oral phase to the laryngeal phase. This SSTS is non-invasive, wireless, easy to set up, and simultaneously measures the dynamics of swallowing from the mouth to the larynx. We propose the SSTS for use as a novel and non-invasive swallowing assessment tool in the clinic.
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http://dx.doi.org/10.1038/s41598-018-23486-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5865167PMC
March 2018

Frequency-specific genetic influence on inferior parietal lobule activation commonly observed during action observation and execution.

Sci Rep 2017 12 15;7(1):17660. Epub 2017 Dec 15.

Division of Functional Diagnostic Science, Graduate School of Medicine, Osaka University, Osaka, 565-0871, Japan.

Brain activity relating to recognition of action varies among subjects. These differences have been hypothesised to originate from genetic and environmental factors although the extent of their effect remains unclear. Effects of these factors on brain activity during action recognition were evaluated by comparing magnetoencephalography (MEG) signals in twins. MEG signals of 20 pairs of elderly monozygotic twins and 11 pairs of elderly dizygotic twins were recorded while they observed finger movements and copied them. Beamformer and group statistical analyses were performed to evaluate spatiotemporal differences in cortical activities. Significant event-related desynchronisation (ERD) of the β band (13-25 Hz) at the left inferior parietal lobule (IPL) was observed for both action observation and execution. Moreover, β-band ERD at the left IPL during action observation was significantly better correlated among monozygotic twins compared to unrelated pairs (Z-test, p = 0.027). β-band ERD heritability at the left IPL was 67% in an ACE model. These results demonstrate that β-band ERD at the IPL, which is commonly observed during action recognition and execution, is affected by genetic rather than environmental factors. The effect of genetic factors on the cortical activity of action recognition may depend on anatomical location and frequency characteristics.
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http://dx.doi.org/10.1038/s41598-017-17662-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5732255PMC
December 2017

Non-invasive detection of language-related prefrontal high gamma band activity with beamforming MEG.

Sci Rep 2017 10 27;7(1):14262. Epub 2017 Oct 27.

Osaka University, Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Suita, 565-0871, Japan.

High gamma band (>50 Hz) activity is a key oscillatory phenomenon of brain activation. However, there has not been a non-invasive method established to detect language-related high gamma band activity. We used a 160-channel whole-head magnetoencephalography (MEG) system equipped with superconducting quantum interference device (SQUID) gradiometers to non-invasively investigate neuromagnetic activities during silent reading and verb generation tasks in 15 healthy participants. Individual data were divided into alpha (8-13 Hz), beta (13-25 Hz), low gamma (25-50 Hz), and high gamma (50-100 Hz) bands and analysed with the beamformer method. The time window was consecutively moved. Group analysis was performed to delineate common areas of brain activation. In the verb generation task, transient power increases in the high gamma band appeared in the left middle frontal gyrus (MFG) at the 550-750 ms post-stimulus window. We set a virtual sensor on the left MFG for time-frequency analysis, and high gamma event-related synchronization (ERS) induced by a verb generation task was demonstrated at 650 ms. In contrast, ERS in the high gamma band was not detected in the silent reading task. Thus, our study successfully non-invasively measured language-related prefrontal high gamma band activity.
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http://dx.doi.org/10.1038/s41598-017-14452-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5660237PMC
October 2017

Preservation of Motor Function After Resection of Lower-Grade Glioma at the Precentral Gyrus and Prediction by Presurgical Functional Magnetic Resonance Imaging and Magnetoencephalography.

World Neurosurg 2017 Nov 4;107:1045.e5-1045.e8. Epub 2017 Aug 4.

Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan.

Background: Intra-axial brain tumors located at anatomically eloquent areas are challenging conditions. On one hand, it is often difficult to pursue maximum extent of resection of tumor in these locations. On the other hand, neuroplasticity occurs in some patients with low-grade glioma, and the primary neural functions are known to sometimes shift from conventional "eloquent cortices."

Case Description: In a patient with a lower-grade glioma located at the precentral gyrus, shift of primary motor function from the precentral gyrus to the postcentral gyrus was detected on magnetoencephalography and functional magnetic resonance imaging. Aggressive removal of the pathologic precentral gyrus was accomplished via awake craniotomy without causing obvious motor function deficit.

Conclusions: This case highlights the importance of preoperative multimodal neurophysiologic imaging in patients with low-grade gliomas in eloquent areas.
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http://dx.doi.org/10.1016/j.wneu.2017.07.152DOI Listing
November 2017

Mapping ECoG channel contributions to trajectory and muscle activity prediction in human sensorimotor cortex.

Sci Rep 2017 03 31;7:45486. Epub 2017 Mar 31.

Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan.

Studies on brain-machine interface techniques have shown that electrocorticography (ECoG) is an effective modality for predicting limb trajectories and muscle activity in humans. Motor control studies have also identified distributions of "extrinsic-like" and "intrinsic-like" neurons in the premotor (PM) and primary motor (M1) cortices. Here, we investigated whether trajectories and muscle activity predicted from ECoG were obtained based on signals derived from extrinsic-like or intrinsic-like neurons. Three participants carried objects of three different masses along the same counterclockwise path on a table. Trajectories of the object and upper arm muscle activity were predicted using a sparse linear regression. Weight matrices for the predictors were then compared to determine if the ECoG channels contributed more information about trajectory or muscle activity. We found that channels over both PM and M1 contributed highly to trajectory prediction, while a channel over M1 was the highest contributor for muscle activity prediction.
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http://dx.doi.org/10.1038/srep45486DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5374467PMC
March 2017

Navigation-assisted trans-inferotemporal cortex selective amygdalohippocampectomy for mesial temporal lobe epilepsy; preserving the temporal stem.

Neurol Res 2017 Mar 9;39(3):223-230. Epub 2017 Jan 9.

f Global Center for Medical Engineering and Informatics Division of Clinical Neuroengineering , Osaka University , Osaka , Japan.

Objective: Selective amygdalohippocampectomy (SAH) can be used to obtain satisfactory seizure control in patients with mesial temporal lobe epilepsy (MTLE). Several SAH procedures have been reported to achieve satisfactory outcomes for seizure control, but none yield fully satisfactory outcomes for memory function. We hypothesized that preserving the temporal stem might play an important role. To preserve the temporal stem, we developed a minimally invasive surgical procedure, 'neuronavigation-assisted trans-inferotemporal cortex SAH' (TITC-SAH).

Methods: TITC-SAH was performed in 23 patients with MTLE (MTLE on the language-non-dominant hemisphere, n = 11). The inferior horn of the lateral ventricle was approached via the inferior or middle temporal gyrus along the inferior temporal sulcus under neuronavigation guidance. The hippocampus was dissected in a subpial manner and resected en bloc together with the parahippocampal gyrus. Seizure control at one year and memory function at 6 months postoperatively were evaluated.

Results: One year after TITC-SAH, 20 of the 23 patients were seizure-free (ILAE class 1), 2 were class 2, and 1 was class 3. Verbal memory improved significantly in 13 patients with a diagnosis of hippocampal sclerosis, for whom WMS-R scores were available both pre- and post-operatively. Improvements were seen regardless of whether the SAH was on the language-dominant or non-dominant hemisphere. No major complication was observed.

Conclusion: Navigation-assisted TITC-SAH performed for MTLE offers a simple, minimally invasive procedure that appears to yield excellent outcomes in terms of seizure control and preservation of memory function, because this procedure does not damage the temporal stem. TITC-SAH should be one of the feasible surgical procedures for MTLE.

Abbreviations: SAH: Amygdalohippocampectomy; MTLE: Mesial temporal lobe epilepsy (MTLE); TITC-SAH: Ttrans-inferotemporal cortex SAH; ILAE: International League Against Epilepsy (ILAE); MRI: Magnetic resonance imaging; EEG: Electroencephalography (EEG); FDG-PET: F-fluorodeoxyglucose (FDG)-positron emission tomography; ECoG: Electrocorticography; MEG: Magnetoencephalography; IMZ-SPECT: N-isopropyl-p(I)-iodoamphetamine single photon emission computed tomography; WMS-R: Wechsler Memory Scale-Revised.
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http://dx.doi.org/10.1080/01616412.2016.1275458DOI Listing
March 2017

Induced sensorimotor brain plasticity controls pain in phantom limb patients.

Nat Commun 2016 10 27;7:13209. Epub 2016 Oct 27.

Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.

The cause of pain in a phantom limb after partial or complete deafferentation is an important problem. A popular but increasingly controversial theory is that it results from maladaptive reorganization of the sensorimotor cortex, suggesting that experimental induction of further reorganization should affect the pain, especially if it results in functional restoration. Here we use a brain-machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. BMI training induces significant plasticity in the sensorimotor cortex, manifested as improved discriminability of movement information and enhanced prosthetic control. Contrary to our expectation that functional restoration would reduce pain, the BMI training with the phantom hand intensifies the pain. In contrast, BMI training designed to dissociate the prosthetic and phantom hands actually reduces pain. These results reveal a functional relevance between sensorimotor cortical plasticity and pain, and may provide a novel treatment with BMI neurofeedback.
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http://dx.doi.org/10.1038/ncomms13209DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5095287PMC
October 2016

Electrical stimulation of the parahippocampal gyrus for prediction of posthippocampectomy verbal memory decline.

J Neurosurg 2016 11 15;125(5):1053-1060. Epub 2016 Jan 15.

Departments of 1 Neurosurgery and.

OBJECTIVE Epilepsy surgery is of known benefit for drug-resistant temporal lobe epilepsy (TLE); however, a certain number of patients suffer significant decline in verbal memory after hippocampectomy. To prevent this disabling complication, a reliable test for predicting postoperative memory decline is greatly desired. Therefore, the authors assessed the value of electrical stimulation of the parahippocampal gyrus (PHG) as a provocation test of verbal memory decline after hippocampectomy on the dominant side. METHODS Eleven right-handed, Japanese-speaking patients with medically intractable left TLE participated in the study. Before surgery, they underwent provocative testing via electrical stimulation of the left PHG during a verbal encoding task. Their pre- and posthippocampectomy memory function was evaluated according to the Wechsler Memory Scale-Revised (WMS-R) and/or Mini-Mental State Examination (MMSE) before and 6 months after surgery. The relationship between postsurgical memory decline and results of the provocative test was evaluated. RESULTS Left hippocampectomy was performed in 7 of the 11 patients. In 3 patients with a positive provocative recognition test, verbal memory function, as assessed by the WMS-R, decreased after hippocampectomy, whereas in 4 patients with a negative provocative recognition test, verbal memory function, as assessed by the WMS-R or MMSE, was preserved. CONCLUSIONS Results of the present study suggest that electrical stimulation of the PHG is a reliable provocative test to predict posthippocampectomy verbal memory decline.
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http://dx.doi.org/10.3171/2015.7.JNS15408DOI Listing
November 2016

Language-related cerebral oscillatory changes are influenced equally by genetic and environmental factors.

Neuroimage 2016 Nov 27;142:241-247. Epub 2016 May 27.

Division of Health Sciences, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan.

Twin studies have suggested that there are genetic influences on inter-individual variation in terms of verbal abilities, and candidate genes have been identified by genome-wide association studies. However, the brain activities under genetic influence during linguistic processing remain unclear. In this study, we investigated neuromagnetic activities during a language task in a group of 28 monozygotic (MZ) and 12 dizygotic (DZ) adult twin pairs. We examined the spatio-temporal distribution of the event-related desynchronizations (ERDs) in the low gamma band (25-50Hz) using beamformer analyses and time-frequency analyses. Heritability was evaluated by comparing the respective MZ and DZ correlations. The genetic and environmental contributions were then estimated by structural equation modeling (SEM). We found that the peaks of the low gamma ERDs were localized to the left frontal area. The power of low gamma ERDs in this area exhibited higher similarity between MZ twins than that between DZ twins. SEM estimated the genetic contribution as approximately 50%. In addition, these powers were negatively correlated with the behavioral verbal scores. These results improve our understanding of how genetic and environmental factors influence cerebral activities during linguistic processes.
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http://dx.doi.org/10.1016/j.neuroimage.2016.05.066DOI Listing
November 2016

Detection of Epileptic Seizures Using Phase-Amplitude Coupling in Intracranial Electroencephalography.

Sci Rep 2016 05 5;6:25422. Epub 2016 May 5.

Osaka University Graduate School of Medicine, Department of Neurosurgery, Suita 565-0871, Osaka, Japan.

Seizure detection using intracranial electroencephalography (iEEG) contributes to improved treatment of patients with refractory epilepsy. For that purpose, a feature of iEEG to characterize the ictal state with high specificity and sensitivity is necessary. We evaluated the use of phase-amplitude coupling (PAC) of iEEG signals over a period of 24 h to detect the ictal and interictal states. PAC was estimated by using a synchronisation index (SI) for iEEG signals from seven patients with refractory temporal lobe epilepsy. iEEG signals of the ictal state was characterised by a strong PAC between the phase of β and the amplitude of high γ. Furthermore, using SI values, the ictal state was successfully detected with significantly higher accuracy than by using the amplitude of high γ alone. In conclusion, PAC accurately distinguished the ictal state from the interictal state.
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http://dx.doi.org/10.1038/srep25422DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857088PMC
May 2016

Common neural correlates of real and imagined movements contributing to the performance of brain-machine interfaces.

Sci Rep 2016 Apr 19;6:24663. Epub 2016 Apr 19.

Department of Neurosurgery, Osaka University Medical School, 2-2 Yamadaoka, Osaka, 565-0871, Japan.

The relationship between M1 activity representing motor information in real and imagined movements have not been investigated with high spatiotemporal resolution using non-invasive measurements. We examined the similarities and differences in M1 activity during real and imagined movements. Ten subjects performed or imagined three types of right upper limb movements. To infer the movement type, we used 40 virtual channels in the M1 contralateral to the movement side (cM1) using a beamforming approach. For both real and imagined movements, cM1 activities increased around response onset, after which their intensities were significantly different. Similarly, although decoding accuracies surpassed the chance level in both real and imagined movements, these were significantly different after the onset. Single virtual channel-based analysis showed that decoding accuracy significantly increased around the hand and arm areas during real and imagined movements and that these are spatially correlated. The temporal correlation of decoding accuracy significantly increased around the hand and arm areas, except for the period immediately after response onset. Our results suggest that cM1 is involved in similar neural activities related to the representation of motor information during real and imagined movements, except for presence or absence of sensory-motor integration induced by sensory feedback.
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http://dx.doi.org/10.1038/srep24663DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4835797PMC
April 2016