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Research ArticleMethods/New Tools, Novel Tools and Methods

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

Ryohei Fukuma, Takufumi Yanagisawa, Masataka Tanaka, Fumiaki Yoshida, Koichi Hosomi, Satoru Oshino, Naoki Tani and Haruhiko Kishima
eNeuro 17 December 2018, 5 (6) ENEURO.0246-18.2018; https://doi.org/10.1523/ENEURO.0246-18.2018
Ryohei Fukuma
1Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
2Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Kyoto, 619-0288, Japan
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Takufumi Yanagisawa
1Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
2Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Kyoto, 619-0288, Japan
3Institute for Advanced Co-creation Studies, Osaka University, Suita, Osaka, 565-0871, Japan
4Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka, 565-0871, Japan
5 JST PRESTO, Suita, Osaka, 565-0871, Japan
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Masataka Tanaka
1Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
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Fumiaki Yoshida
3Institute for Advanced Co-creation Studies, Osaka University, Suita, Osaka, 565-0871, Japan
5 JST PRESTO, Suita, Osaka, 565-0871, Japan
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Koichi Hosomi
1Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
6Department of Neuromodulation and Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
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Satoru Oshino
1Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
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Naoki Tani
1Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
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Haruhiko Kishima
1Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka, 565-0871, Japan
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Article Figures & Data

Figures

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  • Figure 1.
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    Figure 1.

    Feedback system overview. Signals from the DBS electrodes were acquired in real time. The radius of the black circle on the computer screen was controlled based on the β-band power of the acquired bipolar signals from adjacent contacts that were selected in the pre-feedback session.

  • Figure 2.
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    Figure 2.

    Representative DBS signals. DBS signals of Patient 2 during pre-feedback session, and at the beginning and the ending of feedback session were shown. For higher readability, the signals were bandpass filtered between 4 and 80 Hz.

  • Figure 3.
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    Figure 3.

    Power spectra during pre- and post-feedback sessions. Blue and red lines denote the power spectrum of DBS signals during resting state before and after the feedback training, respectively. Shaded areas represent the estimated 95% confidence interval of the power spectrum among 1 s time windows. The horizontal line above the data curves shows the range of β-band used for feedback training. Frequency is shown on a log scale.

  • Figure 4.
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    Figure 4.

    The difference in β-band power between the pre- and post-feedback sessions. The circular markers and red lines denote the down-training condition, whereas the square markers and blue lines indicate the up-training condition.

  • Figure 5.
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    Figure 5.

    Comparison of powers between the pre- and post-feedback sessions. In common frequency bands other than β-band, difference of powers between two sessions was shown. The circular markers and red lines denote the down-training condition, whereas the square markers and blue lines indicate the up-training condition.

  • Figure 6.
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    Figure 6.

    Power spectra of EMG during the pre- and post-feedback sessions. Solid and dashed lines denote the power spectra during resting state before and after the feedback training, respectively. Frequency is shown on a log scale. Each plot shows the patient ID in the title and the difference of β-band power at the selected DBS contacts in the post-feedback session compared to the pre-feedback session. The plots are ordered from left to right, then top panels to bottom panels, so that the differences of β-band power are sorted in ascending order.

Tables

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    Table 1

    Patients and feedback conditions

    Patient IDAge, y (sex)Duration of DBS, yUPDRS-III (On)Feedback condition
    ContactsGroup
    153 (M)1127Lt 1–2Down-training
    270 (M)429Lt 1–2Down-training
    368 (F)67Lt 1–2Down-training
    452 (F)520Lt 0–1Down-training
    562 (F)426Lt 0–1Up-training
    666 (M)927Rt 1–2Up-training
    767 (F)982Rt 1–2Up-training
    866 (F)431Rt 1–2Up-training
    • UPDRS-III, Unified Parkinson’s Disease Rating Scale Part III; Rt, right; Lt, left.

    • View popup
    Table 2.

    DBS parameter settings

    Patient IDContactsFrequency, HzPulse width, μsVoltage
    1Lt 1 − 2− C+130603.4
    Rt 2 − 3− C+603.5
    2Lt 2 − 3− C+130903.0
    Rt 2 − 3− C+902.4
    3Lt 2− C+60603.9
    Rt 0− C+903.8
    4Lt 0 − 1− C+60903.2
    Rt 0− C+903.2
    5Lt 1− C+60904.1
    Rt 1− C+904.1
    6Lt 2 − 3− C+125602.7
    Rt 2− C+902.6
    7Lt 2− C+60903.9
    Rt 2− C+904.0
    8Lt 2 − 3− C+140603.2
    Rt 1 − 2− 3− C+130902.8
    • Rt, Right; Lt, left.

    • View popup
    Table 3.

    Statistical table

    Data structureType of testStatistics
    aNormal distributionOne-tailed unpaired t testPatient 1: t(598) = 3.286, p < 0.001
    Patient 2: t(598) = 2.762, p = 0.003
    Patient 3: t(598) = 3.013, p = 0.001
    Patient 4: t(598) = 4.644, p < 0.001
    Patient 5: t(598) = −1.241, p = 0.108
    Patient 6: t(338) = −3.852, p < 0.001
    Patient 7: t(598) = 0.743, p = 0.771
    Patient 8: t(598) = −1.763, p = 0.039
    bNo assumptionOne-tailed permutation testp = 0.009
    cNo assumptionTwo-tailed permutation testp = 0.627
    dApproximate normal distributionTwo-tailed unpaired t-testt(14) = 0.749, p = 0.466
    • View popup
    Table 4.

    Patients’ reports about feedback training

    Patient IDPatients’ comments after training
    1I tried to make the circle smaller by narrowing my eyes.
    2(This patient did not report.)
    3Doing something hard, but not to the extent of moving my body, made the circle smaller. I think the circle became small.
    4I was expecting the end of the task. I could not find any control strategy.
    5It seemed that narrowing my eyes made the circle smaller.
    I think I performed fairly well.
    6Movements of right limbs seemed to make the circle smaller.
    However, neither moving my eyes nor focusing on an emotion such as happiness or sadness changed the size of the circle.
    7I saw two fixation points.
    Attempting to merge the points into one made the circle smaller.
    8I have no idea how I could make the circle smaller; but I think the circle became small. I was expecting the end of the task.
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Real-Time Neurofeedback to Modulate β-Band Power in the Subthalamic Nucleus in Parkinson’s Disease Patients
Ryohei Fukuma, Takufumi Yanagisawa, Masataka Tanaka, Fumiaki Yoshida, Koichi Hosomi, Satoru Oshino, Naoki Tani, Haruhiko Kishima
eNeuro 17 December 2018, 5 (6) ENEURO.0246-18.2018; DOI: 10.1523/ENEURO.0246-18.2018

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Real-Time Neurofeedback to Modulate β-Band Power in the Subthalamic Nucleus in Parkinson’s Disease Patients
Ryohei Fukuma, Takufumi Yanagisawa, Masataka Tanaka, Fumiaki Yoshida, Koichi Hosomi, Satoru Oshino, Naoki Tani, Haruhiko Kishima
eNeuro 17 December 2018, 5 (6) ENEURO.0246-18.2018; DOI: 10.1523/ENEURO.0246-18.2018
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Keywords

  • beta power
  • deep brain stimulation
  • EEG
  • neurofeedback
  • Parkinson disease
  • voluntary control

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