Frontolimbic alpha activity tracks intentional rest BCI control improvement through mindfulness meditation

被引:12
作者
Jiang, Haiteng [1 ]
Stieger, James [1 ,2 ]
Kreitzer, Mary Jo [2 ]
Engel, Stephen [2 ]
He, Bin [1 ]
机构
[1] Carnegie Mellon Univ, Dept Biomed Engn, Pittsburgh, PA 15213 USA
[2] Univ Minnesota, Minneapolis, MN USA
关键词
D O I
10.1038/s41598-021-86215-0
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Brain-computer interfaces (BCIs) are capable of translating human intentions into signals controlling an external device to assist patients with severe neuromuscular disorders. Prior work has demonstrated that participants with mindfulness meditation experience evince improved BCI performance, but the underlying neural mechanisms remain unclear. Here, we conducted a large-scale longitudinal intervention study by training participants in mindfulness-based stress reduction (MBSR; a standardized mind-body awareness training intervention), and investigated whether and how short-term MBSR affected sensorimotor rhythm (SMR)-based BCI performance. We hypothesize that MBSR training improves BCI performance by reducing mind wandering and enhancing self-awareness during the intentional rest BCI control, which would mainly be reflected by modulations of default-mode network and limbic network activity. We found that MBSR training significantly improved BCI performance compared to controls and these behavioral enhancements were accompanied by increased frontolimbic alpha activity (9-15 Hz) and decreased alpha connectivity among limbic network, frontoparietal network, and default-mode network. Furthermore, the modulations of frontolimbic alpha activity were positively correlated with the duration of meditation experience and the extent of BCI performance improvement. Overall, these data suggest that mindfulness allows participant to reach a state where they can modulate frontolimbic alpha power and improve BCI performance for SMR-based BCI control.
引用
收藏
页数:8
相关论文
共 48 条
  • [1] Neurophysiological predictor of SMR-based BCI performance
    Blankertz, Benjamin
    Sannelli, Claudia
    Haider, Sebastian
    Hammer, Eva M.
    Kuebler, Andrea
    Mueller, Klaus-Robert
    Curio, Gabriel
    Dickhaus, Thorsten
    [J]. NEUROIMAGE, 2010, 51 (04) : 1303 - 1309
  • [2] Meditation experience is associated with differences in default mode network activity and connectivity
    Brewer, Judson A.
    Worhunsky, Patrick D.
    Gray, Jeremy R.
    Tang, Yi-Yuan
    Weber, Jochen
    Kober, Hedy
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2011, 108 (50) : 20254 - 20259
  • [3] Meditation states and traits: EEG, ERP, and neuroimaging studies
    Cahn, BR
    Polich, J
    [J]. PSYCHOLOGICAL BULLETIN, 2006, 132 (02) : 180 - 211
  • [4] The impact of mind-body awareness training on the early learning of a brain-computer interface
    Cassady, Kaitlin
    You, Albert
    Doud, Alex
    He, Bin
    [J]. TECHNOLOGY, 2014, 2 (03): : 254 - 260
  • [5] How reliable are MEG resting-state connectivity metrics?
    Colclough, G. L.
    Woolrich, M. W.
    Tewarie, P. K.
    Brookes, M. J.
    Quinn, A. J.
    Smith, S. M.
    [J]. NEUROIMAGE, 2016, 138 : 284 - 293
  • [6] Consistent resting-state networks across healthy subjects
    Damoiseaux, J. S.
    Rombouts, S. A. R. B.
    Barkhof, F.
    Scheltens, P.
    Stam, C. J.
    Smith, S. M.
    Beckmann, C. F.
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2006, 103 (37) : 13848 - 13853
  • [7] EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
    Delorme, A
    Makeig, S
    [J]. JOURNAL OF NEUROSCIENCE METHODS, 2004, 134 (01) : 9 - 21
  • [8] Noninvasive neuroimaging enhances continuous neural tracking for robotic device control
    Edelman, B. J.
    Meng, J.
    Suma, D.
    Zurn, C.
    Nagarajan, E.
    Baxter, B. S.
    Cline, C. C.
    He, B.
    [J]. SCIENCE ROBOTICS, 2019, 4 (31)
  • [9] Spherical splines and average referencing in scalp electroencephalography
    Ferree, Thomas C.
    [J]. BRAIN TOPOGRAPHY, 2006, 19 (1-2) : 43 - 52
  • [10] Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network
    Greicius, Michael D.
    Supekar, Kaustubh
    Menon, Vinod
    Dougherty, Robert F.
    [J]. CEREBRAL CORTEX, 2009, 19 (01) : 72 - 78