Transcranial Focused Ultrasound to V5 Enhances Human Visual Motion Brain-Computer Interface by Modulating Feature-Based Attention

Paralysis affects roughly 1 in 50 Americans. While there is no cure for the condition, brain-computer interfaces (BCI) can allow users to control a device with their mind, bypassing the paralyzed region. Non-invasive BCIs still have high error rates, which is hypothesized to be reduced with concurrent targeted neuromodulation. This study examines whether transcranial focused ultrasound (tFUS) modulation can improve BCI outcomes, and what the underlying mechanism of action might be through high-density electroencephalography (EEG)-based source imaging (ESI) analyses. V5-targeted tFUS significantly reduced the error for the BCI speller task. ESI analyses showed significantly increased theta activity in the tFUS condition at both V5 and downstream the dorsal visual processing pathway. Correlation analysis indicates that the dorsal processing pathway connection was preserved during tFUS stimulation, whereas extraneous connections were severed. These results suggest that V5-targeted tFUS’ mechanism of action is to raise the brain’s feature-based attention to visual motion.

[1]  T. Hoque,et al.  Mechanisms of theta burst transcranial ultrasound induced plasticity in the human motor cortex , 2023, Brain Stimulation.

[2]  J. Kubanek,et al.  Sustained modulation of primate deep brain circuits with focused ultrasonic waves , 2023, Brain Stimulation.

[3]  Michael A. Pitts,et al.  Human visual consciousness involves large scale cortical and subcortical networks independent of task report and eye movement activity , 2022, Nature Communications.

[4]  J. Millán,et al.  Learning to control a BMI-driven wheelchair for people with severe tetraplegia , 2022, iScience.

[5]  Y. Mitsukura,et al.  Simultaneous multiple-stimulus auditory brain–computer interface with semi-supervised learning and prior probability distribution tuning , 2022, Journal of neural engineering.

[6]  R. Cleveland,et al.  Transcranial ultrasound stimulation to human middle temporal complex improves visual motion detection and modulates electrophysiological responses , 2022, Brain Stimulation.

[7]  R. Andersen,et al.  Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human , 2021, Neuron.

[8]  Joseph G. Makin,et al.  Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria. , 2021, The New England journal of medicine.

[9]  Robert A. Gaunt,et al.  A brain-computer interface that evokes tactile sensations improves robotic arm control , 2021, Science.

[10]  Francis R. Willett,et al.  High-performance brain-to-text communication via handwriting , 2021, Nature.

[11]  Qing-guo Ma,et al.  Dissociable neural oscillatory mechanisms underlying unconscious priming of externally and intentionally initiated inhibition. , 2021, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[12]  Jia Huang,et al.  Transcranial Direct Current Stimulation Enhances Episodic Memory in Healthy Older Adults by Modulating Retrieval-Specific Activation , 2020, Neural plasticity.

[13]  Jingjing Chen,et al.  Doubling the Speed of N200 Speller via Dual-Directional Motion Encoding , 2020, IEEE Transactions on Biomedical Engineering.

[14]  Bin He,et al.  Transcranial Focused Ultrasound Neuromodulation of Voluntary Movement-related Cortical Activity in Humans , 2020, bioRxiv.

[15]  Nader Pouratian,et al.  Dynamic Stimulation of Visual Cortex Produces Form Vision in Sighted and Blind Humans , 2020, Cell.

[16]  L. Chen,et al.  Transcranial focused ultrasound, pulsed at 40 Hz, activates microglia acutely and reduces Aβ load chronically, as demonstrated in vivo , 2020, Brain Stimulation.

[17]  Eric D. Claus,et al.  A comparison of automated and manual co-registration for magnetoencephalography , 2019, bioRxiv.

[18]  Johannes L. Schönberger,et al.  SciPy 1.0: fundamental algorithms for scientific computing in Python , 2019, Nature Methods.

[19]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[20]  Christopher C. Cline,et al.  Noninvasive neuroimaging enhances continuous neural tracking for robotic device control , 2019, Science Robotics.

[21]  Julian R. Brown,et al.  Elimination of peripheral auditory pathway activation does not affect motor responses from ultrasound neuromodulation , 2019, Brain Stimulation.

[22]  J. Gray,et al.  PsychoPy2: Experiments in behavior made easy , 2019, Behavior Research Methods.

[23]  Dejan Draschkow,et al.  Cluster-based permutation tests of MEG/EEG data do not establish significance of effect latency or location. , 2019, Psychophysiology.

[24]  Gopala Krishna Anumanchipalli,et al.  Speech synthesis from neural decoding of spoken sentences , 2018, bioRxiv.

[25]  Bin He,et al.  On the Neuromodulatory Pathways of the In Vivo Brain by Means of Transcranial Focused Ultrasound. , 2018, Current opinion in biomedical engineering.

[26]  Matthew F.S. Rushworth,et al.  Manipulation of Subcortical and Deep Cortical Activity in the Primate Brain Using Transcranial Focused Ultrasound Stimulation , 2018, Neuron.

[27]  Bin He,et al.  Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics. , 2018, Annual review of biomedical engineering.

[28]  Chi Zhang,et al.  A novel P300 BCI speller based on the Triple RSVP paradigm , 2018, Scientific Reports.

[29]  B He,et al.  Combined rTMS and virtual reality brain–computer interface training for motor recovery after stroke , 2018, Journal of neural engineering.

[30]  Laehyun Kim,et al.  Non-invasive transmission of sensorimotor information in humans using an EEG/focused ultrasound brain-to-brain interface , 2017, PloS one.

[31]  Alexandre Gramfort,et al.  Autoreject: Automated artifact rejection for MEG and EEG data , 2016, NeuroImage.

[32]  Bin He,et al.  Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks , 2016, Scientific Reports.

[33]  Heidi G. Fredine,et al.  Prevalence and Causes of Paralysis-United States, 2013. , 2016, American journal of public health.

[34]  Alexandre Gramfort,et al.  Automated rejection and repair of bad trials in MEG/EEG , 2016, 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

[35]  N. Pouratian,et al.  Integrating language models into classifiers for BCI communication: a review , 2016, Journal of neural engineering.

[36]  Nicholas V. Annetta,et al.  Restoring cortical control of functional movement in a human with quadriplegia , 2016, Nature.

[37]  Michela Balconi,et al.  Hemodynamic (fNIRS) and EEG (N200) correlates of emotional inter-species interactions modulated by visual and auditory stimulation , 2016, Scientific Reports.

[38]  Meigen Liu,et al.  Brain-computer interface training combined with transcranial direct current stimulation in patients with chronic severe hemiparesis: Proof of concept study. , 2015, Journal of rehabilitation medicine.

[39]  R. Friedlander,et al.  Transcranial focused ultrasound modulates the activity of primary somatosensory cortex in humans. , 2014, Neurosurgery.

[40]  Niels Birbaumer,et al.  Learned EEG-based brain self-regulation of motor-related oscillations during application of transcranial electric brain stimulation: feasibility and limitations , 2014, Front. Behav. Neurosci..

[41]  Martin Luessi,et al.  MNE software for processing MEG and EEG data , 2014, NeuroImage.

[42]  Martin Luessi,et al.  MEG and EEG data analysis with MNE-Python , 2013, Front. Neuroinform..

[43]  S. Lisanby,et al.  Electric field depth–focality tradeoff in transcranial magnetic stimulation: Simulation comparison of 50 coil designs , 2013, Brain Stimulation.

[44]  Gaby S. Pell,et al.  Commentary on: Deng et al., Electric field depth–focality tradeoff in transcranial magnetic stimulation: Simulation comparison of 50 coil designs , 2013, Brain Stimulation.

[45]  Lyes Bachatene,et al.  Adaptation and Neuronal Network in Visual Cortex , 2012 .

[46]  Slav Petrov,et al.  Syntactic Annotations for the Google Books NGram Corpus , 2012, ACL.

[47]  P. Fries,et al.  Attention Samples Stimuli Rhythmically , 2012, Current Biology.

[48]  Bevil R. Conway,et al.  Toward a Unified Theory of Visual Area V4 , 2012, Neuron.

[49]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[50]  Shangkai Gao,et al.  An online brain–computer interface using non-flashing visual evoked potentials , 2010, Journal of neural engineering.

[51]  Yoshiharu Yamamoto,et al.  Single-trial EEG Power and Phase Dynamics Associated with Voluntary Response Inhibition , 2010, Journal of Cognitive Neuroscience.

[52]  C. Papageorgiou,et al.  Mismatch task conditions and error related ERPs , 2010, Behavioral and Brain Functions.

[53]  Tao Liu,et al.  N200-speller using motion-onset visual response , 2009, Clinical Neurophysiology.

[54]  Helen M. Morgan,et al.  Neural Signatures of Stimulus Features in Visual Working Memory—A Spatiotemporal Approach , 2009, Cerebral cortex.

[55]  E. Callaway,et al.  Parallel processing strategies of the primate visual system , 2009, Nature Reviews Neuroscience.

[56]  Fred L. Drake,et al.  Python 3 Reference Manual , 2009 .

[57]  Xiaorong Gao,et al.  A brain–computer interface using motion-onset visual evoked potential , 2008, Journal of neural engineering.

[58]  J. Polich.  Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[59]  Jonathan R. Folstein,et al.  Influence of cognitive control and mismatch on the N2 component of the ERP: a review. , 2007, Psychophysiology.

[60]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[61]  T. Demiralp,et al.  Comparative analysis of event-related potentials during Go/NoGo and CPT: Decomposition of electrophysiological markers of response inhibition and sustained attention , 2006, Brain Research.

[62]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[63]  John H. R. Maunsell,et al.  Feature-based attention in visual cortex , 2006, Trends in Neurosciences.

[64]  Bernice Porjesz,et al.  Event-Related Oscillations in Offspring of Alcoholics: Neurocognitive Disinhibition as a Risk for Alcoholism , 2006, Biological Psychiatry.

[65]  E Başar,et al.  A new strategy involving multiple cognitive paradigms demonstrates that ERP components are determined by the superposition of oscillatory responses , 2000, Clinical Neurophysiology.

[66]  Erol Başar,et al.  The genesis of human event-related responses explained through the theory of oscillatory neural assemblies , 2000, Neuroscience Letters.

[67]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[68]  Thomas Berlage,et al.  LOCALITE - A Frameless Neuronavigation System for Interventional Magnetic Resonance Imaging Systems , 1999, MICCAI.

[69]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[70]  V. Samar,et al.  Time–Frequency Analysis of Single-Sweep Event-Related Potentials by Means of Fast Wavelet Transform , 1999, Brain and Language.

[71]  J. Pernier,et al.  Oscillatory γ-Band (30–70 Hz) Activity Induced by a Visual Search Task in Humans , 1997, The Journal of Neuroscience.

[72]  Karl J. Friston,et al.  A direct demonstration of functional specialization in human visual cortex , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[73]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[74]  A. Zachor,et al.  Proof of Concept Study , 1981 .

[75]  B. Cragg.  The topography of the afferent projections in the circumstriate visual cortex of the monkey studied by the Nauta method. , 1969, Vision research.

[76]  Dorsal Visual Pathway , 2021, Encyclopedia of Evolutionary Psychological Science.

[77]  H. Woodrow,et al.  : A Review of the , 2018 .

[78]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[79]  Bernice Porjesz,et al.  The role of brain oscillations as functional correlates of cognitive systems: a study of frontal inhibitory control in alcoholism. , 2004, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[80]  A. Milner.  Visual Systems: Dorsal and Ventral , 2001 .

[81]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.