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Clinical Neuroprosthetics and Brain Interaction LabCNBI Lab

 

 

Closed-Loop Brain Stimulation as a Potential Intervention for Cognitive Decline

Project Summary

Cognitive decline in conditions such as Alzheimer disease and related disorders, affects a significant and increasing proportion of our society as a consequence of natural longevity and pathological neurodegenerative processes. Since the prevailing paradigm has viewed cognitive decline as inevitable and irreversible, current interventions are focused on slowing the rate of decline and have been met with limited success. Thus, there is a critical need for disruptive approaches in the quest for effective prevention and treatment of cognitive decline. We propose to develop a novel intervention based on brain-computer interface (BCI) technology and closed-loop brain stimulation, which is safe and non-invasive. Based on previous literature and our own work, our overarching hypothesis is that non-invasive, BCI-driven closed-loop theta-burst stimulation (TBS, a particular modality of transcranial magnetic stimulation) targeting cognitive control areas in the brain will be an effective strategy for improving daily-task performance in older adults (primary outcome), which may also yield sustainable memory enhancement (secondary outcome). This goal will be achieved through three specific aims:

  1. Design a principle for the BCI to trigger TBS over the dorsolateral prefrontal cortex, an area associated with cognitive control. The BCI decodes in real time the presence or absence of contingent negative variation (CNV) in subjects’ electroencephalogram (EEG) while they play a videogame requiring cognitive control abilities (attending and responding to some events while ignoring others). Our working hypothesis is that contingent TBS upon false outputs of the BCI will enhance CNV modulation. We will validate our approach (videogame, BCI and TBS) in young adults (N=20).
  2.  Assess the feasibility, acceptability, and potential therapeutic benefit of our BCI-driven closed-loop TBS intervention in cognitively normal older adults (N=30). Our working hypothesis is that the closed loop TBS condition will yield the greatest CNV modulation and exhibit a trend in improvements in cognitive control as compared to standard TBS and sham TBS. We also postulate that (i) improvements induced by closed-loop TBS will sustain, and (ii) ~90% of participants will complete all 12 sessions and ~85% will rate the intervention as acceptable. Subjects (10 per group: closed-loop TBS, standard TBS and sham TBS), will undergo neuropsychological tests before and after completion of 12 BCI-TBS sessions (3 days/week), and 2 months post-intervention.
  3.  Evaluate the therapeutical benefit of the BCI-driven closed-loop TBS intervention to enhance cognitive control in older
    adults with mild cognitive impairment (N=40). Patients will be randomly assigned to one of 2 groups, closed-loop TBS and the best of either standard or sham TBS in aim 2. Intervention will be similar to aim 2. Our working hypothesis is that the closed loop TBS condition will foster sustained improvements in cognitive control.

Key Personnel:

Jose del R. Millan: lead investigator;  Robin Hilsabeck, PhD: lead investigator,

 

Texas Alzheimer’s Research and Care Consortium funded project

Primary Sidebar

Latest News

  • A Jolt of Innovation for Brain-Computer Interfaces
  • Satyam Kumar Successfully Defended his PhD Thesis. Congratulations!!
  • Hussein Alawieh Successfully Defended his PhD Thesis. Congratulations!!
  • Minsu Zhang recipient of the 2024 KSEA-KUSCO Graduate Scholarship
  • Nature Medicine: The Future of Brain–computer Interfaces in Medicine

Latest Publications

Alawieh H, Liu D, Madera J, Kumar S, Racz FS, Fey AM, Del R Millán J. Electrical Spinal Cord Stimulation Promotes Focal Sensorimotor Activation that Accelerates Brain-computer Interface Skill Learning. Proc Natl Acad Sci U S A. 2025 Jun 17;122(24):e2418920122.

Racz FS, Kumar S, Kaposzta Z, Alawieh H, Liu DH, Liu R, Czoch A, Mukli P, Millán JDR. Combining Detrended Cross-Correlation Analysis with Riemannian Geometry-based Classification for Improved Brain-computer Interface Performance. Front Neurosci. 2024 Mar 14;18:1271831.

Kumar S, Alawieh H, Racz FS, Fakhreddine R, Millán JDR. Transfer Learning Promotes Acquisition of Individual BCI Skills.PNAS Nexus, Volume 3, Issue 2, February 2024, page076.

Iwane F, Billard A, Millán JDR. Inferring Individual Evaluation Criteria for Reaching Trajectories with Obstacle Avoidance from EEG Signals. Sci Rep. 2023 Nov 17;13(1):20163.

S. Kumar S. Kumar, D. H. Liu, F. S. Racz, M. Retana, S. Sharma, F. Iwane, B. P. Murphy, R. O’Keeffe, S. F. Atashzar, F. Alambeigi, J. del R. Millán. CogniDaVinci: Towards Estimating Mental Workload Modulated by Visual Delays During Telerobotic Surgery – An EEG-based Analysis. 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 6789-6794.


For a complete list of publications go to our Publications page!

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