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Jose del R. Millan

The University of Texas at Austin

CNBI Lab

Clinical Neuroprosthetics and Brain Interaction LabCNBI Lab

 

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

Latest News

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  • Stable Electrodes for Long-Term, Wearable Brain-Machine Interface
  • José Del R. Millán Nominated as a Dell Medical School Visionary

Latest Journal Publications

Beraldo, G., Tonin, L., Cesta, A., Menegatti, E., Millán, J.d.R. (2023). Validation of Shared Intelligence Approach for Teleoperating Telepresence Robots Through Inaccurate Interfaces. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham.

Luca Tonin, Serafeim Perdikis, Taylan Deniz Kuzu, Jorge Pardo, Bastien Orset, Kyuhwa Lee, Mirko Aach, Thomas Armin Schildhauer, Ramón Martínez-Olivera, José del R. Millán. Learning to control a BMI-driven wheelchair for people with severe tetraplegia. iScience. 2022. 105418.

Ju-Chun Hsieh, Hussein Alawieh, Yang Li, Fumiaki Iwane, Linran Zhao, […]. A highly stable electrode with low electrode-skin impedance for wearable brain-computer interface. Biosensors and Bioelectronics. 2022, 114756.

JH. Jeong, JH. Cho, YE. Lee, SH. Lee, GH. Shin, YS. Kweon,  J.d.R. Millán, KR. Müller, SW Lee. 2020 International brain-computer interface competition: A review. Front Hum Neurosci. 2022 Jul, 22(16):898300.

F. Dell’Agnola, P.-K. Jao, R. Chavarriaga, J.d.R. Millán, D. Floreano, D. Atienza. (2022). Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones. in IEEE J. of Biomedical and Health Informatics,2022 Sep, 26(9):4751-4762.


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

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