Building Next-Generation Brain/Neural-Machine Interfaces For Restoration of Brain Functions

Project Information

NGBMI

Grant agreement ID: 759370

DOI

10.3030/759370

Project closed

EC signature date 25 April 2018

Start date 1 November 2018

End date 31 October 2024

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 498 125,00

EU contribution

€ 1 498 125,00

Coordinated by

CHARITE - UNIVERSITAETSMEDIZIN BERLIN
Germany

Periodic Reporting for period 4 - NGBMI (Building Next-Generation Brain/Neural-Machine Interfaces For Restoration of Brain Functions)

Reporting period: 2023-05-01 to 2024-10-31

Summary of the context and overall objectives of the project

Today, five out of ten diseases worldwide resulting in long-term disability are related to the central nervous system. Due to the immense complexity and inter-individual variability of the human mind and brain there are still no effective and side effect free treatment options for many serious neuropsychiatric disorders, such as major depression, dementia or schizophrenia. Up to now, many of these disorders are still defined by their clinical picture and not by their exact underlying physiological mechanism.
Recent advancements in sensor technology and computational capacities resulted in the development of brain/neural-machine interfaces (B/NMIs) that translate electric, magnetic or metabolic brain activity into control signals of external devices, robots or machines. Such systems could greatly contribute to improved quality of life, e.g. in severe paralysis when used for restoration of movement or communication. Importantly, it was shown that repeated use of such motor B/NMI can trigger neuroplasticity facilitating motor recovery, even after decades of chronic paralysis, e.g. due to stroke or spinal cord injury. In contrast to chronic paralysis, however, many neuropsychiatric disorders involve other brain functions, e.g. affective and cognitive control or working memory, for which the underlying neural substrates are broadly unknown and thus cannot be targeted effectively with a B/NMI so far. This limits applicability of B/NMI systems in the treatment of neuropsychiatric disorders. A possible solution to identify these neural substrates is to apply brain stimulation, e.g. transcranial electric or magnetic stimulation (TES/TMS), while assessing changes in brain function and physiology. However, such paradigm was regarded unfeasible due to stimulation artifacts. The NGBMI project will overcome these limitations and establish a neurotechnological system that will allow for identifying the individual substrates of various brain functions as well as targeting these substrates with personalized adaptive, closed-loop brain stimulation and feedback.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Since the beginning of the project, we have established a closed-loop B/NMI-TES system to identify and modulate the neural substrates of various brain functions (e.g. memory, motor control, visual perception) (WP1/WP2). To achieve this, the following tasks were performed: First, we integrated the B/NMI hardware combining MEG/EEG with TES into a multi-core real-time machine and established necessary communication protocols to minimize time delays between recorded brain signals, interpretation, and resulting sensory feedback or transcranial stimulation. Besides advanced source reconstruction algorithms improving spatial resolution of the B/NMI system, other approaches were tested. A B/NMI environment was also programmed to allow real-time estimation of phase and amplitude and flexible adjustment of B/NMI output signals, e.g. to adjust TES or feedback signal (WP1). In WP2, we focused on identifying effective stimulation parameters to modulate brain function, characterizing brain stimulation-related artifacts in innovative TES protocols. Moreover, we assessed the boundaries of different stimulation parameters regarding artifact contamination of reconstructed signal sources. We developed a novel stimulation artifact source separation algorithm (SASS) that is real-time compatible and allows recording EEG signals during adaptive, closed-loop electric brain stimulation (CLAM-tACS). The algorithm was made publicly available along with raw data. The system was validated using a phantom model, assessing pulse stimulation durations, lengths, and overlay of multiple continuous stimulation signals oscillating at different frequencies and intensities. We then compiled a stimulation parameter database of safe, tolerable settings that allow reliable reconstruction of brain oscillations. After completing this work, we evaluated the effects of brain-state dependent TES protocols on normal brain physiology. For this, we recruited healthy volunteers and identified the optimal brain-state dependent stimulation parameters to target conscious awareness of bistable percepts. Based on this, we developed a software tool to automatically adapt TES parameters to modulate the targeted brain function. We then applied this methodology to modulate brain states enhancing specific brain functions, e.g. memory formation. In a controlled study with healthy volunteers, we demonstrated the potential of the NGBMI prototype to enhance memory formation (Haslacher et al. 2024, Brain Stimulation). This paradigm was then successfully translated into various patient populations targeting different domains, e.g. the motor domain in stroke or the cognitive/affective domain in mild cognitive disorder (MCI) and alcohol use disorder (AUD). Importantly, the artifact suppression approach was successfully applied in combination with optically pumped magnetometers (OPM) in targeting gamma band responses in the auditory domain.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The NGBMI project could achieve a number of breakthroughs that extend the state-of-the-art across various technological and scientific areas. E.g. we introduced a novel approach to successfully suppress electric brain stimulation-related artifacts during in vivo assessment of brain oscillations. This marks an important milestone for the implementation of closed-loop adaptive transcranial alternating current stimulation (CLAM-tACS), because existing solutions were not real-time compatible or they resulted in incomplete artifact suppression at the target frequency. For the first time, we have successfully applied this new approach to purposefully target key brain functions, such as visual perception or memory formation. To increase spatial resolution of transcranial brain stimulation, we invented a novel stimulation device that uses temporal interference of magnetic fields (TIMS). Existing methods, e.g. tACS, are limited in their spatial resolution because electric currents spread through large areas of the skull and current distribution depends on the (unknown and varying) biophysical properties of different tissues. This makes valid modeling of the electric fields very challenging. In contrast, magnetic fields can pass biological tissues (including bones) undistorted. We expect that this new stimulator device will substantially advance focal targeting of brain activity increasing effectiveness and reliability to transcranial stimulation. Another important progress beyond the state of the art relates to the versatility of brain/neural-machine interface (B/NMI) control. While versatility of noninvasive B/NMI control was rather limited, e.g. to opening and closing a prosthetic device or exoskeleton, we have implemented novel control paradigms that allow for whole-arm exoskeleton control, e.g. to perform activities of daily living (ADL) despite full arm paralysis, e.g. reaching out for a glass and drinking. During the second half of the project, we have systematically tested our closed-loop adaptive B/NCI-TES system across different patient groups and showed that this novel approach can improve brain functions across different domains, including attention, memory and sensorimotor integration.

A brain-controlled exoskeleton attached to the hand of a patient with complete finger paralysis