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

Periodic Reporting for period 2 - NGBMI (Building Next-Generation Brain/Neural-Machine Interfaces For Restoration of Brain Functions)

Reporting period: 2020-05-01 to 2021-10-31

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.
Since the beginning of the project, we have successfully established a closed-loop B/NMI-TES system that allows for identifying the neural substrates of various brain functions (e.g. memory, motor control, visual perception) and their purposeful modulation (WP1/WP2). To achieve this, the following tasks were performed: First, we integrated the B/NMI hardware that combines MEG/EEG with TES into a multi-core real-time machine and established all necessary communication protocols to minimize time delays between recorded brain signals, their interpretation and the resulting sensory feedback or transcranial stimulation. Besides successfully establishing advanced source reconstruction algorithms improving spatial resolution of the B/NMI system, also other approaches to improve spatial resolution were tested. Additionally, a B/NMI environment was programmed that allows for real-time estimation of phase and amplitude and flexible adjustment of B/NMI output signals, e.g. to adjust the TES or feedback signal (WP1). In WP2, we focused on identifying effective stimulation parameters to modulate brain function. Here, characterization of brain stimulation-related artifacts related to innovative TES protocols was of particular importance. Moreover, we assessed the boundaries of different stimulation parameters in regard to artifact contamination of reconstructed signal sources. We successfully developed a novel stimulation artifact source separation algorithm (SASS) that is real-time compatible and allows for the first time to record electroencephalographic (EEG) signals during adaptive, closed-loop electric brain stimulation (cl-tACS). The algorithm was made publicly available along with all raw data. As a next step, the system was validated using a phantom model. This included assessment of pulse stimulation of different durations and lengths as well as overlay of multiple continuous stimulation signals oscillating at different frequencies and intensities. We then compiled a stimulation parameter database that comprises stimulation settings that are safe, tolerable and allow for reliable reconstruction of brain oscillations. After having completed this work, we evaluated the effects of brain-state dependent TES protocols on normal brain physiology. For this, we recruited healthy human volunteers and identified the optimal brain-state dependent stimulation parameters to target conscious awareness of bistable percepts. Based on this approach, we developed a software tool to automatically adapt the TES parameters according to parameter found to be most effective to modulate the targeted brain function. Thereafter, we focused on applying this new methodology to purposefully modulate brain states enhancing specific brain functions, e.g. memory formation. In a first controlled study with healthy volunteers we could successfully exemplify the potential of the NGBMI prototype to enhance memory formation.
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 (cl-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. 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 will systematically test our closed-loop adaptive B/NCI-TES system across different patient groups and expect that this novel approach will improve brain functions across different domains, including attention, memory, emotion regulation and sensorimotor integration.