Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains

“Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains”

“Published online 2013 Apr 3.”

“Abstract
Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer’s intention to stimulate a rat’s brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer’s intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.

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Introduction
Brain-to-computer interface (BCI) refers to the hardware and software environment that detects and translates brain activity to control computers or stored-program architecture devices without involving muscles or the peripheral nervous system [1]. To characterize a specific function of the brain, invasive means such as implantable cortical microelectrode arrays that directly detect the electrical field potentials/spikes from the somatomotor areas have been used, for example, to provide BCI control options for quadriplegic patients [2]. Nicolelis and colleagues explored the method of obtaining the neural electrical signals directly from the motor cortex of primates using an implanted cortical electrode array, and decoded the signals obtained during complex motor intentions, into the appropriate machine control [3]. Velliste et al. used intracortical recording schemes in monkeys to convert motor cortex neural activity into a correlated mechanized prosthetic arm movement used for self-feeding [4]. Other than these BCI methods which require a surgery to implant electrodes to the brain surface, non-invasive functional imaging modalities such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) have also been adopted in implementation of BCI. For example, non-invasive EEG-based BCI, with the combinatory inclusion of navigation algorithms, was successfully implemented to allow for thought processes to control the direction of a wheelchair movement [5]. Yoo and colleagues used fMRI, with real-time processing capabilities, to provide computer cursor directional commands based on spatial patterns of cortical activity that were linked to predetermined thought processes [6]. This ability was later expanded to the generation of computer keyboard commands via combining spatial activation patterns with different temporal hemodynamic patterns associated with the task onset delays controlled by human subjects [7], [8]. Magneto-encephalography (MEG), near infrared spectroscopy (NIRS), and functional trascranial doppler sonography (fTCD) have also emerged recently as potential candidates for non-invasive BCI (reviewed in [9]).

It is notable that the flow of information used in the current implementation of BCI is unidirectional, in the sense that the control commands originating from the brain are directed to operate a computer. To establish the bidirectional interface between the brain and the computer, the creation of a computer-to-brain interface, namely CBI, was sought after, whereby the computer-generated commands can be used to modulate the function of the specific brain area via its direct stimulation/suppression, all without engaging the peripheral nervous system and sensory pathways. The bidirectional interface between the brain and the computer would ultimately lead to the development of a ‘Brain-to-Brain Interface’ (BBI), in which neural activities from individual brains are linked and mediated by computers.

Modern brain stimulation techniques, which typically utilize a computer/electrical circuits for operation, can potentially be used for CBI application under the presence of linkage to a computer. For example, direct electrical stimulation of the motor cortex, achieved by surgically-implanted electrodes, was used to elicit animal limb motion necessary for navigating through complex spatial environments. Deep brain stimulation (DBS) or epicortical stimulation can also be adopted for human application, but would require invasive surgical procedures. Transcranial magnetic stimulation (TMS) confers the non-invasive means of neuromodulation; however, lacks penetration depth and spatial specificity due to its electromagnetically inductive nature.

Transcranial sonication of focused ultrasound (FUS) has emerged as a new breed of non-invasive region-specific brain stimulation technique. Since the seminal feasibility study of Fry et al. back in the late 1950s [12], the neuromodulatory potentials of ultrasound have been demonstrated in ex vivo tissues [13] and more recently in rodent models . Transcranial FUS techniques deliver highly focused acoustic energy to the localized deep regions of the brain, and have been used in thermal ablation of brain tumors and functional neurosurgery. When given in pulsed mode at low acoustic energy, far below the thermal or cavitation threshold which may damage the underlying tissue, FUS is capable of modulating the excitability of sonicated tissues. This ability has been demonstrated in excitation/suppression of rabbit motor/visual cortices. Furthermore, FUS has proven itself as a versatile means of non-invasive neuromodulation in the suppression of chemically-induced epilepsy [18] and in altering the concentrations of extracellular neurotransmitters. Most of the current FUS devices are controlled by a computer, making them favorable candidates for the CBI.

With realization of FUS-based non-invasive neuromodulation as a CBI, we were motivated to implement a novel concept of BBI by combining the EEG-based BCI and FUS-based CBI. Using a processing computer as an interface between the two, the implementation is straightforward. A thought-process (intention to stimulate a rat brain) originating from a human participant is detected in forms of EEG-based steady-state visual evoked potential (SSVEP). Upon detection, a computer triggers the operation of the FUS that stimulates the motor cortex of a rat (Sprague-Dawley), which elicits the subsequent tail movement.

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Materials and Methods
Overview
This study was conducted under approval by the Partners Human Research Committee (Institutional Review Board of Brigham and Women’s Hospital, Partners Healthcare Systems) for the study involving humans, and by the Harvard Medical Area Standing Committee on Animals for the experimental portion involving animals. All experiments were conducted within the premise of Brigham and Women’s Hospital, Harvard Medical School. All the participants provided their written informed consent to participate in the study according to the approved procedures set forth by the IRB. The overall set-up of the BBI configuration is depicted in Figure 1, which consists of BCI and CBI segments. In the BCI segment, EEG signals obtained from the operator via single-montage surface electrodes are processed by a computer. The synchronization of the EEG signal fluctuation with respect to the external visual stimuli occurs only when the individual actively gazes at the stimulus source (thus, generating the SSVEP). This synchronization manifests itself in the form of increased signal amplitude in the EEG bandwidth corresponding to the specific visual stimulation frequency.”

SSVEP is a widely-accepted detection mechanism used in the context of BCI. SSVEP signals are generated only when the participant intentionally gazes at the flickering light source, and the user’s act of actively focusing on the flicker source is indispensible to the actuation of the BCI system. Due to robust responses of SSVEP across test subjects, along with high performance accuracy after only a short training period [24] or even no prior experience [23], SSVEP is considered to provide excellent alternatives to other EEG-base BCI approaches, for example, P300 component or event-related desynchronization [25]. Once detected by a computer algorithm, the SSVEP subsequently triggers the operation of the FUS-based, non-invasive brain stimulation device that stimulates the motor areas of the rat’s brain. The associated tail movement is recorded for further data analysis.”

Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3616031/

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