BRAIN Publication Roundup – August 2019

Noninvasive neuroimaging technology for BCI neuroprosthetics … Genetically-encoded voltage indicator ArcLight maps synaptic activity … Exploring alienation effects of DBS treatment for depression …

Implementing noninvasive neuroimaging technology for BCI neuroprosthetic device control

Brain-computer interfaces (BCI) show promise as therapeutic clinical tools, for example, using signals acquired from intracortical implants to control robotic devices, such as neuroprosthetics. However, invasive recording implants require extensive medical expertise, including high-risk surgical techniques, and are extremely expensive, limiting their use and thus their potential societal impact. While noninvasive counterparts would be ideal in this sense, noninvasive BCI devices have been less effective in capturing and decoding neural signals to the degree needed for functional use. To take on this challenge, Dr. Bin He

 and his team at Carnegie Mellon University have improved both the user learning (“brain” component) and machine learning (“computer” component) of noninvasive  BCI. In this study, the team used electroencephalography (EEG) recordings, with real-time electrical source imaging (ESI)  and new training task strategies to teach individuals to control a cursor and robotic arm in 2D solely with an EEG cap

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. The investigators created two separate training tasks to improve use of a BCI device, asking healthy participants to use motor imagination skills to control a virtual cursor while engaging with a target. One group participated in a traditional BCI discrete trials (DT) task, where they simply had to move a cursor to a static target. The other group participated in a novel, more realistic continuous pursuit (CP) task, using the cursor to chase a continuous, randomly moving target. Analyzing their results, the researchers found that DT training improved performance by 60%. However, the CP task improved performance by 500%, due to its ability to gauge a user’s BCI proficiency and provide more efficient, personalized training. Finally, the researchers tested the practicality of these results by asking participants trained on the CP task to move a robotic arm in 2D instead of a virtual cursor. This task showed a similarly successful performance, indicating a smooth transition from control of a virtual object to a real-world device. Continued improvements in this technology could allow greater access to BCI prosthetics for a wider variety of people with neurological disorders, improving clinical outcomes and restoring once-lost bodily functions without the need for invasive intracortical BCIs.

Using the genetically-encoded voltage indicator ArcLight to map excitatory and inhibitory synaptic activity

Many research experiments currently monitor neuronal activity through calcium imaging, using the concentration of calcium in a cell as an indirect marker for synaptic activity. While this technology has greatly improved scientists’ imaging capabilities, especially through the use of genetically-encoded calcium sensors that are restricted to specific cell types, challenges remain. Calcium imaging provides an indirect and relatively slow measure of synaptic transmission, so it cannot distinguish the nuances of subthreshold or inhibitory changes, ligand-based influx, or spontaneous activity. Dr. Lawrence Cohen

 and colleagues at the Yale School of Medicine and the Korea Institute of Science and Technology have been investigating genetically-encoded voltage indicators (GEVIs) as a more direct approach to map excitatory and inhibitory synaptic connections among populations of neurons. The researchers have expanded research on a previously-developed GEVI, called ArcLight. Applying this imaging technology in the CA1 region of the mouse hippocampus in brain slices, they sought to improve resolution and reduce noise in a region that has known excitatory and inhibitory circuits. To achieve this, the investigators created genetic modifications in mice in order to either encode ArcLight in an experimental group, or encode a calcium sensor, GCaMP6f, in a control group for comparison. Fluorescence changes in ArcLight successfully modeled the characteristic pattern of voltage change caused by action potentials, with initial depolarization followed by hyperpolarization, unlike GCaMP6f. This demonstrated that ArcLight could provide a more robust signal, as it was able to represent both excitatory and inhibitory inputs, in ways consistent with the known circuitry of the region. Following this, the researchers confirmed that these changes were due to voltage changes in the cell, rather than spontaneous synaptic activity. Contrastingly, the activity of GCaMP6f could not represent inhibitory inputs or hyperpolarization and had a much slower flux and response time. Collectively, these results demonstrate a viable approach to mapping neural circuitry, combining structural mapping, which alone does not confirm functional connections, with functional imaging with GEVIs. Future advances in this technology bring hope of creating a full connectome map of the brain, which could improve our understanding of how circuits function, and how their dysfunction impacts human health.

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Exploring the possible alienating effects of DBS treatment for depression

Aligning with the BRAIN Initiative’s vision of advancing novel neurotechnologies and their role in medicine, Deep Brain Stimulation (DBS) has proven effective for certain neurological disorders, and has shown some promise as a treatment for several other neurological and psychiatric conditions. While DBS has shown potential in treating symptoms of depression, previous research has identified possible ethical concerns. For example, some studies have reported patients feeling disconnected from many aspects of their life when considering their states before and after treatment, experiencing alienation and estrangement. As a result, Dr. Gabriel Lazaro-Munoz

 and his team at Baylor College of Medicine aimed to study DBS patients’ experiences of alienation following treatment

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. In their analysis, the researchers made the distinction between alienation from self and alienation from other objects, such as one’s work and personal relationships. They found that many patients were unable to identify with themselves or felt disconnected from targeted aspects of their identity or agency, as a form of self-alienation after DBS treatment for depression. These patients claimed that they “don’t recognize” themselves, felt “like a machine,” or believed their “body is cured” but their “mind is still sick” after DBS treatment, experiencing mental states not characteristic of themselves. Beyond this, some patients failed to identify with previously enjoyed work or relationships, with varying degrees of withdrawal. Finally, the researchers identify patients with depression as a rich source for future evaluations of the relationship between DBS treatment and alienation. Since many symptoms of depression, such as anhedonia, dysphoria, and social withdrawal, can be described as varying degrees of alienation from different objects, patients who have undergone DBS can understand the differences between their feelings of alienation before and after treatment, allowing researchers to distinguish between the specific effects of DBS and general symptoms of depression. Dr. Lazaro-Munoz and his colleagues call for continued research, since alienation can deprive an individual of important improvements in their quality of life, despite potential successful clinical outcomes. While the increased utilization and effectiveness of neurotechnologies like DBS in medicine hold exciting therapeutic potential for millions of people suffering from brain-based disorders, this group has highlighted important neuroethical considerations that can inform future medical research, policy, and practice.