Bidirectional Brain-Machine Interfaces for Modulating Stimulation and Neural Plasticity

In prosthetics, tactile feedback can let us feel how we interact with the environment. Without this, it is extremely difficult to perform a motor task with fine control. The same idea can be applied in the brain-machine interface (BMI), which is an interface that directly connects external devices such as prosthetic limbs to the brain. Bidirectional BMI can deliver a stimulation to the brain as a sensory feedback, which can improve the performance of motor tasks. Such a bidirectional BMI can also serve a different role, if the stimulation encodes different information: if it encodes neural activity from another brain area, for example, then bidirectional BMI can provide a bypass for a damaged neural circuit. This may also affect the neural connectivity, strengthening or weakening the underlying neural connections. In this thesis, we present experiments that explore such applications of bidirectional BMI. First, we describe an experiment for characterizing neural connectivity between different brain areas. We found neural connectivity between supramarginal gyrus (SMG) and PMv (ventral premotor area), and also between anterior intraparietal (AIP) and Brodmann’s area 5 (BA5), characterized by field-field, spike-field, and partial spike-field coherence. Through partial spike-field coherence, we also revealed that the spikes in PMv may drive the activity in SMG, which is obscured in ordinary spike-field coherence. Next, we provide evidence of changes in neural connectivity caused by stimulation in S1. With spike-triggered stimulation, which delivers stimulation in S1 in response to spikes recorded in a selected channel in SMG, we could significantly increase the correlation between SMG and S1, measured by the spike time tilling coefficient (STTC) to avoid dependencies of the correlation on firing rates. Furthermore, we found that not only spike-triggered stimulations, but also random stimulations on multiple channels in S1, can vary partial spike-field coherence in theta and alpha bands within S1; such changes mostly occurred in channel pairs with zero phase difference in partial spike-field coherence. Finally, we demonstrate the possibility of volitional control on stimulation pattern in bidirectional BMI. It is shown that the participants could not only increase or decrease a single-channel firing rate, but also hold the firing rate in a given range, demonstrating a fine control over firing rate. These findings would begin to establish a framework for closed-loop modulation of neural activity with bidirectional BMI and could be used to develop new treatments for neurological damage, such as to promote plasticity in or bridge brain areas affected by stroke.</p

Luciferase-Rose Bengal Conjugates for Single Oxygen Generation by Bioluminescence Resonant Energy Transfer

Conjugates of Rose Bengal and Renilla luciferase generated singlet oxygen upon binding with coelenterazine via bioluminescence resonance energy transfer (BRET). Since the applications of conventional PDT have been limited to superficial lesions due to the limited light penetration in tissue, BRET activated PDT which does not require external light illumination may overcome the limitations of conventional PDT.115Ysciescopu

Proprioceptive and cutaneous sensations in humans elicited by intracortical microstimulation

Pioneering work with nonhuman primates and recent human studies established intracortical microstimulation (ICMS) in primary somatosensory cortex (S1) as a method of inducing discriminable artificial sensation. However, these artificial sensations do not yet provide the breadth of cutaneous and proprioceptive percepts available through natural stimulation. In a tetraplegic human with two microelectrode arrays implanted in S1, we report replicable elicitations of sensations in both the cutaneous and proprioceptive modalities localized to the contralateral arm, dependent on both amplitude and frequency of stimulation. Furthermore, we found a subset of electrodes that exhibited multimodal properties, and that proprioceptive percepts on these electrodes were associated with higher amplitudes, irrespective of the frequency. These novel results demonstrate the ability to provide naturalistic percepts through ICMS that can more closely mimic the body’s natural physiological capabilities. Furthermore, delivering both cutaneous and proprioceptive sensations through artificial somatosensory feedback could improve performance and embodiment in brain-machine interfaces

Proprioceptive and cutaneous sensations in humans elicited by intracortical microstimulation

Pioneering work with nonhuman primates and recent human studies established intracortical microstimulation (ICMS) in primary somatosensory cortex (S1) as a method of inducing discriminable artificial sensation. However, these artificial sensations do not yet provide the breadth of cutaneous and proprioceptive percepts available through natural stimulation. In a tetraplegic human with two microelectrode arrays implanted in S1, we report replicable elicitations of sensations in both the cutaneous and proprioceptive modalities localized to the contralateral arm, dependent on both amplitude and frequency of stimulation. Furthermore, we found a subset of electrodes that exhibited multimodal properties, and that proprioceptive percepts on these electrodes were associated with higher amplitudes, irrespective of the frequency. These novel results demonstrate the ability to provide naturalistic percepts through ICMS that can more closely mimic the body’s natural physiological capabilities. Furthermore, delivering both cutaneous and proprioceptive sensations through artificial somatosensory feedback could improve performance and embodiment in brain-machine interfaces

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages