Integrating Cortical Sensorimotor Representations Across Spatial Scales and Task Contexts

Our understanding of how brains function is stratified between two very different scales: mesoscale (what function a given cortical area performs), measured with tools like fMRI; and microscale (what a given neuron does), measured with implanted microelectrodes. While extensive research has been done to characterize brain activity at both of these spatial scales, describing relationships between these two domains has proven difficult. Identifying ways to integrate findings between these scales is valuable for both research and clinical applications, but is particularly important for intracortical brain-computer interfaces (BCIs), which aim to restore motor function after paralysis or amputation. In humans, the brain is much larger than the available microelectrode arrays, so determining where to place the arrays is a critical aspect of ensuring optimal performance. BCIs preferentially target primary motor and somatosensory cortices, due to their direct relationship to motor control and critical role in skilled and dexterous movements. However, despite these areas displaying a relatively ordered spatial organization, it is difficult to accurately predict the behavior of neurons recorded from a given area for several reasons. Mesoscale activity is overlapping, with activity relating to multiple different movements observed in a single area. Additionally, neurons have flexible behavior, displaying different “tuning” to similar behavior under different contexts. Here I present my research integrating neuroimaging-based cortical mapping with directly-recorded neural activity in human sensorimotor cortex. First, I examine how the large-scale organization of sensorimotor representations measured with fMRI is affected by contextual sensory information. I then examine how spatially separate neural populations recorded with intracortical microelectrode arrays encode different types of movement. Finally, I examine whether how population encoding changes to reflect contextual sensory information using the same task as in the fMRI study. Together, these results provide a foundation for reconciling neural activity across spatial scales and task contexts, and will inform the design and placement of more capable BCI systems

Using metabolic fingerprinting of plants for evaluating nitrogen deposition impacts on the landscape level

Nitrogen emissions and atmospheric deposition are globally significant with the potential to alter ecosystem nutrient balance, provoking changes in vegetation composition. Shifts in plant biochemistry are good indicators of nitrogen pollution and have been used to monitor vegetation health. Fourier transform-infrared (FT-IR) spectroscopy has previously been shown to be a rapid and relatively inexpensive method for evaluating leaf biochemistry. In the present study, FT-IR spectra were collected from Galium saxatile samples taken from sites across the United Kingdom. Spectral changes in the tissue samples were correlated with a gradient of N deposition using partial least squares regression analysis. We show that FT-IR analysis of G. saxatile leaf tissue is an effective way to evaluate nitrogen deposition across the entire UK landscape

Metabolic fingerprinting for bio-indication of nitrogen responses in Calluna vulgaris heath communities

Increased atmospheric deposition of nitrogen (N) over the last 50 years is known to have led to deleterious effects on the health of Calluna vulgaris heathland, with increased proliferation of grasses and loss of species diversity. However, currently it is difficult to attribute damage specifically to N deposition rather than other drivers of change such as inappropriate management. Metabolic fingerprinting using FT-IR offers a rapid, cost-effective and “holistic” means for quantifying foliar biochemistry responses specifically to N deposition. To test the potential of this approach we used a long term lowland heath N addition study in Chesire, England. FT-IR spectra of treated C. vulgaris shoot material showed that responses were detectable above 20 kg N ha−1 year−1. Differentiation was also evident in C. vulgaris metabolic fingerprints due to additional watering. We have shown that FT-IR is able to identify biochemical variations in C. vulgaris related to increases in received N and water. This technique therefore provides a sensitive measure of biochemical change in response to N addition, and allows development towards predictive modelling of N deposition at the landscape level