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Drug Delivery Across the Blood-Brain Barrier using Therapeutic Acoustic Wavelets

Neurological diseases are among the most difficult diseases to treat, because drugs cannot enter the brain, because cerebral capillaries are lined by a blood-brain barrier (BBB).
We are developing a non-invasive and localised method of delivering drugs across the BBB. Using ultrasound and bubbles, we have been able to non-invasively and locally open the BBB for a short duration (less than 10 minutes) (Morse SM, et al., Radiology 2019), which allows drugs into the brain tissue.
The purpose of this project is to evaluate the effectiveness and safety of our acoustic wavelet drug delivery technology. For example, the student may explore the delivery of new and exciting drugs, such as nanobodies, antibodies, nanoparticles, etc; or may study the safety of the procedure, such as whether microglial cells and astrocytes have been activated.
This is amultidisciplinary project that spans physical acoustics, bubble physics, BBB structure and function, and neuroscience. Dr. James Choi will guide the use and development of the acoustic technology and the evaluation of drug delivery while Dr. Magdalena Sastre will guide the analysis of safety.

Ultrafast phenotyping of optically cleared human cortical tissue grafts with lightsheet microscopy and automated image analysis

Model organisms are commonly used for studies of brain physiology and pathogenesis, but the extent to which the principles uncovered can be translated to humans remains unclear. Recently, we established a new approach to study human cortical synaptic networks using transplanted donor-derived cells and intravital longitudinal imaging and discovered new cellular and synaptic phenotypes in Down syndrome (Real et al 2018).
This project aims at developing a new method to characterise the post-mortem cellular content of human cortical tissue grafts. Currently, this is a time-consuming and low-throughput step as it requires slicing the fixed brain. Only a small portion of the graft can be studied by immunohistochemistry with each marker, a process which often takes several weeks to months. Therefore, the specific aims of this project are to: 

1) Apply ScaleS (Hama et al 2015), a reagent which renders brain tissue transparent within hours, to optically clear human cortical tissue grafts;
2) Test the feasibility of using multiple markers on the same tissue;
3) Develop an automated pipeline to analyse and quantify cell density and neurite extension from lightsheet microscopy images (with Anil Bharath). 

These tools will likely accelerate our understanding of how the complexity of the human neocortex is perturbed in several neuropsychiatric conditions.

Model-based estimation of upper limb joint kinematics and kinetics using surface electromyography

Electromyography (EMG) is one of the few methods that provide a window on muscle activity and hence muscle force production during functional movements. As one of its applications, research in myoelectric control has produced active prosthetic devices with multiple degrees-of-freedom (DOFs), paralleled by EMG-based control strategies.
The aim of this project is to evaluate contrasting approaches for estimating upper limb joint kinematics and kinetics using surface EMG. Two such approaches are mathematical modelling (e.g. neural networks) and multibody dynamic musculoskeletal modelling.
From Dr. Farina students will gain the expertise to design and perform experiments to measure and process surface EMG signals. They will also learn how to build mathematical models linking measured EMG and kinematics.
From Dr. Kedgley students will learn to design and perform experiments to measure and process human body kinematics using motion capture systems. Furthermore, they will acquire skills in multibody dynamics modelling and simulation of the human upper limb.
The candidates are required to have strength programming in at least one of Python or MATLAB. Some knowledge of machine learning and biomechanical analysis is desirable.

Mapping the geometry of the “neural manifold” across the primate sensorimotor system

Recent studies of neural population activity during behaviour have consistently uncovered that it is dominated by a handful of —around ten— neural covariation patterns (Gallego et al., 2017; Shenoy et al., 2013). These patterns span a neural manifold, a low-dimensional surface in the high-dimensional space defined by the activity of all recorded neurons. We refer to the neural population activity within the manifold as latent dynamics.

Remarkably, studying the neural manifold and its associated latent dynamics has helped advance our understanding of how the brain controls behaviour, providing insight into questions that had remained elusive when studying single neuron activity. The “manifold approach” has also helped made brain-computer interfaces (BCI) more robust (Gallego, Perich et al., 2020; Pandarinath et al., 2018).

Yet, there are many open questions regarding the geometrical properties of the neural manifold, with important implications both in basic neuroscience and BCIs. The goal of this project is to perform the first systematic comparison of manifold properties across the main areas of the sensorimotor system: premotor cortex, primary motor cortex, and primary sensory cortex. Such comparison will be carried out in a dataset with simultaneous recordings of around a hundred neurons in each of these areas, obtained as monkeys performed the same reaching task —the dataset from (Gallego, Perich et al., 2020). To assess the geometry of the neural manifold, we will fit the neural data with a broad range of manifold models that incorporate different assumptions about the data: from principal component analysis, and factor analysis, to isomap, and autoencoder neural networks. We will then explore the ability to predict the monkey’s behaviour from each of these different manifolds as we vary their assumed dimensionality. We expect this comparative study to inform about the difference in computations performed across these areas during a given behaviour, and on how to improve BCI design.

Dr. Gallego will bring to this project expertise in systems neuroscience, and neural data analysis, in particular focusing on the application of neural manifold approaches. Prof. Clopath is a leading expert in computational neuroscience and the application of mathematical methods and modelling tools to understand neural computations. This project would suit a student with good Python programming skills and a strong mathematical foundation (from electrical or biomedical engineering, physics or mathematics) and a strong desire to learn neuroscience.

References

J.A. Gallego, M.G. Perich, L.E. Miller, S.A. Solla. Neural manifolds for the control of movement. Neuron 94(5):978–84, 2017
J.A. Gallego^*,M.G.Perich^*, R.H. Chowdhury, S.A. Solla, L.E. Miller. Long-term stability of cortical population dynamics underlying consistent behaviour. Nature Neuroscience 23:260-276, 2020.
C. Pandarinath, K.C. Ames, A.A. Russo, A. Farshchian, L.E. Miller, E.L. Dyer, J.C. Kao. Latent factors and dynamics in motor cortex and their application to brain-machine interfaces. Journal of Neuroscience 38(44):9390-9401, 2018.
K.V. Shenoy, M.T. Kaufman, M. Sahani, M.M. Churchland. A dynamical systems view of motor preparation: implications for neural prosthetic system design. Prog Brain Res. 192 33-58, 2011.