My laboratory engages engineers, neuroscientists, mathematicians, and clinicians to create and apply state-of-the-art neural interfaces, kinematic sensors, and biochemical sensors to study neuromotor disease and insult in relevant animal models and humans. Currently, main research goals include (i) Characterization of neuromotor activity during unconstrained, complex motor behavior in non-human primates; and (ii) High-resolution neuromotor disease analysis and quantification.

Contextual dependence of cortical circuits and freely-moving technology development

Context flexibility is critical for performing novel and challenging tasks. Traditional and fundamental views of neural tuning appear to be more complex than originally posed. Studies have shown that the primary motor cortex in fact co-varies with a broad range of parameters of motor performance metrics including target location, hand or joint positioning, and expected torque. Further, a stable representation in one frame of reference can change across behaviors, postures, and movements, uncovering what is referred to as a contextual dependence, highlighting the impressive ability of the cortex to hold dynamic information for movement preparation and execution. Motor cortices process and possess both high- and low-level information about motor action that is dynamically adjusting to its environment. My laboratory uses novel systems of synergistic technologies to explore contextual dependence of internal motor planning and motor execution in the primary and pre-motor cortices. We aim to enable a transformation of traditional non-human primate behavioral neurophysiology into a freely-moving, unconstrained model and to open new fundamental research opportunities in neuroscience and clinical motor disease diagnosis.

Aims
• Develop model of complexity in unconstrained execution of movement in MI and PMd and the relationship to kinematic measurables
• Develop state-of-the-art implantable movement sensors (accelerometers, gyroscopes, chemical sensors, electrodes, etc. )
• Model-based, multi-resolution motion capture for non-human primates
• Explore contextual dependence of MI representation of movement

High-resolution neuromotor disease analysis and quantification

This project demonstrates the utility of a connection-free, multimodal neuromotor analysis platform for translational disease investigation and therapeutic validation, establishing the settings for similar analysis of numerous neuromotor insults (e.g. ALS, SCI, stroke). We leverage a novel, high-resolution wireless neural interface to access brain signals in the primary motor cortex (arm and leg area) during natural movement prior to, and throughout the development of PD in MPTP-treated rhesus monkeys. Upper and lower limb control will be quantified using sophisticated motion capture and wireless electromyography methodologies. Data is analyzed through multifaceted analyses to characterize translational metrics of movement impairments of PD. Neuromorphological alteration of motor cortex connectivity will be characterized in detail through the use of constructs for tract-specific axon and synapse labeling. We first demonstrate the utility of this translational platform is PD research. However, long-term and global aims are to exploit this platform for evaluating safety and optimizing efficacy of neuroprosthetic technologies to improve recovery after neuromotor disorders in general, including spinal cord injury and stroke with the well-defined objective to reach clinical fruition.

Aims
• Characterize through multimodal analysis (kinematic, neural, other) circuitopathy of a neuromotor disease (focus on PD, maybe stroke)
• Build multi-site, multi-area neural recording system / device for flow of information analysis
• Unique platform for basic science research in progression of neuromotor disease
• Build dynamic biomechanics model of disease progression (i.e. what muscles tend to atrophy based on kinematic measures)

Ecole Polytechnique Federal de Lausanne

ENGN 1230 - Instrumentation Design 

ENGN 2912R - Implantable Devices