Blue Sky Research Teams

The McCamish Parkinson’s Disease Innovation Program supports a range of research projects at the interface of basic neuroscience, neuroengineering, data science and machine learning, and clinical neuroscience driven by diverse, collaborative teams across Georgia Tech and Emory University.


Striatal cell-type specific patterns of abnormal activity in L-DOPA-induced dyskinesias

PI: Ellen Hess, Pharmacology & Chemical Biology/Neurology (Emory); Co-PI: Chethan Pandarinath, Biomedical Engineering (Emory)

Parkinson’s disease is typically treated with L-DOPA. However, 80-90% of Parkinson’s disease patients develop L-DOPA-induced dyskinesias (LIDs) as a complication of long-term L-DOPA treatment. This team we will record striatal neuron activity in a model of LIDs and then apply recently-developed machine learning methods to identify specific patterns of neuronal activity that define LIDs.

Developing improved deep brain stimulation designs employing real-time feedback methods to treat motor dysfunction in Parkinson’s disease

PI: Dieter Jaeger, Biology (Emory); Co-PI: Garrett Stanley, Biomedical Engineering (Georgia Tech)

The team is developing innovative deep brain stimulation methods to treat motor symptoms of Parkinson’s disease (PD). Specifically, they are engineering a closed-loop stimulation approach that continuously updates in real-time the parameters of electrical stimulation that feeds back into relevant brain areas to treat PD motor symptoms, based on recorded brain activity.

Human activity recognition to track freezing of gait in Parkinson’s disease

PI: J. Lucas McKay, Biomedical Informatics/Neurology (Emory); Co-PIs: Gari Clifford, Biomedical Informatics/Biomedical Engineering (Emory), Stewart Factor, Neurology (Emory)

One of the most troubling and difficult to treat symptoms of Parkinson’s disease (PD) is Freezing of Gait (FOG). This study will use modern computer vision “human activity recognition” approaches to directly measure FOG in video recordings of PD patients with and without FOG collected and labeled by experts.

Transcutaneous spinal cord stimulation for freezing of gait

PI: Svjetlana Miocinovic, Neurology (Emory); Co-PIs: Nicholas Au Yong, Neurology, (Emory), Stewart Factor, Neurology (Emory)

Freezing of gait (FOG) is a common symptom in patients with Parkinson’s disease (PD) where the ability to walk is abruptly interrupted, often described as if their feet were suddenly “glued” to the floor. This study will examine if lumbar transcutaneous spinal cord stimulation (tSCS), utilizing electrodes on the skin surface to deliver electrical stimulation to the spinal cord, can be used to improve walking and reduce or abort FOG episodes.

Artificial intelligence dynamic network analysis for the multi-factorial and multi-scalar prediction of Parkinsonian disorders

PI: Cassie Mitchell, Biomedical Engineering (Georgia Tech); Co-PIs: Roman Grigoriev, Physics (Georgia Tech), Chao Zhang, Computational Science & Engineering (Georgia Tech), Chad Hales, Neurology (Emory)

The overall objective of this project is to differentiate and prioritize overlapping biomarkers of Parkinsonian disease; treatment targets; and patient risks, like medical history, environment, and lifestyle. The approach is state of the art data mining to construct a biomedical knowledge graph of multifactorial and multi-scalar relationships.

Multimodel meta-optimization for the treatment of Parkinson’s disease

PI: Robert Gross, Neurology (Emory); Co-PI: Matthew Gombolay, Interactive Computing (Georgia Tech)

The combination of drug treatments and electrical brain stimulation is a life changing treatment for patients with Parkinson’s disease, as well as other neurological and psychiatric disorders. This team is developing a data-driven optimization algorithm that uses machine learning to quickly find the optimal stimulation parameters and medication dosage, using the same iterative process currently employed by clinicians – test, evaluate, and test a new parameter to find the best combination.

Development and validation of personal technology for the treatment of communication deficits in people with Parkinson’s disease

PI: Amanda Gillespie, Otolaryngology (Emory); Co-PI: David Anderson, Electrical & Computer Engineering (Georgia Tech); Adam Klein, Otolaryngology (Emory)

Behavioral voice therapy can be effective in Parkinson’s-related voice and speech disorders by increasing vocal loudness and improving speech intelligibility. Clinical and market research demonstrate a dearth of, and need for patient-centered technology to improve communication in patients with Parkinson’s. This multi-disciplinary, multi-institutional team is developing the Speech-Assisting Multi-Microphone System (SAMMS), to isolate, monitor, and analyze vocal output for time and loudness and provide haptic biofeedback to the wearer.

Wearable sensing and artificial intelligence to continuously examine acute and long-term measures of cardiovascular autonomic function in Parkinson’s disease and Multiple System Atrophy

PI: Omer Inan, Electrical and Computer Engineering (Georgia Tech); Co-PI: Chris Rozell, Electrical and Computer Engineering (Georgia Tech); Paul Beach, Neurology (Emory)

Dysfunction of the autonomic nervous system system, or dysautonomia, is common in Parkinson’s disease (PD) and a related condition called Multiple System Atrophy (MSA). Cardiovascular dysautonomia (CVD), such as impaired blood pressure response to standing, is especially associated with worse patient outcomes in both conditions. This team is developing novel wearable devices to allow non-invasive, continuous cardiac and blood pressure monitoring to afford an opportunity for easier assessment of CVD in both clinical (short-term) and real-world (at home/long-term) settings.