Denison Group

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The physiologic controls team explores novel technology to interact with the nervous system, with a goal to restore function in people with neurological and neurodegenerative disorders. Our engineering efforts include the creation of novel research tools for neuroscience discovery, and the application of the resulting discoveries towards developing better disease treatments.

Group Science

Today, many therapies for neurological disorders tacitly assume a fixed brain state. This assumption results in rigid pharmaceutical and stimulation interventions which aim to shift brain state from one point to another, without attention to variations in patient state and environmental demands. As an analogy, the way we currently approach treatments  is akin to attempting to set the temperature in your house (physiological state variable) by setting the boiler at a specific, static output level (the therapy dose), without considering the variation in weather. At best, you can achieve a reasonable steady-state temperature and manage extremes.

The reality is that dynamic fluctuations in disease states can be highly problematic for patient management. Representative examples of how these challenges manifest include:

  • Parkinson’s disease-Gait: fluctuations in the medications and activities of daily living can lead to dyskinesias, postural instability and falls; falls in particular have a high morbidity
  • Epilepsy-Treatment Optimization: episodic variations in network dynamics lead to spontaneous seizures that are difficult to predict and make therapy titration challenging; the uncertainty of seizures makes the disease especially burdensome
  • Mood and Cognition-Co-morbidities: Even in Parkinson’s and epilepsy, drug and physical changes, and events in the patient’s environment, including personal interactions and sleep variance, can impact the patient’s emotional and cognitive state, and compound the network dynamics underlying co-morbidities such as depression.

 

To manage these diseases, clinicians are left in the difficult position of trying to balance worst-case fluctuations, unacceptable side-effects, and non-optimal treatment of symptoms; the final balance depends on the bias of the clinician and patient for toleration of side-effects versus effective management of symptoms.

We believe that better understanding of the dynamics of brain diseases, specifically how changes in physiological signals correlate with changes in symptoms and behaviour, will ultimately improve disease treatment. This understanding includes insights into neural circuit dynamics which might be hidden from externally-visible clinical symptoms, but are important for insight into disease mechanisms. In addition, understanding these dynamics should provide better guidance into how therapies should be managed by a clinician, including algorithms on how to configure a medical device in a more principled way.

Returning to the analogy of house environmental controls, the objective of the physiologic controls lab is to develop the required tools to understand disease mechanisms, and apply this knowledge to create the analogue of a thermostat for dynamic disease management, using a bioelectronic system to automatically correct for challenges that reflect the normal course of human disease.

Selected Publications
Denison T
Consoer K
Santa W
Avestruz AT
Cooley J
Kelly A
2007. IEEE J Solid-St Circ 42(12):2934-45
Rouse AG
Stanslaski SR
Cong P
Jensen RM
Afshar P
Ullestad D
Gupta R
Molnar GF
Moran DW
Denison T

2011.J Neural Eng, 8(3):036018.

Stanslaski SR
Afshar P
Cong P
Giftakis J
Stypulkowski P
Carlson D
Linde D
Ullestad D
Avestruz AT
Denison T

2012.IEEE Trans Neural Syst Rehabil Eng, 20(4):410-21.

Afshar P
Khambhati A
Stanslaski S
Carlson D
Jensen RM
Linde D
Dani S
Lazarewicz M
Cong P
Giftakis J
Stypulkowski P
Denison T

2012.Front Neural Circuits, 6():117.

Capogrosso M
Milekovic T
Borton D
Wagner F
Moraud EM
Mignardot JB
Buse N
Gandar J
Barraud Q
Xing D
Rey E
Duis S
Jianzhong Y
Ko WK
Li Q
Detemple P
Denison T
Micera S
Bezard E
Bloch J
Courtine G
2016.Nature, 539(7628):284-288.
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