Matthew D. Johnson

Headshot of Matthew D. Johnson

Matthew D. Johnson

Associate Professor and Institute for Translational Neuroscience Scholar,
Department of Biomedical Engineering


Nils Hasselmo Hall
Rm 6-134
312 Church St SE




  • BS, Engineering Sciences, Harvard University, 2002

  • MS, Biomedical Engineering, University of Michigan, 2003

  • PhD, Biomedical Engineering, University of Michigan, 2007

  • Post-doctoral Fellow, Lerner Research Institute, Cleveland Clinic, 2007-2009

Research Interests

Neuromodulation mechanisms and technology

My group is primarily interested in developing and refining neural interface technologies to improve the quality of life for people with movement disorders.

Deep brain stimulation (DBS) is one such technology, which over the past 20 years has helped numerous patients with Parkinson’s disease, dystonia, and essential tremor reclaim control over their motor function. The therapy involves placing small electrodes in regions of the brain that exhibit pathological activity, which contributes to the movement disorder, and then stimulating those regions with continuous pulses of electricity.

My lab focuses on understanding how the brain responds and adapts to such stimulation-based therapies from a combination of computational and experimental perspectives. The knowledge gained from these studies in turn provides us with a framework to develop, evaluate, and translate new approaches for improving patient outcomes.

Computational modeling

Computer models are useful for predicting how a neuron or population of neurons might respond to stimulation. Our lab couples 1) finite element models of electric fields generated in neural tissue with 2) neuron models built from sets of mathematical equations that replicate the biophysical properties of membrane and synapse dynamics.

The neuron models range in scale from multi-compartment reconstructions of neurons labeled with histological methods to large-scale neural networks of the sensorimotor system. We use these tools both retrospectively (e.g., relating clinical outcome to targeted pathway) and prospectively (e.g., predicting how stimulation through a new electrode design might impact activity in the brain).

Neurophysiology and behavior

Our lab also investigates the therapeutic mechanisms of neuromodulation experimentally through multi-channel electrophysiological and neurochemical techniques in animal models of movement disorders. We are particularly interested in:

  • How neurons encoding movement are modulated during deep brain stimulation.
  • How stimulation at different therapeutic efficacies influences these neurons.
  • How the modulation of neuronal firing patterns changes during chronic stimulation.

Device development

The design space for neuromodulation technology remains unbounded because we still lack a clear understanding of which neural elements to target for improving each motor symptom.

Indeed, deep brain stimulation in humans and animal models of movement disorders have shown that one can stimulate in any one of several different brain regions and relieve motor symptoms. However, if the electrode(s) are not placed correctly within a given nuclei or fiber pathway, little improvement in motor symptoms will result with stimulation. In such cases, the clinical benefit is often masked by the appearance of unwanted side effects.

We are developing new types of implants and stimulation strategies that are inspired by the underlying neuroscience. Our group evaluates these technologies in our animal models of movement disorders with the goal of translating these therapies from the laboratory to the clinic.

Selected Publications

Peña E, Zhang S, Deyo S, Xiao Y, and Johnson MD. (2017) “Particle swarm optimization for programming deep brain stimulation arrays.” Journal of Neural Engineering, 14(1):016014.

Slopsema J and Johnson MD. (2017) “Deep Brain Stimulation.” Neuroprosthetics: Theory and Practice.  

Neren D, Johnson MD, Legon W, Ling G, and Divani AA. (2016) “Vagus nerve stimulation and other neuromodulation methods for treatment of traumatic brain injury.” Neurocritical Care. 24(2):308-319.

Teplitzky BA, Zitella LM, Xiao Y, and Johnson MD. (2016) “Model-based comparison of deep brain stimulation array functionality with varying number of radial electrodes and machine learning feature sets.” Frontiers in Computational Neuroscience. 10:58.

Xiao Y, Peña E, and Johnson MD. (2016) “Theoretical optimization strategies for directionally segmented deep brain stimulation electrode arrays.” IEEE Transactions on Biomedical Engineering. 63(2):359-371.

Connolly AT, Vetter RJ, Hetke JF, Kipke DR, Pellinen DS, Anderson DJ, Baker KB, Vitek JL, and Johnson MD. (2016) “A novel lead design for modulation and sensing of deep brain structures.” IEEE Transactions on Biomedical Engineering. 63(1): 148-157.

Agnesi F, Muralidharan A, Baker KB, Vitek JL, and Johnson MD. (2015) “Fidelity of frequency and phase entrainment of circuit-level spike activity during DBS.” Journal of Neurophysiology, 114(2):825-834.

Connolly AT, Jensen AL, Baker KB, Vitek JL, and Johnson MD. (2015) “Classification of pallidal oscillations with increasing parkinsonian severity.” Journal of Neurophysiology, 114(1): 209-218.

Connolly AT, Jensen AL, Baker KB, Johnson MD, and Vitek JL. (2015) “Modulations in oscillatory frequency and coupling in globus pallidus with increasing parkinsonian severity.” Journal of Neuroscience, 35(15), 6231-6240.

Zitella LM, Teplitzky BA, Hudson HM, Duchin Y, Harel N, Vitek JL, Baker KB, and Johnson MD. (2015) “Subject-specific computational modeling of DBS in the PPTg area.” Frontiers in Computational Neuroscience, 9:93.