Martin M. Reich
Dr. Martin M. Reich is the Junior Research Group Leader of the visualDBSlab (https://visualdbslab.com/) at the Department of Neurology in the University of Wuerzburg. He is the lead author of a study published in the journal Brain which was selected as an Editor’s choice for the May’s issue.
Q: What motivated you to become a scientist?
I have been motivated to conduct research that has a clinical impact, one in which you can see patient improvement and societal pay-off. I particularly enjoy the collaborative and interdisciplinary aspects of clinical neuroscience, which have also a crucial part of studies directly involving patients. Working in an interdisciplinary environment with individuals that have diverse backgrounds that range from engineering to medicine is something that keeps me motivated and provides an ideal atmosphere for research.
Q: What are the research aims of your group? What are the main findings?
My research group investigates the application of deep brain stimulation as neuromodulation therapy (and tool), meanly focused in movement disorders. Using modern computation tools and combining them with network wide brain imaging and patient outcome we found that high-frequency stimulation within short pulses of 30 µs provides positive outcome. Here we discovered the capability of selectivity in fiber-pathway modulation and we were able to back translate this to our patients suffering from side effects or suboptimal benefit of the treatment. It is important to highlight that stimulation efficacy strongly depends on the targeted brain region and the stimulation parameters. For example, in Parkinson’s disease, delivering high-frequency deep brain stimulation to the subthalamic nucleus and local fiber pathways can suppress many of the movement impairments such as bradykinesia, rigidity and tremor while stimulation of the internal capsule of the globus pallidus has been used for the treatment of dystonia. However, individual outcome is highly variable within 20-25% of non-responders and our aim is to improve this on a patient-specific level. In a current study we were able to introduce a possible future opportunity to greatly reduce individual variability and provide individualized therapy to our patients by using an artificial intelligence programming approach based on anatomic and outcome data of a big cohort of chronically treated patients (Reich et al., 2019).
Q: What do we know about the long-term neural changes as a result of deep brain stimulation?
We know that chronic stimulation leads to long-term plasticity and changes in effective therapeutic value. This can be positive as observed in dystonia, but also really demanding for the patients, like in the case of delayed onset gait disturbances in tremor patients, which our group firstly described. Therefore, understanding how stimulation parameters are correlated with long-term plasticity is key to understand the therapeutic effects of deep brain stimulation. In this regard there are several aspects that would help us determine good patient outcome. One example is understanding the mechanisms of neuromodulation. The current consensus regarding the mechanism of deep brain stimulation is that the high-frequency stimulation modulates neural activity at both afferent and efferent brain regions and that this helps to restore function. However we understand very little about the underlying mechanisms by which this occurs. In particular, what are the network-wide neurochemical and morphological changes that occur as a result of long-term deep brain stimulation and why does modulation of axonal fibers provide better outcome compared to modulation of local neuronal populations.
Q: How do you apply computational modelling to your research?
We have a several computational tools that we apply in our lab. We are able to visualize patient’s stimulation parameters to the individual’s stimulated anatomy, can map the probability of deep brain stimulation effects by using bigger cohorts outcomes. Additionally, we can compute the network-wide individual deep brain stimulation effects by using normative connectome data. These tools are able to picture important network nodes for the effect of the local applied neuromodulation. This computational approach could be a critical future aspect of deep brain stimulation treatment since the choice of the appropriate target location for stimulation could based on the patient’s symptom profile, age and cognitive status.
Q5: What do you think are the big questions to be answered in your field?
What are the mechanisms of action of deep brain stimulation? What are the optimum stimulation parameters and targeted cell populations that can enhance patient outcome? When and how stimulation should be delivered based on a patient’s pathophysiological history? Finding answers to these question will require the development of new technologies that can stimulate specific brain pathways and hardware development by companies that work in close collaboration with clinicians and researchers who apply deep brain stimulation.
Q6: What advice do you find yourself giving to your students and postdocs?
It is important to have mentors during your academic training and select a location in which you can have a collaborative research environment; especially one that can enrich your scientific thinking and career. Also as a clinical scientist, don’t forget your medical training and the time that you spend with patients when addressing research questions. Particularly, the clinical work can define and enrich your scientific questions and will lead you to be aware of the crucial needs of your patients.
Reich, M. M., Horn, A., Lange, F., Roothans, J., Paschen, S., Runge, J., Wodarg, F., Pozzi, N. G., Witt, K., Nickl, R. C., Soussand, L., Ewert, S., Maltese, V., Wittstock, M., Schneider, G. H., Coenen, V., Mahlknecht, P., Poewe, W., Eisner, W., Helmers, A. K., Matthies, C., Sturm, V., Isaias, I. U., Krauss, J. K., Kühn, A. A., Deusch, G., Volkmann, J. (2019). Probabilistic mapping of the antidystonic effect of pallidal neurostimulation: A multicentre imaging study. Brain, 142(5), 1386–1398. https://doi.org/10.1093/brain/awz046