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Computational Neuromodulation: Towards closed-loop Deep Brain Stimulation

Neurosurgeons have used electrical stimulation since the 60's to locate and distinguish specific brain areas. They soon discovered that stimulation of certain brain nuclei suppresses the symptoms of some neurological disorders. Recent efforts on patient-specific therapeutic approaches revealed the importance of computational methods in guiding deep brain stimulation (DBS), a neuromodulation treatment initially applied to motor diseases that is fast expanding to include affective disorders, among others. A major pitfall in DBS is that the result depends on the precise implantation of the stimulation electrode within the affected nucleus, which is a trial-and-error procedure that often cannot elude adverse side effects. Recently, we introduced a functional biomarker that uses the beta band of the intracortical microelectrode recordings to predict the long-term DBS outcome, at least in terms of a heuristic, gross categorization of the response to treatment (Michmizos et al. 2015). We have further expanded this research with a machine learning approach that identified a handful of intraoperatively acquired signal features that predicts the DBS outcome (Kostoglou et al. 2016). Our 10-years research on DBS has also targeted on the debatable relationship between the local field potential and the spike activity (Michmizos et al. 2011, 2012a, 2012b).

This research is supported by the 2016-2018 Charles & Johanna Busch Biomedical Grant Award.

Relevant Publications

K. Kostoglou, K.P. Michmizos, P. Stathis, D. Sakas, K.S. Nikita, G.D. Mitsis, "Classification and prediction of clinical improvement in Deep Brain Stimulation from intraoperative microelectrode recordings," IEEETransactions on Biomedical Engineering , TBME-00631-2016 (in press), 2016 [ link ]

K.P. Michmizos, P. Frangou, P. Stathis, D.E. Sakas, K.S. Nikita "Beta - band frequency peaks inside the subthalamic nucleus as a biomarker for motor improvement after Deep Brain Stimulation in Parkinson’s disease,"IEEE Journal of Biomedical and Health Informatics, 19 (1), 174-180, 2015 [ link ]

K.P. Michmizos, D.E. Sakas, K.S. Nikita, "Parameter identification for a local field potential driven model of the parkinsonian subthalamic nucleus spike activity," Neural Networks, (11 pp.), 2012 [ link ]

K.P. Michmizos, D.E. Sakas, K.S. Nikita, "Prediction of the timing and the rhythm of the parkinsonian subthalamic nucleus neural spikes using the local field potentials," IEEE Transactions of Information Technology in Biomedicine, (8 pp.), 2012 [ link ]

K.P. Michmizos, D.E. Sakas, K.S. Nikita, "Toward relating the subthalamic nucleus spiking activity with the local field potentials acquired intranuclearly," IOP Measurement Science and Technoloy 22(11), (9 pp.),2011 [ link ]