Theodoros P. Zanos, PhD

Assistant Investigator, Center for Bioelectronic Medicine, Neural Decoding and Data Analytics Laboratory,
The Feinstein Institute for Medical Research

Phone: (516) 562-1204
Email: tzanos@northwell.edu

About the Investigator

Dr. Theo Zanos received his Bachelor of Engineering degree in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in Greece in 2004. In 2006 he received his Master of Science and in 2009 his Doctorate in Biomedical Engineering from the University of Southern California, Viterbi School of Engineering. His thesis focused on developing novel machine learning and system identification approaches for Multi-Input Multi-Output hippocampal neural circuits.

In 2009, Dr. Zanos was recruited as a postdoctoral fellow by Dr. Christopher Pack to work at the Montreal Neurological Institute (MNI), McGill, in Montreal, Canada. At the MNI, he worked on high-channel-count primate electrophysiology, estimating correlations and connections between brain areas, detecting spatiotemporal patterns and predicting signals and behaviors based on data true models. He also worked on projects involving neurostimulation experiments that manipulate single neuron firing, intracortical oscillations, connectivity between brain areas and behavior and in 2014 he became a research associate at the MNI.

In 2016, Dr. Zanos accepted a position as an assistant investigator at the Feinstein Institute for Medical Research, where he joined the Center for Bioelectronic Medicine as the lead of the Neural Decoding and Data Analytics lab.

Research Focus

With the recent advance of multielectrode arrays that enable us to record activity from hundreds of neurons simultaneously, interest in the complex ways these ensembles of neurons relate to mental events has emerged. This advance has also allowed for new ways to examine the effects of invasive and non-invasive electrical stimulation methods in both neuronal function and neural circuit connectivity. Thus, we are now able to acquire a deeper understanding, but also active control, of the intricate ways neurons or ensembles of neurons interact and collectively process information and affect behavior. This effort will not only address basic systems neuroscience questions but also provide building blocks for applied biomedical research aimed at the development of new therapeutic applications for existing brain-machine interface and neurostimulation methods and advancement of the Bioelectronic Medicine field.

In the Neural Decoding and Data Analytics lab, we make use of advanced computational methods and engineering principles to address key problems in Neuroengineering. The primary technical challenge involved in understanding the relationship between large neuronal population activity and other physiological or behavioral measures is the sheer volume of data. We have developed a computational framework (Zanos et al., 2006, 2008, 2009, 2011) that addresses this problem by providing quantitative estimates of information flow patterns, linked to physiological and anatomical properties of the system, while at the same time reducing the computational complexity of the analysis. This framework has been applied to a wide range of neural recordings, from in-vitro single-unit recordings from hippocampal slices, in-vivo single-unit recordings from macaque visual area MT, human temporal lobe intracortical recordings, multi-electrode array recordings from rodents (Hippocampus) and primates (areas V4, TEO, PFC), to Vagus Nerve recordings from rodents.

Our goal is to develop and use novel neural data analysis tools and cutting edge neurophysiology recording and stimulating methods, to understand and modulate central and peripheral neuronal circuit function, in order to develop devices that interface with the nervous system and treat disease.

Education

University of Southern California, Viterbi School of Engineering
Degree: PhD
2009
Field of Study: Biomedical Engineering

University of Southern California, Viterbi School of Engineering
Degree: MSc
2006
Field of Study: Biomedical Engineering

Aristotle University of Thessaloniki, Engineering School
Degree: BEng
2004
Field of Study: Electrical and Computer Engineering

Honors and Awards

2012 Jean Timmins Award, Montreal Neurological Institute
2010 Center of Excellence in Commercialization and Research Award, McGill University
2006 Fred Grodins Oral Presentation Award, University of Southern California
2004 Senior Thesis Award, Aristotle University of Thessaloniki

Publications
  1. A. Datta, M. Krause, P. Pilly, J. Choe, T. P. Zanos, C. Thomas, C. C. Pack, “Experimental and modeling evidence for optimized transcranial electrical stimulation in primates”, IEEE Eng in Medicine and Biology Society Conference, Orlando, FL, in press (2016)
  2. T. P. Zanos, P. J. Minaeult, K. T. Nasiotis, D. Guitton, C. C. Pack, “A sensorimotor role for traveling waves in visual cortex”, Neuron, 85, 1-13. (2015) – Journal Cover Story
  3. P. J. Mineault, T. P. Zanos, C. C. Pack, “Local Field Potentials reflect multiple spatial scales in V4”, Frontiers in Computational Neuroscience, 7:21. (2013)
  4. S. Zanos, T. P. Zanos, V. Z. Marmarelis, G. A. Ojemann, E. E. Fetz, “Relationships between spike-free local field potentials and spike timing in human temporal cortex”, Journal of Neurophysiology, Vol. 107(7), pp. 1808-21 (2012)
  5. T. P. Zanos, P. J. Minaeult, C. C. Pack, “Removal of Spurious Correlations between Spikes and Local Field Potentials”, Journal of Neurophysiology, Vol. 105, pp. 474-486. (2011)
  6. T. P. Zanos, P. J. Mineault, J. A. Monteon, C. C. Pack, “Functional Connectivity during surround suppression in macaque area V4”, IEEE Eng in Medicine and Biology Society Conference, Boston, MA, pp. 3342-3345 (2011)
  7. T. P. Zanos, V. Z. Marmarelis, R. E. Hampson, T. W. Berger, S. A. Deadwyler, “Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. Part II: Application to the Hippocampus”, Annals of Biomedical Engineering, Vol. 37 (8), pp. 1668-1682. (2009)
  8. V. Z. Marmarelis, T. P. Zanos, T. W. Berger, “Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. Part I: Theory and Simulations”, Annals of Biomedical Engineering, Vol. 37 (8), pp. 1654-1667. (2009)
  9. T. P. Zanos, S. H. Courellis, T. W. Berger, R. E. Hampson, S. A. Deadwyler, V. Z. Marmarelis, “Nonlinear Modeling of Causal Interrelationships in Neuronal Ensembles”, IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol. 15 (4), pp. 336-352. (2008) – Journal Cover Story
  10. T. P. Zanos, R. E. Hampson, S. A. Deadwyler, T. W. Berger, V. Z. Marmarelis, “Functional Connectivity through Nonlinear Modeling: An Application to the Rat Hippocampus”, IEEE Engineering in Medicine and Biology Society Conference, Vancouver, Canada, pp. 5522-5525 (2008)
  11. T. P. Zanos, S. H. Courellis, R. E. Hampson, S. A. Deadwyler, V. Z. Marmarelis, T. W. Berger, “A multi-input modeling approach to quantify hippocampal nonlinear dynamic transformations”, IEEE Engineering in Medicine and Biology Society Conference, New York, NY, pp. 4967-4970 (2006)
  12. V. Z. Marmarelis, T. P. Zanos, S. H. Courellis, T. W. Berger, “Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs”, IEEE Eng in Medicine and Biology Society Conference, New York, NY, pp. 2114-2117 (2006)
  13. S. H. Courellis, T. P. Zanos, M. C. Hsiao, R. E. Hampson, S. A. Deadwyler, V. Z. Marmarelis, T. W. Berger, “Modeling Hippocampal Nonlinear Dynamic Transformations with Principal Dynamic Modes”, IEEE Engineering in Medicine and Biology Society Conference, New York, NY, pp. 2300-2303 (2006)
  14. M. Hsiao, C. H. Chan, V. Srinivasan, A. Ahuja, G. Erinjippurath, T. P. Zanos, G. Gholmieh, D. Song, J. D. Wills, J. LaCross, S. H. Courelis, A. R. Tanguay, J. J. Granacki, V. Z. Marmarelis, T. W. Berger, “VLSI Implementation of a nonlinear neuronal model: A “Neural Prosthesis” to restore hippocampal trisynaptic dynamics, IEEE Engineering in Medicine and Biology Society Conference, New York, NY, pp. 4396-4399 (2006)

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