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   central nervous system      peripheral nervous system   neural decoding/encoding

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       neurophysiology                  signal processing                machine learning

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artificial intelligence in medicine and healthcare       clinical predictive models

ABOUT

ABOUT THE LAB

Our goal is to develop the algorithms that will enable early diagnosis, disease severity assessment, and personalization and adaptability of the therapy.  To this end, we combine neural and physiological signal processing, machine learning and neurophysiology, and healthcare data analytics. Our two main thrusts are 1) to understand how the nervous system senses the state and affects the function of the immune, metabolic and cardiopulmonary systems, in order to develop neuromodulation devices that are able to diagnose and treat various diseases and conditions by interacting with the nervous system and 2) to combine multiple healthcare data modalities (EHR, continuous vitals, imaging, unstructured notes) with machine learning methods to develop clinical predictive and diagnostic models.

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Summer 2019 (left to right): Ariba Khan, Breanna Huang-Ouyang, Alexander Borca-Tasciuc, Todd Levy, Dimitrios Georgopalis, Shubham Debnath, Theo Zanos, Viktor Tóth
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Spring 2019 (left to right): Viktor Tóth, Shubham Debnath, Todd Levy, Marina Cracchiolo , Theo Zanos
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Summer 2018 (left to right): Theo Zanos, Arielle Gabalski (TNP Lab), Johnny Ciancibello (NB Lab), Ankit Jain, Subash Padmanaban, Aracely Menjivar, Todd Levy, Dimitrios Georgopalis
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Summer 2017(left to right): Chris Ebsch, Subash Padmanaban, Theo Zanos, Ankit Jain, Todd Levy
Lab News
CURRENT PROJECTS

  • Decoding Immune States from Vagal Signals
    Dysregulation of the inflammatory reflex, a critical neural circuit that maintains immunological homeostasis, is a hallmark of inflammation. This neuronal reflex has a sensory and motor component conducted in the vagus nerve. Infection or injury activate immune cells to release cytokines and other mediators of inflammation. Such cytokine production in the spleen is suppressed by efferent neural signals that arise in the vagus nerve and mediate the cholinergic anti-inflammatory pathway. Bioelectronic devices target the motor arc of this reflex by electrically stimulating the vagus nerve. This stimulation reduces cytokine production and inflammation in preclinical animal models of inflammatory diseases and improves disease activity in patients with rheumatoid arthritis and Crohn’s disease. Currently, there is no closed-loop bioelectronic patient specific device targeting the inflammatory reflex. In order to develop such devices, there is a critical need to decode inflammatory related neural sensory signals and define the relationships between the activity of the afferent vagus nerve and the sensing of inflammation. Our long-term goals are to understand the neuronal signatures of the afferent arc of the inflammatory reflex, determine their modulation during sepsis and develop algorithms that will facilitate real time diagnosis of systemic inflammation.

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  • Decoding Metabolic States from Vagal Signals
    Glucose homeostasis is thought to be controlled by the central and peripheral nervous systems, and dysregulation of blood glucose due to a decreased sensitivity to insulin or problems with insulin production results in type 2 diabetes. The vagus transmits signals to and from the pancreas and liver to control the release of hormones such as insulin and glucagon which act to regulate blood glucose concentration. Our long-term goal is to develop bioelectronic devices that can target the afferent arc of this pathway at the cervical level to decode the metabolic state via machine learning algorithms, and close the loop by stimulating the vagus to increase or decrease the blood glucose concentration. 

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  • Closed-loop Optimization of Bioelectronic Therapies
    A major goal of Bioelectronic Medicine is to develop closed-loop interfaces that would not only record neural signals in order to decode levels of biomarkers, but also modulate the signals appropriately by applying parametric nerve stimulation. While the selection of stimulation parameters to consistently up- or down-regulate various biomarkers remains a daunting task, resolving the encoding of these biomarkers by the nervous system can provide valuable insight into the optimization of these parameters. By understanding the signals that encode changes in biomarkers, the ranges of parameter values can be narrowed down and the dimensionality and complexity of the stimulation optimization problem can be reduced significantly.

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  • Noninvasive Bioelectronic Analytics
    Biomarkers that reflect disease presence or intensity, or treatment efficacy are central to medical advance. Recently, modern application of information processing and decoding algorithms permit building on the measurement of tried and true physiological biomarkers like temperature and blood pressure so that investigators can understand broader phenomena of autonomic dysregulation. We want to deploy our expertise in measuring and processing coordinative objective physiological signals with up-to-date devices that simultaneously stream information from the brain, heart, lungs, eyes, skin, and peripheral nerves. We will test the hypothesis that systematic application of advanced data analysis (signal processing and machine learning) that interrogates the relationship of these measurement to disease states will enhance our ability to detect early and significant deflection from the normal state and so enhance current treatment protocols and perhaps suggest new therapeutic opportunities.

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  • Machine Learning applied in Healthcare Data
    In today’s era of personalized medicine, healthcare has evolved from mass treatments, which aren’t effective for all patients, to medicines specifically targeted to patient groups based on companion diagnostic tests. Moreover, with the advent of more sophisticated digital technologies and the rise of artificial intelligence and big data processing, new opportunities arise for creating algorithms that will generate a more holistic view of patient health generated from a wide variety of data sources. These algorithms will be instrumental in new more efficient diagnostic procedures, as well as treatment planning. Our goal is to develop a whole framework of machine learning algorithms to analyze multimodal data captured during a patient's visit for a variety of different conditions, diseases, and applications. We are leveraging the immense amount of clinical data that is generated every day from the whole Northwell Health system and develop these algorithms in-house and roll them out in clinics across the health system to showcase their efficacy and improve healthcare delivery and patient experience within Northwell.

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  • Machine Learning applied in COVID-19 EHR Data
    The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Our goal is to create a suite of Machine Learning-based predictive models that are tailored to COVID-19 patients, can augment current healthcare resources by guiding clinical decision-making, and improve both operations- and patient-centered outcomes. We have access to one of the nation’s largest Electronic Health Record (EHR) databases of COVID-19 patients (by now more than 14000 hospitalized and discharged or expired COVID-19 positive patients) from multiple communities within the current epicenter of the pandemic. We are working on utilizing and augmenting this EHR database, incorporating physician notes, imaging information and remotely, continuously monitored vitals, to develop and deploy algorithms that can inform better clinical decisions at multiple points of the patient care pathway.

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PEOPLE

PEOPLE
Theodoros Zanos, PhD

Head/PI, Associate Professor

Main Interests: Machine Learning in Medicine, neural data analysis, neuroscience,  neurophysiology recording and stimulating methods.

Goal: Understand, monitor and modulate central and peripheral neuronal circuit function, in order to develop devices that interface with the nervous system and treat disease

LinkedIn - Twitter - Personal Webpage - Email

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Todd Levy, MS

Electrical Engineer

Main Interests: Applying novel signal processing and machine learning pipelines to decode biological signals in conjunction with stimulation paradigms for closed-loop control of biological systems in biomedical devices

Goal: Develop models of biological systems, with a focus on the neural circuits, in order to diagnose and treat disease using bioelectronic medicine

LinkedIn - Email

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Avantika Vardhan, PhD

Electrical Engineer

Main Interests: NLP on Electronic Health Records, medical/brain image analysis, machine learning

Goal: using cross-disciplinary expertise in areas such as AI/ML, NLP and 
medical image analysis to contribute towards a deeper understanding of medical records and
radiological images

LinkedIn -  Email

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Shubham Debnath, PhD

Postdoctoral Fellow

Main Interests: Neural data analysis and decoding, machine learning, noninvasive neurophysiology recording methods.

Goal: Build and test systems that can interface with the nervous system to monitor and decode disease states for diagnosis and intervention, develop the future of healthcare that takes advantage of natural signals in the human body

LinkedIn - Email

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Viktor Tóth, MS

Research Associate

Main Interests: Machine learning, neural data analysis, brain-computer synthesis, algorithm design and optimization

Goal: Develop machine learning methods and corresponding software to decode, simulate and stimulate the nervous system

LinkedIn - Twitter - Personal Webpage - Github - Email

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Siavash Bolourani, MD

Elmezzi Scholar, PhD Candidate

Main Interests: Prediction models for surgical outcomes, Machine learning, neural data analysis, algorithm design and optimization

Goal: Develop machine learning methods and corresponding software predict medical and surgical outcomes

Twitter  - Email

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Fylaktis Fylaktou

PhD Student

Main Interests: Signal Processing and Machine Learning in neural and physiological recordings, neural decoding of mental and disease states

Goal: Develop algorithms that will enable better diagnostics and treatments using bioelectronic medicine

LinkedIn - Email

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Michael Scheid, PhD

Biomedical Software Engineer

Main Interests: Machine Learning in Medicine, wearable devices, neural data science, noninvasive neurophysiology recording methods, bioelectronic medicine

Goal: Use vital signals collected from wearable devices worn by hospitalized patients to predict adverse events

LinkedIn - Github - Email

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Panagiotis Grigoriou

Visiting Researcher

Main Interests: Signal processing and system identification models to elucidate physiological relationships between the nervous and the cardiovascular system

Goal: Study of the relationship between neural and cardiac biosignals and its importance on the Autonomic Nervous System

LinkedIn - Email

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Dimitrios Georgopalis

Research Intern

Main Interests: Applying novel signal processing and machine learning pipelines to decode biological signals in conjunction with stimulation paradigms for closed-loop control of biological systems in biomedical devices

Goal: Develop models of biological systems, with a focus on the neural circuits, in order to diagnose and treat disease using bioelectronic medicine

LinkedIn - Email

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Lab Alumni

  • Marina Cracchiolo, Visiting Researcher (2018-2019) - last known whereabouts: The Biorobotics Institute, Sant'Anna

  • Ariba Khan, Summer Research Intern (2019) - last known whereabouts: MIT Computer & Cognitive Science

  • Alexander Borca-Tasciuc, Summer Research Intern (2019) - last known whereabouts: Troy High School

  • Ankit Jain, Research Intern (2017-2018) - last known whereabouts: CS Dept, NYU

  • Subash Padmanaban, Postdoctoral Fellow (2017-2018) - last known whereabouts: InteraXon

  • Aracely Menjivar, Summer Research Intern (2018) - last known whereabouts: Hempstead High School

  • Christopher Ebsch, PhD Summer Research Intern (2017) - last known whereabouts: University of Notre Dame 

  • Leo Potters, Summer Research Intern (2016) - last known whereabouts: Johns Hopkins University

PUBLICATIONS

PUBLICATIONS & PRESENTATIONS

PEER-REVIEWED PUBLICATIONS

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  • Tóth, V., Meytlis, M., Barnaby, D.P., Bock K.R., Oppenheim M.I., Al-Abed Y., McGinn T., Davidson K.W., Becker L.B., Hirsch J.S., Zanos T.P. (2020) Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model. npj Digital Medicine 3, 149. https://doi.org/10.1038/s41746-020-00355-7 link

  • Barish M., Bolourani S., Lau L.F., Shah S., Zanos T.P. (2020) External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19. Nature Machine Intelligence https://doi.org/10.1038/s42256-020-00254-2 link

  • Aranow C., Atish-Fregoso Y., Lesser M., Mackay M., Anderson E., Chavan S,C., Zanos T.P., Datta-Chaudhuri T., Bouton C., Tracey K.J., Diamond B. (2020) Transcutaneous auricular vagus nerve stimulation reduces pain and fatigue in patients with systemic lupus erythematosus: a randomised, double-blind, sham-controlled pilot trial. Annals of the Rheumatic Diseases. doi: 10.1136/annrheumdis-2020-217872. link

  • Chang Y.C., Cracchiolo M., Ahmed U., Mughrabi I., Gabalski A., Daytz A., Rieth L., Becker L., Datta-Chaudhuri T., Al-Abed Y., Zanos T.P., Zanos S. (2020) Quantitative estimation of nerve fiber engagement by vagus nerve stimulation using physiological markers. Brain Stimulation 13(6):1617-1630. doi: 10.1016/j.brs.2020.09.002. PMID: 32956868. link

  • Tóth V., Jayaprakash N., Abbas A., Khan A., Zanos S., Zanos T.P. (2020) Single-axon level automatic segmentation and feature extraction from immuhistochemical images of peripheral nerves, Proceedings of the IEEE Engineering in Medicine and Biology Society, pp. 1859-1862, doi: 10.1109/EMBC44109.2020.9175974. link

  • Debnath S., Barnaby D.P. , Coppa K., Makhnevich A., Kim E.J., Chatterjee S., Tóth V., Levy T.J., Paradis M.d., Cohen S.L., Hirsch J.S., Zanos T.P. & the Northwell COVID-19 Research Consortium (2020) Machine learning to assist clinical decision-making during the COVID-19 pandemic, Bioelectronic Medicine 6,14. link

  • Ahmed, U., Chang, Y., Cracchiolo, M., Lopez M.F., Tomaio J.N., Datta-Chaudhuri T., Zanos T.P., Rieth L., Al-Abed Y, Zanos S. (2020) Anodal block permits directional vagus nerve stimulation. Scientific Reports 10, 9221. link

  • Richardson S., Hirsch J.S., Narasimhan M., Crawford J.M., McGinn T., Davidson K.W., Barnaby D.P., Becker L.B., Chelico J.D., Cohen S.L., Cookingham J., Coppa K., Diefenbach M.A., Dominello A.J., Duer-Hefele J., Falzon L., Gitlin J., Hajizadeh N., Harvin T.G., Hirschwerk D.A., Kim E.J., Kozel Z.M., Marrast L.M., Mogavero J.N., Osorio G.A., Qiu M., Zanos T.P. (2020) Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA. 2020;323(20):2052–2059. link

  • Levy T.J., Ahmed U., Tsaava T., Chang Y.C., Lorraine P.J., Tomaio J.N., Cracchiolo M., Lopez M., Rieth L., Tracey K.J., Zanos S., Zanos T.P. (2020) An impedance matching algorithm for common-mode interference removal in vagus nerve recordings, Journal of Neuroscience Methods, 108467. link

  • Bolourani S., Zanos T.P., Wang P., Tayebi M.A., Lee P.C. (2020) Reply: In machine learning, the devil is in the details. J Thorac Cardiovasc Surg. Nov 15:S0022-5223(20)32869-5. doi: 10.1016/j.jtcvs.2020.10.052. Epub ahead of print. PMID: 33208260.

  • Lampe J.W., Padmanaban S, Becker L.B. and Zanos T.P. (2019) Towards personalized closed-loop mechanical CPR: a model relating carotid blood flow to chest compression rate and duration, IEEE Trans. Biomed. Eng. oi: 10.1109/TBME.2019.2934682. link

  • Masi E.B., Levy T., Tsaava T., Bouton C.E., Tracey K.J., Chavan S.C., Zanos T.P. (2019) Identification of hypoglycemia-specific neural signals by decoding murine vagus nerve activity, Bioelectronic Medicine 2019:5-9. link

  • Zanos, T.P. (2019) Recording and Decoding of Vagal Neural Signals Related to Changes in Physiological Parameters and Biomarkers of Disease. Cold Spring Harbor Perspectives in Medicine, doi:10.1101/cshperspect.a034157. link

  • Zanos, T.P., Silverman H.A., Levy T, Tsaava T., Battinelli E., Lorraine P., Ashe J., Chavan S.S., Tracey K.J., Bouton C.B. (2018) Identification of cytokine-specific sensory neural signals by decoding murine vagus nerve activity. Proceedings of the National Academy of Sciences May 2018, 201719083. link

  • Krause, M.R., Zanos, T.P., Csorba, B.A., Pilly, P.K., Choe, J., Phillips, M.E., Datta, A., and Pack, C.C. (2017) Transcranial direct current stimulation facilitates associative learning and alters functional connectivity in the primate brain. Current Biology, 27, 1–11. link

  • Li, C., Sohal, H.S. , Li, F., Zanos, T.P., Goldman, L., Narayan, R.K., Bouton, C.E. (2017) A new 3D self-adaptive nerve electrode for high density peripheral nerve stimulation and recording. 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS), Kaohsiung, pp. 51-54. link

  • Zanos, T.P., Mineault, P.J., Guitton, D., and Pack, C.C. (2016) Mechanisms of Saccadic Suppression in primate cortical area V4. Journal of Neuroscience, 36, 9227-9239. link

  • Datta, A., Krause, M.R., Pilly, P.K., Choe, J., Zanos, T.P., Thomas, C., and Pack, C.C. (2016) On comparing in vivo intracranial recordings in non-human primates to predictions of optimized transcranial electrical stimulation. Proceedings of the IEEE Engineering in Medicine and Biology Society, 1774-1777. link

 

Before 2016

  • Zanos, T.P., Mineault, P.J., Nasiotis, K., Guitton, D., and Pack, C.C. (2015) A Sensorimotor Role for Traveling Waves in Primate Visual Cortex. Neuron 85, 615-627. link

  • Mineault, P.J., Zanos, T.P., and Pack, C.C. (2013). Local field potentials reflect multiple spatial scales in V4. Front. Comput. Neurosci. 7, 21. link

  • Zanos, S., Zanos, T.P., Marmarelis, V.Z., Ojemann, G.A., and Fetz, E.E. (2012). Relationships between spike-free local field potentials and spike timing in human temporal cortex. J. Neurophysiol. 107, 1808–1821. link

  • Zanos, T.P., Mineault, P.J., and Pack, C.C. (2011). Removal of Spurious Correlations Between Spikes and Local Field Potentials. J. Neurophysiol. 105, 474–486. link

  • Zanos, T.P., Mineault, P.J., Monteon, J.A., and Pack, C.C. (2011). Functional connectivity during surround suppression in macaque area V4. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 3342–3345. link

  • Zanos, T.P., Hampson, R.E., Deadwyler, S.E., Berger, T.W., and Marmarelis, V.Z. (2009). Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. Part II: Application to the Rat Hippocampus. Ann. Biomed. Eng. 37, 1668–1682. link

  • Marmarelis, V.Z., Zanos, T.P., and Berger, T.W. (2009). Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. Part I: Theory and Simulations. Ann. Biomed. Eng. 37, 1654–1667. link

  • Zanos, T.P., Courellis, S.H., Berger, T.W., Hampson, R.E., Deadwyler, S.., and Marmarelis, V.Z. (2008). Nonlinear Modeling of Causal Interrelationships in Neuronal Ensembles. IEEE Trans. Neural Syst. Rehabil. Eng. 16, 336–352. link

  • Zanos, T.P., Hampson, R.E., Deadwyler, S.., Berger, T.W., and Marmarelis, V.Z. (2008). Functional connectivity through nonlinear modeling: An application to the rat hippocampus. In 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008. EMBS 2008, pp. 5522–5525. link

  • Zanos, T.P., Courellis, S.H., Hampson, R.E., Deadwyler, S.., Marmarelis, V.Z., and Berger, T.W. (2006). A multi-input modeling approach to quantify hippocampal nonlinear dynamic transformations. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006. EMBS ’06, pp. 4967–4970. link

  • Courellis, S.H., Zanos, T.P., Min-Chi Hsiao, Hampson, R.E., Deadwyler, S.., Marmarelis, V.Z., and Berger, T.W. (2006). Modeling Hippocampal Nonlinear Dynamic Transformations with Principal Dynamic Modes. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006. EMBS ’06, pp. 2300–2303. link

  • Marmarelis, V.Z., Zanos, T.P., Courellis, S.H., and Berger, T.W. (2006). Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006. EMBS ’06, pp. 2114–2117. link

  • Hsiao, M.-C., Chan, C.-H., Srinivasan, V., Ahuja, A., Erinjippurath, G., Zanos, T.P., Gholmieh, G., Song, D., Wills, J.D., LaCoss, J., et al. (2006). VLSI Implementation of a Nonlinear Neuronal Model: A “Neural Prosthesis” to Restore Hippocampal Trisynaptic Dynamics. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006. EMBS ’06, pp. 4396–4399. link

 

CONFERENCE ABSTRACTS/PRESENTATIONS

 

  • V. Toth, T. Tsaava, E. H. Chang, A. Borca-Tasciuc, T. P. Zanos, "Segmentation of murine cervical vagus nerve fibers from transmission electron microscopy images using domain randomization and deep convolutional neural networks", Society for Neuroscience Conference, Chicago, IL (2019)

  • U. Ahmed, Y.-C. Chang, M. Cracchiolo, J. N. Tomaio, M. F. Lopez, L. Rieth, T. P. Zanos, S. Zanos "Directional biasing of vagal fiber activation using anodal block waveforms", Society for Neuroscience Conference, Chicago, IL (2019)

  • T. J. Levy, H. A. Silverman, E. B. Masi, S. S. Chavan, K. J. Tracey, T. P. Zanos "A framework to isolate and decode neural activity from the rodent vagus nerve to infer immune and metabolic states", IEEE Conference on Neural Engineering, San Fransisco, CA (2019)

  • C. Aranow, M. Lesser, M. Mackay, E. Anderson, T. P. Zanos, T. Datta, C. Bouton, K. J. Tracey, B. Diamond, “Engaging the Cholinergic Anti-Inflammatory Pathway By Stimulating the Vagus Nerve Reduces Pain and Fatigue in Patients with SLE”, Arthritis & Rheumatology, Vol 70 (2018)

  • J. Falcone, T. Liu, J. Wong, M. Ochani, D. Pogue, R. Sharma, T. Levy, T. P. Zanos, T. Datta, L. Reith, H. Sohal, “A novel flexible microelectrode for stimulation and recording in acute and chronic awake models in murine small diameter nerves for bioelectronic medicine”, Society for Neuroscience Conference, San Diego, CA (2018)

  • M. Ma, T. P. Zanos, M. R. Krause, C. C. Pack, T. E. Kennedy, “Quick and consistent method for stability assessment of microelectrodes”, Society for Neuroscience Conference, San Diego, CA (2018)

  • T. P. Zanos, H. Silverman, T. Levy, E. Battinelli, P. Lorraine, J. Ashe, S. S. Chavan, C. E. Bouton, K. J. Tracey, “Identification of cytokine-specific and dose-specific sensory neural signals in murine vagus nerve activity recordings”, Poster Presentation, Shock Society Anual Meeting, Scottsdale, AZ (2018)

  • T. P. Zanos, H. Silverman, T. Levy, E. Battinelli, P. Lorraine, J. Ashe, S. S. Chavan, C. E. Bouton, K. J. Tracey, “Identification of cytokine-specific sensory neural signals in murine vagus nerve activity recordings”, Poster Presentation, American Association of Immunologist Anual Meeting, Austin TX (2018)

  • T. P. Zanos, H. Silverman, T. Levy, E. Battinelli, S. S. Chavan, K. J. Tracey, C. E. Bouton, “Decoding mouse vagus nerve activity for cytokine discrimination”, Poster Presentation, Society for Neuroscience Conference, Washington DC (2017)

  • T. Levy, E. Battinelli, H. Silverman,  S. S. Chavan, K. J. Tracey, C. E. Bouton, T. P. Zanos, "A novel signal processing framework for decoding systemic blood glucose levels from vagus nerve recordings”, Poster Presentation, Society for Neuroscience Conference, Washington DC (2017)

  • E. Battinelli, T. P. Zanos, T. Levy,  C. E. Bouton, S. S. Chavan, K. J. Tracey, "Decoding glucose levels from the cervical vagus nerve”, Poster Presentation, Society for Neuroscience Conference, Washington DC (2017)

  • J.W. Lampe, S. Padmanaban, T.P. Zanos, "Toward Closed-Loop Mechanical CPR: Regression Tree Model Predicts Carotid Blood Flow in a Swine Model of Cardiac Arrest", Poster Presentation, American Heart Association Meeting, Anaheim CA (2017)

  • E. Battinelli, T. P. Zanos, T. Levy,  C. E. Bouton, S. S. Chavan, K. J. Tracey, "Neural decoding of vagus nerve activity for blood glucose level extraction”, Presentation, AAP-ASCI-APSA Meeting, Chicago IL (2017)

  • T. P. Zanos, P. J. Minaeult, , D. Guitton, C. C. Pack, “Visual receptive fields and cortical oscillations during saccadic suppression in area V4”, Poster Presentation, Society for Neuroscience Conference, San Diego CA (2016)

  • M.R. Krause, T. P. Zanos, B. Csorba, M. E. Phillips, P. K. Pilly, “Transcranial direct current stimulation facilitates associative learning and alters functional connectivity in the non-human primate brain”, Poster Presentation, Society for Neuroscience Conference, San Diego CA (2016)

  • A. Richard, T.P. Zanos, F. Dubeau, E. V. Sidani, “The spectrotemporal characteristics of NMDA receptor encephalitis”, Poster Presentation, Canadian Neurological Sciences Federation Congress, Quebec, QC (2016)

 

Before 2016

  • T. P. Zanos, P. J. Minaeult, J. A. Monteon, K. Nasiotis, D. Guitton, C. C. Pack, “Traveling Waves in Macaque V4 Optimize Post-Saccadic Visual Processing”, Dynamic Poster Presentation, Society for Neuroscience Conference, San Diego CA (2013)

  • T. P. Zanos, P. J. Mineault, J. A. Monteon, D. Guitton, C. C. Pack, "Saccades induce LFP waves and alter single-neuron Functional Connectivity in macaque visual cortex", Poster Presentation, Society for Neuroscience Conference, New Orleans LA (2012)

  • P. J. Mineault, T. P. Zanos, C. C. Pack, " Multiple integration scales for LFPs in macaque V4", Poster Presentation, Society for Neuroscience Conference, New Orleans LA (2012)

  • T. P. Zanos, P. J. Mineault, J. A. Monteon, D. Guitton, C. C. Pack, "Traveling Waves Triggered by Saccades in Macaque Visual Cortex", Canadian Society for Brain, Behaviour and Cognitive Science Meeting, Kingston, OT (2012)

  • T. P. Zanos, P. J. Mineault, J. A. Monteon, C. C. Pack, "Rapid reorganization functional connectivity in macaque cortical area V4", Poster Presentation, Society for Neuroscience Conference, Washington DC (2011)

  • P. J. Mineault, T. P. Zanos, J. A. Monteon, C. C. Pack, "Short-term receptive field plasticity in cortical area V4 of the alert macaque monkey", Poster Presentation, Society for Neuroscience Conference, Washington DC (2011)

  • J. A. Monteon, T. P. Zanos, P. J. Mineault, C. C. Pack, “Oculomotor Influences on Visual Responses in Macaque Cortical Area V4”, Canadian Physiological Society Winter Meeting, Sainte-Adele, Canada (2011)

  • P. J. Mineault, T. P. Zanos, J. A. Monteon, C. C. Pack, “Receptive Field Substructure of V4 Neurons”, Canadian Physiological Society Winter Meeting, Sainte-Adele, Canada (2011)

  • T. P. Zanos, J. A. Monteon, P. J. Mineault, C. C. Pack, “Functional Connectivity in Macaque Cortical Area V4”, Canadian Physiological Society Winter Meeting, Sainte-Adele, Canada (2011)

  • T. P. Zanos, P. J. Mineault, C. C. Pack, “Spectral Contamination and efficient removal of spike remnants from Local Field Potentials”, Areadne Research in Encoding and Decoding of Neural Ensembles, Santorini, Greece (2010)

  • T. P. Zanos, R. E. Hampson, S. A. Deadwyler, T. W. Berger, V. Z. Marmarelis, “Functional Connectivity between Neuronal Ensembles through Nonlinear Modeling”, Computational Systems Neuroscience Conference, Salt Lake City, UT (2009)

  • T. P. Zanos, S. P. Zanos, G. A. Ojemann, V. Z. Marmarelis, “Nonlinear relationship between local field potentials and neural discharge in human temporal cortex", Poster Presentation, Society for Neuroscience Conference, Washington DC (2008)

  • T. P. Zanos, S. H. Courellis, T. W. Berger, R. E. Hampson, S. A. Deadwyler, V. Z. Marmarelis, “Functional Connectivity in the rat hippocampus through nonlinear modeling in the context of a neuroprosthetic platform”, Poster Presentation, Society for Neuroscience Conference, San Diego, CA (2007)

  • T. P. Zanos, S. H. Courellis, T. W. Berger, S. A. Deadwyler, R. E. Hampson, V. Z. Marmarelis, “Detecting Functional Connectivity between Neuronal Ensembles through Nonlinear Modeling”, Poster Presentation, Biomedical Engineering Society Annual Meeting, Los Angeles, CA (2007)

  • T. P. Zanos, S. P. Zanos, S. H. Courellis, V. Z. Marmarelis, G. A. Ojemann, “Nonlinear dynamic modeling of the relationship between local field potentials and neural discharge in human temporal cortex”, Poster Presentation, Society for Neuroscience Conference, Atlanta, GA (2006)

  • S. H. Courellis, T. P. Zanos, T. W. Berger, V. Z. Marmarelis, S.A. Deadwyler, R. E. Hampson, “A neural prosthesis for hippocampus: multi-input / multi-output model of the functional relationship between CA3 and CA1 hippocampal neurons in behaving rats”, Poster Presentation, Society for Neuroscience Conference, Atlanta, GA (2006)

  • M. Hsiao, C. Chan, V. Srinivasan, T. P. Zanos, G. Erinjippurath, A. Ahuja, G. Gholmieh, J. D. Wills, J. Lscoss, S. H. Courellis, D. Song, V. Z. Marmarelis, T. W. Berger, “A neural prosthesis for hippocampus: multi-input / multi-output model of the functional relationship between CA3 and CA1 hippocampal neurons in behaving rats”, Poster Presentation, Society for Neuroscience Conference, Atlanta, GA (2006)

  • T. P. Zanos, S. H. Courellis, T. W. Berger, S. A. Deadwyler, R. E. Hampson, V. Z. Marmarelis, “Modeling the effects of interactions between CA3 hippocampal neurons on CA1 neuronal responses in behaving rats”, Oral Presentation, Biomedical Engineering Society Annual Meeting, Chicago, IL (2006)

News & Press

NEWS & PRESS

​​​

  • 11/2020 - Our paper on detailing our Let Sleeping Patients Lie deep learning algorithm to avoid unnecessary overnight vital monitoring, is out! - Link - Press 1 - Press 2 - Press 3 - Press 4 - Press 5 - Press 6 - Press 7 - Press 8 - Press 9 - Press 10

  • 11/2020 - Our Matters arising paper on describing our external validation of a COVID-19 mortality prediction model by Yan et al., is out! - Link - Press 1

  • 11/2020 - New paper from our collaborator Dr, Aranow, on the pilot trial of transcutaneous auricular vagus nerve stimulation for lupus patients. - Link

  • 09/2020 - New paper from our collaborators at the Translational Neurophysiology Lab, co-authored by our visiting researcher Marina Cracchiolo, demonstrating the use of physiological markers to estimate nerve fiber engagement - Link

  • 08/2020 - Our paper on a segmentation algorithm to extract quantitative characteristics of nerve fibers from IHC images is out! - Link

  • 07/2020 - Our perspective piece on the use of ML in clinical decision making for COVID-19 is out! - Link - Press 1 - Press 2

  • 06/2020 - New paper from our collaborators at the Translational Neurophysiology Lab, demonstrating the use of anodal blocks for directional vagus nerve stimulation. - Link

  • 04/2020 - Our simple, accurate survival calculator with early admission information for COVID-19 patients is available for anyone to use here: feinstein.northwell.edu/nocos. The preprint of the paper is available here. Link

  • 04/2020 - New paper from the Northwell COVID-19 consortium on a report of the largest US case series, 5700 NY COVID-19 inpatients is out. - Link

  • 03/2020 - The lab is participating in a health system-wide effort to assist our front line heroes to battle the COVID-19 pandemic, analyzing EMR data and developing predictive algorithms to support clinical decision making.

  • 01/2020 - Our paper on an impedance matching algorithm that helps remove common-mode interference from vagus nerve recordings is out! - Link

  • 01/2020 - Theo offered some insights to Becker's Healthcare website on using artificial intelligence-based methods in medical applications. - Link

  • 10/2019 - Viktor presented his work on using machine learning to segment vagus nerve electron microscopy images at the Society for Neurosciences meeting. - Link

  • 08/2019 - Our paper on closed-loop CPR is out! Great work from Josh Lampe and Subash Padmanaban, first paper from our collaboration with the lab of Lance Becker. - Link 

  • 07/2019 - Our paper on decoding glucose-related vagus nerve recordings is out! Great work from Emily Masi, Todd Levy and the other co-authors and our collaboration with the labs of Sangeeta Chavan and Kevin Tracey. - Link - Press 1 - Press 2 - Press 3 - Press 4 - Press 5 - Press 6 (in Greek) - Press 7 (in Greek) - Press 8 (in Greek)

  • 05/2019 - We presented at the International Neuromodulation Society 14th World Congress in Sydney for the closing plenary session, Thursday, May 30th - Link

  • 04/2019 - Theo participated as the Machine Learning expert Panelist at the Thomson Reuters Long Island Emerging Technology event in Garden City, NY

  • 03/2019 - We were at the IEEE Neural Engineering conference in San Fransisco for our workshop titled "Recording of peripheral nerve signals to decode changes in physiological parameters and biomarkers of disease", our poster and a talk on our framework to decode neural signals at the Bioelectronic Medicine plenary session - Link

  • 01/2019 - New paper out on decoding vagal signals - Link

  • 01/2019 - The lab is officially renamed to Neural and Data Science and the new lab website goes live!

  • 11/2018 - Bioelectronics in Medicine journal interviews Theo about the PNAS study - Link

  • 11/2018 - The lab was in San Diego for the 2018 Neuroscience meeting, where our collaborators presented two posters - Poster 1 - Poster 2

  • 10/2018 - Our paper is featured in an article in Medical Design & Outsourcing - Link

  • 10/2018 - Theo was a featured speaker at Device Talks Boston - Link

  • 09/2018 - We received the Award for Excellence in Research for our PNAS study - Congratulations to the whole team!

  • 07/2018 - Theo gave an invited lecture at the Istanbul Technical University, Department of Electrical and Electronic Engineering in Istanbul, Turkey

  • 06/2018 - Multiple local, national and international news outlets feature our paper - Link 1 - Link 2 - Link 3 - Link 4 - Link 5 - Link 6 - Link 7 - Link 8 - Link 9 - Link 10 - Link 11 - Link 12

  • 05/2018 - Scientific American runs a story in their front page about our PNAS paper - Link

  • 05/2018 - Our paper in collaboration with the Tracey lab is published in the Proceedings of the National Academy of Science - Link - Supplemental Material

  • 05/2018 - Todd gives his insider view/executive insight for Med Device online website on Machine Learning in Bioelectronic Medicine - Link

  • 05/2018 - Theo gave an invited lecture at the Presidents Junior Leadership Council in Mt Kisco, NY

  • 03/2018 - Theo gave an invited lecture at the Whitespace Lab at the Lulumeno Athletica Headquarters in Vancouver, BC

  • 03/2018 - Medium features an article by Avery Bedows talking about our 2017 SfN posters and the lab's work - Link

  • 02/2018 - PBS SciTech Now interviews Theo about our work decoding vagus nerve signals - Link

  • 02/2018 - The Neural Implant Podcast interviews Theo during the 2017 Neuroscience meeting about our posters - Link

  • 11/2017 - The lab was in Washington D.C. for the 2017 Neuroscience meeting, where we presented three posters - Poster 1 - Poster 2 - Poster 3

  • 10/2017 - New paper out on tDCS and associative learning - Link

  • 06/2017 - Theo participated as the Machine Learning expert Panelist at the Thomson Reuters Long Island Emerging Technology event in Garden City, NY

  • 05/2017 - Theo was a guest lecturer for the Bioelectronic Medicine course at the Karolinska Institute in Stockholm, Sweden

  • 01/2017 - Official opening of the Neural Decoding and Data Analytics Lab

Openings

OPENINGS

To apply for the following openings, please forward your CV to tzanos@northwell.edu​
 

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