Dr Steve Mehrkanoon

Postdoctoral Research Fellow

UQ Centre for Clinical Research
Faculty of Medicine
s.mehrkanoon@uq.edu.au
+61 7 334 66012

Overview

Dr Steve Mehrkanoon is computational neuroscientist. He received his BSc (honours; 2003) and MSc in Electronic Engineering (signal processing, 2009), and Ph.D (2014) in computational neuroscience from University of New South Wales (UNSW). During his PhD effort, he contributed significantly to the identification of resting-state and task-dependent models of fucntional connectivity patterns using spatiotemporal and spectral analysis of EEG in humans. From June 2012 – July 2012, he was a visiting researcher at the Functional Brain Imaging Lab, University of Geneva, Switzerland. He has been a Postdoctoral Research Officer at University of Tasmania (Jan 2014 - Dec 2015), Queensland Brain Institute (Feb 2016 - Dec 2016 ) and University of Queensland Centre for Clinical Research (UQCCR) (Jan 2017 - Aug 2017).

He is an Advance Queensland Research Fellow at the University of Queensland Centre for Clinical Research and Perinatal Research Centre, Royal Women's Brisbane Hospital. His passion and research are source to discover the mechanisms that disrupt neuro-typical development in sick newborn infants that are at risk of brain injury shortly after birth. His hope is to develop a diagnostic tool for early detection of disabilities in sick newborn infants. This will allow implementation of early clinical interventions: https://www.facebook.com/uqmedicineresearch/videos/476638539380083/.

He is involved in teaching and advising neuroscience-oriented honours students from bio-engineering, electrical engineering, physics and applied mathematics. He serves as member on national and international computational neuroscience societies such as NeuroEng Australia, OHBM, Society for Neuroscience and IEEE signal processing. He also serves as member on international peer review panels such as Journal of Brain Topography, NeuroImage, Human Movement Science, Fluctuation and Noise Letters (World Scientific), PlosOne and European Journal of Neuroscience (EJN).

Research Interests

  • Computational and mathematical neuroscience, nonlinear dynamics and brain networks, self-organised criticality, neural field theory, and EEG-MEG Signal Processing.
    Dr Steve Mehrkanoon is interested in the dynamics of large-scale brain network organisation in healthy brain and patients with neurological and neuropathological conditions. The ultimate goal of his research is to reveal the principles of the mechanisms that govern multiscale functional organization of the human brain. He uses dynamical system theory, time-frequency signal processing techniques, computational physics and graph theory to investigate the collective cortical oscillations observed in Electroencephalography (EEG) signals, at sensor and source space domains. He has an active interest in understanding of the emergence of the brain’s dynamic functional/effective connectivity (dFC/dEC) patterns at different timescales with distinct spatiotemporal contents. He has particular interest in mathematical neuroscience, where the brain is modeled using network of coupled systems of linear/nonlinear differential equations in order to provide an explanation of how topology of connectivity underpins short and long-term interactions among the brain regions. Dr Steve Mehrkanoon’s research theme also includes neonatal neuroscience, where the aim is to provide significant insights into the functional organisation and patterns of the brain network dynamics in infants born very preterm. He has been recently awarded an Advance Queensland Research Fellowship in the field of Neonatal Neuroscience and Biotechnology entitled “The early diagnosis of neurodevelopmental disabilities using integrated bioengineering technology”. Currently clinical assessments are failed to early diagnose neurodevelopmental disabilities in preterm infants, his project constructs a new diagnostic tool for early prediction of neurodevelopmental disabilities using advanced technologies magnetic resonance imaging (MRI) and EEG.

Research Impacts

Mehrkanoon S, Boonstra TW, Breakspear M, Hinder M, Summers JJ (2016). Upregulation of cortico-cerebellar functional connectivity after motor learning. NeuroImage 128, 252-263.

Mehrkanoon S, Breakspear M, Boonstra TW (2014). Low-dimensional dynamics of resting-state cortical activity. Brain Topography 27: 338-352.

Mehrkanoon S, Breakspear M, Boonstra TW (2014). The reorganization of corticomuscular coherence during a transition between sensorimotor states. NeuroImage 100: 692-702.

Mehrkanoon S, Breakspear M, Britz J, Boonstra TW (2014). Intrinsic coupling modes in source-reconstructed electroencephalography. Brain Connectivity 4: 812-825.

Mehrkanoon S, Mehrkanoon S, Johan Suykens (2014) Parameter estimation of delay dierential equations: an integration-free LS-SVM approach, Communications in Nonlinear Science and Numerical Simulation, 19 (4), 830-841.

Mehrkanoon S, Breakspear M, Daffertshofer A, Boonstra TW (2013). Non-identical smoothing operators for estimating time-frequency interdependence in electrophysiological recordings.EURASIP Journal on Advances in Signal Processing 2013:73.

Boonstra TW, Powell TY, Mehrkanoon S, Breakspear M (2013). Effects of mnemonic load on cortical activity during visual working memory: Linking ongoing brain activity with evoked responses. International Journal of Psychophysiology 89: 409-418.

Moghavvemi M, Mehrkanoon S (2009), Detection of the onset of epileptic seizure signal from scalp EEG using blind signal separation," Biomedical Engineering - Applications, Basis and Communications, vol. 21, 287-290.

Mehrkanoon S, Moghavvemi M (2008). Active Bio-Sensor System, Compatible with Arm Muscle Movement or Blinking Signals in BCI Application, Sensors and Transducers Journal, vol. 92, 144-151.

Mehrkanoon S, M. Breakspear, T. W. Boonstra, Consistent cortical networks in resting-state EEG: Spatial topography and temporal dynamics, Human Brain Mapping (HBM), 2012, China.

Mehrkanoon S, J. Welsh, Juan C. Agüero (2011), Evaluation of measures to detect the synchrony between interacting pendulum oscillator, 5th IEEE International Conference on bioinformatics and Biomedical Engineering, 2011, China

Mehrkanoon S, M. Moghavvemi , H. Fariborzi, Real time ocular and facial muscle artifacts removal from EEG signals using LMS adaptive algorithm," International Conference on Intelligent and Advanced Systems, IEEE, ICIAS 2007, 1245-1250.

Mehrkanoon S, M. Moghavvemi , H. Fariborzi, Real time De-mixing system based on LMS adaptive algorithm for two blind source separation, 5th Student Conference on Research and Development, SCORED, IEEE, Malaysia, 2007, 1-6.

Qualifications

  • Doctor of Philosophy, University of New South Wales

Available Projects

  • New Project (July 2017)

    We will investigate how different types of node interconnectivity patterns affect simulated EEG patterns in network models of brains. We will mathematically and statistically compare the output of the models analysed/simulated by the student with human EEG-driven network patterns. Knowledge of the systems of differential equations, and some numerical methods would be very useful. We will initially study the publication: Which Model to Use for Cortical Spiking Neurons? and extend the ideas to more complex network models such as those used in Dynamics of Networks of Leaky-Integrate-and-Fire Neurons. In particular, dynamics of the Hindmarsh-Rose neuron model will be mathematically analysed and simulated both individually and on networks. Related publications are as follows: Parameter-sweeping techniques for temporal dynamics of neuronal systems: case study of Hindmarsh-Rose model [3], Piecewise-linear approximation of the Hindmarsh–Rose neuron model [4].

    Student involved in this project will gain skills in the following domains:

    1. Network theory and simulation of large-scale brain network.

    2. Have a great opportunity to learn how to develop a MATLAB program in order to generate network models with respect to the nature of EEG features.

    3. Have a window of opportunity to generate a unique multidisciplinary scientific article that embeds mathematics onto neuroscience for better understanding of how the human brain-network operates.

    This project is suitable to applications from students with a background in Applied Mathematics, Physics and Electrical Engineering with ODE/Matlab and/or C/C++ knowledge with an interest in RHD pathway.

    [1] IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 5, SEPTEMBER 2004 1063 Which Model to Use for Cortical Spiking Neurons? Eugene M. Izhikevich

    [2] Dynamics of Networks of Leaky-Integrate-and-Fire Neurons, Chapter Network Science , pp 217-242

    [3] Barrio, Roberto and Shilnikov, Andrey. Parameter-sweeping techniques for temporal dynamics of neuronal systems: case study of Hindmarsh-Rose model, The Journal of Mathematical Neuroscience, 2011,1(1),1-6

    [4] Storace, Marco. and Linaro, Daniele. and Lange, Enno de., The Hindmarsh–Rose neuron model: Bifurcation analysis and piecewise-linear approximations, Chaos: An Interdisciplinary Journal of Nonlinear Science, 2008, 18, 3, 033128-1 033128-10

    If you want to join the team, please express your inetrest by sending an email to "s.mehrkanoon@uq.edu.au".

    • Those prospective domestic PhD applicants who want to join the team through RHD Scholarship, please see here .
  • New Project (July 2017)

    We will investigate how different types of node interconnectivity patterns affect simulated patterns of the brain's electrical activity, the so-called EEG, in network models of brains. The aim of this project is to identify the relationships between network topology and collective dynamics observed in EEG signals. We hypothesize that topology and wiring patterns of a network are central in the emergence of a collective behaviour (or emergent dynamics). The successful candidate will simulate a number of network models to infer the EEG-like dynamics [1].

    1. Wallace E, Benayoun M, van Drongelen W, Cowan JD (2011) Emergent Oscillations in Networks of Stochastic Spiking Neurons. PLOS ONE 6(5): e14804.

    Student involved in this project will gain skills in the following domains:

    1. Network theory and simulation of large-scale brain network.

    2. Have a great opportunity to learn how to develop a MATLAB program in order to generate network models with respect to the nature of EEG features.

    3. Have a window of opportunity to generate a unique multidisciplinary scientific article that embeds mathematics onto neuroscience for better understanding of how human brain-network operates.

    This project is suitable to applications from students with a background in Applied Mathematics, Physics and Electrical Engineering with ODE/Matlab and/or C/C++ knowledge with an interest in RHD pathway.

    • Those prospective domestic PhD applicants who want to join the team through RHD Scholarship, please see here .

View all Available Projects

Grants (Administered at UQ)

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • New Project (July 2017)

    We will investigate how different types of node interconnectivity patterns affect simulated EEG patterns in network models of brains. We will mathematically and statistically compare the output of the models analysed/simulated by the student with human EEG-driven network patterns. Knowledge of the systems of differential equations, and some numerical methods would be very useful. We will initially study the publication: Which Model to Use for Cortical Spiking Neurons? and extend the ideas to more complex network models such as those used in Dynamics of Networks of Leaky-Integrate-and-Fire Neurons. In particular, dynamics of the Hindmarsh-Rose neuron model will be mathematically analysed and simulated both individually and on networks. Related publications are as follows: Parameter-sweeping techniques for temporal dynamics of neuronal systems: case study of Hindmarsh-Rose model [3], Piecewise-linear approximation of the Hindmarsh–Rose neuron model [4].

    Student involved in this project will gain skills in the following domains:

    1. Network theory and simulation of large-scale brain network.

    2. Have a great opportunity to learn how to develop a MATLAB program in order to generate network models with respect to the nature of EEG features.

    3. Have a window of opportunity to generate a unique multidisciplinary scientific article that embeds mathematics onto neuroscience for better understanding of how the human brain-network operates.

    This project is suitable to applications from students with a background in Applied Mathematics, Physics and Electrical Engineering with ODE/Matlab and/or C/C++ knowledge with an interest in RHD pathway.

    [1] IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 5, SEPTEMBER 2004 1063 Which Model to Use for Cortical Spiking Neurons? Eugene M. Izhikevich

    [2] Dynamics of Networks of Leaky-Integrate-and-Fire Neurons, Chapter Network Science , pp 217-242

    [3] Barrio, Roberto and Shilnikov, Andrey. Parameter-sweeping techniques for temporal dynamics of neuronal systems: case study of Hindmarsh-Rose model, The Journal of Mathematical Neuroscience, 2011,1(1),1-6

    [4] Storace, Marco. and Linaro, Daniele. and Lange, Enno de., The Hindmarsh–Rose neuron model: Bifurcation analysis and piecewise-linear approximations, Chaos: An Interdisciplinary Journal of Nonlinear Science, 2008, 18, 3, 033128-1 033128-10

    If you want to join the team, please express your inetrest by sending an email to "s.mehrkanoon@uq.edu.au".

    • Those prospective domestic PhD applicants who want to join the team through RHD Scholarship, please see here .
  • New Project (July 2017)

    We will investigate how different types of node interconnectivity patterns affect simulated patterns of the brain's electrical activity, the so-called EEG, in network models of brains. The aim of this project is to identify the relationships between network topology and collective dynamics observed in EEG signals. We hypothesize that topology and wiring patterns of a network are central in the emergence of a collective behaviour (or emergent dynamics). The successful candidate will simulate a number of network models to infer the EEG-like dynamics [1].

    1. Wallace E, Benayoun M, van Drongelen W, Cowan JD (2011) Emergent Oscillations in Networks of Stochastic Spiking Neurons. PLOS ONE 6(5): e14804.

    Student involved in this project will gain skills in the following domains:

    1. Network theory and simulation of large-scale brain network.

    2. Have a great opportunity to learn how to develop a MATLAB program in order to generate network models with respect to the nature of EEG features.

    3. Have a window of opportunity to generate a unique multidisciplinary scientific article that embeds mathematics onto neuroscience for better understanding of how human brain-network operates.

    This project is suitable to applications from students with a background in Applied Mathematics, Physics and Electrical Engineering with ODE/Matlab and/or C/C++ knowledge with an interest in RHD pathway.

    • Those prospective domestic PhD applicants who want to join the team through RHD Scholarship, please see here .