Projects

  • Capturing time resolved spectral coupling in resting state fMRI for schizophrenia: Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool for assessing functional brain connectivity. Recent studies have focused on shorter-term connectivity and dynamics in the resting state. However, most of the prior work evaluates changes in time-series correlations.

In this study, we focus on time-resolved spectral coupling (assessed via the correlation between power spectra of the windowed time courses) among different brain circuits determined via independent component analysis (ICA).


  • Rhythm generation in sea slugs: Central pattern generators (CPGs) are neural networks which can produce rhythmic activity in isolation and responsible from behaviors like locomotion. We have developed a Hodgkin-Huxley type highly detailed and biologically plausible model using the extensive data intracellularly recorded from sea slug swim CPG and study the rhythmogenesis of oscillatory patterns emerging in network motifs composed of parabolic half-center oscillators. Model and methodology, we have developed, allows for laying down theoretical foundations necessary for devising new detailed phenomenological models of neuronal circuits and making testable predictions of dynamic rhythmic neuronal networks from diverse species.


  • Modeling phytoremediation of metals in soil: Pollution is one of the most significant modern world problems. One of the new innovative methods of eradicating metals from the environment is phytoremediation, which uses plants to absorb metals from soil and detoxify them. This study focuses on using dynamical systems to model plant-metal interactions.


  • Polyrhythmic generation and bifurcation analysis in three node networks: CPGs comprised of coupled

Interneuron circuits underlie wide ranges of natural rhythmic behaviors. Phase lag return maps describing polyrhythmic behavior in three-node reciprocally-inhibitory Fitzhugh-Nagumo networks characterize regimes of robustness and stability like natural patterns. We examine complex rhythm generation in modular network settings coupling such networks.