# Farzan Nadim

### Contact Info

Title: | Professor of Neurobiology |

Email: | farzan.nadim@njit.edu |

Office: | CKB 420E |

Hours: | By appointment. |

Phone: | 973-596-8453 |

Dept: | Biological Sciences |

## About

### About Me

Farzan Nadim, PhD, is a Professor of Neurobiology in the Department for Biological Sciences and a founding Director of the Institute of Brain and Neuroscience Research.

Nadim’s research focuses on understanding the neuronal and circuit mechanisms underlying dynamic activity in the central nervous system, with a focus on oscillatory networks. Elucidating mechanisms of production of network oscillations will help us understand, at a cellular and network level, how widespread synchronous patterns arise in large non-homogeneous networks of the brain. Such widespread synchronization of rhythmic activity among networks of neurons that normally function to produce distinct behavior can lead to disorders such as generalized epilepsy and Parkinson´s disease.

### Education

- Northeastern University, B.A., 1987.
- Boston University, M.A., 1989.
- Boston University, Ph.D., 1994.

### Awards and Grants

**Current:**

**National Institutes of Health** MH-60605 (Principal Investigator) 2000-22; current cycle 2018-22) Regulation of Neuronal Oscillations by Synaptic Dynamics

**National Institutes of Health**NS-083319 (MPI) 2012-18. Regulation of Neuronal Oscillations by Synaptic Dynamics

**Previous:**

**National Science Foundation** DMS-1122291 (Co-PI) 2011-15. Linear conductance-based mechanisms underlying oscillations in neuronal networks

**National Science Foundation**DUE-0436244 (Co-PI) 2004-09 UBM: An undergraduate biology and mathematics training program at NJIT.

**Binational Science Foundation**(co-PI) September 2002 (-2007) Mechanisms of Dose- and State-Dependence of Neuromodulation

**Ellen and Albert Grass Faculty Grant**(PI) Marine Biological Laboratory, 2006

**National Science Foundation** IBN-0090250 (co-I) 2001-06 Research Coordinated Networks: The Pyloric Model Group: Functional Analysis of a Complex, Distributed Biological Neural Network

**National Science Foundation** DMS-0109876 (co-I) 2001-02 Scientific Computing Research Environments for the Mathematical Sciences

**National Science Foundation** IBN-0078966 (PI). 2000-01. Significance of Synaptic Dynamics in Oscillatory Neuronal Circuits

**NJIT Institutional Funding**SBR421140 (Principal Investigator) 1999-2001.

### Affiliations

Professor

- Department of Mathematical Sciences, New Jersey Institute of Technology , Newark, NJ.
- Federated Department of Biological Sciences, NJIT/Rutgers University, Newark, NJ.

Director Institute for Brain and Neuroscience Research, New Jersey Institute of Technology , Newark, NJ.

Member Center for Applied Mathematics and Statistics, New Jersey Institute of Technology, Newark, NJ.

Faculty Member Center for Molecular and Behavioral Neuroscience Graduate Program, Rutgers University and University of Medicine and Dentistry of New Jersey, Newark, NJ.

## Research

## Research Interests

*Co-modulation of neural oscillations by multiple neuromodulators.*

This project examines the rules of interaction of two or more neuromodulators, i.e., co-modulation, on common targets in neural circuits: synaptic currents, electrical coupling and leak and voltage-gated ion-channel currents. We are interested in the quantitative rules of co-modulation, 1) to examine whether co-modulation could be simply described as the sum of the effects of the individual neuromodulators, up to saturation, and 2) to determine whether the co-modulatory effects on different targets follow the same rule. Our current results show that co-modulation of distinct targets in the same cells by the same two neuromodulators follows different rules. Surprisingly, co-modulation of synaptic currents, which may be pre- or postsynaptic, follows the linear summation rule, whereas co-modulation of the voltage-gated ionic current targeted in a single neuron is nonlinear. Currently we are exploring the rules of co-modulation of electrical coupling conductances.

**Li X, Bucher D, Nadim F **(2017) Distinct co-modulation rules of synaptic and voltage-gated currents coordinates interactions of multiple neuromodulators. In review; **doi:** https://doi.org/10.1101/265694

*Short-term synaptic dynamics. *

Although the role of long-term synaptic plasticity in network function is extensively studied, fewer clear functions have been demonstrated for short-term synaptic plasticity. My work showed that all pyloric synapses show short-term depression but with distinct dynamics. We used computational modeling and mathematical analysis to show that, in the context of oscillatory networks, depression of inhibitory synapses always promotes phase constancy in the face of changes in frequency. This finding is of great importance in rhythmic motor systems, such as locomotion, where the behavior may happen at different frequencies (e.g. a fish swimming fast or slow) but the phase relationships between different muscles and therefore different motor units need to be maintained. Current experimental work in my lab shows the synaptic mechanisms that underlie phase maintenance. This work is in preparation for publication. This project was supported from its inception entirely by the current grant.

**Anwar H, Li X, Bucher D, Nadim F** (2017) Functional roles of short-term synaptic plasticity with an emphasis on inhibition. Curr Opin Neurobiol 43:71-8; PMCID: PMC5447482.

**Akcay Z, Bose A, Nadim F** (2014) Effects of synaptic plasticity on phase and period locking in a network of two oscillatory neurons. J Math Neurosci 4:8; PMCID: PMC4003516.

**Rabbah P, Nadim F** (2005) Synaptic dynamics do not determine proper phase of activity in a central pattern generator. J Neurosci 25(49):11269-78.

**Bose A, Manor Y, Nadim F** (2004) The activity phase of postsynaptic neurons in a simplified rhythmic network. J Comput Neurosci 17(2):245-61.

**Manor Y, Bose A, Booth V, Nadim F** (2003) Contribution of synaptic depression to phase maintenance in a model rhythmic network. J Neurophysiol 90(5):3513-28.

*Membrane potential resonance. *

Membrane potential resonance (MPR), or a maximum response at a nonzero frequency to a sinusoidal current input is found in neurons of numerous systems and has been correlated with network oscillations. We established the existence of MPR and synaptic resonances in central pattern generators and demonstrated that synaptic and membrane resonance interact. We showed that MPR frequencies in pacemaker neurons are correlated with the network oscillation frequency. The resonance frequency of these pacemaker neurons is influenced by the voltage range and activity waveforms and is therefore nonlinear. We demonstrated the first causal connection between MPR and network frequency by showing that electrically coupled resonant neurons pull the network frequency toward their MPR frequency. Interestingly, although MPR can be caused by multiple ionic currents, such as the h current, this current does not play a direct role in producing MPR in our system, but rather constrains the parameters of other resonant currents.

**Fox DM, Tseng HA, Smolinski TG, Rotstein HG, Nadim F **(2017) Mechanisms of generation of membrane potential resonance in a neuron with multiple resonant ionic currents. PLoS Comput Biol 13(6):e1005565; PMCID: PMC5476304.

**Chen Y, Li X, Rotstein HG, Nadim F **(2016) Membrane potential resonance frequency directly influences network frequency through electrical coupling. J Neurophysiol 116(4):1554-63; PMCID: PMC5144696.

**Tseng HA, Martinez D, Nadim F** (2014) The frequency preference of neurons and synapses in a recurrent oscillatory network. J Neurosci 34(38):12933-45; PMCID: PMC4166170.

**Tseng HA, Nadim F** (2010) The membrane potential waveform of bursting pacemaker neurons is a predictor of their preferred frequency and the network cycle frequency. J Neurosci 30(32):10809-19; PMCID: PMC2944831.

**Tohidi V, Nadim F** (2009) Membrane resonance in bursting pacemaker neurons of an oscillatory network is correlated with network frequency. J Neurosci 29(20):6427-35; PMCID: PMC2716082.

*The role of axons in neural coding. *

Most axons have a collection of voltage-gated currents, in addition to the standard Hodgkin-Huxley fast Na+ and delayed rectifier K+, which influences their activity. As such, conduction delays along non-myelinated axons can be greatly changed by the history of activity and this history-dependence is subject to neuromodulation. Functionally, history-dependence and other factors such as spike failures and ectopic spiking influences the neural code. Using an unmyelinated motor axon in lobster, my colleague Dirk Bucher and I have used voltage-clamp measurements of ionic currents to build a computational model that demonstrates the nonlinear mechanisms urrents to -clamp measurements of ioniic hanisms of axonal delay nges by dopamine have a huge impact on the synaptic current, iunderlying history-dependence. Axonal history dependence can influence the synaptic readout in the postsynaptic target, in this case the muscle fibers. History dependence of this axon is modulated by tonic (nM) dopamine levels, which also can produce ectopic spiking. The resulting changes by dopamine have a large impact on the synaptic current by amplifying the response to burst inputs and priming effects.

**Zhang Y, Bucher DM, Nadim F** (2017) Ionic mechanisms underlying history-dependence of conduction delay in an unmyelinated axon. eLife 6.

**Ballo AW, Nadim F, Bucher D** (2012) Dopamine modulation of Ih improves temporal fidelity of spike propagation in an unmyelinated axon. J Neurosci 32(15):5106-19; PMCID: 3347488.

**Ballo AW, Keene JC, Troy PJ, Goeritz ML, Nadim F, Bucher D** (2010) Dopamine modulates Ih in a motor axon. J Neurosci 30(25):8425-34; PMCID: 2908950.

**Daur N, Nadim F, Stein W** (2009) Regulation of motor patterns by the central spike-initiation zone of a sensory neuron. Eur J Neurosci 30(5):808-22; PMCID: 2885921.

*Inter-circuit interactions.*

How do oscillatory systems with different frequencies become coordinated? Almost 20 years ago, I used computational and mathematical modeling to show that the faster oscillator can, counter-intuitively, control the frequency of the slower one. We later validated this model prediction was later validated experimentally in collaboration with the lab of Mikey Nusbaum at U. Penn. School of Medicine. Our collaboration has continued to this day and we have 10 joint peer-reviewed papers. Amit Bose, my colleague in the NJIT Math Department became involved in the theoretical aspects of this research and together we demonstrated the mathematical principles underlying the control of the slow oscillator by the fast one.

**Kintos N, Nusbaum MP, Nadim F** (2016) Convergent neuromodulation onto a network neuron can have divergent effects at the network level. J Comput Neurosci 40(2):113-35; PMCID: PMC4786451.

**Kintos N, Nusbaum MP, Nadim F** (2008) A modeling comparison of projection neuron- and neuromodulator-elicited oscillations in a central pattern generating network. J Comput Neurosci 24(3):374-97; PMCID: 2409376.

**Ambrosio-Mouser C, Nadim F, Bose A** (2006) The effects of varying the timing of inputs on a neural oscillator. SIAM J Appl Dyn Syst 5(1):108-39; PMCID: 2968756.

**Bartos M, Manor Y, Nadim F, Marder E, Nusbaum MP** (1999) Coordination of fast and slow rhythmic neuronal circuits. J Neurosci 19(15):6650-60.

**Nadim F, Manor Y, Nusbaum MP, Marder E** (1998) Frequency regulation of a slow rhythm by a fast periodic input. J Neurosci 18(13):5053-67.

*Linear conductance-based mechanisms. *

Slow oscillations of the membrane potential in neurons underlie the bursting activity corresponding to a variety of functions such as motor output and sleep rhythms. Although the role of nonlinear regenerative inward currents in giving rise to slow oscillations and bursting is well studied, experiments from my lab showed the surprising result that only a specific linear component of the I-V curve of the regenerative inward currents—and not the full nonlinear current—can produce stable bursting oscillations. We investigated the role of linear (leak) in the generation and shaping of oscillations in individual neurons.

**Golowasch J, Bose A, Guan Y, Salloum D, Roeser A, Nadim F** (2017) A balance of outward and linear inward ionic currents is required for the generation of slow wave oscillations. J Neurophysiol:jn 00240 2017. PMID: 28539398.

**Bose A, Golowasch J, Guan Y, Nadim F** (2014) The role of linear and voltage-dependent ionic currents in the generation of slow wave oscillations. J Comput Neurosci 37(2):229-42; PMCID: PMC4161634.

**Zhao S, Golowasch J, Nadim F** (2010) Pacemaker neuron and network oscillations depend on a neuromodulator-regulated linear current. Front Behav Neurosci 4:21; PMCID: PMC2876874.

## Publications

### Selected Publications

**Li X, Bucher D, Nadim F **(2018) Distinct co-modulation rules of synaptic and voltage-gated currents coordinates interactions of multiple neuromodulators. In review; **doi:** https://doi.org/10.1101/265694

**Akcay Z, Huang X, Nadim F, Bose A **(2018) Phase-locking and bistability in neuronal networks with synaptic depression, Physica D: Nonlinear Phenomena, (364) 8-21.

**Anwar H, Li X, Bucher D, Nadim F** (2017) Functional roles of short-term synaptic plasticity with an emphasis on inhibition. Curr Opin Neurobiol 43:71-8; PMCID: PMC5447482.

**Akcay Z, Bose A, Nadim F** (2014) Effects of synaptic plasticity on phase and period locking in a network of two oscillatory neurons. J Math Neurosci 4:8; PMCID: PMC4003516.

**Rabbah P, Nadim F** (2005) Synaptic dynamics do not determine proper phase of activity in a central pattern generator. J Neurosci 25(49):11269-78.

**Bose A, Manor Y, Nadim F** (2004) The activity phase of postsynaptic neurons in a simplified rhythmic network. J Comput Neurosci 17(2):245-61.

**Manor Y, Bose A, Booth V, Nadim F** (2003) Contribution of synaptic depression to phase maintenance in a model rhythmic network. J Neurophysiol 90(5):3513-28.

**Fox DM, Tseng HA, Smolinski TG, Rotstein HG, Nadim F** (2017) Mechanisms of generation of membrane potential resonance in a neuron with multiple resonant ionic currents. PLoS Comput Biol 13(6):e1005565; PMCID: PMC5476304.