- Post Doctoral
MIT Unit Affiliation:
- Electrical Engineering & Computer Science
Post Doc Sponsor / Advisor:
Date PhD Completed:
Top 3 Areas of Expertise:
Expected End Date of Post Doctoral Position:
Selected Research Projects:
Decoding and Characterization of Psychiatric Disorders (July 2014-present)
Neuroscience Statistics Research Lab, Massachusetts Institute of Technology, Cambridge, MA
● Characterizing psychiatric disorders and cognitive state estimation from continuous and binary physiological, neural, and behavioral measures
● Developed a signal processing algorithm to recover the stimuli (visual cue, mild electrical shock, emotional response) in Fear Conditioning and Fear Extinction Experiments to understand one’s emotional state.
Real-Time Control of Cortisol Deficiency (June 2014-present)
Neuroscience Statistics Research Lab and Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA
● Developing pulse control algorithms for real-time control of cortisol deficiency
● The ultimate goal is to implement the controller in real-time in cortisol insufficient patients
System Identification of Pulsatile Cortisol Secretion (September 2008 to May 2014)
Laboratory for Information and Decision Systems and Neuroscience Statistics Research Lab, Massachusetts Institute of Technology, Cambridge, MA
● Modeled cortisol secretion using a control feedback model
● Developed an algorithm for deconvolution of pulsatile cortisol serum levels using compressed sensing
● Quantified pituitary-adrenal dynamics and developed an algorithm for deconvolution of concurrent cortisol and adrenocorticotropic hormone data using compressed sensing
● Proposed a physiologically plausible optimization formulation for cortisol secretion
Parameter Estimation and Synchronization of a Novel Spiking Neuron Model (September 2008 to May 2011)
Laboratory for Information and Decision Systems, and Neuroscience Statistics Research Lab, Massachusetts Institute of Technology, Cambridge, MA
● Proposed an extension to the FitzHugh-Nagumo (FHN) model to generate spiking activities that previously could only be obtained by much more complex models
● Devised an algorithm for estimating the spiking threshold in the classical & time-varying FHN models
● Studied synchronization of coupled FHN neurons
Cortisol controls the body's metabolism and response to inflammation and stress. Cortisol is released in pulses from the adrenal glands in response to pulses of adreno-corticotropic hormone (ACTH) released from the anterior pituitary; in return, cortisol has a negative feedback effect on ACTH release. Modeling cortisol secretion and the interactions between ACTH and cortisol allows for quantifying normal and abnormal physiology and can potentially be used for diagnosis and optimal treatment of some cortisol disorders. Due to noise, modeling these interactions using concurrent data from serum ACTH and cortisol levels is challenging. First, using serum cortisol levels, we model cortisol secretion from the adrenal glands by representing the sparse pulses of cortisol using an impulse train. We formulate an optimization problem and successfully recover infusion and clearance rates as well as physiologically plausible cortisol pulses. Then, for serum ACTH and cortisol levels, we model ACTH and cortisol secretion by representing the sparse ACTH pulses using an impulse train. By considering a multi-rate system, we formulate another optimization problem and successfully recover model parameters as well as physiologically plausible ACTH pulses. We solve both optimization problems under the assumption that the number of pulses is between 15 to 22 pulses over 24 hours, and recover the timing and amplitudes of the pulses using compressed sensing, and employ generalized cross validation for determining the number of pulses. In all our studies mentioned above, the datasets we use consist of ACTH and cortisol levels sampled at 10-minute intervals from 10 healthy women. Finally, we present a mathematical characterization of pulsatile cortisol secretion. We hypothesize that there is a controller in the anterior pituitary that leads to pulsatile release of cortisol, and propose a mathematical formulation for such controller. Our proposed controller achieves impulse control, and the obtained impulses and plasma cortisol levels exhibit cortisol circadian and ultradian rhythms that are in agreement with experimental data.
Top 5 Awards and honors (name of award, date received):
5 Recent Papers:
Faghih R.T., Dahleh M.A., Adler G.K., Klerman E.B., and Brown E.N., Quantifying Pituitary Adrenal Dynamics: Deconvolution of Concurrent Cortisol and Adrenocorticotropic Hormone Data by Compressed Sensing. IEEE Transactions on Biomedical Engineering, 62(10): 2379-2388, 2015.
Faghih R.T., Dahleh M.A., and Brown E.N., An Optimization Formulation for Characterization of Pulsatile Cortisol Secretion. Frontiers in Neuroscience, 9: 228, 2015.
Faghih R.T., Dahleh M.A., Adler G., Klerman E., and Brown E.N., Deconvolution of Serum Cortisol Levels by Using Compressed Sensing, PLoS ONE 9(1): e85204. doi:10.1371/journal.pone.0085204, 2014.
Faghih R.T., Savla K., Dahleh M.A., and Brown E.N., Broad Range of Neural Dynamics from a Time-Varying FitzHugh-Nagumo Model and Its Spiking Threshold Estimation IEEE Transactions on Biomedical Engineering, Vol. 59, No. 3, pages 816-823, 2012.
Faghih R.T., Savla K., Dahleh M.A., and Brown E.N., A Feedback Control Model for Cortisol Secretion. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, p.716-719, 2011.