Biomedical engineering, particularly as it applies to neuroscience, has reached a stage of development at which further understanding of complex neural systems, such as the hippocampus and other cortical regions that underlie cognition and higher thought processes, will depend on mathematical modeling as a means to organize experimental data that is known, and to systematically explore the unknown. The research objectives of Core Project #4 are to further develop and apply methodologies based on principles of nonlinear systems theory for experimentally-based, mathematical modeling of neurons and neural systems. This approach leads to what are commonly termed "non-parametric" or "input-output" models, i.e., functional properties that emerge as a consequence of interactions among the internal components of the system - without necessarily describing the internal components themselves. In contrast, "parametric models" represent the mechanistic properties of the system, with parameters that can be interpreted directly with respect to those underlying mechanisms. We will explore parametric modeling of the hippocampus both in the context of a glutamatergic synaptic model (EONS) continued from previous work, and a new project: a large-scale, compartmental neuron model (10[6] neurons, 10[10] synapses) of hippocampus that incorporates much of the quantitative neuroanatomy, synaptic physiology, and topographic connectivity available for that structure. Ultimately our goal is to establish means for the synergistic use of non-parametric and parametric modeling methods, in the context of accelerating multi-scale modeling, to further our understanding of how global system dynamics underlying cognition, and specifically memory, derive from molecular and synaptic mechanisms.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Biotechnology Resource Grants (P41)
Project #
Application #
Study Section
Special Emphasis Panel (ZEB1-OSR-C)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Southern California
Los Angeles
United States
Zip Code
Wu, Ziyue; Kim, Yoon-Chul; Khoo, Michael C K et al. (2014) Evaluation of an independent linear model for acoustic noise on a conventional MRI scanner and implications for acoustic noise reduction. Magn Reson Med 71:1613-20
Zhang, Yaping; Hsu, Cheng-Pang; Lu, Jian-Feng et al. (2014) FLT3 and CDK4/6 inhibitors: signaling mechanisms and tumor burden in subcutaneous and orthotopic mouse models of acute myeloid leukemia. J Pharmacokinet Pharmacodyn 41:675-91
Marmarelis, Vasilis Z; Shin, Dae C; Orme, Melissa et al. (2014) Time-varying modeling of cerebral hemodynamics. IEEE Trans Biomed Eng 61:694-704
Hajjar, Ihab; Marmerelis, Vasilis; Shin, Dae C et al. (2014) Assessment of cerebrovascular reactivity during resting state breathing and its correlation with cognitive function in hypertension. Cerebrovasc Dis 38:10-6
Kim, Yoon-Chul; Lebel, R Marc; Wu, Ziyue et al. (2014) Real-time 3D magnetic resonance imaging of the pharyngeal airway in sleep apnea. Magn Reson Med 71:1501-10
Song, Dong; Harway, Madhuri; Marmarelis, Vasilis Z et al. (2014) Extraction and restoration of hippocampal spatial memories with non-linear dynamical modeling. Front Syst Neurosci 8:97
Marmarelis, V Z; Shin, D C; Song, D et al. (2014) On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes. J Comput Neurosci 36:321-37
Marmarelis, V Z; Shin, D C; Orme, M E et al. (2014) Model-based physiomarkers of cerebral hemodynamics in patients with mild cognitive impairment. Med Eng Phys 36:628-37
Zhou, Alyssa; Pacini, Giovanni; Ahren, Bo et al. (2014) Glucagon clearance is regulated by nutritional state: evidence from experimental studies in mice. Diabetologia 57:801-8
Meel-van den Abeelen, Aisha S S; Simpson, David M; Wang, Lotte J Y et al. (2014) Between-centre variability in transfer function analysis, a widely used method for linear quantification of the dynamic pressure-flow relation: the CARNet study. Med Eng Phys 36:620-7

Showing the most recent 10 out of 71 publications