Recent years have witnessed an explosion of interest in neuroimaging the human brain and in using it to predict brain-based disease. However, the field generally conceives of neuroimaging as revealing disease-specific activation areas or networks, rather than explicitly considering the forces that govern the brain networks’ development. The conceptual transition—from thinking of neuroimaging as providing a static feature to thinking of its ability to capture a dynamic process—is critical for probing how a disease first develops in the brain, as well as for asking why two individuals—with identical clinical diagnoses—might show markedly different prognoses over time.

Most diseases in the body are “dysregulatory,” which means that the negative feedback loops in the body that maintain homeostasis break down in various ways. Importantly, physiological systems often slowly degenerate for years or decades before onset of symptoms.Thus, the ability to identify subtle shifts in the dynamics of those feedback loops would permit treatment of illnesses before they become symptomatic; in the way, for instance, that a glucose tolerance test is currently able to identify pre-diabetes before a person shows any diabetic symptoms. Dr. Lilianne Mujica-Parodi, Associate Professor of Biomedical Engineering at Stony Brook University School of Medicine and Associate Neuroscientist at Massachusetts General Hospital and Harvard Medical School, is developing novel methods to identify subtle signs of dysregulation in the homeostatic control circuits that regulate different brain functions, so that we can identify risk, prognoses, and therefore ultimately more effective treatment, for developing psychiatric and neurological disorders. The ability to "see" dysregulation with the techniques that Dr. Mujica-Parodi and her team at the Laboratory of Computational Neurodiagnostics (LCNeuro) are developing, would have an enormous impact not only for pre-symptomatic diagnosis and treatment, but also for pharmaceutical development and studies of disease genetics.

Thus far, LCNeuro takes an approach almost completely unique throughout the world, in developing methods to use a single patient’s neuroimaging data to derive his or her own “personal computational brain model,” composed of control circuits (described mathematically as a system of coupled differential equations). The advantage of having such a model is that one can, in principle, then feed it any set of new inputs, and thus simulate how the brain’s current dynamical state will evolve over minutes, hours, months, and possibly even years and decades. In so doing, Dr. Mujica-Parodi’s aim is to not only diagnose individuals in their current state, but also to predict that individual’s disease trajectory. In highly interactive clinical settings, Dr. Mujica-Parodi has worked with physicians and patients with a wide range of psychiatric and neurological diseases, including paranoid schizophrenia, clinical anxiety, major depressive disorder, addiction, and epilepsy. In addition, she has worked with the military to develop neurodiagnostic methods designed to predict resilience to combat-related stress for Special Forces populations. Her applied, interdisciplinary research, spanning clinical research, basic neuroscience, engineering, physics, and mathematics, is largely influenced by her interest and background in theoretical physics. Unlike much medical research, which is focused upon working upon compartmentalized areas, Dr. Mujica-Parodi aims to identify critical pathways and dynamics whose perturbation can unify and explain a wide variety of brain-based signs and symptoms. Combining neuroscience with ultra-high field ultra-fast fMRI and taking analytic techniques from control systems engineering, dynamical systems, and chaos theory, Dr. Mujica-Parodi hopes to provide real-world practical applications that can potentially change the face of brain-based medicine.

Current research includes:

• Psychiatry: LCNeuro investigates how different types of homeostatic dysregulation of the brain’s prefrontal-limbic and reward circuits lead to schizophrenia, anxiety, depression, or addiction, with the aim of predicting risk for developing the disease or, once patients are symptomatic, predicting how quickly the disease resolves or degenerates.  
• Neurology: Because the human brain consumes a disproportionately high amount of energy (~22%) per volume (2%), as compared to other organs, it is particularly vulnerable to changes in metabolism. Dietary increase in glycemic load over the past 100 years has led to a national epidemic of insulin resistance (Type 2 diabetes), which has been identified as increasing risk for later-life dementia by 45%. Integrating multi-scale computational modeling to identify emergent properties with respect to brain networks, LCNeuro is currently conducting human studies to understand how diet affects energy constraints at the level of mitochondria, and in turn how mitochondria affect the aging brain’s ability to operate efficiently in response to cognitive demands. Additional research looks at the impact of metabolic dysregulation on the development of seizures. Up to one third of epileptics will see seizure frequency reduce on a ketogenic diet, yet the underlying basis for the phenomenon is still unknown.
• Optimizing Healthy Performance: The same control systems engineering approaches that can be used to predict risk for disease can also be used to predict how the healthy brain may respond to extreme environments. As one example, Dr. Mujica-Parodi has worked closely with elite military organizations in order to develop individual neurodiagnostics designed to predict resilience to combat-related stress, as well as to predict how an individual will make decisions under stress.
• Developing Better Tools: Dr. Mujica-Parodi’s analytical approaches are optimized for a quality of neuroimaging data that is seldom achieved in today’s clinical, or even research, scanners. Thus, one area of intense focus for her laboratory has been not only on designing better algorithms for analyzing data, but in finding ways to acquire the highest-quality data or to develop methods to amplify signal from data obtained from technologies with mainstream clinical usage. Dr. Mujica-Parodi is also interested in applying her analytic approaches to other physiological signals, and thus designed instrumentation for continuously and noninvasively measuring blood glucose levels (GlucoREAD). GlucoREAD is designed to complement her investigation of how regulation of glucose affects the brain, but also has mainstream applications for self-monitoring by diabetics.