Early Detection of Aging Related Alterations in Endogenous Brain Fluctuations

Grant Winners

  • Evan Haskell, PhD – Farquhar College of Arts and Sciences
  • Siddharth Pandya, DO – College of Osteopathic Medicine
  • Naushira Pandya, MD, CMD – College of Osteopathic Medicine
  • Rajeswari Murugan – Farquhar College of Arts and Sciences

Deans

  • Don Rosenblum, PhD – Farquhar College of Arts and Sciences
  • Anthony Silvagni, DO, PharmD – College of Osteopathic Medicine

Abstract

Award Winners

Neurodegenerative disorders such as Alzheimer's disease and vascular dementia contribute a major portion of the rising health care costs in aging populations and adversely effect the quality of life of aging populations. The growing prevalence of dementia in the population underscores the need to develop new tools for early diagnosis and treatment before the neural damage becomes irreversible. Recently fMRI has begun to be utilized as a technique for measuring the endogenous activity of the brain. Understanding the relationship between alterations in neurophysiological mechanisms and connectivity and fMRI measurement due to aging can potentially assist in early diagnosis and treatment of abnormal brain function prior to significant tissue damage resulting in significant improvement in the likelihood of positive outcomes. The coherent spatio-temporal patterns supported by the brain that are revealed by fMRI provide an opportunity for true interdisciplinary study of global brain behavior and alterations due to natural processes of aging and pathological functional abnormalities and diseases. We will develop a mathematical/computational model relating the short and long range interconnectivity of neural networks to the measured fMRI output to explore the functional role of neurophysiological mechanisms and connectivity in fMRI measurements. The resulting data from this model will be interpreted and analyzed by a professional radiologist and clinician to gain new insight into early detection of brain pathology and other functional abnormalities. The exploration of short and long range communication in neural networks is a very promising area of research that is best approached through an interdisciplinary collaboration. The future impact of such research includes the development of new tools for early diagnosis and treatment of functional abnormalities of the brain such as the tools that we develop in this project.