Dr. Hemond's research capitalizes on the use of high-resolution MRI sequences, in particular structural, susceptibility-sensitive (iron), and diffusion imaging to characterize microstructural MS lesion features.

Through these detailed MRI assessments we aim to discover new biomarkers that can be used to improve medical decision-making at the bedside. Iron rim lesions, for example, are seen almost exclusively in MS but not other disease mimics; these markers will help improve the diagnostic process. Microstructural features of the MS lesion likely give insight into the current and future disease severity. Identifying patients who are at high risk for a more severe disease course will allow a more personalized approach to treatment, reducing harms and maximizing benefits of the increasing number of options available in MS care.  Microstructural lesion characteristics may also yield insight into the pathophysiology of MS itself. 

Dr. Hemond's research additionally aims to capture how these microstrucrual aspects of MS lesions evolve over time, based on the use of different MS disease therapies. We anticipate these features can be used as outcome markers in clinical trials, in ongoing work with Dr. Sathish Dundammadappa (UMass neuroradiology) and Dr. Daniel Reich's lab at the NIH. Moreover, in collaboration with immunologists and MS colleagues here at UMass, Dr. Hemond is co-leading projects to leverage this MRI data as a sensitive outcome marker to assist in the development and validation of blood and cerebrospinal fluid biomarker development. Ongoing collaborations include the assessment of Epstein-Barr infection and the dysregulated T-cell response to this virus (with Prof. Liisa Selin and Anna Gil), proteomic biomarker development in cerebrospinal fluid (with Dr. Carolina Ionete and immunologist Jillian Richmond), and deep, molecular immunoprofiling of the infectious "landscape" in MS with faculty neuroscientist Rigel Chan. 

Dr. Hemond also leads projects focused on the psycho-neuro-immuonlogical aspects of MS, in particular how behavioral interventions such as mindfulness based stress reduce reduce the "silent symptoms of MS" (such as anxiety, fatigue, depression) and mechanistically act through top-down modulation of the immune system.

There is a current project opening for using deep learning to train and ultimately perform automatic segmentation for multiple brain structures of interest in Multiple Sclerosis. Helpful background would include some proficiency in shell and Python.  Please email me if interested.