Dr. Charles R. G. Guttmann is an Associate Professor of Radiology at Harvard Medical School, and the Director of the Center for Neurological Imaging, a multi-disciplinary laboratory at Brigham and Women’s Hospital, combining biomedical and computer scientists/biomedical engineers. His research interests focus on the understanding, modeling, and monitoring of neurological diseases, such as multiple sclerosis (MS) and small vessel disease in aging, by integrating imaging-derived biomarkers with clinical, genetic, immunological, and other biomarkers. To this end his laboratory has developed a large scale repository and image analysis workflow management system that has been in use for close to 20 years, and has been populated with images, image-derived metrics, a variety of genetic and blood biomarkers, as well as clinical information inserted at the time of clinical visits. To date the data repository includes over 25,000 magnetic resonance imaging (MRI) exams and data from over 45,000 clinical visits over the past in 8,556 MS patients. This system has enabled multiple large-scale, cross-disciplinary studies addressing a variety of scientific questions, such as genetic determinants of brain atrophy in MS, association studies between risk factors such as smoking and the progression of disease in MS, as well as prospective longitudinal studies to understand the clinical evolution and pathogenesis of MS, as well as to generate predictive models for use in clinical disease management. His current work includes identifying MRI correlates of fatigue and predictors of response to fatigue management interventions. Other projects in Dr. Guttmann's laboratory include studies on the role of perivascular spaces in sleep, sleep deprivation Alzheimer's Disease, and other neurological conditions; lesion-symptom mapping for image-guided focused ultrasound treatment of essential tremor. Dr. Guttmann is also leading the development of SPINE, a web-based virtual laboratory for collaborative scientific research and education. SPINE integrates data federation, image analysis workflows (automated and interactive), as well as statistical modeling. A differentiating aspect of SPINE is the tight interaction between scientific experimentation on one side, and education and image analysis standardization on the other. This approach includes web-based “club-sourcing” of domain experts and ‘crowdsourcing’ of less educated scientific contributors that can be certified in focused analytical tasks, such as outlining an anatomical structure on a CT or MRI image stack.