By the end of next year, approximately 1.25 million children in the United States will be living with autism, 49,000 young adults will suffer their first schizophrenic break, 795,000 individuals will have stroke, 16.2 million people will go through a major depressive episode, and 5.5 million older adults will manifest the early signs of Alzheimer’s disease. Despite decades of research, we have a bare-bones understanding of these disorders, and hence, a limited ability to treat them. In fact, most therapies are still administered on a trial-and-error basis, guided by “physician instinct” and patient behavior. The flipside to this coin is that we are entering a data revolution in clinical neuroscience. Modern-day imaging provides a natural window into brain functionality, both in health and disease. Augmenting the imaging data are a host of behavioral and genetic attributes. However, these snapshots of the brain are confounded by physiological noise, patient variability, and environmental confounds.
The Neural Systems Analysis Laboratory (NSA Lab) at Johns Hopkins University develops new machine learning algorithms that harness the power of noninvasive imaging for targeted biomarker discovery, therapeutic planning, and outcome assessment in clinical neuroscience. Our strategy is to combine hypothesis-driven insights about the brain with data-driven learning techniques; this combination provides interpretable and actionable information, while retaining predictive power. Our work has yielded new insights into debilitating neurological disorders, such as autism, epilepsy and schizophrenia, with the long-term goal of improving patient care.