Dr. Lisa Nickerson is an Associate Professor of Psychiatry at Harvard Medical School and the Director of the Applied Neuroimaging Statistics Research Laboratory at McLean Hospital. Her research focuses on developing and applying innovative machine learning and statistical methodologies for analyzing multimodal neuroimaging data to study Alzheimer’s Disease, substance use, and psychiatric disorders.

Dr. Nickerson’s current work integrates large-scale, open-access neuroimaging datasets, including the Human Connectome Project (HCP), Connectomes Related to Human Disease (CRHD), and Alzheimer’s Disease Neuroimaging Initiative (ADNI). Her research leverages computational techniques such as tensor and matrix decomposition methods, including independent component analysis (ICA) and linked ICA for data fusion, as well as normative modeling, to investigate brain structure and function. She is the principal investigator on a five-year NIH-funded R01 grant aimed at utilizing HCP Lifespan and Connectomes Related to Human Disease datasets to study neuropsychiatric symptoms in Alzheimer’s Disease.

In addition to her research, Dr. Nickerson provides statistical support, training, and mentorship for students and junior scientists. She teaches seminars and courses at Harvard and MIT on topics such as imaging statistics, resting-state and task-based fMRI processing, multivariate data-driven methods, and other advanced neuroimaging techniques. Dr. Nickerson also serves as a biostatistics consultant for Harvard Catalyst and is a faculty member of the Harvard Mind Brain Behavior and Harvard Data Science Initiatives. Through the HDSI MIND project, she mentors students and postdoctoral fellows.

Dr. Nickerson’s lab is actively recruiting, with opportunities for new student trainees and two postdoctoral fellow positions: one through a NIDA T32 program focused on neuroimaging of substance use, and another to work on an NIA-funded R01 project on multimodal computational neuroimaging, big data, neuropsychiatric symptoms, and Alzheimer’s Disease.