Will talk about: The Functional Connectomes Project
Michael P. Milham, MD, PhD, is an internationally recognized neuroscience researcher, a gifted and caring clinician, and the founding director of the Center for the Developing Brain at the Child Mind Institute. Dr. Milham's innovative research techniques signal a sea change in the field and a revolution in discovery science.
Dr. Milham joined the Child Mind Institute after holding the Leon Levy assistant professorship of child and adolescent psychiatry at the NYU School of Medicine, and the associate directorship of the Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center. He is also a research psychiatrist at the Nathan S. Kline Institute for Psychiatric Research of the New York State Office of Mental Health.
As cofounder of the 1000 Functional Connectomes Project (FCP), Dr. Milham helped invigorate the neuroimaging community by aggregating more than 1000 datasets independently collected by imaging sites around the world, and making them publicly available to the scientific community without restriction. He also oversaw the initial feasibility analyses of the FCP datasets, which established the utility of pooling functional imaging data across laboratories. He then went on to found the International Neuroimaging Data-sharing Initiative (INDI), which established a model for the sharing of imaging data prospectively-before it is examined or published-and thus accelerating the pace of research exponentially while expanding the field of scientific disciplines engaged in this endeavor. Most recently, as a founding member and coordinator for the ADHD-200 Consortium, Dr. Milham helped bring together and share more than 800 imaging datasets from studies of attention-deficit hyperactivity disorder (ADHD), sparking a global competition to advance our understanding of the neurobiology of ADHD.
Dr. Milham is also a prolific scientist, with over 60 articles published since 2000, and an average of about 10 publications per year during the past three years. He has published in the most scientifically respected journals, including the American Journal of Psychiatry, Journal of Neuroscience, Biological Psychiatry,Proceedings of the National Academy of Science and the Archives of General Psychiatry.
Dr. Milham received his PhD at the University of Illinois, Urbana-Champaign in cognitive neuroscience, psychophysiology, and clinical neuroscience. He received his medical degree from the University of Illinois Medical School, where he became a member of Alpha Omega Alpha National Medical Honor Society. Dr. Milham completed his general psychiatry residency training at the NYU Langone Medical Center, and his child and adolescent psychiatry fellowship training at the NYU Child Study Center.
Dr. Milham is a member of the Society for Neuroscience, the Organization for Human Brain Mapping, and the Cognitive Neuroscience Society. He has received honors and support from the National Institute of Mental Health (NIMH).
Central to the development of clinical tools for developmental neuropsychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is emerging as a mainstream approach for imaging-based biomarker identification, detecting variations in the human connectome that can be attributed to developmental and clinical variables (e.g., diagnostic status). Despite growing enthusiasm, many challenges remain. I will discuss evidence of the readiness of R- fMRI based functional connectomics to lead to clinically meaningful biomarker identification through the lens of the criteria used to evaluate clinical tests (i.e., validity, reliability, sensitivity, specificity, and applicability). Gaps and needs for R-fMRI- based biomarker identification will be identified, and the potential of emerging conceptual, analytical and cultural innovations (e.g., the Research Domain Criteria Project (RDoC), open science initiatives, and Big Data) to address them will be highlighted. The need to expand future efforts beyond identification of biomarkers for disease status alone will be discussed, with a particular emphasis on the importance of identifying clinical variables related to risk, expected treatment response and prognosis.