Developmental Cognitive Neuroscience in the Era of Networks and Big Data: Strengths, Weaknesses, Opportunities, and Threats
Damien A. Fair, Nico U.F. Dosenbach, Amy H. Moore, Theodore D. Satterthwaite, Michael P. Milham
Abstract
Developmental cognitive neuroscience is being pulled in new directions by network science and big data. Brain imaging [e.g., functional magnetic resonance imaging (fMRI), functional connectivity MRI], analytical advances (e.g., graph theory, machine learning), and access to large computing resources have empowered us to collect and process neurobehavioral datafaster and in larger populations than ever before. The translational potential from these advances is unparalleled, as a better understanding of complex human brain functions is best grounded in the onset of these functions during human development. However, the maturation of developmental cognitive neuroscience has seen the emergence of new challenges and pitfalls, which have significantly slowed progress and need to be overcome to maintain momentum. In this review, we examine the state of developmental cognitive neuroscience in the era of networks and big data. In addition, we provide a discussion of the strengths, weaknesses, opportunities, and threats (SWOT) of the field to advance developmental cognitive neuroscience's scientific and translational potential.