LAUSR

Developmental cognitive neuroscience is a truly interdisciplinary field of research that has the potential to answer critical questions about neural plasticity and neural substrates of learning and behavior across cognitive, affective, and social domains of functioning. It therefore has the potential to not only help us understand trajectories and mechanisms of typical development, but also translate this knowledge to the prevention and treatment of emerging psychopathology and health-risking behaviors. However, to reach these goals our field must be able to model how these processes change within individuals across time. Given how central this methodological issue is to our endeavours, it is surprising that there has been relatively little attention paid to integrating neuroscientific methods with cutting edge statistical techniques for modelling longitudinal change, nor have there been published methodological guidelines on many relevant topics. The current special issue sets out to begin to address this lacuna. Many techniques have been employed to examine the brain across development, including (but not limited to) familiar modalities like structural, functional, and diffusion magnetic resonance imaging (s/f/d MRI), as well as less widespread ones like functional near infrared spectroscopy (fNIRS) or magnetic resonance elastography (MRE). Each of these methods addressed in the special issue have unique strengths and limitations that shape recommendations for data acquisition and analysis, and provide different information about normative and atypical trajectories of brain development. Importantly, researchers are increasingly relying on longitudinal data sets to investigate change within individuals. However, longitudinal studies require special consideration in design, as well as data acquisition, processing, analysis, and interpretation. Despite increasing acknowledgement of methodological issues across modalities and study designs in the cognitive neurosciences, there are relatively limited guidelines available to provide best practices, particularly with developmental populations. This lack of consensus could be contributing to inconsistencies in the literature. For example, recent studies have found that differences in sample composition, quality control procedures, and data analytic approaches affect observed trajectories of brain development (Ducharme et al., 2016; LeWinn et al., 2017). Further, it is unclear how these factors affect associations between brain development and cognition or other behavior. This special issue of Developmental Cognitive Neuroscience, “Methodological Challenges in Developmental Neuroimaging: Contemporary Approaches and Solutions,” presents papers that make headway in understanding and overcoming these methodological concerns, as well as shape strategic research priorities, and suggest guidelines that may serve as best practices for study design, data acquisition, analysis, and dissemination of findings.