Developmental Cognitive Neuroscience in the Era of Networks and Big Data: Strengths, Weaknesses, Opportunities, and Threats

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 data faster 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. Expected final online publication date for the Annual Review of Developmental Psychology, Volume 3 is December 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience

In the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus furthering the potential of Developmental Cognitive Neuroscience (DCN). However, the traditional paradigms used for the analysis of infant fNIRS data are still quite limited. Here, we introduce a multivariate pattern analysis for fNIRS data, xMVPA, that is powered by eXplainable Artificial Intelligence (XAI). The proposed approach is exemplified in a study that investigates visual and auditory processing in six-month-old infants. xMVPA not only identified patterns of cortical interactions, which confirmed the existent literature; in the form of conceptual linguistic representations, it also provided evidence for brain networks engaged in the processing of visual and auditory stimuli that were previously overlooked by other methods, while demonstrating similar statistical performance.

Toward Realizing the Promise of Educational Neuroscience: Improving Experimental Design in Developmental Cognitive Neuroscience Studies

This review presents a critical appraisal of high-quality studies in the field of developmental cognitive neuroscience, focusing on design issues that are critical for establishing effective educational neuroscience. I argue that cognitive neuroscience studies of cognitive development need to respect important experimental constraints. The use of longitudinal and intervention designs is key. The field needs to move beyond simply studying patterns of brain activation to studying brain mechanisms of information encoding and information processing. Indeed, studies at multiple levels of description are required, combining the assessment of individual differences in neural learning, sensory processing, cognitive processing, and children's behavior. Current evidence suggests that the child brain has essentially the same structures as the adult brain, carrying out essentially the same functions via the same mechanisms. This review demonstrates that neural systems that learn the patterns or regularities in environmental input (via statistical learning) can, in principle, acquire complex cognitive structures like language and conceptual knowledge.

Motor development in infants and children

Motor development has been relatively neglected in Developmental Psychology over the past 30 years. A recent renaissance of interest in this domain provides new insights into the dynamic nature of motor development with large individual differences, the myriad of factors influencing motor skill learning, and the long-lasting and important implications of motor activity for cognition, language, and even academic achievement. These behavioral and observational findings raise new questions that need to be addressed by future research. Developmental Cognitive Neuroscience is uniquely positioned to answer open questions about motor development and to contribute to our understanding of the processes underlying the variability, malleability, and generality of motor development. This chapter summarizes select current findings and hopes to stimulate future research using Developmental Cognitive Neuroscience methods.