How to capture developmental brain dynamics: gaps and solutions

Abstract

Capturing developmental and learning-induced brain dynamics is extremely challenging as changes occur interactively across multiple levels and emerging functions. Different levels include the (social) environment, cognitive and behavioral levels, structural and functional brain changes, and genetics, while functions include domains such as math, reading, and executive function. Here, we report the insights that emerged from the workshop “Capturing Developmental Brain Dynamics”, organized to bring together multidisciplinary approaches to integrate data on development and learning across different levels, functions, and time points. During the workshop, current main gaps in our knowledge and tools were identified including the need for: (1) common frameworks, (2) longitudinal, large-scale, multisite studies using representative participant samples, (3) understanding interindividual variability, (4) explicit distinction of understanding versus predicting, and (5) reproducible research. After illustrating interactions across levels and functions during development, we discuss the identified gaps and provide solutions to advance the capturing of developmental brain dynamics.

A methodological perspective on learning in the developing brain

“…Developmental cognitive neuroscience studies have the potential to advance our understanding of learning by combining innovative research methods with longitudinal datasets capturing development from micro (genes, brain) to macro (behavior, environment) levels 48 . That is, (more) rich longitudinal studies are needed to understand learning and learning challenges within individuals, and to address outstanding questions on how interactions between individual characteristics, experience, and environmental influences shape learning across development 26 , 48 – 51 . In the methods discussed in this review, the longitudinal element is sometimes central (such as in intervention studies), whereas in other methodological approaches they have yet to be integrated (such as computational modeling approaches).…”

How Learning to Read Changes the Listening Brain

“…Promising experimental paradigms to disentangle these different explanatory possibilities are learning paradigms, such as artificial script learning and text-based recalibration that permit to trace children’s actual learning trajectories. Ideally, these types of paradigms should be combined with a longitudinal multi-level approach incorporating developmental dynamics at various levels including genetic, social environmental, cortical and subcortical brain changes as well as cognitive and behavioral factors ( van Atteveldt et al, 2021 ). This approach ideally integrates developmental changes across both domain-specific and domain-general functional networks, for example, reading, math, and executive function, thereby acknowledging the multi-deficit spectral view of specific learning disorders including dyslexia ( Pennington, 2006 ; van Bergen et al, 2014 ; Peters and Ansari, 2019 ).…”

Editorial: Capturing developmental brain dynamics

“…The strongly interactive nature of the developing brain is highlighted in this Collection in the meeting report by the workshop organizers 1 and in a methodological perspective on learning and brain development 2 . These articles explore the interactions between maturational changes in the brain (its structure, function, and connectivity) and sensory input from the environment, often called “experience”.…”

“…These articles explore the interactions between maturational changes in the brain (its structure, function, and connectivity) and sensory input from the environment, often called “experience”. Van Atteveldt et al 1 emphasize the importance of interactions between different functions (e.g., reading, math, executive functions), called interactive specialization 3 or mutualism 4 . Van Duijvenvoorde et al 2 illuminate the overlap as well as differences between learning and neurobiological maturational changes.…”

“…Regarding this first suggestion, van Duijvenvoorde et al 2 suggest the use of longitudinal intervention designs as a method that can potentially disentangle maturational and experience-dependent processes in the brain, if neuroimaging and behavioral data are concurrently obtained over time. The meeting report 1 suggests that in addition to multiple timepoints and levels (such as neuroimaging and behavioral data), datasets should also include larger and more representative samples. Regarding the second suggestion, both papers point out that with the widely used analytical strategies that treat individual variability as noise, we miss meaningful types of individual variability.…”