Event segmentation, which involves dividing continuous information into meaningful units, changes as children develop into adolescents. Adolescents tend to segment events more coarsely than adults. This study explores whether adolescents could adjust their segmentation style to resemble that of adults when provided with explicit metacontrol-related instructions. We compared event segmentation in two adolescent groups and one adult group, while simultaneously recording EEG data. One adolescent group was instructed to perform segmentation as finely as possible, whereas the other adolescent group and adults received no specific instructions on segmentation granularity. EEG data were analyzed using multivariate pattern analysis and source reconstruction. The findings revealed that adolescents given fine-grained instructions adjusted their segmentation probability closer to adult levels, although they did not fully match adults in processing multiple simultaneous changes. Neurophysiological results indicated that adolescents with fine-grained instructions exhibited neural decoding performance more similar to adults. Increased activity in the inferior frontal gyrus in these adolescents compared to adults related to this. The results suggest that adolescents with fine-grained instructions demonstrated more persistent cognitive control and enhanced top-down attention than their peers and adults. The study shows that adolescent cognitive processes can be shifted toward adult-like performance through instructions.

Computational neuroscience offers a valuable opportunity to understand the neural mechanisms underlying behavior. However, interpreting individual differences in these mechanisms, such as developmental differences, is less straightforward. We illustrate this challenge through studies that examine individual differences in reinforcement learning. In these studies, a computational model generates an individual-specific prediction error regressor to model activity in a brain region of interest. Individual differences in the resulting regression weight are typically interpreted as individual differences in neural coding. We first demonstrate that the absence of individual differences in neural coding is not problematic, as such differences are already captured in the individual specific regressor. We then review that the presence of individual differences is typically interpreted as individual differences in the use of brain resources. However, through simulations, we illustrate that these differences could also stem from other factors such as the standardization of the prediction error, individual differences in brain networks outside the region of interest, individual differences in the duration of the prediction error response, individual differences in outcome valuation, and in overlooked individual differences in computational model parameters or the type of computational model. To clarify these interpretations, we provide several recommendations. In this manner we aim to advance the understanding and interpretation of individual differences in computational neuroscience.

In response to Covid-19, western governments introduced policies that likely resulted in a reduced variety of facial input. This study investigated how this affected neural representations of face processing: speed of face processing; face categorization (differentiating faces from houses); and emotional face processing (differentiating happy, fearful, and neutral expressions), in infants (five or ten months old) and children (three years old). We compared participants tested before (total N = 462) versus during (total N = 473) the pandemic-related policies, and used electroencephalography to record brain activity. Event Related Potentials showed faster face processing in three-year-olds but not in infants during the policies. However, there were no meaningful differences between the two Covid-groups regarding face categorization, indicating that this fundamental process is resilient despite the reduced variety of input. In contrast, the processing of facial emotions was affected: across ages, while pre-pandemic children showed differential activity, during-pandemic children did not neurocognitively differentiate between happy and fearful expressions. This effect was primarily attributed to a reduced amplitude in response to happy faces. Given that these findings were present only in the later neural components (P400 and Nc), this suggests that post-pandemic children have a reduced familiarity or attention towards happy facial expressions.

Early life adverse experiences, including childhood maltreatment, are major risk factors for psychopathology, including anxiety disorders with dysregulated fear responses. Consistent with human studies, maltreatment by the mother (MALT) leads to increased emotional reactivity in rhesus monkey infants. Whether this persists and results in altered emotion regulation, due to enhanced fear learning or impaired utilization of safety signals as shown in human stress-related disorders, is unclear. Here we used a rhesus model of MALT to examine long-term effects on state anxiety and threat/safety learning in 25 adolescents, using a fear conditioning paradigm (AX+/BX-) with acoustic startle amplitude as the peripheral measure. The AX+/BX- paradigm measures baseline startle, fear-potentiated startle, threat/safety cue discrimination, startle attenuation by safety signals, and extinction. Baseline startle was higher in MALT animals, suggesting elevated state anxiety. No differences in threat learning, or threat/safety discrimination were detected. However, MALT animals showed generalized blunted responses to the conditioned threat cue, regardless of the safety cue presence in the transfer test, and took longer to extinguish spontaneously recovered threat. These findings suggest adverse caregiving experiences have long-term impacts on adolescent emotion regulation, including elevated state anxiety and blunted fear conditioning responses, consistent with reports in children with maltreatment exposure.

Task-related functional MRI (fMRI) studies need to be properly powered with an adequate sample size to reliably detect effects of interest. But for most fMRI studies, it is not straightforward to determine a proper sample size using power calculations based on published effect sizes. Here, we present an alternative approach of sample size estimation with empirical Bayesian updating. First, this method provides an estimate of the required sample size using existing data from a similar task and similar region of interest. Using this estimate researchers can plan their research project, and report empirically determined sample size estimations in their research proposal or pre-registration. Second, researchers can expand the sample size estimations with new data. We illustrate this approach using four existing fMRI data sets where Cohen's d is the effect size of interest for the hemodynamic response in the task condition of interest versus a control condition, and where a Pearson correlation between task effect and age is the covariate of interest. We show that sample sizes to reliably detect effects differ between various tasks and regions of interest. We provide an R package to allow researchers to use Bayesian updating with other task-related fMRI studies.

In older children and adults, cognition builds upon waking sensory experience which is consolidated during sleep. In the fetus and newborn, sensory input is instead largely experienced during sleep. The nature of these sensory inputs differs within sleep, between active and quiet sleep, as well as versus wakefulness. Here, sleep-wake organisation in the fetus and newborn is reviewed, and then its interaction with sensory inputs discussed with a focus on somatosensory and auditory modalities. Next, these ideas are applied to how neurological insults affect early development, using fetal growth restriction as a test case. Finally, the argument is made that taking account of sleep-wake state during perinatal functional neuroimaging can better index sensorimotor, language, and cognitive brain activities, potentially improving its diagnostic and prognostic value. To sum up, sensory and sleep-wake functions go hand in hand during early human development. Perturbation of these twinned functions by neurological insults may mediate later neurodevelopmental deficits. Perinatal neuroimaging has the potential to track these trajectories, feasibly identifying opportunities to therapeutically intervene.

Large diffusion-weighted brain MRI (dMRI) studies in neonates are crucial for developmental neuroscience. Our aim was to investigate the utility of ComBat, an empirical Bayes tool for multisite harmonization, in removing site effects from white matter (WM) dMRI measures in healthy infants born at 37 gestational weeks+ 0 days-42 weeks+ 6 days from the Theirworld Edinburgh Birth Cohort (n = 86) and Developing Human Connectome Project (n = 287). Skeletonized fractional anisotropy (FA), mean, axial and radial diffusivity (MD, AD, RD) maps were harmonized. Differences between voxel-wise metrics, skeleton means and histogram widths (5th-95th percentile) were assessed before and after harmonization, as well as variance associated with gestational age at birth and scan. Before harmonization, large cohort differences were observed. Harmonization removed all voxel-wise differences from MD maps and all metric means and histogram widths, however small voxel-wise differences (<1.5 % of voxels) remained in FA, AD and RD. We detected significant relationships between GA at birth and all metrics. When comparing single site and multisite harmonized datasets of equal sample sizes, harmonized data resulted in smaller standardized regression coefficients. ComBat could enable unprecedented sample sizes in developmental neuroscience, offering new horizons for biomarker discovery and validation, understanding typical and atypical brain development, and assessing neuroprotective therapies.

Our understanding of how visual cortex neural processes mature during infancy and toddlerhood is limited. Using magnetoencephalography (MEG), the present study investigated the development of visual evoked responses (VERs) in cross-sectional and longitudinal samples of infants and toddlers 2 months to 3 years. Brain space analyses focused on N1m and P1m latency, as well as N1m-to-P1m amplitude. Associations between VER measures and developmental quotient (DQ) scores in the cognitive/visual and fine motor domains were also examined. Results showed a nonlinear decrease in N1m and P1m latency as a function of age, characterized by rapid changes followed by slower progression, with the N1m latency plateauing at 6-7 months and the P1m latency plateauing at 8-9 months. The N1m-to-P1m amplitude also exhibited a non-linear decrease, with strong responses observed in younger infants (∼2-3 months) and then a gradual decline. Associations between N1m and P1m latency and fine motor DQ scores were observed, suggesting that infants with faster visual processing may be better equipped to perform fine motor tasks. The present findings advance our understanding of the maturation of the infant visual system and highlight the relationship between the maturation of the visual system and fine motor skills.