Research shows that the theta rhythm in infant electroencephalogram indexes learning processes and is a promising candidate for a marker of early cognitive development. However, a scarcity of studies investigating the stability of individual differences in theta activity in infancy, and a large variability in analytical approaches in existing research constrain the interpretations of research findings. In our large longitudinal study, we related three different indices of frontocentral theta activity (absolute and relative power, and an index of theta modulation by novel content) at 6 and 12 months to cognitive development level, language skills, and visual attention at 24 months. We found an increase in theta power over the course of novel information encoding at 6 and 12 months, replicating prior studies. Both absolute and relative theta power, but not theta modulation index, showed a large degree of stability in individual differences from 6 to 12 months. Finally, absolute theta power at 6 and 12 months was a positive predictor of the general cognitive level, but not of specific skills (selective attention, language) at 24 months. Of note, we observed similar effects for absolute power in the alpha frequency band, suggesting that the effects are not specific to the theta frequency band. Our results support the involvement of the theta rhythm in cognitive development in infancy and point to absolute power as the potentially most sensitive index of individual differences in theta activity.

Multisensory processing and learning shape cognitive and language development, influencing how we perceive and interact with the world from an early age. While multisensory processes mature into adolescence, it remains poorly understood how age influences multisensory associative learning. This study investigated age-related effects on multisensory processing and learning during audio-visual and tactile-visual learning in 67 children (5.7-13 years) by integrating behavioural and neuroimaging data with computational methods. A reinforcement-learning drift diffusion model revealed that older children processed information faster and made more efficient decisions on multisensory associations. These age-related increases coincided with higher activity in brain regions associated with cognitive control, multisensory integration, and memory retrieval, specifically during audio-visual learning. Notably, the bilateral anterior insula exhibited heightened activation in response to lower reward prediction errors, indicative of increased sensitivity to negative feedback with development. Finally, reward prediction errors modulated activation in reward processing and cognitive control regions, with this modulation remaining modality-independent and largely stable across age. In conclusion, while children employ similar learning strategies, older children make decisions more efficiently and engage neural resources more strongly. Our findings reflect ongoing maturation of neural networks supporting multisensory learning in middle childhood, enabling more adaptive learning in later childhood.

Linear mixed effects models (LMEs) have advantages for analyzing mean amplitude event-related potential (ERP) data. Compared to ANOVA and linear regression, LMEs retain more subjects and yield unbiased parameter estimates by accounting for trial-level sources of variability. However, LME analysis of ERP mean amplitude difference waves may be problematic due to the need to pair single trial data to create trial-level difference waves. In both simulated and real pediatric ERP data, the present study compares ERP difference wave results across conventional ANOVA/regression analyses and six trial-level LME approaches in different low trial-count scenarios. We evaluate each approach based on accuracy of estimates and statistical power in simulated data, and magnitude of effect detected in real ERP data from 3- to 5-year-old neurotypical children (N = 64). Two analysis approaches were unbiased: creating trial-level difference waves by pairing trials on all study design features (the 'exact match' approach) and fitting an interaction term; and the interaction term had greater power to detect a significant effect in simulated data. Both simulations and analysis of real preschooler ERP data support using LMEs to analyze difference waves. We also include recommendations for researchers for picking a difference wave approach appropriate for their research question.

Preterm birth is a known risk factor for neurodevelopmental disabilities, but early neurobehavioral assessments and structural imaging often fail to predict long-term outcomes. This limitation underscores the need for alternative biomarkers that reflect early brain organization. Resting-state functional connectivity offers a powerful tool to track functional brain organization by characterizing resting-state networks (RSNs), potentially offering more sensitive biomarkers. However, most fMRI studies in infant populations use group-level analyses that average subject-specific data across several weeks of development, reducing sensitivity to subtle, time-sensitive deviations from typical brain trajectories, particularly in higher-order association networks. Using a recently introduced precision mapping approach, we estimated individual resting-state networks (RSNs) in a large cohort of term and preterm neonates from the developing Human Connectome Project. RSN connectivity strength increased linearly with age at scan, with primary sensory networks maturing earlier and higher-order association networks, including the default mode network (DMN), showing more gradual but pronounced changes, evolving from an immature organization in preterm infants to a more adult-like pattern in term-born infants. Longitudinal data from a subset of preterm infants confirmed ongoing network development shortly after birth. Despite this maturation, preterm infants did not reach the connectivity levels of term-born infants by term-equivalent age. These findings demonstrate that individualized RSN mapping captures heterogeneous developmental trajectories in the neonatal brain and highlights higher-order association networks, particularly the DMN, as promising early markers for monitoring neurodevelopmental outcomes in neonates.

Adolescent substance use (SU) rates remain high, speaking to continued need for enhanced insight into etiological factors. While working memory-related task performance and brain activity have been highlighted as potential predictors, mechanistic links to SU remain unclear. One possible link explored here is alexithymia, which is characterized by difficulty describing, identifying, and recognizing emotions and associated with altered prefrontal cortex (PFC) and superior temporal gyrus (STG) activity.

The Adolescent Brain Cognitive Development (ABCD) Study represents a pioneering initiative that aims to unravel the complexities of behavioral and neural development in youth. In this paper, we address the challenges inherent in extracting meaningful insights from the extensive data compiled by the ABCD initiative. Our focus is on advocating for best practices in reproducible research, interpretation of effect size, and reporting of principled results. Central to this discourse is a detailed examination of effect sizes within the expansive ABCD dataset, and how they can be meaningfully interpreted in the context of large-scale research. We describe the hurdles associated with transitioning from conventional small-sample studies to the opportunities and challenges of large samples, including the phenomenon of statistically significant but practically trivial effects. To promote transparent and rigorous inference, we present a four-part framework to evaluate observed effects: researchers should define a smallest effect size of interest (SESOI), compare estimates to relevant benchmarks, test whether observed effects exceed meaningful thresholds (e.g., through equivalence testing), and visualize results to enhance interpretation and communication. Applying this framework yields a clearer, more cumulative understanding of effect size interpretation and contributes substantively to the refinement of scientific practices within adolescent brain and cognitive development research.

Reduced P3 amplitude during selective attention has been linked to depression in cross-sectional studies primarily with adults. Neurodevelopmental research has yet to examine relations between age-related changes in P3 amplitude, assessed across multiple time points, and the emergence of depressive and anxiety symptoms during adolescence, which may vary by sex. The present study addresses this gap by testing the effects of between- and within-person depressive symptoms, age, and sex on P3 amplitude during the Flanker task, across up to three age time points in a sample of adolescents (N = 190, ages ∼12, 15 and 18) at risk for developing internalizing symptoms. When depression was measured continuously without adjusting for age and sex, higher within-person depressive symptoms emerged as a significant predictor of reduced P3 amplitude. However, when age, sex, and depression (continuous or binary diagnostic status) were modeled together, only age and sex, but not depression, remained significant predictors of P3 amplitude. Specifically, P3 amplitude decreased with age, and males consistently exhibited higher P3 amplitudes than females, with stable age-related decrease across sexes. For anxiety, neither between- nor within-person symptoms were significantly associated with P3 amplitude, with or without age and sex included in the model. Similar to the findings for depression, however, age and sex were significant predictors of P3 amplitude. Thus, previous studies involving a single assessment of P3 amplitude and depression symptoms may be influenced by developmental factors.

Intergenerational neuroimaging, which is used to investigate brain similarities between parent-offspring dyads, promises to elucidate the neural substrates of intergenerational transmission. However, merely identifying similar brain regions or networks between parents and offspring is not sufficient to reveal the mechanisms underlying this transmission. To understand these mechanisms, it is necessary to consider the potential contributions of shared genetic and environmental factors to the development of brain features that are similar between parents and offspring. Sex as a biological variable (SABV), a key factor in intergenerational neuroimaging, provides crucial insights into brain development. Although sex-based differences in brain developmental trajectories have been investigated, the role of SABV in parent-offspring brain similarity has been overlooked. In this narrative review, we summarize the key findings of previous intergenerational neuroimaging studies, grouping them into three categories based on study design: studies of mother-offspring dyads, studies combining fathers and mothers, and studies distinguishing between father-offspring and mother-offspring dyads. Furthermore, we highlight the genetic and environmental factors that may underlie sex-specific parent-offspring brain similarities. Finally, we propose further considerations to clarify the significance of parent-offspring brain similarity in human brain development. Advancements in intergenerational neuroimaging may shed light on mechanisms by which mental health risk is transmitted across generations, potentially providing opportunities for more effective prevention, stratification, and treatment.

The brain's remarkable plasticity during early development makes it highly responsive to environmental input, with early experiences having lasting effects on functioning and development. Both adversity and variations in normative caregiving experiences influence developmental trajectories. Accurately assessing these diverse experiences is crucial for understanding their role in shaping brain development, yet current measurement approaches face significant challenges that limit our ability to capture the complex, multidimensional nature of children's environmental exposures. This review examines seven key challenges in measuring early experiences: (1) Conflation of exposure and response, (2) Oversimplification of complex experiences, (3) Informant bias and reliability issues, (4) Biomarker overinterpretation and inferential leaps, (5) Limited ecological validity, (6) Genetic confounding, and (7) Limited generalizability across cultures and communities. We discuss how these limitations constrain our understanding of how diverse early experiences shape brain development and propose evidence-based approaches to address each challenge. Emerging frameworks that distinguish between different dimensions of adversity, technological advances in passive monitoring, and genetically-informed research designs offer promising paths forward. By advancing precise, high-dimensional approaches to measuring early experiences, researchers can improve understanding of fundamental neurodevelopmental processes while addressing questions of practical significance in education, mental health, and social policy.

Early cannabis initiation during youth is associated with elevated risk for harmful substance use, mental disorders, and cognitive impairments. To account for the complexity behind cannabis use initiation, we performed a data-driven analysis across 151 measurements spanning seven domains from the individual, microsystem, and exosystem level of influences: biobehavior, cognition, brain MRI, family, peer, neighborhood and legal factors. Data were from 450 cannabis-naïve youths from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) (baseline age: 12-21 years). Within an 8-year period, 292 transitioned to first use and 163 to weekly use of cannabis. Random Survival Forest predicted age of first onset (C-index = 0.68; 95 % CI: [0.65,0.71]) and weekly onset (C-index = 0.69; 95 % CI: [0.67-0.71]) with an accuracy significantly higher than chance (i.e., C-index = 0.5). Its prediction patterns consisted of factors from all three levels of influence. The predictive pattern of first onset comprised 13 factors across six domains including lower positive thinking during stress coping, which correlated with earlier use (R²=0.023, p = 0.0090). Three variables were shared with the predictive pattern of weekly use onset: cannabis outlet density, access to alcohol at home, and more positive social expectations of alcohol use forecasting earlier onset (Initial Use: R²=0.031, p = 0.0027; Weekly Use: R²=0.023, p = 0.0090). Weekly use onset was predicted by only four factors suggesting that while many influences contribute to a youth trying cannabis, only a few key factors appear to facilitate escalation to habitual use, some of which represent promising targets for prevention programs.

Adolescence is a sensitive period characterized by significant neurocognitive development, with important implications for learning and decision-making. Working memory and reinforcement learning are essential for decision making and real-life dynamic adaptation to the environment and are often affected in individuals with anxiety and depression. Using a cognitive computational approach, we investigated associations between working memory, reinforcement learning, age, and symptoms of generalized anxiety and depression in 193 adolescents aged 12-24 years. Participants completed the Reinforcement Learning Working Memory (RLWM) task. To gain insight into the dynamics underlying instrumental learning behavior, we employed a computational model that combines the RLWM model with a Linear Ballistic Accumulator (RLWM-LBA), to quantify processes related to working memory and reinforcement learning, as well as choice dynamics underlying reaction times. We observed an age-related increase in task performance, but no differences depending on symptoms. Bayesian regression models revealed strong evidence for an association between age and the start point variability parameter of the LBA module, suggesting reduced choice stochasticity in older adolescents. In line with the behavioral findings, we found anecdotal to moderate evidence for no associations between RLWM-LBA parameters and symptom sum scores. Lastly, we trained regression models testing the utility of RLWM-LBA parameters for predicting age and symptom burden, yielding poor predictive performance. This study highlights differences in choice dynamics underlying age-related improvements in instrumental learning, while finding that cognitive differences identified in case-control studies did not generalize to symptom variation in this sample from the general adolescent population.

Language develops rapidly over the infant and toddler period and has been a key area of research within the field of developmental cognitive neuroscience. Understanding the neural basis of early language development may help us predict delays or disorders, recommend early interventions, and provide a deeper mechanistic understanding of how the brain supports language learning. While the ontogeny of many cognitive functions can be studied in animal models, language development can only be studied in human children. Thus, functional neuroimaging is critical for uncovering the neural basis of language in early development. The purpose of this review is to take stock of some examples of what we have learned so far, and to explore some of the biggest open questions for the next phase of fetal, infant, and toddler neuroimaging research of language development.

Mathematical cognition engages a distributed brain network, but the causal dynamics of information flow within it, particularly how memory circuits interact with other brain regions across development, remain unknown. We examined causal dynamic interactions in typically developing children and adolescents/young adults (AYA) using fMRI during three tasks involving mental arithmetic and symbolic and non-symbolic number comparison. Using multivariate dynamic state-space identification modeling, we found that causal dynamic interactions differed between children and AYA across all three tasks, especially during arithmetic processing. The left medial temporal lobe (MTL) served as a causal signaling hub in AYA across all three tasks, but not in children. The left angular gyrus (AG) maintained consistent hub-like properties during arithmetic task across development. Compared to AYA, children exhibited heightened causal interactions in both the MTL and AG. Moreover, network hub properties of these regions correlated with individual's mathematical achievement specifically during arithmetic processing. Together, we found that the MTL transitioned from heightened, context-dependent, interactions in childhood to a stable causal hub in adulthood, while the AG maintained as a hub during arithmetic processing across development. This dissociation between memory systems, coupled with their task-specific relationship to mathematical abilities, provides novel insights into how brain networks mature to support mathematical cognition.

Anxiety disorders are among the most prevalent mental health conditions, often emerging early in life and leading to substantial impairments across the lifespan. The bed nucleus of the stria terminalis (BNST) plays a central role in threat processing and anxiety regulation, yet its early functional connectivity profile and links to early signs of anxiety remain poorly understood. The current study investigates intrinsic functional connectivity of the BNST in 1-month-old infants using resting-state functional magnetic resonance imaging and explores its longitudinal association with anxiety symptoms later in infancy. We observe that early in development the BNST exhibits intrinsic connectivity with key subcortical regions, including the amygdala, hippocampus, and ventral striatum. However, connectivity patterns observed in human adults, including BNST-frontal cortex connectivity, were not observed in infants. Furthermore, weaker BNST-amygdala connectivity at 1 month was significantly associated with greater anxiety symptoms assessed at 18 months (β=-0.339, 95 % CI [-0.586, -0.093]), highlighting the potential role of early BNST connectivity in later anxiety-related behaviors. These findings provide the earliest evidence to date of BNST functional connectivity in human infancy and its prospective link to later anxiety symptoms, helping to fill a critical gap in our understanding of the early development of anxiety-related neural circuits.

What language a person speaks has been shown to divide even infants' worlds. However, open questions remain about what neural processes are involved in the differentiation of native and foreign speakers in the infant's brain. This study used electroencephalography (EEG) to examine the neural responses related to top-down attention (frontal theta synchronization), action processing (mu desynchronization), and approach-avoidance (frontal alpha asymmetry) of 8- to 12-month-old infants as they observed a native (English) speaker and a foreign (French) speaker perform a goal-directed action (i.e., grasping objects). We further examined whether infants' language exposure modulated these neural responses. We found that monolingual infants exhibited stronger mu desynchronization when observing a native (versus foreign) speaker perform goal-directed actions. In contrast, non-monolingual (i.e., hearing more than one language) infants did not show a difference in mu desynchronization between native and foreign speakers. No language group and exposure effects were found for frontal theta and frontal alpha symmetry. These results suggest that infants' emerging differentiation of native and foreign speakers is also manifested in their neural processing of goal-directed actions and that this neural action processing is shaped by early exposure to different languages.

Adaptively responding to salient stimuli in the environment is a fundamental feature of cognitive development in early life, which is enabled by the developing brain. Understanding individual variability in how the brain supports this fundamental process is essential for uncovering neurodevelopmental trajectories and potential neurodevelopmental risks. In the present study, we used a precision functional imaging approach to probe activation in response to salient auditory stimuli and its relation to brain functional networks in individual infants. A minimum of 60 min of fMRI BOLD data with an auditory oddball paradigm were collected in ten infants with a mean postmenstrual age of 48 weeks. Results demonstrate the feasibility of conducting a precision functional imaging study to investigate individual specific responses to salient stimuli in infants. While responses to the auditory oddball were consistent across individuals in auditory processing areas, responses across the rest of the brain differed across individuals in their magnitude and time to peak. Individual specific response patterns appeared to be relatively stable and differed from other participant's response patterns, despite fluctuations across runs. Commonalities and differences between individuals demonstrated in this sample contribute to our understanding of how the developing brain instantiates processing of salient stimuli. In this context, individual specific response patterns could be a promising target for biomarkers of normative brain and cognitive development.

Preterm birth and socioeconomic status (SES) are associated with brain development in early life, but the contribution of each over time is uncertain. We examined the effects of gestational age (GA) and SES on white matter microstructure in the neonatal period and at five years. Participants included preterm and term children. Diffusion MRI was collected at term-equivalent age (n = 153 preterm, n = 90 term [127/243 female]) and from a subset at five years (n = 26 preterm, n = 32 term [22/58 female]). We assessed linear associations of GA, SES (Scottish Index of Multiple Deprivation [SIMD] and maternal education), and GA×SES interactions on fractional anisotropy (FA) using tract-based spatial statistics. We compared the proportion of voxels with significant associations between timepoints. In preterm neonates, higher GA and higher maternal education, but not SIMD, were associated with higher FA (p corrected for family-wise error rate, p < 0.05). GA-FA associations depended on maternal education and SIMD (β =|0.001-0.005|, p < 0.001). At five years, the strength and direction of GA-FA associations depended on SIMD (β =|0.013-0.028|, p < 0.001), but not maternal education. In term infants, lower SES was associated with higher FA at the neonatal timepoint only (p < 0.05). Preterm birth and SES both shape brain development at birth and continue to do so at five years. The SES measure most strongly associated with FA in preterm infants switches from a family-level (i.e. maternal education) to neighborhood-level (i.e. SIMD) measure between birth and five years, which suggests strategies to mitigate adverse effects of social inequalities on development may require adaptation as children grow.

Preterm birth disrupts the gyrification process during the third trimester of pregnancy. Meanwhile, accumulating studies have highlighted the significant structural and functional differences between the folding patterns of cortical gyri and sulci, suggesting that they may play distinct roles in brain function. This study aimed to explore how preterm birth influences the structural and functional patterns of gyral and sulcal regions. Using a Developing Human Connectome Project (dHCP) open dataset including both full-term and preterm neonates (207 subjects), we parcellated each brain region into gyri and sulci based on the vertex curvature values. Structural connectivity was assessed via diffusion MRI (dMRI) images, and functional differences via fMRI BOLD signals using synchronization measures, nodal degree, and network-based statistics (NBS). Findings revealed that preterm birth reduces structural connectivity between gyri and lowers the ratio of intra-gyri/gyri-sulci connections. This ratio was significantly associated with gestational age, birth weight, and global synchronization. NBS analysis revealed a cluster of hypo-connections, mostly gyri-to-sulci connections. Overall, results suggest that preterm birth affects gyri and sulci differently, potentially disrupting their distinct functional roles, and offering new insights into prematurity's impact on brain function.

Adolescent adversity could have lasting effects on mental health, potentially through neurodevelopmental changes. This study used a random intercept cross-lagged panel model to examine how adverse experiences, brain development, and behavioural and emotional problems are linked over time in the ABCD study (N ≈ 12.000, USA). We found a positive association between family conflict and behavioural and emotional problems: family conflict was related to increased problems at 10 - 12 years (β = 0.06, p = 0.002), and vice versa. At 12 - 14 years, behavioural and emotional problems were also related to increased family conflict (β = 0.20, p < 0.001). Neighbourhood perception was related to behavioural and emotional problems and white matter microstructure. At 10 - 12 years, low neighbourhood safety was related to lower levels of white matter microstructure (β = -0.04, p = 0.041) and vice versa. It was also associated with more behavioural and emotional problems (β = 0.05, p = 0.015) and vice versa. Behavioural and emotional problems were positively associated with neighbourhood perception for adolescents with more friends (χ²(1) = 9.82, p = 0.02). These findings underscore the need to consider socio-environmental adversity when examining adolescent brain development and mental health.

Progressive development of reading comprehension fluency from late childhood to early adolescence is remarkably linked to changes in the temporal dynamics of visual word recognition. EEG/ERP based measures of how an individual participant's cortical timing for visual word recognition change over development are limited by low reliability. We present a novel approach to this challenge that individually models cortical latency to visual word forms by extracting phase values from Steady-State Visual Evoked Potentials (SSVEPs) for each participant. The resulting precise and reliable timing information for neural signatures underlying visual word form processes help account for the development of fluent reading comprehension. Typically developing readers (n=68), aged 8-15 years, viewed streams of four-character stimuli presented at 3 Hz, which evoked large significant power spikes from every participant. Linear phase by frequency functions across harmonics at 3, 6, and 9 Hz were consistent with a delay model, indicating a mean latency of 170 ms. Subject-level latencies revealed (a) high internal consistency (r=.94); (b) stability across variations in character-level (letters, unfamiliar pseudo-characters) and word-form level (words, nonwords, pseudofont strings) manipulations; (c) a linear relationship with age; and most remarkably, (d) a strong relationship with individual variation in the fluency of reading comprehension, that was (e) mediated by word naming speed. Results suggest a promising new approach for investigating the neural basis of reading development across several levels of processes, with temporal precision at the individual level that holds translational significance for promoting population-level fluency in reading comprehension.