Dynamic transient brain states in preschoolers mirror parental report of behavior and emotion regulation.

The temporal dynamics of resting-state networks may represent an intrinsic functional repertoire supporting cognitive control performance across the lifespan. However, little is known about brain dynamics during the preschool period, which is a sensitive time window for cognitive control development. The fast timescale of synchronization and switching characterizing cortical network functional organization gives rise to quasi-stable patterns (i.e., brain states) that recur over time. These can be inferred at the whole-brain level using hidden Markov models (HMMs), an unsupervised machine learning technique that allows the identification of rapid oscillatory patterns at the macroscale of cortical networks. The present study used an HMM technique to investigate dynamic neural reconfigurations and their associations with behavioral (i.e., parental questionnaires) and cognitive (i.e., neuropsychological tests) measures in typically developing preschoolers (4-6 years old). We used high-density EEG to better capture the fast reconfiguration patterns of the HMM-derived metrics (i.e., switching rates, entropy rates, transition probabilities and fractional occupancies). Our results revealed that the HMM-derived metrics were reliable indices of individual neural variability and differed between boys and girls. However, only brain state transition patterns toward prefrontal and default-mode brain states, predicted differences on parental-report questionnaire scores. Overall, these findings support the importance of resting-state brain dynamics as functional scaffolds for behavior and cognition. Brain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers.

Mapping the role of sexuality in adolescent mental health and substance use.

Individuals who belong to a sexual minority are at greater risk of adverse health and social outcomes. These effects are observed during adolescence when many mental health problems, such as depression, first emerge. Here, we used a network analytic approach to better understand the role that sexual minority status plays in the association between depression, interpersonal difficulties and substance use in a large sample of mid-adolescents. In doing so, we used data from 8017 fourteen year olds from the UK's Millennium Cohort Study, of which 490 self-identified as belonging to a sexual minority. We found that sexual minority status was highly central in the network and connected to multiple adverse outcomes, sometimes directly and sometimes indirectly. The largest single association was between sexual minority status and depression, and this link mediated multiple negative associations with being in a sexual minority. The shortest path to drinking, poor social support and closeness with parents and victimization occurred via depression. The shortest path to smoking and drug use occurred via conduct problems. We also identified three distinct profiles of adverse outcomes among those belonging to a sexual minority, highlighting the heterogeneous nature of this group.

Global topology of human connectome is insensitive to early life environments - A prospective longitudinal study of the general population.

The widely acknowledged detrimental impact of early adversity on child development has driven efforts to understand the underlying mechanisms that may mediate these effects within the developing brain. Recent efforts have begun to move beyond associating adversity with the morphology of individual brain regions towards determining if and how adversity might shape their interconnectivity. However, whether adversity effects a global shift in the organisation of whole-brain networks remains unclear. In this study, we assessed this possibility using parental questionnaire and diffusion imaging data from The Avon Longitudinal Study of Parents and Children (ALSPAC, N = 913), a prospective longitudinal study spanning more than 20 years. We tested whether a wide range of adversities-including experiences of abuse, domestic violence, physical and emotional cruelty, poverty, neglect, and parental separation-measured by questionnaire within the first seven years of life were significantly associated with the tractography-derived connectome in young adulthood. We tested this across multiple measures of organisation and using a computational model that simulated the wiring economy of the brain. We found no significant relationships between early exposure to any form of adversity and the global organisation of the structural connectome in young adulthood. We did detect local differences in the medial prefrontal cortex, as well as an association between weaker brain wiring constraints and greater externalising behaviour in adolescence. Our results indicate that further efforts are necessary to delimit the magnitude and functional implications of adversity-related differences in connectomic organization. RESEARCH HIGHLIGHTS: Diverse prospective measures of the early-life environment do not predict the organisation of the DTI tractography-derived connectome in young adulthood Wiring economy of the connectome is weakly associated with externalising in adolescence, but not internalising or cognitive ability Further work is needed to establish the scope and significance of global adversity-related differences in the structural connectome.

What is developmental about developmental prosopagnosia?

Developmental prosopagnosia (DP) is characterised by difficulties recognising face identities and is associated with diverse co-occurring object recognition difficulties. The high co-occurrence rate and heterogeneity of associated difficulties in DP is an intrinsic feature of developmental conditions, where co-occurrence of difficulties is the rule, rather than the exception. However, despite its name, cognitive and neural theories of DP rarely consider the developmental context in which these difficulties occur. This leaves a large gap in our understanding of how DP emerges in light of the developmental trajectory of face recognition. Here, we argue that progress in the field requires re-considering the developmental origins of differences in face recognition abilities, rather than studying the end-state alone. In practice, considering development in DP necessitates a re-evaluation of current approaches in recruitment, design, and analyses.

Understanding divergence: Placing developmental neuroscience in its dynamic context.

Neurodevelopment is not merely a process of brain maturation, but an adaptation to constraints unique to each individual and to the environments we co-create. However, our theoretical and methodological toolkits often ignore this reality. There is growing awareness that a shift is needed that allows us to study divergence of brain and behaviour across conventional categorical boundaries. However, we argue that in future our study of divergence must also incorporate the developmental dynamics that capture the emergence of those neurodevelopmental differences. This crucial step will require adjustments in study design and methodology. If our ultimate aim is to incorporate the developmental dynamics that capture how, and ultimately when, divergence takes place then we will need an analytic toolkit equal to these ambitions. We argue that the over reliance on group averages has been a conceptual dead-end with regard to the neurodevelopmental differences. This is in part because any individual differences and developmental dynamics are inevitably lost within the group average. Instead, analytic approaches which are themselves new, or simply newly applied within this context, may allow us to shift our theoretical and methodological frameworks from groups to individuals. Likewise, methods capable of modelling complex dynamic systems may allow us to understand the emergent dynamics only possible at the level of an interacting neural system.

Parental socioeconomic status weakly predicts specific cognitive and academic skills beyond general cognitive ability.

Parental socioeconomic status (SES) is a well-established predictor of children's neurocognitive development. Several theories propose that specific cognitive skills are particularly vulnerable. However, this can be challenging to test, because cognitive assessments are not pure measures of distinct neurocognitive processes, and scores across different tests are often highly correlated. Aside from one previous study by Tucker-Drob, little research has tested if associations between SES and cognition are explained by differences in general cognitive ability rather than specific cognitive skills. Using structural equation modelling (SEM), we tested if parental SES is associated with individual cognitive test scores after controlling for latent general cognitive ability. Data from three large-scale cohorts totalling over 16,360 participants from the UK and USA (ages 6-19) were used. Associations between SES and cognitive test scores are mainly (but not entirely) explained through general cognitive ability. Socioeconomic advantage was associated with particularly strong vocabulary performance, unexplained by general ability. When controlling for general cognitive ability, socioeconomic disadvantage was associated with better executive functions. Better characterizing relationships between cognition and adversity is a crucial first step toward designing interventions to narrow socioeconomic gaps. RESEARCH HIGHLIGHTS: Understanding environmental influences on cognitive development is a crucial goal for developmental science-parental socioeconomic status (SES) is one of the strongest predictors. Several theories have proposed that specific cognitive skills, such as language or certain executive functions, are particularly susceptible to socioeconomic adversity. Using structural equation modelling, we tested whether SES predicts specific cognitive and academic tests after controlling for latent general cognitive ability across three large-scale cohorts. SES moderately predicted latent general cognitive ability, but associations with specific cognitive skills were mainly small, with a few exceptions.

Testing the triple network model of psychopathology in a transdiagnostic neurodevelopmental cohort.

AIM: The triple network model of psychopathology posits that altered connectivity between the Salience (SN), Central Executive (CEN), and Default Mode Networks (DMN) may underlie neurodevelopmental conditions. However, this has yet to be tested in a transdiagnostic sample of young people. METHOD: We investigated this in 175 children (60 girls) that represent a heterogeneous population who are experiencing neurodevelopmental difficulties in cognition and behavior, and 60 comparison children (33 girls). Hyperactivity/impulsivity and inattention were assessed by parent-report. Resting-state functional Magnetic Resonance Imaging data were acquired and functional connectivity was calculated between independent network components and regions of interest. We then examined whether connectivity between the SN, CEN and DMN was dimensionally related to hyperactivity/impulsivity and inattention, whilst controlling for age, gender, and motion. RESULTS: Hyperactivity/impulsivity was associated with increased functional connectivity between the SN, CEN, and DMN in at-risk children, whereas it was associated with decreased functional connectivity between the CEN and DMN in comparison children. These effects replicated in an adult parcellation of brain function and when using increasingly stringent exclusion criteria for in-scanner motion. CONCLUSION: Triple network connectivity characterizes transdiagnostic neurodevelopmental difficulties with hyperactivity/impulsivity. We suggest that this may arise from delayed network segregation, difficulties sustaining CEN activity to regulate behavior, and/or a heightened developmental mismatch between neural systems implicated in cognitive control relative to those implicated in reward/affect processing.

The adaptive stochasticity hypothesis: Modeling equifinality, multifinality, and adaptation to adversity.

Neural phenotypes are the result of probabilistic developmental processes. This means that stochasticity is an intrinsic aspect of the brain as it self-organizes over a protracted period. In other words, while both genomic and environmental factors shape the developing nervous system, another significant-though often neglected-contributor is the randomness introduced by probability distributions. Using generative modeling of brain networks, we provide a framework for probing the contribution of stochasticity to neurodevelopmental diversity. To mimic the prenatal scaffold of brain structure set by activity-independent mechanisms, we start our simulations from the medio-posterior neonatal rich club (Developing Human Connectome Project, n = 630). From this initial starting point, models implementing Hebbian-like wiring processes generate variable yet consistently plausible brain network topologies. By analyzing repeated runs of the generative process (>107 simulations), we identify critical determinants and effects of stochasticity. Namely, we find that stochastic variation has a greater impact on brain organization when networks develop under weaker constraints. This heightened stochasticity makes brain networks more robust to random and targeted attacks, but more often results in non-normative phenotypic outcomes. To test our framework empirically, we evaluated whether stochasticity varies according to the experience of early-life deprivation using a cohort of neurodiverse children (Centre for Attention, Learning and Memory; n = 357). We show that low-socioeconomic status predicts more stochastic brain wiring. We conclude that stochasticity may be an unappreciated contributor to relevant developmental outcomes and make specific predictions for future research.

Maternal mental health mediates links between socioeconomic status and child development.

The impact of socioeconomic status (SES) on early child development is well-established, but the mediating role of parental mental health is poorly understood. Data were obtained from The Avon Longitudinal Study of Parents and Children (ALSPAC; n = 13,855), including measures of early SES (age 8 months), key aspects of development during mid-late childhood (ages 7-8 years), and maternal mental health during early childhood (ages 0-3 years). In the first year of life, better maternal mental health was shown to weaken the negative association between SES and child mental health. Better maternal mental health was additionally shown to weaken the association between SES and child cognitive ability. These findings highlight the variability and complexity of the mediating role of parental mental health on child development. They further emphasise the importance of proximal factors in the first year of life, such as parental mental health, in mediating key developmental outcomes.

Early adversity changes the economic conditions of mouse structural brain network organization.

Early adversity can change educational, cognitive, and mental health outcomes. However, the neural processes through which early adversity exerts these effects remain largely unknown. We used generative network modeling of the mouse connectome to test whether unpredictable postnatal stress shifts the constraints that govern the organization of the structural connectome. A model that trades off the wiring cost of long-distance connections with topological homophily (i.e., links between regions with shared neighbors) generated simulations that successfully replicate the rodent connectome. The imposition of early life adversity shifted the best-performing parameter combinations toward zero, heightening the stochastic nature of the generative process. Put simply, unpredictable postnatal stress changes the economic constraints that reproduce rodent connectome organization, introducing greater randomness into the development of the simulations. While this change may constrain the development of cognitive abilities, it could also reflect an adaptive mechanism that facilitates effective responses to future challenges.

Toward computational neuroconstructivism: a framework for developmental systems neuroscience.

Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.

Exploring neural heterogeneity in inattention and hyperactivity.

Inattention and hyperactivity are cardinal symptoms of Attention Deficit Hyperactivity Disorder (ADHD). These characteristics have also been observed across a range of other neurodevelopmental conditions, such as autism and dyspraxia, suggesting that they might best be studied across diagnostic categories. Here, we evaluated the associations between inattention and hyperactivity behaviours and features of the structural brain network (connectome) in a large transdiagnostic sample of children (Centre for Attention, Learning, and Memory; n = 383). In our sample, we found that a single latent factor explains 77.6% of variance in scores across multiple questionnaires measuring inattention and hyperactivity. Partial Least-Squares (PLS) regression revealed that variability in this latent factor could not be explained by a linear component representing nodewise properties of connectomes. We then investigated the type and extent of neural heterogeneity in a subset of our sample with clinically-elevated levels of inattention and hyperactivity. Multidimensional scaling combined with k-means clustering revealed two neural subtypes in children with elevated levels of inattention and hyperactivity (n = 232), differentiated primarily by nodal communicability-a measure which demarcates the extent to which neural signals propagate through specific brain regions. These different clusters had similar behavioural profiles, which included high levels of inattention and hyperactivity. However, one of the clusters scored higher on multiple cognitive assessment measures of executive function. We conclude that inattention and hyperactivity are so common in children with neurodevelopmental difficulties because they emerge through multiple different trajectories of brain development. In our own data, we can identify two of these possible trajectories, which are reflected by measures of structural brain network topology and cognition.

Direct and indirect links between children's socio-economic status and education: pathways via mental health, attitude, and cognition.

A child's socio-economic environment can profoundly affect their development. While existing literature focusses on simplified metrics and pair-wise relations between few variables, we aimed to capture complex interrelationships between several relevant domains using a broad assessment of 519 children aged 7-9 years. Our analyses comprised three multivariate techniques that complimented each other, and worked at different levels of granularity. First, an exploratory factor analysis (principal component analysis followed by varimax rotation) revealed that our sample varied along continuous dimensions of cognition, attitude and mental health (from parallel analysis); with potentially emerging dimensions speed and socio-economic status (passed Kaiser's criterion). Second, k-means cluster analysis showed that children did not group into discrete phenotypes. Third, a network analysis on the basis of bootstrapped partial correlations (confirmed by both cross-validated LASSO and multiple comparisons correction of binarised connection probabilities) uncovered how our developmental measures interconnected: educational outcomes (reading and maths fluency) were directly related to cognition (short-term memory, number sense, processing speed, inhibition). By contrast, mental health (anxiety and depression symptoms) and attitudes (conscientiousness, grit, growth mindset) showed indirect relationships with educational outcomes via cognition. Finally, socio-economic factors (neighbourhood deprivation, family affluence) related directly to educational outcomes, cognition, mental health, and even grit. In sum, cognition is a central cog through which mental health and attitude relate to educational outcomes. However, through direct relations with all components of developmental outcomes, socio-economic status acts as a great 'unequaliser'. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-021-02232-2.

Challenges and Solutions to the Measurement of Neurocognitive Mechanisms in Developmental Settings.

Identifying early neurocognitive mechanisms that confer risk for mental health problems is one important avenue as we seek to develop successful early interventions. Currently, however, we have limited understanding of the neurocognitive mechanisms involved in shaping mental health trajectories from childhood through young adulthood, and this constrains our ability to develop effective clinical interventions. In particular, there is an urgent need to develop more sensitive, reliable, and scalable measures of individual differences for use in developmental settings. In this review, we outline methodological shortcomings that explain why widely used task-based measures of neurocognition currently tell us little about mental health risk. We discuss specific challenges that arise when studying neurocognitive mechanisms in developmental settings, and we share suggestions for overcoming them. We also propose a novel experimental approach-which we refer to as "cognitive microscopy"-that involves adaptive design optimization, temporally sensitive task administration, and multilevel modeling. This approach addresses some of the methodological shortcomings outlined above and provides measures of stability, variability, and developmental change in neurocognitive mechanisms within a multivariate framework.

Social and emotional characteristics of girls and young women with DDX3X-associated intellectual disability: a descriptive and comparative study.

DDX3X variants are a common cause of intellectual disability (ID) in females, and have been associated with autism spectrum disorder and emotional-behavioural difficulties. In this study, we compared phenotypic data for 23 females with DDX3X variants, to 23 females with ID and other genetic diagnoses. We found a wide range of adaptive, social and emotional function within the DDX3X group. Autism characteristics did not differ between DDX3X and comparison groups, while levels of anxiety and self-injurious behaviour (SIB) were significantly higher in the DDX3X group. Within the DDX3X group, adaptive function, autism characteristics, anxiety and SIB scores were positively correlated, with evidence for group-specific associations with SIB. Future work is warranted to explore the multilevel mechanisms contributing to social and emotional development in individuals with DDX3X variants.

Testing Deprivation and Threat: A Preregistered Network Analysis of the Dimensions of Early Adversity.

Despite abundant evidence of the detrimental effects of childhood adversity, its nature and underlying mechanisms remain contested. One influential theory, the dimensional model of adversity and psychopathology, proposes deprivation and threat as distinct dimensions of early experience. In this preregistered analysis of data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we used a network and clustering approach to assess the dimensionality of relationships between childhood adversity and adolescent cognition and emotional functioning, and we used recursive partitioning to identify timing effects. We found evidence that deprivation and threat are separate dimensions of adversity and that early experiences of deprivation cluster with later measures of cognition and emotional functioning. This cluster varies by age of exposure; it includes fewer forms of deprivation as children grow from infancy to middle childhood. Our measures did not form a specific cluster linking threat to emotional functioning.

Statistical power for cluster analysis.

BACKGROUND: Cluster algorithms are gaining in popularity in biomedical research due to their compelling ability to identify discrete subgroups in data, and their increasing accessibility in mainstream software. While guidelines exist for algorithm selection and outcome evaluation, there are no firmly established ways of computing a priori statistical power for cluster analysis. Here, we estimated power and classification accuracy for common analysis pipelines through simulation. We systematically varied subgroup size, number, separation (effect size), and covariance structure. We then subjected generated datasets to dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection) and cluster algorithms (k-means, agglomerative hierarchical clustering with Ward or average linkage and Euclidean or cosine distance, HDBSCAN). Finally, we directly compared the statistical power of discrete (k-means), "fuzzy" (c-means), and finite mixture modelling approaches (which include latent class analysis and latent profile analysis). RESULTS: We found that clustering outcomes were driven by large effect sizes or the accumulation of many smaller effects across features, and were mostly unaffected by differences in covariance structure. Sufficient statistical power was achieved with relatively small samples (N = 20 per subgroup), provided cluster separation is large (Δ = 4). Finally, we demonstrated that fuzzy clustering can provide a more parsimonious and powerful alternative for identifying separable multivariate normal distributions, particularly those with slightly lower centroid separation (Δ = 3). CONCLUSIONS: Traditional intuitions about statistical power only partially apply to cluster analysis: increasing the number of participants above a sufficient sample size did not improve power, but effect size was crucial. Notably, for the popular dimensionality reduction and clustering algorithms tested here, power was only satisfactory for relatively large effect sizes (clear separation between subgroups). Fuzzy clustering provided higher power in multivariate normal distributions. Overall, we recommend that researchers (1) only apply cluster analysis when large subgroup separation is expected, (2) aim for sample sizes of N = 20 to N = 30 per expected subgroup, (3) use multi-dimensional scaling to improve cluster separation, and (4) use fuzzy clustering or mixture modelling approaches that are more powerful and more parsimonious with partially overlapping multivariate normal distributions.

[Formula: see text]FarmApp: a new assessment of cognitive control and memory for children and young people with neurodevelopmental difficulties.

We introduce a new touchscreen-based method measuring aspects of cognitive control and memory, in children and young people with neurodevelopmental difficulties, including intellectual disability (ID). FarmApp is a gamified, tablet-based assessment tool measuring go/no-go response speed, response inhibition, visuospatial short-term memory span, and long-term memory. Here, we assessed the feasibility, validity, and utility of the method, including the benefits of measuring change in performance over two weeks. We observed that: 1) a higher proportion of participants completed FarmApp than traditional psychometric tests; 2) this proportion increased when participants had opportunity for two weeks of self-paced testing at home; 3) ADHD-relevant behavioral difficulties were associated with average go/no-go performance across all attempts, and change in go/no-go performance over time, indicating sensitivity of the method to cognitive differences with real-world relevance. We also addressed the potential utility of the FarmApp for exploring links between ID etiology and cognitive processes. We observed differences in go/no-go task between two groups of ID participants stratified by the physiological functions of associated genetic variants (chromatin-related and synaptic-related). Moreover, the synaptic group demonstrated higher degree of improvement in go/no-go performance over time. This outcome is potentially informative of dynamic mechanisms contributing to cognitive difficulties within this group. In sum, FarmApp is a feasible, valid, and useful tool increasing access to cognitive assessment for individuals with neurodevelopmental difficulties of variable severity, with an added opportunity to monitor variation in performance over time and determine capacity to acquire task competence.