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.

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.

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.

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.

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.

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.

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.

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.

Annual Research Review: The transdiagnostic revolution in neurodevelopmental disorders.

Practitioners frequently use diagnostic criteria to identify children with neurodevelopmental disorders and to guide intervention decisions. These criteria also provide the organising framework for much of the research focussing on these disorders. Study design, recruitment, analysis and theory are largely built on the assumption that diagnostic criteria reflect an underlying reality. However, there is growing concern that this assumption may not be a valid and that an alternative transdiagnostic approach may better serve our understanding of this large heterogeneous population of young people. This review draws on important developments over the past decade that have set the stage for much-needed breakthroughs in understanding neurodevelopmental disorders. We evaluate contemporary approaches to study design and recruitment, review the use of data-driven methods to characterise cognition, behaviour and neurobiology, and consider what alternative transdiagnostic models could mean for children and families. This review concludes that an overreliance on ill-fitting diagnostic criteria is impeding progress towards identifying the barriers that children encounter, understanding underpinning mechanisms and finding the best route to supporting them.

Segregation and integration of the functional connectome in neurodevelopmentally 'at risk' children.

Functional connectivity within and between Intrinsic Connectivity Networks (ICNs) transforms over development and is thought to support high order cognitive functions. But how variable is this process, and does it diverge with altered cognitive development? We investigated age-related changes in integration and segregation within and between ICNs in neurodevelopmentally 'at-risk' children, identified by practitioners as experiencing cognitive difficulties in attention, learning, language, or memory. In our analysis we used performance on a battery of 10 cognitive tasks alongside resting-state functional magnetic resonance imaging in 175 at-risk children and 62 comparison children aged 5-16. We observed significant age-by-group interactions in functional connectivity between two network pairs. Integration between the ventral attention and visual networks and segregation of the limbic and fronto-parietal networks increased with age in our comparison sample, relative to at-risk children. Furthermore, functional connectivity between the ventral attention and visual networks in comparison children significantly mediated age-related improvements in executive function, compared to at-risk children. We conclude that integration between ICNs show divergent neurodevelopmental trends in the broad population of children experiencing cognitive difficulties, and that these differences in functional brain organisation may partly explain the pervasive cognitive difficulties within this group over childhood and adolescence.

Testing the specificity of environmental risk factors for developmental outcomes.

Developmental theories often assume that specific environmental risks affect specific outcomes. Canonical Correlation Analysis was used to test whether 28 developmental outcomes (measured at 11-15 years) share the same early environmental risk factors (measured at 0-3 years), or whether specific outcomes are associated with specific risks. We used data from the UK Millennium Cohort Study (N = 10,376, 51% Female, 84% White) collected between 2001 and 2016. A single environment component was mostly sufficient for explaining cognition and parent-rated behavior outcomes. In contrast, adolescents' alcohol and tobacco use were specifically associated with their parents', and child-rated mental health was weakly associated with all risks. These findings suggest that with some exceptions, many different developmental outcomes share the same early environmental risk factors.

A randomized control trial of the effects of home-based online attention training and working memory training on cognition and everyday function in a community stroke sample.

Cognitive difficulties are common following stroke and can have widespread impacts on everyday functioning. Technological advances offer the possibility of individualized cognitive training for patients at home, potentially providing a low-cost, low-intensity adjunct to rehabilitation services. Using this approach, we have previously demonstrated post-training improvements in attention and everyday functioning in fronto-parietal stroke patients. Here we examine whether these benefits are observed more broadly in a community stroke sample. Eighty patients were randomized to either 4 weeks of online adaptive attention training (SAT), working memory training (WMT) or waitlist (WL). Cognitive and everyday function measures were collected before and after the intervention, and after 3 months. During training, weekly measures of patients' subjective functioning were collected. The training was well received and compliance good. No differences in our primary end-point, spatial bias, or other cognitive functions were observed. However, on patient-reported outcomes, SAT participants showed greater levels of improvement in everyday functioning than WMT or WL participants. In line with our previous work, everyday functioning improvements were greatest for patients with spatial impairments and those who received SAT training. Whether attention training can be recommended for stroke survivors depends on whether cognitive test performance or everyday functioning is considered more relevant.

Just a phase? Mapping the transition of behavioural problems from childhood to adolescence.

PURPOSE: Young people change substantially between childhood and adolescence. Yet, the current description of behavioural problems does not incorporate any reference to the developmental context. In the current analysis, we aimed to identify common transitions of behavioural problems between childhood and adolescence. METHOD: We followed 6744 individuals over 6 years as they transitioned from childhood (age 10) into adolescence (age 16). At each stage, we used a data-driven hierarchical clustering method to identify common profiles of behavioural problems, map transitions between profiles and identify factors that predict specific transitions. RESULTS: Common profiles of behavioural problems matched known comorbidity patterns but crucially showed that the presentation of behavioural problems changes markedly between childhood and adolescence. While problems with hyperactivity/impulsivity, motor control and conduct were prominent in childhood, adolescents showed profiles of problems related to emotional control, anxiety and inattention. Transitions were associated with socio-economic status and cognitive performance in childhood CONCLUSION: We show that understanding behavioural difficulties and mental ill-health must take into account the developmental context in which the problems occur, and we establish key risk factors for specific negative transitions as children become adolescents.

Collecting big data with small screens: Group tests of children's cognition with touchscreen tablets are reliable and valid.

Collecting experimental cognitive data with young children usually requires undertaking one-on-one assessments, which can be both expensive and time-consuming. In addition, there is increasing acknowledgement of the importance of collecting larger samples for improving statistical power Button et al. (Nature Reviews Neuroscience 14(5), 365-376, 2013), and reproducing exploratory findings Open Science Collaboration (Science, 349(6251), aac4716-aac4716 2015). One way both of these goals can be achieved more easily, even with a small team of researchers, is to utilize group testing. In this paper, we evaluate the results from a novel tablet application developed for the Resilience in Education and Development (RED) Study. The RED-app includes 12 cognitive tasks designed for groups of children aged 7 to 13 to independently complete during a 1-h school lesson. The quality of the data collected was high despite the lack of one-on-one engagement with participants. Most outcomes from the tablet showed moderate or high reliability, estimated using internal consistency metrics. Tablet-measured cognitive abilities also explained more than 50% of variance in teacher-rated academic achievement. Overall, the results suggest that tablet-based, group cognitive assessments of children are an efficient, reliable, and valid method of collecting the large datasets that modern psychology requires. We have open-sourced the scripts and materials used to make the application, so that they can be adapted and used by others.

Functional network dynamics in a neurodevelopmental disorder of known genetic origin.

Dynamic connectivity in functional brain networks is a fundamental aspect of cognitive development, but we have little understanding of the mechanisms driving variability in these networks. Genes are likely to influence the emergence of fast network connectivity via their regulation of neuronal processes, but novel methods to capture these rapid dynamics have rarely been used in genetic populations. The current study redressed this by investigating brain network dynamics in a neurodevelopmental disorder of known genetic origin, by comparing individuals with a ZDHHC9-associated intellectual disability to individuals with no known impairment. We characterised transient network dynamics using a Hidden Markov Model (HMM) on magnetoencephalography (MEG) data, at rest and during auditory oddball stimulation. The HMM is a data-driven method that captures rapid patterns of coordinated brain activity recurring over time. Resting-state network dynamics distinguished the groups, with ZDHHC9 participants showing longer state activation and, crucially, ZDHHC9 gene expression levels predicted the group differences in dynamic connectivity across networks. In contrast, network dynamics during auditory oddball stimulation did not show this association. We demonstrate a link between regional gene expression and brain network dynamics, and present the new application of a powerful method for understanding the neural mechanisms linking genetic variation to cognitive difficulties.

Mapping differential responses to cognitive training using machine learning.

We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self-organizing maps (SOMs)-a type of simple artificial neural network-to represent multivariate cognitive training data, and then tested whether the way tasks are represented changed as a result of training. The patterns of change we observed in the SOM weight matrices implied that the processes drawn upon to perform WM tasks changed following training. This was then combined with K-means clustering to identify distinct groups of children who respond to the training in different ways. Firstly, the K-means clustering was applied to an independent large sample (N = 616, Mage  = 9.16 years, range = 5.16-17.91 years) to identify subgroups. We then allocated children who had been through cognitive training (N = 179, Mage  = 9.00 years, range = 7.08-11.50 years) to these same four subgroups, both before and after their training. In doing so, we were able to map their improvement trajectories. Scores on a separate measure of fluid intelligence were predictive of a child's improvement trajectory. This paper provides an alternative approach to analysing cognitive training data that go beyond considering changes in individual tasks. This proof-of-principle demonstrates a potentially powerful way of distinguishing task-specific from domain-general changes following training and of establishing different profiles of response to training.

Dissociable effects of attention vs working memory training on cognitive performance and everyday functioning following fronto-parietal strokes.

Difficulties with attention are common following stroke, particularly in patients with frontal and parietal damage, and are associated with poor outcome. Home-based online cognitive training may have the potential to provide an efficient and effective way to improve attentional functions in such patients. Little work has been carried out to assess the efficacy of this approach in stroke patients, and the lack of studies with active control conditions and rigorous evaluations of cognitive functioning pre and post-training means understanding is limited as to whether and how such interventions may be effective. Here, in a feasibility pilot study, we compare the effects of 20 days of cognitive training using either novel Selective Attention Training (SAT) or commercial Working Memory Training (WMT) programme, versus a waitlist control on a range of attentional and working memory tasks. We demonstrate separable effects of each training condition, with SAT leading to improvements in spatial and non-spatial aspects of attention and WMT leading to improvements on closely related working memory tasks. In addition, both training groups reported improvements in everyday functioning, which were associated with improvements in attention, suggesting that improving attention may be of particular importance in maximising functional improvements in this patient group.

Remapping the cognitive and neural profiles of children who struggle at school.

Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within group homogeneity and between group differences, and fails to capture comorbidity. Here, we identify cognitive profiles in a large heterogeneous sample of struggling learners, using unsupervised machine learning in the form of an artificial neural network. Children were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals, irrespective of diagnosis or comorbidity, for problems in attention, memory, language, or poor school progress (n = 530). Children completed a battery of cognitive and learning assessments, underwent a structural MRI scan, and their parents completed behavior questionnaires. Within the network we could identify four groups of children: (a) children with broad cognitive difficulties, and severe reading, spelling and maths problems; (b) children with age-typical cognitive abilities and learning profiles; (c) children with working memory problems; and (d) children with phonological difficulties. Despite their contrasting cognitive profiles, the learning profiles for the latter two groups did not differ: both were around 1 SD below age-expected levels on all learning measures. Importantly a child's cognitive profile was not predicted by diagnosis or referral reason. We also constructed whole-brain structural connectomes for children from these four groupings (n = 184), alongside an additional group of typically developing children (n = 36), and identified distinct patterns of brain organization for each group. This study represents a novel move toward identifying data-driven neurocognitive dimensions underlying learning-related difficulties in a representative sample of poor learners.

Cross-frequency Phase-Amplitude Coupling as a Mechanism for Temporal Orienting of Attention in Childhood.

Temporal orienting of attention operates by biasing the allocation of cognitive and motor resources in specific moments in time, resulting in the improved processing of information from expected compared with unexpected targets. Recent findings have shown that temporal orienting operates relatively early across development, suggesting that this attentional mechanism plays a core role for human cognition. However, the exact neurophysiological mechanisms allowing children to attune their attention over time are not well understood. In this study, we presented 8- to 12-year-old children with a temporal cueing task designed to test (1) whether anticipatory oscillatory dynamics predict children's behavioral performance on a trial-by-trial basis and (2) whether anticipatory oscillatory neural activity may be supported by cross-frequency phase-amplitude coupling as previously shown in adults. Crucially, we found that, similar to what has been reported in adults, children's ongoing beta rhythm was strongly coupled with their theta rhythm and that the strength of this coupling distinguished validly cued temporal intervals, relative to neutral cued trials. In addition, in long trials, there was an inverse correlation between oscillatory beta power and children's trial-by-trial reaction, consistent with oscillatory beta power reflecting better response preparation. These findings provide the first experimental evidence that temporal attention in children operates by exploiting oscillatory mechanism.

Differences in brain morphology and working memory capacity across childhood.

Working memory (WM) skills are closely associated with learning progress in key areas such as reading and mathematics across childhood. As yet, however, little is known about how the brain systems underpinning WM develop over this critical developmental period. The current study investigated whether and how structural brain correlates of components of the working memory system change over development. Verbal and visuospatial short-term and working memory were assessed in 153 children between 5.58 and 15.92 years, and latent components of the working memory system were derived. Fractional anisotropy and cortical thickness maps were derived from T1-weighted and diffusion-weighted MRI and processed using eigenanatomy decomposition. There was a greater involvement of the corpus callosum and posterior temporal white matter in younger children for performance associated with the executive part of the working memory system. For older children, this was more closely linked with the thickness of the occipitotemporal cortex. These findings suggest that increasing specialization leads to shifts in the contribution of neural substrates over childhood, moving from an early dependence on a distributed system supported by long-range connections to later reliance on specialized local circuitry. Our findings demonstrate that despite the component factor structure being stable across childhood, the underlying brain systems supporting working memory change. Taking the age of the child into account, and not just their overall score, is likely to be critical for understanding the nature of the limitations on their working memory capacity.