The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with ...multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition.
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.
The authors supplied the computer algorithm with lots of cognitive testing data from each child, including measures of listening skills, spatial reasoning, problem solving, vocabulary, and memory. Based on these data, the algorithm suggested that the children best fit into four clusters of difficulties. These clusters aligned closely with other data on the children, such as the parents’ reports of their communication difficulties, and educational data on reading and maths. But there was no correspondence with their previous diagnoses.
Abstract
Fluid intelligence is the capacity to solve novel problems in the absence of task-specific knowledge and is highly predictive of outcomes like educational attainment and psychopathology. ...Here, we modeled the neurocognitive architecture of fluid intelligence in two cohorts: the Centre for Attention, Leaning and Memory sample (CALM) (N = 551, aged 5–17 years) and the Enhanced Nathan Kline Institute—Rockland Sample (NKI-RS) (N = 335, aged 6–17 years). We used multivariate structural equation modeling to test a preregistered watershed model of fluid intelligence. This model predicts that white matter contributes to intermediate cognitive phenotypes, like working memory and processing speed, which, in turn, contribute to fluid intelligence. We found that this model performed well for both samples and explained large amounts of variance in fluid intelligence (R2CALM = 51.2%, R2NKI-RS = 78.3%). The relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7–12 years. This age effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that intelligence is part of a complex hierarchical system of partially independent effects.
Background
Behavioural and language difficulties co‐occur in multiple neurodevelopmental conditions. Our understanding of these problems has arguably been slowed by an overreliance on study designs ...that compare diagnostic groups and fail to capture the overlap across different neurodevelopmental disorders and the heterogeneity within them.
Methods
We recruited a large transdiagnostic cohort of children with complex needs (N = 805) to identify distinct subgroups of children with common profiles of behavioural and language strengths and difficulties. We then investigated whether and how these data‐driven groupings could be distinguished from a comparison sample (N = 158) on measures of academic and socioemotional functioning and patterns of global and local white matter connectome organisation. Academic skills were assessed via standardised measures of reading and maths. Socioemotional functioning was captured by the parent‐rated version of the Strengths and Difficulties Questionnaire.
Results
We identified three distinct subgroups of children, each with different levels of difficulties in structural language, pragmatic communication, and hot and cool executive functions. All three subgroups struggled with academic and socioemotional skills relative to the comparison sample, potentially representing three alternative but related developmental pathways to difficulties in these areas. The children with the weakest language skills had the most widespread difficulties with learning, whereas those with more pronounced difficulties with hot executive skills experienced the most severe difficulties in the socioemotional domain. Each data‐driven subgroup could be distinguished from the comparison sample based on both shared and subgroup‐unique patterns of neural white matter organisation. Children with the most pronounced deficits in language, cool executive, or hot executive function were differentiated from the comparison sample by altered connectivity in predominantly thalamocortical, temporal–parietal‐occipital, and frontostriatal circuits, respectively.
Conclusions
These findings advance our understanding of commonly co‐morbid behavioural and language problems and their relationship to behavioural outcomes and neurobiological substrates.
Executive function, an umbrella term used to describe the goal-directed regulation of thoughts, actions, and emotions, is an important dimension implicated in neurodiversity and established malleable ...predictor of multiple adult outcomes. Neurodevelopmental differences have been linked to both executive function strengths and weaknesses, but evidence for associations between specific profiles of executive function and specific neurodevelopmental conditions is mixed. In this exploratory study, we adopt an unsupervised machine learning approach (self-organising maps), combined with k-means clustering to identify data-driven profiles of executive function in a transdiagnostic sample of 566 neurodivergent children aged 8–18 years old. We include measures designed to capture two distinct aspects of executive function: performance-based tasks designed to tap the state-like efficiency of cognitive skills under optimal conditions, and behaviour ratings suited to capturing the trait-like application of cognitive control in everyday contexts. Three profiles of executive function were identified: one had consistent difficulties across both types of assessments, while the other two had inconsistent profiles of predominantly rating- or predominantly task-based difficulties. Girls and children without a formal diagnosis were more likely to have an inconsistent profile of primarily task-based difficulties. Children with these different profiles had differences in academic achievement and mental health outcomes and could further be differentiated from a comparison group of children on both shared and profile-unique patterns of neural white matter organisation. Importantly, children's executive function profiles were not directly related to diagnostic categories or to dimensions of neurodiversity associated with specific diagnoses (e.g., hyperactivity, inattention, social communication). These findings support the idea that the two types of executive function assessments provide non-redundant information related to children's neurodevelopmental differences and that they should not be used interchangeably. The findings advance our understanding of executive function profiles and their relationship to behavioural outcomes and neural variation in neurodivergent populations.
Children and adolescents with developmental problems are at increased risk of experiencing mental health problems. The Strengths and Difficulties Questionnaire (SDQ) is widely used as a screener for ...detecting mental health difficulties in these populations, but its use thus far has been restricted to groups of children with diagnosed disorders (e.g., ADHD). Transdiagnostic approaches, which focus on symptoms and soften or remove the boundaries between traditional categorical disorders, are increasingly adopted in research and practice. The aim of this study was to assess the potential of the SDQ to detect concurrent mental health problems in a transdiagnostic sample of children. The sample were referred by health and educational professionals for difficulties related to learning (
= 389). Some had one diagnosis, others had multiple, but many had no diagnoses. Parent-rated SDQ scores were significantly positively correlated with parent ratings of mental health difficulties on the Revised Child Anxiety and Depression Scale (RCADS). Ratings on the SDQ Emotion subscale significantly predicted the likelihood of having concurrent clinical anxiety and depression scores. Ratings on the Hyperactivity subscale predicted concurrent anxiety levels. These findings suggest the SDQ could be a valuable screening tool for identifying existing mental health difficulties in children recognized as struggling, as it can be in typically developing children and those with specific diagnoses.
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.
•In childhood and adolescence, cognitive ability is ‘best’ explained as consisting of two factors, gc and gf.•White matter tracts provide independent contributions to cognitive ability.•Associations ...between white matter and intelligence differed from childhood to adolescence.
Despite the reliability of intelligence measures in predicting important life outcomes such as educational achievement and mortality, the exact configuration and neural correlates of cognitive abilities remain poorly understood, especially in childhood and adolescence. Therefore, we sought to elucidate the factorial structure and neural substrates of child and adolescent intelligence using two cross-sectional, developmental samples (CALM: N = 551 (N = 165 imaging), age range: 5–18 years, NKI-Rockland: N = 337 (N = 65 imaging), age range: 6–18 years). In a preregistered analysis, we used structural equation modelling (SEM) to examine the neurocognitive architecture of individual differences in childhood and adolescent cognitive ability. In both samples, we found that cognitive ability in lower and typical-ability cohorts is best understood as two separable constructs, crystallized and fluid intelligence, which became more distinct across development, in line with the age differentiation hypothesis. Further analyses revealed that white matter microstructure, most prominently the superior longitudinal fasciculus, was strongly associated with crystallized (gc) and fluid (gf) abilities. Finally, we used SEM trees to demonstrate evidence for developmental reorganization of gc and gf and their white matter substrates such that the relationships among these factors dropped between 7–8 years before increasing around age 10. Together, our results suggest that shortly before puberty marks a pivotal phase of change in the neurocognitive architecture of intelligence.
A substantial proportion of the school-age population experience cognitive-related learning difficulties. Not all children who struggle at school receive a diagnosis, yet their problems are ...sufficient to warrant additional support. Understanding the causes of learning difficulties is the key to developing effective prevention and intervention strategies for struggling learners. The aim of this project is to apply a transdiagnostic approach to children with cognitive developmental difficulties related to learning to discover the underpinning mechanisms of learning problems.
A cohort of 1000 children aged 5 to 18 years is being recruited. The sample consists of 800 children with problems in attention, learning and / memory, as identified by a health or educational professional, and 200 typically-developing children recruited from the same schools as those with difficulties. All children are completing assessments of cognition, including tests of phonological processing, short-term and working memory, attention, executive function and processing speed. Their parents/ carers are completing questionnaires about the child's family history, communication skills, mental health and behaviour. Children are invited for an optional MRI brain scan and are asked to provide an optional DNA sample (saliva). Hypothesis-free data-driven methods will be used to identify the cognitive, behavioural and neural dimensions of learning difficulties. Machine-learning approaches will be used to map the multi-dimensional space of the cognitive, neural and behavioural measures to identify clusters of children with shared profiles. Finally, group comparisons will be used to test theories of development and disorder.
Our multi-systems approach to identifying the causes of learning difficulties in a heterogeneous sample of struggling learners provides a novel way to enhance our understanding of the common and complex needs of the majority of children who struggle at school. Our broad recruitment criteria targeting all children with cognitive learning problems, irrespective of diagnoses and comorbidities, are novel and make our sample unique. Our dataset will also provide a valuable resource of genetic, imaging and cognitive developmental data for the scientific community.
Literacy and numeracy are important skills that are typically learned during childhood, a time that coincides with considerable shifts in large‐scale brain organization. However, most studies ...emphasize focal brain contributions to literacy and numeracy development by employing case‐control designs and voxel‐by‐voxel statistical comparisons. This approach has been valuable, but may underestimate the contribution of overall brain network organization. The current study includes children (N = 133 children; 86 male; mean age = 9.42, SD = 1.715; age range = 5.92–13.75y) with a broad range of abilities, and uses whole‐brain structural connectomics based on diffusion‐weighted MRI data. The results indicate that academic attainment is associated with differences in structural brain organization, something not seen when focusing on the integrity of specific regions. Furthermore, simulated disruption of highly‐connected brain regions known as hubs suggests that the role of these regions for maintaining the architecture of the network may be more important than specific aspects of processing. Our findings indicate that distributed brain systems contribute to the etiology of difficulties with academic learning, which cannot be captured using a more traditional voxel‐wise statistical approach.
Reading and math performance in children who struggle in school is associated with the global organisation of the white matter connectome.
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