Biology subjects

Astle, D. E.

Publications and source records attributed to Astle, D. E..

17 recordsLinked to original sources

bioRxiv2026

Integrating Functional Transdiagnostic Dimensions of Psychopathology with Cortical Organization

Childhood and adolescence represent a critical period in which neurodevelopmental psychiatric conditions emerge; traditional case-control approaches often underestimate the complexity and co-occurrence of psychiatric conditions, calling for a transdiagnostic approach as a complementary measure. Open-access data sharing initiatives provide an opportunity to decipher structural- and functional-based organisational constraints on the relationship between brain connectivity and psychopathology. Using a highly heterogenous and comorbid neurodevelopmental sample of children and adolescents aged between 6 and 17 years old at-risk of neurodevelopmental conditions (N = 174, 114 males, mean age 10.72 {+/-} 2.21 years), and age-matched neurotypical controls (N = 27, 12 males, mean age 10.65 {+/-} 2.07 years), we identified a multivariate association, or latent variable, between resting-state functional connectivity and psychopathology. We extensively benchmarked this latent construct to develop a more parsimonious account of childhood psychopathology though an analytical framework spanning biological maps, brain connectivity, and behaviour. Participant-level expression of this latent brain-behaviour association differed by diagnostic burden and symptomatology, and pre-empted longitudinal psychopathology. Whilst diagnostic status was useful for interpretation, the latent construct transcended traditional diagnostic borders, revealing a neurotypical-neurodivergent continuum. The relationship between functional connectivity and neurodevelopmental psychopathology was circumscribed by functional connectivity networks (visual, fronto-parietal, and dorsal attention) and cytoarchitectonic class (primary/secondary sensory and primary sensory cortices). The latent variable aligned with magnetoencephalography-defined electrophysiological alpha (), high-gamma ({gamma}), and theta ({theta}) frequency bands, and was enriched across cortical distributions of astrocytes and excitatory neurones. The connectivity signature was significantly aligned with the archetypal sensorimotor-to-association axis and validated in an independent sample of pre-adolescents (N = 3504), with the strongest alignment with the principal component of gene expression and myelination and were relatively less enriched in cortical regions related to language, indicating a protective effect, and more positively enriched in regions related to executive functioning, conferring greater risk of psychopathology. Together, our findings suggest that the predictive link between functional connectivity and common symptoms of neurodevelopmental psychopathology are circumscribed by underlying macroscale anatomical, functional, and cognitive-related hierarchies.

neuroscience↗

bioRxiv2025

Right time, right place: Heterochronicity shapes brain network formation

Brain network formation unfolds on a non-uniform developmental timetable, with different cortical regions generating connections at different developmental phases. Generative network models (GNMs) aim to uncover the principles underpinning the organisation of connectomes by creating synthetic networks according to simple computational rules. These models capture the connectomes topology, operationalised here as the overall distributions of network metrics (e.g., modularity, small-worldness, rich-club structure). However, they typically ignore the differential timing of connectivity formation. By omitting this temporal programme, GNMs often misplace topological features in physical space. Here, we add a heterochronous growth term to GNMs and use a new model fitness function that weighs topology and topography equally. Topography refers to the spatial embedding of the network, the actual anatomical positions of tracts. With these advances, we can generate synthetic networks that more faithfully reproduce the spatial layout of diffusion-MRI connectomes from two independent adult cohorts. Compared with classical, temporally agnostic models, heterochronous simulations improve model fit, accurately locate cortical hubs and modules, and converge on a single caudal-to-rostral gradient of brain maturation. Integrating heterochronicity makes GNMs more faithful to brain development, setting the stage for using them to explain and ultimately predict individual differences in network formation.

neuroscience↗

bioRxiv2024

Molecular mechanisms driving divergent development of the human frontal and visual cortex during prenatal development

Key principles of structural brain organization are established very early in fetal development. The frontal cortex is an important hub for integration and control of information, and its integrity and connectivity within the wider neural system are linked to individual differences across multiple cognitive domains and neurodevelopmental conditions. Here we leveraged fetal brain transcriptomics to investigate molecular mechanisms during prenatal development that drive early differences between the two regions at the opposite poles of the physical and representational gradient of the brain - the frontal and visual cortex. We show that the frontal cortex exhibits significantly higher cumulative gene expression for pathways involved in the continued growth and maintenance of larger neurons. These pathways include the gene ontology terms of neuron development and neuronal cell body as well as glucose metabolism important in trophically supporting larger cell sizes. Whole pathways for axonal growth (axonal growth cone, microtubules, filopodia, lamellipodia) and single genes involved in circuit connectivity exhibited increased expression in the frontal cortex. In contrast, in line with the established earlier completion of neurogenesis and lower number of neurons in the anterior cortex, expression of genes involved in DNA replication was significantly lower relative to the visual cortex. We further demonstrate differential cellular composition with higher expression of marker genes for inhibitory neurons in the prenatal frontal cortex. Together, these results suggest that the cellular architecture and composition facilitates earlier connectivity in the frontal cortex which may determine its role as an integrative hub in the global brain organization.

neuroscience↗

bioRxiv2024

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

The temporal dynamics of resting-state networks (RSNs) may represent an intrinsic functional repertoire supporting cognitive control performance across the lifespan (Kupis et al., 2021). 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 macro-scale of cortical networks (Vidaurre et al., 2018). The present study used a 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. KeypointsO_LIHMM-derived metrics are reliable hallmarks of individual neural variability and show gender-related differences. C_LIO_LIBrain state transition patterns toward prefrontal and default-mode brain states predict differences on parental-report questionnaires scores. C_LIO_LIBrain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers. C_LI

neuroscience↗

bioRxiv2023

The entropy of resting-state neural dynamics is a marker of general cognitive ability in childhood

1Resting-state network activity has been associated with the emergence of individual differences across childhood development. However, due to the limitations of time-averaged representations of neural activity, little is known about how cognitive and behavioural variability relates to the rapid spatiotemporal dynamics of these networks. Magnetoencephalography (MEG), which records neural activity at a millisecond timescale, can be combined with Hidden Markov Modelling (HMM) to track the spatial and temporal characteristics of transient neural states. We applied HMMs to resting-state MEG data from (n = 46) children aged 8-13, who were also assessed on their cognitive ability and across multiple parent-report measures of behaviour. We found that entropy-related properties of participants resting-state time-courses were positively associated with cognitive ability. Additionally, cognitive ability was positively correlated with the probability of transitioning into HMM states involving fronto-parietal and somatomotor activation, and negatively associated with a state distinguished by default-mode network suppression. We discuss how using dynamical measures to characterise rapid, spontaneous patterns of brain activity can shed new light on neurodevelopmental processes implicated in the emergence of cognitive differences in childhood. Significance StatementThere is increasing evidence that the function of resting-state brain networks contributes to individual differences in cognition and behaviour across development. However, the relationship between dynamic, transient patterns of switching between resting-state networks and neurodevelopmental diversity is largely unknown. Here, we show that cognitive ability in childhood is related to the complexity of resting-state brain dynamics. Additionally, we demonstrate that the probability of transitioning into and remaining in certain states of brain network activity predicts individual differences in cognitive ability.

neuroscience↗

bioRxiv2022

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 indistinguishable 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 from 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. Research HighlightsO_LIWe investigated variability in structural brain network organisation and its relationship with cognition and behaviour in a sample of 383 children. C_LIO_LIWe did not find linear components of brain structure that explained continuous variations in inattention and hyperactivity across this heterogeneous sample. C_LIO_LIFollowing this, we explored different attributes of brain organisation in children with particularly elevated levels of inattention and hyperactivity (n = 232). C_LIO_LIAmong highly inattentive and hyperactive children, we found two profiles of structural brain organisation ( neurotypes), which were differentiated primarily by the communicability of nodes in frontal and occipital brain areas. C_LIO_LIThese subgroups did not differ on additional measures of behaviour. However, the lower-nodal-communicability group demonstrated weaker performance on cognitive assessments of executive function and visuospatial processing. C_LIO_LIWe discuss the implications that these findings have for our understanding of variability in neurodevelopmental difficulties and related conditions, such as ADHD C_LI

neuroscience↗

bioRxiv2022

Socio-economic disadvantage is associated with alterations in brain wiring economy

The quality of a childs social and physical environment is a key influence on brain development, educational attainment and mental wellbeing. However, there still remains a mechanistic gap in our understanding of how environmental influences converge on changes in the brains developmental trajectory. In a sample of 145 children with structural diffusion tensor imaging data, we used generative network modelling to simulate the emergence of whole brain network organisation. We then applied data-driven clustering to stratify the sample according to socio-economic disadvantage, with one of the resulting clusters containing mostly children living below the poverty line. A formal comparison of the simulated networks from the generative model revealed that the computational principles governing network formation were subtly different for children experiencing socio-economic disadvantage, and that this resulted in significantly altered developmental timing of network modularity emergence. Children in the low socio-economic status (SES) group had a significantly slower time to peak modularity, relative to the higher SES group (t(69) = 3.02, P = 3.50 x 10-4, d = 0.491). In a subsequent simulation we showed that the alteration in generative properties increases the variability in wiring probabilities during network formation (KS test: D = 0.012, P < 0.001). One possibility is that multiple environmental influences such as stress, diet and environmental stimulation impact both the systematic coordination of neuronal activity and biological resource constraints, converging on a shift in the economic conditions under which networks form. Alternatively, it is possible that this stochasticity reflects an adaptive mechanism that creates "resilient" networks better suited to unpredictable environments. Author SummaryWe used generative network models to simulate macroscopic brain network development in a sample of 145 children. Within these models, network connections form probabilistically depending on the estimated "cost" of forming a connection, versus topological "value" that the connection would confer. Tracking the formation of the network across the simulation, we could establish the changes in global brain organisation measures such as integration and segregation. Simulations for children experiencing socio-economic disadvantage were associated with a shift in emergence of a topologically valuable network property, namely modularity.

neuroscience↗

bioRxiv2022

Homophilic wiring principles underpin neuronal network topology in vitro

Economic efficiency has been a popular explanation for how networks self-organize within the developing nervous system. However, the precise nature of the economic negotiations governing this putative organizational principle remains unclear. Here, we address this question further by combining large-scale electrophysiological recordings, to characterize the functional connectivity of developing neuronal networks in vitro, with a generative modeling approach capable of simulating network formation. We find that the best fitting model uses a homophilic generative wiring principle in which neurons form connections to other neurons which are spatially proximal and have similar connectivity patterns to themselves. Homophilic generative models outperform more canonical models in which neurons wire depending upon their spatial proximity either alone or in combination with the extent of their local connectivity. This homophily-based mechanism for neuronal network emergence accounts for a wide range of observations that are described, but not sufficiently explained, by traditional analyses of network topology. Using rodent and human monolayer and organoid cultures, we show that homophilic generative mechanisms can accurately recapitulate the topology of emerging cellular functional connectivity, representing an important wiring principle and determining factor of neuronal network formation in vitro.

neuroscience↗

bioRxiv2021

Testing for implicit emotion regulation in childhood

Although implicit emotion regulation is thought to be critical for psychosocial development and mental wellbeing, few studies have investigated the neural underpinnings of this form of emotion regulation in children. We used a modified emotional Go/NoGo block design fMRI task to explore the neural correlates of implicit emotion regulation and individual differences in a sample of 40 children (50% female, mean age = 8.65 {+/-} 0.77). Conditions included happy, sad, neutral, and scrambled faces as implicit distractors within the actual Go/NoGo targets. We used a relatively standard preprocessing pipeline via fMRIprep, with T-contrasts for response inhibition and emotional effects, and a nonparametric multiple comparisons procedure, with SnPM, for our group-level analysis. There were multiple significant response inhibition effects, including larger NoGo vs Go activation in the IFG, insula, and MCC/ACC. Valence effects showed significantly greater right putamen activity for the Sad NoGo vs Go contrast and greater bilateral putamen and right pallidum activity for the Happy Go vs Sad Go contrast. These results provide preliminary findings of neural substrates, particularly the putamen, that may be associated with implicit emotion regulation in children.

neuroscience↗

bioRxiv2021

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here, we built an interactive resource to benchmark brain morphology, www.brainchart.io, derived from any current or future sample of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3; showed high stability of individual centile scores over longitudinal assessments; and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of individual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of diverse developmental and clinical effects on human brain structure. Furthermore, we provide open resources to support future advances towards adoption of brain charts as standards for quantitative benchmarking of typical or atypical brain MRI scans.

neuroscience↗

bioRxiv2020

A generative network model of neurodevelopment

The emergence 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 govern the diversity of these developmental processes? There are many existing descriptive theories, but to date none are computationally formalized. We provide a mathematical framework that specifies the growth of a brain network over developmental time. 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 development. 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 developmental 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 of childhood brain development, capable of integrating different levels of analysis - from genes to cognition.

neuroscience↗

bioRxiv2020

Gene functional networks and autism spectrum characteristics in young people with intellectual disability

BackgroundGenes associated with Intellectual disability (ID) can be grouped into networks according to gene function. This study asked whether individuals with ID show differences in autism spectrum characteristics (ASC), depending on the functional network membership of their rare, pathogenic de novo genetic variants. MethodsChildren and young people with ID of known genetic origin were allocated to two broad functional network groups: synaptic physiology (n=29) or chromatin regulation (n=23). We applied principle components analysis to the Social Responsiveness Scale to map the structure of ASC in this population, and identified three components - Inflexibility, Social Understanding and Social Motivation. We then used Akaike Information Criterion (AIC) to test the best fitting models for predicting ASC components, including demographic factors (age, gender), non-ASC behavioural factors (global adaptive function, anxiety, hyperactivity, inattention) and gene functional networks. ResultsWe found that, when other factors are accounted for, the chromatin regulation group showed higher levels of Inflexibility. We also observed contrasting predictors of ASC within each network group. Within the chromatin regulation group, Social Understanding was associated with inattention, and Social Motivation was predicted by hyperactivity. Within the synaptic group, Social Understanding was associated with hyperactivity, and Social Motivation was linked to anxiety. ConclusionWe report that gene functional networks can predict Inflexibility, but not other ASC dimensions. Contrasting behavioural associations within each group suggests network-specific developmental pathways from genomic variation to autism. Simple classification of neurodevelopmental disorder genes as high risk or low risk for autism is unlikely to be valid or useful.

genetics↗

bioRxiv2018

Functional network dynamics in a neurodevelopmental disorder of known genetic origin

Cognitive processing depends on the temporal co-ordination of functional brain networks. This fundamental aspect of neurophysiology potentially bridges the genetic regulation of neuronal activity and developmental cognitive impairments. We investigated brain network dynamics in a neurodevelopmental disorder of known genetic origin, by comparing individuals with ZDHHC9-associated intellectual disability to individuals with no known impairment. We used Hidden Markov Modelling on magnetoencephalography (MEG) data, at rest and during auditory oddball stimulation, to characterise transient network dynamics. At rest, network dynamics distinguished the groups, with ZDHHC9 participants showing longer state activation. Crucially, ZDHHC9 gene expression levels predicted the group differences across networks, supporting an association between molecular pathology and neurophysiology. In contrast, network dynamics during auditory oddball stimulation did not show this association. We demonstrate a link between brain network dynamics and regional gene expression, and present a valuable method for understanding the real-time neural mechanisms linking genetic variation to cognitive difficulties.

neuroscience↗

bioRxiv2018

A randomised exploratory investigation of the effects of Attention vs Working Memory Training on cognitive performance and everyday functioning following stroke.

Difficulties with attention are common following stroke and are associated with poor outcome. Home-based online cognitive training may have to 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 we compare the effects of 20 days of active cognitive training using either novel Selective Attention Training (SAT) or commercial Working Memory Training (WMT) programme, versus a waitlist control group, on a wide range of attentional and working memory tasks, as well as on self-reported everyday functioning. We demonstrate separable effects of each of the active training conditions, with SAT leading to improvements in both spatial and non-spatial aspects of attention and WMT leading to improvements only on very closely related working memory tasks. Both training groups reported improvements in everyday functioning, which were associated with improvements in attentional functions, suggesting that improving attention may be of particular importance in maximising functional recovery in this patient group.

clinical trials↗

bioRxiv2018

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 behaviour questionnaires. Within the network, we could identify four groups of children: i) children with broad cognitive difficulties, and severe reading, spelling and maths problems; ii) children with age-typical cognitive abilities and learning profiles; iii) children with working memory problems; and iv) 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 childs 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 organisation for each group. This study represents a novel move towards identifying data-driven neurocognitive dimensions underlying learning-related difficulties in a representative sample of poor learners.\n\nAuthor NoteThe Centre for Attention Learning and Memory (CALM) research clinic is based at and supported by funding from the MRC Cognition and Brain Sciences Unit, University of Cambridge. The Principal Investigators are Joni Holmes (Head of CALM), Susan Gathercole (Chair of CALM Management Committee), Duncan Astle, Tom Manly and Rogier Kievit. Data collection is assisted by a team of researchers and PhD students at the CBSU. This currently includes: Sarah Bishop, Annie Bryant, Sally Butterfield, Fanchea Daily, Laura Forde, Erin Hawkins, Sinead OBrien, Cliodhna OLeary, Joseph Rennie, and Mengya Zhang. The authors wish to thank the many professionals working in childrens services in the South-East and East of England for their support, and to the children and their families for giving up their time to visit the clinic.\n\nResearch HighlightsO_LIfirst study to apply machine learning to understand heterogeneity in struggling learners\nC_LIO_LIlarge sample of struggling learners that includes children with multiple difficulties\nC_LIO_LIrich phenotyping with detailed behavioural, cognitive, and neuroimaging assessments\nC_LI

neuroscience↗

bioRxiv2017

Data-driven brain-types and their cognitive consequences

The canonical approach to exploring brain-behaviour relationships is to group individuals according to a phenotype of interest, and then explore the neural correlates of this grouping. A limitation of this approach is that multiple aetiological pathways could result in a similar phenotype, so the role of any one brain mechanism may be substantially underestimated. Building on advances in network analysis, we used a data-driven community-clustering algorithm to identify robust subgroups based on white-matter microstructure in childhood and adolescence (total N=313, mean age: 11.24 years). The algorithm indicated the presence of two equal-size groups that show a critical difference in FA of the left and right cingulum. These different brain types had profoundly different cognitive abilities with higher performance in the higher FA group. Further, a connectomics analysis indicated reduced structural connectivity in the low FA subgroup that was strongly related to reduced functional activation of the default mode network.\n\nGraphical abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC=\"FIGDIR/small/237859_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (34K):\norg.highwire.dtl.DTLVardef@135e807org.highwire.dtl.DTLVardef@145673org.highwire.dtl.DTLVardef@137cd67org.highwire.dtl.DTLVardef@8e2cff_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2017

Data-driven subtyping of behavioural problems associated with ADHD in children

IntroductionMany developmental disorders are associated with deficits in controlling and regulating behaviour. These difficulties are frequently observed across multiple groups of children including children with diagnoses of attention deficit hyperactivity disorder (ADHD), specific learning difficulties, autistic spectrum disorder, or conduct disorder. The co-occurrence of these behavioural problems across disorders typically leads to comorbid diagnoses and can complicate intervention approaches. An alternative to classifying children on the basis of specific diagnostic criteria is to use a data-driven grouping that identifies dimensions of behaviour that meaningfully distinguish groups of children.\n\nMethodsThe sample consisted of 442 children identified by health and educational professionals as having difficulties in attention, learning and/or memory. The current study applied community clustering, a data-driven clustering algorithm, to group children by similarities across scales on a commonly used rating scale, the Conners-3 questionnaire. Further, the current study investigated if the groups identified by the data-driven algorithm could be identified by white matter connectivity using a structural connectomics approach combined with partial least squares analysis.\n\nResultsThe data-driven clustering yielded three distinct groups of children with symptoms of either: (1) elevated inattention and hyperactivity/impulsivity, and poor executive function, (2) learning problems, and (3) aggressive behaviour and problems with peer relationships. These groups were associated with significant inter-individual variation in white matter connectivity of the prefrontal and anterior cingulate cortex.\n\nConclusionIn sum, data-driven classification of executive function difficulties identifies stable groups of children, provides a good account of inter-individual differences, and aligns closely with underlying neurobiological substrates.

neuroscience↗