Joe Bathelt

Event-related potential measures of executive functioning from preschool to adolescence

Executive functions are a collection of cognitive abilities necessary for behavioural control and regulation, and are important for school success. Executive deficits are common across acquired and developmental disorders in childhood and beyond. This review aims to summarize how studies using event‐related potential (ERP) can provide insight into mechanisms underpinning how executive functions develop in children from preschool to adolescence. We specifically focus on ERP components that are considered to be well‐established markers of executive functioning, including the ability to resist distraction (inhibition, N200), hold scenes in mind (visuospatial working memory, contralateral delay activity), attend to specific stimuli (information processing, P300), follow rules (response monitoring, error‐related negativity [ERN], and error‐related positivity [Pe]), and adjust to feedback (outcome monitoring, feedback‐related negativity). All of these components show developmental changes from preschool to adolescence, in line with behavioural and neuroimaging findings. These ERP markers also show altered developmental trajectories in the context of atypical executive functions. As an example, deficits in executive function are prominently implicated in attention‐deficit–hyperactivity disorder. Therefore, this review highlights ERP studies that have investigated the above ERP components in this population. Overall, ERPs provide a useful marker for the development and dysfunction of executive skills, and provide insight into their neurophysiological basis.

Structural brain abnormalities in a single gene disorder associated with epilepsy, language impairment and intellectual disability

Childhood speech and language deficits are highly prevalent and are a common feature of neurodevelopmental disorders. However, it is difficult to investigate the underlying causal pathways because many diagnostic groups have a heterogeneous aetiology. Studying disorders with a shared genetic cause and shared cognitive deficits can provide crucial insight into the cellular mechanisms and neural systems that give rise to those impairments. The current study investigated structural brain differences of individuals with mutations in ZDHHC9, which is associated with a specific neurodevelopmental phenotype including prominent speech and language impairments and intellectual disability. We used multiple structural neuroimaging methods to characterise neuroanatomy in this group, and observed bilateral reductions in cortical thickness in areas surrounding the temporo-parietal junction, parietal lobule, and inferior frontal lobe, and decreased microstructural integrity of cortical, subcortical-cortical, and interhemispheric white matter projections. These findings are compared to reports for other genetic groups and genetically heterogeneous disorders with a similar presentation. Overlap in the neuroanatomical phenotype suggests a common pathway that particularly affects the development of temporo-parietal and inferior frontal areas, and their connections.

Global and Local Connectivity Differences Converge With Gene Expression in a Neurodevelopmental Disorder of Known Genetic Origin

Knowledge of genetic cause in neurodevelopmental disorders can highlight molecular and cellular processes critical for typical development. Furthermore, the relative homogeneity of neurodevelopmental disorders of known genetic origin allows the researcher to establish the subsequent neurobiological processes that mediate cognitive and behavioral outcomes. The current study investigated white matter structural connectivity in a group of individuals with intellectual disability due to mutations in ZDHHC9. In addition to shared cause of cognitive impairment, these individuals have a shared cognitive profile, involving oromotor control difficulties and expressive language impairment. Analysis of structural network properties using graph theory measures showed global reductions in mean clustering coefficient and efficiency in the ZDHHC9 group, with maximal differences in frontal and parietal areas. Regional variation in clustering coefficient across cortical regions in ZDHHC9 mutation cases was significantly associated with known pattern of expression of ZDHHC9 in the normal adult human brain. The results demonstrate that a mutation in a single gene impacts upon white matter organization across the whole-brain, but also shows regionally specific effects, according to variation in gene expression. Furthermore, these regionally specific patterns may link to specific developmental mechanisms, and correspond to specific cognitive deficits.

Cortical Source Analysis of High-Density EEG Recordings in Children

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited, because the composition and spatial configuration of head tissues changes dramatically over development. In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.

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. Graphical abstract

Whole-brain white matter organization, intelligence, and educational attainment

General cognitive ability, sometimes referred to as intelligence, is associated with educational attainment throughout childhood. Most studies that have explored the neural correlates of intelligence in childhood focus on individual brain regions. This analytical approach is designed to identify restricted sets of voxels that overlap across participants. By contrast, we explored the relationship between white matter connectome organization, intelligence, and education. In both a sample of typically-developing children (N=63) and a sample of struggling learners (N=139), the white matter connectome efficiency was strongly associated with intelligence and educational attainment. Further, intelligence mediated the relationship between connectome efficiency and educational attainment. In contrast, a canonical voxel-wise analysis failed to identify any significant relationships. The results emphasize the importance of distributed brain network properties for cognitive or educational ability in childhood. Our findings are interpreted in the context of a developmental theory, which emphasizes the interaction between different subsystems over developmental time.

The neurocognitive architecture of fluid ability in children and adolescents

Fluid ability 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 modelled the neurocognitive architecture of fluid ability in two cohorts: CALM (N=551, aged 5-17) and NKI-RS (N=335, aged 6-17). We used multivariate Structural Equation Modelling to test a preregistered watershed model of fluid ability. We found that the watershed model fit the data well for both samples: White matter contributed to working memory and processing speed, which, in turn, contributed to fluid ability (R2(CALM)=51.2%, R2(NKI-RS)=78.3%). The relationship between cognitive abilities and white matter differed with age and showed a dip in strength around ages 7-12 years. Speculatively, this age-effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that fluid ability is part of a complex hierarchical system of partially independent effects.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 modelled the neurocognitive architecture of fluid intelligence in two cohorts: CALM (N = 551, aged 5 - 17 years) and NKI-RS (N = 335, aged 6 - 17 years). We used multivariate Structural Equation Modelling 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.

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.

Executive abilities in children with congenital visual impairment in mid-childhood

ABSTRACT The role of vision and vision deprivation in the development of executive function (EF) abilities in childhood is little understood; aspects of EF such as initiative, attention orienting, inhibition, planning and performance monitoring are often measured through visual tasks. Studying the development and integrity of EF abilities in children with congenital visual impairment (VI) may provide important insights into the development of EF and also its possible relationship with vision and non-visual senses. The current study investigates non-visual EF abilities in 18 school-age children of average verbal intelligence with VI of differing levels of severity arising from congenital disorders affecting the eye, retina, or anterior optic nerve. Standard auditory neuropsychological assessments of sustained and divided attention, phonemic, semantic and switching verbal fluency, verbal working memory, and ratings of everyday executive abilities by parents were undertaken. Executive skills were compared to age-matched typically-sighted (TS) typically-developing children and across levels of vision (mild to moderate VI [MVI] or severe to profound VI [SPVI]). The results do not indicate significant differences or deficits on direct assessments of verbal and auditory EF between the groups. However, parent ratings suggest difficulties with everyday executive abilities, with the greatest difficulties in those with SPVI. The findings are discussed as possibly reflecting increased demands of behavioral executive skills for children with VI in everyday situations despite auditory and verbal EF abilities in the typical range for their age. These findings have potential implications for clinical and educational practices.

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

Introduction Many 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. Methods The 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. Results The 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. Conclusion In 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.Many developmental disorders are associated with deficits in controlling and regulating behaviour. These difficulties are foremost associated with attention deficit hyperactivity disorder (ADHD), but are also frequently observed in other groups, including in children with diagnoses of 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 and become specific targets for intervention. The current study applies a novel data-driven clustering algorithm to group children by similarities in their ratings on a parent questionnaire that is commonly used to assess behavioural problems associated with ADHD. The sample consisted of 442 children identified by health and educational professionals as having difficulties in attention, learning and/or memory. The 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 behaviours and problems with peer relationships. These groups were associated with significant inter-individual variation in white matter connectivity of the prefrontal and anterior cingulate. In 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.