Biology subjects

Bathelt, J.

Publications and source records attributed to Bathelt, J..

10 recordsLinked to original sources

bioRxiv2026

Autism Polygenic Score Is Associated With Sex-Dependent Broadening of Brain Network Variability

Background: Autism polygenic scores (PGS) reliably predict case-control status yet explain little variance in autism-related traits. Landscape accounts of neurodevelopmental diversity propose that genetic liability broadens the range of viable neural configurations rather than shifting brain organisation toward dysfunction. We tested whether autism polygenic load is associated with increased variability in functional network organisation among non-autistic adults. Methods: We analysed resting-state functional connectivity from 910 non-autistic adults (aged 22-35) in the Human Connectome Project. Polygenic scores were derived from the iPSYCH autism GWAS at a pre-specified threshold (p = 0.1). Modularity (segregation) and global efficiency (integration) were computed at a pre-selected parcellation size and density (100-node, 20%), and residualised for age, intracranial volume, and head motion. Variance effects were assessed by variance regression including a PGS-by-sex interaction, decile-stratified dispersion trends, and PGS-balanced bootstrap resampling. Edge-wise analyses used false discovery rate correction. Results: Modularity variability broadened with polygenic load in a sex-dependent manner (sex-by-PGS beta = 1.92e-4, p = 0.031). Decile trends (male minus female difference = 0.82, p = 0.034) and balanced-bootstrap trends (difference = 1.19, p = 0.032) both differed by sex: variance increased across polygenic bins in males (r = 0.57, one-tailed p = 0.021) but not females. No comparable effect emerged for global efficiency (all p >= 0.54). Polygenic scores showed no association with social-cognitive difficulty (beta = 0.11, p = 0.209), mean network organisation, or connectivity after correction. Limitations: All participants were non-autistic adults and the analysis was cross-sectional. The identified effects are small and the sample size not sufficient to resolve very small effects often reported in genetics studies. Characterisation of genetic effects in women may be influenced by biases in the data used to calculate polygenic scores. Conclusions: Autism polygenic load broadened modular network configurations in males without shifting mean organisation or its behavioural correlates, offering partial support for landscape accounts.

neuroscience↗

bioRxiv2026

PRISMA: A precision functional imaging dataset of autistic and non-autistic adults

Autism spectrum conditions are characterised by substantial heterogeneity in brain function, yet most neuroimaging studies rely on brief single-session acquisitions that prioritise group-level comparisons over individual characterisation. We present a precision functional imaging dataset with extended neuroimaging acquisitions to advance research on individual differences in autism. The dataset comprises 33 autistic adults between the ages of 18 and 30, and 34 age- and gender-matched comparison participants, each scanned across three sessions with up to 90 minutes of functional magnetic resonance imaging per participant. Functional data were acquired during eyes-open rest, news viewing, and reality television viewing to probe state-dependent differences in network engagement. The dataset includes high-resolution structural imaging, diffusion-weighted imaging with dual phase-encoding, and physiological recordings of cardiac and respiratory activity acquired simultaneously with functional scans. All data are organised following Brain Imaging Data Structure principles and accompanied by comprehensive quality control metrics and preprocessed derivatives. This dataset enables investigation of individual-specific functional brain organisation, test-retest reliability of connectivity patterns, and state-dependency of functional networks in autism, addressing a notable gap in publicly available neuroimaging resources.

neuroscience↗

bioRxiv2023

Multilayer network associations between the exposome and childhood brain development

Growing up in a high poverty neighborhood is associated with elevated risk for academic challenges and health problems. Here, we take a data-driven approach to exploring how measures of childrens environments relate to the development of their brain structure and function in a community sample of children between the ages of 4 and 10 years. We constructed exposomes including measures of family socioeconomic status, childrens exposure to adversity, and geocoded measures of neighborhood socioeconomic status, crime, and environmental toxins. We connected the exposome to two structural measures (cortical thickness and surface area, n = 170) and two functional measures (participation coefficient and clustering coefficient, n = 130). We found dense connections within exposome and brain layers and sparse connections between exposome and brain layers. Lower family income was associated with thinner visual cortex, consistent with the theory that accelerated development is detectable in early-developing regions. Greater neighborhood incidence of high blood lead levels was associated with greater segregation of the default mode network, consistent with evidence that toxins are deposited into the brain along the midline. Our study demonstrates the utility of multilayer network analysis to bridge environmental and neural explanatory levels to better understand the complexity of child development.

neuroscience↗

bioRxiv2020

More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism

Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach to understanding brain function in neuroimaging research. Combining these parallel approaches would enable new insights into the interaction between behaviours and their brain-level correlates. In this paper, we introduce a methodology for combining network psychometrics and network neuro-science. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10-21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. In addition, because the shared variance between behavioural measures is controlled for in the approach, the methodology enables us to isolate mechanisms at the brain-level that are unique to particular behavioural variables. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates.

neuroscience↗

bioRxiv2019

Neurocognitive reorganization between crystallized intelligence, fluid intelligence and white matter microstructure in two age-heterogeneous developmental cohorts

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.

neuroscience↗

bioRxiv2018

The neurocognitive architecture of fluid ability in children and adolescents

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.

neuroscience↗

bioRxiv2018

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.

neuroscience↗

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↗