Biology subjects

Akarca, D.

Publications and source records attributed to Akarca, D..

18 recordsLinked to original sources

bioRxiv2026

Functional connectivity correlates of the hierarchical p-factor model in youth at neurodevelopmental risk

BackgroundEmotional and cognitive difficulties often co-occur in neurodevelopmental conditions. While transdiagnostic, dimensional approaches offer a more precise framework for understanding mental health than diagnostic categories, their neural correlates in youth with learning difficulties remain poorly understood. This study investigates associations between transdiagnostic mental health dimensions and resting-state functional connectivity in struggling learners. MethodsCross-sectional behavioural data from the Centre for Attention, Learning and Memory (CALM) for struggling learners (N = 378) was used to replicate a hierarchical model of mental health from the Conners Parent Rating Short Form, the Revised Childrens Anxiety and Depression Scale and the Strengths and Difficulties Questionnaire. Functional connectomes were derived from resting-state fMRI data (N = 67), and partial least squares regression related mental health dimensions to connectivity within and between large-scale brain networks. ResultsThe replicated model comprised a general p-factor, two broad domains (internalising and externalising), and three specific dimensions (specific internalising, neurodevelopmental and social maladjustment). Symptom severity was associated with two connectivity patterns: greater default mode network coupling to frontoparietal and attention networks, and reduced connectivity between visual and somatomotor systems. These effects were strongest for the neurodevelopmental and social maladjustment dimensions, respectively. ConclusionsThese findings align with population-level evidence linking mental health dimensions to brain network organization, extending it to struggling learners and offering new insight into the neural basis of mental health vulnerability in neurodevelopmentally at-risk youth.

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bioRxiv2025

Human cortical networks trade communication efficiency for computational reliability.

Brains are often described as cost-efficient communication networks, optimally balancing the cost of long connections with the benefits of fast communication. Here, inspired by the "use it or lose it" principle, we present a novel game-theoretic model of self-organizing neural units and show that the brain is, in fact, sub-optimal in both regards: First, we demonstrate that regional competition for connectivity under propagative communication dynamics naturally gives rise to network configurations similar to those derived from the human cortex while being even more efficient and economical. Next, we use a reservoir computing framework to compare the information processing capacity of these networks against those of the brain. Although comparable in performance, the more optimal trade-off comes with a tax on computational reliability. Through synthetic lesions, we show that these networks are fragile because, to optimize for communication, they funnel information through a spatially clustered "oligarchy" comprising a tight set of transmodal hubs. In contrast, the human brain uses a more distributed "rich club" backbone that better resists breakdown following targeted attacks, even when it means higher wiring costs and less efficient communication. This reveals a previously overlooked principle: cortical networks trade both cost and efficiency for reliable computation. Thus, our findings highlight computational reliability as another, and even more prominent driver of brain connectivity compared to wiring cost and communication efficiency. TeaserThe brain avoids fragile efficiency: brain networks favor reliable computation, revealing resilience as a hidden design rule.

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bioRxiv2024

Canonical neurodevelopmental trajectories of structural and functional manifolds

Organisational gradients refer to a continuous low-dimensional embedding of brain regions and can quantify core organisational principles of complex systems like the human brain. Mapping how these organisational principles are altered or refined across development and phenotypes is essential to understanding the relationship between brain and behaviour. Taking a developmental approach and leveraging longitudinal and cross-sectional data from two multi-modal neuroimaging datasets, spanning the full neurotypical-neurodivergent continuum, we charted the organisational variability of structural (610 participants, N = 390 with one observation, N = 163 with two observations, and N = 57 with three) and functional (512 participants, N = 340 with one observation, N = 128 with two observations, and N = 44 with three). Across datasets, despite differing phenotypes, we observe highly similar structural and functional gradients. These gradients, or organisational principles, are highly stable across development, with the exact same ordering across early childhood into mid-adolescence. However, there is substantial developmental change in the strength of embedding within those gradients: by modelling developmental trajectories as non-linear splines, we show that structural and functional gradients exhibit sensitive periods and are refined across development. Specifically, structural gradients gradually contract in low-dimensional space as networks become more integrated, whilst the functional manifold expands, indexing functional specialisation. The coupling of these structural and functional gradients follows a unimodal-association axis and varies across individuals, with developmental effects concentrated in the more plastic higher-order networks. Importantly, these developmental effects on coupling, in these higher-order networks, are attenuated in the neurodivergent sample. Finally, we mapped structure-function coupling onto dimensions of psychopathology and cognition and demonstrate that dimensions of cognition, such as working memory, are robust predictors of coupling. In summary, across clinical and community samples, we demonstrate consistent principles of structural and functional brain organisation, with progressive structural integration and functional segregation. These gradients are established early in life, refined through development, and their coupling is predicted by working memory.

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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.

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bioRxiv2024

Synaptic function and sensory processing in ZDHHC9-associated neurodevelopmental disorder: a mechanistic account

Loss-of-function ZDHHC9 variants are associated with X-linked intellectual disability (XLID), rolandic epilepsy (RE) and developmental language difficulties. This study integrates human neurophysiological data with a computational model to identify a potential neural mechanism explaining ZDHHC9-associated differences in cortical function and cognition. Magnetoencephalography (MEG) data was collected during an auditory roving oddball paradigm from eight individuals with a ZDHHC9 loss-of-function variant (ZDHHC9 group) and seven age-matched individuals without neurological or neurodevelopmental difficulties (control group). Evoked responses to auditory stimulation were larger in amplitude and showed a later peak latency in the ZDHHC9 group but demonstrated normal stimulus-specific properties. Magnetic mismatch negativity (mMMN) amplitude was also increased in the ZDHHC9 group, reflected by stronger neural activation during deviant processing relative to the standard. A recurrent neural network (RNN) model was trained to mimic recapitulate group-level auditory evoked responses, and subsequently perturbed to test the hypothesised impact of ZDHHC9-driven synaptic dysfunction on neural dynamics. Results of model perturbations showed that reducing inhibition levels by weakening inhibitory weights recapitulates the observed group differences in evoked responses. Stronger reductions in inhibition levels resulted in increased peak amplitude and peak latency of RNN prediction relative to the pre-perturbation predictions. Control experiments in which excitatory connections were strengthened by the same levels did not result in consistently stable activity or AEF-like RNN predictions. Together, these results suggest that reduced inhibition is a plausible mechanism by which loss of ZDHHC9 function alters cortical dynamics during sensory processing. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/587155v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1f9bd36org.highwire.dtl.DTLVardef@f54610org.highwire.dtl.DTLVardef@caff66org.highwire.dtl.DTLVardef@145a4d2_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG In the current study, we employed a bottom-up approach to study the impact of synaptic-level alterations associated with ZDHHC9 variants on cortical function in healthy and ZDHHC9-deficient participants. To achieve this, a recurrent neural network model was developed to recapitulate MEG-derived auditory evoked responses and subsequently perturbed in order to determine effects on resulting dynamics. We show that reduced network inhibition recapitulates empirical observations, specifically increased response amplitudes, delayed peak latencies and increased mismatch negativity. These results offered a mechanistic account on the impact of ZDHHC9-associated synaptic alterations on auditory processing.

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bioRxiv2024

A generative model of the connectome with dynamic axon growth

Connectome generative models, otherwise known as generative network models, provide insight into the wiring principles underpinning brain network organization. While these models can approximate numerous statistical properties of empirical networks, they typically fail to explicitly characterize an important contributor to brain organization - axonal growth. Emulating the chemoaffinity guided axonal growth, we provide a novel generative model in which axons dynamically steer the direction of propagation based on distance-dependent chemoattractive forces acting on their growth cones. This simple dynamic growth mechanism, despite being solely geometry-dependent, is shown to generate axonal fiber bundles with brain-like geometry and features of complex network architecture consistent with the human brain, including lognormally distributed connectivity weights, scale-free nodal degrees, small-worldness, and modularity. We demonstrate that our model parameters can be fitted to individual connectomes, enabling connectome dimensionality reduction and comparison of parameters between groups. Our work offers an opportunity to bridge studies of axon guidance and connectome development, providing new avenues for understanding neural development from a computational perspective. Author SummaryGenerative models of the human connectome provide insight into principles driving brain network development. However, current models do not capture axonal outgrowth, which is crucial to the formation of neural circuits. We develop a novel generative connectome model featuring dynamic axonal outgrowth, revealing the contribution of microscopic axonal guidance to the network topology and axonal geometry of macroscopic connectomes. Simple axonal outgrowth rules representing continuous chemoaffinity gradients are shown to generate complex, brain-like topologies and realistic axonal fascicle architectures. Our model is sufficiently sensitive to capture subtle interindividual differences in axonal outgrowth between healthy adults. Our results are significant because they reveal core principles that may give rise to both complex brain networks and brain-like axonal bundles, unifying neurogenesis across scales.

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bioRxiv2023

Population-level genetic variation shapes generative brain mechanisms

The structural organisation of the human brain emerges probabilistically as we develop. Due to the inherent complexity of the human brain, understanding the forces that shape this probabilistic emergence remains one of the central challenges of systems theory and neuroscience. Across 2153 children (9-11 years old) we used a computational model to simulate the formation of structural brain connectivity, conceptualised as a trade-off between the cost of new connections {eta} and their topological value {gamma}. We then triangulated this population-level neuroimaging and computational modelling with genomics. For each participant we assessed their genetic propensity for cognitive ability by calculating polygenic scores. Modelled parameters differed systematically for participants depending upon their genetic propensity. Those with the highest genetic propensity had a significantly weaker {eta} term - put simply, their networks emerged with a weaker distance penalty. Strikingly, this softer distance penalty produces more stochastic, diverse, and efficient networks. Furthermore, across the sample, overlapping biological and cellular pathways between polygenic scores and each childs optimal {eta}-{gamma} trade-off emerged. This application of computational modelling demonstrates a converging genomic basis for structural brain development and cognitive ability across the population, providing a mechanistic explanation of how and why characteristic network topologies emerge from children at the extreme distributions of polygenic scores, and why they might predict cognitive ability.

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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.

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bioRxiv2023

A weighted generative model of the human connectome

Probabilistic generative network models have offered an exciting window into the constraints governing the human connectomes organization. In particular, they have highlighted the economic context of network formation and the special roles that physical geometry and self-similarity likely play in determining the connectomes topology. However, a critical limitation of these models is that they do not consider the strength of anatomical connectivity between regions. This significantly limits their scope to answer neurobiological questions. The current work draws inspiration from the principle of redundancy reduction to develop a novel weighted generative network model. This weighted generative network model is a significant advance because it not only incorporates the theoretical advancements of previous models, but also has the ability to capture the dynamic strengthening or weakening of connections over time. Using a state-of-the-art Convex Optimization Modelling for Microstructure-Informed Tractography (COMMIT) approach, in a sample of children and adolescents (n = 88, aged 8 to 18 years), we show that this model can accurately approximate simultaneously the topology and edge-weights of the connectome (specifically, the MRI signal fraction attributed to axonal projections). We achieve this at both sparse and dense connectome densities. Generative model fits are comparable to, and in many cases better than, published findings simulating topology in the absence of weights. Our findings have implications for future research by providing new avenues for exploring normative developmental trends, models of neural computation and wider conceptual implications of the economics of connectomics supporting human functioning.

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bioRxiv2023

The adaptive stochasticity hypothesis: modelling 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 modelling 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; dHCP, 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; CALM 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.

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bioRxiv2022

Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information processing. To observe the effect of these processes, we introduce the spatially-embedded recurrent neural network (seRNN). seRNNs learn basic task-related inferences while existing within a 3D Euclidean space, where the communication of constituent neurons is constrained by a sparse connectome. We find that seRNNs, similar to primate cerebral cortices, naturally converge on solving inferences using modular small-world networks, in which functionally similar units spatially configure themselves to utilize an energetically-efficient mixed-selective code. As all these features emerge in unison, seRNNs reveal how many common structural and functional brain motifs are strongly intertwined and can be attributed to basic biological optimization processes. seRNNs can serve as model systems to bridge between structural and functional research communities to move neuroscientific understanding forward.

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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

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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.

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bioRxiv2022

Early adversity changes the economic conditions of structural brain network organisation

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 modelling of the mouse connectome to test whether unpredictable postnatal stress shifts the constraints that govern the formation 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 neighbours) generated simulations that replicate the organisation of the rodent connectome. The imposition of early life adversity significantly 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 shape network formation, introducing greater randomness into the structural development of the brain. While this change may constrain the development of cognitive abilities, it could also reflect an adaptive mechanism. In other words, neural development could harness heightened stochasticity to make networks more robust to perturbation, thereby facilitating effective responses to future threats and challenges. Significance statementChildren who experience adversity early in life - such as chronic poverty or abuse - show numerous neural differences that are linked to poorer cognition and mental health later in life. To effectively mitigate the burden of adversity, it is critical to identify how these differences arise. In this paper, we use computational modelling to test whether growing up in an impoverished and unpredictable environment changes the development of structural connections in the mouse brain. We found that early adversity appears to introduce more stochasticity in the formation of neural architecture. Our findings point to a potential mechanism for how early adversity could change the course of child development.

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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.

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bioRxiv2020

Bridging brain and cognition: A multilayer network analysis of brain structural covariance and general intelligence in a developmental sample of struggling learners

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role between brain and behavior. We discuss implications and possible avenues for future studies.

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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.

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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.

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