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.

Global topology of human connectome is insensitive to early life environments - A prospective longitudinal study of the general population.

The widely acknowledged detrimental impact of early adversity on child development has driven efforts to understand the underlying mechanisms that may mediate these effects within the developing brain. Recent efforts have begun to move beyond associating adversity with the morphology of individual brain regions towards determining if and how adversity might shape their interconnectivity. However, whether adversity effects a global shift in the organisation of whole-brain networks remains unclear. In this study, we assessed this possibility using parental questionnaire and diffusion imaging data from The Avon Longitudinal Study of Parents and Children (ALSPAC, N = 913), a prospective longitudinal study spanning more than 20 years. We tested whether a wide range of adversities-including experiences of abuse, domestic violence, physical and emotional cruelty, poverty, neglect, and parental separation-measured by questionnaire within the first seven years of life were significantly associated with the tractography-derived connectome in young adulthood. We tested this across multiple measures of organisation and using a computational model that simulated the wiring economy of the brain. We found no significant relationships between early exposure to any form of adversity and the global organisation of the structural connectome in young adulthood. We did detect local differences in the medial prefrontal cortex, as well as an association between weaker brain wiring constraints and greater externalising behaviour in adolescence. Our results indicate that further efforts are necessary to delimit the magnitude and functional implications of adversity-related differences in connectomic organization. RESEARCH HIGHLIGHTS: Diverse prospective measures of the early-life environment do not predict the organisation of the DTI tractography-derived connectome in young adulthood Wiring economy of the connectome is weakly associated with externalising in adolescence, but not internalising or cognitive ability Further work is needed to establish the scope and significance of global adversity-related differences in the structural connectome.

The adaptive stochasticity hypothesis: Modeling 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 modeling 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, 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; 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.

Early adversity changes the economic conditions of mouse structural brain network organization.

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 modeling of the mouse connectome to test whether unpredictable postnatal stress shifts the constraints that govern the organization 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 neighbors) generated simulations that successfully replicate the rodent connectome. The imposition of early life adversity 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 reproduce rodent connectome organization, introducing greater randomness into the development of the simulations. While this change may constrain the development of cognitive abilities, it could also reflect an adaptive mechanism that facilitates effective responses to future challenges.

Toward computational neuroconstructivism: a framework for developmental systems neuroscience.

Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.

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 similar 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 through 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.

Transdiagnostic profiles of behaviour and communication relate to academic and socioemotional functioning and neural white matter organisation.

BACKGROUND: Behavioural and language difficulties co-occur in multiple neurodevelopmental conditions. Our understanding of these problems has arguably been slowed by an overreliance on study designs that compare diagnostic groups and fail to capture the overlap across different neurodevelopmental disorders and the heterogeneity within them. METHODS: We recruited a large transdiagnostic cohort of children with complex needs (N = 805) to identify distinct subgroups of children with common profiles of behavioural and language strengths and difficulties. We then investigated whether and how these data-driven groupings could be distinguished from a comparison sample (N = 158) on measures of academic and socioemotional functioning and patterns of global and local white matter connectome organisation. Academic skills were assessed via standardised measures of reading and maths. Socioemotional functioning was captured by the parent-rated version of the Strengths and Difficulties Questionnaire. RESULTS: We identified three distinct subgroups of children, each with different levels of difficulties in structural language, pragmatic communication, and hot and cool executive functions. All three subgroups struggled with academic and socioemotional skills relative to the comparison sample, potentially representing three alternative but related developmental pathways to difficulties in these areas. The children with the weakest language skills had the most widespread difficulties with learning, whereas those with more pronounced difficulties with hot executive skills experienced the most severe difficulties in the socioemotional domain. Each data-driven subgroup could be distinguished from the comparison sample based on both shared and subgroup-unique patterns of neural white matter organisation. Children with the most pronounced deficits in language, cool executive, or hot executive function were differentiated from the comparison sample by altered connectivity in predominantly thalamocortical, temporal-parietal-occipital, and frontostriatal circuits, respectively. CONCLUSIONS: These findings advance our understanding of commonly co-morbid behavioural and language problems and their relationship to behavioural outcomes and neurobiological substrates.

Risk of Early Versus Later Rebleeding From Dural Arteriovenous Fistulas With Cortical Venous Drainage.

BACKGROUND: Cranial dural arteriovenous fistulas with cortical venous drainage are rare lesions that can present with hemorrhage. A high rate of rebleeding in the early period following hemorrhage has been reported, but published long-term rates are much lower. No study has examined how risk of rebleeding changes over time. Our objective was to quantify the relative incidence of rebleeding in the early and later periods following hemorrhage. METHODS: Patients with dural arteriovenous fistula and cortical venous drainage presenting with hemorrhage were identified from the multinational CONDOR (Consortium for Dural Fistula Outcomes Research) database. Natural history follow-up was defined as time from hemorrhage to first treatment, rebleed, or last follow-up. Rebleeding in the first 2 weeks and first year were compared using incidence rate ratio and difference. RESULTS: Of 1077 patients, 250 met the inclusion criteria and had 95 cumulative person-years natural history follow-up. The overall annualized rebleed rate was 7.3% (95% CI, 3.2-14.5). The incidence rate of rebleeding in the first 2 weeks was 0.0011 per person-day; an early rebleed risk of 1.6% in the first 14 days (95% CI, 0.3-5.1). For the remainder of the first year, the incidence rate was 0.00015 per person-day; a rebleed rate of 5.3% (CI, 1.7-12.4) over 1 year. The incidence rate ratio was 7.3 (95% CI, 1.4-37.7; P, 0.026). CONCLUSIONS: The risk of rebleeding of a dural arteriovenous fistula with cortical venous drainage presenting with hemorrhage is increased in the first 2 weeks justifying early treatment. However, the magnitude of this increase may be considerably lower than previously thought. Treatment within 5 days was associated with a low rate of rebleeding and appears an appropriate timeframe.

Internal neurolysis: 'nerve combing' for trigeminal neuralgia without neurovascular conflict - early UK outcomes.

INTRODUCTION: Internal neurolysis (INL) is a surgical procedure where trigeminal nerve fibres are separated between the pons and porus trigeminus to relieve trigeminal neuralgia (TN). We report pain and functional outcomes to evaluate its safety and efficacy. MATERIALS AND METHODS: Prospective cohort of all patients undergoing retrosigmoid craniotomy and INL between 2015 and 2017 at University Hospital Southampton. Patients with type I (6) or type II (2) refractory TN and no clear neurovascular conflict were offered INL as an alternative to partial sensory rhizotomy. Barrow Pain Intensity Scale (BNI) and Brief Pain Inventory Facial scores (BPI-Facial) were assessed. Minimum follow-up was 2 years'. RESULTS: Eight patients (7F:1M) underwent INL. Two had MS. Pre-operatively, all had severe pain (BNI grade V) and the median BPI-Facial score was 115 (range 79-123).. There were no unexpected complications. On last follow-up, six (75%) had no pain (BNI grade I), while two (25%) had recurred (at 5 and 27 months). Median BPI-Facial score for all patients on the last follow-up was 20 (range 18-91) reflecting dramatically improved quality of life and activities. CONCLUSIONS: INL is a potentially safe and effective treatment for refractory TN. Long-term efficacy is unknown, but early results are promising.

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.

A generative network model of neurodevelopmental diversity in structural brain organization.

The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition.

Far and wide: Associations between childhood socio-economic status and brain connectomics.

Previous studies have identified localized associations between childhood environment - namely their socio-economic status (SES) - and particular neural structures. The primary aim of the current study was to test whether associations between SES and brain structure are widespread or limited to specific neural pathways. We employed advances in whole-brain structural connectomics to address this. Diffusion tensor imaging was used to construct whole-brain connectomes in 113 6-12 year olds. We then applied an adapted multi-block partial-least squares (PLS) regression to explore how connectome organisation is associated with childhood SES (parental income, education levels, and neighbourhood deprivation). The Fractional Anisotropy (FA) connectome was significantly associated with childhood SES and this effect was widespread. We then pursued a secondary aim, and demonstrated that the connectome mediated the relationship between SES and cognitive ability (matrix reasoning and vocabulary). However, the connectome did not significantly mediate SES relationships with academic ability (maths and reading) or internalising and externalising behavior. This multivariate approach is important for advancing our theoretical understanding of how brain development may be shaped by childhood environment, and the role that it plays in predicting key outcomes. We also discuss the limitations with this new methodological approach.

Surgical Treatment of Chronic Subdural Hematomas in Nonagenarians: Who to Treat?

OBJECTIVE: Although chronic subdural hematomas (cSDH) are often treated surgically it remains plausible that invasive treatment in elderly patients may have a negative effect on survival. The aim of this study was to characterize survival following neurosurgical intervention for cSDH in a selected cohort aged >90 years and to identify prognostic factors that may inform clinical decision-making. METHODS: In total, we identified a cohort of 548 consecutive patients who had undergone burr hole drainage for cSDH in a 5-year period between 2009-2013. Of these patients, 41 were aged >90 years. For each patient, information was gathered from local hospital records, general practice records, and the patients directly. Long-term survival was compared with actuarial data obtained from the National Life Tables. RESULTS: Overall mortality at the time of discharge was 2%. Mortality was 26.8% at 6 months, 36.8% at 1 year, and 47.9% at 2 years. Interestingly, there was no significant difference between the actuarial curve and the survival curve following surgery (hazard ratio, 1.17; confidence interval, 0.67-2.05; P = 0.57). Despite initially departing from the actuarial curve, the survival curve becomes parallel at approximately 1 year. Multivariate analysis showed that preadmission residence and the number of comorbid conditions were significant predictors of survival. CONCLUSIONS: We advocate that neurosurgical intervention for cSDH in selected nonagenarians can be a safe and beneficial procedure. Patients living independently at home and with a limited past medical history were most likely to benefit from the surgery.

Idiopathic Intracranial Hypertension: Shunt Failure and the Role of Obesity.

BACKGROUND: Idiopathic intracranial hypertension (IIH) is a chronic condition characterized by raised intracranial pressure in the absence of a known etiology. IIH typically presents in overweight women of childbearing age. Surgical intervention for IIH involves diversion of cerebrospinal fluid, often by the placement of a shunt. Experience suggests higher shunt failure rates in patients with IIH than shunts placed for other etiologies. Here we sought to both establish and compare failure rates for IIH and non-IIH shunts and to examine association with body mass index (BMI). METHODS: This study was a single-center retrospective consecutive cohort over a 13-year period. There were 1264 non-IIH patients and 116 patients with IIH included in the study. This was a retrospective analysis of time to shunt failure using Kaplan-Meier methods for IIH and non-IIH shunts. Secondary analysis of BMI, shunt type, and sex on IIH shunt failure was also conducted. RESULTS: The median time to failure of the initial IIH shunt was 22.9 months (interquartile range [IQR], 4-55) compared with 57 months (IQR, 12-87) in non-IIH shunts (P < 0.001; 95% confidence interval, 58.6-233.6). In the IIH group, the median shunt survival for BMI above the healthy range (18.5-25 kg/m2) was 18 months relative to 44 months for those with a healthy BMI. CONCLUSIONS: Our study suggests that in IIH, relative to hydrocephalus of other causes, shunts have higher failure rates and often require more frequent revisions. Higher shunt failure rates in patients with IIH may be associated with an unhealthy BMI.

Functional network dynamics in a neurodevelopmental disorder of known genetic origin.

Dynamic connectivity in functional brain networks is a fundamental aspect of cognitive development, but we have little understanding of the mechanisms driving variability in these networks. Genes are likely to influence the emergence of fast network connectivity via their regulation of neuronal processes, but novel methods to capture these rapid dynamics have rarely been used in genetic populations. The current study redressed this by investigating brain network dynamics in a neurodevelopmental disorder of known genetic origin, by comparing individuals with a ZDHHC9-associated intellectual disability to individuals with no known impairment. We characterised transient network dynamics using a Hidden Markov Model (HMM) on magnetoencephalography (MEG) data, at rest and during auditory oddball stimulation. The HMM is a data-driven method that captures rapid patterns of coordinated brain activity recurring over time. Resting-state network dynamics distinguished the groups, with ZDHHC9 participants showing longer state activation and, crucially, ZDHHC9 gene expression levels predicted the group differences in dynamic connectivity across networks. In contrast, network dynamics during auditory oddball stimulation did not show this association. We demonstrate a link between regional gene expression and brain network dynamics, and present the new application of a powerful method for understanding the neural mechanisms linking genetic variation to cognitive difficulties.

Sniffing out causes of gastrointestinal disorders: a review of volatile metabolomic biomarkers.

Distinct changes can be observed in the odor of human excretions during health and disease. Identifying underlying volatile metabolites responsible for these odorous changes can be correlated with the pathological process within the body. Advances in the technology have enabled us to interpret the volatile signature of these changes in the odor. This has opened a promising area to lay the foundations of a rapid, noninvasive and point of care diagnostic tool. This review explores the diagnostic potential of volatile organic metabolites as novel biomarkers and extends the discussion on the clinical applications of these biomarkers in gastrointestinal disorders.

An Evaluation of Commonly Used External Ventricular Drain Securement Methods in a Porcine Model: Recommendations to Improve Practice.

BACKGROUND: External ventricular drain (EVD) dislodgement is common and leads to significant morbidity and mortality. Many securement techniques to prevent this are described. There are, however, no objective studies comparing them. This study aimed to determine the most secure method of securing an EVD. METHODS: A survey was distributed through the British Neurosurgical Trainee Research Collaborative to determine common EVD securement methods and select techniques for testing. Securement methods were tested in a pig cadaver model. Peak pull force before EVD failure was measured. Failure was defined as catheter displacement 1 cm from the insertion site, catheter fracture, or suture fracture. RESULTS: Twenty-three neurosurgical units responded. Five basic EVD securement methods were in common use. These were tested in isolation and in combination so that in total 15 common methods were tested. The most secure method was a triple construct, consisting of an anchoring suture, sutures around a coil of the catheter, and either a soft plastic flange (25.85 N, 95% confidence interval 24.95 N-26.75 N) or a hard plastic flange (29.05 N, 95% confidence interval 25.69 N-32.41 N). Of the individual methods, single anchoring sutures, soft flanges, VentriFix, and staples were found to be the least secure, whereas multiple sutures and hard flanges were the most secure. CONCLUSIONS: An anchoring suture followed by a coil of the catheter and finally a flange is the most secure method for securing EVDs. This simple technique can withstand up to 8.2 times the force of a single anchoring suture, is easily used, and decreases the likelihood of EVD dislodgement and associated complications.