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.

The graded multidimensional geometry of phenotypic variation and progression in neurodegenerative syndromes.

Clinical variants of Alzheimer's disease and frontotemporal lobar degeneration display a spectrum of cognitive-behavioural changes varying between individuals and over time. Understanding the landscape of these graded individual-/group-level longitudinal variations is critical for precise phenotyping; however, this remains challenging to model. Addressing this challenge, we leverage the National Alzheimer's Coordinating Center database to derive a unified geometric framework of graded longitudinal phenotypic variation in Alzheimer's disease and frontotemporal lobar degeneration. We included three time-point, cognitive-behavioural and clinical data from 390 typical, atypical and intermediate Alzheimer's disease and frontotemporal lobar degeneration variants (114 typical Alzheimer's disease; 107 behavioural variant frontotemporal dementia; 42 motor variants of frontotemporal lobar degeneration; and 103 primary progressive aphasia patients). On this data, we applied advanced data-science approaches to derive low-dimensional geometric spaces capturing core features underpinning clinical progression of Alzheimer's disease and frontotemporal lobar degeneration syndromes. To do so, we first used principal component analysis to derive six axes of graded longitudinal phenotypic variation capturing patient-specific movement along and across these axes. Then, we distilled these axes into a visualisable 2D manifold of longitudinal phenotypic variation using Uniform Manifold Approximation and Projection. Both geometries together enabled the assimilation and inter-relation of paradigmatic and mixed cases, capturing dynamic individual trajectories, and linking syndromic variability to neuropathology and key clinical end-points such as survival. Through these low-dimensional geometries, we show that (i) specific syndromes (Alzheimer's disease and primary progressive aphasia) converge over time into a de-differentiated pooled phenotype, while others (frontotemporal dementia variants) diverge to look different from this generic phenotype; (ii) phenotypic diversification is predicted by simultaneous progression along multiple axes, varying in a graded manner between individuals and syndromes; and (iii) movement along specific principal axes predicts survival at 36 months in a syndrome-specific manner and in individual pathological groupings. The resultant mapping of dynamics underlying cognitive-behavioural evolution potentially holds paradigm-changing implications to predicting phenotypic diversification and phenotype-neurobiological mapping in Alzheimer's disease and frontotemporal lobar degeneration.

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.

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.

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.

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.

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.

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.