A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain Monte Carlo (MCMC) simulation

The focus of this study is the development of a statistical modelling procedure for characterising intra-tumour heterogeneity, motivated by recent clinical literature indicating that a variety of tumours exhibit a considerable degree of genetic spatial variability. A formal spatial statistical model has been developed and used to characterise the structural heterogeneity of a number of supratentorial primitive neuroecto-dermal tumours (PNETs), based on diffusionweighted magnetic resonance imaging. Particular attention is paid to the spatial dependence of diffusion close to the tumour boundary, in order to determine whether the data provide statistical evidence to support the proposition that water diffusivity in the boundary region of some tumours exhibits a deterministic dependence on distance from the boundary, in excess of an underlying random 2D spatial heterogeneity in diffusion. Tumour spatial heterogeneity measures were derived from the diffusion parameter estimates obtained using a Bayesian spatial random effects model. The analyses were implemented using Markov chain Monte Carlo (MCMC) simulation. Posterior predictive simulation was used to assess the adequacy of the statistical model. The main observations are that the previously reported relationship between diffusion and boundary proximity remains observable and achieves statistical significance after adjusting for an underlying random 2D spatial heterogeneity in the diffusion model parameters. A comparison of the magnitude of the boundary-distance effect with the underlying random 2D boundary heterogeneity suggests that both are important sources of variation in the vicinity of the boundary. No consistent pattern emerges from a comparison of the boundary and core spatial heterogeneity, with no indication of a consistently greater level of heterogeneity in one region compared with the other. The results raise the possibility that DWI might provide a surrogate marker of intra-tumour genetic regional heterogeneity, which would provide a powerful tool with applications in both patient management and in cancer research

A structural MRI study in monozygotic twins concordant or discordant for attention/hyperactivity problems: Evidence for genetic and environmental heterogeneity in the developing brain.

Several structural brain abnormalities have been reported in patients with Attention Deficit Hyperactivity Disorder (ADHD). However, the etiology of these brain changes is still unclear. To investigate genetic and environmental influences on ADHD related neurobiological changes, we performed Voxel-Based Morphometry on MRI scans from monozygotic (MZ) twins selected from a large longitudinal population database to be highly concordant or highly discordant for ratings on the Child Behavior Checklist Attention Problem scale (CBCL-AP). Children scoring low on the CBCL-AP are at low risk for ADHD, whereas children scoring high on this scale are at high-risk for ADHD. Brain differences between concordant high-risk twin pairs and concordant low-risk twin pairs likely reflect the genetic risk for ADHD; brain differences between the low-risk and high-risk twins from discordant MZ twin pairs reflect the environmental risk for ADHD. A major difference between comparisons of high and low-risk twins from concordant pairs and high/low twins from discordant pairs was found for the prefrontal lobes. The concordant high-risk pairs showed volume loss in orbitofrontal subdivisions. High-risk members from the discordant twin pairs exhibited volume reduction in the right inferior dorsolateral prefontal cortex. In addition, the posterior corpus callosum was compromised in concordant high-risk pairs, only. Our findings indicate that inattention and hyperactivity symptoms are associated with anatomical abnormalities of a distributed action-attentional network. Different brain areas of this network appear to be affected in inattention/hyperactivity caused by genetic (i.e., high concordant MZ pairs) vs. environmental (i.e., high-low discordant MZ pairs) risk factors. These results provide clues that further our understanding of brain alterations in ADHD. © 2007 Elsevier Inc. All rights reserved

Longitudinal segmentation of age-related white matter hyperintensities

Although white matter hyperintensities evolve in the course of ageing, few solutions exist to consider the lesion segmentation problem longitudinally. Based on an existing automatic lesion segmentation algorithm, a longitudinal extension is proposed. For evaluation purposes, a longitudinal lesion simulator is created allowing for the comparison between the longitudinal and the cross-sectional version in various situations of lesion load progression. Finally, applied to clinical data, the proposed framework demonstrates an increased robustness compared to available cross-sectional methods and findings are aligned with previously reported clinical patterns

Integrating Evolutionary, Cultural, and Computational Psychiatry: A Multilevel Systemic Approach

This paper proposes an integrative perspective on evolutionary, cultural and computational approaches to psychiatry. These three approaches attempt to frame mental disorders as multiscale entities and offer modes of explanations and modeling strategies that can inform clinical practice. Although each of these perspectives involves systemic thinking, each is limited in its ability to address the complex developmental trajectories and larger social systemic interactions that lead to mental disorders. Inspired by computational modeling in theoretical biology, this paper aims to integrate the modes of explanation offered by evolutionary, cultural and computational psychiatry in a multilevel systemic perspective. We apply the resulting Evolutionary, Cultural and Computational (ECC) model to Major Depressive Disorder (MDD) to illustrate how this integrative approach can guide research and practice in psychiatry

Genomic and phenotypic signatures of climate adaptation in an Anolis lizard

Integrated knowledge on phenotype, physiology and genomic adaptations is required to understand the effects of climate on evolution. The functional genomic basis of organismal adaptation to changes in the abiotic environment, its phenotypic consequences, and its possible convergence across vertebrates, are still understudied. In this study, we use a comparative approach to verify predicted gene functions for vertebrate thermal adaptation with observed functions underlying repeated genomic adaptations in response to elevation in the lizard Anolis cybotes. We establish a direct link between recurrently evolved phenotypes and functional genomics of altitude-related climate adaptation in three highland and lowland populations in the Dominican Republic. We show that across vertebrates, genes contained in this interactome are expressed within the brain and during development. These results are relevant to elucidate the effect of global climate change across vertebrates, and might aid in furthering insight into gene-environment relationships under disturbances to external homeostasis

Individual differences in sensory sensitivity: a synthesising framework and evidence from normal variation and developmental conditions

For some people, simple sensory stimuli (e.g., noises, patterns) may reliably evoke intense and aversive reactions. This is common in certain clinical groups (e.g., autism) and varies greatly in the neurotypical population. This paper critically evaluates the concept of individual differences in sensory sensitivity, explores its possible underlying neurobiological basis, and presents a roadmap for future research in this area. A distinction is made between subjective sensory sensitivity (self-reported symptoms); neural sensory sensitivity (the degree of neural activity induced by sensory stimuli); and behavioral sensory sensitivity (detection and discrimination of sensory stimuli). Whereas increased subjective and neural sensory sensitivity are assumed to increase together, the status of behavioral sensory sensitivity depends on the extent to which the increased neural activity is linked to signal or noise. A signal detection framework is presented that offers a unifying framework for exploring sensory sensitivity across different conditions. The framework is discussed, in more concrete terms, by linking it to four existing theoretical accounts of atypical sensory sensitivity (not necessarily mutually exclusive): increased excitation-to-inhibition ratio; predictive coding; increased neural noise; and atypical brain connectivity