Guillaume Dumas

The EU-AIMS Longitudinal European Autism Project (LEAP): Design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders

BackgroundThe tremendous clinical and aetiological diversity among individuals with autism spectrum disorder (ASD) has been a major obstacle to the development of new treatments, as many may only be effective in particular subgroups. Precision medicine approaches aim to overcome this challenge by combining pathophysiologically based treatments with stratification biomarkers that predict which treatment may be most beneficial for particular individuals. However, so far, we have no single validated stratification biomarker for ASD. This may be due to the fact that most research studies primarily have focused on the identification of mean case-control differences, rather than within-group variability, and included small samples that were underpowered for stratification approaches. The EU-AIMS Longitudinal European Autism Project (LEAP) is to date the largest multi-centre, multi-disciplinary observational study worldwide that aims to identify and validate stratification biomarkers for ASD.MethodsLEAP includes 437 children and adults with ASD and 300 individuals with typical development or mild intellectual disability. Using an accelerated longitudinal design, each participant is comprehensively characterised in terms of clinical symptoms, comorbidities, functional outcomes, neurocognitive profile, brain structure and function, biochemical markers and genomics. In addition, 51 twin-pairs (of which 36 had one sibling with ASD) are included to identify genetic and environmental factors in phenotypic variability.ResultsHere, we describe the demographic characteristics of the cohort, planned analytic stratification approaches, criteria and steps to validate candidate stratification markers, pre-registration procedures to increase transparency, standardisation and data robustness across all analyses, and share some ‘lessons learnt’. A clinical characterisation of the cohort is given in the companion paper (Charman et al., accepted).ConclusionWe expect that LEAP will enable us to confirm, reject and refine current hypotheses of neurocognitive/neurobiological abnormalities, identify biologically and clinically meaningful ASD subgroups, and help us map phenotypic heterogeneity to different aetiologies.

A de novo microdeletion of SEMA5A in a boy with autism spectrum disorder and intellectual disability

Semaphorins are a large family of secreted and membrane-associated proteins necessary for wiring of the brain. Semaphorin 5A (SEMA5A) acts as a bifunctional guidance cue, exerting both attractive and inhibitory effects on developing axons. Previous studies have suggested that SEMA5A could be a susceptibility gene for autism spectrum disorders (ASDs). We first identified a de novo translocation t(5;22)(p15.3;q11.21) in a patient with ASD and intellectual disability (ID). At the translocation breakpoint on chromosome 5, we observed a 861-kb deletion encompassing the end of the SEMA5A gene. We delineated the breakpoint by NGS and observed that no gene was disrupted on chromosome 22. We then used Sanger sequencing to search for deleterious variants affecting SEMA5A in 142 patients with ASD. We also identified two independent heterozygous variants located in a conserved functional domain of the protein. Both variants were maternally inherited and predicted as deleterious. Our genetic screens identified the first case of a de novo SEMA5A microdeletion in a patient with ASD and ID. Although our study alone cannot formally associate SEMA5A with susceptibility to ASD, it provides additional evidence that Semaphorin dysfunction could lead to ASD and ID. Further studies on Semaphorins are warranted to better understand the role of this family of genes in susceptibility to neurodevelopmental disorders.

Brain-to-brain coupling during handholding is associated with pain reduction

Significance The mechanisms that underlie social touch analgesia are largely unknown. Here, we apply a hyperscanning approach with real-life interaction of dyads to examine the association between brain-to-brain coupling and pain relief. Our findings indicate that hand-holding during pain increases the brain-to-brain coupling network that correlates with the magnitude of the analgesia and the observer’s empathic accuracy. These findings make a unique contribution to our understanding of physiological mechanisms of touch-related analgesia. The mechanisms underlying analgesia related to social touch are not clear. While recent research highlights the role of the empathy of the observer to pain relief in the target, the contribution of social interaction to analgesia is unknown. The current study examines brain-to-brain coupling during pain with interpersonal touch and tests the involvement of interbrain synchrony in pain alleviation. Romantic partners were assigned the roles of target (pain receiver) and observer (pain observer) under pain–no-pain and touch–no-touch conditions concurrent with EEG recording. Brain-to-brain coupling in alpha–mu band (8–12 Hz) was estimated by a three-step multilevel analysis procedure based on running window circular correlation coefficient and post hoc power of the findings was calculated using simulations. Our findings indicate that hand-holding during pain administration increases brain-to-brain coupling in a network that mainly involves the central regions of the pain target and the right hemisphere of the pain observer. Moreover, brain-to-brain coupling in this network was found to correlate with analgesia magnitude and observer’s empathic accuracy. These findings indicate that brain-to-brain coupling may be involved in touch-related analgesia.

A roadmap to computational social neuroscience

To complement experimental efforts toward understanding human social interactions at both neural and behavioral levels, two computational approaches are presented: (1) a fully parameterizable mathematical model of a social partner, the Human Dynamic Clamp which, by virtue of experimentally controlled interactions between Virtual Partners and real people, allows for emergent behaviors to be studied; and (2) a multiscale neurocomputational model of social coordination that enables exploration of social self-organization at all levels—from neuronal patterns to people interacting with each other. These complementary frameworks and the cross product of their analysis aim at understanding the fundamental principles governing social behavior.

Experimenting with reproducibility in bioinformatics

Reproducibility has been shown to be limited in many scientific fields. This question is a fundamental tenet of the scientific activity, but the related issues of reusability of scientific data are poorly documented. Here, we present a case study of our attempt to reproduce a promising bioinformatics method [1] and illustrate the challenges to use a published method for which code and data were available. First, we tried to re-run the analysis with the code and data provided by the authors. Second, we reimplemented the method in Python to avoid dependency on a MATLAB licence and ease the execution of the code on HPCC (High-Performance Computing Cluster). Third, we assessed reusability of our reimplementation and the quality of our documentation. Then, we experimented with our own software and tested how easy it would be to start from our implementation to reproduce the results, hence attempting to estimate the robustness of the reproducibility. Finally, in a second part, we propose solutions from this case study and other observations to improve reproducibility and research efficiency at the individual and collective level. Availability last version of StratiPy (Python) with two examples of reproducibility are available at GitHub [2]. Contact yang-min.kim@pasteur.fr

The Human Dynamic Clamp: A Probe for Coordination Across Neural, Behavioral, and Social Scales

Humans (with their brains, bodies and behaviors) are complex dynamical systems embedded in an environment that includes a multitude of other conspecifics. Moving beyond previous brain- centered views of the human mind requires to develop a parsimonious yet integrative account that relates neural, behavioral, and social scales. Social neuroscience has recently started to acknowledge the importance of relational dynamics when it extended its purview from social stimuli to human-human interactions. Human-machine interactions also constitute promising tools to probe multiple scales in a controlled manner. Inspired by the electrophysiological method of the dynamic clamp, Virtual Partner Interaction (VPI) allows real time interaction between human subjects and their simulations as dynamical system. This provides a new test bed for operationalizing theoretical models in experimental settings. We discuss how VPI can be generalized into a Human Dynamic Clamp (HDC), a paradigm that allows the exploration of the parameter spaces of interactional dynamics in various contexts: from rhythmic and discrete coordination to adaptive and intentional behaviors, including learning. HDC brings humans and machines together to question our understanding of the natural and our theory behind the artificial.

Dark Control: A Unified Account of Default Mode Function by Control Theory and Reinforcement Learning

The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its highest energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an overarching function. Many research streams support an evolutionarily adaptive role in envisioning experience to anticipate the future. The present paper proposes a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. DMN function is recast in mathematical terms of control theory and reinforcement learning based on Markov decision processes. We argue that our formal account of DMN function naturally accommodates as special cases the previously proposed cognitive accounts on (1) predictive coding, (2) semantic associations, and (3) a “sentinel” role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its highest energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an overarching function. Many research streams support an evolutionarily adaptive role in envisioning experience to anticipate the future. The present paper proposes a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. DMN function is recast in mathematical terms of control theory and reinforcement learning based on Markov decision processes. We argue that our formal account of DMN function naturally accommodates as special cases the previously proposed cognitive accounts on (1) predictive coding, (2) semantic associations, and (3) a “sentinel” role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.