Attitudes of the autism community to early autism research

Investigation into the earliest signs of autism in infants has become a significant sub-field of autism research. This work invokes specific ethical concerns such as: use of ‘at-risk’ language; communicating study findings to parents; and the future perspective of enrolled infants when they reach adulthood. The current study aimed to ground this research field in an understanding of the perspectives of members of the autism community. Following focus groups to identify topics, an online survey was distributed to autistic adults, parents of children with autism, and practitioners in health and education settings across eleven European countries. Survey respondents (n=2317) were positively disposed towards early autism research and there was significant overlap in their priorities for the field, and preferred language to describe infant research participants. However there were also differences including overall less favourable endorsement of early autism research by autistic adults relative to other groups and a dislike of the phrase ‘at-risk’ to describe infant participants, in all groups except healthcare practitioners. The findings overall indicate that the autism community in Europe is supportive of early autism research. Researchers should endeavour to maintain this by continuing to take community perspectives into account

G-quadruplex structures mark human regulatory chromatin

G-quadruplex (G4) structural motifs have been linked to transcription, replication and genome instability and are implicated in cancer and other diseases. However, it is crucial to demonstrate the bona fide formation of G4 structures within an endogenous chromatin context. Herein we address this through the development of G4 ChIP-seq, an antibody-based G4 chromatin immunoprecipitation and high-throughput sequencing approach. We find ∼10,000 G4 structures in human chromatin, predominantly in regulatory, nucleosome-depleted regions. G4 structures are enriched in the promoters and 5' UTRs of highly transcribed genes, particularly in genes related to cancer and in somatic copy number amplifications, such as $\textit{MYC}$. Strikingly, $\textit{de novo}$ and enhanced G4 formation are associated with increased transcriptional activity, as shown by HDAC inhibitor-induced chromatin relaxation and observed in immortalized as compared to normal cellular states. Our findings show that regulatory, nucleosome-depleted chromatin and elevated transcription shape the endogenous human G4 DNA landscape.European Molecular Biology Organization (EMBO Long-Term Fellowship), University of Cambridge, Cancer Research UK (Grant ID: C14303/A17197), Wellcome Trust (Grant ID: 099232/z/12/z

QDVS des aidants des survivants d’une tumeur cérébrale primitive à l’impact des séquelles neurocognitives des patients.

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Revue et évaluation des méthodes de méta-analyse sur données corrélées pour l’analyse multivers

National audienceLes chercheurs utilisant des données d'IRMf de tâche disposent d'une vaste gamme d'outils d'analyse pour modéliser l'activité cérébrale. Cette diversité d'approches analytiques signifie qu'il existe de nombreuses variations possibles du même résultat d'imagerie. L'analyse d'un ensemble de données avec une seule approche peut donc être trompeuse. Une solution efficace est d’utiliser une analyse multivers, où plusieurs ensembles de résultats sont obtenus en exécutant différents pipelines sur les mêmes données. Toutefois, le postulat de départ pour les méta-analyses traditionnelles est l'indépendance des données d'entrée. C’est pourquoi nous présentons ici des méthodes de "méta-analyse sur mêmes données" afin d’examiner plusieurs ensembles de résultats de neuro-imagerie issus d'une analyse multivers, tout en tenant compte de la dépendance entre les analyses. La validité de ces méthodes est évaluée et comparée aux méthodes de méta-analyse traditionnelles. Nous évaluons la validité des méthodes sur des simulations ainsi que sur des données réelles provenant de l’étude "NARPS" (Botvinik-Nezer et al. 2020), une analyse multivers avec 70 cartes statistiques issues des mêmes données. Nos résultats montrent que les méthodes développées précisément pour l’analyse multivers sont valides et présentent différents profils de significativité en fonction de leurs spécificités. Ces différents résultats illustrent différents types d’inférences que les analystes pourraient souhaiter mener en fonction des hypothèses sous-jacentes de leurs études

Systematic review and evaluation of meta-analysis methods for same data meta-analyses

International audienceResearchers using fMRI data have a wide range of analysis tools to model brain activity. This diversity of analytical approaches means there are many possible variations of the same imaging result. Thus, analyzing a dataset with a single approach can be misleading. Alternatively a multiverse analysis can be used, where multiple sets of results are obtained from running different pipelines on the same single dataset. The starting assumption for traditional meta-analyses is the independence among input data. Thus, here, we present "same data meta analysis" methods for examining multiple sets of neuroimaging results derived from a multiverse analysis, accounting for the inter-analysis dependence. The validity of this method is evaluated and compared against established meta-analysis methods, and we demonstrate the method on real world data from "NARPS", a multiverse analysis with 70 different statistic maps originating from the same data

Systematic review and evaluation of meta-analysis methods for same data meta-analyses

International audienceResearchers using fMRI data have a wide range of analysis tools to model brain activity. This diversity of analytical approaches means there are many possible variations of the same imaging result. Thus, analyzing a dataset with a single approach can be misleading. Alternatively a multiverse analysis can be used, where multiple sets of results are obtained from running different pipelines on the same single dataset. The starting assumption for traditional meta-analyses is the independence among input data. Thus, here, we present "same data meta analysis" methods for examining multiple sets of neuroimaging results derived from a multiverse analysis, accounting for the inter-analysis dependence. The validity of this method is evaluated and compared against established meta-analysis methods, and we demonstrate the method on real world data from "NARPS", a multiverse analysis with 70 different statistic maps originating from the same data

Systematic review and evaluation of meta-analysis methods for same data meta-analyses in a multiverse setting

International audienceResearchers using task-fMRI data have access to a wide range of analysis tools to model brain activity. This diversity of analytical approaches has been shown to have substantial effects on neuroimaging results. Combined with selective reporting, this analytical flexibility can lead to an inflated rate of false positives and contributes to the irreproducibility of neuroimaging findings. Multiverse analyses are a way to systematically explore and integrate pipeline variation on a given dataset. We focus on the setting where multiple statistic maps are produced as an output of a set of analyses originating from a single datset. Meta-analysis is  a natural approach to extract consensus inferences from these maps, yet the traditional assumption of independence amongst input datasets does not hold. In this work we consider a suite of methods to conduct meta-analysis in the multiverse setting, accounting for inter-pipeline dependence among the results. The validity of these methods were assessed in a set of simulations and evaluated on a real world dataset from "NARPS", a multiverse analysis with 70 different statistic maps originating from the same data, and a multiverse analysis originating form the same HCP data. Our findings demonstrate the validity of our proposed same-data meta-analysis (SDMA) models under inter-pipeline dependence, and provide an array of options for the analysis multiverse data

Systematic review and evaluation of meta-analysis methods for same data meta-analyses in a multiverse setting

Selected for an oral presentationInternational audienceResearchers using task-fMRI data have access to a wide range of analysis tools to model brain activity. This diversity of analytical approaches has been shown to have substantial effects on neuroimaging results. Combined with selective reporting, this analytical flexibility can lead to an inflated rate of false positives and contributes to the irreproducibility of neuroimaging findings. Multiverse analyses are a way to systematically explore and integrate pipeline variation on a given dataset. We focus on the setting where multiple statistic maps are produced as an output of a set of analyses originating from a single datset. Meta-analysis is  a natural approach to extract consensus inferences from these maps, yet the traditional assumption of independence amongst input datasets does not hold. In this work we consider a suite of methods to conduct meta-analysis in the multiverse setting, accounting for inter-pipeline dependence among the results. The validity of these methods were assessed in a set of simulations and evaluated on a real world dataset from "NARPS", a multiverse analysis with 70 different statistic maps originating from the same data, and a multiverse analysis originating form the same HCP data. Our findings demonstrate the validity of our proposed same-data meta-analysis (SDMA) models under inter-pipeline dependence, and provide an array of options for the analysis multiverse data

Same Data Meta Analysis for Neurimaging Multiverse Data

International audienceResearchers using brain MRI data have a wide range of analysis tools to model brain structure and function. This diversity of analytical approaches means there are many possible variations of the same imaging result (Bowring et al., 2019). Analyzing a dataset with a single approach can thus be misleading. Alternatively, a multiverse analysis can be used, where multiple sets of results are obtained from running different pipelines on the same dataset. Such a setting produces multiple outputs in the form of a statistical map. Meta-analysis approaches can then help to effectively extract a unique result from these maps.</div