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@article{Mesmoudi2024,

title = {Transdisciplinary method for exploration and visualization of neuroimaging papers and databases: Application to PTSD},

author = {Salma Mesmoudi and Fanny Dégeilh and Waldo Cancino and Mathieu Rodic and Denis Peschanski and Francis Eustache},

url = {https://www.sciencedirect.com/science/article/pii/S0003448724002142},

doi = {/10.1016/j.amp.2024.06.003},

year  = {2024},

date = {2024-07-14},

urldate = {2024-07-14},

abstract = {A major improvement in MRI techniques has led to an exponential increase in data acquisition and, consequently, in the number of published articles reporting brain impairments and cognitive deficits underlying a disorder. Meta-analysis offers a means of synthesizing the available literature, testing existing models in the light of scientific advances, and revealing unexpected information. However, article selection, author specialization and top-down hypotheses can mask some results and introduce bias into interpretations. LinkRdata is a platform for automated, data-driven, meta-analytical methods suitable for processing large numbers of MRI articles, that can reduce selection and interpretation biases, thereby allowing scientists to review neurocognitive correlates of disorders in relation to their own corpus of articles. To validate our method, we applied it to fMRI studies of post-traumatic stress disorder. Results confirmed LinkRdata's power to uncover findings hidden by the top-down hypothesis approach.



Méthode transdisciplinaire d’exploration et de visualisation d’articles et de bases de données de neuroimagerie : application au stress post-traumatique



Une amélioration majeure des techniques d’IRM a conduit à une augmentation exponentielle de l’acquisition de données et, par conséquent, du nombre d’articles publiés rapportant des altérations cérébrales et des déficits cognitifs liés à un trouble. La méta-analyse offre un moyen de synthétiser la littérature disponible, de tester les modèles existants à la lumière des avancées scientifiques, et de révéler des informations inattendues. Cependant, la sélection des articles, la spécialisation des auteurs et les hypothèses descendantes peuvent masquer certains résultats et introduire des biais dans les interprétations. LinkRdata est une plateforme de méthodes méta-analytiques automatisées et basées sur les données, adaptée au traitement d’un grand nombre d’articles d’IRM, qui peut réduire les biais de sélection et d’interprétation, permettant ainsi aux chercheurs de passer en revue les corrélats neurocognitifs des troubles en relation avec leur propre corpus d’articles. Pour valider notre méthode, nous l’avons appliquée à des études d’IRMf sur le trouble de stress post-traumatique. Les résultats ont confirmé la capacité de LinkRdata à mettre en évidence des résultats cachés par l’approche hypothétique descendante.},

note = {Annales Médico-psychologiques, revue psychiatrique},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Louvel2020,

title = {What’s in a name? The three genealogies of the social insocial epigenetics},

author = {Séverine Louvel},

editor = {SAGE Publications},

doi = {https://doi.org/10.1177/0539018419897001},

year  = {2020},

date = {2020-01-22},

urldate = {2020-01-22},

journal = {Social Science Information},

abstract = {Social  epigenetics  – the  study  of  the  epigenetic  mechanisms  through  which  social  environments  become biologically embodied –   epitomizes recent claims that the boundaries between the natural and the social sciences should be reduced. Relying on a bibliometric study and on a qualitative analysis of publications  in  social  epigenetics,  this   paper   investigates   how   this  research  area defines   and operationalizes  the social dimensions that may have an impact on health status and disease risk. The paper  also  addresses  how  the  social  sciences  engage  with  social  epigenetics.  First,  the  paper  traces  social epigenetics back to five epistemic backgrounds –   two in animal research (on social defeat and early-life  adversity)  and  three  in  human  studies  (on  trauma,  early-life  nutrition  and  social  adversity  over the life-course). Second, it outlines the quest for epigenetic markers of social environments, and the associated expectations and controversies. Third, it analyses the three modes of engagement of the social  sciences  with  human  studies  in  social  epigenetics:  rejection  (social  epigenetics  trapped  in  the  quest  for  a  ‘social  brain’);  warning  and  call  for  responsibility  (social  epigenetics  has  shifted  from socioeconomic   contexts   to   individual   behaviors);   and   support   and   active   contribution   (social   epigenetics may strengthen  social  studies  of  health).  This  paper  argues  that  recent  developments  in  social  epigenetics  could  strengthen  this  third  mode  of  engagement  and  expand  the  scope  of  interdisciplinary collaboration between the natural and the social sciences.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mesmoudi2015linkrbrain,

title = {Linkrbrain: Multi-scale data integrator of the brain},

author = {Salma Mesmoudi and Mathieu Rodic and Claudia Cioli and Jean-Philippe Cointet and Tal Yarkoni and Yves Burnod},

url = {https://doi.org/10.1016/j.jneumeth.2014.12.008},

doi = {10.1016/j.jneumeth.2014.12.008},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Journal of neuroscience methods},

volume = {241},

pages = {44-52},

publisher = {Elsevier},

abstract = {Background LinkRbrain is an open-access web platform for multi-scale data integration and visualization of human brain data. This platform integrates anatomical, functional, and genetic knowledge produced by the scientific community. New method The linkRbrain platform has two major components: (1) a data aggregation component that integrates multiple open databases into a single platform with a unified representation; and (2) a website that provides fast multi-scale integration and visualization of these data and makes the results immediately available. Results LinkRbrain allows users to visualize functional networks or/and genetic expression over a standard brain template (MNI152). Interrelationships between these components based on topographical overlap are displayed using relational graphs. Moreover, linkRbrain enables comparison of new experimental results with previous published works. Comparison with existing methods Previous tools and studies illustrate the opportunities of data mining across multiple tiers of neuroscience and genetic information. However, a global systematic approach is still missing to gather cognitive, topographical, and genetic knowledge in a common framework in order to facilitate their visualization, comparison, and integration. Conclusions LinkRbrain is an efficient open-access tool that affords an integrative understanding of human brain function. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}