Automatic target validation based on neuroscientific literature mining for tractography.

Détails

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Etat: Public
Version: Final published version
ID Serval
serval:BIB_46BD69BD2F2F
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Automatic target validation based on neuroscientific literature mining for tractography.
Périodique
Frontiers In Neuroanatomy
Auteur⸱e⸱s
Vasques X., Richardet R., Hill S.L., Slater D., Chappelier J.C., Pralong E., Bloch J., Draganski B., Cif L.
ISSN
1662-5129 (Electronic)
ISSN-L
1662-5129
Statut éditorial
Publié
Date de publication
2015
Peer-reviewed
Oui
Volume
9
Pages
66
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Résumé
Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.
Pubmed
Web of science
Open Access
Oui
Création de la notice
16/06/2015 9:42
Dernière modification de la notice
20/08/2019 13:52
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