Automatic target validation based on neuroscientific literature mining for tractography
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,...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2015-05-01
|
| Series: | Frontiers in Neuroanatomy |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnana.2015.00066/full |
| _version_ | 1818957676388810752 |
|---|---|
| author | Xavier eVasques Renaud eRichardet Renaud eRichardet Sean L Hill David eSlater Jean-Cedric eChappelier Etienne ePralong Jocelyne eBloch Bogdan eDraganski LAURA eCIF LAURA eCIF |
| author_facet | Xavier eVasques Renaud eRichardet Renaud eRichardet Sean L Hill David eSlater Jean-Cedric eChappelier Etienne ePralong Jocelyne eBloch Bogdan eDraganski LAURA eCIF LAURA eCIF |
| author_sort | Xavier eVasques |
| collection | DOAJ |
| description | 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/. |
| first_indexed | 2024-12-20T11:13:38Z |
| format | Article |
| id | doaj.art-242819bbfc3b450b860dcf2b1472f27d |
| institution | Directory Open Access Journal |
| issn | 1662-5129 |
| language | English |
| last_indexed | 2024-12-20T11:13:38Z |
| publishDate | 2015-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroanatomy |
| spelling | doaj.art-242819bbfc3b450b860dcf2b1472f27d2022-12-21T19:42:40ZengFrontiers Media S.A.Frontiers in Neuroanatomy1662-51292015-05-01910.3389/fnana.2015.00066134659Automatic target validation based on neuroscientific literature mining for tractographyXavier eVasques0Renaud eRichardet1Renaud eRichardet2Sean L Hill3David eSlater4Jean-Cedric eChappelier5Etienne ePralong6Jocelyne eBloch7Bogdan eDraganski8LAURA eCIF9LAURA eCIF10Ecole Polytechnique Fédérale de LausanneEcole Polytechnique Fédérale de LausanneEcole Polytechnique Fédérale de LausanneEcole Polytechnique Fédérale de LausanneCentre Hospitalier Universitaire VaudoisEcole Polytechnique Fédérale de LausanneCentre Hospitalier Universitaire VaudoisCentre Hospitalier Universitaire VaudoisCentre Hospitalier Universitaire VaudoisCHRU MontpellierCentre Hospitalier Universitaire VaudoisTarget 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/.http://journal.frontiersin.org/Journal/10.3389/fnana.2015.00066/fullNucleus AccumbensSubthalamic NucleustractographyNatural Language Processingtext miningglobus pallidus internus |
| spellingShingle | Xavier eVasques Renaud eRichardet Renaud eRichardet Sean L Hill David eSlater Jean-Cedric eChappelier Etienne ePralong Jocelyne eBloch Bogdan eDraganski LAURA eCIF LAURA eCIF Automatic target validation based on neuroscientific literature mining for tractography Frontiers in Neuroanatomy Nucleus Accumbens Subthalamic Nucleus tractography Natural Language Processing text mining globus pallidus internus |
| title | Automatic target validation based on neuroscientific literature mining for tractography |
| title_full | Automatic target validation based on neuroscientific literature mining for tractography |
| title_fullStr | Automatic target validation based on neuroscientific literature mining for tractography |
| title_full_unstemmed | Automatic target validation based on neuroscientific literature mining for tractography |
| title_short | Automatic target validation based on neuroscientific literature mining for tractography |
| title_sort | automatic target validation based on neuroscientific literature mining for tractography |
| topic | Nucleus Accumbens Subthalamic Nucleus tractography Natural Language Processing text mining globus pallidus internus |
| url | http://journal.frontiersin.org/Journal/10.3389/fnana.2015.00066/full |
| work_keys_str_mv | AT xavierevasques automatictargetvalidationbasedonneuroscientificliteratureminingfortractography AT renauderichardet automatictargetvalidationbasedonneuroscientificliteratureminingfortractography AT renauderichardet automatictargetvalidationbasedonneuroscientificliteratureminingfortractography AT seanlhill automatictargetvalidationbasedonneuroscientificliteratureminingfortractography AT davideslater automatictargetvalidationbasedonneuroscientificliteratureminingfortractography AT jeancedricechappelier automatictargetvalidationbasedonneuroscientificliteratureminingfortractography AT etienneepralong automatictargetvalidationbasedonneuroscientificliteratureminingfortractography AT jocelyneebloch automatictargetvalidationbasedonneuroscientificliteratureminingfortractography AT bogdanedraganski automatictargetvalidationbasedonneuroscientificliteratureminingfortractography AT lauraecif automatictargetvalidationbasedonneuroscientificliteratureminingfortractography AT lauraecif automatictargetvalidationbasedonneuroscientificliteratureminingfortractography |