Automated Neuroanatomical Relation Extraction: A Linguistically Motivated Approach with a PVT Connectivity Graph Case Study
Identifying the relations among different regions of the brain is vital for a better understanding of how the brain functions. While a large number of studies have investigated the neuroanatomical and neurochemical connections among brain structures, their specific findings are found in publications...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2016-09-01
|
| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fninf.2016.00039/full |
| _version_ | 1819149444326621184 |
|---|---|
| author | Erinç Gökdeniz Arzucan Özgür Reşit Canbeyli |
| author_facet | Erinç Gökdeniz Arzucan Özgür Reşit Canbeyli |
| author_sort | Erinç Gökdeniz |
| collection | DOAJ |
| description | Identifying the relations among different regions of the brain is vital for a better understanding of how the brain functions. While a large number of studies have investigated the neuroanatomical and neurochemical connections among brain structures, their specific findings are found in publications scattered over a large number of years and different types of publications. Text mining techniques have provided the means to extract specific types of information from a large number of publications with the aim of presenting a larger, if not necessarily an exhaustive picture. By using natural language processing techniques, the present paper aims to identify connectivity relations among brain regions in general and relations relevant to the paraventricular nucleus of the thalamus (PVT) in particular.We introduce a linguistically motivated approach based on patterns defined over the constituency and dependency parse trees of sentences. Besides the presence of a relation between a pair of brain regions, the proposed method also identifies the directionality of the relation, which enables the creation and analysis of a directional brain region connectivity graph. The approach is evaluated over the manually annotated data sets of the WhiteText Project. In addition, as a case study, the method is applied to extract and analyze the connectivity graph of PVT, which is an important brain region that is considered to influence many functions ranging from arousal, motivation, and drug-seeking behavior to attention. The results of the PVT connectivity graph show that PVT may be a new target of research in mood assessment. |
| first_indexed | 2024-12-22T14:01:42Z |
| format | Article |
| id | doaj.art-f78a198649c7483294a43d9dfa3ea959 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-22T14:01:42Z |
| publishDate | 2016-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-f78a198649c7483294a43d9dfa3ea9592022-12-21T18:23:24ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962016-09-011010.3389/fninf.2016.00039186590Automated Neuroanatomical Relation Extraction: A Linguistically Motivated Approach with a PVT Connectivity Graph Case StudyErinç Gökdeniz0Arzucan Özgür1Reşit Canbeyli2Boğaziçi UniversityBoğaziçi UniversityBoğaziçi UniversityIdentifying the relations among different regions of the brain is vital for a better understanding of how the brain functions. While a large number of studies have investigated the neuroanatomical and neurochemical connections among brain structures, their specific findings are found in publications scattered over a large number of years and different types of publications. Text mining techniques have provided the means to extract specific types of information from a large number of publications with the aim of presenting a larger, if not necessarily an exhaustive picture. By using natural language processing techniques, the present paper aims to identify connectivity relations among brain regions in general and relations relevant to the paraventricular nucleus of the thalamus (PVT) in particular.We introduce a linguistically motivated approach based on patterns defined over the constituency and dependency parse trees of sentences. Besides the presence of a relation between a pair of brain regions, the proposed method also identifies the directionality of the relation, which enables the creation and analysis of a directional brain region connectivity graph. The approach is evaluated over the manually annotated data sets of the WhiteText Project. In addition, as a case study, the method is applied to extract and analyze the connectivity graph of PVT, which is an important brain region that is considered to influence many functions ranging from arousal, motivation, and drug-seeking behavior to attention. The results of the PVT connectivity graph show that PVT may be a new target of research in mood assessment.http://journal.frontiersin.org/Journal/10.3389/fninf.2016.00039/fullneuroinformaticsNatural Language Processingtext miningparaventricular nucleus of the thalamusPVTbrain region connectivity graph |
| spellingShingle | Erinç Gökdeniz Arzucan Özgür Reşit Canbeyli Automated Neuroanatomical Relation Extraction: A Linguistically Motivated Approach with a PVT Connectivity Graph Case Study Frontiers in Neuroinformatics neuroinformatics Natural Language Processing text mining paraventricular nucleus of the thalamus PVT brain region connectivity graph |
| title | Automated Neuroanatomical Relation Extraction: A Linguistically Motivated Approach with a PVT Connectivity Graph Case Study |
| title_full | Automated Neuroanatomical Relation Extraction: A Linguistically Motivated Approach with a PVT Connectivity Graph Case Study |
| title_fullStr | Automated Neuroanatomical Relation Extraction: A Linguistically Motivated Approach with a PVT Connectivity Graph Case Study |
| title_full_unstemmed | Automated Neuroanatomical Relation Extraction: A Linguistically Motivated Approach with a PVT Connectivity Graph Case Study |
| title_short | Automated Neuroanatomical Relation Extraction: A Linguistically Motivated Approach with a PVT Connectivity Graph Case Study |
| title_sort | automated neuroanatomical relation extraction a linguistically motivated approach with a pvt connectivity graph case study |
| topic | neuroinformatics Natural Language Processing text mining paraventricular nucleus of the thalamus PVT brain region connectivity graph |
| url | http://journal.frontiersin.org/Journal/10.3389/fninf.2016.00039/full |
| work_keys_str_mv | AT erincgokdeniz automatedneuroanatomicalrelationextractionalinguisticallymotivatedapproachwithapvtconnectivitygraphcasestudy AT arzucanozgur automatedneuroanatomicalrelationextractionalinguisticallymotivatedapproachwithapvtconnectivitygraphcasestudy AT resitcanbeyli automatedneuroanatomicalrelationextractionalinguisticallymotivatedapproachwithapvtconnectivitygraphcasestudy |