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Structural similarities between brain and linguistic data provide evidence of semantic relations in the brain.

ABSTRACT: This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model.

SUBMITTER: Crangle CE

PROVIDER: S-EPMC3682999 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature.ResultsWe extracted semantic relations with the SemRep natural language processing system from 122,421,765 sentences, which came from 21,014,382 MEDLINE citations (i.e., the complete MEDLINE distribution up to the end of 2012). A total of 58,879,300 semantic relation instances were extracted and organized in a relational database. The QA process is implemented as a search in this database, which is accessed through a Web-based application, called SemBT (available at http://sembt.mf.uni-lj.si ). We conducted an extensive evaluation of the proposed methodology in order to estimate the accuracy of extracting a particular semantic relation from a particular sentence. Evaluation was performed by 80 domain experts. In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%).ConclusionsIn this work we propose an innovative methodology for biomedical QA. The system is implemented as a Web-based application that is able to provide precise answers to a wide range of questions. A typical question is answered within a few seconds. The tool has some extensions that make it especially useful for interpretation of DNA microarray results.

Project description:BackgroundAbnormal storage and/or access are among the hypothesized causes of semantic memory deficit in schizophrenia. Neuropsychological and connectionist models have emphasized functional systems that serve the processing of word meaning and frequency: semantic storage disturbance is presumed to result from weak representations of word meaning; defective access is assumed to result from compromises to pathways that activate word frequency knowledge. Candidate biological systems include neuromodulatory pathways that normally function to enhance neural signals (e.g., cholinergic system). Electrophysiological responding may be informative regarding the storage-access distinction for schizophrenia.MethodsVisual event-related potentials were recorded for 14 schizophrenia outpatients receiving atypical antipsychotics, and 14 healthy controls group-matched to patients on age, gender, and demographics. N400 was elicited using an incidental semantic priming paradigm, in which semantic relatedness and word frequency were varied, and a letter probe task.ResultsCompared to controls, patients showed reduced N400 (microV) discrimination of semantic relatedness. Groups also showed different patterns of N400 to word frequency. Controls' N400 increased in negativity as words decreased in frequency of occurrence, while patients did not show a linear relationship between N400 and word frequency. Groups also differed for N400 to frequently occurring words. Patients exhibited increased negativity to high and very high frequency words, compared to controls. A subgroup of patients receiving antipsychotics with known affinity binding for muscarinic receptors (clozapine and olanzapine) showed significant albeit limited N400 priming, but their N400 to word frequency remained nonsignificant.ConclusionsResults suggest a deficit in semantic access for schizophrenia, as well as an influence of neuromodulators on the activation of connections among semantic representations. Cumulative findings indicating only limited N400 priming for patients receiving either typical or atypical antipsychotics support the hypothesis that semantic memory deficit represents a trait marker for schizophrenia.

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Structural similarities between brain and linguistic data provide evidence of semantic relations in the brain.

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