10 research outputs found

    Differential semantic processing in patients with schizophrenia versus bipolar disorder: an N400 study

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    International audienceOBJECTIVE: Both bipolar disorder (BD) and schizophrenia (SZ) are associated with language and thought symptoms that probably reflect a semantic memory-related impairment. We conducted a preliminary study to explore the nature of semantic processing in these disorders, using event-related potentials (ERPs).METHODS: Twelve patients with BD, 10 patients with SZ and a matched group of 21 healthy controls (HC) underwent EEG recording while they heard sentences containing homophones or control words and performed a semantic ambiguity resolution task on congruent or incongruent targets.RESULTS: Mean N400 amplitude differed between groups for homophones. Patients with SZ made more resolution errors than HC and exhibited a greater N400 congruity effect in ambiguous conditions than BD. In BD, the opposite N400 congruity effect was observed in ambiguous conditions.CONCLUSION: Results indicated differences in semantic processing between BD and SZ. Further studies with larger populations are needed in order to develop neurophysiological markers of these disorders

    A comprehensive model of predictors of quality of life in older adults with schizophrenia: results from the CSA study

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    International audienc

    Psychiatric symptoms and quality of life in older adults with schizophrenia spectrum disorder: results from a multicenter study

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    Effects of depression and cognitive impairment on quality of life in older adults with schizophrenia spectrum disorder: Results from a multicenter study

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    International audienc

    Psychiatric and physical outcomes of long-term use of lithium in older adults with bipolar disorder and major depressive disorder: A cross-sectional multicenter study

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    International audienc

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

    No full text
    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press
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