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Category fluency, latent semantic analysis and schizophrenia: a candidate gene approach.

ABSTRACT: BACKGROUND:Category fluency is a widely used task that relies on multiple neurocognitive processes and is a sensitive assay of cortical dysfunction, including in schizophrenia. The test requires naming of as many words belonging to a certain category (e.g., animals) as possible within a short period of time. The core metrics are the overall number of words produced and the number of errors, namely non-members generated for a target category. We combine a computational linguistic approach with a candidate gene approach to examine the genetic architecture of this traditional fluency measure. METHODS:In addition to the standard metric of overall word count, we applied a computational approach to semantics, Latent Semantic Analysis (LSA), to analyse the clustering pattern of the categories generated, as it likely reflects the search in memory for meanings. Also, since fluency performance probably also recruits verbal learning and recall processes, we included two standard measures of this cognitive process: the Wechsler Memory Scale and California Verbal Learning Test (CVLT). To explore the genetic architecture of traditional and LSA-derived fluency measures we employed a candidate gene approach focused on SNPs with known function that were available from a recent genome-wide association study (GWAS) of schizophrenia. The selected candidate genes were associated with language and speech, verbal learning and recall processes, and processing speed. A total of 39 coding SNPs were included for analysis in 665 subjects. RESULTS AND DISCUSSION:Given the modest sample size, the results should be regarded as exploratory and preliminary. Nevertheless, the data clearly illustrate how extracting the meaning from participants' responses, by analysing the actual content of words, generates useful and neurocognitively viable metrics. We discuss three replicated SNPs in the genes ZNF804A, DISC1 and KIAA0319, as well as the potential for computational analyses of linguistic and textual data in other genomics tasks.

SUBMITTER: Nicodemus KK

PROVIDER: S-EPMC4039573 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Category fluency, latent semantic analysis and schizophrenia: a candidate gene approach.

Nicodemus Kristin K KK Elvevåg Brita B Foltz Peter W PW Rosenstein Mark M Diaz-Asper Catherine C Weinberger Daniel R DR

Cortex; a journal devoted to the study of the nervous system and behavior 20131220

<h4>Background</h4>Category fluency is a widely used task that relies on multiple neurocognitive processes and is a sensitive assay of cortical dysfunction, including in schizophrenia. The test requires naming of as many words belonging to a certain category (e.g., animals) as possible within a short period of time. The core metrics are the overall number of words produced and the number of errors, namely non-members generated for a target category. We combine a computational linguistic approach ...[more]

PMID: 24447899

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Project description:Schizophrenia is a chronic psychiatric disorder that affects about 1% of the population globally. A tremendous amount of effort has been expended in the past decade, including more than 2400 association studies, to identify genes influencing susceptibility to the disorder. However, few genes or markers have been reliably replicated. The wealth of this information calls for an integration of gene association data, evidence-based gene ranking, and follow-up replication in large sample. The objective of this study is to develop and evaluate evidence-based gene ranking methods and to examine the features of top-ranking candidate genes for schizophrenia.We proposed a gene-based approach for selecting and prioritizing candidate genes by combining odds ratios (ORs) of multiple markers in each association study and then combining ORs in multiple studies of a gene. We named it combination-combination OR method (CCOR). CCOR is similar to our recently published method, which first selects the largest OR of the markers in each study and then combines these ORs in multiple studies (i.e., selection-combination OR method, SCOR), but differs in selecting representative OR in each study. Features of top-ranking genes were examined by Gene Ontology terms and gene expression in tissues.Our evaluation suggested that the SCOR method overall outperforms the CCOR method. Using the SCOR, a list of 75 top-ranking genes was selected for schizophrenia candidate genes (SZGenes). We found that SZGenes had strong correlation with neuro-related functional terms and were highly expressed in brain-related tissues.The scientific landscape for schizophrenia genetics and other complex disease studies is expected to change dramatically in the next a few years, thus, the gene-based combined OR method is useful in candidate gene selection for follow-up association studies and in further artificial intelligence in medicine. This method for prioritization of candidate genes can be applied to other complex diseases such as depression, anxiety, nicotine dependence, alcohol dependence, and cardiovascular diseases.

Project description:Background: Although performance on the category fluency test (CFT) is influenced by many cognitive functions (i.e., including language, executive functioning and speed of processing), item-level scoring methods of CFT performance might be a promising way to capture aspects of semantic memory that are less influenced by intervenient abilities. One such approach is based on the calculation of correlation coefficients that quantify the association between item-level features and the serial order with which words are recalled (SRO). Methods: We explored the neural underpinnings of 10 of these correlational indices in a sample of 40 healthy adults who completed a classic 1-min CFT and an MRI protocol inclusive of T1-weighted (analysed with voxel-based morphometry) and resting-state fMRI sequences for the evaluation of the default-mode network (DMN). Two sets of linear models were defined to test the association between neural maps and each correlational index: a first set in which major demographic and clinical descriptors were controlled for and a second set in which, additionally, all other 9 correlational indices were regressed out. Results: In the analysis of the DMN, 'SRO-frequency', 'SRO-dominance' and 'SRO-body-object interaction' correlational indices were all negatively associated with the anterior portion of the right temporoparietal junction. The 'SRO-frequency' correlational index was also negatively associated with the right dorsal anterior cingulate and the 'SRO-dominance' correlational index with the right lateral prefrontal cortex. From the second set of models, the 'SRO-typicality' correlational index was positively associated with the left entorhinal cortex. No association was found in relation to grey matter maps. Conclusion: The ability to retrieve more difficult words during CFT performance as measured by the correlational indices between SRO and item-level descriptors is associated with DMN expression in regions deputed to attentional reorienting and processing of salience of infrequent stimuli and dominance status. Of all item-level features, typicality appears to be that most closely linked with entorhinal functioning and may thus play a relevant role in assessing its value in testing procedures for early detection of subtle cognitive difficulties in people with suspected Alzheimer's degeneration. Although exploratory, these findings warrant further investigations in larger cohorts.

Project description:RationalePatient-physician communication plays an essential role in a variety of patient outcomes; however, it is often difficult to operationalize positive patient-physician communication objectively, and the existing evaluation tools are generally time-consuming.ObjectiveThis study proposes semantic similarity of the patient's and physician's language in a medical interaction as a measure of patient-physician communication. Latent semantic analysis (LSA), a mathematical method for modeling semantic meaning, was employed to assess similarity in language during clinical interactions between physicians and patients.MethodsParticipants were 132 Black/African American patients (76% women, Mage = 43.8, range = 18-82) who participated in clinical interactions with 17 physicians (53% women, Mage = 27.1, range = 26-35) in a primary care clinic in a large city in the Midwestern United States.ResultsLSA captured reliable information about patient-physician communication: The mean correlation indicating similarity between the transcripts of a physician and patient in a clinical interaction was 0.142, significantly greater than zero; the mean correlation between a patient's transcript and transcripts of their physician during interactions with other patients was not different from zero. Physicians differed significantly in the semantic similarity between their language and that of their patients, and these differences were related to physician ethnicity and gender. Female patients exhibited greater communication similarity with their physicians than did male patients. Finally, greater communication similarity was predicted by less patient trust in physicians prior to the interaction and greater patient trust after the interaction.ConclusionLSA is a potentially important tool in patient-physician communication research. Methodological considerations in applying LSA to address research questions in patient-physician communication are discussed.

Dataset Information

Category fluency, latent semantic analysis and schizophrenia: a candidate gene approach.

Publications

Category fluency, latent semantic analysis and schizophrenia: a candidate gene approach.

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