Name as Many AEDs as You Can in One Minute: Antiepileptic Drugs and the Disruption of Verbal Fluency Networks.
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ABSTRACT:
SUBMITTER: Chang BS
PROVIDER: S-EPMC5898254 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Semantic verbal fluency (sVF) tasks are commonly used in clinical diagnostic batteries as well as in a research context. When performing sVF tasks to assess executive functions (EFs) the sum of correctly produced words is the main measure. Although previous research indicates potentially better insights into EF performance by the use of finer grained sVF information, this has not yet been objectively evaluated. To investigate the potential of employing a finer grained sVF feature set to predict EF performance, healthy monolingual German speaking participants (n = 230) were tested with a comprehensive EF test battery and sVF tasks, from which features including sum scores, error types, speech breaks and semantic relatedness were extracted. A machine learning method was applied to predict EF scores from sVF features in previously unseen subjects. To investigate the predictive power of the advanced sVF feature set, we compared it to the commonly used sum score analysis. Results revealed that 8 / 14 EF tests were predicted significantly using the comprehensive sVF feature set, which outperformed sum scores particularly in predicting cognitive flexibility and inhibitory processes. These findings highlight the predictive potential of a comprehensive evaluation of sVF tasks which might be used as diagnostic screening of EFs.
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Project description:IntroductionIn recent decades, researchers have defined novel methods for scoring verbal fluency tasks. In this work, we evaluate novel scores based on speed of word responses.MethodsWe transcribed verbal fluency recordings from 641 cases of incident cognitive impairment (ICI) and matched controls, all participants in a large national epidemiological study. Timing measurements of utterances were used to calculate a speed score for each recording. Traditional raw and speed scores were entered into Cox proportional hazards (CPH) regression models predicting time to ICI.ResultsConcordance of the CPH model with speed scores was 0.599, an improvement of 3.4% over a model with only raw scores and demographics. Scores with significant effects included animals raw and speed scores, and letter F speed score.DiscussionNovel verbal fluency scores based on response times could enable use of remotely administered fluency tasks for early detection of cognitive decline.Highlights The current work evaluates prognostication with verbal fluency speed scores. These speed scores improve survival models predicting cognitive decline. Cases with progressive decline have some characteristics suggestive of Alzheimer's disease. The subset of acute decliners is probably pathologically heterogeneous.
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Project description:Background: Verbal fluency (VF) has been associated with several cognitive functions, but the cognitive processes underlying verbal fluency deficits in Multiple Sclerosis (MS) are controversial. Further knowledge about VF could be useful in clinical practice, because these tasks are brief, applicable, and reliable in MS patients. In this study, we aimed to evaluate the cognitive processes related to VF and to develop machine-learning algorithms to predict those patients with cognitive deficits using only VF-derived scores. Methods: Two hundred participants with MS were enrolled and examined using a comprehensive neuropsychological battery, including semantic and phonemic fluencies. Automatic linear modeling was used to identify the neuropsychological test predictors of VF scores. Furthermore, machine-learning algorithms (support vector machines, random forest) were developed to predict those patients with cognitive deficits using only VF-derived scores. Results: Neuropsychological tests associated with attention-executive functioning, memory, and language were the main predictors of the different fluency scores. However, the importance of memory was greater in semantic fluency and clustering scores, and executive functioning in phonemic fluency and switching. Machine learning algorithms predicted general cognitive impairment and executive dysfunction, with F1-scores over 67-71%. Conclusions: VF was influenced by many other cognitive processes, mainly including attention-executive functioning, episodic memory, and language. Semantic fluency and clustering were more explained by memory function, while phonemic fluency and switching were more related to executive functioning. Our study supports that the multiple cognitive components underlying VF tasks in MS could serve for screening purposes and the detection of executive dysfunction.
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Project description:Women are thought to fare better in verbal abilities, especially in verbal-fluency and verbal-memory tasks. However, the last meta-analysis on sex/gender differences in verbal fluency dates from 1988. Although verbal memory has only recently been investigated meta-analytically, a comprehensive meta-analysis is lacking that focuses on verbal memory as it is typically assessed, for example, in neuropsychological settings. On the basis of 496 effect sizes and 355,173 participants, in the current meta-analysis, we found that women/girls outperformed men/boys in phonemic fluency (ds = 0.12-0.13) but not in semantic fluency (ds = 0.01-0.02), for which the sex/gender difference appeared to be category-dependent. Women/girls also outperformed men/boys in recall (d = 0.28) and recognition (ds = 0.12-0.17). Although effect sizes are small, the female advantage was relatively stable over the past 50 years and across lifetime. Published articles reported stronger female advantages than unpublished studies, and first authors reported better performance for members of their own sex/gender. We conclude that a small female advantage in phonemic fluency, recall, and recognition exists and is partly subject to publication bias. Considerable variance suggests further contributing factors, such as participants' language and country/region.
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Project description:OBJECTIVES:Describe novel methods for ascertaining verbal fluency in a large national sample of adults, examine demographic factors influencing performance, and compare scores to studies using in-person assessment. METHODS/DESIGN:Participants were from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a national, population-based, longitudinal study of stroke in adults aged 45 years and older. Letter and semantic fluency were gathered, using Letter "F" and Animal Naming, via a telephone-based assessment with computer-assisted scoring of digital recordings. RESULTS:Initial letter and semantic fluency scores were obtained on 18 505 and 18 072 participants, respectively. For both fluency tests, scores were normally distributed. Younger age and more years of education were associated with better performances (p < 0.0001). The mean and standard deviation for matched subgroups, based on age, gender, and education, were quite comparable with scores reported out of samples using an in-person administration format. Telephone-based assessment also allowed for a level of quality control not available via in-person measurement. CONCLUSIONS:Telephone-based assessment of verbal fluency and computer-assisted scoring programs designed for this study facilitated large-scale data acquisition, storage, and scoring of protocols. The resulting scores have similar characteristics to those obtained by traditional methods. These findings extend validation of cognitive assessment methods, using survey research staff and computer-assisted technology for test administration.
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