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.
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.
Authors: Jordi Peña-Casanova; Rafael Blesa; Miquel Aguilar; Nina Gramunt-Fombuena; Beatriz Gómez-Ansón; Rafael Oliva; José Luis Molinuevo; Alfredo Robles; María Sagrario Barquero; Carmen Antúnez; Carlos Martínez-Parra; Anna Frank-García; Manuel Fernández; Verónica Alfonso; Josep M Sol Journal: Arch Clin Neuropsychol Date: 2009-06-23 Impact factor: 2.813
Authors: Alan J Thompson; Brenda L Banwell; Frederik Barkhof; William M Carroll; Timothy Coetzee; Giancarlo Comi; Jorge Correale; Franz Fazekas; Massimo Filippi; Mark S Freedman; Kazuo Fujihara; Steven L Galetta; Hans Peter Hartung; Ludwig Kappos; Fred D Lublin; Ruth Ann Marrie; Aaron E Miller; David H Miller; Xavier Montalban; Ellen M Mowry; Per Soelberg Sorensen; Mar Tintoré; Anthony L Traboulsee; Maria Trojano; Bernard M J Uitdehaag; Sandra Vukusic; Emmanuelle Waubant; Brian G Weinshenker; Stephen C Reingold; Jeffrey A Cohen Journal: Lancet Neurol Date: 2017-12-21 Impact factor: 44.182