Language networks associated with computerized semantic indices

Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic mem...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Fla.), 2015-01, Vol.104, p.125-137
Hauptverfasser: Pakhomov, Serguei V.S., Jones, David T., Knopman, David S.
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Sprache:eng
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Zusammenfassung:Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic memory and executive control. Previous work relied on manual assessments of semantic relatedness between words and grouping of words into semantic clusters. We investigated a computational linguistic approach to measuring the strength of semantic relatedness between words based on latent semantic analysis of word co-occurrences in a subset of a large online encyclopedia. We computed semantic clustering indices and compared them to brain network connectivity measures obtained with task-free fMRI in a sample consisting of healthy participants and those differentially affected by cognitive impairment. We found that semantic clustering indices were associated with brain network connectivity in distinct areas including fronto-temporal, fronto-parietal and fusiform gyrus regions. This study shows that computerized semantic indices complement traditional assessments of verbal fluency to provide a more complete account of the relationship between brain and verbal behavior involved organization and retrieval of lexical information from memory. •A novel computerized approach to characterize semantic verbal fluency performance•Computerized semantic indices are associated with distinct neural networks•Computerized semantic indices complement traditional assessments
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2014.10.008