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Clustering and switching during a semantic verbal fluency test contribute to differential diagnosis of cognitive impairment.

ABSTRACT: The verbal fluency test (VFT) can be dissociated into "clustering" (generating words within subcategories) and "switching" (shifting between clusters), which may be valuable in differential diagnosis. In the current study, we investigated the validity of VFT in the differential diagnosis of Alzheimer's disease (AD, n = 65), vascular dementia (VaD, n = 65), mild cognitive impairment (MCI, n = 92), and vascular cognitive impairment without dementia (VCIND, n = 76) relative to cognitively normal senior controls (NC, n = 374). We found that in the NC group, the total correct score was significantly correlated with age and education; males generated more subcategories; cluster size increased with education, and subcategory and switching decreased with age. A significantly progressive advantage was observed in VFT scores in the sequence NC > MCI/VCIND > AD/VaD, and this significantly discriminated dementia patients from the other groups. AD patients performed better in all four VFT scores than VaD patients. Subcategory and switching scores significantly distinguished AD from VaD patients (AD > VaD; mean difference, 0.50 for subcategory, P <0.05; 0.71 for switching, P <0.05). MCI patients scored higher than VCIND patients, but the difference did not reach statistical significance. These results suggest that semantic VFT is useful for the detection of MCI and VCIND, and in the differential diagnosis of cognitive impairment.

SUBMITTER: Zhao Q

PROVIDER: S-EPMC5561862 | biostudies-other | 2013 Feb

REPOSITORIES: biostudies-other

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Project description:Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment-subtypes amnestic (aMCI) and amnestic multiple domain (a+mdMCI)-and patients with Alzheimer's disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGA). The individuals were compared when divided in three (NC-MCI-AD) and four (NC-aMCI-a+mdMCI-AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.

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Clustering and switching during a semantic verbal fluency test contribute to differential diagnosis of cognitive impairment.

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