Graph analysis of verbal fluency test discriminate between patients with Alzheimer's disease, mild cognitive impairment and normal elderly controls

Abstract

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.

Keywords: Alzheimer's disease; elderly; graph analysis; mild cognitive impairment; semantic verbal fluency.

Figures

Figure 1

(A) Representation of the word sequence produced on the Semantic Verbal Fluency task. (B) Representations of networks generated by NC, MCI, and AD subjects during the Semantic Verbal Fluency task.

Figure 2

Speech Graph Attributes (SGA) differentiates psychopathological groups. (A) SGA boxplots with significant differences among Alzheimer Disorder (AD), Moderate Cognitive Impairment (MCI) and control groups (N = 25 on AD and C group, N = 50 on MCI group; Kruskal-Wallis test followed by two-sided Wilcoxon Rank-sum test with Bonferroni correction with alpha = 0.0167). (B) Percentage of subjects in each group that made one L3 on the verbal fluency test. AD subjects showed more L3 than MCI subjects (Wilcoxon Rank-sum test with Bonferroni correction with alpha = 0.0167, p = 0.0090). (C) Rating quality measured by AUC, sensitivity and specificity, using MMSE or SGA correlated with clinical symptoms measured with MMSE and Lawton scales (Table 3) (attributes: WC, N, E, Density, Diameter, and ASP). Notice that SGA was more specific than MMSE on triple group sorting, and on MCI diagnosis against the control group. *p = 0.0167.

Figure 3

Speech Graph Attributes (SGA) differentiates psychopathological MCI subgroups. (A) SGA boxplots with significant differences among Alzheimer Disorder (AD), Amnesic Moderate Cognitive Impairment (aMCI), Multiple Domain Moderate Cognitive Impairment (a+mdMCI), and control groups indicated (N = 25 per group; Kruskal-Wallis test followed by two-sided Wilcoxon Rank-sum test with Bonferroni correction with alpha = 0.0083). (B) Rating quality measured by AUC, sensitivity and specificity, using SGA correlated with clinical symptoms measured with MMSE and Lawton scales (Table 4) (attributes: WC, N, E, Density, Diameter, and ASP). Notice that it is possible to sort the MCI subgroups from the NC or AD groups, but not one from another. Classification quality was considered excellent when AUC was higher than 0.8, good when AUC ranged from 0.6 to 0.8, and poor (not above the chance), when AUC was smaller than 0.6. *p = 0.0083.