Language and semantic memory tend to remain stable across the human lifespan in contrast to other cognitive domains, like episodic memory and attention, which usually decline after the 5th decade (Craik and Bialystok, 2006). They are also usually spared in the initial stages of neurodegenerative disorders, such as Alzheimer's disease (AD), though we can still observe milder deficits, e.g., anomia or reduced semantic verbal fluency, which can be identified in a comprehensive neuropsychological evaluation (Henry et al., 2004; Garrard et al., 2005; Nutter-Upham et al., 2008; Taler and Phillips, 2008).

Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval (Nickles, 2001). Verbal fluency tests are experimentally designed to assess this ability through the production of words starting with a specific letter (Phonemic Verbal Fluency) or belonging to a category of knowledge (Semantic Verbal Fluency). Semantic verbal fluency is one of the most commonly used tasks to evaluate language and semantic memory skills in older adults. This task depends on the preservation of language (e.g., words can be spoken correctly during the task), though it is significantly influenced by semantic memory (e.g., the knowledge of the category asked must be intact) and executive function (e.g., the ability to search the asked knowledge) domains (Adlam et al., 2006; Unsworth et al., 2011). This task often activates the temporal lobe, a region broadly related to conceptualization, general information and knowledge about names (Patterson et al., 2007). Semantic verbal fluency contributes to predict future cognitive and functional impairments in the elderly (Salmon et al., 2002; Amieva et al., 2005; Hodges et al., 2006; Aretouli et al., 2011), and predict the progression from MCI to AD (Saxton et al., 2004).

Despite being widely used for neuropsychological assessment in the elderly, the standard measure of the verbal fluency test is restricted to the total of correct words produced in the task (Lezak et al., 2004; Strauss et al., 2006), and does not take into account other clinically-relevant information that may be contained in the patient's specific performance. This task requires the production of words belonging to a specific category, and each subject produced the words following an order of exemplars during the 1-min task. This order of words produced allows the construction of a network based on the temporal link between the words. These temporal links may inform that words produced in a specific temporal sequence are probably conceptually related, as suggested by the semantic association models (McClelland and Rogers, 2003; Griffiths et al., 2007).

Goni et al. (2010) constructed a semantic network using the verbal fluency task applied to an adult sample, and represented the semantic memory as a graph ruled by conceptual constraints. A normal semantic verbal fluency network is represented by a directed graph with only one occurrence for each word. Lerner et al. (2009) investigated the network properties of subjects with MCI and AD, and found that the path lengths of the network decline while the clustering coefficient increases in the MCI and AD subjects compared to healthy elderly controls. These results showed that the normal characteristics of the semantic verbal network are significantly changed in the continuum from normal aging to AD.

The analysis of network properties helps understanding the dynamics and organization of the cognitive and behavioral processes. A graph represents a network with nodes linked by edges (Mota et al., 2012). Formally, a graph is a mathematical representation of a network G = (N, E), with N = {w₁, w₂, … w_n} a set of nodes and E = {(w_i, w_j)} a set of edges or links between words w_i in N and w_j in N. The interpretation of the meaning of a graph depends on what is being represented (Butts, 2009; Mota et al., 2012). We carried out an analysis of the network properties of the semantic verbal fluency of subjects with MCI or AD. We hypothesize that the analysis of the semantic verbal fluency network properties can help to better discriminate between older adults with normal cognitive performance, mild cognitive impairment or Alzheimer's disease. This approach had been used with success to identify patients with schizophrenia and bipolar disorder (Mota et al., 2012, 2014).

Table 1 shows data for socio-demographic data, Mini Mental State Exam (MMSE), total number of produced words in the verbal fluency test, total number of correct words produced, total number of repetitions performed during the task, the percentage of repetitions performed according to the total of produced words, and the errors produced.

The groups did not differ in age and education, and only the control group had a significant difference in gender distributions (X² = 6.76, df = 2, p = 0.009) (Table 1). The results of the groups' comparison on the daily living activities, the global cognitive status are also reported on Table 1. Verbal fluency measures and the Speech Graph Attributes are reported on Table 2.

Despite the lower number of correct words produced by the NC group, it is similar to those observed to Brazilian normative data (Brucki et al., 1997). Moreover, the scores on the verbal fluency test were not taken into account for participant classification into the diagnostic groups.

The groups significantly differed in the performance on ADLs, in general cognitive status, number of correct words and total words produced, and in the Speech Graph measures of word count, nodes, edges, loops of 3 nodes, diameter, average short path and density. As expected, the NC group performed better at ADLs, had higher scores on the MMSE, produced more nodes, a network with larger diameter and less dense, when compared with the MCI and AD groups. The MCI group showed an intermediate performance between NC and AD groups in all measures.

Table 3 and Figure 2A show pairwise comparisons of the 3 diagnosis groups. Statistical significance was set at p < 0.0167, after Bonferroni correction for multiple comparisons.

The comparison of the variables between NC and MCI groups demonstrate that the groups differ in the index of instrumental daily living activities, in the number of correct words produced, number of nodes, diameter, average short path and density of the network. The NC produced less dense graphs with more nodes, and larger Diameter and ASP than the MCI and AD. Furthermore, NC made more edges, total words produced, and had a better general cognitive status than the AD group. The MCI and AD groups differ in all measures, demonstrating that a change in the general cognitive status, functionality, verbal fluency measures and the speech graph attributes (WC, N, E, L3, Diameter, ASP, and Density) (Figure 2B) almost follow a continuous modification as the diagnosis impairs.

Table 4 shows the Spearman correlations between the SGA and the clinical measures of differential diagnosis (global cognitive status—MMSE—and daily living functionality—Katz and Lawton Index). The significance level was established in p = 0.0012 after a Bonferroni correction for 42 comparisons.

We found significant correlations between the MMSE and the SGA of Nodes and Density, indicating that the more cognitively preserved elderly produced a larger number of unique nodes, and networks with a smaller density than cognitively impaired subjects. The correlation between the attributes and the Lawton Index of instrumental daily living activities revealed that the more functionally dependent were the elderly, the less words, nodes and edges they produced, showing networks with a smaller diameter and average short path, but a higher density. These results indicate that functional autonomy correlate more with SGA than with the general cognitive status.

The Naïve Bayes classifier results (Figure 2C) show that a selection of SGA correlated with functional and cognitive impairment measured by other instruments, provided good to excellent classification power, being similar to the MMSE classification power, or even better for the distinction between the NC and MCI groups. When the SGA were associated to the Lawton Index or the MMSE, the power of classification increased; a combination of the 3 measurements provided maximal classification quality (Table 5). Overall, the combination of graph measures and functional dependence yielded very accurate differential classification of the AD (1.00) and MCI (0.78) against the NC group, and between the MCI and AD (0.84).

The additional description of the socio-demographic data, Mini Mental State Exam (MMSE), verbal fluency measures of the two subgroups of MCI are reported on Table 6, and also the results of the four groups' comparison on the sociodemographic variables. Table 7 shows the four group comparison on the verbal fluency and Speech Graph Attributes.

A comparison of the four groups showed significant differences in daily functionality, general cognitive status, total and correct words produced, and in the SGA word count, nodes, edges, diameter, ASP and density (same attributes found in the three-group comparison).

Table 8 and Figure 3A compare the four groups of elderly, with Bonferroni correction for multiple comparisons (alpha = 0.0083).

The pairwise comparison detected no significant differences between MCI subtypes in the measures selected in this study. The difference between the NC and aMCI groups occurred only in instrumental daily living functionality, i.e., NC are more independent than aMCI. The significant differences between the NC and AD and between aMCI and AD are similar; the NC and aMCI groups are less functionally dependent, have better cognitive status, produce more total and correct words, a higher word count, more nodes and edges, higher Diameter and ASP, and less dense networks when compared to the AD group. The NC are more functionally independent, produce more total and correct words, a higher word count, more nodes and edges, and a network less dense than the a+mdMCI group. AD subjects, comparable to the a+mdMCI group, were more functionally dependent, showed general cognitive impairment, and produced fewer nodes and a denser network.

The Naïve Bayes classifier results (Figure 3B) indicate that the selected SGA has a good classification power to the diagnosis of MCI subtypes against cognitive healthy aging, and also a good classification against the dementia group. On the other hand, SGA yielded a poor classification when used to distinguish between the two subtypes of MCI. When SGA were combined with the Lawton Index, we observed an increase in the power of classification across the four groups, except between the two MCI subtypes.

The combination of the SGA with the MMSE, showed less power when compared to the combination with the Lawton index; the combination of these three variables barely improved the classification beyond the SGA and Lawton combination. These results indicate that the combination of graph measures and functional dependence again provides for good classification across the three groups (AUC = 0.71–0.85), except between the MCI subtypes (AUC = 0.47).

The aim of the present study was to assess graph-theoretical differences in the execution of a verbal fluency task among elderly with normal and pathological aging. Our results demonstrate that SGA differed significantly among the AD, MCI, and NC groups and it could be used to classify the groups. The present results show the potential of graph analysis of verbal fluency task to discriminate between these groups in clinical practice.

The correlation between the SGA and the MMSE or the Lawton Index indicate that the SGA are associated with the general cognitive status and functional performance, two important clinical measures used in geriatric assessment. Patients with worse scores in the MMSE produced fewer numbers of nodes and a less dense network. As the functional performance decreases, indicating more severe cognitive impairment stages, the networks became denser, with a smaller diameter and average short path and with fewer numbers of nodes and edges. Their networks became smaller in the number of words, with a small path through the first word to the last one, and their animals have more connection with different neighbors than would be necessary. Subjects more cognitively impaired tended to perform more dependently on their daily activities. Importantly, some attributes of SGA could indicate the progression of cognitive impairment and functional decline, as shown by denser and smaller networks, with a fewer number of nodes, in subjects with more severe cognitive impairment.

Application of speech graph analysis for sorting the groups showed moderate to good classification quality. When selected SGA were combined to the Lawton Index, better classification were obtained, suggesting that the combination of these two simple tools of network measure and functionality can provide to the clinician a good indication of differential diagnosis, except for the contrast between the two MCI subtypes, which spanned a continuum and did not allow the differentiation and classification of the two groups.

The differences prevalent across all groups were in the global attributes of diameter, density and average shortest path (ASP). The results indicate that the networks built by the normal control elderly were more direct, without reoccurrence of words, resulting in a less dense network. Conversely, cognitive impairment corresponded to denser and less direct networks. The density differences across the groups were, among all comparisons, the most uniform result, except for the comparison between the two MCI subgroups, which yielded a pattern of continuous performance. The progressive worsening of cognitive performance within the MCI subtypes is consistent in the literature, indicating that a group of subtle deficits underlie the differential diagnosis (Diniz et al., 2007; Radanovic et al., 2009).

Even the groups that did not differ in total number of word repetitions differ in the occurrence of loops of 3 nodes (L3). Nearly all subjects, as expected, managed to avoid recurrences, but 20% of the AD subjects repeated the same word with only two words of interval (e.g., dog-cat-horse-dog). According to Huntley and Howard (2010), subjects with AD already have working memory deficits at the earliest stages of the disease. The impairment in central executive and episodic buffer functions of working memory probably stems from the difficulty of keeping information in mind while keeping the search for new information. These deficits probably explain the repetition of words in verbal fluency tasks with a very small interval.

The results outline a field that needs to be further explored in future studies, involving the density of the networks and the strength between the words in the semantic memory of elderly with pathological aging. The Parallel Distributed Processing Approach of Semantic Cognition predicts that the decrease in strength of the links between words in a semantic network may allow connections between pairs of words that would not be preferential under normal circumstances (McClelland and Rogers, 2003). Another aspect that deserves further investigation is the absence of difference across the groups in the connectivity attributes (LSC, ATD, and CC). This raises the hypothesis that even very different networks can share a similar structure of local connections, in which a small portion of the words are highly connected with other less connected words, maintaining the integrity of the network's general connection (Bales and Johnson, 2006; De Deyne and Storms, 2008).

Considering the graph analysis performed in this study, build-up in a co-occurrence of the words and based on the temporal link between them, future studies should consider multidimensional scaling and hierarchical clustering analysis. These types of analyses will represent the relation between the variables and combine it into groups, enhancing the results. Future studies should also address the differences between MCI patients and other neurological conditions in which cognitive impairments are quite similar, for example, Temporal Lobe Epilepsy (Holler and Trinka, 2014), as well as the potential association between graph analysis, neuroimaging and other diagnosis instruments. Furthermore, longitudinal studies are also necessary to evaluate whether SGA can help to identify MCI subjects with higher risk of progressing to Alzheimer's disease. In conclusion, the results suggest that SGA may be a useful tool to help in the differential diagnosis between MCI and AD.