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Action and noun fluency testing to distinguishbetween Alzheimer's disease and dementia withLewy bodiesXavier Delbeuck a , Brigitte Debachy a , Florence Pasquier a & Christine Moroni ba Centre Mémoire de Ressources et de Recherche, EA 1046, Université Lille Nord deFrance, Lille, Franceb Laboratoire de Neurosciences Fonctionnelles et Pathologiques, EA 4559,Université Lille Nord de France, Villeneuve d'Ascq, FranceVersion of record first published: 04 Feb 2013.

To cite this article: Xavier Delbeuck , Brigitte Debachy , Florence Pasquier & Christine Moroni (2013): Action andnoun fluency testing to distinguish between Alzheimer's disease and dementia with Lewy bodies, Journal of Clinicaland Experimental Neuropsychology, DOI:10.1080/13803395.2013.763907

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JOURNAL OF CLINICAL AND EXPERIMENTAL NEUROPSYCHOLOGY, 2013http://dx.doi.org/10.1080/13803395.2013.763907

Action and noun fluency testing to distinguish betweenAlzheimer’s disease and dementia with Lewy bodies

Xavier Delbeuck1, Brigitte Debachy1, Florence Pasquier1, and Christine Moroni2

1Centre Mémoire de Ressources et de Recherche, EA 1046, Université Lille Nord de France, Lille,France2Laboratoire de Neurosciences Fonctionnelles et Pathologiques, EA 4559, Université Lille Nord deFrance, Villeneuve d’Ascq, France

The objective of the present study was to establish whether performance in an action fluency task is of value inthe differential diagnosis of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB). After collectingnormative data on performance in an action fluency task and a conventional animal fluency task in a cohortof French-speaking healthy controls, we assessed AD and DLB patients. Only the action fluency score differedsignificantly between the two demented groups, with DLB patients performing worse than AD patients. However,a composite action and animal fluency score was found to be more effective for discriminating between these twogroups.

Keywords: Alzheimer disease; Lewy body disease; Verbal fluency; Action fluency; Normative data.

Verbal fluency tasks are commonly used inneuropsychological evaluations to assess executivefunctions and verbal abilities (notably when a neu-rodegenerative illness is suspected). More specifi-cally, semantic and phonologic fluency are used tocharacterize the cognitive profile of Alzheimer’s dis-ease (AD) patients (Henry, Crawford, & Phillips,2004) and to distinguish the latter from individ-uals with dementia from other etiologies, suchas frontotemporal dementia (Pasquier, Lebert,Grymonprez, & Petit, 1995; Rascovsky, Salmon,Hansen, Thal, & Galasko, 2007), dementia withLewy bodies (DLB; Lambon Ralph et al., 2001),and subcortical dementia (Tröster et al., 1998).In this respect, it has been found that AD patientsshow greater impairments in semantic/categoryfluency tasks than in phonologic/literal fluencytasks, whereas patients with other types ofdementias perform poorly but similarly in the twofluency tasks (Levy & Chelune, 2007). This obser-vation is usually interpreted as an additive effect ofsemantic memory disturbance on category fluencytask performance in AD patients. However, this

We thank Samuel Lepoittevin for his help with data collection.Address correspondence to Xavier Delbeuck, Centre Mémoire de Ressources et de Recherche, Hopital Roger Salengro, CHRU,

F-59037 Lille CEDEX (E-mail: xavier.delbeuck@chru-lille.fr).

discrepancy in AD patients has been challenged ina meta-analysis showing that the same profile canbe observed in healthy subjects (Laws, Duncan, &Gale, 2010). This finding calls into question thespecificity of this fluency profile in AD patients.

In addition to semantic and phonologic fluency,the assessment of action fluency has recentlyyielded interesting results in the field of dementia.In Parkinson’s disease (PD), it has been shownthat action fluency is more impaired in dementedpatients than in nondemented patients (Piatt,Fields, Paolo, Koller, & Tröster, 1999). The authorsof the latter study suggested that PD patients’ per-formance in an action fluency task may be an indi-cator of progression to dementia. This hypothesiswas challenged by the results of a two-year follow-up study in nondemented PD patients, showing thatthe action fluency task impairment was stable overtime (Signorini & Volpato, 2006). However, noneof these patients converted to dementia during thefollow-up, and thus it remains to be shown whetheraction fluency is truly an indicator of conversionto dementia in PD. In research in other patient

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groups, it has been found that (a) HIV-infectedpatients performed less well than controls in anaction fluency task while they performed as well ascontrols in an animal fluency task (Woods, Carey,Tröster, & Grant, 2005), and (b) action fluencyperformance was associated with the HIV-infectedpatients’ instrumental activities of daily living score(Woods, Morgan, Dawson, Cobb Scott, & Grant,2006).

Impaired action fluency in these pathologies hasusually been explained as the consequence of afrontostriatal disturbance and has been consid-ered as a potential marker of executive impair-ment (Piatt, Fields, Paolo, & Tröster, 1999). Thishypothesis has been strengthened by subsequentresearch in which AD patients were compared withother demented patients with a primarily dysexec-utive profile. The AD patients performed less wellthan PD patients in all fluency tasks other thanan action fluency task, in which the PD patientsshowed a more pronounced impairment (McDowdet al., 2011). Moreover, the action and categoryfluency profile in AD patients differed from thoseof patients with subcortical dementia (due tonormal pressure hydrocephalus) and frontotempo-ral lobar degeneration (behavioral frontotemporaldementia or progressive nonfluent aphasia; Daviset al., 2010). The AD patients showed similarperformance levels when comparing action andnoun fluency tasks, whereas the other dementedpatients showed lower scores for action fluencythan for noun fluency. These differing perfor-mance patterns in AD and other demented patientswith frontal/subcortical involvement suggests thataction fluency requires greater executive involve-ment than noun fluency does. However, McDowdet al. (2011) reported that processing speed (ratherthan executive function) accounted for a largerproportion of the variation in action fluency per-formance (i.e., the number of correct answers).The researchers also reported that verbal abilitieshave little influence on fluency (and even semanticfluency).

The objective of the present study was to furtherstudy action and noun fluency in demented AD andDLB patients. In fact, DLB has clinical featuresof AD and PD diseases and has been described asan intermediate syndrome. In the cognitive sphere,DLB patients have more severe executive/attentionimpairments than AD patients do (Kraybill et al.,2005), even at the predementia stage (Jicha et al.,2010). However, the constructive apraxia and visualperception disturbances observed in DLB patients(but not in dementia-free PD patients) reflects theimpairment of cortical structures (Aarsland et al.,2003; Calderon et al., 2001). The latter disturbances

are more severe in DLB than in AD. Moreover,DLB patients may have much the same episodicmemory performance levels as AD patients in freerecall tasks (Hamilton et al., 2004) and do notperform better than AD patients in tasks eval-uating semantic memory (Lambon Ralph et al.,2001). Considering these overlaps and differencesin cognitive functioning in AD and DLB (for areview, see Metzler-Baddeley, 2007), we hypothe-sized that disease-related differences in noun andaction generation would be of value in distinguish-ing the two conditions. We further hypothesizedthat (a) DLB patients would perform less well thanAD patients in an action fluency test (given theirattention/executive impairments), and (b) the twopatient groups would perform similarly in a cat-egorical fluency task (given the semantic memorydisturbances reported in the two pathologies).

We addressed these hypotheses in a two-stepprocess. First, we collected normative data from151 healthy controls, in order to measure interindi-vidual variations in performance and study theinfluence of demographic variables on task per-formance (Experiment 1). Given that output foraction fluency (AF) is lower than that for nounfluency (NF) in healthy participants (Woods, Carey,et al., 2005), we chose to score the number of cor-rect words produced in each fluency task (i.e., NFand AF scores) and to calculate a composite score(C score, indicating the standard interval betweenthe NF and AF scores). In the second part of thestudy, we studied groups of AD and DLB patientsand measured their performance in the two fluencytasks. The patients’ NF, AF, and C scores weretransformed into z scores (on the basis of the resultsin the healthy group) and were compared.

EXPERIMENT 1: NORMATIVE DATAIN ELDERLY, HEALTHY CONTROLS

Method

Participants

One hundred and fifty-one healthy adult volun-teers were recruited from a variety of settings (suchas senior-citizen associations and the caregivers ofpatients attending our memory clinic). The partic-ipants’ mean age was 69.81 ± 8.82 years, and 53%were women. The healthy participants’ distributionby age, gender, and educational level is describedin Table 1. We observed a number of differencesrelated to educational level: Distribution differedaccording to this factor with age (p value of theFischer’s exact test = .01 with a significant effect

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TABLE 1Description of the group of healthy participants sampleaccording to their age, gender, and educational level

Age

55–69 years Over 69 years

Full-time education Men Women Men Women

!9 years 10 12 10 2510 to 12 years 10 10 9 13"13 years 21 14 11 6

size index, Cramer’s V = 0.25, p = .01) and alsowith gender (p value of the Fischer’s exact test =.02 with a significant effect size index, Cramer’sV = 0.23, p = .02). Note that there is no signif-icant difference between distributions for age andgender (p value of the Fischer’s exact test = .14 withan effect size index, phi coefficient = .13, p = .12).Indeed, in the most elderly age group (>69 yearsof age), for example, the proportion of women with9 or fewer years of full-time education was signif-icantly higher than the proportion of women with13 or more years of full-time education. However,this is also the case in the general population.

All participants were native French speakers withnormal or corrected hearing and vision at thetime of the testing. Participants with a historyof specific cognitive complaints, neurological dis-ease (traumatic brain injury, stroke, seizures, etc.),psychiatric illness or the consumption of drugsof abuse were excluded. No participant consumedmore than three glasses of alcohol per day. Normalglobal cognitive function (as assessed by scoreson the Mini Mental State Examination (MMSE;Folstein, Folstein, & McHugh, 1975) and the MattisDementia Rating Scale (DRS; Mattis, 1973) wasan inclusion criteria. The normative scores for thecognitive tests were those reported by Kalafat,Hugonot-Diener, and Poitrenaud (2003), for theMMSE and by Pedraza et al. (2010) for the DRS.The healthy controls’ mean scores were 28.41 ±1.31 for the MMSE and 138.45 ± 3.78 for the DRS.Study subjects did not receive any financial com-pensation for their participation in the research.

Test procedures

All participants were administered two fluencytasks. In the action fluency task (based on the pro-cedure described by Piatt, Fields, Paolo, & Tröster,2004), participants were asked to generate as manywords describing actions (i.e., the verb) as possi-ble during a one-minute test period. Single wordswere requested (rather than sentences), and par-ticipants were not allowed to generate the same

verb with different endings. The AF score corre-sponded to the number of correct items (i.e., verbs)pronounced. In the noun fluency task, participantswere asked to generate as many names of animalsas possible during a one-minute test period. TheNF score corresponded to the number of correctitems (i.e., animal names) pronounced. We then cal-culated the C score in accordance with the followingequation: C score = (NF score – AF score)/(NFscore + AF score).

Statistical analyses

All statistical analyses were carried out withSPSS software (Version 18.0 for Macintosh, SPSS,Inc. Chicago, IL). To determine the associationbetween the three task variables (AF, NF, andC scores) and age, Pearson’s correlation coeffi-cients were calculated for the entire sample. Effectsizes were then examined. The effect of educationallevel (in terms of years of full-time education) onthe three task variables was assessed in the sameway. Potential gender effects were assessed witha parametric, independent-samples comparison ofthe means.

In order to correct for score variability due todemographic variables, normative data were calcu-lated using a regression-based approach. Standardmultiple regressions were performed between AF,NF, and C scores as dependent variables, andage and educational level as independent variables.The threshold for statistical significance was set top < .05.

Results

AF, NF, and C scores: The influenceof demographic variables

The correlations between age on one hand andAF and NF scores on the other were both nega-tive and statistically significant (r = #.27 and #.33,respectively, all p values ! .001). However, the cor-relation between age and the C score was not sta-tistically significant (r = .01, p = .90). Concerningthe effect of education, we used partial correlationto remove the effect of age identified in our previousanalyses. The partial correlations between the num-ber of years in full-time education and our threefluency scores were all significant (pr = .54 for AF,pr = .38 for NF, and pr = #.27 for C; p ! .001 in allcases). The men and women, however, did not dif-fer significantly in terms of the three mean (± SD)scores: AF, 15.99 ± 6.21 for men and 14.46 ± 5.7 forwomen, t(149) = #1.57, p = .11 with a Cohen’s

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4 DELBEUCK ET AL.

d = #0.26; NF, 18.99 ± 5.32 for men and 18.15± 5.62 for women, t(149) = #0.93, p = .35 witha Cohen’s d = #0.15; C, .09 ± .16 for men and0.12 ± 0.17 for women, t(149) = 0.85, p = .39 witha Cohen’s d = 0.14.

Regression-based normative data

Since both age and educational level were asso-ciated with the three scores, the two variableswere considered separately in a step-by-step, lin-ear regression analyses. Our models are presentedin Table 2. Educational level appeared to exert asignificant influence on the variance of each score(33% for the AF score, 18% for the NF score, and7% for the C score). However, the influence of agewas weak and was only observed for the NF score(accounting for just 5% of the variance).

When a regression-based approach is used in aclinical application, a person’s raw scores are con-verted into standardized residuals in three steps:The person’s predicted scores are calculated, andthe residuals (eid) are then calculated and stan-dardized: zi = eid/standard deviation (residual).The following example illustrates the use of thismethod: A 65-year-old man with 9 years of full-time education produced 4 words in the AF taskand 18 words in the NF task. Hence, the AFscore is 4, the NF score is 18, and the C scoreis 0.63. To determine whether or not this partici-pant obtained a normal C score, a predicted scorewas calculated using the following equation: 0.24– (0.01 $ number of years in full-time education),yielding 0.24 – (0.01 $ 9) = 0.15. The residual was0.48 (= 0.63 # 0.15), and the standardized residualwas 3.03 (= 0.48/0.16). This can be considered tobe an abnormal level of performance. Calculationsfor all the AF, NF, and C scores are presented in theAppendix.

To facilitate the clinical application of our nor-mative data, we created an examiner’s manualand a technical manual. The documents can bedownloaded from http://nca.recherche.univ-lille3.fr/index.php?page = materiel-de-test-2

EXPERIMENT 2

Method

Participants

Patients with probable Alzheimer’s disease (AD,n = 34) and patients with probable dementia withLewy bodies (DLB, n = 18) participated in thisstudy. All patients were attending the MemoryClinic at Lille University Medical Center. Thepatients in the AD group (14 men and 20 women;mean ± SD age: 71.64 ± 9.53 years; range:49–87 years) met the NINCDS-ADRDA (NationalInstitute of Neurological and CommunicativeDisorders and Stroke–Alzheimer’s Disease andRelated Disorders Association) criteria for prob-able AD (McKhann et al., 2011), whereas thepatients in the DLB group (11 men and 7 women;mean ± SD age: 69.26 ± 10.02 years; range:47–85 years) met the criteria for probable DLBgiven by McKeith et al. (2005). Diagnoses of ADand DLB were based on a general medical, neuro-logical, imaging, and neuropsychological examina-tion. The two groups of patients were similar withregard to age, t(50) = #0.84, p = .40 with a Cohen’sd = #0.24, and the number of years in full-timeeducation, t(50) = #0.4, p = .68 with a Cohen’sd = #0.11. In the AD group, the mean MMSEand DRS scores were 23.68 ± 2.43 and 117.24± 8.86, respectively. In the DLB group, the meanMMSE and DRS scores were 25.11 ± 2.88 and122.11 ± 9.73, respectively. There were no signifi-cant differences between the patient groups in termsof the scores [MMSE: t(50) = 1.89, p = .06 with aCohen’s d = 0.53; DRS: t(50) = 1.82, p = .07 witha Cohen’s d = 0.51]. Given that these comparisonsapproached statistical significance and consider-ing that there were significant correlations betweenDRS, NF (r = .46, p < .01), and C (r = .29, p = .03)scores, the DRS score was considered as a covari-ate in the subsequent analyses. It should be notedthat we preferred the DRS score over the MMSEscore in this respect because the DRS includes anexecutive function subtest.

TABLE 2Multiple regression analyses with the AF, NF, or C scores as the dependent variable and age and educational level as

independent variables

Educational level Age

Score ! R2 ! R2 F SD residuals ! Total R2

AF .89 .33%%% — — 75.42 4.87 5.13 .33%%%

NF .52 .18%% #.14 .05%% 23.36 4.78 22.97 .23%%%

C #.01 .07%%% — — 11.89 .16 .24 .07%%%

Note. AF = action fluency. NF = noun fluency. C = composite. Age in years. Educational level: number of years in full-time education.%%p < .01. %%%p < .001.

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ACTION AND NOUN FLUENCY IN AD AND DLB 5

Test procedures

The AF and SF tasks were administeredaccording to the procedure described above forExperiment 1.

Statistical analyses

All statistical analyses were carried out withSPSS software (Version 18.0 for Macintosh, SPSS,Inc. Chicago, IL). Using the regression-basedmethod from Experiment 1, the raw AF, NF, andC scores obtained by AD and DLB patients wereconverted into a z score. Intergroup differenceswere analyzed in a one-way analysis of covariance(ANCOVA), with the DRS score as a covariate.The assumptions for a valid ANCOVA were eval-uated, and all our scores were found to be normallydistributed; skewness values always fell within theacceptable range (i.e., between #1 and 1), Levene’stests were never statistically significant, and theregression slopes were homogeneous. The thresh-old for statistical significance was set to p < .05.It should be noted that a chi-squared test (withYates’s correction, when applicable) was performedto compare the two groups of patients in termsof the frequency of impairment in each fluencyscore.

Results

The subjects’ raw scores in the AF and NF tasksare given in Table 3. The corresponding z scoresare displayed in Figure 1. The ANCOVA of the AFand NF scores revealed a group effect for the for-mer score but not for the latter, F(1, 49) = 7.13,p < .01, "2 = .13, and F(1, 49) = 1.34, p = .25,"2 = .03, respectively. For the AF score, the ADpatients obtained a better mean z score than theDLB patients did (–1.03 ± 0.83 and #1.60 ± 0.88,respectively). Overall, 44% of the DLB patients(n = 8) and only 18% of the AD patients (n = 6)failed in this task by obtaining a z score above theabnormal threshold of #1.65, #2(1) = 4.3, p = .04.

TABLE 3Mean raw AF, NF, and C scores in the AD and DLB groups

Patients nAF rawscore

NF rawscore C raw score

AD 34 9.26 (4.41) 9.94 (5.08) 0.01 (0.39)DLB 18 6.17 (3.59) 12.67 (5.06) 0.37 (0.2)

Note. AF = action fluency. NF = noun fluency. C = composite.AD = Alzheimer’s disease. DLB = dementia with Lewy bodies.Standard deviations in parentheses.

For the NF score, there was no statistical differ-ence between the DLB and AD groups (–1.74 ±1.01 and #1.20 ± 0.93, respectively). Overall, 33%of the DLB patients (n = 6) and 41% of the ADpatients (n = 14) failed the NF task, #2(1) = 0.3,p = .58.

The C score z scores obtained by each partici-pant are presented in Figure 2. Raw C scores arepresented in Table 3. A z score in the range from#1.65 to 1.65 reflects a normal interval between theAF and NF scores. A z score above 1.65 reflectsa pathologic interval, with an AF score that ismarkedly lower than the NF score. Likewise, a zscore below #1.65 is pathologic and reflects an AFscore that is markedly higher than the NF score.An ANCOVA of the C scores revealed an effect ofgroup, F(1, 49) = 10.19, p < .01, "2 = .17. Halfof the AD patients (n = 17) had a pathological zscore. Overall, 18% of the AD patients (n = 6) hada pathological z score above 1.65, and 32% (n = 11)had a pathological z score below #1.65. ConcerningDLB patients, 39% (n = 7) had a pathological Cscore (above 1.65 in all cases, reflecting an AF scorethat was markedly lower than the NF score). We usea chi-squared test to examine differences betweenDLB and AD patients in terms of the frequency ofC score impairments: A trend towards a significantdifference was obtained, #2(1) = 2.8, p = .09, for az score above 1.65, whereas the difference was sig-nificant for a score below #1.65: Yates #2(1) = 5.6,p = .02. It should be noted that the intergroup dif-ference was not significant, #2(1) = 0.59, p = .44,when considering patients with scores above 1.65 orbelow #1.65 as a whole.

Lastly, we analyzed the overlap between individ-uals with abnormal fluency scores (NF and AF)and those with an abnormal C score. When onlyone fluency score was abnormal, and the other wasin the normal range, the C score always providedthe same information as the NF and AF scorestaken together (n = 12). However, some patientswith abnormal C scores had normal AF and NFfluency scores (with better performance for AF overNF (n = 2) or vice versa (n = 2). Moreover, whenboth AF and NF scores were abnormal, the C scorewas still useful for indicating whether AF fluencywas disproportionately impaired (relative to NFfluency; n = 1) or vice versa (n = 6).

GENERAL DISCUSSION

The present study’s initial objective was to compileand compare normative data for healthy, elderly,French-speaking participants in an action fluencytask and a noun fluency task. The comparison was

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Figure 1. The z scores obtained by patients in the AD (Alzheimer’s disease) and DLB (dementia with Lewy bodies) groups for the AF(action fluency) and NF (noun fluency) tasks.

performed by means of a C score that reflectedthe differences in performance of the two tasksand constituted a fluency performance profile forhealthy controls. Our analysis revealed an effect ofage on the individual fluency scores but not for theC score. This age effect was still statistically sig-nificant, for noun fluency only, when educationallevel was included in the regression as an indepen-dent factor. This lack of association between ageand action fluency is consistent with previous find-ings in a similar group of healthy adults aged 55 orover (Piatt et al., 2004). However, these findingsmay have been biased by the fact that neither ourstudy nor that by Piatt et al. included young adults.However, Woods et al. also failed to see an effectof age on action fluency in subjects aged between18 and 66 years (Woods, Scott, et al., 2005). Thetwo literature studies and the present study all failedto see an effect of gender on action fluency; in fact,gender influenced neither action nor noun fluencyin our study. However, a significant effect of edu-cational level was consistently noted in the threestudies. Indeed, in our study, educational level was

the main factor of variation in the NF score andthe C score. The latter was solely influenced bythe educational level and to a lesser extent thanaction and noun fluency taken separately. In clin-ical populations, the C score usefully enables thesubsequent comparison of independent changes inaction and noun fluency and also serves as a controlfor potential bias in the interpretation of deficitsto one fluency testing considered in isolation. Thiskind of bias has already been identified for dif-ferences between semantic and phonemic fluency.The differences have been discussed as a somewhatspecific observation in AD patients but have, infact, also been observed to some extent in healthyparticipants (Laws et al., 2010).

On the basis of these normative data, we fur-ther sought to characterize the performance ofAD and DLB patients in the two fluency tasks bytransforming the AF and NF scores into z scoresand also by analyzing the C score. In accordancewith previous reports, we did not observed anydifference between the AD and DLB groups interms of noun fluency (Lambon Ralph et al., 2001).

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ACTION AND NOUN FLUENCY IN AD AND DLB 7

Figure 2. The z scores obtained by patients in the AD (Alzheimer’s disease) and DLB (dementia with Lewy bodies) groups for thecomposite score.

However, there was a significant intergroup differ-ence for action fluency, with DLB patients namingfewer verbs than AD patients. This intergroup dif-ference was even greater for C score, since DLBpatients generally presented a greater impairmentin action fluency than in noun fluency. Seven of the18 of DLB patients showed a significant impair-ment (relative to controls) in this respect (i.e., a zscore above 1.65 for the C score). In contrast, ADpatients showed a more heterogeneous pattern ofperformance for the two fluency tasks; some ADpatients showed the opposite pattern (i.e., a greaterimpairment in noun fluency than in action fluency),which was not observed in any DLB patients. Thisobservation was confirmed by the values of "2 equalto .13 and .17, respectively, for the AF and Cscores obtained in the ANCOVA. Although thisassociation remained weak, it should be borne inmind that this result was obtained with two 1-min

fluency tasks and that taken conjointly with otherneuropsychological and clinical observations, thedistinction between the demented groups should beeven more effective.

Our results on action fluency would bring thisDLB syndrome closer to what has been observedin Parkinson’s disease (McDowd et al., 2011) orto other conditions with frontostriatal involvement(Davis et al., 2010). As it has been previouslysuggested, this performance profile in DLB mightbe due to a greater need for executive involve-ment when generating actions than when generat-ing nouns (Piatt, Fields, Paolo, & Tröster, 1999).However, it has also been shown that processingspeed is significantly involved in fluency perfor-mance (McDowd et al., 2011). Both executive func-tion and processing speed are more greatly impairedin DLB than in AD (Metzler-Baddeley, 2007).Thus, future studies of DLB versus AD differences

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in action fluency should include measures of execu-tive function and processing speed.

Moreover, our results emphasize the value ofusing a C score, which may be more informa-tive than the fluency scores taken separately. Thisvaluable property of such a procedure has alreadybeen demonstrated by using stepwise regression(Oda, Yamamoto, & Maeda, 2009) and multivari-ate modeling (Ferman et al., 2006) to identify thecombination of scores that best distinguished DLBfrom AD. Compared with these techniques, thepresent C score has the advantage of giving a spe-cific normative value that reflects the extent towhich a patient’s score differs from control val-ues. Similarly, a ratio between the scores in testsevaluating executive and perceptual functions hasbeen used to distinguish between frontotemporaldementia and AD with good sensitivity and speci-ficity (Mendez, McMurtray, Licht, & Saul, 2009).Our present results emphasized the value of usinga specific fluency performance ratio for distinguish-ing between DLB and AD. Even when both fluencyscores were normal or when both were impaired,the C score enabled us to identify a performancepattern and determine when a patient was moreimpaired for one type of fluency than the other.

Lastly, the C score also highlighted another dis-criminative pattern between the patients for thesefluency tasks. When considering the fluency tasksalone, one could conceivably conclude that actionfluency was a better marker for identifying DLBpatients (relative to AD patients) than noun fluencybecause there were no intergroup differences forthe latter. However, this was mainly the case whenconsidering the numerical values of the actionfluency scores themselves (i.e., there was a statisti-cal difference between the groups in terms of themean z scores). When the proportion of patientswith impaired action fluency is considered (i.e.,those with a z score below or above #1.65), thediscrimination is somewhat less specific for DLB(although it is still statistically significant), with18% of AD patients and 44% of DLB patientsclassified as abnormal. However, when consider-ing the proportion of patients with an impaired Cscore, a different discrimination pattern emerged.The most significant intergroup difference in termsof the C score was that 32% of AD patients had agreater impairment in noun fluency than in actionfluency, whereas this was not the case for any of theDLB patients. Consequently, the C score appearsto be a more specific indicator of AD versus DLB.However, this finding needs to be replicated on anindependent sample. Future work should also iden-tify factors associated with this fluency profile inAD, since some AD patients may show the oppositeperformance pattern.

This study had several limitations. As mentionedabove, the effect size was small ("2 = .17) butthe test was quick to perform. The addition ofcognitive scores or clinical characteristics (notablyconcerning the presence of visual hallucinationsand/or visuoperceptive disorders) might haveincreased the effect size for distinguishing betweenthe groups (Tiraboschi et al., 2006). Anotherlimitation relates to the fact that we do not haveneuropathologic confirmation of our patients’clinical diagnoses, and so the potential for mis-diagnosis in each of the two groups cannot beruled out. Moreover, the difference between thenumber of AD patients (n = 34) and the numberof DLB patients (n = 18) reflects the fact thatthese dementias are not observed with the samefrequency in the general population. In view of therelatively low number of DLB patients, our resultsneed to be replicated in a larger, independentsample. Our study groups comprised consecutivepatients seen in our clinic between 2005 and 2010.The groups were younger than those reported inthe literature, for two main reasons: (a) we excludedpatients with significant vascular lesions (whichare more frequently observed in elderly adultswith dementia), and (b) our memory clinic special-izes in the management of early-onset dementia.Consequently, extrapolation of our presentresults in older patients would require additionalresearch.

In conclusion, we observed a specific impairmentin action fluency in DLB, relative to AD. Thisimpairment was confirmed when considering a Cscore for action and noun fluency. However, analy-sis of the latter score also revealed that AD patientsare more likely than DLB patients to display bet-ter action fluency scores than noun fluency scores.Consequently, the administration of a one-minuteaction fluency task may be of great value whencharacterizing the cognitive profile of a patient withdementia.

Original manuscript received 9 February 2012Revised manuscript accepted 27 December 2012

First published online 4 February 2013

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APPENDIX

Calculations of scores

To determine whether or not a participant hasobtained normal scores, the following calculationsare performed.

Step 1: Collate the participant’s performances inthe AF and NF tasks:

A 65-year-old man with 9 years of full-timeeducation produced 4 words in the AF task and18 words in the NF task. The C score is (NF score –AF score)/(NF score + AF score). Hence, for thisparticipant, the AF score is 4, the NF score is 18,and the C score is 0.63.

Step 2: Calculate the participant’s predicted scoreby applying a specific regression equation.

Score EquationPredicted

score

AF score y = 5.13 + (0.89 $ number of yearsin full-time education)

y = 13

y = 5.13 + (0.89 $ 9)NF score y = 22.97 + (0.52 $ number of years

in full-time education) # (0.14 $age)

y = 18.55

y = 22.97 + (0.52 $ 9) # (0.14 $ 65)C score y = 0.24 # (0.01 $ number of years

in full-time education)y = 0.15

y = 0.24 # (0.01 $ 9)

Step 3: Calculate the residuals and the z score.

Scores

eid = (observedscore – predicted

score)

Residualstandarddeviation z-score

Clinicaldecision

AF score 4 – 13 = #9 4.87 #1.87 Impaireda

NF score 18 – 18.55 = #0.55 4.78 #0.11 NormalC score 0.63 – 0.15 = 0.48 0.16 3.03 Impaireda

aAbnormal thresholds (i.e., z score < #1.65 or > 1.65).

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