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Differential diagnosis of depression and Alzheimer’s disease with the Addenbrooke's Cognitive Examination-Revised (ACE-R)

Abstract

Background

One of the usual problems psychologists and clinicians face in clinical practice is differential diagnostics of Alzheimer’s disease and depression. It has been reported that the ACE and ACE-R could discriminate the cognitive dysfunctions due to depression from that due to dementia, although this is not uniform in all studies. The current study aimed to evaluate the utility of the ACE-R to differentiate late-life onset depression (with severe episode) from mild-moderate Alzheimer’s Disease (AD).

Methods

This study received approval from the Lithuanian Bioethics Committee. All participants were older than 50 years (mean age = 66.52 (±8.76) years). The study sample consisted of 295 individuals: 117 with severe depression, 85 with mild-moderate Alzheimer’s disease (AD), and 94 age, gender and education matched participants of control group.

Results

The ACE-R had high sensitivity (100%) and specificity (81%) at detecting cognitive impairments related to AD. Patients with late-life onset depression (ACE-R mean 76.82, SD = 7.36) performed worse than controls (ACE-R mean 85.08, SD = 7.2), but better than the AD group (ACE-R mean 54.74, SD = 12.19). Participants with late-life onset depression were differentiated by mild impairment in the ACE-R total score with mild memory (13.79, SD = 6.29) and greater deficits in letter fluency (3.65, SD = 1.21) than in semantic fluency (4.68, SD = 1.23). Participants with AD were differentiated by severely impaired performance on attention and orientation (11.80, SD = 2.93), memory (8.25, SD = 3.47) and language subtests (17.21, SD = 4.04), and moderately impaired performance on verbal fluency (6.07, SD = 2.74).

Conclusions

ACE-R has diagnostic accuracy in detecting people with AD and can be used in differential diagnostics of late-life onset depression (severe episode) and AD. Diagnostic accuracy may be improved by analyzing the neuropsychological profiles and using lower cutoffs for different age groups.

Peer Review reports

Background

The development of novel treatments for AD, aimed at ameliorating symptoms and modifying disease processes are likely to be the the most successful early in the disease, which increases the need for early diagnosis [1]. The early differential diagnosis between Alzheimer’s disease (AD) and late-life-onset depression remains a diagnostic challenge in neurology and psychiatry [2]. Patients presenting with combinations of cognitive, affective, and behavioral problems pose a clinical conundrum. In some cases, it is difficult to establish whether the cognitive impairment is secondary to an affective disorder, or to organic dementing process. A small proportion of depressed individuals present with significant cognitive dysfunction, formerly known as depressive pseudodementia, also termed “functional dementia”, “memory disorder in the context of depressive illness”, or “the dementia syndrome of depression” [3]. Accurate diagnosis is difficult in older adults for several reasons: patient and family members may give confusing and conflicting information; depression and dementia may be attributed to the normal effects of aging; dementia and depression often present as co-morbid conditions [3,4]. In clinical practice, differential diagnosis of severe depression from early dementia remains difficult, which leads to misdiagnosis of severe depression as early dementia.

The differential diagnosis of early stage AD and late life onset severe depression is especially difficult, because these diseases greatly overlap in cognitive impairments. Mild AD is characterized in the early stages by deficits in episodic memory [5], which are particularly clear on tasks requiring learning and retention of either verbal or nonverbal information [6,7]. However, patients with severe depression also tend to perform poorly on both verbal and nonverbal memory tests [8-10]. To complicate matters even further, difficult verbal episodic memory tasks such as recall tasks often fail to discriminate accurately severe depression patients from mild AD patients [11].

One important aspect of the clinical assessment of cognitive impairment in depression should be identifying cognitive screening instruments that differentiate the cognitive deficits most characteristic of depression from those that are most likely to reflect AD [12]. Unfortunately, there are few empirical data on screening measures that effectively discriminate between the cognitive presentations of AD and depression. In a study that illustrates the diagnostic problem, the Short Cognitive Evaluation Battery was found to demonstrate 94% sensitivity and 85% specificity for discriminating AD from non-demented and non-depressed controls, but there was only 63% sensitivity with 96% specificity discriminating AD from individuals with depression symptoms [13]. Similar issues of test insensitivity in discriminating depression are present in the Mini-Mental State Examination [14], which is widely used to estimate the severity of cognitive impairment, but which is less sensitive to milder cognitive impairment that might be expected in depression because of a low ceiling of difficulty, narrow range of cognitive abilities assessed, and differential sensitivity to age, education and ethnicity [15]. Based on current evidence, there are no screening measures that are sufficiently valid for distinguishing among depression and AD in a clinical setting, and this is even more of an issue when depression and cognitive impairment occur together. There is a need for multidimensional and easily accessible dementia screening tools that would accurately identify people suffering from AD and differentiate them from those suffering from depression.

The Addenbrooke‘s Cognitive Examination – Revised (ACE-R) is a brief cognitive dementia screening test battery recently adapted to Lithuanian population, which could be recommended as the most appropriate tool for dementia screening and possibly differential diagnosis from depression. It has been reported that the ACE-R could discriminate the cognitive dysfunctions due to depression from that due to dementia [16], although this is not uniform in all cultural backgrounds [17]. We argue that ACE-R can be used to differentiate depression from AD. For the ACE-R to be in differential diagnostics of depression and AD, culture specific cutoff scores have to be adjusted [18-20]. The Lithuanian language is different from English in the length of its words. Lithuanian words have more syllabeles than their English equivalents. This could explain the lower cut-off poins for the ACE-R in the Lithuanian-speaking population. In the validation study of version of the ACE-R in the Lithuanian-speaking population we identified a lower cuf-off score of 74 for the detection of dementia [21]. When the lower cut-off score of 74 was used, the sensitivity of the ACE-R to detect dementia was 91%. We argue that the Lithuanian version of ACE-R with a lower cut-off score of 74 for dementia could be used for differential diagnosis of AD and depression. Our study sought to investigate the ability of the ACE-R to accurately differentiate mild-moderate AD from severe depression.

Methods

Participants

We recruited the following participants: 85 participants with early mild-moderate AD, 117 participants with late-life onset depression (with severe episode), and 94 healthy controls. Consecutive referrals to the Neurology Department of the Vilnius University Hospital Santariskiu Clinics were screened for possible inclusion into the study. Outpatients with late-life onset depression (with severe episode) fulfilling the criteria for participation in our study were recruited from the Vilnius City Mental Health Centre. The inclusion criteria for all the groups are displayed in Table 1. Participants were excluded from the study, if they had a concurrent degenerative CNS disease (for example, Parkinson’s disease) or other primary nervous system diseases (for example, epilepsy), an acute stroke, primary psychiatric disorder (for example, schizophrenia), clinically significant kidney or liver disease, thyroid dysfunction or vitamin B12 deficiency. All participants were between 50 and 88 years old at the time of recruitment and were well matched for age, sex, and education. All participants had at least 4 years of education. Spouses or friends of the participating participants were recruited as healthy controls. All participants had sufficient knowledge of Lithuanian language to participate in the study. The majority of control participants were able to perform all of the tasks in the test. Participants who had visual problems were asked to wear glasses. None of the participants had severe hearing or other sensory impairments. The study and informed consent form was approved by the Lithuanian Bioethics Committee. Written informed consent for participation in the study was obtained from all participants.

Table 1 Inclusion criteria for the participant recruitment

Instrument

Addenbrooke’s Cognitive Examination-Revised (ACE-R)

The ACE-R is a brief, 15–20-min test battery originally designed to detect and classify different kinds of dementia, particularly AD and frontotemporal dementia, without the use of specialized test equipment [22]. The ACE-R takes between 12 and 20 min (average 16) to administer and score in a clinical setting. It contains 5 subtests, each one representing one cognitive domain: attention/orientation (18points), memory (26 points), fluency (14 points), language (26 points) and visuospatial (16 points). ACE-R maximum score is 100, composed by the addition of the all subtests.

Statistical analysis

The statistical analysis was carried out with the SPSS for Windows package. The possible influence of demographic factors (age, gender and education) on the ACE-R scores was investigated. A general linear regression model was formed in order to test whether gender, age and education have an effect on the ACE-R test scores. To analyze the extent of utility of the ACE-R scores in prediction of the presence or absence of clinical diagnosis the binominal logistic regression analysis was used. We applied a receiver operating characteristic curve (ROC) analysis to examine the sensitivity and specificity of our measures. One-way ANOVAs and Mann–Whitney U-tests were carried out to compare the relevant group means of performance on the ACE-R test. The Chi square test was used to compare relevant group frequencies.

Results

Demographics

Demographic characteristics of the patient and control groups are summarized in Table 2. The groups were matched on age (one-way ANOVA, F[2, 293] = 0.154; p = 0.857), years of education (one-way ANOVA, F[2, 293] = 1.376; p = 0.254) and gender (χ2, p = 0.663).

Table 2 Demographic characteristics of the patient and control groups

To evaluate whether the demographic variables had an effect on performance on the ACE-R test scores, we formed general linear regression models for the patient and control groups. In AD group neither age (F = 1.288; Beta = −0.178; p = 0.110), nor gender (F = 7.588; Beta = −0.034; p = 0.759) nor education (F = 3.366; Beta = 0.149; p = 0.182) had an effect on ACE-R scores. In depression group both age had an effect (F = 12.111; Beta = −0.321; p < 0.001), while education (F = 0.339; Beta = −0.113, p = 0.211) and gender did not (F = 0.200; Beta = −0.061; p = 0.499). In control group both age (F = 2.174; Beta = −0.387; p < 0.001) and education (F = 2.869; Beta = 0.454; p < 0.001) had an effect, while gender did not (F = 0.101; Beta = 0.025; p = 0.769).

ACE-R clinical utility

We carried out a logistic-regression analysis with two target variables: patients with AD group versus no-AD group (depression and healthy controls). The total ACE-R score correctly classified 93.9% of the cases.

We carried out a ROC analysis with two target variables: patients with AD group versus no-AD group (depression and healthy controls). The trade-off between sensitivity (true positive rate) and 1–specificity (false positive rate) of the ACE-R in diagnosing AD in a patient population with and without a later confirmed AD dementia is shown in the ROC curve in Figure 1. The area under the ROC curve is 0.977, which suggests that the ACE-R has a high specificity for a large range of sensitivities. At 74, the previously recommended cut-off score [21] for clinical use in the detection of dementia, the ACE-R showed a sensitivity of 81%, and a specificity of 100% for AD in our study.

Figure 1
figure 1

Receiver Operating Characteristics (ROC) of the Addenbrooke’s Cognitive Examination-Revised (ACE-R) as a Test for AD. Note. The blue line is the ROC curve, the black line is the diagonal line.

ACE-R performance in participants with depression, AD and control groups

The mean scores on the ACE-R of the patient and control groups are shown in Table 3. A one-way ANOVA showed a significant between-group difference on ACE-R. To explore this further, we carried out post-hoc pairwise comparisons. The AD groups showed significant impairment relative to the control group (Bonferroni p < 0.001). The AD group showed significant impairment relative to depression group (Bonferroni; p < 0.001). The depression group showed significant impairment relative to control group (Bonferroni; p < 0.001). Participants with AD tend to fail the ACE-R (score below the recommended cut-off of 74 points) significantly more often than participants with severe depression or controls (chi square; p < 0.001). In addition, more participants with depression fail ACE-R than controls (chi square; p < 0.001). In the AD group 100% of participants fell below the cut-off score, in the depression group – 23.9%, in the control group – 8.5%.

Table 3 Addenbrooke’s Cognitive Examination - Revised (ACE-R) and its subtests means, and standard deviations in patient and control groups (in parenthesis SD)*

Because the depression group was likely to score statistically significantly lower than the control group, we ran a series of ANOVAs to test for differences between group performances in each of the ACE-R subtests. To explore this further, we carried out post-hoc pairwise comparisons. The AD groups showed significant impairment relative to the control group (Bonferroni p < 0.001). The impairment was severe, because the AD group scored in the range of more than three standard deviations below performance of controls. Also, the AD group showed significant impairment relative to depression group (Bonferroni; p < 0.001). The depression group showed no significant impairment relative to control group (Bonferroni; p = 0.067). The AD groups showed significant impairment relative to the control group (Bonferroni p < 0.001). The impairment was severe, because the AD group scored in the range of more than three standard deviations below performance of controls. Also, the AD group showed significant impairment relative to depression group (Bonferroni; p < 0.001). The depression group showed significant impairment relative to control group (Bonferroni; p < 0.001). The impairment was considered mild, because the depression group scored in the range of one to two standard deviations below the controls. The AD groups showed significant impairment relative to the control group (Bonferroni p < 0.001). The impairment was moderate, because the AD group scored in the range of two to three standard deviations below performance of controls. Also, the AD group showed significant impairment relative to depression group (Bonferroni; p < 0.001). The depression group showed significant impairment relative to control group (Bonferroni; p < 0.001). The impairment was considered mild, because the depression group scored in the range of one to two standard deviations below the controls. The AD groups showed significant impairment relative to the control group (Bonferroni p < 0.001). The impairment was mild, because the AD group scored in the range of two to three standard deviations below performance of controls. Also, the AD group showed significant impairment relative to depression group (Bonferroni; p < 0.001). The depression group showed significant impairment relative to control group (Bonferroni; p < 0.001). The impairment was considered mild, because the depression group scored in the range of one to two standard deviations below the controls. To explore this further, we carried out post-hoc pairwise comparisons. The AD groups showed significant impairment relative to the control group (Bonferroni p < 0.001). The impairment was severe, because the AD group scored in the range of more than three standard deviations below performance of controls. Also, the AD group showed significant impairment relative to depression group (Bonferroni; p < 0.001). The depression group showed no significant impairment relative to control group (Bonferroni; p = 1). To explore this further, we carried out post-hoc pairwise comparisons. The AD groups showed significant impairment relative to the control group (Bonferroni p < 0.001). The impairment was mild, because the AD group scored in the range of one to two standard deviations below performance of controls. Also, the AD group showed significant impairment relative to depression group (Bonferroni; p < 0.001). The depression group showed no significant impairment relative to control group (Bonferroni; p = 1).

Because we found mild impairments of orientation and attention, memory and verbal fluency subtests in the depression group, we compared the differences between the groups on memory and verbal fluency tasks. We used Mann–Whitney U-test to compare ACE-R performance of AD and depression groups. A series of Mann–Whitney U-tests revealed significant mean differences (Table 4) between the groups for all memory tasks. We ran a series of ANOVAs to test for differences between group performances in each of the verbal fluency subtest tasks. A one-way ANOVA showed a significant between-group difference on the letter fluency task (Table 5). The AD groups showed significant impairment relative to the control group (Bonferroni p < 0.001). Also, the depression group showed significant impairment relative to the control group (Bonferroni; p < 0.001). The AD group showed no significant impairment relative to the depression group (Bonferroni; p = 1). A one-way ANOVA showed a significant between-group difference on the category fluency task (Table 5). The AD groups showed significant impairment relative to the control group (Bonferroni p < 0.001). Also, the AD group showed significant impairment relative to the depression group (Bonferroni; p < 0.001). The depression group showed no significant impairment relative to the control group (Bonferroni; p < 0.001).

Table 4 Means and standard deviations of memory subtest tasks in the AD and depression groups (in parenthesis SD)
Table 5 Means and standard deviations of verbal fluency tasks in the subject groups (in parenthesis SD)*

Because analysis of the neuropsychological profiles’ of depression and AD showed that these diseases have distinct neuropsychological impairments on the ACE-R, we invenstigated, how diagnostic accuracy may be improved by analyzing the neuropsychological profiles. A logistic-regression analysis using the domain scores of orientation, attention, category fluency, memory and language of the ACE-R again to predict membership in the target groups with and without a progressive-degenerative dementia indicated a satisfactorily high proportion, 97.8%, of the observed cases correctly predicted. A higher proportion of observed cases were correctly by using the identified domain scores.

Discussion

The earlier studies established the Lithuanian version of Addenbrooke’s Cognitive Examination-Revised to be a sensitive and reliable tool to detect cognitive decline due to organic pathology [21]. We have extended this work by investigating, how ACE-R can be used in differential diagnostics in depression and AD.

The demographic differences had no effect on the study results, because the AD, depression and control groups did not differ significantly by age, gender or education. In our study age had a significant influence on ACE-R performance in depression and control groups (in line with previous findings by Margeviciute et al., [21]), which again emphasizes the need for age-specific ACE-R norms. Having the importance of age on overall performance in mind, it appears to be worthwhile to consider establishing different ACE-R cutoff points for the young-old and the old-old groups in Lithuanian-speaking population later on in the result analysis, as had been done in the Pigliautille and colleagues’ [23] adaptation of ACE-R. Future research in this field is needed to test this hypothesis.

In this study we have shown ACE-R to be a useful tool, which with high accuracy was able to detect participants with cognitive deficits due to AD and differentiate them from participants with late-life onset severe depression or healthy controls. The ACE-R displayed high clinical utility (high sensitivity and specificity). Despite this, participants with depression were likely to have a lower score in ACE-R than the control group. This pertained that participants with depression were statistically significantly more likely than the healthy controls to be identified as having dementia with ACE-R. This way we have replicated in our sample the problem of dementia-depression differentiation that is common in clinical practice. This led to a further analysis of the ACE-R subtests scores, how depression influenced the performance on ACE-R.

Further analysis of the neuropsychological profiles’ of depression and AD showed that these diseases have distinct neuropsychological impairments on the ACE-R. AD was characterized by severely impaired performance on attention and orientation, memory and language subtests, and moderately impaired performance on verbal fluency subtest. Meanwhile, mild impairment in the total ACE-R score, along with a low score on the memory and verbal fluency subtest tasks, characterized participants with depression. Memory and verbal fluency impairments found in depression are distinct from those found in AD. Memory deficits in AD group are different from those found in depression group. AD was characterized by more severe impairment of category fluency, while the depression was characterized by more severe selective impairment of letter fluency.

Considering the neuropsychological profile of the AD group on the ACE-R, it is consistent with the research on cognitive decline in early AD. Memory decline is the commonest complaint of participants and, more often, of their caregivers in AD. This is most commonly seen in the domain of anterograde episodic memory, that is the encoding storage, retention, and recall of new information about day-to-day personal experiences, which are accompanied by mild impairments in retrograde memory with a temporal gradient such that more distant memories are the most intact [24,25]. Consistent with previous research, we found accompanying deficits in attentional mechanisms, language and category fluency [25-27]. Considering visuospatial abilities in AD, visuoperceptual and visuospatial deficits are seldom clinically evident in the early stages of AD, with the notable exception of those participants who present with visual agnosia, the visual variant of AD [28]. This explains the occasionally found mild visuospatial deficits in AD group.

Considering the neuropsychological profile of depression, the results are mostly consistent with previous research. As expected memory impairment in AD seems to be more severe than in depression [29]. Memory tasks in the ACE-R could discriminate AD from depression. Although participants with late-life onset severe depression tended to score significantly lower than controls, they are likely to score statistically significantly higher than participants with AD. Moving to the verbal fluency findings, the depression group was characterized by letter fluency deficits and unimpaired semantic fluency, while in AD group category fluency deficits were significantly more severe than letter fluency deficits. Semantic memory impairments are detected in mild-moderate AD. On tests of verbal fluency, category fluency performance of patients with AD is more impaired than letter fluency, indicating difficulty accessing the semantic lexicon of word meanings [27]. In comparison to the extensive literature of letter- and category-based fluency in AD, relatively little attention has been paid to verbal fluency in affective disorders. Some researchers have found no significant difference between depressed and normal-comparison subjects [30], whereas others have reported impairment [31,32]. Studies consistent with our research have reported impaired letter but unimpaired semantic fluency in depressed elderly subjects [33], but more research is needed.

Although participants scoring below the cut-off score for dementia on ACE-R are likely to have AD not depression, we argue that these disorders should be differentiated by analyzing the neuropsychological profile of impairments. Mild impairment in the ACE-R total score with mild memory and greater deficits in letter fluency than in category fluency is indicative of depression not AD. Meanwhile, severe attention and orientation, memory and language deficits, greater deficits in semantic fluency than in letter fluency, and occasionally found mild impairments in visuospatial abilities are features attributable to AD.

Conclusions

ACE-R has diagnostic accuracy in detecting people with AD and can be used in differential diagnostics of depression and AD. When interpreting the results it is important to compare the overall pattern of performance across all five subtests to verify the diagnosis, because depression and AD have distinct neuropsychological profiles. AD was characterized by severely impaired performance on attention and orientation, memory and language subtests, and moderately impaired performance on verbal fluency subtest. Meanwhile, mild impairment in the total ACE-R score, along with a low score on the memory and verbal fluency subtest tasks, characterized participants with depression. Memory and verbal fluency impairments found in depression are distinct from those found in AD. Memory deficits in depression are less severe than in AD, letter fluency is more impaired than category fluency, while in AD category fluency is more impaired than letter fluency. To sum up, ACE-R can be used in differential diagnostics of AD and depression.

References

  1. Ritchie CW, Ames D, Clayton T, Lai R. Metaanalysis of randomized trials of the efficacy and safety of donepezil, galantamine and rivastigmine for the treatment of Alzheimer disease. Am J Geriatr Psychiatry. 2004;12:358–69.

    Article  PubMed  Google Scholar 

  2. Potter G, Steffens D. Contribution of depression to cognitive impairment and dementia in older adults. Neurologist. 2007;13:105–17.

    Article  PubMed  Google Scholar 

  3. DesRosiers G. Primary or depressive dementia: hedonic tone and memory. Ph.D. dissertation, University of Cambridge, Cambridge, UK, 1994.

  4. Leplaire J, Buntinx G. The link between depression and dementia in the elderly. Clin Geriatrics. 1999;15:82–7.

    Google Scholar 

  5. Grober E, Dickson D, Sliwinski MJ, Buschke H, Katz M, Crystal H, et al. Memory and mental status correlates of modified Braak staging. Neurobiol Aging. 1999;20(6):573.

    Article  CAS  PubMed  Google Scholar 

  6. Dudas RB, Clague F, Thompson SA, Graham KS, Hodges JR. Episodic and semantic memory in mild cognitive impairment. Neuropsychologia. 2005;43(9):1266–76.

    Article  CAS  PubMed  Google Scholar 

  7. Perry RJ, Watson P, Hodges JR. The nature and staging of attention dysfunction in early (minimal and mild) Alzheimer’s disease: Relationship to episodic and semantic memory impairment. Neuropsychologia. 2000;38(3):252–71.

    Article  CAS  PubMed  Google Scholar 

  8. Abas MA, Sahakian BJ, Levy R. Neuropsychological deficits and CT scan changes in nine elderly depressives. Psycholl Med. 1990;20(3):507–20.

    Article  CAS  Google Scholar 

  9. Burt DB, Zembar MJ, Niederehe G. Depression and memory impairment: A meta-analysis of the association, its pattern, and specificity. Psychol Bull. 1995;117(2):285–305.

    Article  CAS  PubMed  Google Scholar 

  10. Jean L, Simard M, van Reekum R, Clarke DE. Differential cognitive impairment in subjects with geriatric depression who will develop Alzheimer’s disease and other dementias: A retrospective study. Int Psychogeriatr. 2005;17(2):289–301.

    Article  PubMed  Google Scholar 

  11. O'Carroll RE, Conway S, Ryman A, Prentice N. Performance on the delayed word recall (DWR) fails to differentiate clearly between depression and Alzheimer’s disease. Psychol Med. 1997;27(4):967–71.

    Article  PubMed  Google Scholar 

  12. Steffens DC, Potter GG. Geriatric depression and cognitive impairment. Psychol Med. 2008;38(2):163–75.

    Article  CAS  PubMed  Google Scholar 

  13. Robert PH, Schuck S, Dubois B, Olie JP, Lepine JP, Gallarda T, et al. Screening for Alzheimer’s disease with the short cognitive evaluation battery. Dement Geriatr Cogn Disord. 2003;15:92–8.

    Article  PubMed  Google Scholar 

  14. Folstein M, Folstein S, Fanjiang G. Mini-Mental State Examination: Clinical Guide and User’s Guide. Lutz, FL: Psychological Assessment Resources; 2001.

    Google Scholar 

  15. Tombaugh TN, McIntyre NJ. The Mini-Mental State Examination: a comprehensive review. J Am Geriatr Soc. 1992;40:922–35.

    Article  CAS  PubMed  Google Scholar 

  16. Larner AJ. Dementia in Clinical Practice: A Neurological Perspective. In: Pragmatic Studies in the Cognitive Function Clinic. 2nd ed. London: Springer; 2014. p. 168–9.

    Google Scholar 

  17. Stokholm J, Vogel A, Johannsen P, Waldemar G. Validation of the Danish Addenbrooke’s Cognitive Examination as a screening test in a memory clinic. Dement Geriatr Cogn Disord. 2009;27(4):361–5.

    Article  PubMed  Google Scholar 

  18. Larner AJ. An audit of the Addenbrooke’s Cognitive Examination (ACE) in clinical practice: Longitudinal change. Int J Geriatr Psychiatry. 2006;21:698–9.

    Article  CAS  PubMed  Google Scholar 

  19. Garcia-Caballero A, Garcia-Lado I, Gonzalez-Hermida J, Recimil M, Area R, Manes F, et al. Validation of the Spanish version of the Addenbrooke’s Cognitive Examination in a rural community in Spain. Int J Geriatr Psychiatry. 2006;21:239–45.

    Article  CAS  PubMed  Google Scholar 

  20. Larner AJ. Addenbrooke’s Cognitive Examination-Revised (ACE-R) in day-to-day clinical practice. Age Ageing. 2007;36:685–6.

    Article  CAS  PubMed  Google Scholar 

  21. Margevičiūtė R, Bagdonas A, Butkus K, Kuzmickienė J, Vaitkevičius A, Kaubrys GF, et al. Adenbruko kognityvinio tyrimo metodikos – taisytos adaptacija lietuviškai kalbantiems gyventojams (ACE-RLT). Neurologijos seminarai. 2013;1(55):29–51.

    Google Scholar 

  22. Mioshi E, Dawson K, Mitchell J, Arnold R, Hodges JR. The Addenbrooke’s Cognitive Examination Revised (ACE-R): a brief cognitive test battery for dementia screening. Int J Geriatr Psychiatry. 2006;21(11):1078–85.

    Article  PubMed  Google Scholar 

  23. Pigliautile M, Ricci M, Mioshi E, Ercolani S, Mangialasche F, Monastero R, et al. Validation Study of the Italian Addenbrooke’s Cognitive Examination Revised in a Young-Old and Old-old Population. Dement Geriatr Cogn Disord. 2011;32(5):301–7.

    Article  CAS  PubMed  Google Scholar 

  24. Overman AA, Becker JT. Information processing defects in episodic memory in Alzheimer’s disease. In: Morris RG, Becker JT, editors. Cognitive Neuropsychology of Alzheimer’s Disease. 2nd ed. Oxford: Oxford University Press; 2004. p. 121–40.

    Google Scholar 

  25. Bright P, Kopelman MD. Remote memory in Alzheimer’s disease. In: Morris RG, Becker JT, editors. Cognitive Neuropsychology of Alzheimer’s Disease. 2nd ed. Oxford: Oxford University Press; 2004. p. 141–51.

    Google Scholar 

  26. Parasuraman R. Attentional functioning in Alzheimer’s disease. In: Morris RG, Becker JT, editors. Cognitive Neuropsychology of Alzheimer’s Disease. 2nd ed. Oxford: Oxford University Press; 2004. p. 81–102.

    Google Scholar 

  27. Kertesz A. Language in Alzheimer’s disease. In: Morris RG, Becker JT, editors. Cognitive Neuropsychology of Alzheimer’s Disease. 2nd ed. Oxford: Oxford University Press; 2004. p. 197–218.

    Google Scholar 

  28. Levine DN, Lee JM, Fisher CM. The visual variant of Alzheimer’s disease: a clinicopathologic case study. Neurology. 1993;43:305–13.

    Article  CAS  PubMed  Google Scholar 

  29. Christensen H, Griffiths K, Mackinnon A, Jacomb P. A quantitative review of cognitive deficits in depression and Alzheimer-type dementia. JINS. 1997;3:631–51.

    CAS  PubMed  Google Scholar 

  30. Fossati P, Deweer B, Raoux N, Allilaire JF. Deficits in memory retrieval: an argument in favour of frontal subcortical dysfunction in depression. Encéphale. 1995;21:295–305.

    CAS  PubMed  Google Scholar 

  31. Geffen G, Bate A, Wright M, Rozenbilds U, Geffen L. A comparison of cognitive impairments in dementia of the Alzheimer type and depression in the elderly. Dementia. 1993;4:294–300.

    CAS  PubMed  Google Scholar 

  32. Kuzis G, Sabe L, Tiberti C, Leiguarda R, Starkstein SE. Cognitive functions in major depression and Parkinson disease. Arch Neurol. 1997;54:982–6.

    Article  CAS  PubMed  Google Scholar 

  33. Ravdin LD, Katzen HL, Agrawal P, Relkin NR. Letter and semantic fluency in older adults: effects of mild depressive symptoms and age-stratified normative data. Clin Neuropsychol. 2003;17:195–202.

    Article  CAS  PubMed  Google Scholar 

  34. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA work group under the auspices of the Department of Health and Human Services task force on Alzheimer’s disease. Neurology. 1984;34(7):939–44.

    Article  CAS  PubMed  Google Scholar 

  35. Folstein MF, Folstein SE, McHugh PR. “Mini-Mental State:” a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–98.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We would like to thank all patients and controls for their participation.

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Corresponding author

Correspondence to Augustinas Rotomskis.

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The authors declare that they have no competing interests.

Authors’ contributions

AR substantially contributed to the study concept and design, acquisition of data, analysis and interpretation of data and drafting of the manuscript. RM substantially contributed to the study concept and design, analysis and interpretation of data and critical revision of the manuscript. AG substantially contributed to the critical revision of the manuscript. GK substantially contributed to the study concept and design, analysis and interpretation of data and critical revision of the manuscript. VB substantially contributed to the critical revision of the manuscript. AB substantially contributed to the analysis and interpretation of data and critical revision of the manuscript. All authors read and approved the final manuscript.

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Rotomskis, A., Margevičiūtė, R., Germanavičius, A. et al. Differential diagnosis of depression and Alzheimer’s disease with the Addenbrooke's Cognitive Examination-Revised (ACE-R). BMC Neurol 15, 57 (2015). https://doi.org/10.1186/s12883-015-0315-3

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