Skip to main content
Download PDF

Clinical validity of the Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB) in Mild Cognitive Impairment and Alzheimer’s Disease

Alzheimer's Research & Therapy volume 16, Article number: 98 (2024) Cite this article

Abstract

Background

The identification and staging of Alzheimer’s Disease (AD) represent a challenge, especially in the prodromal stage of Mild Cognitive Impairment (MCI), when cognitive changes can be subtle. Worldwide efforts were dedicated to select and harmonize available neuropsychological instruments. In Italy, the Italian Network of Neuroscience and Neuro-Rehabilitation has promoted the adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB), collecting normative data from 433 healthy controls (HC).

Here, we aimed to explore the ability of I-UDSNB to differentiate between a) MCI and HC, b) AD and HC, c) MCI and AD.

Methods

One hundred thirty-seven patients (65 MCI, 72 AD) diagnosed after clinical-neuropsychological assessment, and 137 HC were included. We compared the I-UDSNB scores between a) MCI and HC, b) AD and HC, c) MCI and AD, with t-tests. To identify the test(s) most capable of differentiating between groups, significant scores were entered in binary logistic and in stepwise regressions, and then in Receiver Operating Characteristic curve analyses.

Results

Two episodic memory tests (Craft Story and Five Words test) differentiated MCI from HC subjects; Five Words test, Semantic Fluency (vegetables), and TMT-part B differentiated AD from, respectively, HC and MCI.

Conclusions

Our findings indicate that the I-UDSNB is a suitable tool for the harmonized and concise assessment of patients with cognitive decline, showing high sensitivity and specificity for the diagnosis of MCI and AD.

Background

The identification and staging of individuals with Alzheimer’s Disease (AD) still represent a major challenge for clinicians, especially in the prodromal stage of Mild Cognitive Impairment (MCI), when cognitive changes can be subtle and arduous to detect [1]. Along with biomarker positivity, a specific neuropsychological profile is of cardinal importance for diagnosing the disease and tracking its progression over time [2]. According to current criteria, individuals receive a diagnosis of dementia when the impairment affects at least two cognitive domains and interferes with daily living [3]. Conversely, a diagnosis of MCI is proposed when individuals manifest concerns about their changes in cognition (respect to a previous level of functioning), show mild deficits affecting one or more cognitive domains, but are otherwise independent on daily functional activities [4].

The use of validated neuropsychological measures, assessing episodic memory and other cognitive domains, is recommended for a diagnosis of MCI [4]. Notably, combining neuropsychological tests tapping different cognitive domains provides richer information compared to the use of a single test. For instance, the combined use of episodic memory measures with semantic fluency [5], or executive functions tests [6], or language, executive functions, and visuo-perceptual tests [7], achieves high sensitivity and specificity in predicting the conversion to AD. In this vein, several efforts have been dedicated to survey, select, and harmonize the available neuropsychological instruments to identify MCI subjects and AD patients. This goal has been pursued by several initiatives, for example in Europe [8], China [9], and U.S.A., in the latter case for both English and Spanish speaking individuals [10,11,12,13]. In particular, in the U.S.A., the Uniform Data Set initiative of the National Alzheimer's Coordinating Center [14] has led to the development and validation of a neuropsychological battery (UDSNB), now in its 3rd version [12]. In Italy, a recent initiative of the Virtual Dementia Institute of the Italian Network of Neuroscience and Neuro-Rehabilitation (RIN) has promoted the translation and adaptation of the UDSNB. The Italian version of the battery (I-UDSNB) is now available, together with normative data collected from 433 healthy individuals [15]. The battery is administrable via a tablet-based application and consists of tests covering different cognitive domains, i.e., memory, attention, language, executive functions, and visuospatial skills.

To foster the use of the I-UDSNB in clinical and research settings it is of pivotal importance to ascertain its clinical validity in staging the continuum between normal cognition, MCI, and AD. Previous studies have addressed the clinical validity of the UDSNB in MCI and AD cohorts, taking into account only some tests, such as the picture naming test [16, 17] or considering composite measures, such as an executive functioning score [18], and the global score [19].

The current multicenter Italian initiative was intended to explore the usefulness of the I-UDSNB to differentiate MCI and mild AD subjects from healthy individuals, as well as MCI from mild AD patients. To assess the diagnostic value of the I-UDSNB, we compared the raw test scores for the listed comparisons by means of a series of t-tests. Significant scores were entered in binary logistic regression models and in stepwise regressions to identify the test(s) most capable of differentiating between groups. For the scores emerging as significant in the previous analysis, we then calculated the Area Under the Curve (AUC), sensitivity, specificity, and cut-off values using Receiver Operating Characteristic (ROC) curve analyses.

The identification of the neuropsychological test(s) with the best diagnostic performance may offer noteworthy insights for the diagnosis, management, and monitoring of these patients in Memory Clinics.

Methods

Participants

A sample of 137 patients were recruited in thirteen Italian Hospitals belonging to the RIN network. Specifically, 65 subjects had received a diagnosis of MCI and 72 patients were diagnosed as mild probable AD. The diagnosis was formulated by expert clinicians in each center and was based on core clinical criteria [3, 4]. Data from the I-UDSNB were not used for diagnosis, and all the subjects were previously administered a comprehensive neuropsychological examination according to local practice.

Participants were excluded if they had prior/current cerebrovascular disorders; a history of traumatic brain injury, brain tumors, stroke; concomitant medical, sensory and/or motor deficits possibly affecting the performance; a history of alcohol and/or drug abuse; use of medications influencing cognitive functions. As we were specifically interested in the diagnostic usefulness of the test in the prodromal and early dementia stage, participants with a performance in the MMSE below 20 (score corrected for age and education) were excluded [20].

Biomarker information was available for 107 patients, specifically 47 with MCI and 60 with AD, and was collected by means of CSF in 67 cases (32 MCI, 35 AD), amyloid-PET in 36 cases (12 MCI, 24 AD), or both in 4 cases (3 MCI, 1 AD). In the case of PET data only a qualitative evaluation of amyloid positivity was available. For CSF data heterogeneity emerged in the measures considered and the reference values. Most of the centers assessed Aβ42, T-Tau, and P-Tau (in 8 centers each), Aβ42/Aβ40 was assessed in 5 centers, while other ratios between the aforementioned measures were collected in a minority of cases (i.e., 1 or 2 centers). Reference values were also variable. For Aβ42 normal reference values were > 600 pg/ml in 5 centers, > 640 pg/ml in 2 centers, while one center used both. For T-Tau normal reference values were < 404 pg/ml in 4 centers, < 580 pg/ml in 2 centers, < 450 pg/ml in 1 center, and > 275 pg/ml in 1 center. For P-Tau, reference values were < 56.50 pg/ml in most centers (n = 4), < 61 pg/ml in 2 centers, < 50 pg/ml and < 63 pg/ml in one center each. Finally, Aβ42/Aβ40 normal reference values were > 0.07 in all the centers. In total, 98.1% of patients with available biomarker information (i.e., 105 out of 107) showed positivity for AD, and specifically 95.7% (i.e., 45 out of 47) of MCI patients and 100% (i.e., 60 out of 60) of AD patients.

MCI and AD groups were matched for age (t(135) = 0.353, p = 0.725), years of education (t(135) = 0.366, p = 0.907), and sex (Chi-square = 0.118, p = 0.863). A lower score in the Mini-Mental State Examination (MMSE) was found in AD compared to MCI (t(135) = 7.795, p < 0.001). See Table 1 for the samples’ demographic information.

Table 1 Demographic information of the patients
Full size table

The study was approved by the local ethics committees in compliance with the provisions of the Declaration of Helsinki. All patients gave written informed consent to participate.

Data from 137 healthy controls (HC) from our previous norming study [15] were also included, divided into two groups (n = 65 and n = 72, respectively), and used for the comparison with MCI and AD groups (see below). The two groups of HC were matched between each other, and with the MCI and AD samples, respectively, for age, years of education, and sex (all p-values at least > 0.156).

I-UDSNB administration

All the participants were administered the I-UDSNB, encompassing tests assessing memory, language, executive functions, processing speed, and visuo-constructional abilities. The battery includes the following tests (in order of administration): Craft Story, Benson Figure (Copy, Recall), Digit Span Forward and Backward, Semantic Fluency, Trail Making Test A and B (TMT-part A, TMT-part B), Picture Naming, Phonemic Fluency, and a short encoding controlled cued recall test (Five Words Test). The administration of the I-UDSNB takes approximately 45 min and it is supported by a newly developed tablet-based application, aiding the experimenter in administration and scoring [15].

Data analysis

Data of TMT-part A and part B were excluded for 3 (1 AD and 2 MCI) and 10 patients (6 AD, 4 MCI), respectively, who were not able to perform the task, i.e. they were unable to follow task instructions.

The analyses proceeded according to three steps.

  1. 1.

    We assessed the differences between a) MCI and HC, b) AD and HC, and c) MCI and AD by comparing the raw scores in each test (n = 54 scores), using Mann–Whitney non parametric tests with Bonferroni correction (α = 0.05/54 = 0.0009). Non parametric tests were adopted because the majority of data were skewed and showed a non-normal distribution. A maximum of six significant scores in different tests with the highest effect size (Hedges' g) were selected for further analyses. We decided to keep six scores in order to approximately maintain a 1:10 ratio between the number of independent variables (i.e., the selected scores) and the number of observations per group in the regression models described below. The criterion of choosing scores belonging to different tests was intended to avoid multicollinearity.

  2. 2.

    The six significant scores were entered as predictors in a binary logistic regression, with the group membership as dependent variable, i.e., a) MCI and HC, b) AD and HC, and c) MCI and AD, respectively. Significant predictors were then used in a stepwise binary logistic regression. Specifically, they were entered following all the possible order combinations (e.g., first predictor A, then predictor B, and vice-versa), to ascertain whether each score would have significantly improved group membership prediction. Significant predictors were then retained for the third step.

  3. 3.

    Receiver Operating Characteristic (ROC) analyses were adopted to assess the ability of the scores selected from the previous analysis to differentiate between: a) MCI and HC, b) AD and HC, and c) MCI and AD, respectively. In order to facilitate the comparison between tests, whenever a higher score corresponded to a worse performance (e.g., TMT), this was inverted prior to the analysis. We evaluated the ability of the scores in performing the diagnostic classification using the Area Under the Curve (AUC). Significant AUC were used to estimate the optimal cut-off, calculated as the trade-off between sensitivity and specificity by means of the Youden Index (J) (i.e., sensitivity + specificity -1). We considered as acceptable the values of J above 0.5 [22], otherwise the cut-off was manually determined.

ROC analyses were also performed taking into account a composite measure obtained by summing the scores emerging as significant in point 1 described above. Whenever a higher score corresponded to a worse performance (e.g., TMT), this was inverted prior to entering the composite measure.

The same analyses were conducted taking into account only the sub-sample of patients showing biomarker positivity, i.e. 45 MCI and 60 AD patients. Biomarker positive patients and patients without biomarker information did not show any significant difference in demographic variables (age, years of education, and sex), in MMSE, and in the I-UDSNB tests scores; additionally, biomarker positive MCI and biomarker positive AD were matched between each other and with the respective control group for demographic variables (age, years of education, and sex).

Results

MCI vs HC

The six significant scores with the highest effect size unveiling differences between MCI and HC were: Craft Story recall paraphrase (g = 2.632), Five Words test delayed total weighted recall (g = 2.593), Benson figure recall (g = 1.896), Semantic Fluency total correct score (g = 1.412), TMT (part B-A) (g = 1.121), and Picture Naming total correct score (g = 1.002). After being entered in the binary logistic regression, only Craft Story recall paraphrase (B = -0.293, p-value = 0.008) and Five Words test delayed total weighted recall (B = -0.447, p-value = 0.014) could significantly predict group membership (i.e., MCI, HC). In the stepwise binary logistic regression both of them significantly contributed to the prediction and were consequently retained for the ROC analysis. The latter unveiled that Craft Story recall paraphrase and Five Words test delayed total weighted recall differentiated MCI from HC with high sensitivity (0.846 and 0.954, respectively) and specificity (0.908 and 0.831, respectively). See Fig. 1 for the results of the ROC analysis.

Fig. 1
figure 1

Results of the Receiver Operating Characteristic (ROC) analysis for the comparison between a MCI and HC, b AD and HC, and c MCI and AD; HC = healthy controls; MCI = Mild Cognitive Impairment; AD = Alzheimer’s Disease; AUC = area under the curve; N = Youden Index below the reference value of 0.5

Full size image

The composite measure also significantly differentiated between MCI and HC (AUC: 0.879, p-value < 0.001), with a sensitivity of 0.862 and a specificity of 0.803.

The analyses including only biomarker positive MCI patients identified the same six scores emerging in the main analysis and significantly differentiating patients and HC as suggested by the non parametric tests. When considering the single scores for the subsequent analyses, there were no significant results, with only a trend towards significance in the binary logistic regression analysis for Craft Story recall paraphrase (p-value = 0.077) and Five Words test delayed total weighted recall (p-value = 0.080). Conversely, the composite measure successfully differentiated between biomarker positive MCI patients and HC (AUC: 0.901, p-value < 0.001), with a sensitivity of 0.862 and a specificity of 0.881.

AD vs HC

The six significant scores with the highest effect size differentiating AD and HC were: Five Words test delayed total weighted recall (g = 3.788), Craft Story recall paraphrase (g = 2.661), Benson figure recall (g = 2.136), Semantic Fluency vegetables total correct score (g = 2.048), TMT (part B) (g = 1.623), and Picture Naming correct without cue score (g = 1.378). After being entered in the binary logistic regression, only Five Words test delayed total weighted recall (B = -1.126, p-value = 0.003), Semantic Fluency vegetables total correct score (B = -0.634, p-value = 0.019), and TMT (part B) (B = 0.020, p-value = 0.026), significantly predicted group membership (i.e., AD, HC). In the stepwise binary logistic regression all of them significantly contributed to the prediction and were used for the ROC analysis. The latter indicated that Five Words test delayed total weighted recall (sensitivity: 0.972 or 0.931, specificity: 0.917 or 0.958, see Fig. 1 for the respective cut-off value), Semantic Fluency vegetables total correct score (sensitivity: 0.861, specificity: 0.861), and TMT (part B) (sensitivity: 0.833, specificity: 0.803) successfully differentiated AD and HC. See Fig. 1 for the results of the ROC analysis.

The composite measure also significantly differentiated between AD and HC (AUC: 0.918, p-value < 0.001), with a sensitivity of 0.806 and a specificity of 0.894. The analyses taking into account only biomarker positive AD patients led to comparable results (all sensitivity and specificity values above 0.764).

MCI vs AD

Five scores significantly differentiated MCI and AD showing the highest effect size: TMT (part B) (g = 0.887), Semantic Fluency vegetables (correct < 30 s) (g = 0.878), Five Words total weighted recall (g = 0.768), Phonemic Fluency letter F (correct < 30 s) (g = 0.587), and Picture Naming correct without cue score (g = 0.577). After being entered in the binary logistic regression, TMT (part B) (B = 0.006, p-value = 0.003), Five Words total weighted recall (B = -0.135, p-value = 0.038), and Semantic Fluency vegetables (correct < 30 s) (B = -0.192, p-value = 0.043) could significantly predict group membership (i.e., MCI, AD). In the stepwise binary logistic regression all of them significantly contributed to the prediction and were thus retained for the ROC analysis. The latter suggested that TMT (part B), Five Words total weighted recall, and Semantic Fluency vegetables (correct < 30 s) differentiated MCI from AD with good sensitivity (0.639, 0.723, and 0.723, respectively) and specificity (0.773, 0.583, and 0.667, respectively). See Fig. 1 for the results of the ROC analysis.

The composite measure also significantly differentiated between MCI and AD (AUC: 0.749, p-value < 0.001), with a sensitivity of 0.639 and a specificity of 0.803.

The analyses including only biomarker positive patients suggested that Semantic Fluency vegetables total correct score (g = 0.924) and TMT (part B) (g = 0.779) differentiated MCI and AD with the highest effect size, and the regression analyses suggested that both significantly contributed to the group membership prediction. ROC analysis indicated that Semantic Fluency vegetables total correct score and TMT (part B) differentiated MCI from AD with acceptable sensitivity (0.689 and 0.619, respectively) and specificity (0.773 and 0.617, respectively). The composite measure also significantly differentiated between biomarker positive MCI and AD (AUC: 0.725, p-value < 0.001), with a sensitivity of 0.619 and a specificity of 0.839.

See Supplementary Information for the descriptive statistics of the HC, MCI, and AD groups in the I-UDSNB tests, the percentage of patients showing a pathological performance according to the cut-off established from the normative data, and the results of the non-parametric tests comparing a) MCI and HC, b) AD and HC, and c) MCI and AD.

Discussion

Our findings indicate high sensitivity and specificity of the I-UDS neuropsychological battery for the diagnosis of MCI and mild AD, and for differentiating between the two patients’ groups. The study sample had been diagnosed in 13 research hospitals of the Italian Ministry of Health Network of Neuroscience and Neuro-Rehabilitation across Italy, using the neuropsychological protocols currently adopted within each center. Our results suggest that the I-UDSNB is a suitable tool for the harmonized assessment of subjects with suspected cognitive impairment. Considering the referral diagnosis as the gold standard, all the probable AD patients had an impaired performance in at least one I-UDS subtest, and only 1 out of 65 MCI subjects had a profile fully within normal limits. As expected, the scores with the highest diagnostic value in the case of MCI individuals were those of verbal episodic memory tests. In the case of patients with a clinical diagnosis of AD dementia, tests evaluating semantic memory (semantic fluency) and executive functioning (TMT-part B), in association with a verbal episodic memory score, gave the highest contribution in differentiating AD from MCI and HC.

Our results fit well with the current literature, indicating that disturbances in episodic memory, namely in the ability to learn, store, and retrieve new information, represent the most common hallmark of MCI [23] and AD. Of note, verbal episodic memory performance is related to grey matter volume in medial temporal lobe regions, especially the entorhinal and hippocampal cortices [24, 25], known to show the earliest histological alterations during the course of AD [26, 27].

Despite the importance of episodic memory assessment, there is still a lack of consensus on which neuropsychological test is the most suitable instrument to diagnose MCI and AD. According to some proposals, the Free and Cued Selective Reminding Test (FCSRT) outperforms other verbal episodic memory tests, e.g., word list and paragraph recall, in differentiating MCI from AD [28], and in predicting amyloid positivity in MCI [29]. This superiority is advocated in virtue of two aspects characterizing the FCSRT, derived from the Grober-Buschke paradigm [30], namely the possibility to control for encoding and the presence of semantic cues during retrieval, thereby allowing to isolate and target the affected memory storage (versus retrieval) abilities. Amyloid status in MCI is indeed predicted by a diminished sensitivity to semantic cuing [31], which is progressively reduced as AD reaches the most advanced stages [32]. Nevertheless, the superiority of cued recall tests has been deemed as controversial [33] or nonexistent [34] by literature revisions. The latter work indeed concludes that all tests targeting verbal episodic memory, independently of material (e.g., word list vs paragraph) and test conditions (e.g., immediate vs delayed recall, presence vs absence of encoding and of cuing) show high sensitivity and specificity in predicting the progression from MCI to AD [34]. The discrepant results may be motivated by different factors, such as the heterogeneity of the patients’ groups, including disease severity as well as the qualitative profile of the impaired cognitive domains [33, 34]. In the current study, our results suggest that the Five Words test outperforms the logical memory test in the diagnostic classification of AD patients. Conversely, we found a comparable diagnostic performance of cued word recall (Five Words test) and of logical memory (Craft Story) in a population of MCI-core clinical criteria patients [4]. We suggest that the diagnostic superiority of cued word recall and its ability to isolate hippocampal damage are reduced in the case of a heterogeneous population of MCI patients defined on the basis of core clinical criteria (see, for example, [35]). Of note, when restricting the analyses to biomarker positive MCI individuals, even if a composite measure still differentiated between patients and controls, no significant results were found considering the single test scores, plausibly due to the reduced sample size as well as to the heterogeneity in the measures and the reference values used to assess biomarker positivity among centers.

Notwithstanding episodic memory deficits are usually the first and most prominent symptom, additional cognitive disturbances may be present from the beginning of the disease or may eventually appear and worsen during the progression of AD pathology to other brain structures [26]. This is clearly indicated by the high diagnostic value of semantic fluency and TMT-part B for the diagnosis of mild AD, and specifically to differentiate AD from both HC and MCI individuals.

For instance, impairments in semantic fluency have been reported in AD and usually ascribed to a deterioration of semantic knowledge [36, 37], as suggested by the associated hypometabolic pattern encompassing the temporal areas implicated in conceptual processing, e.g. the inferior temporal gyrus [38]. On the other hand, category fluency typically undergoes only a subtle decline in MCI [39, 40], and the underlying cause remains debated and attributed either to a deterioration of semantic knowledge [41], or to difficulties in lexical retrieval [42].

Additionally, in line with our results, deficits in TMT-part B have been previously reported in AD [43], fitting into the set of impairments that affect the multiple facets of executive functions, encompassing attention [44], working memory [45], inhibitory control, and cognitive flexibility [46]. These deficits have been suggested to correlate with the emergence of impairments in daily living [47], and to reflect tau deposition [48], as well as structural and functional alterations in fronto-parietal networks [44, 49]. In MCI, executive functions abilities may be compromised, albeit to a lesser extent than AD [44, 45], or preserved but associated with compensatory neural activations [50].

We acknowledge some limitations of this study. First, we included only mild AD patients and, although the I-UDSNB tests and the composite measures allowed to distinguish between the considered groups, the inclusion of AD patients with a more advance stage of dementia would have likely resulted in a better differentiation between MCI and AD. Moreover, heterogeneity emerged in the measures and reference values used to assess biomarker positivity across the centers, that may account for the absence of effects when restricting the analyses to biomarker positive MCI individuals, specifically when considering the individual tests scores. However, also in this case, a composite measure successfully differentiated between groups. An additional limitation pertains to the inclusion of a sample of healthy controls from which we previously obtained the normative data, which may represent a source of bias in the results and partially account for the overall high sensitivity and specificity in differentiating patients from HC. Finally, we have to mention that data on TMT-part A and B were excluded for 3 and 10 patients, respectively, thus overall reducing the sample size.

Conclusion

In conclusion, the current multicenter study speaks in favor of the validity of the I-UDSNB as a harmonized and concise battery, able to adequately diagnose both MCI and AD patients. Limitations for MCI diagnosis [19], possibly due to the use of a logical memory test as a unique assessment of verbal episodic memory, were likely overcome by the addition of a brief cued word recall test, allowing to achieve high diagnostic sensitivity and specificity.

Finally, this study represents a direct continuation of our previous work leading to the creation of I-UDSNB and the collection of normative data [15]. Future initiatives may be dedicated to the employment of the I-UDSNB to address the longitudinal tracking of cognitive change, and to design modules for other types of dementia, e.g. Lewy bodies dementia and frontotemporal lobar degeneration.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author after a reasonable request.

Abbreviations

AD:

Alzheimer’s Disease

MCI:

Mild Cognitive Impairment

UDSNB:

Neuropsychological Test Battery of the Uniform Data Set

I-UDSNB:

Italian adaptation of Neuropsychological Test Battery of the Uniform Data Set

RIN:

Italian Network of Neuroscience and Neuro-Rehabilitation

TMT:

Trail Making Test

MMSE:

Mini Mental State Examination

SD:

Standard deviation

References

  1. Jutten RJ, Thompson L, Sikkes SA, Maruff P, Molinuevo JL, Zetterberg H, Snyder PJ. A neuropsychological perspective on defining cognitive impairment in the clinical study of Alzheimer’s disease: Towards a more continuous approach. J Alzheimer’s Dis. 2022;86(2):511–24.

    Article  CAS  Google Scholar 

  2. Frisoni GB, Boccardi M, Barkhof F, Blennow K, Cappa S, Chiotis K, Winblad B. Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers. The Lancet Neurology. 2017;16(8):661–76.

  3. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Phelps CH. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):263–9.

    Article  PubMed  Google Scholar 

  4. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Phelps CH. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011;7(3):270–9.

    Article  Google Scholar 

  5. Gallagher D, Mhaolain AN, Coen R, Walsh C, Kilroy D, Belinski K, Lawlor BA. Detecting prodromal Alzheimer’s disease in mild cognitive impairment: utility of the CAMCOG and other neuropsychological predictors. Int J Geriatr Psychiatry. 2010;25(12):1280–7.

    Article  PubMed  Google Scholar 

  6. Tabert MH, Manly JJ, Liu X, Pelton GH, Rosenblum S, Jacobs M, Devanand DP. Neuropsychological prediction of conversion to Alzheimer disease in patients with mild cognitive impairment. Arch Gen Psychiatry. 2006;63(8):916–24.

    Article  PubMed  Google Scholar 

  7. Belleville S, Gauthier S, Lepage É, Kergoat MJ, Gilbert B. Predicting decline in mild cognitive impairment: A prospective cognitive study. Neuropsychology. 2014;28(4):643.

    Article  PubMed  Google Scholar 

  8. Boccardi M, Monsch AU, Ferrari C, Altomare D, Berres M, Bos I, ... & Consortium for the Harmonization of Neuropsychological Assessment for Neurocognitive Disorders. Harmonizing neuropsychological assessment for mild neurocognitive disorders in Europe. Alzheimers Dement. 2022;18(1):29–42

  9. Wang H, Fan Z, Shi C, Xiong L, Zhang H, Li T, Yu X. Consensus statement on the neurocognitive outcomes for early detection of mild cognitive impairment and Alzheimer dementia from the Chinese Neuropsychological Normative (CN-NORM) Project. J Glob Health. 2019;9(2):020320.

  10. Acevedo A, Krueger KR, Navarro E, Ortiz F, Manly JJ, Padilla-Vélez MM, Mungas D. The Spanish translation and adaptation of the uniform data set of the National Institute on Aging Alzheimer’s Disease Centers. Alzheimer Disease and associated disorders. 2009;23(2):102.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Benson G, de Felipe J, Sano M. Performance of Spanish-speaking community-dwelling elders in the United States on the Uniform Data Set. Alzheimers Dement. 2014;10:S338–43.

    Article  PubMed  Google Scholar 

  12. Weintraub S, Besser L, Dodge HH, Teylan M, Ferris S, Goldstein FC, Morris JC. Version 3 of the Alzheimer Disease Centers’ neuropsychological test battery in the Uniform Data Set (UDS). Alzheimer Dis Assoc Disord. 2018;32(1):10.

  13. Weintraub S, Salmon D, Mercaldo N, Ferris S, Graff-Radford NR, Chui H, Morris JC. The Alzheimer’s disease centers’ uniform data set (UDS): The neuropsychological test battery. Alzheimer Dis Assoc Disord. 2009;23(2):91.

  14. Beekly DL, Ramos EM, Lee WW, Deitrich WD, Jacka ME, Wu J, Kukull WA. The National Alzheimer’s Coordinating Center (NACC) database: the uniform data set. Alzheimer Dis Assoc Disord. 2007;21(3):249–58.

    Article  PubMed  Google Scholar 

  15. Conca F, Esposito V, Rundo F, Quaranta D, Muscio C, Manenti R, Cappa SF. Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB 10): development and normative data. Alzheimer’s Research & Therapy. 2022;14(1):113.

    Article  Google Scholar 

  16. Ivanova I, Salmon DP, Gollan TH. The multilingual naming test in Alzheimer’s disease: clues to the origin of naming impairments. J Int Neuropsychol Soc. 2013;19(3):272–83.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Stasenko A, Jacobs DM, Salmon DP, Gollan TH. The Multilingual Naming Test (MINT) as a measure of picture naming ability in Alzheimer’s disease. J Int Neuropsychol Soc. 2019;25(8):821–33.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Staffaroni AM, Asken BM, Casaletto KB, Fonseca C, You M, Rosen HJ, Kramer JH. Development and validation of the Uniform Data Set (v3. 0) executive function composite score (UDS3-EF). Alzheimers Dement. 2021;17(4):574–83.

    Article  PubMed  Google Scholar 

  19. Mathews M, Abner E, Kryscio R, Jicha G, Cooper G, Smith C, Schmitt FA. Diagnostic accuracy and practice effects in the National Alzheimer’s Coordinating Center Uniform Data Set neuropsychological battery. Alzheimers Dement. 2014;10(6):675–83.

    Article  PubMed  Google Scholar 

  20. Perneczky R, Wagenpfeil S, Komossa K, Grimmer T, Diehl J, Kurz A. Mapping scores onto stages: mini-mental state examination and clinical dementia rating. Am J Geriatr Psychiatry. 2006;14(2):139–44.

    Article  PubMed  Google Scholar 

  21. Measso G, Cavarzeran F, Zappalà G, Lebowitz BD, Crook TH, Pirozzolo FJ, Grigoletto F. The mini-mental state examination: Normative study of an Italian random sample. Dev Neuropsychol. 1993;9(2):77–85.

    Article  Google Scholar 

  22. Fluss R, Faraggi D, Reiser B. Estimation of the Youden Index and its associated cutoff point. Biom J. 2005;47(4):458–72.

    Article  PubMed  Google Scholar 

  23. de Jager CA, Hogervorst E, Combrinck M, Budge MM. Sensitivity and specificity of neuropsychological tests for mild cognitive impairment, vascular cognitive impairment and Alzheimer’s disease. Psychol Med. 2003;33(6):1039–50.

    Article  PubMed  Google Scholar 

  24. Di Paola M, Macaluso E, Carlesimo GA, Tomaiuolo F, Worsley KJ, Fadda L, Caltagirone C. Episodic memory impairment in patients with Alzheimer’s disease is correlated with entorhinal cortex atrophy: A voxel-based morphometry study. J Neurol. 2007;254:774–81.

    Article  PubMed  Google Scholar 

  25. Sánchez-Benavides G, Gómez-Ansón B, Molinuevo JL, Blesa R, Monte GC, Buschke H, Peña-Casanova J. Medial temporal lobe correlates of memory screening measures in normal aging, MCI, and AD. J Geriatr Psychiatry Neurol. 2010;23(2):100–8.

    Article  PubMed  Google Scholar 

  26. Braak H, Del Tredici K. Alzheimer’s pathogenesis: is there neuron-to-neuron propagation? Acta Neuropathol. 2011;121:589–95.

    Article  CAS  PubMed  Google Scholar 

  27. Igarashi KM. Entorhinal cortex dysfunction in Alzheimer's disease. Trends in Neurosci. 2023;46(2):124–36.

  28. Lemos R, Cunha C, Marôco J, Afonso A, Simões MR, Santana I. F ree and C ued S elective R eminding T est is superior to the W echsler M emory S cale in discriminating mild cognitive impairment from A lzheimer’s disease. Geriatr Gerontol Int. 2015;15(8):961–8.

    Article  PubMed  Google Scholar 

  29. Wagner M, Wolf S, Reischies FM, Daerr M, Wolfsgruber S, Jessen F, Wiltfang J. Biomarker validation of a cued recall memory deficit in prodromal Alzheimer disease. Neurology. 2012;78(6):379–86.

    Article  CAS  PubMed  Google Scholar 

  30. Grober E, Buschke H. Genuine memory deficits in dementia. Dev Neuropsychol. 1987;3(1):13–36.

    Article  Google Scholar 

  31. Spallazzi M, Michelini G, Barocco F, Dieci F, Copelli S, Messa G, Caffarra P. The role of free and cued selective reminding test in predicting [18F] Florbetaben PET results in mild cognitive impairment and mild dementia. J Alzheimers Dis. 2020;73(4):1647–59.

    Article  PubMed  Google Scholar 

  32. Tounsi H, Deweer B, Ergis AM, Van der Linden M, Pillon B, Michon A, Dubois B. Sensitivity to semantic cuing: an index of episodic memory dysfunction in early Alzheimer disease. Alzheimer Dis Assoc Disord. 1999;13(1):38–46.

    Article  CAS  PubMed  Google Scholar 

  33. Carlesimo GA, Perri R, Caltagirone C. Category cued recall following controlled encoding as a neuropsychological tool in the diagnosis of Alzheimer’s disease: a review of the evidence. Neuropsychol Rev. 2011;21:54–65.

    Article  PubMed  Google Scholar 

  34. Belleville, S., Fouquet, C., Hudon, C., Zomahoun, H. T. V., Croteau, J., & Consortium for the Early Identification of Alzheimer’s Disease-Quebec. Neuropsychological measures that predict progression from mild cognitive impairment to Alzheimer’s type dementia in older adults: a systematic review and meta-analysis. Neuropsychol Rev. 2017;27:328–53.

    Article  Google Scholar 

  35. Jicha GA, Parisi JE, Dickson DW, Johnson K, Cha R, Ivnik RJ, Petersen RC. Neuropathologic outcome of mild cognitive impairment following progression to clinical dementia. Arch Neurol. 2006;63(5):674–81.

    Article  PubMed  Google Scholar 

  36. Verma M, Howard RJ. Semantic memory and language dysfunction in early Alzheimer’s disease: a review. Int J Geriatr Psychiatry. 2012;27(12):1209–17.

    Article  CAS  PubMed  Google Scholar 

  37. Weintraub S, Wicklund AH, Salmon DP. The neuropsychological profile of Alzheimer disease. Cold Spring Harb Perspect Med. 2012;2(4): a006171.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Melrose RJ, Campa OM, Harwood DG, Osato S, Mandelkern MA, Sultzer DL. The neural correlates of naming and fluency deficits in Alzheimer’s disease: an FDG-PET study. Int J Geriatric Psychiatry. 2009;24(8):885–93.

    Article  Google Scholar 

  39. McDonnell M, Dill L, Panos S, Amano S, Brown W, Giurgius S, Miller K. Verbal fluency as a screening tool for mild cognitive impairment. Int Psychogeriatr. 2020;32(9):1055–62.

    Article  PubMed  Google Scholar 

  40. Murphy KJ, Rich JB, Troyer AK. Verbal fluency patterns in amnestic mild cognitive impairment are characteristic of Alzheimer’s type dementia. J Int Neuropsychol Soc. 2006;12(4):570–4.

    Article  PubMed  Google Scholar 

  41. Gainotti G, Quaranta D, Vita MG, Marra C. Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease. J Alzheimers Dis. 2014;38(3):481–95.

    Article  CAS  PubMed  Google Scholar 

  42. Ahn H, Yi D, Chu K, Joung H, Lee Y, Jung G, Lee DY. Functional Neural Correlates of Semantic Fluency Task Performance in Mild Cognitive Impairment and Alzheimer’s Disease: An FDG-PET Study. J Alzheimer’s Dis. 2022;85(4):1689–700.

    Article  CAS  Google Scholar 

  43. Ashendorf L, Jefferson AL, O’Connor MK, Chaisson C, Green RC, Stern RA. Trail Making Test errors in normal aging, mild cognitive impairment, and dementia. Arch Clin Neuropsychol. 2008;23(2):129–37.

    PubMed  Google Scholar 

  44. Zhang Z, Zheng H, Liang K, Wang H, Kong S, Hu J, ... & Sun G. Functional degeneration in dorsal and ventral attention systems in amnestic mild cognitive impairment and Alzheimer’s disease: an fMRI study. Neuroscience Lett.  2015;585:160–165.

  45. Kirova AM, Bays RB, Lagalwar S. Working memory and executive function decline across normal aging, mild cognitive impairment, and Alzheimer’s disease. BioMed Res Int. 2015;2015:1–9.

  46. Guarino A, Favieri F, Boncompagni I, Agostini F, Cantone M, Casagrande M. Executive functions in Alzheimer disease: A systematic review. Front Aging Neurosci. 2019;10:437.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Martyr A, Clare L. Executive function and activities of daily living in Alzheimer’s disease: a correlational meta-analysis. Dement Geriatr Cogn Disord. 2012;33(2–3):189–203.

    Article  CAS  PubMed  Google Scholar 

  48. Bejanin A, Schonhaut DR, La Joie R, Kramer JH, Baker SL, Sosa N, ... & Rabinovici GD. Tau pathology and neurodegeneration contribute to cognitive impairment in Alzheimer’s disease. Brain. 2017;140(12):3286–3300.

  49. Nho K, Risacher SL, Crane PK, DeCarli C, Glymour MM, Habeck C, Alzheimer’s Disease Neuroimaging Initiative (ADNI). Voxel and surface-based topography of memory and executive deficits in mild cognitive impairment and Alzheimer’s disease. Brain Imaging Behav. 2012;6:551–67.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Van Dam NT, Sano M, Mitsis EM, Grossman HT, Gu X, Park Y, Fan J. Functional neural correlates of attentional deficits in amnestic mild cognitive impairment. PLoS One. 2013;8(1):e54035.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

Italian Ministry of Health Grant RCR-2022-23682290.

Author information

Authors and Affiliations

  1. ICoN Cognitive Neuroscience Center, Institute for Advanced Studies, IUSS, Pavia, Italy

    Francesca Conca, Eleonora Catricalà & Stefano Francesco Cappa

  2. IRCCS Mondino Foundation, Pavia, Italy

    Valentina Esposito, Matteo Cotta Ramusino & Stefano Francesco Cappa

  3. Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

    Rosa Manenti & Maria Cotelli

  4. Neurology Unit, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy

    Federica L’Abbate, Davide Quaranta, Guido Maria Giuffrè & Camillo Marra

  5. Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy

    Davide Quaranta, Camillo Marra & Sonia Di Tella

  6. IRCCS Fondazione Don Carlo Gnocchi, ONLUS, Milan, Italy

    Federica Rossetto & Sonia Di Tella

  7. Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, Milan, Italy

    Federica Solca, Barbara Poletti & Vincenzo Silani

  8. Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy

    Beatrice Orso & Matteo Pardini

  9. Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy

    Emanuela Inguscio, Valeria Crepaldi, Pietro Tiraboschi & Fabrizio Tagliavini

  10. IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy

    Maddalena De Matteis, Michelangelo Stanzani Maserati & Raffaele Lodi

  11. Neurodegenerative Diseases Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy

    Emanuela Rotondo & Andrea Arighi

  12. Istituti Clinici Scientifici Maugeri IRCCS, Psychology Unit Pavia-Montescano, Pavia Institute, Pavia, Italy

    Marina Manera

  13. Neuropsychiatric Laboratory, Clinical Neuroscience and Neurorehabilitation Department, IRCCS Fondazione Santa Lucia, Rome, Italy

    Giulia Caruso & Federica Piras

  14. Oasi Research Institute-IRCCS, Troina, Italy

    Valentina Catania & Francesco Rundo

  15. Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Elisa Canu & Massimo Filippi

  16. Neurology Unit, Neurophysiology Service, Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Massimo Filippi

  17. Vita-Salute San Raffaele University, Milan, Italy

    Massimo Filippi

  18. Istituti Clinici Scientifici Maugeri IRCCS, Neurophysiopatology Unit Pavia-Montescano, Pavia Institute, Pavia, Italy

    Cira Fundarò

  19. IRCCS Ospedale Policlinico S. Martino, Genoa, Italy

    Matteo Pardini

  20. Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy

    Barbara Poletti

  21. “Dino Ferrari” Center, Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy

    Vincenzo Silani

Authors
  1. Francesca Conca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Valentina Esposito
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eleonora Catricalà
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rosa Manenti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Federica L’Abbate
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Davide Quaranta
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Guido Maria Giuffrè
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Federica Rossetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Federica Solca
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Beatrice Orso
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Emanuela Inguscio
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Valeria Crepaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Maddalena De Matteis
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Emanuela Rotondo
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Marina Manera
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Giulia Caruso
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Valentina Catania
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Elisa Canu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Francesco Rundo
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Matteo Cotta Ramusino
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Massimo Filippi
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Cira Fundarò
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Federica Piras
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Andrea Arighi
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Pietro Tiraboschi
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Michelangelo Stanzani Maserati
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Matteo Pardini
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Barbara Poletti
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Vincenzo Silani
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Camillo Marra
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Sonia Di Tella
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Maria Cotelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Raffaele Lodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Fabrizio Tagliavini
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Stefano Francesco Cappa
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F.C. and E.Cat. contributed to the design of the work, acquisition, analysis, and interpretation of the data and drafted the work; V.E. contributed to the design of the work and acquisition of the data; R.M., F.L.A., G.M.G., F.R., F.S., B.O., E.I., V.Cr., M.D.M., E.R., M.M., G.C., V.Ca., M.C.R. contributed to the acquisition of the data; F.R. contributed to the creation and maintenance of the software used in the work; D.Q., E.Can., M.F., C.F., F.P., A.A., P.T., M.S.M., M.P., B.P., V.S., C.M., S.D.T., M.C. revised the work; R.L., F.T. contributed to the design of the work and revised the work; S.F.C. contributed to the design of the work, interpretation of the data and drafted the work. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Eleonora Catricalà.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the local ethics committees (Ethic committee of Pavia, IRCCS Policlinico “San Matteo”, Pavia, Italy) and complied with the provisions of the Declaration of Helsinki. All subjects gave written informed consent to participate.

Consent for publication

Not applicable.

Competing interests

V. Silani received compensation for consulting services and/or speaking activities from AveXis, Cytokinetics, Italfarmaco, Liquidweb S.r.l., Novartis Pharma AG and Zambon Biotech SA; he receives or has received research supports form the Italian Ministry of Health, AriSLA, E-Rare Joint Transnational Call, and the ERN Euro-NMD. He is in the Editorial Board of Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, European Neurology, American Journal of Neurodegenerative Diseases, Frontiers in Neurology, and Exploration of Neuroprotective Therapy. E. Canu receives research supports from the Italian Ministry of Health. M. Filippi is Editor-in-Chief of the Journal of Neurology, Associate Editor of Human Brain Mapping, Neurological Sciences, and Radiology; he received compensation for consulting services from Alexion, Almirall, Biogen, Merck, Novartis, Roche, Sanofi and for speaking activities from Bayer, Biogen, Celgene, Chiesi Italia SpA, Eli Lilly, Genzyme, Janssen, Merck-Serono, Neopharmed Gentili, Novartis, Novo Nordisk, Roche, Sanofi, Takeda, and TEVA; participation in Advisory Boards for Alexion, Biogen, Bristol-Myers Squibb, Merck, Novartis, Roche, Sanofi, Sanofi-Aventis, Sanofi-Genzyme, Takeda; scientific direction of educational events for Biogen, Merck, Roche, Celgene, Bristol-Myers Squibb, Lilly, Novartis, Sanofi-Genzyme; he receives research support from Biogen Idec, Merck-Serono, Novartis, Roche, the Italian Ministry of Health, the Italian Ministry of University and Research, and Fondazione Italiana Sclerosi Multipla.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Conca, F., Esposito, V., Catricalà, E. et al. Clinical validity of the Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB) in Mild Cognitive Impairment and Alzheimer’s Disease. Alz Res Therapy 16, 98 (2024). https://doi.org/10.1186/s13195-024-01465-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13195-024-01465-0

Keywords

Alzheimer's Research & Therapy

ISSN: 1758-9193