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Parkinson’s disease (PD) is a neurodegenerative disorder that causes motor and nonmotor symptoms including cognitive symptoms. This study examines cognition, in particular visuoconstruction with fine motor skills, visuospatial relations and memory. The Vienna Visuoconstructive Test 3.0 (VVT 3.0) using three tasks (VVT3.0 Screening Copy - VVT3.0-SCO, VVT3.0 Screening Delayed Recall - VVT3.0 - SDR, VVT3.0 Screening Quotient - VVT3.0SQU) to test these abilities. The aim of the study was to evaluate the potential of the VVT 3.0 screening by comparing the visuoconstructive abilities of PD patients and control subjects (HC) and examining differences in other neuropsychological tests and sociodemographic characteristics.
Material and methods
A retrospective study with 287 participants (74 PD, 213 HC) was conducted. Descriptive and inferential statistical analyses were performed. An ROC analysis assessed the discriminatory power of the VVT 3.0 screening scores.
Results
A significant difference in VVT3.0-SCO was found between the groups (r=0.25). The difference showed a small effect (r=0.23). VVT3.0-SQU showed a large effect (d=0.74).
Conclusion
The main finding of the present study was that PD leads to a decrease in test performance in VVT3.0-SQU.
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Introduction
The cognitive symptoms of Parkinson’s disease (PD) represent a severe impact for those affected and their relatives, which is why diagnostics in this respect are of great relevance in order to ensure effective treatment. Drawing tests are often used for this purpose.
The PD is a neurodegenerative disorder primarily affecting motor-related brain areas [1] but also causing nonmotor symptoms including cognitive deficits [2]. The Movement Disorders Society defined four core cognitive domains: attention, executive functioning, free recall memory and visuospatial functioning [3, 4].
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The Vienna Visuoconstructive Test 3.0 Screening (Vienna Visuoconstructive Test 3.0 Screening Copy (VVT3.0-SCO), Vienna Visuoconstructive Test 3.0 Screening Delayed Recall (VVT3.0-SDR), Vienna Visuoconstructive Test 3.0 Screening Quotient (VVT3.0-SQU) was developed to examine visuoconstructive abilities by using copying tasks [5‐9]. Studies have demonstrated its ability to differentiate neurodegenerative disorders such as PD and Alzheimer’s disease (AD). The VVT3.0-SCO performance declines with age while sex showed no influence. Formal years of education were shown to positively effect VVT3.0-SCO performance. Depressive symptoms did not significantly affect results. Patients generally performed worse on the delayed recall score (VVT3.0-SDR) compared to the VVT3.0-SCO. Significant differences regarding the VVT3.0-SQU had been observed in patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI) and AD but PD remained underexplored [6, 9‐15].
The aim of this study was to examine whether the VVT3.0-SCO, VVT3.0-SDR and VVT3.0-SQU could be used to differentiate PD patients from healthy controls (HC). In addition, the study investigated the extent to which patients’ VVT3.0-SCO, VVT3.0-SDR and VVT3.0-SQU scores and clinical as well as demographic variables are associated in PD.
Material and methods
Study design and participants
Data were collected from patients at the Department of Neurology, Medical University of Vienna, assessed for movement disorders. The study followed the Declaration of Helsinki and was approved by the Medical University of Vienna ethics committee (EC-No. 2190/2021).
The PD patients came to the outpatient clinic for motor dysfunctions at the Department of Neurology and underwent a neurological examination and a neuropsychological assessment. Healthy controls were recruited by means of public advertisements. Healthy controls needed to score above 26 on the Mini Mental State Examination (MMSE). Study participation was voluntary, with written consent and withdrawal was possible at any time.
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Inclusion criteria were age ≥ 30 years and an MMSE score of at least 23 in PD. Patients with PD were diagnosed based on the UK Parkinson’s Disease Society brain bank clinical diagnostic criteria [16]. All participants had to have completed the VVT3.0-SCO. Exclusion criteria were neurological conditions (e.g., stroke, brain injuries), severe medical conditions affecting cognition, major psychiatric disorders (except depressive symptoms) and significant sensory, language or motor deficits [9].
The study included 213 HC and 74 PD patients. Sociodemographic and clinical data are listed in Table 1. The proportion of men in the PD group was 63.5% (95% confidence interval, CI 52.5–74.5%), with a significant sex distribution difference (χ2(1) = 19.974, p < 0.001). The median age difference between HC and PD was significant (6.1 years, p < 0.001), but no significant age differences were found between male and female participants (p = 0.990). The variable years of formal of education (YFE) was formed by asking the test subjects about the duration of their school and university attendance and adding up all the years.
Table 1
Key values and significance assessment of neuropsychological testing and frequencies of categorical parameters considering complete protocols of the two subgroups
Total
HC
PD
p-value
Age in years (N)
287
213
74
–
M ± SD
58.9 ± 14.2
57.1 ± 14.8
64.2 ± 10.9
–
Min-max
30.0–90.5
30.0–88.0
30.6–90.5
–
Md (IQR)
59.2 (49.3; 70.2)
56.8 (46.0; 68.2)
62.9 (56.8; 72.5)
–
Female (N, %)
168 (59%)
141 (66%)
27 (36%)
–
M ± SD
58.9 ± 14.2
58.1 ± 14.6
63.1 ± 11.3
–
Min-max
30.1–88.0
30.1–88.0
30.6–79.2
–
Md (IQR)
59.9 (48.7; 70.8)
57.7 (47.0; 69.0)
63.1 (57.6; 71.5)
–
Male (N, %)
119 (41%)
72 (34%)
47 (64%)
–
M ± SD
58.9 ± 14.4
55.1 ± 15.2
64.8 ± 10.8
–
Min-max
30.0–90.5
30.0–82.0
40.8–90.5
–
Md (IQR)
58.9 (49.7; 69.0)
54.4 (43.1; 67.0)
62.6 (56.8; 73.0)
–
YFE in years (N)
264
190
74
–
M ± SD
13.9 ± 4.7
14.6 ± 4.6
12.0 ± 4.4
< 0.001**
Min-max
4–27
4–27
7–23
r = 0.28
Md (IQR)
13 (10; 18)
14 (11; 18)
11 (8; 15)
–
MMSE (N)
119
45
74
–
M ± SD
28.07 ± 1.75
28.84 ± 1.00
27.59 ± 1.94
< 0.001**
Min–max
23–30
27–30
23–30
r = 0.33
Md (IQR)
28 (27; 29)
29 (28; 30)
28 (26; 29)
–
BDI-II (N)
198
135
63
–
M ± SD
6.79 ± 6.90
5.61 ± 7.09
9.32 ± 5.72
< 0.001**
Min-max
0–48
0–48
0–26
r = 0.37
Md (IQR)
5.0 (2.0; 10.0)
4.0 (1.0; 8.0)
9.0 (5.0; 12.0)
–
BDI-II category
–
–
–
–
0–13
177 (89.4%)
126 (93.3%)
51 (81.0%)
0.008**
≥ 14 depression
21 (10.6%)
9 (6.8%)
12 (19.0%)
VVT3.0-SCO (N)
287
213
74
–
M ± SD
9.43 ± 1.04
9.58 ± 0.71
8.97 ± 1.56
< 0.001**
Min-max
0–10
5–10
0–10
r = 0.24
Md (IQR)
10 (9; 10)
10 (9; 10)
9 (9; 10)
–
VVT3.0-SDR (N)
184
162
22
–
M ± SD
8.24 ± 1.96
8.44 ± 1.79
6.77 ± 2.53
0.002**
Min-max
1–10
1–10
2–10
r = 0.23
Md (IQR)
9 (7; 10)
9 (8; 10)
6.5 (5; 9)
–
VVT3.0-SQU (N)
184
162
22
–
M ± SD
0.864 ± .190
0.881 ± .175
0.744 ± .252
Welch t‑test
95% CI (LB;UB)
0.835; 0.891
0.851; 0.908
0.633; 0.846
0.022*
min-max
0.100–1.125
0.100–1.125
0.286–1.111
d = 0.74
Md (IQR)
0.900 (0.778; 1.0)
0.900 (0.800; 1.0)
0.750 (0.556; 1.0)
–
HC healthy control, PD Parkinson’s disease, N total number of observations, M Mean, SD Standard deviation, min minimum, max maximum, Md median, IQR interquartile range, YFE years of formal education, MMSE Mini Mental State Examination, BDI-II Beck’s Depressions Inventory, VVR3.0-SCO Vienna Visuoconstructive Test 3.0 Screening Copy , VVT3.0-SDR Vienna Visuoconstructive Test 3.0 Screening Delayed Recall, VT3.0-SQU Vienna Visuoconstructive Test 3.0 Screening quotient, CI Confidence Interval, LB Lower Bound, UB Upper Bound
**p ≤ 0.01, *p ≤ 0.05
The PD-specific measures included age at diagnosis, disease duration, motor function (part III of the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale, MDS-UPDRS-III, focusing on bradykinesia, rigidity, tremor, and postural stability) [17] and Hoehn and Yahr staging [18, 19]. Among 74 PD patients, Hoehn and Yahr staging was available for 30 cases: 10% stage 1, 63.3% stage 2, and 26.7% stage 3. Motor scores (UPDRS) were documented for 28 patients (mean 20.4 ± 10.9, range 6–53). Onset age (mean 54.2 ± 10.2; min 36–max 82) and disease duration (mean 127.7 ± 70.9 months; min 34–max 360; Md 120, IQR 71; 174) were recorded for 31 patients.
Participants underwent the MMSE for cognitive assessment [10, 20] and the Beck Depression Inventory-II (BDI-II) for assessing depressive symptoms [18]. The VVT3.0 was developed as a neuropsychological tool to assess visuoconstructive abilities in neurocognitive disorders [21]. Participants copy a clock with a 12-digit face set to “10 past 11.” Scoring evaluates digit placement, hand positioning and proportionality. Accuracy in copying overlapping pentagons, including correct angles and intersections, is also assessed. Participants copy a three-dimensional cube, with correct angles and parallel lines contributing to the score. The copy score (VVT3.0-SCO) is based on immediate performance, while the delayed recall score 30 min later (VVT3.0-SDR) assesses memory retention. Maximum score is 10 points for the copy score as well as the delayed recall score. The quotient score (VVT3.0-SQU), calculated by dividing the VVT3.0-SDR score by the VVT3.0-SCO score can also be above 1, when participants get a better result in the second session and reflects stability in test performance [10, 21]. The VVT3.0 can be obtained from www.psimistri.com. Baseline characteristics of the subgroups are detailed in Table 1.
Statistical analyses
Statistical analyses were conducted using IBM SPSS® Statistics 29.0.0 (IBM Coporation, Armonk, New York) and Microsoft Excel 2409 (Microsoft Corporation, Redmond, Washington) with an alpha level of 0.05. Missing values were excluded per variable.
Cohen’s standardized effect sizes (small: r ≥ 0.10, moderate: r ≥ 0.30, substantial: r ≥ 0.50) were calculated for practical relevance. For Mann-Whitney U tests, effect size was determined using r = z : √N. Descriptive statistics included mean (M), standard deviation (SD), minimum (min), maximum (max), median (Md), and interquartile range (IQR).
A nonparametric Mann-Whitney U test was applied to compare metric variables between groups. The two-way mixed repeated ANOVA evaluated VVT3.0-SCO, VVT3.0-SDR, VVT3.0-SQU changes across subgroups, using F distribution and degrees of freedom. Post hoc pairwise comparisons employed paired t‑tests and Welch tests, χ2-tests assessed relationships between nominal variables and tested for equal distribution.
Multiple binary logistic regression examined metric and binary predictors on binary outcomes, using Hosmer-Lemeshow for model fit and Nagelkerke’s R2 (≥ 20% acceptable). Odds ratios (OR) were categorized as small (≥ 2), moderate (≥ 3) and substantial (≥ 7). Receiver operating characteristic (ROC) analysis, with the area under the curve (AUC), assessed prediction accuracy, using the Youden Index for cut-off efficiency.
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Results
The PD patients had significantly less years of formal education (p < 0.001, small effects: r = 0.28), lower cognitive functioning (p < 0.001, moderate effects: r = 0.33) and more depressive symptoms (p < 0.001, moderate effects: r = 0.33) compared to HC.
The VVT3.0-SCO score showed significant differences between HC and PD (p < 0.001, small effects: r = 0.24) with HC performing better. The VVT3.0-SDR score showed significant differences between HC and PD (p < 0.001, small effects: r = 0.23) with HC again performing better. To assess performance stability the VVT3.0-SQU quotient was calculated reflecting performance changes. It showed a moderate effect (p < 0.001, d = 0.74). See Table 1 for details.
Using 162 HC and 22 PD patients, a 2 × 2 mixed repeated ANOVA was used tpo analyze changes in VVT3.0-SCO and VVT3.0-SDR in performance across HC and PD groups. A weak interaction was found, F (1, 182) = 7.667, p = 0.006 (η2 = 0.04), requiring separate interpretation of time and group effects. At T1, Welch testing showed a moderate effect (t (22.91) = 2.093, p = 0.048, d = 0.72) between subgroups. At T2, delayed recall scores indicated a substantial effect (t (23.96) = 2.993, p = 0.006, d = 0.88). Paired t‑tests assessed changes within subgroups. For HC, t (161) = 8.755, p < 0.001, d = 0.69 indicated a moderate effect, while for PD, t (21) = 4.839, p < 0.001, d = 1.03 showed a substantial effect, confirming a decline in both groups. Among 162 HC participants, 51.9% deteriorated, 4.3% improved, and 43.8% remained stable. In 22 PD patients, 68.2% declined, 9.1% improved, and 22.7% showed no change.
The discriminatory potential of VVT3.0-SCO, VVT3.0-SDR, VVT3.0-SQU was assessed using ROC analysis providing cut-off scores, sensitivity, specificity using the Youden Index with scores for demonstrating sufficient validity to differentiate HC and PD by all three scores. See Table 2 for details.
Table 2
Key values of the ROC functions for AUC and validity for VVT 3.0 screening scores
F
AUC
SE
p-value
95% CI AUC
Sensitivity
Specificity
YI
Cut-off
LB
UB
VVT3.0-SCO (n = 287)
0.638
0.038
< 0.001**
0.562
0.713
0.545
0.679
0.22
9.5
VVT3.0-SDR (n = 184)
0.632
0.068
0.045*
0.498
0.766
0.727
0.623
0.35
8.5
VVT3.0-SQU (n = 184)
0.695
0.068
0.003**
0.562
0.829
0.636
0.716
0.35
0.85
AUC Area Under the Curve, SE Standard Error, LB/UB Lower/Upper Bound, YI Youden Index, VVT3.0-SCO Vienna Visuoconstructive Test 3.0 Screening Copy, VVT3.0-SDR Vienna Visuoconstructive Test 3.0 Screening Delayed Recall, VT3.0-SQU Vienna Visuoconstructive Test 3.0 Screening quotient
(* p<0.05; ** p>0.01)
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The ROC analysis using the VVT3.0-SQU yielded an AUC of 0.695 at a cut-off of 0.85, with sensitivity = 0.636 and specificity = 0.716. For the VVT3.0-SCO score, sensitivity and specificity were 0.545 and 0.697, and for the VVT3.0-SDR score, 0.727 and 0.623. These results are illustrated in Fig. 1.
Fig. 1
ROC-AUC functions of the Vienna Visuoconstructive Test 3.0 Screening Copy (VVT3.0-SCO), Vienna Visuoconstructive Test 3.0 Screening Delayed Recall (VVT3.0-SDR) Vienna Visuoconstructive Test 3.0 Screening Quotient (VT3.0-SQU scores regarding the PD criterion). The colored circles indicate the Youden Index for cut-off efficiency which optimizes sensitivity and specificity
Cut-offs for higher specificity (0.90) were: VVT3.0-SCO score < 8.5, VVT3.0-SDR score < 5.5, and VVT3.0-SQU score < 0.58, reducing false positives to 10%.
A hierarchical binary regression on 108 cases with complete protocols predicted PD vs. HC using VVT3.0-SQU, age, sex, YFE, BDI-II scores as predictors. As listed in Table 3 the model’s Nagelkerke R2 increased from 8.6% to 34.4%. The VVT3.0-SQU score showed a protective effect (OR = 0.057), while male sex (OR = 9.71) and depression (OR = 1.059) were risk factors.
Table 3
Coefficients of the predictors in the model of the PD vs. HC criterion (n = 108)
Block
Predictor
B
SE
Wald χ2 (df = 1)
p-value
OR
95% CI OR
LB
UB
1
VVT3.0-SQU
−2.960
1.239
5.710
0.017*
0.052
0.005
0.587
Constant
0.844
1.024
0.680
0.410
2.327
–
–
–
Nagelkerke’s R2 8.6%
–
–
–
–
–
–
2
VVT3.0-SQU
−2.785
1.393
3.996
0.046*
0.062
0.004
0.947
Age (years)
0.048
0.023
4.221
0.040*
1.049
1.002
1.098
Sex
−2.004
0.644
9.674
0.002**
0.135
0.038
0.477
Constant
−1.212
1.977
0.376
0.540
0.298
–
–
–
Nagelkerke’s R2 30.3%
–
–
–
–
–
–
3
VVT3.0-SQU
−2.931
1.431
4.192
0.041*
0.053
0.003
0.882
Age (years)
0.042
0.024
2.875
0.090°
1.042
0.994
1.094
Sex
−2.102
0.665
9.978
0.002**
0.122
0.033
0.450
School years
−0.053
0.069
0.587
0.444
0.949
0.829
1.086
Constant
0.082
2.609
0.001
0.975
1.086
0.003
1.023
–
Nagelkerke’s R2 31.1%
–
–
–
–
–
–
4
VVT3.0-SQU
−2.859
1.470
3.780
0.052°
0.057
0.003
1.023
Age (years)
0.041
0.025
2.650
0.104
1.042
0.992
1.094
Sex
−2.272
0.703
10.434
0.001**
0.103
0.026
0.409
School years
−0.056
0.070
0.632
0.427
0.946
0.823
1.086
BDI-II
0.058
0.034
2.832
0.092°
1.059
0.991
1.133
Constant
−0.312
2.688
0.013
0.908
0.732
–
–
–
Nagelkerke’s R2 34.4%
–
–
–
–
–
–
B regression coefficient, SE Standard error, df Degrees of freedom, CI Confidence Interval, OR Odds Ratio, LB/UB Lower/Upper Bound, BDI-II Beck’s Depressions Inventory, VVT3.0-SCO Vienna Visuoconstructive Test 3.0 Screening Copy , VVT3.0-SDR Vienna Visuoconstructive Test 3.0 Screening Delayed Recall, VT3.0-SQU Vienna Visuoconstructive Test 3.0 Screening Quotient
**p ≤ 0.01, *p ≤ 0.05, ° p ≤ 0.10 (tendency)
Discussion
This study examined visuoconstructive and visual memory skills in PD and assessed the Vienna Visuoconstructive Test 3.0 Screening to differentiate them from healthy controls (HC). Unlike prior studies, it focused only on PD, without investigating Alzheimer’s disease (AD).
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The HC group had more women (66.2%), consistent with healthcare-seeking behavior [22] and PD predominantly affects men, as reflected in the 63.5% male PD group [23]. The PD patients had fewer years of education, which can probably only be explained multifactorially, lower cognitive functioning (MMSE) and higher depression levels (BDI-II), which is consistent with PD patients’ cognitive impairment [24] and depression [25].
The VVT3.0-SDR distinguished PD from HC better than the VVT3.0-SCO, as cognitive impairments in PD effects memory to a greater degree. The VVT3.0-SQU, with an AUC of 69.5, was the most effective, showing moderate sensitivity (63.6%) and specificity (71.6%). A cut-off of < 0.58 increased specificity to ≥ 90%, reducing false positives. This AUC confirms that the VVT3.0-SQU may be able to distinguish between the two groups [26].
Comparing this to Fok et al. [10], the present study confirmed similar results for the VVT3.0-SCO. Their study found an AUC of 0.693 (cut-off 9.5, sensitivity 0.67, specificity 0.69), while the present study found an AUC = 0.638 (sensitivity 0.55, specificity 0.68). The VVT3.0-SQU score showed superior diagnostic accuracy, supporting it as the most reliable tool for PD differentiation.
Model testing suggested a VVT3.0-SQU score > 0.58 might protect against PD (8.6% explained variance). Study parameters explained 34.4% of variance, confirming male sex (OR = 9.77) and depression (OR = 1.059) as PD risk factors; however, this result has to be viewed with caution because due to missing values only 108 participants were available for analysis.
Existing research [6, 9, 14, 15] found VVT3.0-SCO scores distinguish AD from HC, with AD patients performing worse. One study [11] showed differences between HC, AD, and PD but AD and PD were not significantly different. The latest VVT3.0-SCO study [15] found similar results, with HC having a median copy score of 10 and PD 9, suggesting AD patients perform worse.
This research suggested the VVT3.0-SDR score to be superior for detecting AD, while the present study highlights the VVT3.0-SQU score for detecting PD [15].
The study of Fok et al. [10] found an AUC of 0.74 for the MMSE and 0.72 for the CDT, demonstrating that VVT3.0-SQU may differentiate as effectively as both these tests.
The present study also has some limitations, i.e., Hoehn and Yahr staging was only available for 30 PD patients. Thus, clinical heterogeneity within the group as might be indicated by differing motor score data and HY staging was insufficiently assessed. The relatively small PD sample is a further limitation. In future studies larger more diverse samples are needed for validation. Furthermore, the monocentric design may limit the generalizability of the results. Medication might have an influence on cognitive functioning; however, medication was not assessed. Future studies should include medication status. Due to the fact that the VVT3.0-SDR was included later in the study only 22 PD patients had VVT3.0-SDR and VVT3.0-SQU scores available. Future studies should use equal numbers of test assessments. The lower educational level of PD patients might have influenced the results. In future studies with larger patient samples this effect should be controlled. Data for a more comprehensive neurocognitive assessment were not available which is a limitation of the study. Future studies should incorporate formal neuropsychological testing in order to control for differences in cognitive functioning between groups.
To conclude, alongside motor symptoms, cognitive symptoms should also be given attention in PD patients. The use of the VVT3.0-SCO, VVT3.0-SDR and VVT3.0-SQU might be a good way of assessing visuoconstruction and visual memory in PD. The VVT3.0-SQU score represents stability of the test performance and may enable differentiation between PD patients and healthy individuals.
Funding
No funding was available for the study.
Declarations
Conflict of interest
J. Lehrner is CEO of psimistri GmbH which is the owner of www.psimistri.com. M. Hofmann declares that she has no competing interests.
Ethical standards
This study was approved by the Ethics Committee of the Medical University Vienna. All patients gave informed consent in accordance with the Declaration of Helsinki.
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