Analysis of the Use of Colour for Early Detection of Dementia

Thomas Ostermann

, Sibylle Robens

, Petra Heymann², Sebastian Unger

, Stephan Müller

Christoph Laske

and Ulrich Elbing

Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany

University of Applied Sciences Nürtingen-Geislingen, Germany

Department of Psychiatry and Psychotherapy, Eberhard Karls University, Tübingen, Germany

stephan.mueller@med.uni-tuebingen.de, christoph.laske@med.uni-tuebingen.de, ulrichelbing@arcor.de

Keywords: Alzheimer’s Disease, Free Drawing Task, Colour Perception, Mental Status and Dementia Tests.

Abstract: Cognitive visuo-constructive impairments, which can be detected by drawing tasks are early signs of

Alzheimer’s disease (AD). Additionally, several studies revealed deficits in colour perception for patients

with AD. In a former analysis of the impact of digital tree-drawing parameters on the screening of early

dementia, a logistic regression revealed the number of colours together with the drawing velocity and the

number of line widths changes as discrimination characteristics (ROC AUC=0.90, sensitivity=.86,

specificity=0.82). To analyse the diagnostic importance of colour variations in drawings, a reanalysis of these

data was done with 67 healthy subjects (25 females, mean age 66 ± 10 yrs.) and 56 subjects with early AD

(40 females, mean age 73 ± 9 yrs.). The exclusion of colour variables resulted in a good discrimination of

healthy and AD (ROC AUC=0.89, specificity=0.89) but in a reduction of sensitivity to .77 compared to the

former model. This suggest that the analysis of colour variations in drawings has an important diagnostic

impact.

1 INTRODUCTION

Although the neurodegenerative process of AD

cannot be stopped yet, an early diagnosis allows for

the application of symptomatic therapies which can

temporally reduce symptoms and maintain the

patient’s level of life quality and functioning

(Alzheimer's Association, 2018).

Several drawing tests, such as Shulman’s clock

drawing test (Shulman, Gold, Cohen, & Zucchero,

1993) or the Rey–Osterrieth complex figure test

(Shin, Park, Park, Seol, & Kwon, 2006), have been

established to screen for dementia in assessing several

cognitive functions, e.g. the eye-hand coordination,

concentration, visuospatial and planning skills, and

short-term memory (Freedman et al., 1994). These

screening tests all have in common that the patient

draws with a pen or pencil on a paper sheet and that

the resulting picture is analysed by dementia

specialists.

Actual studies suggest that the digital assessment

of the total drawing process, including time and

kinematic variables as well as texture features,

provides additional prognostic information in

detecting mild cognitive impairment and early AD.

(Muller, Preische, Heymann, Elbing, & Laske, 2017;

Robens, Heymann, et al., 2019; Robens, Ostermann,

et al., 2019; Souillard-Mandar et al., 2016).

In the digital tree-drawing test (dTDT), which was

firstly introduced by Heyman et al. (Heymann et al.,

2018), patients drew a tree from memory with a

digital pen on a Microsoft Surface Pro 3 digitizer

without time restrictions and optionally 12 colours

and 3 line widths. The dTDT analysis of digitally

recorded grey-level texture features revealed

significant differences between cognitive healthy

subjects and patients with early AD, indicating more

homogeneity and less contrast in the pictures of the

dementia patients (Robens, Ostermann, et al., 2019).

Furthermore a current study on the dTDT analysed 19

dTDT characteristics with stepwise logistic

regression models and identified the average painting

velocity in combination with the variation in the use

of colours and line widths as significant predictors for

early AD (Robens, Heymann, et al., 2019). These

results are in accordance with neuropsychological

findings on colour vision deficiencies in AD patients

(Safar & Press, 2011; Wijk, Berg, Sivik, & Steen,

316

Ostermann, T., Robens, S., Heymann, P., Unger, S., Müller, S., Laske, C. and Elbing, U.

Analysis of the Use of Colour for Early Detection of Dementia.

DOI: 10.5220/0008881303160320

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 316-320

ISBN: 978-989-758-398-8; ISSN: 2184-4305

1999) and case reports on artists’ drawings before and

with AD (Lee, Tsai, & Chen, 2015; Maurer &

Prvulovic, 2004).

As nearly all actual screening drawing tasks are

based on pencil-paper drawings, the main question of

the current analysis is, whether the exclusion of

colour variables in a digital tree drawing task does

influence the test’s sensitivity.

2 MATERIAL AND METHODS

The participants came to the clinical ambulance of the

Memory Clinic at the University Hospital of

Tübingen from July 2015 to July 2016 in order to

check their cognitive skills. All participants

underwent neuropsychological testing. The healthy

control subjects had no signs of cognitive

impairments besides normal cognitive aging.

Participants fulfilled the inclusion criteria of normal

or corrected-to-normal visual acuity, of no severe

hearing impairments and had the ability to perform

tests and drawings without physical restrictions. The

study was approved by the local ethical committee of

the University Hospital of Tübingen and all

participants signed an informed consent form after

receiving a detailed explanation of the study.

The diagnostic criteria for early dementia of

Alzheimer’s type (eAD) were defined according to

the National Institute of Neurological and

Communicative Disorders and Stroke Alzheimer’s

Disease and Related Disorders Association

(McKhann et al., 1984). All 56 patients diagnosed

with eAD had a score of 3 or 4 on the Global

Deterioration Scale (Reisberg, Ferris, de Leon, &

Crook, 1982).

In addition to the conventional

neuropsychological tests, participants performed the

dTDT (Heymann et al., 2018). They were asked to

draw a tree from their memory with a digital pen on a

Microsoft Surface Pro 3 digitizer with no time limit

for painting. Windows 8.1 Pro software was

implemented on this multi-touch digital device with

an Intel Core i7-4650U processor (1.7 - 3.3 GHz).

The size of the display area was 25.4 times 16.9 cm

with a resolution of 2160 x 1440. The participants

could hold the display upright or crosswise by

drawing and were able to choose between 3 lines

widths and 12 different colours. The participants

became familiar with the device in drawing one

sample-tree before the actual test started.

The impact of dTDT variables was analysed using

logistic regression models adjusted for age, education

level and gender. Receiver operating characteristic

(ROC) curves of the logistic models and areas under

curves (AUCs) were calculated and compared.

Sensitivities and specificities of different models

based on Youden-index cut-points were calculated.

All statistical calculations were done using SAS

(Version 9.4) and p-values < .05 were considered

significant.

3 RESULTS

The study population included 67 cognitive healthy

subjects (Controls; mean age 65.9 ± 10.3 years; 25

females) and 56 patients with early Alzheimer’s

disease (early AD; mean age 72.7 ± 9.2 years; 40

females).

The years of education were on average 3 years

lower in the early AD (11 years) compared to control

(14 years). The percentage of females was higher in

the group of early AD (71%) compared to the control

group (37%) and early AD subjects were on average

6.8 years older. These demographic differences

suggest an adjustment in the logistic model analysis

for age, gender and education. A brief summary of the

subjects’ characteristics is given in Table 1.

Table 1: Means, standard deviations (SD), median (Med)

and interquartile range (IQR) of clinical and demographic

characteristics.

N Mean SD Med IQR

Control  

Male/female 42/25  

Age(yrs.) 67 65.9 10.3 65 [59,74]

Education(yrs.) 66 14.1 3.0 15 [12,17]

GDS 67 2.1 2.0 1 [0,4]

MMSE 67 29.3 0.8 29 [29,30]

CDT 67 1.2 0.6 1 [1,1]

EarlyAD

Male/female 16/40

Age(yrs.) 56 72.7 9.2 74 [67,80]

Education(yrs.) 56 11.1 2.9 11 [8,13]

GDS 56 3.1 2.2 3 [2,4.5]

MMSE 55 22.3 2.9 22 [20,25]

CDT 56 2.8 0.9 3 [3,3]

GDS=Geriatric Depression Scale; MMSE=Mini Ment al  State

Examination;CDT=ClockDrawingTest.



Figure 1 illustrates examples of tree drawings

from two subjects of the control group, and two

subjects from the early AD group. As can be seen, the

pictures clearly differ in their use of colours.

However, there are also obvious differences in the

shaping and the complexity of the pictures.

Analysis of the Use of Colour for Early Detection of Dementia

317

Figure 1: Examples of digital tree drawings of two healthy

controls and two early AD participants.

Number of Colour- and Line Width Changes

There were significant differences in the number of

line width and colour changes during the painting

process between the subject groups (Figure 2, all p-

values of chi-square test < .0001) with less colour and

line width changes in cognitive impaired patients

compared to healthy subjects. Fifty percent of the

patients with early AD only used one colour

compared to 10% in the control group.

Number of Different Colours and Line Widths

In both subject groups, the most used colours were

green and brown, followed by yellow-orange. Except

for black, all colours were more often used by healthy

subjects (Figure 3).

There was a significant difference in the mean

number of used colours and line widths between

healthy and early AD subjects (Table 2, p-values

<.001), indicating less colour and line width

variations in cognitive impaired individuals.

Figure 2: Number and percentages (in brackets) of subjects

with number of colour and line width changes during the

drawing process.

Figure 3: Percentage of subjects using a specific colour,

grouped by healthy control and early AD (eAD).

Table 2: Means, standard deviations (SD), median (Med)

and interquartile range (IQR) of dTDT characteristics.

Mean SD Med IQR

Control  

No.ofcolours 4.6 1.8 4 [3,6]

No.ofline

widths 2.2 0.8 2 [2,3]

EarlyAD  

No.ofcolours 3.3 1.6 3 [2.5,4]

No.ofline

widths 1.6 0.7 1 [1,2]

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Figure 4: ROC-curves of model 1 (a) with number of

colours, number of line widths and average velocity as

factors and model 2 (b) with number of line widths and

average velocity as factors. Both models were adjusted for

age, gender and years of education.

Table 3 summarizes the results of the two logistic

regression models with and without colour variables

included (model 1 and model 2, respectively). Both

models were adjusted by age, gender and education

level by including these variables as covariates. In

both models, the number of used line widths is a

significant and stable predictor with ORs of 0.328

(model 1) and 0.283 (model 2). The same stability

holds for the average velocity of the strokes, which is

quite comparable within both models (ORs of 0.840

and 0.850). The AUC’s of the ROC-curves based on

both logistic regression models (figure 4) ranged

between 0.89 (model 2, without colours) and 0.90

(model 1), indicating only a slight loss of

discrimination power for model 2. Youden-Index

calculations resulted in sensitivities of 86% (model 1)

and 77% (model 2) and in specificities of 82% (model

1) and 89% (model 2).

Table 3: Results of logistic regression of healthy controls

(n=67) versus patients with early AD (n=56). Models

adjusted for gender, age and education.

Model

AUC



Spec.

%

Sens.

%

Variable OR 95%CI



1 .90 82 86 Colours 0.67 [0.47,0.97]

 Line

widths

0.33 [0.17,0.64]

 Velocity 0.84 [0.74,0.96]

2 .89 89 77 Line

widths

0.28 [0.15,0.54]

 Velocity 0.85 [0.74,0.97]

AUC: Area under curve of receiver operating curve, ß: Estimated

logistic regression coefficient, OR: Odds ratio, CI

: Confidence

Interval of odds ratio; Specificity (Spec.) and Sensitivity (Sens.)

are based on Youden-Index calculations.

4 CONCLUSIONS

In accordance with the literature we observed reduced

colour variations in the images of early AD patients

and found a good separation of healthy from early AD

subjects by combining the number of used colours

with the average painting velocity and the number of

line width changes (ROC AUC=0.90).

After omitting the colour information from the

logistic regression the corresponding AUC was still

good and only slightly smaller (AUC= 0.89), but the

Youden-index calculation revealed a reduction in

sensitivity (0.77).

Several drawing deficits have been reported with

early stages of AD, e.g. image simplifications,

reduced image sizes and disorders of spatial relations

and perspectives (Gragnaniello, Kessler, Bley, &

Mielke, 1998; Heymann et al., 2018; Trojano &

Gainotti, 2016). Paintings of AD patients have been

described as a reduction in colour variations with a

preference to yellow-red in early stages and a

tendency to darker colours in later stages (Lee et al.,

2015; Maurer & Prvulovic, 2004). Results of Wijk et

al. (Wijk et al., 1999) and Pache et al. (Pache et al.,

2003) suggest colour vision deficiencies in AD

patients with difficulties in discriminating between

blue-green colours.

One limitation of the current study is its small

sample size and the results have to be validated in

larger clinical settings. The differences concerning

demographic parameters as age, gender, and

education level, indicated an adjustment in the

logistic regression model. With larger sample sizes,

pre analysis matching of subjects can be applied to

avoid demographic differences between subject

groups.

Analysis of the Use of Colour for Early Detection of Dementia

319

In further analyses, the percentage composition of

colours should be examined, too, as this might be an

important characteristic for identifying patients with

early AD.

Our results suggest that the number of colours

used in a free tree-drawing task has an impact on

discriminating healthy subjects from patients with

early AD. Nevertheless, a good separation of both

subject groups was also achieved by only including

the average velocity and the number of stroke

changes in the ROC-curve analysis.

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