Performance of the Benton Judgment of Line Orientation test across patients with different types of dementia

Abstract

Background

The Benton Judgment of Line Orientation (JLO) test is one of the most frequently used tests for assessing visuospatial function.

Objective

This study aimed to determine the diagnostic and differential performance of JLO for different types of dementia.

Methods

A total of 258 participants, including 68 patients with Alzheimer's disease (AD), 86 with subcortical ischemic vascular dementia (SIVD), 30 with frontotemporal lobar degeneration (FTLD), 22 with Lewy body dementia (LBD), and 52 cognitively unimpaired (CU) controls, were enrolled from a memory clinic. The total scores and error types in the JLO test were compared between groups. Receiver operating characteristic curve analyses were used to estimate the diagnostic and differential abilities of the JLO test for patients with different types of dementia.

Results

We found that the JLO score was significantly lower in patients with AD, SIVD, FTLD, or LBD than in CU controls (12.90 ± 8.72 versus 17.06 ± 6.14 versus 15.47 ± 8.39 versus 9.23 ± 8.96 versus 21.69 ± 3.72, respectively; all p < 0.05). In particular, for patients with LBD, the JLO score was significantly lower than that for patients in the other groups (all p < 0.05) and showed excellent performance in distinguishing LBD patients from CU controls, with an AUC of 0.888 (sensitivity 72.73% and specificity 94.23%) at a cutoff value of 16. Intraquadrant oblique error was the most common type of error in dementia patients.

Conclusions

The JLO test is an effective tool for evaluating visuospatial function in patients with dementia, particularly for identifying LBD patients.

Keywords

Alzheimer's disease frontotemporal lobar degeneration judgment of line orientation Lewy body dementia subcortical ischemic vascular dementia visuospatial function

Introduction

Cognitive impairment, including but not limited to learning and memory, attention and information processing, executive function, language, and visuospatial function, is the main symptom of patients with dementia. Since the characteristics and trajectory of cognitive decline vary across types of dementia due to the pathologically specific involvement of structural and functional brain regions and networks, a comprehensive neuropsychological assessment and interpretation is valuable in the clinical diagnosis, differentiation and evaluation of patients with dementia.

Visuospatial function, which operates upon perceptual stimuli and mental images and allows individuals to interact with the environment,^1,2 is associated with various brain regions, from a basic level of perception mainly related to occipital cortices to a more complex and extended level of integration related to temporal, parietal, and frontal regions based on the ventral (also called “what”) and the dorsal (also called “where”) paths.³ Most visuospatial abilities, including object perception (the ability to recognize familiar objects such as household items or faces) and spatial perception (the ability to recognize the physical location of objects either alone or in relation to other objects), remain intact as individuals age.⁴ In contrast, visuospatial dysfunction is common in patients with dementia, particularly Lewy body dementia (LBD), which includes dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) and is attributed to the predominant impact of synucleinopathy on posterior cerebral regions.^5–7 Visuospatial and constructional impairments have been found in 74% of LBD patients at early stages and could be sensitive cognitive markers for patients with DLB; these impairments have a negative predictive value of 90% for discriminating patients with LBD from patients with Alzheimer's disease (AD),⁸ although visuospatial dysfunction also occurs at mild to moderate stages in patients with AD. Patients with frontotemporal lobar degeneration (FTLD), which primarily affects the frontal and anterior temporal lobes at early stages, usually exhibit relatively preserved visuospatial function. This cognitive feature could improve diagnostic accuracy.^9,10 Our previous study demonstrated that patients with subcortical ischemic vascular dementia (SIVD) had better visuospatial function than did AD patients, although the score on the Benton Judgment of Line Orientation (JLO) test was significantly lower in SIVD patients than in cognitively unimpaired (CU) controls.¹¹

Since visuospatial functions are affected in different ways in patients with cognitive impairment, identifying a tool that is easy to use in the clinical evaluation of dementia patients is important. The JLO test,¹² which measures a person's ability to match the angle and orientation of lines in space, is a widely used neuropsychological tool for assessing visuospatial deficits, particularly visuo-perceptive, in neurological and mental disorders.^13–15 To our knowledge, there are few reports on visuospatial dysfunctions in patients with different types of dementia and their relationships with JLO scores. The JLO test has been shown to have high test-retest reliability¹⁶ and good neuropsychological construct validity, as shown in neuroanatomical localization studies.¹⁷ In addition, unlike other visuospatial tests, the JLO test has the advantages of requiring minimal motor response and avoiding confounding factors, such as constructional praxis and information processing speed.¹⁶ Although there are other visuospatial tasks that also require minimal motor response, such as motion coherence,¹⁸ these tasks may require the use of other cognitive and physical functions, e.g., strong selective attention and oculomotor control for fixations on moving dots over random noise dots.

In addition to a global score, a qualitative analysis of performance in the JLO test has also been proposed as a complementary procedure to measure visuospatial perception functions.¹⁹ Patients with different central nervous system diseases might tend to experience different types of errors in the JLO test, which are attributed to the localization of the brain injury. For example, patients with PD tend to make a greater proportion of severe intraquadrant oblique errors than either patients with AD or healthy controls.²⁰ Apart from severe intraquadrant oblique errors, another study also revealed a greater proportion of horizontal errors in PD patients than in healthy controls.¹⁶ Moreover, compared with healthy controls, PD patients with mild cognitive impairment were shown to make a greater proportion of severe intraquadrant oblique errors and interquadrant errors.²¹ However, previous findings in patients with dementia are limited.

Although the JLO test has been investigated in patients with AD and LBD, the diagnosis was made based only on clinical information in early studies, and comparisons with other common types of dementia, such as SIVD and FTLD, are lacking. In addition, the characteristic types of errors in the JLO test made by individuals with different types of dementia are unknown. In the present study, the JLO test was validated in a Chinese population, given that it comprises simple lines and is rarely influenced by cultural background. We preliminarily explored the effects of age, sex and education on the JLO score in elderly CU individuals and identified the diagnostic and differential value of the JLO score for patients with different types of dementia, including AD, SIVD, FTLD, and LBD; these patients were diagnosed according to recent criteria, including magnetic resonance imaging (MRI) and positron emission tomography (PET) biomarkers.

Methods

Participants

This study was approved by the Ethics Committee of Tianjin Medical University General Hospital. Written informed consent was obtained from all participants. A total of 258 participants, including 68 patients with AD (66 with typical AD and 2 with the logopenic variant of AD), 86 patients with SIVD, 30 patients with FTLD, 22 patients with LBD (18 with DLB and 4 with PDD), and 52 CU controls, were enrolled from the memory clinic of Tianjin Medical University General Hospital from June 2017 to September 2022. All participants were aged 50 to 85 years and underwent a comprehensive evaluation, including medical history, physical examination, clinical laboratory tests, brain MRI, and neuropsychological assessments.

AD patients met the diagnostic criteria of the International Working Group-2,²² with a positive result for ¹¹C-labeled Pittsburgh compound B (PiB) PET. SIVD patients met the diagnostic criteria for vascular behavioral and cognitive disorders²³ and had evidence of subcortical ischemic lesions, such as white matter hyperintensities (WMHs), lacunes, enlarged perivascular spaces (PVSs), and microbleeds, on MRI. FTLD patients met the diagnostic criteria for the behavioral variant of FTD (bvFTD),²⁴ nonfluent/agrammatic variant primary progressive aphasia (nfvPPA), or semantic variant primary progressive aphasia (svPPA) according to the classification recommendations for PPA,²⁵ supported by specific atrophy or hypometabolism features on MRI or PET. DLB patients met the revised criteria for the clinical diagnosis of probable DLB,²⁶ with two or more core clinical features, including fluctuating cognition, recurrent visual hallucinations, rapid eye movement sleep behavior disorder, and spontaneous cardinal features of parkinsonism. PDD patients met the Movement Disorder Society (MDS) criteria,²⁷ exhibiting both Parkinson's disease and a dementia syndrome characterized by impairment in at least two of the four core cognitive domains, including attention, executive functions, visuospatial functions, and free-recall memory, and the presence of at least one behavioral symptom, including apathy, depressed or anxious mood, hallucinations, delusions, and excessive daytime sleepiness. All patients had a Mini-Mental State Examination (MMSE) score within the range of 10–26, a Clinical Dementia Rating (CDR) scale score of 0.5–2 and a 17-item Hamilton Rating Scale for Depression (HAMD-17) score less than 7. Patients whose cognitive decline was caused by other neurological diseases, mental disorders, or medical conditions, such as multiple sclerosis, severe depression, vitamin B₁₂ deficiency, or thyroid dysfunction, were excluded. According to comprehensive neuropsychological assessments, the CU controls had no cognitive complaints or objective cognitive impairment and demonstrated independence in execution of activities of daily living, with a CDR = 0 and an MMSE >26.

Neuropsychological assessment

All participants underwent cognitive and functional assessments with a neuropsychological battery as previously described.²⁸ For the JLO test (Supplemental Figure 1), the participants had to make judgments regarding the relative spatial orientation of pairs of line segments in 5 practice items followed by 30 test items. For each item, there is a test card that appears on the top page of the booklet and a reference card that appears on the bottom page of the booklet. The five practice items include full length lines at the top of the page, whilst the test items consist of a pair of partial lines. During the test, the rater faced the subject and placed the cards on the table so that the subject could clearly see both the test card and the reference card. The participants can point at the test booklet but are not allowed to touch it or move it. Feedback was provided during the five practice questions, and the instructions and practice cards could be repeated until the subject could answer correctly before the formal test began. The test was discontinued if a participant did not improve or did not demonstrate understanding of the task. The number of items for which judgments for both lines were correct was calculated as the total score, ranging from 0 to 30.

In addition, the errors were classified into four main types using the procedure developed by Ska et al. (1990).

Intraquadrant oblique error (QO) corresponds to an error occurring between lines from the same quadrant (a quadrant is constituted by half of the 11-line array, e.g., lines numbered from 2 to 5 and lines numbered from 7 to 10). There are four subtypes of intraquadrant oblique error: an oblique confused with another oblique difference by only one spacing of 18° (QO1) or by two or three spacings (QO2); both obliques displaced to one or two spacings in the same direction maintaining the initial spacing (QO3) or without maintaining the initial spacing (QO4).

Vertical and horizontal errors are divided into three subtypes: a vertical error involving an incorrect identification of the vertical line numbered 6 (V); a horizontal error involving an incorrect identification of one horizontal line numbered 1 or 11 (H); and a vertical and horizontal error involving simultaneous incorrect identification of the vertical and one of the horizontal lines (VH).

Interquadrant oblique errors were defined as the displacement of one line from one quadrant to the other quadrant (IQO).

Combined oblique interquadrant and vertical or horizontal errors were divided into two subtypes: combined oblique interquadrant and vertical errors involving incorrect answers combined with an IQO error and a V error (IQOV) and combined oblique interquadrant and horizontal errors involving incorrect answers combining an IQO error and an H error (IQOH).

To qualitatively analyze the performance of the JLO model, the proportion of each error type was calculated by the following equation: (No. of errors in one type/No. of total errors) × 100.

Statistical analyses

Statistical analyses were conducted using the SPSS statistical package version 25.0 and MedCalc 19.1. The level of significance was set at α = 0.05. Raw scores from the cognitive tests were used for the analyses in this study. We assessed the normality of the data with the Shapiro‒Wilk test. For continuous variables, differences between groups were compared using one-way analysis of variance, followed by post hoc least significant difference (LSD) tests (normally distributed data, including age, education, age at onset, disease duration and cognitive test scores) or Kruskal‒Wallis tests followed by the Mann‒Whitney U test (skewed data, including the proportion of JLO error types), with the Bonferroni correction for multiple comparisons applied. Differences in sex and handedness distributions between groups were compared using Pearson's χ² test. The G*Power software was used to calculate post hoc power. The associations between the JLO score and the combined scores of the visuospatial items on the MMSE (drawing item) and the MoCA (cube copy item and clock drawing test) were analyzed using Spearman's correlation coefficient.

Stepwise multiple linear regression models were applied to determine the effects of age, sex and education level on the JLO score in CU participants. The interactions between age, sex and education across the JLO scores were then analyzed using a general linear model, in which CU participants were stratified into three groups according to age (58–69, 70–79, and 80–90 years) and three groups according to education level (0–9, 10–14, and 15 or more years of formal education). Receiver operating characteristic (ROC) analyses were performed to estimate the diagnostic ability of the JLO, MMSE and MoCA scores for discriminating between patients with different types of dementia and CU controls. The optimal cutoff scores were calculated based on the maximum sum of the sensitivity and specificity using the ROC curve.

Results

Demographic and clinical characteristics of all participants

The demographic and clinical data of the patients in the AD, SIVD, FTLD, LBD, and CU groups are shown in Table 1. There were significant differences in age (69.01 ± 7.92 versus 73.26 ± 7.37 versus 65.37 ± 7.92 versus 72.23 ± 6.64 versus 70.17 ± 6.21); sex distribution (proportion of females: 70.6% versus 39.5% versus 60.0% versus 27.3% versus 59.6%); years of education (10.90 ± 3.58 versus 11.38 ± 3.28 versus 10.90 ± 3.98 versus 10.36 ± 3.02 versus 12.46 ± 3.36) between the five groups; and age at onset (64.27 ± 7.69 versus 67.58 ± 7.11 versus 66.50 ± 4.97 versus 68.23 ± 6.80) between the patient groups. Post-hoc power calculation showed that the samples were sufficiently powered (1-β = 0.908 for continuous variables and 0.952 for sex and handedness) to detect differences in demographic and clinical characteristics. Specifically, patients with FTLD were significantly younger than patients in the other groups and CU controls were (p < 0.05), and patients with SIVD were older than those with AD, FTLD, and CU controls were (p < 0.05). There was a greater proportion of females in the AD group than in the SIVD group (p < 0.05) and the LBD group (p < 0.05). Patients with AD, FTLD, and LBD had a lower education level than did the CU controls (p < 0.05). For age at onset, patients with AD were younger than patients with SIVD or LBD were (p < 0.05). There was no significant difference in handedness (p = 0.282) or disease duration (p = 0.08) across groups.

Table 1.

Demographic and clinical characteristics of each group.

	AD ( N = 68)	SIVD ( N = 86)	FTLD ( N = 30)	LBD ( N = 22)	CUs ( N = 52)	F/χ²	p
Age, y	69.01 ± 7.92^cd	73.26 ± 7.37^abd	65.37 ± 7.92^abc	72.23 ± 6.64^d	70.17 ± 6.21^cd	7.785	<0.001
Sex, F (%)	52 (70.6) ^c	34 (39.5) ^b	18 (60.0)	6 (27.3) ^b	31 (59.6) ^c	22.058	0.003
Education, y	10.90 ± 3.58^a	11.38 ± 3.28	10.90 ± 3.98^a	10.36 ± 3.02^a	12.46 ± 3.36^bd	2.229	0.066
Age at onset, y	64.27 ± 7.69^c	67.58 ± 7.11^b	66.50 ± 4.97	68.23 ± 6.80^b	−	2.954	0.034
Handedness, R (%)	60(88.2)	79(91.9)	26(86.7)	20(90.9)	51(98.1)	9.761	0.282
Disease duration, y	5.11 ± 2.87	5.55 ± 3.04	3.17 ± 1.72	4.05 ± 3.57	−	2.289	0.080
MMSE	18.21 ± 5.80^ac	21.93 ± 4.65^abd	18.77 ± 6.78^ac	19.09 ± 5.69^ac	27.60 ± 1.58^bcd	30.826	<0.001
MoCA	13.90 ± 5.31^ac	16.89 ± 5.47^abd	13.80 ± 6.45^ac	14.45 ± 5.06^ac	24.69 ± 2.62^bcd	37.866	<0.001
JLO	12.90 ± 8.72^ac	17.06 ± 6.14^ab	15.47 ± 8.39^a	9.23 ± 8.96^abcd	21.69 ± 3.72^bcd	17.153	<0.001

Variables are presented as the mean ± SD, except for sex and handedness, as specifically indicated. CU: cognitively unimpaired; AD: Alzheimer's disease; SIVD: subcortical ischemic vascular dementia; FTLD: frontotemporal lobar degeneration; LBD: Lewy body dementia; MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment Scale; JLO: Judgment of Line Orientation.

p < 0.05 versus CU.

p < 0.05 versus AD.

p < 0.05 versus SIVD.

p < 0.05 versus FTLD.

The MMSE (18.21 ± 5.80 versus 21.93 ± 4.65 versus 18.77 ± 6.78 versus 19.09 ± 5.69 versus 27.60 ± 1.58, p < 0.001) and MoCA (13.90 ± 5.31 versus 16.89 ± 5.47 versus 13.80 ± 6.45 versus 14.45 ± 5.06 versus 24.69 ± 2.62, p < 0.001) scores were significantly lower in the AD, SIVD, FTLD, and LBD groups than in the CU group. A comparison of the patient groups revealed that the SIVD group had higher MMSE and MoCA scores than did the AD, FTLD, and LBD groups (all p < 0.05). The JLO scores were positively correlated with the combined scores of visuospatial items on the MMSE and MoCA (r = 0.455, p < 0.001).

Total JLO score across groups and age, sex, and education effects

The mean JLO scores are presented in Table 1 and Figure 1(a). There was a significant main effect of JLO score on the group comparison (p < 0.001). Post hoc analyses revealed that the JLO score was significantly lower in each patient group than in the CU group (12.90 ± 8.72 versus 17.06 ± 6.14 versus 15.47 ± 8.39 versus 9.23 ± 8.96 versus 21.69 ± 3.72, all p < 0.05). A comparison of patient groups revealed that the LBD group had a markedly lower JLO score than the other patient groups did (all p < 0.05).

Figure 1.

The JLO score and its diagnostic performance in patients with different types of dementia. The JLO score was lower in all patient groups than in the CU group; the LBD group had a much lower JLO score than the other patient groups, and the AD group had a lower JLO score than the SIVD group (a). According to the ROC curve, the JLO score could be used to discriminate patients with all types of dementia effectively (b), patients with AD (c), patients with SIVD (d), patients with FTLD (e), and, in particular, patients with LBD (f) from CU controls. AUC: area under the curve; CU: cognitively unimpaired; AD: Alzheimer's disease; SIVD: subcortical ischemic vascular dementia; FTLD: frontotemporal lobar degeneration; LBD: Lewy body dementia; ROC: receiver operating characteristic. ***p < 0.001, **p < 0.01, *p < 0.05.

We identified the effects of age, sex and education on the JLO score in the CU group, with ages ranging between 58 and 85 years. After stepwise multiple linear regression analyses, the JLO score was demonstrated to be correlated with sex (R²= 0.224, p < 0.001) and education level (R²= 0.114, p = 0.006) but not age (Table 2). Education accounted for only 11.4% of the shared variance, while sex had a more important effect (22.4%), with males exhibiting better performance than females (B = −3.512, p < 0.001; male as the reference). There were no significant interaction effects of sex*education (F = 0.206, p = 0.815), age*education (F = 0.361, p = 0.699) or age*sex (F = 0.049, p = 0.952) on the JLO score.

Table 2.

Effects of age, sex, and education on JLO scores in cognitively unimpaired individuals.

VARIABLE	B	CI	ΔR²	p
Sex (male as reference)	−3.512	−5.266∼−1.759	0.224	<0.001
Education	0.374	0.115∼0.632	0.114	0.006
Constant	22.643	18.256∼27.030		<0.001

Excluded variable: age. A stepwise multiple linear regression model was used.

Diagnostic and differential performance of the JLO score

JLO had a sensitivity of 51.94% and a specificity of 94.23% at a cutoff value of 16, with an area under the curve (AUC) of 0.770 for discriminating all patients with dementia from CUs (Figure 1(b)). For discriminating AD patients from CUs (Figure 1(c)), the JLO had a satisfactory AUC of 0.801 with a sensitivity of 54.41% and a specificity of 94.23% at a cutoff value of 16. For discriminating SIVD patients (Figure 1(d)) and FTLD patients (Figure 1(e)) from CUs, the JLO had moderate AUCs of 0.727 (sensitivity 46.51%, specificity 94.23%) and 0.734 (sensitivity 46.67%, specificity 94.23%), respectively, at a cutoff value of 16. The JLO showed excellent performance, with an AUC of 0.888, sensitivity of 72.73%, and specificity of 94.23% at a cutoff value of 16 for identifying patients with LBD from CUs (Figure 1(f)).

For discriminating patients with different types of dementia, the JLO score had an AUC of 0.744 (sensitivity 45.5%, specificity 98.8%) and 0.689 (sensitivity 45.5%, specificity 86.7%) at a cutoff value of 3 for distinguishing SIVD patients and FTLD patients from LBD patients (Figure 2(b,c)) and an AUC of 0.627 (sensitivity 91.9%, specificity 32.4%) at a cutoff value of 7 for distinguishing AD patients from SIVD patients (Figure 2(d)). The JLO score could not be used to effectively distinguish AD patients from LBD patients (AUC = 0.618; Figure 2(a)) or to distinguish AD patients and SIVD patients from FTLD patients (AUC = 0.535 to 0.576; Figure 2(e,f)). The diagnostic and differential performance metrics of the JLO test are presented in Table 3. Although the JLO score had a greater AUC in discriminating LBD patients from patients with other types of dementia than did the MMSE and MoCA scores, there was no significant difference in the differential ability for all types of dementia among the three tests (Supplemental Figure 2).

Figure 2.

Differential performance of the JLO for different types of dementia. According to the ROC curves, the JLO score could not be effectively used to distinguish patients with AD from patients with LBD (a) but could distinguish patients with SIVD (b) and patients with FTLD (c) from patients with LBD; it could also distinguish patients with AD from patients with SIVD (d) but could not distinguish patients with AD (e) or patients with SIVD (f) from patients with FTLD. AUC: area under the curve; CU: cognitively unimpaired; AD: Alzheimer's disease; SIVD: subcortical ischemic vascular dementia; FTLD: frontotemporal lobar degeneration; LBD: Lewy body dementia; ROC: receiver operating characteristic.

Table 3.

Diagnostic performance of the JLO test.

	AUC	Cutoff score	Sensitivity	Specificity	p
Dementia versus CU	0.770	≤16	51.94	94.23	<0.001
AD versus CU	0.801	≤16	54.41	94.23	<0.001
SIVD versus CU	0.727	≤16	46.51	94.23	<0.001
FTLD versus CU	0.734	≤16	46.67	94.23	<0.001
LBD versus CU	0.888	≤16	72.73	94.23	<0.001
AD versus LBD	0.618	≤3	45.45	75.00	0.088
SIVD versus LBD	0.744	≤3	45.45	98.84	<0.001
FTLD versus LBD	0.689	≤3	45.45	86.67	0.011
AD versus SIVD	0.627	>7	91.86	32.35	0.006
AD versus FTLD	0.576	>15	66.67	51.47	0.229
SIVD versus FTLD	0.535	≤10	30.00	84.88	0.598

AUC: area under the curve; CU: cognitively unimpaired; AD: Alzheimer's disease; SIVD: subcortical ischemic vascular dementia; FTLD: frontotemporal lobar degeneration; LBD: Lewy body dementia.

Error type analysis of the JLO between different groups

Of all 258 participants, 30 (AD = 14, SIVD = 1, FTLD = 4, and LBD = 11) were excluded from the error type analysis because there was no specific answer to any item on the JLO test. There were significant differences in the percentages of QO errors (H = 25.219, p < 0.001), V or H errors (H = 17.636, p = 0.001), and IQO errors (H = 17.551, p = 0.002) between the five groups (Table 4). Although patient groups also tended to have more combined errors than did CU controls, the difference between all groups did not reach statistical significance. However, the sample size was not powered enough to accurately detect differences for the comparison of error types between patient groups (1-β = 0.261–0.796).

Table 4.

Relative frequency of error types among groups.

	AD ( N = 54)	SIVD ( N = 85)	FTLD ( N = 26)	LBD ( N = 11)	CUs ( N = 52)	H	p
QO error	100.00 (87.05,100.00) ^a	100.00 (90.69,100.00) ^a	100.00 (89.38,100.00) ^a	100.00 (85.71,100.00)	100.00 (100.00,100.00) ^bcd	25.219	<0.001
QQ1	66.67 (45.87,78.33) ^a	71.43 (50.00,86.61) ^a	72.08 (59.58,81.82)	72.73 (68.75,78.57)	80.91 (70.00,100.00) ^bc	19.175	0.001
QQ2	0.00 (0.00,14.42)	0.00 (0.00,8.89)	0.00 (0.00,8.27)	0.00 (0.00,10.53)	0.00 (0.00,0.00)	7.319	0.120
QQ3	12.77 (7.33,20.00)	14.29 (8.33,21.58)	14.29 (6.61,20.00)	16.67(6.25, 30.00)	10.00(0.00, 18.18)	4.537	0.338
QQ4	6.46 (0.00,12.63) ^a	0.00 (0.00,9.55)	0.00 (0.00,10.28)	0.00 (0.00,7.14)	0.00 (0.00, 7.33) ^b	10.658	0.031
V and/or H error	0.00 (0.00,1.04) ^a	0.00 (0.00,4.76) ^a	0.00 (0.00,5.49)	0.00 (0.00,0.00)	0.00 (0.00,0.00) ^c	17.636	0.001
V	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	7.198	0.126
H	0.00 (0.00,0.00)^a	0.00 (0.00,0.00) ^a	0.00 (0.00,5.49)	0.00 (0.00,0.00)	0.00 (0.00,0.00) ^bc	14.603	0.006
VH	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	1.682	0.794
IQO errors	0.00 (0.00,4.89) ^a	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,7.14) ^a	0.00 (0.00,0.00) ^b	17.551	0.002
Combined errors	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	6.741	0.150
IQOV	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	4.516	0.341
IQOH	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	0.00 (0.00,0.00)	5.537	0.237

Variables are presented as the median (interquartile range). CU: cognitively unimpaired; AD: Alzheimer's disease; SIVD: subcortical ischemic vascular dementia; FTLD: frontotemporal lobar degeneration; LBD: Lewy body dementia; QO: intraquadrant oblique error; V: vertical error; H: horizontal error; IQO: interquadrant oblique error; IQOV: combined oblique interquadrant and vertical error; IQOH, combined oblique interquadrant and horizontal error.

p < 0.005 versus CU; ^bp < 0.005 versus AD; ^cp < 0.005 versus SIVD; ^dp < 0.005 versus FTLD.

No CUs made visual perception mistakes involving V and/or H errors, IQO errors or combined errors (IQOH or IQOV). QO1 errors were the most common mistakes in all groups, accounting for 66.67%, 71.43%, 72.08%, and 72.73% of all mistakes made by AD, SIVD, FTLD, and LBD patients, respectively, and more than 80% of all mistakes made by CUs. The percentages of QO1 errors were lower in AD patients and SIVD patients than in CUs (both p < 0.05). Compared with CUs, AD patients also made more severe intraquadrant errors (QO4) (p < 0.01), accounting for 6.46% of these errors in the AD group. A greater percentage of H errors was detected in AD and SIVD patients than in CUs (both p < 0.05). AD and LBD patients made more IQO errors than did CUs (both p < 0.05). Compared with CUs, FTLD patients had a lower percentage of total QO errors (p < 0.05). However, there was no significant difference in any error type between patient groups.

Discussion

This study provides evidence of JLO performance in a Chinese elderly population that included CU individuals and patients with different types of dementia. The JLO score was correlated with sex and education level and was significantly lower in patients with AD, SIVD, FTLD, and LBD. In particular, for patients with LBD, the JLO score showed good diagnostic performance (AUC = 0.888), with a sensitivity of 72.73% and a high specificity of 94.23%. In addition, patients with different types of dementia tended to make different types of errors in the JLO.

We observed a similar cutoff score for JLO (16 versus 15–19) for discriminating patients with dementia from healthy elderly individuals to those previously obtained in English-, French-, or Spanish-speaking populations.^29,30 In this study, the mean JLO score was 21.8 in cognitively unimpaired healthy elderly individuals, which is slightly lower than that reported in previous studies in English-speaking populations.^20,29,30 This discrepancy might be attributed to differences in demographic factors, such as sex and education, which were demonstrated to influence the JLO score in this study.^20,31,32 JLO scores showed a moderate correlation with visuospatial items in the MMSE and MoCA, further validating the reliability of the JLO test in visuospatial evaluation. However, compared with copy drawing and clock drawing in the MMSE and MoCA, which mainly assess visuo-constructive abilities and require additional cognitive resources such as action planning (executive functions), recall (memory), and motor control,³³ the JLO test primarily assesses visuo-perceptual and visuospatial processing, with no or minimal requirements for additional cognitive or physical functions.

We found that sex was the main demographic factor contributing to JLO performance in elderly individuals, which is consistent with previous findings.³⁴ Sex differences in visuospatial function might be associated with differences in brain organization. Gur and colleagues reported that while the line orientation task activated both hemispheres in men, it chiefly acted on the right hemisphere in women.³⁵ In addition, our results indicated that education influenced JLO performance, although its effect was relatively small. A previous study³⁶ also revealed a modest impact of education on JLO scores in older adults, suggesting higher scores for more highly educated individuals. However, we did not find a correlation between age and the JLO score. This result is supported by general recognition and most of the previous evidence that aging has little influence on visuospatial functions and has no significant effect on JLO performance.^37,38

In this study, the JLO scores were lower in patients with each type of dementia. Visual spatial ability is composed of a multifaceted set of functions mediated by a predominantly right-hemisphere network of widely distributed brain regions, including the parietal lobes, lateral prefrontal cortex, medial temporal lobes, inferior temporal cortex, and occipital cortex.³⁹ The right parietal and occipital regions, as well as bilateral frontal areas, have been shown to be activated on functional MRI in people performing the JLO test.^40,41 Moreover, poor performance on the JLO was shown to be associated with lesions in the right superior parietal region,⁴² the right posterior parietal region,¹⁷ the right supramarginal gyrus, the right frontal lobe, and the right superior temporal lobe⁴³ using a lesion mapping technique. Another study revealed that JLO performance was best predicted by cortical thickness in the right temporal lobe.³²

Our results showed that the JLO score was more prominently decreased in patients with LBD than in patients with other common types of dementia; additionally, the JLO score had excellent performance (AUC of 0.888, sensitivity of 72.73%, specificity of 94.23%) for distinguishing patients with LBD from CUs and differentiating patients with LBD from patients with SIVD and FTLD under conditions of similar global cognition. The JLO score also showed good performance (AUC of 0.801, sensitivity of 54.41%, specificity of 94.23%) for distinguishing patients with AD from CUs and was lower in AD patients than in SIVD patients. Although both LBD and AD patients showed hypoperfusion and hypometabolism in the parietal and temporal cortex, LBD patients also had obvious functional changes in the occipital lobes, including the primary visual cortex,^6,44–47 which is the most critical cortical region for the early steps of visual processing. The dorsal stream (where/action) pathway is particularly vulnerable to AD pathology, with visuospatial and motion perceptual impairment occurring early in the disease course, even in the absence of posterior cortical atrophy (PCA) syndrome.^3,39,48 In this study, the JLO score was lower in LBD patients than in AD patients, but these two types of dementia could not be efficiently distinguished.

SIVD patients and FTLD patients also had lower JLO scores than did CUs. Visuospatial dysfunction was previously observed in SIVD patients.^11,49 Lesions associated with vascular disease might directly or indirectly damage visuospatial processing networks, such as the extrastriatal, posterior parietal, and posterior temporal regions.³ A previous study also revealed that patients with bvFTD had worse performance than healthy persons on almost all visuospatial tasks.⁵⁰ Although primary visual spatial processing regions, such as the parietal, inferior temporal, and occipital cortex, are relatively spared in the early stages of FTLD pathology, visual discrimination reversal learning and inhibition of spatial attention, which are predominantly associated with ventral and medial aspects of the prefrontal cortex, could be impaired.^51,52 Therefore, whether executive function or actual visuospatial dysfunction is the cause of worse performance in visuospatial tasks in bvFTD patients is under debate.^53–57 Taken together, these findings suggest that distinguishing between AD, SIVD and FTLD patients based on the JLO test alone is difficult.

Previous findings concerning the types of JLO error made by dementia patients are limited and inconsistent. AD patients were more likely to have different quadrants of misjudgment than CU controls were in one study¹⁹ but not in another study.²⁰ In our study, dementia patients made more errors of all error types than did CU controls. Only healthy elderly individuals made QO errors, most of which were the mildest type of error. In contrast, dementia patients made a greater proportion of severe errors, such as V and/or H errors (AD and SIVD), IQO errors (AD and DLB) and combined errors in addition to QO errors (AD, SIVD and FTLD), suggesting a substantial deficit in visuospatial perception that was not only caused by inattention. However, we did not find any difference in the proportion of error types between different types of dementia. An early study showed that DLB patients suffering from psychosis made more VH and IQOH errors than did AD patients.⁵⁸ Accordingly, the feature of the JLO error type in different types of dementia is still unclear and influenced by several confounders, such as clinical phenotype and disease stage.

This study has several limitations. Although comprehensive evaluations, such as neuropsychological assessment, multimodal brain MRI, and additional amyloid PET for AD patients, were conducted on all participants, specific biomarkers were not used to exclude potential AD pathology for all patients with other types of dementia and CU controls. Furthermore, structural and functional imaging, which are substantially helpful in revealing and understanding the neural basis of visuospatial dysfunction in patients with different types of dementia, were not analyzed in the present study. Finally, since a proportion of patients did not give any specific answers during JLO testing due to severe impairment in visuospatial function, not all participants (e.g., only half of the LBD patients) were analyzed for error type. Conclusions regarding the types of errors on the JLO test among patients with different types of dementia could not be drawn based on the current findings because of the small sample size.

In conclusion, this study confirmed that the JLO is effective at detecting visuospatial deficits in Chinese patients with different common types of dementia and is influenced mainly by sex. Specifically, the JLO showed excellent performance in identifying patients with LBD.

Supplemental Material

sj-docx-1-alz-10.1177_13872877241289382 - Supplemental material for Performance of the Benton Judgment of Line Orientation test across patients with different types of dementia

Supplemental material, sj-docx-1-alz-10.1177_13872877241289382 for Performance of the Benton Judgment of Line Orientation test across patients with different types of dementia by Huifeng Chen, Lijun Wang, Feifan Chen, Wenhao Sun, Siqi Wang and Nan Zhang in Journal of Alzheimer's Disease

Footnotes

Acknowledgments

We thank all the participants who agreed to participate in this study and all the members involved in the selection and assessment.

ORCID iD

Nan Zhang

Author contributions

Huifeng Chen (Data curation; Formal analysis; Validation; Writing – original draft); Lijun Wang (Formal analysis; Funding acquisition; Methodology); Feifan Chen (Data curation; Investigation); Wenhao Sun (Formal analysis; Methodology); Siqi Wang (Data curation; Investigation); Nan Zhang (Conceptualization; Funding acquisition; Methodology; Project administration; Supervision; Writing – review & editing).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Science and Technology Innovation 2030—Major Project (2021ZD0201805), the Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-004A), and the Tianjin Health Research Project (ZC20073).

Declaration of conflicting interests

Nan Zhang is an Associate Editor of this journal but was not involved in the peer-review process of this article or had access to any information regarding its peer-review status. The other authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data supporting the findings of this study are available upon request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Supplemental material

Supplemental material for this article is available online.

References

Hill

Stuart

Lord

, et al. Vision, visuo-cognition and postural control in Parkinson's disease: an associative pilot study. Gait Posture 2016; 48: 74–76.

Trojano

Siciliano

Pedone

, et al. Italian Normative data for the battery for visuospatial abilities (TERADIC). Neurol Sci 2015; 36: 1353–1361.

Pelak

. The clinical approach to the identification of higher-order visual dysfunction in neurodegenerative disease. Curr Neurol Neurosci Rep 2022; 22: 229–242.

Harada

Natelson Love

Triebel

. Normal cognitive aging. Clin Geriatr Med 2013; 29: 737–752.

Spillantini

Goedert

. The alpha-synucleinopathies: Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. Ann N Y Acad Sci 2000; 920: 16–27.

Sato

Hanyu

Hirao

, et al. Deep gray matter hyperperfusion with occipital hypoperfusion in dementia with Lewy bodies. Eur J Neurol 2007; 14: 1299–1301.

Walker

Possin

Boeve

, et al. Lewy body dementias. Lancet 2015; 386: 1683–1697.

Tiraboschi

Salmon

Hansen

, et al.

What best differentiates Lewy body from Alzheimer's disease in early-stage dementia?

Brain 2006; 129: 729–735.

Harciarek

Jodzio

. Neuropsychological differences between frontotemporal dementia and Alzheimer's disease: a review. Neuropsychol Rev 2005; 15: 131–145.

10.

Stopford

Snowden

Thompson

, et al. Variability in cognitive presentation of Alzheimer's disease. Cortex 2008; 44: 185–195.

11.

Tian

Zhang

Liu

, et al. A neuropsychological profile and its correlation with neuroimaging markers in patients with subcortical ischaemic vascular dementia. Int J Geriatr Psychiatry 2023; 38: e5900.

12.

Benton

Varney

Hamsher

. Visuospatial judgment. A clinical test. Arch Neurol 1978; 35: 364–367.

13.

Bilo

Santangelo

Improta

, et al. Neuropsychological profile of adult patients with nonsymptomatic occipital lobe epilepsies. J Neurol 2013; 260: 445–453.

14.

Silver

Goodman

. Verbal as well as spatial working memory predicts visuospatial processing in male schizophrenia patients. Schizophr Res 2008; 101: 210–217.

15.

Valenti

Charidimou

Xiong

, et al. Visuospatial functioning in cerebral amyloid angiopathy: a pilot study. J Alzheimers Dis 2017; 56: 1223–1227.

16.

Montse

Pere

Carme

, et al. Visuospatial deficits in Parkinson's disease assessed by judgment of line orientation test: error analyses and practice effects. J Clin Exp Neuropsychol 2001; 23: 592–598.

17.

Tranel

Vianna

Manzel

, et al. Neuroanatomical correlates of the Benton facial recognition test and judgment of line orientation test. J Clin Exp Neuropsychol 2009; 31: 219–233.

18.

Merabet

Manley

Pamir

, et al. Motion and form coherence processing in individuals with cerebral visual impairment. Dev Med Child Neurol 2023; 65: 1379–1386.

19.

Ska

Poissant

Joanette

. Line orientation judgment in normal elderly and subjects with dementia of Alzheimer's type. J Clin Exp Neuropsychol 1990; 12: 695–702.

20.

Finton

Lucas

Graff-Radford

, et al. Analysis of visuospatial errors in patients with Alzheimer's disease or Parkinson's disease. J Clin Exp Neuropsychol 1998; 20: 186–193.

21.

Galtier

Nieto

Mata

, et al. Analyses of visuospatial and visuoperceptual errors as predictors of dementia in Parkinson's disease patients with subjective cognitive decline and mild cognitive impairment. J Int Neuropsychol Soc 2021; 27: 722–732.

22.

Dubois

Feldman

Jacova

, et al. Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. Lancet Neurol 2014; 13: 614–629.

23.

Sachdev

Kalaria

O'Brien

, et al. Diagnostic criteria for vascular cognitive disorders: a VASCOG statement. Alzheimer Dis Assoc Disord 2014; 28: 206–218.

24.

Rascovsky

Hodges

Knopman

, et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 2011; 134: 2456–2477.

25.

Gorno-Tempini

Hillis

Weintraub

, et al. Classification of primary progressive aphasia and its variants. Neurology 2011; 76: 1006–1014.

26.

McKeith

Boeve

Dickson

, et al. Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB consortium. Neurology 2017; 89: 88–100.

27.

Emre

Aarsland

Brown

, et al. Clinical diagnostic criteria for dementia associated with Parkinson's disease. Mov Disord 2007; 22: 1689–1707. quiz 1837.

28.

Meng

Liu

, et al. The identification and cognitive correlation of perfusion patterns measured with arterial spin labeling MRI in Alzheimer's disease. Alzheimers Res Ther 2023; 15: 75.

29.

Hudon

Belleville

Belzile

, et al. Normative data for the Judgment of Line Orientation Test (long and short forms) in the Quebec-French population aged between 50 and 89 years. Arch Clin Neuropsychol 2023. doi: https://doi.org/10.1093/arclin/acad077

30.

Pena-Casanova

Quintana-Aparicio

Quinones-Ubeda

, et al. Spanish multicenter normative studies (NEURONORMA project): norms for the visual object and space perception battery-abbreviated, and judgment of line orientation. Arch Clin Neuropsychol 2009; 24: 355–370.

31.

Gullett

Price

Nguyen

, et al. Reliability of three Benton Judgment of Line Orientation short forms in idiopathic Parkinson's disease. Clin Neuropsychol 2013; 27: 1167–1178.

32.

Zink

Miller

Caldwell

JZK

, et al. The relationship between neuropsychological tests of visuospatial function and lobar cortical thickness. J Clin Exp Neuropsychol 2018; 40: 518–527.

33.

Liebermann-Jordanidis

Roheger

Boosfeld

, et al. Which test is the best to assess visuo-cognitive impairment in patients with Parkinson's disease with mild cognitive impairment and dementia? A systematic review and meta-analysis. J Parkinsons Dis 2022; 12: 1749–1782.

34.

Caparelli-Daquer

Oliveira-Souza

Moreira Filho

. Judgment of line orientation depends on gender, education, and type of error. Brain Cogn 2009; 69: 116–120.

35.

Gur

Alsop

Glahn

, et al. An fMRI study of sex differences in regional activation to a verbal and a spatial task. Brain Lang 2000; 74: 157–170.

36.

Lucas

Ivnik

Smith

, et al. Mayo's older African Americans normative studies: norms for Boston naming test, controlled oral word association, category fluency, animal naming, token test, WRAT-3 Reading, trail making test, stroop test, and judgment of line orientation. Clin Neuropsychol 2005; 19: 243–269.

37.

Eslinger

Benton

. Visuoperceptual performances in aging and dementia: clinical and theoretical implications. J Clin Neuropsychol 1983; 5: 213–220.

38.

Eslinger

Damasio

Benton

, et al. Neuropsychologic detection of abnormal mental decline in older persons. JAMA 1985; 253: 670–674.

39.

Possin

. Visual spatial cognition in neurodegenerative disease. Neurocase 2010; 16: 466–487.

40.

Kesler

Haberecht

Menon

, et al. Functional neuroanatomy of spatial orientation processing in Turner syndrome. Cereb Cortex 2004; 14: 174–180.

41.

Lee

Liu

Hung

, et al. The cerebellum's involvement in the judgment of spatial orientation: a functional magnetic resonance imaging study. Neuropsychologia 2005; 43: 1870–1877.

42.

Bullmore

de Zubicaray

, et al. Identifying rate-limiting nodes in large-scale cortical networks for visuospatial processing: an illustration using fMRI. J Cogn Neurosci 2001; 13: 537–545.

43.

Biesbroek

van Zandvoort

Kuijf

, et al. The anatomy of visuospatial construction revealed by lesion-symptom mapping. Neuropsychologia 2014; 62: 68–76.

44.

Liu

Shi

, et al. The value of FDG combined with PiB PET in the diagnosis of patients with cognitive impairment in a memory clinic. CNS Neurosci Ther 2024; 30: e14418.

45.

Lobotesis

Fenwick

Phipps

, et al. Occipital hypoperfusion on SPECT in dementia with Lewy bodies but not AD. Neurology 2001; 56: 643–649.

46.

Minoshima

Foster

Sima

, et al. Alzheimer's disease versus dementia with Lewy bodies: cerebral metabolic distinction with autopsy confirmation. Ann Neurol 2001; 50: 358–365.

47.

Shimizu

Hanyu

Hirao

, et al. Value of analyzing deep gray matter and occipital lobe perfusion to differentiate dementia with Lewy bodies from Alzheimer's disease. Ann Nucl Med 2008; 22: 911–916.

48.

Mapstone

Steffenella

Duffy

. A visuospatial variant of mild cognitive impairment: getting lost between aging and AD. Neurology 2003; 60: 802–808.

49.

Kandiah

Narasimhalu

Lee

, et al. Differences exist in the cognitive profile of mild Alzheimer's disease and subcortical ischemic vascular dementia. Dement Geriatr Cogn Disord 2009; 27: 399–403.

50.

Salimi

Irish

Foxe

, et al. Visuospatial dysfunction in Alzheimer's disease and behavioural variant frontotemporal dementia. J Neurol Sci 2019; 402: 74–80.

51.

Kertesz

. Clinical features and diagnosis of frontotemporal dementia. Front Neurol Neurosci 2009; 24: 140–148.

52.

Rahman

Sahakian

Hodges

, et al. Specific cognitive deficits in mild frontal variant frontotemporal dementia. Brain 1999; 122: 1469–1493.

53.

Delgado-Alvarez

Cabrera-Martin

Valles-Salgado

, et al. Neural basis of visuospatial tests in behavioral variant frontotemporal dementia. Front Aging Neurosci 2022; 14: 963751.

54.

Floris

Borghero

Cannas

, et al. Constructional apraxia in frontotemporal dementia associated with the C9orf72 mutation: broadening the clinical and neuropsychological phenotype. Amyotroph Lateral Scler Frontotemporal Degener 2015; 16: 8–15.

55.

Garcia-Gutierrez

Delgado-Alvarez

Delgado-Alonso

, et al. Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms. Int J Geriatr Psychiatry 2022; 37. doi: https://doi.org/10.1002/gps.5667

56.

Hallam

Jacova

Hsiung

, et al. Early neuropsychological characteristics of progranulin mutation carriers. J Int Neuropsychol Soc 2014; 20: 694–703.

57.

Ranasinghe

Rankin

Lobach

, et al. Cognition and neuropsychiatry in behavioral variant frontotemporal dementia by disease stage. Neurology 2016; 86: 600–610.

58.

Simard

van Reekum

Myran

. Visuospatial impairment in dementia with Lewy bodies and Alzheimer's disease: a process analysis approach. Int J Geriatr Psychiatry 2003; 18: 387–391.

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