Abstract

Differences in the memory characteristics of patients with Alzheimer's disease (AD), Huntington's disease (HD), and Parkinson's disease (PD) were investigated with tests that assess learning and retention of words, line-drawn objects, and locations. Large groups of AD, HD, and PD patients were administered the Hopkins Verbal Learning Test-Revised (HVLT-R) and the Hopkins Board (HB). Eight learning and memory measures were subjected to discriminant function analysis. A 91% classification accuracy was achieved for the separation of cortical and subcortical dementias and 79% accuracy for the discrimination of the three groups. The delayed recall of items was the best discriminator. Receiver-operating curve analysis indicated up to 90% sensitivity and 90% specificity in differentiating the three diseases using the discriminant scores. Individual learning and memory measures of the HVLT-R and the HB provided very high sensitivity and specificity in distinguishing cortical versus subcortical dementias and modest accuracy in separating the two subcortical diseases.

Introduction

Group differences in the memory performances of patients with Alzheimer's disease (AD), Huntington's disease (HD), and Parkinson's disease (PD) have been described often (Delis et al., 2005; Hodges, Salmon, & Butters, 1990; Kramer, Levin, Brandt, & Delis, 1989; Noe et al., 2004; Paulsen et al., 1995; Pillon, Deweer, Agid, & Dubois, 1993; Salmon, Kwo-on-Yuen, Heindel, Butters, & Thal, 1989; Tröster, Jacobs, Butters, Cullum, & Salmon, 1989). However, exactly how robust those differences are, and how reliably individual performance on memory tests can predict the group membership, have rarely been tested. Very few studies have attempted to quantify the accuracy with which the prototypical “cortical” dementia (AD) and the prototypical “subcortical” dementias (HD and PD) can be differentiated.

Certain aspects of verbal learning have been found to differ between the cortical and subcortical dementias. On the California Verbal Learning Test (CVLT), AD patients typically display lower recall on learning trials, higher intrusion rates, and worse recognition performance than either PD and HD patients, when groups are matched for either severity of dementia or overall memory impairment (Delis et al., 1991; Kramer et al., 1989). Performance on subtests of the Wechsler Memory Scale (WMS) reveals more rapid forgetting after a 30-min delay in AD patients than in HD patients (Tröster, Butters et al., 1993; Tröster, Jacobs et al., 1989) or PD patients (Litvan, Mohr, Williams, Gomez, & Chase, 1991) matched for dementia severity. However, on the Rey-Auditory Verbal Learning Test (RAVLT), AD and PD patients have been shown to exhibit similar levels of impairment on the learning trials and the recognition condition, and only on delayed recall do AD patients perform worse than PD (Litvan et al., 1991). Moreover, recognition accuracy on the Hopkins Verbal Learning Test (HVLT) was found to be similar in groups of HD and AD patients matched for dementia severity (Brandt, Corwin, & Krafft, 1992). Finally, a more recent study failed to reveal differences in any aspect of verbal memory in genetically confirmed familial AD and HD patients (Arango-Lasprilla et al., 2006).

Subtle differences in verbal learning and memory have been reported even among groups with primarily subcortical disease, although the evidence here is even more equivocal. Kramer and colleagues (1989) found higher rates of forgetting in PD patients than in HD patients, whereas Massman, Delis, Butters, Levin, and Salmon (1990) reported worse recall and lower performance on learning trials in HD than PD patients. HD patients have been reported to make more perseverations across learning trials than PD patients, but their recognition accuracy is often higher (Kramer et al., 1989; Massman et al., 1990; Zizak et al., 2005).

The vast majority of studies of differential memory impairment in the dementias have used verbal memoranda (word lists or stories). On short-term spatial memory tasks, AD patients perform worse than PD patients, although both patient groups display severely compromised learning and memory for locations (Sahakian et al., 1988). On the other hand, some researchers have found AD patients to perform better than HD patients. The latter group had shorter spatial spans, needed more trials, and eventually learned the location of fewer abstract visual stimuli than AD patients matched for the level of dementia and premorbid IQ (Lange, Sahakian, Quinn, Marsden, & Robbins, 1995). Similarly, Brandt, Shpritz, Munro, Marsh, and Rosenblatt (2005) found that HD patients have a disproportionate impairment in memory for locations when compared with both AD and PD patients.

Despite the inconsistencies in the reported findings, the extant literature overall provides compelling evidence that there are reliable differences in memory among the three disease groups. However, it does not allow us to determine whether these differences are large enough to be detected on an individual level in clinical practice. The purpose of the present study was to examine whether individual AD, HD, and PD patients could be correctly categorized (i.e., diagnosed) based on verbal and visuo-spatial learning and memory characteristics and, thus, to contribute to the theoretical understanding of the nature of the memory impairments in each disease. AD patients were expected to be identified by greater impairment in verbal learning and memory measures (especially on the delayed recall and recognition condition) compared with the patients with subcortical diseases. It was further hypothesized that HD would be differentiated from both the AD and the PD patients by their disproportionate impairment in memory for spatial locations. In order to test these hypotheses, we retrieved archival data on two brief tests of learning and memory, the Hopkins Verbal Learning Test-Revised (HVLT-R) (Brandt & Benedict, 2001) and the Hopkins Board (HB) (Brandt, 2003; Brandt et al., 2005) from large samples of AD, HD, and PD patients and performed statistical classification analyses. To our knowledge, only a few studies have examined the ability of the HVLT-R to discriminate among dementing disorders (Brandt et al., 1992; De Jager, Hogervorst, Combrinck, & Budge, 2003; Shapiro, Benedict, Schretlen, & Brandt, 1999), and no study has examined the discriminative accuracy of the HB.

Phase 1 Analysis

Objective

The purpose of Phase 1 was to determine whether learning and memory of words, line-drawn objects, and spatial location can differentiate patients with AD, HD, and PD and to identify the aspects of test performance that best discriminate the three disorders.

Materials and Methods

Participants

AD Patients

One hundred thirty-five patients with the clinical diagnosis of probable AD were recruited from the Johns Hopkins Alzheimer's Disease Research Center (ADRC). The diagnosis was made by an experienced behavioral neurologist or neuropsychiatrist according to the criteria of the National Institute of Neurological Disorder and Stroke and Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA criteria) (McKhann et al., 1984).

HD Patients

Ninety patients with definite HD were recruited during their annual research visits to the Baltimore Huntington's Disease Project at the Johns Hopkins Hospital. Diagnosis was based on a positive family history, the presence of clinical symptoms, and genetic testing. All patients had choreiform movements or voluntary motor impairment, cognitive or emotional changes (Folstein, Leigh, Parhad, & Folstein, 1986), and all had an expanded triplet repeat mutation (CAG >36) in the huntingtin gene (MacDonald et al., 1993; Myers, 2004).

PD Patients

One hundred eleven patients with idiopathic PD were studied as part of their baseline evaluations in the Johns Hopkins Parkinson's Disease Research Center (PDRC). The diagnosis was made by movement disorder specialists using the UK Brain Bank clinical criteria (Hughes, Daniel, Kilford, & Lees, 1992).

Subjects were excluded if they reported any history of central nervous system disorder other than their group-defining illness, or active systemic illness (e.g., cancer, hepatic disease). No patient had any evidence of more than one of the conditions being studied. Formal neuropsychological testing was not part of the diagnostic process for any of the groups. Administration of the HVLT-R and the HB was part of the “clinical core” of these studies for already diagnosed patients.

Procedures

Dementia Severity

Overall level of cognitive impairment was evaluated using the Mini-Mental State Exam (MMSE; Folstein, Folstein, & McHugh, 1975).

Memory Tests

The HVLT-R (Brandt & Benedict, 2001) is a word-list learning and memory test that uses 12 words from three semantic categories. One of five forms of the HVLT-R was randomly selected and administered according to standard instructions. The list is read to the participant on three consecutive learning trials. After each trial, recall of the words from the list is requested. A delayed recall trial follows 20–25 min after the third learning trial. Finally, a yes/no recognition condition is administered during which the participant is asked to identify the 12 target words intermixed with 12 distractor words.

The following scores were calculated: (a) sum of words correctly recalled on the three learning trials, (b) number of words recalled on the delayed recall trial, and (c) recognition discrimination index (true positive minus false positive responses). In addition, we calculated a new learning score recently proposed by Foster and colleagues (2009) as a very sensitive indicator of total learning capacity, the cumulative word learning score (CWL). CWL is calculated by summing the words recalled on the three learning trials and multiplying this by the difference between the higher of the second or third learning trial and the first trial. Thus: 

formula

The HB (Brandt, 2003; Brandt et al., 2005) is a test of verbal and visuospatial learning and memory. Nine cards, each containing a line drawing of a common, easily namable object, are shown to the subject. The participant is first asked to name each item and then to watch carefully as the card is placed on a 3 × 3 grid board. The subject's task is to memorize each item's location. She/he is provided up to 10 trials to achieve two consecutive errorless placements of the cards on the grid. Each wrong placement is corrected. Recall of the items and reproduction of their location is requested after 20–30 min. Four learning scores are derived from the HB: (a) errors to learning criterion, (b) trials to learning criterion, (c) delayed recall of items, and (d) delayed recall of locations.

Measures of Functional Ability

Functional abilities were measured with three different functional impairment scales that were used in the three research centers from which the patients were drawn. Specifically, the Clinical Dementia Rating (CDR) scale (Hughes, Berg, Danziger, Coben, & Martin, 1982), the Huntington's Disease Activities of Daily Living (HD-ADL) scale (Bylsma, Rothlind, Hall, Folstein, & Brandt, 1993; Rothlind, Bylsma, Peyser, Folstein, & Brandt, 1993) and the Instrumental Activities of Daily Living (IADL) scale (Lawton & Brody, 1969) assessed the functional abilities in the AD, HD, and PD groups, respectively.

Motor abnormalities were measured with the Quantified Neurological Exam (QNE) (Folstein, Jensen, Leigh, & Folstein, 1983) in the HD patients and the Unified Parkinson's Disease Rating Scale-Part III (motor exam) (UPDRS) (Fahn & Elton, 1987) in the PD patients.

The data in the present study were collected retrospectively from patients who participated in the “clinical core” of the ADRC, PDRC, or HD Center from 1994 until 2007. In each case, testing was completed within a 1-month period. The Johns Hopkins University Institutional Review Board fully reviewed and approved the study protocols from which these data were drawn. Written informed consent was obtained from all participants or their legally authorized representatives.

Data Analysis

Statistical analyses were performed using SPSS 15. One-way ANOVAs were used to compare the demographic and clinical characteristics of the three groups as well as their performances on the memory test variables. Post-hoc tests were used to determine the locus of between-group differences on continuous variables. Age, education, and MMSE were entered as covariates in the analyses of learning and memory test variables, because the groups differed on these characteristics.

Linear stepwise discriminant analyses with jackknife classification matrices were conducted to determine whether the HVLT-R and HB can discriminate (i) patients with cortical dementia (AD) from those with subcortical dementia (HD + PD) and (ii) the three neurological groups from each other (AD, PD, and HD). These linear discriminant function models classify each case based on the discriminant functions derived from all cases other than that case. They are thus more conservative, minimize bias, and provide a more realistic estimate of the ability of predictors to separate groups (Tabachnick & Fidell, 2007) than discriminant functions derived from non-cross validated classification analyses (where the “coefficients used to assign a case to a group are derived, in part, from the case” (Tabachnick & Fidell, 2007, p 405). Because the patient groups differed in their demographic characteristics and dementia severity, we computed two models for each discriminant analysis. In the first, the independent variables were age, sex, education, and MMSE score. In the second, the eight learning and memory measures (HVLT-R: Total learning, CWL, delayed free recall, and recognition discrimination index; HB: Learning trials to criterion, errors to criterion, delayed recall of objects and delayed recall of spatial location) were added to the initial independent variables. For both models, variables were entered in a stepwise fashion (entry criterion F > 3.84; removal criterion F < 2.71). The prior probabilities of group membership were set to .50 for the discriminant analysis of cortical versus subcortical and .33 for the disease-specific analysis.

Results

Group Demographic and Clinical Characteristics

As expected, there were large age differences among the groups, F(2,336) = 141.83, p < .001, η2 = 0.460. AD patients were older than both HD and PD patients, and PD patients were older than HD patients (Table 1). The groups also differed in the levels of education, F(2,336) = 37.77, p < .001, η2 = 0.185, with our PD patients having more years of schooling than AD patients. The groups differed in sex distribution, x2(2) = 19.76, p < .001, V = 0.242; men predominated among PD patients and women among AD patients. Finally, mean MMSE score differed among groups, F(2,336) = 146.15, p < .001, η2 = 0.467. PD patients had the highest MMSE scores and AD patients had the lowest.

Table 1.

Clinical and demographic characteristics of Study 1 participants. Means (SD)

 Alzheimer's disease (n = 135) Huntington's disease (n = 90) Parkinson's disease (n = 111) p 
Age (years) 74.64 (8.47) 50.56 (13.16) 65.78 (10.34) <.001 
Education (years) 12.83 (3.97) 14.82 (2.81) 16.57 (2.96) <.001 
Sex ratio (M:F) 55:80 40:50 75:36 <.001 
Disease duration (years) 3.91 (2.82) 6.97 (4.37) 9.59 (6.62) <.001 
MMSE 19.72 (4.98) 26.13 (3.02) 27.47 (2.48) <.001 
Functional ability* 0.95 (0.49) (range = 0.50–3.00) 14.64 (10.50) (range = 0–39) 12.56 (5.90) (range = 8–30)  
Motor abnormalities** N/A 29.91 (16.08) (range = 6–88) 26.14 (14.35) (range = 3–71)  
 Alzheimer's disease (n = 135) Huntington's disease (n = 90) Parkinson's disease (n = 111) p 
Age (years) 74.64 (8.47) 50.56 (13.16) 65.78 (10.34) <.001 
Education (years) 12.83 (3.97) 14.82 (2.81) 16.57 (2.96) <.001 
Sex ratio (M:F) 55:80 40:50 75:36 <.001 
Disease duration (years) 3.91 (2.82) 6.97 (4.37) 9.59 (6.62) <.001 
MMSE 19.72 (4.98) 26.13 (3.02) 27.47 (2.48) <.001 
Functional ability* 0.95 (0.49) (range = 0.50–3.00) 14.64 (10.50) (range = 0–39) 12.56 (5.90) (range = 8–30)  
Motor abnormalities** N/A 29.91 (16.08) (range = 6–88) 26.14 (14.35) (range = 3–71)  

*AD: Clinical Dementia Rating Scale; HD: Huntington's Disease Activities of Daily Living Questionnaire; PD: Instrumental Activities of Daily Living (IADL) Scale.**HD: Quantified Neurologic Exam; PD: Unified Parkinson's Disease Rating Scale-Part III.

Group Performances on Learning and Memory Tasks

Even after adjusting for age, sex, education, and MMSE score, the performance on most of the HVLT-R and HB measures was poorest in the AD group, whereas the performances of the HD and PD groups were comparable on all measures, except the trials to criterion of the HB (Table 2). More specifically, AD patients performed worse than both subcortical groups (HD and PD) on the following variables: HVLT-R CWL, HVLT-R delayed recall, HVLT-R discrimination index, HB total errors to criterion, HB delayed recall of items, and HB delayed recall of locations. AD patients performed worse than PD patients also on HVLT-R total learning. Finally, PD patients needed fewer trials to criterion than both AD and HD patients on the HB.

Table 2.

Group performances on learning and memory tasks (unadjusted and adjusted for age, education and MMSE scores). Means (SE)

 Alzheimer's disease (n = 135) Huntington's disease (n = 90) Parkinson's disease (n = 111) p η2  
HVLT-R total learning 
 Unadjusted scores 10.66 (0.52) 19.81 (0.64) 21.21 (0.57)    
 Adjusted scores 15.09 (0.56) 16.84 (0.65) 18.13 (0.54) .002 0.037 AD <PD 
HVLT-R cumulative word learning 
 Unadjusted scores 22.60 (3.22) 58.61 (3.95) 72.26 (3.56)    
 Adjusted scores 35.11 (4.12) 52.43 (4.78) 61.88 (3.96) <.001 0.051 AD < HD, PD 
HVLT-R delayed recall 
 Unadjusted scores 0.65 (0.23) 6.76 (0.28) 7.09 (0.25)    
 Adjusted scores 2.28 (0.27) 5.57 (0.32) 6.07 (0.26) <.001 0.205 AD < HD, PD 
HVLT-R discrimination 
 Unadjusted scores 3.88 (0.27) 9.46 (0.33) 9.54 (0.29)    
 Adjusted scores 5.41 (.33) 8.38 (0.38) 8.54 (0.32) <.001 0.111 AD < HD, PD 
HB total errors to criterion 
 Unadjusted scores 38.56 (1.33) 12.73 (1.64) 9.27 (1.47)    
 Adjusted scores 30.32 (1.61) 18.30 (1.87) 14.91 (1.55) <.001 0.106 AD > HD, PD 
HB trials to criterion 
 Unadjusted scores 9.32 (0.23) 7.18 (0.28) 6.43 (0.25)    
 Adjusted scores 8.21 (0.29) 8.10 (0.33) 7.03 (0.28) .006 0.031 AD, HD > PD 
HB delayed recall of items 
 Unadjusted scores 1.90 (0.18) 7.47 (0.22) 7.37 (0.20)    
 Adjusted scores 3.18 (0.20) 6.55 (0.24) 6.51 (0.20) <.001 0.278 AD < HD, PD 
HB delayed recall of locations 
 Unadjusted scores 3.91 (0.19) 7.34 (0.24) 8.03 (0.21)    
 Adjusted scores 4.93 (0.24) 6.69 (0.28) 7.30 (0.23) <.001 0.113 AD < HD, PD 
 Alzheimer's disease (n = 135) Huntington's disease (n = 90) Parkinson's disease (n = 111) p η2  
HVLT-R total learning 
 Unadjusted scores 10.66 (0.52) 19.81 (0.64) 21.21 (0.57)    
 Adjusted scores 15.09 (0.56) 16.84 (0.65) 18.13 (0.54) .002 0.037 AD <PD 
HVLT-R cumulative word learning 
 Unadjusted scores 22.60 (3.22) 58.61 (3.95) 72.26 (3.56)    
 Adjusted scores 35.11 (4.12) 52.43 (4.78) 61.88 (3.96) <.001 0.051 AD < HD, PD 
HVLT-R delayed recall 
 Unadjusted scores 0.65 (0.23) 6.76 (0.28) 7.09 (0.25)    
 Adjusted scores 2.28 (0.27) 5.57 (0.32) 6.07 (0.26) <.001 0.205 AD < HD, PD 
HVLT-R discrimination 
 Unadjusted scores 3.88 (0.27) 9.46 (0.33) 9.54 (0.29)    
 Adjusted scores 5.41 (.33) 8.38 (0.38) 8.54 (0.32) <.001 0.111 AD < HD, PD 
HB total errors to criterion 
 Unadjusted scores 38.56 (1.33) 12.73 (1.64) 9.27 (1.47)    
 Adjusted scores 30.32 (1.61) 18.30 (1.87) 14.91 (1.55) <.001 0.106 AD > HD, PD 
HB trials to criterion 
 Unadjusted scores 9.32 (0.23) 7.18 (0.28) 6.43 (0.25)    
 Adjusted scores 8.21 (0.29) 8.10 (0.33) 7.03 (0.28) .006 0.031 AD, HD > PD 
HB delayed recall of items 
 Unadjusted scores 1.90 (0.18) 7.47 (0.22) 7.37 (0.20)    
 Adjusted scores 3.18 (0.20) 6.55 (0.24) 6.51 (0.20) <.001 0.278 AD < HD, PD 
HB delayed recall of locations 
 Unadjusted scores 3.91 (0.19) 7.34 (0.24) 8.03 (0.21)    
 Adjusted scores 4.93 (0.24) 6.69 (0.28) 7.30 (0.23) <.001 0.113 AD < HD, PD 

Cortical–Subcortical Classification

Demographic variables (age, sex, education) and the MMSE were entered into a stepwise discriminant function analysis to determine how well these variables predicted cortical-subcortical group membership. The standardized canonical coefficient for the MMSE was 0.754, for age was −0.550 and for education was 0.168. The overall model was significant (Wilks' λ = 0.445, x2(3) = 268.95, p < .001). Eighty-two percent of the AD patients were classified as having a “cortical” disease and 89.1% of HD and PD patients as having a “subcortical” disease. The overall cross-validated classification accuracy for the entire sample was 86.3%.

A second stepwise discriminant analysis with the addition of eight scores from the HVLT-R and the HB as potential independent variables revealed that four measures improved classification accuracy by only 4.5%. Standardized canonical discriminant function coefficients and significance of the model at each step is presented in Table 3. HB delayed recall of items entered in the first step, and the HVLT-R delayed recall contributed most to the discriminant function in the final model. The resulting equation accounted for an overall 90.8% correct jackknifed classification of patients (88.9% with cortical dementia and 92% with subcortical) (Wilks' λ = 0.289; x2(7) = 410.68, p < .001).

Table 3.

Predictors, standardized canonical discriminant function coefficients, and significance of the cortical–subcortical dementias discriminant analysis at each step

Step Variable Standardized canonical coefficients Wilks' Lambda p 
HB delayed recall of items 0.577 0.362 <.001 
HVLT-R delayed recall of items 0.612 0.323 <.001 
Age (years) −0.275 0.308 <.001 
Education (years) 0.158 0.302 <.001 
HVLT-R total learning −0.331 0.296 <.001 
MMSE 0.209 0.292 <.001 
HB trials to criterion 0.150 0.289 <.001 
Step Variable Standardized canonical coefficients Wilks' Lambda p 
HB delayed recall of items 0.577 0.362 <.001 
HVLT-R delayed recall of items 0.612 0.323 <.001 
Age (years) −0.275 0.308 <.001 
Education (years) 0.158 0.302 <.001 
HVLT-R total learning −0.331 0.296 <.001 
MMSE 0.209 0.292 <.001 
HB trials to criterion 0.150 0.289 <.001 

Disease-Specific Classification

Demographic variables (age, sex, education) and the MMSE were entered into the first stepwise jackknifed discriminant function analysis to determine how well these variables predicted membership in one of the three disease groups. The overall model was significant (Wilks' λ = 0.319, x2(6) = 379.50, p < .001). Age, MMSE score, and education accounted for an overall 74.1% correct jackknifed classification of the patients (77% of AD, 73.3% of HD, and 71.2% of PD). Sex did not contribute to the separation of the groups.

The addition of three HVLT-R and HB measures improved classification accuracy by 4.2%. Specifically, the HB delayed recall of items, the HVLT-R delayed recall, and the HVLT-R total learning resulted in 78.3% of cross-validated grouped cases correctly classified (Wilks' λ = 0.211; x2(10) = 514.28, p < .001). 86.7% of AD, 74.4% of HD, and 71.2% of PD patients were correctly classified. In general, AD patients who were misclassified into one of the other disease groups were younger and had higher MMSE scores than those correctly classified. PD patients who were misclassified were also younger and had fewer years of education. HD patients who were misclassified were older and more highly educated than those correctly identified. HD patients were more likely to be misclassified as PD (22.2%) than AD (3.3%). PD patients were more likely to be misclassified as HD (19.8%) than AD (9%). Standardized canonical discriminant function coefficients and significance of the model at each step is presented in Table 4. Delayed recall measures had the highest standardized coefficients in the first function, and age and education had the highest coefficients in the second function. The first discriminant function accounted for 86.5% of the between-group-explained variance whereas the second accounted for the remaining 13.5% between-group variance. Fig. 1 illustrates the combined group plot for the discrimination among AD, HD, and PD patients.

Table 4.

Predictors, standardized canonical discriminant function coefficients, and significance of the disease-specific discriminant analysis at each step

Step Variable Standardized canonical coefficients (Function 1) Standardized canonical coefficients (Function 2) Wilks' Lambda p 
HB delayed recall of items 0.638 0.148 0.362 <.001 
Age (years) −0.329 0.912 0.257 <.001 
HVLT-R delayed recall 0.523 0.200 0.230 <.001 
Education (years) 0.150 0.403 0.217 <.001 
HVLT-R total learning −0.256 0.160 0.211 <.001 
Step Variable Standardized canonical coefficients (Function 1) Standardized canonical coefficients (Function 2) Wilks' Lambda p 
HB delayed recall of items 0.638 0.148 0.362 <.001 
Age (years) −0.329 0.912 0.257 <.001 
HVLT-R delayed recall 0.523 0.200 0.230 <.001 
Education (years) 0.150 0.403 0.217 <.001 
HVLT-R total learning −0.256 0.160 0.211 <.001 
Fig. 1.

Combined groups plot for the discrimination among Alzheimer's, Parkinson's and Huntington's disease groups.

Fig. 1.

Combined groups plot for the discrimination among Alzheimer's, Parkinson's and Huntington's disease groups.

Discussion

Consistent with a number of studies suggesting patients with cortical dementias have more severe learning and memory impairments than patients with subcortical dementias (Noe et al., 2004; Paulsen et al., 1995), our findings show that AD patients performed worse than those with PD and HD on two tests of new learning and memory, even when the overall dementia severity was statistically controlled. The magnitude of the differences among the groups was sufficiently large that 91% of patients could be correctly classified as having a primarily cortical or subcortical dementia and 79% could be assigned to their specific disease group. Although differences also emerged between patients with PD and HD, the two subcortical dementias were more similar to one another than they were to AD. Thus, PD patients were more likely to be misclassified as HD than as AD, and HD patients were more likely to be misclassified as PD than AD.

A caveat to this interpretation of our findings is that age, education, and MMSE alone resulted in a high classification accuracy of the groups and the addition of learning and memory measures improved the cortical–subcortical dementia and the disease-specific group assignment only by 4–5%. However, in the cortical–subcortical dementia classification, the delayed recall of items from both tests contributed most to the discriminant function. Similarly, in the disease-specific discrimination analysis, a delayed memory factor underlied the first discriminant function, which accounted for most of the between-group variance. Age and education contributed most to the second discriminant function, which explained just 15% of the between-group variance. Moreover, the dementia severity measure that contributed significantly to the classification of the patients in the first discriminant models (the MMSE) includes items assessing working memory (serial 7s) and short delayed recall (3-word recall). In addition, specific MMSE subscores have been shown to correlate highly with measures of episodic memory (Carcaillon, Amieva, Auriacombe, Helmer, & Dartigues, 2009). Thus, the episodic memory items included in the MMSE might have contributed to the high separation of the groups in the first discriminant analysis models.

Delayed recall of items on both the HVLT-R and the HB were the episodic memory measures that best discriminated the groups. The performances of the three groups also differed on measures of learning (HVLT-R total learning, CWL, HB trials to criterion). However, scores on these measures made only modest unique contributions to the separation of the groups in both the dementia-type (cortical vs. subcortical) and disease-specific classification. More specifically, total learning of the HVLT-R and trials to criterion made subtle contributions to the separation of the cortical–subcortical groups, whereas only HVLT-R total learning added to the classification of the three diseases. Moreover, CWL, which is alleged to be a purer measure of learning capacity (in that it quantifies the rate of acquisition across trials regardless of the overall level of immediate recall performances [Foster et al., 2009]), did not contribute at all to the discrimination of the groups. The HVLT-R total-learning score, the number of words recalled immediately after successive presentations of the stimuli, is a global measure of immediate free-recall performance that relies heavily on learning capacity. However, it seems to have more discriminative value than the ability to learn per se and the rate at which words are acquired across trials.

A possible limitation in the interpretation of our findings is that the three groups differed on demographic characteristics; for example, PD patients had more years of education than AD patients. However, all of our analyses were adjusted for these differences.

Phase 2 (Follow-Up) Analysis

Objective

The goal of the Phase 2 was to determine the sensitivity and specificity with which empirically derived cutting scores on the HVLT-R and the HB can distinguish AD, HD, and PD patients matched for overall dementia severity.

Methods

Participants

Subsamples of AD, HD, and PD patients who participated in Phase 1 were selected so that the groups would be matched for MMSE, F(2,88) = 0.731, p > .05. Our selection criterion was an MMSE between 22 and 27, indicating early or mild dementia. As a result, groups were also matched on education, F(2,88) = 0.512, p > .05, and sex distributions, x2(2) = 4.89, p > .05. The AD and PD groups were of equivalent age (p > .05), but the HD patients were significantly younger (p < .001). Matching the groups on age would have affected the representativeness of the sample. Our final sample included 29 AD, 30 HD, and 18 PD patients (see Table 5).

Table 5.

Clinical and demographic characteristics of the three patient groups in Study 2. Means (SD)

 Alzheimer's disease (n = 29) Huntington's disease (n = 30) Parkinson's disease (n = 18) p 
Age (years) 72.13 (7.99) 51.02 (14.77) 73.62 (9.36) <.001a 
Education (years) 13.66 (3.18) 13.87 (2.37) 15.22 (1.90) .119 
Sex ratio (M:F) 17:12 11:19 12:6 .087 
Disease duration (years) 4.94 (4.40) 6.59 (4.06) 10.91 (6.19) .003b 
MMSE 24.45 (0.99) 24.67 (1.18) 24.67 (1.07) .691 
 Alzheimer's disease (n = 29) Huntington's disease (n = 30) Parkinson's disease (n = 18) p 
Age (years) 72.13 (7.99) 51.02 (14.77) 73.62 (9.36) <.001a 
Education (years) 13.66 (3.18) 13.87 (2.37) 15.22 (1.90) .119 
Sex ratio (M:F) 17:12 11:19 12:6 .087 
Disease duration (years) 4.94 (4.40) 6.59 (4.06) 10.91 (6.19) .003b 
MMSE 24.45 (0.99) 24.67 (1.18) 24.67 (1.07) .691 

aAD, PD > HD.

bAD, HD < PD.

Procedures

The same procedures as in Phase 1.

Data Analysis

Receiver-operating characteristic (ROC) curves were constructed in order to characterize the overall discriminating value of each test and to determine the cut-off points that best distinguish the cortical group from each of the subcortical groups (positive groups: HD, PD; negative group: AD), as well as the two subcortical groups from each other (positive group: PD; negative group: HD). In addition, ROC curves were constructed for the discriminant scores generated in Phase 1. It was predicted that the variables that contributed most to the discriminant function, namely the HVLT-R delayed recall and the HB delayed recall of items, would provide the cutting scores with the highest sensitivity and specificity.

Results

The optimal cut-off scores (the score/point that maximized the sum of the sensitivity and specificity) are presented in Table 6. The Function 1 discriminant score provided the highest level of discrimination of AD from HD patients. The next highest discrimination accuracy was provided by the HVLT-R delayed recall score. The optimal cutting score on this measure was 3.5, yielding a sensitivity of 0.83 and a specificity of 0.83 (Fig. 2a). Thus, 83% of HD patients obtained a score of 4 or more on the HVLT-R delayed recall, whereas 83% of the AD patients recalled 3 or fewer words. The next best measure for discriminating AD from HD patients was the HB delayed recall of items, followed by the HVLT-R discrimination index, and then HVLT-R total learning.

Table 6.

ROC curves statistics for HVLT-R and HB scores

 AUC Optimal cutting score Se Sp CI (95%) 
AD versus HD 
 HVLT-R total learning 0.772 12.50 0.97 0.52 0.653–0.890 
 HVLT-R delayed recall 0.913 3.50 0.83 0.83 0.841–0.984 
 HVLT-R cumulative word learning 0.672 54.00 0.43 0.90 0.532–0.813 
 HVLT-R discrimination 0.832 7.50 0.84 0.72 0.727–0.937 
 HB total errors to criteriona 0.707 21.50 0.66 0.80 0.565–0.850 
 HB trials to criteriona 0.616 9.50 0.83 0.47 0.469–0.763 
 HB delayed recall of items 0.868 5.50 0.87 0.69 0.776–0.961 
 HB delayed recall of locations 0.667 6.50 0.60 0.66 0.527–0.806 
 Discriminant score (function1) 0.953 0.24 0.90 0.90 0.903–1.000 
 Discriminant score (function2)a 0.754 −0.79 0.90 0.60 0.626–0.882 
AD versus PD 
 HVLT-R total learning 0.640 12.50 0.72 0.52 0.480–0.800 
 HVLT-R delayed recall 0.793 0.50 0.89 0.62 0.659–0.927 
 HVLT-R cumulative word learning 0.639 43.50 0.44 0.86 0.469–0.809 
 HVLT-R discrimination 0.780 5.50 1.0 0.55 0.650–0.909 
 HB total errors to criteriona 0.698 21.50 0.66 0.78 0.548–0.848 
 HB trials to criteriona 0.581 9.00 0.83 0.39 0.411–0.752 
 HB delayed recall of items 0.747 4.50 0.83 0.62 0.608–0.889 
 HB delayed recall of locations 0.687 7.50 0.50 0.79 0.533–0.841 
 Discriminant score (Function 1) 0.791 0.93 0.83 0.66 0.658–0.924 
 Discriminant score (Function 2) 0.715 0.59 0.61 0.83 0.560–0.870 
PD versus HD 
 HVLT-R total learning 0.656 13.50 0.87 0.44 0.485–0.826 
 HVLT-R delayed recall 0.703 5.50 0.60 0.83 0.543–0.863 
 HVLT-R cumulative word learning 0.543 26.00 0.73 0.50 0.370–0.715 
 HVLT-R discrimination 0.641 7.50 0.83 0.44 0.480–0.802 
 HB total errors to criteriona 0.514 20.50 0.39 0.73 0.337–0.691 
 HB trials to criteriona 0.541 7.50 0.72 0.33 0.372–0.710 
 HB delayed recall of items 0.665 6.50 0.70 0.61 0.495–0.834 
 HB delayed recall of locationsa 0.542 7.50 0.50 0.67 0.368–0.716 
 Discriminant score (Function 1) 0.831 1.00 0.80 0.89 0.710–0.953 
 Discriminant score (Function 2)a 0.883 0.18 0.83 0.80 0.792–0.975 
 AUC Optimal cutting score Se Sp CI (95%) 
AD versus HD 
 HVLT-R total learning 0.772 12.50 0.97 0.52 0.653–0.890 
 HVLT-R delayed recall 0.913 3.50 0.83 0.83 0.841–0.984 
 HVLT-R cumulative word learning 0.672 54.00 0.43 0.90 0.532–0.813 
 HVLT-R discrimination 0.832 7.50 0.84 0.72 0.727–0.937 
 HB total errors to criteriona 0.707 21.50 0.66 0.80 0.565–0.850 
 HB trials to criteriona 0.616 9.50 0.83 0.47 0.469–0.763 
 HB delayed recall of items 0.868 5.50 0.87 0.69 0.776–0.961 
 HB delayed recall of locations 0.667 6.50 0.60 0.66 0.527–0.806 
 Discriminant score (function1) 0.953 0.24 0.90 0.90 0.903–1.000 
 Discriminant score (function2)a 0.754 −0.79 0.90 0.60 0.626–0.882 
AD versus PD 
 HVLT-R total learning 0.640 12.50 0.72 0.52 0.480–0.800 
 HVLT-R delayed recall 0.793 0.50 0.89 0.62 0.659–0.927 
 HVLT-R cumulative word learning 0.639 43.50 0.44 0.86 0.469–0.809 
 HVLT-R discrimination 0.780 5.50 1.0 0.55 0.650–0.909 
 HB total errors to criteriona 0.698 21.50 0.66 0.78 0.548–0.848 
 HB trials to criteriona 0.581 9.00 0.83 0.39 0.411–0.752 
 HB delayed recall of items 0.747 4.50 0.83 0.62 0.608–0.889 
 HB delayed recall of locations 0.687 7.50 0.50 0.79 0.533–0.841 
 Discriminant score (Function 1) 0.791 0.93 0.83 0.66 0.658–0.924 
 Discriminant score (Function 2) 0.715 0.59 0.61 0.83 0.560–0.870 
PD versus HD 
 HVLT-R total learning 0.656 13.50 0.87 0.44 0.485–0.826 
 HVLT-R delayed recall 0.703 5.50 0.60 0.83 0.543–0.863 
 HVLT-R cumulative word learning 0.543 26.00 0.73 0.50 0.370–0.715 
 HVLT-R discrimination 0.641 7.50 0.83 0.44 0.480–0.802 
 HB total errors to criteriona 0.514 20.50 0.39 0.73 0.337–0.691 
 HB trials to criteriona 0.541 7.50 0.72 0.33 0.372–0.710 
 HB delayed recall of items 0.665 6.50 0.70 0.61 0.495–0.834 
 HB delayed recall of locationsa 0.542 7.50 0.50 0.67 0.368–0.716 
 Discriminant score (Function 1) 0.831 1.00 0.80 0.89 0.710–0.953 
 Discriminant score (Function 2)a 0.883 0.18 0.83 0.80 0.792–0.975 

Note: AUC = Area under the curve; Se = sensitivity; Sp = specificity.

aReversed positive value/reference group. On all other measures the subcortical groups are the reference group. In the PD versus HD analysis, PD is the reference group, unless otherwise specified.

Fig. 2.

(a) ROC curve for HVLT-R delayed recall scores for discrimination of AD and HD patients. (b) ROC curve for HVLT-R delayed recall scores for discrimination of AD and PD patients. (c) ROC curve for HVLT-R delayed recall scores for discrimination of PD and HD patients.

Fig. 2.

(a) ROC curve for HVLT-R delayed recall scores for discrimination of AD and HD patients. (b) ROC curve for HVLT-R delayed recall scores for discrimination of AD and PD patients. (c) ROC curve for HVLT-R delayed recall scores for discrimination of PD and HD patients.

The highest level of discrimination of AD from PD patients was obtained with the HVLT-R delayed recall score (Fig. 2b), followed by the Function 1 discriminant score. The HVLT-R recognition discrimination index, the HB delayed recall of items, and the discriminant score derived from Function 2 were the next best discriminators.

The ROC curve analysis of the HVLT-R and the HB for the distinction of the HD and PD patients indicated that the discriminant scores derived from Function 2 provided high discrimination accuracy, followed by those from Function 1. Scores from individual test measures resulted in a less accurate classification. Among them, HVLT-R delayed recall score was the best discriminator of the two groups (Fig. 2c), followed by the HB delayed recall of items, HVLT-R total learning, and the HVLT-R discrimination index. All other variables resulted in classification only marginally above the chance level.

Discussion

The HVLT-R and the HB provided very high sensitivity and specificity in distinguishing patients with AD from patients with subcortical dementias, even when groups are matched for overall dementia severity and the level of education. However, they are less useful in discriminating among patients with specific subcortical disorders. Phase 2 analysis confirmed our interpretation of the results from Phase 1 that performance on episodic memory measures underlies the high discrimination of the groups and not dementia severity or demographic characteristics. One might argue that age differences might have contributed to performance differences between the AD and the younger HD patients. We opted against matching these patient groups on age, because a very young AD sample and a very old HD sample would not be representative of these clinical conditions. Moreover, AD patients were age matched with PD patients who displayed similar patterns of learning and memory scores as those of HD patients. In addition, although HD and PD patients were not age matched, their performances were still more difficult to distinguish from each other than from AD patients' performances.

The individual measures of the HVLT-R and the HB achieved only a modest separation of the subcortical groups. However, the discriminant scores, which represent the most effective combination of weighted predictor variables to maximize differences between groups, did accomplish a high degree of separation of all groups. After the discriminant scores, the next best discriminator among groups was performance on delayed recall for both words and line-drawn objects. Finally, yes/no recognition accuracy also provided very good sensitivity and specificity in distinguishing AD from both HD and PD patients.

General Discussion

Memory impairments are among the earliest and most devastating clinical symptoms of dementia (Brandt & Munro, 2002; Paulsen et al., 1995; Salmon et al., 1989). Consistent with previous studies (Butters, Delis, & Lucas, 1995; Kramer et al., 1989; Noe et al., 2004; Pillon et al., 1993), our findings indicate that different degenerative dementias have distinct patterns of memory impairments, probably reflecting different pathological substrates. AD patients displayed more severe impairments in learning and memory of both items' names and locations, regardless of mode of testing (immediate recall, delayed recall, yes/no recognition), or modality of stimulus presentation (visual or auditory) than PD and HD patients. PD patients needed fewer trials to learn the location of items than HD patients, but otherwise performed comparably with HD patients. Thus, the subcortical dementias (HD and PD) were more similar to one another in pattern of memory performance than they were to the prototypical cortical dementia (AD). Indeed, based on their characteristics of learning and memory, an HD patient was more likely to be misclassified as a PD patient than an AD patient, and a PD patient was more likely to be misclassified as having HD than AD. Our findings therefore provide further support for the cortical–subcortical dichotomy (at least at the level of the cognitive syndrome).

Specific measures of episodic memory, such as delayed free recall, were more effective than others in distinguishing cortical and subcortical patterns of performances. In addition, delayed recall of names of items, presented either verbally or visually, differentiated all three dementias better than did delayed recall of locations. Previous studies have demonstrated the deterioration of organization and content of semantic memory in AD patients (Chertkow & Bub, 1990; Rohrer, Salmon, Wixted, & Paulsen, 1999; Salmon, Heindel, & Lange, 1999). The storage of new information into episodic memory is directly associated with, and dependent upon, the recovery of information from semantic memory (Steven, Takashi, & Roberto, 2007; Tulving, 1995). This may help explain why AD patients perform more poorly on delayed recall of items compared with delayed recall of locations. Moreover, memory of items presented visually may depend more critically on the inferior temporal lobe, where AD pathology abounds (Arnold, Hyman, Flory, Damasio, & Van Hoesen, 1991), than does memory for spatial location. Why the same episodic memory measure distinguishes between the two subcortical dementias is less clear and warrants further investigation. Finally, the ROC curves revealed that the recognition index (a score that reflects the ability to identify target items and reject distractor items) also had discriminative power. This measure did not, however, remain in the final model in the stepwise discriminant analysis probably because it shared a considerable variance with other independent variables.

The superiority of delayed recall over recognition accuracy in distinguishing primarily cortical from primarily subcortical diseases has been debated. Several studies support the effectiveness of recognition accuracy in distinguishing between patients with cortical and subcortical dysfunction (Delis, Kramer, Kaplan, & Ober, 2000; Massman, Delis, & Butters, 1993). Whereas AD, PD, and HD patients typically perform poorly on delayed free recall, only HD or PD patients exhibit substantial improvement on measures of recognition discrimination (Butters et al., 1995; Delis et al., 1991; Deweer et al., 1994; Glosser, Friedman, Grugan, Lee, & Grossman, 1998). On the other hand, several studies have shown that both recall and recognition are compromised in HD and PD patients (Brandt & Munro, 2002; Brandt et al., 2005; Montoya et al., 2006; Solomon et al., 2007; Zizak et al., 2005) and the best overall discriminator between groups with cortical and subcortical dementia is delayed recall (Noe et al., 2004; Paulsen et al., 1995; Welsh, Butters, Hughes, Mohs, & Heyman, 1991). Our results suggest that both delayed recall and recognition differentiate distinct dementias, but delayed recall has greater discriminative power.

Two main implications, both theoretical and clinical, can be drawn from the present study. First, our findings provide insight into the nature of memory impairments in different degenerative diseases that can result in a dementia syndrome. Prominent disease-specific memory impairments that can be captured by brief memory tests add to the existent evidence that there are probably distinct underlying neurocognitive mechanisms that contribute to the differential impairment in the learning of new information. Second, our findings are unlikely to be useful for differential diagnosis; the distinctions among AD, HD, and PD are profound, and the diseases are not usually confused with each other. However, having HD or PD does not preclude the possibility that a patient could at some point also develop AD. It is well documented in neuropathological studies that coincident AD pathology may be present in PD patients (Boller, Mizutani, Roessmann, & Gambetti, 1980; Braak et al., 1996; Emre, 2003). In a patient with a primary subcortical disease (PD and HD), a severe verbal-learning impairment may be indicative of superimposed AD pathology.

A major finding of the present study concerns the effectiveness of both the HVLT-R and the HB in differentiating AD, HD, and PD patients. In the present study, the inclusion of three variables from the HVLT-R and HB resulted in an 80% correct classification of the AD, HD, and PD patients, and four measures from the same tasks produced a 91% correct classification of diseases as primarily cortical or subcortical. Kramer and colleagues (1989) reported 76% classification accuracy of AD, HD, and PD patients matched for their levels of immediate recall performances using nine measures of learning characteristics (e.g., rates of forgetting) and error types (intrusions) from the CVLT. Massman and colleagues (1993) showed that the CVLT recognition discriminability index resulted in a correct classification rate of 90% between patients with AD and those with HD, whereas Delis and colleagues (1991) reported around 85% accuracy in classifying the same patient groups. However, a more recent study by Delis and colleagues (2005) showed that traditional recall conditions of the CVLT-II (both short and long delayed recall conditions, with and without cues) failed to differentiate AD and HD patients matched for dementia rating scale scores. The Buschke Selective Reminding Test produced a 68% accuracy of group classification of AD and HD subjects (Paulsen et al., 1995). Finally, Tröster and colleagues (1993) reported a 79% classification accuracy of AD and HD patients using indices of the WMS-R. Thus, the HVLT-R and the HB were as good as or better than other memory tests that require more time to administer in distinguishing different dementias.

Strengths of the present study are the large sample sizes, as well as the inclusion of three different dementias. The former allows us to conduct the appropriate discriminant analysis and the latter to test the hypothesis that the pattern of memory impairment is disease-specific as opposed to only reflecting differences in cortical and subcortical degenerative processes. A conceptual and methodological issue that remains is how best to match groups of patients with different dementias (Brandt & Munro, 2002). The failure to match groups on demographic characteristics, such as age and education, and especially on dementia severity, or the application of different matching procedures may result in heterogeneous patient samples and contribute to the discrepant findings (Graham, Emery, & Hodges, 2004). In our study, we defined dementia severity based on MMSE scores. Although it has received criticism, MMSE is still recommended and used as the primary instrument for tracking/screening for dementia and describing dementia severity (Dubois et al., 2007). In addition, we followed two different methodological approaches that resulted in consistent findings. First, in the discriminant analysis, we included all patients, but controlled statistically for demographic differences and dementia severity. Second, in the ROC curves analyses, we selected subgroups matched for dementia severity and education. Both methods revealed not only group differences, but also distinct patterns of memory performances that predict group membership with very high accuracy. Thus, we conclude that our findings that AD, HD, and PD patients display different patterns of memory impairment are robust.

An important caveat when interpreting our findings is the diagnostic accuracy of the AD and PD diseases. Although we applied the standard clinical criteria to diagnose AD and PD, our patients had no autopsy that would pathologically confirm their diagnosis. Consequently, some AD patients might ultimately have a different type of dementing disease and some PD patients, especially those with major memory problems, might also have AD pathology. In addition, our HD and PD patients had various degrees of cognitive impairment, as can be inferred from their MMSE scores and their ratings on the functional scales. We opted against classifying patients with HD and PD as having mild cognitive impairment, dementia, or neither, because (at one level) they all have cognitive impairments in more than one domain and functional impairments and might therefore all have some degree of dementia. We thus consider such distinctions problematic (Brandt & Munro, 2002).

In summary, AD, HD, and PD patients display differences in patterns of memory performances of such magnitude that can predict their disease-defining group membership. Delayed memory recall provides the highest discrimination accuracy especially when comparing primarily cortical with subcortical dementias. Finally, both the HVLT-R and the HB can accurately discriminate the three diseases.

Funding

This work was supported by grants of the National Institutes of Health to the Johns Hopkins University School of Medicine [P01-NS16375 to the Huntington's Disease Research Center, P50-AG05146 to the Alzheimer's Disease Research Center, and P50-NS58377 to the Parkinson's Disease Research Center].

Conflict of Interest

J.B. receives royalties from Psychological Assessment Resources, Inc., on sales of the Hopkins Verbal Learning Test-Revised. His relationship with the company is managed by the Johns Hopkins University in accordance with its established conflict of interest policies. E.A. has no conflict of interest.

Acknowledgement

We thank Cynthia A. Munro, Ph.D. for her helpful comments on the manuscript.

References

Arango-Lasprilla
J. C.
Rogers
H.
Lengenfelder
J.
Deluca
J.
Moreno
S.
Lopera
F.
Cortical and subcortical diseases: Do true neuropsychological differences exist?
Archives of Clinical Neuropsychology
 , 
2006
, vol. 
21
 (pg. 
29
-
40
)
[PubMed]
Arnold
S. E.
Hyman
B. T.
Flory
J.
Damasio
A. R.
Van Hoesen
G. W.
The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer's disease
Cerebral Cortex
 , 
1991
, vol. 
1
 (pg. 
103
-
116
)
Boller
F.
Mizutani
T.
Roessmann
U.
Gambetti
P.
Parkinson disease, dementia, and Alzheimer disease: Clinicopathological correlations
Annals of Neurology
 , 
1980
, vol. 
7
 (pg. 
329
-
335
)
Braak
H.
Braak
E.
Yilmazer
D.
De Vos
R. A.
Jansen
E. N.
Bohl
J.
New aspects of pathology in Parkinson's disease with concomitant incipient Alzheimer's disease
Journal of Neural Transmission
 , 
1996
, vol. 
48 (Suppl.)
 (pg. 
1
-
6
)
Brandt
J.
The Hopkins Board, professional manual
 , 
2003
Author: Baltimore, MD
Brandt
J.
Benedict
R. H. B.
Hopkins Verbal Learning Test—Revised, professional manual
 , 
2001
Lutz, FL
Psychological Assessment Resources
Brandt
J.
Corwin
J.
Krafft
L.
Is verbal recognition memory really different in Huntington's and Alzheimer's disease?
Journal of Clinical and Experimental Neuropsychology
 , 
1992
, vol. 
14
 (pg. 
773
-
784
)
Brandt
J.
Munro
C.
Baddeley
A.
Kopelman
M.
Wilson
B.
Memory disorders in subcortical dementia
The handbook of memory disorders
 , 
2002
Chichester
John Wiley & Sons, Ltd
(pg. 
591
-
614
)
Brandt
J.
Shpritz
B.
Munro
C. A.
Marsh
L.
Rosenblatt
A.
Differential impairment of spatial location memory in Huntington's disease
Journal of Neurology, Neurosurgery, and Psychiatry
 , 
2005
, vol. 
76
 (pg. 
1516
-
1519
)
Butters
N.
Delis
D. C.
Lucas
J. A.
Clinical assessment of memory disorders in amnesia and dementia
Annual Review of Psychology
 , 
1995
, vol. 
46
 (pg. 
493
-
523
)
Bylsma
F. W.
Rothlind
J.
Hall
M. R.
Folstein
S. E.
Brandt
J.
Assessment of adaptive functioning in Huntington's disease
Movement Disorders
 , 
1993
, vol. 
8
 (pg. 
183
-
190
)
Carcaillon
L.
Amieva
H.
Auriacombe
S.
Helmer
C.
Dartigues
J. F.
A subtest of the MMSE as a valid test of episodic memory? Comparison with the Free and Cued Reminding Test
Dementia and Geriatric Cognitive Disorders
 , 
2009
, vol. 
27
 (pg. 
429
-
438
)
Chertkow
H.
Bub
D.
Semantic memory loss in dementia of Alzheimer's type. What do various measures measure?
Brain
 , 
1990
, vol. 
113
 
Pt. 2
(pg. 
397
-
417
)
[PubMed]
De Jager
C. A.
Hogervorst
E.
Combrinck
M.
Budge
M. M.
Sensitivity and specificity of neuropsychological tests for mild cognitive impairment, vascular cognitive impairment and Alzheimer's disease
Psychological Medicine
 , 
2003
, vol. 
33
 
6
(pg. 
1039
-
1050
)
Delis
D. C.
Kramer
J. H.
Kaplan
E.
Ober
B. A.
California Verbal Learning Test—second edition
 , 
2000
San Antonio, TX
Psychological Corporation
Delis
D. C.
Massman
P. J.
Butters
N.
Salmon
D. P.
Cernak
L. S.
Kramer
J. H.
Profiles of demented and amnesic patients on the California Verbal Learning Test: Implications for the assessment of memory disorders
Psychological Assessment: A Journal of Consulting and Clinical Psychology
 , 
1991
, vol. 
3
 (pg. 
19
-
26
)
Delis
D. C.
Wetter
S. R.
Jacobson
M. W.
Peavy
G.
Hamilton
J.
Gongvatana
A.
, et al.  . 
Recall discriminability: Utility of a new CVLT-II measure in the differential diagnosis of dementia
Journal of the International Neuropsychological Society
 , 
2005
, vol. 
11
 (pg. 
708
-
715
)
[PubMed]
Deweer
B.
Ergis
A. M.
Fossati
P.
Pillon
B.
Boller
F.
Agid
Y.
, et al.  . 
Explicit memory, procedural learning and lexical priming in Alzheimer's disease
Cortex
 , 
1994
, vol. 
30
 (pg. 
113
-
126
)
[PubMed]
Dubois
B.
Burn
D.
Goetz
C.
Aarsland
D.
Brown
R. G.
Broe
G. A.
, et al.  . 
Diagnostic procedures for Parkinson's disease dementia: Recommendations from the movement disorder society task force
Movement Disorders
 , 
2007
, vol. 
22
 (pg. 
2314
-
2324
)
Emre
M.
Dementia associated with Parkinson's disease
The Lancet Neurology
 , 
2003
, vol. 
2
 (pg. 
229
-
237
)
Fahn
S.
Elton
R.
the Members of the UPDRS Development Committee
David Marsden
C.
Fahn
S.
Calne
D. B.
Unified Parkinson's Disease Rating Scale
Recent developments in Parkinson's disease
 , 
1987
London
MacMillan
(pg. 
153
-
163
)
Folstein
M. F.
Folstein
S. E.
McHugh
P. R.
“Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician
Journal of Psychiatric Research
 , 
1975
, vol. 
12
 (pg. 
189
-
198
)
Folstein
S.
Jensen
B.
Leigh
R.
Folstein
M.
The measurement of abnormal movement: Methods developed for Huntington's disease
Neurobehavioral Toxicology & Teratology
 , 
1983
, vol. 
5
 
6
(pg. 
605
-
609
)
Folstein
S. E.
Leigh
R. J.
Parhad
I. M.
Folstein
M. F.
The diagnosis of Huntington's disease
Neurology
 , 
1986
, vol. 
36
 
10
(pg. 
1279
-
1283
)
[PubMed]
Foster
P. S.
Drago
V.
Crucian
G. P.
Rhodes
R. D.
Shenal
B. V.
Heilman
K. M.
Verbal learning in Alzheimer's disease: Cumulative word knowledge gains across learning trials
Journal of the International Neuropsychological Society
 , 
2009
, vol. 
15
 (pg. 
730
-
739
)
Glosser
G.
Friedman
R. B.
Grugan
P. K.
Lee
J. H.
Grossman
M.
Lexical semantic and associative priming in Alzheimer's disease
Neuropsychology
 , 
1998
, vol. 
12
 (pg. 
218
-
224
)
Graham
N. L.
Emery
T.
Hodges
J. R.
Distinctive cognitive profiles in Alzheimer's disease and subcortical vascular dementia
Journal of Neurology, Neurosurgery, and Psychiatry
 , 
2004
, vol. 
75
 (pg. 
61
-
71
)
[PubMed]
Hodges
J. R.
Salmon
D. P.
Butters
N.
Differential impairment of semantic and episodic memory in Alzheimer's and Huntington's diseases: A controlled prospective study
Journal of Neurology, Neurosurgery, and Psychiatry
 , 
1990
, vol. 
53
 (pg. 
1089
-
1095
)
Hughes
A. J.
Daniel
S. E.
Kilford
L.
Lees
A. J.
Accuracy of clinical diagnosis of idiopathic Parkinson's disease: A clinico-pathological study of 100 cases
Journal of Neurology, Neurosurgery, and Psychiatry
 , 
1992
, vol. 
55
 (pg. 
181
-
184
)
Hughes
C. P.
Berg
L.
Danziger
W. L.
Coben
L. A.
Martin
R. L.
A new clinical scale for the staging of dementia
British Journal of Psychiatry
 , 
1982
, vol. 
140
 (pg. 
566
-
572
)
Kramer
J. H.
Levin
B. E.
Brandt
J.
Delis
D. C.
Differentiation of Alzheimer's, Huntington's, and Parkinson's disease patients on the basis of verbal learning characteristics
Neuropsychology
 , 
1989
, vol. 
3
 (pg. 
111
-
120
)
Lange
K. W.
Sahakian
B. J.
Quinn
N. P.
Marsden
C. D.
Robbins
T. W.
Comparison of executive and visuospatial memory function in Huntington's disease and dementia of Alzheimer type matched for degree of dementia
Journal of Neurology, Neurosurgery, and Psychiatry
 , 
1995
, vol. 
58
 (pg. 
598
-
606
)
Lawton
M. P.
Brody
E. M.
Assessment of older people: Self-maintaining and instrumental activities of daily living
Gerontologist
 , 
1969
, vol. 
9
 (pg. 
179
-
186
)
[PubMed]
Litvan
I.
Mohr
E.
Williams
J.
Gomez
C.
Chase
T. N.
Differential memory and executive functions in demented patients with Parkinson's and Alzheimer's disease
Journal of Neurology, Neurosurgery, and Psychiatry
 , 
1991
, vol. 
54
 (pg. 
25
-
29
)
MacDonald
M. E.
Ambrose
C. M.
Duyao
M. P.
Myers
R. H.
Lin
C.
Srinidhi
L.
, et al.  . 
A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington's disease chromosomes
Cell
 , 
1993
, vol. 
72
 (pg. 
971
-
983
)
Massman
P. J.
Delis
D. C.
Butters
N.
Does impaired primacy recall equal impaired long-term storage? Serial position effects in Huntington's disease and Alzheimer's disease
Developmental Neuropsychology
 , 
1993
, vol. 
9
 (pg. 
1
-
15
)
Massman
P. J.
Delis
D. C.
Butters
N.
Levin
B. E.
Salmon
D. P.
Are all subcortical dementias alike? Verbal learning and memory in Parkinson's and Huntington's disease patients
Journal of Clinical and Experimental Neuropsychology
 , 
1990
, vol. 
12
 (pg. 
729
-
744
)
McKhann
G.
Drachman
D.
Folstein
M.
Katzman
R.
Price
D.
Stadlan
E. M.
Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease
Neurology
 , 
1984
, vol. 
34
 (pg. 
939
-
944
)
[PubMed]
Montoya
A.
Pelletier
M.
Menear
M.
Duplessis
E.
Richer
F.
Lepage
M.
Episodic memory impairment in Huntington's disease: A meta-analysis
Neuropsychologia
 , 
2006
, vol. 
44
 (pg. 
1984
-
1994
)
Myers
R. H.
Huntington's disease genetics
NeuroRx
 , 
2004
, vol. 
1
 (pg. 
255
-
262
)
Noe
E.
Marder
K.
Bell
K. L.
Jacobs
D. M.
Manly
J. J.
Stern
Y.
Comparison of dementia with Lewy bodies to Alzheimer's disease and Parkinson's disease with dementia
Movement Disorders
 , 
2004
, vol. 
19
 (pg. 
60
-
67
)
Paulsen
J. S.
Salmon
D.
Monsch
A.
Butters
N.
Swenson
M.
Bondi
M.
Discrimination of cortical from subcortical dementias on the basis of memory and problem-solving tests
Journal of Clinical Psychology
 , 
1995
, vol. 
51
 (pg. 
48
-
58
)
Pillon
B.
Deweer
B.
Agid
Y.
Dubois
B.
Explicit memory in Alzheimer's, Huntington's, and Parkinson's diseases
Archives of Neurology
 , 
1993
, vol. 
50
 (pg. 
374
-
379
)
[PubMed]
Rohrer
D.
Salmon
D. P.
Wixted
J. T.
Paulsen
J. S.
The disparate effects of Alzheimer's disease and Huntington's disease on semantic memory
Neuropsychology
 , 
1999
, vol. 
13
 (pg. 
381
-
388
)
Rothlind
J. C.
Bylsma
F. W.
Peyser
C.
Folstein
S. E.
Brandt
J.
Cognitive and motor correlates of everyday functioning in early Huntington's disease
The Journal of Nervous and Mental Disease
 , 
1993
, vol. 
181
 (pg. 
194
-
199
)
Sahakian
B. J.
Morris
R. G.
Evenden
J. L.
Heald
A.
Levy
R.
Philpot
M.
, et al.  . 
A comparative study of visuospatial memory and learning in Alzheimer-type dementia and Parkinson's disease
Brain
 , 
1988
, vol. 
111
 (pg. 
695
-
718
)
Salmon
D. P.
Heindel
W. C.
Lange
K. L.
Differential decline in word generation from phonemic and semantic categories during the course of Alzheimer's disease: Implications for the integrity of semantic memory
Journal of the International Neuropsychological Society
 , 
1999
, vol. 
5
 (pg. 
692
-
703
)
[PubMed]
Salmon
D. P.
Kwo-on-Yuen
P. F.
Heindel
W. C.
Butters
N.
Thal
L. J.
Differentiation of Alzheimer's disease and Huntington's disease with the Dementia Rating Scale
Archives of Neurology
 , 
1989
, vol. 
46
 (pg. 
1204
-
1208
)
[PubMed]
Shapiro
A. M.
Benedict
R. H. B.
Schretlen
D.
Brandt
J.
Construct and concurrent validity of the Hopkins Verbal Learning Test-Revised
The Clinical Neuropsychologist
 , 
1999
, vol. 
13
 (pg. 
348
-
358
)
[PubMed]
Solomon
A. C.
Stout
J. C.
Johnson
S. A.
Langbehn
D. R.
Aylward
E. H.
Brandt
J.
, et al.  . 
Verbal episodic memory declines prior to diagnosis in Huntington's disease
Neuropsychologia
 , 
2007
, vol. 
45
 (pg. 
1767
-
1776
)
Steven
E. P.
Takashi
T.
Roberto
C.
Distinguishing the neural correlates of episodic memory encoding and semantic memory retrieval
Psychological Science
 , 
2007
, vol. 
18
 (pg. 
144
-
151
)
[PubMed]
Tabachnick
B. G.
Fidell
L. S.
Using multivariate statistics
 , 
2007
5th ed.
Needham Heights, MA
Pearson Education, Inc
Tröster
A. I.
Butters
N.
Salmon
D. P.
Cullum
C. M.
Jacobs
D.
Brandt
J.
, et al.  . 
The diagnostic utility of savings scores: Differentiating Alzheimer's and Huntington's diseases with the logical memory and visual reproduction tests
Journal of Clinical and Experimental Neuropsychology
 , 
1993
, vol. 
15
 (pg. 
773
-
788
)
Tröster
A. I.
Jacobs
D.
Butters
N.
Cullum
M.
Salmon
D. P.
Differentiating Alzheimer's disease from Huntington's disease with the Wechsler Memory Scale-Revised
Clinics in Geriatric Medicine
 , 
1989
, vol. 
5
 (pg. 
611
-
632
)
[PubMed]
Tulving
E.
Gazzaniga
M. S.
Organization of memory: Quo vadis?
The cognitive neurosciences
 , 
1995
Cambridge, MA
MIT Press
Welsh
K.
Butters
N.
Hughes
J.
Mohs
R.
Heyman
A.
Detection of abnormal memory decline in mild cases of Alzheimer's disease using CERAD neuropsychological measures
Archives of Neurology
 , 
1991
, vol. 
48
 
3
(pg. 
278
-
281
)
[PubMed]
Zizak
V. S.
Filoteo
J. V.
Possin
K. L.
Lucas
J. A.
Rilling
L. M.
Davis
J. D.
, et al.  . 
The ubiquity of memory retrieval deficits in patients with frontal-striatal dysfunction
Cognitive and Behavioral Neurology
 , 
2005
, vol. 
18
 
4
(pg. 
198
-
205
)