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Arch Clin Neuropsychol. 2009 May; 24(3): 237–244.
Published online 2009 July 8. doi:  10.1093/arclin/acp029
PMCID: PMC2765349

The Contribution of Executive Control on Verbal-Learning Impairment in Patients with Parkinson's Disease with Dementia and Alzheimer's Disease


Deficits in learning, memory, and executive functions are common cognitive sequelae of Parkinson's disease with dementia (PDD) and Alzheimer's disease (AD); however, the pattern of deficits within these populations is distinct. Hierarchical regression was used to investigate the contribution of two measures with executive function properties (Verbal Fluency and CLOX) on list-learning performance (CVLT-II total words learned) in a sample of 25 PDD patients and 25 matched AD patients. Executive measures were predictive of list learning in the PDD group after the contribution of overall cognition and contextual verbal learning was accounted for, whereas in the AD group the addition of executive measures did not add to prediction of variance in CVLT-II learning. These findings suggest that deficits in executive functions play a vital role in learning impairments in patients with PDD; however, for AD patients, learning difficulties appear relatively independent of executive dysfunction.

Keywords: Parkinson's disease with dementia, Alzheimer's disease, Executive function, List learning, Neuropsychologic tests, Comparative studies


Parkinson's disease (PD) is a neurodegenerative condition affecting 110–190 individuals per 100,000 (Mayeux et al., 1995), with hallmark motor deficits, including bradykinesia, tremor, rigidity, and postural instability. PD is pathologically characterized by a loss of neurons in the pars compacta of the substantia nigra, which results in a disruption of the dopaminergic input into the basal ganglia as well as the function of the frontal-subcortical feedback loops (Mendez & Cummings, 2003; Middleton & Strick, 2001; Wichmann & DeLong, 2003). In addition to the hallmark motor symptoms, in as many as 55% of PD patients, cognitive deficits may occur that impair functional abilities, sufficient to meet diagnostic criteria of dementia (Aarsland, Andersen, Larsen, Lolk, & Kragh-Sorensen, 2003). Although there are a number of clinical entities that may cause dementia in PD, including Lewy Body disease and Alzheimer's disease (AD), a specific condition, Parkinson's disease with dementia (PDD), exists that involves the onset of a progressing dementing illness at least 1 year after the appearance of the movement disorder (McKeith et al., 2005).

The cognitive sequelae characteristic of PDD include deficits in executive functions, visual-spatial abilities, decreased verbal fluency with slow, pressured speech, and impaired learning and retrieval of verbal memories (Lezak, Howeson, & Loring, 2004). Decreases in executive functioning are the most prominent cognitive deficits in PD and PDD, and include impairments in planning and problem-solving (Brown, Schneider, & Lidsky, 1997), difficulty adapting to novelty (Taylor, Saint-Cyr, & Lang, 1990), and decreased set shifting abilities (Owen et al., 1992). Executive dysfunction has been implicated as an underlying factor of other cognitive deficits typical of PD, such as memory. Impairments of memory and learning in PD and PDD are characterized by poor performance on free recall tasks with relatively normal performance on recognition and cued recall tasks (Taylor et al., 1990). This pattern suggests that the memory impairments associated with PD may be due to a lack of effective search and retrieval skills, which are aspects of executive control, rather than ineffective encoding and consolidation.

The primary clinical symptom of AD is memory impairment. Unlike PDD, the pattern of learning and memory impairments typical of mild AD is characterized by reduced learning, rapid forgetting, and poor recognition along with little or no benefit from cueing (Cummings, 1988; Elias et al., 2000; Grober, Lipton, Hall, & Crystal, 2000). This configuration of deficits indicates a profound reduction in the ability to encode and consolidate new information. Although memory impairment is the hallmark feature of AD, deficits in executive functions are also common (Baddeley, Baddeley, Bucks, & Wilcock, 2001; Lezak et al., 2004). This includes decreased working memory capacity, as well as impaired set shifting, planning, and divided attention (Albert, Moss, Tanzi, & Jones, 2001; Baddeley et al., 2001; Lezak et al., 2004).

Several studies have investigated the relationship between executive functions and verbal (Duff, Schoenberg, Scott, & Adams, 2005; Tremont, Halpert, Javorsky, & Stern, 2000) and visual memory (Busch et al., 2005; Temple, Davis, Silverman, & Tremont, 2006) in mixed samples. Clearly a strong relationship between these cognitive domains exists, yet the differential patterns of associations between disease groups remains unclear. In a sample of non-demented PD patients, Bondi, Kaszniak, Bayles, and Vance (1993) found that when executive functioning was controlled as a covariate, memory scores were within the range of the healthy control group's performance. However, executive function performance of PD patients remained significantly different from controls when memory performance was used as a covariate. Higginson and colleagues (2003) discovered that measures of working memory, an aspect of executive function, related to verbal encoding and retrieval, predicted performance on a wordlist-learning task in PD patients. Using a stepwise regression analysis, these authors showed that this relationship holds when other aspects of cognition are controlled. For the AD population, there are few studies that attempt to link the memory deficit to executive dysfunction. One study (Baudic et al., 2006) found significant correlations between memory scores (recall and recognition on Buschke's Selective Reminding Test) and the number of perseverative errors made on the Modified Card Sorting Task.

Many investigations into the differential cognitive profiles between AD and PDD are available (Cummings, 1988; Starkstein et al., 1996; Stern, Gur, Saykin, & Hurtig, 1986; Stern, Richards, Sano, & Mayeux, 1993; Stern et al., 1998). However, there is a lack of studies that compare the relationship between executive control and learning in these two populations. Kensinger, Shearer, Locascio, Growdon, and Corkin (2003) assessed the relationship between inhibitory abilities and short-term verbal memory in samples of early AD and non-demented PD patients, finding no significant correlations in either group.

The purpose of this study is to investigate the relationship between executive dysfunction and verbal learning in a sample of PDD patients in comparison with a mild AD group matched for age and dementia severity. It is hypothesized that deficits in executive control will be predictive of performance on a learning task in PDD after the contribution of overall cognition and contextual memory (when to-be-remembered material is provided within a structured context, such as a story narrative) are accounted for. Furthermore, it is expected that in the AD patients, decreased executive functions will not predict list-learning performance after the contribution of overall cognition and contextual memory are accounted for.

Materials and Methods


Informed consent was obtained from all study participants, and the procedures were approved by, and performed in compliance with the UAB Institutional Review Board for Research Involving Human Subjects. Participants were selected from the participant pool of the UAB Alzheimer's Disease Research Center (ADRC). Patients at the ADRC undergo an evaluation consisting of neurologic and neurocognitive examinations. Diagnosis is assigned by a consensus conference comprised of neurologists, neuropsychologists, and health professionals from the ADRC. General exclusion criteria for ADRC participants include neurologic, psychiatric, medical, or chemical conditions that could present as cognitive impairment. These exclusionary conditions include metabolic disease, head injuries, large vessel strokes, and neurodevelopmental conditions.

Parkinson'a Disease with Dementia

Twenty-five individuals with PDD were recruited from the UAB Movement Disorders Clinic into the ADRC and were determined to have idiopathic PD with no clear cause for their movement disorder. Dementia status was determined at the time of ADRC consensus conference. Patients given a diagnosis of PDD were considered to have dementia secondary to their PD based on a clinical diagnosis of a movement disorder for at least 1 year prior to onset of dementia, family or caregiver report of functional declines, as well as neurologic and neuropsychologic findings. Criteria are generally consistent with DSM-IV criteria for dementia and current clinical standards for a diagnosis of PDD (Emre et al., 2007; Galvin, Pollack, & Morris, 2006). Individuals in this study were considered to have mild dementia, with a Clinical Dementia Rating (DRS; Morris, 1993) staging of no greater than 1.0. Individuals with evidence of Lewy Body Dementia, atypical PD, or Parkinsonian symptoms with a known etiology were excluded from consideration in this study. Patients with a comorbid neurologic or severe psychiatric disorder were also excluded. Mildly depressed individuals were not excluded from consideration.

Alzheimer's Disease

Twenty-five community dwelling participants with AD were recruited from the UAB Memory Disorders Clinic into the ADRC. These individuals underwent a similar baseline assessment as PDD patients, consisting of neurologic and neuropsychologic examinations. Likewise, diagnosis was reached during the ADRC consensus conference consisting of neurologists, neuropsychologists, and health care professionals. A diagnosis of probable AD is based on the National Institute of Neurological and Communicative Disorders and Stroke—Alzheimer's Disease and Related Disorders Association (NINCDS/ADRDA) criteria. As with PDD, individuals with mild dementia, based upon clinical consensus, with a CDR rating no greater than 1.0, were considered for the study.

The 25 participants with AD were selected from a pool of 101 potential subjects with AD in order to match the PDD sample on age, gender, race, level of education, and MMSE score. A propensity score-matching algorithm (Painter, 2004) was used to obtain the overall best fit between groups. Matching variables were entered into a binary logistic regression as predictors with group membership as the dependent variable. Probability scores were obtained and used as the propensity score in the algorithm. An individual with AD was selected from the ADRC subject pools based on how closely the propensity score matched the score of a PDD case. This was continued until all PDD cases were matched with the best fit of the remaining AD cases.


As mentioned earlier, patients in the ADRC undergo a neurocognitive assessment as part of the diagnostic process. Although the results of the neuropsychologic profile are considered at the time of diagnosis, all diagnoses are made as part of a consensus conference that utilized neurologic examinations, psychiatric assessments, radiographic findings, and laboratory results in addition to the neuropsychologic profile. The neurocognitive examination used at the ADRC includes a battery of widely used assessment tools that measure learning and memory, attention, executive functioning, language, visual-spatial skills, and general intellectual functioning. The following measures were selected from the neuropsychologic battery for analysis in this study.

The California Verbal Learning Test—Second Edition (CVLT-II; Delis, Kramer, Kaplan, & Ober, 2000) was used as the primary measure of verbal list-learning. The total number of words learned across the five trials is the main dependent variable in this study. Two covariates were used to control for contextual verbal learning and overall cognition. The Logical Memory subtest from the Wechsler Memory Scale—Revised Edition (Wechsler, 1987) was used as the measure of overall contextual verbal learning during the regression analyses. As the information is provided within the context of a story, this measure is thought to contain less of an executive component than a list-learning task such as the CVLT-II (Lezak et al., 2004; Tremont et al., 2000). The Dementia Rating Scale—2 (DRS-2; Mattis, 1988) was used to control for the effects of overall cognitive status. Measures of executive functioning included the semantic component of the word fluency task as a measure of executive search and retrieval strategies, and the generation component of the Clock Drawing Test (CLOX-1; Royall, Chordes, & Polk, 1998) was used to measure subjects' planning and constructional abilities.

Motor functioning was assessed through the Purdue Pegboard (Tiffin, 1968) and the Motor subscale of the Unified Parkinson's Disease Rating Scale (UPDRS; Fahn, Elton, & Committee, 1987). The UPDRS is a rating scale that assesses the severity of signs and symptoms of PD, and is comprised of three components: (a) mentation, behavior, and mood, (b) activities of daily living, and (c) motor symptoms. The UPDRS Motor subscale was administered to all participants in the ADRC, including the participants with AD. The geriatric depression scale was administered to assess the level of depression and was not included as a predictor during regression analyses.

Statistical Analysis

A preliminary analysis was performed to ensure that the matching algorithm described earlier resulted in no group inequalities on demographic variables. Independent sample t-tests were performed with continuous variables (age, education, MMSE, geriatric depression, and UPDRS), and the χ2 statistic was computed for categorical variables (gender and race).

The executive measures inherently contain some motor component, and impairments in motor functions, such as those typically seen in PD, may negatively affect performance. Therefore, in order to minimize variability in the executive measures attributable to motor deficits, scores on the Purdue Pegboard task (using the dominant hand) and the UPDRS motor components were regressed on the executive measures, and unstandardized residual scores were obtained. The procedure was performed and residual scores were calculated for both the PDD and AD groups so that between-group comparisons could be made. After these procedures, an examination of neuropsychologic variables was performed using independent samples t-tests.

A series of hierarchical multiple regressions was computed to compare the relationship between executive functions and verbal learning among the patients with PDD and AD. The unique variance of list-learning scores attributable to a contextual verbal-learning measure (Logical Memory I) was determined after the measure of overall cognition was entered into the model. The addition of this measure within the second step allowed for the assessment of the contribution of the executive measures in predicting variance in list-learning performance beyond what can be accounted for by memory impairment alone. This was followed by entering executive measures into the equation, and the unique variance attributable to executive control was determined after accounting for variance in list-learning measures due to overall cognition and verbal contextual learning measures. An alpha level of .05 for significance was adopted for all analyses.


Demographic and Neuropsychological Test Comparisons

A summary of the demographic measures is presented in Table 1. The two groups were matched on gender, race, age, level of education, and MMSE score; however, the PDD group had higher geriatric depression scores. As expected, the PDD group also had higher UPDRS scores than the AD group. The two dementia groups were matched on nearly all neuropsychologic measures (see Table 2). Not surprisingly, the PDD group had lower scores on the Purdue Pegboard task when compared with the AD group. There were no group differences on CVLT measures of serial, semantic, or subjective clustering; however, the PDD group exhibited more efficient learning across the five trials than the AD group (t = 2.123, df = 48, p = 0.039, d = 0.61).

Table 1.
Mean ± standard deviation of demographic variables
Table 2.
Mean ± standard deviation of neuropsychologic variables

Since many of the neuropsychologic variables are typically considered measures of executive functioning, intercorrrelation and multicolinearity among the variables is a concern. A univariate correlation matrix for all included variables was examined and displayed in Table 3. Among the executive predictors, CLOX 1 significantly correlated with word fluency in the PDD group; however, the degree of association was within acceptable limits (Tabachnick & Fidell, 2001). Additionally, colinearity diagnostics were performed, and were found to be within acceptable limits (Tabachnick & Fidell, 2001).

Table 3.
Univariate correlation matrix of neuropsychologic variables

Hierarchical Regression Models

A three-step hierarchical regression model was performed to investigate the ability of executive measures in predicting list learning after variance has been attributed to contextual verbal learning in addition to overall cognition (see Table 4). For PDD patients, the measure of contextual verbal learning did not significantly increase prediction of variance in list learning after attributing variance to overall cognition. However, the executive measures significantly increased prediction of variance in CVLT scores after the variance attributed to overall cognition and contextual verbal learning was accounted for. This model was able to account for nearly 86% of the variance in CVLT trials 1–5 scores.

Table 4.
Results of hierarchical regression of contextual learning and executive measures on CVLT learning scores

An investigation into the beta weights revealed that the two measures of executive control, word fluency, (β = −1.048, t = −5.338, p < 0.001, d = 2.96) and CLOX-1, (β = 0.918, t = 5.472, p < 0.002, d = 3.04), were the most important predictors in accounting for variance in list learning in PDD. Although to a lesser degree, DRS total score (β = 0.837, t = 5.031, p < 0.001, d = 2.79) and Logical Memory I (β = 0.586, t = 4.259, p = 0.001, d = 2.36) were also important when predicting variance in CVLT scores of individuals with PDD.

In the sample of AD patients, contextual learning scores significantly increased prediction of variance in list learning after variance was attributed to overall cognition. However, the measures of executive functioning did not significantly add to prediction of variance in CVLT scores after variance had been attributed to overall cognition and contextual verbal learning in AD patients. This pattern is in contrast to that observed in PDD patients, as in PDD contextual learning failed to predict variance in CVLT scores while the executive measures were able to make a significant prediction. The three-step regression model was able to account for 64% of the variance in CVLT scores.

Alternative models using the DRS Construction subscale as an executive function predictor instead of CLOX did not reveal any significant predictive ability for this measure (analyses not shown).


This study investigated the relationship between deficits in executive control and verbal list-learning impairments in PDD in contrast to patients with mild AD. Strong support was found for our hypothesis that executive dysfunction is a primary determinant of verbal learning impairment in patients with PDD. We found that measures of executive control (including Word Fluency and CLOX-1) significantly accounted for variance in CVLT-II scores after variance was attributed to overall cognition (measured by DRS total score) and contextual verbal learning (Logical Memory I). Furthermore, the results of this study suggest that list-learning impairments in patients with mild AD are comparatively unrelated to executive dysfunction. In the AD group, the executive measures did not account for significant variance in list-learning scores after the contribution of overall cognition and contextual verbal learning was accounted for.

Previous literature has established that an association exists between executive dysfunction and learning in various populations, which the current study supports. Duff and colleagues (2005) found measures of executive functioning shared as much as 60% of their variance with a wide array of visual and verbal learning variables in a sample of patients in a neuropsychology clinic. Furthermore, executive measures with a strong visual-spatial component (Trails B and Wisconsin Card Sorting Test) have been shown to be predictive of performance on visual memory tasks (Temple et al., 2006). Finally, poorer performance on the CVLT has been displayed in mixed samples of neurologic patients with psychometrically documented executive dysfunctions (Tremont et al., 2000) as well as frontal lobe lesion patients (Alexander, Stuss, & Fansabedian, 2003).

The current findings demonstrate that a robust relationship exists between executive function and list learning in patients with PDD. Previous studies have found some evidence for relationships between executive dysfunction and verbal learning in non-demented PD patients (Bondi et al., 1993; Higginson et al., 2003). Bondi and colleagues demonstrated that impairments on learning tasks (including word learning, procedural learning, perceptual learning, and continuous verbal recognition) were no longer apparent after using statistical covariation of executive measures, including the Wisconsin Card Sorting Task (WCST), California Sorting Test, and measures of verbal temporal ordering and verbal fluency. Higginson et al. (2003) utilized a step-wise regression approach to demonstrate that a measure of working memory (Letter-Number Sequencing from the Wechsler Intelligence Scales) predicted CVLT delayed free recall scores. Other executive measures, including WCST, stroop color-word test, digit span, and letter fluency (FAS), did not significantly predict variance in list learning. We found that CLOX-1, a planning and construction task, and word fluency, a search and retrieval task, were the most important predictors of verbal learning in our sample of PDD patients. It is clear that a range of executive functions likely play a role in verbal-learning impairments in patients with PD (both with and without dementia).

Although the current study is one of the first to document an association between verbal learning and executive dysfunction in PDD patients, Levy and colleagues (2002) demonstrated that impairments in these two cognitive domains are associated with the development of dementia in PD patients. The current study adds to the growing body of evidence, suggesting that the executive dysfunction contributes to the deficits in verbal learning and memory. Given the clinical significance of these cognitive domains in predicting long-term outcomes, it is vital to further explore the relationship between deficits in executive control and memory in the PD population.

Furthermore, our findings provide evidence of comparatively different mechanisms of verbal learning impairment in PDD and mild AD patients. In PDD, executive measures were significantly associated with verbal list-learning after controlling for the contribution of contextual learning. On the other hand, in the AD group, the unique contribution of contextual learning was significant after accounting for the contribution of overall cognition, while the addition of unique variance attributable to the executive measures did not provide a significant contribution in the prediction of variance over that provided by overall cognition and contextual learning. None of the beta coefficients of the executive predictors approached significance levels in the AD patient model. PDD patients typically display poor learning, yet relatively spared cued and recognition memory; whereas AD patients show insufficient registration ability with poor learning, retrieval, and recognition memory (Cummings, 1988). These patterns suggest a different mechanism of learning deficits in the two populations, which is supported by the current results.

Consistent with previous literature, we found no group differences among patients with AD and PDD, on measures of verbal learning and executive functions (Song, Kim, Yoo, Song, & Lee, 2008; Starkstein et al., 1996; Stern et al., 1993, 1998). However, there is little prior support for a link between executive dysfunction and verbal-learning impairment in AD. Baudic et al. (2006) found significant correlations between scores on the Buschke Selective Reminding Task, a measure of verbal list-learning abilities, and executive measures (number of perseverative errors on Modified Card Sorting Task and WAIS Similarities) in mild AD patients. Unlike the current study, Baudic and colleagues did not account for overall cognition or other measures of verbal learning in their analyses. Furthermore, while their study reported significant correlations, they did not use a multiple regression approach which would help determine the contributions of cognitive processes to learning. Recently, Grober and colleagues (2008) found memory impairments precede deficits in executive functions by 4–5 years in preclinical AD patients. Therefore, it appears that memory and learning deficits exist independently of executive dysfunctions in mild AD patients.

From our perspective, the literature suggests that several CVLT-II measures may differ between patients with AD and PDD, primarily on the basis of the mnemonic processes that are eroded in these dementias (i.e., encoding, consolidation, retrieval). Our understanding of the literature is that in AD the primary deficit is one of the deficient consolidations of information, such that immediately learned material is rapidly lost and that retrieval cues do not enhance performance. In contrast, we understand memory loss in PDD as representing the prototypical “subcortical” pattern, whereby encoding is impoverished and retrieval is inefficient, such that learning may be slow but that with retrieval cues the information that is learned can be retained. We see this pattern as primarily implicating executive dysfunction. It should be noted that in practice such patterns are relative rather than absolute, and that all aspects of mnemonic function can be impaired in AD, but that consolidation is disproportionately impaired.

There are a number of limitations and weaknesses inherit in this study that suggests avenues for future research. The most prominent weakness lies with the limited sample size. The authors wanted to utilize a well-defined and well-matched sample of PDD and AD patients. This resulted in a more modest sample size due to the rigors of study methods in the diagnostic process. It should also be noted that the specification of PDD patients may not generalize to the dementia with Lewy Bodies or non-demented PD populations. As with any analysis based on correlation, the generalizability of results is of further concern as all regression-based analyses are dependent on the measures used and sample characteristics. The authors chose to utilize widely administered measures of executive functioning and verbal learning, along with highly characterized samples, in order to minimize these concerns. However, replication with other measures and samples is needed.

In summary, this study provides evidence of distinct patterns of list-learning impairment in PDD and AD. This is the first study that we are aware of that specifically compares the influence of executive control on list-learning performance in these two populations using the same measures in a matched sample. The data support the notion that executive dysfunction plays a distinct role in verbal learning impairment in PDD but less of a role in patients with AD. Overall cognition and contextual learning are the key predictors of verbal list-learning performance in AD. More research into comparative cognitive deficits in PDD and AD is strongly desirable to better understand the underlying neurocognitive phenomena behind neuropsychologic impairments in these patient populations.


This research was supported by grants from the National Institute on Aging (Alzheimer's Disease Research Center) (1P50 AG16582-10: Marson, PI).

Conflict of Interest

None declared.


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