Abstract

Recent research supports the utility of process variables in understanding mechanisms underlying memory impairments. The Hopkins Verbal Learning Test-Revised (HVLT-R) was administered to 84 patients with left (LTL, n = 58) or right temporal lobe glioma (RTL, n = 26) prior to surgical resection. Primary HVLT-R measures of learning and memory and numerous learning process indices were computed. Both groups exhibited frequent memory impairment (>30%), with greater severity in the LTL group. Patients with LTL glioma also exhibited lower semantic clustering scores than RTL patients, which were highly associated with Total Recall (ρ = 0.83) and Delayed Recall (ρ = 0.68). Learning slope and a novel measure of learning efficiency were also significantly associated with primary memory measures, though scores were similar across the LTL and RTL groups. While lesions to either temporal lobe impact verbal memory, semantic encoding appears to depend upon the integrity of LTL structures in particular.

Introduction

Characterization of memory functioning in clinical and research settings frequently involves administration of standardized list learning tasks. The Hopkins Verbal Learning Test-Revised (HVLT-R; Benedict, Schretlen, Groninger, & Brandt, 1998) is a list learning task with demonstrated reliability and validity (De Jager, Hogervorst, Combrinck, & Budge, 2003; Kuslansky et al., 2004; Lacritz, Cullum, Weiner, & Rosenberg, 2001; Shapiro, Benedict, Schretlen, & Brandt, 1999). Administration involves the auditory presentation of 12 words from 3 semantic categories, repeated across 3 learning trials. A delayed recall trial is administered ∼20 min after the learning trials, followed by a yes/no recognition discrimination trial involving the presentation of the original list interspersed among foils. While the primary measures of learning (Total Recall) and long-term storage and retrieval (Delayed Recall, Retention, and Recognition Discrimination Index) are sensitive to verbal memory impairment in various neurological populations (Bruce & Echemendia, 2003; Kuslansky et al., 2004; Lacritz et al., 2001), they provide limited information regarding the component processes underlying observed deficits. Supplementary “component process” indices quantify the underlying mechanisms that facilitate verbal memory, aiding the interpretation of primary measures on list learning tests such as the HVLT-R (Woods et al., 2005b).

Process indices include measures of the efficiency and consistency of new learning, as well as indices of the extent to which different encoding strategies are employed. Specifically, “learning slope” represents the rate at which new information is acquired in short-term storage through repetition of material. In healthy individuals, a highly positive learning slope is expected as more information is encoded through rehearsal (Lamar, Charlton, Zhang, & Kumar, 2012). Indicators of “serial position” (e.g., percent primacy, middle, and recency) describe how newly presented information is extracted and encoded. Specifically, when encoding unstructured information, healthy individuals tend to retain the most when the material falls early in presentation order (primacy effect) followed by material presented last (recency effect), while information within the middle of the order is recalled least well (Moser et al., 2014). Measures of encoding strategy use include “serial clustering” (the extent to which items are grouped by order of presentation) and “semantic clustering” (the extent to which items are grouped by category). The use of strategies during verbal learning facilitates the storage and retrieval of acquired information (Stricker, Brown, Wixted, Baldo, & Delis, 2002), with semantic clustering supporting learning and recall better than the serial strategy (Delis, Kramer, Kaplan, & Ober, 1987; Gaines, Shapiro, Alt, & Benedict, 2006).

Increasing research supports the utility of HVLT-R process indices in examining components of verbal learning (Bruce & Echemendia, 2003; Woods et al., 2005a); however, these indices have been more commonly examined with the California Verbal Learning Test (CVLT; Delis et al., 1987; CVLT-II; Delis, Kramer, Kaplan, & Ober, 2000). Evidence suggests that various neurological populations exhibit abnormal learning as indicated by evaluation of CVLT process variables (Lezak, Howieson, & Loring, 2004). Decreased semantic clustering is associated with reduced learning in patients with Parkinson's disease (Bronnick, Alves, Aarsland, Tysnes, & Larsen, 2011), multiple sclerosis (Arnett et al., 1997), traumatic brain injury (Millis & Ricker, 1994), vascular dementia (Vanderploeg, Yuspeh, & Schinka, 2001), and dementia of the Alzheimer's type (Simon, Leach, Winocur, & Moscovitch, 1994). Data also suggest that process measures may be useful in differential diagnosis, as patients with Alzheimer's disease tend to exhibit a diminished primacy effect relative to normal controls and those with mild cognitive impairment (Moser et al., 2014). Similar to studies with the CVLT, various populations exhibit learning deficits associated with reduced encoding strategy use on the HVLT-R, including amnestic-MCI (Malek-Ahmadi, Raj, & Small, 2011; Price et al., 2010), Alzheimer's disease (Gaines et al., 2006), mild TBI (Bruce and Echemendia, 2003), and HIV-1 (Woods et al., 2005c). However, much of the clinical literature regarding HVLT-R process variables focuses on semantic encoding (e.g., Gaines et al., 2006; Malek-Ahmadi et al., 2011), while other process variables are studied less frequently.

It has been well established that memory impairment is frequent in patients with brain tumors (Taphoorn & Klein, 2004; Tucha, Smely, Preier, & Lange, 2000; Wefel et al., 2011), though no prior studies examined verbal learning process variables in this population. Additionally, most studies regarding neurocognitive functioning in patients with brain tumors are confounded by anatomical variability in tumor size and location, in addition to the effects of treatment. The present study aims to characterize verbal learning processes on the HVLT-R in treatment-naïve patients with glioma of the left (LTL) and right (RTL) temporal lobes. In light of literature implicating bilateral temporal structures in verbal memory processes (Kennepohl, Sziklas, Garver, Wagner, & Jones-Gotman, 2007), it is hypothesized that both LTL and RTL groups will exhibit frequent impairment on selected primary learning and memory measures from the HVLT-R, though impairment is expected to be greatest for the LTL group. It is also expected that LTL patients will exhibit less efficient semantic encoding compared to the RTL group, given associations between LTL structures and semantic processing (Jackson & Schacter, 2004; Habib, Nyberg, & Tulving, 2003). An exploratory aim is to investigate differences in less studied process indices across LTL and RTL groups, as well as their relationships with primary verbal learning and memory measures from the HVLT-R.

Materials and Methods

Participants

Adult patients with glioma of the LTL or RTL were identified in The University of Texas MD Anderson Cancer Center (MDACC) neuropsychology and neurosurgery databases. Specialist neuropathologists at MDACC made the histopathological diagnoses. A single neurosurgeon (M.Z.) reviewed MRI scans of the identified patients. Cases were considered for inclusion if they had unifocal tumors restricted to one of the temporal lobes, as indicated on T1-weighted or gadolinium-enhanced T1-weighted sequences. Patients were excluded if tumors extended beyond the bounds of the temporal lobe on T1-weighted imaging sequences. Those with a history of other neurological disease were also excluded, with the exception of seizure disorder secondary to tumor. Patients with a history of open surgical resection, radiotherapy, or any type of chemotherapy prior to completion of neurocognitive testing were excluded. One hundred and three clinically referred patients underwent neuropsychological evaluation between 2001 and 2010 before initiation of treatment. Of those, 84 were administered the HVLT-R with examinee response order recorded to facilitate calculation of process variables. Glioma were restricted to the LTL in 58 patients and the RTL in 26 patients. The MDACC Institutional Review Board approved this study.

Data Collection and Coding

Temporal lobe segmentation

Three distinct temporal lobe areas were defined to describe tumor location, including the lateral anterior, lateral posterior, and medial regions (see Fig. 1). The anterior region comprised the temporal pole, in addition to the area lateral to the temporal horn of the ventricle extending ∼30–35 mm posteriorly from the pole. The posterior region consisted of the lateral region extending posteriorly from that point, not to exceed 99 mm from the temporal pole. The medial region was designated as the area medial to the temporal horn of the lateral ventricle, including the hippocampal formation and parahippocampal gyrus. Tumors were described as localized in a region based on their extension on T1-weighted or gadolinium-enhanced T1-weighted MRI. The tumor boundary was defined as the entire occupied space on T1-weighted images or the enhancing area on gadolinium-enhanced T1-weighted images. For cases in which the temporal horn was compressed, estimated locations were obtained using the horn of the tumor-free temporal lobe as a reference point. A fourth group, multi-region, included tumors with extension into two or more regions. More precise segmentation was attempted, but was not useful due to sample size restrictions.

Fig. 1.

Temporal lobe segmentation. A, lateral anterior; P, lateral posterior; M, medial.

Fig. 1.

Temporal lobe segmentation. A, lateral anterior; P, lateral posterior; M, medial.

Lesion size

Volumetric analysis was performed on MRI scans with MedVision 1.41 software, as previously described (Shi, Wildrick, & Sawaya, 1998). T1-weighted volume was defined as the greater of the hypointense region on T1-weighted MRI, or the hyperintense area on gadolinium-enhanced T1-weighted MRI. Fluid-attenuated inversion recovery (FLAIR) volume was defined as the area of hyperintensity identified on the FLAIR MRI sequence.

Neurocognitive testing

The HVLT or HVLT-R was administered by a neuropsychologist or a trained neuropsychology staff member (i.e., psychometrist or neuropsychology fellow) supervised by a neuropsychologist, as part of a neuropsychological evaluation for clinical purposes. Measures of interest included Total Recall, Delayed Recall, and the Recognition Discrimination Index (Recognition). These scores were standardized using published normative data stratified by patient age (Benedict et al., 1998) and converted to z-scores (M = 0, SD = 1). Approximately half of the total sample did not have data for HVLT-R Delayed Recall and Recognition, as clinic practices initially utilized an earlier version of the HVLT that did not include the delayed trials. Nonetheless, Total Recall trials are identical between HVLT versions and HVLT-R normative data (Benedict et al., 1998) were used for calculation of all primary variables.

For a subset of patients with other test data available, age-adjusted z-scores were computed across diverse measures to describe broader neurocognitive functioning. These measures included tasks assessing auditory attention (Digit Span subtest of the Wechsler Adult Intelligence Scale-Revised or Third Edition; Wechsler, 1997, 1981), processing speed and executive functioning (Trail Making Test Parts A and B; Tombaugh, 2004), naming to visual confrontation (Visual Naming from the Multilingual Aphasia Examination or Boston Naming Test; Benton, Hamsher, & Sivan, 2000; Heaton, Miller Taylor, & Grant, 2004), and visuoconstruction (Block Design subtest of the Wechsler Adult Intelligence Scale-Revised or Third Edition; Wechsler, 1997, 1981). For all HVLT-R and additional measures, performances were considered impaired for z-scores at or below −1.5.

Verbal Learning Component Process Indices

In addition to the primary learning and memory measures from the HVLT, the following verbal learning process indices were computed.

Learning slope

Two methods were used to compute learning slope. The subtraction method from the HVLT-R (Benedict, Schretlen, Groninger, & Brandt, 1998) calculates learning slope (Standard Slope) as the greatest number of words correctly recalled on the second or third learning trial minus the number of words correctly recalled on the first learning trial. Learning slope was also calculated with a variation of the method common to the CVLT (Delis et al., 1987). This measure (Average Slope) represents the average number of new words correctly recalled across the three learning trials.

Cumulative word learning

Cumulative word learning is a novel measure of learning capacity first proposed by Foster and colleagues (2009). Cumulative word learning is purported to be a more pure measure of learning capacity than traditional indices of slope and Total Recall, as it quantifies the rate of acquisition across learning trials while accounting for overall immediate recall performance. More specifically, cumulative word learning is the sum of words correctly recalled on the three learning trials multiplied by the higher of the second or third trial minus the first trial.

Semantic clustering

Semantic clustering calculations were performed according to the method described by Gaines and colleagues (2006). The raw semantic clustering score (Semantic Clusters) reflects the number of times a patient correctly recalled two consecutive words within the same category (e.g., “lion” followed by “cow”) summed across the three learning trials. A semantic clustering ratio was also calculated by dividing Semantic Clusters by the total number of words correctly recalled on the learning trials.

Serial clustering

Serial clustering was calculated with a modified version of the method described by Meijs and colleagues (2009). The raw serial clustering score (Serial Clusters) is the number of times a patient correctly recalled two consecutive words in the same sequential order as initial presentation, summed across the three learning trials. A serial clustering ratio was also calculated by dividing Serial Clusters by the total number of correctly recalled words on the learning trials.

Serial position

The serial position effect was quantified with a variation of the method utilized with the CVLT (Delis et al., 1987). Specifically, percentages of words correctly recalled from the first, middle, and last third of the list were computed. These variables were designated Percent Primacy, Percent Middle, and Percent Recency, respectively.

Statistical Analysis

Independent-samples t-tests or Pearson χ2 tests were used to compare differences in demographics, clinical characteristics, and rates of neurocognitive impairment between LTL and RTL glioma patient groups. Fisher's exact tests were utilized for categorical comparisons in which more than 20% of cells had frequencies <5. For normally distributed neurocognitive variables (Kolmogrov–Smirnov p-values > .05), independent-samples t-tests were used to compare performances across the LTL and RTL groups. Non-normally distributed variables were analyzed with independent-samples Mann–Whitney U-tests. Effect sizes were measured with the Cohen's d statistic. Using Cohen's convention, d-values of 0.2, 0.5, and 0.8 correspond to small, medium, and large effect sizes, respectively (Cohen, 1988). Associations between primary and process HVLT-R indices were determined with Spearman's rank order correlations (ρ). Correlational analyses were performed for the LTL and RTL groups separately and collapsed into a single group. Using Cohen's guidelines, correlation coefficients of 0.1, 0.3, and 0.5 correspond to small, medium, and large associations, respectively (Cohen, 1988).

Sensitivity analyses were conducted with independent-samples t-tests comparing verbal learning and memory indices by seizure status and medication use, in addition to correlational analyses (ρ) determining associations between verbal learning and memory with tumor (T1-weighted) and overall lesion (FLAIR) volume. Partial correlations were also computed between primary and process variables from the HVLT-R controlling for performances on other frequently impaired neurocognitive measures to determine the independence of relationships. All statistical analyses were performed with SPSS 21.0 (IBM Corp., 2012). Given the exploratory nature of the study, two-sided tests were used with a significance level of p ≤ .05 for all analyses.

Results

Demographic and Clinical Characteristics

The overall study sample (n = 84) was predominantly white (88%) and right-handed (86%), with a mean education commensurate with a few years of college (M = 14.5, SD = 2.5). The majority of patients were diagnosed with high grade tumors (79% WHO grade III or IV). Histology revealed that most tumors were glioblastoma (55%), astrocytoma (20%), or oligodendroglioma (16%). Tumors were most frequently located in medial (41%) and posterior (29%) temporal lobe regions. Seizure disorder within the context of brain tumor was identified in 40% of patients. Over half the sample was prescribed antiepileptic drugs (68%) and/or steroids (54%) at the time of assessment. Demographic, clinical characteristics, tumor distribution, and tumor/lesion volume did not significantly differ between the LTL and RTL groups (Table 1).

Table 1.

Demographic and clinical characteristics

 LTL (n = 58) RTL (n = 26) p-value* 
Age in years 
 Mean (SD51.5 (13.6) 54.3 (10.9) .352 
 Median 52 57  
 Range 20–75 25–73  
Male, n (%) 33 (57) 17 (65) .484 
White, n (%) 50 (86) 24 (92) .424 
Right hand dominant, n (%) 50 (86) 22 (85) .847 
Education, years 
 Mean (SD14.5 (2.7) 14.7 (2.0) .678 
 Median 15 15  
 Range 7–20 11–19  
Histology, n (%) 
 Glioblastoma 30 (53) 14 (54) .731 
 Oligodendroglioma 11 (19) 2 (6)  
 Astrocytoma 10 (17) 6 (25)  
 Other 6 (11) 4 (15)  
WHO Grade, n (%) 
 IV 32 (55) 15 (58) .778 
 III 15 (26) 5 (19)  
 II 11 (19) 6 (23)  
Temporal lobe region, n (%) 
 Anterior 9 (16) 4 (15) .720 
 Posterior 18 (31) 6 (23)  
 Medial 21 (36) 13 (50)  
 Multi 10 (17) 3 (12)  
MRI volume, cm3 
 T1-Weighted, mean (SD)a 25.6 (27.0) 34.5 (29.1)  
 FLAIR, mean (SD)b 50.8 (46.3) 58.9 (49.0) .175 
Seizure history   .476 
 Yes, n (%) 26 (45) 8 (31)  
Antiepileptic drugc   .225 
 Yes, n (%) 39 (67) 18 (71)  
Steroidd   .759 
 Yes, n (%) 31 (54) 14 (55) .976 
 LTL (n = 58) RTL (n = 26) p-value* 
Age in years 
 Mean (SD51.5 (13.6) 54.3 (10.9) .352 
 Median 52 57  
 Range 20–75 25–73  
Male, n (%) 33 (57) 17 (65) .484 
White, n (%) 50 (86) 24 (92) .424 
Right hand dominant, n (%) 50 (86) 22 (85) .847 
Education, years 
 Mean (SD14.5 (2.7) 14.7 (2.0) .678 
 Median 15 15  
 Range 7–20 11–19  
Histology, n (%) 
 Glioblastoma 30 (53) 14 (54) .731 
 Oligodendroglioma 11 (19) 2 (6)  
 Astrocytoma 10 (17) 6 (25)  
 Other 6 (11) 4 (15)  
WHO Grade, n (%) 
 IV 32 (55) 15 (58) .778 
 III 15 (26) 5 (19)  
 II 11 (19) 6 (23)  
Temporal lobe region, n (%) 
 Anterior 9 (16) 4 (15) .720 
 Posterior 18 (31) 6 (23)  
 Medial 21 (36) 13 (50)  
 Multi 10 (17) 3 (12)  
MRI volume, cm3 
 T1-Weighted, mean (SD)a 25.6 (27.0) 34.5 (29.1)  
 FLAIR, mean (SD)b 50.8 (46.3) 58.9 (49.0) .175 
Seizure history   .476 
 Yes, n (%) 26 (45) 8 (31)  
Antiepileptic drugc   .225 
 Yes, n (%) 39 (67) 18 (71)  
Steroidd   .759 
 Yes, n (%) 31 (54) 14 (55) .976 

Notes: LTL = left temporal lobe; RTL = right temporal lobe.

aLTL n = 58; RTL n = 26.

bLTL n = 56; RTL n = 25.

cLTL n = 52; RTL n = 24.

dLTL n = 48; RTL n = 22.

*Comparisons performed with independent-samples t-tests, Pearson χ2 tests, or Fisher's exact tests (for variables with 20% of cell sizes <5).

Learning, Memory, and Other Neurocognitive Performances

Table 2 displays the means, standard deviations, and effect sizes for all HVLT-R variables of interest and other selected neurocognitive measures compared across the LTL and RTL groups. The LTL group performed significantly worse than the RTL group on HVLT-R Total Recall (p = .020), with a medium effect size. No significant between-group differences were found on other primary HVLT-R measures, though the mean HVLT-R Delayed Recall and Recognition z-scores were lower for the LTL group. Rates of impairment on primary measures did not significantly differ between groups for HVLT-R Total Recall (LTL, 45%; RTL, 35%), Delayed Recall (LTL, 33%; RTL, 40%), and Recognition (LTL, 19%; RTL, 0%), though the number of RTL patients completing Delayed Recall and Recognition was small (n = 10) due to changes in clinical practice (i.e., switching from the HVLT to the HVLT-R). Regarding process indices, LTL patients exhibited significantly fewer Semantic Clusters (p = .016) and a lower semantic clustering ratio (p = .014) than the RTL group, with medium effect sizes. No other HVLT-R process variables significantly differed between groups.

Table 2.

Neurocognitive performances of left temporal lobe and right temporal lobe glioma patients

Measure LTL
 
RTL
 
Cohen's d 
n Mean (SDn Mean (SD
Primary HVLT measures 
 Total recall 58 −1.44 (1.61) 26 −0.60 (1.06) −0.62* 
 Delayed recall 33 −1.17 (1.89) 10 −0.92 (1.38) −0.15 
 Recognition 33 −0.67 (1.63) 10 −0.30 (0.93) −0.28 
HVLT learning process indicesa 
 Standard slope 58 3.19 (1.86) 26 3.31 (1.62) −0.07 
 Average slope 58 1.49 (1.03) 26 1.54 (0.82) −0.05 
 Cumulative word learning 58 75.41 (51.37) 26 85.00 (43.45) −0.20 
 Semantic clusters 58 7.81 (5.71) 26 11.19 (6.89) −0.53* 
 Semantic clustering ratio 58 0.31 (0.18) 26 0.42 (0.20) −0.58* 
 Serial clusters 58 4.07 (2.53) 26 4.50 (2.94) −0.16 
 Serial clustering ratio 58 0.19 (0.11) 26 0.19 (0.13) 0.00 
 Percent primacy 58 38.05 (10.79) 26 37.04 (6.73) 0.11 
 Percent middle 58 26.76 (10.07) 26 29.81 (5.54) −0.38 
 Percent recency 58 35.19 (13.33) 26 33.04 (6.30) 0.21 
Other measures 
 Digit span 58 −0.62 (0.74) 26 −0.05 (1.03) −0.63* 
 Block design 57 −0.15 (0.91) 25 −0.03 (0.84) −0.13 
 Namingb 57 −0.95 (1.12) 25 −0.09 (1.32) −0.67* 
 Trail making test-A 57 −0.40 (2.33) 25 −0.21 (1.37) −0.08 
 Trail making test-B 54 −1.15 (2.37) 24 −0.84 (1.49) −0.14 
Measure LTL
 
RTL
 
Cohen's d 
n Mean (SDn Mean (SD
Primary HVLT measures 
 Total recall 58 −1.44 (1.61) 26 −0.60 (1.06) −0.62* 
 Delayed recall 33 −1.17 (1.89) 10 −0.92 (1.38) −0.15 
 Recognition 33 −0.67 (1.63) 10 −0.30 (0.93) −0.28 
HVLT learning process indicesa 
 Standard slope 58 3.19 (1.86) 26 3.31 (1.62) −0.07 
 Average slope 58 1.49 (1.03) 26 1.54 (0.82) −0.05 
 Cumulative word learning 58 75.41 (51.37) 26 85.00 (43.45) −0.20 
 Semantic clusters 58 7.81 (5.71) 26 11.19 (6.89) −0.53* 
 Semantic clustering ratio 58 0.31 (0.18) 26 0.42 (0.20) −0.58* 
 Serial clusters 58 4.07 (2.53) 26 4.50 (2.94) −0.16 
 Serial clustering ratio 58 0.19 (0.11) 26 0.19 (0.13) 0.00 
 Percent primacy 58 38.05 (10.79) 26 37.04 (6.73) 0.11 
 Percent middle 58 26.76 (10.07) 26 29.81 (5.54) −0.38 
 Percent recency 58 35.19 (13.33) 26 33.04 (6.30) 0.21 
Other measures 
 Digit span 58 −0.62 (0.74) 26 −0.05 (1.03) −0.63* 
 Block design 57 −0.15 (0.91) 25 −0.03 (0.84) −0.13 
 Namingb 57 −0.95 (1.12) 25 −0.09 (1.32) −0.67* 
 Trail making test-A 57 −0.40 (2.33) 25 −0.21 (1.37) −0.08 
 Trail making test-B 54 −1.15 (2.37) 24 −0.84 (1.49) −0.14 

Notes: LTL = left temporal lobe; RTL = right temporal lobe; HVLT = Hopkins Verbal Learning Test.

aRaw scores. All others z-scores.

bEither Multilingual Aphasia Examination Visual Naming or Boston Naming Test.

*Significant difference between the LTL and RTL groups, p ≤ .05.

Regarding other selected measures, the LTL group performed significantly worse than the RTL group on Digit Span (p = .005) and Naming (p = .004), with medium effect sizes. While rates of impairment did not significantly differ between the LTL and RTL groups, a trend was observed in which patients with LTL tumors also showed more frequent impairment on Digit Span (LTL, 24%; RTL, 8%) and Naming (LTL, 23%; RTL, 4%) tasks. Impairment rates were comparable across groups on Block Design (LTL, 9%; RTL 4%), Trail Making Test-A (LTL, 11%, RTL, 16%), and Trail Making Test-B (LTL, 32%; RTL, 33%). Performances did not differ by antiepileptic or steroid use, and patients with seizures did not exhibit worse performances than those without for all verbal learning and memory variables. Learning and memory variables were not significantly associated with tumor or lesion size.

Associations Between Primary HVLT-R Measures and Process Indices

The relationships between HVLT-R process and primary variables are reported in Table 3. Large associations were noted between Semantic Clusters and HVLT-R Total Recall (p < .001) and Delayed Recall (p < .001). Large associations were also found between Semantic Clustering Ratio and HVLT-R Total Recall (p < .001) and Delayed Recall (p < .001). Medium to large relationships were noted between Percent Middle and HVLT-R Total Recall (p < .001) and Delayed Recall (p = .001), as well as between Cumulative Word Learning and HVLT-R Total Recall (p < .001), Delayed Recall (p = .005), and Recognition (p = .001). Additional small to medium associations were observed between more traditional measures of learning slope and primary HVLT-R variables. Similar relationships were noted when each group was treated separately, though slightly stronger associations were generally observed for the LTL group compared with patients with RTL glioma, potentially reflective of sample size differences.

Table 3.

Associations between HVLT-R primary and process variables

HVLT-R variable Total recall
 
Delayed recall
 
Recognition
 
ρ (n = 84) Partial (n = 32) ρ (n = 70) Partial (n = 22) ρ (n = 70) Partial (n = 22) 
Learning process indices 
 Standard slope 0.26* 0.01 0.30** 0.21 0.35* 0.25 
 Average slope 0.30** 0.13 0.13 0.25 0.35* 0.27 
 Cumulative word learning 0.54** 0.57*** 0.42** 0.40* 0.48*** 0.17 
 Semantic clusters 0.83*** 0.74*** 0.68*** 0.48** 0.28 0.15 
 Semantic clustering ratio 0.69*** 0.59*** 0.52*** 0.29 0.19 0.07 
 Serial clusters 0.20 0.00 0.09 0.15 0.25 0.27 
 Serial clustering ratio −0.23* −0.37* −0.19 −0.10 0.09 0.08 
 Percent primacy −0.16 −0.22 −0.24 0.03 −0.26 −0.13 
 Percent middle 0.50*** 0.38** 0.48*** 0.15 0.34* 0.24 
 Percent recency −0.25* −0.24 −0.18 −0.18 −0.08 −0.16 
HVLT-R variable Total recall
 
Delayed recall
 
Recognition
 
ρ (n = 84) Partial (n = 32) ρ (n = 70) Partial (n = 22) ρ (n = 70) Partial (n = 22) 
Learning process indices 
 Standard slope 0.26* 0.01 0.30** 0.21 0.35* 0.25 
 Average slope 0.30** 0.13 0.13 0.25 0.35* 0.27 
 Cumulative word learning 0.54** 0.57*** 0.42** 0.40* 0.48*** 0.17 
 Semantic clusters 0.83*** 0.74*** 0.68*** 0.48** 0.28 0.15 
 Semantic clustering ratio 0.69*** 0.59*** 0.52*** 0.29 0.19 0.07 
 Serial clusters 0.20 0.00 0.09 0.15 0.25 0.27 
 Serial clustering ratio −0.23* −0.37* −0.19 −0.10 0.09 0.08 
 Percent primacy −0.16 −0.22 −0.24 0.03 −0.26 −0.13 
 Percent middle 0.50*** 0.38** 0.48*** 0.15 0.34* 0.24 
 Percent recency −0.25* −0.24 −0.18 −0.18 −0.08 −0.16 

Notes: HVLT-R; Hopkins Verbal Learning Test-Revised. Data represent Spearman's ρ or partial coefficients (controlling for Digit Span, Naming, and Trail Making Test-B).

*p ≤ .05. **p ≤ .01. ***p ≤ .001.

In light of the possible impact of broader neurocognitive dysfunction on the relationships between learning process indices and primary HVLT-R measures, partial correlations were calculated controlling for performances on measures of attention (Digit Span), object naming (Naming), and executive functioning (Trail Making Test-B). Relationships between most learning process variables and HVLT-R Total Recall were slightly reduced but of similar magnitude when controlling for other neurocognitive performances, for both the LTL and RTL groups. Greater variability in associations was observed between process indices and HVLT-R delayed memory measures, likely related to the reduced sample sizes for these analyses.

Discussion

In confirmation of our initial hypothesis, patients with either LTL or RTL glioma exhibited frequent verbal learning and memory impairment (>30%) on primary measures (HVLT-R Total and Delayed Recall). While rates of impairment were similar across groups, LTL patients showed significantly lower overall verbal learning than the RTL group on HVLT-R Total Recall. Also, a trend was observed in which the LTL group exhibited lower scores than RTL patients on measures of delayed memory functioning. While some of the differences in verbal learning and memory across groups may relate to the somewhat more generalized impairment observed in those with LTL glioma, these results are consistent with findings in other focal neurologic populations, such as temporal lobe epilepsy and stroke (Gregoire et al., 2011; Jones-Gotman et al., 2010; Snaphaan, Rijpkema, van Uden, Fernandez, & de Leeuw, 2009). Interestingly, patients with RTL glioma also exhibited frequent verbal learning and memory impairment, despite exhibiting relatively intact performances across most other domains. Additionally, learning and memory were not significantly related to tumor or lesion size. Taken together, these results suggest a role for the RTL in verbal memory functioning and question the extent to which verbal learning and memory are wholly localized to the LTL.

While interpretation of functional neuroimaging results (e.g., signal representing activation vs. inhibition) and their integration with lesion studies are challenging, these results generally support the growing neuroscientific literature suggesting that verbal memory processes are subserved by networks involving bilateral temporal lobe structures (Baker, Sanders, Maccotta, & Buckner, 2001; Jackson & Schacter, 2004; Johnson, Saykin, Flashman, McAllister, & Sparling, 2001; Powell et al., 2005; Rosazza et al., 2009). Consistent with a more distributed conception of verbal learning and memory functions, Johnson and colleagues (2001) reported that healthy subjects showed bilateral cerebral activation on fMRI during CVLT task performance. In addition to the commonly reported associations between memory and anterior and medial LTL structures, they noted that processing of novel words was significantly associated with activity in the right anterior hippocampus, and recognition performance with the right dorsolateral prefrontal cortex. Further, increased bilateral activation was related to better memory performances, suggesting that more efficient encoding and retrieval occurs with recruitment of additional right hemisphere structures. The bilaterality of verbal learning and memory functions is also supported by evidence that compression of the right inferior longitudinal fasciculus by glioma of the RTL is associated with reduced verbal memory (Shinoura et al., 2011). While additional study is needed to further minimize potential confounds, these findings suggest that verbal learning and memory involve bilateral temporal structures; however, the results also indicate that the component learning processes they support differ by hemisphere.

The hemispheric encoding and retrieval asymmetry (HERA) model describes processing asymmetry of frontal lobe memory functions (Habib et al., 2003). While disagreement exists regarding the adequacy of the HERA model (Owen, 2003), functional imaging and clinical studies provide support to the general concept of hemispheric asymmetry, with the left prefrontal cortex appearing more involved in episodic memory encoding and the right prefrontal cortex more related to memory retrieval regardless of material type (Berlingeri, Danelli, Bottini, Sberna, & Paulesu, 2013; Opitz, Mecklinger, & Friederici, 2000; Propper, McGraw, Brunye, & Weiss, 2013). Kennepohl and colleagues (2007) found similar asymmetries in the temporal lobes. Specifically, they reported bilateral medial temporal lobe activation during visual and verbal memory tasks, though greater BOLD activation was found in the left versus right entorhinal cortex. Interestingly, there was no hemisphere by material type interaction, indicating that the processing asymmetry was independent of the verbal or nonverbal nature of the stimulus.

The present findings provide further support for processing asymmetry in verbal learning between the temporal lobes. As hypothesized, significant differences were observed between the LTL and RTL groups on semantic processing variables from the HVLT-R. Specifically, the LTL glioma group had significantly fewer Semantic Clusters than RTL patients across learning trials. Groups also differed significantly on Semantic Clustering Ratio scores, which accounted for the total number of words recalled on each trial. Further, semantic encoding indices (Semantic Clusters, Semantic Clustering Ratio) exhibited large associations with primary measures of verbal learning and memory (Total and Delayed Recall). Importantly, these relationships held even when statistically controlling for other commonly impaired domains, namely attention, object naming, and executive functioning. Taken together, the results suggest that both RTL and LTL tumors impact verbal memory, though patients with LTL glioma show greater memory problems relative to the RTL group, which may relate to the greater disruption of semantic encoding processes in those with LTL lesions.

A secondary aim was to explore relationships between other verbal learning process indices and primary HVLT-R memory measures across patients with LTL and RTL glioma. Other process indices did not significantly differ between the LTL and RTL groups, though a number of interesting relationships were observed. Both patient groups showed the expected serial position effect, in which Percent Primacy and Percent Recency were greater than Percent Middle. Also, Percent Middle showed medium to large associations with primary memory measures on the HVLT-R, suggesting that the position effect may be attenuated by more efficient learning and memory processes. Indeed, Percent Middle showed significant and medium strength associations with semantic clustering indices, implying that patients utilizing the semantic encoding strategy were able to recall more information throughout the entire list with less dependence on serial encoding.

Foster and colleagues (2009) reported that cumulative word learning was a more pure and sensitive index of verbal learning than Total Recall and traditional measures of learning slope. Our findings lend some support to this contention, as cumulative word learning exhibited stronger associations with primary measures of learning and memory than more traditional indices of learning slope. However, unlike HVLT-R Total Recall and the semantic encoding indices, cumulative word learning scores did not differ between the LTL and RTL groups. As such, this index may not be as sensitive to lateralized temporal lobe pathology as these other variables. Additionally, Aretouli and Brandt (2010) reported that continuous word learning failed to discriminate between Alzheimer's, Parkinson's, and Huntington's disease patient groups, whereas HVLT-R Total Recall made a significant contribution to the discriminability. Accordingly, the utility of Continuous Word Learning requires further investigation.

It is notable that the use of a serial clustering strategy (Serial Clustering Ratio) had a significant and negative association with primary learning on HVLT-R Total Recall. The Serial Clustering Ratio also showed a significant negative relationship with semantic clustering indices (Semantic Clusters and Semantic Clustering Ratio). As expected, the serial clustering strategy appears to be a less efficient encoding technique than semantic clustering. These results are similar to those of Woods and colleagues (2005c) in an HIV-1 population. Given these findings, training in cognitive compensatory strategies focusing on the active organization of material (i.e., semantic clustering) may be a useful technique for rehabilitation of memory difficulties in temporal lobe glioma patients.

Unfortunately, few patients had data on the HVLT-R Delayed Recall and Recognition variables. As such, lack of differences in delayed verbal memory across groups may relate to the limited sample size, particularly in light of the sizeable difference noted in verbal learning (Total Recall) performances between patients with LTL and RTL glioma. Similarly, correlational analyses between delayed memory measures and learning process indices should be interpreted with caution given the sample limitations. Despite these limitations, the tumor grade groups were of similar if not favorable size compared with existing studies of glioma and neurocognitive functioning. Further, a significant strength of the study is the strict control of patient and tumor characteristics, including lesion size and location, medication use, and seizure status, all of which were similar across the LTL and RTL groups. Accordingly, variability in such clinical characteristics cannot account for differences in verbal learning and memory between patients with LTL and RTL glioma, supporting a primary role for tumor laterality in the present findings.

It should be noted that although the LTL group exhibited less efficient learning (i.e., decreased semantic clustering and lower Total Recall) than RTL patients, it is unclear whether the RTL group also showed some attenuation of semantic clustering, due to lack of a control group. Nonetheless, the Semantic Clustering Ratio of RTL patients (M = 0.42, SD = 0.20) was similar to that of a healthy control sample (M = 0.42, SD = 0.18) of comparable demographic background reported elsewhere (Gaines et al., 2006). Accordingly, it is likely that verbal memory impairment in the RTL group is attributable to factors other than reduced semantic encoding, though the present study was unable to identify candidate processes. Additionally, while the focus of the study was verbal memory processes in particular, inclusion of a visuospatial memory measure may have aided understanding of processing asymmetries between the temporal lobes, though it is less clear how to quantify visuospatial learning processes for most existing measures. Finally, given the preliminary and exploratory nature of the study, follow-up studies are needed to replicate the findings and to determine the impact of surgical resection on learning and memory processes in the postoperative period.

In sum, these findings add to the growing literature indicating that HVLT-R process indices are useful tools to identify the breakdown in component processes underlying memory dysfunction across a variety of neurologic populations. More specifically, indices of semantic clustering appear helpful in explaining discrepancies between verbal learning and memory performances in patients with lateralized pathology of the temporal lobes. The study also demonstrated that significant learning impairment is common in temporal lobe glioma patients regardless of hemisphere involved, suggesting that verbal memory processes may be subserved by both temporal lobes. As such, routine neuropsychological assessment should be considered for patients with tumors of the temporal lobes, even when pathology is restricted to RTL structures. Additionally, identification of verbal memory deficits specifically related to reduced learning efficiency may allow for remediation with training in semantic encoding strategies.

Funding

This work was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number R01NR014195 (J.S.W.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Conflict of Interest

None declared.

References

Aretouli
E.
,
Brandt
J.
(
2010
).
Episodic memory in dementia: Characteristics of new learning that differentiate Alzheimer's, Huntington's, and Parkinson's diseases
.
Archives of Clinical Neuropsychology
 ,
25
,
396
409
.
Arnett
P. A.
,
Rao
S. M.
,
Grafman
J.
,
Bernardin
L.
,
Luchetta
T.
,
Binder
J. R.
et al
. (
1997
).
Executive functions in multiple sclerosis: An analysis of temporal ordering, semantic encoding, and planning abilities
.
Neuropsychology
 ,
11
,
535
544
.
Baker
J. T.
,
Sanders
A. L.
,
Maccotta
L.
,
Buckner
R. L.
(
2001
).
Neural correlates of verbal memory encoding during semantic and structural processing tasks
.
Neuro-Report
 ,
12
,
1251
1256
.
Benedict
R. H. B.
,
Schretlen
D.
,
Groninger
L.
,
Brandt
J.
(
1998
).
Hopkins Verbal Learning Test Revised: Normative data and analysis of inter-form and test–retest reliability
.
The Clinical Neuropsychologist
 ,
12
,
43
55
.
Benton
A.
,
Hamsher
K.
,
Sivan
A.
(
2000
).
Multilingual aphasia examination manual
 .
Iowa City, IA
:
AJA Associates Inc
.
Berlingeri
M.
,
Danelli
L.
,
Bottini
G.
,
Sberna
M.
,
Paulesu
E.
(
2013
).
Reassessing the HAROLD model: Is the hemispheric asymmetry reduction in older adults a special case of compensatory-related utilization of neural circuits?
Experimental Brain Research
 ,
224
,
393
410
.
Bronnick
K.
,
Alves
G.
,
Aarsland
D.
,
Tysnes
O.
,
Larsen
J. P.
(
2011
).
Verbal memory in drug-naive, newly diagnosed Parkinson's disease. The retrieval deficit hypothesis revisited
.
Neuropsychology
 ,
25
,
114
124
.
Bruce
J. M.
,
Echemendia
R. J.
(
2003
).
Delayed-onset deficits in verbal encoding strategies among patients with mild traumatic brain injury
.
Neuropsychology
 ,
17
,
622
629
.
Cohen
J.
(
1988
).
Statistical power analyses for the behavioral sciences
  (
2nd ed.
).
Mahwah, NJ
:
Lawrence Erlbaum Associates, Inc
.
De Jager
C. A.
,
Hogervorst
E.
,
Combrinck
M.
,
Budge
M. M.
(
2003
).
Sensitivity and specificity of neuropsychological tests for mild cognitive impairment, vascular cognitive impairment, and Alzheimer's disease
.
Psychological Medicine
 ,
33
,
1039
1050
.
Delis
D. C.
,
Kramer
J. H.
,
Kaplan
E.
,
Ober
B.
(
1987
).
California verbal learning test: Adult version. Professional manual
 .
San Antonio, TX
:
The Psychological Corporation
.
Delis
D. C.
,
Kramer
J. H.
,
Kaplan
E.
,
Ober
B.
(
2000
).
California verbal learning test—Second edition. Professional manual
 .
San Antonio, TX
:
The Psychological Corporation
.
Foster
P. S.
,
Drago
V.
,
Crucian
G. P.
,
Rhodes
R. D.
,
Shenal
B. V.
,
Heilman
K. M.
(
2009
).
Verbal learning in Alzheimer's disease: Cumulative word knowledge gains across learning trials
.
Journal of the International Neuropsychological Society
 ,
15
,
730
739
.
Gaines
J. J.
,
Shapiro
A.
,
Alt
M.
,
Benedict
R. H. B.
(
2006
).
Semantic clustering indexes for the Hopkins Verbal Learning Test-Revised: Initial exploration in elder control and dementia groups
.
Applied Neuropsychology
 ,
13
,
213
222
.
Gregoire
S.
,
Kebets
V.
,
Scheffler
G.
,
Smith
K.
,
Benjamin
M.
,
Brown
M. M.
et al
. (
2011
).
13 neuroimaging correlates of vascular cognitive impairment: Prevalence and clinical relevance of mesial temporal lobe atrophy in a stroke service cohort
.
Journal of Neurology, Neurosurgery, and Psychiatry
 ,
82
,
e1
.
Habib
R.
,
Nyberg
L.
,
Tulving
E.
(
2003
).
Hemispheric asymmetries of memory: The HERA model revisited
.
TRENDS in Cognitive Sciences
 ,
7
,
241
245
.
Heaton
R. K.
,
Miller
S. W.
,
Taylor
M. J.
,
Grant
I.
(
2004
).
Revised comprehensive norm for an expanded Halstead-Reitan battery: Demographically adjusted neuropsychological norms for African American and Caucasian adults
 .
Lutz, FL
:
Psychological Assessment Resources, Inc
.
IBM Corp.
(
2012
).
IBM SPSS statistics for windows,
 
Version 21.0
.
Armonk, NY
:
IBM Corp
.
Jackson
O.
III
,
Schacter
D. L.
(
2004
).
Encoding activity in anterior medial temporal lobe supports subsequent associative recognition
.
NeuroImage
 ,
21
,
456
462
.
Johnson
S. C.
,
Saykin
A. J.
,
Flashman
L. A.
,
McAllister
T. W.
,
Sparling
M. B.
(
2001
).
Brain activation on fMRI and verbal memory ability: Functional neuroanatomic correlates of CVLT performance
.
Journal of the International Neuropsychological Society
 ,
7
,
55
62
.
Jones-Gotman
M.
,
Smith
M. L.
,
Risse
G. L.
,
Westerveld
M.
,
Swanson
S. J.
,
Giovagnoli
A. R.
et al
. (
2010
).
The contribution of neuropsychology to diagnostic assessment in epilepsy
.
Epilepsy and Behavior
 ,
18
,
3
12
.
Kennepohl
S.
,
Sziklas
V.
,
Garver
K. E.
,
Wagner
D. D.
,
Jones-Gotman
M.
(
2007
).
Memory and the medial temporal lobe: Hemispheric specialization reconsidered
.
NeuroImage
 ,
36
,
969
978
.
Kuslansky
G.
,
Katz
M.
,
Verghese
J.
,
Hall
C. B.
,
Lapuerta
P.
,
LaRuffa
G.
et al
. (
2004
).
Detecting dementia with the Hopkins Verbal Learning Test and the Mini-Mental State Examination
.
Archives of Clinical Neuropsychology
 ,
19
,
89
104
.
Lacritz
L. H.
,
Cullum
C. M.
,
Weiner
M. F.
,
Rosenberg
R. N.
(
2001
).
Comparison of the HopkinsVerbal Learning Test-Revised to the California Verbal Learning Test in Alzheimer's disease
.
Applied Neuropsychology
 ,
8
,
180
184
.
Lamar
M.
,
Charlton
R.
,
Zhang
A.
,
Kumar
A.
(
2012
).
Differential associations between types of verbal memory and prefrontal brain structure in healthy aging and late life depression
.
Neuropsychologia
 ,
50
,
1823
1829
.
Lezak
M. D.
,
Howieson
D. B.
,
Loring
D. W.
(
2004
).
Neuropsychological assessment
  (
4th ed.
).
New York
:
Oxford
.
Malek-Ahmadi
M.
,
Raj
A.
,
Small
B. J.
(
2011
).
Semantic clustering as a neuropsychological predictor for amnestic MCI
.
Aging, Neuropsychology, and Cognition
 ,
18
,
280
292
.
Meijs
C. J. C.
,
Hurks
P. P. M.
,
Kalff
A. C.
,
Slaats-Willemse
D. I. E.
,
Rozendaal
N.
,
Jolles
J.
(
2009
).
Differential development of learning strategies on a pictorial verbal learning test (PVLT) in primary-school children
.
Child Neuropsychology
 ,
15
,
247
261
.
Millis
S. R.
,
Ricker
J. H.
(
1994
).
Verbal learning patterns in moderate and severe traumatic brain injury
.
Journal of Clinical & Experimental Neuropsychology
 ,
16
,
498
507
.
Moser
B.
,
Deisenhammer
E. A.
,
Marksteiner
J.
,
Papousek
I.
,
Fink
A.
,
Weiss
E. M.
(
2014
).
Serial position effects in patients with mild cognitive impairment and early and moderate Alzheimer's disease compared with healthy comparison subjects
.
Dementia and Geriatric Cognitive Disorders
 ,
37
,
19
26
.
Opitz
B.
,
Mecklinger
A.
,
Friederici
A. D.
(
2000
).
Functional asymmetry of human prefrontal cortex: Encoding and retrieval of verbally and nonverbally coded information
.
Learning and Memory
 ,
7
,
85
96
.
Owen
A. M.
(
2003
).
HERA today, gone tomorrow?
TRENDS in Cognitive Science
 ,
7
,
383
384
.
Powell
H. W.
,
Koepp
M. R.
,
Symms
P. A.
,
Boulby
A.
,
Salek-Haddadi
P. J.
,
Thompson
J. S.
et al
. (
2005
).
Material-specific lateralization of memory encoding in the medial temporal lobe: Blocked versus event-related design
.
NeuroImage
 ,
27
,
231
239
.
Price
S. E.
,
Kinsella
G. J.
,
Ong
B.
,
Mullaly
E.
,
Phillips
M.
,
Pagnadasa-Fox
L.
et al
. (
2010
).
Learning and memory in amnestic mild cognitive impairment: Contribution of working memory
.
Journal of the International Neuropsychological Society
 ,
16
,
342
351
.
Propper
R. E.
,
McGraw
S. E.
,
Brunye
T. T.
,
Weiss
M.
(
2013
).
Getting a grip on memory: Unilateral hand clenching alters episodic recall
.
PLoS One
 ,
24
,
e62474
.
Rosazza
C.
,
Minati
L.
,
Ghielmetti
F.
,
Macagnano
E.
,
Erbetta
A.
,
Villani
F.
et al
. (
2009
).
Engagement of the medial temporal lobe in verbal and nonverbal memory: Assessment with functional MR imaging in healthy subjects
.
American Journal of Neuroradiology
 ,
30
,
1134
1141
.
Shapiro
A. M.
,
Benedict
R. H. B.
,
Schretlen
D.
,
Brandt
J.
(
1999
).
Construct and concurrent validity of the Hopkins Verbal Learning Test-Revised
.
The Clinical Neuropsychologist
 ,
13
,
348
358
.
Shi
W. M.
,
Wildrick
D. M.
,
Sawaya
R.
(
1998
).
Volumetric measurement of brain tumors from MR imaging
.
Journal of Neurooncology
 ,
37
,
87
93
.
Shinoura
N.
,
Midorikawa
A.
,
Kurokawa
K.
,
Onodera
T.
,
Tsukada
M.
,
Yamada
R.
et al
. (
2011
).
Right temporal lobe plays a role in verbal memory
.
Neurological Research
 ,
33
,
734
738
.
Simon
E.
,
Leach
L.
,
Winocur
G.
,
Moscovitch
M.
(
1994
).
Intact primary memory in mild to moderate Alzheimer disease: Indices from the California Verbal Learning Test
.
Journal of Clinical & Experimental Neuropsychology
 ,
16
,
414
422
.
Snaphaan
L.
,
Rijpkema
M.
,
van Uden
I.
,
Fernandez
G.
,
de Leeuw
F.
(
2009
).
Reduced medial temporal lobe functionality in stroke patients: A functional magnetic resonance imaging study
.
Brain
 ,
132
,
1882
1888
.
Stricker
J. L.
,
Brown
G. G.
,
Wixted
J.
,
Baldo
J. V.
,
Delis
D. C.
(
2002
).
New semantic and serial clustering indices for the California Verbal Learning Test—Second Edition: Background, rationale, and formulae
.
Journal of the International Neuropsychological Society
 ,
8
,
425
435
.
Taphoorn
M. J. B.
,
Klein
M.
(
2004
).
Cognitive deficits in adult patients with brain tumors
.
Lancet Neurology
 ,
3
,
159
168
.
Tombaugh
T. N.
(
2004
).
Trail Making Test A and B: Normative data stratified by age and education
.
Archives of Clinical Neuropsychology
 ,
19
,
203
214
.
Tucha
O.
,
Smely
C.
,
Preier
M.
,
Lange
K. W.
(
2000
).
Cognitive deficits before treatment among patients with brain tumors
.
Neurosurgery
 ,
47
,
324
333
.
Vanderploeg
R. D.
,
Yuspeh
R. L.
,
Schinka
J. A.
(
2001
).
Differential episodic and semantic memory performance in Alzheimer's disease and vascular dementias
.
Journal of the International Neuropsychological Society
 ,
7
,
563
573
.
Wechsler
D.
(
1981
).
Wechsler adult intelligence scale—Revised
 .
San Antonio
:
The Psychological Corporation
.
Wechsler
D.
(
1997
).
Wechsler adult intelligence scale—III
 .
San Antonio
:
The Psychological Corporation
.
Wefel
J. S.
,
Cloughesy
T.
,
Zazzali
J. L.
,
Zheng
M.
,
Prados
M.
,
Wen
P. Y.
et al
. (
2011
).
Neurocognitive function in patients with recurrent glioblastoma treated with bevacizumab
.
Neuro-Oncology
 ,
13
,
660
668
.
Woods
S. P.
,
Rippeth
J. D.
,
Conover
E.
,
Gongvatana
A.
,
Gonzalez
R.
,
Carey
C. L.
, &
HIV Neurobehavioral Research Center Group
. (
2005a
).
Deficient strategic control of verbal encoding and retrieval in individuals with methamphetamine dependence
.
Neuropsychology
 ,
19
,
35
43
.
Woods
S. P.
,
Scott
J. C.
,
Conover
E.
,
Marcotte
T. D.
,
Heaton
R. K.
,
Grant
I.
, &
HIV Neurobehavioral Research Center Group
. (
2005b
).
Test–retest reliability of component process variables within the Hopkins Verbal Learning Test-Revised
.
Assessment
 ,
12
,
96
100
.
Woods
S. P.
,
Scott
J. C.
,
Dawson
M. S.
,
Morgan
E. E.
,
Carey
C. L.
,
Heaton
R. K.
, &
HIV Neurobehavioral Research Center (HNRC) Group
. (
2005c
).
Construct validity of Hopkins Verbal Learning Test-Revised component process measures in an HIV-1 sample
.
Archives of Clinical Neuropsychology
 ,
20
,
1061
1071
.