Verbal Learning and Memory in Older Adults with Minor
and Major Depression
Raquelle I. Mesholam-Gately1,*, Anthony J. Giuliano1, Eric A. Zillmer2, Lamia P. Barakat3,
Anand Kumar4, Ruben C. Gur5,6, Lisa M. McAndrew7, Warren B. Bilker5,8,
Virginia Elderkin-Thompson9, Paul J. Moberg5
1Harvard Medical School Department of Psychiatry, Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess Medical
Center, Boston, MA, USA
2Department of Psychology, Drexel University, Philadelphia, PA, USA
3Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
4Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
5Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
6Philadelphia Veterans Administration Medical Center, Philadelphia, PA, USA
7Department of Veteran Affairs New Jersey Healthcare System, War Related Illness and Injury Study Center, East Orange, NJ, USA
8Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
9Semel Institute of Neuropsychiatry and Human Behavior, University of California-Los Angeles, Los Angeles, CA, USA
*Corresponding author at: Harvard Medical School Department of Psychiatry, Massachusetts Mental Health Center Public Psychiatry Division of the Beth
Israel Deaconess Medical Center, 75 Fenwood Road, 5th Floor, Boston, MA 02115, USA. Tel.: +1-617-754-1233; fax: +1-617-754-1250.
E-mail address: email@example.com (R.I. Mesholam-Gately).
Accepted 13 November 2011
Late-life minor depression (miD) is a prevalent but poorly understood illness. Verbal learning and memory profiles have commonly been
used to characterize neuropsychiatric disorders. This study compared the performance of 27 older adults with miD on the California Verbal
Learning Test (CVLT) with 26 age-matched individuals with Major Depressive Disorder (MDD) and 36 non-depressed controls. Results
revealed that the miD group performed comparably with controls and significantly better than the MDD group on several CVLT indices.
Moreover, cluster analysis revealed three distinct groups, consistent with theoretical representations of “normal,” “subcortical,” and “cor-
tical” verbal learning and memory profiles. The majority of the miD group showed “normal” profiles (74%), whereas most individuals
with MDD displayed “subcortical” profiles (54%). The findings suggest that depression in the elderly is a heterogeneous entity and that
the CVLT may be a useful tool for characterizing learning and memory in late-onset depressive disorders.
Keywords: Depression; Minor; Elderly; Late-onset; CVLT; Verbal learning and memory
Depression is a common symptom and disorder among elderly individuals. Within the spectrum of mental health disorders
in older adults, it is second only to dementia in its incidence (Alexopoulos, 2005) and is often a better predictor of overall
functioning than demographic and medical variables (Denihan et al., 2000; Sharma, Copeland, Dewey, Lowe, & Davidson,
1998). Epidemiological surveys have revealed that depressive syndromes not meeting criteria for Major Depressive
Disorder (MDD) are most prevalent in elderly populations, and range between 15% and 23% (see Lavretsky, Kurbanyan,
& Kumar, 2004, for a review), with 10% meeting criteria for minor depression (miD) (Beekman, Copeland, & Prince,
1999). In contrast, the most recent WHO mental health survey (Kessler et al., 2009) reported a point prevalence of 2.6%
for MDD in individuals older than 64 in developed countries. Similarly, approximately 5% of community-dwelling elderly
have reported a lifetime diagnosis of MDD, whereas 32% indicated a history of either minor or recurrent brief depression
Archives of Clinical Neuropsychology 27 (2012) 196–207
# The Author 2011. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org.
doi:10.1093/arclin/acr106 Advance Access publication on 21 December 2011
(per ICD-10 and Diagnostic and Statistical Manual of Mental Disorders-IV, Text Revision [DSM-IV-TR], intermittent major
depressive episodes lasting ,14 days) (Heun, Papassotiropoulos, & Ptok, 2000).
According to the DSM-IV-TR (American Psychiatric Association, 2000), miD is classified under the “Criteria Sets and
Axes Provided for Further Study” as a depressive disorder of heterogeneous etiology with less severity than MDD (2–4 symp-
toms for miD compared with at least five symptoms for MDD) and less chronicity than Dysthymic Disorder (DyD; 2 weeks for
miD vs. at least 2 years for DyD). DSM-V proposes a re-classification of miD as a “subsyndromal depression that meets dur-
ation criteria but not symptom count criteria for Major Depressive Episode” (American Psychiatric Association, 2010). Despite
conceptualizations of miD as a subsyndromal form of major depression, the public health impact of miD is significant, as miD
is associated with substantial functional disability and reduced productivity (Cuijpers et al., 2007; Judd & Akiskal, 2002),
worse social and role functioning (Howland et al., 2008; Rapaport et al., 2002), poor quality of life (Ruo et al., 2003), increased
mortality (Penninx et al., 1999), and greater bodily pain than individuals with major chronic medical conditions (Wells et al.,
1989). Furthermore, untreated miD is a significant risk factor for future onset of MDD (Cuijpers, Smit, & Willemse, 2005; Judd
& Akiskal, 2002), with one recent study reporting a 7-fold risk for developing MDD at 1-year follow-up compared with non-
depressed controls (Lyness, Chapman, McGriff, Drayer, & Duberstein, 2009).
The limited number of studies that have examined miD in older adults have sought to better clarify, define, and distinguish
miD as an affective diathesis through the examination of cognitive and neuroimaging patterns. Although normal aging alone
contributes to attention and executive functioning difficulties (e.g., working memory, encoding, free recall, organizational
strategies, memory for source, and temporal order) (Craik & Bialystok, 2006; Keys & White, 2000), these and other cognitive
difficulties are exacerbated by miD. In a study examining a broad range of cognitive abilities in older adults with late-onset miD
and MDD (Elderkin-Thompson et al., 2003), only executive functioning differentiated miD and control groups, with the miD
group performing significantly worse on executive function measures. However, their performance was similar to controls and
significantly better than those with MDD on tests of verbal recall (California Verbal Learning Test [CVLT] indices and
semantic fluency) and maintenance of set (scores from the Modified Card Sorting Test); scores typically fell intermediate
between control and MDD groups. Better performance generally correlated with less severe depression, although significant
correlations were not observed for verbal recall when each patient group was analyzed separately, possibly due to the restricted
More recently, in an in-depth probe of the findings from Elderkin-Thompson and colleagues (2003), CVLT performance
was found to be similarly impaired in those with late-life miD and MDD on selected learning indices, though the groups
did not perform significantly worse than controls on delayed recall trials and recognition hits; however, at least half of both
patient groups had prior depressive episodes (Elderkin-Thompson, Mintz, Haroon, Lavretsky, & Kumar, 2007). In addition,
CVLT scores appeared to be mediated by executive functioning (i.e., semantic clustering) with the most robust mediation oc-
curring on Trial 5 among late-onset patients. MRI studies have revealed significantly reduced prefrontal cortex volumes of
elderly miD compared with age-matched, non-depressed controls, with volumes falling intermediate between controls and
those with MDD (Kumar et al., 1997; Kumar, Jin, Bilker, Udupa, & Gottlieb, 1998).
In contrast to elderly miD, the neuropsychological sequelae of late-life MDD have been the focus of considerable research.
Elderly individuals with MDD tend to exhibit the most difficulty on free recall measures of memory that require effortful pro-
cessing (Lockwood, Alexopoulos, & van Gorp, 2002; see McClintock, Husain, Greer, & Cullum, 2010, for a broad recent
review of cognition in MDD, including late-life MDD). While several neuroanatomical regions have been implicated in the
pathophysiology of elderly MDD, including the hippocampus (Ballmaier et al., 2008), evidence from neuroimaging and neuro-
pathological studies suggests a subcortical-frontal network syndrome (Coffey et al., 1993; Cummings, 1993;
Elderkin-Thompson, Hellemann, Pham, & Kumar, 2009; Kumar et al., 2002). In addition to these neurobiologic markers, it
is noteworthy that depression frequently coexists with subcortical dementia and that depressive symptoms often precede the
onset of motor disability (Caine & Shoulson, 1983; Mayeux, Stern, Rosen, & Leventhal, 1981).
Moreover, the performance of patients with MDD on neuropsychological tasks often resembles that of frontal-subcortical
dementias such as Parkinson’s disease and Huntington’s disease (HD), including psychomotor slowing, poor attention,
problem-solving difficulties, and a general encoding and retrieval deficit that is typified by impaired recall but not retention
and recognition deficits (Cummings, 1986; Kramer, Levin, Brandt, & Delis, 1989; Massman, Delis, Butters, Levin, &
Salmon, 1990). Implicit memory, which is also associated with subcortical networks, does not appear to be impacted by de-
pression (Elderkin-Thompson, Moody, Knowlton, Hellemann, & Kumar, 2011). In contrast, individuals who suffer from “cor-
tical dementias,” such as Alzheimer’s disease (AD) and Korsakoff’s disease, typically demonstrate profound memory
disruptions that predominantly affect encoding and storage, with deficits in recall, retention, and recognition performance
(Butters, Granholm, Salmon, Grant, & Wolfe, 1987; Paulsen et al., 1995). They also exhibit highly elevated intrusion rates,
which are posited to be a consequence of a bottom-up deterioration of semantic knowledge (Paulsen et al., 1995).
R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207 197
Several investigators have found good discriminability between subcortical and cortical disorders based on CVLT perform-
ance (e.g., Kramer et al., 1989; Paulsen et al., 1995). Of note is the application of a subcortical dysfunction hypothesis of verbal
learning and memory deficits to a subgroup of middle-aged adults with MDD (n ¼ 40) and Bipolar I disorder (n ¼ 9)
(Massman, Delis, Butters, Dupont, & Gillin, 1992). CVLT profiles of 49 depressed patients were compared with mildly de-
mented patients with a prototypical cortical dementia (AD), a prototypical subcortical dementia (HD), and healthy controls.
Discriminant function analysis yielded three key verbal learning and memory variables that differentiated the HD, AD, and
healthy control groups: (a) total recall on Trials 1–5 of List A, (b) intrusion errors produced on cued recall of List A, and
(c) the difference between recognition discriminability and Trial 5 free recall. This analysis classified 49% of depressed
patients as “normal,” 29% as HD, and none as AD; 22% were not well-classified. Age, years of education, medication
status, prior alcohol use, previous hospitalizations, and severity of depression were not related to the classification group.
The findings of this study suggested that there is heterogeneity in the verbal learning and memory abilities of people with de-
pression and that only a subgroup exhibited learning and memory deficits indicative of subcortical dysfunction.
The present work sought to better characterize miD in the elderly by examining its impact on verbal learning and memory
processes and discriminating their pattern of performance from MDD and non-depressed control groups. We were also inter-
ested in further exploring heterogeneity in verbal learning and memory functions, both between and within the three groups.
Moreover, we investigated whether a subcortical dysfunction hypothesis of verbal learning and memory deficits in MDD
extended to elderly adults with late-onset miD and MDD. While CVLT performance has been studied in elderly miD
(Elderkin-Thompson et al., 2003, 2007), several issues remain unresolved regarding the characteristics and heterogeneity of
verbal learning and memory profiles in late-onset miD and MDD. For example, the earlier study (Elderkin-Thompson
et al., 2003) found no significant differences in performance between miD participants and controls, with scores for the
miD group falling intermediate between the control and MDD groups. The later study (Elderkin-Thompson et al., 2007)
reported similarly deficient scores between miD and MDD groups on selected learning indices, though the groups did not
perform significantly worse than controls on delayed recall trials and recognition hits. At least half of both patient groups
in the Elderkin-Thompson and colleagues (2007) study had previous depressive episodes, whereas the index episode was
the first mood episode in the Elderkin-Thompson and colleagues (2003) sample. In the Elderkin-Thompson and colleagues
(2003) study, individual CVLT test scores were not examined; rather a “verbal recall” composite score was analyzed,
which consisted of seven selected CVLT indices (which only partially overlap with this study) and a semantic fluency
measure. Neither study examined all the selected CVLT variables in this study nor included memory profile analyses.
We hypothesized that CVLT performance would be significantly worse in the MDD group in comparison to the miD and
healthy control groups. While we hypothesized that performance in the miD group would be somewhat reduced compared with
controls, we did not expect the differences in scores to be significant. We expected that most persons in the MDD group would
demonstrate a “subcortical” verbal learning and memory profile, while the majority of the miD group would show “normal”
learning and memory with comparable, though somewhat lower, scores than controls. Few, if any, participants from any of the
study groups were expected to show CVLT scores consistent with a “cortical” profile.
Participants in this study were a subset of a larger sample recruited at the University of Pennsylvania Medical Center for
neuropsychological, pharmacological, and neuroanatomical studies of late-onset depression (operationally defined as onset
≥60 years of age) (Elderkin-Thompson et al., 2003). These studies were approved by the University of Pennsylvania’s
Committee on Studies involving Human Beings. Twenty-seven elderly individuals with miD, 26 with MDD, and 36 controls
participated. The MDD participants were recruited from ambulatory and inpatient geropsychiatry programs. The miD and
control groups were volunteers who responded to community advertisements.
Both controls and patients underwent a screening evaluation consisting of medical, psychiatric, and mental status examina-
tions. The three groups included individuals with stable comorbid medical disorders of comparable severity, such as hyperten-
sion, diabetes, and arthritis, as assessed by the Cumulative Illness Rating Scale (Linn, Linn, & Gurel, 1968). A psychiatric
diagnostic evaluation was conducted with the Structured Clinical Interview for the DSM-IV (First, Spitzer, Gibbon, &
Williams, 1996), including completion of a DSM-IV Research Diagnostic Criteria (RDC) checklist for miD (American
Psychiatric Association, 1994). Exclusion criteria for all participants included: any new prescription or significant change
in the regimen of an existing prescription in the 3 months prior to study participation for medications with potential psycho-
tropic action (e.g., thyroxine, propranolol); history of alcohol or other substance use disorder; any history of schizophrenia or
other psychotic disorder; history of CNS disease (e.g., dementia, tumor, stroke); history of ECT treatment; hospitalization for
198 R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207
depression or any other psychiatric disorder in the past 12 months; a change in a medical condition requiring urgent re-
evaluation or hospitalization in the prior 3 months; or the presence of a life-threatening condition (e.g., renal or hepatic failure).
In the two patient groups, information about depressive episodes was obtained from participants and caregivers. While all
participants in the miD sample met the DSM-IV RDC criteria of low mood and/or loss of interest in activities and at least one
additional depressive symptom from the DSM-IV checklist, the duration criterion was increased from 2 weeks to 1 month to
enhance the likelihood that patients were not merely experiencing a transient dysphoria. Six patients who concurrently met
DSM-IV criteria for DyD (minor depressive symptoms of at least 2 years duration) were included in the miD group.
However, those with a history of earlier major depressive episodes were excluded. All miD patients reported the index
episode as their first episode of a mood disturbance, and their scores on the Hamilton Depression Rating Scale (HDRS;
Hamilton, 1967) were required to be within the 8–16 range. Most patients with miD were drug naı ¨ve, and all were free of
psychotropic medications for at least 2 weeks prior to study participation.
In addition to meeting the standard DSM-IV diagnostic criteria for MDD, MDD participants obtained an HDRS score that
was ≥15. Diagnosis of patients scoring 15 or 16 on the HDRS was decided based on clinical evaluation by a board-certified
geriatric psychiatrist (AK) per DSM-IV research criteria. About one third of the patients in the MDD group were on a com-
bination of low-dose anxiolytics and/or antidepressants in therapeutic dosages at the time of the study. The healthy elderly
controls were screened using criteria similar to those for the patient groups except for no presently observed depressive symp-
toms and no history of depression.
Demographic characteristics of the three groups are summarized in Table 1. Results of univariate analyses of variance
(ANOVAs) with subsequent Tukey’s HSD analyses revealed a diagnosis-by-age interaction; the mean age of MDD participants
was significantly higher than healthy controls (p ¼ .01). A diagnosis-by-education interaction was also observed, with MDD
patients having significantly less years of formal schooling than those in both the miD (p ¼ .03) and control (p ¼ .001) groups.
Age and years of education were thus used as covariates in all subsequent analyses.
Table 1. Demographic characteristics of the miD, MDD, and control groups
CharacteristicmiDMDD ControlsTest statistic
Years of education
Age of onset
Illness duration (years)
F (2, 86) ¼ 4.29, p ¼ .017
x2(2) ¼ 0.30, p ¼ .859
F (2, 86) ¼ 7.40, p ¼ .001
x2(2) ¼ 4.45, p ¼ .348
x2(2) ¼ 1.39, p ¼ .499
t (51) ¼ 0.91, p ¼ .367
1 month–13 years
1 month–15 years
t (51) ¼ 1.50, p ¼ .140
t (39.5) ¼ 9.44, p , .001
Notes: miD ¼ minor depression; MDD ¼ Major Depressive Disorder; HDRS ¼ Hamilton Depression Rating Scale.
R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207199
The CVLT (Delis, Kramer, Kaplan, & Ober, 1987) provides an assessment of multiple processes involved in learning and
remembering orally presented verbal material. Sixteen shopping items (List A) which comprise four semantic categories of
four words each are read to examinees five times. After each of the five trials, examinees attempt free recall of the shopping
items. An interference trial is then presented in which the examiner reads another 16-item shopping list (List B), also compris-
ing four semantic categories of four words each, and asks for recall. Examinees subsequently recall as much of List A as pos-
sible (short-delay free recall). Following this free recall, the examiner offers semantic cues for List A (e.g., “tell me the fruits”).
Free and cued recalls are repeated after a 20-min delay (long-delay). Nonverbal tasks are administered during the delay inter-
val. Finally, a recognition trial is presented in which participants must discriminate List A words from distractors.
After determination of study eligibility, participants were administered the CVLT as part of a larger neuropsychological
battery (Elderkin-Thompson et al., 2003). Neuropsychological testing was conducted by blinded psychological technicians
trained in standardized neuropsychological test administration under the supervision of neuropsychology faculty. The
CVLT protocols were scored with a computer program that calculated raw scores, semantic and serial clustering ratios
(observed/expected), learning slope, discriminability and response bias for recognition memory, and other relevant indices
(Fridlund & Delis, 1987).
Raw scores on these indices were converted to z-scores by using the healthy controls as a reference group for the two patient
groups. We first focused on the six CVLT indices most consistently used to discriminate between healthy individuals and those
with cortical and subcortical disorders: (a) total recall on Trials 1–5 of List A (TOT), (b) rate of learning (Slope), (c) semantic
clustering ratio (SEM), (d) short-delay free recall (SD), (e) the difference between recognition discriminability (accounting for
false-positive errors) and Trial 5 recall (DIFF), and (f) intrusion errors produced on the cued recall of List A (CUE) (Kramer
et al., 1989; Massman et al., 1992; Paulsen et al., 1995). As women typically outperform men on verbal learning and memory
measures at all ages, both in healthy (Kramer, Delis, & Daniel, 1988; Kramer, Delis, Kaplan, O’Donnell, & Prifitera, 1997;
Lamar, Resnick, & Zonderman, 2003) and depressed patient (Otto et al., 1994) samples, gender was included as an independ-
ent variable along with diagnostic classification in all analyses.
To investigate whether a subcortical dysfunction hypothesis of verbal learning and memory deficits in MDD extended to
elderly adults with late-onset miD and MDD, we also performed a k-means cluster analysis on three CVLT variables: (a)
TOT, (b) CUE, and (c) DIFF. These three indices have successfully differentiated among young healthy adults and those
with cortical and subcortical pathology (Massman et al., 1992).
Effort and motivation are important factors to consider in interpreting cognitive findings, and have been the focus of con-
sideration in individuals with depression for some time (Millis, Putnam, Adams, & Ricker, 1995; Richards & Ruff, 1989;
Rohling, Green, Allen, & Iverson, 2002). To address the potential role of reduced effort as an explanation for some of the
lower CVLT scores in the MDD and miD groups, we applied a prediction formula for the CVLT developed by Millis and
Volinsky (2001). Millis and Volinsky developed their prediction formula based on Bayesian model averaging for use in indi-
viduals with traumatic brain injury. We also included recognition discriminability in the detection algorithm, because it has
been specifically associated with effort and applied established cutoff scores (Millis et al., 1995; S.R. Millis, personal commu-
nication, April 21, 2011). This post hoc method identified five individuals (four from the MDD group and one from the miD
group) whose CVLT performance may have been lowered due to reduced effort. We thus re-ran all of our analyses to determine
if any differences would emerge between the full and attenuated samples. Re-analysis revealed no statistically significant or
meaningful differences between the results obtained with the full and attenuated clinical samples. Given the absence of differ-
ences in the results and the absence of specific effort assessment methods for the CVLT in late-life depression (D. Delis, per-
sonal communications, October 16 and 21, 2011), we chose to report findings based on the full clinical sample.
Multivariate analysis of covariance (MANCOVA), controlling for age and education with diagnosis and gender entered as
predictor variables, was conducted to assess for differences among the three participant groups and between genders on CVLT
z-scores of (a) TOT, (b) Slope, (c) SEM, (d) SD, (e) DIFF, and (f) CUE. Results revealed a significant effect for diagnosis,
200R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207
F(12, 152) ¼ 2.23, p ¼ .01, but no main effect for gender and no diagnosis-by-gender interaction. Follow-up ANOVAs
revealed significant differences among the three groups on (a) TOT, F(2, 81) ¼ 4.20, p , .05, (b) Slope, F(2, 81) ¼ 6.29,
p , .01, (c) SD, F(2, 81) ¼ 3.31, p , .05, and (d) DIFF, F(2, 81) ¼ 3.75, p , .05, but not SEM or CUE. Post hoc Tukey’s
HSD procedures demonstrated that the MDD group performed significantly higher than the control group on DIFF (p ,
.001; better recognition performance compared with Trial 5 free recall), and significantly lower than both the miD and
control groups on TOT (p ¼ .003 compared with miD and p , .001 compared with controls), Slope (p , .001 compared
with miD and p ¼ .01 compared with controls), and SD (p ¼ .002 compared with miD and p , .001 compared with controls).
The miD and control groups did not significantly differ from each other on any of these indices. Table 2 provides the means and
standard deviations of z-scores on the six CVLT indices.
A k-means cluster analysis was used to classify the miD and MDD patients into three verbal learning and memory subgroups
based on their z-scores on three CVLT variables of (a) TOT, (b) CUE and (c) DIFF. These indices represent measures of initial
learning, susceptibility to intrusions, and improvement in recall when provided with recognition prompts, respectively. The
three CVLT indices and the three clusters were set a priori in order to reflect theoretical distinctions between cortical dementia,
subcortical dementia, and normal verbal learning and memory profiles (Massman et al., 1992). The cluster analysis sorted the
patients into three distinct subgroups based on their pattern of performance on the CVLT indices; that is, each cluster was
defined by those scores that exhibited the least possible variability from each other and maximal variability between scores
from the other two clusters.
As shown in Fig. 1, three clusters emerged that resembled theoretical representations of normal, subcortical, and cortical
verbal learning and memory profiles. Cluster 1 represents a prototypical “normal” verbal learning and memory profile, as per-
formance on all three CVLT variables was average. In contrast, persons classified to Cluster 2 exhibited a classic “subcortical”
verbal learning and memory profile. Although their acquisition and learning were poor, they demonstrated an average number
of cued intrusions and an above average ability to benefit from recognition cues. Cluster 3 is characteristic of a “cortical” verbal
learning and memory profile. Individuals assigned to this cluster showed reduced acquisition and learning, a considerably large
number of cued intrusions and a lower ability to benefit from recognition prompts than persons grouped in Cluster 2. The
cluster means and standard deviations of z-scores for each CVLT variable are presented in Table 3. ANOVAs revealed
Table 2. Standard (z) scores for the six selected CVLT indices
CVLT index Diagnostic classificationdfF
M SDM SDM SD
Total recall on Trials 1–5, A
Semantic clustering ratio
Discriminability versus Trial 5 FR
Intrusions on cued recall, A
Six indices combined (MANCOVA)
M SDM SD
Total recall on Trials 1–5, A
Semantic clustering ratio
Discriminability versus Trial 5 FR
Intrusions on cued recall, A
Six indices combined (MANCOVA)
Notes: All z-scores indicate the level of performance relative to the control group. miD ¼ minor depression; MDD ¼ Major Depressive Disorder; CVLT ¼
California Verbal Learning Test; FR ¼ free recall; MANCOVA ¼ multivariate analysis of covariance.
aAs no significant diagnosis-by-gender interaction was observed, z-scores for each gender represent scores across three groups.
*p ≤ .05.
**p ≤ .01.
R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207201
significant differences among the three clusters on all three CVLT variables of interest: (a) TOT, F(2, 86) ¼ 65.9, p , .001, (b)
CUE, F(2, 86) ¼ 75.7, p , .001, and (c) DIFF, F(2, 86) ¼ 27.5, p , .001.
As shown in Table 4, cluster assignments for the three participant groups differed significantly: x2(4) ¼ 16.76, p ¼ .002.
Cluster assignments for the miD and control groups were very similar, with the majority of participants in each group assigned
to Cluster 1 (“normal” profile). In contrast, MDD participants largely fell into Cluster 2 (“subcortical” profile).
Finally, we examined the presence of any cluster-by-diagnosis interactions in each of the patient groups on gender and se-
verity of depression (as defined by HDRS scores). Within the miD and MDD groups, MANOVAs revealed no significant dif-
ferences between the three clusters in severity of depression. Chi-square analysis showed that gender did not significantly differ
by cluster assignment.
While depression is a heterogeneous entity, findings from this study suggest that the CVLT may be a useful tool for char-
acterizing and differentiating between learning and memory processes in late-onset MDD and the more prevalent miD in older
adults. As expected, those with miD performed comparably with controls and significantly better than those with MDD on the
six CVLT indices most consistently used to differentiate among healthy individuals and those with cortical and subcortical
Table 4. Cluster classification rates for each participant group
Group Cluster No. 1 (“normal”; n ¼ 54)
74.1% (n ¼ 20)
30.8% (n ¼ 8)
72.2% (n ¼ 26)
Cluster No. 2 (“subcortical”; n ¼ 23)
14.8% (n ¼ 4)
53.8% (n ¼ 14)
13.9% (n ¼ 5)
Cluster No. 3 (“cortical”; n ¼ 12)
11.1% (n ¼ 3)
15.4% (n ¼ 4)
13.9% (n ¼ 5)
Notes: miD ¼ minor depression; MDD ¼ Major Depressive Disorder.
Fig. 1. Mean z-scores (+SD) for each cluster. CVLT ¼ California Verbal Learning Test; TOT ¼ Total recall on Trials 1–5 of List A; CUE ¼ cued recall of
List A; DIFF ¼ difference between recognition discriminability and Trial 5 recall.
Table 3. Standard (z) scores for the three selected CVLT indices on the three clusters
CVLT index Cluster No. 1 (“normal”; n ¼ 54)
Cluster No. 2 (“subcortical”; n ¼ 23)
Cluster No. 3 (“cortical”; n ¼ 12)
M SD SD SD
Notes: All z-scores indicate the level of performance relative to the control group. CVLT ¼ California Verbal Learning Test; TOT ¼ Total recall on Trials 1–5
of List A; CUE ¼ cued recall of List A; DIFF ¼ difference between recognition discriminability and Trial 5 recall.
202 R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207
Specifically, the MDD group scored significantly lower than both the miD and control groups in acquisition (total recall on
Trials 1–5 of List A), rate of learning, and short-delay free recall. Compared with controls, the MDD group also showed sig-
nificantly greater improvement in recall when provided with recognition prompts. This latter finding supports a retrieval deficit
in MDD and bolsters the notion that subcortical pathology is likely to result in reduced performance on more effortful recall
tasks while recognition is intact. Semantic clustering and intrusion errors on cued recall of List A were the only two of the six
CVLT indices thatdid not differ significantly among the three groups. The lackof significant differences on semantic clustering
is consistent with the results of two other studies (Elderkin-Thompson et al., 2003, 2007) examining CVLT performance in
late-life miD and MDD when the index episode was the first mood episode, as it was in the current study; in
Elderkin-Thompson and colleagues (2007), semantic clustering did significantly differ between the groups when individuals
with a history of depressive episodes were also included in the analyses. Intrusions on cued recall were not specifically exam-
ined in these studies (Elderkin-Thompson et al., 2003, 2007). Since the semantic clustering ratio did not discriminate among
the groups in this or the two prior studies by Elderkin-Thompson and colleagues (2003, 2007), it is possible that the CVLT
findings from this study may readily be generalized to other verbal list learning tests that do not measure semantic organization
(e.g., Rey Auditory Verbal Learning Test). However, this warrants further study with similar samples, particularly in light of
the findings from Elderkin-Thompson and colleagues (2007) that semantic clustering appeared to mediate scores on some
The findings also provide support for the use of verbal learning and memory classification groups to characterize the het-
erogeneity among elderly individuals with depressive disorders. Three distinct verbal learning and memory profiles emerged
from cluster analysis of the three CVLT indices previously used to discriminate among “normal” abilities and subcortical and
cortical pathologies (total recall on Trials 1–5 of List A, the difference between recognition discriminability and Trial 5 recall,
and intrusion errors produced on the cued recall of List A). Within the miD group, roughly 74% were classified as normal,
nearly 15% were categorized as subcortical, and approximately 11% were classified as cortical. The majority of MDD patients
(?54%) were assigned to the subcortical verbal learning and memory profile, almost 31% were classified as normal, and about
15% were categorized as cortical. The proportion of individuals assigned to each cluster differed significantly, and the three
clusters resembled theoretical representations of normal, cortical, and subcortical profiles, as hypothesized.
Consistent with this study’s hypotheses, most individuals in the miD group demonstrated normal verbal learning and
memory profiles. While MRI studies have documented that prefrontal lobe volumes in elderly individuals with miD fall
between age-matched controls and those with MDD (Kumar et al., 1997, 1998), such findings do not appear to translate
into performance differences in verbal memory between those with miD and controls. Although Elderkin-Thompson and col-
leagues (2007) found similarly deficient CVLT performance between miD and MDD groups, at least half of both of these
groups had previous depressive episodes, and the study did not examine cued intrusions or the difference between recognition
discriminability and free recall.
The pattern of performance exhibited by the MDD group is also consistent with the study hypotheses, as well as a subcor-
tical dysfunction hypothesis of verbal learning and memory deficits for a subgroup of depressed patients (Massman et al.,
1992). Compared with Massman’s group, this study’s MDD sample showed a higher proportion of subcortical and cortical
profiles. This discrepancy may be due to the compounding effects of age and depression on cognition of MDD patients in
this study (e.g., King, Caine, Conwell, & Cox, 1991). The finding that classification status for both patient groups was not sig-
nificantly influenced by severity of depression was also congruent with findings from Massman and colleagues (1992) and
other investigators (Sweeney, Wetzler, Stokes, & Kocsis, 1989).
It is notable that while there were significant differences in verbal learning and memory profile cluster assignments
(“normal,” “subcortical,” or “cortical”) between the groups, there was heterogeneity in profile assignments within each
group. While some variability may be due to moderating influences not examined in this study (e.g., use of estrogen replace-
ment therapy [ERT] in women, non-psychotropic medications, psychotherapy, previous hospitalizations), it should also be con-
sidered that elderly individuals with miD are at a significant risk for subsequent development of MDD (Cuijpers et al., 2005;
Judd & Akiskal, 2002; Lyness et al., 2009). Furthermore, MDD may present as an early sign of a later developing dementia
(e.g., Agbayewa, 1986; Kral & Emery, 1989; Nussbaum, Kaszniak, Allender, & Rapcsak, 1995). Indeed, Lyness and collea-
gues (2009) reported that untreated miD conferred a 7-fold risk for developing MDD at 1-year follow-up compared with non-
depressed controls. Thus, within both patient groups, perhaps neurobiologically, those who showed normal profiles were ex-
periencing miD, while those with subcortical profiles had MDD and those with cortical profiles had an incipient dementing
This study found no significant gender differences on the six selected CVLT indices as a group. There were also no signifi-
cant gender differences in cluster assignments to normal, subcortical, or cortical profiles. Reduced power due to the modest
sample size in this study may have limited the potential of detecting an interaction with gender. Direct comparison with
gender effects in the Massman and colleagues (1992) study, on which this study’s classification strategy was based, was
R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207203
not possible as nearly all depressed participants from the Massman study were male. However, when examining the six CVLT
indices individually, women performed significantly better than men on TOT and SDFR. Other studies have reported similar
sex differences on TOT and SDFR indices, both in healthy (Kramer et al., 1988, 1997; Lamar et al., 2003; Saykin et al., 1995)
and depressed samples (Otto et al., 1994). While females have also been found to use semantic clustering strategies to a sig-
nificantly greater extent than males, findings have either been restricted to children and adolescents (Kramer et al., 1997) or
represented a broad age range and were not specific to older adults (Kramer et al., 1988). In the two previous studies of
CVLT performance in late-life miD (Elderkin-Thompson et al., 2003, 2007), the influence of gender has been equivocal as
it was either not specifically examined (Elderkin-Thompson et al., 2007) or no gender effects were found on several CVLT
indices when analyzed as a group (effects were not assessed on individual indices) (Elderkin-Thompson et al., 2003).
These results should also be interpreted in the context of limitations related to sample size and characteristics, and the po-
tential influence of moderator variables. As this study focused on community-dwelling elderly patients who were predomin-
antly Caucasian and had a variety of comorbid medical illnesses, the findings can only be generalized to similar populations.
However, the study may provide clinical utility by the inclusion of outpatients with diverse medical problems as clinicians are
often asked to treat such individuals (Lichtenberg, Ross, Millis, & Manning, 1995).
Another limitation is that the effect of psychotropic medication (low-dose antidepressants and/or anxiolytics in therapeutic
dosages) on one third of the MDD group is unknown. Improvement in cognitive functioning resulting from antidepressant med-
ications has been documented (Van den Berg, Oldehinkel, Brilman, Bouhuys, & Ormel, 2000); however, any such substantive
effects in this sample seem unlikely because the pattern of cognitive findings fits the hypotheses. Although these medications,
particularly the anxiolytics, may have contributed to reduced CVLT performance of MDD participants (Mintzer & Griffiths,
2000; Settle, 1998), most studies have found such effects to be small (Berg & Dellasega, 1996). Notably, the scatterplots of
CVLT scores did not show subsets of MDD participants who were outliers in either direction, although medication effects
cannot be entirely ruled out. Participants in the miD group were drug-free, and most were drug naı ¨ve.
Third, an ongoing consideration in the assessment of memory and cognition in adults with depression is the issue of reduced
effort, which itself might be reasonably regarded as a feature of depression (Rohling et al., 2002). Our study did not include
embedded or stand-alone cognitive effort tests. However, in an attempt to address this important issue, we applied a post hoc
detection method developed by Millis and colleagues for the CVLT with individuals with traumatic brain injury (Millis et al.,
1995; Millis & Volinsky, 2001). Based on the results of this approach, reduced effort was determined to be an unlikely source
of influence on our findings. Nonetheless, future studies, particularly those employing the CVLT-II, should take care to index
effort in accord with emerging standards of practice (Heilbronner et al., 2009).
Finally, not all factors that may have influenced CVLT performance were analyzed. For example, psychotherapeutic inter-
ventions in the miD and MDD were not assessed. It was also not known whether women in this study were receiving ERT, and
estrogen levels were not obtained. Additionally, variables such as comorbid medical problems, the use of multiple non-
psychotropic medications, and hospitalizations prior to 1 year before the study may have the potential to affect CVLT
scores. Although Massman and colleagues (1992) did not find any significant relationships between these variables and
verbal learning and memory profile classification status, their depressive sample was younger than the elderly individuals
who participated in this study.
To address these limitations, future investigations of cognition in late-onset miD should be replicated with a larger sample
size, which is more ethnically and racially diverse, to confirm the findings and to consider the influence of aforementioned
moderating variables including potential interactions with gender. It is also important that researchers operationally define
miD explicitly. The current DSM-IV research criteria are tentative, and there are no consistent guidelines that researchers
follow when investigating correlates of miD. As noted, the miD duration criterion in this study was increased from 2 weeks
to 1 month to enhance the likelihood that patients were not merely experiencing a transient dysphoria. Additional efforts to
examine the neurobiological (e.g., neuroimaging) and neuropsychological correlates of miD will likely reveal more specific
characteristics that are common to this putative and heterogeneous disorder. Comparing cognitive profiles of miD to DyD,
as well as longitudinal studies of cognition in miD, may be valuable for better understanding the functional impact of miD
and the extent to which cognition may vary as a function of mood state.
In summary, the findings from this study suggest that although elderly individuals with miD may manifest similar mood
symptoms to those with MDD (e.g., depressive mood, anhedonia), their verbal learning and memory profiles are different
and have different functional implications. The miD group performed comparably with controls on the CVLT, both in
terms of magnitude and patterns, and significantly better than the MDD group. Furthermore, the majority of participants in
both the miD and control groups showed prototypical “normal” verbal learning and memory profiles, while the majority of
those in the MDD group exhibited prototypical “subcortical” profiles. These findings highlight the value of assessing both
mood symptoms and cognition in older adults with depression, particularly given the considerable public health impact of
both miD and MDD, including evidence that untreated miD is a significant risk factor for developing MDD (Cuijpers
204R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207
et al., 2005; Judd & Akiskal, 2002; Lyness et al., 2009) and that MDD may be an early sign of progression to dementia (e.g.,
Agbayewa, 1986; Kral & Emery, 1989; Nussbaum et al., 1995). The results also raise important treatment considerations for
clinicians. For example, therapeutic interventions for those with verbal learning and memory deficits would need to incorporate
strategies to monitor medication adherence. Positive behavior supports that help individuals compensate for memory problems
(e.g., prompts, cues, environmental modifications) would also be beneficial, as would education of family members about cog-
nitive difficulties associated with the illness and compensatory adaptations.
While there is heterogeneity in learning and memory functions that is not sufficiently explained by diagnosis alone, clin-
icians can generally assume that mild or subsyndromal forms of depression in the elderly typically do not include memory
problems, while memory problems do accompany MDD and require specific attention. However, 25% of the miD sample
in this study exhibited either subcortical or cortical profiles, suggesting that a substantial minority of older adults with miD
have memory problems at a rate exceeding expectations related to normal aging alone. Establishing the implications for
risk of progression to MDD and dementia, as well as its functional impact and potential for compensation, require additional
research attention with longitudinal investigations.
This work was supported by a grant from The Dana Foundation Consortium on Memory Loss and Aging; and, in part, by
Massachusetts Department of Mental Health research support to the Commonwealth Research Center; and CIDAR grant
P50MH080272 from the National Institute of Mental Health to Dr. Mesholam-Gately.
The work presented in this paper represents part of a larger study by the University of Pennsylvania Medical Center for
neuropsychological, pharmacological and neuroanatomical studies of late-onset depression. This paper was based on research
conducted for Dr. Mesholam-Gately’s thesis at Drexel University.
Conflict of Interest
Agbayewa, M. O. (1986). Earlier psychiatric morbidity in patients with Alzheimer’s disease. Journal of the American Geriatrics Society, 34, 561–564.
Alexopoulos, G. S. (2005). Depression in the elderly. Lancet, 365, 1961–1970.
American Psychiatric Association. (1994). Diagnostic and Statistical Manual of Mental Disorders (4th ed.). Washington, DC: American Psychiatric
American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental Disorders (4th ed.), text revision. Washington, DC: American
American Psychiatric Association. (2010). Depressive conditions not elsewhere classified. Retrieved from http://www.dsm5.org/ProposedRevisions/Pages/
Ballmaier, M., Narr, K. L., Toga, A. W., Elderkin-Thompson, V., Thompson, P. M., Hamilton, L., et al. (2008). Hippocampal morphology and distinguishing
late-onset from early-onset elderly depression. American Journal of Psychiatry, 165 (2), 229–237.
Beekman, A. T., Copeland, J. R., & Prince, M. J. (1999). Review of community prevalence of depression in later life. British Journal of Psychiatry, 174, 307–
Berg, S., & Dellasega, C. (1996). The use of psychoactive medications and cognitive function in older adults. Journal of Aging and Health, 8 (1), 136–149.
Butters, N., Granholm, E., Salmon, D. P., Grant, I., & Wolfe, J. (1987). Episodic and semantic memory: A comparison of amnesic and demented patients.
Journal of Clinical and Experimental Psychology, 9, 479–497.
Caine, E. D., & Shoulson, I. (1983). Psychiatric syndromes in Huntington’s disease. American Journal of Psychiatry, 140, 728–733.
Coffey, C. E., Wilkinson, W. E., Weiner, R. D., Parshos, I. A., Djang, W. T., Webb, M. C., et al. (1993). Quantitative cerebral anatomy in depression: A con-
trolled magnetic resonance imaging study. Archives of General Psychiatry, 50, 7–16.
Craik, F. I., & Bialystok, E. (2006). Cognition through the lifespan: Mechanisms of change. Trends in Cognitive Sciences, 10 (3), 131–138.
Cuijpers, P., Smit, F., Oostenbrink, J., de Graaf, R., Ten Have, M., & Beekman, A. (2007). Economic costs of minor depression: A population-based study. Acta
Psychiatrica Scandinavica, 115 (3), 229–236.
Cuijpers, P., Smit, F., & Willemse, G. (2005). Predicting the onset of major depression in subjects with subthreshold depression in primary care: A prospective
study. Acta Psychiatrica Scandinavica, 111 (2), 133–138.
Cummings, J. L. (1986). Subcortical dementia: Neuropsychology, neuropsychiatry, and pathophysiology. British Journal of Psychiatry, 149, 682–697.
Cummings, J. L. (1993). The neuroanatomy of depression. Journal of Clinical Psychiatry, 54 (11), 14–20.
Delis, D. C., Kramer, J. H., Kaplan, E., & Ober, B. A. (1987). The California Verbal Learning Test. San Antonio, TX: The Psychological Corporation.
Denihan, A., Kirby, M., Bruce, I., Cunningham, C., Coakley, D., & Lawlor, B. A. (2000). Three-year prognosis of depression in the community-dwelling
elderly. British Journal of Psychiatry, 176, 453–457.
R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207205
Elderkin-Thompson, V., Hellemann, G., Pham, D., & Kumar, A. (2009). Prefrontal brain morphology and executive function in healthy and depressed elderly.
International Journal of Geriatric Psychiatry, 24 (5), 459–468.
Elderkin-Thompson, V., Kumar, A., Bilker, W. B., Dunkin, J. J., Mintz, J., Moberg, P. J., et al. (2003). Neuropsychological deficits among patients with
late-onset minor and major depression. Archives of Clinical Neuropsychology, 18 (5), 529–549.
Elderkin-Thompson, V., Mintz, J., Haroon, E., Lavretsky, H., & Kumar, A. (2007). Executive dysfunction and memory in older patients with major and minor
depression. Archives of Clinical Neuropsychology, 22 (2), 261–270.
Elderkin-Thompson, V., Moody, T., Knowlton, B., Hellemann, G., & Kumar, A. (2011). Explicit and implicit memory in late-life depression. American
Journal of Geriatric Psychiatry, 19 (4), 249–255.
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1996). Structured Clinical Interview for DSM-IVAxis I Disorders, Clinician Version (SCID-CV).
Washington, DC: American Psychiatric Press.
Fridlund, A., & Delis, D. C. (1987). CVLT research edition administration and scoring software. New York: The Psychological Corporation.
Hamilton, M. (1967). Development of a rating scale for primary care depressive illness. British Journal of Social and Clinical Psychology, 6, 278–296.
Heilbronner, R. L., Sweet, J. J., Morgan, J. E., Larrabee, G. J., & Millis, S. R., & Conference Participants. (2009). American Academy of Clinical
Neuropsychology Consensus Conference Statement on the neuropsychological assessment of effort, response bias, and malingering. The Clinical
Neuropsychologist, 23 (7), 1093–1129.
Heun, R., Papassotiropoulos, A., & Ptok, U. (2000). Subthreshold depressive and anxiety disorders in the elderly. European Psychiatry, 15 (3), 173–182.
Howland, R. H., Schettler, P. J., Rapaport, M. H., Mischoulon, D., Schneider, T., Fasiczka, A., et al. (2008). Clinical features and functioning of patients with
minor depression. Psychotherapy and Psychosomatics, 77 (6), 384–389.
Judd, L. L., & Akiskal, H. S. (2002). The clinical and public health relevance of current research on subthreshold depressive symptoms to elderly patients.
American Journal of Geriatric Psychiatry, 10 (3), 233–238.
Kessler, R. C., Birnbaum, H. G., Shahly, V., Bromet, E., Hwang, I., McLaughlin, K. A., et al. (2009). Age differences in the prevalence and co-morbidity of
DSM-IV major depressive episodes: Results from the WHO world mental health survey initiative. Depression and Anxiety, 27 (4), 351–364.
Keys, B. A., & White, D. A. (2000). Exploring the relationship between age, executive abilities, and psychomotor speed. Journal of the International
Neuropsychological Society, 6 (1), 76–82.
King, D. A., Caine, E. D., Conwell, Y., & Cox, C. (1991). The neuropsychology of depression in the elderly: A comparative study of normal aging and
Alzheimer’s disease. Journal of Neuropsychiatry and Clinical Neurosciences, 3, 163–168.
Kral, V. A., & Emery, O. B. (1989). Long-term follow-up of depressive pseudodementia of the aged. Canadian Journal of Psychiatry, 34, 445–446.
Kramer, J. H., Delis, D. C., & Daniel, M. H. (1988). Sex differences in verbal learning. Journal of Clinical Psychology, 44 (6), 907–915.
Kramer, J. H., Delis, D. C., Kaplan, E., O’Donnell, L., & Prifitera, A. (1997). Developmental sex differences in verbal learning. Neuropsychology, 11 (4), 577–
Kramer, J. H., Levin, B. E., Brandt, J., & Delis, D. C. (1989). Differentiation of Alzheimer’s, Huntington’s, and Parkinson’s disease patients on the basis of
verbal learning characteristics. Neuropsychology, 3, 111–120.
Kumar, A., Jin, Z., Bilker, W., Udupa, J., & Gottlieb, G. (1998). Late-onset minor and major depression: Early evidence for common neuroanatomical sub-
strates detected by using MRI. Proceedings of the National Academy of Sciences of the USA, 95 (13), 7654–7658.
Kumar, A., Schweizer, E., Jin, Z., Miller, D., Bilker, W., Swan, L. L., et al. (1997). Neuroanatomical substrates of late-life minor depression. A quantitative
magnetic resonance imaging study. Archives of Neurology, 54 (5), 613–617.
Kumar, A., Thomas, A., Lavretsky, H., Yue, K., Huda, A., Curran, J., et al. (2002). Frontal white matter biochemical abnormalities in late-life major depression
detected with proton magnetic resonance spectroscopy. American Journal of Psychiatry, 159 (4), 630–636.
Lamar, M., Resnick, S. M., & Zonderman, A. B. (2003). Longitudinal changes in verbal memory in older adults: Distinguishing the effects of age from repeat
testing. Neurology, 60 (1), 82–86.
Lavretsky, H., Kurbanyan, K., & Kumar, A. (2004). The significance of subsyndromal depression in geriatrics. Current Psychiatry Reports, 6 (1), 25–31.
Lichtenberg, P. A., Ross, T., Millis, S. R., & Manning, C. A. (1995). The relationship between depression and cognition in older adults: A cross-validation
study. Journal of Gerontology: Psychological Sciences, 50B (1), P25–P32.
Linn, B. S., Linn, B. W., & Gurel, L. (1968). Cumulative Illness Rating Scale. Journal of the American Geriatrics Society, 16, 622–626.
Lockwood, K. A., Alexopoulos, G. S., & van Gorp, W. G. (2002). Executive dysfunction in geriatric depression. American Journal of Psychiatry, 159 (7),
Lyness, J. M., Chapman, B. P., McGriff, J., Drayer, R., & Duberstein, P. R. (2009). One-year outcomes of minor and subsyndromal depression in older primary
care patients. International Psychogeriatrics, 21 (1), 60–68.
Massman, P. J., Delis, D. C., Butters, N., Dupont, R. M., & Gillin, J. C. (1992). The subcortical dysfunction hypothesis of memory deficits in depression:
Neuropsychological validation in a subgroup of patients. Journal of Clinical and Experimental Neuropsychology, 14 (5), 687–706.
Massman, P. J, Delis, D. C., Butters, N., Levin, B. E., & Salmon, D. P. (1990). Are all subcortical dementias alike? Verbal learning and memory in Parkinson’s
and Huntington’s disease patients. Journal of Clinical and Experimental Neuropsychology, 12 (5), 729–744.
Mayeux, R., Stern, Y., Rosen, J., & Leventhal, J. (1981). Depression, intellectual impairment, and Parkinson’s disease. Neurology, 31, 64–650.
McClintock, S. M., Husain, M. M., Greer, T. L., & Cullum, C. M. (2010). Association between depression severity and neurocognitive function in major de-
pressive disorder: A review and synthesis. Neuropsychology, 24 (1), 9–34.
Millis, S. R., Putnam, S. H., Adams, K. M., & Ricker, J. H. (1995). The California Verbal Learning Test in the detection of incomplete effort in neuropsycho-
logical evaluation. Psychological Assessment, 7 (4), 463–471.
Millis, S. R., & Volinsky, C. T. (2001). Assessment of response bias in mild head injury: Beyond malingering tests. Journal of Clinical and Experimental
Neuropsychology, 23 (6), 809–828.
Mintzer, M. Z., & Griffiths, R. R. (2000). Acute effects of triazolam on false recognition. Memory and Cognition, 28 (8), 1357–1365.
Nussbaum, P. D., Kaszniak, A. W., Allender, J., & Rapcsak, S. (1995). Depression and cognitive decline in the elderly: A follow-up study. The Clinical
Neuropsychologist, 9 (2), 101–111.
Otto, M. W., Bruder, G. E., Fava, M., Delis, D. C., Quitkin, F. M., & Rosenbaum, J. F. (1994). Norms for depressed patients for the California Verbal Learning
Test: Associations with depression severity and self-report of cognitive difficulties. Archives of Clinical Neuropsychology, 9, 81–88.
206 R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207
Paulsen, J. S., Butters, N., Sadek, J. R., Johnson, S. A., Salmon, D. P., Swerdlow, N. R., et al. (1995). Distinct cognitive profiles of cortical and subcortical Download full-text
dementia in advanced illness. Neurology, 45, 951–956.
Penninx, B. W., Geerlings, S. W., Deeg, D. J., van Eijk, J. T., van Tilburg, W., & Beekman, A. T. (1999). Minor and major depression and the risk of death in
older persons. Archives of General Psychiatry, 56 (10), 889–895.
Rapaport, M. H., Judd, L. L., Schettler, P. J., Yonkers, K. A., Thase, M. E., Kupfer, D. J., et al. (2002). A descriptive analysis of minor depression. American
Journal of Psychiatry, 159 (4), 637–643.
Richards, P. M., & Ruff, R. M. (1989). Motivational effects on neuropsychological functioning: comparison of depressed versus nondepressed individuals.
Journal of Consulting and Clinical Psychology, 57 (3), 396–402.
Rohling, M. L., Green, P., Allen, L. M., 3rd, & Iverson, G. L. (2002). Depressive symptoms and neurocognitive test scores in patients passing symptom validity
tests. Archives of Clinical Neuropsychology, 17 (3), 205–222.
Ruo, B., Rumsfeld, J. S., Hlatky, M. A., Liu, H., Browner, W. S., & Whooley, M. A. (2003). Depressive symptoms and health-related quality of life: The heart
and soul study. Journal of the American Medical Association, 290 (2), 215–221.
Saykin, A. J., Gur, R. C., Gur, R. E., Shtasel, D. L., Flannery,K. A., Mozley, L. H., et al. (1995) Normativeneuropsychological test performance: Effects of age,
education, gender and ethnicity. Applied Neuropsychology, 2, 79–88.
Settle, E. C. (1998). Antidepressant drugs: Disturbing and potentially dangerous adverse effects. Journal of Clinical Psychiatry, 59(Suppl. 16), 25–42.
Sharma, V. K., Copeland, J. R., Dewey, M. E., Lowe, D., & Davidson, I. (1998). Outcome of the depressed elderly living in the community in liverpool: A
5-year follow-up. Psychological Medicine, 28 (6), 1329–1337.
Sweeney, J. A., Wetzler, S., Stokes, P., & Kocsis, J. (1989). Cognitive functioning in depression. Journal of Clinical Psychology, 45 (6), 836–842.
Van den Berg, M. D., Oldehinkel, A. J., Brilman, E. I., Bouhuys, A. L., & Ormel, J. (2000). Correlates of symptomatic, minor and major depression in the
elderly. Journal of Affective Disorders, 60 (2), 87–95.
Wells, K. B., Stewart, A., Hays, R. D., Burnam, M. A., Rogers, W., Daniels, M., et al. (1989). The functioning and well-being of depressed patients. Results
from the medical outcomes study. Journal of the American Medical Association, 262 (7), 914–919.
R.I. Mesholam-Gately et al. / Archives of Clinical Neuropsychology 27 (2012) 196–207207