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. Author manuscript; available in PMC: 2015 Nov 4.
Published in final edited form as: Psychol Med. 2015 Jul 14;45(14):3111–3120. doi: 10.1017/S0033291715001075

Cognitive Control, Reward Related Decision Making and Outcomes of Late-Life Depression Treated with an Antidepressant

George S Alexopoulos 1, Kevin Manning 2, Dora Kanellopoulos 1, Amanda McGovern 1, Joanna K Seirup 1, Samprit Banerjee 3, Faith Gunning 1
PMCID: PMC4631680  NIHMSID: NIHMS710011  PMID: 26169527

Abstract

Background

Executive processes consist of at least two sets of functions: one concerned with cognitive control and the other with reward-related decision making. Abnormal performance in both sets occurs in late-life depression. This study tested the hypothesis that only abnormal performance in cognitive control tasks predicts poor outcomes of late-life depression treated with escitalopram.

Methods

We studied older subjects with major depression (N=53) and non-depressed subjects (N=30). Executive functions were tested with the Iowa Gambling Test (IGT), Stroop Color/Word test, Tower of London, and Dementia Rating Scale-Initiation/Perseveration Domain (DRS-IP). After a 2-week placebo washout, depressed subjects received escitalopram (target daily dose: 20 mg) for 12 weeks.

Results

There were no significant differences between depressed and non-depressed subjects on executive function tests. Hierarchical cluster analysis of depressed subjects identified a Cognitive Control Cluster (abnormal Stroop, Tower, DRS-IP), a Reward-Related Cluster (IGT), and an Executively Unimpaired Cluster. Decline in depression was greater in the Executively Unimpaired (t=−2.09, df=331, p=0.0375) and the Reward-Related Cluster (t=−2.33, df=331,p=0.0202) than the Cognitive Control Cluster. The Executively Unimpaired Cluster (t=2.17, df=331, p=0.03) and the Reward-Related Cluster (t=2.03, df=331, p=0.0433) had a higher probability of remission than the Cognitive Control Cluster.

Conclusions

Dysfunction of cognitive control functions, but not reward-related decision making, may influence the decline of symptoms and the probability of remission of late-life depression treated with escitalopram. If replicated, simple to administer cognitive control tests may be used to select depressed older patients at risk for poor outcomes to SSRIs who may require structured psychotherapy.

INTRODUCTION

Late-life depression is classified as a mood disorder, yet abnormalities in various executive functions often occur during depressive episodes (Elliott et al., 1998, Eshel and Roiser, 2010, Vrieze et al., 2013). A large body of literature suggests that executive processes consist of two distinct sets of cognitive functions: one concerned with cognitive control and the other with reward-related decision making (Glascher et al., 2012, Roiser and Sahakian, 2013). Cognitive control processes include response inhibition, planning, problem solving, and working memory. Reward-related decision making processes include valuation, reward learning, and decision making. Cognitive control and reward-related decision making are instantiated in distinct neuroanatomical circuits, which interact to generate adaptive behavior. Abnormalities in both cognitive control and reward-related decision making tasks (Elliott et al., 1998, Eshel and Roiser, 2010, Vrieze et al., 2013) have been reported in depression. Determining which of these functions is central to perpetuating the syndrome of late-life depression is an important heuristic and clinical question.

Impairment in some cognitive control functions has been associated with poor outcomes of late-life depression when treated with antidepressants. In particular, tests of initiation/perseveration, cognitive inhibition, and semantic clustering have been associated with poor or slow improvement of late-life depression to antidepressants (Alexopoulos et al., 2005, Morimoto et al., 2012, Morimoto et al., 2011, Pimontel et al., 2012, Sneed et al., 2010). Performance in these tests requires integrity of the anterior cingulate cortex (ACC) and the dorsolateral prefrontal cortices (DLPFC) (Beauchamp et al., 2003, Dagher et al., 1999, Elliott et al., 1997, Goethals et al., 2004, MacDonald et al., 2000, Ruocco et al., 2014, van den Heuvel et al., 2003). These neuropsychological findings parallel structural and functional neuroanatomical changes associated both with cognitive control dysfunction and with poor outcomes of late-life depression treated with antidepressants. These include white matter hyperintensities (Gunning-Dixon et al., 2010), microstructural white matter changes abnormalities (Alexopoulos et al., 2008), low volume of the anterior cingulate (Gunning et al., 2009), hypoactivation of the cognitive control network in response to a cognitive control challenge (Aizenstein et al., 2005), and reduced resting functional connectivity of the cognitive control network (Alexopoulos et al., 2012). Taken together, these findings lend support to the hypothesis that depression with cognitive control dysfunction is a distinct syndrome of late-life depression (Alexopoulos, 2001) with poor outcomes following treatment with antidepressants.

In addition to cognitive control dysfunction, abnormal performance in reward related decision making tasks has been reported in depression (Eshel and Roiser, 2010, Vrieze et al., 2013). Performance in such tasks requires integrity of the ventromedial prefrontal cortex (VMPFC) (Rogalsky et al., 2012). Neuroimaging, neuropathologic, and lesion analysis findings implicate the VMPFC in the pathophysiology of major depression (Drevets, 2007). Severity of depression is inversely correlated with physiological activity in parts of the posterior lateral and medial orbitofrontal cortex and cerebrovascular lesions in this region predispose to depression. The posterior lateral and medial orbitofrontal cortex function may also be impaired in mood disorders, as these patients have low grey-matter volume, histopathologic abnormalities, and altered hemodynamic responses to emotionally valenced stimuli, probabilistic reversal learning, and reward processing. Impairment in reward-related decision making is mediated by the VMPFC and in late-life depression is associated with critical clinical outcomes including functional impairment and suicidality (Dombrovski et al., 2012, Jollant et al., 2005, Jollant et al., 2010). Despite these findings, it is unknown whether abnormal performance in tasks requiring integrity of the VMPFC is linked to change of late-life depression during treatment with antidepressants.

The goal of this study was to examine whether performance in cognitive control and reward-related decision making tasks predicts change of symptoms and signs of late-life major depression during treatment with a selective serotonin reuptake inhibitor (SSRI). To this end, we used a set of cognitive control tasks (Stroop Color Word, Tower of London, and Dementia Rating Scale, Initiation/Perseveration Domain) and a reward-related decision making task (Iowa Gambling Test). Based on earlier literature (Alexopoulos et al., 2005, Morimoto et al., 2012, Morimoto et al., 2011, Pimontel et al., 2012, Sneed et al., 2010), we hypothesized that depressed older patients with abnormal performance in cognitive control tasks constitute a group with poor outcomes (decline in depressive symptoms and probability of remission) to treatment with the SSRI escitalopram, while patients with abnormal performance in a reward-related decision making task, but relatively unimpaired cognitive control performance, will have outcomes similar to that of patients with unimpaired executive functions (null hypothesis).

METHODS

Subjects

The subjects were consecutively recruited older adults with major depression and non-depressed comparison subjects who had completed the following four executive function tests during their baseline assessment: Iowa Gambling Test (Bechara, 2007), Stroop Color/Word test (Golden, 1978), Tower of London (Culbertson and Zillmer, 2001), and Dementia Rating Scale-Initiation/Perseveration Domain (Mattis, 1989). Additional inclusion criteria for the depressed subjects were: 1) age ≥ 60 years; 2) unipolar, non-psychotic major depression (by SCID (First, 1995), DSM-IV); 3) Mini-Mental State Examination (Folstein et al., 1975) (MMSE) ≥ 24; 4) capacity to consent. Exclusion Criteria were: 1) intent or plan to attempt suicide in the near future; 2) history or presence of psychiatric diagnoses other than unipolar, non-psychotic major depression or generalized anxiety disorder; and 3) use of psychotropic drugs or cholinesterase inhibitors other than mild doses of benzodiazepines. The inclusion criteria for the non-depressed comparison group were: 1) age ≥ 60 years; 2) absence of presence or history of psychiatric disorders; and 3) use of psychotropic agents. The study was approved by the Weill Cornell Medical College Institutional Review Board.

Treatment

Depressed subjects had a single-blind, 2-week drug-washout phase during which they received placebo identical to escitalopram tablets. This phase was followed by treatment with escitalopram 10 mg daily for one week followed by an increase to the target dose of 20 mg daily. Subjects who were unable to tolerate 20 mg daily received 15 mg or 10 mg daily. Subjects unable to tolerate 10 mg exited the study. The primary treatment outcome was severity of depression assessed with the 24-item Hamilton Depression Rating Scale (HAM-D)(Hamilton, 1960).

Assessment of Executive Functions

Stroop Color/Word

Response inhibition was tested with the Stroop Color-Word test. Subjects are presented with a list of the words “red”, “blue”, and “green” printed with an incongruent ink color, e.g., the word “red” printed with blue ink. Subjects were instructed to name the ink color of each word and inhibit the prepotent competing response of reading the word. Scores represent the number of correct responses in 45 seconds. We used the Stroop Interference Score, which takes into consideration processing speed (Golden 1978). To this end, we first calculated the Predicted Color-Word score (Color Score × Word Score/ Color Score + Word Score). The Interference Score consists of the Color Word Score minus the Predicted Color-Word Score. Functional neuroimaging studies have shown ACC activation during Stroop performance (MacDonald et al., 2000). Abnormal Stroop-induced ACC activation has been reported in fMRI studies of depressed individuals (Kikuchi et al., 2012, Liotti and Mayberg, 2001).

Tower of London

Planning was tested with the Tower of London, 2nd Edition (Culbertson and Zillmer, 2001). The test consists of two pegboards with three pegs of different lengths. Each pegboard has three beads of different colors (red, green, and blue), and subjects are asked to move their beads one at a time to replicate the bead pattern on the examiner’s board. After a demonstration and two practice trials, subjects conduct 10 trials replicating increasingly difficult bead configurations. The score for each trial consists of moves made above the minimum required to replicate that bead configuration. The total moves for each of the 10 trials are summed for the total reported score. Performance in the Tower task activates the DLPFC and caudate nucleus (Beauchamp et al., 2003, Dagher et al., 1999, Elliott et al., 1997, Goethals et al., 2004, Ruocco et al., 2014, van den Heuvel et al., 2003).

DRS-IP

The IP domain tests: 1) verbal initiation/perseveration, i.e., naming of all things one can buy in a supermarket over 1 minute; 2) alternating movements; and 3) graphomotor design, e.g., reproduce XOXO. The IP has high criterion validity against standard neuropsychological measures of verbal initiation and preservation (Marson et al., 1997). Functional neuroimaging studies suggest that functions tested by the IP subscale require integrity of circuitry including the ACC and the DLPFC (Jueptner et al., 1997, Sakai et al., 1998).

Iowa Gambling Test (IGT)

This test consists of 100 playing cards from four decks (A, B, C, and D) (Bechara, 2007). Some cards are followed by reward only (monetary gain), whereas others are followed by punishment (monetary loss). Subjects are instructed to choose cards from one of the four decks with the aim to win as much money as possible. Decks with higher immediate reward (A and B) have higher long-term punishment, yielding an overall net loss. Decks (C and D) with lower immediate gain have lower long-term punishment, yielding an overall net gain (advantageous decks). A performance score is calculated by subtracting the number of risky deck choices (A and B) from the number of conservative deck choices (C and D), i.e. [(C + D)−(A + B)]. Performance in the IGT predicts performance on other decision making tasks such as temporal discounting (Halfmann et al., 2014) and consumer decision making (Denburg et al., 2007). Human lesion (Glascher et al., 2012) and neuroimaging studies (Rogalsky et al., 2012) have shown that performance in the IPT requires integrity of the VMPFC.

In addition to executive function tests, overall cognitive impairment was assessed with the Mini-Mental State Examination (MMSE) (Folstein et al., 1975) and memory was assessed with the Hopkins Verbal Learning Test-Revised (HVLT) (Benedict et al., 1998).

Assessment of Psychopathology, Medical Burden, and Disability

Diagnosis was assigned in research conferences by agreement of two clinician investigators after review of history and the SCID-R (First, 1995). Age at the onset of first episode of major depression was derived from the SCID-R. All other research data were obtained by interviewers trained by the Weill Cornell Institute of Geriatric Psychiatry.

Anxiety was assessed with the Clinical Anxiety Scale (CAS) (Snaith et al., 1982), hopelessness with the Beck Hopelessness Scale (BHS) (Beck et al., 1974), neuroticism with the 12-item subscale of the NEO (Costa and McCrae, 1992), and life satisfaction with the 13-item Life Satisfaction Index (Wood et al., 1969). Disability was quantified by the interviewer-administered World Health Organization Disability Assessment Schedule II-12 item (WHODAS) (Epping-Jordan and Ustun, 2000). The WHODAS yields a composite score of disability after assessing the domains of: understanding and communicating, getting around, self-care, getting along with others, household and work activities, and participation in society. Medical burden was quantified with the Charlson Comorbidity Index (CCI) (Charlson et al., 1987).

After baseline assessment, the HAM-D was assessed weekly for 4 weeks then every over week until week 12. Payment for transportation or transportation arrangements were provided to all meetings. Compensation was offered for time spent in assessments.

Data Analysis

We conducted agglomerative hierarchical cluster analysis using Ward’s method (Ward, 1963) with squared Euclidean distance to identify clusters based on performance on four tests of executive function; all subjects had data on the variables used in the cluster analysis. This method classifies subjects into clusters and aims to increase within cluster homogeneity and increase between cluster heterogeneity, i.e., within each cluster subjects have similar test performance but clusters are distinct from each other. The choice of the number of clusters was based on maximizing the Calinski-Harabasz Index (CH-Index) (Calinski and Harabasz, 1974), a ratio of between-cluster to within-cluster variation, size of each cluster (≥10) and clinical judgment. Group comparisons (depressed vs. non-depressed and comparisons among the three clusters) of demographic and clinical characteristics was performed with Analysis of Variance (ANOVA). Age and education were used as covariates in all comparisons of cognitive performance tests.

We used mixed-effects linear regression analyses (Laird and Ware, 1982) to compare HAM-D scores among the resultant clusters over a period of 12 weeks. The model included random effects for intercept and slope and fixed effects for cluster, time trend parameter(s) and time × cluster interaction. We examined a model which included a cluster-specific random intercept and nested random intercept for patients within clusters, but the estimate for cluster-specific random intercept was zero and this model was not selected.

To evaluate remission of depression (HAM-D ≤ 10), we used a mixed effects logistic regression model to analyze the longitudinal trajectory of the probability of remission. HAM-D ≤ 10 is commonly used to define remission of late-life depression (Lecrubier, 2002). Age and gender were included as covariates in all analyses and retained in the model if significant or improved model fit.

RESULTS

A total of 83 subjects met criteria for this analysis. Of these, 53 met criteria for major depression and 30 had no psychopathology. The depressed subjects had greater severity of depression and disability and worse performance in the memory task (HVLT) and in one of the executive function tasks (DRS-IP) than non-depressed subjects (Table 1). However, there were no differences between depressed and non-depressed subjects in demographics, medical burden, and overall cognitive impairment (MMSE).

Table 1.

Clinical and Cognitive Functioning Characteristics of 53 Participants with Major Depression and 30 Non-Depressed Participants

Variable Group Statistics
Demographics Depressed (N = 53) Non-Depressed (N = 30) F df p
  Age (years) 72.18 (7.56) 72.83 (5.95) 0.165 1 .686
  Education (years) 16.30 (2.96) 16.33 (2.12) 0.003 1 .959
Psychopathology
  HAM-D 23.40 (3.90) 0.60 (0.89) 990.764 1 <.001
Medical Burden
  Charlson Comorbidity Index 1.55 (1.29) 1.30 (1.60) 0.587 1 .446
Cognition^
  Mini Mental State Examination 28.34 (1.74) 28.57 (0.86) 0.672 1 .415
  DRS-IP* −0.20 (1.16) 0.35 (0.47) 6.319 1 .014
  Stroop Interference Score* 0.04 (0.91) −0.08 (1.16) 0.204 1 .653
  Tower of London* 0.14 (1.02) −0.25 (0.92) 3.236 1 .076
  Iowa Gambling Task* −0.04 (1.03) 0.07 (0.96) 0.254 1 .616
  HVLT Learning Total* −0.16 (1.05) 0.28 (0.86) 4.988 1 .028
  HVLT Recall Total* −.021 (1.06) 0.37 (0.78) 8.469 1 .005
Disability
  WHODAS 37.14 (13.02) 22.21 (3.62) 37.61 1 <.001
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^

Controlled for age and education

*

Z-scores

HAM-D: 24-item Hamilton Depression Rating Scale

DRS: Dementia Rating Scale-Initiation/Perseveration Domain

HVLT: Hopkins Verbal Learning Test

WHODAS: World Health Organization: Disability Assessment Scale II

Exploratory Cluster Analysis

We used exploratory hierarchical cluster analysis to classify the depressed subjects according to their performance on four executive function tests, i.e., the Stroop Color/Word, the Tower of London Test, the DRS-IP, and the IGT. Based on a CH-Index (30.9) and clinical/biological relevance, a three clusters solution was chosen: a Cognitive Control Cluster (abnormal Stroop, Tower, and DRS-IP), a Reward-Related Cluster (abnormal IGT), and an Executively Unimpaired Cluster. A two cluster solution (Cognitive Control vs. Combined Reward-Related and the Executively Unimpaired clusters combined into one) had a slightly higher CH-Index (31.4) but the three cluster solution was chosen because the Reward-Related Cluster and Executively Unimpaired Cluster represent heuristically distinct groups.

Clinical Profile

Subjects in the three clusters had similar age and education (Table 2). Age at depression onset, severity of depression (HAM-D), severity of anxiety (CAS), medical burden (CCI), overall cognitive impairment (MMSE), and disability (WHODAS) were similarly distributed across the three clusters. The Cognitive Control Cluster had lower hopelessness (BHS) scores than the Reward Related Cluster and the Cognitively Unimpaired Cluster (F=3.64, df=2, p<0.034).

Table 2.

Clinical Characteristics in Three Clusters of Executive Functions of 53 Older Patients with Major Depression

Cognitive Function Clusters
Demographics Reward-Related
Cluster (N = 30)		 Cognitive Control
Cluster (N=10)		 Executively Unimpaired
Cluster (N = 13)
  Age (years) 70.88 (7.44) 79.47 (6.96) 69.59 (4.68)
  Education (years) 16.67 (3.22) 14.70 (3.53) 16.69 (0.95)
Psychopathology
  Age of Depression Onset (years) 48.59 (22.89) 57.10 (27.25) 52.23 (23.20)
  HAM-D 23.47 (4.59) 23.30 (1.64) 23.31 (3.59)
  Clinical Anxiety Scale 4.31 (4.88) 3.60 (3.69) 2.69 (2.84)
  Beck Hopelessness Scale 9.17 (5.86) 5.20 (3.91) 11.38 (5.59)
  NEO Neuroticism 27.00 (8.49) 20.44 (8.58) 25.38 (7.14)
  Life Satisfaction 3.97 (2.93) 4.56 (2.35) 4.15 (2.38)
Medical Burden
  Charlson Comorbidity Index 1.53 (1.38) 2.10 (1.29) 1.15 (0.99)
Cognition
  Mini-Mental State Examination 28.30 (1.75) 27.80 (2.44) 28.85 (0.90)
  DRS-IP* −0.28 (1.37) −0.55 (1.08) 0.25 (0.27)
  Stroop Interference Score* 0.08 (0.83) −0.44 (1.05) 0.34 (0.88)
  Tower of London* −0.07 (0.59) 1.66 (0.73) −0.54 (0.87)
  Iowa Gambling Test* −0.62 (0.68) 0.03 (0.90) 1.24 (0.47)
  HVLT Learning* −0.19 (1.05) −0.74 (1.11) 0.37 (0.74)
  HVLT Recall* −0.25 (1.05) −0.78 (1.18) 0.33 (0.74)
Disability
  WHODAS 36.67 (13.95) 40.65 (10.23) 35.54 (13.14)
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*

Z-scores

HAM-D: 24-item Hamilton Depression Rating Scale

DRS-IP: Dementia Rating Scale-Initiation/Perseveration Domain

HVLT: Hopkins Verbal Learning Test

WHODAS: World Health Organization: Disability Assessment Scale II

Treatment

Of the 53 depressed subjects who participated in this analysis, one exited the study prior to receiving any treatment. The remaining subjects were treated with escitalopram. Of the 52 treated subjects, 88% (46/52) received the target dose of 20 mg daily, 4% (2/52) received 15 mg and 8% (4/52), received 10 mg. Both dosages and duration of treatment were similarly distributed across the three clusters. Specifically, subjects within the Cognitive Control Cluster received a mean dose of 19.00 mg (SD: 3.16), subjects within the Reward-Related Cluster 18.97 mg (SD: 2.80), and subjects within the Executively Unimpaired Cluster 19.23 mg (SD: 2.77). The corresponding means of duration of escitalopram treatment were 11.80 weeks (SD: 0.63), 10.38 weeks (SD: 3.62), and 12.00 weeks (SD: 0).

Treatment Outcomes

We compared the trajectory of depression severity (HAM-D) of the three clusters with a linear mixed model with fixed effects for linear week, quadratic week and week × cluster interaction and subject-specific random effects for intercept and slope. Week and quadratic week were significantly different from zero indicating that all clusters had a reduction in HAM-D over time. The week × cluster interaction was significantly different (F2, 331=3.03, p=0.0497) indicating different HAM-D progression in the paths of each cluster (Figure 1). The decline in severity of depression (HAM-D) was greater in the Executively Unimpaired Cluster (t = −2.09, df=331, p=0.0375) and the Reward-Related Cluster (t=−2.33, df=331, p=0.0202) than the Cognitive Control Cluster.

Figure 1.

Figure 1

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Trajectory of 24-item Hamilton Depression Rating Scale (HAM-D) Scores in Three Clusters of Older Patients with Major Depression (N=53) Treated with Escitalopram

Remission was achieved in 1 subject (out of 10) in the Cognitive Control Cluster, 18 (out of 30) in the Reward-Related Cluster, and 8 (out of 13) in the Executively Unimpaired Cluster. A mixed effects analysis of depression remission (HAM-D≤10) trajectory as a binary outcome was performed with fixed effects for linear week, quadratic week, cluster, and cluster×week interaction and a subject specific random intercept. There was a week × cluster interaction (F2, 331=4.31, p=0.0648). The Cognitive Control Cluster had a lower probability of remission than the Executively Unimpaired Cluster (t = 2.17, df=331, p = 0.03) and the Reward-Related Cluster (t=2.03, df=331, p=0.0433).

DISCUSSION

The principal finding of this study is that older adults with major depression and abnormal performance in cognitive control tasks had less decline of depressive symptoms during treatment with escitalopram than depressed subjects with abnormal reward-related decision making or executively unimpaired patients. Moreover, subjects of the abnormal cognitive control cluster had lower probability of remission during the 12-week escitalopram treatment period.

To our knowledge, this is the first study to observe that abnormality in tasks of cognitive control, but not in reward-related decision making, influence the trajectory of symptoms and the attainment of remission of late-life depression during treatment with an antidepressant. This observation is consistent with neurobiological findings suggesting that these two sets of functions rely on distinct brain networks. Cognitive control functions, such as those tested by the Stroop Color/Word, the Tower of London and the DRS-IP, are mediated by a rostro-caudal hierarchy of structures organized for behavioral control and planning (Glascher et al., 2012). This hierarchy includes the DLPFC and its connections to posterior cortical areas in the parietal lobe. The rostral ACC has been found to be activated by cognitive control tasks of set shifting and error detection in fMRI studies (Braver et al., 2001, Lie et al., 2006) and lesions in the anterior sectors of ACC impair rule-switching in primates (Buckley et al., 2009). More posterior regions within the dorsal ACC and the DLPFC may be recruited during error detection (Braver et al., 2001) and conflict monitoring (Botvinick et al., 2001, MacDonald et al., 2000), while parietal regions are responsible for selective attention (Roberts and Hall, 2008). Reward-related decision making, such as that tested by the Iowa Gambling Test, is mainly mediated by ventromedial structures of the prefrontal cortex and has strong connections to the limbic system. Lesion studies have shown that damage of the VMPFC impairs performance in the Iowa Gambling Test but spares performance in cognitive control tasks (Glascher et al., 2012, Stuss et al., 2000). Both the cognitive control and the reward-related networks converge at the ACC, which serves as the point for their interaction (Glascher et al., 2012) and plays a role in symptom change during treatment with antidepressants (Drevets et al., 2008, Liston et al., 2014, McGrath et al., 2014, Seminowicz et al., 2004). It is tempting to speculate that facilitation of decline in symptoms of depression during treatment with antidepressants is mediated by input of cognitive control structures to the ACC, which in turn exerts control over limbic structures. Input of VMPFC structures to ACC, although important for reward-related decision making, may not be related to outcomes of antidepressant drug treatment.

Our findings are consistent with reports that impairment on some cognitive control tasks predicts little change in symptoms of late-life depression during treatment with antidepressant drugs (Alexopoulos et al., 2005, Alexopoulos et al., 2004, Morimoto et al., 2011). It has been suggested that this relationship is rather specific to cognitive control tasks. A study of old-old patients with major depression treated with citalopram found that performance on the Stroop Color/Word task, but not overall cognitive impairment or poor performance in choice reaction time, spatial judgment, or selective reminding, influenced negatively the decline of depressive symptoms (Sneed et al., 2008). Our findings further support the specificity of the relationship between cognitive control abnormalities on the one hand and change in depressive symptoms and remission rate during treatment with escitalopram.

The findings of this study should be viewed in the context of its limitations. These include the small sample, the uncertainty of hierarchical clustering in identifying clusters, the limited number of neuropsychological tests, and the effect of processing speed on these tests. Processing speed moderates performance on other neuropsychological instruments (Butters et al., 2004). Therefore, we cannot exclude that slow processing did not influence the relationship of cognitive control abnormalities to change in depressive symptoms during escitalopram treatment. Other limitations include the absence of a placebo-treated group, and the use of a single antidepressant. However, testing of depressed subjects occurred after a 2-week placebo/wash-out phase that might have reduced the influence of prior psychotropic treatment on test performance and the inclusion of placebo responders. Further, most of our subjects tolerated high dosages of escitalopram, and there were no significant dosage and length of drug treatment differences among the three clusters. Finally, there was high retention of subjects in all three groups. Therefore, differences in change in depressive symptoms and in time in remission may not be attributed to under-dosage, unequal intensity or length of treatment, or selective drop-out. Nonetheless, replication of this study is necessary.

The clinical significance of this study’s findings is their potential use for treatment selection. While a convergence of findings suggests that poor performance in cognitive control tasks is a rather specific predictor of poor outcome to treatment with at least some antidepressants (Alexopoulos et al., 2005, Alexopoulos et al., 2004, Morimoto et al., 2011, Sneed et al., 2008), it is possible that such patients may do well with other antidepressant strategies, e.g. other psychotropic agents, transcranial magnetic stimulation, other psychotherapies. In fact, problem solving therapy improved depression (Arean et al., 2010) and disability (Alexopoulos et al., 2011) in older adults with major depression and cognitive control dysfunction more than supportive therapy even though performance in a cognitive control tests improved equally in the two treatment groups (Mackin et al., 2013).

In sum, impairment in cognitive control functions, but not in reward-related decision making, appears to adversely influence outcomes of geriatric depression to escitalopram. The theoretical significance of this finding is that it provides a target for neuroimaging studies of outcomes of antidepressants focusing on the structural and functional connectivity of cognitive control structures to rostral ACC and to limbic structures. In addition to its theoretical significance, if our finding is replicated, simple to administer cognitive control tests may be used to select depressed older patients at risk for poor outcomes with some SSRI antidepressants who may require a structured, learning-based therapy.

Figure 2.

Figure 2

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Longitudinal Trajectory of the Probability of Remission (HAM-D≤10) in Three Clusters of Older Patients with Major Depression (N=53) Treated with Escitalopram

Acknowledgments

Personnel cost for this work was supported by NIMH grants R01 MH065653, R01 MH079414, T32 MH019132 and the Sanchez Foundation. Escitalopram and placebo were provided free of cost by Forest Pharmaceuticals, Inc.

REFERENCES

  1. Aizenstein HJ, Butters MA, Figurski JL, Stenger VA, Reynolds CF, 3rd, Carter CS. Prefrontal and striatal activation during sequence learning in geriatric depression. Biol Psychiatry. 2005;58:290–296. doi: 10.1016/j.biopsych.2005.04.023. [DOI] [PubMed] [Google Scholar]
  2. Alexopoulos GS. "The depression-executive dysfunction syndrome of late life": a specific target for D3 agonists? Am J Geriatr Psychiatry. 2001;9:22–29. [PubMed] [Google Scholar]
  3. Alexopoulos GS, Hoptman MJ, Kanellopoulos D, Murphy CF, Lim KO, Gunning FM. Functional connectivity in the cognitive control network and the default mode network in late-life depression. J Affect Disord. 2012;139:56–65. doi: 10.1016/j.jad.2011.12.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Alexopoulos GS, Kiosses DN, Heo M, Murphy CF, Shanmugham B, Gunning-Dixon F. Executive dysfunction and the course of geriatric depression. Biol Psychiatry. 2005;58:204–210. doi: 10.1016/j.biopsych.2005.04.024. [DOI] [PubMed] [Google Scholar]
  5. Alexopoulos GS, Kiosses DN, Murphy C, Heo M. Executive dysfunction, heart disease burden, and remission of geriatric depression. Neuropsychopharmacology. 2004;29:2278–2284. doi: 10.1038/sj.npp.1300557. [DOI] [PubMed] [Google Scholar]
  6. Alexopoulos GS, Murphy CF, Gunning-Dixon FM, Latoussakis V, Kanellopoulos D, Klimstra S, Lim KO, Hoptman MJ. Microstructural white matter abnormalities and remission of geriatric depression. Am J Psychiatry. 2008;165:238–244. doi: 10.1176/appi.ajp.2007.07050744. [DOI] [PubMed] [Google Scholar]
  7. Alexopoulos GS, Raue PJ, Kiosses DN, Mackin RS, Kanellopoulos D, McCulloch C, Arean PA. Problem-solving therapy and supportive therapy in older adults with major depression and executive dysfunction: effect on disability. Arch Gen Psychiatry. 2011;68:33–41. doi: 10.1001/archgenpsychiatry.2010.177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Arean PA, Raue P, Mackin RS, Kanellopoulos D, McCulloch C, Alexopoulos GS. Problem-solving therapy and supportive therapy in older adults with major depression and executive dysfunction. Am J Psychiatry. 2010;167:1391–1398. doi: 10.1176/appi.ajp.2010.09091327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Beauchamp MH, Dagher A, Aston JA, Doyon J. Dynamic functional changes associated with cognitive skill learning of an adapted version of the Tower of London task. Neuroimage. 2003;20:1649–1660. doi: 10.1016/j.neuroimage.2003.07.003. [DOI] [PubMed] [Google Scholar]
  10. Bechara A. Iowa Gambling Task (IGT) Professional Manual. Lutz, FL: Psychological Assessment Resources; 2007. [Google Scholar]
  11. Beck AT, Weissman A, Lester D, Trexler L. The measurement of pessimism: the hopelessness scale. J Consult Clin Psychol. 1974;42:861–865. doi: 10.1037/h0037562. [DOI] [PubMed] [Google Scholar]
  12. Benedict RHB, Schretlen D, Groninger L, Brandt J. Hopkins Verbal Learning Test Revised: Normative data and analysis of inter-form and test-retest reliability. Clinical Neuropsychologist. 1998;12:43–55. [Google Scholar]
  13. Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen JD. Conflict monitoring and cognitive control. Psychol Rev. 2001;108:624–652. doi: 10.1037/0033-295x.108.3.624. [DOI] [PubMed] [Google Scholar]
  14. Braver TS, Barch DM, Gray JR, Molfese DL, Snyder A. Anterior cingulate cortex and response conflict: effects of frequency, inhibition and errors. Cereb Cortex. 2001;11:825–836. doi: 10.1093/cercor/11.9.825. [DOI] [PubMed] [Google Scholar]
  15. Buckley MJ, Mansouri FA, Hoda H, Mahboubi M, Browning PG, Kwok SC, Phillips A, Tanaka K. Dissociable components of rule-guided behavior depend on distinct medial and prefrontal regions. Science. 2009;325:52–58. doi: 10.1126/science.1172377. [DOI] [PubMed] [Google Scholar]
  16. Butters MA, Whyte EM, Nebes RD, Begley AE, Dew MA, Mulsant BH, Zmuda MD, Bhalla R, Meltzer CC, Pollock BG, Reynolds CF, 3rd, Becker JT. The nature and determinants of neuropsychological functioning in late-life depression. Arch Gen Psychiatry. 2004;61:587–595. doi: 10.1001/archpsyc.61.6.587. [DOI] [PubMed] [Google Scholar]
  17. Calinski RB, Harabasz J. A dendrite method for cluster analysis. Communications in Statistics. 1974;3:1–27. [Google Scholar]
  18. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–383. doi: 10.1016/0021-9681(87)90171-8. [DOI] [PubMed] [Google Scholar]
  19. Costa PT, McCrae RR. NEO PI-R Professional Manual. Odessa, FL: Psychological Assessment Resources; 1992. [Google Scholar]
  20. Culbertson WC, Zillmer EA. Tower of London Drexel University (TOL DX): Technical manual. Multi-Health Systems Incorporated (MHS); 2001. [Google Scholar]
  21. Dagher A, Owen AM, Boecker H, Brooks DJ. Mapping the network for planning: a correlational PET activation study with the Tower of London task. Brain. 1999;122(Pt 10):1973–1987. doi: 10.1093/brain/122.10.1973. [DOI] [PubMed] [Google Scholar]
  22. Denburg NL, Cole CA, Hernandez M, Yamada TH, Tranel D, Bechara A, Wallace RB. The orbitofrontal cortex, real-world decision making, and normal aging. Ann N Y Acad Sci. 2007;1121:480–498. doi: 10.1196/annals.1401.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Dombrovski AY, Siegle GJ, Szanto K, Clark L, Reynolds CF, Aizenstein H. The temptation of suicide: striatal gray matter, discounting of delayed rewards, and suicide attempts in late-life depression. Psychol Med. 2012;42:1203–1215. doi: 10.1017/S0033291711002133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Drevets WC. Orbitofrontal cortex function and structure in depression. Ann N Y Acad Sci. 2007;1121:499–527. doi: 10.1196/annals.1401.029. [DOI] [PubMed] [Google Scholar]
  25. Drevets WC, Savitz J, Trimble M. The subgenual anterior cingulate cortex in mood disorders. CNS Spectr. 2008;13:663–681. doi: 10.1017/s1092852900013754. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Elliott R, Baker SC, Rogers RD, O'Leary DA, Paykel ES, Frith CD, Dolan RJ, Sahakian BJ. Prefrontal dysfunction in depressed patients performing a complex planning task: a study using positron emission tomography. Psychol Med. 1997;27:931–942. doi: 10.1017/s0033291797005187. [DOI] [PubMed] [Google Scholar]
  27. Elliott R, Sahakian BJ, Michael A, Paykel ES, Dolan RJ. Abnormal neural response to feedback on planning and guessing tasks in patients with unipolar depression. Psychol Med. 1998;28:559–571. doi: 10.1017/s0033291798006709. [DOI] [PubMed] [Google Scholar]
  28. Epping-Jordan JA, Ustun TB. The WHODAS-II: leveling the playing field for all disorders. WHO Mental Health Bulletin. 2000;6:5–6. [Google Scholar]
  29. Eshel N, Roiser JP. Reward and punishment processing in depression. Biol Psychiatry. 2010;68:118–124. doi: 10.1016/j.biopsych.2010.01.027. [DOI] [PubMed] [Google Scholar]
  30. First MB, Spitzer RL, Williams JBW, Gibbon M. Structured clinical interview for DSM-IV - patient version (SCID-P) Washington: American Psychiatric Press; 1995. [Google Scholar]
  31. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–198. doi: 10.1016/0022-3956(75)90026-6. [DOI] [PubMed] [Google Scholar]
  32. Glascher J, Adolphs R, Damasio H, Bechara A, Rudrauf D, Calamia M, Paul LK, Tranel D. Lesion mapping of cognitive control and value-based decision making in the prefrontal cortex. Proc Natl Acad Sci U S A. 2012;109:14681–14686. doi: 10.1073/pnas.1206608109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Goethals I, Audenaert K, Jacobs F, Van de Wiele C, Pyck H, Ham H, Vandierendonck A, van Heeringen C, Dierckx R. Application of a neuropsychological activation probe with SPECT: the 'Tower of London' task in healthy volunteers. Nucl Med Commun. 2004;25:177–182. doi: 10.1097/00006231-200402000-00015. [DOI] [PubMed] [Google Scholar]
  34. Golden CJ. The Stroop Color and Word Test (Manual) Chicago: Stoetling; 1978. [Google Scholar]
  35. Gunning-Dixon FM, Walton M, Cheng J, Acuna J, Klimstra S, Zimmerman ME, Brickman AM, Hoptman MJ, Young RC, Alexopoulos GS. MRI signal hyperintensities and treatment remission of geriatric depression. J Affect Disord. 2010;126:395–401. doi: 10.1016/j.jad.2010.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Gunning FM, Cheng J, Murphy CF, Kanellopoulos D, Acuna J, Hoptman MJ, Klimstra S, Morimoto S, Weinberg J, Alexopoulos GS. Anterior cingulate cortical volumes and treatment remission of geriatric depression. Int J Geriatr Psychiatry. 2009;24:829–836. doi: 10.1002/gps.2290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Halfmann K, Hedgcock W, Bechara A, Denburg NL. Functional neuroimaging of the Iowa Gambling Task in older adults. Neuropsychology. 2014;28:870–880. doi: 10.1037/neu0000120. [DOI] [PubMed] [Google Scholar]
  38. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. doi: 10.1136/jnnp.23.1.56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Jollant F, Bellivier F, Leboyer M, Astruc B, Torres S, Verdier R, Castelnau D, Malafosse A, Courtet P. Impaired decision making in suicide attempters. Am J Psychiatry. 2005;162:304–310. doi: 10.1176/appi.ajp.162.2.304. [DOI] [PubMed] [Google Scholar]
  40. Jollant F, Lawrence NS, Olie E, O'Daly O, Malafosse A, Courtet P, Phillips ML. Decreased activation of lateral orbitofrontal cortex during risky choices under uncertainty is associated with disadvantageous decision-making and suicidal behavior. Neuroimage. 2010;51:1275–1281. doi: 10.1016/j.neuroimage.2010.03.027. [DOI] [PubMed] [Google Scholar]
  41. Jueptner M, Stephan KM, Frith CD, Brooks DJ, Frackowiak RS, Passingham RE. Anatomy of motor learning. I. Frontal cortex and attention to action. J Neurophysiol. 1997;77:1313–1324. doi: 10.1152/jn.1997.77.3.1313. [DOI] [PubMed] [Google Scholar]
  42. Kikuchi T, Miller JM, Schneck N, Oquendo MA, Mann JJ, Parsey RV, Keilp JG. Neural responses to incongruency in a blocked-trial Stroop fMRI task in major depressive disorder. J Affect Disord. 2012;143:241–247. doi: 10.1016/j.jad.2012.05.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Laird N, Ware J. Random-effects models for longitudinal data. Biometrics. 1982;38:963–974. [PubMed] [Google Scholar]
  44. Lecrubier Y. How do you define remission? Acta Psychiatr Scand Suppl. 2002:7–11. doi: 10.1034/j.1600-0447.106.s415.2.x. [DOI] [PubMed] [Google Scholar]
  45. Lie CH, Specht K, Marshall JC, Fink GR. Using fMRI to decompose the neural processes underlying the Wisconsin Card Sorting Test. Neuroimage. 2006;30:1038–1049. doi: 10.1016/j.neuroimage.2005.10.031. [DOI] [PubMed] [Google Scholar]
  46. Liotti M, Mayberg HS. The role of functional neuroimaging in the neuropsychology of depression. J Clin Exp Neuropsychol. 2001;23:121–136. doi: 10.1076/jcen.23.1.121.1223. [DOI] [PubMed] [Google Scholar]
  47. Liston C, Chen AC, Zebley BD, Drysdale AT, Gordon R, Leuchter B, Voss HU, Casey BJ, Etkin A, Dubin MJ. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol Psychiatry. 2014;76:517–526. doi: 10.1016/j.biopsych.2014.01.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. MacDonald AW, 3rd, Cohen JD, Stenger VA, Carter CS. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science. 2000;288:1835–1838. doi: 10.1126/science.288.5472.1835. [DOI] [PubMed] [Google Scholar]
  49. Mackin RS, Nelson JC, Delucchi K, Raue P, Byers A, Barnes D, Satre DD, Yaffe K, Alexopoulos GS, Arean PA. Cognitive Outcomes after Psychotherapeutic Interventions for Major Depression in Older Adults with Executive Dysfunction. Am J Geriatr Psychiatry. 2013 doi: 10.1016/j.jagp.2013.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Marson DC, Dymek MP, Duke LW, Harrell LE. Subscale validity of the Mattis Dementia Rating Scale. Arch Clin Neuropsychol. 1997;12:269–275. [PubMed] [Google Scholar]
  51. Mattis S. Dementia Rating Scale. Odesa Fl: PAR; 1989. [Google Scholar]
  52. McGrath CL, Kelley ME, Dunlop BW, Holtzheimer PE, 3rd, Craighead WE, Mayberg HS. Pretreatment brain states identify likely nonresponse to standard treatments for depression. Biol Psychiatry. 2014;76:527–535. doi: 10.1016/j.biopsych.2013.12.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Morimoto SS, Gunning FM, Kanellopoulos D, Murphy CF, Klimstra SA, Kelly RE, Jr, Alexopoulos GS. Semantic organizational strategy predicts verbal memory and remission rate of geriatric depression. Int J Geriatr Psychiatry. 2012;27:506–512. doi: 10.1002/gps.2743. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Morimoto SS, Gunning FM, Murphy CF, Kanellopoulos D, Kelly RE, Alexopoulos GS. Executive function and short-term remission of geriatric depression: the role of semantic strategy. Am J Geriatr Psychiatry. 2011;19:115–122. doi: 10.1097/JGP.0b013e3181e751c4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Pimontel MA, Culang-Reinlieb ME, Morimoto SS, Sneed JR. Executive dysfunction and treatment response in late-life depression. Int J Geriatr Psychiatry. 2012;27:893–899. doi: 10.1002/gps.2808. [DOI] [PubMed] [Google Scholar]
  56. Roberts KL, Hall DA. Examining a supramodal network for conflict processing: a systematic review and novel functional magnetic resonance imaging data for related visual and auditory stroop tasks. J Cogn Neurosci. 2008;20:1063–1078. doi: 10.1162/jocn.2008.20074. [DOI] [PubMed] [Google Scholar]
  57. Rogalsky C, Vidal C, Li X, Damasio H. Risky decision-making in older adults without cognitive deficits: an fMRI study of VMPFC using the Iowa Gambling Task. Soc Neurosci. 2012;7:178–190. doi: 10.1080/17470919.2011.588340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Roiser JP, Sahakian BJ. Hot and cold cognition in depression. CNS Spectr. 2013;18:139–149. doi: 10.1017/S1092852913000072. [DOI] [PubMed] [Google Scholar]
  59. Ruocco AC, Rodrigo AH, Lam J, Di Domenico SI, Graves B, Ayaz H. A problem-solving task specialized for functional neuroimaging: validation of the Scarborough adaptation of the Tower of London (S-TOL) using near-infrared spectroscopy. Front Hum Neurosci. 2014;8:185. doi: 10.3389/fnhum.2014.00185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sakai K, Hikosaka O, Miyauchi S, Takino R, Sasaki Y, Putz B. Transition of brain activation from frontal to parietal areas in visuomotor sequence learning. J Neurosci. 1998;18:1827–1840. doi: 10.1523/JNEUROSCI.18-05-01827.1998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Seminowicz DA, Mayberg HS, McIntosh AR, Goldapple K, Kennedy S, Segal Z, Rafi-Tari S. Limbic-frontal circuitry in major depression: a path modeling metanalysis. Neuroimage. 2004;22:409–418. doi: 10.1016/j.neuroimage.2004.01.015. [DOI] [PubMed] [Google Scholar]
  62. Snaith RP, Baugh SJ, Clayden AD, Husain A, Sipple MA. The Clinical Anxiety Scale: an instrument derived from the Hamilton Anxiety Scale. Br J Psychiatry. 1982;141:518–523. doi: 10.1192/bjp.141.5.518. [DOI] [PubMed] [Google Scholar]
  63. Sneed JR, Culang ME, Keilp JG, Rutherford BR, Devanand DP, Roose SP. Antidepressant medication and executive dysfunction: a deleterious interaction in late-life depression. Am J Geriatr Psychiatry. 2010;18:128–135. doi: 10.1097/JGP.0b013e3181c796d2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Sneed JR, Keilp JG, Brickman AM, Roose SP. The specificity of neuropsychological impairment in predicting antidepressant non-response in the very old depressed. Int J Geriatr Psychiatry. 2008;23:319–323. doi: 10.1002/gps.1889. [DOI] [PubMed] [Google Scholar]
  65. Stuss DT, Levine B, Alexander MP, Hong J, Palumbo C, Hamer L, Murphy KJ, Izukawa D. Wisconsin Card Sorting Test performance in patients with focal frontal and posterior brain damage: effects of lesion location and test structure on separable cognitive processes. Neuropsychologia. 2000;38:388–402. doi: 10.1016/s0028-3932(99)00093-7. [DOI] [PubMed] [Google Scholar]
  66. van den Heuvel OA, Groenewegen HJ, Barkhof F, Lazeron RH, van Dyck R, Veltman DJ. Frontostriatal system in planning complexity: a parametric functional magnetic resonance version of Tower of London task. Neuroimage. 2003;18:367–374. doi: 10.1016/s1053-8119(02)00010-1. [DOI] [PubMed] [Google Scholar]
  67. Vrieze E, Pizzagalli DA, Demyttenaere K, Hompes T, Sienaert P, de Boer P, Schmidt M, Claes S. Reduced reward learning predicts outcome in major depressive disorder. Biol Psychiatry. 2013;73:639–645. doi: 10.1016/j.biopsych.2012.10.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Ward JH. Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association. 1963;58:236–244. [Google Scholar]
  69. Wood V, Wylie ML, Sheafor B. An analysis of a short self-report measure of life satisfaction: correlation with rater judgments. J Gerontol. 1969;24:465–469. doi: 10.1093/geronj/24.4.465. [DOI] [PubMed] [Google Scholar]

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