- Email: [email protected]
Dimensions of Poststroke Depression and Neuropsychological Deficits in Older Adults
Journal Pre-proof
Dimensions of post-stroke depression and neuropsychological deficits in older adults Dora Kanellopoulos Ph.D. , Victoria Wilkins Ph.D. , Jimmy Avari M.D. , Lauren Oberlin Ph.D. , Lindsay Arader BA , Merete Chaplin BA , Samprit Banerjee Ph.D., M. Stat , George S. Alexopoulos M.D. PII: DOI: Reference:
S1064-7481(20)30022-1 https://doi.org/10.1016/j.jagp.2020.01.009 AMGP 1401
To appear in:
The American Journal of Geriatric Psychiatry
Received date: Revised date: Accepted date:
8 November 2019 18 January 2020 21 January 2020
Please cite this article as: Dora Kanellopoulos Ph.D. , Victoria Wilkins Ph.D. , Jimmy Avari M.D. , Lauren Oberlin Ph.D. , Lindsay Arader BA , Merete Chaplin BA , Samprit Banerjee Ph.D., M. Stat , George S. Alexopoulos M.D. , Dimensions of post-stroke depression and neuropsychological deficits in older adults, The American Journal of Geriatric Psychiatry (2020), doi: https://doi.org/10.1016/j.jagp.2020.01.009
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc. on behalf of American Association for Geriatric Psychiatry.
Dimensions of post-stroke depression and neuropsychological deficits in older adults
Dora Kanellopoulos,1Ph.D., Victoria Wilkins,1Ph.D., Jimmy Avari,1M.D., Lauren Oberlin, 1
Ph.D., Lindsay Arader,1BA, Merete Chaplin,1BA, Samprit Banerjee,1Ph.D., M. Stat, George S.
Alexopoulos, 1M.D.
1
Weill Cornell Medicine, Weill Cornell Institute of Geriatric Psychiatry
Corresponding author: George S. Alexopoulos, M.D. S.P. Tobin and A.M. Cooper Professor Director, Weill-Cornell Institute of Geriatric Psychiatry 21 Bloomingdale Road White Plains, NY 10605
Email: [email protected] Phone: 914-997-5767 Fax: 914-682-6979
Keywords: Post-stroke depression, apathy, older adults
1
Highlights What is the primary question addressed by this study?
Post- stroke depression (PSD) is prevalent, challenging to diagnose and often presents with widespread cognitive deficits. This study characterizes dimensions of post-stroke depression in an older adult sample with moderate to moderately severe depression.
What is the main finding of this study?
Among the three dimensions identified, apathy was related to worse functioning on multiple cognitive domains, while sadness and distress were not related to any cognitive variables.
What is the meaning of this finding?
Our findings indicate that PSD is a heterogeneous entity with clinically meaningful dimensions that if further explored can inform personalized therapeutic interventions.
ABSTRACT Objective: Post-stroke depression (PSD) has a heterogeneous presentation and is often accompanied by cognitive impairment. This study aimed to identify distinct dimensions of depressive symptoms in older adults with PSD and to evaluate their relationship to cognitive functioning. Design: Cross-sectional factor and correlational analyses of patients with post-stroke depression. Setting: Patients were recruited from the community and from acute inpatient stroke rehabilitation hospitals. Participants: Participants had suffered a stroke and met DSM-IV criteria for major depression (≥18 Montgomery Åsberg Depression Scale; MADRS). 2
Intervention: None Measurements: MADRS was used to quantify depression severity at study entry. Neuropsychological assessment at the time of study entry consisted of measures Global Cognition, Attention, Executive Function, Processing Speed, Immediate Memory, Delayed Memory, and Language. Results: The participants were 135 older adults (age ≥50) with PSD and varying degrees of cognitive impairment (MMSE Total ≥ 20). Factor analysis of the MADRS identified three factors, i.e. Sadness, Distress, and Apathy. Items comprising each factor were totaled and correlated with neuropsychological domain z-score averages. Symptoms of the apathy factor (lassitude, inability to feel) were significantly associated with greater impairment in executive dysfunction, memory, and global cognition. Symptoms of the sadness and distress factors had no relationship to cognitive impairment. Conclusions: PSD consists of three correlated dimensions of depressive symptoms. Apathy symptoms are associated with cognitive impairment across several neuropsychological domains. PSD patients with prominent apathy may benefit from careful attention to cognitive functions and by interventions that address both psychopathology and behavioral deficits resulting from cognitive impairment.
INTRODUCTION Stroke is the leading cause of both serious and long-term disability and the fifth leading cause of death in the United States.(1, 2) Post-stroke depression (PSD) afflicts approximately one-fourth of stroke survivors and increases the likelihood of persistent disability.(3-6) Prompt identification and treatment of PSD can improve affective symptoms and stroke related disability.(7, 8) 3
PSD often occurs in patients presenting emotional lability, anosognosia and cognitive impairment leading to a clinical presentation far more complex than that of idiopathic depression. Despite the apparent heterogeneity of PSD, few studies to date have attempted to identify distinct dimensions of depressive symptoms in PSD. One prior PSD study of adults with a wide age range(9) diagnosed with major depression due to medical reasons, used the post-stroke depression scale and reported three dimensions of depressive symptom patterns, i.e., depressive/anxious, lack of emotional control, and reduced motivation. Reduced motivation (i.e., a combination of symptoms of apathy and anhedonia) was significantly associated with older age in PSD patients.(8) Little is known about the clinical heterogeneity of PSD in older adults. In the only study examining symptom patterns of PSD in older adults, a multidimensional profile emerged.(8)Factor analysis of items of the Montgomery Åsberg Depression Rating Scale (MADRS) in 163 patients with PSD identified three factors, i.e. sadness, agitation and anhedonia.(8) The subjects of this study had mild depression and there have been no studies of more severe PSD to date. The relationship of depressive symptoms to cognitive impairment is especially meaningful in PSD, as post-stroke cognitive deficits are common and widespread.(10, 11) Cognitive deficits in PSD include memory difficulties, executive dysfunction, poor attention, slowed information processing speed, and visuoperceptual/visuoconstructional impairments.(12-16) Executive dysfunction in particular is a persistent, chronic and prevalent cognitive deficit among stroke survivors(14, 17) and may worsen deficits in other cognitive domains (e.g., learning and memory).(18) Among executive functions, verbal fluency and conceptualization are impaired in 33%-66% of stroke patients.(18) Aging compounds stroke related cognitive deficits leading to worsening outcomes in older adults with PSD.(19) While cognitive impairments improve or stabilize in many stroke survivors, cognitive function in patients with PSD often worsens.(12) Earlier reports indicate that patients with prominent 4
anhedonia experience greater cognitive impairment, while those with prominent sadness exhibit greater sensorimotor and cranial nerve deficits.(8) Characterizing the relationship of distinct dimensions of depressive symptoms with cognitive impairments in older adults with PSD may help elucidate aspects of the neurobiology of distinct presentations of PSD, guide the selection of interventions and increase personalization of treatment. This study sought to identify dimensions of depressive symptoms in older adults with moderate to moderately severe PSD. Based on the limited, available literature, we hypothesized that: 1) the PSD syndrome of older adults has several distinct dimensions; and 2) the dimensions of the PSD syndrome have different relationships to cognitive deficits.
METHODS Participants The participants were recruited from local inpatient stroke rehabilitation units and from the community. The inclusion criteria were: 1) Age of 50 years and older; 2) history of ischemic, embolic or hemorrhagic stroke established by medical record review; 3) diagnosis of major depression by DSM-IV criteria; and 4) Montgomery Åsberg Depression Scale (MADRS)(20) score of 18 or higher. Exclusion criteria were: 1) Moderate or severe dementia (Mini Mental State Examination (MMSE(21)) score < 20); 2) greater than moderate aphasia (NIH Stroke Scale(22) Best Language > 1); 3) psychotic depression or bipolar disorder (by DSM-IV); 4) suicide intent or plan; 5) plans to enter a nursing home for treatment in the near future; and 6) inability to speak English. All participants signed informed consent approved by the Weill Cornell Institutional Review Board. Assessment
5
Diagnostic evaluation was conducted with the Structured Clinical Interview- Revised (SCIDR).(23) Severity of depression was quantified with the MADRS.(20) The MADRS is a reliable and valid 10-item clinician rated scale, which places less emphasis on somatic symptoms and is frequently used as a depression severity rating instrument for older adults who have experienced a stroke.(24-27) Global cognitive dysfunction was assessed with the MMSE,(21) a reliable and valid brief screening instrument, and with the Dementia Rating Scale(28) a more extensive instrument of cognitive dysfunction in older adults. Participants also received a neuropsychological battery consisting of scales assessing the following cognitive domains: 1) Executive Function (Controlled Oral Word Association Test (COWAT), Dementia Rating Scale (DRS) Initiation/Perseveration (IP) subscale, DRS Conceptualization subscale, Stroop Color Word Interference, Weschler Adult Intelligence Scale (WAIS –III) Digit Span Backward Span); 2) Processing speed (Stroop Color and Stroop Word subtests); 3) Attention (WAIS–III Digit Span- Digits Forward Total Span, DRS Attention subscale), 4) Immediate Memory (Hopkins Verbal Learning Test-Revised (HVLT-R) Immediate Recall), and 5) Delayed memory (HVLT-R Delayed Recall, DRS Memory subscale); and 6) Language (Semantic fluency/Animal Naming Test (ANT)). To account for effort as a confounder of performance, reliable digit span (RDS) was used and optimal effort was indicated by RDS>6.(29) Statistical Analysis Exploratory principal axis factoring(30) was performed on the 10 items of the MADRS scale using an oblique rotation procedure in SPSS 25.0 (Direct Oblimin). Three factors that explained 46.2% of the total variation were extracted by visual inspection of the scree plot and by requiring factor eigenvalues to be > 1. Factor loadings greater or equal to 0.2 were used to identify MADRS factors of depression. Factor (dimensions) totals were created by adding the score on each MADRS item within each factor grouping into a sum. 6
Multiple imputation(31) by Markov Chain Monte Carlo method (MCMC) was used to account for missing data within each neuropsychological measure (see table 2 for list of measures and missing data estimates by variable). Neuropsychological data were, then, normatively corrected according to respective normative tables (age and education corrected: FAS (32), ANT(32); age corrected: HVLT-R(33), WAIS-III Digit Span Backward and Forward(34), DRS (28), Stroop Color Word Interference Test(35). Individual normative scores were translated into z-scores. Domains of cognitive functioning were created by averaging z-scores of measures within each neuropsychological domain as defined above (Table 2). MADRS factor (dimensions) scores were, then, correlated with each neuropsychological domain. Descriptive and correlational analyses derived by pooled analysis of five imputation models. All tests were two-tailed, with results considered significant at p≤0.05. RESULTS The participants were 135 consecutively recruited older adults with major depression and history of stroke (Table 1). The severity of their depression (MADRS) was in the moderate to moderately severe range (MADRS Total Range 18-35). Their global cognitive impairment (MMSE) scores ranged from 21 to 30 with 21 participants having an MMSE score below 24. Effort on neuropsychological measures was assessed by the reliable digit span index and deemed to be adequate (RDS score >6)(29) in 86% of the sample. Participant neuropsychological performance fell within the impaired to the average ranges (Table 2). [Insert Table 1: Clinical and Demographic Characteristics of the study sample (N = 135).] [Insert Table 2: Table 2. Baseline Neuropsychological Characteristics by Cognitive Domain after Multiple Imputation.]
Exploratory factor analysis of MADRS items resulted in a three-factor solution explaining 46.2% of the variance (Table 3). We labeled each factor based on item content and prior literature(8, 9)
7
as: 1) Sadness (apparent sadness, reported sadness, pessimistic thoughts, suicidal thoughts;17.5% variance explained) 2) Distress (inner tension concentration difficulties, and reduced appetite; 15.9% variance explained) and 3) Apathy (lassitude, inability to feel;12.8% variance explained). Correlations between factors were as follows: Sadness and Distress r= -0.04, Sadness and Apathy r=-0.05, Distress and Apathy r= -0.02), One MADRS item (reduced sleep) did not load well on any one factor and was removed from the analysis. [Insert Table 3: Principal Factor Analysis Pattern Matrix MADRS Item Factor (factor items in bold)] There was no significant correlation between effort during neuropsychological assessment (RDS performance) and Sadness (Spearman r=-0.03, N=135 p=0.8), Distress (Spearman r=0.06, N=135, p=0. 53) or Apathy (Spearman r=-0.11, N=135, p=0.24). Apathy was associated with greater executive dysfunction, worse immediate and delayed memory performance, worse language functioning and worse general cognitive functioning (Table 4). Neither Sadness nor Distress dimensions were significantly correlated with performance on any cognitive measures. [Table 4: Pooled Spearman correlations between MADRS Subgroups and Neuropsychological Domains (N = 135).]
DISCUSSION The principal finding of this study is that PSD has a heterogeneous presentation with symptoms clustered along three dimensions, i.e. sadness, distress, and apathy. Apathy was the only symptom cluster of PSD with a significant relationship to impairment in executive function, memory, and global cognitive function. To our knowledge this is the first study to characterize the dimensions of PSD in older adults with moderate to severe depression. Its findings are consistent with reports of previous studies, which factor analyzed the presentation of PSD in wide age range samples 8
with mild severity of depression.(8, 9) Further, our observations are in line with earlier findings suggesting that post-stroke apathy is associated with cognitive impairment.(8, 36) Apathy afflicts 20% and 25% of stroke patients.(36) As in post-stroke depressed patients of this study, post-stroke apathy has been associated with cognitive impairment.(36) Apathy is a disturbance of motivation(37) clinically expressed as paucity of goal-directed, behavior.(38) Goal-directed behavior requires focusing of attention on meaningful information, holding this information in working memory, suppressing irrelevant or conflicting information, and selecting appropriate responses.(39) Brain lesion studies in humans suggest that apathy results, in part, from failed encoding of salience (meaning) of an ongoing or forthcoming behavior(40) leading to disruption of the processing that attributes motivational value of a behavior and that orients decision-making. These processes are served by interactions of the salience network with the cognitive control network. The salience network includes an anterior insular and anterior cingulate cortical (ACC) circuit that monitors the environment for motivationally salient stimuli and transforms salient signals into an orienting response that selectively increases arousal and engages networks that govern attention, and working-memory.(41) Subsequent behavioral responses are mediated by activation of the cognitive control network, which unlike the salience network, is equipped to operate on identified salience. Thus the salience network, with its connections to limbic and subcortical structures, serves as a bottom-up processor of salient cognitive, homeostatic, or emotional information (39) that triggers subsequent activation of the cognitive control network.(42, 43) Based on this understanding, the association of apathy with executive dysfunction in PSD may be understood as a consequence of lesions that disrupt the salience-cognitive control network communication along with pathways serving other cognitive functions. Consistent with this view are imaging studies suggesting that apathy mostly occurs after frontal or basal ganglia strokes and is associated with disruption of neural networks connecting the anterior cingulate gyrus, the
9
dorsomedial frontal cortex, and the frontal pole with the ventral aspects of the caudate nucleus, the anterior and ventral globus pallidus, and the dorsomedial and intralaminar thalamic nuclei.(36), (44) Executive dysfunction may impair learning and memory by disrupting organization during information storage and retrieval and account in part for the association of apathy with memory impairment. Apathy is common in PSD(7) but also in idiopathic late-life depression.(45) Studies of resting state functional connectivity (rsFC) in apathy of idiopathic late-life depression suggest a disruption of communication between and within the salience, reward, and cognitive control networks.(46) Relative to non-apathetic depressed elderly, depressed apathetic subjects had decreased rsFC of the right anterior insula (central node of the salience network) to dorsal ACC and to subcortical/limbic components of the salience network. Depressed elderly subjects with high apathy also had increased rsFC of the right anterior insula to right dorsolateral prefrontal cortex and right posterior cingulate cortex when compared to non-apathetic depressed elderly.(47) Further, depressed older apathetic patients had lower rsFC of the nucleus accumbens with the amydgala and with subcortical structures than non-apathetic patients and increased rsFC with the dorsomedial prefrontal cortex, the superior frontal cortex, and the insula.(48) Both apathy and executive dysfunction are disabling. Post-stroke apathy has a chronic course characterized by progressive disability and it responds poorly to antidepressants.(36) Even in idiopathic late-life depression, apathy is correlated with disability.(45) During antidepressant treatment, change in apathy remains correlated with disability, but improvement of apathy is at best modest.(45, 49) Similarly, executive dysfunction is associated with disability (50) and predicts poor response of late-life depression to antidepressants.(51-53) The findings of this study may guide the selection of interventions for PSD. The association of apathy with executive and other cognitive dysfunctions and its modest response to antidepressants points to the need for alternative treatment strategies. Given the relationship between PSD, apathy 10
and executive dysfunction in our sample, augmenting traditional interventions for PSD may improve outcomes. Small clinical studies and anecdotal reports on dopaminergic agonists(54) and cholinesterase inhibitors(55) suggest that these treatments may improve response of apathy symptoms of PSD to antidepressants. However, high quality randomized controlled trials are lacking. Adherence to medications in individuals with PSD is challenging.(56) Cuing interventions using reminders, checklists, and electronic devices may help apathetic PSD patients to initiate action. Cognitive remediation is a promising approach found to reduce apathy in healthy older adults(57) and reduce depression in older adults with executive dysfunction.(58, 59) Problem solving therapy can reduce both depression (60) and disability (61) in patients with idiopathic major depression and executive dysfunction and needs to be studied in patients with apathetic PSD and executive dysfunction. A reasonable approach in PSD patients with prominent sadness may be a streamlined behavioral activation treatment, (62, 63) while PSD patients with prominent distress may benefit from relaxation training.(64, 65) The findings of this study should be viewed in the context of its limitations. The study included participants with and without history of depression prior to stroke. This decision was based on findings that history of depressive episodes predisposes to depression after stroke.(66) However, some of this study’s subjects may have had depression unrelated to stroke. Another limitation is the exclusion of individuals with aphasia. It is possible that depressive profiles may differ in patients with aphasic syndromes secondary to stroke, limiting the generalizability of our findings to PSD without pronounced aphasia. Further, our use of the MADRS to identify dimensions of PSD may limit generalizability of our findings to this particular instrument; it is possible that use of another scale to quantify depression in PSD could have identified different symptom dimensions. It is conceivable that variability in symptoms of depression may be related to variables other than neuropsychological function (e.g., disability, stroke location); however, examining these relationship was beyond the scope of our analysis and should be explored further
11
in future studies. Finally, our study is cross-sectional and it does not offer information on the stability of the relationship between depressive symptoms and cognitive impairments. This study has sought to clarify the relationship of affective and cognitive symptoms in PSD. Our findings highlight the variability in symptom presentation of PSD, and underscore apathy as a meaningful dimension of PSD related to executive and other cognitive impairments. Findings from non-PSD populations implicate a disruption of communication between and within the salience, reward, cognitive control networks in apathy but studies of the neurobiology of apathetic in PSD are needed. Antidepressants have modest efficacy in both depression with apathy and executive dysfunction. However, behavioral interventions are available for apathetic syndromes. Preliminary findings suggest that some pharmacological agents may improve apathy and need to be investigated in apathy of PSD.
Author’s contributions: Author G.S.A conceived the original project and designed the study. Author D.K. generated the study hypothesis and wrote the manuscript with support from G.S.A, J.A., V.W., and L.O. Author V.W. was involved in supervising data collection. Authors V.W., L.A and M.C. participated in data collection. All authors participated in quality assurance. Authors D.K. and S.B. analyzed the data. All authors provided critical feedback, contributed to the interpretation of results and contributed to the final draft of the manuscript.
Disclosures and Sources of Funding: In the last three years, Dr. Alexopoulos has been a consultant to Allergan, Otsuka and Takeda-Lundbeck. For the remaining authors, no conflicts of interest were declared. Please credit P50 MH113838, T32 MH019132, R01 MH096685 and the Sanchez Foundation
12
Preliminary data were presented at the Annual Meeting of the International Neuropsychological Society February 20-23, 2019. New York City, USA.
REFERENCES 1.
Murphy SK, KD; Xu, JQ; Arias, E: Mortality in the United States, 2014, Hyattsville,
MD, 2015 2.
Mozaffarian D, Benjamin EJ, Go AS, et al: Heart disease and stroke statistics—2016
update: a report from the American Heart Association. Circulation 2016; 133:e38-e360 3.
Ayerbe L, Ayis S, Wolfe CD, et al: Natural history, predictors and outcomes of
depression after stroke: systematic review and meta-analysis. The British journal of psychiatry : the journal of mental science 2013; 202:14-21 4.
Hackett ML,Anderson CS: Predictors of depression after stroke: a systematic review of
observational studies. Stroke 2005; 36:2296-2301 5.
Hackett ML,Pickles K: Part I: frequency of depression after stroke: an updated systematic
review and meta-analysis of observational studies. International journal of stroke : official journal of the International Stroke Society 2014; 9:1017-1025 6.
Teasell RW, Foley NC, Bhogal SK, et al: An evidence-based review of stroke
rehabilitation. Topics in stroke Rehabilitation 2003; 10:29-58 7.
Hama S, Yamashita H, Yamawaki S, et al: Post-stroke depression and apathy:
Interactions between functional recovery, lesion location, and emotional response. Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society 2011; 11:68-76 8.
Farner L, Wagle J, Flekkoy K, et al: Factor analysis of the Montgomery Aasberg
Depression Rating Scale in an elderly stroke population. International journal of geriatric psychiatry 2009; 24:1209-1216 13
9.
Quaranta D, Marra C,Gainotti G: Post-stroke depression: Main phenomenological
clusters and their relationships with clinical measures. Behavioural neurology 2012; 25:303-310 10.
Hackett ML, Yapa C, Parag V, et al: Frequency of depression after stroke: a systematic
review of observational studies. Stroke 2005; 36:1330-1340 11.
Murata Y, Kimura M,Robinson RG: Does cognitive impairment cause post-stroke
depression? The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2000; 8:310-317 12.
Hochstenbach JB, den Otter R,Mulder TW: Cognitive recovery after stroke: a 2-year
follow-up. Archives of physical medicine and rehabilitation 2003; 84:1499-1504 13.
Lesniak M, Bak T, Czepiel W, et al: Frequency and prognostic value of cognitive
disorders in stroke patients. Dementia and geriatric cognitive disorders 2008; 26:356-363 14.
Levine DA, Galecki AT, Langa KM, et al: Trajectory of Cognitive Decline After Incident
Stroke. Jama 2015; 314:41-51 15.
Nys GM, van Zandvoort MJ, de Kort PL, et al: The prognostic value of domain-specific
cognitive abilities in acute first-ever stroke. Neurology 2005; 64:821-827 16.
Stephens S, Kenny RA, Rowan E, et al: Neuropsychological characteristics of mild
vascular cognitive impairment and dementia after stroke. International journal of geriatric psychiatry 2004; 19:1053-1057 17.
Barker-Collo S, Feigin VL, Parag V, et al: Auckland Stroke Outcomes Study. Part 2:
Cognition and functional outcomes 5 years poststroke. Neurology 2010; 75:1608-1616 18.
Duff K, Schoenberg MR, Scott JG, et al: The relationship between executive functioning
and verbal and visual learning and memory. Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists 2005; 20:111-122 19.
Lokk J,Delbari A: Management of depression in elderly stroke patients. Neuropsychiatric
disease and treatment 2010; 6:539-549
14
20.
Montgomery SA,Asberg M: A new depression scale designed to be sensitive to change.
The British journal of psychiatry : the journal of mental science 1979; 134:382-389 21.
Lezak MD: Neuropsychological assessment, 3rd. New York, Oxford University Press,
1995 22.
Goldstein LB, Bertels C,Davis JN: Interrater reliability of the NIH stroke scale. Archives
of neurology 1989; 46:660-662 23.
First MB, Spitzer RL, Gibbon M, et al: The Structured Clinical Interview for Dsm-Iii-R
Personality-Disorders (Scid-Ii) .1. Description. J Pers Disord 1995; 9:83-91 24.
Singh A, Black SE, Herrmann N, et al: Functional and neuroanatomic correlations in
poststroke depression: the Sunnybrook Stroke Study. Stroke 2000; 31:637-644 25.
Suenkeler IH, Nowak M, Misselwitz B, et al: Timecourse of health-related quality of life
as determined 3, 6 and 12 months after stroke. Relationship to neurological deficit, disability and depression. Journal of neurology 2002; 249:1160-1167 26.
Wiart L, Petit H, Joseph PA, et al: Fluoxetine in early poststroke depression: a double-
blind placebo-controlled study. Stroke 2000; 31:1829-1832 27.
Herrmann N, Black SE, Lawrence J, et al: The Sunnybrook Stroke Study: a prospective
study of depressive symptoms and functional outcome. Stroke 1998; 29:618-624 28.
Jurica P, Leitten C,Mattis S: Dementia Rating Scale-2: Professional Manual, Lutz, FL,
Psychological Assessment Resources, 2001 29.
Zenisek R, Millis SR, Banks SJ, et al: Prevalence of below-criterion Reliable Digit Span
scores in a clinical sample of older adults. Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists 2016; 31:426-433 30.
MacCallum RC, Widaman KF, Zhang SB, et al: Sample size in factor analysis. Psychol
Methods 1999; 4:84-99 31.
Mackinnon A: The use and reporting of multiple imputation in medical research - a
review. J Intern Med 2010; 268:586-593 15
32.
Tombaugh TN, Kozak J,Rees L: Normative data stratified by age and education for two
measures of verbal fluency: FAS and animal naming. Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists 1999; 14:167-177 33.
Benedict RHB, Schretlen D, Groninger L, et al: Hopkins Verbal Learning Test Revised:
Normative data and analysis of inter-form and test-retest reliability. Clin Neuropsychol 1998; 12:43-55 34.
Saklofske DH,Schoenberg MR: Wechsler Adult Intelligence Scale (All Versions), in
Encyclopedia of Clinical Neuropsychology. Edited by Kreutzer JS, DeLuca J,Caplan B. New York, NY, Springer New York, 2011, pp 2675-2680 35.
Golden CJ,Freshwater SM: The Stroop Color and Word Test: A Manual for Clinical and
Experimental Uses, Stoelting, 1998 36.
Jorge RE, Starkstein SE,Robinson RG: Apathy following stroke. Canadian journal of
psychiatry. Revue canadienne de psychiatrie 2010; 55:350-354 37.
Marin RS,Wilkosz PA: Disorders of diminished motivation. The Journal of head trauma
rehabilitation 2005; 20:377-388 38.
Marin RS: Apathy: Concept, Syndrome, Neural Mechanisms, and Treatment. Seminars in
clinical neuropsychiatry 1996; 1:304-314 39.
Seeley WW, Menon V, Schatzberg AF, et al: Dissociable intrinsic connectivity networks
for salience processing and executive control. The Journal of neuroscience : the official journal of the Society for Neuroscience 2007; 27:2349-2356 40.
Levy R,Dubois B: Apathy and the functional anatomy of the prefrontal cortex-basal
ganglia circuits. Cerebral cortex 2006; 16:916-928 41.
Menon V,Uddin LQ: Saliency, switching, attention and control: a network model of
insula function. Brain structure & function 2010; 214:655-667
16
42.
Sridharan D, Levitin DJ,Menon V: A critical role for the right fronto-insular cortex in
switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences of the United States of America 2008; 105:12569-12574 43.
Supekar K,Menon V: Developmental maturation of dynamic causal control signals in
higher-order cognition: a neurocognitive network model. PLoS computational biology 2012; 8:e1002374 44.
Tang WK, Wong LK, Mok VC, et al: Apathy after stroke: potential risk factors and
magnetic resonance imaging markers. Hong Kong medical journal = Xianggang yi xue za zhi 2018; 24 Suppl 3:18-20 45.
Yuen GS, Bhutani S, Lucas BJ, et al: Apathy in late-life depression: common, persistent,
and disabling. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2015; 23:488-494 46.
Pimontel MA, Kanellopoulos D,Gunning FM: Neuroanatomical Abnormalities in Older
Depressed Adults With Apathy: A Systematic Review. Journal of geriatric psychiatry and neurology 2019; 891988719882100 47.
Yuen GS, Gunning-Dixon FM, Hoptman MJ, et al: The salience network in the apathy of
late-life depression. International journal of geriatric psychiatry 2014; 29:1116-1124 48.
Alexopoulos GS, Hoptman MJ, Yuen G, et al: Functional connectivity in apathy of late-
life depression: a preliminary study. Journal of affective disorders 2013; 149:398-405 49.
Yuen GS, Gunning FM, Woods E, et al: Neuroanatomical correlates of apathy in late-life
depression and antidepressant treatment response. Journal of affective disorders 2014; 166:179186 50.
Kiosses DN, Klimstra S, Murphy C, et al: Executive dysfunction and disability in elderly
patients with major depression. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2001; 9:269-274
17
51.
Alexopoulos GS, Kiosses DN, Heo M, et al: Executive dysfunction and the course of
geriatric depression. Biological psychiatry 2005; 58:204-210 52.
Alexopoulos GS, Manning K, Kanellopoulos D, et al: Cognitive control, reward-related
decision making and outcomes of late-life depression treated with an antidepressant. Psychological medicine 2015; 45:3111-3120 53.
Sheline YI, Pieper CF, Barch DM, et al: Support for the vascular depression hypothesis
in late-life depression: results of a 2-site, prospective, antidepressant treatment trial. Archives of general psychiatry 2010; 67:277-285 54.
Kant R,Smith-Seemiller L: Assessment and treatment of apathy syndrome following head
injury. NeuroRehabilitation 2002; 17:325-331 55.
Whyte EM, Lenze EJ, Butters M, et al: An open-label pilot study of acetylcholinesterase
inhibitors to promote functional recovery in elderly cognitively impaired stroke patients. Cerebrovascular diseases 2008; 26:317-321 56.
Villa RF, Ferrari F,Moretti A: Post-stroke depression: Mechanisms and pharmacological
treatment. Pharmacology & therapeutics 2018; 184:131-144 57.
Montoya-Murillo G, Ibarretxe-Bilbao N, Pena J, et al: Effects of Cognitive Rehabilitation
on Cognition, Apathy, Quality of Life, and Subjective Complaints in the Elderly: A Randomized Controlled Trial. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2019; 58.
Morimoto SS, Gunning FM, Wexler BE, et al: Executive Dysfunction Predicts Treatment
Response to Neuroplasticity-Based Computerized Cognitive Remediation (nCCR-GD) in Elderly Patients with Major Depression. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2016; 24:816-820 59.
Morimoto SS, Wexler BE,Alexopoulos GS: Neuroplasticity-based computerized
cognitive remediation for geriatric depression. International journal of geriatric psychiatry 2012; 27:1239-1247 18
60.
Arean PA, Raue P, Mackin RS, et al: Problem-solving therapy and supportive therapy in
older adults with major depression and executive dysfunction. The American journal of psychiatry 2010; 167:1391-1398 61.
Alexopoulos GS, Raue PJ, Kiosses DN, et al: Problem-solving therapy and supportive
therapy in older adults with major depression and executive dysfunction: effect on disability. Archives of general psychiatry 2011; 68:33-41 62.
Alexopoulos GS,Arean P: A model for streamlining psychotherapy in the RDoC era: the
example of 'Engage'. Molecular psychiatry 2014; 19:14-19 63.
Alexopoulos GS, Raue PJ, Gunning F, et al: "Engage" Therapy: Behavioral Activation
and Improvement of Late-Life Major Depression. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2016; 24:320-326 64.
Golding K, Fife-Schaw C,Kneebone I: Twelve month follow-up on a randomised
controlled trial of relaxation training for post-stroke anxiety. Clinical rehabilitation 2017; 31:1164-1167 65.
Golding K, Fife-Schaw C,Kneebone I: A pilot randomized controlled trial of self-help
relaxation to reduce post-stroke depression. Clinical rehabilitation 2018; 32:747-751 66.
Robinson RG,Jorge RE: Post-Stroke Depression: A Review. The American journal of
psychiatry 2016; 173:221-231
Table 1. Clinical and Demographic Characteristics of the study sample (N = 135) Mean (SD) Age, years
19
70.1 (11.2)
Education, years
15.7 (3.6)
Female, % (n)
59%
Living alone % (n)
36%
On Antidepressant Medication %
56%
MADRS*
23.2 (4.0)
Duration of Depressive Episode (Months)
17.5 (23.6)
Number of Previous Depressive Episodes
3.4 (10.8)
Adequate Effort % (RDS**)
86%
*MADRS=Montgomery Aspberg Depression Rating Scale **RDS=Reliable Digit Span; adequate effort defined as RDS>6
Table 2. Baseline Neuropsychological Characteristics by Cognitive Domain after Multiple Imputation N=135.
Pooled Descriptive Statistics
multiple imputation)
zscore
z-score SD
Global Cognition
-0.5
1.3
Neuropsychological Domains (missing data prior to
Mean
SD
MMSE* Total (2 missing)
27.2
2.7
-0.5
1.9
Dementia Rating Scale (DRS) Total (24 missing)
129.2
13.1
-0.6
1.2
20
Attention
0.3
0.7
WAIS-III+ Forward Digit Span (25 missing)
6.3
1.5
0.1
1.1
DRS Attention Total (14 missing)
35.5
2.5
0.4
0.8
-0.4
0.6
Executive Function DRS Initiation/Perseveration (19 missing)
31.5
6.1
-0.8
1.1
DRS Conceptualization (13 missing)
34.6
4.1
-0.2
0.9
Stroop Color/Word Interference++(30 missing)
2.7
6.6
0.3
0.7
WAIS-III Backward Digit Span (25 missing)
4.0
1.2
-0.3
1.0
Controlled Oral Word Association Test (FAS) Total
28.6
13.3
-1.0
1.1
-1.5
0.7
(14 missing) Processing Speed Stroop Word Total++(26 missing)
84.0
15.5
-1.2
0.7
Stroop Color Total++(30 missing)
53.5
12.6
-1.7
0.7
-0.6
0.8
-0.6
0.8
-1.5
1.3
-1.5
1.3
Visuoconstruction DRS Construction (20 missing)
5.2
1.4
Immediate Memory (Learning) Hopkins Verbal Learning Test- Revised (HVLT-R) Immediate Recall (11 missing)
21
19.3
6.2
Delayed Memory (Retention)
-0.9
1.1
HVLT-R Delayed Recall (14 missing)
5.8
3.3
-1.6
1.5
DRS Memory (13 missing)
22.4
3.1
-0.2
1.2
-0.8
1.2
-0.5
1.3
Language Animal Naming Test Total (12 missing)
15.4
5.5
*MMSE=MiniMental State Examination +Wechsler Adult Intelligence Scales – Third Edition ++Age Corrected raw score
Table 3. Principal Factor Analysis Pattern Matrix MADRS Item Factor (factor items in bold) Factor 1
Factor 2
Factor 3
Sadness
Distress
Apathy
Apparent Sadness
0.57
-0.03
0.20
Reported Sadness
0.72
0.07
0.03
Pessimistic Thoughts
0.30
0.30
-0.03
Suicidal Thoughts
0.22
-0.01
-0.07
Reduced appetite
0.12
-0.72
0.00
Concentration difficulties
-0.26
0.47
0.17
Inner Tension
0.08
0.39
-0.07
Lassitude
-0.02
0.00
0.31
Inability to feel
0.14
-0.04
0.70
Items
22
Table 4. Pooled Spearman correlations between MADRS Subgroups and Neuropsychological Domains (N = 135)
Neuropsychological Domains
Sadness
Distress
Apathy
Global Cognition
-0.00
0.04
-0.34**
Attention
-0.04
0.00
-0.11
Executive Function
-0.00
0.16
-0.20*
Processing Speed
0.13
0.12
-0.00
Visuoconstruction
-0.10
0.02
-0.08
Immediate Memory
-0.07
0.08
-0.30**
Delayed Memory
-0.05
0.15
-0.23**
Language
0.11
0.07
-0.22*
* Correlation is significant at the p<0.05 level (two-tailed) ** Correlation is significant at the p<0.01 level
23
Dimensions of post-stroke depression and neuropsychological deficits in older adults Dora Kanellopoulos Ph.D. , Victoria Wilkins Ph.D. , Jimmy Avari M.D. , Lauren Oberlin Ph.D. , Lindsay Arader BA , Merete Chaplin BA , Samprit Banerjee Ph.D., M. Stat , George S. Alexopoulos M.D. PII: DOI: Reference:
S1064-7481(20)30022-1 https://doi.org/10.1016/j.jagp.2020.01.009 AMGP 1401
To appear in:
The American Journal of Geriatric Psychiatry
Received date: Revised date: Accepted date:
8 November 2019 18 January 2020 21 January 2020
Please cite this article as: Dora Kanellopoulos Ph.D. , Victoria Wilkins Ph.D. , Jimmy Avari M.D. , Lauren Oberlin Ph.D. , Lindsay Arader BA , Merete Chaplin BA , Samprit Banerjee Ph.D., M. Stat , George S. Alexopoulos M.D. , Dimensions of post-stroke depression and neuropsychological deficits in older adults, The American Journal of Geriatric Psychiatry (2020), doi: https://doi.org/10.1016/j.jagp.2020.01.009
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc. on behalf of American Association for Geriatric Psychiatry.
Dimensions of post-stroke depression and neuropsychological deficits in older adults
Dora Kanellopoulos,1Ph.D., Victoria Wilkins,1Ph.D., Jimmy Avari,1M.D., Lauren Oberlin, 1
Ph.D., Lindsay Arader,1BA, Merete Chaplin,1BA, Samprit Banerjee,1Ph.D., M. Stat, George S.
Alexopoulos, 1M.D.
1
Weill Cornell Medicine, Weill Cornell Institute of Geriatric Psychiatry
Corresponding author: George S. Alexopoulos, M.D. S.P. Tobin and A.M. Cooper Professor Director, Weill-Cornell Institute of Geriatric Psychiatry 21 Bloomingdale Road White Plains, NY 10605
Email: [email protected] Phone: 914-997-5767 Fax: 914-682-6979
Keywords: Post-stroke depression, apathy, older adults
1
Highlights What is the primary question addressed by this study?
Post- stroke depression (PSD) is prevalent, challenging to diagnose and often presents with widespread cognitive deficits. This study characterizes dimensions of post-stroke depression in an older adult sample with moderate to moderately severe depression.
What is the main finding of this study?
Among the three dimensions identified, apathy was related to worse functioning on multiple cognitive domains, while sadness and distress were not related to any cognitive variables.
What is the meaning of this finding?
Our findings indicate that PSD is a heterogeneous entity with clinically meaningful dimensions that if further explored can inform personalized therapeutic interventions.
ABSTRACT Objective: Post-stroke depression (PSD) has a heterogeneous presentation and is often accompanied by cognitive impairment. This study aimed to identify distinct dimensions of depressive symptoms in older adults with PSD and to evaluate their relationship to cognitive functioning. Design: Cross-sectional factor and correlational analyses of patients with post-stroke depression. Setting: Patients were recruited from the community and from acute inpatient stroke rehabilitation hospitals. Participants: Participants had suffered a stroke and met DSM-IV criteria for major depression (≥18 Montgomery Åsberg Depression Scale; MADRS). 2
Intervention: None Measurements: MADRS was used to quantify depression severity at study entry. Neuropsychological assessment at the time of study entry consisted of measures Global Cognition, Attention, Executive Function, Processing Speed, Immediate Memory, Delayed Memory, and Language. Results: The participants were 135 older adults (age ≥50) with PSD and varying degrees of cognitive impairment (MMSE Total ≥ 20). Factor analysis of the MADRS identified three factors, i.e. Sadness, Distress, and Apathy. Items comprising each factor were totaled and correlated with neuropsychological domain z-score averages. Symptoms of the apathy factor (lassitude, inability to feel) were significantly associated with greater impairment in executive dysfunction, memory, and global cognition. Symptoms of the sadness and distress factors had no relationship to cognitive impairment. Conclusions: PSD consists of three correlated dimensions of depressive symptoms. Apathy symptoms are associated with cognitive impairment across several neuropsychological domains. PSD patients with prominent apathy may benefit from careful attention to cognitive functions and by interventions that address both psychopathology and behavioral deficits resulting from cognitive impairment.
INTRODUCTION Stroke is the leading cause of both serious and long-term disability and the fifth leading cause of death in the United States.(1, 2) Post-stroke depression (PSD) afflicts approximately one-fourth of stroke survivors and increases the likelihood of persistent disability.(3-6) Prompt identification and treatment of PSD can improve affective symptoms and stroke related disability.(7, 8) 3
PSD often occurs in patients presenting emotional lability, anosognosia and cognitive impairment leading to a clinical presentation far more complex than that of idiopathic depression. Despite the apparent heterogeneity of PSD, few studies to date have attempted to identify distinct dimensions of depressive symptoms in PSD. One prior PSD study of adults with a wide age range(9) diagnosed with major depression due to medical reasons, used the post-stroke depression scale and reported three dimensions of depressive symptom patterns, i.e., depressive/anxious, lack of emotional control, and reduced motivation. Reduced motivation (i.e., a combination of symptoms of apathy and anhedonia) was significantly associated with older age in PSD patients.(8) Little is known about the clinical heterogeneity of PSD in older adults. In the only study examining symptom patterns of PSD in older adults, a multidimensional profile emerged.(8)Factor analysis of items of the Montgomery Åsberg Depression Rating Scale (MADRS) in 163 patients with PSD identified three factors, i.e. sadness, agitation and anhedonia.(8) The subjects of this study had mild depression and there have been no studies of more severe PSD to date. The relationship of depressive symptoms to cognitive impairment is especially meaningful in PSD, as post-stroke cognitive deficits are common and widespread.(10, 11) Cognitive deficits in PSD include memory difficulties, executive dysfunction, poor attention, slowed information processing speed, and visuoperceptual/visuoconstructional impairments.(12-16) Executive dysfunction in particular is a persistent, chronic and prevalent cognitive deficit among stroke survivors(14, 17) and may worsen deficits in other cognitive domains (e.g., learning and memory).(18) Among executive functions, verbal fluency and conceptualization are impaired in 33%-66% of stroke patients.(18) Aging compounds stroke related cognitive deficits leading to worsening outcomes in older adults with PSD.(19) While cognitive impairments improve or stabilize in many stroke survivors, cognitive function in patients with PSD often worsens.(12) Earlier reports indicate that patients with prominent 4
anhedonia experience greater cognitive impairment, while those with prominent sadness exhibit greater sensorimotor and cranial nerve deficits.(8) Characterizing the relationship of distinct dimensions of depressive symptoms with cognitive impairments in older adults with PSD may help elucidate aspects of the neurobiology of distinct presentations of PSD, guide the selection of interventions and increase personalization of treatment. This study sought to identify dimensions of depressive symptoms in older adults with moderate to moderately severe PSD. Based on the limited, available literature, we hypothesized that: 1) the PSD syndrome of older adults has several distinct dimensions; and 2) the dimensions of the PSD syndrome have different relationships to cognitive deficits.
METHODS Participants The participants were recruited from local inpatient stroke rehabilitation units and from the community. The inclusion criteria were: 1) Age of 50 years and older; 2) history of ischemic, embolic or hemorrhagic stroke established by medical record review; 3) diagnosis of major depression by DSM-IV criteria; and 4) Montgomery Åsberg Depression Scale (MADRS)(20) score of 18 or higher. Exclusion criteria were: 1) Moderate or severe dementia (Mini Mental State Examination (MMSE(21)) score < 20); 2) greater than moderate aphasia (NIH Stroke Scale(22) Best Language > 1); 3) psychotic depression or bipolar disorder (by DSM-IV); 4) suicide intent or plan; 5) plans to enter a nursing home for treatment in the near future; and 6) inability to speak English. All participants signed informed consent approved by the Weill Cornell Institutional Review Board. Assessment
5
Diagnostic evaluation was conducted with the Structured Clinical Interview- Revised (SCIDR).(23) Severity of depression was quantified with the MADRS.(20) The MADRS is a reliable and valid 10-item clinician rated scale, which places less emphasis on somatic symptoms and is frequently used as a depression severity rating instrument for older adults who have experienced a stroke.(24-27) Global cognitive dysfunction was assessed with the MMSE,(21) a reliable and valid brief screening instrument, and with the Dementia Rating Scale(28) a more extensive instrument of cognitive dysfunction in older adults. Participants also received a neuropsychological battery consisting of scales assessing the following cognitive domains: 1) Executive Function (Controlled Oral Word Association Test (COWAT), Dementia Rating Scale (DRS) Initiation/Perseveration (IP) subscale, DRS Conceptualization subscale, Stroop Color Word Interference, Weschler Adult Intelligence Scale (WAIS –III) Digit Span Backward Span); 2) Processing speed (Stroop Color and Stroop Word subtests); 3) Attention (WAIS–III Digit Span- Digits Forward Total Span, DRS Attention subscale), 4) Immediate Memory (Hopkins Verbal Learning Test-Revised (HVLT-R) Immediate Recall), and 5) Delayed memory (HVLT-R Delayed Recall, DRS Memory subscale); and 6) Language (Semantic fluency/Animal Naming Test (ANT)). To account for effort as a confounder of performance, reliable digit span (RDS) was used and optimal effort was indicated by RDS>6.(29) Statistical Analysis Exploratory principal axis factoring(30) was performed on the 10 items of the MADRS scale using an oblique rotation procedure in SPSS 25.0 (Direct Oblimin). Three factors that explained 46.2% of the total variation were extracted by visual inspection of the scree plot and by requiring factor eigenvalues to be > 1. Factor loadings greater or equal to 0.2 were used to identify MADRS factors of depression. Factor (dimensions) totals were created by adding the score on each MADRS item within each factor grouping into a sum. 6
Multiple imputation(31) by Markov Chain Monte Carlo method (MCMC) was used to account for missing data within each neuropsychological measure (see table 2 for list of measures and missing data estimates by variable). Neuropsychological data were, then, normatively corrected according to respective normative tables (age and education corrected: FAS (32), ANT(32); age corrected: HVLT-R(33), WAIS-III Digit Span Backward and Forward(34), DRS (28), Stroop Color Word Interference Test(35). Individual normative scores were translated into z-scores. Domains of cognitive functioning were created by averaging z-scores of measures within each neuropsychological domain as defined above (Table 2). MADRS factor (dimensions) scores were, then, correlated with each neuropsychological domain. Descriptive and correlational analyses derived by pooled analysis of five imputation models. All tests were two-tailed, with results considered significant at p≤0.05. RESULTS The participants were 135 consecutively recruited older adults with major depression and history of stroke (Table 1). The severity of their depression (MADRS) was in the moderate to moderately severe range (MADRS Total Range 18-35). Their global cognitive impairment (MMSE) scores ranged from 21 to 30 with 21 participants having an MMSE score below 24. Effort on neuropsychological measures was assessed by the reliable digit span index and deemed to be adequate (RDS score >6)(29) in 86% of the sample. Participant neuropsychological performance fell within the impaired to the average ranges (Table 2). [Insert Table 1: Clinical and Demographic Characteristics of the study sample (N = 135).] [Insert Table 2: Table 2. Baseline Neuropsychological Characteristics by Cognitive Domain after Multiple Imputation.]
Exploratory factor analysis of MADRS items resulted in a three-factor solution explaining 46.2% of the variance (Table 3). We labeled each factor based on item content and prior literature(8, 9)
7
as: 1) Sadness (apparent sadness, reported sadness, pessimistic thoughts, suicidal thoughts;17.5% variance explained) 2) Distress (inner tension concentration difficulties, and reduced appetite; 15.9% variance explained) and 3) Apathy (lassitude, inability to feel;12.8% variance explained). Correlations between factors were as follows: Sadness and Distress r= -0.04, Sadness and Apathy r=-0.05, Distress and Apathy r= -0.02), One MADRS item (reduced sleep) did not load well on any one factor and was removed from the analysis. [Insert Table 3: Principal Factor Analysis Pattern Matrix MADRS Item Factor (factor items in bold)] There was no significant correlation between effort during neuropsychological assessment (RDS performance) and Sadness (Spearman r=-0.03, N=135 p=0.8), Distress (Spearman r=0.06, N=135, p=0. 53) or Apathy (Spearman r=-0.11, N=135, p=0.24). Apathy was associated with greater executive dysfunction, worse immediate and delayed memory performance, worse language functioning and worse general cognitive functioning (Table 4). Neither Sadness nor Distress dimensions were significantly correlated with performance on any cognitive measures. [Table 4: Pooled Spearman correlations between MADRS Subgroups and Neuropsychological Domains (N = 135).]
DISCUSSION The principal finding of this study is that PSD has a heterogeneous presentation with symptoms clustered along three dimensions, i.e. sadness, distress, and apathy. Apathy was the only symptom cluster of PSD with a significant relationship to impairment in executive function, memory, and global cognitive function. To our knowledge this is the first study to characterize the dimensions of PSD in older adults with moderate to severe depression. Its findings are consistent with reports of previous studies, which factor analyzed the presentation of PSD in wide age range samples 8
with mild severity of depression.(8, 9) Further, our observations are in line with earlier findings suggesting that post-stroke apathy is associated with cognitive impairment.(8, 36) Apathy afflicts 20% and 25% of stroke patients.(36) As in post-stroke depressed patients of this study, post-stroke apathy has been associated with cognitive impairment.(36) Apathy is a disturbance of motivation(37) clinically expressed as paucity of goal-directed, behavior.(38) Goal-directed behavior requires focusing of attention on meaningful information, holding this information in working memory, suppressing irrelevant or conflicting information, and selecting appropriate responses.(39) Brain lesion studies in humans suggest that apathy results, in part, from failed encoding of salience (meaning) of an ongoing or forthcoming behavior(40) leading to disruption of the processing that attributes motivational value of a behavior and that orients decision-making. These processes are served by interactions of the salience network with the cognitive control network. The salience network includes an anterior insular and anterior cingulate cortical (ACC) circuit that monitors the environment for motivationally salient stimuli and transforms salient signals into an orienting response that selectively increases arousal and engages networks that govern attention, and working-memory.(41) Subsequent behavioral responses are mediated by activation of the cognitive control network, which unlike the salience network, is equipped to operate on identified salience. Thus the salience network, with its connections to limbic and subcortical structures, serves as a bottom-up processor of salient cognitive, homeostatic, or emotional information (39) that triggers subsequent activation of the cognitive control network.(42, 43) Based on this understanding, the association of apathy with executive dysfunction in PSD may be understood as a consequence of lesions that disrupt the salience-cognitive control network communication along with pathways serving other cognitive functions. Consistent with this view are imaging studies suggesting that apathy mostly occurs after frontal or basal ganglia strokes and is associated with disruption of neural networks connecting the anterior cingulate gyrus, the
9
dorsomedial frontal cortex, and the frontal pole with the ventral aspects of the caudate nucleus, the anterior and ventral globus pallidus, and the dorsomedial and intralaminar thalamic nuclei.(36), (44) Executive dysfunction may impair learning and memory by disrupting organization during information storage and retrieval and account in part for the association of apathy with memory impairment. Apathy is common in PSD(7) but also in idiopathic late-life depression.(45) Studies of resting state functional connectivity (rsFC) in apathy of idiopathic late-life depression suggest a disruption of communication between and within the salience, reward, and cognitive control networks.(46) Relative to non-apathetic depressed elderly, depressed apathetic subjects had decreased rsFC of the right anterior insula (central node of the salience network) to dorsal ACC and to subcortical/limbic components of the salience network. Depressed elderly subjects with high apathy also had increased rsFC of the right anterior insula to right dorsolateral prefrontal cortex and right posterior cingulate cortex when compared to non-apathetic depressed elderly.(47) Further, depressed older apathetic patients had lower rsFC of the nucleus accumbens with the amydgala and with subcortical structures than non-apathetic patients and increased rsFC with the dorsomedial prefrontal cortex, the superior frontal cortex, and the insula.(48) Both apathy and executive dysfunction are disabling. Post-stroke apathy has a chronic course characterized by progressive disability and it responds poorly to antidepressants.(36) Even in idiopathic late-life depression, apathy is correlated with disability.(45) During antidepressant treatment, change in apathy remains correlated with disability, but improvement of apathy is at best modest.(45, 49) Similarly, executive dysfunction is associated with disability (50) and predicts poor response of late-life depression to antidepressants.(51-53) The findings of this study may guide the selection of interventions for PSD. The association of apathy with executive and other cognitive dysfunctions and its modest response to antidepressants points to the need for alternative treatment strategies. Given the relationship between PSD, apathy 10
and executive dysfunction in our sample, augmenting traditional interventions for PSD may improve outcomes. Small clinical studies and anecdotal reports on dopaminergic agonists(54) and cholinesterase inhibitors(55) suggest that these treatments may improve response of apathy symptoms of PSD to antidepressants. However, high quality randomized controlled trials are lacking. Adherence to medications in individuals with PSD is challenging.(56) Cuing interventions using reminders, checklists, and electronic devices may help apathetic PSD patients to initiate action. Cognitive remediation is a promising approach found to reduce apathy in healthy older adults(57) and reduce depression in older adults with executive dysfunction.(58, 59) Problem solving therapy can reduce both depression (60) and disability (61) in patients with idiopathic major depression and executive dysfunction and needs to be studied in patients with apathetic PSD and executive dysfunction. A reasonable approach in PSD patients with prominent sadness may be a streamlined behavioral activation treatment, (62, 63) while PSD patients with prominent distress may benefit from relaxation training.(64, 65) The findings of this study should be viewed in the context of its limitations. The study included participants with and without history of depression prior to stroke. This decision was based on findings that history of depressive episodes predisposes to depression after stroke.(66) However, some of this study’s subjects may have had depression unrelated to stroke. Another limitation is the exclusion of individuals with aphasia. It is possible that depressive profiles may differ in patients with aphasic syndromes secondary to stroke, limiting the generalizability of our findings to PSD without pronounced aphasia. Further, our use of the MADRS to identify dimensions of PSD may limit generalizability of our findings to this particular instrument; it is possible that use of another scale to quantify depression in PSD could have identified different symptom dimensions. It is conceivable that variability in symptoms of depression may be related to variables other than neuropsychological function (e.g., disability, stroke location); however, examining these relationship was beyond the scope of our analysis and should be explored further
11
in future studies. Finally, our study is cross-sectional and it does not offer information on the stability of the relationship between depressive symptoms and cognitive impairments. This study has sought to clarify the relationship of affective and cognitive symptoms in PSD. Our findings highlight the variability in symptom presentation of PSD, and underscore apathy as a meaningful dimension of PSD related to executive and other cognitive impairments. Findings from non-PSD populations implicate a disruption of communication between and within the salience, reward, cognitive control networks in apathy but studies of the neurobiology of apathetic in PSD are needed. Antidepressants have modest efficacy in both depression with apathy and executive dysfunction. However, behavioral interventions are available for apathetic syndromes. Preliminary findings suggest that some pharmacological agents may improve apathy and need to be investigated in apathy of PSD.
Author’s contributions: Author G.S.A conceived the original project and designed the study. Author D.K. generated the study hypothesis and wrote the manuscript with support from G.S.A, J.A., V.W., and L.O. Author V.W. was involved in supervising data collection. Authors V.W., L.A and M.C. participated in data collection. All authors participated in quality assurance. Authors D.K. and S.B. analyzed the data. All authors provided critical feedback, contributed to the interpretation of results and contributed to the final draft of the manuscript.
Disclosures and Sources of Funding: In the last three years, Dr. Alexopoulos has been a consultant to Allergan, Otsuka and Takeda-Lundbeck. For the remaining authors, no conflicts of interest were declared. Please credit P50 MH113838, T32 MH019132, R01 MH096685 and the Sanchez Foundation
12
Preliminary data were presented at the Annual Meeting of the International Neuropsychological Society February 20-23, 2019. New York City, USA.
REFERENCES 1.
Murphy SK, KD; Xu, JQ; Arias, E: Mortality in the United States, 2014, Hyattsville,
MD, 2015 2.
Mozaffarian D, Benjamin EJ, Go AS, et al: Heart disease and stroke statistics—2016
update: a report from the American Heart Association. Circulation 2016; 133:e38-e360 3.
Ayerbe L, Ayis S, Wolfe CD, et al: Natural history, predictors and outcomes of
depression after stroke: systematic review and meta-analysis. The British journal of psychiatry : the journal of mental science 2013; 202:14-21 4.
Hackett ML,Anderson CS: Predictors of depression after stroke: a systematic review of
observational studies. Stroke 2005; 36:2296-2301 5.
Hackett ML,Pickles K: Part I: frequency of depression after stroke: an updated systematic
review and meta-analysis of observational studies. International journal of stroke : official journal of the International Stroke Society 2014; 9:1017-1025 6.
Teasell RW, Foley NC, Bhogal SK, et al: An evidence-based review of stroke
rehabilitation. Topics in stroke Rehabilitation 2003; 10:29-58 7.
Hama S, Yamashita H, Yamawaki S, et al: Post-stroke depression and apathy:
Interactions between functional recovery, lesion location, and emotional response. Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society 2011; 11:68-76 8.
Farner L, Wagle J, Flekkoy K, et al: Factor analysis of the Montgomery Aasberg
Depression Rating Scale in an elderly stroke population. International journal of geriatric psychiatry 2009; 24:1209-1216 13
9.
Quaranta D, Marra C,Gainotti G: Post-stroke depression: Main phenomenological
clusters and their relationships with clinical measures. Behavioural neurology 2012; 25:303-310 10.
Hackett ML, Yapa C, Parag V, et al: Frequency of depression after stroke: a systematic
review of observational studies. Stroke 2005; 36:1330-1340 11.
Murata Y, Kimura M,Robinson RG: Does cognitive impairment cause post-stroke
depression? The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2000; 8:310-317 12.
Hochstenbach JB, den Otter R,Mulder TW: Cognitive recovery after stroke: a 2-year
follow-up. Archives of physical medicine and rehabilitation 2003; 84:1499-1504 13.
Lesniak M, Bak T, Czepiel W, et al: Frequency and prognostic value of cognitive
disorders in stroke patients. Dementia and geriatric cognitive disorders 2008; 26:356-363 14.
Levine DA, Galecki AT, Langa KM, et al: Trajectory of Cognitive Decline After Incident
Stroke. Jama 2015; 314:41-51 15.
Nys GM, van Zandvoort MJ, de Kort PL, et al: The prognostic value of domain-specific
cognitive abilities in acute first-ever stroke. Neurology 2005; 64:821-827 16.
Stephens S, Kenny RA, Rowan E, et al: Neuropsychological characteristics of mild
vascular cognitive impairment and dementia after stroke. International journal of geriatric psychiatry 2004; 19:1053-1057 17.
Barker-Collo S, Feigin VL, Parag V, et al: Auckland Stroke Outcomes Study. Part 2:
Cognition and functional outcomes 5 years poststroke. Neurology 2010; 75:1608-1616 18.
Duff K, Schoenberg MR, Scott JG, et al: The relationship between executive functioning
and verbal and visual learning and memory. Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists 2005; 20:111-122 19.
Lokk J,Delbari A: Management of depression in elderly stroke patients. Neuropsychiatric
disease and treatment 2010; 6:539-549
14
20.
Montgomery SA,Asberg M: A new depression scale designed to be sensitive to change.
The British journal of psychiatry : the journal of mental science 1979; 134:382-389 21.
Lezak MD: Neuropsychological assessment, 3rd. New York, Oxford University Press,
1995 22.
Goldstein LB, Bertels C,Davis JN: Interrater reliability of the NIH stroke scale. Archives
of neurology 1989; 46:660-662 23.
First MB, Spitzer RL, Gibbon M, et al: The Structured Clinical Interview for Dsm-Iii-R
Personality-Disorders (Scid-Ii) .1. Description. J Pers Disord 1995; 9:83-91 24.
Singh A, Black SE, Herrmann N, et al: Functional and neuroanatomic correlations in
poststroke depression: the Sunnybrook Stroke Study. Stroke 2000; 31:637-644 25.
Suenkeler IH, Nowak M, Misselwitz B, et al: Timecourse of health-related quality of life
as determined 3, 6 and 12 months after stroke. Relationship to neurological deficit, disability and depression. Journal of neurology 2002; 249:1160-1167 26.
Wiart L, Petit H, Joseph PA, et al: Fluoxetine in early poststroke depression: a double-
blind placebo-controlled study. Stroke 2000; 31:1829-1832 27.
Herrmann N, Black SE, Lawrence J, et al: The Sunnybrook Stroke Study: a prospective
study of depressive symptoms and functional outcome. Stroke 1998; 29:618-624 28.
Jurica P, Leitten C,Mattis S: Dementia Rating Scale-2: Professional Manual, Lutz, FL,
Psychological Assessment Resources, 2001 29.
Zenisek R, Millis SR, Banks SJ, et al: Prevalence of below-criterion Reliable Digit Span
scores in a clinical sample of older adults. Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists 2016; 31:426-433 30.
MacCallum RC, Widaman KF, Zhang SB, et al: Sample size in factor analysis. Psychol
Methods 1999; 4:84-99 31.
Mackinnon A: The use and reporting of multiple imputation in medical research - a
review. J Intern Med 2010; 268:586-593 15
32.
Tombaugh TN, Kozak J,Rees L: Normative data stratified by age and education for two
measures of verbal fluency: FAS and animal naming. Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists 1999; 14:167-177 33.
Benedict RHB, Schretlen D, Groninger L, et al: Hopkins Verbal Learning Test Revised:
Normative data and analysis of inter-form and test-retest reliability. Clin Neuropsychol 1998; 12:43-55 34.
Saklofske DH,Schoenberg MR: Wechsler Adult Intelligence Scale (All Versions), in
Encyclopedia of Clinical Neuropsychology. Edited by Kreutzer JS, DeLuca J,Caplan B. New York, NY, Springer New York, 2011, pp 2675-2680 35.
Golden CJ,Freshwater SM: The Stroop Color and Word Test: A Manual for Clinical and
Experimental Uses, Stoelting, 1998 36.
Jorge RE, Starkstein SE,Robinson RG: Apathy following stroke. Canadian journal of
psychiatry. Revue canadienne de psychiatrie 2010; 55:350-354 37.
Marin RS,Wilkosz PA: Disorders of diminished motivation. The Journal of head trauma
rehabilitation 2005; 20:377-388 38.
Marin RS: Apathy: Concept, Syndrome, Neural Mechanisms, and Treatment. Seminars in
clinical neuropsychiatry 1996; 1:304-314 39.
Seeley WW, Menon V, Schatzberg AF, et al: Dissociable intrinsic connectivity networks
for salience processing and executive control. The Journal of neuroscience : the official journal of the Society for Neuroscience 2007; 27:2349-2356 40.
Levy R,Dubois B: Apathy and the functional anatomy of the prefrontal cortex-basal
ganglia circuits. Cerebral cortex 2006; 16:916-928 41.
Menon V,Uddin LQ: Saliency, switching, attention and control: a network model of
insula function. Brain structure & function 2010; 214:655-667
16
42.
Sridharan D, Levitin DJ,Menon V: A critical role for the right fronto-insular cortex in
switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences of the United States of America 2008; 105:12569-12574 43.
Supekar K,Menon V: Developmental maturation of dynamic causal control signals in
higher-order cognition: a neurocognitive network model. PLoS computational biology 2012; 8:e1002374 44.
Tang WK, Wong LK, Mok VC, et al: Apathy after stroke: potential risk factors and
magnetic resonance imaging markers. Hong Kong medical journal = Xianggang yi xue za zhi 2018; 24 Suppl 3:18-20 45.
Yuen GS, Bhutani S, Lucas BJ, et al: Apathy in late-life depression: common, persistent,
and disabling. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2015; 23:488-494 46.
Pimontel MA, Kanellopoulos D,Gunning FM: Neuroanatomical Abnormalities in Older
Depressed Adults With Apathy: A Systematic Review. Journal of geriatric psychiatry and neurology 2019; 891988719882100 47.
Yuen GS, Gunning-Dixon FM, Hoptman MJ, et al: The salience network in the apathy of
late-life depression. International journal of geriatric psychiatry 2014; 29:1116-1124 48.
Alexopoulos GS, Hoptman MJ, Yuen G, et al: Functional connectivity in apathy of late-
life depression: a preliminary study. Journal of affective disorders 2013; 149:398-405 49.
Yuen GS, Gunning FM, Woods E, et al: Neuroanatomical correlates of apathy in late-life
depression and antidepressant treatment response. Journal of affective disorders 2014; 166:179186 50.
Kiosses DN, Klimstra S, Murphy C, et al: Executive dysfunction and disability in elderly
patients with major depression. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2001; 9:269-274
17
51.
Alexopoulos GS, Kiosses DN, Heo M, et al: Executive dysfunction and the course of
geriatric depression. Biological psychiatry 2005; 58:204-210 52.
Alexopoulos GS, Manning K, Kanellopoulos D, et al: Cognitive control, reward-related
decision making and outcomes of late-life depression treated with an antidepressant. Psychological medicine 2015; 45:3111-3120 53.
Sheline YI, Pieper CF, Barch DM, et al: Support for the vascular depression hypothesis
in late-life depression: results of a 2-site, prospective, antidepressant treatment trial. Archives of general psychiatry 2010; 67:277-285 54.
Kant R,Smith-Seemiller L: Assessment and treatment of apathy syndrome following head
injury. NeuroRehabilitation 2002; 17:325-331 55.
Whyte EM, Lenze EJ, Butters M, et al: An open-label pilot study of acetylcholinesterase
inhibitors to promote functional recovery in elderly cognitively impaired stroke patients. Cerebrovascular diseases 2008; 26:317-321 56.
Villa RF, Ferrari F,Moretti A: Post-stroke depression: Mechanisms and pharmacological
treatment. Pharmacology & therapeutics 2018; 184:131-144 57.
Montoya-Murillo G, Ibarretxe-Bilbao N, Pena J, et al: Effects of Cognitive Rehabilitation
on Cognition, Apathy, Quality of Life, and Subjective Complaints in the Elderly: A Randomized Controlled Trial. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2019; 58.
Morimoto SS, Gunning FM, Wexler BE, et al: Executive Dysfunction Predicts Treatment
Response to Neuroplasticity-Based Computerized Cognitive Remediation (nCCR-GD) in Elderly Patients with Major Depression. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2016; 24:816-820 59.
Morimoto SS, Wexler BE,Alexopoulos GS: Neuroplasticity-based computerized
cognitive remediation for geriatric depression. International journal of geriatric psychiatry 2012; 27:1239-1247 18
60.
Arean PA, Raue P, Mackin RS, et al: Problem-solving therapy and supportive therapy in
older adults with major depression and executive dysfunction. The American journal of psychiatry 2010; 167:1391-1398 61.
Alexopoulos GS, Raue PJ, Kiosses DN, et al: Problem-solving therapy and supportive
therapy in older adults with major depression and executive dysfunction: effect on disability. Archives of general psychiatry 2011; 68:33-41 62.
Alexopoulos GS,Arean P: A model for streamlining psychotherapy in the RDoC era: the
example of 'Engage'. Molecular psychiatry 2014; 19:14-19 63.
Alexopoulos GS, Raue PJ, Gunning F, et al: "Engage" Therapy: Behavioral Activation
and Improvement of Late-Life Major Depression. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2016; 24:320-326 64.
Golding K, Fife-Schaw C,Kneebone I: Twelve month follow-up on a randomised
controlled trial of relaxation training for post-stroke anxiety. Clinical rehabilitation 2017; 31:1164-1167 65.
Golding K, Fife-Schaw C,Kneebone I: A pilot randomized controlled trial of self-help
relaxation to reduce post-stroke depression. Clinical rehabilitation 2018; 32:747-751 66.
Robinson RG,Jorge RE: Post-Stroke Depression: A Review. The American journal of
psychiatry 2016; 173:221-231
Table 1. Clinical and Demographic Characteristics of the study sample (N = 135) Mean (SD) Age, years
19
70.1 (11.2)
Education, years
15.7 (3.6)
Female, % (n)
59%
Living alone % (n)
36%
On Antidepressant Medication %
56%
MADRS*
23.2 (4.0)
Duration of Depressive Episode (Months)
17.5 (23.6)
Number of Previous Depressive Episodes
3.4 (10.8)
Adequate Effort % (RDS**)
86%
*MADRS=Montgomery Aspberg Depression Rating Scale **RDS=Reliable Digit Span; adequate effort defined as RDS>6
Table 2. Baseline Neuropsychological Characteristics by Cognitive Domain after Multiple Imputation N=135.
Pooled Descriptive Statistics
multiple imputation)
zscore
z-score SD
Global Cognition
-0.5
1.3
Neuropsychological Domains (missing data prior to
Mean
SD
MMSE* Total (2 missing)
27.2
2.7
-0.5
1.9
Dementia Rating Scale (DRS) Total (24 missing)
129.2
13.1
-0.6
1.2
20
Attention
0.3
0.7
WAIS-III+ Forward Digit Span (25 missing)
6.3
1.5
0.1
1.1
DRS Attention Total (14 missing)
35.5
2.5
0.4
0.8
-0.4
0.6
Executive Function DRS Initiation/Perseveration (19 missing)
31.5
6.1
-0.8
1.1
DRS Conceptualization (13 missing)
34.6
4.1
-0.2
0.9
Stroop Color/Word Interference++(30 missing)
2.7
6.6
0.3
0.7
WAIS-III Backward Digit Span (25 missing)
4.0
1.2
-0.3
1.0
Controlled Oral Word Association Test (FAS) Total
28.6
13.3
-1.0
1.1
-1.5
0.7
(14 missing) Processing Speed Stroop Word Total++(26 missing)
84.0
15.5
-1.2
0.7
Stroop Color Total++(30 missing)
53.5
12.6
-1.7
0.7
-0.6
0.8
-0.6
0.8
-1.5
1.3
-1.5
1.3
Visuoconstruction DRS Construction (20 missing)
5.2
1.4
Immediate Memory (Learning) Hopkins Verbal Learning Test- Revised (HVLT-R) Immediate Recall (11 missing)
21
19.3
6.2
Delayed Memory (Retention)
-0.9
1.1
HVLT-R Delayed Recall (14 missing)
5.8
3.3
-1.6
1.5
DRS Memory (13 missing)
22.4
3.1
-0.2
1.2
-0.8
1.2
-0.5
1.3
Language Animal Naming Test Total (12 missing)
15.4
5.5
*MMSE=MiniMental State Examination +Wechsler Adult Intelligence Scales – Third Edition ++Age Corrected raw score
Table 3. Principal Factor Analysis Pattern Matrix MADRS Item Factor (factor items in bold) Factor 1
Factor 2
Factor 3
Sadness
Distress
Apathy
Apparent Sadness
0.57
-0.03
0.20
Reported Sadness
0.72
0.07
0.03
Pessimistic Thoughts
0.30
0.30
-0.03
Suicidal Thoughts
0.22
-0.01
-0.07
Reduced appetite
0.12
-0.72
0.00
Concentration difficulties
-0.26
0.47
0.17
Inner Tension
0.08
0.39
-0.07
Lassitude
-0.02
0.00
0.31
Inability to feel
0.14
-0.04
0.70
Items
22
Table 4. Pooled Spearman correlations between MADRS Subgroups and Neuropsychological Domains (N = 135)
Neuropsychological Domains
Sadness
Distress
Apathy
Global Cognition
-0.00
0.04
-0.34**
Attention
-0.04
0.00
-0.11
Executive Function
-0.00
0.16
-0.20*
Processing Speed
0.13
0.12
-0.00
Visuoconstruction
-0.10
0.02
-0.08
Immediate Memory
-0.07
0.08
-0.30**
Delayed Memory
-0.05
0.15
-0.23**
Language
0.11
0.07
-0.22*
* Correlation is significant at the p<0.05 level (two-tailed) ** Correlation is significant at the p<0.01 level
23