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. Author manuscript; available in PMC: 2022 Dec 1.
Published in final edited form as: J Affect Disord. 2021 Aug 24;295:298–304. doi: 10.1016/j.jad.2021.08.019

Age moderates the relationship between affective response inhibition and bipolar disorder in adults

Sarah Rose Slate 1, Jessica N Busler 1,2, Pamela B Mahon 1,*, Katherine E Burdick 1,*,+
PMCID: PMC8577218  NIHMSID: NIHMS1737915  PMID: 34482062

Abstract

Background:

Patients with bipolar disorder (BD) often have impairments in neurocognition, including affective processing and affective response inhibition. While studies suggest that cognitive control in general may decline with age in BD, less is known about age-related changes in response inhibition to emotionally salient information.

Methods:

258 participants with BD and 54 healthy controls, ages 18–70, completed the Cambridge Neuropsychological Test Automated Battery (CANTAB) Affective Go/No-Go task to assess affective response inhibition to positive and negative valenced stimuli. We examined the relationship between BD and affective response inhibition (number of commission and omission errors and reaction time), as well as a potential moderating effect of age, using mixed effects linear regression models.

Results:

The BD group made more omission and commission errors overall than the control group (p<0.018). We observed a significant 3-way group-by-age-by-valence interaction for reaction time (p=0.006). Within BD, a slower reaction time to negative than positive stimuli was found in middle and older age groups (p<0.012), but not in the younger age group. No significant moderating effect of age was observed within the control group.

Conclusions:

These cross-sectional findings indicate that compared with healthy controls, individuals with BD display differential and age-related effects in inhibition to emotionally salient information that is valence-dependent. The observed pattern of a switch in bias from negative to positive stimuli with age in BD may aid in our understanding of the progression of neurocognitive changes with aging in BD, as well as inform targeted treatments for cognitive symptoms.

Keywords: bipolar disorder, affective response inhibition, aging, neurocognition

INTRODUCTION

Bipolar Disorder (BD) is a common and debilitating disorder (Ferrari et al., 2016) Up to 40% of patients with BD show broad neurocognitive deficits in domains including attention. (Burdick et al., 2011). These deficits persist during affective remission (Gopin et al., 2011; Gruber et al., 2007) and contribute significantly to functional disability (Burdick et al., 2015). While much is known about neurocognitive deficits in BD, the trajectory of this dysfunction with aging remains unclear (van Rheenen et al., 2019).

In healthy aging, neurocognitive functioning declines over time, including in the domain of attention (Harada et al., 2013; Hedden & Gabrieli, 2004). Adults with BD also show increased cognitive decline over the course of the illness (Cardoso et al., 2015) though it is less clear whether those with BD show a more accelerated decline in cognition with aging than healthy controls. Although some longitudinal studies following older adults suggest a more accelerated cognitive decline in patients with BD compared with healthy controls (Dhingra & Rabins, 1991; Gildengers et al., 2009; Torrent et al., 2012), evidence is not consistent (Delaloye et al., 2011; Schouws et al., 2012). Several cross-sectional studies looking at both younger and older adults have shown steeper age-related declines in processing speed and cognitive flexibility in patients with BD as compared to controls (Dev et al., 2017; Lewandowski et al., 2014; Seelye et al., 2019). In addition, a history of BD may be associated with an increased risk of dementia compared to other psychiatric disorders and controls (Diniz et al., 2017). Taken together, these studies suggest differential cognitive decline in patients with BD compared with healthy controls.

One neurocognitive domain of particular interest in BD is affective processing, which involves the cognitive processing of emotionally relevant stimuli in order to generate a response (Elliott et al., 2011). Patients with BD often display impaired affective processing, and these deficits can negatively impact social and interpersonal functioning (Johnson et al., 2016; Miskowiak et al., 2019). Affective response inhibition, a subset of affective processing, involves the ability to attend to emotionally salient information while inhibiting a response to an irrelevant distractor stimulus (Murphy & Sahakian, 2001). Individuals with mood disorders have shown deficits in this type of inhibitory control, as well as response biases to information related to their affective state (Mathews & MacLeod, 2005). Research on cognitive bias modification (CBM), a process designed to experimentally modify attentional response to emotional stimuli, has further illustrated the relationship between information processing biases and psychopathology (Koster et al., 2009). Indeed, in pediatric BD, patients have been shown to exhibit more response errors to both positive and negative emotional stimuli on response inhibition tasks than healthy children (Bauer et al., 2015; Seymour et al., 2015). Adults with BD also display deficits in affective response inhibition, showing biases towards negatively valenced stimuli and difficulty in processing positive information (Brand et al., 2012; Gopin et al., 2011; Wessa et al., 2007) as well as slower reaction times to emotional stimuli than healthy adults (Bauer et al., 2016).

Recent research has begun to look at affective processing in BD as a function of age. One study (Weisenbach et al., 2014) compared young and older-middle-aged BD participants on several cognitive domains and found that young adults with BD performed similarly to age-matched controls on an emotion processing task; however, older middle-aged adults with BD were impaired relative to age-matched controls. These results, although cross-sectional, indicate a possible aging-related accelerated decline in affective processing in BD patients. However, another study reported performance on an emotional response inhibition task was similar in older adults with BD and age-matched controls (Bauer et al., 2018).

In contrast to what may be considered an age-related decline in BD patients on some aspects of affective processing, there is evidence that healthy older adults’ performance may actually improve on tasks involving processing emotionally positive salient information. A “positivity effect” is seen in normal aging, wherein older adults increasingly show a processing bias towards positive versus negative information (Mather & Carstensen, 2005). The purpose of the current study was to examine age-related differences in affective processing in a large sample of participants with BD, across a broad range of ages (18–70). We hypothesized that age-related effects in affective processing would differ between participants with BD and healthy controls, and specifically that participants with BD would not exhibit the same shift in bias from negative stimuli towards positive stimuli that has been observed in healthy controls. Understanding changes in affective processing with aging in BD may help inform targeted treatment strategies.

METHODS

Participants

258 participants with Bipolar Disorder (BD), ages 18–70, were recruited at Icahn School of Medicine at Mount Sinai from 2013–2018. 54 healthy controls were recruited using the same methods, for comparison and to assist with interpretation of the findings in BD. All procedures were approved by the Institutional Review Board and written informed consent was obtained from all participants.

Inclusion criteria for BD participants were: 1) diagnosis of BD I or BD II from the Structured Clinical Interview for DSM-IV (SCID-IV) (First et al., 2002), 2) 18–70 years of age, and 3) affectively stable, defined as an outpatient not in an acute episode. The inclusion criteria for control participants were: 1) no evidence of an Axis I disorder and 2) 18–70 years of age. Exclusion criteria for all participants were: 1) history of central nervous system trauma, neurological disorder, or attention-deficit hyperactivity disorder, 2) recent substance use/dependence disorder (past three months), 3) electroconvulsive therapy (ECT) in the past 12 months, 4) active, unstable medical problem, and 5) estimated premorbid IQ < 70 (from the Wide Range Achievement Test-3rd edition [WRAT-3] Reading (Wilkinson & Robertson, 2006)).

Clinical Assessment

Highly trained clinical coordinators and postdoctoral fellows administered the SCID-IV, which was used to assess: DSM-IV BD diagnosis (or absence of Axis I diagnosis in controls), BD subtype (I vs II), presence of lifetime psychotic features, length of illness, number of prior episodes, and psychiatric medication use. Manic and depressive symptoms were assessed by the Clinician Administered Rating Scale for Mania (CARS-M) (Altman et al., 1994) and the Hamilton Rating Scale for Depression (HRSD) (Hamilton, 1960). Clinical ratings were administered no more than one week prior to completion of the affective processing task.

Affective Response Inhibition Task

Participants completed the Cambridge Neuropsychological Test Automated Battery Affective Go/No-Go task (CANTAB AGN: https://www.cambridgecognition.com/cantab/cognitive-tests/affective-go-no-go-agn/) to assess affective response inhibition. The task consists of words presented in the center of a screen, with each word having positive, negative or neutral valence. Participants are asked to respond as quickly as possible to target words with a given valence (e.g. positive) and to not respond to distractor words with either of the other two valences (e.g. negative or neutral). Each word is displayed for 300ms, with an inter-word interval of 900ms. There are 2 practice blocks followed by 18 task blocks, comprised of 6 blocks for each target valence (positive, negative, neutral). In each block, 9 target words and 9 distractor words are presented.

Affective response inhibition was measured by number of omission errors (non-responses to target word), commission errors (responses to distractor word), and reaction time (RT) in milliseconds (ms). Of the participants recruited, a total of 204 participants with BD completed the CANTAB AGN and are included in the analysis. One control participant with average reaction time of <200ms was dropped from analysis, for a total of 53 controls included in the analysis.

Data Analysis

We tested group differences on demographic and clinical variables using t-tests for continuous variables and chi-square tests for dichotomous variables. To assess consistency of our data with findings reported in the literature, the relationship between group (BD, control) and each outcome measure (omission, commission, reaction time) was assessed using mixed effects linear regression models, with target valence (positive, negative) as a within-subject repeated measure and group (BD, control) as a between-subjects measure. All analyses were adjusted for sex, WRAT premorbid IQ, and years of education. For our primary analyses of interest, a potential effect of age as moderating the relationship between group and performance on the task was tested in a three-way interaction (age × group × valence). When this three-way interaction was significant, post hoc tests of two-way interactions and pairwise comparisons were conducted. Joint tests of interaction effects and main effects were examined using chi-square tests. Nominal p-values are presented throughout. Significance was set to p<0.016 using Bonferroni correction to account for testing 3 outcome measures (omission, commission, reaction time). Results with 0.016<p<0.05 were considered to have trend-level significance. All analyses were performed in STATA 14.

RESULTS

Participants

A total of 203 participants with BD and 53 controls completed the CANTAB Affective Go/No-Go task and were included in the analyses. Demographic and clinical features of the study groups are shown in Table 1. Relative to the control group, the BD group was an average of 4.5 years older (t252=−2.41, p=0.017), had fewer years of education (t252=2.99, p=0.003), lower estimated premorbid IQ (t252=2.01, p=0.045) and were more likely to be white (χ2=6.63, p=0.010). The BD and control groups were similar with regard to sex distribution (χ2=0.23, p=0.629). As expected, the BD group endorsed significantly more symptoms of depression (t251=−7.79, p<.001) and mania (t251=−5.21, p<.001) than the control group, despite relative affective stability. Psychotropic medications were being taken by 79% of BD participants.

Table 1.

Participant characteristics, by diagnosisa

BD (N=203) Controls (N=53) Statistical test, p-value
Sex (% female) 50.99% 54.72% χ2=0.23, p=0.629
Age (yrs) 43.60 (11.90) 39.06 (13.46) t252=−2.41, p=0.017
Race (% white) 48.02% 28.30% χ2=6.63, p=0.010
Education (yrs) 14.35 (2.63) 15.49 (1.75) t252=2.99, p=0.003
WRAT IQ 102.33 (13.55) 106.47 (12.40) t252=2.01, p=0.045
HRSD 5.89 (4.9898) 0.51 (1.17) t251=−77.79, p<.001
CARS-M, mania subscale 3.09 (3.7373) 0.23 (0.78) t251=−55.21, p<.001
Psychotropic medication (% yes) 78.71% -- --
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a

Results are presented as mean(s.d.) or %. BD=bipolar disorder, WRAT=Weschler’s Reading Aptitude Test, HRSD=Hamilton Rating Scale for Depression, CARS-M=Clinician Administered Rating Scale for Mania

Age

Participants were divided into three age cohorts (young, middle and older adult) by cutting age into tertiles. The three age cohorts were similar with regard to years of education (F2,251=0.95, p=0.387), proportion of participants who were white (χ2=0.53, p=0.768) and symptoms of mania (F2,250=0.42, p=0.656). There was a significant difference by age group on sex (χ2=10.60, p=0.005), premorbid IQ (F2,250=4.38, p=0.014) and depressive symptoms (F2.250=3.70, p=0.026). Post-hoc testing indicated the older age cohort comprised a larger proportion of males than the other two age groups combined (χ2=10.58, p=0.001), while the younger age cohort had a higher mean estimated premorbid IQ (t251=2.95, p=0.004) and fewer symptoms of depression (t251=−2.06, p=0.041).

CANTAB AGN

Reaction Time

There was no significant main effect of either valence (χ12=1.28, p=0.258) or diagnosis (χ12=0.04, p=0.835) on reaction time and the two-way interaction between diagnosis and valence was also non-significant (χ12=1.05, p=0.307).

Age

There was a main effect of age on reaction time (χ22=9.48, p=0.009). The three-way interaction between age (young, middle and older adult tertiles), valence, and diagnosis was significant for reaction time (χ22=10.32, p=0.006; Figure 2). Follow-up investigation of this three-way interaction showed that the two-way interaction between valence and age was significant within BD (χ22 = 11.64, p=0.003), but not within controls (χ22 = 3.18, p=0.204). Pairwise comparisons within BD showed a significantly slower reaction time to negative than positive targets in the middle (χ12 = 8.70, p=0.003) and older adult tertiles (χ12 = 6.35, p=0.012), but not in the younger adult tertile (χ12 = 2.34, p=0.126). Results were similar considering age as a continuous variable.

Figure 2.

Figure 2.

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Three-way interaction between age, diagnosis and valence (p=0.006). Shown are adjusted means and standard errors for reaction time in milliseconds (ms) by valence and age tertile for a) BD and b) controls. *p<0.012

Depressive and Manic Symptoms

To better understand the findings related to age and reaction times in BD, we examined post hoc correlations between current depressive and manic symptom scores and reaction times. Among participants with BD, there was no observed significant correlation between either depressive or manic symptom scores and reaction time to either negative or positive valenced targets overall or in any of the three age cohorts (r=−0.19 – 0.05, p>0.078).

Omission Errors (non-responses to target word)

We observed a trend for a main effect of diagnosis, such that participants with BD made more omission errors overall than control participants (χ12=5.56, p=0.018; Figure 1b). We did not detect a significant main effect of valence (χ12=3.10, p=0.078) or of a two-way interaction between diagnosis and valence (χ12=0.33, p=0.564). In the model considering age for omission errors, there was no evidence of a main effect of age (χ22=1.03, p=0.597), two-way interaction between age and diagnosis (χ22 =0.27, p=0.876), two-way interaction between age and valence (χ22 =0.39, p=0.825), or three-way interaction between age, diagnosis and valence (χ22 =0.35, p=0.838).

Figure 1.

Figure 1.

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Affective go/no-go performance, by diagnosis and valence for a) mean reaction time, b) mean number of omission errors, and c) mean number of commission errors. Shown are adjusted means and standard errors.

Commission Errors (responses to distractor word)

Results showed a main effect of diagnosis on commission errors, such that participants with BD made more commission errors overall than control participants (χ12 =6.12, p=0.013; Figure 1c). The main effect of valence was non-significant (χ12 =2.89, p=0.089), as was the two-way interaction between diagnosis and valence (χ12 =0.29, p=0.588). For the model investigating age, there was no significant main effect of age (χ22 =0.79, p=0.672), two-way interaction of age and diagnosis (χ22 =1.47, p=0.481), two-way interaction of age and valence (χ22 =0.18, p=0.9136), or three-way-diagnosis-valence interaction for commission errors (χ74692).

DISCUSSION

In this study, we examined age-related differences in affective processing in a large sample of participants with BD compared to controls across the adult lifespan. We found that BD participants were impaired on an affective go/no-go task, making more commission and omission errors than the control group, consistent with previous literature (Gopin et al., 2011). We further observed an age-dependent shift in bias towards positive targets within individuals with BD such that middle-aged and older adults with BD responded more quickly to positive than to negative stimuli, a difference not seen in younger adults with BD. These results support an age-related shift away from the frequently reported negative attentional bias in BD, and are in contrast with our a priori hypothesis. These findings are consistent with a “positivity effect” in BD similar to that commonly reported in healthy aging (Reed & Carstensen, 2012).

In healthy aging, studies have shown a positivity effect in multiple domains of information processing. An early study using a dot-probe visual attention paradigm showed that older adults looked towards positive faces and away from negative faces more than younger adults (Mather & Carstensen, 2005). Other studies have found the positivity effect in selective attention and memory using eye tracking and word recognition tasks (Reed et al., 2014; Sasse et al., 2014). These findings are in contrast to a documented negativity bias in healthy young people (Baumeister et al., 2001), as well as a bias towards negatively valenced stimuli seen in cognitive processing in BD and other mood disorders. Thus, results of the current study showing significant differences in processing of positive and negative affective stimuli in middle age and older adults as compared to younger adults may reflect an aging-related shift away from negative emotional bias and towards a positive emotional bias in BD (Bauer et al., 2016; Brand et al., 2012; Murphy & Sahakian, 2001).

Our results build on those from a few previous studies that examined affective processing and aging in BD. A recent pilot study using the same task as the current study in older adults with BD and healthy controls did not detect a group difference on either reaction time or accuracy on positive or negative stimuli, which could be consistent with a normal aging pattern of performance (e.g. a shift away from a negativity bias in older age in BD (Bauer et al., 2018)). However, that study did not include comparison groups of younger or middle-aged adults and so did not explicitly examine aging-related changes in affective response inhibition. In contrast, in a study of disease burden on cognition in young adults and older adults with and without BD utilizing an emotion processing task that required participants to rate and judge facial and auditory affective stimuli, older adults with BD performed worse than age-matched healthy controls (Weisenbach et al., 2014). However, that study did not report on the effect of the valence of the stimuli and so the presence or absence of a potential positivity effect could not be assessed. The observation of a positivity effect with aging in BD may depend on the type of measure used to assess affective processing as positivity effects have been shown to be stronger during tasks requiring implicit rather than explicit processing of emotionally salient information (Reed et al., 2014). In the current study, the task involved aspects of both explicit and implicit affective processing, with inhibition of emotionally salient information, as the goal was to select emotionally valenced words consistent with a target valence category whilst inhibiting response to competing stimuli. It has also been suggested that the strength of the positivity effect may be larger in studies including a wider age range of participants, as the effect may be observed as a gradual shift towards positivity with aging (Reed et al., 2014). In our study, we included a broad range of adults aged 18–70. Thus, our results build on previous findings by utilizing an affective response inhibition task in a sample with a broad age range and by showing a valence-specific effect in the group with BD.

The observed age-related changes in affective processing in BD may also be understood in the context of underlying neuropathological changes with aging in key regions involved in affective processing including fronto-limbic and cingulate cortices. In a large meta-analysis of structural imaging, the ENIGMA consortium reported age-related reduced surface area in the posterior cingulate cortex and reduced volume of hippocampus (Hibar et al., 2016, 2018). Moreover, a progressive loss of grey matter with aging in the hippocampus, fusiform gyrus, and cerebellum has been observed in BD compared to controls, and these changes have been shown to be associated with a decline in cognitive functioning (Moorhead et al., 2007). Disrupted activation of fronto-limbic regions in response to emotion processing and emotion regulation paradigms is evident in BD, as well as dysfunction in resting state functional connectivity in these regions, though the relationship with aging is less clear (Syan et al., 2018; Townsend et al., 2012). In healthy aging, and partly in contrast to age-related cognitive decline, evidence suggests that emotion regulation processes in older compared to younger adults may be enhanced by differential recruitment of prefrontal cortex regions, including increased brain connectivity from the dorsocaudal anterior cingulate cortex to the ventromedial prefrontal and orbitofrontal cortices, and may potentially underlie the positivity effect (Allard & Kensinger, 2014). Gaining a better understanding of how brain networks involved in affective response inhibition change with age in BD could help detect differences in neurofunctional adaptations across the lifespan that underly shifts in affective processing bias. Such information can aid in identifying critical periods for interventions, such as affective bias training or cognitive behavior therapy to enhance the use of explicit emotion regulation strategies towards more favorable biases such as a positivity bias, in order to improve outcomes.

There are several limitations to the current study. First, our healthy control group was relatively small, which may have contributed to our not being able to detect a significant positivity bias with aging within the control group, as might have been expected. Second, results from this single site study may not be generalizable and would benefit from replication in additional populations. Third, the cross-sectional design limited our ability to directly assess changes over time. Although we assessed a broad range of ages in order to examine group differences in affective processing at different ages, longitudinal studies are needed to examine patterns of within-individual changes in affective response inhibition with aging in BD.

Taken together, our data support an age-related shift that reflects a change in bias towards positive over negative stimuli in mid-to-late life, an effect that is seen in our BD cohort despite the presence of a recurrent major mood disorder.

Highlights.

  • Individuals with BD display differential and age-related effects in inhibition to emotionally salient information that is valence-dependent

  • There is a switch in bias from negative to positive stimuli with age in BD

ROLE OF THE FUNDING SOURCE

This work was supported by grants from the National Institutes of Health R01MH100125 (KEB) and R01MH110797 (PBM).

Footnotes

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CONFLICT OF INTEREST

All authors declare no conflict of interest.

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