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Reduced willingness to expend effort for rewards is associated with risk for conversion and negative symptom severity in youth at clinical high-risk for psychosis

Published online by Cambridge University Press:  14 June 2021

Gregory P. Strauss ,
Lisa A. Bartolomeo  and
Lauren Luther
Gregory P. Strauss*
Affiliation:
Department of Psychology, University of Georgia, Athens, GA, USA
Lisa A. Bartolomeo
Affiliation:
Department of Psychology, University of Georgia, Athens, GA, USA
Lauren Luther
Affiliation:
Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
*
Author for correspondence: Gregory P. Strauss, E-mail: gstrauss@uga.edu
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Abstract

Background

Schizophrenia (SZ) is typically preceded by a prodromal (i.e. pre-illness) period characterized by attenuated positive symptoms and declining functional outcome. Negative symptoms are prominent among individuals at clinical high-risk (CHR) for psychosis (i.e. those with prodromal syndromes) and predictive of conversion to illness. Mechanisms underlying negative symptoms are unclear in the CHR population.

Methods

The current study evaluated whether CHR participants demonstrated deficits in the willingness to expend effort for rewards and whether these impairments are associated with negative symptoms and greater risk for conversion. Participants included 44 CHR participants and 32 healthy controls (CN) who completed the Effort Expenditure for Reward Task (EEfRT).

Results

Compared to CN, CHR participants displayed reduced likelihood of exerting high effort for high probability and magnitude rewards. Among CHR participants, reduced effort expenditure was associated with greater negative symptom severity and greater probability of conversion to a psychotic disorder on a cross-sectional risk calculator.

Conclusions

Findings suggest that effort-cost computation is a marker of illness liability and a transphasic mechanism underlying negative symptoms in the SZ spectrum.

Keywords

Negative symptomsprodromereward processingschizophrenia

Information

Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 3 , February 2023 , pp. 714 - 721
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Introduction

Schizophrenia (SZ) is a leading medical cause of functional disability in the world and is associated with extremely high public healthcare costs (e.g. annual cost of ~$94–102 billion in the United States alone) (Charlson et al., Reference Charlson, Ferrari, Santomauro, Diminic, Stockings, Scott and Whiteford2018; Chong et al., Reference Chong, Teoh, Wu, Kotirum, Chiou and Chaiyakunapruk2016). Given that few individuals achieve functional or symptomatic recovery after the onset of SZ (Harrow, Grossman, Jobe, & Herbener, Reference Harrow, Grossman, Jobe and Herbener2005), there has been increased interest in the early identification and prevention of psychosis (Fusar-Poli et al., Reference Fusar-Poli, Bonoldi, Yung, Borgwardt, Kempton, Valmaggia and McGuire2012, Reference Fusar-Poli, Borgwardt, Bechdolf, Addington, Riecher-Rössler, Schultze-Lutter and Seidman2013, Reference Fusar-Poli, Tantardini, De Simone, Ramella-Cravaro, Oliver, Kingdon and Millan2017; Fusar-Poli, Yung, McGorry, & Van Os, Reference Fusar-Poli, Yung, McGorry and Van Os2014). SZ is often preceded by a prodromal (i.e. pre-illness) period that is characterized by attenuated positive symptoms and gradually worsening functional outcome that serves as a window for identifying mechanisms related to illness onset (Beiser, Erickson, Fleming, & Iacono, Reference Beiser, Erickson, Fleming and Iacono1993; Yung et al., Reference Yung, McGorry, McFarlane, Jackson, Patton and Rakkar1996). It is now possible to identify youth with prodromal syndromes using structured clinical interviews; however, it is unclear which pathophysiological processes are most predictive of psychosis risk (Fusar-Poli et al., Reference Fusar-Poli, Tantardini, De Simone, Ramella-Cravaro, Oliver, Kingdon and Millan2017). As such, there is a pressing need to develop alternative approaches to identifying pathophysiological mechanisms, which may serve as novel targets for early identification and prevention.

The current study aimed to address this gap in the literature by examining mechanisms related to negative symptoms. There are several reasons to suspect that a focus on mechanisms underlying negative symptoms may lead to the identification of novel treatment targets. First, negative symptoms are highly prevalent during the prodromal stage, with 82% of clinical high-risk (CHR) participants displaying clinically significant negative symptoms in the NAPLS study (Piskulic et al., Reference Piskulic, Addington, Cadenhead, Cannon, Cornblatt, Heinssen and Walker2012). Second, negative symptoms are often a key factor that initially motivates individuals at CHR and their families to come into contact with the medical system and pursue treatment (Addington & Heinssen, Reference Addington and Heinssen2012). They are also one of the earliest markers of psychosis risk, often occurring years prior to the emergence of attenuated positive symptoms (Carrión et al., Reference Carrión, Demmin, Auther, McLaughlin, Olsen, Lencz and Cornblatt2016; Häfner, Löffler, Maurer, Hambrecht, & Heiden, Reference Häfner, Löffler, Maurer, Hambrecht and Heiden1999). This suggests that determining mechanisms underlying negative symptoms may facilitate the goal of identifying individuals at the earliest phase of the prodromal period so they can be monitored and prevention efforts can be applied (McGorry, Hickie, Yung, Pantelis, & Jackson, Reference McGorry, Hickie, Yung, Pantelis and Jackson2006; McGorry, Nelson, Goldstone, & Yung, Reference McGorry, Nelson, Goldstone and Yung2010). Third, negative symptoms predict conversion to psychosis (Demjaha, Valmaggia, Stahl, Byrne, & McGuire, Reference Demjaha, Valmaggia, Stahl, Byrne and McGuire2010; Johnstone, Ebmeier, Miller, Owens, & Lawrie, Reference Johnstone, Ebmeier, Miller, Owens and Lawrie2005; Lencz, Smith, Auther, Correll, & Cornblatt, Reference Lencz, Smith, Auther, Correll and Cornblatt2004; Piskulic et al., Reference Piskulic, Addington, Cadenhead, Cannon, Cornblatt, Heinssen and Walker2012; Schlosser et al., Reference Schlosser, Jacobson, Chen, Sugar, Niendam, Li and Cannon2012; Valmaggia et al., Reference Valmaggia, Stahl, Yung, Nelson, Fusar-Poli, McGorry and McGuire2013; Yung & McGorry, Reference Yung and McGorry1996) and deterioration in functional outcome (Corcoran et al., Reference Corcoran, Kimhy, Parrilla-Escobar, Cressman, Stanford, Thompson and Malaspina2011; Meyer et al., Reference Meyer, Carrión, Cornblatt, Addington, Cadenhead and Cannon2014; Pelletier-Baldelli, Strauss, Visser, & Mittal, Reference Pelletier-Baldelli, Strauss, Visser and Mittal2017; Schlosser et al., Reference Schlosser, Jacobson, Chen, Sugar, Niendam, Li and Cannon2012; Valmaggia et al., Reference Valmaggia, Stahl, Yung, Nelson, Fusar-Poli, McGorry and McGuire2013) among CHR individuals. These studies suggest that an increased understanding of mechanisms underlying negative symptoms in CHR youth may lead to the identification of novel treatment targets that could facilitate early intervention and prevention efforts. Unfortunately, almost no studies have examined processes underlying negative symptoms in the CHR population.

Several conceptual models of negative symptoms have been developed for SZ, which may guide exploration of negative symptom mechanisms in CHR participants. These models propose that negative symptoms result from dysfunctional cortico-striatal interactions that lead to a series of reward processing deficits that impede decision-making processes needed to generate motivated behavior (Barch & Dowd, Reference Barch and Dowd2010; Kring & Barch, Reference Kring and Barch2014; Strauss, Waltz, & Gold, Reference Strauss, Waltz and Gold2013). Paramount among these reward processing mechanisms is effort-cost computation (i.e. determining whether the benefits associated with an action outweigh the costs needed to obtain them) (Strauss et al., Reference Strauss, Waltz and Gold2014). The mechanisms underlying effort-cost computation dysfunction are well-delineated in the translational neuroscience literature. Numerous rodent studies implicate dopaminergic dysfunction, demonstrating that focal dopamine depletion in the nucleus accumbens results in reduced willingness to exert high effort for higher value rewards (Cousins & Salamone, Reference Cousins and Salamone1994; Salamone, Correa, Farrar, Nunes, & Pardo, Reference Salamone, Correa, Farrar, Nunes and Pardo2009; Salamone, Cousins, & Bucher, Reference Salamone, Cousins and Bucher1994). Additionally, increasing dopamine via amphetamine administration reverses this pattern and results in increased likelihood of performing high effort behaviors for high value rewards (Bardgett, Depenbrock, Downs, Points, & Green, Reference Bardgett, Depenbrock, Downs, Points and Green2009). Similar findings have been demonstrated in humans, such that d-amphetamine administration increases effortful behavior and individual differences in dopamine release predict willingness to exert high effort for high value rewards (Treadway et al., Reference Treadway, Buckholtz, Cowan, Woodward, Li, Ansari and Zald2012; Wardle, Treadway, Mayo, Zald, & de Wit, Reference Wardle, Treadway, Mayo, Zald and de Wit2011). The anterior cingulate cortex (ACC) has also been found to be critical to willingness to work for rewards, with studies demonstrating that ACC structural and functional abnormalities predict willingness to expend effort for higher value rewards (Croxson, Walton, O'Reilly, Behrens, & Rushworth, Reference Croxson, Walton, O'Reilly, Behrens and Rushworth2009; Endepols et al., Reference Endepols, Sommer, Backes, Wiedermann, Graf and Hauber2010; Prévost, Pessiglione, Météreau, Cléry-Melin, & Dreher, Reference Prévost, Pessiglione, Météreau, Cléry-Melin and Dreher2010; Walton et al., Reference Walton, Groves, Jennings, Croxson, Sharp, Rushworth and Bannerman2009). Adults in the chronic and first-episode stages of SZ have consistently been shown to have deficits in willingness to work for rewards using tasks that require exerting either physical or cognitive effort to earn rewards of differing magnitude (Culbreth, Moran, & Barch, Reference Culbreth, Moran and Barch2018; Green, Horan, Barch, & Gold, Reference Green, Horan, Barch and Gold2015). Neuroimaging studies suggest that impairments in willingness to work for rewards in SZ are associated with reduced activation of the ventral striatum, dorsolateral prefrontal cortex, medial frontal gyrus, and posterior cingulate cortex (Huang et al., Reference Huang, Yang, Lan, Zhu, Liu, Wang and Chan2016; Wolf et al., Reference Wolf, Satterthwaite, Kantrowitz, Katchmar, Vandekar, Elliott and Ruparel2014). Furthermore, hypoactivation of the ventral striatum (Culbreth, Moran, Kandala, Westbrook, & Barch, Reference Culbreth, Moran, Kandala, Westbrook and Barch2020; Huang et al., Reference Huang, Yang, Lan, Zhu, Liu, Wang and Chan2016) during effort tasks and reduced willingness to expend effort for higher value rewards have been linked to greater severity of negative symptoms, particularly avolition and anhedonia (Barch, Treadway, & Schoen, Reference Barch, Treadway and Schoen2014; Culbreth, Westbrook, & Barch, Reference Culbreth, Westbrook and Barch2016; Hartmann et al., Reference Hartmann, Hager, Reimann, Chumbley, Kirschner, Seifritz and Kaiser2015; Horan et al., Reference Horan, Reddy, Barch, Buchanan, Dunayevich, Gold and Green2015; Serper, Payne, Dill, Portillo, & Taliercio, Reference Serper, Payne, Dill, Portillo and Taliercio2017; Strauss et al., Reference Strauss, Whearty, Morra, Sullivan, Ossenfort and Frost2016; Wang et al., Reference Wang, Huang, Yang, Lui, Cheung and Chan2015). Although several studies show an association between willingness to work for rewards and negative symptoms in SZ and first-episode psychosis (Culbreth et al., Reference Culbreth, Moran and Barch2018), there are inconsistencies in the literature that might relate to psychometric properties, tolerability, whether physical or cognitive effort is required, and the manipulation of reward probability and magnitude across tasks (Reddy et al., Reference Reddy, Horan, Barch, Buchanan, Dunayevich, Gold and Green2015). Despite these inconsistencies related to methodology, deficits in the willingness to work for rewards have been proposed to play a critical role in the etiology of negative symptoms in SZ (Strauss et al., Reference Strauss, Waltz and Gold2014).

The current study evaluated whether deficits in the willingness to work for rewards are also present in those at CHR and whether they are associated with greater severity of negative symptoms. Based on studies in chronic and first-episode SZ (Barch et al., Reference Barch, Treadway and Schoen2014; Chang et al., Reference Chang, Chu, Treadway, Strauss, Chan, Lee and Chen2019b; Culbreth et al., Reference Culbreth, Westbrook and Barch2016, Reference Culbreth, Moran and Barch2018, Reference Culbreth, Moran, Kandala, Westbrook and Barch2020; Gold et al., Reference Gold, Strauss, Waltz, Robinson, Brown and Frank2013; Green et al., Reference Green, Horan, Barch and Gold2015; Huang et al., Reference Huang, Yang, Lan, Zhu, Liu, Wang and Chan2016; McCarthy, Treadway, Bennett, & Blanchard, Reference McCarthy, Treadway, Bennett and Blanchard2016; Strauss et al., Reference Strauss, Whearty, Morra, Sullivan, Ossenfort and Frost2016; Treadway, Peterman, Zald, & Park, Reference Treadway, Peterman, Zald and Park2015), it was hypothesized that:

  1. (1) CHR youth would demonstrate reduced likelihood of selecting the high effort option when reward probability and magnitude were high in comparison with healthy controls (CN).

  2. (2) In the CHR group, greater severity of clinically rated negative symptoms would be associated with reduced likelihood of choosing to engage in high effort behavior when reward probability and magnitude were high.

  3. (3) Reduced likelihood of choosing to engage in high effort behavior when reward probability and magnitude were high would be associated with greater cross-sectional risk for developing a psychotic disorder on a psychosis risk prediction algorithm (Zhang et al., Reference Zhang, Xu, Tang, Li, Tang, Cui and Liu2018).

Method

Participants

Participants included 44 CHR participants and 32 healthy controls (CN). CHR participants were recruited from psychosis risk evaluation programs in Georgia and New York that received referrals from local clinicians (e.g. psychiatrists, psychologists, social workers, and school psychiatrists) to perform diagnostic assessment and monitoring evaluations for youth displaying psychotic experiences. Additional recruitment methods included online and print advertisements, in-person presentations to community mental health centers, and calls or in-person meetings with members of the local school system (e.g. superintendent and principals). All CHR youth met criteria for a prodromal syndrome on the Structured Interview for Prodromal Syndromes (SIPS) (Miller et al., Reference Miller, McGlashan, Woods, Stein, Driesen and Corcoran1999). Participants met criteria for the following non-exclusive prodromal syndromes: Attenuated Positive Symptoms (i.e. SIPS score of at least 3–5 on at least one positive symptom item; n = 42), Brief Intermittent Psychosis Syndrome (i.e. SIPS score of 6 on at least one positive symptom item, with symptoms present at least several minutes a day at a frequency of at least once per month; n = 1), and Genetic Risk and Deterioration Syndrome (i.e. first-degree relative with a psychotic disorder and decline in global functioning over the past year) and Attenuated Positive Symptoms (n = 1). Thirty-eight CHR participants met SIPS criteria for progression and six for persistence. None of the CHR participants met lifetime criteria for a DSM-5 psychotic disorder as determined via SCID-5 interview (First, Williams, Karg, & Spitzer, Reference First, Williams, Karg and Spitzer2015b). Three CHR participants were prescribed an antipsychotic at the time of testing.

CN participants were recruited from the local community using posted flyers, newspapers advertisements, and electronic advertisements. CN participants had no current major psychiatric disorder diagnoses and no SZ-spectrum personality disorders as established by the SCID-5 (First, Williams, Karg, & Spitzer, Reference First, Williams, Karg and Spitzer2015c) and SCID-5-PD (First, Williams, Benjamin, & Spitzer, Reference First, Williams, Benjamin and Spitzer2015a), no family history of psychosis, and were not taking psychotropic medications. All participants were free from lifetime neurological disease.

Groups did not significantly differ on age, race, sex, or parental education; CHR had lower personal education than CN (see Table 1).

Table 1.

Demographic and clinical characteristics

CHR, clinical high-risk group; CN, control group; MCCB, MATRICS Consensus Cognitive Battery; SIPS, Structured Interview for Prodromal Syndromes; BNSS, Brief Negative Symptom Scale; GFS: S, Global Functioning Scale: Social; GFS: R, Global Functioning Scale: Role.

*p < 0.05.

Procedure

Participants provided written informed consent and received monetary compensation for their participation. Study procedures were approved by the University of Georgia and Binghamton University Institutional Review Boards. Participants completed a structured clinical interview to rate the SCID-5 (First et al., Reference First, Williams, Karg and Spitzer2015c) and SCID-5-PD (First et al., Reference First, Williams, Benjamin and Spitzer2015a), SIPS (Miller et al., Reference Miller, McGlashan, Woods, Stein, Driesen and Corcoran1999), Brief Negative Symptom Scale, Clinical High-Risk Adaptation version (BNSS; Strauss and Chapman, Reference Strauss and Chapman2018), Global Functioning Scale: Social (GFS:S; Auther, Smith, & Cornblatt, Reference Auther, Smith and Cornblatt2006), and Global Functioning Scale: Role (GFS:R; Niendam et al., Reference Niendam, Bearden, Johnson, Cannon and :2006). Interviews were conducted by a licensed clinical psychologist (GPS) or examiners trained to reliability standards (>0.80) using gold standard training videos developed by the PI. In cases of the latter, examiners consulted with the PI for consensus.

A cross-sectional conversion risk score was calculated for CHR participants using an algorithm by Zhang et al. (Reference Zhang, Xu, Tang, Li, Tang, Cui and Liu2018) that calculates risk using scores on SIPS items that significantly predicted conversion in the initial validation study. This includes functional decline, positive, negative, and general symptom scale items, such that greater decline in Global Assessment of Functioning (GAF) scores, high positive symptom scores, high negative symptom scores, and low general symptom scores predict greater risk for conversion. A composite score is calculated for each of these domains by summing individual item ratings. Estimated risk is determined based on whether the magnitude of each composite score surpasses a relevant threshold, with each domain contributing to the overall risk estimate. Calculated risk is based solely on SIPS scores and reflects an individual's current risk for conversion that can be used in clinical decision-making. After the interview, participants completed the Effort Expenditure for Reward Task (EEfRT; Treadway, Buckholtz, Schwartzman, Lambert, & Zald, Reference Treadway, Buckholtz, Schwartzman, Lambert and Zald2009).

Effort expenditure for reward task

The EEfRT was used to measure willingness to work for rewards. It requires participants to choose between performing a low effort task (30 button presses within 7 s with the dominant hand index finger) for a lower reward value ($1) v. a high effort option (100 button presses within 21 s with the non-dominant hand little finger) for higher reward values ($1.24–$4.30). Probability of reward receipt is manipulated across trials with cues at the start of each trial indicating a high (88%), medium (50%), or low (12%) probability of receiving money on that trial. Participants were told that they would receive a proportion of money earned as result of their decisions at the end of the task; all participants received $5 bonus for task completion. The task was performed for 20 min. The key dependent variable is the rate of selecting the high effort choice across probability and magnitude levels.

Data analysis

Hypothesis 1 was evaluated using two separate mixed models analyses of variance (ANOVAs) examining the effects of reward probability and magnitude. The effects of probability were tested with a 2 (Group: CHR, CN) × 3 (Reward Probability: 12, 55, 88%) ANOVA, while Magnitude was evaluated using a 2 (Group: CHR, CN) × 4 (Reward Magnitude: low, medium, high, highest) ANOVA (cut points for magnitude categories were derived from Barch et al., Reference Barch, Treadway and Schoen2014). Significant interactions were followed up by post hoc one way ANOVAs. Hypothesis 2 was evaluated using bivariate correlations between EEfRT scores for 88% probability and very high magnitude conditions and the two BNSS dimension scores [i.e. motivation and pleasure (MAP) and diminished expression (EXP)]. Exploratory correlations were also conducted between EEfRT scores and BNSS domain scores, measures of positive, disorganized, and general symptoms from the SIPS and functioning on the GFS:S and GFS:R scales. Hypothesis 3 was evaluated using bivariate correlations between the SHARP risk calculator score and the 88% probability and very high magnitude EEfRT scores. The highest reward probability and magnitude variables were selected as key dependent variables because they have most consistently been associated with negative symptoms and shown impairment in past SZ studies. The Benjamini and Hochberg correction for multiple comparisons was applied to account for false-discovery rate. Instances where significant effects did not survive correction for multiple comparisons are noted.

Results

Group differences in EEfRT scores

Reward probability

The Group × Probability interaction was significant, F (2,74) = 6.88, p < 0.001, as were the main effects of Probability, F (2,74) = 182.67, p < 0.001, and Group, F (1,74) = 4.08, p < 0.05. Post hoc one way ANOVAs indicated that CHR were less likely to select the high effort option at the 50% F (1,74) = 8.27, p < 0.01, and 88%, F (1,74) = 4.91, p = 0.03, probability levels; however, groups did not differ at 12% probability, F (1,74) = 0.34, p = 0.56 (see Fig. 1).

Fig. 1.

Effort expenditure for reward performance in CHR and CN participants. A, effort expenditure by group and reward magnitude; B, effort expenditure by group and reward probability; CHR, clinical high-risk group; CN, healthy control group.

Reward magnitude

The Group × Magnitude interaction was significant, F (3,74) = 2.73, p = 0.045, as were the main effects of Magnitude, F (2,74) = 233.10, p < 0.001, and Group, F (1,74) = 3.95, p = 0.05. Post hoc one way ANOVAs indicated that CHR were less likely to select the high effort option at the very high magnitude, F (1,74) = 5.76, p = 0.019; however, groups did not differ at the low, F (1,74) = 0.03, p = 0.86, medium F (1,74) = 3.74, p = 0.057, or high F (1,74) = 3.32, p = 0.07 magnitudes (see Fig. 1).

Correlations between EEfRT scores and clinical outcomes

In CHR, lower effort expenditure on the very high reward magnitude condition was associated with higher negative symptoms on the BNSS total score, BNSS motivation and pleasure (MAP) dimension, and the BNSS asociality domain. Associations with other variables were non-significant (see Table 2 and online Supplementary materials). The MAP and EXP dimensions did not differ significantly in the magnitude of correlation.

Table 2.

Correlations between EEfRT performance and clinical outcomes

SIPS, Structured Interview for Prodromal Syndromes; BNSS, Brief Negative Symptom Scale

*p < 0.05, **p < 0.01.

Correlations between EEfRT scores and SHARP risk calculator

In CHR, greater cross-sectional risk of conversion on the SHARP risk calculator was significantly associated with reduced effort expenditure on the very high magnitude condition (r = −0.33, p = 0.034) but not the 88% probability condition.

Antipsychotics

When analyses were reconducted with the three participants receiving antipsychotics removed, the results were very similar and the significance values changed minimally (see online Supplementary materials).

Discussion

Findings supported the three hypotheses: (1) CHR participants exerted less effort than CN on high probability and very high magnitude reward conditions; (2) higher negative symptom severity was associated with reduced effort expenditure for rewards on the very high magnitude reward condition, and (3) greater cross-sectional risk on the SHARP risk calculator was associated with reduced effort expenditure for rewards on the very high magnitude condition.

Reduced effort expenditure for high probability and high magnitude conditions is consistent with prior studies in first-episode and chronic SZ on this task. For example, prior studies have demonstrated that those with established psychotic disorders allocate less effort than healthy controls as reward magnitude or probability increases (Barch et al., Reference Barch, Treadway and Schoen2014; Culbreth et al., Reference Culbreth, Moran and Barch2018; Green et al., Reference Green, Horan, Barch and Gold2015; McCarthy et al., Reference McCarthy, Treadway, Bennett and Blanchard2016; Strauss et al., Reference Strauss, Whearty, Morra, Sullivan, Ossenfort and Frost2016). Similar findings of reduced effort allocation in participants with first-episode psychosis have been attributed to impaired reward valuation, based on evidence that patients discount rewards more steeply as demands on cognitive effort increase (Chang et al., Reference Chang, Chu, Treadway, Strauss, Chan, Lee and Chen2019b).

The observation that reduced effort expenditure for rewards is associated with greater negative symptom severity is also consistent with prior studies in chronic SZ and first-episode psychosis. Specifically, two recent studies in first-episode psychosis and several studies in chronic SZ have demonstrated that compared to those with low negative symptoms, those with high levels of negative symptoms are less likely to exert effort for high magnitude and high probability rewards (Chang et al., Reference Chang, Westbrook, Strauss, Chu, Chong, Siu and Chen2020; Chang et al., Reference Chang, Chu, Treadway, Strauss, Chan, Lee and Chen2019b; Gold et al., Reference Gold, Strauss, Waltz, Robinson, Brown and Frank2013; Hartmann et al., Reference Hartmann, Hager, Reimann, Chumbley, Kirschner, Seifritz and Kaiser2015; Serper et al., Reference Serper, Payne, Dill, Portillo and Taliercio2017). Several, but not all (c.f. Docx et al., Reference Docx, de la Asuncion, Sabbe, Hoste, Baeten, Warnaerts and Morrens2015; Fervaha et al., Reference Fervaha, Duncan, Foussias, Agid, Faulkner and Remington2015), prior studies have also demonstrated that reduced willingness to exert greater effort for high probability and magnitude rewards is associated with higher levels of negative symptoms (Barch et al., Reference Barch, Treadway and Schoen2014; Fervaha et al., Reference Fervaha, Graff-Guerrero, Zakzanis, Foussias, Agid and Remington2013; Horan et al., Reference Horan, Reddy, Barch, Buchanan, Dunayevich, Gold and Green2015; Serper et al., Reference Serper, Payne, Dill, Portillo and Taliercio2017; Wang et al., Reference Wang, Huang, Yang, Lui, Cheung and Chan2015). Furthermore, the significant association with MAP but not EXP negative symptom dimensions is consistent with the majority of prior literature indicating this pattern in SZ. For example, several prior studies in SZ by our group (Strauss et al., Reference Strauss, Whearty, Morra, Sullivan, Ossenfort and Frost2016), as well as Hartmann et al. (Reference Hartmann, Hager, Reimann, Chumbley, Kirschner, Seifritz and Kaiser2015) and Wolf et al. (Reference Wolf, Satterthwaite, Kantrowitz, Katchmar, Vandekar, Elliott and Ruparel2014), have demonstrated that decreased willingness to expend effort for rewards is more robustly associated with greater avolition and anhedonia symptoms than with expressive negative symptoms. Thus, our study extends prior findings showing that deficits in effort expenditure for rewards are critical mechanisms underlying MAP negative symptoms in first episode and chronic psychosis to those with CHR; as proposed by Gold et al. (Reference Gold, Strauss, Waltz, Robinson, Brown and Frank2013), these deficits may result from difficulty using mental representations of value to guide decision-making.

The significant correlation with the SHARP risk calculator indicates that deficits in the willingness to work for rewards are also associated with liability for conversion to a full psychotic disorder. This suggests that a key mechanism underlying negative symptoms may be valuable for predicting which CHR cases will transition. Other variables associated with negative symptom mechanisms have not been extensively examined in this regard (e.g. reinforcement learning, hedonic response, and value representation) but would be worth examining in future studies. Other mechanisms more closely tied to positive symptoms and cognitive impairment may also be promising, as several psychological and neural abnormalities have been predictive of conversion [e.g. attenuated positive symptoms, neurocognitive deficits (particularly in verbal fluency, visual learning, and memory (Addington et al., Reference Addington, Liu, Perkins, Carrion, Keefe and Woods2017; De Herdt et al., Reference De Herdt, Wampers, Vancampfort, De Hert, Vanhees, Demunter and Probst2013; Giuliano et al., Reference Giuliano, Li, Mesholam-Gately, Sorenson, Woodberry and Seidman2012)), poor global and social functioning (Addington et al., Reference Addington, Liu, Perkins, Carrion, Keefe and Woods2017, Reference Addington, Farris, Stowkowy, Santesteban-Echarri, Metzak and Kalathil2019; Addington, Farris, Devoe, & Metzak, Reference Addington, Farris, Devoe and Metzak2020; Fusar-Poli et al., Reference Fusar-Poli, Borgwardt, Bechdolf, Addington, Riecher-Rössler, Schultze-Lutter and Seidman2013; Oliver et al., Reference Oliver, Reilly, Baccaredda Boy, Petros, Davies, Borgwardt and Fusar-Poli2020; Riecher-Rössler & Studerus, Reference Riecher-Rössler and Studerus2017), increased rate of cortical grey matter loss (Cannon et al., Reference Cannon, Chung, He, Sun, Jacobson, van Erp and Heinssen2015; Chung et al., Reference Chung, Addington, Bearden, Cadenhead, Cornblatt, Mathalon and Cannon2018), cerebello–thalamic–cortical hyperconnectivity (Bernard, Orr, & Mittal, Reference Bernard, Orr and Mittal2017; Cao et al., Reference Cao, Chén, Chung, Forsyth, McEwen, Gee and Cannon2018), reduced mismatch negativity (Bodatsch, Brockhaus-Dumke, Klosterkötter, & Ruhrmann, Reference Bodatsch, Brockhaus-Dumke, Klosterkötter and Ruhrmann2015), and P300 event-related potential responses (Hamilton et al., Reference Hamilton, Roach, Bachman, Belger, Carrion, Duncan and Mathalon2019; van Tricht et al., Reference van Tricht, Nieman, Koelman, Bour, van der Meer, van Amelsvoort and de Haan2011)]. Exploring the joint predictive power of processes underlying negative symptoms, positive symptoms, disorganized symptoms, and cognitive impairment may be a novel approach to enhancing risk prediction algorithms.

Certain limitations should be considered. First, the data reported are cross-sectional. Longitudinal data collection is in progress to determine whether effort expenditure predicts conversion at long-term follow-ups. Second, three CHR participants were prescribed antipsychotics and all subjects were therefore not antipsychotic naive. Although antipsychotic use was not significantly associated with EEfRT performance, a potential influence of antipsychotics cannot be ruled out. Third, only a single effort task was administered. The EEfRT is a well-validated translational neuroscience task that assesses willingness to expend physical effort only. Future studies will want to administer cognitive effort tasks, as these have also been validated and associated with negative symptoms in some SZ studies (Chang et al., Reference Chang, Westbrook, Strauss, Chu, Chong, Siu and Chen2020; Culbreth et al., Reference Culbreth, Westbrook and Barch2016; Wolf et al., Reference Wolf, Satterthwaite, Kantrowitz, Katchmar, Vandekar, Elliott and Ruparel2014). Fourth, the effect of smoking status could not be reliably determined because rates of smoking were low in this sample. Fifth, results were purely behavioral and conclusions regarding underlying neural mechanisms were not possible. Future studies will want to consider administering an effort task while collecting neurophysiological response via electroencephalography or functional magnetic resonance imaging. Finally, cognitive measures were not collected concurrently to determine the extent to which effort expenditure deficits relate to working memory, as has been demonstrated previously (Gold et al., Reference Gold, Strauss, Waltz, Robinson, Brown and Frank2013); this should be examined in future CHR studies. To our knowledge, this is the first study to examine willingness to work for rewards in CHR. As such, it is unclear whether the inconsistent associations observed between effort tasks and negative symptoms will also occur in this population as it has in SZ. It is possible that the significant relationship observed here relates to the good psychometric properties of the EEfRT or the conceptual and psychometric advantages of the BNSS; notably the association with the SIPS was non-significant, which may reflect conceptual limitations of its negative subscale (Strauss et al., Reference Strauss, Pelletier-Baldelli, Visser, Walker and Mittal2020).

Despite these limitations, several important conclusions can be drawn. First, deficient effort expenditure for rewards is a transphasic mechanism of negative symptoms in the SZ spectrum. Although correlational findings with effort tasks have not been observed across all studies, there does appear to be general support that greater negative symptom severity is associated with reduced willingness to expend effort for rewards in CHR, FEP, and chronic phases (Chang et al., Reference Chang, Westbrook, Strauss, Chu, Chong, Siu and Chen2020, Reference Chang, Chu, Treadway, Strauss, Chan, Lee and Chen2019b; Culbreth et al., Reference Culbreth, Moran and Barch2018; Luther, Firmin, Lysaker, Minor, & Salyers, Reference Luther, Firmin, Lysaker, Minor and Salyers2018). This implies that mechanisms underlying impaired effort expenditure for rewards may be a viable treatment target for negative symptoms transphasically. At the neural level, these mechanisms include dopaminergic function, anterior cingulate function, and cortico-striatal connectivity (Huang et al., Reference Huang, Yang, Lan, Zhu, Liu, Wang and Chan2016; Wolf et al., Reference Wolf, Satterthwaite, Kantrowitz, Katchmar, Vandekar, Elliott and Ruparel2014). Transcranial direct current stimulation or repetitive transcranial magnetic stimulation has successfully been used to stimulate areas that have been implicated in impaired willingness to expend effort for rewards (e.g. dorsolateral prefrontal cortex); it may be worth exploring whether activation of these areas improves negative symptoms (Cheng et al., Reference Cheng, Louie, Wong, Wong, Leung, Nitsche and Chan2020; Osoegawa et al., Reference Osoegawa, Gomes, Grigolon, Brietzke, Gadelha, Lacerda and de Jesus2018). Second, the EEfRT appears to be a reliable behavioral index of negative symptoms that could be considered an intermediate phenotype. Developing computerized behavioral tasks capable of tapping into negative symptom mechanisms may be particularly valuable given that structured clinical interviews of prodromal symptoms take substantial training, can typically only be administered at specialized CHR clinics, and are too lengthy to be practical for use in routine clinical practice in many settings (Gold et al., Reference Gold, Corlett, Strauss, Schiffman, Ellman, Walker and Mittal2020). The creation of a battery of computerized behavioral tasks capable of reliably measuring mechanisms underlying negative, positive, and disorganized symptoms that can output a risk calculator score similar to the SHARP and NAPLS risk calculators might allow for more efficient CHR screening in instances where specialty services are not available. The EEfRT would appear to be a valuable measure to include in such a battery. Efforts are currently underway to pursue this line of work developing a computerized battery of behavioral measures in the multisite CAPR Consortium (Computerized Assessment of Psychosis Risk) (Gold et al., Reference Gold, Corlett, Strauss, Schiffman, Ellman, Walker and Mittal2020).

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S003329172100204X

Financial support

This research was supported by a NARSAD Young Investigator Grant from Brain & Behavior Research Foundation to Dr Strauss.

Conflict of interest

G.P.S. is an original developer of the Brief Negative Symptom Scale (BNSS) and receive royalties and consultation fees from Medavante-ProPhase LLC in connection with commercial use of the BNSS and other professional activities. G.P.S. has consulted for Minerva, Acadia, Otsuka, and Lundbeck pharmaceutical companies. All other authors have no relevant conflicts of interest to report.

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Figure 0

Table 1. Demographic and clinical characteristics

Figure 1

Fig. 1. Effort expenditure for reward performance in CHR and CN participants. A, effort expenditure by group and reward magnitude; B, effort expenditure by group and reward probability; CHR, clinical high-risk group; CN, healthy control group.

Figure 2

Table 2. Correlations between EEfRT performance and clinical outcomes

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