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Corticostriatal Control of Goal-Directed Action Is Impaired in Schizophrenia

Open AccessPublished:June 17, 2014DOI:https://doi.org/10.1016/j.biopsych.2014.06.005

      Abstract

      Background

      Goal-directed actions depend on our capacity to integrate the anticipated consequences of an action with the value of those consequences, with the latter derived from direct experience or inferred from predictive stimuli. Schizophrenia is associated with poor goal-directed performance, but whether this reflects a deficit in experienced or predicted value or in integrating these values with action-outcome information is unknown, as is the locus of any associated neuropathology.

      Methods

      We assessed the contribution of these sources of value to goal-directed actions in people with schizophrenia (SZ) (n = 18) and healthy adults (n = 18). Participants learned to use specific actions to liberate snack foods from a vending machine. They also learned about the reward value of the foods, changes in reward value, and the relationship between various predictive stimuli and food delivery. We then evaluated the ability of subjects to use experienced or predicted value to guide goal-directed actions while undergoing functional magnetic resonance imaging.

      Results

      Acquisition and sensitivity to experienced changes in outcome value did not differ in SZ and healthy adults. The SZ were, however, deficient in their ability to integrate action-outcome learning with outcome values to guide choice, more so when actions were guided by experienced than by predicted values. These effects were differentially associated with reductions in activity in caudate and limbic structures, respectively.

      Conclusions

      This novel assessment of goal-directed learning revealed dysfunction in corticostriatal control associated with a profound deficit in integrating changes in experienced value with the action-outcome association in schizophrenia.

      Keywords

      The performance of goal-directed actions depends on the ability to integrate knowledge of the causal consequences of specific actions with the experienced value of those consequences (
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      • Dowd E.C.
      • Barch D.M.
      Pavlovian reward prediction and receipt in schizophrenia: Relationship to anhedonia.
      ,
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      • Gold J.M.
      • Kurup P.K.
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      Patients with schizophrenia have a reduced neural response to both unpredictable and predictable primary reinforcers.
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      Dysfunction of ventral striatal reward prediction in schizophrenia.
      ).
      Recent studies have distinguished the influence of experienced and predicted reward value on the control of goal-directed actions. The ability of reward-related cues to motivate and guide actions is demonstrated by Pavlovian-instrumental transfer in which Pavlovian cues that predict a particular reward bias choice toward actions that earn that reward (
      • Dickinson A.
      • Balleine B.
      The role of learning in the operation of motivational systems.
      ). This specific transfer effect engages a circuit involving the orbitofrontal cortex (OFC), amygdala, and nucleus accumbens in humans (
      • Bray S.
      • Rangel A.
      • Shimojo S.
      • Balleine B.
      • O’Doherty J.P.
      The neural mechanisms underlying the influence of pavlovian cues on human decision making.
      ,
      • Gottfried J.A.
      • O’Doherty J.
      • Dolan R.J.
      Encoding predictive reward value in human amygdala and orbitofrontal cortex.
      ,
      • Prevost C.
      • Liljeholm M.
      • Tyszka J.M.
      • O’Doherty J.P.
      Neural correlates of specific and general Pavlovian-to-Instrumental Transfer within human amygdalar subregions: A high-resolution fMRI study.
      ,
      • Talmi D.
      • Seymour B.
      • Dayan P.
      • Dolan R.J.
      Human pavlovian-instrumental transfer.
      ), and damage to this circuit renders choices indifferent to predictive stimuli (
      • Corbit L.H.
      • Balleine B.W.
      Double dissociation of basolateral and central amygdala lesions on the general and outcome-specific forms of pavlovian-instrumental transfer.
      ,
      • Corbit L.H.
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      The role of the nucleus accumbens in instrumental conditioning: Evidence of a functional dissociation between accumbens core and shell.
      ,
      • Ostlund S.B.
      • Balleine B.W.
      The contribution of orbitofrontal cortex to action selection.
      ). Outcome devaluation tests have established that experienced reward values also influence choice: devaluing a food reward can reduce the performance of actions associated with that food relative to other actions (
      • Adams C.D.
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      Instrumental responding following reinforcer devaluation.
      ,
      • Colwill R.M.
      • Rescorla R.A.
      Postconditioning devaluation of a reinforcer affects instrumental responding.
      ). Such goal-directed actions engage a prefrontal cortical–dorsomedial striatal circuit in humans (
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      Determining a role for ventromedial prefrontal cortex in encoding action-based value signals during reward-related decision making.
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      Neural correlates of instrumental contingency learning: Differential effects of action-reward conjunction and disjunction.
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      Determining the neural substrates of goal-directed learning in the human brain.
      ), and damage to this circuit renders choices insensitive to changes in outcome value and abolishes goal-directed action control (
      • Corbit L.H.
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      The role of prelimbic cortex in instrumental conditioning.
      ,
      • Yin H.H.
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      Blockade of NMDA receptors in the dorsomedial striatum prevents action-outcome learning in instrumental conditioning.
      ).
      In this study, we sought to establish whether deficits in goal-directed action associated with schizophrenia are due to an inability to use reward-related cues or previous experiences of reward value to select the best action. We assessed the influence of 1) cues predicting food rewards in a Pavlovian-instrumental transfer test; and 2) an outcome devaluation test in people with schizophrenia (SZ) and in healthy adults (HA). Neural hemodynamic responses were assessed (functional magnetic resonance imaging [fMRI]) during each test. We predicted that SZ would be unable flexibly to update their choices on the basis of changes in either predicted or experienced reward values. Furthermore, we predicted deficits in transfer associated with aberrant hemodynamic responses in the limbic and medial OFC (mOFC) regions, described above and known to be critical for cue-guided action selection. In contrast, we predicted deficits in outcome devaluation associated with abnormal responses in the prefrontal cortex–dorsomedial striatal circuit.

      Methods and Materials

      All participants provided written informed consent according to the approval requirements of the Human Research Ethics Committee of Sydney University (HREC #12812).
      See Supplement 1 for a full description of the methods and results.

      Participants

      Eighteen healthy adults and 18 people with schizophrenia (n = 12) or schizoaffective disorder (n = 6) and no other Axis 1 disorder (Table 1) were included after meeting the inclusion criteria (cf. Supplementary Methods in Supplement 1). Participants were assessed with the Diagnostic Interview for Psychosis to establish a lifetime diagnosis of schizophrenia or schizoaffective disorder according to DSM-IV criteria (
      • Castle D.J.
      • Jablensky A.
      • McGrath J.J.
      • Carr V.
      • Morgan V.
      • Waterreus A.
      • et al.
      The diagnostic interview for psychoses (DIP): Development, reliability and applications.
      ,
      • Jablensky A.
      • McGrath J.
      • Herrman H.
      • Castle D.
      • Gureje O.
      • Evans M.
      • et al.
      Psychotic disorders in urban areas: An overview of the Study on Low Prevalence Disorders.
      ).
      Table 1Clinical and Neuropsychological Results, Mean (SD)
      Schizophrenia (n = 18)
      Antipsychotic drug treatment of schizophrenia: aripiprazole n = 2; clozapine n = 4; olanzapine n = 6; paliperidone n = 2; quetiapine n = 1; risperidone n = 1; ziprasidone n = 2.
      Healthy (n = 18)t Value (df = 34)p Value
      Age45.3 (11.4)39.9 (12.9)1.36.18
      Female Subjects99
      Edinburgh Handedness Score73.9 (27.7)79.1 (29.7).56.58
      Years of Education14.4 (3.4)15.1 (2.3).71.48
      WASI IQ99.2 (15.5)112.3 (14.5)2.69.01
      WTAR IQ102.4 (15.9)108.2 (7.8)1.43.16
      DASS-21 Scores
       Depression13.1 (8.9)4.4 (4.8)3.60.00
       Anxiety12.0 (9.1)3.2 (2.5)4.10.00
       Stress14.7 (10.7)6.0 (5.4)3.09.00
      BIS/BAS Scores
       BIS16.5 (5.4)16.3 (4.3).15.89
       BAS-reward subscale11.3 (4.8)12.1 (3.9).52.60
       BAS-drive subscale9.5 (2.6)11.5 (2.4)2.31.03
       BAS-fun-seeking subscale9.9 (3.2)10.1 (2.3).17.87
      SAPS25.1 (14.6)
      SANS34.4 (14.2)
      BAS, Behavioral Approach System; BIS, Behavioral Inhibition System; DASS-21, Depression Anxiety Stress Scale-21; SANS, Scale for the Assessment of Negative Symptoms; SAPS, Scale for the Assessment of Positive Symptoms; WASI, Wechsler Abbreviated Scale of Intelligence; WTAR, Wechsler Test of Adult Reading.
      a Antipsychotic drug treatment of schizophrenia: aripiprazole n = 2; clozapine n = 4; olanzapine n = 6; paliperidone n = 2; quetiapine n = 1; risperidone n = 1; ziprasidone n = 2.

      Stimuli

      Visual stimuli during the scan were presented via a projector positioned at the back of the MRI scanner. A Lumina MRI-compatible two-button response pad (Cedrus, California) recorded each response. Food rewards consisted of sweet and salty snack foods (chocolate candy, chocolate chip cookies, and barbecue flavored crackers).

      Procedure

      Instrumental Training

      Before the MRI scan, participants rated the desirability of the three different snack foods by answering the question “How much do you want this snack right now?” on a 7-point Likert scale and were then trained to liberate two of the snack foods from a virtual vending machine presented on a laptop computer (Figure 1A). Left and right button presses earned a different snack food (food A and food B, respectively). Participants were provided with the relevant snack food and allowed to eat it. After every three rewards, a probe question to assess knowledge of the instrumental contingencies was posed on the screen. After getting six questions correct in a row, instrumental training ended. For further details, see Supplementary Methods in Supplement 1.
      Figure thumbnail gr1
      Figure 1Examples of trial types from each stage of training and testing. (A) Actions (tilting the vending machine to the left or right) were freely available during instrumental training. (B) No actions were available during Pavlovian training. No outcomes were delivered during the transfer test (C) or the devaluation test (D) to prevent further learning in these stages. CS, conditioned stimulus; ITI, intertrial interval; MRI, magnetic resonance imaging.

      Pavlovian Training

      In the next stage (Figure 1B), the virtual snack machine was presented and participants learned the predictive relationship between colored lights (red, green, blue, or yellow) presented on the front of the virtual machine and snack food delivery (foods A, B, C, and empty, respectively). Each cue lasted for 6 seconds, after which a snack fell out of the virtual machine. Participants were allowed to eat each snack when it appeared. After every four trials, a probe question was posed to test knowledge of the Pavlovian contingencies. Feedback was provided and training ended when six correct answers in a row occurred.
      The next two stages took place during the fMRI scan.

      Pavlovian-Instrumental Transfer Test

      Once the participant had entered the scanner and instructions were provided (Supplementary Methods in Supplement 1), the virtual snack machine was presented and one of the reward cues (red, green, blue, or yellow colored lights) was displayed (Figure 1C). The reward cues were presented for 6 seconds on the front of the virtual machine every 18 seconds (0- to 4-second random jitter). There were 28 trials in each condition (specific, general, and neutral). The participant was able to tilt the virtual machine at will during each cue, as well as during the intertrial intervals (i.e., an active baseline was employed). No food or food stimuli were presented during the test. The duration of the test was 1525 seconds.

      Devaluation Test

      The participant watched a movie for 4 minutes depicting one of the snack foods (food A or B, counterbalanced) infested with cockroaches (Figure 1D). Then, after instructions (Supplementary Methods in Supplement 1), the virtual machine was displayed for 10 blocks of 12 seconds and the participant could tilt the machine at will during each block. Before each block, a fixation cross was presented for 12 seconds. The duration of the session was 264 seconds, including a 24-second prescan fixation period. No food or food stimuli were presented.
      After exiting the scanner, each participant was asked to rate the three snack foods again and were administered a six-item multiple-choice test assessing their knowledge of the instrumental and Pavlovian contingencies.

      fMRI Acquisition

      Scanning occurred in a 3T GE Discovery and a 32-channel head coil (GE Healthcare, Little Chalfont, Buckinghamshire, United Kingdom). The functional images were acquired with a 2910-msec repetition time; 20-msec echo time; 90° flip angle; 240-mm field of view; and 128 × 128 matrix with a SENSE (Sensitivity Encoding) factor of 2. A T1-weighted high-resolution anatomical scan was acquired for each participant for registration and screening: 7200-msec repetition time; 2700-msec echo time; 176 slices in the sagittal plane; 1-mm slice thickness (no gap); 256-mm field of view; and 256 × 256 matrix. We acquired 525 and 88 whole-brain T2*-weighted echo planar images in the first and second runs, respectively. Each volume consisted of 52 axial slices, 2-mm thick with a .2 mm gap. The acquired voxel dimensions were 1.87 mm2.

      Data Analysis

      Images

      For preprocessing details, see Supplementary Methods in Supplement 1. The first-level general linear model for the specific transfer test included responses as a stick function and each reward cue as a boxcar function. Following Prevost et al. (
      • Prevost C.
      • Liljeholm M.
      • Tyszka J.M.
      • O’Doherty J.P.
      Neural correlates of specific and general Pavlovian-to-Instrumental Transfer within human amygdalar subregions: A high-resolution fMRI study.
      ), a parametric regressor modulated the cues for food A and B by the number of responses for the same outcome to reveal neural activity correlated with the influence of reward cues over choice (specific transfer). Cues for food C (CS+) and no food (CS−) were parametrically modulated by the number of total responses to reveal neural activity correlated with a general incentive effect of reward cues (general transfer) (
      • Talmi D.
      • Seymour B.
      • Dayan P.
      • Dolan R.J.
      Human pavlovian-instrumental transfer.
      ).
      The first-level general linear model for the devaluation test included the 10 block durations as a boxcar function and two response regressors (valued and devalued) across the 10 blocks, in which each response was modeled as a stick function. On average, there were approximately 25 responses per block. A contrast between valued and devalued responses was performed to identify neural regions involved in the comparison between the new action values for each group (
      • Valentin V.V.
      • Dickinson A.
      • O’Doherty J.P.
      Determining the neural substrates of goal-directed learning in the human brain.
      ). The beta image for the valued actions was used to identify group differences in neural activity during goal-directed actions.

      Region of Interest Analysis

      Two regions of interest (ROIs) were constructed to test for Pavlovian-instrumental transfer effects within a limbic-OFC circuit, and outcome devaluation effects in a caudate-PFC circuit (Figure S1 in Supplement 1). ROIs were selected on the basis of prior human imaging studies (Table 2). Significant effects were identified with a voxel level familywise error rate correction p = .05 for multiple comparisons in each ROI (small volume corrected). To confirm the direction of significant group differences, parameter estimates at significant voxels were calculated for each group (
      • Wilkinson L.
      • Inference T.F.S.
      Statistical methods in psychology journals - Guidelines and explanations.
      ).
      Table 2MNI Coordinates of Regions-of-Interest and Independent Studies
      TestRegionxyzReference
      Outcome Devaluation ROIMedial OFC−230−20Tanaka et al. (2008) (
      • Tanaka S.C.
      • Balleine B.W.
      • O’Doherty J.P.
      Calculating consequences: Brain systems that encode the causal effects of actions.
      )
      −336−24Valentin et al. (2007) (
      • Valentin V.V.
      • Dickinson A.
      • O’Doherty J.P.
      Determining the neural substrates of goal-directed learning in the human brain.
      )
      −1251−18Gottfried et al. (2003) (
      • Gottfried J.A.
      • O’Doherty J.
      • Dolan R.J.
      Encoding predictive reward value in human amygdala and orbitofrontal cortex.
      )
      333−19Liljeholm et al. (2011) (
      • Liljeholm M.
      • Tricomi E.
      • O’Doherty J.P.
      • Balleine B.W.
      Neural correlates of instrumental contingency learning: Differential effects of action-reward conjunction and disjunction.
      )
      −438−20Average
      Coordinates used to define regions of interest.
      Medial PFC−652−10Tanaka et al. (2008) (
      • Tanaka S.C.
      • Balleine B.W.
      • O’Doherty J.P.
      Calculating consequences: Brain systems that encode the causal effects of actions.
      )
      1257−6Liljeholm et al. (2011) (
      • Liljeholm M.
      • Tricomi E.
      • O’Doherty J.P.
      • Balleine B.W.
      Neural correlates of instrumental contingency learning: Differential effects of action-reward conjunction and disjunction.
      )
      355−8Average
      Coordinates used to define regions of interest.
      Right caudate6102Tanaka et al. (2008) (
      • Tanaka S.C.
      • Balleine B.W.
      • O’Doherty J.P.
      Calculating consequences: Brain systems that encode the causal effects of actions.
      )
      9164Tricomi et al. (2004) (
      • Tricomi E.M.
      • Delgado M.R.
      • Fiez J.A.
      Modulation of caudate activity by action contingency.
      )
      15915Liljeholm et al. (2011) (
      • Liljeholm M.
      • Tricomi E.
      • O’Doherty J.P.
      • Balleine B.W.
      Neural correlates of instrumental contingency learning: Differential effects of action-reward conjunction and disjunction.
      )
      10127Average
      Coordinates used to define regions of interest.
      Left caudate−12118Tricomi et al. (2004) (
      • Tricomi E.M.
      • Delgado M.R.
      • Fiez J.A.
      Modulation of caudate activity by action contingency.
      )
      −9015Liljeholm et al. (2011) (
      • Liljeholm M.
      • Tricomi E.
      • O’Doherty J.P.
      • Balleine B.W.
      Neural correlates of instrumental contingency learning: Differential effects of action-reward conjunction and disjunction.
      )
      −11612Average
      Coordinates used to define regions of interest.
      Pavlovian-to-Instrumental Transfer ROILeft amygdala−18−3−22Prevost et al. (2012) (
      • Prevost C.
      • Liljeholm M.
      • Tyszka J.M.
      • O’Doherty J.P.
      Neural correlates of specific and general Pavlovian-to-Instrumental Transfer within human amygdalar subregions: A high-resolution fMRI study.
      )
      −30−10−18Talmi et al. (2008) (
      • Talmi D.
      • Seymour B.
      • Dayan P.
      • Dolan R.J.
      Human pavlovian-instrumental transfer.
      )
      −15−6−18Gottfried et al. (2003) (
      • Gottfried J.A.
      • O’Doherty J.
      • Dolan R.J.
      Encoding predictive reward value in human amygdala and orbitofrontal cortex.
      )
      −24−12−12Prevost et al. (2012) (
      • Prevost C.
      • Liljeholm M.
      • Tyszka J.M.
      • O’Doherty J.P.
      Neural correlates of specific and general Pavlovian-to-Instrumental Transfer within human amygdalar subregions: A high-resolution fMRI study.
      )
      −22−8−18Average
      Coordinates used to define regions of interest.
      Right amygdala20−6−18Talmi et al. (2008) (
      • Talmi D.
      • Seymour B.
      • Dayan P.
      • Dolan R.J.
      Human pavlovian-instrumental transfer.
      )
      Right putamen29−2−6Prevost et al. (2012) (
      • Prevost C.
      • Liljeholm M.
      • Tyszka J.M.
      • O’Doherty J.P.
      Neural correlates of specific and general Pavlovian-to-Instrumental Transfer within human amygdalar subregions: A high-resolution fMRI study.
      )
      27−3−3Bray et al. (2008) (
      • Bray S.
      • Rangel A.
      • Shimojo S.
      • Balleine B.
      • O’Doherty J.P.
      The neural mechanisms underlying the influence of pavlovian cues on human decision making.
      )
      24−180Bray et al. (2008) (
      • Bray S.
      • Rangel A.
      • Shimojo S.
      • Balleine B.
      • O’Doherty J.P.
      The neural mechanisms underlying the influence of pavlovian cues on human decision making.
      )
      28−3−5Average
      Coordinates used to define regions of interest.
      Left putamen−27−15−3Bray et al. (2008) (
      • Bray S.
      • Rangel A.
      • Shimojo S.
      • Balleine B.
      • O’Doherty J.P.
      The neural mechanisms underlying the influence of pavlovian cues on human decision making.
      )
      Coordinates used to define regions of interest.
      Ventral striatum48−2Talmi et al. (2008) (
      • Talmi D.
      • Seymour B.
      • Dayan P.
      • Dolan R.J.
      Human pavlovian-instrumental transfer.
      )
      Coordinates used to define regions of interest.
      Medial OFC−438−20Valentin et al. (2007) (
      • Valentin V.V.
      • Dickinson A.
      • O’Doherty J.P.
      Determining the neural substrates of goal-directed learning in the human brain.
      )
      Coordinates used to define regions of interest.
      MNI, Montreal Neurological Institute; OFC, orbitofrontal cortex; PFC, prefrontal cortex; ROI, region of interest.
      a Coordinates used to define regions of interest.
      Relationship between symptoms and hemodynamic responses in SZ were examined by multiple regression. Mean voxel activity for subregions with significant aberrant activity (e.g., 12 mm sphere in the mOFC centered on coordinates in Table 2) were extracted and entered as a dependent variable in SPSS (IBM, Armonk, New York), with the Scale for the Assessment of Positive Symptoms and Scale for the Assessment of Negative Symptoms subscale scores included as predictor variables. Significant associations with individual symptoms were confirmed outside the context of the linear regression and the other symptoms by Spearman correlation. See Supplementary Methods in Supplement 1 for further details.

      Results

      For details of the participant demographics and pretest and posttest food ratings, see Supplementary Results in Supplement 1.

      Behavior and Food Ratings During Training

      Instrumental Conditioning

      There were no significant group differences in acquisition during instrumental training; the mean (SEM) number of snacks earned by HA and SZ were 37 (±5.2) and 43 (±5.6) respectively, p = .51, d = .22. Both HA and SZ tilted the vending machine at a higher rate for their preferred snack during instrumental training, as indicated by a positive correlation between the food ratings and number of choices for that food in HA (r = 0.75, p < .001) and SZ (r = .77, p < .001). Thus, both groups learned the response-outcome contingencies and the initial reward value influenced choice.

      Pavlovian Conditioning

      Learning the four stimulus-outcome contingencies during Pavlovian training of the reward cues was successful in both groups. The average (SEM) number of trials-to-criterion (and snacks delivered) was 50 (±5.2) and 46 (±5.4) for HA and SZ, respectively, and the group difference was not significant, p = .60, d = .18.

      Retention of Instrumental and Pavlovian Contingencies

      The tests of explicit memory administered at the end of the experiment indicated both HA and SZ acquired and retained the Pavlovian and instrumental contingencies during training and testing. The mean (SEM) percent correct were 93 (±3.0) and 98 (±1.8) percent for SZ and HA, respectively. Only four SZ and two HA scored less than 100 percent and the group difference was not significant, p = .23, d = .41.

      Choices During the Transfer Test

      The Effect of Reward-Related Cues Was Weaker in SZ

      The influence of the predictive cues on instrumental choices for food during the transfer test are shown in Figure 2A. The cues predicting food A or food B biased choice toward the action previously earning the same food outcome; i.e., specific transfer (response-type main effect, F1,34 = 39.71, p < .001, ηp2= .54); however, significantly less specific transfer occurred among SZ than HA (response-type by group interaction F1,34 = 6.96, p = .01, ηp2= .17). Despite the significant interaction, SZ still chose the action that delivered the same outcome in training as that predicted by the stimulus significantly more than the other action (t17 = 2.70, p = .016, d = .89), indicating cues predicting specific food rewards influenced choice for that food in SZ. Similar results emerged in people with schizophrenia and schizoaffective disorder (Figure S5 in Supplement 1).
      Figure thumbnail gr2
      Figure 2Results of the transfer test. (A) Reward cues increased choices for the same outcome in both groups (i.e., specific transfer). However, the effect was greater among healthy adults (**interaction p < .01, *p < .05). (B) A reward cue (CS+) increased reward-seeking actions over a nonreward cue (CS−) among healthy adults, but not among people with schizophrenia (**interaction p < .01, *p < .05). (C) Positive correlation between mean medial orbitofrontal cortex (OFC) activity and Scale for the Assessment of Positive Symptoms (SAPS) subscale score (delusions) in people with schizophrenia. Diff, different; ITI, intertrial interval.

      Evidence of Aberrant Incentive Motivation in SZ

      Figure 2B shows incentive motivation induced by the predictive cue for food C (CS+) during the general transfer test relative to the unpaired cue predicting no food (CS−) (cue-type main effect, F1,34 = 16.44, p < .001, ηp2= .35). Food C was never associated with any response during instrumental training; the cue for food C (CS+) cannot systematically bias responding and the increased button presses during this cue are consistent with a general incentive motivational effect of reward cues on responding (i.e., general transfer). Less general transfer occurred in SZ (cue-type by group interaction F1,34 = 7.60, p < .01, ηp2= .186); however, SZ responded more during the cue predicting no food than HA, t34 = 2.33, p = .03, d = .78. Thus, the group difference was due to a deficit withholding responding during the cue predicting no food rather than during the predictive cue. There were no significant group differences in button presses during the active baseline intertrial interval, p = .26, d = .38.

      Neuroimaging Results from the Transfer Test

      Reduced Limbic Activity During Cue-Guided Choices in SZ

      Figure 3A shows a significant deficit occurred in the bilateral amygdala of SZ during cues predicting foods A and B relative to HA (cf. Table 3). The parameter estimates from the right amygdala confirmed this was due to a reduced neural response in SZ (Figure 3A). Thus, pathology in this limbic region may prevent predictive cues from signaling the salient properties of the relevant outcome to effectively guide choice in schizophrenia.
      Figure thumbnail gr3
      Figure 3Aberrant hemodynamic responses in people with schizophrenia (SZ) during the transfer test (image threshold familywise error p = .05). (A) Hypoactivity among SZ relative to healthy adults (HA) in the bilateral amygdala during specific transfer. Parameter estimates (betas) at the peak voxel in the right amygdala confirmed the group difference was due to deficient activity in SZ. (B) Hyperactivity in SZ relative to HA in the medial orbitofrontal cortex during nonreward cue (CS−). Betas from the peak voxel confirmed group difference was due to hyperactivity in SZ. Note: Betas are in-sample estimates provided for illustration only, in line with American Psychological Association recommendations, and should not be taken to indicate an unbiased estimate of the true effect size. a.u., arbitrary units.
      Table 3ROI Results
      TestRegionxyzt ValueFWE pkContrast
      Specific Pavlovian-to-Instrumental TransferLeft amygdala−22−4−143.90.00989HA > SZ
      Right amygdala202−174.38.00846HA
      15−7−183.68.01596HA > SZ
      General Pavlovian-to-Instrumental TransferRight amygdala18−4−264.63.00989HA
      Ventral striatum316−26.69.001143HA
      24−85.64.01159HA
      Medial OFC−634−145.19.004393HA
      635−263.92.01520SZ > HA (CS−)
      Outcome DevaluationMedial PFC125805.28.003315HA val over dev
      464−56.78<.00190SZ val over dev
      Left caudate−107113.61.041129HA val over dev
      −91022.88.06049HA > SZ valued actions
      Right caudate161425.63.00294HA val over dev
      151723.63.013117HA > SZ valued actions
      CS−, nonreward cue; dev, devalued; FWE, familywise error; HA, healthy adults; OFC, orbitofrontal cortex; PFC, prefrontal cortex; ROI, region of interest; SZ, people with schizophrenia; val, valued.

      Aberrant Medial OFC Responses in SZ Associated with Incentive Motivation

      The no-reward cue (CS−) induced an abnormal incentive effect on responses in SZ, so we tested whether the parametric modulator of CS− was associated with neural hyperactivity in SZ relative to HA. Figure 3B shows the parametric modulator revealed significantly greater activity in the medial OFC of SZ over HA (Table 3). Thus, aberrant neural activity in this cortical region co-occurred with the aberrant incentive effect of the nonreward-related cue in SZ.

      Aberrant Neural Responses in the OFC Were Related to Positive Symptoms in SZ

      The multiple regression with symptoms indicated a significant relationship between positive symptom subscores and mOFC activity during the CS− (Montreal Neurological Institute: −2 30 −20; F = 22.02, p = .016). The coefficients for hallucinations, delusion severity, bizarre behavior, thought disorder, and attention were significant (Table S1 in Supplement 1). Only hallucinations, delusions, and attention had positive coefficients, and delusions had the largest coefficient. A scatterplot of the delusion subscale scores against the mean voxel activity in the mOFC confirmed higher delusion scores were related to larger (aberrant) responses in the mOFC (Figure 2C).

      Choices and Food Ratings During the Outcome Devaluation Test

      Outcome Devaluation Produced a Similar Effect on Food Ratings in HA and SZ

      Food ratings taken after the devaluation test found ratings were decreased for the devalued snack in each group (Figure 4A). The effect of devaluation was significantly greater on the devalued snack food; there was a devaluation main effect, F1,34 = 17.69, p < .001, ηp2= .34, but no effect of group or a devaluation × group interaction, Fs < 1, ηp2= .02 and 0, respectively. The effect of devaluation in each group was also confirmed with follow-up t tests, t17 = 2.38 and 3.20, ps < .05, d = .79 and 1.07, for HA and SZ, respectively. There was no significant change in hunger ratings after devaluation, ps > .3, ds = .35. (see Supplementary Results in Supplement 1). Thus, our outcome devaluation procedure selectively reduced the reward value of the devalued snack food in each group.
      Figure thumbnail gr4
      Figure 4Results of the outcome devaluation test. (A) Reductions in food ratings for the devalued food (Dev) relative to the valued food (Val) in both groups after devaluation (***main effect of devaluation p < .001). (B) Devaluation also reduced the proportion of choices for the devalued food in healthy adults (HA) but not people with schizophrenia (SZ) (***interaction p < .001). (C) Negative correlation between mean anterior caudate activity and Scale for the Assessment of Negative Symptoms (SANS) subscale score (avolition) in SZ.

      SZ Showed a Complete Deficit in Instrumental Outcome Devaluation

      The mean (±SEM) rates of responding in HA and SZ were similar during the outcome devaluation test (24.6 ± 3.9 and 25.8 ± 3.6 per 12-second block, respectively, p = .88, d = .05); however, the effect of devaluation on action selection was different in each group. Devaluation reduced actions for the devalued snack food in HA but had little effect in SZ (Figure 4B). Analysis of variance found a significant group × devaluation interaction, F1,34 = 21.64, p < .001, ηp2= .39, and follow-up t tests confirmed the effect of devaluation was significant in HA, t17 = 5.62, p < .001, d = 1.88, but not SZ, t17 = .52, p = .61, d = .17. This absence of any effect of devaluation on instrumental choice is in marked contrast to the robust effect of devaluation on food ratings in SZ and is novel evidence of a profound deficit in integrating changes in experienced value with the action-outcome association in schizophrenia.
      Although there was no effect of devaluation on choices in SZ, their responses were not random. The correlation between choices during the devaluation test and instrumental training was r = .80, indicating the preferred choice in SZ persisted from initial training. This correlation was significantly higher than the correlation among HA (r = −.18), p < .05.

      Neuroimaging Results from the Outcome Devaluation Test

      Caudate and Medial PFC Activity Tracked Action Values in HA

      Figure 5A shows hemodynamic responses in the right and left caudate, as well as medial PFC, of HA were greater during valued relative to devalued actions (Table 3). No significant activity in the striatum was related to devalued actions. Thus, neural responses in HA were consistent with the role of the PFC-caudate circuit in goal-directed action.
      Figure thumbnail gr5
      Figure 5(A) Hemodynamic responses in healthy adults (HA) during valued actions in the left and right anterior caudate and medial prefrontal cortex. Betas in right caudate confirmed activity was due to valued actions (Val) over devalued actions (Dev). (B) Deficient activity in the left and right anterior caudate during valued actions in people with schizophrenia (SZ) relative to HA. Betas illustrate the group difference was due to deficient activity in SZ. Image threshold familywise error p = .05. a.u., arbitrary units.

      SZ Showed Hypoactivity in Caudate During Valued Actions

      No significant activity in the striatum was revealed by the contrast of valued actions over devalued in SZ, although the medial PFC was significant (Table 3). Figure 5B shows a group comparison during valued actions (HA > SZ) revealed significantly less activity in the right caudate of SZ. The group difference in the left caudate approached, but did not exceed, our conservative criterion. Inspection of the group beta values from the right caudate confirmed this was due to deficient activity among the SZ group, concurrent with poor goal-directed performance in this group.

      Activity in Caudate Related to Negative Symptoms in SZ

      A significant relationship between negative symptom severity and neural responses was found in the right caudate of SZ (Montreal Neurological Institute: 24 23 −2; F = 10.45, p = .045). The coefficients for avolition and alogia scores were significant (Table S1 in Supplement 1). The correlation between avolition and the mean voxel activity in the right anterior caudate confirmed higher avolition scores were related to reduced caudate activity in schizophrenia (Figure 4C). Whole-brain analyses are presented in Supplementary Results and Figures S2, S3, and S4 in Supplement 1.

      Discussion

      The present results suggest a specific deficit in goal-directed action in schizophrenia; essentially a failure to integrate causal knowledge about the action-outcome association with changes in outcome value to modify action selection. Although similar judgments regarding the action-outcome contingency and changes in food ratings after devaluation were observed in HA and SZ, the latter were unable to integrate these sources of information to guide choice. We propose this represents a failure of corticostriatal control based on the deficit found in neural activity in the caudate during goal-directed action in SZ. Although there was no evidence of altered cortical activity in either medial OFC or medial PFC during the devaluation test (Figure 5B), the importance of input from these structures to the dorsomedial striatum for goal-directed control (
      • Liljeholm M.
      • Tricomi E.
      • O’Doherty J.P.
      • Balleine B.W.
      Neural correlates of instrumental contingency learning: Differential effects of action-reward conjunction and disjunction.
      ,
      • Tanaka S.C.
      • Balleine B.W.
      • O’Doherty J.P.
      Calculating consequences: Brain systems that encode the causal effects of actions.
      ) suggests this pathway may be affected in schizophrenia. Secondly, the predictive value of reward cues exerted some control on choice in SZ (Figure 3A), albeit not to the extent of HA, likely due to the deficit observed in amygdala activity (Figure 3A), a structure known to control the encoding of predicted values (
      • Parkes S.L.
      • Balleine B.W.
      Incentive memory: Evidence the basolateral amygdala encodes and the insular cortex retrieves outcome values to guide choice between goal-directed actions.
      ). Finally, although the motivational impact of a reward-related cue appeared to be intact, its effects generalized to an unpaired cue in SZ (Figure 3B), perhaps due to overactivity in the mesoaccumbal pathway (Figure 3B). Such generalization could have hampered the ability of SZ to direct efforts toward the most relevant predictors of goal events.
      The results of the outcome devaluation test have significant implications for theories of reward processing in schizophrenia. On the basis of performance in cued reward tasks, poor goal-directed performance in schizophrenia has been generally thought to be due to a failure to anticipate reward value (
      • Gold J.M.
      • Waltz J.A.
      • Prentice K.J.
      • Morris S.E.
      • Heerey E.A.
      Reward processing in schizophrenia: A deficit in the representation of value.
      ,
      • Gard D.E.
      • Kring A.M.
      • Gard M.G.
      • Horan W.P.
      • Green M.F.
      Anhedonia in schizophrenia: Distinctions between anticipatory and consummatory pleasure.
      ,
      • Heerey E.A.
      • Gold J.M.
      Patients with schizophrenia demonstrate dissociation between affective experience and motivated behavior.
      ,
      • Barch D.M.
      • Dowd E.C.
      Goal representations and motivational drive in schizophrenia: The role of prefrontal-striatal interactions.
      ). However, such tasks do not isolate the predictive value of cues from the value of actions to guide choice [but see (
      • Heerey E.A.
      • Gold J.M.
      Patients with schizophrenia demonstrate dissociation between affective experience and motivated behavior.
      )]. As a consequence, whether the impairment reflects a deficit in cue-reward or action-reward predictions has remained unclear [see (
      • Griffiths K.R.
      • Morris R.W.
      • Balleine B.W.
      Translational studies of goal-directed action as a framework for classifying deficits across psychiatric disorders.
      ) for discussion]. By separating the assessment of changes in reward value from the test of the ability to use those values to guide choice, we were able to establish that the deficit in SZ lies in the latter capacity. This was unlikely to be due to a deficit in working memory; a problem in encoding or maintaining reward values in memory should have produced a similar effect on posttest food ratings as well as choice. The dissociation we observed between food ratings and goal-directed choices suggests instead that reward values are preserved and correctly updated in schizophrenia, evidently in the prefrontal cortex, but that the new reward value is not integrated appropriately with prior causal learning to guide choice. This represents a more specific deficit in translating the updated reward value into value-based decisions, consistent with a generally observed failure to appropriately integrate cognitive and affective information in schizophrenia (
      • Heerey E.A.
      • Gold J.M.
      Patients with schizophrenia demonstrate dissociation between affective experience and motivated behavior.
      ,
      • Heerey E.A.
      • Bell-Warren K.R.
      • Gold J.M.
      Decision-making impairments in the context of intact reward sensitivity in schizophrenia.
      ).
      The results of the specific transfer test revealed patients were able to choose actions reliably on the basis of predictive cues, consistent with a variety of evidence from stimulus-based reinforcement learning tasks finding that patients are able to use cues predicting reward to guide their decisions, albeit not as well as healthy adults (
      • Gold J.M.
      • Waltz J.A.
      • Prentice K.J.
      • Morris S.E.
      • Heerey E.A.
      Reward processing in schizophrenia: A deficit in the representation of value.
      ,
      • Keri S.
      • Nagy O.
      • Kelemen O.
      • Myers C.E.
      • Gluck M.A.
      Dissociation between medial temporal lobe and basal ganglia memory systems in schizophrenia.
      ,
      • Weickert T.W.
      • Terrazas A.
      • Bigelow L.B.
      • Malley J.D.
      • Hyde T.
      • Egan M.F.
      • et al.
      Habit and skill learning in schizophrenia: Evidence of normal striatal processing with abnormal cortical input.
      ). The performance deficit observed here was also associated with decreases in limbic activity in or near the basolateral amygdala. Substantial rat and human work has found evidence that the basolateral amygdala mediates cue-guided choice (
      • Prevost C.
      • Liljeholm M.
      • Tyszka J.M.
      • O’Doherty J.P.
      Neural correlates of specific and general Pavlovian-to-Instrumental Transfer within human amygdalar subregions: A high-resolution fMRI study.
      ,
      • Corbit L.H.
      • Balleine B.W.
      Double dissociation of basolateral and central amygdala lesions on the general and outcome-specific forms of pavlovian-instrumental transfer.
      ); however, the spatial resolution of our imaging data did not allow us to confirm the precise anatomical location of the deficit within the amygdala complex in SZ. Nevertheless, the results distinguished the neural circuits mediating cue-guided choice in a limbic-striatal circuit versus goal-directed choice in a corticostriatal circuit (
      • Balleine B.W.
      • O’Doherty J.P.
      Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action.
      ,
      • Griffiths K.R.
      • Morris R.W.
      • Balleine B.W.
      Translational studies of goal-directed action as a framework for classifying deficits across psychiatric disorders.
      ) and established that they are differentially affected in schizophrenia.
      Although these results provide evidence of a critical functional deficit in SZ, it is important to note a few caveats. First, although the sample size and test durations used here were sufficient to replicate behavioral and neural effects previously reported in healthy adults (
      • Liljeholm M.
      • Tricomi E.
      • O’Doherty J.P.
      • Balleine B.W.
      Neural correlates of instrumental contingency learning: Differential effects of action-reward conjunction and disjunction.
      ,
      • Tanaka S.C.
      • Balleine B.W.
      • O’Doherty J.P.
      Calculating consequences: Brain systems that encode the causal effects of actions.
      ), recent statistical arguments suggest that the effects sizes observed in our SZ group should be interpreted with caution (
      • Button K.S.
      • Ioannidis J.P.
      • Mokrysz C.
      • Nosek B.A.
      • Flint J.
      • Robinson E.S.
      • Munafò M.R.
      Power failure: Why small sample size undermines the reliability of neuroscience.
      ). Second, although relatively novel in the current context, the methods used here were drawn from prior studies in healthy adults (
      • Prevost C.
      • Liljeholm M.
      • Tyszka J.M.
      • O’Doherty J.P.
      Neural correlates of specific and general Pavlovian-to-Instrumental Transfer within human amygdalar subregions: A high-resolution fMRI study.
      ), based originally on those in rodents (
      • Balleine B.W.
      • Dickinson A.
      Goal-directed instrumental action: Contingency and incentive learning and their cortical substrates.
      ,
      • Parkes S.L.
      • Balleine B.W.
      Incentive memory: Evidence the basolateral amygdala encodes and the insular cortex retrieves outcome values to guide choice between goal-directed actions.
      ), and were designed to isolate action-outcome learning from other forms of learning, particularly stimulus-response learning, which are often confounded in standard instrumental paradigms. As such, mapping the neural circuits revealed by standard tests onto the circuits distinguished here will depend on the extent to which standard tests engage action-outcome learning over stimulus-response learning. Third, all SZ participants were treated with second generation antipsychotics, as detailed in Table 1. Nevertheless, whole-brain linear regression analyses (Table S1 in Supplement 1) did not find any significant association between chlorpromazine equivalent dose and neural activity from either test (even at a liberal threshold of p < .05, uncorrected), nor was dose correlated with behavior. Thus, although we cannot estimate the effect of antipsychotic treatment on our results, it is worth noting that the deficits we observed emerged in the presence of medication. It is also worth noting that data collected regarding functional deficits in our schizophrenia patients, particularly the number of hours of paid work they had engaged in over the prior month, correlated highly with the deficit in outcome devaluation that we describe here (Figure S6 in Supplement 1), suggesting this test may be relevant for assessing functional capacity, something worth pursuing in future research.

      Acknowledgments and Disclosures

      This study was supported by a Laureate Fellowship from the Australian Research Council #FL0992409 to BWB; a National Alliance for Research on Schizophrenia and Depression Young Investigator Grant from the Brain & Behavior Research Foundation, and a Grant-In-Aid from the Schizophrenia Research Institute awarded to RWM with infrastructure funding from NSW Health. MJG was supported by a Future Fellowship from the Australian Research Council (FT0991511) and a National Health and Medical Research Council R.D. Wright Biomedical Career Development Award (APP1061875). Participants were recruited from the Australian Schizophrenia Research Bank participant register, which is supported by the National Health and Medical Research Council of Australia, the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. The programs used in the behavioral tasks are available for download at http://balleinelab.com.
      The authors declare no biomedical financial interests or potential conflicts of interest.

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