Patients with schizophrenia demonstrate deficits in motivation and learning that suggest impairment in different aspects of the reward system. In this article, we present the results of 8 converging experiments that address subjective reward experience, the impact of rewards on decision making, and the role of rewards in guiding both rapid and long-term learning. All experiments compared the performance of stably treated outpatients with schizophrenia and demographically matched healthy volunteers. Results to date suggest (1) that patients have surprisingly normal experiences of positive emotion when presented with evocative stimuli, (2) that patients show reduced correlation, compared with controls, between their own subjective valuation of stimuli and action selection, (3) that decision making in patients appears to be compromised by deficits in the ability to fully represent the value of different choices and response options, and (4) that rapid learning on the basis of trial-to-trial feedback is severely impaired whereas more gradual learning may be surprisingly preserved in many paradigms. The overall pattern of findings suggests compromises in the orbital and dorsal prefrontal structures that play a critical role in the ability to represent the value of outcomes and plans. In contrast, patients often (but not always) approach normal performance levels on the slow learning achieved by the integration of reinforcement signals over many trials, thought to be mediated by the basal ganglia.

Background Decision-making studies show that response selection is influenced by the “effort cost” associated with response alternatives. These effort-cost calculations seem to be mediated by a distributed neural circuit including the anterior cingulate cortex and subcortical targets of dopamine neurons. On the basis of evidence of dysfunction in these systems in schizophrenia (SZ), we examined whether effort-cost computations were impaired in SZ patients and whether these deficits were associated with negative symptoms. Methods Effort-cost decision-making performance was evaluated in 44 patients with SZ and 36 demographically matched control subjects. Subjects performed a computerized task where they were presented with a series of 30 trials in which they could choose between making 20 button presses for $1 or 100 button presses for higher amounts (varying from $3 to $7 across trials). Probability of reward receipt was also manipulated to determine whether certain (100%) or uncertain (50%) reward affected effort-based decision-making. Results Patients were less likely than control subjects to select the high-effort response alternative during the 100% probability condition, particularly when the value payoff was highest (i.e., $6 and $7). Patients were also less likely to select the high-effort option on trials after reward in the 50% probability condition. Furthermore, these impairments in effort-cost computations were greatest among patients with elevated negative symptoms. There was no association with haloperidol equivalent dosage. Conclusions The motivational impairments of SZ might be associated with abnormalities in estimating the “cost” of effortful behavior. This increased effort cost might undermine volition.

Synchronized low-frequency spontaneous fluctuations of the functional MRI (fMRI) signal have recently been applied to investigate large-scale neuronal networks of the brain in the absence of specific task instructions. However, the underlying neural mechanisms of these fluctuations remain largely unknown. To this end, electrophysiological recordings and resting-state fMRI measurements were conducted in ␣-chloralose-anesthetized rats. Using a seed-voxel analysis strategy, region-specific, anesthetic dosedependent fMRI resting-state functional connectivity was detected in bilateral primary somatosensory cortex (S1FL) of the resting brain. Cortical electroencephalographic signals were also recorded from bilateral S1FL; a visual cortex locus served as a control site. Results demonstrate that, unlike the evoked fMRI response that correlates with power changes in the ␥ bands, the resting-state fMRI signal correlates with the power coherence in low-frequency bands, particularly the ␦ band. These data indicate that hemodynamic fMRI signal differentially registers specific electrical oscillatory frequency band activity, suggesting that fMRI may be able to distinguish the ongoing from the evoked activity of the brain.electroencephalogram ͉ spontaneous fluctuations ͉ functional connectivity T he human brain is thought to be composed of multiple coherent neuronal networks of variable scales that support sensory, motor, and cognitive functions (1). The traditional approach to studying such networks has been to use specific tasks to probe neurobiological responses. In contrast, recent studies have demonstrated the existence of spontaneous, low-frequency (i.e., Ͻ0.1 Hz) fluctuations in the functional MRI (fMRI) signal of the resting brain that exhibit coherence patterns within specific neuronal networks in the absence of overt task performance or explicit attentional demands (2-4). Such precisely patterned spontaneous activity has been reported in both awake human and anesthetized nonhuman primates (5). Recently, ''resting-state'' fMRI has been applied to study alterations in brain networks under such pathological conditions as Alzheimer's disease (6), multiple sclerosis (7), and spatial neglect syndrome (8). These studies collectively suggest that, rather than simple physiological artifacts induced by cardiac pulsations or respiration, as was originally suspected, these widely distributed coherent low-frequency fMRI fluctuations have a direct neural basis (9, 10). However, more than a decade since they were first identified, the linkage between neuronal activity and restingstate fMRI signal remains largely unknown, underscoring the clear and critical need for well controlled animal models to investigate this phenomenon.Across various states of vigilance, the electrical activity of neuronal networks is known to oscillate at various frequencies and amplitudes, with high-frequency oscillations confined to local networks, whereas large networks are recruited during slow oscillations (11,12). Imposed tasks alter local field potent...

Context Negative symptoms are a core feature of schizophrenia, but their pathophysiology remains unclear. Objective Negative symptoms are defined by the absence of normal function. However, there must be a productive mechanism that leads to this absence. Here, we test a reinforcement learning account suggesting that negative symptoms result from a failure to represent the expected value of rewards coupled with preserved loss avoidance learning. Design Subjects performed a probabilistic reinforcement learning paradigm involving stimulus pairs in which choices resulted in either reward or avoidance of loss. Following training, subjects indicated their valuation of the stimuli in a transfer task. Computational modeling was used to distinguish between alternative accounts of the data. Setting A tertiary care research outpatient clinic. Patients A total of 47 clinically stable patients with a diagnosis of schizophrenia or schizoaffective disorder and 28 healthy volunteers participated. Patients were divided into high and low negative symptom groups. Main Outcome measures 1) The number of choices leading to reward or loss avoidance and 2) performance in the transfer phase. Quantitative fits from three different models were examined. Results High negative symptom patients demonstrated impaired learning from rewards but intact loss avoidance learning, and failed to distinguish rewarding stimuli from loss-avoiding stimuli in the transfer phase. Model fits revealed that high negative symptom patients were better characterized by an “actor-critic” model, learning stimulus-response associations, whereas controls and low negative symptom patients incorporated expected value of their actions (“Q-learning”) into the selection process. Conclusions Negative symptoms are associated with a specific reinforcement learning abnormality: High negative symptoms patients do not represent the expected value of rewards when making decisions but learn to avoid punishments through the use of prediction errors. This computational framework offers the potential to understand negative symptoms at a mechanistic level.