Dual system theories suggest that behavioral control is parsed between a deliberative “model-based” and a more reflexive “model-free” system. A balance of control exerted by these systems is thought to be related to dopamine neurotransmission. However, in the absence of direct measures of human dopamine, it remains unknown whether this reflects a quantitative relation with dopamine either in the striatum or other brain areas. Using a sequential decision task performed during functional magnetic resonance imaging, combined with striatal measures of dopamine using [¹⁸F]DOPA positron emission tomography, we show that higher presynaptic ventral striatal dopamine levels were associated with a behavioral bias toward more model-based control. Higher presynaptic dopamine in ventral striatum was associated with greater coding of model-based signatures in lateral prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum. Thus, interindividual variability in ventral striatal presynaptic dopamine reflects a balance in the behavioral expression and the neural signatures of model-free and model-based control. Our data provide a novel perspective on how alterations in presynaptic dopamine levels might be accompanied by a disruption of behavioral control as observed in aging or neuropsychiatric diseases such as schizophrenia and addiction. Significance Whether humans make choices based on a deliberative “model-based” or a reflexive “model-free” system of behavioral control remains an ongoing topic of research. Dopamine is implicated in motivational drive as well as in planning future actions. Here, we demonstrate that higher presynaptic dopamine in human ventral striatum is associated with more pronounced model-based behavioral control, as well as an enhanced coding of model-based signatures in lateral prefrontal cortex and diminished coding of model-free learning signals in ventral striatum. Our study links ventral striatal presynaptic dopamine to a balance between two distinct modes of behavioral control in humans. The findings have implications for neuropsychiatric diseases associated with alterations of dopamine neurotransmission and a disrupted balance of behavioral control.

Differentiation between self-produced tactile stimuli and touch by others is necessary for social interactions and for a coherent concept of “self.” The mechanisms underlying this distinction are unknown. Here, we investigated the distinction between self- and other-produced light touch in healthy volunteers using three different approaches: fMRI, behavioral testing, and somatosensory-evoked potentials (SEPs) at spinal and cortical levels. Using fMRI, we found self–other differentiation in somatosensory and sociocognitive areas. Other-touch was related to activation in several areas, including somatosensory cortex, insula, superior temporal gyrus, supramarginal gyrus, striatum, amygdala, cerebellum, and prefrontal cortex. During self-touch, we instead found deactivation in insula, anterior cingulate cortex, superior temporal gyrus, amygdala, parahippocampal gyrus, and prefrontal areas. Deactivation extended into brain areas encoding low-level sensory representations, including thalamus and brainstem. These findings were replicated in a second cohort. During self-touch, the sensorimotor cortex was functionally connected to the insula, and the threshold for detection of an additional tactile stimulus was elevated. Differential encoding of self- vs. other-touch during fMRI correlated with the individual self-concept strength. In SEP, cortical amplitudes were reduced during self-touch, while latencies at cortical and spinal levels were faster for other-touch. We thus demonstrated a robust self–other distinction in brain areas related to somatosensory, social cognitive, and interoceptive processing. Signs of this distinction were evident at the spinal cord. Our results provide a framework for future studies in autism, schizophrenia, and emotionally unstable personality disorder, conditions where symptoms include social touch avoidance and poor self-vs.-other discrimination.

Abnormalities in reinforcement learning are a key finding in schizophrenia and have been proposed to be linked to elevated levels of dopamine neurotransmission. Behavioral deficits in reinforcement learning and their neural correlates may contribute to the formation of clinical characteristics of schizophrenia. The ability to form predictions about future outcomes is fundamental for environmental interactions and depends on neuronal teaching signals, like reward prediction errors. While aberrant prediction errors, that encode non-salient events as surprising, have been proposed to contribute to the formation of positive symptoms, a failure to build neural representations of decision values may result in negative symptoms. Here, we review behavioral and neuroimaging research in schizophrenia and focus on studies that implemented reinforcement learning models. In addition, we discuss studies that combined reinforcement learning with measures of dopamine. Thereby, we suggest how reinforcement learning abnormalities in schizophrenia may contribute to the formation of psychotic symptoms and may interact with cognitive deficits. These ideas point toward an interplay of more rigid versus flexible control over reinforcement learning. Pronounced deficits in the flexible or model-based domain may allow for a detailed characterization of well-established cognitive deficits in schizophrenia patients based on computational models of learning. Finally, we propose a framework based on the potentially crucial contribution of dopamine to dysfunctional reinforcement learning on the level of neural networks. Future research may strongly benefit from computational modeling but also requires further methodological improvement for clinical group studies. These research tools may help to improve our understanding of disease-specific mechanisms and may help to identify clinically relevant subgroups of the heterogeneous entity schizophrenia.

Theoretical and animal work has proposed that prefrontal cortex (PFC) glutamate inhibits dopaminergic inputs to the ventral striatum (VS) indirectly, whereas direct VS glutamatergic afferents have been suggested to enhance dopaminergic inputs to the VS. In the present study, we aimed to investigate relationships of glutamate and dopamine measures in prefrontostriatal circuitries of healthy humans. We hypothesized that PFC and VS glutamate, as well as their balance, are differently associated with VS dopamine. Glutamate concentrations in the left lateral PFC and left striatum were assessed using 3-Tesla proton magnetic resonance spectroscopy. Striatal presynaptic dopamine synthesis capacity was measured by fluorine-18-l-dihydroxyphenylalanine (F-18-FDOPA) positron emission tomography. First, a negative relationship was observed between glutamate concentrations in lateral PFC and VS dopamine synthesis capacity (n = 28). Second, a positive relationship was revealed between striatal glutamate and VS dopamine synthesis capacity (n = 26). Additionally, the intraindividual difference between PFC and striatal glutamate concentrations correlated negatively with VS dopamine synthesis capacity (n = 24). The present results indicate an involvement of a balance in PFC and striatal glutamate in the regulation of VS dopamine synthesis capacity. This notion points toward a potential mechanism how VS presynaptic dopamine levels are kept in a fine-tuned range. A disruption of this mechanism may account for alterations in striatal dopamine turnover as observed in mental diseases (e.g., in schizophrenia). SIGNIFICANCE STATEMENT The present work demonstrates complementary relationships between prefrontal and striatal glutamate and ventral striatal presynaptic dopamine using human imaging measures: a negative correlation between prefrontal glutamate and presynaptic dopamine and a positive relationship between striatal glutamate and presynaptic dopamine are revealed. The results may reflect a regulatory role of prefrontal and striatal glutamate for ventral striatal presynaptic dopamine levels. Such glutamate–dopamine relationships improve our understanding of neurochemical interactions in prefrontostriatal circuits and have implications for the neurobiology of mental disease.

The striatum is known to play a key role in reinforcement learning, specifically in the encoding of teaching signals such as reward prediction errors (RPEs). It has been proposed that aberrant salience attribution is associated with impaired coding of RPE and heightened dopamine turnover in the striatum, and might be linked to the development of psychotic symptoms. However, the relationship of aberrant salience attribution, RPE coding, and dopamine synthesis capacity has not been directly investigated. Here we assessed the association between a behavioral measure of aberrant salience attribution, the salience attribution test, to neural correlates of RPEs measured via functional magnetic resonance imaging while healthy participants (n = 58) performed an instrumental learning task. A subset of participants (n = 27) also underwent positron emission tomography with the radiotracer [¹⁸F]fluoro-l-DOPA to quantify striatal presynaptic dopamine synthesis capacity. Individual variability in aberrant salience measures related negatively to ventral striatal and prefrontal RPE signals and in an exploratory analysis was found to be positively associated with ventral striatal presynaptic dopamine levels. These data provide the first evidence for a specific link between the constructs of aberrant salience attribution, reduced RPE processing, and potentially increased presynaptic dopamine function.