Dopamine, a neurotransmitter critical for mammalian brain function and behavior, plays a pivotal role in various human brain disorders, including substance use disorders, depression, and Parkinson’s disease. The intricate web of dopamine signaling influences distributed brain networks controlling processes such as reward learning, motor planning, motivation, and emotion. Understanding the neural basis of dopamine function is essential for unraveling the mechanisms underlying these disorders.
Dopamine Neurons and Reward Prediction Errors
Human Evidence for Dopaminergic Involvement
In humans, direct evidence of dopamine neurons signaling RPEs has been limited. Firing rate changes of putative dopamine neurons and blood oxygen–level–dependent signals in dopaminergic-rich regions suggest physiological processing of RPEs. However, indirect measures like spiking rates and hemodynamic signals do not offer direct evidence of dopamine release signaling RPEs in target regions.
Direct Measurement of Dopamine Release in Humans
Recent advancements in technology have enabled the direct measurement of dopamine release in the human brain with high temporal resolution. Studies utilizing voltametric methods have revealed subsecond changes in dopamine levels reflecting both actual and counterfactual error signals during decision-making processes. These findings suggest that dopamine release in the human brain encodes a complex interplay of reward and punishment information.
Testing Hypotheses with Voltametric Studies
Reinforcement Learning Models and Hypotheses
Two mutually exclusive reinforcement learning models were tested to elucidate the role of dopamine in encoding RPEs and punishment prediction errors (PPEs). The first model proposed a unidimensional valence system, suggesting dopamine release encodes either RPEs or PPEs. The second model suggested a valence-partitioned system, positing that appetitive and aversive stimuli are processed independently, allowing the simultaneous learning of statistically independent stimuli.
Discussion
In this discussion, we delve into the key findings and implications of our study, highlighting the significance of subsecond dopamine fluctuations in the caudate nucleus, their association with reward prediction errors (RPEs) and punishment prediction errors (PPEs), and the broader implications for understanding human decision-making processes.
Confirmation of VPRL Framework:
Integration of Reward and Punishment Information:
Our findings challenge existing ideas suggesting the integration of rewards and punishments during learning, leading to a “zero-sum” prediction error signaled by dopamine neurons only in the case of positive prediction errors (rewarding outcomes). Instead, the VPRL framework suggests that dopamine release encodes reward and punishment learning simultaneously, in parallel, through independent neural systems.
Temporal Dynamics of Dopamine Signals:
Our observation of distinct temporal patterns in dopamine responses is consistent with the neuroanatomical circuitry involved in modulating dopamine neuron activity in response to rewarding and aversive stimuli. Early dopamine responses (0 to 300 ms) signaled RPEs, while later responses (400 to 700 ms) signaled PPEs. This temporal coding of valence information suggests that the regulation of goal-directed decision-making by basal ganglia and distributed cortical brain regions depends on the timing of dopamine fluctuations.
Challenges in Data Analysis and Variability:
Averaging dopamine time series across patients revealed VP-RPEs and VP-PPEs, yet we acknowledge challenges in data analysis. Human volunteers exhibit significant variation in life experience and genetic background, potentially influencing dopamine signals. Moreover, the microanatomical environment of recording electrodes within the caudate introduces variability. Future studies must address these challenges for a more comprehensive understanding of subsecond dopamine fluctuations.
Implications for Neuromodulation and Behavior:
Despite the constraints imposed by neurosurgical procedures, our data extend traditional Temporal Difference Reinforcement Learning (TDRL)-based algorithms, offering insights into how humans process affective information. Understanding the role of dopamine in human behavior, decision-making, and subjective experience is crucial for advancing our knowledge of psychiatric and neurological disorders. Rapid dopaminergic signals may be pivotal in conditions such as essential tremor (ET) and Parkinson’s disease, warranting further investigation.
Human-Specific Aspects of Dopaminergic Function:
While acknowledging the contributions of nonhuman models, our study emphasizes the need to explore fundamentally human phenomena. The confirmation of dopamine’s role in encoding aversive feedback extends prior nonhuman model organism findings. The parallel processing of aversive outcomes by dopamine aligns with models of the reinforcement learning system’s role in anxiety disorders, analogous to addiction, depression, and obsessive-compulsive disorder.
In conclusion, our study sheds light on the intricate dynamics of dopamine signaling in the human brain during decision-making. By confirming and expanding upon existing frameworks, we provide a foundation for future investigations into the role of dopamine in neuromodulation, human behavior, and the pathophysiology of psychiatric and neurological disorders.
reference link : https://www.science.org/doi/10.1126/sciadv.adi4927
