Kenneth T. Kishida

Aberrant interval timing in the millisecond-to-second range is a consistent feature of neurological and neuropsychiatric disorders involving striatal dysfunction, yet its neurochemical substrates in the human brain remain poorly characterized. Striatal dopamine has long been implicated in temporal processing, but evidence from animal models and human pharmacological studies has proved difficult to reconcile, in part because endogenous dopamine dynamics in humans have been inaccessible at relevant timescales. Patients with Parkinson’s disease (n = 5, aged 62–73 years; two females) completed a temporal bisection task (300 trials), judging whether visual stimulus intervals (500–1100 ms) were closer to a learned short (500 ms) or long (1100 ms) reference. Dopamine and serotonin concentrations were measured in the caudate nucleus during awake deep brain stimulation surgery using fast-scan cyclic voltammetry. Elastic-net machine-learning models trained on in vitro voltammetry data were applied to human recordings to derive dopamine and serotonin time series at 10 Hz. We used cluster-based permutation analysis to identify stimulus-aligned time windows in which dopamine or serotonin differed between short and long judgements, and entered mean concentrations from these windows as predictors of temporal performance in trial-level hierarchical mixed-effects models. Slower steady-state dopaminergic and serotonergic tone, quantified as the mean concentration within successive multi-minute periods, was also assessed as a predictor of temporal behaviour. Finally, behavioural performance was compared with that of a non-clinical control cohort (n = 17, aged 50–66 years; seven females). Elevated phasic dopamine (625–670 ms after stimulus onset) were associated with a greater likelihood of short judgements and reduced temporal overestimation bias. This association was independent of the objective clock time, with an identical analysis yielding non-significant results for the short versus the long stimulus intervals. By contrast, tonic dopamine levels covaried with sensitivity in discriminating stimulus intervals (temporal precision). Lower tonic dopamine was associated with poorer temporal precision both within patients over the experimental session and in patients relative to healthy controls. No corresponding effects were observed for serotonin, suggesting dopamine specificity across phasic and tonic timescales. By resolving endogenous striatal dopamine with sub-second precision during conscious human behaviour, this study links temporally distinct dopaminergic dynamics to dissociable aspects of timing behaviour in Parkinson’s disease. This challenges accounts of dopamine as a single modulatory influence and situates these findings within the literature on partly separable effects of phasic and tonic dopaminergic signalling, reconciling discrepancies between human and animal evidence, and advancing understanding of the neurochemical basis of human time perception.

Recent advances in deep brain stimulation (DBS) of the subcallosal cingulate (SCC) show promise in mitigating the symptoms of treatment-resistant depression (TRD) in humans ^1–3 . Monoamines, such as dopamine and serotonin, mediate the effects of pharmacological treatments of depression. However, their roles in recovery following DBS remain elusive, largely due to technical limitations of measuring these neurotransmitters in the living human brain. Here, by leveraging machine learning-enhanced electrochemistry ^4–7 , we show that dopamine and serotonin signaling following DBS to the SCC predicted later depressive symptom relief in humans with TRD. We found that both dopamine and serotonin levels increased following subtherapeutic intraoperative SCC stimulation, with each neurotransmitter showing selective responses to distinct decision-making tasks. Furthermore, acute dopamine increases predicted later mood improvements during a social decision-making task, while serotonin enhancement predicted faster responses during a non-social learning task longitudinally. Critically, changes in dopamine and serotonin levels during the social decision-making task jointly predicted depressive symptom remission at 6-month follow-up. These findings illustrate the contribution of both dopamine and serotonin signaling in predicting behavioral improvement and depressive symptom remission in humans with TRD. Such neurochemical plasticity may serve as potential mechanistic biomarkers for SCC DBS mechanism and TRD treatment response. Significance statement: Dopamine and serotonin levels increased following acute DBS to the SCC in humans. Acute dopamine and serotonin changes predicted later mood and response speed changes. Sustained TRD recovery was predicted by acute increases in both dopamine and serotonin estimates.

Dynamic changes in dopamine, noradrenaline, and serotonin release are believed to causally contribute to the neural computations that support reward-based decision making. Accordingly, changes in signaling by these systems are hypothesized to underwrite multiple cognitive and behavioral symptoms observed in many neurological disorders. Here, we characterize the release of these neurotransmitters measured concurrently in the caudate of patients with Parkinson's disease or essential tremor undergoing deep brain stimulation surgery as they played a social exchange game. We show that violations in the expected value of monetary offers are encoded by opponent patterns of dopamine and serotonin release in essential tremor, but not Parkinson's disease, patients. We also demonstrate that these changes in serotonin signaling comprise a neurochemical boundary that subsegments these two neuromotor diseases. Our combined results point to a neural signature of altered reward processing that can be used to understand the signaling deficiencies that underwrite these diseases.

The neurobiological mechanisms underlying human time perception remain elusive. Evidence has consistently linked striatal dopamine to timing behaviors, but it is still uncertain how rapid changes in dopamine may modulate human time perception. Many tasks designed to measure time perception utilize instrumental conditioning paradigms that reinforce correctly timed intervals. In these tasks, subjects are shown to improve their performance following repeated presentations of temporal cues – a phenomenon known as ‘temporal learning’. We sought to determine the association between rapid changes in human dopamine levels and temporal learning on an interval timing task that tested the reproduction of 1000ms, 3000ms, and 5000ms intervals in the presence and absence of monetary reinforcement. We utilized human voltammetry to measure real-time dopamine concentrations from the striatum of patients with Parkinson’s disease while they performed the interval timing task. We first compared task behavior between patients with Parkinson’s disease and neurologically healthy controls and found significant differences in the reproduction of 1000ms intervals, but not 3000ms or 5000ms intervals of time. Further, we observed that during 1000ms intervals, increases in striatal dopamine concentrations were associated with increases in temporal errors, but only during the expectation of monetary reinforcement. We also demonstrated that as temporal errors decrease overtime during temporal learning, so do striatal dopamine concentrations. These results suggest that dopamine may be driving temporal learning through the generation of temporal errors in response to positive reinforcement. These findings may have significant implications in our understanding of the role that dopamine plays in time perception. Significance Statement: Human time perception is a fundamental cognitive ability but the interaction between how the human brain perceives time and dopamine’s role is unclear. This study is the first of its kind to apply human voltammetry to measure rapid changes in dopamine levels associated with temporal learning in the presence and absence of positive reinforcement. We demonstrate that temporal learning may be affected by moment-to-moment changes in dopamine levels, which are also counterintuitively related to the generation of temporal errors in the presence of expectations of rewarding feedback.

Expectations shape our perception, profoundly influencing how we interpret the world. Positive expectations about sensory stimuli can alleviate distress and reduce pain (e.g., placebo effect), while negative expectations may heighten anxiety and exacerbate pain (e.g., nocebo effect). To investigate the impact of the (an)hedonic aspect of expectations on subjective experiences, we measured neurobehavioral responses to the taste of hot sauce among participants with heterogeneous taste preferences. By identifying participants who “liked” versus those who strongly “disliked” spicy flavors and by providing contextual cues about the spiciness of the sauce to be tasted, we dissociated the effects of positive and negative expectations from sensory stimuli (i.e., visual and gustatory stimuli), which were the same across all participants. Our results indicate that positive expectations lead to modulations in the intensity of subjective experience. These modulations were accompanied by increased activity in brain regions previously linked to information integration and the placebo effect, including the anterior insula, dorsolateral prefrontal cortex, and dorsal anterior cingulate cortex, as well as a predefined “pleasure signature.” In contrast, negative expectations decreased hedonic experience and increased neural activity in the previously validated “Neurological Pain Signature” network. These findings demonstrate that hedonic aspects of one’s expectations asymmetrically shape how the brain processes sensory input and associated behavioral reports of one’s subjective experiences of intensity, pleasure, and pain. Our results suggest a dissociable impact of hedonic information: positive expectations facilitate higher-level information integration and reward processing, while negative expectations prime lower-level nociceptive and affective processes. This study demonstrates the powerful role of hedonic expectations in shaping subjective reality and suggests potential avenues for consumer and therapeutic interventions targeting expectation-driven neural processes.

KEY POINTS: QUESTION : Can neurocomputational depictions of learning and affective behavior characterize patients with treatment-resistant depression before electroconvulsive therapy? FINDINGS : In this observational study, computational models were used to quantify the behavioral dynamics of 1) adaptive choice behavior as individuals learned from feedback and 2) associated changes in affective self-report. These models provided quantitative parameters that were associated with specific neural and behavioral changes in patients with treatment-resistant depression and may be sufficient to independently identify patients with depression. MEANING : Computational models that describe hypothesized mechanisms underlying adaptive behavior and affective experience may provide a means to quantitatively phenotype individual differences in major depression pathophysiology. IMPORTANCE: Globally, treatment-resistant depression affects approximately one-third of all patients diagnosed with major depressive disorder. Currently, there are neither behavioral nor neural measures that quantitatively phenotype characteristics underlying treatment-resistant depression. OBJECTIVE: Determine whether neurocomputational models that integrate information about adaptive behavior and associated self-reported feelings can characterize differences in patients with treatment-resistant depression. DESIGN: In this observational study, data were collected over two research visits from 2020-2023 that occurred before and after standard-of-care electroconvulsive therapy (ECT) for treatment-resistant depression. This report focuses on “visit 1”, which occurred after patients consented to ECT but before their initial treatment. SETTING: Wake Forest University School of Medicine; Atrium Health Wake Forest Baptist Psychiatric Outpatient Center; Atrium Health Wake Forest Hospital. PARTICIPANTS: Participants planning to receive ECT for depression (“pre-ECT”) and participants not planning to receive ECT with (“non-ECT”) or without depression (“no-depression”), were recruited from the Psychiatric Outpatient Center and community. EXPOSURES: Computerized delivery of a ‘Probabilistic Reward and Punishment with Subjective Rating’ task during fMRI. MAIN OUTCOMES AND MEASURES: Computational modeling of choice behavior provided parameters that characterized learning dynamics and associated affect dynamics expressed through intermittent Likert scale self-reports. Multivariate statistical analyses relating model parameters, neurobehavioral responses, and clinical assessments. RESULTS: Pre-ECT (N=29; 55.2% female), non-ECT (N=40; 70% female), and no-depression (N=41; 65.9% female). Parameters derived from computational models fit to behavior elicited during learning and the expression of affective experiences clearly differentiates the three groups. Reinforcement Learning model parameters alone do not perform as well as models that incorporate affective self-reports. Notably, the set of model parameters that include learning and affective dynamics demonstrated excellent, cross-validated, diagnostic classification of depression diagnosis. Prior to ECT, neurobehavioral responses associated with learning and affective experiences about ‘punishing’ events were significantly impaired in pre-ECT compared to non-ECT and no-depression cohorts. CONCLUSIONS AND RELEVANCE: Computational models of behavioral dynamics associated with learning and affect can describe specific hypotheses about neurocomputational-mechanisms underlying treatment-resistant depression. The present work suggests differences in processing of emotionally negative states and suggests a potential model-based behavioral diagnostic for individuals with major depression. Such models may eventually be used to augment the diagnosis of treatment-resistant depression or possibly determine phenotype-genotype relationships for disease status and progression.

IMPORTANCE: Electroconvulsive therapy (ECT) is an effective medical procedure for patients with treatment-resistant depression. However, quantitative neural and behavioral measures that characterize how patients respond to ECT treatment are largely lacking. OBJECTIVE: Determine whether neurocomputational models that integrate information about adaptive learning behavior and associated affective experiences can characterize neurobehavioral changes in patients whose depression improves following ECT treatment. DESIGN: This observational study included two research visits from 2020-2023 that occurred before and after standard-of-care ECT for treatment-resistant depression. This report focuses on “visit 2”, which occurred after patients received their initial ECT treatment series. SETTING: Wake Forest University School of Medicine; Atrium Health Wake Forest Baptist Psychiatric Outpatient Center; Atrium Health Wake Forest Hospital. PARTICIPANTS: Participants who received ECT for treatment-resistant depression (“ECT”), and participants not receiving ECT but with depression (“non-ECT”) or without depression (“no-depression”) were recruited from the Psychiatric Outpatient Center and community, respectively. EXPOSURES: Computerized delivery of a Probabilistic Reward and Punishment with Subjective Rating task with functional magnetic resonance imaging. MAIN OUTCOMES AND MEASURES: Computational modeling of choice behavior provided parameters that characterized learning dynamics and associated affect dynamics expressed through intermittent Likert scale self-reports. Multivariate statistical analyses relating model parameters, neurobehavioral responses, and clinical assessments. RESULTS: ECT (N=21; 47.6% female), non-ECT (N=36; 69.4% female), and no-depression (N=38; 65.8% female) participants. Parameters derived from computational models fit to behavior elicited during learning and the expression of affective experiences for all groups reveled specific changes in patients who responded favorably to ECT. ECT-responders demonstrated increased rates of learning from rewarding trials, normalized affective response to punishments, and an increase in the influence of counterfactual ‘missed opportunities’ on affective behavior. Additionally, ECT-responders’ showed changes in BOLD activity regions specific to each of these parameters. ECT-responders’ BOLD-responses to surprising punishments and counterfactual missed opportunities were altered from visit 1 to visit 2 in the inferior frontal operculum, Rolandic operculum, precentral gyrus, and caudate. CONCLUSIONS AND RELEVANCE: Computational models of neurobehavioral dynamics associated with learning and affect can describe specific hypotheses about neurocomputational-mechanisms underlying favorable responses to treatment-resistant depression. Our results suggest computational estimates of learning and affective dynamics may aid in identifying depression phenotypes and treatment outcomes in psychiatric medicine where objective measures are largely lacking. KEY POINTS: QUESTION: How does ECT treatment change neurocomputational measures of learning and affective experiences in patients with treatment-resistant depression? FINDINGS: In this observational study, computational models were used to quantify the behavioral dynamics of learning and associated changes in subjective feelings in patients who underwent ECT treatment for treatment resistant depression and controls. In ECT-responders we observed increases in reward-based learning, normalized affective responses to surprising positive and negative outcomes, and associated changes in fMRI-measured BOLD-responses. MEANING: Computational phenotyping of task behavior and associated brain responses provides quantification of complex neurobehavioral dynamics and provides specific insight into the neurobehavioral mechanisms underlying successful ECT treatment.

For over four decades, fast‐scan cyclic voltammetry (FSCV) has been used to selectively measure neurotransmitters such as dopamine (DA) with high spatial and temporal resolution, providing detailed information about the regulation of DA in the extracellular space. FSCV is an optimal method for determining concentrations of stimulus‐evoked DA in brain tissue. When modelling diseases involving disturbances in DA transmission, preclinical rodent models are especially useful because of the availability of specialized tools and techniques that serve as a foundation for translational research. There is known heterogeneity in DA dynamics between and within DA‐innervated brain structures and between males and females. However, systematic evaluations of sex‐ and species‐differences across multiple areas are lacking. Therefore, using FSCV, we captured a broad range of DA dynamics across five sub‐regions of the dorsal and ventral striatum of males and females of both rats and mice that reflect the functional heterogeneity of DA kinetics and dynamics within these structures. While numerous differences were found, in particular, we documented a strong, consistent pattern of increased DA transporter activity in females in all of the regions surveyed. The data herein are intended to be used as a resource for further investigation of DA terminal function.

Time perception is often investigated in animal models and in humans using instrumental paradigms where reinforcement learning (RL) and associated dopaminergic processes have modulatory effects. For example, interval timing, which includes the judgment of relatively short intervals of time (ranging from milliseconds to minutes), has been shown to be modulated by manipulations of striatal dopamine. The ‘expected value of reward’ (EV) and ‘reward prediction errors’ (RPEs) are key variables described in RL-theory that explain dopaminergic signals during reward processing during instrumental learning. Notably, the underlying connection between RL-processes and time perception in humans is relatively underexplored. Herein, we investigated the impact of EV and RPEs on interval timing in humans. We tested the hypotheses that EV and RPEs modulate the experience of short time intervals. We demonstrate that expectations of monetary gains or losses increases the initial performance error for 1000ms intervals. Temporal learning over repeated trials is observed with accelerated learning of non-reinforced 1000ms intervals; however, RPEs – specifically about rewards and not punishments – appear to reinforce performance errors, which effectively interferes with the rate at which (reinforced) 1000ms intervals were learned. These effects were not significant for 3000ms and 5000ms intervals. Our results demonstrate that EV and RPEs influence human behavior about 1000ms time intervals. We discuss our results considering model-free ‘temporal difference RL-theory’, which suggests the hypothesis that interval timing may be mediated by dopaminergic signals that reinforce the learning and prediction of dynamic state-transitions which could be encoded without an explicit reference to ‘time’ intervals.

This perspective encompasses a focused review of the literature leading to a tipping point in electroanalytical chemistry. We tie together the threads of a "revolution" quietly in the making for years through the work of many authors. Long-held misconceptions about the use of background subtraction in fast voltammetry are addressed. We lay out future advantages that accompany background-inclusive voltammetry, particularly when paired with modern machine-learning algorithms for data analysis.