The Protective Action Encoding of Serotonin Transients in the Human Brain

Erasing "bad memories": reversing aberrant synaptic plasticity as therapy for neurological and psychiatric disorders

Dopamine modulates corticostriatal plasticity in both the direct and indirect pathways of the cortico-striato-thalamo-cortical (CSTC) loops. These gradual changes in corticostriatal synaptic strengths produce long-lasting changes in behavioral responses. Under normal conditions, these mechanisms enable the selection of the most appropriate responses while inhibiting others. However, under dysregulated dopamine conditions, including a lack of dopamine release or dopamine signaling, these mechanisms could lead to the selection of maladaptive responses and/or the inhibition of appropriate responses in an experience-dependent and task-specific manner. In this review, we propose that preventing or reversing such maladaptive synaptic strengths and erasing such aberrant "memories" could be a disease-modifying therapeutic strategy for many neurological and psychiatric disorders. We review evidence from Parkinson's disease, drug-induced parkinsonism, L-DOPA-induced dyskinesia, obsessive-compulsive disorder, substance use disorders, and depression as well as research findings on animal disease models. Altogether, these studies allude to an emerging theme in translational neuroscience and promising new directions for therapy development. Specifically, we propose that combining pharmacotherapy with behavioral therapy or with deep brain stimulation (DBS) could potentially cause desired changes in specific neural circuits. If successful, one important advantage of correcting aberrant synaptic plasticity is long-lasting therapeutic effects even after treatment has ended. We will also discuss the potential molecular targets for these therapeutic approaches, including the cAMP pathway, proteins involved in synaptic plasticity as well as pathways involved in new protein synthesis. We place special emphasis on RNA binding proteins and epitranscriptomic mechanisms, as they represent a new frontier with the distinct advantage of rapidly and simultaneously altering the synthesis of many proteins locally.

Carbon microelectrodes for the measurement of neurotransmitters with fast-scan cyclic voltammetry: methodology and applications

Carbon microelectrodes (CMEs) have emerged as pivotal tools in the field of neurochemical sensing, enabling precise, real-time monitoring of neurotransmitters in both research and clinical contexts. The current review explores the design, fabrication, and application of CMEs, emphasizing recent advancements in material science and electrochemical techniques that enhance their sensitivity, selectivity, and biocompatibility. Innovations such as the incorporation of nanomaterials, including graphene and carbon nanotubes, and the adoption of advanced fabrication methods like three-dimensional (3D) printing and chemical vapor deposition, are discussed in detail. These developments have led to significant improvements in electrode performance, the reduction of biofouling and interferants, while enabling the detection of low concentrations of neurochemicals in complex biological systems. This review further highlights the potential of CMEs to address clinical challenges such as diagnosing and monitoring neurological disorders such as Parkinson's Disease and depression. By integrating advanced surface modifications, polymer coatings, and method development strategies, CMEs demonstrate high durability, reduced fouling, and enhanced specificity. Despite these advancements, challenges remain related to long-term in vivo stability, batch fabrication, and reproducibility, thus necessitating further research and optimization. This review highlights the transformative potential of CMEs in both research and therapeutic applications, providing a comprehensive overview of their current state and future directions. By addressing existing limitations and leveraging emerging technologies, CMEs have the potential to further enhance neurochemical sensing and contribute to breakthroughs in neuroscience and biomedical science.

Emotional words evoke region- and valence-specific patterns of concurrent neuromodulator release in human thalamus and cortex

Words represent a uniquely human information channel-humans use words to express thoughts and feelings and to assign emotional valence to experience. Work from model organisms suggests that valence assignments are carried out in part by the neuromodulators dopamine, serotonin, and norepinephrine. Here, we ask whether valence signaling by these neuromodulators extends to word semantics in humans by measuring sub-second neuromodulator dynamics in the thalamus (N = 13) and anterior cingulate cortex (N = 6) of individuals evaluating positive, negative, and neutrally valenced words. Our combined results suggest that valenced words modulate neuromodulator release in both the thalamus and cortex, but with region- and valence-specific response patterns, as well as hemispheric dependence for dopamine release in the anterior cingulate. Overall, these experiments provide evidence that neuromodulator-dependent valence signaling extends to word semantics in humans, but not in a simple one-valence-per-transmitter fashion.

In Vivo Electrochemical Biosensing Technologies for Neurochemicals: Recent Advances in Electrochemical Sensors and Devices

In vivo electrochemical sensing of neurotransmitters, neuromodulators, and metabolites plays a critical role in real-time monitoring of various physiological or psychological processes in the central nervous system. Currently, advanced electrochemical biosensors and technologies have been emerging as prominent ways to meet the surging requirements of in vivo monitoring of neurotransmitters and neuromodulators ranging from single cells to brain slices, even the entire brain. This review introduces the fundamental working principles and summarizes the achievements of in vivo electrochemical biosensing technologies including voltammetry, amperometry, potentiometry, field-effect transistor (FET), and organic electrochemical transistor (OECT). According to the elaborate feature of sensing technology, versatile strategies have been devoted to solve critical issues associated with the sensing of neurochemicals under an intricate physiological environment. Voltammetry is a universal technique to investigate electrochemical processes in complex matrices which could realize the miniaturization of electrodes, while amperometry serves as a well-suited approach offering high temporal resolution which is favorable for the fast oxidation-reduction kinetics of neurochemicals. Potentiometry realizes quantitative analysis by recording the potential difference with reduced invasiveness and high compatibility. FET and OECT serve as amplification strategies with higher sensitivity than traditional technologies. Furthermore, we point out the current shortcomings and address the challenges and perspectives of in vivo electrochemical biosensing technologies.

A review of the role of graphene-based nanomaterials in tackling challenges posed by the COVID-19 pandemic

In 2020, the World Health Organization (WHO) declared a pandemic due to the emergence of the coronavirus disease (COVID-19) which had resulted by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). At present, the emergence of many new variants and mutants were found to be more harmful compared to the previous strains. As a result, research scientists around the world had devoted significant efforts to understand the mechanism, causes and transmission due to COVID-19 along with the treatment to cure these diseases. However, despite achieving several findings, much more was unknown and yet to be explored. Hence, along with these developments, it is also extremely essential to design effective systems by incorporating smart materials to battle the COVID-19. Therefore, several approaches have been implemented to combat against COVID-19. Recently, the graphene-based materials have been explored for the current COVID-19 and future pandemics due to its superior physicochemical properties, providing efficient nanoplatforms for optical and electrochemical sensing and diagnostic applications with high sensitivity and selectivity. Moreover, based on the photothermal effects or reactive oxygen species formation, the carbon-based nanomaterials have shown its potentiality for targeted antiviral drug delivery and the inhibitory effects against pathogenic viruses. Therefore, this review article sheds light on the recent progress and the most promising strategies related to graphene and related materials and its applications for detection, decontamination, diagnosis, and protection against COVID-19. In addition, the key challenges and future directives are discussed in detail for fundamental design and development of technologies based on graphene-based materials along with the demand aspects of graphene-based products and lastly, our personal opinions on the appropriate approaches to improve these technologies respectively.

Waveform Optimization for the In Vitro Detection of Caffeic Acid by Fast-Scan Cyclic Voltammetry

Caffeic acid is a polyphenol of critical importance in plants, involved in a variety of physiological processes including lignin formation, cellular growth, stress response, and external signaling. This small molecule also acts as a powerful antioxidant and thus has therapeutic potential for a variety of health conditions. Traditional methods of detecting caffeic acid lack appropriate temporal resolution to monitor real time concentration changes on a subsecond time scale with nM detection limits. Here we report on the first usage of fast-scan cyclic voltammetry with carbon fiber microelectrodes for the detection of caffeic acid. Through the use of flow injection analysis, the optimal waveform for its detection under acidic conditions at a scan rate of 400 V/s was determined to be sawtooth-shaped, from 0 to 1.4 to -0.4 to 0 V. Signal was linear with concentration up to 1 μM with a sensitivity of 44.8 ± 1.3 nA/μM and a detection limit of 2.3 ± 0.2 nM. The stability of its detection was exceptional, with an average of 0.96% relative standard deviation across 32 consecutive injections. This waveform was also successful in detecting other catechol-based plant antioxidants including 5-chlorogenic acid, oleuropein, rosmarinic acid, chicoric acid, and caffeic acid phenethyl ester. Finally, we show the successful use of fast-scan cyclic voltammetry in monitoring the degradation of caffeic acid by polyphenol oxidase on a subsecond time scale via a novel modification of a Ramsson cell. This work demonstrates that fast-scan cyclic voltammetry can be used to successfully monitor real-time dynamic changes in the concentrations of catechol-containing plant polyphenols.

Resolution of tonic concentrations of highly similar neurotransmitters using voltammetry and deep learning

With advances in our understanding regarding the neurochemical underpinnings of neurological and psychiatric diseases, there is an increased demand for advanced computational methods for neurochemical analysis. Despite having a variety of techniques for measuring tonic extracellular concentrations of neurotransmitters, including voltammetry, enzyme-based sensors, amperometry, and in vivo microdialysis, there is currently no means to resolve concentrations of structurally similar neurotransmitters from mixtures in the in vivo environment with high spatiotemporal resolution and limited tissue damage. Since a variety of research and clinical investigations involve brain regions containing electrochemically similar monoamines, such as dopamine and norepinephrine, developing a model to resolve the respective contributions of these neurotransmitters is of vital importance. Here we have developed a deep learning network, DiscrimNet, a convolutional autoencoder capable of accurately predicting individual tonic concentrations of dopamine, norepinephrine, and serotonin from both in vitro mixtures and the in vivo environment in anesthetized rats, measured using voltammetry. The architecture of DiscrimNet is described, and its ability to accurately predict in vitro and unseen in vivo concentrations is shown to vastly outperform a variety of shallow learning algorithms previously used for neurotransmitter discrimination. DiscrimNet is shown to generalize well to data captured from electrodes unseen during model training, eliminating the need to retrain the model for each new electrode. DiscrimNet is also shown to accurately predict the expected changes in dopamine and serotonin after cocaine and oxycodone administration in anesthetized rats in vivo. DiscrimNet therefore offers an exciting new method for real-time resolution of in vivo voltammetric signals into component neurotransmitters.

Recent advances in graphene-based electroanalytical devices for healthcare applications

Graphene, with its outstanding mechanical, electrical, and biocompatible properties, stands out as an emerging nanomaterial for healthcare applications, especially in building electroanalytical biodevices. With the rising prevalence of chronic diseases and infectious diseases, such as the COVID-19 pandemic, the demand for point-of-care testing and remote patient monitoring has never been greater. Owing to their portability, ease of manufacturing, scalability, and rapid and sensitive response, electroanalytical devices excel in these settings for improved healthcare accessibility, especially in resource-limited settings. The development of different synthesis methods yielding large-scale graphene and its derivatives with controllable properties, compatible with device manufacturing - from lithography to various printing methods - and tunable electrical, chemical, and electrochemical properties make it an attractive candidate for electroanalytical devices. This review article sheds light on how graphene-based devices can be transformative in addressing pressing healthcare needs, ranging from the fundamental understanding of biology in in vivo and ex vivo studies to early disease detection and management using in vitro assays and wearable devices. In particular, the article provides a special focus on (i) synthesis and functionalization techniques, emphasizing their suitability for scalable integration into devices, (ii) various transduction methods to design diverse electroanalytical device architectures, (iii) a myriad of applications using devices based on graphene, its derivatives, and hybrids with other nanomaterials, and (iv) emerging technologies at the intersection of device engineering and advanced data analytics. Finally, some of the major hurdles that graphene biodevices face for translation into clinical applications are discussed.

Multiomic foundations of human prefrontal cortex tissue function

The prefrontal cortex (PFC) is a region of the brain that in humans is involved in the production of higher-order functions such as cognition, emotion, perception, and behavior. Neurotransmission in the PFC produces higher-order functions by integrating information from other areas of the brain. At the foundation of neurotransmission, and by extension at the foundation of higher-order brain functions, are an untold number of coordinated molecular processes involving the DNA sequence variants in the genome, RNA transcripts in the transcriptome, and proteins in the proteome. These "multiomic" foundations are poorly understood in humans, perhaps in part because most modern studies that characterize the molecular state of the human PFC use tissue obtained when neurotransmission and higher-order brain functions have ceased (i.e., the postmortem state). Here, analyses are presented on data generated for the Living Brain Project (LBP) to investigate whether PFC tissue from individuals with intact higher-order brain function has characteristic multiomic foundations. Two complementary strategies were employed towards this end. The first strategy was to identify in PFC samples obtained from living study participants a signature of RNA transcript expression associated with neurotransmission measured intracranially at the time of PFC sampling, in some cases while participants performed a task engaging higher-order brain functions. The second strategy was to perform multiomic comparisons between PFC samples obtained from individuals with intact higher-order brain function at the time of sampling (i.e., living study participants) and PFC samples obtained in the postmortem state. RNA transcript expression within multiple PFC cell types was associated with fluctuations of dopaminergic, serotonergic, and/or noradrenergic neurotransmission in the substantia nigra measured while participants played a computer game that engaged higher-order brain functions. A subset of these associations - termed the "transcriptional program associated with neurotransmission" (TPAWN) - were reproduced in analyses of brain RNA transcript expression and intracranial neurotransmission data obtained from a second LBP cohort and from a cohort in an independent study. RNA transcripts involved in TPAWN were found to be (1) enriched for RNA transcripts associated with measures of neurotransmission in rodent and cell models, (2) enriched for RNA transcripts encoded by evolutionarily constrained genes, (3) depleted of RNA transcripts regulated by common DNA sequence variants, and (4) enriched for RNA transcripts implicated in higher-order brain functions by human population genetic studies. In PFC excitatory neurons of living study participants, higher expression of the genes in TPAWN tracked with higher expression of RNA transcripts that in rodent PFC samples are markers of a class of excitatory neurons that connect the PFC to deep brain structures. TPAWN was further reproduced by RNA transcript expression patterns differentiating living PFC samples from postmortem PFC samples, and significant differences between living and postmortem PFC samples were additionally observed with respect to (1) the expression of most primary RNA transcripts, mature RNA transcripts, and proteins, (2) the splicing of most primary RNA transcripts into mature RNA transcripts, (3) the patterns of co-expression between RNA transcripts and proteins, and (4) the effects of some DNA sequence variants on RNA transcript and protein expression. Taken together, this report highlights that studies of brain tissue obtained in a safe and ethical manner from large cohorts of living individuals can help advance understanding of the multiomic foundations of brain function.

Human Substantia Nigra Neurons Encode Reward Expectations

Dopamine (DA) signals originating from substantia nigra (SN) neurons are centrally involved in the regulation of motor and reward processing. DA signals behaviorally relevant events where reward outcomes differ from expectations (reward prediction errors, RPEs). RPEs play a crucial role in learning optimal courses of action and in determining response vigor when an agent expects rewards. Nevertheless, how reward expectations, crucial for RPE calculations, are conveyed to and represented in the dopaminergic system is not fully understood, especially in the human brain where the activity of DA neurons is difficult to study. One possibility, suggested by evidence from animal models, is that DA neurons explicitly encode reward expectations. Alternatively, they may receive RPE information directly from upstream brain regions. To address whether SN neuron activity directly reflects reward expectation information, we directly examined the encoding of reward expectation signals in human putative DA neurons by performing single-unit recordings from the SN of patients undergoing neurosurgery. Patients played a two-armed bandit decision-making task in which they attempted to maximize reward. We show that neuronal firing rates (FR) of putative DA neurons during the reward expectation period explicitly encode reward expectations. First, activity in these neurons was modulated by previous trial outcomes, such that FR were greater after positive outcomes than after neutral or negative outcome trials. Second, this increase in FR was associated with shorter reaction times, consistent with an invigorating effect of DA neuron activity during expectation. These results suggest that human DA neurons explicitly encode reward expectations, providing a neurophysiological substrate for a signal critical for reward learning.

Maximizing Electrochemical Information: A Perspective on Background-Inclusive Fast Voltammetry

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.

Dopamine and serotonin in human substantia nigra track social context and value signals during economic exchange

Dopamine and serotonin are hypothesized to guide social behaviours. In humans, however, we have not yet been able to study neuromodulator dynamics as social interaction unfolds. Here, we obtained subsecond estimates of dopamine and serotonin from human substantia nigra pars reticulata during the ultimatum game. Participants, who were patients with Parkinson's disease undergoing awake brain surgery, had to accept or reject monetary offers of varying fairness from human and computer players. They rejected more offers in the human than the computer condition, an effect of social context associated with higher overall levels of dopamine but not serotonin. Regardless of the social context, relative changes in dopamine tracked trial-by-trial changes in offer value-akin to reward prediction errors-whereas serotonin tracked the current offer value. These results show that dopamine and serotonin fluctuations in one of the basal ganglia's main output structures reflect distinct social context and value signals.

Electrochemical and biosensor techniques to monitor neurotransmitter changes with depression

Depression is a common mental illness. However, its current treatments, like selective serotonin reuptake inhibitors (SSRIs) and micro-dosing ketamine, are extremely variable between patients and not well understood. Three neurotransmitters: serotonin, histamine, and glutamate, have been proposed to be key mediators of depression. This review focuses on analytical methods to quantify these neurotransmitters to better understand neurological mechanisms of depression and how they are altered during treatment. To quantitatively measure serotonin and histamine, electrochemical techniques such as chronoamperometry and fast-scan cyclic voltammetry (FSCV) have been improved to study how specific molecular targets, like transporters and receptors, change with antidepressants and inflammation. Specifically, these studies show that different SSRIs have unique effects on serotonin reuptake and release. Histamine is normally elevated during stress, and a new inflammation hypothesis of depression links histamine and cytokine release. Electrochemical measurements revealed that stress increases histamine, decreases serotonin, and leads to changes in cytokines, like interleukin-6. Biosensors can also measure non-electroactive neurotransmitters, including glutamate and cytokines. In particular, new genetic sensors have shown how glutamate changes with chronic stress, as well as with ketamine treatment. These techniques have been used to characterize how ketamine changes glutamate and serotonin, and to understand how it is different from SSRIs. This review briefly outlines how these electrochemical techniques work, but primarily highlights how they have been used to understand the mechanisms of depression. Future studies should explore multiplexing techniques and personalized medicine using biomarkers in order to investigate multi-analyte changes to antidepressants.

Sub-second and multi-second dopamine dynamics underlie variability in human time perception

Timing behaviour and the perception of time are fundamental to cognitive and emotional processes in humans. In non-human model organisms, the neuromodulator dopamine has been associated with variations in timing behaviour, but the connection between variations in dopamine levels and the human experience of time has not been directly assessed. Here, we report how dopamine levels in human striatum, measured with sub-second temporal resolution during awake deep brain stimulation surgery, relate to participants' perceptual judgements of time intervals. Fast, phasic, dopaminergic signals were associated with underestimation of temporal intervals, whereas slower, tonic, decreases in dopamine were associated with poorer temporal precision. Our findings suggest a delicate and complex role for the dynamics and tone of dopaminergic signals in the conscious experience of time in humans.

Synthetic surprise as the foundation of the psychedelic experience

Psychedelic agents, such as LSD and psilocybin, induce marked alterations in consciousness via activation of the 5-HT2A receptor (5-HT2ARs). We hypothesize that psychedelics enforce a state of synthetic surprise through the biased activation of the 5-HTRs system. This idea is informed by recent insights into the role of 5-HT in signaling surprise. The effects on consciousness, explained by the cognitive penetrability of perception, can be described within the predictive coding framework where surprise corresponds to prediction error, the mismatch between predictions and actual sensory input. Crucially, the precision afforded to the prediction error determines its effect on priors, enabling a dynamic interaction between top-down expectations and incoming sensory data. By integrating recent findings on predictive coding circuitry and 5-HT2ARs transcriptomic data, we propose a biological implementation with emphasis on the role of inhibitory interneurons. Implications arise for the clinical use of psychedelics, which may rely primarily on their inherent capacity to induce surprise in order to disrupt maladaptive patterns.

Techniques for Measurement of Serotonin: Implications in Neuropsychiatric Disorders and Advances in Absolute Value Recording Methods

Serotonin (5-HT) is a monoamine neurotransmitter in the peripheral, enteric, and central nervous systems (CNS). Within the CNS, serotonin is principally involved in mood regulation and reward-seeking behaviors. It is a critical regulator in CNS pathologies such as major depressive disorder, addiction, and schizophrenia. Consequently, in vivo serotonin measurements within the CNS have emerged as one of many promising approaches to investigating the pathogenesis, progression, and treatment of these and other neuropsychiatric conditions. These techniques vary in methods, ranging from analyte sampling with microdialysis to voltammetry. Provided this diversity in approach, inherent differences between techniques are inevitable. These include biosensor size, temporal/spatial resolution, and absolute value measurement capabilities, all of which must be considered to fit the prospective researcher's needs. In this review, we summarize currently available methods for the measurement of serotonin, including novel voltammetric absolute value measurement techniques. We also detail serotonin's role in various neuropsychiatric conditions, highlighting the role of phasic and tonic serotonergic neuronal firing within each where relevant. Lastly, we briefly review the present clinical application of these techniques and discuss the potential of a closed-loop monitoring and neuromodulation system utilizing deep brain stimulation (DBS).

Subsecond fluctuations in extracellular dopamine encode reward and punishment prediction errors in humans

In the mammalian brain, midbrain dopamine neuron activity is hypothesized to encode reward prediction errors that promote learning and guide behavior by causing rapid changes in dopamine levels in target brain regions. This hypothesis (and alternatives regarding dopamine's role in punishment-learning) has limited direct evidence in humans. We report intracranial, subsecond measurements of dopamine release in human striatum measured, while volunteers (i.e., patients undergoing deep brain stimulation surgery) performed a probabilistic reward and punishment learning choice task designed to test whether dopamine release encodes only reward prediction errors or whether dopamine release may also encode adaptive punishment learning signals. Results demonstrate that extracellular dopamine levels can encode both reward and punishment prediction errors within distinct time intervals via independent valence-specific pathways in the human brain.

Noradrenaline tracks emotional modulation of attention in human amygdala

The noradrenaline (NA) system is one of the brain's major neuromodulatory systems; it originates in a small midbrain nucleus, the locus coeruleus (LC), and projects widely throughout the brain.1,2 The LC-NA system is believed to regulate arousal and attention3,4 and is a pharmacological target in multiple clinical conditions.5,6,7 Yet our understanding of its role in health and disease has been impeded by a lack of direct recordings in humans. Here, we address this problem by showing that electrochemical estimates of sub-second NA dynamics can be obtained using clinical depth electrodes implanted for epilepsy monitoring. We made these recordings in the amygdala, an evolutionarily ancient structure that supports emotional processing8,9 and receives dense LC-NA projections,10 while patients (n = 3) performed a visual affective oddball task. The task was designed to induce different cognitive states, with the oddball stimuli involving emotionally evocative images,11 which varied in terms of arousal (low versus high) and valence (negative versus positive). Consistent with theory, the NA estimates tracked the emotional modulation of attention, with a stronger oddball response in a high-arousal state. Parallel estimates of pupil dilation, a common behavioral proxy for LC-NA activity,12 supported a hypothesis that pupil-NA coupling changes with cognitive state,13,14 with the pupil and NA estimates being positively correlated for oddball stimuli in a high-arousal but not a low-arousal state. Our study provides proof of concept that neuromodulator monitoring is now possible using depth electrodes in standard clinical use.

Sub-second Dopamine Signals during Risky Decision-Making in Patients with Impulse Control Disorder

Background

Impulse Control Disorder (ICD) in Parkinson's disease is a behavioral addiction arising secondary to dopaminergic therapies, most often dopamine receptor agonists. Prior research implicates changes in striatal function and heightened dopaminergic activity in the dorsal striatum of patients with ICD. However, this prior work does not possess the temporal resolution required to investigate dopaminergic signaling during real-time progression through various stages of decision-making involving anticipation and feedback.

Methods

We recorded high-frequency (10Hz) measurements of extracellular dopamine in the striatum of patients with (N=3) and without (N=3) a history of ICD secondary to dopamine receptor agonist therapy for Parkinson's disease symptoms. These measurements were made using carbon fiber microelectrodes during awake DBS neurosurgery and while participants performed a sequential decision-making task involving risky investment decisions and real monetary gains and losses. Per clinical standard-of-care, participants withheld all dopaminergic medications prior to the procedure.

Results

Patients with ICD invested significantly more money than patients without ICD. On each trial, patients with ICD made smaller adjustments to their investment levels compared to patients without ICD. In patients with ICD, dopamine levels rose or fell on sub-second timescales in anticipation of investment outcomes consistent with increased or decreased confidence in a positive outcome, respectively; dopamine levels in patients without ICD were significantly more stable during this phase. After outcome revelation, dopamine levels in patients with ICD rose significantly more than in inpatients without ICD for better-than-expected gains. For worse-than-expected losses, dopamine levels in patients with ICD remained level whereas dopamine levels in patients without ICD fell.

Conclusion

We report significantly increased risky behavior and exacerbated phasic dopamine signaling, on sub-second timescales, anticipating and following the revelation of the outcomes of risky decisions in patients with ICD. Notably, these results were obtained when patients who had demonstrated ICD in the past but were, at the time of surgery, in an off-medication state. Thus, it is unclear whether observed signals reflect an inherent predisposition for ICD that was revealed when dopamine receptor agonists were introduced or whether these observations were caused by the introduction of dopamine receptor agonists and the patients having experienced ICD symptoms in the past. Regardless, future work investigating dopamine's role in human cognition, behavior, and disease should consider the signals this system generates on sub-second timescales.

New Opportunities of Electrochemistry for Monitoring, Modulating, and Mimicking the Brain Signals

In vivo electrochemistry is a powerful key for unlocking the chemical consequences in neural networks of the brain. The past half-century has witnessed the technology revolutionization in this field along with innovations in electrochemical concepts, principles, methods, and devices. Present applications of electrochemical approaches have extended from measuring neurochemical concentrations to modulating and mimicking brain signals. In this Perspective, newly reported strategies for tackling long-standing challenges of in vivo electrochemical brain monitoring (i.e., basal level measurement, electroactivity dependence, in vivo stability, neuron compatibility, multiplexity, and implantable device fabrication) are highlighted. Moreover, recent progress on neuromodulation tools and neuromorphic devices in electrochemical frameworks is introduced. A glimpse of future opportunities for electrochemistry in brain research is offered at last.

Invasive Computational Psychiatry

Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction-invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.

Valence-partitioned learning signals drive choice behavior and phenomenal subjective experience in humans

How the human brain generates conscious phenomenal experience is a fundamental problem. In particular, it is unknown how variable and dynamic changes in subjective affect are driven by interactions with objective phenomena. We hypothesize a neurocomputational mechanism that generates valence-specific learning signals associated with 'what it is like' to be rewarded or punished. Our hypothesized model maintains a partition between appetitive and aversive information while generating independent and parallel reward and punishment learning signals. This valence-partitioned reinforcement learning (VPRL) model and its associated learning signals are shown to predict dynamic changes in 1) human choice behavior, 2) phenomenal subjective experience, and 3) BOLD-imaging responses that implicate a network of regions that process appetitive and aversive information that converge on the ventral striatum and ventromedial prefrontal cortex during moments of introspection. Our results demonstrate the utility of valence-partitioned reinforcement learning as a neurocomputational basis for investigating mechanisms that may drive conscious experience.

Hierarchical resampling for bagging in multistudy prediction with applications to human neurochemical sensing

We propose the "study strap ensemble", which combines advantages of two common approaches to fitting prediction models when multiple training datasets ("studies") are available: pooling studies and fitting one model versus averaging predictions from multiple models each fit to individual studies. The study strap ensemble fits models to bootstrapped datasets, or "pseudo-studies." These are generated by resampling from multiple studies with a hierarchical resampling scheme that generalizes the randomized cluster bootstrap. The study strap is controlled by a tuning parameter that determines the proportion of observations to draw from each study. When the parameter is set to its lowest value, each pseudo-study is resampled from only a single study. When it is high, the study strap ignores the multi-study structure and generates pseudo-studies by merging the datasets and drawing observations like a standard bootstrap. We empirically show the optimal tuning value often lies in between, and prove that special cases of the study strap draw the merged dataset and the set of original studies as pseudo-studies. We extend the study strap approach with an ensemble weighting scheme that utilizes information in the distribution of the covariates of the test dataset. Our work is motivated by neuroscience experiments using real-time neurochemical sensing during awake behavior in humans. Current techniques to perform this kind of research require measurements from an electrode placed in the brain during awake neurosurgery and rely on prediction models to estimate neurotransmitter concentrations from the electrical measurements recorded by the electrode. These models are trained by combining multiple datasets that are collected in vitro under heterogeneous conditions in order to promote accuracy of the models when applied to data collected in the brain. A prevailing challenge is deciding how to combine studies or ensemble models trained on different studies to enhance model generalizability. Our methods produce marked improvements in simulations and in this application. All methods are available in the studyStrap CRAN package.

Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders

In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission.

Software for near-real-time voltammetric tracking of tonic neurotransmitter levels in vivo

Tonic extracellular neurotransmitter concentrations are important modulators of central network homeostasis. Disruptions in these tonic levels are thought to play a role in neurologic and psychiatric disease. Therefore, ways to improve their quantification are actively being investigated. Previously published voltammetric software packages have implemented FSCV, which is not capable of measuring tonic concentrations of neurotransmitters in vivo. In this paper, custom software was developed for near-real-time tracking (scans every 10 s) of neurotransmitters’ tonic concentrations with high sensitivity and spatiotemporal resolution both in vitro and in vivo using cyclic voltammetry combined with dynamic background subtraction (M-CSWV and FSCAV). This software was designed with flexibility, speed, and user-friendliness in mind. This software enables near-real-time measurement by reducing data analysis time through an optimized modeling algorithm, and efficient memory handling makes long-term measurement possible. The software permits customization of the cyclic voltammetric waveform shape, enabling experiments to detect a specific analyte of interest. Finally, flexibility considerations allow the user to alter the fitting parameters, filtering characteristics, and size and shape of the analyte kernel, based on data obtained live during the experiment to obtain accurate measurements as experimental conditions change. Herein, the design and advantages of this near-real-time voltammetric software are described, and its use is demonstrated in in vivo experiments.

Accurate and stable chronic in vivo voltammetry enabled by a replaceable subcutaneous reference electrode

In vivo sensing of neurotransmitters has provided valuable insight into both healthy and diseased brain. However, chronically implanted Ag/AgCl reference electrodes suffer from degradationgradation, resulting in errors in the potential at the working electrode. Here, we report a simple, effective way to protect in vivo sensing measurements from reference polarization with a replaceable subcutaneously implanted reference. We compared a brain-implanted reference and a subcutaneous reference and observed no difference in impedance or dopamine redox peak separation in an acute preparation. Chronically, peak background potential and dopamine oxidation potential shifts were eliminated for three weeks. Scanning electron microscopy shows changes in surface morphology and composition of chronically implanted Ag/AgCl electrodes, and postmortem histology reveals extensive cell death and gliosis in the surrounding tissue. As accurate reference potentials are critical to in vivo electrochemistry applications, this simple technique can improve a wide and diverse assortment of in vivo preparations.

Neurochemical Concentration Prediction Using Deep Learning vs Principal Component Regression in Fast Scan Cyclic Voltammetry: A Comparison Study

Neurotransmitters, such as dopamine and serotonin, are responsible for mediating a wide array of neurologic functions, from memory to motivation. From measurements using fast scan cyclic voltammetry (FSCV), one of the main tools used to detect synaptic efflux of neurochemicals in vivo, principal component regression (PCR), has been commonly used to predict the identity and concentrations of neurotransmitters. However, the sensitivity and discrimination performance of PCR have room for improvement, especially for analyzing mixtures of similar oxidizable neurochemicals. Deep learning may be able to address these challenges. To date, there have been a few studies to apply machine learning to FSCV, but no attempt to apply deep learning to neurotransmitter mixture discrimination and no comparative study have been performed between PCR and deep learning methods to demonstrate which is more accurate for FSCV analysis so far. In this study, we compared the neurochemical identification and concentration estimation performance of PCR and deep learning in an analysis of FSCV recordings of catecholamine and indolamine neurotransmitters. Both analysis methods were tested on in vitro FSCV data with a single or mixture of neurotransmitters at the desired concentration. In addition, the estimation performance of PCR and deep learning was compared in incorporation with in vivo experiments to evaluate the practical usage. Pharmacological tests were also conducted to see whether deep learning would track the increased amount of catecholamine levels in the brain. Using conventional FSCV, we used five electrodes and recorded in vitro background-subtracted cyclic voltammograms from four neurotransmitters, dopamine, epinephrine, norepinephrine, and serotonin, with five concentrations of each substance, as well as various mixtures of the four analytes. The results showed that the identification accuracy errors were reduced 5-20% by using deep learning compared to using PCR for mixture analysis, and the two methods were comparable for single analyte analysis. The applied deep-learning-based method demonstrated not only higher identification accuracy but also better discrimination performance than PCR for mixtures of neurochemicals and even for in vivo testing. Therefore, we suggest that deep learning should be chosen as a more reliable tool to analyze FSCV data compared to conventional PCR methods although further work is still needed on developing complete validation procedures prior to widespread use.

Multiplexed Monitoring of Neurochemicals via Electrografting-Enabled Site-Selective Functionalization of Aptamers on Field-Effect Transistors

Neurochemical corelease has received much attention in understanding brain activity and cognition. Despite many attempts, the multiplexed monitoring of coreleased neurochemicals with spatiotemporal precision and minimal crosstalk using existing methods remains challenging. Here, we report a soft neural probe for multiplexed neurochemical monitoring via the electrografting-assisted site-selective functionalization of aptamers on graphene field-effect transistors (G-FETs). The neural probes possess excellent flexibility, ultralight mass (28 mg), and a nearly cellular-scale dimension of 50 μm × 50 μm for each G-FET. As a demonstration, we show that G-FETs with electrochemically grafted molecular linkers (-COOH or -NH₂) and specific aptamers can be used to monitor serotonin and dopamine with high sensitivity (limit of detection: 10 pM) and selectivity (dopamine sensor >22-fold over norepinephrine; serotonin sensor >17-fold over dopamine). In addition, we demonstrate the feasibility of the simultaneous monitoring of dopamine and serotonin in a single neural probe with minimal crosstalk and interferences in phosphate-buffered saline, artificial cerebrospinal fluid, and harvested mouse brain tissues. The stability studies show that multiplexed neural probes maintain the capability for simultaneously monitoring dopamine and serotonin with minimal crosstalk after incubating in rat cerebrospinal fluid for 96 h, although a reduced sensor response at high concentrations is observed. Ex vivo studies in harvested mice brains suggest potential applications in monitoring the evoked release of dopamine and serotonin. The developed multiplexed detection methodology can also be adapted for monitoring other neurochemicals, such as metabolites and neuropeptides, by simply replacing the aptamers functionalized on the G-FETs.

A tissue-like neurotransmitter sensor for the brain and gut

Neurotransmitters play essential roles in regulating neural circuit dynamics both in the central nervous system as well as at the peripheral, including the gastrointestinal tract^1-3. Their real-time monitoring will offer critical information for understanding neural function and diagnosing disease^1-3. However, bioelectronic tools to monitor the dynamics of neurotransmitters in vivo, especially in the enteric nervous systems, are underdeveloped. This is mainly owing to the limited availability of biosensing tools that are capable of examining soft, complex and actively moving organs. Here we introduce a tissue-mimicking, stretchable, neurochemical biological interface termed NeuroString, which is prepared by laser patterning of a metal-complexed polyimide into an interconnected graphene/nanoparticle network embedded in an elastomer. NeuroString sensors allow chronic in vivo real-time, multichannel and multiplexed monoamine sensing in the brain of behaving mouse, as well as measuring serotonin dynamics in the gut without undesired stimulations and perturbing peristaltic movements. The described elastic and conformable biosensing interface has broad potential for studying the impact of neurotransmitters on gut microbes, brain-gut communication and may ultimately be extended to biomolecular sensing in other soft organs across the body.

Letting the little light of mind shine: Advances and future directions in neurochemical detection

Synaptic transmission via neurochemical release is the fundamental process that integrates and relays encoded information in the brain to regulate physiological function, cognition, and emotion. To unravel the biochemical, biophysical, and computational mechanisms of signal processing, one needs to precisely measure the neurochemical release dynamics with molecular and cell-type specificity and high resolution. Here we reviewed the development of analytical, electrochemical, and fluorescence imaging approaches to detect neurotransmitter and neuromodulator release. We discussed the advantages and practicality in implementation of each technology for ease-of-use, flexibility for multimodal studies, and challenges for future optimization. We hope this review will provide a versatile guide for tool engineering and applications for recording neurochemical release.

Pushing the frontiers: tools for monitoring neurotransmitters and neuromodulators

Neurotransmitters and neuromodulators have a wide range of key roles throughout the nervous system. However, their dynamics in both health and disease have been challenging to assess, owing to the lack of in vivo tools to track them with high spatiotemporal resolution. Thus, developing a platform that enables minimally invasive, large-scale and long-term monitoring of neurotransmitters and neuromodulators with high sensitivity, high molecular specificity and high spatiotemporal resolution has been essential. Here, we review the methods available for monitoring the dynamics of neurotransmitters and neuromodulators. Following a brief summary of non-genetically encoded methods, we focus on recent developments in genetically encoded fluorescent indicators, highlighting how these novel indicators have facilitated advances in our understanding of the functional roles of neurotransmitters and neuromodulators in the nervous system. These studies present a promising outlook for the future development and use of tools to monitor neurotransmitters and neuromodulators.

Optogenetic Stimulation of Midbrain Dopamine Neurons Produces Striatal Serotonin Release

Targeting neurons with light-driven opsins is widely used to investigate cell-specific responses. We transfected midbrain dopamine neurons with the excitatory opsin Chrimson. Extracellular basal and stimulated neurotransmitter levels in the dorsal striatum were measured by microdialysis in awake mice. Optical activation of dopamine cell bodies evoked terminal dopamine release in the striatum. Multiplexed analysis of dialysate samples revealed that the evoked dopamine was accompanied by temporally coupled increases in striatal 3-methoxytyramine, an extracellular dopamine metabolite, and in serotonin. We investigated a mechanism for dopamine-serotonin interactions involving striatal dopamine receptors. However, the evoked serotonin associated with optical stimulation of dopamine neurons was not abolished by striatal D1- or D2-like receptor inhibition. Although the mechanisms underlying the coupling of striatal dopamine and serotonin remain unclear, these findings illustrate advantages of multiplexed measurements for uncovering functional interactions between neurotransmitter systems. Furthermore, they suggest that the output of optogenetic manipulations may extend beyond opsin-expressing neuronal populations.

Aversive motivation and cognitive control

Aversive motivation plays a prominent role in driving individuals to exert cognitive control. However, the complexity of behavioral responses attributed to aversive incentives creates significant challenges for developing a clear understanding of the neural mechanisms of this motivation-control interaction. We review the animal learning, systems neuroscience, and computational literatures to highlight the importance of experimental paradigms that incorporate both motivational context manipulations and mixed motivational components (e.g., bundling of appetitive and aversive incentives). Specifically, we postulate that to understand aversive incentive effects on cognitive control allocation, a critical contextual factor is whether such incentives are associated with negative reinforcement or punishment. We further illustrate how the inclusion of mixed motivational components in experimental paradigms enables increased precision in the measurement of aversive influences on cognitive control. A sharpened experimental and theoretical focus regarding the manipulation and assessment of distinct motivational dimensions promises to advance understanding of the neural, monoaminergic, and computational mechanisms that underlie the interaction of motivation and cognitive control.

Defining a Path Toward the Use of Fast-Scan Cyclic Voltammetry in Human Studies

Fast Scan Cyclic Voltammetry (FSCV) has been used for decades as a neurochemical tool for in vivo detection of phasic changes in electroactive neurotransmitters in animal models. Recently, multiple research groups have initiated human neurochemical studies using FSCV or demonstrated interest in bringing FSCV into clinical use. However, there remain technical challenges that limit clinical implementation of FSCV by creating barriers to appropriate scientific rigor and patient safety. In order to progress with clinical FSCV, these limitations must be first addressed through (1) appropriate pre-clinical studies to ensure accurate measurement of neurotransmitters and (2) the application of a risk management framework to assess patient safety. The intent of this work is to bring awareness of the current issues associated with FSCV to the scientific, engineering, and clinical communities and encourage them to seek solutions or alternatives that ensure data accuracy, rigor and reproducibility, and patient safety.

Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression

Many voltammetry methods have been developed to monitor brain extracellular dopamine levels. Fewer approaches have been successful in detecting serotonin in vivo. No voltammetric techniques are currently available to monitor both neurotransmitters simultaneously across timescales, even though they play integrated roles in modulating behavior. We provide proof-of-concept for rapid pulse voltammetry coupled with partial least squares regression (RPV-PLSR), an approach adapted from multi-electrode systems (i.e., electronic tongues) used to identify multiple components in complex environments. We exploited small differences in analyte redox profiles to select pulse steps for RPV waveforms. Using an intentionally designed pulse strategy combined with custom instrumentation and analysis software, we monitored basal and stimulated levels of dopamine and serotonin. In addition to faradaic currents, capacitive currents were important factors in analyte identification arguing against background subtraction. Compared to fast-scan cyclic voltammetry-principal components regression (FSCV-PCR), RPV-PLSR better differentiated and quantified basal and stimulated dopamine and serotonin associated with striatal recording electrode position, optical stimulation frequency, and serotonin reuptake inhibition. The RPV-PLSR approach can be generalized to other electrochemically active neurotransmitters and provides a feedback pipeline for future optimization of multi-analyte, fit-for-purpose waveforms and machine learning approaches to data analysis.

Deep Learning for Voltammetric Sensing in a Living Animal Brain

Numerous neurochemicals have been implicated in the modulation of brain function, making them appealing analytes for sensors and diagnostics. However, it is a grand challenge to selectively measure multiple neurochemicals simultaneously in vivo because of their great variations in concentrations, dynamic nature, and composition. Herein, we present a deep learning-based voltammetric sensing platform for the highly selective and simultaneous analysis of three neurochemicals in a living animal brain. The system features a carbon fiber electrode capable of capturing the mixed dynamics of a neurotransmitter, neuromodulator, and ions. Then a powerful deep neural network is employed to resolve individual chemical and spatial-temporal information. With this, a single electrochemical measurement reveals an interplaying concentration changes of dopamine, ascorbate, and ions in living rat brain, which is unobtainable with existing analytical methodologies. Our strategy provides a powerful means to expedite research in neuroscience and empower sensing-aided diagnostic applications.

Biocompatible reference electrodes to enhance chronic electrochemical signal fidelity in vivo

In vivo electrochemistry is a vital tool of neuroscience that allows for the detection, identification, and quantification of neurotransmitters, their metabolites, and other important analytes. One important goal of in vivo electrochemistry is a better understanding of progressive neurological disorders (e.g., Parkinson's disease). A complete understanding of such disorders can only be achieved through a combination of acute (i.e., minutes to hours) and chronic (i.e., days or longer) experimentation. Chronic studies are more challenging because they require prolonged implantation of electrodes, which elicits an immune response, leading to glial encapsulation of the electrodes and altered electrode performance (i.e., biofouling). Biofouling leads to increased electrode impedance and reference electrode polarization, both of which diminish the selectivity and sensitivity of in vivo electrochemical measurements. The increased impedance factor has been successfully mitigated previously with the use of a counter electrode, but the challenge of reference electrode polarization remains. The commonly used Ag/AgCl reference electrode lacks the long-term potential stability in vivo required for chronic measurements. In addition, the cytotoxicity of Ag/AgCl adversely affects animal experimentation and prohibits implantation in humans, hindering translational research progress. Thus, a move toward biocompatible reference electrodes with superior chronic potential stability is necessary. Two qualifying materials, iridium oxide and boron-doped diamond, are introduced and discussed in terms of their electrochemical properties, biocompatibilities, fabrication methods, and applications. In vivo electrochemistry continues to advance toward more chronic experimentation in both animal models and humans, necessitating the utilization of biocompatible reference electrodes that should provide superior potential stability and allow for unprecedented chronic signal fidelity when used with a counter electrode for impedance mitigation.

A Competition of Critics in Human Decision-Making

Recent experiments and theories of human decision-making suggest positive and negative errors are processed and encoded differently by serotonin and dopamine, with serotonin possibly serving to oppose dopamine and protect against risky decisions. We introduce a temporal difference (TD) model of human decision-making to account for these features. Our model involves two critics, an optimistic learning system and a pessimistic learning system, whose predictions are integrated in time to control how potential decisions compete to be selected. Our model predicts that human decision-making can be decomposed along two dimensions: the degree to which the individual is sensitive to (1) risk and (2) uncertainty. In addition, we demonstrate that the model can learn about the mean and standard deviation of rewards, and provide information about reaction time despite not modeling these variables directly. Lastly, we simulate a recent experiment to show how updates of the two learning systems could relate to dopamine and serotonin transients, thereby providing a mathematical formalism to serotonin's hypothesized role as an opponent to dopamine. This new model should be useful for future experiments on human decision-making.

Expression and co-expression of serotonin and dopamine transporters in social anxiety disorder: a multitracer positron emission tomography study

Serotonin and dopamine are putatively involved in the etiology and treatment of anxiety disorders, but positron emission tomography (PET) studies probing the two neurotransmitters in the same individuals are lacking. The aim of this multitracer PET study was to evaluate the regional expression and co-expression of the transporter proteins for serotonin (SERT) and dopamine (DAT) in patients with social anxiety disorder (SAD). Voxel-wise binding potentials (BP_ND) for SERT and DAT were determined in 27 patients with SAD and 43 age- and sex-matched healthy controls, using the radioligands [¹¹C]DASB (3-amino-4-(2-dimethylaminomethylphenylsulfanyl)-benzonitrile) and [¹¹C]PE2I (N-(3-iodopro-2E-enyl)-2beta-carbomethoxy-3beta-(4'-methylphenyl)nortropane). Results showed that, within transmitter systems, SAD patients exhibited higher SERT binding in the nucleus accumbens while DAT availability in the amygdala, hippocampus, and putamen correlated positively with symptom severity. At a more lenient statistical threshold, SERT and DAT BP_ND were also higher in other striatal and limbic regions in patients, and correlated with symptom severity, whereas no brain region showed higher binding in healthy controls. Moreover, SERT/DAT co-expression was significantly higher in SAD patients in the amygdala, nucleus accumbens, caudate, putamen, and posterior ventral thalamus, while lower co-expression was noted in the dorsomedial thalamus. Follow-up logistic regression analysis confirmed that SAD diagnosis was significantly predicted by the statistical interaction between SERT and DAT availability, in the amygdala, putamen, and dorsomedial thalamus. Thus, SAD was associated with mainly increased expression and co-expression of the transporters for serotonin and dopamine in fear and reward-related brain regions. Resultant monoamine dysregulation may underlie SAD symptomatology and constitute a target for treatment.

Improving serotonin fast-scan cyclic voltammetry detection: new waveforms to reduce electrode fouling

Serotonin is a neuromodulator implicated in depression that is often measured in real-time by fast-scan cyclic voltammetry (FSCV). A specialized "Jackson" waveform (JW, 0.2, 1.0 V, -0.1 V, 0.2 V, 1000 V s-1) was developed to reduce serotonin fouling, but the 1.0 V switching potential limits sensitivity and electrodes still foul. The goal of this study was to test the effects of extending the FSCV switching potential to increase serotonin sensitivity and decrease fouling. We compared the Jackson waveform, the dopamine waveform (DA, -0.4 V, 1.3 V, 400 V s-1), and two new waveforms: the extended serotonin waveform (ESW, 0.2, 1.3, -0.1, 0.2, 1000 V s-1) and extended hold serotonin waveform (EHSW, 0.2, 1.3 (hold 1 ms), -0.1, 0.2, 400 V s-1). The EHSW was the most sensitive (LOD = 0.6 nM), and the JW the least sensitive (LOD = 2.4 nM). With the Jackson waveform, electrode fouling was significant with repeated injections of serotonin or exposure to its metabolite, 5-hydroxyindoleacetic acid (5-HIAA). Using the extended waveforms, electrodes fouled 50% less than with the Jackson waveform for both analytes. No electrode fouling was observed with the dopamine waveform because of the negative holding potential. The Jackson waveform was the most selective for serotonin over dopamine (800×), and the ESW was also highly selective. All waveforms were useful for measuring serotonin with optogenetic stimulation in Drosophila larvae. These results provide new FSCV waveforms to measure dynamic serotonin changes with different experimental requirements, like high sensitivity (EHSW), high selectivity (ESW, JW), or eliminating electrode fouling (DA).

A single dose of escitalopram blunts the neural response in the thalamus and caudate during monetary loss

BACKGROUND

Selective serotonin reuptake inhibitors (SSRIs) show acute effects on the neural processes associated with negative affective bias in healthy people and people with depression. However, whether and how SSRIs also affect reward and punishment processing on a similarly rapid time scale remains unclear.

METHODS

We investigated the effects of an acute and clinically relevant dose (20 mg) of the SSRI escitalopram on brain response during reward and punishment processing in 19 healthy participants. In a doubleblind, placebo-controlled study using functional MRI, participants performed a well-established monetary reward task at 3 time points: at baseline; after receiving placebo or escitalopram; and after receiving placebo or escitalopram following an 8-week washout period.

RESULTS

Acute escitalopram administration reduced blood-oxygen-level-dependent (BOLD) response during punishment feedback in the right thalamus (family-wise error corrected [FWE] p = 0.013 at peak level) and the right caudate head (pFWE = 0.011 at peak level) compared to placebo. We did not detect any significant BOLD changes during reward feedback.

LIMITATIONS

We included only healthy participants, so interpretation of findings are limited to the healthy human brain and require future testing in patient populations. The paradigm we used was based on monetary stimuli, and results may not be generalizable to other forms of reward.

CONCLUSION

Our findings extend theories of rapid SSRI action on the neural processing of rewarding and aversive stimuli and suggest a specific and acute effect of escitalopram in the punishment neurocircuitry.

Growing points in attachment disorganization: looking back to advance forward

In this special issue paper we reflect on the next generation of attachment research with a focus on disorganization, a central but still poorly understood topic in this area. We suggest that progress will be facilitated by a return to attachment theory's evolutionary roots, and to the emphasis on biological function that inspired Bowlby's original thinking. Increased interdisciplinary cross-fertilization and collaborations would enable novel and generative research on some of the long-standing questions surrounding attachment disorganization. Accordingly, we present an agenda for future research that encompasses contributions of modern ethology and neurobiology, novel hypotheses based on the concept of adaptive decanalization, connections with neurodevelopmental vulnerability and risk for mental disorders such as schizophrenia, and the possibility of sex differences in the behavioral manifestations of attachment disorganization. We believe that these avenues of theory and research offer exciting potential for innovative work in attachment disorganization in the years ahead.

Serotonergic inhibition of responding for conditioned but not primary reinforcers

Serotonin is widely implicated as a modulator of brain reward function. However, laboratory studies have not yielded a consensus on which specific reward-related processes are influenced by serotonin and in what manner. Here we explored the role of serotonin in cue-reward learning in mice. In a first series of experiments, we found that acute administration of the serotonin reuptake inhibitors citalopram, fluoxetine, or duloxetine all reduced lever pressing reinforced on an FR1 schedule with presentation of a cue that had been previously paired with delivery of food. However, citalopram had no effect on responding that was reinforced with both cue and food on an FR1 schedule. Furthermore, citalopram did not affect nose poke responses that produced no auditory, visual, or proprioceptive cues but were reinforced with food pellets on a progressive ratio schedule. We next performed region-specific knock out of tryptophan hydroxylase-2 (Tph2), the rate-limiting enzyme in serotonin synthesis. Viral delivery of Cre recombinase was targeted to dorsal or median raphe nuclei (DRN, MRN), the major sources of ascending serotonergic projections. MRN but not DRN knockouts were impaired in development of cue-elicited approach during Pavlovian conditioning; both groups were subsequently hyper-responsive when lever pressing for cue presentation. The inhibitory effect of citalopram was attenuated in DRN but not MRN knockouts. Our findings are in agreement with prior studies showing serotonin to suppress responding for conditioned reinforcers. Furthermore, these results suggest an inhibitory role of MRN serotonin neurons in the initial attribution of motivational properties to a reward-predictive cue, but not in its subsequent maintenance. In contrast, the DRN appears to promote the reduction of motivational value attached to a cue when it is presented repeatedly in the absence of primary reward.

Knockout serotonin transporter in rats moderates outcome and stimulus generalization

Understanding the common dimension of mental disorders (such as anxiety, depression, and drug addiction) might contribute to the construction of biological frameworks (Research Domain Criteria, RDoC) for novel ways of treatment. One common dimension at the behavioral level observed across these disorders is a generalization. Testing generalization in serotonin transporter (5-HTT) knockout (KO) rats, an animal model showing depression/anxiety-like behaviors and drug addiction-like behaviors, could therefore provide more insights into this framework. We tested the outcome and stimulus generalization in wild-type (WT) and 5-HTT KO rats. Using a newly established touchscreen-based task, subjects directly responded to visual stimuli (Gabor patch images). We measured the response time and outcome in a precise manner. We found that 5-HTT KO rats processed visual information faster than WT rats during outcome generalization. Interestingly, during stimulus generalization, WT rats gradually responded faster to the stimuli as the sessions progressed, while 5-HTT KO rats responded faster than WT in the initial sessions and did not change significantly as the sessions progressed. This observation suggests that KO rats, compared to WT rats, may be less able to update changes in information. Taken together, KO 5-HTT modulates information processing when the environment changes.

Influence of ovarian hormones on value-based decision-making systems: Contribution to sexual dimorphisms in mental disorders

Women and men exhibit differences in behavior when making value-based decisions. Various hypotheses have been proposed to explain these findings, stressing differences in functional lateralization of the brain, functional activation, neurotransmitter involvement and more recently, sex hormones. While a significant interaction of neurotransmitter systems and sex hormones has been shown for both sexes, decision-making in women might be particularly affected by variations of ovarian hormones. In this review we have gathered information from animal and human studies on how ovarian hormones affect decision-making processes in females by interacting with neurotransmitter systems at functionally relevant brain locations and thus modify the computation of decision aspects. We also review previous findings on impaired decision-making in animals and clinical populations with substance use disorder and depression, emphasizing how little we know about the role of ovarian hormones in aberrant decision-making.

Acute stress impairs reward learning in men

Acute stress is ubiquitous in everyday life, but the extent to which acute stress affects how people learn from the outcomes of their choices is still poorly understood. Here, we investigate how acute stress impacts reward and punishment learning in men using a reinforcement-learning task. Sixty-two male participants performed the task whilst under stress and control conditions. We observed that acute stress impaired participants' choice performance towards monetary gains, but not losses. To unravel the mechanism(s) underlying such impairment, we fitted a reinforcement-learning model to participants' trial-by-trial choices. Computational modeling indicated that under acute stress participants learned more slowly from positive prediction errors - when the outcomes were better than expected - consistent with stress-induced dopamine disruptions. Such mechanistic understanding of how acute stress impairs reward learning is particularly important given the pervasiveness of stress in our daily life and the impact that stress can have on our wellbeing and mental health.

Evaluation of electrochemical methods for tonic dopamine detection in vivo

Dysfunction in dopaminergic neuronal systems underlie a number of neurologic and psychiatric disorders such as Parkinson's disease, drug addiction, and schizophrenia. Dopamine systems communicate via two mechanisms, a fast "phasic" release (sub-second to second) that is related to salient stimuli and a slower "tonic" release (minutes to hours) that regulates receptor tone. Alterations in tonic levels are thought to be more critically important in enabling normal motor, cognitive, and motivational functions, and dysregulation in tonic dopamine levels are associated with neuropsychiatric disorders. Therefore, development of neurochemical recording techniques that enable rapid, selective, and quantitative measurements of changes in tonic extracellular levels are essential in determining the role of dopamine in both normal and disease states. Here, we review state-of-the-art advanced analytical techniques for in vivo detection of tonic levels, with special focus on electrochemical techniques for detection in humans.