Sub-second Dopamine and Serotonin Signaling in Human Striatum during Perceptual Decision-Making

Serotonergic and Dopaminergic Function in Neuropsychiatrically Asymptomatic People With HIV on Antiretroviral Therapy

OBJECTIVE

People with HIV (PWH) on antiretroviral therapy (ART) still experience neurocognitive dysfunction and accelerated brain volume loss. To assess whether the serotonergic and dopaminergic systems are affected, we used [11C]DASB positron emission tomography (PET) to assess presynaptic serotonergic function and [18F]FDOPA PET to measure presynaptic dopaminergic reserve in neurocognitively and psychiatrically asymptomatic PWH on ART, compared to seronegative controls (SCs).

METHODS

We compared [11C]DASB binding (BPND) (n = 17 PWH/19 SCs) and [18F]FDOPA influx constant (Ki) (n = 20 PWH/19 SCs) of PWH and SCs. We assessed correlations of Ki and BPND with CSF cytokines and imaging/clinical/neuropsychological outcomes using multivariable and univariable models.

RESULTS

BPND, Ki, and neurocognitive and psychiatric scores did not differ between PWH and SCs. Higher BPND correlated with better neurocognitive scores in the whole group. Ki did not correlate with neurocognitive scores. Neither BPND nor Ki correlated with depression scores. Caudate and putamen MRI volumes trended smaller in PWH. While CSF inflammatory cytokines were higher in PWH, they did not correlate with either PET measure.

INTERPRETATIONS

Presynaptic serotonin transporter density (measured by [11C]DASB) is a good correlate of the neurocognitive function in PWH and SCs. Despite long-term ART, PWH still showed trends for basal ganglia volume loss, consistent with known disproportionate global and regional volume loss in PWH compared to SCs. Aspects of serotonergic and dopaminergic function, namely, presynaptic SERT density and dopaminergic reserve, however, are not different in this PWH cohort on ART from SCs, despite persistent neuroinflammation and trends of volume loss. This could reflect a functional compensatory process versus minimal, below-detection-level dysfunction.

Stabilized carbon coating on microelectrodes for scalable and interoperable neurotransmitter sensing

Real-time monitoring of neurotransmitters is essential in driving basic neuroscience understandings and creating treatments for various brain disorders. However, current neurotransmitter sensing devices are highly limited in their spatiotemporal resolution and ability to integrate with neuronal recording. Here, we introduce a unique carbon coating approach to achieve high-performance voltammetry electrodes with extraordinary scalability and interoperability. Surprisingly, we discovered that mild annealing drastically improves the electrochemical stability of graphene-based carbon coating, enabling the transformation of conventional neuroelectrodes into fast-scan-cyclic-voltammetry-stable carbon sensors. We successfully validated sub-second detection of nanomolar dopamine in vivo using carbon-coated microelectrodes (CCMs) in rodents and demonstrated arrays of one hundred CCMs with high yield and uniformity. Furthermore, we developed a dual-modal neural probe that integrates the CCM with electrophysiological recording sites, allowing us to demonstrate that dopamine fluctuation in the ventral striatum of awake rats strongly correlates with the high gamma power in the brain with sub-second-level precision. Together, these advances pave the way for spatiotemporally scalable and multiplexed brain interfacing, with also broad applicability in electrochemical-related diagnostic and interventional approaches.

Simultaneous, real-time tracking of many neuromodulatory signals with Multiplexed Optical Recording of Sensors on a micro-Endoscope

Dozens of extracellular molecules jointly impact a given neuron, yet we lack methods to simultaneously record many such signals in real time. We developed a probe to track ten or more neuropeptides and neuromodulators using spatial multiplexing of genetically encoded fluorescent sensors. Cultured cells expressing one sensor at a time are immobilized at the front of a gradient refractive index (GRIN) lens for 3D two-photon imaging in vitro and in vivo . The sensor identity and detection sensitivity of each cell are determined via robotic dipping of the probe into wells containing various ligands and concentrations. Using this probe, we detected stimulation-evoked release of multiple neuromodulators in acute brain slices. We also tracked endogenous and drug-evoked changes in cerebrospinal fluid composition in the awake mouse lateral ventricle, which triggered downstream activation of the choroid plexus epithelium. Our approach offers a first step towards quantitative, real-time, high-dimensional tracking of brain fluid composition.

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.

Predictive coding for the actions and emotions of others and its deficits in autism spectrum disorders

Traditionally, the neural basis of social perception has been studied by showing participants brief examples of the actions or emotions of others presented in randomized order to prevent participants from anticipating what others do and feel. This approach is optimal to isolate the importance of information flow from lower to higher cortical areas. The degree to which feedback connections and Bayesian hierarchical predictive coding contribute to how mammals process more complex social stimuli has been less explored, and will be the focus of this review. We illustrate paradigms that start to capture how participants predict the actions and emotions of others under more ecological conditions, and discuss the brain activity measurement methods suitable to reveal the importance of feedback connections in these predictions. Together, these efforts draw a richer picture of social cognition in which predictive coding and feedback connections play significant roles. We further discuss how the notion of predicting coding is influencing how we think of autism spectrum disorder.

Wideband ratiometric measurement of tonic and phasic dopamine release in the striatum

Reward learning, cognition, and motivation are supported by changes in neurotransmitter levels across multiple timescales. Current measurement technologies for various neuromodulators (such as dopamine and serotonin) do not bridge timescales of fluctuations, limiting the ability to define the behavioral significance, regulation, and relationship between fast (phasic) and slow (tonic) dynamics. To help resolve longstanding debates about the behavioral significance of dopamine across timescales, we developed a novel quantification strategy, augmenting extensively used carbon-fiber Fast Scan Cyclic Voltammetry (FSCV). We iteratively engineered the FSCV scan sequence to rapidly modify electrode sensitivity within a sampling window and applied ratiometric analysis for wideband dopamine measurement. This allowed us to selectively eliminate artifacts unrelated to electrochemical detection (i.e., baseline drift), overcoming previous limitations that precluded wideband dopamine detection from milliseconds to hours. We extensively characterize this approach in vitro, validate performance in vivo with simultaneous microdialysis, and deploy this technique to measure wideband dopamine changes across striatal regions under pharmacological, optogenetic, and behavioral manipulations. We demonstrate that our approach can extend to additional analytes, including serotonin and pH, providing a robust platform to assess the contributions of multi-timescale neuromodulator fluctuations to cognition, learning, and motivation.

Neural basis of concurrent deliberation toward a choice and degree of confidence

Decision confidence plays a key role in flexible behavior and (meta)cognition, but its underlying neural mechanisms remain elusive. To uncover the latent dynamics of confidence formation at the level of population activity, we designed a decision task for nonhuman primates that measures choice, reaction time, and confidence with a single eye movement on every trial. Monkey behavior was well fit by a bounded accumulator model instantiating parallel processing of evidence, rejecting a serial model in which the choice is resolved first followed by post-decision accumulation for confidence. Neurons in area LIP reflected concurrent accumulation, exhibiting covariation of choice and confidence signals across the population, and within-trial dynamics consistent with parallel updating at near-zero time lag. The results demonstrate that monkeys can process a single stream of evidence in service of two computational goals simultaneously-a categorical decision and associated level of confidence-and illuminate a candidate neural substrate for this ability.

Genetically encoded sensors for in vivo detection of neurochemicals relevant to depression

Depressive disorders are a common and debilitating form of mental illness with significant impacts on individuals and society. Despite the high prevalence, the underlying causes and mechanisms of depressive disorders are still poorly understood. Neurochemical systems, including serotonin, norepinephrine, and dopamine, have been implicated in the development and perpetuation of depressive symptoms. Current treatments for depression target these neuromodulator systems, but there is a need for a better understanding of their role in order to develop more effective treatments. Monitoring neurochemical dynamics during depressive symptoms is crucial for gaining a better a understanding of their involvement in depressive disorders. Genetically encoded sensors have emerged recently that offer high spatial-temporal resolution and the ability to monitor neurochemical dynamics in real time. This review explores the neurochemical systems involved in depression and discusses the applications and limitations of current monitoring tools for neurochemical dynamics. It also highlights the potential of genetically encoded sensors for better characterizing neurochemical dynamics in depression-related behaviors. Furthermore, potential improvements to current sensors are discussed in order to meet the requirements of depression research.

Temporal regularities shape perceptual decisions and striatal dopamine signals

Perceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that male mice adapt their perceptual choice history biases to different temporal regularities of visual stimuli. This adaptation was slow, evolving over hundreds of trials across several days. It occurred alongside a fast non-adaptive choice history bias, limited to a few trials. Both fast and slow trial history effects are well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that dorsal striatal dopamine tracks predictions of the model and behavior, suggesting that striatal dopamine reports reward predictions associated with adaptive choice history biases. Our results reveal the adaptive nature of perceptual choice history biases and shed light on their underlying computational principles and neural correlates.

Evaluation of In Vitro Serotonin-Induced Electrochemical Fouling Performance of Boron Doped Diamond Microelectrode Using Fast-Scan Cyclic Voltammetry

Fast-scan cyclic voltammetry (FSCV) is an electrochemical sensing technique that can be used for neurochemical sensing with high spatiotemporal resolution. Carbon fiber microelectrodes (CFMEs) are traditionally used as FSCV sensors. However, CFMEs are prone to electrochemical fouling caused by oxidative byproducts of repeated serotonin (5-HT) exposure, which makes them less suitable as chronic 5-HT sensors. Our team is developing a boron-doped diamond microelectrode (BDDME) that has previously been shown to be relatively resistant to fouling caused by protein adsorption (biofouling). We sought to determine if this BDDME exhibits resistance to electrochemical fouling, which we explored on electrodes fabricated with either femtosecond laser cutting or physical cleaving. We recorded the oxidation current response after 25 repeated injections of 5-HT in a flow-injection cell and compared the current drop from the first with the last injection. The 5-HT responses were compared with dopamine (DA), a neurochemical that is known to produce minimal fouling oxidative byproducts and has a stable repeated response. Physical cleaving of the BDDME yielded a reduction in fouling due to 5-HT compared with the CFME and the femtosecond laser cut BDDME. However, the femtosecond laser cut BDDME exhibited a large increase in sensitivity over the cleaved BDDME. An extended stability analysis was conducted for all device types following 5-HT fouling tests. This analysis demonstrated an improvement in the long-term stability of boron-doped diamond over CFMEs, as well as a diminishing sensitivity of the laser-cut BDDME over time. This work reports the electrochemical fouling performance of the BDDME when it is repeatedly exposed to DA or 5-HT, which informs the development of a chronic, diamond-based electrochemical sensor for long-term neurotransmitter measurements in vivo.

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.

Distinct neural markers of evidence accumulation index metacognitive processing before and after simple visual decisions

Perceptual decision-making is affected by uncertainty arising from the reliability of incoming sensory evidence (perceptual uncertainty) and the categorization of that evidence relative to a choice boundary (categorical uncertainty). Here, we investigated how these factors impact the temporal dynamics of evidence processing during decision-making and subsequent metacognitive judgments. Participants performed a motion discrimination task while electroencephalography was recorded. We manipulated perceptual uncertainty by varying motion coherence, and categorical uncertainty by varying the angular offset of motion signals relative to a criterion. After each trial, participants rated their desire to change their mind. High uncertainty impaired perceptual and metacognitive judgments and reduced the amplitude of the centro-parietal positivity, a neural marker of evidence accumulation. Coherence and offset affected the centro-parietal positivity at different time points, suggesting that perceptual and categorical uncertainty affect decision-making in sequential stages. Moreover, the centro-parietal positivity predicted participants' metacognitive judgments: larger predecisional centro-parietal positivity amplitude was associated with less desire to change one's mind, whereas larger postdecisional centro-parietal positivity amplitude was associated with greater desire to change one's mind, but only following errors. These findings reveal a dissociation between predecisional and postdecisional evidence processing, suggesting that the CPP tracks potentially distinct cognitive processes before and after a decision.

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.

Toward robust quantification of dopamine and serotonin in mixtures using nano-graphitic carbon sensors

Monitoring the coordinated signaling of dopamine (DA) and serotonin (5-HT) is important for advancing our understanding of the brain. However, the co-detection and robust quantification of these signals at low concentrations is yet to be demonstrated. Here, we present the quantification of DA and 5-HT using nano-graphitic (NG) sensors together with fast-scan cyclic voltammetry (FSCV) employing an engineered N-shape potential waveform. Our method yields 6% error in quantifying DA and 5-HT analytes present in in vitro mixtures at concentrations below 100 nM. This advance is due to the electrochemical properties of NG sensors which, in combination with the engineered FSCV waveform, provided distinguishable cyclic voltammograms (CVs) for DA and 5-HT. We also demonstrate the generalizability of the prediction model across different NG sensors, which arises from the consistent voltammetric fingerprints produced by our NG sensors. Curiously, the proposed engineered waveform also improves the distinguishability of DA and 5-HT CVs obtained from traditional carbon fiber (CF) microelectrodes. Nevertheless, this improved distinguishability of CVs obtained from CF is inferior to that of NG sensors, arising from differences in the electrochemical properties of the sensor materials. Our findings demonstrate the potential of NG sensors and our proposed FSCV waveform for future brain studies.

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.

Serotonin as a biomarker of toxin-induced Parkinsonism

BACKGROUND

Loss of dopaminergic neurons underlies the motor symptoms of Parkinson's disease (PD). However stereotypical PD symptoms only manifest after approximately 80% of dopamine neurons have died making dopamine-related motor phenotypes unreliable markers of the earlier stages of the disease. There are other non-motor symptoms, such as depression, that may present decades before motor symptoms.

METHODS

Because serotonin is implicated in depression, here we use niche, fast electrochemistry paired with mathematical modelling and machine learning to, for the first time, robustly evaluate serotonin neurochemistry in vivo in real time in a toxicological model of Parkinsonism, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP).

RESULTS

Mice treated with acute MPTP had lower concentrations of in vivo, evoked and ambient serotonin in the hippocampus, consistent with the clinical comorbidity of depression with PD. These mice did not chemically respond to SSRI, as strongly as control animals did, following the clinical literature showing that antidepressant success during PD is highly variable. Following L-DOPA administration, using a novel machine learning analysis tool, we observed a dynamic shift from evoked serotonin release in the hippocampus to dopamine release. We hypothesize that this finding shows, in real time, that serotonergic neurons uptake L-DOPA and produce dopamine at the expense of serotonin, supporting the significant clinical correlation between L-DOPA and depression. Finally, we found that this post L-DOPA dopamine release was less regulated, staying in the synapse for longer. This finding is perhaps due to lack of autoreceptor control and may provide a ground from which to study L-DOPA induced dyskinesia.

CONCLUSIONS

These results validate key prior hypotheses about the roles of serotonin during PD and open an avenue to study to potentially improve therapeutics for levodopa-induced dyskinesia and depression.

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.

Molecular organization of neuronal cell types and neuromodulatory systems in the zebrafish telencephalon

The function of neuronal networks is determined not only by synaptic connectivity but also by neuromodulatory systems that broadcast information via distributed connections and volume transmission. To understand the molecular constraints that organize neuromodulatory signaling in the telencephalon of adult zebrafish, we used transcriptomics and additional approaches to delineate cell types, to determine their phylogenetic conservation, and to map the expression of marker genes at high granularity. The combinatorial expression of GPCRs and cell-type markers indicates that all neuronal cell types are subject to modulation by multiple monoaminergic systems and distinct combinations of neuropeptides. Individual cell types were associated with multiple (typically >30) neuromodulatory signaling networks but expressed only a few diagnostic GPCRs at high levels, suggesting that different neuromodulatory systems act in combination, albeit with unequal weights. These results provide a detailed map of cell types and brain areas in the zebrafish telencephalon, identify core components of neuromodulatory networks, highlight the cell-type specificity of neuropeptides and GPCRs, and begin to decipher the logic of combinatorial neuromodulation.

Dopamine precursor depletion affects performance and confidence judgements when events are timed from an explicit, but not an implicit onset

Dopamine affects processing of temporal information, but most previous work has tested its role in prospective tasks, where participants know in advance when the event to be timed starts. However, we are often exposed to events whose onset we do not know in advance. We can evaluate their duration after they have elapsed, but mechanisms underlying this ability are still elusive. Here we contrasted effects of acute phenylalanine and tyrosine depletion (APTD) on both forms of timing in healthy volunteers, in a within-subject, placebo-controlled design. Participants were presented with a disc moving around a circular path and asked to reproduce the duration of one full revolution and to judge their confidence in performance. The onset of the revolution was either known in advance (explicit onset) or revealed only at the end of the trial (implicit onset). We found that APTD shortened reproduced durations in the explicit onset task but had no effect on temporal performance in the implicit onset task. This dissociation is corroborated by effects of APTD on confidence judgements in the explicit task only. Our findings suggest that dopamine has a specific role in prospective encoding of temporal intervals, rather than the processing of temporal information in general.

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.

Transdiagnostic computations of uncertainty: towards a new lens on intolerance of uncertainty

People radically differ in how they cope with uncertainty. Clinical researchers describe a dispositional characteristic known as "intolerance of uncertainty", a tendency to find uncertainty aversive, reported to be elevated across psychiatric and neurodevelopmental conditions. Concurrently, recent research in computational psychiatry has leveraged theoretical work to characterise individual differences in uncertainty processing. Under this framework, differences in how people estimate different forms of uncertainty can contribute to mental health difficulties. In this review, we briefly outline the concept of intolerance of uncertainty within its clinical context, and we argue that the mechanisms underlying this construct may be further elucidated through modelling how individuals make inferences about uncertainty. We will review the evidence linking psychopathology to different computationally specified forms of uncertainty and consider how these findings might suggest distinct mechanistic routes towards intolerance of uncertainty. We also discuss the implications of this computational approach for behavioural and pharmacological interventions, as well as the importance of different cognitive domains and subjective experiences in studying uncertainty processing.

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.

Digitally embodied lifespan neurocognitive development and Tactile Internet: Transdisciplinary challenges and opportunities

Mechanisms underlying perceptual processing and inference undergo substantial changes across the lifespan. If utilized properly, technologies could support and buffer the relatively more limited neurocognitive functions in the still developing or aging brains. Over the past decade, a new type of digital communication infrastructure, known as the "Tactile Internet (TI)," is emerging in the fields of telecommunication, sensor and actuator technologies and machine learning. A key aim of the TI is to enable humans to experience and interact with remote and virtual environments through digitalized multimodal sensory signals that also include the haptic (tactile and kinesthetic) sense. Besides their applied focus, such technologies may offer new opportunities for the research tapping into mechanisms of digitally embodied perception and cognition as well as how they may differ across age cohorts. However, there are challenges in translating empirical findings and theories about neurocognitive mechanisms of perception and lifespan development into the day-to-day practices of engineering research and technological development. On the one hand, the capacity and efficiency of digital communication are affected by signal transmission noise according to Shannon's (1949) Information Theory. On the other hand, neurotransmitters, which have been postulated as means that regulate the signal-to-noise ratio of neural information processing (e.g., Servan-Schreiber et al., 1990), decline substantially during aging. Thus, here we highlight neuronal gain control of perceptual processing and perceptual inference to illustrate potential interfaces for developing age-adjusted technologies to enable plausible multisensory digital embodiments for perceptual and cognitive interactions in remote or virtual environments.

Voglibose and saxagliptin ameliorate the post-surgical stress and cognitive dysfunction in chronic anaesthesia exposed diabetic MCAo induced ischemic rats

Background

Chronic surgical anaesthesia and uncontrolled hyperglycemia are bidirectional risk factors for the development of psychiatric, cerebrovascular, and cardiovascular diseases.

Objective

The current study was designed to elucidate the neuroprotective effects of anti-diabetic agents in pre and post-surgical anaesthesia exposure on diabetic ischemic rats.

Methods

The diabetes type-2 was induced and rats having more than 250 gm/dl blood glucose levels were considered for study. Administration of anaesthetic agents (ketamine 100 mg/kg IP, xylazine 10 mg/kg IP) were done pre and post MCAo surgery for 7 days. The treatment with anti-diabetic agents (voglibose, saxagliptin, repaglinide, dapagliflozin) was carried out after 7 days of Post MCAo surgery for one week. After treatment, assessment of neurobehavioral function was carried out using Morris Water Maze. After that, brains were excised and bloods were collected from all groups subjected for assessment of neuromodulator levels, oxidative stress parameters, serum biochemical biomarkers.

Results

The treatment with voglibose and saxagliptin not only improved neuromodulator levels statistically significant (p < 0.001) and cognitive profile but also significantly improved (p < 0.01) overall stroke serum biomarkers (Serum Glucose, GGT, CRP, CK-MB, LDH).

Conclusion

The results of the present study, suggested that chronic exposure of anaesthesia worsens the cognition and increases risk of stroke biomarkers in diabetic conditions. We can conclude that voglibose, saxagliptin, and dapagliflozin can significantly improve the postoperative mortality, morbidity, and cognitive dysfunction caused by post-surgical stress and chronic anaesthesia-induced cognitive dysfunction.

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.

Nigrostriatal dopamine pathway regulates auditory discrimination behavior

The auditory striatum, the tail portion of dorsal striatum in basal ganglia, is implicated in perceptual decision-making, transforming auditory stimuli to action outcomes. Despite its known connections to diverse neurological conditions, the dopaminergic modulation of sensory striatal neuronal activity and its behavioral influences remain unknown. We demonstrated that the optogenetic inhibition of dopaminergic projections from the substantia nigra pars compacta to the auditory striatum specifically impairs mouse choice performance but not movement in an auditory frequency discrimination task. In vivo dopamine and calcium imaging in freely behaving mice revealed that this dopaminergic projection modulates striatal tone representations, and tone-evoked striatal dopamine release inversely correlated with the evidence strength of tones. Optogenetic inhibition of D1-receptor expressing neurons and pharmacological inhibition of D1 receptors in the auditory striatum dampened choice performance accuracy. Our study uncovers a phasic mechanism within the nigrostriatal system that regulates auditory decisions by modulating ongoing auditory perception.

The auditory striatum, the tail portion of dorsal striatum, is implicated in decision-making. This study uncovers a phasic mechanism within the nigrostriatal system that regulates auditory decisions by modulating ongoing auditory perception.

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.

Comparative anatomical analysis of dopamine systems in Mus musculus and Peromyscus californicus

Dopamine plays important roles in motivational and social behaviors in mammals, and it has been implicated in several human neurological and psychiatric disorders. Rodents are used extensively as experimental models to study dopamine function in health and disease. However, interspecies differences of dopamine systems remain incompletely characterized. Here, we assessed whether the commonly referenced anatomical organization of dopamine systems in Mus musculus differs from another rodent species, Peromyscus californicus, which exhibits unique social behaviors such as biparental care. We applied tyrosine hydroxylase immunofluorescence labeling and high-throughput microscopy to establish whole-brain maps of dopamine systems in P. californicus. By comparing these maps to those from M. musculus, we identified unexpected anatomical similarity and difference between these two species. A sex difference in dopamine neurons at the anteroventral periventricular nucleus of hypothalamus, which has been implicated in regulating the maternal behaviors of the uniparental M. musculus, is similarly present in the biparental P. californicus. In contrast, major interspecies differences from M. musculus are found in the ventral midbrain and striatum of P. californicus, including the expansion of midbrain dopamine neurons into the ventral substantia nigra and the presence of an internal capsule-like white matter tract that demarcates a dorsomedial area from the rest of the striatum. These features identified in P. californicus resemble the anatomical organization of the primate brain more closely compared to those in M. musculus. Our findings suggest that P. californicus is a unique model organism for studying the evolution of dopamine systems in mammals and the disorders of dopamine systems.

Indoor Air Pollution and Decision-Making Behavior: An Interdisciplinary Review

The human brain is constantly exposed to air pollutants, some of which might be disruptive or even lethal to certain neurons implicated in abstract features of cognitive function. In this review, we present new evidence from behavioral and neural studies in humans, suggesting a link between indoor fine particulate matter and decision-making behavior. To illustrate this relationship, we use qualitative sources, such as historical documents of the Vietnam War to develop hypotheses of how aerial transmission of pollutants might obstruct alternative choices during the evaluation of policy decisions. We first describe the neural circuits driving decision-making processes by addressing how neurons and their cognate receptors directly evaluate and transduce physical phenomena into sensory perceptions that allow us to decide the best course of action among competing alternatives. We then raise the possibility that indoor air pollutants might also impact cell-signaling systems outside the brain parenchyma to further obstruct the computational analysis of the social environment. We also highlight how particulate matter might be pathologically integrated into the brain to override control of sensory decisions, and thereby perturb selection of choice. These lines of research aim to extend our understanding of how inhalation of airborne particulates and toxicants in smoke, for example, might contribute to cognitive impairment and negative health outcomes.

Serotonin and dopamine modulate aging in response to food odor and availability

An organism's ability to perceive and respond to changes in its environment is crucial for its health and survival. Here we reveal how the most well-studied longevity intervention, dietary restriction, acts in-part through a cell non-autonomous signaling pathway that is inhibited by the presence of attractive smells. Using an intestinal reporter for a key gene induced by dietary restriction but suppressed by attractive smells, we identify three compounds that block food odor effects in C. elegans, thereby increasing longevity as dietary restriction mimetics. These compounds clearly implicate serotonin and dopamine in limiting lifespan in response to food odor. We further identify a chemosensory neuron that likely perceives food odor, an enteric neuron that signals through the serotonin receptor 5-HT1A/SER-4, and a dopaminergic neuron that signals through the dopamine receptor DRD2/DOP-3. Aspects of this pathway are conserved in D. melanogaster. Thus, blocking food odor signaling through antagonism of serotonin or dopamine receptors is a plausible approach to mimic the benefits of dietary restriction.

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.

Invariant neural subspaces maintained by feedback modulation

Sensory systems reliably process incoming stimuli in spite of changes in context. Most recent models accredit this context invariance to an extraction of increasingly complex sensory features in hierarchical feedforward networks. Here, we study how context-invariant representations can be established by feedback rather than feedforward processing. We show that feedforward neural networks modulated by feedback can dynamically generate invariant sensory representations. The required feedback can be implemented as a slow and spatially diffuse gain modulation. The invariance is not present on the level of individual neurons, but emerges only on the population level. Mechanistically, the feedback modulation dynamically reorients the manifold of neural activity and thereby maintains an invariant neural subspace in spite of contextual variations. Our results highlight the importance of population-level analyses for understanding the role of feedback in flexible sensory processing.

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.

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 Role of BDNF in the Depression Pathogenesis and a Potential Target as Antidepressant: The Modulator of Stress Sensitivity "Shati/Nat8l-BDNF System" in the Dorsal Striatum

Depression is one of the most common mental diseases, with increasing numbers of patients globally each year. In addition, approximately 30% of patients with depression are resistant to any treatment and do not show an expected response to first-line antidepressant drugs. Therefore, novel antidepressant agents and strategies are required. Although depression is triggered by post-birth stress, while some individuals show the pathology of depression, others remain resilient. The molecular mechanisms underlying stress sensitivity remain unknown. Brain-derived neurotrophic factor (BDNF) has both pro- and anti-depressant effects, dependent on brain region. Considering the strong region-specific contribution of BDNF to depression pathogenesis, the regulation of BDNF in the whole brain is not a beneficial strategy for the treatment of depression. We reviewed a novel finding of BDNF function in the dorsal striatum, which induces vulnerability to social stress, in addition to recent research progress regarding the brain regional functions of BDNF, including the prefrontal cortex, hippocampus, and nucleus accumbens. Striatal BDNF is regulated by Shati/Nat8l, an N-acetyltransferase through epigenetic regulation. Targeting of Shati/Nat8l would allow BDNF to be striatum-specifically regulated, and the striatal Shati/Nat8l-BDNF pathway could be a promising novel therapeutic agent for the treatment of depression by modulating sensitivity to stress.

Dopaminergic restoration of prefrontal cortico-putaminal network in gene therapy for aromatic l-amino acid decarboxylase deficiency

Aromatic l-amino acid decarboxylase (AADC) is an essential dopamine-synthesizing enzyme. In children with AADC deficiency, the gene delivery of AADC into the putamen, which functionally interacts with cortical regions, was found to improve motor function and ameliorate dystonia. However, how the restoration of dopamine in the putamen in association with cortico-putaminal networks leads to therapeutic effects remains unclear. Here, we examined neuroimaging data of eight patients with AADC deficiency (five males and three females, age range 4-19 years) who received the AADC gene therapy of the bilateral putamen in an open-label phase 1/2 study. Using high-resolution positron emission tomography with a specific AADC tracer, 6-[¹⁸F]fluoro-l-m-tyrosine (FMT), we showed that FMT uptake increased in the broad area of the putamen over the years. Then, with the structural connectivity-based parcellation of the putaminal area, we found that motor improvement is associated with dopaminergic restoration of the putaminal area that belongs to the prefrontal cortico-putaminal network. The prefrontal area dominantly belongs to the frontoparietal control network, which contributes to cognitive-motor control function, including motor initiation and planning. The results suggest that putaminal dopamine promotes the development of an immature motor control system, particularly in the human prefrontal cortex that is primarily affected by AADC deficiency.

A mosaic of cost-benefit control over cortico-striatal circuitry

Dopamine contributes to cognitive control through well-established effects in both the striatum and cortex. Although earlier work suggests that dopamine affects cognitive control capacity, more recent work suggests that striatal dopamine may also impact on cognitive motivation. We consider the emerging perspective that striatal dopamine boosts control by making people more sensitive to the benefits versus the costs of cognitive effort, and we discuss how this sensitivity shapes competition between controlled and prepotent actions. We propose that dopamine signaling in distinct cortico-striatal subregions mediates different types of cost-benefit tradeoffs, and also discuss mechanisms for the local control of dopamine release, enabling selectivity among cortico-striatal circuits. In so doing, we show how this cost-benefit mosaic can reconcile seemingly conflicting findings about the impact of dopamine signaling on cognitive control.

Automatic and Reliable Quantification of Tonic Dopamine Concentrations In Vivo Using a Novel Probabilistic Inference Method

Dysregulation of the neurotransmitter dopamine (DA) is implicated in several neuropsychiatric conditions. Multiple-cyclic square-wave voltammetry (MCSWV) is a state-of-the-art technique for measuring tonic DA levels with high sensitivity (<5 nM), selectivity, and spatiotemporal resolution. Currently, however, analysis of MCSWV data requires manual, qualitative adjustments of analysis parameters, which can inadvertently introduce bias. Here, we demonstrate the development of a computational technique using a statistical model for standardized, unbiased analysis of experimental MCSWV data for unbiased quantification of tonic DA. The oxidation current in the MCSWV signal was predicted to follow a lognormal distribution. The DA-related oxidation signal was inferred to be present in the top 5% of this analytical distribution and was used to predict a tonic DA level. The performance of this technique was compared against the previously used peak-based method on paired in vivo and post-calibration in vitro datasets. Analytical inference of DA signals derived from the predicted statistical model enabled high-fidelity conversion of the in vivo current signal to a concentration value via in vitro post-calibration. As a result, this technique demonstrated reliable and improved estimation of tonic DA levels in vivo compared to the conventional manual post-processing technique using the peak current signals. These results show that probabilistic inference-based voltammetry signal processing techniques can standardize the determination of tonic DA concentrations, enabling progress toward the development of MCSWV as a robust research and clinical tool.

Dynamic Changes of Cytoskeleton-Related Proteins Within Reward-Related Brain Regions in Morphine-Associated Memory

Drug-induced memory engages complex and dynamic processes and is coordinated at multiple reward-related brain regions. The spatiotemporal molecular mechanisms underlying different addiction phases remain unknown. We investigated the role of β-actin, as well as its potential modulatory protein activity-regulated cytoskeletal-associated protein (Arc/Arg3.1) and extracellular signal-regulated kinase (ERK), in reward-related associative learning and memory using morphine-induced conditioned place preference (CPP) in mice. CPP was established by alternate morphine (10 mg/kg) injections and extinguished after a 10-day extinction training, while the withdrawal group failed to extinguish without training. In the nucleus accumbens (NAc), morphine enhanced the level of β-actin and Arc only during extinction, while p-ERK1/2 was increased during both CPP acquisition and extinction phases. In the dorsal hippocampus, morphine induced an upregulation of p-ERK only during extinction, while p-β-actin was elevated during both CPP establishment and extinction. In the dorsal hippocampus, Arc was elevated during CPP formation and suppressed during extinction. Compared with the NAc and dorsal hippocampus, dynamic changes in the medial prefrontal cortex (mPFC) and caudate putamen (CPu) were not very significant. These results suggested region-specific changes of p-β-actin, Arc/Arg3.1, and p-ERK1/2 protein during establishment and extinction phases of morphine-induced CPP. These findings unveiled a spatiotemporal molecular regulation in opiate-induced plasticity.

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.