Optimal ensemble construction for multistudy prediction with applications to mortality estimation.

It is increasingly common to encounter prediction tasks in the biomedical sciences for which multiple datasets are available for model training. Common approaches such as pooling datasets before model fitting can produce poor out-of-study prediction performance when datasets are heterogeneous. Theoretical and applied work has shown multistudy ensembling to be a viable alternative that leverages the variability across datasets in a manner that promotes model generalizability. Multistudy ensembling uses a two-stage stacking strategy which fits study-specific models and estimates ensemble weights separately. This approach ignores, however, the ensemble properties at the model-fitting stage, potentially resulting in performance losses. Motivated by challenges in the estimation of COVID-attributable mortality, we propose optimal ensemble construction, an approach to multistudy stacking whereby we jointly estimate ensemble weights and parameters associated with study-specific models. We prove that limiting cases of our approach yield existing methods such as multistudy stacking and pooling datasets before model fitting. We propose an efficient block coordinate descent algorithm to optimize the loss function. We use our method to perform multicountry COVID-19 baseline mortality prediction. We show that when little data is available for a country before the onset of the pandemic, leveraging data from other countries can substantially improve prediction accuracy. We further compare and characterize the method's performance in data-driven simulations and other numerical experiments. Our method remains competitive with or outperforms multistudy stacking and other earlier methods in the COVID-19 data application and in a range of simulation settings.

A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments.

Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials. We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. Our method identifies two dopamine components with distinct temporal dynamics that may be hard to explain under currently competing learning theories. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package implementing our framework.

Nonparametric causal inference for optogenetics: sequential excursion effects for dynamic regimes.

Optogenetics is a powerful neuroscience technique for studying how neural circuit manipulation affects behavior. Standard analysis conventions discard information and severely limit the scope of the causal questions that can be probed. To address this gap, we 1) draw connections to the causal inference literature on sequentially randomized experiments, 2) propose a non-parametric framework for analyzing "open-loop" (static regime) optogenetics behavioral experiments, 3) derive extensions of history-restricted marginal structural models for dynamic treatment regimes with positivity violations for "closed-loop" designs, and 4) propose a taxonomy of identifiable causal effects that encompass a far richer collection of scientific questions compared to standard methods. From another view, our work extends "excursion effect" methods, popularized recently in the mobile health literature, to enable estimation of causal contrasts for treatment sequences in the presence of positivity violations. We describe sufficient conditions for identifiability of the proposed causal estimands, and provide asymptotic statistical guarantees for a proposed inverse probability-weighted estimator, a multiply-robust estimator (for two intervention timepoints), a framework for hypothesis testing, and a computationally scalable implementation. Finally, we apply our framework to data from a recent neuroscience study and show how it provides insight into causal effects of optogenetics on behavior that are obscured by standard analyses.

Dissociable encoding of motivated behavior by parallel thalamo-striatal projections.

The successful pursuit of goals requires the coordinated execution and termination of actions that lead to positive outcomes. This process is thought to rely on motivational states that are guided by internal drivers, such as hunger or fear. However, the mechanisms by which the brain tracks motivational states to shape instrumental actions are not fully understood. The paraventricular nucleus of the thalamus (PVT) is a midline thalamic nucleus that shapes motivated behaviors via its projections to the nucleus accumbens (NAc)1-8 and monitors internal state via interoceptive inputs from the hypothalamus and brainstem3,9-14. Recent studies indicate that the PVT can be subdivided into two major neuronal subpopulations, namely PVTD2(+) and PVTD2(-), which differ in genetic identity, functionality, and anatomical connectivity to other brain regions, including the NAc4,15,16. In this study, we used fiber photometry to investigate the in vivo dynamics of these two distinct PVT neuronal types in mice performing a reward foraging-like behavioral task. We discovered that PVTD2(+) and PVTD2(-) neurons encode the execution and termination of goal-oriented actions, respectively. Furthermore, activity in the PVTD2(+) neuronal population mirrored motivation parameters such as vigor and satiety. Similarly, PVTD2(-) neurons, also mirrored some of these parameters but to a much lesser extent. Importantly, these features were largely preserved when activity in PVT projections to the NAc was selectively assessed. Collectively, our results highlight the existence of two parallel thalamo-striatal projections that participate in the dynamic regulation of goal pursuits and provide insight into the mechanisms by which the brain tracks motivational states to shape instrumental actions.

Dissociable encoding of motivated behavior by parallel thalamo-striatal projections.

The successful pursuit of goals requires the coordinated execution and termination of actions that lead to positive outcomes. This process relies on motivational states that are guided by internal drivers, such as hunger or fear. However, the mechanisms by which the brain tracks motivational states to shape instrumental actions are not fully understood. The paraventricular nucleus of the thalamus (PVT) is a midline thalamic nucleus that shapes motivated behaviors via its projections to the nucleus accumbens (NAc)1,2,3,4,5,6,7,8 and monitors internal state via interoceptive inputs from the hypothalamus and brainstem.3,9,10,11,12,13,14 Recent studies indicate that the PVT can be subdivided into two major neuronal subpopulations, namely PVTD2(+) and PVTD2(-), which differ in genetic identity, functionality, and anatomical connectivity to other brain regions, including the NAc.4,15,16 In this study, we used fiber photometry to investigate the in vivo dynamics of these two distinct PVT neuronal types in mice performing a foraging-like behavioral task. We discovered that PVTD2(+) and PVTD2(-) neurons encode the execution and termination of goal-oriented actions, respectively. Furthermore, activity in the PVTD2(+) neuronal population mirrored motivation parameters such as vigor and satiety. Similarly, PVTD2(-) neurons also mirrored some of these parameters, but to a much lesser extent. Importantly, these features were largely preserved when activity in PVT projections to the NAc was selectively assessed. Collectively, our results highlight the existence of two parallel thalamo-striatal projections that participate in the dynamic regulation of goal pursuits and provide insight into the mechanisms by which the brain tracks motivational states to shape instrumental actions.

Reliability and predictability of phenotype information from functional connectivity in large imaging datasets.

One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as latent phenotypes from resting state functional connectivity. For this task, we predicted phenotypes in two large neuroimaging datasets: the Human Connectome Project (HCP) and the Philadelphia Neurodevelopmental Cohort (PNC). We illustrate the importance of regressing out confounds, which could significantly influence phenotype prediction. Our findings reveal that both phenotypes and their corresponding latent phenotypes yield similar predictive performance. Interestingly, only the first five latent phenotypes were reliably identified, and using just these reliable phenotypes for predicting phenotypes yielded a similar performance to using all latent phenotypes. This suggests that the predictable information is present in the first latent phenotypes, allowing the remainder to be filtered out without any harm in performance. This study sheds light on the intricate relationship between functional connectivity and the predictability and reliability of phenotypic information, with potential implications for enhancing predictive modeling in the realm of neuroimaging research.

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.

Surprise-induced enhancements in the associability of Pavlovian cues facilitate learning across behavior systems.

Surprising violations of outcome expectancies have long been known to enhance the associability of Pavlovian cues; that is, the rate at which the cue enters into further associations. The adaptive value of such enhancements resides in promoting new learning in the face of uncertainty. However, it is unclear whether associability enhancements reflect increased associative plasticity within a particular behavior system, or whether they can facilitate learning between a cue and any arbitrary outcome, as suggested by attentional models of conditioning. Here, we show evidence consistent with the latter hypothesis. Violating the outcome expectancies generated by a cue in an appetitive setting (feeding behavior system) facilitated subsequent learning about the cue in an aversive setting (defense behavior system). In addition to shedding light on the nature of associability enhancements, our findings offer the neuroscientist a behavioral tool to dissociate their neural substrates from those of other, behavior system- or valence-specific changes. Moreover, our results present an opportunity to utilize associability enhancements to the advantage of counterconditioning procedures in therapeutic contexts. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Protocol for Outcome Evaluation of Ayahuasca-Assisted Addiction Treatment: The Case of Takiwasi Center.

The present study describes the protocol for the Ayahuasca Treatment Outcome Project (ATOP) with a special focus on the evaluation of addiction treatment services provided through Takiwasi Center, the first ATOP study site. The goal of the project is to assess treatment outcomes and understand the therapeutic mechanisms of an Ayahuasca-assisted, integrative treatment model for addiction rehabilitation in the Peruvian Amazon. The proposed intervention protocol highlights the significance of treatment setting in the design, delivery, and efficacy of an addiction rehabilitation program that involves the potent psychedelic tea known as Ayahuasca. After describing the context of the study, we put forth details about our mixed-methods approach to data collection and analysis, with which we seek to gain an understanding of why, how, and for whom this specific ayahuasca-assisted treatment program is effective across a range of outcomes. The ATOP protocol employs qualitative research methods as a means to determine which aspects of the setting are meaningful to clients and practitioners, and how this may correlate with outcome measures. This paper delineates the core principles, methods, and measures of the overall ATOP umbrella, then discusses the role of ATOP in the context of the literature on long-term residential programs. To conclude, we discuss the strengths and limitations of the protocol and the intended future of the project.

Operant self-stimulation of thalamic terminals in the dorsomedial striatum is constrained by metabotropic glutamate receptor 2.

Dorsal striatal manipulations including stimulation of dopamine release and activation of medium spiny neurons (MSNs) are sufficient to drive reinforcement-based learning. Glutamatergic innervation of the striatum by the cortex and thalamus is a critical determinant of MSN activity and local regulation of dopamine release. However, the relationship between striatal glutamatergic afferents and behavioral reinforcement is not well understood. We evaluated the reinforcing properties of optogenetic stimulation of thalamostriatal terminals, which are associated with vesicular glutamate transporter 2 (Vglut2) expression, in the dorsomedial striatum (DMS), a region implicated in goal-directed behaviors. In mice expressing channelrhodopsin-2 (ChR2) under control of the Vglut2 promoter, optical stimulation of the DMS reinforced operant lever-pressing behavior. Mice also acquired operant self-stimulation of thalamostriatal terminals when ChR2 expression was virally targeted to the intralaminar thalamus. Stimulation trains that supported operant responding evoked dopamine release in the DMS and excitatory postsynaptic currents in DMS MSNs. Our previous work demonstrated that the presynaptic G protein-coupled receptor metabotropic glutamate receptor 2 (mGlu2) robustly inhibits glutamate and dopamine release induced by activation of thalamostriatal afferents. Thus, we examined the regulation of thalamostriatal self-stimulation by mGlu2. Administration of an mGlu2/3 agonist or an mGlu2-selective positive allosteric modulator reduced self-stimulation. Conversely, blockade of these receptors increased thalamostriatal self-stimulation, suggesting that endogenous activation of these receptors negatively modulates the reinforcing properties of thalamostriatal activity. These findings demonstrate that stimulation of thalamic terminals in the DMS is sufficient to reinforce a self-initiated action, and that thalamostriatal reinforcement is constrained by mGlu2 activation.

Dopamine D2 receptor signaling on iMSNs is required for initiation and vigor of learned actions.

Striatal dopamine D2 receptors (D2Rs) are important for motor output. Selective deletion of D2Rs from indirect pathway-projecting medium spiny neurons (iMSNs) impairs locomotor activities in a task-specific manner. However, the role of D2Rs in the initiation of motor actions in reward seeking and taking is not fully understood, and there is little information about how receptors contribute under different task demands and with different outcome types. The iMSN-D2Rs modulate neuronal activity and synaptic transmission, exerting control on circuit functions that may play distinct roles in action learning and performance. Selective deletion of D2Rs on iMSNs resulted in slower action initiation and response rate in an instrumental conditioning task, but only when performance demand was increased. The iMSN-Drd2KO mice were also slower to initiate swimming in a T-maze procedural learning task but were unimpaired in cognitive function and behavioral flexibility. In contrast, in a Pavlovian discrimination learning task, iMSN-Drd2KO mice exhibited normal acquisition and extinction of rewarded responding. The iMSN-Drd2KO mice showed performance deficits at all phases of rotarod skill learning. These findings reveal that dopamine modulation through iMSN-D2Rs influences the ability to self-initiate actions, as well as the willingness and/or vigor with which these responses are performed. However, these receptors seem to have little influence on simple associative learning or on stimulus-driven responding. The loss of normal D2R roles may contribute to disorders in which impaired dopamine signaling leads to hypokinesia or impaired initiation of specific voluntary actions.

Low knowledge and perceived Hepatitis C risk despite high risk behaviour among injection drug users in Kathmandu, Nepal.

BACKGROUND: In Nepal, prevalence of Hepatitis C (HCV) among injecting drug users (IDUs) has been measured at 50% and knowledge of the virus is low. Rehabilitation and harm reduction attendees constitute populations to whom health care providers can deliver services. As such, characterizing their drug use and risk profiles is important for developing targeted service delivery. We measured drug use and risk patterns of IDUs participating in residential rehabilitation as well as those contacted through needle exchanges to identify correlates of drug use frequency, risky injection practices as well as HCV testing, knowledge and perceived risk. METHODS: We collected cross-sectional data from one-on-one structured interviews of IDUs contacted through needle-exchange outreach workers (n=202) and those attending rehabilitation centres (behaviour immediately prior to joining rehabilitation) (n=167). RESULTS: Roughly half of participants reported injecting at least 30 times in the past 30 days and individuals with previous residential rehabilitation experience reported frequent injection far more than those without it. About one in fourteen respondents reported past week risky injection practices. Participants were over three times as likely to report risky injection if they consumed alcohol daily (17.2%) than if they did not (5.0%) (p=0.002). Those who reported injecting daily reported risky injection practices (11.9%) significantly more than non-daily injectors (1.8%) (p<0.001). Respondents reported high HCV infection rates, low perceived risk, testing history and knowledge. HCV knowledge was not associated with differences in risky injecting. CONCLUSION: Treatment centres should highlight the link between heavy drinking, frequent injection and risky injecting practices. The link between rehabilitation attendance and frequent injection may suggest IDUs with more severe use patterns are more likely to attend rehabilitation. Rehabilitation centres and needle exchanges should provide testing and education for HCV. Education alone may not be sufficient to initiate change since knowledge did not predict lower risk.

Phasic mesolimbic dopamine release tracks reward seeking during expression of Pavlovian-to-instrumental transfer.

BACKGROUND: Recent theories addressing mesolimbic dopamine's role in reward processing emphasize two apparently distinct functions, one in reinforcement learning (i.e., prediction error) and another in incentive motivation (i.e., the invigoration of reward seeking elicited by reward-paired cues). Here, we evaluate the latter. METHODS: Using fast-scan cyclic voltammetry, we monitored, in real time, dopamine release in the nucleus accumbens core of rats (n = 9) during a Pavlovian-to-instrumental transfer task in which the effects of a reward-predictive cue on an independently trained instrumental action were assessed. Voltammetric data were parsed into slow and phasic components to determine whether these forms of dopamine signaling were differentially related to task performance. RESULTS: We found that a reward-paired cue, which increased reward-seeking actions, induced an increase in phasic mesolimbic dopamine release and produced slower elevations in extracellular dopamine. Interestingly, phasic dopamine release was temporally related to and positively correlated with lever-press activity generally, while slow dopamine changes were not significantly related to such activity. Importantly, the propensity of the reward-paired cue to increase lever pressing was predicted by the amplitude of phasic dopamine release events, indicating a possible mechanism through which cues initiate reward-seeking actions. CONCLUSIONS: These data suggest that those phasic mesolimbic dopamine release events thought to signal reward prediction error may also be related to the incentive motivational impact of reward-paired cues on reward-seeking actions.

Methamphetamine-induced dopamine terminal deficits in the nucleus accumbens are exacerbated by reward-associated cues and attenuated by CB1 receptor antagonism.

Methamphetamine (METH) exposure is primarily associated with deleterious effects to dopaminergic neurons. While several studies have implicated the endocannabinoid system in METH's locomotor, rewarding and neurochemical effects, a role for this signaling system in METH's effects on dopamine terminal dynamics has not been elucidated. Given that CB1 receptor blockade reduces the acute potentiation of phasic extracellular dopamine release from other psychomotor stimulant drugs and that the degree of acute METH-induced increases in extracellular dopamine levels is related to the severity of dopamine depletion, we predicted that pretreatment with the CB1 receptor antagonist rimonabant would reduce METH-induced alterations at dopamine terminals. Furthermore, we hypothesized that administration of METH in environments where reward associated-cues were present would potentiate METH's acute effects on dopamine release in the nucleus accumbens and exacerbate changes in dopamine terminal activity. Fast-scan cyclic voltammetry was used to measure electrically-evoked dopamine release in the nucleus accumbens and revealed markers of compromised dopamine terminal integrity nine days after a single dose of METH. These were exacerbated in animals that received METH in the presence of reward-associated cues, and attenuated in rimonabant-pretreated animals. While these deficits in dopamine dynamics were associated with reduced operant responding on days following METH administration in animals treated with only METH, rimonabant-pretreated animals exhibited levels of operant responding comparable to control. Moreover, dopamine release correlated significantly with changes in lever pressing behavior that occurred on days following METH administration. Together these data suggest that the endocannabinoid system is involved in the subsecond dopaminergic response to METH.