Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment.

Significant evidence supports the view that dopamine shapes learning by encoding reward prediction errors. However, it is unknown whether striatal targets receive tailored dopamine dynamics based on regional functional specialization. Here, we report wave-like spatiotemporal activity patterns in dopamine axons and release across the dorsal striatum. These waves switch between activational motifs and organize dopamine transients into localized clusters within functionally related striatal subregions. Notably, wave trajectories were tailored to task demands, propagating from dorsomedial to dorsolateral striatum when rewards are contingent on animal behavior and in the opponent direction when rewards are independent of behavioral responses. We propose a computational architecture in which striatal dopamine waves are sculpted by inference about agency and provide a mechanism to direct credit assignment to specialized striatal subregions. Supporting model predictions, dorsomedial dopamine activity during reward-pursuit signaled the extent of instrumental control and interacted with reward waves to predict future behavioral adjustments.

Subthalamic control of impulsive actions: insights from deep brain stimulation in Parkinson's disease.

Control of actions allows adaptive, goal-directed behaviour. The basal ganglia, including the subthalamic nucleus, are thought to play a central role in dynamically controlling actions through recurrent negative feedback loops with the cerebral cortex. Here, we summarize recent translational studies that used deep brain stimulation to record neural activity from and apply electrical stimulation to the subthalamic nucleus in people with Parkinson's disease. These studies have elucidated spatial, spectral and temporal features of the neural mechanisms underlying the controlled delay of actions in cortico-subthalamic networks and demonstrated their causal effects on behaviour in distinct processing windows. While these mechanisms have been conceptualized as control signals for suppressing impulsive response tendencies in conflict tasks and as decision threshold adjustments in value-based and perceptual decisions, we propose a common framework linking decision-making, cognition and movement. Within this framework subthalamic deep brain stimulation can lead to suboptimal choices by reducing the time that patients take for deliberation before committing to an action. However, clinical studies have consistently shown that the occurrence of impulse control disorders is reduced, not increased, after subthalamic deep brain stimulation surgery. This apparent contradiction can be reconciled when recognizing the multifaceted nature of impulsivity, its underlying mechanisms and modulation by treatment. While subthalamic deep brain stimulation renders patients susceptible to making decisions without proper forethought, this can be disentangled from effects related to dopamine comprising sensitivity to benefits vs. costs, reward delay aversion and learning from outcomes. Alterations in these dopamine-mediated mechanisms are thought to underlie the development of impulse control disorders, and can be relatively spared with reduced dopaminergic medication after subthalamic deep brain stimulation. Together, results from studies using deep brain stimulation as an experimental tool have improved our understanding of action control in the human brain and have important implications for treatment of patients with Neurological disorders.

Prolonged exposure to dexamethasone alters the proteome and cellular phenotype of human testicular peritubular cells.

Human testicular peritubular cells (HTPCs) are smooth muscle cells, which in the testis form a small compartment surrounding the seminiferous tubules. Contractions of HTPCs are responsible for sperm transport, HTPCs contribute to spermatogenesis, have immunological roles and are a site of glucocorticoid receptor expression. Importantly, HTPCs maintain their characteristics in vitro, and thus can serve as an experimental window into the male gonad. Previously we reported consequences of 3-day treatment with Dexamethasone (Dex), a synthetic glucocorticoid and multi-purpose anti-inflammatory drug. However, as glucocorticoid therapies in man often last longer, we now studied consequences of a prolonged 7-day exposure to 1 µM Dex. Combining live cell imaging with quantative proteomics of samples taken from men, we confirmed our recent findings but more importantly, found numerous novel proteomic alterations induced by prolonged Dex treatment. The comparison of the 7-day treatment with the 3-day treatment dataset revealed that extracellular matrix- and focal adhesion-related proteins become more prominent after 7 days of treatment. In contrast, extended stimulation is, for example, associated with a decrease of proteins related to cholesterol and steroid metabolism. Our dataset, which describes phenotypic and proteomic alterations, is a valuable resource for further research projects investigating effects of Dex on human testicular cells.

Neural Index of Reinforcement Learning Predicts Improved Stimulus-Response Retention under High Working Memory Load.

Human learning and decision-making are supported by multiple systems operating in parallel. Recent studies isolating the contributions of reinforcement learning (RL) and working memory (WM) have revealed a trade-off between the two. An interactive WM/RL computational model predicts that although high WM load slows behavioral acquisition, it also induces larger prediction errors in the RL system that enhance robustness and retention of learned behaviors. Here, we tested this account by parametrically manipulating WM load during RL in conjunction with EEG in both male and female participants and administered two surprise memory tests. We further leveraged single-trial decoding of EEG signatures of RL and WM to determine whether their interaction predicted robust retention. Consistent with the model, behavioral learning was slower for associations acquired under higher load but showed parametrically improved future retention. This paradoxical result was mirrored by EEG indices of RL, which were strengthened under higher WM loads and predictive of more robust future behavioral retention of learned stimulus-response contingencies. We further tested whether stress alters the ability to shift between the two systems strategically to maximize immediate learning versus retention of information and found that induced stress had only a limited effect on this trade-off. The present results offer a deeper understanding of the cooperative interaction between WM and RL and show that relying on WM can benefit the rapid acquisition of choice behavior during learning but impairs retention.SIGNIFICANCE STATEMENT Successful learning is achieved by the joint contribution of the dopaminergic RL system and WM. The cooperative WM/RL model was productive in improving our understanding of the interplay between the two systems during learning, demonstrating that reliance on RL computations is modulated by WM load. However, the role of WM/RL systems in the retention of learned stimulus-response associations remained unestablished. Our results show that increased neural signatures of learning, indicative of greater RL computation, under high WM load also predicted better stimulus-response retention. This result supports a trade-off between the two systems, where degraded WM increases RL processing, which improves retention. Notably, we show that this cooperative interplay remains largely unaffected by acute stress.

Bi-allelic variants in IPO8 cause a connective tissue disorder associated with cardiovascular defects, skeletal abnormalities, and immune dysregulation.

Dysregulated transforming growth factor TGF-ß signaling underlies the pathogenesis of genetic disorders affecting the connective tissue such as Loeys-Dietz syndrome. Here, we report 12 individuals with bi-allelic loss-of-function variants in IPO8 who presented with a syndromic association characterized by cardio-vascular anomalies, joint hyperlaxity, and various degree of dysmorphic features and developmental delay as well as immune dysregulation; the individuals were from nine unrelated families. Importin 8 belongs to the karyopherin family of nuclear transport receptors and was previously shown to mediate TGF-ß-dependent SMADs trafficking to the nucleus in vitro. The important in vivo role of IPO8 in pSMAD nuclear translocation was demonstrated by CRISPR/Cas9-mediated inactivation in zebrafish. Consistent with IPO8's role in BMP/TGF-ß signaling, ipo8-/- zebrafish presented mild to severe dorso-ventral patterning defects during early embryonic development. Moreover, ipo8-/- zebrafish displayed severe cardiovascular and skeletal defects that mirrored the human phenotype. Our work thus provides evidence that IPO8 plays a critical and non-redundant role in TGF-ß signaling during development and reinforces the existing link between TGF-ß signaling and connective tissue defects.

The role of translation equivalents in bilingual word learning.

Bilingual environments present an important context for word learning. One feature of bilingual environments is the existence of translation equivalents (TEs)-words in different languages that share similar meanings. Documenting TE learning over development may give us insight into the mechanisms underlying word learning in young bilingual children. Prior studies of TE learning have often been confounded by the fact that increases in overall vocabulary size with age lead to greater opportunities for learning TEs. To address this confound, we employed an item-level analysis, which controls for the age trajectory of each item independently. We used Communicative Development Inventory data from four bilingual datasets (two English-Spanish and two English-French; total N = 419) for modeling. Results indicated that knowing a word's TE increased the likelihood of knowing that word for younger children and for TEs that are more similar phonologically. These effects were consistent across datasets, but varied across lexical categories. Thus, TEs may allow bilingual children to bootstrap their early word learning in one language using their knowledge of the other language. RESEARCH HIGHLIGHTS: Bilingual children must learn words that share a common meaning across both languages, that is, translation equivalents, like dog in English and perro in Spanish. Item-level models explored how translation equivalents affect word learning, in addition to child-level (e.g., exposure) and item-level (e.g., phonological similarity) factors. Knowing a word increased the probability of knowing its corresponding translation equivalent, particularly for younger children and for more phonologically-similar translation equivalents. These findings suggest that young bilingual children use their word knowledge in one language to bootstrap their learning of words in the other language.

Difficult airway simulation-based training for anaesthesiologists: efficacy and skills retention within six months.

BACKGROUND: The aim of this study was to evaluate how anaesthesiologists manage a "cannot intubate, can ventilate" (CI) and "cannot intubate, cannot ventilate" (CICV) scenarios, and how following simulation training will affect their guideline adherence, skills and decision-making immediately after training and 6 months later. METHODS: A prospective controlled study was conducted from July to December 2022. Anaesthesiologists who applied for the continuous medical education course "Difficult Airway Management" were involved in the study. Each volunteer participated in two simulation scenarios (CI, CICV) with structural debriefing after each scenario. After the first simulation round, volunteers were trained in difficult airway management according to DAS guidelines, using the same equipment as during the simulation. The participants repeated the simulation scenarios the day after the training and six months later. The primary and secondary endpoints were compared between three rounds: initial simulation (Group 1), immediately after training (Group 2), and six months after training (Group 3). RESULTS: A total of 24 anaesthesiologists consented to participate in the study and completed the initial survey form. During the first session, 83.3% of participants had at least one major deviation from the DAS protocol. During the first CICV scenario, 79% of participants made at least one deviation from the DAS protocol. The second time after simulation training, significantly better results were achieved: the number of anaesthesiologists, who attempted more than 3 laryngoscopies decreased (OR = 7 [1.8-26.8], p = 0.006 right after training and OR = 3.9 [1.06-14.4], p = 0.035 6 month later); the number, who skipped the supralaryngeal device attempt, call for help and failure to initiate surgical airway also decreased. Simulation training also significantly decreases the time to call for help, cricothyroidotomy initiation time, and mean desaturation time and increases the odds ratio of successful cricothyroidotomy (OR 0.02 [0.003-0.14], p < 0.0001 right after training and OR = OR 0.02 [0.003-0.16] 6 months after training). CONCLUSIONS: Anaesthesiologists usually display major deviations from DAS guidelines while managing CI and CICV scenarios. Simulation training improves their guideline adherence, skills, and decision-making when repeating the simulation immediately after training and 6 months later. STUDY REGISTRATION: NCT05913492, clinicaltrials.gov, 22/06/2023.

Unsupervised neural network models of the ventral visual stream.

Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today's best supervised methods and that the mapping of these neural network models' hidden layers is neuroanatomically consistent across the ventral stream. Strikingly, we find that these methods produce brain-like representations even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. We also find that semisupervised deep contrastive embeddings can leverage small numbers of labeled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results illustrate a use of unsupervised learning to provide a quantitative model of a multiarea cortical brain system and present a strong candidate for a biologically plausible computational theory of primate sensory learning.

Comparing apples to manzanas and oranges to naranjas: A new measure of English-Spanish vocabulary for dual language learners.

The valid assessment of vocabulary development in dual-language-learning infants is critical to developmental science. We developed the Dual Language Learners English-Spanish (DLL-ES) Inventories to measure vocabularies of U.S. English-Spanish DLLs. The inventories provide translation equivalents for all Spanish and English items on Communicative Development Inventory (CDI) short forms; extended inventories based on CDI long forms; and Spanish language-variety options. Item-Response Theory analyses applied to Wordbank and Web-CDI data (n = 2603, 12-18 months; n = 6722, 16-36 months; half female; 1% Asian, 3% Black, 2% Hispanic, 30% White, 64% unknown) showed near-perfect associations between DLL-ES and CDI long-form scores. Interviews with 10 Hispanic mothers of 18- to 24-month-olds (2 White, 1 Black, 7 multi-racial; 6 female) provide a proof of concept for the value of the DLL-ES for assessing the vocabularies of DLLs.

Can perioperative pCO2 gaps predict complications in patients undergoing major elective abdominal surgery randomized to goal-directed therapy or standard care? A secondary analysis.

The difference between venous and arterial carbon dioxide pressure (pCO2 gap), has been used as a diagnostic and prognostic tool. We aimed to assess whether perioperative pCO2 gaps can predict postoperative complications. This was a secondary analysis of a multicenter RCT comparing goal-directed therapy (GDT) to standard care in which 464 patients undergoing high-risk elective abdominal surgery were included. Arterial and central venous blood samples were simultaneously obtained at four time points: after induction, at the end of surgery, at PACU/ICU admission, and PACU/ICU discharge. Complications within the first 30 days after surgery were recorded. Similar pCO2 gaps were found in patients with and without complications, except for the pCO2 gap at the end of surgery, which was higher in patients with complications (6.0 mmHg [5.0-8.0] vs. 6.0 mmHg [4.1-7.5], p = 0.005). The area under receiver operating characteristics curves for predicting complications from pCO2 gaps at all time points were between 0.5 and 0.6. A weak correlation between ScvO2 and pCO2 gaps was found for all timepoints (ρ was between - 0.40 and - 0.29 for all timepoints, p < 0.001). The pCO2 gap did not differ between GDT and standard care at any of the selected time points. In our study, pCO2 gap was a poor predictor of major postoperative complications at all selected time points. Our research does not support the use of pCO2 gap as a prognostic tool after high-risk abdominal surgery. pCO2 gaps were comparable between GDT and standard care. Clinical trial registration Netherlands Trial Registry NTR3380.

Biophysical and Architectural Mechanisms of Subthalamic Theta under Response Conflict.

The cortico-basal ganglia circuit is needed to suppress prepotent actions and to facilitate controlled behavior. Under conditions of response conflict, the frontal cortex and subthalamic nucleus (STN) exhibit increased spiking and theta band power, which are linked to adaptive regulation of behavioral output. The electrophysiological mechanisms underlying these neural signatures of impulse control remain poorly understood. To address this lacuna, we constructed a novel large-scale, biophysically principled model of the subthalamopallidal (STN-globus pallidus externus) network and examined the mechanisms that modulate theta power and spiking in response to cortical input. Simulations confirmed that theta power does not emerge from intrinsic network dynamics but is robustly elicited in response to cortical input as burst events representing action selection dynamics. Rhythmic burst events of multiple cortical populations, representing a state of conflict where cortical motor plans vacillate in the theta range, led to prolonged STN theta and increased spiking, consistent with empirical literature. Notably, theta band signaling required NMDA, but not AMPA, currents, which were in turn related to a triphasic STN response characterized by spiking, silence, and bursting periods. Finally, theta band resonance was also strongly modulated by architectural connectivity, with maximal theta arising when multiple cortical populations project to individual STN "conflict detector" units because of an NMDA-dependent supralinear response. Our results provide insights into the biophysical principles and architectural constraints that give rise to STN dynamics during response conflict, and how their disruption can lead to impulsivity and compulsivity.SIGNIFICANCE STATEMENT The subthalamic nucleus exhibits theta band power modulation related to cognitive control over motor actions during conditions of response conflict. However, the mechanisms of such dynamics are not understood. Here we developed a novel biophysically detailed and data-constrained large-scale model of the subthalamopallidal network, and examined the impacts of cellular and network architectural properties that give rise to theta dynamics. Our investigations implicate an important role for NMDA receptors and cortico-subthalamic nucleus topographical connectivities in theta power modulation.

Self-judgment dissected: A computational modeling analysis of self-referential processing and its relationship to trait mindfulness facets and depression symptoms.

Cognitive theories of depression, and mindfulness theories of well-being, converge on the notion that self-judgment plays a critical role in mental health. However, these theories have rarely been tested via tasks and computational modeling analyses that can disentangle the information processes operative in self-judgments. We applied a drift-diffusion computational model to the self-referential encoding task (SRET) collected before and after an 8-week mindfulness intervention (n = 96). A drift-rate regression parameter representing positive-relative to negative-self-referential judgment strength positively related to mindful awareness and inversely related to depression, both at baseline and over time; however, this parameter did not significantly relate to the interaction between mindful awareness and nonjudgmentalness. At the level of individual depression symptoms, at baseline, a spectrum of symptoms (inversely) correlated with the drift-rate regression parameter, suggesting that many distinct depression symptoms relate to valenced self-judgment between subjects. By contrast, over the intervention, changes in only a smaller subset of anhedonia-related depression symptoms showed substantial relationships with this parameter. Both behavioral and model-derived measures showed modest split-half and test-retest correlations. Results support cognitive theories that implicate self-judgment in depression and mindfulness theories, which imply that mindful awareness should lead to more positive self-views.

Combining amperometry and mass spectrometry as a dual detection approach for capillary electrophoresis.

Dual detection concepts (DDCs) are becoming more and more popular in analytical chemistry. In this work, we describe a novel DDC for capillary electrophoresis (CE) consisting of an amperometric detector (AD) and a mass spectrometer (MS). This detector combination has a good complementarity as the AD exhibits high sensitivity, whereas the MS provides excellent selectivity. Both detectors are based on a destructive detection principle, making a serial detector arrangement impossible. Thus, for the realization of the DDC, the CE flow was divided into two parts with a flow splitter. The DDC was characterized in a proof-of-concept study with ferrocene derivates and a nonaqueous background electrolyte. We could show that splitting the CE flow was a suitable method for the instrumental realization of the DDC consisting of two destructive detectors. By lowering the height of the AD compared to the MS, it was possible to synchronize the detector responses. Additionally, for the chosen model system, we confirmed that the AD was much more reproducible and had lower limits of detection (LODs) than the MS. The LODs were identical for the DDC and the single-detection arrangements, indicating no sensitivity decrease due to the CE flow splitting. The DDC was successfully applied to determine the drug and doping agent trimetazidine.

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits.

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This "associative cluster-dependent chain" (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous "Thunderstruck" song intro and then flexibly play it in a "bossa nova" rhythm without further training.

An individual differences perspective on pragmatic abilities in the preschool years.

Pragmatic abilities are fundamental to successful language use and learning. Individual differences studies contribute to understanding the psychological processes involved in pragmatic reasoning. Small sample sizes, insufficient measurement tools, and a lack of theoretical precision have hindered progress, however. Three studies addressed these challenges in three- to 5-year-old German-speaking children (N = 228, 121 female). Studies 1 and 2 assessed the psychometric properties of six pragmatics tasks. Study 3 investigated relations among pragmatics tasks and between pragmatics and other cognitive abilities. The tasks were found to measure stable variation between individuals. Via a computational cognitive model, individual differences were traced back to a latent pragmatics construct. This presents the basis for understanding the relations between pragmatics and other cognitive abilities. RESEARCH HIGHLIGHTS: Individual differences in pragmatic abilities are important to understanding variation in language development. Research in this domain lacks a precise theoretical framework and psychometrically high-quality measures. We present six tasks capturing a wide range of pragmatic abilities with excellent re-test reliability. We use a computational cognitive model to provide a substantive theory of individual differences in pragmatic abilities.

Computational Psychiatry Needs Time and Context.

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry.

Registered Reports in Child Development: Introduction to the Special Section.

Registered Reports (RRs) are an emerging format for publishing empirical journal articles in which the decision to publish an article is based on sound conceptualization, methods, and planned analyses rather than the specific nature of the results. This article introduces the Special Section on Registered Reports in Child Development by describing what RRs are and why they are necessary, outlining the thought process that guided the Special Section, describing key thematic insights across the eight articles included in the collection, and providing recommendations for developmental researchers interested in publishing via the RR format. This article also serves as a formal announcement that RRs will be a standard publishing option at Child Development, effective immediately.

Left ventricular remodelling post-myocardial infarction: pathophysiology, imaging, and novel therapies.

Most patients survive acute myocardial infarction (MI). Yet this encouraging development has certain drawbacks: heart failure (HF) prevalence is increasing and patients affected tend to have more comorbidities worsening economic strain on healthcare systems and impeding effective medical management. The heart's pathological changes in structure and/or function, termed myocardial remodelling, significantly impact on patient outcomes. Risk factors like diabetes, chronic obstructive pulmonary disease, female sex, and others distinctly shape disease progression on the 'road to HF'. Despite the availability of HF drugs that interact with general pathways involved in myocardial remodelling, targeted drugs remain absent, and patient risk stratification is poor. Hence, in this review, we highlight the pathophysiological basis, current diagnostic methods and available treatments for cardiac remodelling following MI. We further aim to provide a roadmap for developing improved risk stratification and novel medical and interventional therapies.

The BabyView camera: Designing a new head-mounted camera to capture children's early social and visual environments.

Head-mounted cameras have been used in developmental psychology research for more than a decade to provide a rich and comprehensive view of what infants see during their everyday experiences. However, variation between these devices has limited the field's ability to compare results across studies and across labs. Further, the video data captured by these cameras to date has been relatively low-resolution, limiting how well machine learning algorithms can operate over these rich video data. Here, we provide a well-tested and easily constructed design for a head-mounted camera assembly-the BabyView-developed in collaboration with Daylight Design, LLC., a professional product design firm. The BabyView collects high-resolution video, accelerometer, and gyroscope data from children approximately 6-30 months of age via a GoPro camera custom mounted on a soft child-safety helmet. The BabyView also captures a large, portrait-oriented vertical field-of-view that encompasses both children's interactions with objects and with their social partners. We detail our protocols for video data management and for handling sensitive data from home environments. We also provide customizable materials for onboarding families with the BabyView. We hope that these materials will encourage the wide adoption of the BabyView, allowing the field to collect high-resolution data that can link children's everyday environments with their learning outcomes.

Peekbank: An open, large-scale repository for developmental eye-tracking data of children's word recognition.

The ability to rapidly recognize words and link them to referents is central to children's early language development. This ability, often called word recognition in the developmental literature, is typically studied in the looking-while-listening paradigm, which measures infants' fixation on a target object (vs. a distractor) after hearing a target label. We present a large-scale, open database of infant and toddler eye-tracking data from looking-while-listening tasks. The goal of this effort is to address theoretical and methodological challenges in measuring vocabulary development. We first present how we created the database, its features and structure, and associated tools for processing and accessing infant eye-tracking datasets. Using these tools, we then work through two illustrative examples to show how researchers can use Peekbank to interrogate theoretical and methodological questions about children's developing word recognition ability.