Listening to the Data: Computational Approaches to Addiction and Learning.

Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.

Similar network compositions, but distinct neural dynamics underlying belief updating in environments with and without explicit outcomes.

Classic decision theories typically assume the presence of explicit value-based outcomes after action selections to update beliefs about action-outcome contingencies. However, ecological environments are often opaque, and it remains unclear whether the neural dynamics underlying belief updating vary under conditions characterized by the presence or absence of such explicit value-based information, after each choice selection. We investigated this question in healthy humans (n = 28) using Bayesian inference and two multi-option fMRI tasks: a multi-armed bandit task, and a probabilistic perceptual task, respectively with and without explicit value-based feedback after choice selections. Model-based fMRI analysis revealed a network encoding belief updating which did not change depending on the task. More precisely, we found a confidence-building network that included anterior hippocampus, amygdala, and medial prefrontal cortex (mPFC), which became more active as beliefs about action-outcome probabilities were confirmed by newly acquired information. Despite these consistent responses across tasks, dynamic causal modeling estimated that the network dynamics changed depending on the presence or absence of trial-by-trial value-based outcomes. In the task deprived of immediate feedback, the hippocampus increased its influence towards both amygdala and mPFC, in association with increased strength in the confidence signal. However, the opposite causal relations were found (i.e., from both mPFC and amygdala towards the hippocampus), in presence of immediate outcomes. This finding revealed an asymmetric relationship between decision confidence computations, which were based on similar computational models across tasks, and neural implementation, which varied depending on the availability of outcomes after choice selections.

Compatibilization of Polylactide/Poly(ethylene 2,5-furanoate) (PLA/PEF) Blends for Sustainable and Bioderived Packaging.

Despite the advantages of polylactide (PLA), its inadequate UV-shielding and gas-barrier properties undermine its wide application as a flexible packaging film for perishable items. These issues are addressed in this work by investigating the properties of melt-mixed, fully bioderived blends of polylactide (PLA) and poly(ethylene furanoate) (PEF), as a function of the PEF weight fraction (1-30 wt %) and the amount of the commercial compatibilizer/chain extender Joncryl ADR 4468 (J, 0.25-1 phr). J mitigates the immiscibility of the two polymer phases by decreasing and homogenizing the PEF domain size; for the blend containing 10 wt % of PEF, the PEF domain size drops from 0.67 ± 0.46 µm of the uncompatibilized blend to 0.26 ± 0.14 with 1 phr of J. Moreover, the increase in the complex viscosity of PLA and PLA/PEF blends with the J content evidences the effectiveness of J as a chain extender. This dual positive contribution of J is reflected in the mechanical properties of PLA/PEF blends. Whereas the uncompatibilized blend with 10 wt % of PEF shows lower mechanical performance than neat PLA, all the compatibilized blends show higher tensile strength and strain at break, while retaining their high elastic moduli. The effects of PEF on the UV- and oxygen-barrier properties of PLA are also remarkable. Adding only 1 wt % of PEF makes the blend an excellent barrier for UV rays, with the transmittance at 320 nm dropping from 52.8% of neat PLA to 0.4% of the sample with 1 wt % PEF, while keeping good transparency in the visible region. PEF is also responsible for a sensible decrease in the oxygen transmission rate, which decreases from 189 cc/m2·day for neat PLA to 144 cc/m2·day with only 1 wt % of PEF. This work emphasizes the synergistic effects of PEF and J in enhancing the thermal, mechanical, UV-shielding, and gas-barrier properties of PLA, which results in bioderived blends that are very promising for packaging applications.

A change of mind: Globus pallidus activity and effective connectivity during changes in choice selections.

The basal ganglia are a group of interconnected subcortical nuclei that plays a key role in multiple motor and cognitive processes, in a close interplay with several cortical regions. Two conflicting theories postulate that the basal ganglia pathways can either foster or suppress the cortico-striatal output or, alternatively, they can stabilize or destabilize the cortico-striatal circuit dynamics. These different approaches significantly impact the understanding of observable behaviours and cognitive processes in healthy, as well as clinical populations. We investigated the predictions of these models in healthy participants (N = 28), using dynamic causal modeling of fMRI BOLD activity to estimate time- and context-dependent changes in the indirect pathway effective connectivity, in association with repetitions or changes of choice selections. We used two multi-option tasks that required the participants to adapt to uncontrollable environmental changes, by performing sequential choice selections, with and without value-based feedbacks. We found that, irrespective of the task, the trials that were characterized by changes in choice selections (switch trials) were associated with a neural response that mostly overlapped with a network commonly described for the encoding of uncertainty. More interestingly, dynamic causal modeling and family-wise model comparison identified with high likelihood a directed causal relation from the external to the internal part of the globus pallidus (i.e., the short indirect pathway in the basal ganglia), in association with the switch trials. This finding supports the hypothesis that the short indirect pathway in the basal ganglia drives instability in the network dynamics, resulting in changes in choice selection.

An insula-driven network computes decision uncertainty and promotes abstinence in chronic cocaine users.

The anterior insular cortex (AIC) and its interconnected brain regions have been associated with both addiction and decision-making under uncertainty. However, the causal interactions in this uncertainty-encoding neurocircuitry and how these neural dynamics impact relapse remain elusive. Here, we used model-based fMRI to measure choice uncertainty in a motor decision task in 61 individuals with cocaine use disorder (CUD) and 25 healthy controls. CUD participants were assessed before discharge from a residential treatment program and followed for up to 24 weeks. We found that choice uncertainty was tracked by the AIC, dorsal anterior cingulate cortex (dACC) and ventral striatum (VS), across participants. Stronger activations in these regions measured pre-discharge predicted longer abstinence after discharge in individuals with CUD. Dynamic causal modeling revealed an AIC-to-dACC-directed connectivity modulated by uncertainty in controls, but a dACC-to-AIC connectivity in CUD participants. This reversal was mostly driven by early relapsers (<30 days). Furthermore, CUD individuals who displayed a stronger AIC-to-dACC excitatory connection during uncertainty encoding remained abstinent for longer periods. These findings reveal a critical role of an AIC-driven, uncertainty-encoding neurocircuitry in protecting against relapse and promoting abstinence.

Value encoding in the globus pallidus: fMRI reveals an interaction effect between reward and dopamine drive.

The external part of the globus pallidus (GPe) is a core nucleus of the basal ganglia (BG) whose activity is disrupted under conditions of low dopamine release, as in Parkinson's disease. Current models assume decreased dopamine release in the dorsal striatum results in deactivation of dorsal GPe, which in turn affects motor expression via a regulatory effect on other nuclei of the BG. However, recent studies in healthy and pathological animal models have reported neural dynamics that do not match with this view of the GPe as a relay in the BG circuit. Thus, the computational role of the GPe in the BG is still to be determined. We previously proposed a neural model that revisits the functions of the nuclei of the BG, and this model predicts that GPe encodes values which are amplified under a condition of low striatal dopaminergic drive. To test this prediction, we used an fMRI paradigm involving a within-subject placebo-controlled design, using the dopamine antagonist risperidone, wherein healthy volunteers performed a motor selection and maintenance task under low and high reward conditions. ROI-based fMRI analysis revealed an interaction between reward and dopamine drive manipulations, with increased BOLD activity in GPe in a high compared to low reward condition, and under risperidone compared to placebo. These results confirm the core prediction of our computational model, and provide a new perspective on neural dynamics in the BG and their effects on motor selection and cognitive disorders.

Toughness Evolution of Flax-Fiber-Reinforced Composites under Repeated Salt Fog-Dry Aging Cycles.

This research examined the response of flax-fiber-reinforced composites (FFRCs) to simulated outdoor conditions involving repeated exposure to salt fog and drying. The study investigated the effect of cycles on the toughness of the FFRCs. To achieve this, the composites were exposed to humidity (salt fog) for 10 days, followed by 18 days of drying in cycles. A total of up to 3 cycles, each lasting 4 weeks, were conducted over a 12-week period. Throughout this process, changes in the material's weight, water absorption, and mechanical properties were monitored by water uptake and three-point bending tests. The findings revealed the significant impact of these humid-dry cycles on the mechanical response of the FFRCs. When exposed to humid environments without drying, the composite's toughness increased significantly, due to a weakening effect more pronounced for stiffness, with strength reductions of about 20%. However, subsequent drying partially restored the material's performance. After 18 days of drying, the composite regained most of its initial performance.

Aberrant neural computation of social controllability in nicotine-dependent humans.

Social controllability, defined as the ability to exert influence when interacting with others, is crucial for optimal decision-making. Inability to do so might contribute to maladaptive behaviors such as drug use, which often takes place in social settings. Here, we examined nicotine-dependent humans using fMRI, as they made choices that could influence the proposals from simulated partners. Computational modeling revealed that smokers under-estimated the influence of their actions and self-reported a reduced sense of control, compared to non-smokers. These findings were replicated in a large independent sample of participants recruited online. Neurally, smokers showed reduced tracking of forward projected choice values in the ventromedial prefrontal cortex, and impaired computation of social prediction errors in the midbrain. These results demonstrate that smokers were less accurate in estimating their personal influence when the social environment calls for control, providing a neurocomputational account for the social cognitive deficits in this population.

Aberrant neural computation of social controllability in nicotine-dependent humans.

Social controllability, or the ability to exert control during social interactions, is crucial for optimal decision-making. Inability to do so might contribute to maladaptive behaviors such as smoking, which often takes place in social settings. Here, we examined social controllability in nicotine-dependent humans as they performed an fMRI task where they could influence the offers made by simulated partners. Computational modeling revealed that smokers under-estimated the influence of their actions and self-reported a reduced sense of control, compared to non-smokers. These findings were replicated in a large independent sample of participants recruited online. Neurally, smokers showed reduced tracking of forward projected choice values in the ventromedial prefrontal cortex, and impaired computation of social prediction errors in the midbrain. These results demonstrate that smokers were less accurate in estimating their personal influence when the social environment calls for control, providing a neurocomputational account for the social cognitive deficits in this population. Pre-registrations: OSF Registries|How interoceptive state interacts with value-based decision-making in addiction (fMRI study). OSF Registries|COVID-19: social cognition, mental health, and social distancing (online study).

Nicotine-related beliefs induce dose-dependent responses in the human brain.

Beliefs have a powerful influence on our behavior, yet their neural mechanisms remain elusive. Here we investigate whether beliefs could impact brain activities in a way akin to pharmacological dose-dependent effects. Nicotine-dependent humans were told that nicotine strength in an electronic cigarette was either 'low', 'medium' or 'high', while nicotine content was held constant. After vaping, participants underwent functional neuroimaging and performed a decision-making task known to engage neural circuits affected by nicotine. Beliefs about nicotine strength induced dose-dependent responses in the thalamus, a key binding site for nicotine, but not in other brain regions such as the striatum. Nicotine-related beliefs also parametrically modulated the connectivity between the thalamus and ventromedial prefrontal cortex, a region important for decision-making. These findings reveal a high level of precision in the way beliefs influence the brain, offering mechanistic insights into humans' heterogeneous responses to drugs and a pivotal role of beliefs in addiction.

Flax-Glass Fiber Reinforced Hybrid Composites Exposed to a Salt-Fog/Dry Cycle: A Simplified Approach to Predict Their Performance Recovery.

Despite natural fibers gaining significant attention in recent decades, their limited performance and poor durability under humid environments cannot allow them to fully replace their synthetic counterparts as reinforcement for structural composites. In such a context, this paper aims to investigate how exposure to a humid/dry cycle affects the mechanical response of epoxy laminates reinforced with flax and glass fibers. In particular, the main goal is to assess the performance evolution of a glass-flax hybridized stacking sequence in comparison with the full glass and flax fiber reinforced composites. To this end, the investigated composites were first exposed to salt-fog for 15 or 30 days and then to dry conditions (i.e., 50% R.H. and 23 °C) for up to 21 days. The presence of glass fibers in the stacking sequence significantly stabilizes the mechanical performance of composites during the humid/dry cycle. Indeed, hybridization of inner flax laminae with outer glass ones, acting as a protective shield, hinders the composite degradation due to the humid phase also promoting performance recovery during the dry phase. Hence, this work showed that a tailored hybridization of natural fibers with glass fibers represents a suitable approach to extend the service-life of natural fiber reinforced composites exposed to discontinuous humid conditions, thus allowing their employment in practical indoor and outdoor applications. Finally, a simplified theoretical pseudo-second-order model that aimed to forecast the performance recovery shown by composites was proposed and experimentally validated, highlighting good agreement with the experimental data.

Towards a neurocomputational account of social controllability: From models to mental health.

Controllability, or the influence one has over their surroundings, is crucial for decision-making and mental health. Traditionally, controllability is operationalized in sensorimotor terms as one's ability to exercise their actions to achieve an intended outcome (also termed "agency"). However, recent social neuroscience research suggests that humans also assess if and how they can exert influence over other people (i.e., their actions, outcomes, beliefs) to achieve desired outcomes ("social controllability"). In this review, we will synthesize empirical findings and neurocomputational frameworks related to social controllability. We first introduce the concepts of contextual and perceived controllability and their respective relevance for decision-making. Then, we outline neurocomputational frameworks that can be used to model social controllability, with a focus on behavioral economic paradigms and reinforcement learning approaches. Finally, we discuss the implications of social controllability for computational psychiatry research, using delusion and obsession-compulsion as examples. Taken together, we propose that social controllability could be a key area of investigation in future social neuroscience and computational psychiatry research.

Testing hypotheses about the harm that capitalism causes to the mind and brain: a theoretical framework for neuroscience research.

In this paper, we will attempt to outline the key ideas of a theoretical framework for neuroscience research that reflects critically on the neoliberal capitalist context. We argue that neuroscience can and should illuminate the effects of neoliberal capitalism on the brains and minds of the population living under such socioeconomic systems. Firstly, we review the available empirical research indicating that the socio-economic environment is harmful to minds and brains. We, then, describe the effects of the capitalist context on neuroscience itself by presenting how it has been influenced historically. In order to set out a theoretical framework that can generate neuroscientific hypotheses with regards to the effects of the capitalist context on brains and minds, we suggest a categorization of the effects, namely deprivation, isolation and intersectional effects. We also argue in favor of a neurodiversity perspective [as opposed to the dominant model of conceptualizing neural (mal-)functioning] and for a perspective that takes into account brain plasticity and potential for change and adaptation. Lastly, we discuss the specific needs for future research as well as a frame for post-capitalist research.

Computational models of behavioral addictions: State of the art and future directions.

Non-pharmacological behavioral addictions, such as pathological gambling, videogaming, social networking, or internet use, are becoming major public health concerns. It is not yet clear how behavioral addictions could share many major neurobiological and behavioral characteristics with substance use disorders, despite the absence of direct pharmacological influences. A deeper understanding of the neurocognitive mechanisms of addictive behavior is needed, and computational modeling could be one promising approach to explain intricately entwined cognitive and neural dynamics. This review describes computational models of addiction based on reinforcement learning algorithms, Bayesian inference, and biophysical neural simulations. We discuss whether computational frameworks originally conceived to explain maladaptive behavior in substance use disorders can be effectively extended to non-substance-related behavioral addictions. Moreover, we introduce recent studies on behavioral addictions that exemplify the possibility of such extension and propose future directions.

Impaired belief updating and devaluation in adult women with bulimia nervosa.

Recent models of bulimia nervosa (BN) propose that binge-purge episodes ultimately become automatic in response to cues and insensitive to negative outcomes. Here, we examined whether women with BN show alterations in instrumental learning and devaluation sensitivity using traditional and computational modeling analyses of behavioral data. Adult women with BN (n = 30) and group-matched healthy controls (n = 31) completed a task in which they first learned stimulus-response-outcome associations. Then, participants were required to repeatedly adjust their responses in a "baseline test", when different sets of stimuli were explicitly devalued, and in a "slips-of-action test", when outcomes instead of stimuli were devalued. The BN group showed intact behavioral sensitivity to outcome devaluation during the slips-of-action test, but showed difficulty overriding previously learned stimulus-response associations on the baseline test. Results from a Bayesian learner model indicated that this impaired performance could be accounted for by a slower pace of belief updating when a new set of previously learned responses had to be inhibited (p = 0.036). Worse performance and a slower belief update in the baseline test were each associated with more frequent binge eating (p = 0.012) and purging (p = 0.002). Our findings suggest that BN diagnosis and severity are associated with deficits in flexibly updating beliefs to withhold previously learned responses to cues. Additional research is needed to determine whether this impaired ability to adjust behavior is responsible for maintaining automatic and persistent binge eating and purging in response to internal and environmental cues.

Hypothyroidism in older adults: A narrative review.

INTRODUCTION: The prevalence of hypothyroidism increases along with aging, resulting in one of the most common comorbidities among patients over 75 years. The leading causes of hypothyroidism in older adults are iatrogenic, Hashimoto's thyroiditis, and medications. AIMS: The narrative review aimed to discuss the clinical characteristics of hypothyroidism in older adults and the impact of hormonal replacement therapy on survival rates. RESULTS: Thyroid function declines over time due to physiological changes in the thyroid stimulating hormone signaling, iodine absorption and metabolism, thyroid hormone metabolism, and activity at peripheral sites. A serum TSH value over the upper limit of the normal reference range is not necessarily attributable to hypothyroidism. However, an appropriate diagnostic work-up is required to rule out true hypothyroidism and discriminate the etiology (i.e., thyroid autoimmune diseases, iodine deficiency, drug-induced hypothyroidism). Levothyroxine treatment should be considered in cases of overt hypothyroidism. A complete risk-to-benefit assessment, particularly considering the overall health status, life expectancy, cognitive function, mood, and cardiovascular and neurological background, should be considered before treating subclinical hypothyroidism with more potential benefits in patients under 75 years old. Levothyroxine formulations facilitating hormone absorption and increasing compliance to long-term treatment should be preferred. TSH target should usually be set over 3 mIU/ml. CONCLUSION: Defining optimal diagnostic approaches and targeted therapeutic strategies should be considered in the personalized management of aged patients with hypothyroidism.

Dyslipidemia and Cardiovascular Prevention in the Elderly: A Balance between Benefits and Risks of Statin Treatment in a Specific Population.

INTRODUCTION: Atherosclerotic Cardiovascular Diseases (CVD) are among the most relevant causes of morbidity and mortality worldwide, especially in aged people. Statins are one of the leading pharmacological interventions against atherosclerosis and are widely used to reduce the risk of occurring coronary artery diseases and related outcomes in both primary and secondary prevention. The management of chronic diseases is improved considerably over time, leading to an increase in life expectancy despite heavier comorbidity-related burdens in the elderly. AIMS: The paper focused on the role of statins in the management of atherosclerosis and related burdens in elderly patients. RESULTS: Statins are essential in reducing the risk of CVD in secondary and primary prevention, particularly in high-risk individuals. Guidelines encourage using specific algorithms with age-specific cutoffs to assess individual cardiovascular risk irrespective of baseline age, as the expansion of life expectancy produces favorable effects of statin treatment in those over 70. DISCUSSION: Besides the estimation of baseline CV risk, a specific age-related assessment is also necessary before prescribing statin treatment in aged people focusing on frailty, potential pharmacological interactions due to polypharmacotherapy, cognitive impairment, and background chronic comorbidities, such as diabetes mellitus. Before starting statin therapy, an accurate choice of type and dose of statins is needed as potential adverse events are more prevalent with high-dose than low-to-moderatedose regimens and with lipophile than hydrophile statins (e.g., potential implication on intra-cerebral cholesterol metabolism). CONCLUSION: Despite possible adverse events, elderly patients should receive statins, when appropriate, to avoid the first occurrence of recurrent cardiovascular events and related burdens.

Drilling characteristics and properties analysis of fiber reinforced polymer composites: A comprehensive review.

Fiber-reinforced polymer (FRP) composites play a vital role in the production of structural and semi-structural components for engineering applications. The drilling process is a commonly employed machining process for FRP composites to join the FRP structural elements. Usually, the FRP composites possess a heterogeneous nature because of their multi-layered structure, hybridization, and the presence of multi-phase materials. Hence, common problems like delaminations, fuzzing, buckling, cracking, matrix and fiber burning occur during the drilling operations. These problems cause dimensional inaccuracy, poor surface finish, and tool wear and reduce the mechanical strength of the composites. The optimum drilling parameters (drill geometry, speed, feed, and depth of cut) selection for the specific materials is good to achieve effective drilling performance and better surface quality of the holes. Yet, little study has been done on how all of these factors affect the size of the drilled hole. The majority of drilling studies on FRPCs in the past have focused on how to improve the hole quality by maximizing processing conditions, and there has been little discussion on the correlation between drilling conditions, physical properties, and production techniques. This is what motivated to review the characteristics and properties analysis of FRP composites. As a consequence of this research, it is anticipated that scientists and researchers would place a greater emphasis on the drilling characteristic of the workpieces made from FRPCs than on other attributes. This review clearly presents an overview of FRP composites drilling that had progressed from 2000 to 2021. The analysis of different drilling conditions and parameters like thrust force, drill geometry, temperature, speed, and feed also includes the post-drilling analysis through delaminations, thermal damage, and surface roughness. Furthermore, the recent developments in carbon, glass, and natural fiber reinforced polymer composites are studied with both conventional and nonconventional drilling techniques. Based on the above studies, some future challenges and conclusions are drawn from this review.

An Interpretable and Predictive Connectivity-Based Neural Signature for Chronic Cannabis Use.

BACKGROUND: Cannabis is one of the most widely used substances in the world, with usage trending upward in recent years. However, although the psychiatric burden associated with maladaptive cannabis use has been well established, reliable and interpretable biomarkers associated with chronic use remain elusive. In this study, we combine large-scale functional magnetic resonance imaging with machine learning and network analysis and develop an interpretable decoding model that offers both accurate prediction and novel insights into chronic cannabis use. METHODS: Chronic cannabis users (n = 166) and nonusing healthy control subjects (n = 124) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic users and nonusers based on whole-brain functional connectivity. Network analysis was used to identify the most predictive regions and communities. RESULTS: We obtained high (∼80% out-of-sample) accuracy across 4 different classification models, demonstrating that task-evoked connectivity can successfully differentiate chronic cannabis users from nonusers. We also identified key predictive regions implicating motor, sensory, attention, and craving-related areas, as well as a core set of brain networks that contributed to successful classification. The most predictive networks also strongly correlated with cannabis craving within the chronic user group. CONCLUSIONS: This novel approach produced a neural signature of chronic cannabis use that is both accurate in terms of out-of-sample prediction and interpretable in terms of predictive networks and their relation to cannabis craving.

Computational mechanisms underlying illusion of control in delusional individuals.

Humans navigate complex situations that require the accurate estimation of the controllability of the environment. Aberrant controllability computation might lead to maladaptive behaviors and poor mental health outcomes. Illusion of control, which refers to a heightened sense of control while the environment is uncontrollable, is one such manifestation and has been conceptually associated with delusional ideation. Nevertheless, this association has not yet been formally characterized in a computational framework. To address this, we used a computational psychiatry approach to quantify illusion of control in human participants with high (n = 125) or low (n = 126) trait delusion. Participants played a two-party exchange game in which their choices either did ("Controllable condition") or did not ("Uncontrollable condition") influence the future monetary offers made by simulated partners. We found that the two groups behaved similarly in model-agnostic measures (i.e., offer size, rejection rate). However, computational modeling revealed that compared to the low trait delusion group, the high delusion group overestimated their influence ("expected influence" parameter) over the offers made by their partners under the Uncontrollable condition. Highly delusional individuals also reported a stronger sense of control than those with low trait delusion in the Uncontrollable condition. Furthermore, the expected influence parameter and self-reported beliefs about controllability were significantly correlated in the Controllable condition in individuals with low trait delusion, whereas this relationship was diminished in those with high trait delusion. Collectively, these findings demonstrate that delusional ideation is associated with aberrant computation of and belief about environmental controllability, as well as a belief-behavior disconnect.