Computational Modeling Approach Research Articles

Effects of plantar fascia stiffness on the internal mechanics of idiopathic pes cavus by finite element analysis: implications for metatarsalgia

Metatarsalgia occurring in individuals with pes cavus is typically associated with abnormal loading patterns in the forefoot resulting from structural alterations. Simultaneously, the frequent overstress of the plantar fascia (PF) caused by the persistence of this foot deformity may further exacerbate the chronic pain induced by metatarsal overload. We aimed to investigate and quantify the effects of PF stiffness on the internal biomechanics of pes cavus using a computational modelling approach. A patient-specific finite element model of the foot-ankle complex using the actual three-dimensional geometry of idiopathic pes cavus bones and soft tissues was reconstructed. A sensitivity study was conducted to evaluate the effects of varying elastic modulus (0–700 MPa) of the PF on the metatarsal stress distribution, and force transmission through the metatarsophalangeal (MTP) and tarsometatarsal (TMT) joints in the pes cavus. The results indicated that variations in PF stiffness led to stress redistribution in the metatarsal region. Peak stress gradually reduced with decreasing stiffness until the PF was released, eventually resulting in a reduction of 22.39% compared to the reference value of 350 MPa. Furthermore, adjusting the PF stiffness to twice the reference value (700 MPa) increased the contact forces through the TMT and MTP joints by up to 23% and 116%, respectively. The reduction of PF stiffness alleviated focal metatarsal loading, and therefore, surgical fascia release can be considered to alleviate metatarsalgia in patients with pes cavus.

On the interest of artificial intelligence approaches in solving the IoT coverage problem

This survey deals with the 3D indoor deployment in IoT collection networks to identify the right locations of the IoT connected objects and, subsequently, to manage the coverage holes while guaranteeing other objectives such as localization and connectivity of IoT devices. These coverage holes result generally from the initial random distribution of the objects. Unlike the existing studies, the present survey does not only focus on the use cases and applications of the different deployment issues but, it also analyzes the problem of deployment by showing its different models and hypotheses presented in the literature and highlighting the used evaluation and performance criteria. The aim of the study is to highlight the relevance of artificial intelligence techniques; especially meta-heuristics, evolutionary optimization, machine learning and reinforcement learning; in finding coverage solutions, better than other methods. Through experimental validation, the performances of the various deployment and redeployment approaches in a 3D indoor context are compared. These approaches involve computational geometry, virtual force, clustering, mathematical modeling, and evolutionary optimization-based approaches. Afterwards, they are investigated and categorized according to their specifications into different summary tables. Statistical and complexity tests are performed to evaluate the complexity of these approaches. In addition, current 3D deployment trends are presented, and some outstanding deployment issues are discussed. Based on the experimental and simulation findings, the behaviors of evolutionary optimization algorithms are compared to those of the other deployment techniques. The obtained findings demonstrate that using artificial intelligence, specifically many-objective optimization algorithms for 3D deployment of IoT networks is more beneficial as it allows enhancing the quality of deployment compared to the other deployment approaches.

Assessing structural insights into in-house arylsulfonyl L-(+) glutamine MMP-2 inhibitors as promising anticancer agents through structure-based computational modelling approaches

ABSTRACT MMP-2 is potentially contributing to several cancer progressions including leukaemias. Therefore, considering MMP-2 as a promising target, novel anticancer compounds may be designed. Here, 32 in-house arylsulfonyl L-(+) glutamines were subjected to various structure-based computational modelling approaches to recognize crucial structural attributes along with the spatial orientation for higher MMP-2 inhibition. Again, the docking-based 2D-QSAR study revealed that the Coulomb energy conferred by Tyr142 and total interaction energy conferred by Ala84 was crucial for MMP-2 inhibition. Importantly, the docking-dependent CoMFA and CoMSIA study revealed the importance of favourable steric, electrostatic, and hydrophobic substituents at the terminal phenyl ring. The MD simulation study revealed a lower fluctuation in the RMSD, RMSF, and Rg values indicating stable binding interactions of MMP-2 and these molecules. Moreover, the residual hydrogen bond and their interaction analysis disclosed crucial amino acid residues responsible for forming potential hydrogen bonding for higher MMP-2 inhibition. The results can effectively aid in the design and discovery of promising small-molecule drug-like MMP-2 inhibitors with greater anticancer potential in the future.

Reduced Bone Regeneration in Rats With Type 2 Diabetes Mellitus as a Result of Impaired Stromal Cell and Osteoblast Function-A Computer Modeling Study.

Bone has the fascinating ability to self-regenerate. However, under certain conditions, such as type 2 diabetes mellitus (T2DM), this ability is impaired. T2DM is a chronic metabolic disease known by the presence of elevated blood glucose levels that is associated with reduced bone regeneration capability, high fracture risk, and eventual non-union risk after a fracture. Several mechanical and biological factors relevant to bone regeneration have been shown to be affected in a diabetic environment. However, whether impaired bone regeneration in T2DM can be explained due to mechanical or biological alterations remains unknown. To elucidate the relevance of either one, the aim of this study was to investigate the relative contribution of T2DM-related alterations on either cellular activity or mechanical stimuli driving bone regeneration. A previously validated in silico computer modeling approach that was capable of explaining bone regeneration in uneventful conditions of healing was further developed to investigate bone regeneration in T2DM. Aspects analyzed included the presence of mesenchymal stromal cells (MSCs), cellular migration, proliferation, differentiation, apoptosis, and cellular mechanosensitivity. To further verify the computer model findings against in vivo data, an experimental setup was replicated, in which regeneration was compared in healthy and diabetic after a rat femur bone osteotomy stabilized with plate fixation. We found that mechanical alterations had little effect on the reduced bone regeneration in T2DM and that alterations in MSC proliferation, MSC migration, and osteoblast differentiation had the highest effect. In silico predictions of regenerated bone in T2DM matched qualitatively and quantitatively those from ex vivo μCT at 12 weeks post-surgery when reduced cellular activities reported in previous in vitro and in vivo studies were included in the model. The presented findings here could have clinical implications in the treatment of bone fractures in patients with T2DM. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

An optimization framework for targeted spinal cord stimulation

Objective. Spinal cord stimulation (SCS) is a common neurostimulation therapy to manage chronic pain. Technological advances have produced new neurostimulation systems with expanded capabilities in an attempt to improve the clinical outcomes associated with SCS. However, these expanded capabilities have dramatically increased the number of possible stimulation parameters and made it intractable to efficiently explore this large parameter space within the context of standard clinical programming procedures. Therefore, in this study, we developed an optimization approach to define the optimal current amplitudes or fractions across individual contacts in an SCS electrode array(s). Approach. We developed an analytic method using the Lagrange multiplier method along with smoothing approximations. To test our optimization framework, we used a hybrid computational modeling approach that consisted of a finite element method model and multi-compartment models of axons and cells within the spinal cord. Moreover, we extended our approach to multi-objective optimization to explore the trade-off between activating regions of interest (ROIs) and regions of avoidance (ROAs). Main results. For simple ROIs, our framework suggested optimized configurations that resembled simple bipolar configurations. However, when we considered multi-objective optimization, our framework suggested nontrivial stimulation configurations that could be selected from Pareto fronts to target multiple ROIs or avoid ROAs. Significance. We developed an optimization framework for targeted SCS. Our method is analytic, which allows for the fast calculation of optimal solutions. For the first time, we provided a multi-objective approach for selective SCS. Through this approach, we were able to show that novel configurations can provide neural recruitment profiles that are not possible with conventional stimulation configurations (e.g. bipolar stimulation). Most importantly, once integrated with computational models that account for sources of interpatient variability (e.g. anatomy, electrode placement), our optimization framework can be utilized to provide stimulation settings tailored to the needs of individual patients.

Studies on the Impact of Seam Gas Composition on Proactive Goaf Inertization Strategies for Spontaneous Combustion Management—a Computational Modeling Approach

Studies on the Impact of Seam Gas Composition on Proactive Goaf Inertization Strategies for Spontaneous Combustion Management—a Computational Modeling Approach

Reconnoitering the sequence and structural analysis of Staphylococcus aureus “A” protein

BackgroundThe Staphylococcus aureus “A” protein plays an essential role in the pathogenicity and virulence of this bacterial species. To gain deeper insights into the protein's characteristics, we conducted an in-depth analysis of its sequence and structure. ObjectiveThis study aimed to unravel the underlying genetic and structural components that contribute to the protein's functional properties. ResultsUtilizing various bioinformatics tools and techniques, we first examined the protein's primary sequence, identifying key amino acid residues and potential functional domains. Additionally, we employed computational modeling and simulation approaches to determine the tertiary structure of the “A” protein. Through this comprehensive analysis, we discovered novel features and interactions within the protein's structure, shedding light on its potential mechanisms of action. Furthermore, we investigated the protein's evolutionary conservation and compared it with related proteins from other bacterial species. ConclusionsOverall, our findings provide valuable insights into the sequence and structure of the Staphylococcus aureus “A” protein, which may have implications for understanding its role in pathogenicity and guiding the development of novel therapeutic strategies.

QSPR Analysis of Diverse Drugs Using Linear Regression for Predicting Physical Properties

The study focused on QSPR (Quantitative Structure-Property Relationship) analysis using a variety of topological indices on the medications Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Through the use of computational modeling approaches, the study sought to determine how these medications’ chemical structures relate to their individual qualities. The discovered results provided information on the quantitative correlations between structural characteristics and pharmacological qualities, allowing for better comprehension and forecast of their behavior. The results of this study make a positive contribution to the field of medication discovery and design by offering important knowledge about the structure-property correlations of these medicinal molecules. In this article, we focus on using topological indices and a linear regression model to successfully predict various pharmacological features. This method enables more effective drug discovery and development by providing insights into the connection between molecular structure and pharmacological characteristics. We can improve our comprehension of drug behavior and assist targeted drug design by utilizing topological indices and regression analysis.

Adsorption behavior, different green solvent effect and surface enhanced Raman spectra (SERS) investigation on inhibition of SARS-CoV-2 by antineoplastic drug Carmofur with silver/gold/platinum loaded silica nanocomposites: A combined computational analysis and molecular modelling approach

In recent times, extensive research has been carried out to uncover effective drug treatments for coronavirus disease (COVID-19). The Carmofur (HCFU) molecule is primarily known for its antineoplastic properties and its use in cancer treatment. Nowadays, the antineoplastic drug Carmofur has shown its effectiveness in inhibiting the SARS-CoV-2 virus. The primary objective of this article is to utilize theoretical knowledge to explore the absorption mechanism and interaction between the HCFU molecule and the noble metal (Ag/Au/Pt)-loaded silica nanocomposites (HCFU + Ag/Au/Pt…SiO2). The optimized geometry, frequency, and intensity of the vibrational bands of the HCFU molecule and its complexes were obtained from the DFT method with the LANL2DZ basis set. The SERS technique was carried out to explore the adsorption of HCFU molecule with metal (Ag/Au/Pt)-loaded silica nanocomposites. The chemical reactivity and the charge distribution within the molecule were analysed with the help of HOMO-LUMO, Molecular Electrostatic Potential (MEP), Electron Localized Function (ELF), and Localized Orbital Locator (LOL) studies. The polar protic (ethanol, methanol, and water) and polar aprotic (DMSO) solvents were used to analyse the UV–Visible spectra of the molecules using the TD-DFT method. The drug-likeness properties of the molecules were calculated utilizing Lipinski’s rule, and finally, the molecular docking prediction was carried out for the Carmofur (HCFU) and its complexes with the selected proteins.

Development of molecular cluster models to probe pyrite surface reactivity.

The recent discovery that anaerobic methanogens can reductively dissolve pyrite and utilize dissolution products as a source of iron and sulfur to meet their biosynthetic demands for these elements prompted the development of atomic-scale nanoparticle models, as maquettes of reactive surface sites, for describing the fundamental redox steps that take place at the mineral surface during reduction. The given report describes our computational approach for modeling n(FeS2 ) nanoparticles originated from mineral bulk structure. These maquettes contain a comprehensive set of coordinatively unsaturated Fe(II) sites that are connected via a range of persulfide (S2 2- ) ligation. In addition to the specific maquettes with n = 8, 18, and 32 FeS2 units, we established guidelines for obtaining low-energy structures by considering the pattern of ionic, covalent, and magnetic interactions among the metal and ligand sites. The developed models serve as computational nano-reactors that can be used to describe the reductive dissolution mechanism of pyrite to better understand the reactive sites on the mineral, where microbial extracellular electron-transfer reactions can occur.

Numerical investigation of residual stresses in thin-walled additively manufactured structures from selective laser melting

Selective laser melting (SLM), a metal laser powder bed fusion additive manufacturing method, involves several cycles of very high temperature gradient heating and cooling during the solidification of each layer, which can cause the accumulation of detrimental residual stresses in the 3D printed structure. This work uses a thermo-mechanical computational modeling approach to investigate the formation of residual stresses in thin-walled structures and also investigates the effects of varying taper on the evolution of residual stress profiles of the build. Three material grades; namely, Titanium alloy (Ti64), Stainless steel (SS316L) and Inconel (IN718) have been used for this study. The results show that varying taper thickness up to a certain value has a considerable effect on residual stress evolutions in thin-wall structures, however, beyond a certain value of the taper level, the residual stresses are observed to converge. Also, it is observed that the tensile stresses at the edges of the wall are almost equal or exceed the yield stress of the materials. Among the three material grades considered, the magnitude of residual stress was higher in Ti64 and the stresses are dominant in the build direction. The simulation framework is also applied to analyze the effect of residual stresses on the mechanical properties of complex thin-wall structures such as TPMS (Triply Periodic Minimum Surfaces) lattice structure, using Schwarz Primitive (SP) as a case study. It is observed that the residual stresses lower the effective elastic properties of the lattice structures by 6% ∼ 10% for the three material grades but has no effect on the effective plastic behavior of the material.

Thermodynamically consistent hydrodynamic phase-field computational modeling for fluid-structure interaction with moving contact lines

This paper proposes a novel computational modeling approach to investigate the fluid-structure interactions with moving contact lines. By embracing the generalized Onsager principle, a coupled hydrodynamics and phase field system is introduced that can describe the fluid-structure interactions with moving contact lines in a thermodynamically consistent manner. The resulting partial differential equation (PDE) model consists of the Navier-Stokes equation and a nonlinear Allen-Cahn type equation. Volume conservation is enforced through an additional penalty term. A fully-discrete structure-preserving numerical scheme is proposed by combining several techniques to solve this coupled PDE system effectively and accurately. For the temporal discretization, we utilize the supplementary variable method for preserving the thermodynamic structure and the projection approach for reducing the problem size. Then, we use the finite difference method on the staggered grid for spatial discretization. Furthermore, we have rigorously proved that the proposed numerical scheme based on the second-order backward difference formula respects the original energy stability, i.e., the scheme is energy stable. Additionally, with the aid of the supplementary variable method, the resultant scheme can be transformed into a constrained optimization problem, where the solutions of the supplementary variables are the arguments of the objective function that reaches the optimality. Then the augmented Lagrangian method is introduced in part to bring robustness and efficiency to solving such a constrained optimization problem. Finally, various numerical simulations verify the model's capability and demonstrate the scheme's effectiveness, accuracy, and stability.

Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modeling and event-related potentials.

It has been hypothesized that lonely individuals demonstrate hypervigilance toward social threats. However, recent studies have raised doubts about the reliability of tasks commonly used to measure attentional biases toward threats. Two alternative approaches have been suggested to overcome the limitations of traditional analysis of attentional bias. First, the neurophysiological indicators of orienting to threats were shown to have superior psychometric characteristics compared to overt measures of behavioral performance. The second approach involves utilizing computational modeling to isolate latent components corresponding to specific cognitive mechanisms from observable data. To test the usefulness of these approaches in loneliness research, we analyzed behavioral and electroencephalographic (EEG) data from 26 lonely and 26 non-lonely participants who performed a dot-probe task using a computational modeling approach. We applied the Drift Diffusion Model (DDM) and extracted N2pc-an event-related potential that serves as an indicator of spatial attention. No evidence for social threat hypervigilance has been found in DDM parameters nor in N2pc characteristics in the current study. However, we did observe decreased drift rate and increased variability in drift rate between trials within the lonely group, indicating reduced efficiency in perceptual decision-making among lonely individuals. These effects were not detected using standard behavioral measures used in the dot-probe paradigm. Given that DDM indicators were sensitive to differences in perceptual discrimination between the two groups, even when no overt differences were found in standard behavioral measures, it may be postulated that computational approaches offer a more comprehensive understanding of cognitive processes.

Computational modelling of the mechanical performance of nitinol guidewires in an idealised tortuous path for medical device applications

Guidewires are critically important components in medical implant and device delivery systems and their desired clinical performance or “steerability” requires a good torque response, i.e., the rotation of the (distal) guidewire tip should follow exactly the (proximal) applied input rotation. However, guidewires can suffer from phenomena known as lag (tip rotation is significantly less than the input rotation) and whip (the lag is suddenly recovered). Nitinol is a common guidewire material that can give superior performance but has been known to exhibit lag and whip depending on the specific material formulation and processing route. In this study, the torsional response of Nitinol guidewires is investigated using computational modelling (the finite element method) and analytical approaches, with a view to gaining a fundamental understanding of the mechanisms behind the lag and whip phenomena and how these relate to the specific material properties of a range of Nitinol variants and the geometrical configuration of the wire during torsion. An idealised vascular geometry is considered; this consists of a curved section and a straight section of varying length where the curved section is representative of geometries that are encountered in tortuous path navigation in the vasculature.The results capture the experimentally observed phenomena and reveal the relationship between material properties and guidewire performance. The stress state in the wire, which is dictated by the stress plateaus in the Nitinol material response, leads to the generation of a net moment within the wire which requires net work to be done during rotation of the wire. Analysis of idealised hypothetical materials show that the transformation strain is also an important parameter. The analysis reduced the performance of the guidewire material to a single metric that is given in terms of the energy dissipated during transformation, i.e., the area of the hysteresis loop. The results show that the combination of torsion and the bending of the wire in the curved path are critically important in the generation of lag and whip, and that both are accentuated by increasing the length of the straight section once the phenomena are active.

A Computational Fluid Dynamics Modelling Approach for the Numerical Verification of the Bioclimatic Design of a Public Urban Area in Greece

Recent recordings of urban overheating reveal a dramatic increase in cities’ population exposure to heatwaves. Heatwaves’ implications are escalated in urban environments due to the intensification of the Urban Heat Island (UHI) effect. To combat the overheating and UHI adverse effects, novel urban rehabilitation actions are needed based on reliable predictions of appropriate Key Performance Indicators (KPIs) (such as pedestrian-level air temperature and thermal comfort) for alternative design scenarios. The objective of the current study is to present the application of a previously developed Computational Fluid Dynamics (CFD) model for the calculation of urban microclimatic conditions for the assessment of the UHI and thermal-comfort conditions in a central urban area in the city of Heraklion in Crete, Greece. Empirical validation of the model is performed through comparisons with monitored microclimate data (i.e., air temperature, relative humidity, wind speed) and actual sensation vote recordings found in another research study. In compliance with the latter, the validation campaign is conducted for a typical hot summer day in July 2009 from 10:00 to 16:00. The model is then used to assess the UHI effects for both the existing urban configuration and a given suggested environmental upgrade of the space. Simulations of the existing situation reveal that the squares located in the studied area already stand for efficient resistances to urban overheating, and heat vulnerabilities are detected mainly in non-shaded traffic and pedestrian roads perimetric to the squares. Based on the CFD simulations, the suggested environmental upgrading plan exhibits a reduction in the peak mean air temperature of 0.46 °C, and thermal comfort is improved by at least 5% (based on SET) throughout the studied area in summer. At the same time, simulations of winter conditions suggest that there are limited potential pedestrian thermal-sensation and building heating penalties under the considered renovation scenario.

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes.

Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.

An approach to study recruitment/derecruitment dynamics in a patient-specific computational model of an injured human lung.

We present a new approach for physics-based computational modeling of diseased human lungs. Our main object is the development of a model that takes the novel step of incorporating the dynamics of airway recruitment/derecruitment into an anatomically accurate, spatially resolved model of respiratory system mechanics, and the relation of these dynamics to airway dimensions and the biophysical properties of the lining fluid. The importance of our approach is that it potentially allows for more accurate predictions of where mechanical stress foci arise in the lungs, since it is at these locations that injury is thought to arise and propagate from. We match the model to data from a patient with acute respiratory distress syndrome (ARDS) to demonstrate the potential of the model for revealing the underlying derangements in ARDS in a patient-specific manner. To achieve this, the specific geometry of the lung and its heterogeneous pattern of injury are extracted from medical CT images. The mechanical behavior of the model is tailored to the patient's respiratory mechanics using measured ventilation data. In retrospective simulations of various clinically performed, pressure-driven ventilation profiles, the model adequately reproduces clinical quantities measured in the patient such as tidal volume and change in pleural pressure. The model also exhibits physiologically reasonable lung recruitment dynamics and has the spatial resolution to allow the study of local mechanical quantities such as alveolar strains. This modeling approach advances our ability to perform patient-specific studies in silico, opening the way to personalized therapies that will optimize patient outcomes.

Impaired Punishment Learning in Conduct Disorder

Conduct disorder (CD) has been associated with deficits in the use of punishment to guide reinforcement learning (RL) and decision making. This may explain the poorly planned and often impulsive antisocial and aggressive behavior in affected youths. Here, we used a computational modeling approach to examine differences in RL abilities between CD youths and typically developing controls (TDCs). Specifically, we tested 2 competing hypotheses that RL deficits in CD reflect either reward dominance (also known as reward hypersensitivity) or punishment insensitivity (also known as punishment hyposensitivity). The study included 92 CD youths and 130 TDCs (aged 9-18 years, 48% girls) who completed a probabilistic RL task with reward, punishment, and neutral contingencies. Using computational modeling, we investigated the extent to which the 2 groups differed in their learning abilities to obtain reward and/or to avoid punishment. RL model comparisons showed that a model with separate learning rates per contingency explained behavioral performance best. Importantly, CD youths showed lower learning rates than TDCs specifically for punishment, whereas learning rates for reward and neutral contingencies did not differ. Moreover, callous-unemotional (CU) traits did not correlate with learning rates in CD. CD youths have a highly selective impairment in probabilistic punishment learning, regardless of their CU traits, whereas reward learning appears to be intact. In summary, our data suggest punishment insensitivity rather than reward dominance in CD. Clinically, the use of punishment-based intervention techniques to achieve effective discipline in patients with CD may be a less helpful strategy than reward-based techniques.

Suicide prevention and ketamine: insights from computational modeling.

Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.

Metacognitive judgments during visuomotor learning reflect the integration of error history.

People form metacognitive representations of their own abilities across a range of tasks. How these representations are influenced by errors during learning is poorly understood. Here, we ask how metacognitive confidence judgments of performance during motor learning are shaped by the learner's recent history of errors. Across four motor learning experiments, our computational modeling approach demonstrated that people's confidence judgments are best explained by a recency-weighted averaging of visually observed errors. Moreover, in the formation of these confidence estimates, people appear to reweight observed motor errors according to a subjective cost function. Confidence judgments were adaptive, incorporating recent motor errors in a manner that was sensitive to the volatility of the learning environment, integrating a shallower history when the environment was more volatile. Finally, confidence tracked motor errors in the context of both implicit and explicit motor learning but only showed evidence of influencing behavior in the latter. Our study thus provides a novel descriptive model that successfully approximates the dynamics of metacognitive judgments during motor learning.NEW & NOTEWORTHY This study examined how, during visuomotor learning, people's confidence in their performance is shaped by their recent history of errors. Using computational modeling, we found that confidence incorporated recent error history, tracked subjective error costs, was sensitive to environmental volatility, and in some contexts may influence learning. Together, these results provide a novel model of metacognitive judgments during motor learning that could be applied to future computational and neural studies at the interface of higher-order cognition and motor control.