Analytical Decisions Research Articles

Validating the Influences of Methodological Decisions on Assessing the Spatiotemporal Stability of Speech Movement Sequences Using Children's Speech Data.

Prior research introduced quantifiable effects of three methodological parameters (number of repetitions, stimulus length, and parsing error) on the spatiotemporal index (STI) using simulated data. Critically, these parameters often vary across studies. In this study, we validate these effects, which were previously only demonstrated via simulation, using children's speech data. Kinematic data were collected from 30 typically developing children and 15 children with developmental language disorder, all spanning the ages of 6-8 years. All children repeated the sentence "buy Bobby a puppy" multiple times. Using these data, experiments were designed to mirror the previous simulated experiments as closely as possible to assess the effects of analytic decisions on the STI. Experiment 1 manipulated number of repetitions, Experiment 2 manipulated stimulus length (or the number of movement units in the target phrase), and Experiment 3 manipulated precision of parsing of the articulatory trajectories. The findings of all three experiments closely mirror those of the prior simulation. Experiment 1 showed consistent underestimation of STI values from smaller repetition counts consistent with the theoretical model for all three participant groups. Experiment 2 found speech segments containing fewer movements yield lower STI values than longer ones. Finally, Experiment 3 showed even small parsing errors are found to significantly increase measured STI values. The results of this study are consistent with the findings of prior simulations in showing that the number of repetitions, length of stimuli, and amount of parsing error can all strongly influence the STI independent of behavioral factors. These results further confirm the importance of closely considering the design of experiments, which employ the STI.

Issues in systematizing the elicitation and analysis of syllable structure

Abstract This reply explores issues with documenting prosodic structures given their unique relationship with metalinguistic knowledge. New evidence and perspectives are incorporated into a deeper investigation of Budai Rukai and the analytical decisions that underlie its reanalyzed prosodic system. This case study is then taken as a basis for discussion of a possible standardized protocol for the documentation of prosodic structures, in which diverse types of phonological evidence are integrated and contextualized.

Go with your gut! The beneficial mood effects of intuitive decisions.

People make countless decisions every day. We explored the self-regulatory function of decisions and assumed that the very act of making a decision in everyday life enhances people's mood. We expected that this decision-related mood change would be more pronounced for intuitive decisions than for analytical ones. The ease of making a decision and the feeling of rightness were expected to mediate the effect of intuitive (vs. analytical) decisions on participants' mood. In a preregistered experimental experience sampling study, participants from the general population were asked to report when they were about to make an everyday decision over the course of 14 days (N = 256 participants, 6,779 decisions). For each decision, participants were randomly instructed to decide either based on their intuition or based on careful analysis. We assessed several variables before and immediately after the decision. Participants also reported retrospectively on their choices in voluntary follow-up assessments. Making a decision per se immediately enhanced participants' mood. This mood enhancement was stronger for intuitive compared to analytic decisions and persisted until follow-up. Ease of decision, but not feeling of rightness, mediated this effect. Intuitive decisions compared to analytic decisions were more likely to be implemented and led to greater satisfaction and pleasantness of the chosen option. Having more options for a particular decision led to generally higher mood improvement and satisfaction. This is the first empirical demonstration showing that using one's gut has beneficial effects in everyday life. Study limitations and implications for theory and practice are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A cross-linguistic review of citation tone production studies: Methodology and recommendations.

The study of citation tones, lexical tones produced in isolation, is one of the first steps towards understanding speech prosody in tone languages. However, methodologies for investigating citation tones vary significantly, often leading to limited comparability of tone inventories, both within and across languages. This paper presents a systematic review of research methods and practices in 136 citation tone studies on 129 tonal language varieties in China, including 99 studies published in Chinese, which are therefore not easily available to an international scientific readership. The review provides an overview of possible analytical decisions along the research pipeline, and unveils considerable variation in data collection, analysis, and reporting conventions, particularly in how f0, the primary acoustic correlate for tone, is operationalised and reported across studies. Key methodological issues are identified, including small sample sizes and inadequate transparency in communicating methodological decisions and procedure. This paper offers a clear road map for citation tone production research and proposes a range of recommendations on speaker sampling, experimental design, acoustic processing techniques, f0 analysis, and result reporting, with the goal of facilitating future tonal research and enhancing resources for underrepresented tonal varieties.

Replicable and robust cellular and biochemical blood marker signatures of depression and depressive symptoms

Background: Identification of replicable and robust peripheral blood-based markers associated with depression remains elusive, given that studies frequently identify potential biomarkers that ultimately fail to replicate in other studies, impeding progress in psychiatric research. Peripheral biochemical and cellular markers (PBCs; e.g., albumin) may play an important role in depression. Methods: Using a test-replication design including participants from the NHANES community cohort (ntest=17,450, nreplication=17,449), we examined 42 PBCs to identify PBCs that were both replicably and robustly associated with either overall depression severity or individual symptoms of depression across both cohorts across a wide range of possible combinations of analytic decisions (n's = 17,000+). Results: We found that a small set of PBCs (e.g., bilirubin) were robustly and replicably associated with overall depression severity, with unique signatures of PBCs linked with individual symptoms of depression when stratified by gender. A varying degree of correlation was found between measures of replicability. Conclusions: We identified replicable and robust cellular biochemical blood marker signatures associated with both overall depression severity and individual symptoms of depression. Our findings can be used to enhance other researchers’ abilities to better understand factors associated with depression and potentially drive the development of effective treatments for depression.

Impact of analytic decisions on test–retest reliability of individual and group estimates in functional magnetic resonance imaging: A multiverse analysis using the monetary incentive delay task

Abstract Empirical studies reporting low test–retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction, and contrast selection, may improve estimates of test–retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual- and group-level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options), and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 – .55 across the 3 samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization, and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared with using the Neutral cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test–retest reliability may not always improve interpretability of the estimated BOLD signal.

Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions

BackgroundMissing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions (“missing completely at random”, “missing at random” [MAR], “missing not at random”) are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.MethodsWe searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.ResultsOf the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.ConclusionEffort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.

Individual differences in changes in subjective well-being: The role of event characteristics after negative life events.

Negative life events can lead to lasting changes in subjective well-being (SWB). However, people change differently in their SWB after negative life events, and our understanding of factors explaining these individual differences is still limited-possibly because research so far has neglected to investigate differences in the characteristics of the experienced events (e.g., perceived impact, causes of the event). To address this gap, we examined whether perceived event characteristics and objective-descriptive characteristics of negative life events can explain individual differences in changes in SWB. We used data from a longitudinal study in which the SWB of participants (N = 1,068) who had recently experienced a negative life event was assessed at five measurement occasions over 6 months. Perceived event characteristics and objective-descriptive event characteristics were significantly related to each other. Furthermore, both kinds of event characteristics were associated with individual differences in changes in SWB. Finally, specification curve analyses illustrated that several analytical decisions (e.g., the examined SWB component) influenced the association between an event characteristic and changes in SWB. Results from these specification curve analyses can be accessed via a ShinyApp (https://life-event-research.shinyapps.io/EventCharacteristics/). Our findings provide insights into possible causes of the event perception and show that both perceived event characteristics and objective-descriptive event characteristics can help to better understand individual differences in the reaction to major life events. However, as effects seem to depend on several analytical decisions, future research is needed to identify the important characteristics of life events for different events and outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Specifying a target trial protocol to estimate the effect of preconception treatment of periodontitis on time-to-pregnancy: A commentary and applied example.

The target trial framework was developed as a strategy to design and analyze observational epidemiologic studies with the aim of reducing bias due to analytic decisions. It involves designing a hypothetical randomized trial to answer a question of interest and systematically considering how to use observational data to emulate each trial component. The primary aim of this paper is to provide a detailed example of the application of the target trial framework to a research question in oral epidemiology. We describe the development of a hypothetical target trial and emulation protocol to evaluate the effect of preconception periodontitis treatment on time-to-pregnancy. We leverage data from Pregnancy Study Online (PRESTO), a preconception cohort, to ground our example in existing observational data. We discuss the decision-making process for each trial component, as well as limitations encountered. Our target trial application revealed data limitations that precluded us from carrying out the proposed emulation. Implications for data quality are discussed and we provide recommendations for researchers interested in conducting trial emulations in the field of oral epidemiology. The target trial framework has the potential to improve the validity of observational research in oral health, when properly applied. We encourage the broad adoption of the target trial framework to the field of observational oral health research and demonstrate its value as a tool to identify directions for future research.

Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task.

Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.

A Systematic Review of Data Analysis Decisions in Counseling Single-Case Research Designs to Inform Best Practices

Single-case research designs (SCRDs) are a vital tool to study a variety of topics pertinent to the counseling profession. Furthermore, SCRDs are a flexible methodology that counselors can use to assess the effectiveness of their treatments, thus acting as scientist-practitioners. We aimed to report the types of data analytic procedures used in counseling SCRDs to evaluate intervention effects. We searched 42 counseling affiliated journals between the years 2015 to 2021 and identified 50 SCRD studies. Results suggested all studies used visual analysis, yet few studies reported quantitative information that align with the recommended data characteristics to evaluate for visual analysis. The most prevalent effect sizes used were non-overlap indices, with percentage of data exceeding the median being the most frequent. No studies reported between-case metrics and few cases reported within-case metrics that capture magnitude of change. We discuss recommendations for making a priori data analytic decisions as well as strengths and limitations in available options.

Same model, same data, but different outcomes: Evaluating the impact of method choices in structural equation modeling

AbstractScientific research demands robust findings, yet variability in results persists due to researchers' decisions in data analysis. Despite strict adherence to state‐of the‐art methodological norms, research results can vary when analyzing the same data. This article aims to explore this variability by examining the impact of researchers' analytical decisions when using different approaches to structural equation modeling (SEM), a widely used method in innovation management to estimate cause–effect relationships between constructs and their indicator variables. For this purpose, we invited SEM experts to estimate a model on absorptive capacity's impact on organizational innovation and performance using different SEM estimators. The results show considerable variability in effect sizes and significance levels, depending on the researchers' analytical choices. Our research underscores the necessity of transparent analytical decisions, urging researchers to acknowledge their results' uncertainty, to implement robustness checks, and to document the results from different analytical workflows. Based on our findings, we provide recommendations and guidelines on how to address results variability. Our findings, conclusions, and recommendations aim to enhance research validity and reproducibility in innovation management, providing actionable and valuable insights for improved future research practices that lead to solid practical recommendations.

Prevalence, Inequalities, and Impact of Bullying in Adolescence: Insights from the #BeeWell Study

AbstractMeasurement is fundamental to understanding and preventing bullying, but approaches in the field are inconsistent, producing much conflicting evidence. We illustrate this by demonstrating the sensitivity of findings to researcher-led analytical decisions (exposure threshold and type(s) of bullying considered) in a study addressing the following aims: (i) to determine the prevalence of bullying; (ii) to establish the nature and extent of inequalities in bullying exposure between different socio-demographic groups; (iii) to examine the relationship between bullying exposure and internalising symptoms; and (iv) to establish if this relationship varies between socio-demographic groups. Adolescents aged 12–15 (N = 35,825) attending 147 secondary schools in the #BeeWell study completed measures of bullying and internalising mental health difficulties. These data were linked to information on their socio-demographic characteristics (e.g. socio-economic disadvantage). A series of pre-registered analyses were undertaken. With regard to the first aim, the prevalence of bullying victimisation was found to range between 5 and 16%. In relation to the second aim, disparities in exposure to bullying were consistently found among gender and sexual minorities (vs cisgender heterosexual boys), those with special educational needs (vs those without special educational needs), younger students (vs older students), and those from more disadvantaged neighbourhoods (vs those from less disadvantaged neighbourhoods), irrespective of the bullying exposure threshold or type being considered. However, disparities among cisgender heterosexual girls (vs cisgender heterosexual boys) and ethnic minority groups (vs White students) varied by exposure threshold and type of bullying. Pertaining to the third aim, the population attributable fraction for the association between bullying exposure and internalising symptoms was found to range between 6 and 19%, with the odds ratio ranging between 3.55 and 4.20. Finally, in terms of the fourth aim, there was limited evidence that the magnitude of the impact of bullying victimisation varied across socio-demographic subgroups, except that bullying exposure was more strongly associated with internalising symptoms among LGBTQ+ young people and cisgender heterosexual girls (vs cisgender heterosexual boys), and less strongly associated with internalising symptoms among Black students (vs White students). Our findings speak to the importance of developing more consistent measurement practices in bullying research, with consequent implications for prevention and intervention. These implications are contextualised by consideration of study strengths and limitations.

Exploring the multiverse of analysis options for the alcohol Stroop

The alcohol Stroop is a widely used task in addiction science to measure the theoretical concept of attentional bias (a selective attention to alcohol-related cues in the environment), which is thought to be associated with clinical outcomes (craving and consumption). However, recent research suggests findings from this task can be equivocal. This may be because the task has many different potential analysis pipelines, which increase researcher degrees of freedom when analysing data and reporting results. These analysis pipelines largely come from how outlying reaction times on the task are identified and handled (e.g. individual reaction times > 3 standard deviations from the mean are removed from the distribution; removal of all participant data if > 25% errors are made). We used specification curve analysis across two alcohol Stroop datasets using alcohol-related stimuli (one published and one novel) to examine the robustness of the alcohol Stroop effect to different analytical decisions. We used a prior review of this research area to identify 27 unique analysis pipelines. Across both data sets, the pattern of results was similar. The alcohol Stroop effect was present and largely robust to different analysis pipelines. Increased variability in the Stroop effect was observed when implementing outlier cut-offs for individual reaction times, rather than the removal of participants. Stricter outlier thresholds tended to reduce the size of the Stroop interference effect. These specification curve analyses are the first to examine the robustness of the alcohol Stroop to different analysis strategies, and we encourage researchers to adopt such analytical methods to increase confidence in their inferences across cognitive and addiction science.

Medicine utilization studies in Australian individual-level dispensing data: A blinded, multi-center replicated analysis.

Medicine dispensing data require extensive preparation when used for research and decisions during this process may lead to results that do not replicate between independent studies. We conducted an experiment to examine the impact of these decisions on results of a study measuring discontinuation, intensification, and switching in a cohort of patients initiating metformin. Four Australian sites independently developed a HARmonized Protocol template to Enhance Reproducibility (HARPER) protocol and executed their analyses using the Australian Pharmaceutical Benefits Scheme 10% sample dataset. Each site calculated cohort size and demographics and measured treatment events including discontinuation, switch to another diabetes medicine, and intensification (addition of another diabetes medicine). Time to event and hazard ratios for associations between cohort characteristics and each event were also calculated. Concordance was assessed by measuring deviations from the calculated median of each value across the sites. Good agreement was found across sites for the number of initiators (median: 53 127, range: 51 848-55 273), gender (56.9% female, range: 56.8%-57.1%) and age group. Each site employed different methods for estimating days supply and used different operational definitions for the treatment events. Consequently, poor agreement was found for incidence of discontinuation (median 55%, range: 34%-67%), switching (median 3.5%, range: 1%-7%), intensification (median 8%, range: 5%-12%), time to event estimates and hazard ratios. Differences in analytical decisions when deriving exposure from dispensing data affect replicability. Detailed analytical protocols, such as HARPER, are critical for transparency of operational definitions and interpretations of key study parameters.

Contrasting stances at the crossroads of debugging learning opportunities.

Moments of failure during learning present a wide range of opportunities for growth. However, experimental research and meta reviews focused on failure and learning tend to target singular valued learning processes, such as efficient fixes or transfer of conceptual understanding. These analytical decisions conflict with research documenting that teachers and students pursue heterogeneous learning processes following impasses, including preparing to curb recurring problems and preparing for novel impasses. This work focuses on weekend and summer computer science education workshops for middle school students (roughly ages 10-14) in a large city in the United States. Drawing on discursive psychology, interaction analysis and portraiture, we examine the selection and prioritization of five valued learning processes in naturalistic discourse around impasses, especially when students and teachers disagree with one another. In three case studies, our approach blends detailed analyses of conversations with longitudinal portraits of students across a year invested in learning coding. We document how moments of failure not only create openings for numerous learning opportunities, but also occasion argumentation between students and instructors about which valued learning process they view as worthy of prioritizing and pursuing in the moment, including their rationale for foregrounding a particular process. We argue that this work throws light on an under-examined, yet central tension around which goals and supports - and whose perspective on those goals and supports - to foreground in research and teaching that pursue productive learning from moments of failure.

Does information reduce interpersonal violence? Evidence from prisons

Abstract Why does interpersonal violence erupt? A key source is conflict over resources and a little-studied mechanism for solving such cases concerns the availability of credible information about individuals’ abilities and willingness to use force. Its core prediction is that when such credible information abounds, the outcome of a potential struggle can be anticipated and thus there is no need to fight. I test this prediction using a nationally representative survey of male prisoners (Ninmates = 10,768, Nfacilities = 207) incarcerated in US state correctional facilities. I also make two methodologically orientated contributions. First, recent research highlights the important consequences that data analysis choices can have in determining quantitative findings. To overcome this, I draw on multiverse analysis and specification curve analysis to map out the key analytical decisions I make and run 262,144 plausible regression models. By doing so, I classify associations that are robust, mixed, or entirely frail according to analytic decisions and find partial support for the informational mechanism of interpersonal violence. Second, in contrast to much quantitative prison research, which uses only official or institutionally punished assaults data, I compare self-recognized fights to self-reported institutionally punished fights and highlight key differences.

Is the perceptual disfluency effect moderated by working memory capacity? Direct replication of Lehmann et al. (2016)

According to an aptitude-treatment interaction experiment (Lehmann et al., Metacognition and Learning,11, 89–105, 2016, N = 47, published in Metacognition and Learning), perceptually disfluent texts facilitated retention and comprehension performance (but not transfer performance) only for learners with higher working memory capacity (WMC). No effects of WMC for a fluent text were found (albeit theoretically, fluency may be more advantageous for learners with lower WMC). The findings of our (pre-registered) direct replication (supervised online sample of N = 96) show a substantial deviation from the original results: In contrast to the interaction effect (disfluency and WMC) of the primary study, we obtained null results for disfluency, WMC, and their interaction for all learning outcomes. Our replication data are not indicative of WMC as a boundary condition moderating the disfluency effect on learning. We discuss discrepancies in the results of the primary study and our direct replication regarding particular methodological and analytical decisions, questioning the robustness and generalizability of Lehman et al.’s results beyond their primary study.

Neural connectivity moderators and mechanisms of ketamine treatment among treatment-resistant depressed patients: a randomized controlled trial

Intravenous (IV) ketamine has emerged as a rapid and effective treatment for TRD. However, the specific neural mechanisms of ketamine's effects in humans remains unclear. Although neuroplasticity is implicated as a mechanism of action in animal models, relatively few randomized controlled trials (RCTs) in TRD patients have examined ketamine's impact on functional connectivity, a posited functional marker of neuroplasticity-particularly in the context of a mood-induction paradigm (termed miFC). 152 adults with TRD (63% female; 37% male) were randomly allocated to receive a single infusion of ketamine or saline in a 2:1 ratio. We examined changes in connectivity (from baseline to 24-h post-infusion) that differed by treatment, and whether clinical treatment response at 24-h post-infusion was uniquely related (among patients allocated to ketamine relative to saline) to (1) pre-treatment connectivity and (2) changes in connectivity. We examined both miFC and rsFC, using prefrontal cortex and limbic seed regions. We also conducted a multiverse analysis to examine findings most robust against analytic decisions. Across both miFC and rsFC, ketamine was associated with greater in prefrontal/limbic connectivity compared to saline, and lower baseline connectivity of limbic and prefrontal regions predicted greater treatment response in patients receiving ketamine. Greater connectivity increases in participants receiving ketamine was uniquely related to greater treatment response. In addition, certain findings were identified as being reproducible against different analytic decisions in multiverse analyses. Our findings identify specific neural connectivity patterns impacted by ketamine and were uniquely related to outcomes following ketamine (relative to saline). These findings generally support prominent neuroplasticity models of ketamine's therapeutic efficacy. These findings lay new groundwork for understanding how to enhance and optimize ketamine treatments and develop novel rapid-acting treatments for depression. This research was supported by NIH grant R01MH113857 and by the Clinical and Translational Sciences Institute at the University of Pittsburgh (UL1-TR-001857).

Novel design of nonlinear autoregressive with exogenous noise-based intelligent networks for dynamics of MHD Casson–Williamson models involving heat generation and viscous dissipation

In the current arena, the role of artificial intelligence (AI) has gained immense impact in the field of computational fluid dynamics (CFD) due to mesh improvement efficiency, optimal manual intervention, reliable forecasting, comprehending data analytic decisions, and utilization of machine automation. The study under investigation treatise on CFD problem representing the flow of Casson and Williamson fluids (CWFs) model with heat generation and viscous dissipation by using AI-based knacks through nonlinear autoregressive exogenous (NARX) networks backpropagated with Bayesian regularization technique (BRT), i.e. NARX-BRT. The design NARX-BRT procedure implemented on data generated with Adams numerical scheme for CWFs by carefully altering parameters i.e. Darcy number, Casson fluid, mass convective Williamson, mixed convection, thermal conductivity, thermal convective, and Eckert number, with fixed values Prandtl and Schmidt numbers. Results invariably matched with the reference/standard numerical solutions for the CWF model having significantly reduced magnitude of error for each anticipated CWF scenario. The effectiveness of projected CWF is portrayed exhaustively through mean square error (MSE) based iterative learning curves, analysis of adaptive controlling factors, error histogram plots, and regression metrics for variants of nonlinear differential order systems of CWF-based CFD.