Active Testing of Executive Functions: Toward More Efficient and Equitable Individual Behavioral Modeling

Inferences about executive functions are commonly drawn through serial administration of various individual assessments that often take a long time to complete and cannot capture complex trends across multiple variables. In an attempt to improve upon current methods used to estimate latent brain constructs, this thesis makes two primary contributions to the field of behavioral modeling. First, it brings attention to sequential designs for more efficient diagnostic testing of fluctuations in executive functions with respect to a baseline level. It was shown that a sequential framework was successfully capable of detecting significant differences in cognitive performance more rapidly than conventional fixed approaches. Second, it introduces a scalable Gaussian Process estimator that can build individual psychometric models of task performance without requiring prohibitive amounts of data. This probabilistic machine learning classifier was capable of obtaining fully predictive models of working memory capacity person by person with high confidence

Accelerating Executive Function Assessments With Group Sequential Designs

Inferences about executive functions (EFs) are commonly drawn via lengthy serial administration of simple independent assessments. Classical methods for EF estimation often require excessive measurements and provide little or no flexibility to dynamically adjust test length for each individual. In order to decrease test duration and mitigate respondent burden, active testing modalities that incorporate more efficient data collection strategies are indispensable. To this end, we propose sequential analysis to improve upon traditional testing methods in behavioral science. In this paper, we show that sequential testing can be used to rapidly screen for a difference in the EF of a given individual with respect to a baseline level. In cognitive tests consisting of repeated identical tasks, a sequential framework can be utilized to actively detect significant differences in cognitive performance with high confidence more rapidly than conventional non-sequential approaches. Ultimately, sequential analysis could be applied to a variety of problems in cognitive and perceptual domains to improve efficiency gains and achieve substantial test length reduction

Evaluation of limited-sampling strategies to calculate AUC(0–24) and the role of CYP3A5 in Chilean pediatric kidney recipients using extended-release tacrolimus

Background: Kidney transplantation (KTx) requires immunosuppressive drugs such as Tacrolimus (TAC) which is mainly metabolized by CYP3A5. TAC is routinely monitored by trough levels (C0) although it has not shown to be a reliable marker. The area-under-curve (AUC) is a more realistic measure of drug exposure, but sampling is challenging in pediatric patients. Limited-sampling strategies (LSS) have been developed to estimate AUC. Herein, we aimed to determine AUC(0–24) and CYP3A5 genotype in Chilean pediatric kidney recipients using extended-release TAC, to evaluate different LSS-AUC(0–24) formulas and dose requirements.Patients and methods: We analyzed pediatric kidney recipients using different extended-release TAC brands to determine their trapezoidal AUC(0–24) and CYP3A5 genotypes (SNP rs776746). Daily TAC dose (TAC-D mg/kg) and AUC(0–24) normalized by dose were compared between CYP3A5 expressors (*1/*1 and *1/*3) and non-expressors (*3/*3). We evaluated the single and combined time-points to identify the best LSS-AUC(0–24) model. We compared the performance of this model with two pediatric LSS-AUC(0–24) equations for clinical validation.Results: Fifty-one pharmacokinetic profiles were obtained from kidney recipients (age 13.1 ± 2.9 years). When normalizing AUC(0–24) by TAC-D significant differences were found between CYP3A5 expressors and non-expressors (1701.9 vs. 2718.1 ng*h/mL/mg/kg, p < 0.05). C0 had a poor fit with AUC(0–24) (r2 = 0.5011). The model which included C0, C1 and C4, showed the best performance to predict LSS-AUC(0–24) (r2 = 0.8765) and yielded the lowest precision error (7.1% ± 6.4%) with the lowest fraction (9.8%) of deviated AUC(0–24), in comparison to other LSS equations.Conclusion: Estimation of LSS-AUC(0–24) with 3 time-points is an advisable and clinically useful option for pediatric kidney recipients using extended-release TAC to provide better guidance of decisions if toxicity or drug inefficacy is suspected. The different CYP3A5 genotypes associated with variable dose requirements reinforce considering genotyping before KTx. Further multi-centric studies with admixed cohorts are needed to determine the short- and long-term clinical benefits

Scalable Probabilistic Modeling of Working Memory Performance

A standard approach for evaluating a cognitive variable involves designing a test procedure targeting that variable and then validating test results in a sample population. To extend this functionality to other variables, additional tests are designed and validated in the same way. Test batteries are constructed by concatenating individual tests. This approach is convenient for the designer because it is modular. However, it is not scalable because total testing time grows proportionally with test count, limiting the practical size of a test battery. Cross-test models can inform the relationships between explicit or implicit cognitive variables but do not shorten test time and cannot readily accommodate subpopulations who exhibit different relationships than average. An alternate modeling framework using probabilistic machine learning can rectify these shortcomings, resulting in item-level prediction from individualized models while requiring fewer data points than current methods. To validate this approach, a Gaussian process probabilistic classifier was used to model young adult and simulated spatial working memory task performance as a psychometric function. This novel test instrument was evaluated for accuracy, reliability and efficiency relative to a conventional method recording the maximum spatial sequence length recalled. The novel method exhibited extremely low bias, as well as test-retest reliability 30% higher than the conventional method under standard testing conditions. Efficiency was consistent with other adaptive psychometric threshold estimation strategies, with 30–50 samples needed for consistently reliable estimates. While these results demonstrate that similar spatial working memory tasks can be effectively modeled as psychometric functions by any method, the advantage of the novel method is that it is scalable to accommodate much more complex models, such as those including additional executive functions. Further, it was designed with tremendous flexibility to incorporate informative theory, ancillary data, previous cohort performance, previous individual performance, and/or current individual performance for improved predictions. The result is a promising method for behavioral modeling that can be readily extended to capture complex individual task performance