All tests are imperfect: Accounting for false positives and false negatives using Bayesian statistics

Use of multi-perturbation Shapley analysis in lesion studies of functional networks: The case of upper limb paresis

Understanding the impact of variation in lesion topography on the expression of functional impairments following stroke is important, as it may pave the way to modeling structure-function relations in statistical terms while pointing to constraints for adaptive remapping and functional recovery. Multi-perturbation Shapley-value analysis (MSA) is a relatively novel game-theoretical approach for multivariate lesion-symptom mapping. In this methodological paper, we provide a comprehensive explanation of MSA. We use synthetic data to assess the method's accuracy and perform parameter optimization. We then demonstrate its application using a cohort of 107 first-event subacute stroke patients, assessed for upper limb (UL) motor impairment (Fugl-Meyer Assessment scale). Under the conditions tested, MSA could correctly detect simulated ground-truth lesion-symptom relationships with a sensitivity of 75% and specificity of ~90%. For real behavioral data, MSA disclosed a strong hemispheric effect in the relative contribution of specific regions-of-interest (ROIs): poststroke UL motor function was mostly contributed by damage to ROIs associated with movement planning (supplementary motor cortex and superior frontal gyrus) following left-hemispheric damage (LHD) and by ROIs associated with movement execution (primary motor and somatosensory cortices and the ventral brainstem) following right-hemispheric damage (RHD). Residual UL motor ability following LHD was found to depend on a wider array of brain structures compared to the residual motor ability of RHD patients. The results demonstrate that MSA can provide a unique insight into the relative importance of different hubs in neural networks, which is difficult to obtain using standard univariate methods.

OXTR polymorphisms and parental bonding modulate alexithymia: The main effects and interaction

Alexithymia is a subclinical personality disorder characterized by difficulties in identifying and expressing one's own emotion. Individual differences in alexithymia are influenced by both genetic and environmental factors. However, the interplay between these factors and their influences on alexithymia are unclear. Considering that oxytocin plays important roles in emotion processing and that parental bonding influences the development of alexithymia, we explored the associations between OXTR polymorphisms and alexithymia and examined whether the potential associations are moderated by parental bonding. To this end, we genotyped the OXTR polymorphisms (rs53576 and rs1042778) and scored alexithymia and parental bonding with the Toronto Alexithymia Scale and the Parental Bonding Instrument. Results indicated that: (1) the characteristics of alexithymia were negatively associated with parental care and positively with parental overprotection; (2) the OXTR rs53576 was significantly associated with the characteristics of alexithymia, such that the AA genotype was associated with fewer difficulties in identifying feelings and describing feelings than the AG/GG genotypes; and (3) the OXTR rs1042778 interacted with parental care in alexithymia with the vantage sensitivity model: the GG genotype was related to less severity of alexithymia than the AG/GG genotypes only in individuals with higher parental care. Overall, these findings suggest that the OXTR is related to alexithymia and that the quality of parental care influences the relationship. However, considering that the evidence from this study is weak, more research is needed to understand the roles of OXTR in alexithymia.

Sample Adequacy Control (SAC) Lowers False Negatives and Increases the Quality of Screening: Introduction of "Non-Competitive" SAC for qPCR Assays

Sample Adequacy Control (SAC) has critical analytical, clinical and epidemiological value that increases confidence in a negative test result. The SAC is an integral qPCR assay control, which ensures that all pre-analytical and analytical steps are adequate for accurate testing and reporting. As such, a negative SAC with a negative result on pathogen screen specifies that the result should be reported as inconclusive instead of negative. Despite this, many regulatory approved tests do not incorporate SAC into their assay design. Herein, we emphasize the universal value of SAC and offer for the first time, a simple technical strategy to introduce non-competitive SAC which does not interfere with the limit of detection for the screened pathogen. Integration of SAC can provide key benefits towards identifying, isolating, quarantining and contact tracing infected individuals and in turn can improve worldwide efforts in infection control.

Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing

From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study.