Background: The delayed matching-to-sample test assesses short-term memory performance in a variety of species, including humans allowing for cross species translation. We tested a novel statistical model to evaluate the effect of prenatal and childhood lead exposure on children's rate of forgetting as an illustration. Methods: We analyzed data from 550 children participating in the PROGRESS study, a longitudinal birth cohort in Mexico. Children performed a delayed matching-to-sample tests at 6-8 years of age. Lighted shapes appear followed by a variable time delay from a few seconds up to 3 minutes, then the comparison stimuli appear as 3 choices, one of which is correct. Correct choices are rewarded with a coin. Blood lead was measured at 2nd trimester, and at 4-6 years of age. We used a nonlinear modified power function to predict the rates of forgetting at each time delay, and constructed separate models for prenatal and childhood blood lead. Results: Prenatal and childhood blood lead predicted increased rates of forgetting; indicating a faster forgetting as blood lead levels increased (prenatal: beta=-0.02; 95%CI: -0.05, -0.01, and childhood: beta=-0.05; 95%CI: -0.09, -0.01). In the prenatal lead model, higher maternal IQ and child's age were significantly associated with a slower rate of forgetting (Maternal IQ: beta= 0.01; 95%CI: 0.01, 0.02, and child's age: beta= 0.01; 95%CI: 0.01, 0.03). Similar results were found with childhood lead except children's age was not significant. Plots based on the model estimates showed that either high prenatal or high childhood blood lead (90th percentile) with low maternal IQ (10th percentile) had the greatest effect on increased rate of forgetting. Conclusion: We validated our novel power functions statistical for rates of forgetting using lead exposure, a paradigm neurotoxin. Future work will evaluate other environmental exposures on children's forgetting rates including mixed exposures.

A one and a half day workshop on behavioral testing was conducted in order to discuss experimental procedures and practices that may help enhance the utility of behavioral data as a reliable index of neurotoxicity and in the safety evaluation of chemical substances. The workshop was open to participation by all sectors of the neuroscience community including academia, government, testing laboratories, and industry. The level of confidence with which changes in behavior can reliably signal adverse effects on the nervous system depends, in part, on the scientific quality of the data generated. With an emphasis on education and problem solving, the workshop focused on the practical aspects and scientific rationale underlying valid and high quality testing. In behavioral testing, there are numerous experimental factors that may impact on the quality of data. These include such elements as experimental design, selection of test methods, the care and precision in the conduct of behavioral testing, procedures to minimize bias and potential confounds, appropriateness of statistical analyses, and data interpretation. In plenary session investigators experienced in behavioral testing discussed the significance of these various experimental factors to data quality, outlined problematic issues, and presented a synopsis of approaches for addressing each of the factors as outlined in a draft of a primer developed by the Interagency Committee on Neurotoxicology (ICON). During the remainder of the workshop, open discussions in small breakout groups were used to address the problematic issues identified by the plenary speakers and explore alternative approaches for dealing with them. Finally, all workshop participants were reconvened in plenary session for summation of breakout group discussions and final recommendations. Information from the workshop was used to form the basis of this manuscript and will be used to help finalize a behavioral test methods primer being drafted by the ICON. The overall conclusions from the workshop were that consensus can be reached on the fundamentals of behavioral assessment, and that aspects of behavioral assessment including experimental design, test method selection, training, validation, control of confounds, data variability, data analysis, and data interpretation need to be carefully considered in the planning and conduct of behavioral safety assessments.