Understanding Individual Subject Differences through Large Behavioral Datasets: Analytical and Statistical Considerations

A core feature of behavior analysis is the single-subject design, in which each subject serves as its own control. This approach is powerful for identifying manipulations that are causal to behavioral changes but often fails to account for individual differences, particularly when coupled with a small sample size. It is more common for other subfields of psychology to use larger-N approaches; however, these designs also often fail to account for the individual by focusing on aggregate-level data only. Moving forward, it is important to study individual differences to identify subgroups of the population that may respond differently to interventions and to improve the generalizability and reproducibility of behavioral science. We propose that large-N datasets should be used in behavior analysis to better understand individual subject variability. First, we describe how individual differences have been historically treated and then outline practical reasons to study individual subject variability. Then, we describe various methods for analyzing large-N datasets while accounting for the individual, including correlational analyses, machine learning, mixed-effects models, clustering, and simulation. We provide relevant examples of these techniques from published behavioral literature and from a publicly available dataset compiled from five different rat experiments, which illustrates both group-level effects and heterogeneity across individual subjects. We encourage other behavior analysts to make use of the substantial advancements in online data sharing to compile large-N datasets and use statistical approaches to explore individual differences.

Discriminative-stimulus effects of cannabidiol oil in Sprague-Dawley rats

Cannabidiol (CBD) is one of the major centrally active phytocannabinoid components of cannabis, and has been approved by the FDA only for the treatment of seizures associated with three rare disorders. It has also been touted as a potential treatment for anxiety in place of more traditional treatments like benzodiazepines. Although there is some evidence of anxiolytic effects of CBD, its suitability as a substitute for benzodiazepines is unknown. This experiment was designed to assess the extent to which CBD shares interoceptive discriminative-stimulus properties with the anxiolytic drug chlordiazepoxide (CDP), a benzodiazepine. In the present experiment, a range of doses (0-1569 mg/kg) of over-the-counter CBD oil was administered (i.g.) in male Sprague-Dawley rats trained to discriminate 5.6 mg/kg CDP from saline. Due to the long time-course effects of CBD, generalization tests were conducted at 90 and 120 min post-CBD administration. The two highest doses of CBD tested (1064 and 1569 mg/kg) were found to partially substitute for 5.6 mg/kg CDP, with mean percent responding on the CDP-associated lever reaching above 20% at time 2 (120 min post-CBD administration), suggesting that high doses of the over-the-counter CBD oils used in this experiment share interoceptive discriminative-stimulus properties to some degree with CDP. These results are novel in comparison to existing research into stimulus effects of CBD, in which substitution for benzodiazepines has not previously been observed.

Acute gut microbiome changes after traumatic brain injury are associated with chronic deficits in decision-making and impulsivity in male rats

The mechanisms underlying chronic psychiatric-like impairments after traumatic brain injury (TBI) are currently unknown. The goal of the present study was to assess the role of diet and the gut microbiome in psychiatric symptoms after TBI. Rats were randomly assigned to receive a high-fat diet (HFD) or calorie-matched low-fat diet (LFD). After 2 weeks of free access, rats began training on the rodent gambling task (RGT), a measure of risky decision-making and motor impulsivity. After training, rats received a bilateral frontal TBI or a sham procedure and continued postinjury testing for 10 weeks. Fecal samples were collected before injury and 3-, 30-, and 60 days postinjury to evaluate the gut microbiome. HFD altered the microbiome, but ultimately had low-magnitude effects on behavior and did not modify functional outcomes after TBI. Injury-induced functional deficits were far more robust; TBI substantially decreased optimal choice and increased suboptimal choice and motor impulsivity on the RGT. TBI also affected the microbiome, and a model comparison approach revealed that bacterial diversity measured 3 days postinjury was predictive of chronic psychiatric-like deficits on the RGT. A functional metagenomic analysis identified changes to dopamine and serotonin synthesis pathways as a potential candidate mechanism. Thus, the gut may be a potential acute treatment target for psychiatric symptoms after TBI, as well as a biomarker for injury and deficit severity. However, further research will be needed to confirm and extend these findings. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Large-N Rat Data Enables Phenotyping of Risky Decision-Making: A Retrospective Analysis of Brain Injury on the Rodent Gambling Task

Decision-making is substantially altered after brain injuries. Patients and rats with brain injury are more likely to make suboptimal, and sometimes risky choices. Such changes in decision-making may arise from alterations in how sensitive individuals are to outcomes. To assess this, we compiled and harmonized a large dataset from four studies of TBI, each of which evaluated behavior on the Rodent Gambling Task (RGT). We then determined whether the following were altered: (1) sensitivity to overall contingencies, (2) sensitivity to immediate outcomes, or (3) general choice phenotypes. Overall sensitivity was evaluated using the matching law, immediate sensitivity by looking at the probability of switching choices given a win or loss, and choice phenotypes by k-means clustering. We found significant reductions in sensitivity to the overall outcomes and a bias toward riskier alternatives in TBI rats. However, the substantial individual variability led to poor overall fits in matching analyses. We also found that TBI caused a significant reduction in the tendency to repeatedly choose a given option, but no difference in win- or loss-specific sensitivity. Finally, clustering revealed 5 distinct decision-making phenotypes and TBI reduced membership in the “optimal” type. The current findings support a hypothesis that TBI reduces sensitivity to contingencies. However, in the case of tasks such as the RGT, this is not a simple shift to indiscriminate or less discriminate responding. Rather, TBI rats are more likely to develop suboptimal preferences and frequently switch choices. Treatments will have to consider how this behavior might be corrected.