Meta-analysis of biological variables' impact on spinal motoneuron electrophysiology data.

Experimental, methodological, and biological variables must be accounted for statistically to maximize accuracy and comparability of published neuroscience data. However, accounting for all variables is nigh impossible. Thus we aimed to identify particularly influential variables within published neurological data, from cat, rat, and mouse studies, via a robust statistical process. Our goal was to develop tools to improve rigor in the collection and analysis of data. We strictly constrained experimental and methodological variables and then assessed four key biological variables within motoneuron research: species, age, sex, and cell type. We quantified intraexperimental and interexperimental variances in 11 commonly reported electrophysiological properties of spinal motoneurons. We first assessed variances without accounting for biological variables and then reassessed them while accounting for all four variables. We next assessed variances with all possible combinations of these four variables. We concluded that some motoneuron properties have low intraexperimental, but high interexperimental, variance; that individual motoneuron properties are impacted differently by biological variables; and that some unexplained variances still remain. We report here the optimal combinations of biological variables to reduce interexperimental variance for all 11 parameters. We also rank each parameter by intra- and interexperimental consistency. We expect these results to assist with design of experimental and analytical methods, and to support accuracy in simulations. Furthermore, although demonstrated on spinal motoneuron electrophysiology literature, our approach is applicable to biological data from all fields of neuroscience. This approach represents an important aid to experimental design, comparison of reported data, and reduction of unexplained variance in neuroscience data.