Statistical analysis of dose-response curves in extracellular electrophysiological studies of single neurons

Authors

  • David K. Pitts,

    1. Laboratory of Neurophysiology, Center for Cell Biology, Sinai Research Institute, and The Cellular and Clinical Neurobiology Program, Department, of Psychiatry, Wayne State University, School of Medicine, Detroit, Michigan 48235
    Search for more papers by this author
  • Mark D. Kelland,

    1. Laboratory of Neurophysiology, Center for Cell Biology, Sinai Research Institute, and The Cellular and Clinical Neurobiology Program, Department, of Psychiatry, Wayne State University, School of Medicine, Detroit, Michigan 48235
    Search for more papers by this author
  • Roh-Yu Shen,

    1. Laboratory of Neurophysiology, Center for Cell Biology, Sinai Research Institute, and The Cellular and Clinical Neurobiology Program, Department, of Psychiatry, Wayne State University, School of Medicine, Detroit, Michigan 48235
    Search for more papers by this author
  • Arthur S. Freeman,

    1. Laboratory of Neurophysiology, Center for Cell Biology, Sinai Research Institute, and The Cellular and Clinical Neurobiology Program, Department, of Psychiatry, Wayne State University, School of Medicine, Detroit, Michigan 48235
    Search for more papers by this author
  • Louis A. Chiodo

    1. Laboratory of Neurophysiology, Center for Cell Biology, Sinai Research Institute, and The Cellular and Clinical Neurobiology Program, Department, of Psychiatry, Wayne State University, School of Medicine, Detroit, Michigan 48235
    Search for more papers by this author

Abstract

The application of polynomial regression and the analysis of covariance (ANCOVA) to dose-response (DR) data derived from extracellular electrophysiological studies of midbrain dopamine neurons and noradrenergic locus coeruleus neurons in vivo is demonstrated and discussed. Third-order polyomial regression was found to be a better method for estimating ED50 values than probit analysis or linear regression. ANCOVA provides a more powerful statistical method than ANOVA for detecting significant differences in ED50 values or DR curves when a confounding variable such as basal discharge rate is present. The methods of analysis presented herein should be useful in the analysis of other types of neurons in electrophysiol studies.

Ancillary