title: A novel model incorporating two variability sources for describing motor evoked potentials.
abstract: OBJECTIVE
  Motor evoked potentials (MEPs) play a pivotal role in transcranial magnetic stimulation (TMS), e.g., for determining the motor threshold and probing cortical excitability. Sampled across the range of stimulation strengths, MEPs outline an input-output (IO) curve, which is often used to characterize the corticospinal tract. More detailed understanding of the signal generation and variability of MEPs would provide insight into the underlying physiology and aid correct statistical treatment of MEP data.
  
  
  METHODS
  A novel regression model is tested using measured IO data of twelve subjects. The model splits MEP variability into two independent contributions, acting on both sides of a strong sigmoidal nonlinearity that represents neural recruitment. Traditional sigmoidal regression with a single variability source after the nonlinearity is used for comparison.
  
  
  RESULTS
  The distribution of MEP amplitudes varied across different stimulation strengths, violating statistical assumptions in traditional regression models. In contrast to the conventional regression model, the dual variability source model better described the IO characteristics including phenomena such as changing distribution spread and skewness along the IO curve.
  
  
  CONCLUSIONS
  MEP variability is best described by two sources that most likely separate variability in the initial excitation process from effects occurring later on. The new model enables more accurate and sensitive estimation of the IO curve characteristics, enhancing its power as a detection tool, and may apply to other brain stimulation modalities. Furthermore, it extracts new information from the IO data concerning the neural variability-information that has previously been treated as noise.
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA. Electronic address: stefan.goetz@duke.edu.
authors.forename: Stefan M
authors.initials: SM
authors.lastname: Goetz
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Department of Psychology and Neuroscience, Duke University, USA.
authors.forename: Bruce
authors.initials: B
authors.lastname: Luber
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Department of Psychology and Neuroscience, Duke University, USA.
authors.forename: Sarah H
authors.initials: SH
authors.lastname: Lisanby
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Department of Biomedical Engineering, Duke University, USA; Department of Electrical and Computer Engineering, Duke University, USA.
authors.forename: Angel V
authors.initials: AV
authors.lastname: Peterchev
mesh_terms: D000328:Adult; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009044:Motor Cortex / Q000166:cytology / Q000502:physiology; D011712:Pyramidal Tracts / Q000166:cytology / Q000502:physiology; D012044:Regression Analysis; D050781:Transcranial Magnetic Stimulation
keywords: Brain stimulation; Dual-variability source model; Input–output curve (IO); Motor evoked potentials (MEP); Neural recruitment; Response distribution; Transcranial magnetic stimulation (TMS); Trial-to-trial variability