EEG {MANOVA.RM}

EEG Measurements in Patients with Alzheimer's Disease (long format)

Description

At the Department of Neurology, University Clinic of Salzburg, 160 patients were diagnosed with either AD, MCI, or SCC, based on neuropsychological diagnostics. This data set contains z-scores for brain rate and Hjorth complexity, each measured at frontal, temporal and central electrode positions and averaged across hemispheres. In addition to standardization, complexity values were multiplied by -1 in order to make them more easily comparable to brain rate values: For brain rate we know that the values decrease with age and pathology, while Hjorth complexity values are known to increase with age and pathology. The three between-subjects factors considered were sex (men vs. women), diagnosis (AD vs. MCI vs. SCC), and age (< 70 vs. >= 70 years). Additionally, the within-subjects factors region (frontal, temporal, central) and feature (brain rate, complexity) structure the response vector.

Usage

data(EEG)

Format

A data frame with 960 rows and 7 variables:

resp

EEG measurements

sex

sex of the patient

age

age of the patient, coded as 0 for less than 70 years and 1 for >= 70 years

diagnosis

neuropsychological diagnosis, AD for Alzheimer's Disease, MCI for mild cognitive impairment or SCC for subjective cognitive complaints without clinically significant deficits

region

brain region of the EEG measurements, one of "temporal", "frontal" and "central"

feature

feature of the EEG measurements, either "brainrate" or "complexity"

id

Subject id

Source

Bathke, A., Friedrich, S., Konietschke, F., Pauly, M., Staffen, W., Strobl, N. and Hoeller, Y. (2018). Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions. Multivariate Behavioral Research. Doi: 10.1080/00273171.2018.1446320.

Examples

if(requireNamespace("ggplot2")){ 
  library(ggplot2)
  ggplot(EEG, aes(x=sex, y=resp)) + geom_point(alpha=0.5) + facet_grid(region+feature~diagnosis) +
     stat_summary(fun.y = mean, fun.ymin = min, fun.ymax = max, colour = "red")
  }