NeuroCog: Neurocognitive Measures in Psychiatric Groups
In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

Description Usage Format Details Source References Examples

The primary purpose of the study (Hartman, 2016, Heinrichs etal. (2015)) was to evaluate patterns and levels of performance on neurocognitive measures among individuals with schizophrenia and schizoaffective disorder using a well-validated, comprehensive neurocognitive battery specifically designed for individuals with psychosis (Heinrichs etal. (2008))

The main interest was in determining how well these measures distinguished among all groups and whether there were variables that distinguished between the schizophrenia and schizoafective groups.

1	data("NeuroCog")

A data frame with 242 observations on the following 10 variables.

Dx: Diagnostic group, a factor with levels Schizophrenia Schizoaffective Control
Speed: Speed of processing domain T score, a numeric vector
Attention: Attention/Vigilance Domain T score, a numeric vector
Memory: Working memory a numeric vector
Verbal: Verbal Learning Domain T score, a numeric vector
Visual: Visual Learning Domain T score, a numeric vector
ProbSolv: Reasoning/Problem Solving Domain T score, a numeric vector
SocialCog: Social Cognition Domain T score, a numeric vector
Age: Subject age, a numeric vector
Sex: Subject gender, a factor with levels Female Male

Neurocognitive function was assessed using the MATRICS Consensus Cognitive Battery (MCCB; Nuechterlein et al., 2008). The MCCB consists of 10 individually administered tests that measure cognitive performance in seven domains: speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning and problem solving, and social cognition.

The clinical sample comprised 116 male and female patients who met the following criteria: 1) a diagnosis of schizophrenia (n = 70) or schizoaffective disorder (n = 46) confirmed by the Structured Clinical Interview for DSM-IV-TR Axis I Disorders; 2) outpatient status; 3) a history free of developmental or learning disability; 4) age 18-65; 5) a history free of neurological or endocrine disorder; and 6) no concurrent DSM-IV-TR diagnosis of substance use disorder.

Non-psychiatric control participants (n = 146) were screened for medical and psychiatric illness and history of substance abuse. Patients were recruited from three outpatient clinics in Hamilton, Ontario, Canada. Control participants were recruited through local newspaper and online classified advertisements for paid research participation.

Hartman, L. I. (2016). Schizophrenia and Schizoaffective Disorder: One Condition or Two? Unpublished PhD dissertation, York University.

Heinrichs, R.W., Pinnock, F., Muharib, E., Hartman, L.I., Goldberg, J.O., & McDermid Vaz, S. (2015). Neurocognitive normality in schizophrenia revisited. Schizophrenia Research: Cognition, 2 (4), 227-232. doi: 10.1016/j.scog.2015.09.001

Heinrichs, R. W., Ammari, N., McDermid Vaz, S. & Miles, A. (2008). Are schizophrenia and schizoaffective disorder neuropsychologically distinguishable? Schizophrenia Research, 99, 149-154.

Nuechterlein KH, Green MF, Kern RS, Baade LE, Barch D, Cohen J, Essock S, Fenton WS, Frese FJ, Gold JM, Goldberg T, Heaton R, Keefe RSE, Kraemer H, Mesholam-Gately R, Seidman LJ, Stover E, Weinberger DR, Young AS, Zalcman S, Marder SR. (2008) The MATRICS Consensus Cognitive Battery, Part 1: Test selection, reliability, and validity. American Journal of Psychiatry, 165 (2), 203-213. http://www.ncbi.nlm.nih.gov/pubmed/18172019.

data(NeuroCog)
NC.mlm <- lm(cbind( Speed, Attention, Memory, Verbal, Visual, ProbSolv) ~ Dx,
               data=NeuroCog)
Anova(NC.mlm)

# test contrasts
contrasts(NeuroCog$Dx)
print(linearHypothesis(NC.mlm, "Dx1"), SSP=FALSE)
print(linearHypothesis(NC.mlm, "Dx2"), SSP=FALSE)

# pairwise HE plots
pairs(NC.mlm, var.cex=1.5)

# canonical discriminant analysis
if (require(candisc)) {
  NC.can <- candisc(NC.mlm)
  NC.can
  
  plot(NC.can, ellipse=TRUE, rev.axes=c(TRUE,FALSE), pch=c(7,9,10))
}