heplots (version 1.3-8)

NeuroCog: Neurocognitive Measures in Psychiatric Groups

Description

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 et al. (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 schizoaffective groups.

Usage

data("NeuroCog")

Arguments

Format

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

Details

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.

References

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

Nuechterlein K.H., Green M.F., Kern R.S., Baade L.E., Barch D., Cohen J., Essock S., Fenton W.S., Frese F.J., Gold J.M., Goldberg T., Heaton R., Keefe R.S.E., Kraemer H., Mesholam-Gately R., Seidman L.J., Stover E., Weinberger D.R., Young A.S., Zalcman S., Marder S.R. (2008) The MATRICS Consensus Cognitive Battery, Part 1: Test selection, reliability, and validity. American Journal of Psychiatry, 165 (2), 203-213. https://pubmed.ncbi.nlm.nih.gov/18172019/.

Examples

# NOT RUN {
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))
}

# }