Negative affect predicts subsequent cognitive change in multiple sclerosis

J Int Neuropsychol Soc. 2009 Jan;15(1):53-61. doi: 10.1017/S135561770809005X.

Abstract

Baseline predictors of cognitive change were explored in a sample of persons with multiple sclerosis (MS). Potential predictors included demographic features, baseline clinical characteristics, and psychological state. Participants were 38 individuals diagnosed with either relapsing remitting or secondary progressive MS who did not meet criteria for a current major depressive episode. Subjects were tested at baseline and approximately 1 year in an ongoing longitudinal study of cognition in MS. Participants completed neuropsychological tests sensitive to impairment in MS. They also completed self-report measures of depression, anxiety, fatigue, apathy, and positive and negative affect. Baseline measures of negative affect (e.g., depressed mood, state anxiety, and negative affective state) consistently predicted cognitive change over the course of the study. Higher baseline levels of negative affect were associated with greater relative declines in cognitive performance. This longitudinal relation occurred in the absence of a cross-sectional relation between negative affect and overall cognition. High baseline negative affect particularly predicted a relative decline in episodic memory for newly learned verbal and visuospatial information. The negative affect measures were unique in their predictive value among all the baseline measures assessed. (JINS, 2009, 15, 53-61.).

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Affect / physiology*
  • Cognition / physiology
  • Female
  • Humans
  • Longitudinal Studies
  • Male
  • Middle Aged
  • Mood Disorders / etiology
  • Mood Disorders / psychology
  • Multiple Sclerosis / complications*
  • Multiple Sclerosis / psychology*
  • Neuropsychological Tests
  • Predictive Value of Tests
  • Prognosis
  • Psychomotor Performance / physiology
  • Regression Analysis
  • Surveys and Questionnaires