Cognitive styles in individuals with bipolar disorders

Area of Science:

• Psychiatry
• Clinical Psychology
• Cognitive Science

Background:

• Existing research on emotional processing and cognitive style in bipolar disorders often lacks sufficient sample size or clinical representativeness.
• There is a need for larger studies using clinically relevant samples to better understand bipolar disorder.
• This study addresses the gap by investigating cognitive styles in a clinically representative sample of bipolar disorder patients.

Purpose of the Study:

• To compare cognitive styles, including self-esteem and dysfunctional attitudes, between unipolar and bipolar disorders.
• To examine how cognitive styles vary across different phases of bipolar disorder (remitted, depressed, hypomanic).
• To identify predictors of one-year outcome, specifically relapse, in bipolar disorder.

Main Methods:

• A comparative study involving individuals with unipolar disorder (N=16) and bipolar disorder (N=77).
• Assessment of self-esteem, dysfunctional attitudes, and personality style across different phases of bipolar disorder (remitted, depressed, hypomanic).
• One-year outcome, including relapse, was monitored in a subset of bipolar subjects (N=36).

Main Results:

• Bipolar disorder patients exhibited significantly lower mean levels of negative self-esteem compared to unipolar disorder patients.
• In bipolar disorder, dysfunctional beliefs were highest in the depressed phase, intermediate in hypomania, and lowest in remission.
• Hypomanic states were associated with the highest levels of both negative and positive self-esteem.
• Negative self-esteem emerged as a significant predictor of relapse within one year in bipolar disorder patients.

Conclusions:

• Cognitive styles in unipolar and bipolar disorders show notable similarities.
• Hypomania in bipolar disorder is characterized by a cognitive style that falls between remission and depression, not as an extreme opposite.
• These findings have implications for psychological and neural network models of mood disorders.