Treatment of bipolar depression: a survey of Canadian psychiatrists.

OBJECTIVE: This study was conducted to examine how Canadian psychiatrists manage bipolar depression. METHOD: A questionnaire specific to the treatment of bipolar depression was mailed to 1639 active members of the Canadian Psychiatric Association. RESULTS: Seven hundred and sixty-six completed questionnaires were returned (46.7%). Most psychiatrists indicated that a combination of psychotherapy and somatic therapy was their preferred approach. For bipolar disorder, depressed, lithium carbonate and selective serotonin reuptake inhibitors (SSRIs) were the preferred treatment strategies. For substitution, tricyclic antidepressants (TCAs) were the favoured choice. Lithium carbonate was the preferred choice for augmentation and, addition. CONCLUSION: These findings indicate that a combination of psychotherapy and somatic therapy is the preferred treatment approach for bipolar depression. Lithium carbonate and SSRIs are the favoured somatic therapies.

Factor analysis of open-field behavior in the rat (Rattus norvegicus): application of the three-way PARAFAC model to a longitudinal data set.

We examined the multivariate nature of open-field behavior in adult male rats (n = 26) by subjecting a longitudinal data set, obtained during 4 open-field test sessions (4 min in duration and spaced 48 h apart), to a three-way PARAFAC analysis. Unlike conventional two-way factor analytical models, the PARAFAC procedure allows for the direct factor analysis of 3-dimensional arrays, which then provided a unique factor solution to the longitudinal data set. The PARAFAC analysis extracted 2 factors: i) emotional reactivity and ii) exploratory behavior. These two factors changed in temporal prominence, with animals showing greater emotional reactivity on the first test session, and greater levels of exploration on the third and fourth test sessions. These results are in general agreement with previous findings which used more conventional factor analytic approaches. These findings indicate that multivariate procedures, such as the PARAFAC analysis, can be helpful in the quantitative characterization of behavioral phenomena in a more 'realistic' manner.

Some behavioral factors related to the effects of cold-restraint stress in rats: a factor analytic-multiple regression approach.

Recently, some individual differences in rats have been shown to be related to stress-induced physiological responses. As yet there has been no attempt to incorporate measurement techniques from the psychometric field to this line of research. The present study was conducted to examine the utility of applying such methods to animal research. Physiological responses to cold-restraint stress in 26 male rats were investigated using a factor analytic-multiple regression procedure. Nineteen behavioral and physiological measures obtained during open-field testing, motor activity monitoring, and passive avoidance learning were subjected to a principle components factor analysis. Five factors were extracted which reflected exploratory behavior, general activity level, metabolic rate, behavioral reactivity, and autonomic reactivity. The obtained factor scores were used to predict physiological responses to four hours of supine cold-restraint using a step-wise multiple regression procedure. General activity level was the best predictor of adrenal weight and temperature loss. Autonomic reactivity was the best predictor of ulcer incidence and severity. Applications of these statistical procedures are discussed.

The measurement and integration of behavioral variables: aggregation and complexity as important issues.

The measurement of motor activity in animals is central to the description of animal behavior in general. A wide variety of measurement procedures have been used in the past and various researchers have suggested that multiple measures or "behavioral profiles" be used to describe the effects of various types of experimental manipulations. However, many of the measures used have been found to be unreliable. The principle of aggregation, which states that the sum of a set of multiple measurements is a more stable and representative estimator than any single measurement, allows for construction of variables which are more reliable and have greater generalizability. Examples are provided which show that the application of aggregation can substantially increase correlations within variables over time as well as between variables. Application of factor analytic procedures to multivariate motor activity data is suggested to be a useful method of reducing many variables into fewer, more complex variables, which have greater phenomenon realism. Examples of the application of such factor analytic approaches are provided, both for the traditional two-way factor analytic methods as well as a more recent procedure (PARAFAC model) developed for analysis of three-way longitudinal data sets. It is suggested that multivariate analytic procedures are appropriate for data reduction and description in the area of behavioral neuroscience.