Monitoring of circadian rhythms of heart rate, locomotor activity, and temperature for diagnosis and evaluation of response to treatment in an animal model of depression.

Depressive disorders affect approximately 5% of the population in developed countries each year. Current antidepressant treatment usually requires several weeks to obtain response or remission and is only effective in about half of depressed patients. Objective diagnostic tools and detection of symptom relief by physiological biomarkers may assist in the clinical decision-making process regarding the selection, replacing, and augmenting of antidepressants. Furthermore, such biomarkers may enable early prediction of the appropriateness of a specific antidepressant for a particular patient. Here, we examined a new non-invasive method for objective diagnosis of depressive-like behavior and for the purpose of predicting antidepressant (paroxetine and desipramine) treatment effectiveness. This method employed a genetic rat model of depression and mathematical analysis of physiological parameters, of circadian rhythms of heart rate, locomotor activity, and temperature for diagnosis and evaluation of response to treatment in an animal model of depression. By utilizing this method, we were able to discern, in a rat model, between depressive and non-depressive individuals and to predict beneficial response to the antidepressants. Mathematical analysis of physiological parameters such as heart rate, locomotor activity, and temperature circadian rhythms can be used for objective diagnosis of depressive-like behavior and for early prediction of response to antidepressant treatment.