Differentiation of women with premenstrual dysphoric disorder, recurrent brief depression, and healthy controls by daily mood rating dynamics

Abstract

Enhanced statistical characterization of mood-rating data holds the potential to more precisely classify and sub-classify recurrent mood disorders like premenstrual dysphoric disorder (PMDD) and recurrent brief depressive disorder (RBD). We applied several complementary statistical methods to differentiate mood rating dynamics among women with PMDD, RBD, and normal controls (NC). We compared three subgroups of women: NC (n=8); PMDD (n=15); and RBD (n=9) on the basis of daily self-ratings of sadness, study lengths between 50 and 120 days. We analyzed mean levels; overall variability, SD; sequential irregularity, approximate entropy (ApEn); and a quantification of the extent of brief and staccato dynamics, denoted 'Spikiness'. For each of SD, irregularity (ApEn), and Spikiness, we showed highly significant subgroup differences, ANOVA0.001 for each statistic; additionally, many paired subgroup comparisons showed highly significant differences. In contrast, mean levels were indistinct among the subgroups. For SD, normal controls had much smaller levels than the other subgroups, with RBD intermediate. ApEn showed PMDD to be significantly more regular than the other subgroups. Spikiness showed NC and RBD data sets to be much more staccato than their PMDD counterparts, and appears to suitably characterize the defining feature of RBD dynamics. Compound criteria based on these statistical measures discriminated diagnostic subgroups with high sensitivity and specificity. Taken together, the statistical suite provides well-defined specifications of each subgroup. This can facilitate accurate diagnosis, and augment the prediction and evaluation of response to treatment. The statistical methodologies have broad and direct applicability to behavioral studies for many psychiatric disorders, and indeed to similar analyses of associated biological signals across multiple axes.