Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determining effective personalized treatments.

To identify a data-driven pattern of clinical improvement in MDD and to quantify neural-to-symptom relationships according to antidepressant treatment, we performed a secondary analysis of the publicly available dataset EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care). In EMBARC, participants with MDD were treated either by sertraline or placebo for 8 weeks (Stage 1), and then switched to bupropion according to clinical response (Stage 2). We computed a univariate measure of clinical improvement through a principal component (PC) analysis on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas, and manic-like symptoms. We then investigated how initial clinical and neural factors predicted this measure during Stage 1 by running a linear model for each brain parcel’s resting-state global brain connectivity (GBC) with individual improvement scores during Stage 1.

The first PC (PC1) was similar across treatment groups at stages 1 and 2, suggesting a shared pattern of symptom improvement. PC1 patients’ scores significantly differed according to treatment, whereas no difference in response was evidenced between groups with the Clinical Global Impressions Scale. Baseline GBC correlated with Stage 1 PC1 scores in the sertraline but not in the placebo group.

Using data-driven reduction of symptom scales, we identified a common profile of symptom improvement with distinct intensity between sertraline and placebo.

Mapping from data-driven symptom improvement onto neural circuits revealed treatment-responsive neural profiles that may aid in optimal patient selection for future trials.

Subthreshold hypomania during a major depressive episode challenges the bipolar-unipolar dichotomy. In our study we employed a cross-diagnostic cluster analysis - to identify distinct subgroups within a cohort of depressed patients.

A k-means cluster analysis— based on the domain scores of the Mood Spectrum Self-Report (MOODS-SR) questionnaire—was performed on a data set of 300 adults with either bipolar or unipolar depression. After identifying groups, between-clusters comparisons were conducted on MOODS-SR domains and factors and on a set of sociodemographic, clinical and psychometric variables.

Three clusters were identified: one with intermediate depressive and poor manic symptomatology (Mild), one with severe depressive and poor manic symptomatology (Moderate), and a third one with severe depressive and intermediate manic symptomatology (Mixed). Across the clusters, bipolar patients were significantly less represented in the Mild one, while the DSM-5 “Mixed features” specifier did not differentiate the groups. When compared to the other patients, those of Mixed cluster exhibited a stronger association with most of the illness-severity, quality of life, and outcomes measures considered. After performing pairwise comparisons significant differences between “Mixed” and “Moderate” clusters were restricted to: current and disease-onset age, psychotic ideation, suicidal attempts, hospitalization numbers, impulsivity levels and comorbidity for Cluster B personality disorder.

In the present study, a clustering approach based on a spectrum exploration of mood symptomatology led to the identification of three transdiagnostic groups of patients. Consistent with our hypothesis, the magnitude of subthreshold (hypo)manic symptoms was related to a greater clinical severity, regardless of the main categorical diagnosis.

To explore relationships among post-traumatic stress disorder (PTSD), depressive spectrum symptoms, and intrusiveness in subjects who survived the crash of a train derailed carrying liquefied petroleum gas and exploded causing a fire.

A sample of 111 subjects was enrolled in Viareggio, Italy. AMOS version 21 (IBM Corp, 2012) was utilized for a structural equation model-path analysis to model the direct and indirect links between the exposure to the traumatic event, the occurrence of depressive symptoms, and intrusiveness. Subjects were administered with the SCID-IV (Structured Clinical Interview for DSM-IV), the Questionnaire for Mood Spectrum (MOODS-SR)-Last Month version, the Trauma and Loss Spectrum Questionnaire (TALS-SR), and the Impact of Event Scale-Revised version (IES-R).

Sixty-six (66/111; 59.4%) subjects met SCID-IV criteria for PTSD. Indices of goodness of fit were as followed: χ2/df = 0.2 P = .6; comparative fit index = 1 and root mean square error of approximation = 0.0001. A significant path coefficient for direct effect of potential traumatic events on depressive symptoms (β = 0.25; P < .04) and from depressive symptoms to intrusiveness (β = 0.34; P < .003) was found. An indirect effect was also observed: standardized value of potential traumatic events on intrusiveness was 0.86. The mediating factor of this indirect effect path was represented by depressive symptoms. Potential traumatic events explained 6.2% of the variance of depressive symptoms; 11.8% of the variance of intrusiveness was accounted for traumatic event and depressive symptoms.

Path analysis led us to speculate that depression symptoms might have mediated the relationship between the exposure to potential traumatic events and intrusiveness for the onset of PTSD.

To investigate if sleep disturbances may affect treatment outcomes of patients with panic disorder (PD).

Eighty-five PD outpatients with no Axis I comorbidity for mood disorders completed a baseline assessment (T1) and were evaluated after 3 (T2), 6 (T3) and 12 months (T4), with the Panic Disorder Severity Scale (PDSS) total score as outcome measure during a 12-month naturalistic follow-up. Patients were assessed with the Mood Spectrum Self-Report (MOODS-SR, Lifetime Version), and the PDSS.

Forty-three patients (50.5%) met criteria for remission (PDSS<5) and 42 (49.5%) for no remission. In a logistic regression model with remission as the dependent variable, MOODS-SR sleep disturbances was the only determinant for a lower likelihood of PD remission. The items accounting for this result were the following: Repeated difficulty falling asleep (chi-square = 4.4; df = 1; p = 0.036), and Repeatedly waking up in the middle of the night (chi-square = 5.2; df = 1; p = 0.022).

Lifetime sleep disturbances would represent a cue of mood spectrum (in absence of overt affective comorbidity) that may impair remission in PD.

High levels of comorbidity between separation anxiety disorder (SEPAD) and panic disorder (PD) have been found in clinical settings. In addition, there is some evidence for a relationship involving bipolar disorder (BD) and combined PD and SEPAD. We aim to investigate the prevalence and correlates of SEPAD among patients with PD and whether the presence of SEPAD is associated with frank diagnoses of mood disorders or with mood spectrum symptoms.

Adult outpatients (235) with PD were assessed by the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), the Panic Disorder Severity Scale (PDSS), the Structured Clinical Interview for Separation Anxiety Symptoms (SCI-SAS), and the Mood Spectrum Self-Report Instrument (MOODS-SR, lifetime version).

Of ther 235 subjects, 125 (53.2%) were categorized as having SEPAD and 110 (46.8%) as not. Groups did not differ regarding onset of PD, lifetime prevalence of obsessive compulsive disorder (OCD), social phobia, simple phobia, BD I and II, or major depressive disorder (MDD). SEPAD subjects were more likely to be female and younger; they showed higher rates of childhood SEPAD, higher PDSS scores, and higher MOODS-SR total and manic component scores than subjects without SEPAD.

SEPAD is highly prevalent among PD subjects. Patients with both PD and SEPAD show higher lifetime mood spectrum symptoms than patients with PD alone. Specifically, SEPAD is correlated with the manic/hypomanic spectrum component.

Our data confirm the high prevalence of SEPAD in clinical settings. Moreover, our findings corroborate a relationship between mood disorders and SEPAD, highlighting a relationship between lifetime mood spectrum symptoms and SEPAD.