Relations of gray matter volume to dimensional measures of cognition and affect in mood disorders

Understanding the relationship between brain measurements and behavioral performance is an important step in developing approaches for early identification of any psychiatric difficulties and interventions to modify these challenges. Conventional methods to identify associations between regional brain volume and behavioral measures are not optimized, either in scale, scope, or specificity. To find meaningful associations between brain and behavior with greater sensitivity and precision, we applied data-driven factor analytic models to identify and extract individual differences in latent cognitive functions embedded across several computerized cognitive tasks. Furthermore, we simultaneously utilized a keyword-based neuroimaging meta-analytic tool (i.e., NeuroSynth), restricted atlas-parcel matching, and factor-analytic models to narrow down the scope of search and to further aggregate gray matter volume (GMV) data into empirical clusters. We recruited an early adult community cross-sectional sample (Total n = 177, age 18-30) that consisted of individuals with no history of any mood disorder (healthy controls, n = 44), those with remitted major depressive disorder (rMDD, n = 104), and those with a diagnosis of bipolar disorder currently in euthymic state (eBP, n = 29). Study participants underwent structural magnetic resonance imaging (MRI) scans and separately completed behavioral testing using computerized measures. Factor-analyzing five computerized tasks used to assess aspects of cognitive and affective processing resulted in seven latent dimensions: (a) Emotional Memory, (b) Interference Resolution, (c) Reward Sensitivity, (d) Complex Inhibitory Control, (e) Facial Emotion Sensitivity, (f) Sustained attention, and (g)Simple Impulsivity/Response Style. These seven dimensions were then labeled with specific keywords which were used to create neuroanatomical maps using NeuroSynth. These masks were further subdivided into GMV clusters. Using regression, we identified GMV clusters that were predictive of individual differences across each of the aforementioned seven cognitive dimensions. We demonstrate that a dimensional approach consistent with core principles of RDoC can be utilized to identify structural variability predictive of critical dimensions of human behavior.

Mechanisms of rumination change in adolescent depression (RuMeChange): study protocol for a randomised controlled trial of rumination-focused cognitive behavioural therapy to reduce ruminative habit and risk of depressive relapse in high-ruminating adolescents

BACKGROUND

Adolescent-onset depression often results in a chronic and recurrent course, and is associated with worse outcomes relative to adult-onset depression. Targeting habitual depressive rumination, a specific known risk factor for relapse, may improve clinical outcomes for adolescents who have experienced a depressive episode. Randomized controlled trials (RCTs) thus far have demonstrated that rumination-focused cognitive behavioral therapy (RFCBT) reduces depressive symptoms and relapse rates in patients with residual depression and adolescents and young adults with elevated rumination. This was also observed in a pilot RCT of adolescents at risk for depressive relapse. Rumination can be measured at the self-report, behavioral, and neural levels- using patterns of connectivity between the Default Mode Network (DMN) and Cognitive Control Network (CCN). Disrupted connectivity is a putative important mechanism for understanding reduced rumination via RFCBT. A feasibility trial in adolescents found that reductions in connectivity between DMN and CCN regions following RFCBT were correlated with change in rumination and depressive symptoms.

METHOD

This is a phase III two-arm, two-stage, RCT of depression prevention. The trial tests whether RFCBT reduces identified risk factors for depressive relapse (rumination, patterns of neural connectivity, and depressive symptoms) in adolescents with partially or fully remitted depression and elevated rumination. In the first stage, RFCBT is compared to treatment as usual within the community. In the second stage, the comparator condition is relaxation therapy. Primary outcomes will be (a) reductions in depressive rumination, assessed using the Rumination Response Scale, and (b) reductions in resting state functional magnetic resonance imaging connectivity of DMN (posterior cingulate cortex) to CCN (inferior frontal gyrus), at 16 weeks post-randomization. Secondary outcomes include change in symptoms of depression following treatment, recurrence of depression over 12 months post-intervention period, and whether engagement with therapy homework (as a dose measure) is related to changes in the primary outcomes.

DISCUSSION

RFCBT will be evaluated as a putative preventive therapy to reduce the risk of depressive relapse in adolescents, and influence the identified self-report, behavioral, and neural mechanisms of change. Understanding mechanisms that underlie change in rumination is necessary to improve and further disseminate preventive interventions.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03859297 , registered 01 March 2019.

Progesterone inhibits cytokine/TNF-α production by porcine CL macrophages via the genomic progesterone receptor

In pigs, luteolytic sensitivity to PGF-2α (=LS) is delayed until d 13 of the estrous cycle. While the control of LS is unknown, it is temporally associated with macrophage (MAC; which secretes tumor necrosis factor [TNF]-α) infiltration into the corpora lutea (CL), and previous studies have shown that TNF-α induces LS in porcine luteal cells (LCs) in culture. This study was designed to explore the control of LS by CL macrophage (CL MAC)/TNF-α by progesterone (P4), and to examine the hypothesis that P4 acting via the genomic P4 receptor (PGR) inhibits CL MAC TNF-α and thus plays a key role in regulating LS during the pig estrous cycle. In experiment 1, the effects of LCs on CL MAC cytokine/TNF-α mRNA expression in co-culture were examined (MID cycle; ~d 7-12; no LS); results showed that LC was inhibitory to cytokine/TNF-α. In experiment 2, the effects of P4 or R5020 (PGR-agonist) on CL MAC cytokine/TNF-α mRNA expression were examined (MID cycle; ~d 7-12; no LS); results showed that both P4 and R5020 dose-dependently inhibited TNF-α. In experiment 3, CL MACs were isolated from CL at MID (~d 7-12; no LS) and LATE (~d 13-18; + LS) cycle, and TNF-α/PGR mRNA measured. Results indicated that while TNF-α mRNA was 4.2-fold greater in CL MACs from LATE vs MID cycle, PGR mRNA was 4.5-fold greater in CL MACs from MID vs LATE cycle. These data support our hypothesis and suggest that progesterone, acting via PGR, plays a critical physiological role in the control of TNF-α production by CL MACs and LS during the pig estrous cycle.

Prostaglandin synthesis by the porcine corpus luteum: effect of tumor necrosis factor-α

The porcine corpus luteum (CL) displays delayed sensitivity to PGF-2α (luteolytic sensitivity, [LS]) until days 12 to 13 of cycle. The control of LS is unknown, but it is temporally associated with macrophage (which secrete tumor necrosis factor-α; TNF-α) infiltration into the CL. Other studies showed that TNF-α induces LS in vitro and that prostaglandins (PGs) may be involved in this mechanism. In experiment 1, PGF-2α and PGE secretion by luteal cells (LCs) was measured on days 4 to 14 of the estrous cycle, and the expression of PTGFS/AKR1B1 and PTGES/mPGES-1, determined by Western blot, before (day 7) vs after (day 13) the onset of LS. Results showed that the PGF-2α:PGE ratio increased significantly (P < 0.05) from day 4 to 13-14, and PTGFS/AKR1B1 and PTGES/mPGES-1 were significantly increased (P < 0.05) on day 13 (vs day 7). In experiment 2, LCs were collected from porcine CL at early (∼days 4-6) or mid (∼days 7-12) stages of the estrous cycle and cultured with 0, 0.1, 1, or 10 ng/mL TNF-α. Results showed that TNF-α significantly increased (P < 0.05) messenger RNA (mRNA) expression of cyclooxygenase (COX)-2 and mPGES-1 but not AKR1B1. TNF-α had no significant effects on AKR1B1 or mPGES protein abundance. TNF-α significantly increased (P < 0.05) PGE-2 but had no effect on PGF-2α secretion or on the PGF-2α:PGE2 ratio. In conclusion, although TNF-α increased COX2 and mPGES-1 mRNA, and PGE-2 secretion in vitro, it did not increase the PGF-2α:PGE2 ratio. Studies are currently directed toward exploring other pathways (eg, FP receptor signaling) by which TNF-α induces LS in the porcine CL.