Women with primary dysmenorrhea are vulnerable to develop a depressive disorder, which is a common form of psycho-disturbance. However, clinical findings are inconsistent across studies, and the evidence has not been previously synthesized. This study aims to investigate whether primary dysmenorrhea is associated with a higher risk of depression via a cumulative analysis. Four electronic databases were systematically searched for the eligible studies. The combined effect was assessed by analyzing the relative risk (RR) and standard mean differences (SMD) with a 95% confidence interval (CI). This cumulative analysis was registered on the PROSPERO (ID: CRD42020169601). Of 972 publications, a total of 10 studies involving 4,691 participants were included. Pooled results from six included studies showed that primary dysmenorrhea was associated with a significant depressive disorder (RR = 1.72, 95%CI: 1.44 to 2.0, P < 0.001; heterogeneity: I2 = 0%, P = 0.544). In addition, synthesis results from two studies provided the BDI scores suggested that dysmenorrhea had significantly higher scores when compared to non-dysmenorrhea (SMD = 0.47, 95% CI: 0.31–0.62, P < 0.001; heterogeneity: I2 = 0%, P = 0.518). However, in the two studies providing the PROMIS T-Score, the pooled result showed that there was no significant difference between women with dysmenorrhea and those without dysmenorrhea (P = 0.466). The overall quality of the evidence in our study was judged to MODERATE. The present study has confirmed the positive relationship between primary dysmenorrhea and depression. Social supports and medical help from pain management physicians or psychologists are important interventions for women with dysmenorrhea-suffering depressive disorder.
Background: The findings of many neuroimaging studies in patients with first-episode major depressive disorder (MDD), and even those of previous meta-analysis, are divergent. To quantitatively integrate these studies, we performed a meta-analysis of gray matter volumes using voxel-based morphometry (VBM).
Methods: We performed a comprehensive literature search for relevant studies and traced the references up to May 1, 2021 to select the VBM studies between first-episode MDD and healthy controls (HC). A quantitative meta-analysis of VBM studies on first-episode MDD was performed using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) method, which allows a familywise error rate (FWE) correction for multiple comparisons of the results. Meta-regression was used to explore the effects of demographics and clinical characteristics.
Results: Nineteen studies, with 22 datasets comprising 619 first-episode MDD and 707 HC, were included. The pooled and subgroup meta-analysis showed robust gray matter reductions in the left insula, the bilateral parahippocampal gyrus extending into the bilateral hippocampus, the right gyrus rectus extending into the right striatum, the right superior frontal gyrus (dorsolateral part), the left superior frontal gyrus (medial part) and the left superior parietal gyrus. Meta-regression analyses showed that higher HDRS scores were significantly more likely to present reduced gray matter volumes in the right amygdala, and the mean age of MDD patients in each study was negatively correlated with reduced gray matter in the left insula.
Conclusions: The present meta-analysis revealed that structural abnormalities in the fronto-striatal-limbic and fronto-parietal networks are essential characteristics in first-episode MDD patients, which may become a potential target for clinical intervention.
With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3–85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni_refacer, deepdefacer, mri_deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni_refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3–20 years) for afni_refacer and the oldest (44–85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files.
Background: Study-level meta-analyses have demonstrated the efficacy of cognitive–behavioural therapy for psychosis (CBTp). Limitations of conventional meta-analysis may be addressed using individual-participant-data (IPD). We aimed to determine a) whether results from IPD were consistent with study-level meta-analyses and b) whether demographic and clinical characteristics moderate treatment outcome.
Methods: We systematically searched PubMed, Embase, PsychInfo and CENTRAL. Authors of RCTs comparing CBTp with other psychological interventions were contacted to obtain original databases. Hierarchical mixed effects models were used to examine efficacy for psychotic symptoms. Patient characteristics were investigated as moderators of symptoms at post-treatment. Sensitivity analyses were conducted for risk of bias, treatment format and study characteristics.
Results: We included 14 of 23 eligible RCTs in IPD meta-analyses including 898 patients. Ten RCTs minimised risk of bias. There was no significant difference in efficacy between RCTs providing IPD and those not (p >0.05). CBTp was superior vs. other interventions for total psychotic symptoms and PANSS general symptoms. No demographic or clinical characteristics were robustly demonstrated as moderators of positive, negative, general or total psychotic symptoms at post-treatment. Sensitivity analyses demonstrated that number of sessions moderated the impact of treatment assignment (CBTp or other therapies) on total psychotic symptoms (p = 0.02).
Conclusions: IPD suggest that patient characteristics, including severity of psychotic symptoms, do not significantly influence treatment outcome in psychological interventions for psychosis while investing in sufficient dosage of CBTp is important. IPD provide roughly equivalent efficacy estimates to study-level data although significant benefit was not replicated for positive symptoms. We encourage authors to ensure IPD is accessible for future research.
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