Depression has been linked to disruptions in resting-state networks (RSNs). However, inconsistent findings on RSN disruptions, with variations in reported connectivity within and between RSNs, complicate the understanding of the neurobiological mechanisms underlying depression.

A systematic literature search of PubMed and Web of Science identified studies that employed resting-state functional magnetic resonance imaging (fMRI) to explore RSN changes in depression. Studies using seed-based functional connectivity analysis or independent component analysis were included, and coordinate-based meta-analyses were performed to evaluate alterations in RSN connectivity both within and between networks.

A total of 58 studies were included, comprising 2321 patients with depression and 2197 healthy controls. The meta-analysis revealed significant alterations in RSN connectivity, both within and between networks, in patients with depression compared with healthy controls. Specifically, within-network changes included both increased and decreased connectivity in the default mode network (DMN) and increased connectivity in the frontoparietal network (FPN). Between-network findings showed increased DMN–FPN and limbic network (LN)–DMN connectivity, decreased DMN–somatomotor network and LN–FPN connectivity, and varied ventral attention network (VAN)–dorsal attentional network (DAN) connectivity. Additionally, a positive correlation was found between illness duration and increased connectivity between the VAN and DAN.

These findings not only provide a comprehensive characterization of RSN disruptions in depression but also enhance our understanding of the neurobiological mechanisms underlying depression.

Medication nonadherence is a public health concern and can impact clinical trial data quality. Traditional compliance collection (pill counts, diaries) can be unreliable in central nervous system trials. As such, strategies such as adherence technologies may play a key role in trial outcomes. AiCure, a computer vision-assisted dosing mobile application (app), collects dosing data and connects patients to sites for dosing support. Phone-based computer vision algorithms confirm dosing and transfer videos for artificial intelligence and human review. Boehringer Ingelheim is partnering with AiCure on pilot trials using AiCure adherence data to improve patient retention and clinical trial data quality. Here we report initial findings.

This pilot used data from two Phase II trials on the efficacy and safety of BI 409306 in people with schizophrenia (NCT03351244) or Attenuated Psychosis Syndrome (NCT03230097). The AiCure mobile app alerted participants to dosing protocols. The dose event was visually confirmed, providing sites a real-time view of adherence and allowing for targeted outreach and intervention. Adherence data from the first 2 weeks generated quantitative, machine-learning models to predict the individual adherence over the trial. Predictive modeling explored different monitoring periods (7-, 10-, and 14-day) and adherence cutoff points (0.8, 0.7, 0.6).

Initial AiCure assessment identified 43% of participants in NCT03351244 as ≤80% compliant (definition of compliance >80% compliant). Variance in adherence rates between electronic case report forms (eCRF; 78%) and AiCure (26%) data was also observed in the highly compliant/adherent group in NCT03230097. Using the first 2 weeks of adherence data (both studies combined), a participant’s adherence predicted their average adherence for the remainder of the trial. Observation of a participant’s adherence for the latest 4 weeks predicted the probability of premature dropout from the trial. There were further correlations of lower predicted adherence with actual disposition-based dropouts.

The early adherence predictive model (0.6 adherence cutoff) identified 22%, 20%, and 19% of patients for trial NCT03351244 (total n=235) as high-risk patients (low-adherence prediction) across 7-, 10-, and 14-day monitoring periods, respectively. Of those high-risk patients, 81%, 90%, and 96%, respectively, were truly nonadherent based on actual adherence data. The 14-day monitoring period model provided the lowest false omission rate, indicative of a better performing model.

AiCure data provided insights into patient behavior and adherence patterns which would not be available via CRF. Predictive models developed with AiCure adherence data can identify and predict future poor adherers. This creates opportunities to plan interventions and mitigation strategies to improve patient adherence during trials, thereby providing test drugs the best opportunity at proving efficacy.

Boehringer Ingelheim International GmbH (NCT03351244/1289-0049 and NCT03230097/1289-0032)

Depressive symptoms, functional impairment, and decreased quality of life (QOL) are three important domains of major depressive disorder (MDD). However, the possible causal relationship between these factors has yet to be elucidated. Moreover, it is not known whether certain symptoms of MDD are more impairing than others. The network approach is a promising solution to these shortfalls.

The baseline data of a multicenter prospective project conducted in 11 governances of China were analyzed. In total, 1385 patients with MDD were included. Depressive symptoms, functioning disability, and QOL were evaluated by the 17-item Hamilton Depression Rating Scale (HAMD-17), the Sheehan Disability Scale (SDS), and the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF). The network was estimated through the graphical Least Absolute Shrinkage and Selection Operator (LASSO) technique in combination with the directed acyclic graph.

Three centrality metrics of the graphical LASSO showed that social life dysfunction, QOL, and late insomnia exhibited the highest strength centrality. The network accuracy and stability were estimated to be robust and stable. The Bayesian network indicated that some depressive symptoms were directly associated with QOL, while other depressive symptoms showed an indirect association with QOL mediated by impaired function. Depressed mood was positioned at the highest level in the model and predicted the activation of functional impairment and anxiety.

Functional disability mediated the relationship between depressive symptoms and QOL. Family functionality and suicidal symptoms were directly related to QOL. Depressed mood played the predominant role in activating both anxiety symptom and functional impairment.

Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).

CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.

The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.

These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

The relationship of a diet low in fibre with mortality has not been evaluated. This study aims to assess the burden of non-communicable chronic diseases (NCD) attributable to a diet low in fibre globally from 1990 to 2019.

All data were from the Global Burden of Disease (GBD) Study 2019, in which the mortality, disability-adjusted life-years (DALY) and years lived with disability (YLD) were estimated with Bayesian geospatial regression using data at global, regional and country level acquired from an extensively systematic review.

All data sourced from the GBD Study 2019.

All age groups for both sexes.

The age-standardised mortality rates (ASMR) declined in most GBD regions; however, in Southern sub-Saharan Africa, the ASMR increased from 4·07 (95 % uncertainty interval (UI) (2·08, 6·34)) to 4·60 (95 % UI (2·59, 6·90)), and in Central sub-Saharan Africa, the ASMR increased from 7·46 (95 % UI (3·64, 11·90)) to 9·34 (95 % UI (4·69, 15·25)). Uptrends were observed in the age-standardised YLD rates attributable to a diet low in fibre in a number of GBD regions. The burden caused by diabetes mellitus increased in Central Asia, Southern sub-Saharan Africa and Eastern Europe.

The burdens of disease attributable to a diet low in fibre in Southern sub-Saharan Africa and Central sub-Saharan Africa and the age-standardised YLD rates in a number of GBD regions increased from 1990 to 2019. Therefore, greater efforts are needed to reduce the disease burden caused by a diet low in fibre.

Based on hubs of neural circuits associated with addiction and their degree centrality (DC), this study aimed to construct the addiction-related brain networks for patients diagnosed with heroin dependence undertaking stable methadone maintenance treatment (MMT) and further prospectively identify the ones at high risk for relapse with cluster analysis.

Sixty-two male MMT patients and 30 matched healthy controls (HC) underwent brain resting-state functional MRI data acquisition. The patients received 26-month follow-up for the monthly illegal-drug-use information. Ten addiction-related hubs were chosen to construct a user-defined network for the patients. Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse.

Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical−striatal−thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse.

MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.

To evaluate the relationship between religious beliefs and mental state, care burden, and quality of life in parents of infantile patients with CHD.

A cross-sectional study was conducted at a provincial hospital in Fujian, China. In this study, 114 parents of infant patients with CHD were successfully enrolled. Data were collected using the Duke University Religion Index, Hospital Anxiety and Depression Scale, Zarit Caregiver Burden Interview, and 36-Item Short-Form Health Survey.

The organisational religious activity, non-organisational religious activity, and intrinsic religiosity of parents were significantly related to the care burden and quality of life, and the two dimensions of non-organisational religious activity and intrinsic religiosity of parents were significantly related to their anxiety symptoms. No association was found between parents’ religious beliefs and their depressive symptoms. Among Buddhist parents, non-organisational religious activity and intrinsic religiosity reduced the care burden and improved quality of life. Among Christian parents, organisational religious activity and non-organisational religious activity were found to reduce the care burden, while organisational religious activity and intrinsic religiosity were found to improve quality of life. There was no correlation between the sub-dimensions of religious beliefs and a negative impact on the care process in Muslim parents.

Religious beliefs have a protective effect on the parents of infant patients with CHD. They help relieve parents’ anxiety, reduce their care burden, and improve their quality of life. In addition, different religious beliefs have different dimensions of influence on caregivers.

To explore the feasibility and superiority of applying the WeChat platform in a midterm follow-up of surgical repair for ventricular septal defects in infants.

Eighty-six infants with VSD who underwent surgical repair were divided into an outpatient follow-up group and a WeChat follow-up group. The clinical data, including complications, economic cost, time spent, loss to follow-up rate, and parents’ satisfaction at the 3-month and 1-year follow-ups, were recorded and analysed.

There was no significant difference in the incidence of post-operative complications between the two groups. Although the loss to follow-up rate in the WFU group was lower than that of the OFU group, the difference was not statistically significant. The economic cost and time spent in the 3 months and 1 year after discharge in the WFU group were significantly lower than those in the OFU group. One year after discharge, the PSQ-18 score of the WFU group was significantly higher than that of the OFU group.

Compared with outpatient follow-up, the WeChat platform at the midterm follow-up after surgical repair of VSDs in infants has the advantages of saving time and economic costs and improves parents’ satisfaction.