Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders.

BACKGROUND: Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine. METHODS: We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort. RESULTS: Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable. CONCLUSIONS: Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

Unmasking the mental health scars of COVID-19: A longitudinal investigation of children and adolescents in post-lockdown China.

The long-term mental health consequences of COVID-19 in children and adolescents remain unclear. We investigated the impact of COVID-19 infection on mental health after China's zero-COVID policy relaxation, focusing on symptom-specific and social-family risk factors for mental health issues in children and adolescents. In a longitudinal study, 8348 youths (aged 10-18) were assessed twice (T1: September to October 2022 and T2: April to May 2023). Mental health changes (Δ=T1-T2) were compared between COVID-19-infected (COVID+, n = 4108) and non-infected (COVID-, n = 4240). After balancing social-family confounding factors at T1 with propensity score-based inverse probability weights, multivariable logistic regression was employed to assess associations between COVID-19 infection and the onset/worsening of mental health symptoms. Multivariable logistic regression was conducted to explore specific acute COVID-19 symptoms and social-family risk factors associated with the onset/worsening of mental health symptoms in COVID + group. Compared to COVID- group, COVID + group exhibited lower overall mental health improvement (Δ). COVID + group was associated with increased risks of depression worsening (OR 1.20, 95 % CI 1.04-1.39), anxiety worsening (OR 1.30, 95 % CI 1.15-1.47), stress worsening (OR 1.23, 95 % CI 1.03-1.46), insomnia worsening (OR 1.21, 95 % CI 1.05-1.39), and emotional symptoms worsening (OR 1.72, 95 % CI 1.27-2.33). Moderate-to-severe difficulty thinking, breathlessness, and gastrointestinal symptoms were specific COVID-19 symptoms associated with worsening of various mental health outcomes. Furthermore, academic difficulties, economic disadvantages, family conflicts, food addiction, and alcohol consumption were identified as social-family risk factors for worsening mental health symptoms in COVID + youths. COVID-19 infection leaves lasting mental health scars in youths, extending beyond the acute phase. Specific symptoms, particularly cognitive dysfunction and respiratory/gastrointestinal distress play a significant role in this vulnerability. Social-family factors further modulate these effects, highlighting the need for comprehensive interventions that address both biological and psychosocial aspects. This study provides valuable insights for tailoring mental health support to youths navigating the consequences of the COVID-19 pandemic.

Integrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder.

Several lines of evidence support the involvement of transcriptomic and epigenetic mechanisms in the brain structural deficits of major depressive disorder (MDD) separately. However, research in these two areas has remained isolated. In this study, we proposed an integrative strategy that combined neuroimaging, brain-wide gene expression, and peripheral DNA methylation data to investigate the genetic basis of gray matter abnormalities in MDD. The MRI T1-weighted images and Illumina 850 K DNA methylation microarrays were obtained from 269 patients and 416 healthy controls, and brain-wide transcriptomic data were collected from Allen Human Brain Atlas. The between-group differences in gray matter volume (GMV) and differentially methylated CpG positions (DMPs) were examined. The genes with their expression patterns spatially related to GMV changes and genes with DMPs were overlapped and selected. Using principal component regression, the associations between DMPs in overlapped genes and GMV across individual patients were investigated, and the region-specific correlations between methylation status and gene expression were examined. We found significant associations between the decreased GMV and DMPs methylation status in the anterior cingulate cortex, inferior frontal cortex, and fusiform face cortex regions. These DMPs genes were primarily enriched in the neurodevelopmental and synaptic transmission process. There was a significant negative correlation between DNA methylation and gene expression in genes associated with GMV changes of the frontal cortex in MDD. Our findings suggest that GMV abnormalities in MDD may have a transcriptomic and epigenetic basis. This imaging-transcriptomic-epigenetic integrative analysis provides spatial and biological links between cortical morphological deficits and peripheral epigenetic signatures in MDD.

Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation.

BACKGROUND: Depression is often accompanied by changes in behavior, including dietary behaviors. The relationship between dietary behaviors and depression has been widely studied, yet previous research has relied on self-reported data which is subject to recall bias. Electronic device-based behavioral monitoring offers the potential for objective, real-time data collection of a large amount of continuous, long-term behavior data in naturalistic settings. OBJECTIVE: The study aims to characterize digital dietary behaviors in depression, and to determine whether these behaviors could be used to detect depression. METHODS: A total of 3310 students (2222 healthy controls [HCs], 916 with mild depression, and 172 with moderate-severe depression) were recruited for the study of their dietary behaviors via electronic records over a 1-month period, and depression severity was assessed in the middle of the month. The differences in dietary behaviors across the HCs, mild depression, and moderate-severe depression were determined by ANCOVA (analyses of covariance) with age, gender, BMI, and educational level as covariates. Multivariate logistic regression analyses were used to examine the association between dietary behaviors and depression severity. Support vector machine analysis was used to determine whether changes in dietary behaviors could detect mild and moderate-severe depression. RESULTS: The study found that individuals with moderate-severe depression had more irregular eating patterns, more fluctuated feeding times, spent more money on dinner, less diverse food choices, as well as eating breakfast less frequently, and preferred to eat only lunch and dinner, compared with HCs. Moderate-severe depression was found to be negatively associated with the daily 3 regular meals pattern (breakfast-lunch-dinner pattern; OR 0.467, 95% CI 0.239-0.912), and mild depression was positively associated with daily lunch and dinner pattern (OR 1.460, 95% CI 1.016-2.100). These changes in digital dietary behaviors were able to detect mild and moderate-severe depression (accuracy=0.53, precision=0.60), with better accuracy for detecting moderate-severe depression (accuracy=0.67, precision=0.64). CONCLUSIONS: This is the first study to develop a profile of changes in digital dietary behaviors in individuals with depression using real-world behavioral monitoring. The results suggest that digital markers may be a promising approach for detecting depression.