Biological Psychiatry最新文献

Background: Pregnancy-related disorders, such as hypertensive disorders of pregnancy (HDPs) and postpartum depression, have consequences for maternal health, increasing risk for major depressive disorder (MDD) and Alzheimer's disease (AD). Observational studies show intertwined pathophysiologies and shared cardiovascular factors. However, genetic links of cardiovascular factors with pregnancy-related disorders, MDD, and AD, as well as the genetic mechanisms between the disorders, have not been fully established.

Methods: Using summary statistics from female-specific genome-wide association studies, we estimated genetic correlations and causal associations, using Mendelian randomization, between cardiovascular factors (C-reactive protein, high-density lipoprotein [HDL] cholesterol, low-density lipoprotein [LDL] cholesterol, and triglycerides), pregnancy-related disorders (HDPs and postpartum depression), MDD, and AD. For significant associations, body mass index (BMI), as a known confounder, was included in multivariable Mendelian randomization analyses. Furthermore, we applied causal mixture models (MiXeR) to explore polygenic overlap between pregnancy-related disorders, MDD, and BMI.

Results: We found widespread genetic correlations between cardiovascular factors, pregnancy-related disorders, and MDD. Using Mendelian randomization, higher triglycerides and lower HDL cholesterol were causally linked to higher HDP risk, and higher LDL cholesterol was linked to higher AD risk. When BMI was included, only the effect of triglycerides on HDP remained significant. Trivariate MiXeR estimated substantial polygenic overlap of pregnancy-related disorders with MDD and BMI.

Conclusions: Using multiple genetic approaches, our findings indicate some shared cardiovascular factors associated with pregnancy-related disorders, MDD, and AD, partially driven by BMI. BMI should be further explored as a modifiable factor genetically linked to pregnancy-related, mental, and brain disorders. Our findings highlight the relevance of early prevention of genetically interconnected disorders across the female lifespan.

The brain is the quintessential complex dynamic system. Static analytic approaches-such as time-averaged connectivity or correlations-remain widely used but cannot characterize dynamic processes that are central to brain function and dysfunction. Fluctuations in state are central to psychiatric illness; so constraining analysis to time-averaged, static techniques fundamentally limits insight. Dynamic modeling approaches address this gap by quantifying temporal complexity, identifying causal influences, compressing activity into latent trajectories in abstract representational spaces, and simulating whether hypothesized principles can reproduce observed data. Early methods like sliding-window correlations and temporal ICA have progressed to more advanced frameworks such as dynamic causal modeling, recurrent neural networks, and neural differential equations. This review organizes current dynamic analytic approaches with respect to four broad research goals: (1) describing patterns of brain activity over time, (2) inferring causal mechanisms, (3) decoding latent dynamics, and (4) simulating complex neural processes. For each of these broad goals, we highlight representative methods, their assumptions, clinical applications, and limitations. In each case, we provide links to available open-access tools. Together, these approaches provide a framework for testing theories of brain function directly in clinical populations. By aligning analytical tools with systems-level theories, dynamic modeling approaches represent more than technical progress-they reflect a conceptual shift from static, data-driven descriptions to theory-informed tests of brain processes as they unfold over time.