Dataset Information

Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis.

ABSTRACT: Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.

SUBMITTER: Regonia PR

PROVIDER: S-EPMC8656401 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis.

Regonia Paul Rossener PR Takamura Masahiro M Nakano Takashi T Ichikawa Naho N Fermin Alan A Okada Go G Okamoto Yasumasa Y Yamawaki Shigeto S Ikeda Kazushi K Yoshimoto Junichiro J

Frontiers in psychiatry 20211125

Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, b ...[more]

PMID: 34899435

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Project description:Liquid-liquid phase separation (LLPS) inside the cell often results in biological condensates that can critically impact cell homeostasis. Such phase separation events occur in multiple parts of cells, including the cell membranes, where the so-called "lipid raft" hypothesis posits the formation of ordered domains floating in a sea of disordered lipids. The resulting lipid domains often have functional roles. However, the thermodynamics of lipid phase separation and their resulting mechanistic effects on cell function and dysfunction are poorly understood. Understanding such complex phenomena in cell membranes, with their diverse lipid compositions, is exceptionally difficult. For this reasons, simple model systems that can recapitulate similar behavior are widely used to study this phenomenon. Despite these simplifications, the timescale and and length scales of domain formation pose a challenge for molecular dynamics (MD) simulations. Thus, most MD studies focus on spontaneous lipid phase separation - essentially measuring the sign (but not the amplitude) of the free energy change upon separation - rather than directly interrogating the thermodynamics. Here, we propose a proof-of-concept pipeline that can directly measure this free energy by combining coarse-grained MD with enhanced sampling protocols using a novel collective variable. This approach will be a useful tool to help connect the thermodynamics of phase separation with the mechanistic insights already available from molecular dynamics simulations.SignificanceStandard molecular dynamics simulations can determine the sign the free energy change upon phase separation, but not the amplitude. We present a new method to determine the phase separation free energy for lipid membranes, based on a enhanced sampling using the weighted ensemble method combined with a novel collective variable, validated using coarse-grained simulations applied to several simple systems. The new method will be valuable as a way to develop models that connect molecular-level structural features to the thermodynamics of phase separation.

Dataset Information

Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis.

Publications

Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis.

Similar Datasets

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