Socoró-Garrigosa Marcel, Perl Yonatan Sanz, Kringelbach Morten L, Erritzoe David, Nutt David J, Carhart-Harris Robin, Vohryzek Jakub, Deco Gustavo
Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Department of Physics, University of Buenos Aires, Buenos Aires, Argentina.
Ann N Y Acad Sci. 2025 Aug;1550(1):255-272. doi: 10.1111/nyas.15391. Epub 2025 Jul 21.
Determining the scale of neural representations is a central challenge in neuroscience. While localized representations have traditionally dominated, evidence suggests information is also encoded in distributed, hierarchical networks. Recent research indicates that the hierarchy of causal influences shaping functional patterns serves as a signature of distinct brain states, with implications for neuropsychiatric disorders. Here, we first explore how whole-brain models, guided by the thermodynamics of mind framework, estimate brain hierarchy and how perturbing such models enables the study of in-silico transitions represented by static functional connectivity. We then apply this to major depressive disorder, where different brain hierarchical reconfigurations emerge following psilocybin and escitalopram treatments. We build resting-state whole-brain models of depressed patients before and after interventions and conduct a dynamic sensitivity analysis to explore brain states' susceptibility-measuring their capacity to change-and their drivability to healthier states. We show that susceptibility is on average reduced by escitalopram and increased by psilocybin, and that both treatments promote healthier transitions. These results align with the post-treatment window of plasticity opened by serotonergic psychedelics and the similar clinical efficacy of both drugs in trials. Overall, this work demonstrates how whole-brain models of brain hierarchy can inform in-silico neurostimulation protocols for neuropsychiatric disorders.
确定神经表征的规模是神经科学中的一项核心挑战。虽然传统上局部表征占主导地位，但有证据表明信息也编码在分布式的层级网络中。最近的研究表明，塑造功能模式的因果影响层级作为不同脑状态的标志，对神经精神疾病具有启示意义。在此，我们首先探讨在心智热力学框架指导下的全脑模型如何估计脑层级，以及对这些模型进行扰动如何能够研究由静态功能连接所代表的计算机模拟转变。然后我们将此应用于重度抑郁症，在该疾病中，裸盖菇素和艾司西酞普兰治疗后会出现不同的脑层级重构。我们构建干预前后抑郁症患者的静息态全脑模型，并进行动态敏感性分析，以探索脑状态的易感性——测量它们改变的能力——以及它们向更健康状态的可驱动性。我们表明，艾司西酞普兰平均降低易感性，裸盖菇素增加易感性，并且两种治疗都促进更健康的转变。这些结果与血清素能迷幻剂开启的治疗后可塑性窗口以及两种药物在试验中的相似临床疗效一致。总体而言，这项工作展示了脑层级的全脑模型如何为神经精神疾病的计算机模拟神经刺激方案提供信息。
