Sepúlveda Pradyumna, Aitsahalia Ines, Kumar Krishan, Atkin Tobias, Iigaya Kiyohito
Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York; Italian Academy for Advanced Studies, Columbia University, New York, New York.
Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York; Center for Theoretical Neuroscience and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Mar 7. doi: 10.1016/j.bpsc.2025.02.014.
Anticipation of future experiences is a crucial cognitive function impacted in various psychiatric conditions. Despite significant research advancements, the mechanisms that underlie altered anticipation remain poorly understood, and effective targeted treatments are largely lacking. In this review, we propose an integrated computational psychiatry approach to addressing these challenges. We begin by outlining how altered anticipation presents across different psychiatric conditions, including schizophrenia, major depressive disorder, anxiety disorders, substance use disorders, and eating disorders, and summarizing the insights that have been gained from extensive research using self-report scales and task-based neuroimaging despite notable limitations. Then, we explore how emerging computational modeling approaches, such as reinforcement learning and anticipatory utility theory, could overcome these limitations and offer deeper insights into underlying mechanisms and individual variations. We propose that integrating these interdisciplinary methodologies can offer comprehensive transdiagnostic insights, aiding the discovery of new therapeutic targets and advancing precision psychiatry.
对未来经历的预期是一种关键的认知功能，在各种精神疾病中都会受到影响。尽管研究取得了重大进展，但预期改变背后的机制仍知之甚少，而且基本上缺乏有效的靶向治疗方法。在这篇综述中，我们提出一种综合计算精神病学方法来应对这些挑战。我们首先概述了预期改变在不同精神疾病中的表现，包括精神分裂症、重度抑郁症、焦虑症、物质使用障碍和饮食失调，并总结了尽管存在显著局限性，但通过使用自我报告量表和基于任务的神经影像学进行广泛研究所获得的见解。然后，我们探讨新兴的计算建模方法，如强化学习和预期效用理论，如何能够克服这些局限性，并对潜在机制和个体差异提供更深入的见解。我们提出，整合这些跨学科方法可以提供全面的跨诊断见解，有助于发现新的治疗靶点并推动精准精神病学的发展。
