Invasive Computational Psychiatry.
Invasive Computational Psychiatry.
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DOI：
10.1016/j.biopsych.2022.09.032
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发表时间：
2023-04-15
影响因子：
10.6
通讯作者：
中科院分区：
文献类型：
--
作者：
计算精神病学是一个相对较新但多产的领域，旨在用关于大脑的正式理论来理解精神疾病，在过去十年中取得了巨大的增长。尽管最初令人兴奋，但计算精神病学的实际进展似乎停滞不前。与此同时，颅内神经科学的最新进展极大地促进了对人类大脑的理解。具体而言，侵入性技术，如立体定向脑电图，皮质电描记术和脑深部电刺激提供了一个独特的机会，精确测量和因果调节神经生理活动的生活人类大脑。本文综述了计算精神病学和侵入性电生理学的研究进展和不足，并提出两者的结合将为侵入性计算精神病学提供一个非常有前途的新方向。这种方法的价值至少是双重的。首先，它通过提供对神经活动的时空精确描述来推进我们对精神状态的神经计算的机械理解，这是传统上使用非侵入性技术对人类受试者无法实现的。其次，它提供了一种直接和即时的方式来通过刺激算法定义的神经区域和电路来调节大脑状态（即，算法靶向），从而提供因果和治疗见解。然后，我们将抑郁症作为一个用例，其中计算和侵入性方法的结合已经显示出初步的成功。最后，我们概述了未来的发展方向，作为这个令人兴奋的新领域的路线图，以及提出有关问题的警告，如伦理问题和研究结果的普遍性。
Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction—invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.
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影响因子：
7.7
作者：
Cecchi, Romane;Vinckier, Fabien;Hammer, Jiri;Marusic, Petr;Nica, Anca;Rheims, Sylvain;Trebuchon, Agnes;Barbeau, Emmanuel J.;Denuelle, Marie;Maillard, Louis;Minotti, Lorella;Kahane, Philippe;Pessiglione, Mathias;Bastin, Julien
通讯作者：
Bastin, Julien
影响因子：
82.9
作者：
Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
通讯作者：
Liston C
影响因子：
5.8
作者：
Banker, Sarah M.;Na, Soojung;Beltran, Jacqueline;Koenigsberg, Harold W.;Foss-Feig, Jennifer H.;Gu, Xiaosi;Schiller, Daniela
通讯作者：
Schiller, Daniela
影响因子：
16.2
作者：
Daw ND;Gershman SJ;Seymour B;Dayan P;Dolan RJ
通讯作者：
Dolan RJ
影响因子：
13.7
作者：
Camerer, CF;Ho, TH;Chong, JK
通讯作者：
Chong, JK