用于丘脑底-中-前额叶回路行为转换的人类颅内θ活动的最佳水平-动脉网

Optimal level of human intracranial theta activity for behavioral switching in the subthalamo-medio-prefrontal circuit

Nature 等信源发布 2024-09-07 21:41

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AbstractThe ability to switch between rules associating stimuli and responses depend on a circuit including the dorsomedial prefrontal cortex (dmPFC) and the subthalamic nucleus (STN). However, the precise neural implementations of switching remain unclear. To address this issue, we recorded local field potentials from the STN and from the dmPFC of neuropsychiatric patients during behavioral switching.

摘要在刺激和反应规则之间切换的能力取决于包括背内侧前额叶皮层（dmPFC）和丘脑底核（STN）的电路。然而，开关的精确神经实现仍不清楚。为了解决这个问题，我们在行为转换过程中记录了神经精神病患者的STN和dmPFC的局部场电位。

Drift-diffusion modeling revealed that switching is associated with a shift in the starting point of evidence accumulation. Theta activity increases in dmPFC and STN during successful switch trials, while temporally delayed and excessive levels of theta lead to premature switch errors. This seemingly opposing impact of increased theta in successful and unsuccessful switching is explained by a negative correlation between theta activity and the starting point.

。在成功的开关试验期间，dmPFC和STN中的θ活性增加，而时间延迟和θ水平过高会导致过早的开关错误。θ活动与起点之间的负相关解释了θ增加对成功和不成功转换的这种看似相反的影响。

Together, these results shed a new light on the neural mechanisms underlying the rapid reconfiguration of stimulus-response associations, revealing a Goldilocks’ effect of theta activity on switching behavior..

总之，这些结果为刺激-反应关联快速重构的神经机制提供了新的思路，揭示了θ活动对转换行为的影响。。

IntroductionBehavioral switching allows us to rapidly adapt to changing demands and depends on a set of high-level cognitive functions from the anticipated application of abstract rules and the inhibition of no longer relevant choices to the selection and execution of the most adaptive response1,2. However, the neuro-computational mechanisms underlying our ability to rapidly adjust our behavior in response to unpredictable and sudden environmental changes remains poorly understood.Early evidence suggests that the hyperdirect pathway connecting the dorso-medial prefrontal cortex (dmPFC) to the subthalamic nucleus (STN) implements behavioral switching, action stopping or response conflict3,4,5,6.

引言行为转换使我们能够快速适应不断变化的需求，并依赖于一系列高级认知功能，从抽象规则的预期应用和不再相关的选择的抑制到最适应性反应的选择和执行1,2。然而，我们能够快速调整行为以应对不可预测和突然的环境变化的神经计算机制仍然知之甚少。早期证据表明，连接背内侧前额叶皮层（dmPFC）和丘脑底核（STN）的超直接通路实现了行为转换，动作停止或反应冲突3,4,5,6。

Yet, previous studies have mainly focused on action stopping or speed-accuracy trade-off adjustments to “conflict” (also referred to as choice difficulty or choice uncertainty)7,8,9. Previous human intracranial studies showed that human dmPFC and STN neurons’ firing rate, as well as theta ( ~ 5–10 Hz) prefronto-subthalamic activity, increase when the best response is more difficult to select and require longer decision time10,11,12,13,14.

然而，以前的研究主要集中在动作停止或速度准确性权衡调整“冲突”（也称为选择难度或选择不确定性）7,8,9。先前的人类颅内研究表明，当最佳反应更难选择且需要更长的决策时间时，人类dmPFC和STN神经元的放电频率以及θ（〜5-10Hz）前额叶-丘脑底活动会增加10,11,12,13,14。

Conversely, action stopping increases both neurons firing rate and beta-band activity ( ~ 15–30 Hz) in the same dmPFC-STN circuit15,16,17,18,19,20,21,22. By contrast, non-invasive electrophysiological studies of task switching yield mixed findings in terms of temporal dynamics, frequency regime and putative brain circuits23,24,25, highlighting the need for more direct human electrophysiological recordings to establish a precise mapping between prefronto-subthalamic neural dynamics and key cognitive processes underlying behavioral switching.Drift diffusion models posit that response selection proceeds by accumulating evidence up to a decision thr.

相反，在相同的dmPFC STN电路15,16,17,18,19,20,21,22中，动作停止会增加神经元的放电频率和β波段活动（15-30Hz）。相比之下，任务转换的非侵入性电生理学研究在时间动力学，频率机制和假定的大脑回路方面产生了混合的发现23,24,25，突出了需要更直接的人类电生理记录来建立前额叶-丘脑底神经动力学和行为转换的关键认知过程之间的精确映射。漂移扩散模型认为，响应选择是通过积累证据进行决策的。

(1)

when considering a, t, v constant and independent to task condition (switch or non-switch). Here, k0 and k1 are coefficients with k0 equivalent to the intercept and Task refers to the trial type (0 for non-switch, 1 for switch). Markov chain Monte Carlo sampling was used for Bayesian approximation of the posterior distribution of model parameters by generating 150,000 samples of which the first 100,000 samples were discarded.

当考虑a，t，v常数且独立于任务条件（开关或非开关）时。这里，k0和k1是系数，k0等于截距，任务是指试验类型（0表示非开关，1表示开关）。马尔可夫链蒙特卡罗抽样用于贝叶斯近似模型参数的后验分布，生成150000个样本，其中前100000个样本被丢弃。

Thus we obtain a final chain of 50,000 samples and the posterior of all parameters [a,t,v,z] for each subject and for the group. We checked traces of model parameters, their autocorrelation and the model convergence. To test the significance of parameter differences across conditions (i.e., switch vs.

因此，我们为每个受试者和该组获得了50000个样本的最终链和所有参数[a，t，v，z]的后验值。我们检查了模型参数的轨迹，它们的自相关性和模型收敛性。测试不同条件下参数差异的重要性（即开关vs。

non-switch trials), we computed the proportion of samples in which starting point for switch condition was lower than in non-switch condition over the full simulated posterior distribution. Model parameters were judged significantly different from 0 if ≥95% of the samples drawn from the full posterior were below/above from zero.

非切换试验），我们计算了在完全模拟的后验分布中，切换条件的起点低于非切换条件的样本比例。如果从整个后部抽取的样本中≥95%低于/高于零，则判断模型参数与0显着不同。

Even though such posterior probabilities are distinct from classical parametric statistics (e.g., in a t-test), they can be interpreted in a similar manner28,30.HDDM with neural dataIn a second step, we analyzed neuro-computational effects during the task. We consider the neural fluctuation through trials and add this information to the previous model.

尽管这种后验概率不同于经典的参数统计（例如，在t检验中），但它们可以以类似的方式解释28,30。在第二步中，我们分析了任务期间的神经计算效应。我们通过试验考虑神经波动，并将此信息添加到先前的模型中。

We use the mean value of the power during a specific time window as reflecting the neural state of each trial. We define the initial level of evidence as follow:$${{{\rm{z}}}}={{{{\rm{k}}}}}_{0}+{{{{\rm{k}}}}}_{1}\, {{{{\rm{\theta }}}}}_{\left(\frac{{{{\bf{sw}}}}}{{{{\bf{nsw}}}}}\right)}\, ({{{\rm{M}}}}2)\, {{{\rm{also}}}}\; {{{\rm{described}}}}\; {{{\rm{with}}}}.

我们使用特定时间窗口内功率的平均值来反映每次试验的神经状态。我们将初始证据级别定义为：$$${{{{\rm{z}}}}={{{\rm{k}}}}}}}{{{{{\rm{k}}}}}}}}}}}{{{{\rm{\theta}}}}}}}}}}}}}}}}}}}{{{{{}}}}}}}}}}}{{{{{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}{{{{{}}}}}}{\ bf{nsw}}}\右）}\，（{{\ rm{M}}}}2）\，{{{\ rm{也}}}}\；{{{\rm{描述}}}}\；{{{\rm{with}}}}。

(2)

where k0 and k1 are coefficients, θ the mean power for the frequency range 5,6,7,8,9,10 Hz. k0 acts as an intercept and represents the reference value of the initial level of evidence calculated for all the trials. k1 is the coefficient of regression associated with θ which is assigned a certain distribution for switch trials and another for the non-switch trials.

其中k0和k1是系数，θ是频率范围5,6,7,8,9,10 Hz的平均功率。k0作为截距，代表为所有试验计算的初始证据水平的参考值。k1是与θ相关的回归系数，θ为开关试验分配了一定的分布，为非开关试验分配了另一个分布。

Here, we generated a chain of 150,000 samples of which the first 100 000 samples were discarded. Thus, we obtain a chain of 50,000 samples. This time, we calculated the posterior of all parameters for the group only to facilitate model convergence. Indeed, we widely increase the complexity of the model by adding the neural coefficient of regression.

在这里，我们生成了150000个样本的链，其中前10万个样本被丢弃。因此，我们获得了50000个样本链。这次，我们计算了该组所有参数的后验值，以促进模型收敛。实际上，我们通过添加回归的神经系数，大大增加了模型的复杂性。

To calculate θ value, we used the theta single trial estimation means during the significant increase time window (Figs. 2c and 4c). It gives us a theta estimation per trials which we normalized by subtracting the mean value and dividing by the standard deviation per subject and per trial type39,42.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article..

为了计算θ值，我们在显着增加的时间窗口内使用了θ单次试验估计方法（图2c和4c）。它为我们提供了每个试验的θ估计值，我们通过减去平均值并除以每个受试者和每个试验类型的标准差来归一化。报告摘要有关研究设计的更多信息，请参阅与本文相关的Nature Portfolio Reporting Summary。。

Data availability

数据可用性

The behavior and neural data generated in this study are available from the Figshare database [source data: https://doi.org/10.6084/m9.figshare.26363740].

本研究中产生的行为和神经数据可从Figshare数据库获得[源数据：https://doi.org/10.6084/m9.figshare.26363740]。

Code availability

代码可用性

The custom codes used to generate the figures and statistics are available from the lead contact (JB) upon request.

用于生成数字和统计数据的自定义代码可根据要求从lead contact（JB）获得。

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Download referencesAcknowledgementsThis work benefited from University Grenoble Alpes ‘Investissements d’Avenir’ program (ANR-17-CE37-0018; ANR-18-CE28-0016; ANR-22-CE17-0057; ANR-23-CE17-0070) awarded to JB; PK and MP and from an ‘Agence Nationale de la Recherche’ young investigator grant awarded to PD (ANR-21-CE37-0004-01).

下载参考文献致谢这项工作受益于格勒诺布尔阿尔卑斯大学“Avenir投资”计划（ANR-17-CE37-0018；ANR-18-CE28-0016；ANR-22-CE17-0057；ANR-23-CE17-0070）授予JB；PK和MP以及授予PD的“国家研究机构”青年研究员补助金（ANR-21-CE37-0004-01）。

The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank all the patients who participated to the study and Grenoble Teaching Hospital epilepsy, psychiatry and neurosurgery clinical teams for their support. We thank Dr. E. Boorman and N.

资助者在研究设计，数据收集和分析，决定发表或准备手稿方面没有任何作用。我们感谢所有参与研究的患者以及格勒诺布尔教学医院癫痫，精神病学和神经外科临床团队的支持。我们感谢E.Boorman博士和N。

Kolling for their helpful comments on an early version of this manuscript and Dr. M. Laubach for his suggestion about the Goldilocks’ expression.Author informationAuthor notesThese authors contributed equally: Philippe Domenech, Julien Bastin.Authors and AffiliationsUniv. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, F-38000, Grenoble, FranceMaëva Laquitaine & Julien BastinUniv.

科尔林（Kolling）对这份手稿的早期版本发表了有益的评论，劳巴赫（M.Laubach）博士（Dr.M.Laubach）对金发姑娘的表达提出了建议。作者信息作者注意到这些作者做出了同样的贡献：Philippe Domenech，Julien Bastin。作者和附属机构IV。格勒诺布尔阿尔卑斯山，Inserm，U1216，格勒诺布尔神经科学研究所，GIN，F-38000，格勒诺布尔，FranceMaëva Laquitaine＆Julien BastinUniv。

Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, 38000, Grenoble, FranceMircea Polosan, Philippe Kahane & Stephan ChabardesCognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin center, F-91191, Gif/Yvette, FranceJérôme Yelnik, Sara Fernandez-Vidal & Philippe DomenechInstitut de Neuromodulation, Pole Hospitalo-Universitaire 15, Groupe Hospitalo-Universitaire Paris, Psychiatrie et Neurosciences, Université Paris Cité, Paris, FrancePhilippe DomenechAuthorsMaëva LaquitaineView author publicationsYou can also search for this author in.

格勒诺布尔阿尔卑斯山，Inserm，U1216，CHU格勒诺布尔阿尔卑斯，格勒诺布尔神经科学研究所，38000，格勒诺布尔，Francemircea Polosan，Philippe Kahane&Stephan ChabardesCognitive神经成像单元，CEA，Inserm，巴黎萨克莱大学，神经脊柱中心，F-91191，GIF/Yvette，FranceJérôme Yelnik，Sara Fernandez Vidal&PhilippeDomenechinstitut de Neuromodulation，Pole Hospitalo Universitaire 15，巴黎大学医院集团，精神病学和神经科学，巴黎城市大学，Franceplippe Domenechauthorsmaëva LaquitaineView作者出版物您也可以在中搜索此作者。

PubMed Google ScholarMircea PolosanView author publicationsYou can also search for this author in

PubMed Google Scholarmarcea PolosanView作者出版物您也可以在

PubMed Google ScholarPhilippe KahaneView author publicationsYou can also search for this author in

PubMed Google ScholarPhilippe KahaneView作者出版物您也可以在

PubMed Google ScholarStephan ChabardesView author publicationsYou can also search for this author in

PubMed Google ScholarStephan ChabardesView作者出版物您也可以在

PubMed Google ScholarJérôme YelnikView author publicationsYou can also search for this author in

PubMed Google ScholarJérôme YelnikView作者出版物您也可以在

PubMed Google ScholarSara Fernandez-VidalView author publicationsYou can also search for this author in

PubMed Google ScholarSara Fernandez VidalView作者出版物您也可以在

PubMed Google ScholarPhilippe DomenechView author publicationsYou can also search for this author in

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PubMed谷歌学者Julien BastinView作者出版物您也可以在

PubMed Google ScholarContributionsJ.B. designed the experiment and collected the data. J.B., P.D., J.Y., S.F.V. and M.L. provided preprocessing scripts and anatomical location of dmPFC/STN sites. M.L., J.B. and P.D. performed the data analysis. M.P., P.K., and S.C. did the clinical investigation.

PubMed谷歌学术贡献。B、设计实验并收集数据。J、 B.，P.D.，J.Y.，S.F.V.和M.L.提供了dmPFC/STN位点的预处理脚本和解剖位置。M、 L.，J.B.和P.D.进行了数据分析。M、 P.，P.K。和S.C.进行了临床研究。

M.L., P.D. and J.B. wrote the manuscript. All the authors discussed the results and commented on the manuscript.Corresponding authorsCorrespondence to.

M、 L.，P.D.和J.B.撰写了手稿。。通讯作者通讯。

Philippe Domenech or Julien Bastin.Ethics declarations

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Nature Communications thanks Ishita Basu, Vincenzo Fiore, Alekhya Mandali and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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Reprints and permissionsAbout this articleCite this articleLaquitaine, M., Polosan, M., Kahane, P. et al. Optimal level of human intracranial theta activity for behavioral switching in the subthalamo-medio-prefrontal circuit.

转载和许可本文引用本文Laquitaine，M.，Polosan，M.，Kahane，P。等人。人类颅内θ活动的最佳水平，用于丘脑下-中-前额叶回路的行为转换。

Nat Commun 15, 7827 (2024). https://doi.org/10.1038/s41467-024-52290-wDownload citationReceived: 30 August 2023Accepted: 29 August 2024Published: 07 September 2024DOI: https://doi.org/10.1038/s41467-024-52290-wShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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认知控制计算模型决策人类行为

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