Neurophysiologie Clinique/Clinical Neurophysiology最新文献

英文中文

Electrocardiogram (ECG)-based seizure detection using supervised machine-learning 使用监督机器学习的基于心电图（ECG）的癫痫检测

IF 2.4 4区医学 Q2 CLINICAL NEUROLOGY

Neurophysiologie Clinique/Clinical Neurophysiology

Pub Date : 2025-08-21 DOI: 10.1016/j.neucli.2025.103098

Eva Diab , William Gacquer , Carole Nouboue , Derambure Philippe , Bertille Périn , Simone Chen , Julien De Jonckheere , William Szurhaj

Background

We conducted a pilot study utilizing automatic delineation of electrocardiogram (ECG) and machine learning that considered all components of the ECG complex for seizure detection. The primary outcome was to assess the feasibility of this method. The secondary outcome was to identify the most effective machine learning algorithm.

Methods

We screened ECG recordings from patients included in the EPICARD cohort who underwent video-electroencephalogram monitoring. A total of 47 seizures from 32 patients were selected. Epochs of 90 min surrounding the seizures were retained. Each ECG was converted into a sequence of heartbeats modeled as a P-Q-R-S-T succession. Derivative quantities measuring time variations between the inner and outer components of heartbeats were computed, designated as δ_X and ΔX. Our algorithm monitored 3 to 60 successive heartbeats within a sliding window. An alarm was triggered when more than N heartbeats were classified as in-seizure (N between 3 and 20). Heartbeats were categorized as in-seizure by trained neurophysiologists. We used automated machine learning (auto-ML) platforms (Dataiku & Flaml) to assess six different algorithms: Random Forest, LightGBM, XGBoost, Decision Tree, K-Nearest Neighbors, and Extra Trees.

Results

The Extra Trees algorithm provided the best seizure detection performance regardless of the validation method used. Although longer-window models enhance detection sensitivity, they do so at the cost of delayed identification. A model analyzing 60 heartbeats with a trigger of 20 achieved 86 % sensitivity and 99.9 % specificity.

Discussion

Automatic delineation is reliable, however the false alarm rate remains high (1.5 per hour). Future work should focus on personalizing detection algorithms to improve this false alarm rate.

我们利用心电图（ECG）的自动描述和机器学习进行了一项试点研究，该研究考虑了ECG复合体的所有组成部分来检测癫痫发作。主要结果是评估该方法的可行性。次要结果是确定最有效的机器学习算法。方法：我们筛选EPICARD队列中接受视频脑电图监测的患者的心电图记录。选取32例患者的47次癫痫发作。保留癫痫发作前后90分钟的epoch。每个心电图被转换成一个心跳序列，按照P-Q-R-S-T序列建模。计算了测量心跳内外分量时间变化的导数量，分别为δX和ΔX。我们的算法在一个滑动窗口内监测3到60次连续的心跳。当超过N次心跳被归类为癫痫发作时（N次在3到20之间），就会触发警报。心跳被训练有素的神经生理学家归类为癫痫发作。我们使用自动机器学习（auto-ML）平台（Dataiku & Flaml）来评估六种不同的算法：Random Forest、LightGBM、XGBoost、Decision Tree、K-Nearest Neighbors和Extra Trees。结果无论采用何种验证方法，Extra Trees算法都能提供最佳的癫痫检测性能。虽然较长的窗口模型提高了检测灵敏度，但这样做的代价是延迟识别。一个模型分析了60次心跳，触发次数为20次，灵敏度为86%，特异性为99.9%。自动描述是可靠的，但是误报率仍然很高（每小时1.5次）。未来的工作应侧重于个性化检测算法，以提高这一虚警率。

{"title":"Electrocardiogram (ECG)-based seizure detection using supervised machine-learning","authors":"Eva Diab , William Gacquer , Carole Nouboue , Derambure Philippe , Bertille Périn , Simone Chen , Julien De Jonckheere , William Szurhaj","doi":"10.1016/j.neucli.2025.103098","DOIUrl":"10.1016/j.neucli.2025.103098","url":null,"abstract":"<div><h3>Background</h3><div>We conducted a pilot study utilizing automatic delineation of electrocardiogram (ECG) and machine learning that considered all components of the ECG complex for seizure detection. The primary outcome was to assess the feasibility of this method. The secondary outcome was to identify the most effective machine learning algorithm.</div></div><div><h3>Methods</h3><div>We screened ECG recordings from patients included in the EPICARD cohort who underwent video-electroencephalogram monitoring. A total of 47 seizures from 32 patients were selected. Epochs of 90 min surrounding the seizures were retained. Each ECG was converted into a sequence of heartbeats modeled as a P-Q-R-S-T succession. Derivative quantities measuring time variations between the inner and outer components of heartbeats were computed, designated as δ<sub>X</sub> and ΔX. Our algorithm monitored 3 to 60 successive heartbeats within a sliding window. An alarm was triggered when more than N heartbeats were classified as in-seizure (N between 3 and 20). Heartbeats were categorized as in-seizure by trained neurophysiologists. We used automated machine learning (auto-ML) platforms (Dataiku & Flaml) to assess six different algorithms: Random Forest, LightGBM, XGBoost, Decision Tree, K-Nearest Neighbors, and Extra Trees.</div></div><div><h3>Results</h3><div>The Extra Trees algorithm provided the best seizure detection performance regardless of the validation method used. Although longer-window models enhance detection sensitivity, they do so at the cost of delayed identification. A model analyzing 60 heartbeats with a trigger of 20 achieved 86 % sensitivity and 99.9 % specificity.</div></div><div><h3>Discussion</h3><div>Automatic delineation is reliable, however the false alarm rate remains high (1.5 per hour). Future work should focus on personalizing detection algorithms to improve this false alarm rate.</div></div>","PeriodicalId":19134,"journal":{"name":"Neurophysiologie Clinique/Clinical Neurophysiology","volume":"55 5","pages":"Article 103098"},"PeriodicalIF":2.4,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Is the decrement pattern in myasthenia gravis due to muscle-specific kinase antibodies different to that due to acetylcholine receptor antibodies? 肌肉特异性激酶抗体与乙酰胆碱受体抗体在重症肌无力中的衰减模式是否不同？

IF 2.4 4区医学 Q2 CLINICAL NEUROLOGY

Neurophysiologie Clinique/Clinical Neurophysiology

Pub Date : 2025-07-31 DOI: 10.1016/j.neucli.2025.103092

Antoine Pegat , Antoine Gavoille , Maxime Bonjour , Florent Cluse , Martin Moussy , Juliette Svahn , Ludivine Kouton , Aude-Marie Grapperon , Annie Verschueren , Emilien Delmont , Emmanuelle Salort-Campana , Shahram Attarian , Etienne Fortanier , Françoise Bouhour

Objective

A decrement on repetitive nerve stimulation (RNS) is essential for the diagnosis of myasthenia gravis (MG). The decrement pattern is typically “U-shaped” in MG caused by acetylcholine receptor antibodies (AChR-MG) but is less well described in MG caused by muscle-specific kinase antibodies (MuSK-MG). The aim of this study was to investigate RNS abnormalities in MuSK-MG, and to describe the differences in the decrement pattern as compared to AChR-MG.

Methods

This retrospective case-control study included patients diagnosed with generalized MuSK-MG, compared to a control group of generalized AChR-MG. The five most frequently explored nerve-muscle pairs in RNS were analyzed: radial-anconeus, fibular nerve–tibialis anterior (TA), XI-trapezius, XII/V-submental complex (SMC), and VII-orbicularis oculi (OO). Decreased amplitude between the 1st and 4th responses (early decrement) and the late/early ratio were calculated (late/early ratio <100 %=U-shaped pattern, and ≥100 %=progressive pattern).

Results

For MuSK-MG, 25 patients were included and compared to 35 AChR-MG patients. An early decrement was present in 38/83 (54.2 %) muscles in MuSK-MG compared to 88/130 (67.7 %) muscles in AChR-MG; and in MuSK-MG was less frequently found in anconeus (4/22 [18.2 %] vs 27/31 [87.1 %], p < 0.001) and in TA (0/12 [0.0 %] vs 9/30 [30 %], p = 0.04). A progressive pattern was more frequent in MuSK-MG (19/38 [50.0 %] of muscles vs 15/88 [17.0 %], p < 0.001). The late/early ratio was greater in MuSK-MG (median value was 98.4 % [IQR, 86.8–106.8] vs 89.7 % [IQR, 79.5–96.5]). The first response with minimal amplitude during the RNS (Amin) was significantly different between the two groups (p < 0.001).

Conclusion

Compared to AChR-MG, RNS in MuSK-MG showed fewer affected muscles, with less frequent involvement of anconeus and TA in particular; and a more progressive decrement pattern.

目的反复神经刺激（RNS）减少对重症肌无力（MG）的诊断有重要意义。在乙酰胆碱受体抗体（AChR-MG）引起的MG中，衰减模式典型地呈“u形”，但在肌肉特异性激酶抗体（MuSK-MG）引起的MG中，衰减模式描述较少。本研究的目的是研究麝香- mg的RNS异常，并描述与AChR-MG相比，其衰减模式的差异。方法本回顾性病例对照研究纳入诊断为全身性麝香- mg的患者，并与全身性AChR-MG的对照组进行比较。我们分析了RNS中最常发现的5对神经-肌肉：桡侧-寰枢肌、腓骨神经-胫前肌（TA）、xi -斜方肌、XII/ v -颏下复合体（SMC）和vii -眼轮匝肌（OO）。计算第1和第4次反应之间的下降幅度（早期衰减）和晚/早比（晚/早比<； 100% = u型模式，≥100% =渐进式模式）。结果MuSK-MG纳入25例患者，而AChR-MG纳入35例患者。与AChR-MG的88/130（67.7%）肌肉相比，MuSK-MG的38/83（54.2%）肌肉出现早期衰退；MuSK-MG在anconeus中的发生率较低(4/22[18.2%]比27/31 [87.1%],p <；0.001)和TA （0/12 [0.0%] vs 9/30 [30%], p = 0.04）。MuSK-MG肌肉的进行性模式更为常见(19/38 [50.0%]vs 15/88 [17.0%], p <；0.001)。MuSK-MG的晚期/早期比例更高（中位值为98.4% [IQR, 86.8-106.8] vs 89.7% [IQR, 79.5-96.5]）。两组间RNS时最小振幅第一反应（Amin）差异有统计学意义(p <；0.001)。结论与AChR-MG相比，麝香- mg的RNS受累肌肉较少，尤其是肘部和TA受累较少；以及更渐进的递减模式。

{"title":"Is the decrement pattern in myasthenia gravis due to muscle-specific kinase antibodies different to that due to acetylcholine receptor antibodies?","authors":"Antoine Pegat , Antoine Gavoille , Maxime Bonjour , Florent Cluse , Martin Moussy , Juliette Svahn , Ludivine Kouton , Aude-Marie Grapperon , Annie Verschueren , Emilien Delmont , Emmanuelle Salort-Campana , Shahram Attarian , Etienne Fortanier , Françoise Bouhour","doi":"10.1016/j.neucli.2025.103092","DOIUrl":"10.1016/j.neucli.2025.103092","url":null,"abstract":"<div><h3>Objective</h3><div>A decrement on repetitive nerve stimulation (RNS) is essential for the diagnosis of myasthenia gravis (MG). The decrement pattern is typically “U-shaped” in MG caused by acetylcholine receptor antibodies (AChR-MG) but is less well described in MG caused by muscle-specific kinase antibodies (MuSK-MG). The aim of this study was to investigate RNS abnormalities in MuSK-MG, and to describe the differences in the decrement pattern as compared to AChR-MG.</div></div><div><h3>Methods</h3><div>This retrospective case-control study included patients diagnosed with generalized MuSK-MG, compared to a control group of generalized AChR-MG. The five most frequently explored nerve-muscle pairs in RNS were analyzed: radial-anconeus, fibular nerve–tibialis anterior (TA), XI-trapezius, XII/V-submental complex (SMC), and VII-orbicularis oculi (OO). Decreased amplitude between the 1st and 4th responses (early decrement) and the late/early ratio were calculated (late/early ratio <100 %=<em>U</em>-shaped pattern, and ≥100 %=progressive pattern).</div></div><div><h3>Results</h3><div>For MuSK-MG, 25 patients were included and compared to 35 AChR-MG patients. An early decrement was present in 38/83 (54.2 %) muscles in MuSK-MG compared to 88/130 (67.7 %) muscles in AChR-MG; and in MuSK-MG was less frequently found in anconeus (4/22 [18.2 %] <em>vs</em> 27/31 [87.1 %], <em>p</em> < 0.001) and in TA (0/12 [0.0 %] <em>vs</em> 9/30 [30 %], <em>p</em> = 0.04). A progressive pattern was more frequent in MuSK-MG (19/38 [50.0 %] of muscles <em>vs</em> 15/88 [17.0 %], <em>p</em> < 0.001). The late/early ratio was greater in MuSK-MG (median value was 98.4 % [IQR, 86.8–106.8] <em>vs</em> 89.7 % [IQR, 79.5–96.5]). The first response with minimal amplitude during the RNS (Amin) was significantly different between the two groups (<em>p</em> < 0.001).</div></div><div><h3>Conclusion</h3><div>Compared to AChR-MG, RNS in MuSK-MG showed fewer affected muscles, with less frequent involvement of anconeus and TA in particular; and a more progressive decrement pattern.</div></div>","PeriodicalId":19134,"journal":{"name":"Neurophysiologie Clinique/Clinical Neurophysiology","volume":"55 6","pages":"Article 103092"},"PeriodicalIF":2.4,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short-interval intracortical inhibition (SICI): effect of target tracking on variability of responses for 1 mV and 200µV test-alone targets 短间隔皮质内抑制（SICI）：目标跟踪对1 mV和200µV单独测试目标反应变异性的影响

IF 2.7 4区医学 Q2 CLINICAL NEUROLOGY

Neurophysiologie Clinique/Clinical Neurophysiology

Pub Date : 2025-07-09 DOI: 10.1016/j.neucli.2025.103091

Gaia Fanella , Hugh Bostock , Gintaute Samusyte , Anna Bystrup Jacobsen , James Howells , Bülent Cengiz , Hasan Kılınç , Martin Koltzenburg , Agessandro Abrahao , Lorne Zinman , Benjamin Bardel , Jean-Pascal Lefaucheur , Lucía Del Valle , José Manuel Matamala , Hatice Tankisi

Objective

To evaluate whether continuously tracking unconditioned thresholds for maintaining constant motor-evoked potential (MEP) amplitudes improves the variability of amplitude-based short-interval intracortical inhibition (SICI) measurements.

Methods

Fifty-five healthy subjects were tested twice on two days with six SICI protocols. Conditioning stimulus (CS) intensity was set to 70 % of the resting motor threshold for a 200µV target (RMT200), while test stimulus (TS) intensity targeted MEP of either 1 mV or 200µV. Protocols included conventional A-SICI (fixed CS and TS), hybrid A-SICI (fixed CS and updated TS by threshold tracking); tracked A-SICI (both CS and TS updated by threshold tracking). Variability in unconditioned and conditioned responses was analyzed across interstimulus intervals (ISIs) of 1, 2.5, and 3 ms.

Results

Threshold-tracking reduced variability of the unconditioned responses measured by geometric standard deviation (expressed as a factor) for 1 mV (×/÷1.61 to 1.39; p<0.0001) and 200µV targets (×/÷2.21 to 1.30; p<0.0001). However, variability of inhibition measures did not differ significantly across protocols. Inhibition with the 200µV MEP target was significantly less than with 1 mV across all ISIs (p<0.001). The A-SICI 200µV tracked protocol showed reliability comparable to A-SICI fixed 1 mV, suggesting it may be a practical alternative in clinical populations where achieving a 1 mV MEP is challenging, such as in patients with severe muscle denervation.

Conclusions

While threshold-tracking enhances unconditioned MEP reproducibility, it does not reduce the variability of SICI, which is highly dependent on target MEP size. These findings point towards two distinct mechanisms underlying conditioned and unconditioned responses and refine understanding of SICI variability.

目的评价连续跟踪维持恒定运动诱发电位（MEP）振幅的非条件阈值是否能改善基于振幅的短间隔皮质内抑制（SICI）测量结果的可变性。方法55名健康受试者采用6种SICI方案，在2天内进行2次检测。条件刺激（CS）强度设定为200µV目标（RMT200）静息运动阈值的70%，而测试刺激（TS）强度设定为1 mV或200µV。协议包括传统的A-SICI（固定CS和TS），混合A-SICI（固定CS和更新阈值跟踪TS）；跟踪A-SICI （CS和TS都通过阈值跟踪更新）。在刺激间隔（ISIs）为1、2.5和3 ms时，分析了非条件反应和条件反应的变异性。结果阈值跟踪将几何标准差（以因子表示）测量的非条件反应的变异性在1 mV （x /÷1.61）至1.39；p<0.0001)和200µV靶(×/÷2.21至1.30；术中,0.0001)。然而，抑制措施的可变性在不同的方案中没有显著差异。在所有ISIs中，200µV MEP靶的抑制作用明显小于1 mV （p<0.001）。a - sici 200µV跟踪方案显示出与a - sici固定1mv相当的可靠性，这表明它可能是临床人群中实现1mv MEP具有挑战性的实用替代方案，例如严重肌肉失神经患者。结论阈值跟踪虽然提高了无条件MEP的再现性，但并没有降低SICI的变异性，后者高度依赖于目标MEP的大小。这些发现指出了条件反应和非条件反应的两种不同机制，并完善了对SICI变异性的理解。

{"title":"Short-interval intracortical inhibition (SICI): effect of target tracking on variability of responses for 1 mV and 200µV test-alone targets","authors":"Gaia Fanella , Hugh Bostock , Gintaute Samusyte , Anna Bystrup Jacobsen , James Howells , Bülent Cengiz , Hasan Kılınç , Martin Koltzenburg , Agessandro Abrahao , Lorne Zinman , Benjamin Bardel , Jean-Pascal Lefaucheur , Lucía Del Valle , José Manuel Matamala , Hatice Tankisi","doi":"10.1016/j.neucli.2025.103091","DOIUrl":"10.1016/j.neucli.2025.103091","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate whether continuously tracking unconditioned thresholds for maintaining constant motor-evoked potential (MEP) amplitudes improves the variability of amplitude-based short-interval intracortical inhibition (SICI) measurements.</div></div><div><h3>Methods</h3><div>Fifty-five healthy subjects were tested twice on two days with six SICI protocols. Conditioning stimulus (CS) intensity was set to 70 % of the resting motor threshold for a 200µV target (RMT200), while test stimulus (TS) intensity targeted MEP of either 1 mV or 200µV. Protocols included conventional A-SICI (fixed CS and TS), hybrid A-SICI (fixed CS and updated TS by threshold tracking); tracked A-SICI (both CS and TS updated by threshold tracking). Variability in unconditioned and conditioned responses was analyzed across interstimulus intervals (ISIs) of 1, 2.5, and 3 ms.</div></div><div><h3>Results</h3><div>Threshold-tracking reduced variability of the unconditioned responses measured by geometric standard deviation (expressed as a factor) for 1 mV (×/÷1.61 to 1.39; <em>p</em><0.0001) and 200µV targets (×/÷2.21 to 1.30; <em>p</em><0.0001). However, variability of inhibition measures did not differ significantly across protocols. Inhibition with the 200µV MEP target was significantly less than with 1 mV across all ISIs (<em>p</em><0.001). The A-SICI 200µV tracked protocol showed reliability comparable to A-SICI fixed 1 mV, suggesting it may be a practical alternative in clinical populations where achieving a 1 mV MEP is challenging, such as in patients with severe muscle denervation.</div></div><div><h3>Conclusions</h3><div>While threshold-tracking enhances unconditioned MEP reproducibility, it does not reduce the variability of SICI, which is highly dependent on target MEP size. These findings point towards two distinct mechanisms underlying conditioned and unconditioned responses and refine understanding of SICI variability.</div></div>","PeriodicalId":19134,"journal":{"name":"Neurophysiologie Clinique/Clinical Neurophysiology","volume":"55 5","pages":"Article 103091"},"PeriodicalIF":2.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The neurophysiology of aggressiveness: From adaptive behavior to pathology and deep brain stimulation 攻击性的神经生理学：从适应性行为到病理学和深部脑刺激

IF 2.7 4区医学 Q2 CLINICAL NEUROLOGY

Neurophysiologie Clinique/Clinical Neurophysiology

Pub Date : 2025-07-03 DOI: 10.1016/j.neucli.2025.103090

Marie des Neiges Santin

Objectives

Aggressiveness is a complex social behavior that ranges from adaptive to pathological forms. This review synthesizes current knowledge of the neural circuits underlying aggression and explores how this informs neurosurgical strategies for severe, treatment-resistant cases.

Methods

We reviewed recent experimental and clinical studies of the anatomical, functional, and neurochemical bases of aggression, focusing on reactive and proactive subtypes. Emphasis was placed on animal models, optogenetics, and human deep brain stimulation (DBS) approaches.

Results

Internal states – such as hormonal status, energy balance, and prior experience – modulate the threshold for aggression. The ventrolateral part of the ventromedial nucleus of the hypothalamus (VMHvl), particularly its ERα-expressing neurons, plays a central role in triggering aggressive behavior. The core aggression circuit (CAC) includes the VMHvl, amygdala, bed nucleus of the stria terminalis, and ventral premammillary nucleus, under modulation by prefrontal inputs. Aggression is expressed through a direct VMHvl–periaqueductal gray (PAG) pathway for innate actions and an indirect, dopamine-dependent striatal pathway for learned aggression. Serotonin inhibits, while dopamine promotes, proactive aggression.

Discussion

Pathological impulsive aggression, often linked to neurodevelopmental disorders and intellectual disability, may become refractory to pharmacotherapy. In such cases, neurosurgical approaches targeting the Sano triangle—originally described as part of the posterior hypothalamus—have shown promise. Understanding the connectivity and functional role of this region is essential for optimizing targeted interventions. Viewing aggression as a disorder of internal state regulation within defined circuits provides a framework for ethical and effective neuromodulation.

攻击是一种复杂的社会行为，从适应性到病态形式都有。这篇综述综合了目前关于攻击背后的神经回路的知识，并探讨了这如何为严重的、治疗抵抗的病例的神经外科策略提供信息。方法回顾了近年来关于攻击行为的解剖学、功能和神经化学基础的实验和临床研究，重点介绍了反应性和主动性亚型。重点放在动物模型、光遗传学和人类深部脑刺激（DBS）方法上。结果内部状态——如荷尔蒙状态、能量平衡和先前经验——调节攻击的阈值。下丘脑腹内侧核（VMHvl）的腹外侧部分，特别是其表达er α的神经元，在引发攻击行为中起着核心作用。核心攻击回路（CAC）在前额叶输入调制下，包括中脑下丘脑、杏仁核、终纹床核和腹侧乳前核。先天攻击通过直接的vmhv1 -导水管周围灰质（PAG）通路表达，习得攻击通过间接的多巴胺依赖纹状体通路表达。血清素抑制主动攻击，而多巴胺促进主动攻击。病理性冲动攻击通常与神经发育障碍和智力残疾有关，可能对药物治疗难以治愈。在这种情况下，针对萨诺三角（最初被描述为下丘脑后部的一部分）的神经外科方法已经显示出希望。了解该区域的连通性和功能作用对于优化有针对性的干预措施至关重要。将攻击性视为一种内在状态调节的紊乱，这为道德和有效的神经调节提供了一个框架。

{"title":"The neurophysiology of aggressiveness: From adaptive behavior to pathology and deep brain stimulation","authors":"Marie des Neiges Santin","doi":"10.1016/j.neucli.2025.103090","DOIUrl":"10.1016/j.neucli.2025.103090","url":null,"abstract":"<div><h3>Objectives</h3><div>Aggressiveness is a complex social behavior that ranges from adaptive to pathological forms. This review synthesizes current knowledge of the neural circuits underlying aggression and explores how this informs neurosurgical strategies for severe, treatment-resistant cases.</div></div><div><h3>Methods</h3><div>We reviewed recent experimental and clinical studies of the anatomical, functional, and neurochemical bases of aggression, focusing on reactive and proactive subtypes. Emphasis was placed on animal models, optogenetics, and human deep brain stimulation (DBS) approaches.</div></div><div><h3>Results</h3><div>Internal states – such as hormonal status, energy balance, and prior experience – modulate the threshold for aggression. The ventrolateral part of the ventromedial nucleus of the hypothalamus (VMHvl), particularly its ERα-expressing neurons, plays a central role in triggering aggressive behavior. The core aggression circuit (CAC) includes the VMHvl, amygdala, bed nucleus of the stria terminalis, and ventral premammillary nucleus, under modulation by prefrontal inputs. Aggression is expressed through a direct VMHvl–periaqueductal gray (PAG) pathway for innate actions and an indirect, dopamine-dependent striatal pathway for learned aggression. Serotonin inhibits, while dopamine promotes, proactive aggression.</div></div><div><h3>Discussion</h3><div>Pathological impulsive aggression, often linked to neurodevelopmental disorders and intellectual disability, may become refractory to pharmacotherapy. In such cases, neurosurgical approaches targeting the Sano triangle—originally described as part of the posterior hypothalamus—have shown promise. Understanding the connectivity and functional role of this region is essential for optimizing targeted interventions. Viewing aggression as a disorder of internal state regulation within defined circuits provides a framework for ethical and effective neuromodulation.</div></div>","PeriodicalId":19134,"journal":{"name":"Neurophysiologie Clinique/Clinical Neurophysiology","volume":"55 5","pages":"Article 103090"},"PeriodicalIF":2.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Relationships between arm nerve signals measured by somatosensory evoked potentials and functional testing in unilateral cerebral palsy 单侧脑瘫患者躯体感觉诱发电位测量臂神经信号与功能检测的关系

IF 2.7 4区医学 Q2 CLINICAL NEUROLOGY

Neurophysiologie Clinique/Clinical Neurophysiology

Pub Date : 2025-06-26 DOI: 10.1016/j.neucli.2025.103089

Guillaume Zavard , Laura Routier , Fabrice Wallois , Jean-François Catanzariti , Séverine Fritot , Emeline Cailliau , Laurent Béghin , Laurence Gottrand

引用次数: 0

Late-onset brachial plexopathy following an implantable cardioverter defibrillator placement: Expect the unexpected 植入式心律转复除颤器放置后的迟发性臂丛病：期待意外

IF 2.7 4区医学 Q2 CLINICAL NEUROLOGY

Neurophysiologie Clinique/Clinical Neurophysiology

Pub Date : 2025-06-23 DOI: 10.1016/j.neucli.2025.103088

Berkay Yalçınkaya, Ahmet Furkan Çolak, Alp Çetin

引用次数: 0

The potential of OPM-based magnetoencephalography in pre-surgical evaluation of drug-resistant epilepsy 基于opm的脑磁图在耐药癫痫术前评估中的潜力

IF 2.7 4区医学 Q2 CLINICAL NEUROLOGY

Neurophysiologie Clinique/Clinical Neurophysiology

Pub Date : 2025-06-18 DOI: 10.1016/j.neucli.2025.103087

Congyan Chen , Pengfei Teng , Qiujian Meng , Yuying Jiang , Rui Li , Jing Wang , Jiangfen Wu , Yuguang Guan , Mengyang Wang , Jian Zhou , Tianfu Li , Jingwei Sheng , Jia-Hong Gao , Xiongfei Wang , Guoming Luan

引用次数: 0

Transforming spontaneous premature neonatal EEG to spontaneous fetal MEG using a novel machine learning approach 利用一种新的机器学习方法将早产儿自发性脑电图转化为胎儿自发性脑电信号

IF 2.7 4区医学 Q2 CLINICAL NEUROLOGY

Neurophysiologie Clinique/Clinical Neurophysiology

Pub Date : 2025-06-11 DOI: 10.1016/j.neucli.2025.103086

Alban Gallard , Benoit Brebion , Katrin Sippel , Amer Zaylaa , Hubert Preissl , Sahar Moghimi , Yael Fregier , Fabrice Wallois

Objectives

The spontaneous neural activity of premature neonates has been characterized with electroencephalography (EEG). However, evaluation of normal and pathological fetal brain development is still largely unknown. Fetal magnetoencephalography (fMEG) is currently the only available technique to record fetal neural activity. Benefiting from progress in machine learning and artificial intelligence, we aimed to transfer premature EEG to fMEG, to characterize the manifestation of spontaneous activity using the knowledge obtained from premature EEG.

Methods

In this study, 30 high-resolution EEG recordings from premature newborns and 44 fMEG recordings were used to develop a transfer function to predict the spontaneous neural activity of the fetus. After preprocessing, bursts of spontaneous activity were detected using the non-linear energy operator. Next, we proposed a CycleGAN-based model to transform the premature EEG to fMEG and evaluated its performance with both time and frequency measurements.

Results

In the time domain, the values were similar for the mean square error (< 5 %) and correlation (0.91 ± 0.05 and 0.89 ± 0.08) for both transformations between the original data and that generated by CycleGAN. However, considering the frequency content, the CycleGAN-based model modulated the frequency content of EEG to MEG transformed signals relative to the original signals by increasing the power, on average, in all frequency bands, except for the slow delta frequency band.

Conclusion

Our developed model showed promising potential to generate a priori signatures of fMEG manifestations related to spontaneous neural activity. Collectively, this study represents the first steps toward identifying neurobiomarkers of fetal brain development.

目的用脑电图（EEG）对早产儿的自发神经活动进行表征。然而，对正常和病理胎儿大脑发育的评估在很大程度上仍然是未知的。胎儿脑磁图（fMEG）是目前唯一可用的记录胎儿神经活动的技术。得益于机器学习和人工智能的进步，我们的目标是将早产儿脑电图转换为fMEG，利用从早产儿脑电图中获得的知识来表征自发活动的表现。方法采用30张早产儿高分辨率脑电图记录和44张fMEG记录，建立预测胎儿自发神经活动的传递函数。预处理后，利用非线性能量算子检测自发活动爆发。接下来，我们提出了一种基于cyclegan的模型将早熟脑电信号转换为fMEG，并通过时间和频率测量对其性能进行了评估。结果在时域内，均方误差(<；（5%），原始数据与CycleGAN生成数据的相关性分别为0.91±0.05和0.89±0.08。然而，考虑到频率含量，基于cyclegan的模型在除慢δ频带外的所有频带中，平均通过增加功率来调制脑电到脑磁图变换信号相对于原始信号的频率含量。结论该模型在生成与自发神经活动相关的fMEG表现的先验特征方面具有很大的潜力。总的来说，这项研究代表了鉴定胎儿大脑发育的神经生物标志物的第一步。

{"title":"Transforming spontaneous premature neonatal EEG to spontaneous fetal MEG using a novel machine learning approach","authors":"Alban Gallard , Benoit Brebion , Katrin Sippel , Amer Zaylaa , Hubert Preissl , Sahar Moghimi , Yael Fregier , Fabrice Wallois","doi":"10.1016/j.neucli.2025.103086","DOIUrl":"10.1016/j.neucli.2025.103086","url":null,"abstract":"<div><h3>Objectives</h3><div>The spontaneous neural activity of premature neonates has been characterized with electroencephalography (EEG). However, evaluation of normal and pathological fetal brain development is still largely unknown. Fetal magnetoencephalography (fMEG) is currently the only available technique to record fetal neural activity. Benefiting from progress in machine learning and artificial intelligence, we aimed to transfer premature EEG to fMEG, to characterize the manifestation of spontaneous activity using the knowledge obtained from premature EEG.</div></div><div><h3>Methods</h3><div>In this study, 30 high-resolution EEG recordings from premature newborns and 44 fMEG recordings were used to develop a transfer function to predict the spontaneous neural activity of the fetus. After preprocessing, bursts of spontaneous activity were detected using the non-linear energy operator. Next, we proposed a CycleGAN-based model to transform the premature EEG to fMEG and evaluated its performance with both time and frequency measurements.</div></div><div><h3>Results</h3><div>In the time domain, the values were similar for the mean square error (< 5 %) and correlation (0.91 ± 0.05 and 0.89 ± 0.08) for both transformations between the original data and that generated by CycleGAN. However, considering the frequency content, the CycleGAN-based model modulated the frequency content of EEG to MEG transformed signals relative to the original signals by increasing the power, on average, in all frequency bands, except for the slow delta frequency band.</div></div><div><h3>Conclusion</h3><div>Our developed model showed promising potential to generate a priori signatures of fMEG manifestations related to spontaneous neural activity. Collectively, this study represents the first steps toward identifying neurobiomarkers of fetal brain development.</div></div>","PeriodicalId":19134,"journal":{"name":"Neurophysiologie Clinique/Clinical Neurophysiology","volume":"55 5","pages":"Article 103086"},"PeriodicalIF":2.7,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of electrode size in phrenic nerve CMAP 电极尺寸对膈神经CMAP的影响

IF 2.7 4区医学 Q2 CLINICAL NEUROLOGY

Neurophysiologie Clinique/Clinical Neurophysiology

Pub Date : 2025-05-15 DOI: 10.1016/j.neucli.2025.103078

José Castro , Miguel Oliveira Santos , Mamede de Carvalho

引用次数: 0

The F-wave fingerprint of amyotrophic lateral sclerosis 肌萎缩侧索硬化症的f波指纹图谱

IF 2.7 4区医学 Q2 CLINICAL NEUROLOGY

Neurophysiologie Clinique/Clinical Neurophysiology

Pub Date : 2025-05-13 DOI: 10.1016/j.neucli.2025.103080

Mamede de Carvalho

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Neurophysiologie Clinique/Clinical Neurophysiology

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀