Neurophysiologie Clinique/Clinical Neurophysiology最新文献

{"title":"Electrocardiogram (ECG)-based seizure detection using supervised machine-learning","authors":"Eva Diab ,&nbsp;William Gacquer ,&nbsp;Carole Nouboue ,&nbsp;Derambure Philippe ,&nbsp;Bertille Périn ,&nbsp;Simone Chen ,&nbsp;Julien De Jonckheere ,&nbsp;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_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 &amp; 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}

{"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 ,&nbsp;Antoine Gavoille ,&nbsp;Maxime Bonjour ,&nbsp;Florent Cluse ,&nbsp;Martin Moussy ,&nbsp;Juliette Svahn ,&nbsp;Ludivine Kouton ,&nbsp;Aude-Marie Grapperon ,&nbsp;Annie Verschueren ,&nbsp;Emilien Delmont ,&nbsp;Emmanuelle Salort-Campana ,&nbsp;Shahram Attarian ,&nbsp;Etienne Fortanier ,&nbsp;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 &lt;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> &lt; 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> &lt; 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> &lt; 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}

{"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 ,&nbsp;Hugh Bostock ,&nbsp;Gintaute Samusyte ,&nbsp;Anna Bystrup Jacobsen ,&nbsp;James Howells ,&nbsp;Bülent Cengiz ,&nbsp;Hasan Kılınç ,&nbsp;Martin Koltzenburg ,&nbsp;Agessandro Abrahao ,&nbsp;Lorne Zinman ,&nbsp;Benjamin Bardel ,&nbsp;Jean-Pascal Lefaucheur ,&nbsp;Lucía Del Valle ,&nbsp;José Manuel Matamala ,&nbsp;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>&lt;0.0001) and 200µV targets (×/÷2.21 to 1.30; <em>p</em>&lt;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>&lt;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}

{"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}

{"title":"Relationships between arm nerve signals measured by somatosensory evoked potentials and functional testing in unilateral cerebral palsy","authors":"Guillaume Zavard ,&nbsp;Laura Routier ,&nbsp;Fabrice Wallois ,&nbsp;Jean-François Catanzariti ,&nbsp;Séverine Fritot ,&nbsp;Emeline Cailliau ,&nbsp;Laurent Béghin ,&nbsp;Laurence Gottrand","doi":"10.1016/j.neucli.2025.103089","DOIUrl":"10.1016/j.neucli.2025.103089","url":null,"abstract":"","PeriodicalId":19134,"journal":{"name":"Neurophysiologie Clinique/Clinical Neurophysiology","volume":"55 4","pages":"Article 103089"},"PeriodicalIF":2.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481431","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}

{"title":"Late-onset brachial plexopathy following an implantable cardioverter defibrillator placement: Expect the unexpected","authors":"Berkay Yalçınkaya,&nbsp;Ahmet Furkan Çolak,&nbsp;Alp Çetin","doi":"10.1016/j.neucli.2025.103088","DOIUrl":"10.1016/j.neucli.2025.103088","url":null,"abstract":"","PeriodicalId":19134,"journal":{"name":"Neurophysiologie Clinique/Clinical Neurophysiology","volume":"55 4","pages":"Article 103088"},"PeriodicalIF":2.7,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364474","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}

{"title":"The potential of OPM-based magnetoencephalography in pre-surgical evaluation of drug-resistant epilepsy","authors":"Congyan Chen ,&nbsp;Pengfei Teng ,&nbsp;Qiujian Meng ,&nbsp;Yuying Jiang ,&nbsp;Rui Li ,&nbsp;Jing Wang ,&nbsp;Jiangfen Wu ,&nbsp;Yuguang Guan ,&nbsp;Mengyang Wang ,&nbsp;Jian Zhou ,&nbsp;Tianfu Li ,&nbsp;Jingwei Sheng ,&nbsp;Jia-Hong Gao ,&nbsp;Xiongfei Wang ,&nbsp;Guoming Luan","doi":"10.1016/j.neucli.2025.103087","DOIUrl":"10.1016/j.neucli.2025.103087","url":null,"abstract":"","PeriodicalId":19134,"journal":{"name":"Neurophysiologie Clinique/Clinical Neurophysiology","volume":"55 4","pages":"Article 103087"},"PeriodicalIF":2.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307475","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}

{"title":"Transforming spontaneous premature neonatal EEG to spontaneous fetal MEG using a novel machine learning approach","authors":"Alban Gallard ,&nbsp;Benoit Brebion ,&nbsp;Katrin Sippel ,&nbsp;Amer Zaylaa ,&nbsp;Hubert Preissl ,&nbsp;Sahar Moghimi ,&nbsp;Yael Fregier ,&nbsp;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 (&lt; 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}

{"title":"Effect of electrode size in phrenic nerve CMAP","authors":"José Castro ,&nbsp;Miguel Oliveira Santos ,&nbsp;Mamede de Carvalho","doi":"10.1016/j.neucli.2025.103078","DOIUrl":"10.1016/j.neucli.2025.103078","url":null,"abstract":"","PeriodicalId":19134,"journal":{"name":"Neurophysiologie Clinique/Clinical Neurophysiology","volume":"55 3","pages":"Article 103078"},"PeriodicalIF":2.7,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070693","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}

{"title":"The F-wave fingerprint of amyotrophic lateral sclerosis","authors":"Mamede de Carvalho","doi":"10.1016/j.neucli.2025.103080","DOIUrl":"10.1016/j.neucli.2025.103080","url":null,"abstract":"","PeriodicalId":19134,"journal":{"name":"Neurophysiologie Clinique/Clinical Neurophysiology","volume":"55 4","pages":"Article 103080"},"PeriodicalIF":2.7,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942247","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}