Pub Date : 2026-01-01Epub Date: 2026-02-06DOI: 10.1016/j.cnp.2026.02.001
Sampsa Lohi , Petro Julkunen , Reetta Kälviäinen , Esa Mervaala
Objective
We present ‘Epicurrents’, an open-source JavaScript library for processing and displaying neurophysiological signal data in a web browser.
Methods
The library follows a modular architecture to enable support for multiple clinical neurophysiology modalities. It supports open standards such as the European Data Format (EDF) and Digital Imaging and Communications in Medicine (DICOM), with optional Python and Open Neural Network Exchange (ONNX) integrations for scientific signal processing. The application presented in this article is platform agnostic, requires no installation, and is usable both online and offline as a progressive web application.
Results
The library has been tested in real-world educational and research projects and is used by the European Academy of Neurology for hands-on EEG-education in their congresses. While JavaScript’s memory management poses limitations for processing large recordings, architectural workarounds such as shared memory buffers and asynchronous processing have resulted in improved performance.
Conclusions
The application presented here is not intended nor certified for clinical diagnostics, but its accessibility and extensibility make it a promising tool for neurophysiology education and research.
Significance
Epicurrents is the first modular JavaScript library for clinical neurophysiology education and illustrates how web technologies can also enhance collaborative scientific research in the field of clinical neurophysiology.
{"title":"An open-source JavaScript clinical neurophysiology library for education and clinical research","authors":"Sampsa Lohi , Petro Julkunen , Reetta Kälviäinen , Esa Mervaala","doi":"10.1016/j.cnp.2026.02.001","DOIUrl":"10.1016/j.cnp.2026.02.001","url":null,"abstract":"<div><h3>Objective</h3><div>We present ‘Epicurrents’, an open-source JavaScript library for processing and displaying neurophysiological signal data in a web browser.</div></div><div><h3>Methods</h3><div>The library follows a modular architecture to enable support for multiple clinical neurophysiology modalities. It supports open standards such as the European Data Format (EDF) and Digital Imaging and Communications in Medicine (DICOM), with optional Python and Open Neural Network Exchange (ONNX) integrations for scientific signal processing. The application presented in this article is platform agnostic, requires no installation, and is usable both online and offline as a progressive web application.</div></div><div><h3>Results</h3><div>The library has been tested in real-world educational and research projects and is used by the European Academy of Neurology for hands-on EEG-education in their congresses. While JavaScript’s memory management poses limitations for processing large recordings, architectural workarounds such as shared memory buffers and asynchronous processing have resulted in improved performance.</div></div><div><h3>Conclusions</h3><div>The application presented here is not intended nor certified for clinical diagnostics, but its accessibility and extensibility make it a promising tool for neurophysiology education and research.</div></div><div><h3>Significance</h3><div>Epicurrents is the first modular JavaScript library for clinical neurophysiology education and illustrates how web technologies can also enhance collaborative scientific research in the field of clinical neurophysiology.</div></div>","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"11 ","pages":"Pages 115-125"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-05-17DOI: 10.1016/j.cnp.2026.05.002
Khalil S. Husari , Michael Porambo , Vivek S. Yedavalli , Peter W. Kaplan
Over the past decade, Computed Tomography perfusion (CTP) and Magnetic Resonance Perfusion (MRP) imaging have become increasingly valuable tools in the assessment of acute neurological emergencies, including stroke and stroke mimics. These modalities can demonstrate seizure-related alterations in regional cerebral blood flow across the spectrum of epileptic conditions. While ictal states classically manifest as regional hyperperfusion and interictal periods typically show hypoperfusion or normal perfusion, the perfusion patterns are more nuanced and influenced by several variables. This review examines the current literature on CTP and MRP applications in status epilepticus, seizures, and ictal-interictal continuum patterns, with particular emphasis on the comparative diagnostic accuracy across different clinical scenarios and imaging techniques.
{"title":"Brain perfusion imaging in patients with status epilepticus, seizures, and IIC patterns","authors":"Khalil S. Husari , Michael Porambo , Vivek S. Yedavalli , Peter W. Kaplan","doi":"10.1016/j.cnp.2026.05.002","DOIUrl":"10.1016/j.cnp.2026.05.002","url":null,"abstract":"<div><div>Over the past decade, Computed Tomography perfusion (CTP) and Magnetic Resonance Perfusion (MRP) imaging have become increasingly valuable tools in the assessment of acute neurological emergencies, including stroke and stroke mimics. These modalities can demonstrate seizure-related alterations in regional cerebral blood flow across the spectrum of epileptic conditions. While ictal states classically manifest as regional hyperperfusion and interictal periods typically show hypoperfusion or normal perfusion, the perfusion patterns are more nuanced and influenced by several variables. This review examines the current literature on CTP and MRP applications in status epilepticus, seizures, and ictal-interictal continuum patterns, with particular emphasis on the comparative diagnostic accuracy across different clinical scenarios and imaging techniques.</div></div>","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"11 ","pages":"Pages 359-373"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147991522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-10-19DOI: 10.1016/j.cnp.2026.10.007
Marjan Makhsous, Maryam Izadpanahi, Ehsan Rezayat
Objective
This systematic review synthesized evidence on quantitative EEG (qEEG) correlates of Parkinson's disease (PD) across five domains: cognition, motor function, pharmacological interventions, neuromodulation, and sleep/non-motor features, evaluating qEEG's potential as a biomarker across these clinical domains.
Methods
Following PRISMA, we searched PubMed, Embase, Web of Science, and Scopus for studies published from 1991 to 2025. We included studies with ≥10 idiopathic PD patients, resting-state qEEG, and empirical data. Methodological quality was assessed using JBI checklists and AMSTAR.
Results
We included 89 studies. Spectral slowing – reduced posterior dominant frequency and increased theta power – was the most consistent correlate of cognitive impairment. Connectivity measures (PLV, graph metrics) showed stronger associations with motor dysfunction, gait impairment, and freezing than spectral power alone. qEEG changed with dopaminergic drugs (increased alpha/beta) and neuromodulation (DBS, tDCS, rTMS, neurofeedback). Sleep studies revealed spindle density reductions linked to dementia risk.
Conclusions
qEEG is a promising biomarker for PD, with distinct electrophysiological correlates across cognitive, motor, pharmacological, and neuromodulatory domains. However, methodological standardization is needed before clinical use.
Significance
With standardized protocols and multicenter validation, qEEG could aid diagnosis, progression tracking, and personalized therapy in PD.
目的本系统综述综合了定量脑电图（qEEG）在认知、运动功能、药物干预、神经调节和睡眠/非运动特征五个领域与帕金森病（PD）相关的证据，评估了定量脑电图（qEEG）在这些临床领域作为生物标志物的潜力。方法采用PRISMA软件，检索PubMed、Embase、Web of Science和Scopus，检索1991 - 2025年间发表的研究。我们纳入了≥10例特发性PD患者、静息状态qEEG和经验数据的研究。使用JBI检查表和AMSTAR评估方法学质量。结果纳入89项研究。谱变慢——后优势频率降低和θ波功率增加——是认知障碍最一致的关联。连通性测量（PLV，图形度量）显示与运动功能障碍，步态障碍和冻结的关联比单独的频谱功率更强。qEEG随多巴胺能药物（α / β增加）和神经调节（DBS、tDCS、rTMS、神经反馈）而改变。睡眠研究显示纺锤体密度降低与痴呆风险有关。结论脑电图在认知、运动、药理和神经调节领域具有明显的电生理相关性，是一种很有前景的帕金森病生物标志物。然而，在临床应用之前，方法需要标准化。通过标准化的协议和多中心验证，qEEG可以帮助PD的诊断、进展跟踪和个性化治疗。
{"title":"Decoding Parkinson's disease: a systematic review of qEEG biomarkers across clinical domains","authors":"Marjan Makhsous, Maryam Izadpanahi, Ehsan Rezayat","doi":"10.1016/j.cnp.2026.10.007","DOIUrl":"10.1016/j.cnp.2026.10.007","url":null,"abstract":"<div><h3>Objective</h3><div>This systematic review synthesized evidence on quantitative EEG (qEEG) correlates of Parkinson's disease (PD) across five domains: cognition, motor function, pharmacological interventions, neuromodulation, and sleep/non-motor features, evaluating qEEG's potential as a biomarker across these clinical domains.</div></div><div><h3>Methods</h3><div>Following PRISMA, we searched PubMed, Embase, Web of Science, and Scopus for studies published from 1991 to 2025. We included studies with ≥10 idiopathic PD patients, resting-state qEEG, and empirical data. Methodological quality was assessed using JBI checklists and AMSTAR.</div></div><div><h3>Results</h3><div>We included 89 studies. Spectral slowing – reduced posterior dominant frequency and increased theta power – was the most consistent correlate of cognitive impairment. Connectivity measures (PLV, graph metrics) showed stronger associations with motor dysfunction, gait impairment, and freezing than spectral power alone. qEEG changed with dopaminergic drugs (increased alpha/beta) and neuromodulation (DBS, tDCS, rTMS, neurofeedback). Sleep studies revealed spindle density reductions linked to dementia risk.</div></div><div><h3>Conclusions</h3><div>qEEG is a promising biomarker for PD, with distinct electrophysiological correlates across cognitive, motor, pharmacological, and neuromodulatory domains. However, methodological standardization is needed before clinical use.</div></div><div><h3>Significance</h3><div>With standardized protocols and multicenter validation, qEEG could aid diagnosis, progression tracking, and personalized therapy in PD.</div></div>","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"11 ","pages":"Pages 900-908"},"PeriodicalIF":2.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149252107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-03-01DOI: 10.1016/j.cnp.2026.02.007
Ana Calzada-Reyes , Lidice Galán-García , Trinidad Virués-Alba , Lidia Charroó-Ruiz , Laura Perez-Mayo , Maria Luisa Bringas-Vega , Jorge Bosch-Bayard , Yanely Acosta-Ymas , Eduardo Aubert-Vázquez , Mayrim Vega-Hernández , Joel Gutiérrez-Gil , Antonio Caballero-Moreno , Annette Valdés-Virués , Mitchell Valdés-Sosa , Peng Ren , Dezhong Yao , Luo Cheng , Roberto Rodriguez-Labrada , Pedro Valdés-Sosa
Objective
This study compared EEG findings in COVID-19 survivors, close contacts, and a normative database using visual EEG inspection, quantitative EEG (QEEG), and source density analysis.
Methods
Resting-state EEG, QEEG, and VARETA inverse solution were analyzed in 173 participants (87 COVID-19 cases, 86 close contacts) alongside the Cuban EEG normative database. Clinical evaluations included neurological exams and neuropsychiatric assessments.
Results
COVID-19 survivors showed significant EEG abnormalities, including background abnormalities, focal abnormalities and diffuse slow activity. QEEG revealed excess theta, alpha, and beta activity in COVID-19 and symptomatic groups, while close contacts had elevated alpha and beta. EEG source analysis identified functional impairments in key brain regions: COVID-19 patients had abnormalities in the supramarginal and angular gyri, while close contacts showed abnormalities in the supramarginal, postcentral, and superior temporal gyri. Symptomatic individuals exhibited superior temporal gyrus abnormalities, whereas asymptomatic cases had impairments in the supramarginal, angular, and postcentral gyri.
Conclusions
Long-term COVID-19 impacts brain function, with QEEG and VARETA revealing region-specific vulnerabilities linked to viral exposure. These tools help assess neurological dysfunction in post-COVID cases.
Significance
This approach is crucial for understanding the COVID-19 long-term effects on brain function and guiding potential therapeutic interventions.
{"title":"EEG Signatures of COVID-19 Survival compared to close contacts and the Cuban EEG normative database","authors":"Ana Calzada-Reyes , Lidice Galán-García , Trinidad Virués-Alba , Lidia Charroó-Ruiz , Laura Perez-Mayo , Maria Luisa Bringas-Vega , Jorge Bosch-Bayard , Yanely Acosta-Ymas , Eduardo Aubert-Vázquez , Mayrim Vega-Hernández , Joel Gutiérrez-Gil , Antonio Caballero-Moreno , Annette Valdés-Virués , Mitchell Valdés-Sosa , Peng Ren , Dezhong Yao , Luo Cheng , Roberto Rodriguez-Labrada , Pedro Valdés-Sosa","doi":"10.1016/j.cnp.2026.02.007","DOIUrl":"10.1016/j.cnp.2026.02.007","url":null,"abstract":"<div><h3>Objective</h3><div>This study compared EEG findings in COVID-19 survivors, close contacts, and a normative database using visual EEG inspection, quantitative EEG (QEEG), and source density analysis.</div></div><div><h3>Methods</h3><div>Resting-state EEG, QEEG, and VARETA inverse solution were analyzed in 173 participants (87 COVID-19 cases, 86 close contacts) alongside the Cuban EEG normative database. Clinical evaluations included neurological exams and neuropsychiatric assessments.</div></div><div><h3>Results</h3><div>COVID-19 survivors showed significant EEG abnormalities, including background abnormalities, focal abnormalities and diffuse slow activity. QEEG revealed excess theta, alpha, and beta activity in COVID-19 and symptomatic groups, while close contacts had elevated alpha and beta. EEG source analysis identified functional impairments in key brain regions: COVID-19 patients had abnormalities in the supramarginal and angular gyri, while close contacts showed abnormalities in the supramarginal, postcentral, and superior temporal gyri. Symptomatic individuals exhibited superior temporal gyrus abnormalities, whereas asymptomatic cases had impairments in the supramarginal, angular, and postcentral gyri.</div></div><div><h3>Conclusions</h3><div>Long-term COVID-19 impacts brain function, with QEEG and VARETA revealing region-specific vulnerabilities linked to viral exposure. These tools help assess neurological dysfunction in post-COVID cases.</div></div><div><h3>Significance</h3><div>This approach is crucial for understanding the COVID-19 long-term effects on brain function and guiding potential therapeutic interventions.</div></div>","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"11 ","pages":"Pages 174-186"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-03-03DOI: 10.1016/j.cnp.2026.02.009
José Castro
{"title":"Evaluating diaphragm motor response variability in electric and magnetic phrenic nerve stimulations during passive expiration","authors":"José Castro","doi":"10.1016/j.cnp.2026.02.009","DOIUrl":"10.1016/j.cnp.2026.02.009","url":null,"abstract":"","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"11 ","pages":"Pages 172-173"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Discrimination between EMG activity such as fibrillation potentials/positive sharp waves (Fib/PSW), end plate spikes (EPS), fasciculation potentials (FP), and contaminating voluntary motor unit potentials (MUP) is mandatory for EMG diagnosis. Discharge rhythm is the key for discrimination. We devised a new parameter, Random Index (RI), which quantifies the rhythm and takes a value from 0 to 1, smaller for regular trains of discharges. This study evaluated the utility of RI as well as modified versions of the regularity indices proposed in past reports.
Methods
EMG records of patients with amyotrophic lateral sclerosis were retrospectively reviewed. EPS were collected also from a healthy volunteer. The EMG activity was classified by an expert. RI and other regularity indices as well as the median instantaneous firing rate (IFRm) were calculated.
Results
Analyzed sequences were 73 Fib/PSW, 27 EPS, 24 FP, and 36 MUP. The four types were clearly separated over the 2-dimensional plots of regularity indices vs. IFRm. Especially, Fib/PSW and EPS were far separated in these plots. RI achieved significantly better discrimination between Fib/PSW and MUP than other indices.
Conclusion
RI is a robust tool for discriminating EMG activity.
Significance
RI and other regularity indices would be useful for educational purpose.
{"title":"Discrimination of spontaneous activity in needle EMG based on the quantitative assessment of the discharge rhythm using “Random Index”","authors":"Taiji Mukai , Takamichi Kanbayashi , Masahito Kobayashi , Masamichi Peter Okano , Keisuke Tachiyama , Yosuke Miyaji , Yuki Hatanaka , Shunsuke Kobayashi , Masahiro Sonoo","doi":"10.1016/j.cnp.2026.01.003","DOIUrl":"10.1016/j.cnp.2026.01.003","url":null,"abstract":"<div><h3>Objective</h3><div>Discrimination between EMG activity such as fibrillation potentials/positive sharp waves (Fib/PSW), end plate spikes (EPS), fasciculation potentials (FP), and contaminating voluntary motor unit potentials (MUP) is mandatory for EMG diagnosis. Discharge rhythm is the key for discrimination. We devised a new parameter, Random Index (RI), which quantifies the rhythm and takes a value from 0 to 1, smaller for regular trains of discharges. This study evaluated the utility of RI as well as modified versions of the regularity indices proposed in past reports.</div></div><div><h3>Methods</h3><div>EMG records of patients with amyotrophic lateral sclerosis were retrospectively reviewed. EPS were collected also from a healthy volunteer. The EMG activity was classified by an expert. RI and other regularity indices as well as the median instantaneous firing rate (IFRm) were calculated.</div></div><div><h3>Results</h3><div>Analyzed sequences were 73 Fib/PSW, 27 EPS, 24 FP, and 36 MUP. The four types were clearly separated over the 2-dimensional plots of regularity indices vs. IFRm. Especially, Fib/PSW and EPS were far separated in these plots. RI achieved significantly better discrimination between Fib/PSW and MUP than other indices.</div></div><div><h3>Conclusion</h3><div>RI is a robust tool for discriminating EMG activity.</div></div><div><h3>Significance</h3><div>RI and other regularity indices would be useful for educational purpose.</div></div>","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"11 ","pages":"Pages 65-71"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-24DOI: 10.1016/j.cnp.2025.12.008
Yuichiro Shirota , Juuri Otsuka , Masashi Hamada
Objective
To investigate precision, accuracy, and reliability of a threshold hunting method to estimate resting motor threshold (RMT) using parameter estimation by sequential testing and maximum likelihood estimation (PEST-MLE).
Methods
In 53 healthy participants, single pulse transcranial magnetic stimulation was delivered to estimate RMT with cutoff values of 0.05 mV and 0.2 mV. RMT was inferred from 30-trial PEST-MLE algorithm for at maximum two days with two estimation sessions per day, comprising Sessions 1 to 4. Precision and accuracy were assessed within each session. Repeatability was assessed using intraclass correlation coefficient (ICC), reproducibility coefficient (RC), and standard error of measurement (SEM).
Results
For both 0.05 mV and 0.2 mV, 18 trials were needed to have good accuracy. ICC greater than 0.8 was achieved for within-day comparison but the ICC of between-day comparison was lower. RC and SEM were around 10 % and 5 %, respectively.
Conclusions
At 18th trial the estimations were converged in terms of precision and accuracy, and good reliability was achieved at that stage.
Significance
RMT estimation with the PEST-MLE is a rapid and reliable approach that can be implemented for future clinical and scientific studies.
{"title":"Precision, accuracy, and reliability of a threshold hunting method for transcranial magnetic stimulation","authors":"Yuichiro Shirota , Juuri Otsuka , Masashi Hamada","doi":"10.1016/j.cnp.2025.12.008","DOIUrl":"10.1016/j.cnp.2025.12.008","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate precision, accuracy, and reliability of a threshold hunting method to estimate resting motor threshold (RMT) using parameter estimation by sequential testing and maximum likelihood estimation (PEST-MLE).</div></div><div><h3>Methods</h3><div>In 53 healthy participants, single pulse transcranial magnetic stimulation was delivered to estimate RMT with cutoff values of 0.05 mV and 0.2 mV. RMT was inferred from 30-trial PEST-MLE algorithm for at maximum two days with two estimation sessions per day, comprising Sessions 1 to 4. Precision and accuracy were assessed within each session. Repeatability was assessed using intraclass correlation coefficient (ICC), reproducibility coefficient (RC), and standard error of measurement (SEM).</div></div><div><h3>Results</h3><div>For both 0.05 mV and 0.2 mV, 18 trials were needed to have good accuracy. ICC greater than 0.8 was achieved for within-day comparison but the ICC of between-day comparison was lower. RC and SEM were around 10 % and 5 %, respectively.</div></div><div><h3>Conclusions</h3><div>At 18th trial the estimations were converged in terms of precision and accuracy, and good reliability was achieved at that stage.</div></div><div><h3>Significance</h3><div>RMT estimation with the PEST-MLE is a rapid and reliable approach that can be implemented for future clinical and scientific studies.</div></div>","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"11 ","pages":"Pages 24-31"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-03-20DOI: 10.1016/j.cnp.2026.03.003
Ivan Chakalov
{"title":"Evaluating diaphragm motor response variability in electric and magnetic phrenic nerve stimulations during passive expiration","authors":"Ivan Chakalov","doi":"10.1016/j.cnp.2026.03.003","DOIUrl":"10.1016/j.cnp.2026.03.003","url":null,"abstract":"","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"11 ","pages":"Pages 197-198"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147537692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-02-19DOI: 10.1016/j.cnp.2026.02.003
William T. Creel, Colleen A. Brenner, Richard E Hartman
Objective
Entropy analysis of electroencephalographic (EEG) data provides insight into the complexity of neural activity. This study primarily examined whether cannabis use frequency is associated with alterations in multiscale entropy (MSE) in the prefrontal cortex (PFC) and mapped baseline MSE differences across cortical regions.
Methods
Resting-state EEG was collected from 57 adults: non-users (n = 18), low-frequency users (≤ 1x/week; n = 24), and frequent users (≥ 2x/week; n = 15). MSE values were binned into fine, medium, coarse, and very-coarse scale ranges. Linear mixed-effects models assessed group × scale bin interactions in the PFC and regional differences among lobes, irrespective of cannabis use.
Results
Entropy increased with coarser scales in all groups, but the slope was significantly flatter in frequent users. From the medium bin onward, their PFC scale–entropy slope was ∼ 0.12 bits lower than in non-users, widening to ∼ 0.16 bits at very-coarse scales (FDR-corrected q < 0.012). Across all participants, the PFC exhibited lower MSE than parietal, occipital, and temporal lobes, with regional gaps expanding at coarser scales.
Conclusions
The PFC exhibits intrinsically reduced signal complexity compared with other cortical regions, with further attenuation in frequent cannabis users.
Significance
MSE captures regional cortical dynamics in resting EEG and detects cannabis-related reductions in prefrontal signal complexity at longer temporal scales.
{"title":"Regional and cannabis-related differences in prefrontal multiscale entropy of resting-state EEG","authors":"William T. Creel, Colleen A. Brenner, Richard E Hartman","doi":"10.1016/j.cnp.2026.02.003","DOIUrl":"10.1016/j.cnp.2026.02.003","url":null,"abstract":"<div><h3>Objective</h3><div>Entropy analysis of electroencephalographic (EEG) data provides insight into the complexity of neural activity. This study primarily examined whether cannabis use frequency is associated with alterations in multiscale entropy (MSE) in the prefrontal cortex (PFC) and mapped baseline MSE differences across cortical regions.</div></div><div><h3>Methods</h3><div>Resting-state EEG was collected from 57 adults: non-users (n = 18), low-frequency users (≤ 1x/week; n = 24), and frequent users (≥ 2x/week; n = 15). MSE values were binned into fine, medium, coarse, and very-coarse scale ranges. Linear mixed-effects models assessed group × scale bin interactions in the PFC and regional differences among lobes, irrespective of cannabis use.</div></div><div><h3>Results</h3><div>Entropy increased with coarser scales in all groups, but the slope was significantly flatter in frequent users. From the medium bin onward, their PFC scale–entropy slope was ∼ 0.12 bits lower than in non-users, widening to ∼ 0.16 bits at very-coarse scales (FDR-corrected q < 0.012). Across all participants, the PFC exhibited lower MSE than parietal, occipital, and temporal lobes, with regional gaps expanding at coarser scales.</div></div><div><h3>Conclusions</h3><div>The PFC exhibits intrinsically reduced signal complexity compared with other cortical regions, with further attenuation in frequent cannabis users.</div></div><div><h3>Significance</h3><div>MSE captures regional cortical dynamics in resting EEG and detects cannabis-related reductions in prefrontal signal complexity at longer temporal scales.</div></div>","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"11 ","pages":"Pages 135-141"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147327711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-04-07DOI: 10.1016/j.cnp.2026.04.003
Øystein Dunker , Leonora Onarheim Bergsjø , Gro-Hilde Severinsen , Henrik Glette , Kristian Bernhard Nilsen
Objective
To investigate attitudes, trust, expectations and concerns among patients and healthcare personnel regarding the use of AI-based clinical decision support systems (AI-CDSS) in clinical neurophysiology.
Methods
We iteratively designed a questionnaire and conducted a national survey of patients and healthcare personnel in Norway. Patients were recruited from a quality registry, while healthcare personnel and technicians were recruited from hospital laboratories. Items included binary and Likert-scale questions addressing attitudes, expectations, concerns and trust, as well as a scenario-based trust assessment. Between-group comparisons were performed using non-parametric and categorical statistical tests.
Results
Both groups reported positive attitudes toward AI-CDSS. Patients more often expected AI-CDSS to improve diagnostic accuracy (p = 0.005), while healthcare personnel anticipated improved equity of services across regions (p = 0.017). Key concerns in both groups were related to responsibility, doctor-patient relationships, AI literacy, overreliance on AI, and potential deskilling. The scenario-based question revealed lower trust when AI-CDSS directly influenced decision-making without physician involvement. Trust in public institutions was high, whereas trust in commercial or industry use of health data was consistently low.
Conclusions
Attitudes toward AI-CDSS in clinical neurophysiology are positive, but diagnostic accuracy isn’t enough: Acceptance is conditional on human oversight, delineated responsibilities, and steps taken to improve transparency and minimize deskilling. Patients want to share health data for AI purposes, but the commercial sector needs to build trust.
Significance
AI-CDSS in clinical neurophysiology is promising, but design and implementation need to address key concerns shared by both patients and health care personnel.
{"title":"Is accuracy enough? trust and barriers to AI-based clinical decision support in clinical neurophysiology","authors":"Øystein Dunker , Leonora Onarheim Bergsjø , Gro-Hilde Severinsen , Henrik Glette , Kristian Bernhard Nilsen","doi":"10.1016/j.cnp.2026.04.003","DOIUrl":"10.1016/j.cnp.2026.04.003","url":null,"abstract":"<div><h3>Objective</h3><div>To investigate attitudes, trust, expectations and concerns among patients and healthcare personnel regarding the use of AI-based clinical decision support systems (AI-CDSS) in clinical neurophysiology.</div></div><div><h3>Methods</h3><div>We iteratively designed a questionnaire and conducted a national survey of patients and healthcare personnel in Norway. Patients were recruited from a quality registry, while healthcare personnel and technicians were recruited from hospital laboratories. Items included binary and Likert-scale questions addressing attitudes, expectations, concerns and trust, as well as a scenario-based trust assessment. Between-group comparisons were performed using non-parametric and categorical statistical tests.</div></div><div><h3>Results</h3><div>Both groups reported positive attitudes toward AI-CDSS. Patients more often expected AI-CDSS to improve diagnostic accuracy (p = 0.005), while healthcare personnel anticipated improved equity of services across regions (p = 0.017). Key concerns in both groups were related to responsibility, doctor-patient relationships, AI literacy, overreliance on AI, and potential deskilling. The scenario-based question revealed lower trust when AI-CDSS directly influenced decision-making without physician involvement. Trust in public institutions was high, whereas trust in commercial or industry use of health data was consistently low.</div></div><div><h3>Conclusions</h3><div>Attitudes toward AI-CDSS in clinical neurophysiology are positive, but diagnostic accuracy isn’t enough: Acceptance is conditional on human oversight, delineated responsibilities, and steps taken to improve transparency and minimize deskilling. Patients want to share health data for AI purposes, but the commercial sector needs to build trust.</div></div><div><h3>Significance</h3><div>AI-CDSS in clinical neurophysiology is promising, but design and implementation need to address key concerns shared by both patients and health care personnel.</div></div>","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"11 ","pages":"Pages 262-270"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147649501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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