Clinical EEG and Neuroscience最新文献

{"title":"Information Contained in EEG Allows Characterization of Cognitive Decline in Neurodegenerative Disorders.","authors":"Sebastian M Keller,&nbsp;Cornelius Reyneke,&nbsp;Ute Gschwandtner,&nbsp;Peter Fuhr","doi":"10.1177/15500594221120734","DOIUrl":"https://doi.org/10.1177/15500594221120734","url":null,"abstract":"<p><p>Over the last few decades, electroencephalography (EEG) has evolved from being a method that purely relies on visual inspection into a quantitative method. Quantitative EEG, or QEEG, enables the assessment of neurological disorders based on spectral features, dynamic characterizations of EEG resting-state activity, brain connectivity analyzes or quantification of EEG signal complexity. The information contained in EEG is multidimensional: Electrodes, positioned at different scalp locations, provide a spatial dimension to the analysis of EEG while time provides a dynamic dimension: This multidimensional property of EEG makes its quantification a challenging task. In this narrative review we present quantitative models focused on different aspects of EEG: While microstate models focus more on the quantification of the dynamic aspects of EEG, spectral methods, connectivity analysis and entropy based models are more concerned with its spatial aspects. Nevertheless, these diverse approaches have provided neurophysiology based biomarkers, especially for monitoring and predicting the course of various neurodegenerative disorders. However, their translation into clinical practice crucially depends on the ability to automate the analysis of EEG in a user-friendly manner, without compromising on the validity of the provided results. Once this has been accomplished, EEG would provide an inexpensive and widely available method for monitoring disease progression, identifying patients at risk of neurodegeneration-especially before the onset of clinical symptoms, and predicting future cognition. For stratification of patients to clinical trials, EEG would allow shortening the trial duration and lowering the number of necessary participants by identifying patients at risk of fast cognitive decline.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"54 4","pages":"391-398"},"PeriodicalIF":2.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9696224","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":"Oscillatory Activities in Multiple Frequency Bands in Patients with Schizophrenia During Motion Perception.","authors":"C Başar-Eroğlu,&nbsp;K M Küçük,&nbsp;L Rürup,&nbsp;C Schmiedt-Fehr,&nbsp;B Mathes","doi":"10.1177/15500594221141825","DOIUrl":"https://doi.org/10.1177/15500594221141825","url":null,"abstract":"<p><p>Patients with schizophrenia show impairment in binding stimulus features into coherent objects, which are reflected in disturbed oscillatory activities. This study aimed to identify disturbances in multiple oscillatory bands during perceptual organization of motion perception in patients with schizophrenia. EEG was recorded from healthy controls and patients with schizophrenia during continuous presentation of a motion stimulus which induces reversals between two exogenously generated perceptions. This stimulus was used to investigate differences in motion binding processes between healthy controls and patients with schizophrenia. EEG signals were transformed into frequency components by means of the Morlet wavelet transformation in order to analyse inter-trial coherences (ITC) in the delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), and gamma (28-48 Hz) frequency bands during exogenous motion binding. Patients showed decreased delta-ITC in occipital and theta-ITC in central and parietal areas, while no significant differences were found for neither alpha nor gamma-ITCs. The present study provides one of the first insights on the oscillatory synchronizations related with the motion perception in schizophrenia. The ITC differences revealed alterations in the consistency of large-scale integration and transfer functions in patients with schizophrenia.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"54 4","pages":"349-358"},"PeriodicalIF":2.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9696240","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":"Mental Activity as the Bridge between Neural Biomarkers and Symptoms of Psychiatric Illness.","authors":"Justin Riddle,&nbsp;Flavio Frohlich","doi":"10.1177/15500594221112417","DOIUrl":"https://doi.org/10.1177/15500594221112417","url":null,"abstract":"<p><p>The Research Domain Criteria (RDoC) initiative challenges researchers to build neurobehavioral models of psychiatric illness with the hope that such models identify better targets that will yield more effective treatment. However, a guide for building such models was not provided and symptom heterogeneity within Diagnostic Statistical Manual categories has hampered progress in identifying endophenotypes that underlie mental illness. We propose that the best chance to discover viable biomarkers and treatment targets for psychiatric illness is to investigate a triangle of relationships: severity of a specific psychiatric symptom that correlates to mental activity that correlates to a neural activity signature. We propose that this is the minimal model complexity required to advance the field of psychiatry. With an understanding of how neural activity relates to the experience of the patient, a genuine understanding for how treatment imparts its therapeutic effect is possible. After the discovery of this three-fold relationship, causal testing is required in which the neural activity pattern is directly enhanced or suppressed to provide causal, instead of just correlational, evidence for the biomarker. We suggest using non-invasive brain stimulation (NIBS) as these techniques provide tools to precisely manipulate spatial and temporal activity patterns. We detail how this approach enabled the discovery of two orthogonal electroencephalography (EEG) activity patterns associated with anhedonia and anxiosomatic symptoms in depression that can serve as future treatment targets. Altogether, we propose a systematic approach for building neurobehavioral models for dimensional psychiatry.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"54 4","pages":"399-408"},"PeriodicalIF":2.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10100601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Relationship Between Cannabinoids and Neural Oscillations: How Cannabis Disrupts Sensation, Perception, and Cognition.","authors":"Shariful A Syed,&nbsp;Ashley M Schnakenberg Martin,&nbsp;Jose A Cortes-Briones,&nbsp;Patrick D Skosnik","doi":"10.1177/15500594221138280","DOIUrl":"https://doi.org/10.1177/15500594221138280","url":null,"abstract":"<p><p>Disruptions in neural oscillations are believed to be one critical mechanism by which cannabinoids, such as delta-9-tetrahyrdrocannabinol (THC; the primary psychoactive constituent of cannabis), perturbs brain function. Here we briefly review the role of synchronized neural activity, particularly in the gamma (30-80 Hz) and theta (4-7 Hz) frequency range, in sensation, perception, and cognition. This is followed by a review of clinical studies utilizing electroencephalography (EEG) which have demonstrated that both chronic and acute cannabinoid exposure disrupts neural oscillations in humans. We also offer a hypothetical framework through which endocannabinoids modulate neural synchrony at the network level. This also includes speculation on how both chronic and acute cannabinoids disrupt functionally relevant neural oscillations by altering the fine tuning of oscillations and the inhibitory/excitatory balance of neural circuits. Finally, we highlight important clinical implications of such oscillatory disruptions, such as the potential relationship between cannabis use, altered neural synchrony, and disruptions in sensation, perception, and cognition, which are perturbed in disorders such as schizophrenia.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"54 4","pages":"359-369"},"PeriodicalIF":2.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9688626","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":"Special Issue: Update on Neural Oscillations in Neuropsychiatric Disorders.","authors":"Bahar Güntekin,&nbsp;Brian F O'Donnell","doi":"10.1177/15500594231181523","DOIUrl":"https://doi.org/10.1177/15500594231181523","url":null,"abstract":"The present issue highlights recent progress in the analysis of oscillatory activity for the assessment, understanding and remediation of psychiatric and neurologic disorders. Contemporary interest in neural synchrony and oscillations in neuropsychiatric disorders has been motivated by investigations of cellular and network oscillations, modeling of neural networks and advances in signal analysis. Hebb proposed that synchronous activation between two neurons strengthened connectivity between them; and that a reverberatory neural circuit could maintain a representation of a stimulus in memory. More recent findings implicate neural synchrony and oscillations in feature binding, attentional selection, arousal, memory operations and consciousness. Consequently, disturbances of oscillations within and between neural ensembles may contribute to sensory, behavioral and cognitive deficits in neuropsychiatric disorders. Because human EEG and MEG activity are generated by synchronized inhibitory and excitatory postsynaptic potentials within large ensembles of neurons, they are well suited for the detection of neural synchrony and oscillations with millisecond temporal resolution. In the present issue, investigators describe alterations of spontaneous or evoked oscillatory activity in a wide range of conditions, including Parkinson’s disease (Bayraktaroglu et al, 2023), Alzheimer’s disease (Fide et al, 2023), neurodegenerative disorders (Keller et al, 2023) depression (Riddle et al, 2023), schizophrenia (Basar Eroglu et al, 2023; Lundin et al, 2023; Peterson et al, 2023; Roach et al, 2023) and cannabis use disorders (Syed et al, 2023). These studies are broadly consistent with the hypothesis that disorders that affect signaling or connectivity among neurons will be associated with aberrant oscillatory activity. Moreover, these data indicate the potential of oscillatory measures for probing neuropathological mechanisms, identifying biomarkers, tracking course and predicting outcomes. Advances in signal analysis have been critical for the characterization of oscillatory activity in neuropsychiatric populations. At a single channel, time frequency analysis yields measures of frequency, amplitude and phase. Time-frequency analysis allows the assessment of the temporal dynamics of event-related oscillations before and after an event of interest (Delorme and Makeig, 2004; Keil et al, 2022). The papers in this issue draw on a rich array of methods, including resting power spectrum, event-related phase locking, phase delay, event-related power spectrum, coherence, and resting or event-related cross-frequency coupling. Lundin et al (2023) investigated the event-related power spectrum in patients with schizophrenia and Fide et al (2023) examined patients with Alzheimer’s disease patients. Event-related phase locking, also termed inter-trial coherence, is a measure of phase consistency across trials relative to a time locking event for a specific frequency band. In th","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"54 4","pages":"347-348"},"PeriodicalIF":2.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9696319","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":"Transcranial Alternating Current Stimulation Alters Auditory Steady-State Oscillatory Rhythms and Their Cross-Frequency Couplings.","authors":"S. de la Salle, J. Choueiry, Mark Payumo, Matt Devlin, Chelsea Noel, A. Abozmal, M. Hyde, Renée Baysarowich, B. Duncan, V. Knott","doi":"10.2139/ssrn.4081702","DOIUrl":"https://doi.org/10.2139/ssrn.4081702","url":null,"abstract":"Auditory cortical plasticity deficits in schizophrenia are evidenced with electroencephalographic (EEG)-derived biomarkers, including the 40-Hz auditory steady-state response (ASSR). Aiming to understand the underlying oscillatory mechanisms contributing to the 40-Hz ASSR, we examined its response to transcranial alternating current stimulation (tACS) applied bilaterally to the temporal lobe of 23 healthy participants. Although not responding to gamma tACS, the 40-Hz ASSR was modulated by theta tACS (vs sham tACS), with reductions in gamma power and phase locking being accompanied by increases in theta-gamma phase-amplitude cross-frequency coupling. Results reveal that oscillatory changes induced by frequency-tuned tACS may be one approach for targeting and modulating auditory plasticity in normal and diseased brains.","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"1 1","pages":"15500594231179679"},"PeriodicalIF":2.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44036347","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":"Experimental Validation of the Cumulative MDRM in theP300 Speller Machine.","authors":"Fodil Zerrouki,&nbsp;Salah Haddab","doi":"10.1177/15500594221078166","DOIUrl":"https://doi.org/10.1177/15500594221078166","url":null,"abstract":"<p><p>The P300 speller Machine is among the leading applications of the electroencephalography (EEG)-based brain computer interfaces (BCIs), it is still a benchmark and a persistent challenge for the BCI Community. EEG signal classification represents the key piece of a BCI chain. The minimum distance to Riemannian mean (MDRM) belongs to these classification methods emerging in different BCI applications such as text spelling by thought. Based on a binary classification of each covariance matrix separately, character prediction is done according to the highest score across the whole set of all repetitions. Minimum cumulative distance to Riemannian mean (MCDRM) is a Cumulative variant of the MDRM, perfectly adapted to the P300 Speller Machine. The power of this variant is that prediction takes a more global proceeding involving the <i>n</i> repetitions together. Indeed, thanks to cumulative distances selected row and column are those having the covariance matrices both closer to the Target barycenter and farther from the non-Target one. This variant overcomes the main MDRM limitations as it improves inter-sessional generalization, allows optimal use of all repetitions and reduces considerably the risk of conflict appearing during the selection of rows and columns leading to character prediction. We applied this variant to the raw signals of Data set II-b of Berlin BCI and compared to the published results the MCDRM offers significantly higher results: 97.5% of correct predictions compared to the 96.5% of the competition winner. The MCDRM fits best with the P300 Speller machine, especially when dealing with noisy signals that requires intelligent and optimal usage of the <i>n</i> repetitions.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"54 3","pages":"238-246"},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9315368","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":"Early Neuroprognostication Using Frontal Spectrograms in Moderately Sedated Cardiac Arrest Patients.","authors":"Jae Hoon Lee","doi":"10.1177/15500594221074888","DOIUrl":"https://doi.org/10.1177/15500594221074888","url":null,"abstract":"<p><p><i>Introduction.</i> The integrated suppression ratio throughout all electroencephalography (EEG) patterns has rarely been studied. The aim of this study was to evaluate the clinical utility of the suppression ratio and hyperactivity of EEG on spectrograms. <i>Methods.</i> This prospective observational study included 73 cardiac arrest patients. Hardwired frontal EEG monitoring with spectrograms (color density spectral arrays, CDSA) was used to predict neurological outcomes. The mean suppression ratio (MSR) and hyperactivity in the high-frequency band (HHF) in the spectrogram were investigated in moderately sedated patients. Sedative doses were considered to estimate the MSR, which was automatically measured. <i>Results.</i> Using propofol 30 to 40 µg/kg/min and remifentanil 0.1 to 0.15 µg/kg/min, all the patients with an MSR >30% died. At day 2, the MSR in patients with a good outcome was 0%. The cut off values were different as an MSR >30% at day 1 (AUC 0.815) and an MSR >1% at day 2 (AUC 0.891). Of the patients with an MSR ≤30%, HHF was the greatest predictor of a poor outcome (OR 12.858, <i>P</i> = .006). The best predictors of a poor outcome using the spectrogram were suppression ratio (SR) >30% or HHF at day 1 (AUC 0.88) and SR >1% or HHF at day 2 (AUC 0.909). <i>Conclusions.</i> The use of MSR and HHF in frontal spectrograms is convenient and may be successfully employed for early neuroprognostication in moderately sedated cardiac arrest patients. However, spectrograms should be used with electroencephalogram considering the effects of sedatives because of the imperfect detection of electrographic seizures and artifacts.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"54 3","pages":"281-288"},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9267351","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":"Borderline and Depression: A Thin EEG Line.","authors":"Jakša Vukojević,&nbsp;Damir Mulc,&nbsp;Ivana Kinder,&nbsp;Eda Jovičić,&nbsp;Krešimir Friganović,&nbsp;Aleksandar Savić,&nbsp;Mario Cifrek,&nbsp;Domagoj Vidović","doi":"10.1177/15500594211060830","DOIUrl":"https://doi.org/10.1177/15500594211060830","url":null,"abstract":"<p><p>In everyday clinical practice, there is an ongoing debate about the nature of major depressive disorder (MDD) in patients with borderline personality disorder (BPD). The underlying research does not give us a clear distinction between those 2 entities, although depression is among the most frequent comorbid diagnosis in borderline personality patients. The notion that depression can be a distinct disorder but also a symptom in other psychopathologies led our team to try and delineate those 2 entities using 146 EEG recordings and machine learning. The utilized algorithms, developed solely for this purpose, could not differentiate those 2 entities, meaning that patients suffering from MDD did not have significantly different EEG in terms of patients diagnosed with MDD and BPD respecting the given data and methods used. By increasing the data set and the spatiotemporal specificity, one could have a more sensitive diagnostic approach when using EEG recordings. To our knowledge, this is the first study that used EEG recordings and advanced machine learning techniques and further confirmed the close interrelationship between those 2 entities.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"54 3","pages":"224-227"},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9266861","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":"Pitfalls in EEG Analysis in Patients With Nonconvulsive Status Epilepticus: A Preliminary Study.","authors":"Ying Wang,&nbsp;Ivan C Zibrandtsen,&nbsp;Richard H C Lazeron,&nbsp;Johannes P van Dijk,&nbsp;Xi Long,&nbsp;Ronald M Aarts,&nbsp;Lei Wang,&nbsp;Johan B A M Arends","doi":"10.1177/15500594211050492","DOIUrl":"https://doi.org/10.1177/15500594211050492","url":null,"abstract":"Objective: Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. Methods: We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals’ clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Results: Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. Conclusion: A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":"54 3","pages":"255-264"},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10084519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9277303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}