Clinical EEG and Neuroscience最新文献

The report of the electroencephalogram (EEG) results has traditionally been made using free-text formats with a huge variation in descriptions due to several factors. Recently, the International Federation of Clinical Neurophysiology (IFCN) endorsed the use of the Standardized Computer-based Organized Reporting of EEG (SCORE). This system has many advantages, but only some concerns have been investigated so far. This study aimed to assess the end-users acceptability of this proposed EEG report format. A 16-item electronic survey was sent to physicians who use EEG services of a medical diagnosis clinic. Physicians had been receiving the EEG reports in free-text formats from the same three board-certified electroencephalographers for the past three years. In January 2019, the report changed to the SCORE format. The survey assessed five main topics: physician information and historical use of EEG; personal preferences; comparative aspects of the formats; impact of the new format on clinical decision-making; and satisfaction. Thirty-two of 52 have responded to the survey (61%). On average, 81% of the responders have received enough reports with the new format to reliably complete the survey. Every responder prefers the standardized compared to the free-text format. Twenty-five responders like the inclusion of the head model, and interestingly, five suggest including another legend to differentiate "slow activity" from "other abnormal activity". Virtually all responders would recommend the new format, but one-third read only the conclusion. Our findings suggest high acceptability of this standardized report format. Despite the limitations of this study, we hope these findings contribute to the improvement and expansion of standardized EEG reporting systems.

Objective. To measure the EEG signals of the people with chronic stroke in eyes-closed and eyes-open condition and study their relationship with the cognitive function and mental wellbeing. Methods. The investigators would conduct cognitive and mental wellbeing tests on recruited subjects. Their EEG signal was acquired by the 16-channel EEG system. The absolute power under different frequency bands and EEG indices (delta alpha ratio and pairwise derived brain symmetry index) in different eye conditions was calculated. Pearson's correlation was conducted to investigate the association between the clinical tests and the EEG index. Results. 32 subjects were recruited for the study. There was a significant correlation between the pairwise derived brain symmetry index (pdBSI) in eyes-open condition with the Stroop Test (p = .002), Paced Auditory Serial Addition Test-3 s (p = .008)/2 s (p = .002) and WHO-5 well-being scale (p = .023). Conclusions. There is a significant correlation between the brain symmetry index and the cognitive and wellbeing assessment. Brain symmetry index over the delta frequency has been found to be the most useful parameter relating to the clinical score.Significance:It is recommended to use EEG as an adjunctive neuropsychological assessment in clinics for people with chronic stroke, especially for clients who could not undertake conventional assessments (eg aphasia, attention problem).Highlights: There is a significant correlation between the EEG index and the clinical neuropsychological assessmentPairwise Derived Brain Symmetry index in delta frequency range correlated with most of the neuropsychological outcome.It is feasible for us to adopt EEG as an adjunctive assessment in clinical settings.

Background. Biallelic pathogenic variants in the FRRS1L gene are now known to cause developmental and epileptic encephalopathy-37 (DEE37). It can also be associated with chorea and continuous spikes and waves during sleep (CSWS). CSWS is a rare age-related epileptic encephalopathy syndrome of childhood that is characterized by seizures, neurocognitive regression and electrical status epilepticus during sleep (ESES) on electroencephalogram (EEG) that evolves in four stages. Seizures start during the prodromal phase but the ESES on EEG appears only during acute stage and this is the stage when the diagnosis of CSWS can be made. Methods. We present two patients with FRRS1L mutation causing DEE37 with CSWS. We also review twenty-nine cases of DEE37 described in the literature before and discuss its association with CSWS in the total cohort of thirty-one cases. Results. Developmental regression was found in 80% of the patients, mean age of seizure onset was 18 months, ESES or slow spike and wave on the EEG were reported mostly in the older patients (median age of 11 years) and hypsarrhythmia was reported in younger patients (median age of 4 years). This could suggest that if the younger patients were followed longer their EEG would have evolved into ESES during the acute stage of this syndrome and a diagnosis of CSWS could be made. Conclusion. Recognizing ESES and the natural evolution of CSWS is important in diagnosis and proper management of these patients. More detailed report of EEG findings and the evolution of epilepsy and development are needed to further characterize this syndrome.

Purpose: Leptomeningeal carcinomatosis (LC) is a devastating condition in patients with systemic malignancies or primary brain tumors. Although much is known about neuro-radiologic investigations, there is very little information about EEG findings in these patients. Whether EEG is correlated with cranial magnetic resonance imaging (MRI) results and survival has not been investigated. Methods: Medical records of 2340 adult patients with the diagnosis of brain tumor, either metastatic (Group 1) or primary (Group 2), between 2000-2021 were reviewed for the presence of LC and seizures. Demographic and clinical features, laboratory results and Karnofsky performance scores of included patients were noted. Available routine EEG recordings were re-evaluated. Any possible correlation between EEG findings-MRI and EEG findings-survival were investigated statistically. Results: Sixty-six patients with LC and seizures were identified. The most common malignancies were lung cancer and glioblastoma multiforme. Twenty-six EEG recordings of 17 patients in Group 1, and 13 EEGs of 9 patients in Group 2 were available for final analysis. The most common EEG characteristic was background slowing (73%). The most frequent findings were rhythmic periodic patterns or spike wave activity (27%). Sporadic epileptiform discharges (8%) or ictal recordings (4%) were very rare. None of the EEG features correlated with MRI results or survival. Conclusion: There are various EEG patterns in patients with LC and seizures. The most common findings are related to background activity, with rhythmic periodic patterns or spike wave activity being observed less commonly. EEG characteristics do not predict MRI findings or survival.

Objectives. Suicide is the second leading cause of adolescent deaths and may be linked to difficulties with inhibitory and emotional processing. This study assessed the neural correlates of cognitive inhibition during emotional processing in adolescents hospitalized for a suicidal crisis. Methods. Event-related potentials were recorded during an emotional Go/NoGo task in 12 adolescents who attempted suicide and 12 age- and sex-matched healthy controls. Results. Compared to the control group, the suicidal group showed significantly reduced positivity at the time of the P3d (difference waveform reflecting NoGo minus Go trials) in response to happy and neutral, but not sad stimuli. For happy stimuli, this group difference was restricted to the right hemisphere. Further analyses indicated that the suicidal group had a reversed pattern of P3 amplitude in response to inhibition, with lower amplitudes in the NoGo compared to the Go conditions. Suicidal symptoms severity strongly correlated with lower amplitude of the P3d in response to neutral faces. Conclusions. These findings provide more insight into inhibition difficulties in adolescents with acute suicidal risk. Interactions between emotional and inhibition processing should be considered when treating acutely suicidal youths.

Introduction. The use of concurrent, single lead electrocardiograph (ECG) recording, when performing a routine electroencephalograph (EEG), has been standard practice for many years. Previous studies have reported on the usefulness of concurrent EEG in assessing syncope and the detection of newly identified cardiac dysrhythmia but have relied on specialist cardiologist interpretation of the ECG trace. This study expands the understanding of concurrent ECG and provides demographic information regarding the incidence, nature of ECG changes and diagnostic utility of ECG interpretation, during routine EEGs, as evaluated by neurologists. Methods. A single center, retrospective study of routine concurrent EEG and ECG recordings was performed. All routine EEGs, performed within a 12 month period were analysed. Demographic data, underlying comorbidities, reasons for referral and ECG changes were assessed. Results. ECG abnormalities were identified in 147 (13.5%) of concurrent ECG/EEG routine recordings. The presence of ECG abnormalities was significantly associated with the reason for referral, namely being assessed for the evaluation of seizure activity and with increasing patient age. Thirty-eight patients (3.5%) had newly identified ECG abnormalities, of which atrial fibrillation (AF) (12 patients) and sinus bradycardia (9 patients) were the most common. Five patients (0.5%) had a change in their management consequent to the identified ECG changes. Conclusions. These findings support the value of neurologists' interpretation and need for ongoing concurrent ECGs, during routine EEG recordings. The study raises concern about the requesting clinician's response to the identification of newly diagnosed cardiac dysrhythmias.

Event-related oscillations (EROs) may represent sensitive biomarkers or endophenotypes for disorders that underlie risk behaviors such as suicidal thoughts and actions. In this study, young adults of American Indian (AI) (n = 821) and Mexican American (MA) (n = 721) ancestry (age 18-30 yrs) were clinically assessed for internalizing and externalizing disorders, and an internalizing scale was generated by extracting core diagnostic items from 6 lifetime DSM5-compatible diagnoses (social phobia, panic disorder, agoraphobia, obsessive compulsive disorder, post-traumatic stress disorder, major depressive episode) and symptoms of suicidality. EROs were generated to sad, happy and neutral faces, and energy and phase locking of delta ERO oscillations were assessed in frontal areas. An increase in delta ERO energy was found in the frontal lead (FZ) following presentation of the sad facial expressions in those with a history of 10 or more internalizing symptoms compared to those with no symptoms. Increases in delta ERO energy in FZ were also associated with a diagnosis of major depressive disorder (MDD), but not with anxiety disorders or antisocial personality disorder/conduct disorders (ASP). Major depression was also associated with increases in cross-cortical phase-locking (FZ-PZ). A decrease in the percentage of correctly identified neutral faces also was seen among those with 10 or more internalizing symptoms compared to those without internalizing symptoms, and in those with anxiety disorders, but not in those with ASP or MDD as compared to their controls. These findings suggest ERO measures may represent important potential biomarkers of depressive disorders as well as risk indicators for suicidal behaviors.

Diagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/f^X) shape in the power spectrum. In this paper, we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic components revealed that the steepness of the power spectrum better-predicted group status than traditional band-limited oscillatory power in classification analysis. Aperiodic activity also outperformed the predictions made using participants' behavioral responses. Additionally, the differences in aperiodic activity were highly consistent across all electrodes. In sum, compared to oscillations the aperiodic activity appears to be a more accurate and more robust way to differentiate patients with schizophrenia from healthy controls.

Parkinson's disease (PD) is a movement disorder caused by degeneration in dopaminergic neurons. During the disease course, most of PD patients develop mild cognitive impairment (PDMCI) and dementia, especially affecting frontal executive functions. In this study, we tested the hypothesis that PDMCI patients may be characterized by abnormal neurophysiological oscillatory mechanisms coupling frontal and posterior cortical areas during cognitive information processing. To test this hypothesis, event-related EEG oscillations (EROs) during counting visual target (rare) stimuli in an oddball task were recorded in healthy controls (HC; N = 51), cognitively unimpaired PD patients (N = 48), and PDMCI patients (N = 53). Hilbert transform served to estimate instantaneous phase and amplitude of EROs from delta to gamma frequency bands, while modulation index computed ERO phase-amplitude coupling (PAC) at electrode pairs. As compared to the HC and PD groups, the PDMCI group was characterized by (1) more posterior topography of the delta-theta PAC and (2) reversed delta-low frequency alpha PAC direction, ie, posterior-to-anterior rather than anterior-to-posterior. These results suggest that during cognitive demands, PDMCI patients are characterized by abnormal neurophysiological oscillatory mechanisms mainly led by delta frequencies underpinning functional connectivity from frontal to parietal cortical areas.

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.