Clinical EEG and neuroscience最新文献

Objective: Post-stroke seizures (PSS) are one of the major stroke-related complications. Early therapeutic interventions are critical therefore using electroencephalography (EEG) as a predictive tool for future recurrence may be helpful. We aimed to assess frequencies of different EEG patterns in patients with PSS and their association with seizure recurrence and functional outcomes. Methods: All patients admitted with PSS were included and underwent interictal EEG recording during their admission and monitored for seizure recurrence for 24 months. Results: PSS was reported in 106 patients. Generalized slow wave activity (GSWA) was the most frequent EEG pattern observed (n  =  62, 58.5%), followed by Focal sharp wave discharges (FSWDs) (n  =  57, 55.8%), focal slow wave activity (FSWA) (n  =  56, 52.8%), periodic discharges (PDs) (n  =  13, 12.3%), and ictal epileptiform abnormalities (n  =  6, 5.7%). FSWA and ictal EAs were positively associated with seizure recurrence (p < .001 and p  =  .015 respectively) and it remained significant even after adjusting for age, sex, stroke severity, stroke subtype, or use of anti-seizure medications (ASMs). Other positive associations were status epilepticus (SE) (p  =  .015), and use of older ASM (p < .001). FSWA and GSWA in EEG were positively associated with severe functional disability (p  =  .055, p  =  .015 respectively). Other associations were; Diabetes Mellitus (p  =  .034), Chronic Kidney Disease (p  =  .002), use of older ASMs (p  =  .037), presence of late PSS (p  =  .021), and those with Ischemic stroke (p  =  .010). Conclusions: Recognition and documentation of PSS-related EEG characteristics are important, as certain EEG patterns may help to identify the patients who are at risk of developing recurrence or worse functional outcomes.

Over the past decade, the Diagnostic and Statistical Manual's method of prescribing medications based on presenting symptoms has been challenged. The shift toward precision medicine began with the National Institute of Mental Health and culminated with the World Psychiatric Association's posit that a paradigm shift is needed. This study supports that shift by providing evidence explaining the high rate of psychiatric medication failure and suggests a possible first step toward precision medicine. A large psychiatric practice began collecting electroencephalograms (EEGs) for this study in 2012. The EEGs were analyzed by the same neurophysiologist (board certified in electroencephalography) on 1,233 patients. This study identified 4 EEG biomarkers accounting for medication failure in refractory patients: focal slowing, spindling excessive beta, encephalopathy, and isolated epileptiform discharges. Each EEG biomarker suggests underlying brain dysregulation, which may explain why prior medication attempts have failed. The EEG biomarkers cannot be identified based on current psychiatric assessment methods, and depending upon the localization, intensity, and duration, can all present as complex behavioral or psychiatric issues. The study highlights that the EEG biomarker identification approach can be a positive step toward personalized medicine in psychiatry, furthering the clinical thinking of "testing the organ we are trying to treat."

Objective. Neurophysiological tools remain indispensable instruments in the assessment of psychiatric disorders. These techniques are widely available, inexpensive and well tolerated, providing access to the assessment of brain functional alterations. In the clinical psychiatric context, electrophysiological techniques are required to provide important information on brain function. While there is an immediate benefit in the clinical application of these techniques in the daily routine (emergency assessments, exclusion of organic brain alterations), these tools are also useful in monitoring the progress of psychiatric disorders or the effects of therapy. There is increasing evidence and convincing literature to confirm that electroencephalography and related techniques can contribute to the diagnostic workup, to the identification of subgroups of disease categories, to the assessment of long-term causes and to facilitate response predictions. Methods and Results. In this report we focus on 3 different novel developments of the use of neurophysiological techniques in 3 highly prevalent psychiatric disorders: (1) the value of EEG recordings and machine learning analyses (deep learning) in order to improve the diagnosis of dementia subtypes; (2) the use of mismatch negativity in the early diagnosis of schizophrenia; and (3) the monitoring of addiction and the prevention of relapse using cognitive event-related potentials. Empirical evidence was presented. Conclusion. Such information emphasized the important role of neurophysiological tools in the identification of useful biological markers leading to a more efficient care management. The potential of the implementation of machine learning approaches together with the conduction of large cross-sectional and longitudinal studies was also discussed.

The term visual working memory (VWM) refers to the temporary storage of visual information. In electrophysiological recordings during the change detection task which relates to VWM, contralateral negative slow activity was detected. It was found to occur during the information is kept in memory and it was called contralateral delay activity. In this study, the characteristics of electroencephalogram frequencies of the contralateral and ipsilateral responses in the retention phase of VWM were evaluated by using time-frequency analysis (discrete wavelet transform [DWT]) in the change detection task. Twenty-six volunteers participated in the study. Event-related brain potentials (ERPs) were examined, and then a time-frequency analysis was performed. A statistically significant difference between contralateral and ipsilateral responses was found in the ERP. DWT showed a statistically significant difference between contralateral and ipsilateral responses in the delta and theta frequency bands range. When volunteers were grouped as either high or low VWM capacity the time-frequency analysis between these groups revealed that high memory capacity groups have a significantly higher negative coefficient in alpha and beta frequency bands. This study showed that during the retention phase delta and theta bands may relate to visual memory retention and alpha and beta bands may reflect individual memory capacity.

Objective: Severe pain and other symptoms in complex regional pain syndrome (CRPS), such as allodynia and hyperalgesia, are associated with abnormal resting-state brain network activity. No studies to date have examined resting-state brain networks in CRPS patients using electroencephalography (EEG), which can clarify the temporal dynamics of brain networks. Methods: We conducted microstate analysis using resting-state EEG signals to prospectively reveal direct correlations with pain intensity in CRPS patients (n = 17). Five microstate topographies were fitted back to individual CRPS patients' EEG data, and temporal microstate measures were subsequently calculated. Results: Our results revealed five distinct microstates, termed microstates A to E, from resting EEG data in patients with CRPS. Microstates C, D and E were significantly correlated with pain intensity before pain treatment. Particularly, microstates D and E were significantly improved together with pain alleviation after pain treatment. As microstates D and E in the present study have previously been related to attentional networks and the default mode network, improvement in these networks might be related to pain relief in CRPS patients. Conclusions: The functional alterations of these brain networks affected the pain intensity of CRPS patients. Therefore, EEG microstate analyses may be used to identify surrogate markers for pain intensity.

Background. Predicting neurological outcomes after hypoxic-ischemic brain injury (HIBI) is difficult. Objective. Electroencephalography (EEG) can identify acute and subacute brain abnormalities after hypoxic brain injury and predict HIBI recovery. We examined EEG's ability to predict neurologic outcomes following HIBI. Method. A PRISMA-compliant search was conducted in the Medline, Embase, Cochrane, and Central databases until January 2023. EEG-predicted neurological outcomes in HIBI patients were selected from relevant perspective and retrospective cohort studies. RevMan did meta-analysis, while QDAS2 assessed research quality. Results. Eleven studies with 3761 HIBI patients met the inclusion and exclusion criteria. We aggregated study-level estimates of sensitivity and specificity for EEG patterns determined a priori using random effect bivariate and univariate meta-analysis when appropriate. Positive indicators and anatomical area heterogeneity impacted prognosis accuracy. Funnel plots analyzed publication bias. Significant heterogeneity of greater than 80% was among the included studies with P < 0.001. The area under the curve was 0.94, the threshold effect was P < 0.001, and the sensitivity and specificity, with 95% confidence intervals, were 0.91 (0.84-0.99) and 0.86 (0.75-0.97). EEG detects status epilepticus and burst suppression with good sensitivity, specificity, and little probability of false-negative impairment result attribution. Study quality varied by domain, but patient flow and timing were well conducted in all. Conclusion. EEG can predict the outcome of HIBI with good prognostic accuracy, but more standardized cross-study protocols and descriptions of EEG patterns are needed to better evaluate its prognostic use for patients with HIBI.

To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves and identification of abnormal electrical discharges and slowing of basic waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in neuropsychiatric diseases. However, the recently developed field of mathematics "information geometry" indicates that EEG has another dimension orthogonal to power dimension - that of normalized power variance (NPV). In addition, by introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. Research into this NPV coordinate has been limited thus far. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. Our NPV analysis of eLORETA detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Considering our present and past research findings about NPV, we also discuss the advantage of this application of NPV representing a sensitive early warning signal of cortical impairment. Overall, our findings demonstrated that EEG has another dimension - that of NPV, which contains a lot of information about cortical electrical activity that can be useful in clinical practice.

Background. Persons with a history of traumatic brain injury (TBI) may exhibit short- and long-term cognitive deficits as well as psychiatric symptoms. These symptoms often reflect functional anomalies in the brain that are not detected by standard neuroimaging. In this context, quantitative electroencephalography (qEEG) is more suitable to evaluate non-normative activity in a wide range of clinical settings. Method. We searched the literature using the "Medline" and "Web of Science" online databases. The search was concluded on February 23, 2023, and revised on July 12, 2023. It returned 134 results from Medline and 4 from Web of Science. We then applied the PRISMA method, which led to the selection of 31 articles, the most recent one published in March 2023. Results. The qEEG method can detect functional anomalies in the brain occurring immediately after and even years after injury, revealing in most cases abnormal power variability and increases in slow (delta and theta) versus decreases in fast (alpha, beta, and gamma) frequency activity. Moreover, other findings show that reduced beta coherence between frontoparietal regions is associated with slower processing speed in patients with recent mild TBI (mTBI). More recently, machine learning (ML) research has developed highly reliable models and algorithms for the detection of TBI, some of which are already integrated into commercial qEEG equipment. Conclusion. Accumulating evidence indicates that the qEEG method may improve the diagnosis and management of TBI, in many cases revealing long-term functional anomalies in the brain or even neuroanatomical insults that are not revealed by standard neuroimaging. While FDA clearance has been obtained only for some of the commercially available equipment, the qEEG method allows for systematic, cost-effective, non-invasive, and reliable investigations at emergency departments. Importantly, the automated implementation of intelligent algorithms based on multimodally acquired, clinically relevant measures may play a key role in increasing diagnosis reliability.