Electroencephalogram discharges in atypical cognitive development.
Ontology highlight
ABSTRACT:
SUBMITTER: Frye RE
PROVIDER: S-EPMC2896832 | biostudies-other | 2010 May
REPOSITORIES: biostudies-other
Publications
Electroencephalogram discharges in atypical cognitive development.
Frye Richard E RE Butler Ian I Strickland David D Castillo Edwardo E Papanicolaou Andrew A
Journal of child neurology 20100318 5
To investigate the significance of electroencephalogram (EEG) discharges and their treatment, we retrospectively reviewed the charts of 22 children with atypical cognitive development that did not respond to standard educational therapy and demonstrated discharges on EEG. Most children demonstrated no obvious symptoms of seizures, and developmental regression and/or fluctuations were uncommon. The majority of children demonstrated a language and attention disorder and autism symptomatology and h ...[more]
Similar Datasets
Project description:Importance:Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability. Objective:To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs. Design, Setting, and Participants:A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13?262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built. Main Outcomes and Measures:SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation. Results:SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). Conclusions and Relevance:In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.
| S-EPMC6806668 | biostudies-literature
Project description:ObjectiveCognitive impairment occurs frequently in Parkinson's disease (PD) and negatively impacts the patient's quality of life. However, its pathophysiological mechanism remains unclear, hindering the development of new therapies. Changes in brain connectivity are related to cognitive impairment in patients with PD, with the dorsolateral prefrontal cortex (DLPFC) being considered the essential region related to PD cognitive impairment. Nevertheless, few studies have focused on the global connectivity responsible for communication with the DLPFC node, the posterior division of the middle frontal gyrus (PMFG) in patients with PD; this was the focus of this study.MethodsWe applied resting-state electroencephalography (EEG) and calculated a reliable functional connectivity measurement, the debiased weighted phase lag index (dWPLI), to examine inter-regional functional connectivity in 68 patients with PD who were classified into two groups according to their cognitive condition.ResultsWe observed that altered left and right PMFG-based functional connectivity associated with cognitive impairment in patients with PD in the theta frequency bands under the eyes closed condition (r = -0.426, p < 0.001 and r = -0.437, p < 0.001, respectively). Exploratory results based on the MoCA subdomains indicated that poorer visuospatial function was associated with higher right PMFG-based functional connectivity (r = -0.335, p = 0.005), and poorer attention function was associated with higher left and right PMFG-based functional connectivity (r = -0.380, p = 0.001 and r = -0.256, p = 0.035, respectively). Further analysis using logistic regression and receiver operating characteristic (ROC) curves found that this abnormal functional connectivity was an independent risk factor for cognitive impairment [odds ratio (OR): 2.949, 95% confidence interval (CI): 1.294-6.725, p = 0.01 for left PMFG; OR: 11.278, 95% CI: 2.578-49.335, p = 0.001 for right PMFG, per 0.1 U], and provided moderate classification power to discriminate between cognitive abilities in patients with PD [area under the ROC curve (AUC) = 0.770 for left PMFG; AUC = 0.809 for right PMFG].ConclusionThese preliminary findings indicate that abnormal PMFG-based functional connectivity patterns associated with cognitive impairment in the theta frequency bands under the eyes closed condition and altered functional connectivity patterns have the potential to act as reliable biomarkers for identifying cognitive impairment in patients with PD.
| S-EPMC8281812 | biostudies-literature
Project description:During adulthood, electroencephalogram (EEG) recordings are used to distinguish wake, non-rapid eye movement sleep, and rapid eye movement sleep states. The close association between behavioral states and EEG rhythms is reached late during development, after birth in humans and by the end of the second postnatal week in rats and mice. This critical time is also when cortical activity switches from a discontinuous to a continuous pattern. We review the major cellular and network changes that can account for this transition. After this close link is established, new evidence suggests that the slow waves of non-rapid eye movement sleep may function as markers to track cortical development. However, before the EEG can be used to identify behavioral states, two distinct sleep phases--quiet sleep and active sleep--are identified based on behavioral criteria and muscle activity. During this early phase of development, cortical activity is far from being disorganized, despite the presence of long periods of neuronal silence and the poor modulation by behavioral states. Specific EEG patterns, such as spindle bursts and gamma oscillations, have been identified very early on and are believed to play a significant role in the refinement of brain circuits. Because most early EEG patterns do not map to a specific behavioral state, their contribution to the presumptive role of sleep in brain maturation remains to be established and should be a major focus for future research.
| S-EPMC4444390 | biostudies-literature
Project description:BackgroundPostoperative delirium is a common complication that hinders recovery after surgery. Intraoperative electroencephalogram suppression has been linked to postoperative delirium, but it is unknown if this relationship is causal or if electroencephalogram suppression is merely a marker of underlying cognitive abnormalities. The hypothesis of this study was that intraoperative electroencephalogram suppression mediates a nonzero portion of the effect between preoperative abnormal cognition and postoperative delirium.MethodsThis is a prespecified secondary analysis of the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) randomized trial, which enrolled patients age 60 yr or older undergoing surgery with general anesthesia at a single academic medical center between January 2015 and May 2018. Patients were randomized to electroencephalogram-guided anesthesia or usual care. Preoperative abnormal cognition was defined as a composite of previous delirium, Short Blessed Test cognitive score greater than 4 points, or Eight Item Interview to Differentiate Aging and Dementia score greater than 1 point. Duration of intraoperative electroencephalogram suppression was defined as number of minutes with suppression ratio greater than 1%. Postoperative delirium was detected via Confusion Assessment Method or chart review on postoperative days 1 to 5.ResultsAmong 1,113 patients, 430 patients showed evidence of preoperative abnormal cognition. These patients had an increased incidence of postoperative delirium (151 of 430 [35%] vs.123 of 683 [18%], P < 0.001). Of this 17.2% total effect size (99.5% CI, 9.3 to 25.1%), an absolute 2.4% (99.5% CI, 0.6 to 4.8%) was an indirect effect mediated by electroencephalogram suppression, while an absolute 14.8% (99.5% CI, 7.2 to 22.5%) was a direct effect of preoperative abnormal cognition. Randomization to electroencephalogram-guided anesthesia did not change the mediated effect size (P = 0.078 for moderation).ConclusionsA small portion of the total effect of preoperative abnormal cognition on postoperative delirium was mediated by electroencephalogram suppression. Study precision was too low to determine if the intervention changed the mediated effect.
| S-EPMC7228853 | biostudies-literature
Project description:The effects of interictal epileptiform discharges on neurocognitive development in children with medically-intractable epilepsy are poorly understood. Such discharges may have a deleterious effect on the brain's intrinsic connectivity networks, which reflect the organization of functional networks at rest, and in turn on neurocognitive development. Using a combined functional magnetic resonance imaging-magnetoencephalography approach, we examine the effects of interictal epileptiform discharges on intrinsic connectivity networks and neurocognitive outcome. Functional magnetic resonance imaging was used to determine the location of regions comprising various intrinsic connectivity networks in 26 children (7-17 years), and magnetoencephalography data were reconstructed from these locations. Inter-regional phase synchronization was then calculated across interictal epileptiform discharges and graph theoretical analysis was applied to measure event-related changes in network topology in the peri-discharge period. The magnitude of change in network topology (network resilience/vulnerability) to interictal epileptiform discharges was associated with neurocognitive outcomes and functional magnetic resonance imaging networks using dual regression. Three main findings are reported: (i) large-scale network changes precede and follow interictal epileptiform discharges; (ii) the resilience of network topologies to interictal discharges is associated with stronger resting-state network connectivity; and (iii) vulnerability to interictal discharges is associated with worse neurocognitive outcomes. By combining the spatial resolution of functional magnetic resonance imaging with the temporal resolution of magnetoencephalography, we describe the effects of interictal epileptiform discharges on neurophysiological synchrony in intrinsic connectivity networks and establish the impact of interictal disruption of functional networks on cognitive outcome in children with epilepsy. The association between interictal discharges, network changes and neurocognitive outcomes suggests that it is of clinical importance to suppress discharges to foster more typical brain network development in children with focal epilepsy.
| S-EPMC4163036 | biostudies-literature
Project description:Some esophageal cancer patients complicated with depression exhibit cognitive impairments. Frontal electroencephalogram (EEG) may be used as a reliable biomarker for prefrontal-mediated cognitive functions. This study was to investigate alterations of EEG and frontal cognitive impairment in esophageal cancer patients complicated with depression and to assess their correlation.Sixty-five esophageal cancer patients with depression (study group) and 62 healthy controls (control group) were included in this study. The study group were assigned into psychotic depressed (PD, n = 32) and nonpsychotic depressed (NPD, n = 33) subgroups based on complication with psychotic symptoms (Brief Psychiatric Rating Scale [BPRS] >35). EEG examination, Beck self-rating depression scale, and BPRS were used to assess clinical symptoms. Chi-square test, two independent sample t-test, one-way analysis of variance, and Kruskal-Wallis test were utilized to compare the variables between two groups. EEG abnormalities and scores of frontal cognitive function test were analyzed by partial correlation analysis in the PD and NPD subgroups.Compared with control group, the study group displayed greater scores either in the Stroop test (19.89 ± 2.05 vs. 24.12 ± 2.19, P = 0.006) or Color Trails Test (CTT; 11.92 ± 1.01 vs. 15.02 ± 1.63, P = 0.008), and reduced score (35.05 ± 2.01 vs. 32.11 ± 2.38, P = 0.007) in the verbal fluency test (VFT). Compared to NPD subgroup, PD subgroup exhibited increased scores in Stroop test (22.89 ± 2.07 vs. 25.38 ± 2.32, P = 0.009) and CTT (13.16 ± 1.71 vs. 15.82 ± 1.13, P = 0.008). Moreover, increased scores in Stroop test and CTT as well as scores in VFT were associated with the severity of depression. The study group had an abnormal frontal EEG, such as α forward, α asymmetry, α moderation, and increased θ activity relative to control group. Similarly, compared with NPD subgroup, PD subgroup displayed α forward, α asymmetry, and α moderation. The correlation test revealed that α forward and α asymmetry were negatively associated with VFT score, but positively correlated with the scores of CTT and the Stroop test in PD subgroup. In addition, α asymmetry in NPD subgroup was positively related to CTT scores.This study indicated that frontal cognitive impairment in esophageal cancer patients complicated with depression is associated with EEG alterations.
| S-EPMC5547829 | biostudies-other
Project description:Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.
| S-EPMC5448783 | biostudies-literature
Project description:Mild cognitive impairment (MCI) as a precursor of dementia with Lewy bodies (DLB) is the focus of recent research, trying to explore the early mechanisms and possible biomarkers of DLB. Quantitative electroencephalogram (QEEG) methods are able to differentiate early DLB from Alzheimer's disease (AD). The aim of the present study was to assess whether QEEG abnormalities, characterized by dominant frequency <8 Hz and dominant frequency variability >1.5 Hz, typical of early DLB, are already present at the stage of MCI and to evaluate whether EEG abnormalities can predict the development of DLB. Forty-seven MCI subjects were followed for 3 years. EEG recordings were obtained at admission and at the end of the study. At the end of follow-up, 20 subjects had developed probable DLB (MCI-DLB), 14 had probable AD (MCI-AD), 8 did not convert to dementia, 5 developed a non-AD/DLB dementia. One hundred percent of MCI-DLB showed EEG abnormalities at admission. Ninety three percent of MCI-AD maintained a normal EEG throughout the study. QEEG may represent a powerful tool to predict the progression from MCI to DLB with a sensitivity and specificity close to 100%.
| S-EPMC4270449 | biostudies-literature
Project description:Asymmetric patterns of frontal brain electrical activity reflect approach and avoidance tendencies, with stability of relative right activation associated with withdrawal emotions/motivation and left hemisphere activation linked with approach and positive affect. However, considerable shifts in approach/avoidance-related lateralization have been reported for children not targeted because of extreme temperament. In this study, dynamic effects of frontal electroencephalogram (EEG) power within and across hemispheres were examined throughout early childhood. Specifically, EEG indicators at 5, 10, 24, 36, 48, and 72 months-of-age (n = 410) were analyzed via a hybrid of difference score and panel design models, with baseline measures and subsequent time-to-time differences modeled as potentially influencing all subsequent amounts of time-to-time change (i.e., predictively saturated). Infant sex was considered as a moderator of dynamic developmental effects, with temperament attributes measured at 5 months examined as predictors of EEG hemisphere development. Overall, change in left and right frontal EEG power predicted declining subsequent change in the same hemisphere, with effects on the opposing neurobehavioral system enhancing later growth. Infant sex moderated the pattern of within and across-hemisphere effects, wherein for girls more prominent left hemisphere influences on the right hemisphere EEG changes were noted and right hemisphere effects were more salient for boys. Largely similar patterns of temperament prediction were observed for the left and the right EEG power changes, with limited sex differences in links between temperament and growth parameters. Results were interpreted in the context of comparable analyses using parietal power values, which provided evidence for unique frontal effects.
| S-EPMC6893078 | biostudies-literature
Project description:Recent studies have made substantial progress in understanding the interactions between cognitive functions, from language to cognitive control, attention, and memory. However, dissociating these functions has been hampered by the close proximity of regions involved, as in the case in the prefrontal and parietal cortex. In this article, we review a series of studies that investigated the relationship between language and other cognitive functions in an alternative way - by examining their functional (co-)lateralization. We argue that research on the hemispheric lateralization of language and its link with handedness can offer an appropriate starting-point to shed light on the relationships between different functions. Besides functional interactions, anatomical asymmetries in non-human primates and those underlying language in humans can provide unique information about cortical organization. Finally, some open questions and criteria are raised for an ideal theoretical model of the cortex based on hemispheric specialization.
| S-EPMC5563700 | biostudies-other