Adult EEG

EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis

Background

At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application.

Methods

We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality.

Results

EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT.

Conclusion

This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.

Advanced perioperative assessment of neurological function in acute Stanford A aortic dissection

OBJECTIVE

Acute Stanford Type A aortic dissection (AAAD) is a critical condition in vascular surgery, and total aortic arch replacement surgery is the preferred method to save patients' lives. In recent years, as clinical research has advanced, there has been a growing realization of the close association between poor postoperative outcomes in patients and neurological functional deficits. Neurological function monitoring is a medical technique used to evaluate and monitor the functional status of the nervous system.

METHODS

This monitoring involves the assessment of various aspects of the nervous system, including but not limited to nerve conduction velocity, neuromuscular function, electroencephalographic activity, and sensory nerve transmission. Neurological function monitoring has broad clinical applications and can be used to diagnose and monitor many neurological disorders, helping physicians understand patients' neurological functional status and guide treatment plans. During the postoperative recovery process, neurological function monitoring can assist physicians in assessing the potential impact of surgery on the nervous system and monitor the recovery of patients' neurological function.

RESULTS

Studies have shown that neurological function monitoring holds promise in predicting neurological functional prognosis and interventions for patients with aortic dissection.

CONCLUSION

Therefore, the primary objective of this study is to evaluate the effectiveness and reliability of various intraoperative neurological monitoring techniques, neuroimaging examinations, and biomarkers in predicting and assessing postoperative neurological outcomes in patients undergoing AAAD surgery.

Sex differences in sleep architecture in a mouse model of Huntington's disease

Sleep and circadian rhythm disturbances are common features of Huntington's disease (HD). HD is an autosomal dominant neurodegenerative disorder that affects men and women in equal numbers, but some epidemiological studies as well as preclinical work indicate there may be sex differences in disease presentation and progression. Since sex differences in HD could provide important insights to understand cellular and molecular mechanism(s), we used the bacterial artificial chromosome transgenic mouse model of HD (BACHD) to examine whether sex differences in sleep/wake cycles are detectable in an animal model of the disease. Electroencephalography/electromyography (EEG/EMG) was used to measure sleep/wake states and polysomnographic patterns in young adult (12-week-old) male and female wild-type and BACHD mice. Our findings show that male, but not female, BACHD mice exhibited increased variation in phases of the rhythms as compared to age- and sex-matched wild-types. For both rapid-eye movement (REM) and non-rapid eye movement (NREM) sleep, genotypic and sex differences were detected. In particular, the BACHD males spent less time in NREM sleep and exhibited a more fragmented sleep than the other groups. Finally, in response to 6 h of sleep deprivation, both genotypes and sexes displayed the predicted homeostatic responses to sleep loss. These findings suggest that females are relatively protected early in disease progression in this HD model.

Serum neuronal pentraxin 2 is related to cognitive dysfunction and electroencephalogram slow wave/fast wave frequency ratio in epilepsy

BACKGROUND

Cognitive dysfunction in epileptic patients is a high-incidence complication. Its mechanism is related to nervous system damage during seizures, but there is no effective diagnostic biomarker. Neuronal pentraxin 2 (NPTX2) is thought to play a vital role in neurotransmission and the maintenance of synaptic plasticity. This study explored how serum NPTX2 and electroencephalogram (EEG) slow wave/fast wave frequency ratio relate to cognitive dysfunction in patients with epilepsy.

AIM

To determine if serum NPTX2 could serve as a potential biomarker for diagnosing cognitive impairment in epilepsy patients.

METHODS

The participants of this study, conducted from January 2020 to December 2021, comprised 74 epilepsy patients with normal cognitive function (normal group), 37 epilepsy patients with cognitive dysfunction [epilepsy patients with cognitive dysfunction (ECD) group] and 30 healthy people (control group). The mini-mental state examination (MMSE) scale was used to evaluate cognitive function. We determined serum NPTX2 levels using an enzyme-linked immunosorbent kit and calculated the signal value of EEG regions according to the EEG recording. Pearson correlation coefficient was used to analyze the correlation between serum NPTX2 and the MMSE score.

RESULTS

The serum NPTX2 level in the control group, normal group and ECD group were 240.00 ± 35.06 pg/mL, 235.80 ± 38.01 pg/mL and 193.80 ± 42.72 pg/mL, respectively. The MMSE score was lowest in the ECD group among the three, while no significant difference was observed between the control and normal groups. In epilepsy patients with cognitive dysfunction, NPTX2 level had a positive correlation with the MMSE score (r = 0.367, P = 0.0253) and a negative correlation with epilepsy duration (r = -0.443, P = 0.0061) and the EEG slow wave/fast wave frequency ratio value in the temporal region (r = -0.339, P = 0.039).

CONCLUSION

Serum NPTX2 was found to be related to cognitive dysfunction and the EEG slow wave/fast wave frequency ratio in patients with epilepsy. It is thus a potential biomarker for the diagnosis of cognitive impairment in patients with epilepsy.

Early Prediction of Epilepsy after Encephalitis in Childhood Based on EEG and Clinical Features

Objective

The present study was designed to establish and evaluate an early prediction model of epilepsy after encephalitis in childhood based on electroencephalogram (ECG) and clinical features.

Methods

255 patients with encephalitis were randomly divided into training and verification sets and were divided into postencephalitic epilepsy (PE) and no postencephalitic epilepsy (no-PE) according to whether epilepsy occurred one year after discharge. Univariate and multivariate logistic regression analyses were used to screen the risk factors for PE. The identified risk factors were used to establish and verify a model.

Results

This study included 255 patients with encephalitis, including 209 in the non-PE group and 46 in the PE group. Univariate and multiple logistic regression analysis showed that hemoglobin (OR = 0.968, 95% CI = 0.951-0.958), epilepsy frequency (OR = 0.968, 95% CI = 0.951-0.958), and ECG slow wave/fast wave frequency (S/F) in the occipital region were independent influencing factors for PE (P < 0.05).The prediction model is based on the above factors: -0.031 × hemoglobin -2.113 × epilepsy frequency + 7.836 × occipital region S/F + 1.595. In the training set and the validation set, the area under the ROC curve (AUC) of the model for the diagnosis of PE was 0.835 and 0.712, respectively.

Conclusion

The peripheral blood hemoglobin, the number of epileptic seizures in the acute stage of encephalitis, and EEG slow wave/fast wave frequencies can be used as predictors of epilepsy after encephalitis.

Insights into Advanced Neurological Dysfunction Mechanisms Following DBS Surgery in Parkinson's Patients: Neuroinflammation and Pyroptosis

Parkinson's disease is a severe neurodegenerative disorder. Currently, deep brain electrical stimulation (DBS) is the first line of surgical treatment. However, serious neurological impairments such as speech disorders, disturbances of consciousness, and depression after surgery limit the efficacy of treatment. In this review, we summarize the recent experimental and clinical studies that have explored the possible causes of neurological deficits after DBS. Furthermore, we tried to identify clues from oxidative stress and pathological changes in patients that could lead to the activation of microglia and astrocytes in DBS surgical injury. Notably, reliable evidence supports the idea that neuroinflammation is caused by microglia and astrocytes, which may contribute to caspase-1 pathway-mediated neuronal pyroptosis. Finally, existing drugs and treatments may partially ameliorate the loss of neurological function in patients following DBS surgery by exerting neuroprotective effects.

Sex Differences in Sleep Phenotypes in the BACHD Mouse Model of Huntington's Disease

Sleep and circadian rhythm disturbances are common features of Huntington's disease (HD). HD is an autosomal dominant neurodegenerative disorder that affects men and women in equal numbers, but some epidemiological studies as well as preclinical work indicate there may be sex differences in disease progression. Since sex differences in HD could provide important insights to understand cellular and molecular mechanism(s), we used the bacterial artificial chromosome transgenic mouse model of HD (BACHD) to examine whether sex differences in sleep/wake cycles are detectable in an animal model of the disease. Electroencephalography/electromyography (EEG/EMG) was used to measure sleep/wake states and polysomnographic patterns in young adult (12 week-old) male and female wild-type and BACHD mice. Our findings show that male, but not female, BACHD mice exhibited increased variation in phases of the rhythms as compared to age and sex matched wild-types. For both Rapid-eye movement (REM) and Non-rapid eye movement (NREM) sleep, genotypic and sex differences were detected. In particular, the BACHD males spent less time in NREM and exhibited a more fragmented sleep than the other groups. Both male and female BACHD mice exhibited significant changes in delta but not in gamma power compared to wild-type mice. Finally, in response to a 6-hrs sleep deprivation, both genotypes and sexes displayed predicted homeostatic responses to sleep loss. These findings suggest that females are relatively protected early in disease progression in this HD model.

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring

Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG's signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.

Normative Structure of Resting-State EEG in Bipolar Derivations for Daily Clinical Practice: A Pilot Study

We used numerical methods to define the normative structure of resting-state EEG (rsEEG) in a pilot study of 37 healthy subjects (10-74 years old), using a double-banana bipolar montage. Artifact-free 120-200 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum was calculated and used to compute the area for delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands and was log-transformed. Furthermore, Shannon's spectral entropy (SSE) and coherence by bands were computed. Finally, we also calculated the main frequency and amplitude of the posterior dominant rhythm. According to the age-dependent distribution of the bands, we divided the patients in the following three groups: younger than 20; between 21 and 50; and older than 51 years old. The distribution of bands and coherence was different for the three groups depending on the brain lobes. We described the normative equations for the three age groups and for every brain lobe. We showed the feasibility of a normative structure of rsEEG picked up with a double-banana montage.

Evaluation of the Patient With Paroxysmal Spells Mimicking Epileptic Seizures

BACKGROUND

The diagnostic issue of paroxysmal spells, including epileptic seizure (ES) mimics, is one that neurologists frequently encounter. This review provides an up-to-date overview of the most common causes of ES mimics encountered in the outpatient setting.

REVIEW SUMMARY

Paroxysmal spells are characterized by changes in awareness, attention, perception, or abnormal movements. These can be broadly classified as ES and nonepileptic spells (NES). NES mimics ES but are distinguished by their symptomatology and lack of epileptiform activity on electroencephalography. NES may have psychological or physiological underpinnings. Psychogenic non-ES are the most common mimics of ES. Physiological causes of NES include syncope, cerebrovascular, movement, and sleep-related disorders.

CONCLUSIONS

Distinguishing NES from ES at times may be challenging even to the most experienced clinicians. However, detailed history with an emphasis on the clinical clues, including taking a moment-by-moment history of the event from the patient and observers and physical examination, helps create an appropriate differential diagnosis to guide further diagnostic testing. An accurate diagnosis of NES prevents iatrogenic harm, including unnecessary exposure to antiseizure medications and overuse of health care resources. It also allows for the correct specialist referral and appropriate treatment.

Utilization of quantitative electroencephalogram in China: an online questionnaire survey

Background

Quantitative electroencephalogram (QEEG) is a tool that uses a computer to analyze brain activity monitored by electroencephalogram (EEG) according to measurements such as frequency, amplitude, and slope. The purpose of this study was to understand the current situation of QEEG utilization in China and further compare the situations among different regions and different levels of hospitals.

Methods

An online questionnaire comprising 14 questions was designed. Statistical description and analysis were made for the results of the questionnaire survey.

Results

A total of 158 people from 134 medical institutions participated in the survey. The participants came from 21 provinces, accounting for 61.76% (21/34) of the 34 provincial administrative regions in China. The Eastern China region accounted for 66.42% (89/134) of all the medical institutions that participated in this survey. Among the institutions surveyed, QEEG was routinely used in only 23.88% (32/134) of them. Among the medical institutions in which QEEG was routinely used, 87.50% (28/32) of them were 3A-grade hospitals. Among the institutions with routine use of QEEG, 56.25% (18/32) were affiliated hospitals of medical schools. There was a significant difference in the utilization of QEEG between the 3A-grade and non-3A-grade hospitals (P = 0.040) and between the hospitals affiliated to medical schools and those non-affiliated to medical schools (P = 0.020).

Conclusions

The utilization of QEEG is still limited in China. There are differences in the use of QEEG among different hospitals and regions.

EEG background frequency is associated with discharge outcomes in non-ICU hospitalized patients with COVID-19

Objective

To assess risk factors for encephalopathy in non-ICU hospitalized patients with COVID-19 and the effect of encephalopathy on short-term outcomes.

Methods

We collected clinical and electrophysiological characteristics of fifty patients with COVID-19 infection admitted to a ward service and who had an electroencephalogram (EEG) performed. Associations with short-term outcomes including hospital length of stay and discharge disposition were determined from univariate and multivariate statistical analysis.

Results

Clinical delirium was associated with encephalopathy on EEG, cefepime use was associated with increased length of stay, and of all factors analyzed, background frequency on EEG alone was correlated with discharge disposition.

Conclusion

Encephalopathy is one of the major determinants of short-term outcomes in hospitalized non-ICU patients with COVID-19.

Electroencephalography Findings in Older Adults Undergoing Geriatric Treatment: A Surrogate for the Outcome?

Background: Comprehensive geriatric care (CGC) is a multidisciplinary approach developed to meet the needs of older patients. Electroencephalography (EEG) provides valuable information for monitoring the cerebral function. As a surrogate, EEG findings may help to estimate the course of diseases as well as the treatment outcomes. Objectives: Therefore, the aim of the present study is to investigate EEG findings in older patients receiving CGC. Methods: Patients with an initial EEG in a geriatric unit between May 2019 and April 2020 and treated using the CGC approach were analyzed. EEG abnormalities were defined as generalized (diffuse) background slowing and/or intermittent/persistent focal slowing and/or epileptiform discharges. Assessment results for the Barthel index (BI), Tinetti Balance and Gait test (TBGT), and Timed Up and Go test (TUG) were analyzed in relation to the presence of EEG abnormalities. Results: The study included 398 patients (mean age: 83.0 ± 6.57 years, 69.3% were female). Abnormal EEG patterns were found in 94 (23.6%) patients. Patients with EEG abnormalities had a mean age of 83.4 years versus a mean of 82.8 years in those without (p = 0.451). Based on all calculated scores, the majority of the patients improved after CGC, with a tendency to higher-grade improvements in those without EEG abnormalities (BI: 86.2% vs. 75.5%, p = 0.024; TUG: 53.3% vs. 31.9%, p < 0.001); for TBGT, only a gradual difference was detected (TBGT: 79.9% vs. 71.3%, p = 0.088). The presence of EEG abnormalities was associated with the parameters dementia (36.2% vs. 22.4%, p = 0.010), known epilepsy/seizure (19.1% vs. 5.9%, p < 0.001), structural brain lesion (47.9% vs. 19.7%, p < 0.001), and delirium (9.6% vs. 3.6%, p = 0.030) during hospitalization. Conclusions: We found EEG abnormalities in almost a quarter of the patients treated in the geriatric unit. In older patients, the presence of EEG abnormalities is associated with lower improvements after CGC.

A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

Utility of Routine EEG in Emergency Department and Inpatient Service

Objective

The most important indication for EEGs is the investigation of epileptic and nonepileptic seizures. However, it is unclear whether EEG in the emergency depatment (ED) can be useful in managing other conditions. Our objective was to investigate the usefulness of EEGs in the ED.

Methods

We performed an observational, descriptive, retrospective study based on clinical records between 2018 and 2019. We evaluated patients admitted to our ED or hospital wards who underwent an EEG. We defined the EEG results as useful when they prompted changes in antiepileptic drug (AED) treatment or clinical management.

Results

We gathered information from 236 patients with a mean age of 59.23 years (SD ±22.6), of whom 47.9% were women. In patients with seizures, 18.2% were generalized, 27.1% were focal, and 18.6% were unknown. Overall, 25.8% of the EEGs were abnormal. However, in patients with a history of predisposing conditions for epileptic seizures or encephalopathies, the tracing was abnormal in 47.5%. The most frequent alteration on the abnormal EEGs was generalized slowing (18.2%). The EEG was useful in 76.7% of patients: AEDs changed in 8.4% and clinical management changed in 76.2% of patients. The usefulness of EEGs associated with acute ischemic lesions on CT (p = 0.023) and with the diagnosis of vasovagal syncope (p = 0.022).

Conclusions

Routine EEG is useful in the ED, even in patients with a normal CT or MR brain image, because it helps determine clinical management or AED changes.

Pedunculopontine Nucleus--Rapid Eye Movement Sleep--Electroencephalogram--Desynchronization (PRED) Axis in the Evolution of Epilepsy: A Novel Concept

Epilepsy is one of the commonest and oldest neurological diseases in the history of mankind, the exact pathophysiology of the evolution of which still remains elusive. The intimate and intriguing relation between epilepsy and sleep has been known for a long time. Rapid eye movement sleep (REMS) is well documented to exert potent antiepileptic action in human epilepsies and the underlying mechanism of which is largely based on its property to induce widespread electroencephalogram (EEG)-desynchronization. The pedunculopontine nucleus (PPN) owing to its property to enhance REMS has recently been under study for its potential role in intractable epilepsy (IE) and has been proposed as a novel deep brain stimulation target in IE. This brief paper unfolds the existing role of PPN, REMS, and EEG-desynchronization (PRED) in the evolution of epilepsy in an axial manner, the realization and comprehension of which is likely to open new avenues for further understanding of epileptogenesis, improved treatment of epilepsy and reducing the risk of IE.

Electroencephalography in Orthostatic Tremor: A Prospective Study of 30 Patients

Background

Orthostatic tremor (OT) is characterized by a sensation of instability while standing, associated with high frequency (1318 Hz) tremor in the legs. Small retrospective series have reported electroencephalography (EEG) findings in OT with discordant results.

Methods

We prospectively enrolled 30 OT subjects. Mean age = 68.3 (range 5487) with mean disease duration 16.3 years (range 444). A modified 1020 system EEG recording with additional midline electrodes was obtained. EMG electrodes were placed on quadricep muscles. EEG recording was performed at rest, during sleep and while standing unassisted.

Results

In all subjects, EEG showed normal background, normal drowsiness and/or stage 2 sleep, and normal responses to hyperventilation and photic stimulation. These normal results persisted during stance. EEG abnormalities were found in 3 subjects (anterior-mid temporal slow activity), but were not position-dependent and were judged unlikely to be related to OT. Tremor artifact while standing was noted in all subjects, however it was measurable in 26 with frequency in the OT range in 25. When compared with EMG, the average difference in frequency was small at 1.2 Hz (range 0.52.5, p 0.46). Visual EEG analysis in OT patients did not reveal electrographic abnormalities even upon standing unassisted.

Discussion

EEG was normal on this prospective, relatively large OT series. Clinicians interpreting video-EEGs should be aware of the OT artifact that can be seen in EEG and EKG leads mostly while standing.

When should we obtain a routine EEG while managing people with epilepsy?

More than eight decades after its discovery, routine electroencephalogram (EEG) remains a safe, noninvasive, inexpensive, bedside test of neurological function. Knowing when a routine EEG should be obtained while managing people with epilepsy is a critical aspect of optimal care. Despite advances in neuroimaging techniques that aid diagnosis of structural lesions in the central nervous system, EEG continues to provide critical diagnostic evidence with implications on treatment. A routine EEG performed after a first unprovoked seizure can support a clinical diagnosis of epilepsy and differentiate those without epilepsy, classify an epilepsy syndrome to impart prognosis, and characterize seizures for antiseizure management. Despite a current viral pandemic, EEG services continue, and the value of routine EEG is unchanged.

Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers

The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.

Neurophysiologic Characterization of Resting State Connectivity Abnormalities in Schizophrenia Patients

Background: Patients with schizophrenia show abnormal spontaneous oscillatory activity in scalp-level electroencephalographic (EEG) responses across multiple frequency bands. While oscillations play an essential role in the transmission of information across neural networks, few studies have assessed the frequency-specific dynamics across cortical source networks at rest. Identification of the neural sources and their dynamic interactions may improve our understanding of core pathophysiologic abnormalities associated with the neuropsychiatric disorders. Methods: A novel multivector autoregressive modeling approach for assessing effective connectivity among cortical sources was developed and applied to resting-state EEG recordings obtained from n = 139 schizophrenia patients and n = 126 healthy comparison subjects. Results: Two primary abnormalities in resting-state networks were detected in schizophrenia patients. The first network involved the middle frontal and fusiform gyri and a region near the calcarine sulcus. The second network involved the cingulate gyrus and the Rolandic operculum (a region that includes the auditory cortex). Conclusions: Schizophrenia patients show widespread patterns of hyper-connectivity across a distributed network of the frontal, temporal, and occipital brain regions. Results highlight a novel approach for characterizing alterations in connectivity in the neuropsychiatric patient populations. Further mechanistic characterization of network functioning is needed to clarify the pathophysiology of neuropsychiatric and neurological diseases.