Coupling between resting cerebral perfusion and EEG

Novel EEG Metric Correlates with Intracranial Pressure in an Animal Model

BACKGROUND

Intracranial pressure (ICP) can be continuously and reliably measured using invasive monitoring through an external ventricular catheter or an intraparenchymal probe. We explore electroencephalography (EEG) to identify a reliable real-time noninvasive ICP correlate.

METHODS

Using a previously described porcine model of intracranial hypertension, we examined the cross correlation between ICP time series and the slope of the EEG power spectral density as described by ϕ. We calculated ϕ as tan-1 (slope of power spectral density) and normalized it by π, where slope is that of the power-law fit (log frequency vs. log power) to the power spectral density of the EEG signal. Additionally, we explored the relationship between the ϕ time series and cerebral perfusion pressure. A total of 11 intracranial hypertension episodes across three different animals were studied.

RESULTS

The mean correlation between ϕ angle and ICP was - 0.85 (0.15); the mean correlation with cerebral perfusion pressure was 0.92 (0.02). Significant correlation occurred at zero lag. In the absence of intracranial hypertension, the absolute value of the ϕ angle was greater than 0.9 (mean 0.936 radians). However, during extreme intracranial hypertension causing cerebral circulatory arrest, the ϕ angle is on average below 0.9 radians (mean 0.855 radians).

CONCLUSIONS

EEG ϕ angle is a promising real-time noninvasive measure of ICP/cerebral perfusion using surface electroencephalography.

Distinct Spectral Profiles of Awake Resting EEG in Disorders of Consciousness: The Role of Frequency and Topography of Oscillations

The prolonged disorders of consciousness (PDOC) pose a challenge for an accurate clinical diagnosis, mainly due to patients' scarce or ambiguous behavioral responsiveness. Measurement of brain activity can support better diagnosis, independent of motor restrictions. Methods based on spectral analysis of resting-state EEG appear as a promising path, revealing specific changes within the internal brain dynamics in PDOC patients. In this study we used a robust method of resting-state EEG power spectrum parameter extraction to identify distinct spectral properties for different types of PDOC. Sixty patients and 37 healthy volunteers participated in this study. Patient group consisted of 22 unresponsive wakefulness patients, 25 minimally conscious patients and 13 patients emerging from the minimally conscious state. Ten minutes of resting EEG was acquired during wakefulness and transformed into individual power spectra. For each patient, using the spectral decomposition algorithm, we extracted maximum peak frequency within 1-14 Hz range in the centro-parietal region, and the antero-posterior (AP) gradient of the maximal frequency peak. All patients were behaviorally diagnosed using coma recovery scale-revised (CRS-R). The maximal peak frequency in the 1-14 Hz range successfully predicted both neurobehavioral capacity of patients as indicated by CRS-R total score and PDOC diagnosis. Additionally, in patients in whom only one peak within the 1-14 Hz range was observed, the AP gradient significantly contributed to the accuracy of prediction. We have identified three distinct spectral profiles of patients, likely representing separate neurophysiological modes of thalamocortical functioning. Etiology did not have significant influence on the obtained results.

EEG Source Imaging of Infarct Core and Penumbra for Ischemic Stroke Patients

A quantitative method of analyzing EEG signals after stroke onset can help monitor disease progression and tailor treatments. In this work, we present an EEG-based imaging algorithm to estimate the location and size of the stroke infarct core and penumbra tissues. Building on recent advancements in localizing neural silences, we develop an algorithm that utilizes known spectral properties of the infarct core and penumbra to separately localize them. Our algorithm uses these properties to estimate source contributions to the scalp EEG recordings in different frequency bands. Subsequently, it utilizes optimization techniques to search for the affected brain sources iteratively. We test our algorithm on simulated datasets using a realistic MRI head model, achieving center-of-mass error of 12.80mm and 17.24mm, and size estimation error of 21.78% and 36.62% for infarct core and penumbra respectively.

Neuropsychiatric Symptoms in Dementia: Considering a Clinical Role for Electroencephalography

OBJECTIVE

Degenerative dementia is characterized by progressive cognitive decline and neuropsychiatric symptoms. People with Alzheimer's disease (AD), the most common cause of dementia, show synaptic loss and disruption of functional brain networks along with neuritic plaques and neurofibrillary tangles. Electroencephalography (EEG) directly reflects synaptic activity, and among patients with AD it is associated with slowing of background activity. The purpose of this study was to identify associations between neuropsychiatric symptoms and EEG in patients with dementia and to determine whether EEG parameters could be used for clinical assessment of pharmacological treatment of neuropsychiatric symptoms in dementia (NPSD) with galantamine or risperidone.

METHODS

Seventy-two patients with EEG recordings and a score ≥10 on the Neuropsychiatric Inventory (NPI) were included. Clinical assessments included administration of the NPI, the Mini-Mental State Examination (MMSE), and the Cohen-Mansfield Agitation Inventory (CMAI). Patients underwent EEG examinations at baseline and after 12 weeks of treatment with galantamine or risperidone. EEG frequency analysis was performed. Correlations between EEG and assessment scale scores were statistically examined, as were EEG changes from baseline to the week 12 visit and the relationship with NPI, CMAI, and MMSE scores.

RESULTS

Significant correlations were found between NPI agitation and delta EEG frequencies at baseline and week 12. No other consistent and significant relationships were observed between NPSD and EEG at baseline, after NPSD treatment, or in the change in EEG from baseline to follow-up.

CONCLUSIONS

The limited informative findings in this study suggest that there exists a complex relationship between NPSD and EEG; hence, it is difficult to evaluate and use EEG for clinical assessment of pharmacological NPSD treatment.

Beneficial Effect of Sodium Nitrite on EEG Ischaemic Markers in Patients with Subarachnoid Haemorrhage

Subarachnoid haemorrhage (SAH) is associated with long-term disability, serious reduction in quality of life and significant mortality. Early brain injury (EBI) refers to the pathological changes in cerebral metabolism and blood flow that happen in the first few days after ictus and may lead on to delayed cerebral ischaemia (DCI). A disruption of the nitric oxide (NO) pathway is hypothesised as a key mechanism underlying EBI. A decrease in the alpha-delta power ratio (ADR) of the electroencephalogram has been related to cerebral ischaemia. In an experimental medicine study, we tested the hypothesis that intravenous sodium nitrite, an NO donor, would lead to increases in ADR. We studied 33 patients with acute aneurysmal SAH in the EBI phase. Participants were randomised to either sodium nitrite or saline infusion for 1 h. EEG measurements were taken before the start of and during the infusion. Twenty-eight patients did not develop DCI and five patients developed DCI. In the patients who did not develop DCI, we found an increase in ADR during sodium nitrite versus saline infusion. In the five patients who developed DCI, we did not observe a consistent pattern of ADR changes. We suggest that ADR power changes in response to nitrite infusion reflect a NO-mediated reduction in cerebral ischaemia and increase in perfusion, adding further evidence to the role of the NO pathway in EBI after SAH. Our findings provide the basis for future clinical trials employing NO donors after SAH.

Transcranial Doppler Combined With Quantitative Electroencephalography Brain Function Monitoring for Estimating the Prognosis of Patients With Posterior Circulation Cerebral Infarction

Posterior circulation cerebral infarction (PCCI) can lead to deceased infratentorial cerebral blood flow (CBF) and metabolism. Neural activity is closely related to regional cerebral blood flow both spatially and temporally. Transcranial Doppler (TCD) combined with quantitative electroencephalography (QEEG) is a technique that evaluates neurovascular coupling and involves synergy between the metabolic and vascular systems. This study aimed to monitor brain function using TCD-QEEG and estimate the efficacy of TCD-QEEG for predicting the prognosis of patients with PCCI. We used a TCD-QEEG recording system to perform quantitative brain function monitoring; we recorded the related clinical variables simultaneously. The data were analyzed using a Cox proportional hazards regression model. Receiver-operating characteristic (ROC) curve analysis was used to evaluate the cut-off for the diastolic flow velocity (VD) and (delta + theta)/(alpha + beta) ratio (DTABR). The area under the ROC curve (AUROC) was calculated to assess the predictive validity of the study variables. Forty patients (aged 63.7 ± 9.9 years; 30 men) were assessed. Mortality at 90 days was 40%. The TCD indicators of VD [hazard ratio (HR) 0.168, confidence interval (CI) 0.047-0.597, p = 0.006] and QEEG indicators of DTABR (HR 12.527, CI 1.637-95.846, p = 0.015) were the independent predictors of the clinical outcomes. The AUROC after combination of VD and DTABR was 0.896 and showed better predictive accuracy than the Glasgow Coma Scale score (0.75), VD (0.76), and DTABR (0.781; all p < 0.05). TCD-QEEG provides a good understanding of the coupling mechanisms in the brain and can improve our ability to predict the prognosis of patients with PCCI.

The Use of Electroencephalography in Patients with Sepsis: a Review of The Literature

Sepsis-associated encephalopathy (SAE) is the term used to define brain dysfunction related to infections that are principally located outside the central nervous system (CNS). A number of published studies report that electroencephalography (EEG) has been used in the evaluation of patients with sepsis, alone or usually in combination, to evoked potentials and neuroimaging. This was in an effort to assess if EEG can be a tool in the diagnosis and monitoring of the neurological status in sepsis patients. Although there is no specific test for the diagnosis and prognosis of sepsis related encephalopathy, our literature review suggests that EEG has a role in the assessment of this clinical entity. Due to its low cost and simplicity in its performance, EEG could be a potential aid in the assessment of sepsis neurological complications even in the early, subclinical stages of the syndrome. The aim of this review is to summarize the published literature regarding the application and utility of electroencephalography in adult patients with sepsis.

The Changes of qEEG Approximate Entropy during Test of Variables of Attention as a Predictor of Major Depressive Disorder

Evaluating brain function through biosignals remains challenging. Quantitative electroencephalography (qEEG) outcomes have emerged as a potential intermediate biomarker for diagnostic clarification in psychological disorders. The Test of Variables of Attention (TOVA) was combined with qEEG to evaluate biomarkers such as absolute power, relative power, cordance, and approximate entropy from covariance matrix images to predict major depressive disorder (MDD). EEG data from 18 healthy control and 18 MDD patients were monitored during the resting state and TOVA. TOVA was found to provide aspects for the evaluation of MDD beyond resting electroencephalography. The results showed that the prefrontal qEEG theta cordance of the control and MDD groups were significantly different. For comparison, the changes in qEEG approximate entropy (ApEn) patterns observed during TOVA provided features to distinguish between participants with or without MDD. Moreover, ApEn scores during TOVA were a strong predictor of MDD, and the ApEn scores correlated with the Beck Depression Inventory (BDI) scores. Between-group differences in ApEn were more significant for the testing state than for the resting state. Our results provide further understanding for MDD treatment selection and response prediction during TOVA.

Rotated spiral RARE for high spatial and temporal resolution volumetric arterial spin labeling acquisition

Background:

Arterial Spin Labeling (ASL) MRI can provide quantitative images that are sensitive to both time averaged blood flow and its temporal fluctuations. 3D image acquisitions for ASL are desirable because they are more readily compatible with background suppression to reduce noise, can reduce signal loss and distortion, and provide uniform flow sensitivity across the brain. However, single-shot 3D acquisition for maximal temporal resolution typically involves degradation of image quality through blurring or noise amplification by parallel imaging. Here, we report a new approach to accelerate a common stack of spirals 3D image acquisition by pseudo golden-angle rotation and compressed sensing reconstruction without any degradation of time averaged blood flow images.

Methods:

28 healthy volunteers were imaged at 3T with background-suppressed unbalanced pseudo-continuous ASL combined with a pseudo golden-angle Stack-of-Spirals 3D RARE readout. A fully-sampled perfusion-weighted volume was reconstructed by 3D non-uniform Fast Fourier Transform (nuFFT) followed by sum-of-squares combination of the 32 individual channels. Coil sensitivities were estimated followed by reconstruction of the 39 single-shot volumes using an L₁-wavelet Compressed-Sensing reconstruction. Finally, brain connectivity analyses were performed in regions where BOLD signal suffers from low signal-to-noise ratio and susceptibility artifacts.

Results:

Image quality, assessed with a non-reference 3D blurring metric, of full time averaged blood flow was comparable to a conventional interleaved acquisition. The temporal resolution provided by the acceleration enabled identification and quantification of resting-state networks even in inferior regions such as the amygdala and inferior frontal lobes, where susceptibility artifacts can degrade conventional resting-state fMRI acquisitions.

Conclusion:

This approach can provide measures of blood flow modulations and resting-state networks for free within any research or clinical protocol employing ASL for resting blood flow.

Normalization of reduced functional connectivity after revascularization of asymptomatic carotid stenosis

Internal carotid artery stenosis is a risk factor for ischemic stroke. Even in the absence of visible structural brain changes, patients with asymptomatic stenosis are prone to cognitive impairment. On a neuronal level, it was suggested that stenosis may lead to disturbed functional brain connectivity. If so, carotid revascularization should have an effect on hypothesized brain network disturbances. We studied functional connectivity in a motor network by resting-state electroencephalography in 12 patients with high grade asymptomatic carotid stenosis before and after interventional or surgical revascularization as compared to 23 controls. In patients with stenosis, functional connectivity of neural oscillations was significantly decreased prior and improved returning to normal connectivity after revascularization. In a subgroup of patients, also studied by contrast perfusion magnetic resonance imaging, reduced connectivity was associated with decreased regional brain perfusion reflected by increased mean transit time in the middle cerebral artery borderzone. Cognitive testing revealed only minor differences between patients and controls. In summary, we identified oscillatory connectivity changes in patients with asymptomatic carotid stenosis correlating with regional hypoperfusion, which both normalized after revascularization. Hence, electrophysiological changes might be a reversible precursor preceding macroscopic structural brain damage and behavioral impairment in patients with asymptomatic carotid stenosis.

Assessment of a Non Invasive Brain Oximeter in Volunteers Undergoing Acute Hypoxia

INTRODUCTION

Research in traumatic brain injury suggests better patient outcomes when invasive oxygen monitoring is used to detect and correct episodes of brain hypoxia. Invasive brain oxygen monitoring is, however, not routinely used due to the risks, costs and technical challengers. We are developing a non-invasive brain oximeter to address these limitations. The monitor uses the principles of pulse oximetry to record a brain photoplethysmographic waveform and oxygen saturations. We undertook a study in volunteers to assess the new monitor.

PATIENTS AND METHODS

We compared the temporal changes in the brain and skin oxygen saturations in six volunteers undergoing progressive hypoxia to reach arterial saturations of 70%. This approach provides a method to discriminate potential contamination of the brain signal by skin oxygen levels, as the responses in brain and skin oxygen saturations are distinct due to the auto-regulation of cerebral blood flow to compensate for hypoxia. Conventional pulse oximetry was used to assess skin oxygen levels. Blood was also collected from the internal jugular vein and correlated with the brain oximeter oxygen levels.

RESULTS

At baseline, a photoplethysmographic waveform consistent with that expected from the brain was obtained in five subjects. The signal was adequate to assess oxygen saturations in three subjects. During hypoxia, the brain's oximeter oxygen saturation fell to 74%, while skin saturation fell to 50% (P<0.0001). The brain photoplethysmographic waveform developed a high-frequency oscillation of ~7 Hz, which was not present in the skin during hypoxia. A weak correlation between the brain oximeter and proximal internal jugular vein oxygen levels was demonstrated, R2=0.24, P=0.01.

CONCLUSION

Brain oximeter oxygen saturations were relatively well preserved compared to the skin during hypoxia. These findings are consistent with the expected physiological responses and suggest skin oxygen levels did not markedly contaminate the brain oximeter signal.

Reduced Brain Electric Activity and Functional Connectivity in Bipolar Euthymia: An sLORETA Source Localization Study

Bipolar disorder (BD) is a chronic illness with a relapsing and remitting time course. Relapses are manic or depressive in nature and intermitted by euthymic states. During euthymic states, patients lack the criteria for a manic or depressive diagnosis, but still suffer from impaired cognitive functioning as indicated by difficulties in executive and language-related processing. The present study investigated whether these deficits are reflected by altered intracortical activity in or functional connectivity between brain regions involved in these processes such as the prefrontal and the temporal cortices. Vigilance-controlled resting state EEG of 13 euthymic BD patients and 13 healthy age- and sex-matched controls was analyzed. Head-surface EEG was recomputed into intracortical current density values in 8 frequency bands using standardized low-resolution electromagnetic tomography. Intracortical current densities were averaged in 19 evenly distributed regions of interest (ROIs). Lagged coherences were computed between each pair of ROIs. Source activity and coherence measures between patients and controls were compared (paired t tests). Reductions in temporal cortex activity and in large-scale functional connectivity in patients compared to controls were observed. Activity reductions affected all 8 EEG frequency bands. Functional connectivity reductions affected the delta, theta, alpha-2, beta-2, and gamma band and involved but were not limited to prefrontal and temporal ROIs. The findings show reduced activation of the temporal cortex and reduced coordination between many brain regions in BD euthymia. These activation and connectivity changes may disturb the continuous frontotemporal information flow required for executive and language-related processing, which is impaired in euthymic BD patients.

Acute EEG Patterns Associated With Transient Ischemic Attack

BACKGROUND

Transient ischemic attack (TIA) is characterized by stroke-like neurologic signs and symptoms in the absence of demonstrable structural neuropathology. There is no test for TIA, with classification often reliant on subjective, retrospective report. Functional brain measures such as the electroencephalogram (EEG) may be helpful in objectively detecting and describing the pathophysiology of TIA, but this has not been adequately examined.

METHODS

EEG was obtained from a single electrode over the left frontal lobe during 3-minute resting-state and auditory oddball conditions administered to consecutive patients within 72 hours of admission to the acute stroke ward of a tertiary hospital. Separately, patients were classified by their treating team as having suffered either an ischemic stroke (n = 10) or a TIA (n = 10). Relative power of delta, theta, alpha, and beta EEG frequency bands were extracted for comparison between the 2 clinical groups and an existing normative sample of 10 healthy, age-, gender-, and education-matched older adults.

RESULTS

Analysis of variance with post hoc testing identified pronounced delta activity in stroke patients, while alpha and beta power were elevated in TIA patients. Both patient groups exhibited attenuated theta activity compared with healthy controls. Receiver operating characteristic curve analysis identified thresholds for each EEG frequency capable of distinguishing the 3 participant groups.

CONCLUSIONS

TIA, ischemic stroke, and healthy aging are each associated with distinct electrophysiological profiles. These preliminary findings suggest that acute EEG may be helpful in elucidating the pathophysiology and reversibility of TIA symptoms, and further exploration of the value of this unique functional brain data is encouraged.

Cross-Frequency Coupling Between Cerebral Blood Flow Velocity and EEG in Ischemic Stroke Patients With Large Vessel Occlusion

Background: Neurovascular coupling enables a rapid adaptation of cerebral blood flow (CBF) to support neuronal activities. Whether this mechanism is compromised during the acute phase after ischemic stroke remains unknown. In this study, we applied a phase-amplitude cross-frequency coupling (PAC) algorithm to investigate multimodal neuro signals including CBF velocity (CBFV), and electroencephalography (EEG). Methods: Acute ischemic stroke patients admitted to the Neurointensive Care Unit, Tiantan Hospital, Capital Medical University (Beijing, China) with continuous monitoring of 8-lead EEG (F3-C3, T3-P3, P3-O1, F4-C4, T4-P4, P4-O2), non-invasive arterial blood pressure (ABP), and bilateral CBFV of the middle cerebral arteries or posterior cerebral arteries were retrospectively analyzed. PAC was calculated between the phase of CBFV in frequency bands (0-0.05 and 0.05-0.15 Hz) and the EEG amplitude in five bands (δ, θ, α, β, γ). The global PAC was calculated as the sum of all PACs across the six EEG channels and five EEG bands for each patient. The hemispherical asymmetry of cross-frequency coupling (CFC) was calculated as the difference between left and right PAC. Results: Sixteen patients (3 males) met our inclusion criteria. Their age was 60.9 ± 7.9 years old. The mean ABP, mean left CBFV, and mean right CBFV were 90.2 ± 31.2 mmHg, 57.3 ± 20.6 cm/s, and 68.4 ± 20.9 cm/s, respectively. The PAC between CBFV and EEG was significantly higher in β and γ bands than in the other three bands. Occipital region (P3-O1 and P4-O2 channels) showed stronger PAC than the other regions. The deceased group tended to have smaller global PAC than the survival group (the area under the receiver operating characteristic curve [AUROC] was 0.81, p = 0.57). The unfavorable outcome group showed smaller global PAC than the favorable group (AUROC = 0.65, p = 0.23). The PAC asymmetry between the two brain hemispheres correlates with the degree of stenosis in stroke patients (p = 0.01). Conclusion: We showed that CBFV interacts with EEG in β and γ bands through a phase-amplitude CFC relationship, with the strongest PAC found in the occipital region and that the degree of hemispherical asymmetry of CFC correlates with the degree of stenosis.

Global fluctuations of cerebral blood flow indicate a global brain network independent of systemic factors

Global synchronization across specialized brain networks is a common feature of network models and in-vivo electrical measurements. Although the imaging of specialized brain networks with blood oxygenation sensitive resting state functional magnetic resonance imaging (rsfMRI) has enabled detailed study of regional networks, the study of globally correlated fluctuations with rsfMRI is confounded by spurious contributions to the global signal from systemic physiologic factors and other noise sources. Here we use an alternative rsfMRI method, arterial spin labeled perfusion MRI, to characterize global correlations and their relationship to correlations and anti-correlations between regional networks. Global fluctuations that cannot be explained by systemic factors dominate the fluctuations in cerebral blood flow. Power spectra of these fluctuations are band limited to below 0.05 Hz, similar to prior measurements of regional network fluctuations in the brain. Removal of these global fluctuations prior to measurement of regional networks reduces all regional network fluctuation amplitudes to below the global fluctuation amplitude and changes the strength and sign of inter network correlations. Our findings support large amplitude, globally synchronized activity across networks that require a reassessment of regional network amplitude and correlation measures.

Electroencephalographic Abnormalities in Sepsis Patients in Correlation to the Calculated Prognostic Scores: A Case Series

Objective

To evaluate the electroencephalographic (EEG) findings and correlate EEG findings with inflammatory biomarkers and the sepsis prognostic scores SOFA, SAPS II and APACHE II in patients who present in the Emergency Department with sepsis without clinical central nervous system involvement.

Methods

The study included seventeen patients (< 70 years old) with sepsis without central nervous system involvement presenting in the Emergency Department of the University Hospital of Patras, Greece. All patients underwent neurologic examination and EEG analysis on admission to the hospital and were treated according to the international guideline protocols for sepsis.

Results

Six of seventeen sepsis patients had mild or moderate EEG abnormalities. We did not find any significant correlation between EEG abnormalities and inflammatory biomarkers (CRP, WBC) or commonly used prognostic sepsis scores.

Conclusions

EEG could serve as a useful tool to identify brain alterations at an early stage in sepsis, before clinical sings of encephalopathy can be detected. However, the presence of EEG abnormalities does not correlate with sepsis severity as measured by the commonly used prognostic sepsis scores SOFA, APACHE II or SAPS II. Because this was a small single center observational study, large multi-center studies are warranted to confirm these findings.

Theta resting EEG in TPJ/pSTS is associated with individual differences in the feeling of being looked at

Direct eye gaze is a powerful stimulus in social interactions, yet people vary considerably in the range of gaze lines that they accept as being direct (cone of direct gaze, CoDG). Here, we searched for a possible neural trait marker of these individual differences. We measured the width of the CoDG in 137 healthy participants and related their individual CoDG to their neural baseline activation as measured with resting electroencephalogram. Using a source-localization technique, we found that resting theta current density in the left temporo-parietal junction (TPJ) and adjacent posterior superior temporal sulcus (pSTS) was associated with the width of CoDG. Our findings suggest that the higher the baseline cortical activation in the left TPJ/pSTS, the wider the CoDG and thus the more liberal the individuals’ judgments were in deciding whether a looker stimulus was making eye contact or not. This is a first demonstration of the neural signatures underlying individual differences in the feeling of being looked at.

Interrogating the Relationship Between Schizotypy, the Catechol-O-Methyltransferase (COMT) Val158Met Polymorphism, and Neuronal Oscillatory Activity

The COMT Val158Met polymorphism affects the availability of synaptic dopamine in the prefrontal cortex and has been widely studied as a genetic risk factor for psychosis. Schizotypy is associated with an increased risk of psychosis, with some studies implicating similar neurobiological mechanisms to schizophrenia. The present study sought to interrogate the link between the COMT Val158Met polymorphism and schizotypy using electroencephalogram (EEG) to identify neurophysiological mechanisms underpinning psychosis risk. Neurotypical (N = 91) adults were genotyped for the COMT Val158Met polymorphism, completed the Schizotypal Personality Questionnaire (SPQ), and had eyes open resting-state EEG recorded for 4 min. SPQ suspiciousness subscale scores were higher for individuals homozygous for Val/Val and Met/Met versus Val/Met genotypes. Delta, theta, alpha-2, beta-1, and beta-2 amplitudes were lower for Val/Val than Met/Met individuals. Lower theta amplitudes were correlated with higher total SPQ scores (P = 0.050), and multiple regression revealed that higher delta, and lower theta and beta-2 amplitudes (but not COMT genotype) best predicted total SPQ scores (P = 0.014). This study demonstrates the importance of COMT genotype in determining trait suspiciousness and EEG oscillatory activity. It also highlights relationships between dopaminergic alterations, EEG and schizotypy that are dissimilar to those observed in schizophrenia.

Pre-treatment EEG signal variability is associated with treatment success in depression

Background

Previous work suggests that major depressive disorder (MDD) is associated with disturbances in global connectivity among brain regions, as well as local connectivity within regions. However, the relative importance of these global versus local changes for successful antidepressant treatment is unknown. We used multiscale entropy (MSE), a measure of brain signal variability, to examine how the propensity for local (fine scale MSE) versus global (coarse scale MSE) neural processing measured prior to antidepressant treatment is related to subsequent treatment response.

Methods

We collected resting-state EEG activity during eyes-open and closed conditions from unmedicated individuals with MDD prior to antidepressant pharmacotherapy (N = 36) as well as from non-depressed controls (N = 36). Treatment response was assessed after 12 weeks of treatment using the Montgomery-Åsberg Depression Rating Scale (MADRS), at which time participants with MDD were characterized as either responders (≥ 50% MADRS decrease) or non-responders. MSE was calculated from baseline EEG, and compared between controls, future treatment responders and non-responders. Putative interactions with the well-documented age effect on signal variability (increased reliance on local neural communication with increasing age, indexed by greater finer-scale variability) were assessed.

Results

Only in responders, we found that reduced MSE at fine temporal scales (especially fronto-centrally) and increased MSE diffusely at coarser temporal scales was related to the magnitude of the antidepressant response. In controls and MDD non-responders, but not MDD responders, there was an increase in MSE with age at fine temporal scales and a decrease in MSE with age at coarse temporal scales.

Conclusion

Our results suggest that an increased propensity toward global processing, indexed by greater MSE at coarser timescales, at baseline appears to facilitate eventual antidepressant treatment response.

•

We measured resting-state EEG prior to antidepressant pharmacotherapy.

•

We examined global vs. local processing in relation to antidepressant response.

•

Greater response was linked with increased global processing.

•

Age-related decreases in global communication were absent in future responders.

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Baseline brain dynamics in those who are/are not responsive to antidepressants differ.

Neural Cross-Frequency Coupling Functions

Although neural interactions are usually characterized only by their coupling strength and directionality, there is often a need to go beyond this by establishing the functional mechanisms of the interaction. We introduce the use of dynamical Bayesian inference for estimation of the coupling functions of neural oscillations in the presence of noise. By grouping the partial functional contributions, the coupling is decomposed into its functional components and its most important characteristics-strength and form-are quantified. The method is applied to characterize the δ-to-α phase-to-phase neural coupling functions from electroencephalographic (EEG) data of the human resting state, and the differences that arise when the eyes are either open (EO) or closed (EC) are evaluated. The δ-to-α phase-to-phase coupling functions were reconstructed, quantified, compared, and followed as they evolved in time. Using phase-shuffled surrogates to test for significance, we show how the strength of the direct coupling, and the similarity and variability of the coupling functions, characterize the EO and EC states for different regions of the brain. We confirm an earlier observation that the direct coupling is stronger during EC, and we show for the first time that the coupling function is significantly less variable. Given the current understanding of the effects of e.g., aging and dementia on δ-waves, as well as the effect of cognitive and emotional tasks on α-waves, one may expect that new insights into the neural mechanisms underlying certain diseases will be obtained from studies of coupling functions. In principle, any pair of coupled oscillations could be studied in the same way as those shown here.

Electroencephalographic Response to Sodium Nitrite May Predict Delayed Cerebral Ischemia After Severe Subarachnoid Hemorrhage

Supplemental Digital Content is available in the text.

Objectives:

Aneurysmal subarachnoid hemorrhage often leads to death and poor clinical outcome. Injury occurring during the first 72 hours is termed “early brain injury,” with disruption of the nitric oxide pathway playing an important pathophysiologic role in its development. Quantitative electroencephalographic variables, such as α/δ frequency ratio, are surrogate markers of cerebral ischemia. This study assessed the quantitative electroencephalographic response to a cerebral nitric oxide donor (intravenous sodium nitrite) to explore whether this correlates with the eventual development of delayed cerebral ischemia.

Design:

Unblinded pilot study testing response to drug intervention.

Setting:

Neuroscience ICU, John Radcliffe Hospital, Oxford, United Kingdom.

Patients:

Fourteen World Federation of Neurosurgeons grades 3, 4, and 5 patients (mean age, 52.8 yr [range, 41–69 yr]; 11 women).

Interventions:

IV sodium nitrite (10 μg/kg/min) for 1 hour.

Measurements and Main Results:

Continuous electroencephalographic recording for 2 hours. The alpha/delta frequency ratio was measured before and during IV sodium nitrite infusion. Seven of 14 patients developed delayed cerebral ischemia. There was a +30% to +118% (range) increase in the alpha/delta frequency ratio in patients who did not develop delayed cerebral ischemia (p < 0.0001) but an overall decrease in the alpha/delta frequency ratio in those patients who did develop delayed cerebral ischemia (range, +11% to –31%) (p = 0.006, multivariate analysis accounting for major confounds).

Conclusions:

Administration of sodium nitrite after severe subarachnoid hemorrhage differentially influences quantitative electroencephalographic variables depending on the patient’s susceptibility to development of delayed cerebral ischemia. With further validation in a larger sample size, this response may be developed as a tool for risk stratification after aneurysmal subarachnoid hemorrhage.

Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states

Objective

Epilepsy is a neuronal disorder for which the electrical discharge in the brain is synchronized, abnormal and excessive. To detect the epileptic seizures and to analyse brain activities during different mental states, various methods in non-linear dynamics have been proposed. This study is an attempt to quantify the complexity of control and epileptic subject with and without seizure as well as to distinguish eye-open (EO) and eye-closed (EC) conditions using threshold-based symbolic entropy.

Methods

The threshold-dependent symbolic entropy was applied to distinguish the healthy and epileptic subjects with seizure and seizure-free intervals (i.e. interictal and ictal) as well as to distinguish EO and EC conditions. The original time series data was converted into symbol sequences using quantization level, and word series of symbol sequences was generated using a word length of three or more. Then, normalized corrected Shannon entropy (NCSE) was computed to quantify the complexity. The NCSE values were not following the normal distribution, and the non-parametric Mann–Whitney–Wilcoxon (MWW) test was used to find significant differences among various groups at 0.05 significance level. The values of NCSE were presented in a form of topographic maps to show significant brain regions during EC and EO conditions. The results of the study were compared to those of the multiscale entropy (MSE).

Results

The results indicated that the dynamics of healthy subjects are more complex compared to epileptic subjects (during seizure and seizure-free intervals) in both EO and EC conditions. The comparison of the dynamics of epileptic subjects revealed that seizure-free intervals are more complex than seizure intervals. The dynamics of healthy subjects during EO conditions are more complex compared to those during EC conditions. Further, the results clearly demonstrated that threshold-dependent symbolic entropy outperform MSE in distinguishing different physiological and pathological conditions.

Conclusion

The threshold symbolic entropy has provided improved accuracy in quantifying the dynamics of healthy and epileptic subjects during EC an EO conditions for each electrode compared to the MSE.

Abnormal brain oscillations persist after recovery from bipolar depression

When directly perturbed in healthy subjects, premotor cortical areas generate electrical oscillations in the beta range (20–40 Hz). In schizophrenia, major depressive disorder and bipolar disorder (BD), these oscillations are markedly reduced, in terms of amplitude and frequency. However, it still remains unclear whether these abnormalities can be modulated over time, or if they can be still observed after treatment. Here, we employed transcranial magnetic stimulation (TMS) combined with EEG to assess the frontal oscillatory activity in eighteen BD patients before/after antidepressant treatments (sleep deprivation and light therapy), relative to nine healthy controls. In order to detect dominant frequencies, event related spectral perturbations (ERSP) were computed for each TMS/EEG session in all participants, using wavelet decomposition. The natural frequency at which the cortical circuit oscillates was calculated as the frequency value with the largest power across 300 ms post-stimulus time interval. Severity of depression markedly decreased after treatment with 12 patients achieving response and nine patients achieving remission. TMS/EEG resulted in a significant activation of the beta/gamma band response (21–50 Hz) in healthy controls. In patients, the main frequencies of premotor EEG responses to TMS did not significantly change before/after treatment and were always significantly lower than those of controls (11–27 Hz) and comparable in patients achieving remission and in those not responding to treatment. These results suggest that the reduction of natural frequencies is a trait marker of BD, independent from the clinical status of the patients. The present findings shed light on the neurobiological underpinning of severe psychiatric disorders and demonstrate that TMS/EEG represents a unique tool to develop biomarkers in psychiatry.

Modalities of Thinking: State and Trait Effects on Cross-Frequency Functional Independent Brain Networks

Functional states of the brain are constituted by the temporally attuned activity of spatially distributed neural networks. Such networks can be identified by independent component analysis (ICA) applied to frequency-dependent source-localized EEG data. This methodology allows the identification of networks at high temporal resolution in frequency bands of established location-specific physiological functions. EEG measurements are sensitive to neural activity changes in cortical areas of modality-specific processing. We tested effects of modality-specific processing on functional brain networks. Phasic modality-specific processing was induced via tasks (state effects) and tonic processing was assessed via modality-specific person parameters (trait effects). Modality-specific person parameters and 64-channel EEG were obtained from 70 male, right-handed students. Person parameters were obtained using cognitive style questionnaires, cognitive tests, and thinking modality self-reports. EEG was recorded during four conditions: spatial visualization, object visualization, verbalization, and resting. Twelve cross-frequency networks were extracted from source-localized EEG across six frequency bands using ICA. RMANOVAs, Pearson correlations, and path modelling examined effects of tasks and person parameters on networks. Results identified distinct state- and trait-dependent functional networks. State-dependent networks were characterized by decreased, trait-dependent networks by increased alpha activity in sub-regions of modality-specific pathways. Pathways of competing modalities showed opposing alpha changes. State- and trait-dependent alpha were associated with inhibitory and automated processing, respectively. Antagonistic alpha modulations in areas of competing modalities likely prevent intruding effects of modality-irrelevant processing. Considerable research suggested alpha modulations related to modality-specific states and traits. This study identified the distinct electrophysiological cortical frequency-dependent networks within which they operate.

Perturbation of Brain Oscillations after Ischemic Stroke: A Potential Biomarker for Post-Stroke Function and Therapy

Brain waves resonate from the generators of electrical current and propagate across brain regions with oscillation frequencies ranging from 0.05 to 500 Hz. The commonly observed oscillatory waves recorded by an electroencephalogram (EEG) in normal adult humans can be grouped into five main categories according to the frequency and amplitude, namely δ (1-4 Hz, 20-200 μV), θ (4-8 Hz, 10 μV), α (8-12 Hz, 20-200 μV), β (12-30 Hz, 5-10 μV), and γ (30-80 Hz, low amplitude). Emerging evidence from experimental and human studies suggests that groups of function and behavior seem to be specifically associated with the presence of each oscillation band, although the complex relationship between oscillation frequency and function, as well as the interaction between brain oscillations, are far from clear. Changes of brain oscillation patterns have long been implicated in the diseases of the central nervous system including ischemic stroke, in which the reduction of cerebral blood flow as well as the progression of tissue damage have direct spatiotemporal effects on the power of several oscillatory bands and their interactions. This review summarizes the current knowledge in behavior and function associated with each brain oscillation, and also in the specific changes in brain electrical activities that correspond to the molecular events and functional alterations observed after experimental and human stroke. We provide the basis of the generations of brain oscillations and potential cellular and molecular mechanisms underlying stroke-induced perturbation. We will also discuss the implications of using brain oscillation patterns as biomarkers for the prediction of stroke outcome and therapeutic efficacy.

Frequency-specific alterations of large-scale functional brain networks in patients with Alzheimer's disease

BACKGROUND

Previous studies have indicated that the cognitive deficits in patients with Alzheimer's disease (AD) may be due to topological deteriorations of the brain network. However, whether the selection of a specific frequency band could impact the topological properties is still not clear. Our hypothesis is that the topological properties of AD patients are also frequency-specific.

METHODS

Resting state functional magnetic resonance imaging data from 10 right-handed moderate AD patients (mean age: 64.3 years; mean mini mental state examination [MMSE]: 18.0) and 10 age and gender-matched healthy controls (mean age: 63.6 years; mean MMSE: 28.2) were enrolled in this study. The global efficiency, the clustering coefficient (CC), the characteristic path length (CpL), and "small-world" property were calculated in a wide range of thresholds and averaged within each group, at three different frequency bands (0.01-0.06 Hz, 0.06-0.11 Hz, and 0.11-0.25 Hz).

RESULTS

At lower-frequency bands (0.01-0.06 Hz, 0.06-0.11 Hz), the global efficiency, the CC and the "small-world" properties of AD patients decreased compared to controls. While at higher-frequency bands (0.11-0.25 Hz), the CpL was much longer, and the "small-world" property was disrupted in AD, particularly at a higher threshold. The topological properties changed with different frequency bands, suggesting the existence of disrupted global and local functional organization associated with AD.

CONCLUSIONS

This study demonstrates that the topological alterations of large-scale functional brain networks in AD patients are frequency dependent, thus providing fundamental support for optimal frequency selection in future related research.

The functional significance of EEG microstates--Associations with modalities of thinking

The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, subjective interoceptive-autonomic processing, and attention reorientation, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared microstate parameters between conditions. TANOVAs compared microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions included particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with microstate parameters except verbal cognitive style which correlated negatively with microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG microstate class parameter changes based on their hypothesized functional significance. Beyond the associations of microstate classes A and B with visual and verbal processing, respectively, our results suggest that a finely-tuned interplay between all four EEG microstate classes is necessary for the continuous formation of visual and verbal thoughts. Our results point to the possibility that the EEG microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis.

Topographic distribution of EEG alpha attractor correlation dimension values in wake and drowsy states in humans

Organization of resting state cortical networks is of fundamental importance for the phenomenon of awareness, which is altered in the first part of hypnagogic period (Hori stages 1-4). Our aim was to investigate the change in brain topography pattern of EEG alpha attractor correlation dimension (CD) in the period of transition from Hori stage 1 to 4. EEG of ten healthy adult individuals was recorded in the wake and drowsy states, using a 14 channel average reference montage, from which 91 bipolar channels were derived and filtered in the wider alpha (6-14 Hz) range. Sixty 1s long epochs of each state and individual were subjected to CD calculation according to the Grassberger-Procaccia method. For such a collection of signals, two embedding dimensions, d={5, 10}, and 22 time delays τ=2-23 samples were explored. Optimal values were d=10 and τ=18, where both saturation and second zero crossing of the autocorrelation function occurred. Bipolar channel CD underwent a significant decrease during the transition and showed a positive linear correlation with electrode distance, stronger in the wake individuals. Topographic distribution of bipolar channels with above median CD changed from longitudinal anterior-posterior pattern (awake) to a more diagonal pattern, with localization in posterior regions (drowsiness). Our data are in line with the literature reporting functional segregation of neuronal assemblies in anterior and posterior regions during this transition. Our results should contribute to understanding of complex reorganization of the cortical part of alpha generators during the wake/drowsy transition.

Brain electrical source imaging in manic and depressive episodes of bipolar disorder

OBJECTIVE

Bipolar disorder (BD) electroencephalographic (EEG) studies have reported varying results. The present study compared EEG in BD during manic and depressive episodes, using brain electrical source imaging [standardized low-resolution electromagnetic tomography (sLORETA)] to assess the cortical spatial distribution of the sources of EEG oscillation frequencies.

METHODS

Two independent datasets (a total of 95 patients with bipolar I disorder, of whom 59 were female) were analyzed. Dataset #1 comprised 14 patients in a manic as well as a depressive episode. Dataset #2 comprised 26 patients in a manic episode and 55 patients in a depressive episode. From the head surface-recorded EEG, sLORETA cortical activity was computed in eight EEG frequency bands, and compared between mood states in both datasets. The results from the two datasets were combined using conjunction analysis.

RESULTS

Conjunction analysis yielded significant differences between mood states: In manic compared to depressive states, patients had lesser theta frequency band activity (right-hemispheric lateral lower prefrontal and anterior temporal, mainly Brodmann areas 13, 38, and 47), and greater beta-2 and beta-3 frequency band activity (extended bilateral prefrontal-to-parietal, mainly Brodmann area 6, and the cingulate).

CONCLUSIONS

The spatial organization of the brain's electrical oscillations differed in patients with BD between manic and depressive mood states. The brain areas implementing the main functions that show opposing abnormalities during manic and depressive episodes were affected by unduly increased or decreased activity (beta or theta). The discussion considers that facilitating (beta) or inhibiting (theta) electrical activity can in either case result in behavioral facilitation or inhibition, depending on the function of the brain area.

Zazen meditation and no-task resting EEG compared with LORETA intracortical source localization

Meditation is a self-induced and willfully initiated practice that alters the state of consciousness. The meditation practice of Zazen, like many other meditation practices, aims at disregarding intrusive thoughts while controlling body posture. It is an open monitoring meditation characterized by detached moment-to-moment awareness and reduced conceptual thinking and self-reference. Which brain areas differ in electric activity during Zazen compared to task-free resting? Since scalp electroencephalography (EEG) waveforms are reference-dependent, conclusions about the localization of active brain areas are ambiguous. Computing intracerebral source models from the scalp EEG data solves this problem. In the present study, we applied source modeling using low resolution brain electromagnetic tomography (LORETA) to 58-channel scalp EEG data recorded from 15 experienced Zen meditators during Zazen and no-task resting. Zazen compared to no-task resting showed increased alpha-1 and alpha-2 frequency activity in an exclusively right-lateralized cluster extending from prefrontal areas including the insula to parts of the somatosensory and motor cortices and temporal areas. Zazen also showed decreased alpha and beta-2 activity in the left angular gyrus and decreased beta-1 and beta-2 activity in a large bilateral posterior cluster comprising the visual cortex, the posterior cingulate cortex and the parietal cortex. The results include parts of the default mode network and suggest enhanced automatic memory and emotion processing, reduced conceptual thinking and self-reference on a less judgmental, i.e., more detached moment-to-moment basis during Zazen compared to no-task resting.

Functionally aberrant electrophysiological cortical connectivities in first episode medication-naive schizophrenics from three psychiatry centers

Functional dissociation between brain processes is widely hypothesized to account for aberrations of thought and emotions in schizophrenic patients. The typically small groups of analyzed schizophrenic patients yielded different neurophysiological findings, probably because small patient groups are likely to comprise different schizophrenia subtypes. We analyzed multichannel eyes-closed resting EEG from three small groups of acutely ill, first episode productive schizophrenic patients before start of medication (from three centers: Bern N = 9; Osaka N = 9; Berlin N = 12) and their controls. Low resolution brain electromagnetic tomography (LORETA) was used to compute intracortical source model-based lagged functional connectivity not biased by volume conduction effects between 19 cortical regions of interest (ROIs). The connectivities were compared between controls and patients of each group. Conjunction analysis determined six aberrant cortical functional connectivities that were the same in the three patient groups. Four of these six concerned the facilitating EEG alpha-1 frequency activity; they were decreased in the patients. Another two of these six connectivities concerned the inhibiting EEG delta frequency activity; they were increased in the patients. The principal orientation of the six aberrant cortical functional connectivities was sagittal; five of them involved both hemispheres. In sum, activity in the posterior brain areas of preprocessing functions and the anterior brain areas of evaluation and behavior control functions were compromised by either decreased coupled activation or increased coupled inhibition, common across schizophrenia subtypes in the three patient groups. These results of the analyzed three independent groups of schizophrenics support the concept of functional dissociation.

The handyman's brain: a neuroimaging meta-analysis describing the similarities and differences between grip type and pattern in humans

BACKGROUND

Handgrip is a ubiquitous human movement that was critical in our evolution. However, the differences in brain activity between grip type (i.e. power or precision) and pattern (i.e. dynamic or static) are not fully understood. In order to address this, we performed Activation Likelihood Estimation (ALE) analysis between grip type and grip pattern using functional magnetic resonance imaging (fMRI) data. ALE provides a probabilistic summary of the BOLD response in hundreds of subjects, which is often beyond the scope of a single fMRI experiment.

METHODS

We collected data from 28 functional magnetic resonance data sets, which included a total of 398 male and female subjects. Using ALE, we analyzed the BOLD response during power, precision, static and dynamic grip in a range of forces and age in right handed healthy individuals without physical impairment, cardiovascular or neurological dysfunction using a variety of grip tools, feedback and experimental training.

RESULTS

Power grip generates unique activation in the postcentral gyrus (areas 1 and 3b) and precision grip generates unique activation in the supplementary motor area (SMA, area 6) and precentral gyrus (area 4a). Dynamic handgrip generates unique activation in the precentral gyrus (area 4p) and SMA (area 6) and of particular interest, both dynamic and static grip share activation in the area 2 of the postcentral gyrus, an area implicated in the evolution of handgrip. According to effect size analysis, precision and dynamic grip generates stronger activity than power and static, respectively.

CONCLUSION

Our study demonstrates specific differences between grip type and pattern. However, there was a large degree of overlap in the pre and postcentral gyrus, SMA and areas of the frontal-parietal-cerebellar network, which indicates that other mechanisms are potentially involved in regulating handgrip. Further, our study provides empirically based regions of interest, which can be downloaded here within, that can be used to more effectively study power grip in a range of populations and conditions.

sLORETA intracortical lagged coherence during breath counting in meditation-naïve participants

We investigated brain functional connectivity comparing no-task resting to breath counting (a meditation exercise but given as task without referring to meditation). Functional connectivity computed as EEG coherence between head-surface data suffers from localization ambiguity, reference dependence, and overestimation due to volume conduction. Lagged coherence between intracortical model sources addresses these criticisms. With this analysis approach, experienced meditators reportedly showed reduced coherence during meditation, meditation-naïve participants have not yet been investigated. 58-channel EEG from 23 healthy, right-handed, meditation-naïve males during resting [3 runs] and breath counting [2 runs] was computed into sLORETA time series of intracortical electrical activity in 19 regions of interest (ROI) corresponding to the cortex underlying 19 scalp electrode sites, for each of the eight independent EEG frequency bands covering 1.5–44 Hz. Intracortical lagged coherences and head-surface conventional coherences were computed between the 19 regions/sites. During breath counting compared to resting, paired t-tests corrected for multiple testing revealed four significantly lower intracortical lagged coherences, but four significantly higher head-surface conventional coherences. Lowered intracortical lagged coherences involved left BA 10 and right BAs 3, 10, 17, 40. In conclusion, intracortical lagged coherence can yield results that are inverted to those of head-surface conventional coherence. The lowered functional connectivity between cognitive control areas and sensory perception areas during meditation-type breath counting compared to resting conceivably reflects the attention to a bodily percept without cognitive reasoning. The reductions in functional connectivity were similar but not as widespread as the reductions reported during meditation in experienced meditators.

Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

Alzheimer's disease (AD) is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI) is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG) biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a 2-year period. We followed 86 patients initially diagnosed with MCI for 2 years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13–30 Hz) can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/). We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers) also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention.