Delta power during sleep is modulated by EEG-gated auricular vagal afferent nerve stimulation (EAVANS).

Vagus nerve stimulation (VNS) has many clinical applications under development. In particular, there is a large interest in transcutaneous auricular VNS (taVNS) because it is non-invasive and provides easy access to neuromodulation. The present study proposes a novel approach for electroencephalography (EEG)-gated taVNS, with the ultimate goal of enhancing therapeutic outcomes, including for the treatment of delirium. Delirium arises from an altered state of consciousness and is the most common neuropsychiatric disorder observed in hospitalized patients, especially the elderly. Delirium has been linked to specific disturbances in EEG rhythms. Here, we propose an EEG-gated auricular vagal afferent nerve stimulation (EAVANS) approach to deliver stimulation targeting a specific instantaneous phase of the EEG Delta rhythm to modulate arousal and downstream reduction of neuroinflammation, two of the contributing factors to delirium. We hypothesize that treatment with EAVANS will modulate Delta power, which has been linked with delirium. As dominant Delta power is also a typical feature of non-rapid eye movement (NREM) sleep, we applied a prototype of an EAVANS device on healthy volunteers during sleep to establish preliminary validation. We successfully employed our closed-loop approach to target vagal afference during the rising Delta phase in the range [-π/2 0] radians. We found a significant reduction in Delta wave power for stimulation during the rising Delta phase compared to 1) absence of stimulation, 2) active stimulation during the descending Delta phase, and 3) active stimulation targeting non-vagal territory (i.e. greater auricular nerve) during the rising Delta phase. Further validation of our EEG-gated taVNS approach in the peri-operative period will be needed. As there is presently a lack of effective treatments for delirium, our non-pharmacological and non-invasive approach, if validated, could be easily deployed in clinical settings.Clinical Relevance- Given the serious health consequences and costs associated with delirium, and the absence of effective non-pharmacological treatments, the proposed neuromodulatory approach may be a promising option for reducing delirium and other disorders of consciousness. Our EAVANS prototype system has been tested on healthy volunteers during a NREM sleep state and will require further validation in different patient populations to optimize the proposed technology and gather more evidence to support its clinical utility. This novel non-pharmacological and non-invasive closed-loop neuromodulatory device could be used peri-operatively and in inpatient hospital settings to treat patients at risk of developing delirium. For instance, in a pre-operative setting, this technology may provide an effective preventative "pre-habilitation" approach for patients at high risk of developing delirium. Post-operatively, our technology may help manage patients with delirium more effectively.

Electroencephalographic markers of brain development during sevoflurane anaesthesia in children up to 3 years old.

BACKGROUND: General anaesthetics generate spatially defined brain oscillations in the EEG that relate fundamentally to neural-circuit architecture. Few studies detailing the neural-circuit activity of general anaesthesia in children have been described. The study aim was to identify age-related changes in EEG characteristics that mirror different stages of early human brain development during sevoflurane anaesthesia. METHODS: Multichannel EEG recordings were performed in 91 children aged 0-3 yr undergoing elective surgery. We mapped spatial power and coherence over the frontal, parietal, temporal, and occipital cortices during maintenance anaesthesia. RESULTS: During sevoflurane exposure: (i) slow-delta (0.1-4 Hz) oscillations were present in all ages, (ii) theta (4-8 Hz) and alpha (8-12 Hz) oscillations emerge by ∼4 months, (iii) alpha oscillations increased in power from 4 to 10 months, (iv) frontal alpha-oscillation predominance emerged at ∼6 months, (v) frontal slow oscillations were coherent from birth until 6 months, and (vi) frontal alpha oscillations became coherent ∼10 months and persisted in older ages. CONCLUSIONS: Key developmental milestones in the maturation of the thalamo-cortical circuitry likely generate changes in EEG patterns in infants undergoing sevoflurane general anaesthesia. Characterisation of anaesthesia-induced EEG oscillations in children demonstrates the importance of developing age-dependent strategies to monitor properly the brain states of children receiving general anaesthesia. These data have the potential to guide future studies investigating neurodevelopmental pathologies involving altered excitatory-inhibitory balance, such as epilepsy or Rett syndrome.

The Ageing Brain: Age-dependent changes in the electroencephalogram during propofol and sevoflurane general anaesthesia.

BACKGROUND: Anaesthetic drugs act at sites within the brain that undergo profound changes during typical ageing. We postulated that anaesthesia-induced brain dynamics observed in the EEG change with age. METHODS: We analysed the EEG in 155 patients aged 18-90 yr who received propofol (n=60) or sevoflurane (n=95) as the primary anaesthetic. The EEG spectrum and coherence were estimated throughout a 2 min period of stable anaesthetic maintenance. Age-related effects were characterized by analysing power and coherence as a function of age using linear regression and by comparing the power spectrum and coherence in young (18- to 38-yr-old) and elderly (70- to 90-yr-old) patients. RESULTS: Power across all frequency bands decreased significantly with age for both propofol and sevoflurane; elderly patients showed EEG oscillations ∼2- to 3-fold smaller in amplitude than younger adults. The qualitative form of the EEG appeared similar regardless of age, showing prominent alpha (8-12 Hz) and slow (0.1-1 Hz) oscillations. However, alpha band dynamics showed specific age-related changes. In elderly compared with young patients, alpha power decreased more than slow power, and alpha coherence and peak frequency were significantly lower. Older patients were more likely to experience burst suppression. CONCLUSIONS: These profound age-related changes in the EEG are consistent with known neurobiological and neuroanatomical changes that occur during typical ageing. Commercial EEG-based depth-of-anaesthesia indices do not account for age and are therefore likely to be inaccurate in elderly patients. In contrast, monitoring the unprocessed EEG and its spectrogram can account for age and individual patient characteristics.

Ageing delays emergence from general anaesthesia in rats by increasing anaesthetic sensitivity in the brain.

BACKGROUND: Little is known about ageing-related changes in the brain that affect emergence from general anaesthesia. We used young adult and aged Fischer 344 rats to test the hypothesis that ageing delays emergence from general anaesthesia by increasing anaesthetic sensitivity in the brain. METHODS: Time to emergence was determined for isoflurane (1.5 vol% for 45 min) and propofol (8 mg kg(-1) i.v.). The dose of isoflurane required to maintain loss of righting (LOR) was established in young adult and aged rats. The efficacy of methylphenidate to reverse LOR from general anaesthesia was tested. Separate young adult and aged rats with implanted electroencephalogram (EEG) electrodes were used to test whether ageing increases sensitivity to anaesthetic-induced burst suppression. RESULTS: Mean time to emergence from isoflurane anaesthesia was 47 s [95% CI 33, 60; young adult) compared with 243 s (95% CI 185, 308; aged). For propofol, mean time to emergence was 13.1 min (95% CI 11.9, 14.0; young adult) compared with 23.1 min (95% CI 18.8, 27.9; aged). These differences were statistically significant. When methylphenidate was administered after propofol, the mean time to emergence decreased to 6.6 min (95% CI 5.9, 7.1; young adult) and 10.2 min (95% CI 7.9, 12.3; aged). These reductions were statistically significant. Methylphenidate restored righting in all rats during continuous isoflurane anaesthesia. Aged rats had lower EEG power and were more sensitive to anaesthetic-induced burst suppression. CONCLUSIONS: Ageing delays emergence from general anaesthesia. This is due, at least in part, to increased anaesthetic sensitivity in the brain. Further studies are warranted to establish the underlying causes.

Age-dependency of sevoflurane-induced electroencephalogram dynamics in children.

BACKGROUND: General anaesthesia induces highly structured oscillations in the electroencephalogram (EEG) in adults, but the anaesthesia-induced EEG in paediatric patients is less understood. Neural circuits undergo structural and functional transformations during development that might be reflected in anaesthesia-induced EEG oscillations. We therefore investigated age-related changes in the EEG during sevoflurane general anaesthesia in paediatric patients. METHODS: We analysed the EEG recorded during routine care of patients between 0 and 28 yr of age (n=54), using power spectral and coherence methods. The power spectrum quantifies the energy in the EEG at each frequency, while the coherence measures the frequency-dependent correlation or synchronization between EEG signals at different scalp locations. We characterized the EEG as a function of age and within 5 age groups: <1 yr old (n=4), 1-6 yr old (n=12), >6-14 yr old (n=14), >14-21 yr old (n=11), >21-28 yr old (n=13). RESULTS: EEG power significantly increased from infancy through ∼6 yr, subsequently declining to a plateau at approximately 21 yr. Alpha (8-13 Hz) coherence, a prominent EEG feature associated with sevoflurane-induced unconsciousness in adults, is absent in patients <1 yr. CONCLUSIONS: Sevoflurane-induced EEG dynamics in children vary significantly as a function of age. These age-related dynamics likely reflect ongoing development within brain circuits that are modulated by sevoflurane. These readily observed paediatric-specific EEG signatures could be used to improve brain state monitoring in children receiving general anaesthesia.

Beamforming approach to phase-amplitude modulation analysis of multi-channel EEG.

Phase-amplitude modulation is a form of cross frequency coupling where the phase of one frequency influences the amplitude of another higher frequency. It has been observed in neurophysiological recordings during sensory, motor, and cognitive tasks, as well as during general anesthesia. In this paper, we describe a novel beamforming procedure to improve estimation of phase-amplitude modulation. We apply this method to 64-channel EEG data recorded during propofol general anesthesia. The method improves the sensitivity of phase-amplitude analyses, and can be applied to a variety of multi-channel neuroscience data where phase-amplitude modulation is present.

A fast iterative greedy algorithm for MEG source localization.

Recent dynamic source localization algorithms for the Magnetoencephalographic inverse problem use cortical spatio-temporal dynamics to enhance the quality of the estimation. However, these methods suffer from high computational complexity due to the large number of sources that must be estimated. In this work, we introduce a fast iterative greedy algorithm incorporating the class of subspace pursuit algorithms for sparse source localization. The algorithm employs a reduced order state-space model resulting in significant computational savings. Simulation studies on MEG source localization reveal substantial gains provided by the proposed method over the widely used minimum-norm estimate, in terms of localization accuracy, with a negligible increase in computational complexity.

A spatially-regularized dynamic source localization algorithm for EEG.

Cortical activity can be estimated from electroencephalogram (EEG) or magnetoencephalogram (MEG) data by solving an ill-conditioned inverse problem that is regularized using neuroanatomical, computational, and dynamic constraints. Recent methods have incorporated spatio-temporal dynamics into the inverse problem framework. In this approach, spatio-temporal interactions between neighboring sources enforce a form of spatial smoothing that enhances source localization quality. However, spatial smoothing could also occur by way of correlations within the state noise process that drives the underlying dynamic model. Estimating the spatial covariance structure of this state noise is challenging, particularly in EEG and MEG data where the number of underlying sources is far greater than the number of sensors. However, the EEG/MEG data are sparse compared to the large number of sources, and thus sparse constraints could be used to simplify the form of the state noise spatial covariance. In this work, we introduce an empirically tailored basis to represent the spatial covariance structure within the state noise processes of a cortical dynamic model for EEG source localization. We augment the method presented in Lamus, et al. (2011) to allow for sparsity enforcing priors on the covariance parameters. Simulation studies as well as analysis of real data reveal significant gains in the source localization performance over existing algorithms.

PARAMETER ESTIMATION AND DYNAMIC SOURCE LOCALIZATION FOR THE MAGNETOENCEPHALOGRAPHY (MEG) INVERSE PROBLEM.

Dynamic estimation methods based on linear state-space models have been applied to the inverse problem of magnetoencephalography (MEG), and can improve source localization compared with static methods by incorporating temporal continuity as a constraint. The efficacy of these methods is influenced by how well the state-space model approximates the dynamics of the underlying brain current sources. While some components of the state-space model can be inferred from brain anatomy and knowledge of the MEG instrument noise structure, parameters governing the temporal evolution of underlying current sources are unknown and must be selected on an ad-hoc basis or estimated from data. In this work, we apply the Expectation-Maximization (EM) algorithm to estimate parameters and sources in an MEG state-space model, and demonstrate in simulation studies that the resulting source estimates are superior to those provided by static methods or dynamic methods employing ad hoc parameter selection.

Correlating acupuncture FMRI in the human brainstem with heart rate variability.

Past neuroimaging studies of acupuncture have demonstrated variable results for important brainstem nuclei. We have employed cardiac-gated fMRI with T1-variability correction to study the processing of acupuncture by the human brain. Furthermore, our imaging experiments collected simultaneous ECG data in order to correlate heart rate variability (HRV) with fMRI signal intensity. Subjects experienced one of three stimulations over a 31.5 minute fMRI run: (1) electro-acupuncture at 2Hz/15Hz over the acupoint ST-36 (2) electro-acupuncture at a sham non-acupoint, or (3) sensory control tapping over ST-36. The ECG was analyzed with power spectral methods for low frequency and high frequency components, which reflect the balance in the autonomic nervous system. The HRV data was then correlated with the time-varying fMRI signal intensity. Our data suggests that fMRI activity in the hypothalamus, the dorsal raphe nucleus, the periaqueductal gray, and the rostroventral medulla showed significant correlation with LF/HF ratio calculated from simultaneous HRV data. The correlation of time-varying fMRI response with physiological parameters may provide insight into connections between acupuncture modulation of the autonomic nervous system and neuroprocessing.

Locally regularized spatiotemporal modeling and model comparison for functional MRI.

In this work we treat fMRI data analysis as a spatiotemporal system identification problem and address issues of model formulation, estimation, and model comparison. We present a new model that includes a physiologically based hemodynamic response and an empirically derived low-frequency noise model. We introduce an estimation method employing spatial regularization that improves the precision of spatially varying noise estimates. We call the algorithm locally regularized spatiotemporal (LRST) modeling. We develop a new model selection criterion and compare our model to the SPM-GLM method. Our findings suggest that our method offers a better approach to identifying appropriate statistical models for fMRI studies.

A signal estimation approach to functional MRI.

In the last half decade, fast methods of magnetic resonance imaging have led to the possibility, for the first time, of non-invasive dynamic brain imaging. This has led to an explosion of work in the Neurosciences. From a signal processing viewpoint the problems are those of nonlinear spatio-temporal system identification. In this paper, we develop new methods of identification using novel spatial regularization. We also develop a new model comparison technique and use that to compare our method with existing techniques on some experimental data.

Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel-level false-positive rates in fMRI.

Statistical mapping within a binary hypothesis testing framework is the most widely used analytical method in functional MRI of the brain. A common assumption in this kind of analysis is that the fMRI time series are independent and identically distributed in time, yet we know that fMRI data can have significant temporal correlation due to low-frequency physiological fluctuation (Weisskoff et al. [1993]; Proc Soc Magn Reson Med 9:7; Biswal et al. [1995]: Mag Reson Med 34:537-541). Furthermore, since the signal-to-noise ratio will vary with imaging rate, we should expect that the degree of correlation will vary with imaging rate. In this paper, we investigate the effect of temporal correlation and experimental paradigm on false-positive rates (type I error rates), using data synthesized through a simple autoregressive plus white-noise model whose parameters were estimated from real data over a range of imaging rates. We demonstrate that actual false-positive rates can be biased far above or below the assumed significance level alpha when temporal autocorrelation is ignored in a way that depends on both the degree of correlation as well as the paradigm frequency. Furthermore, we present a simple method, based on the noise model described above, for correcting such distortions, and relate this method to the extended general linear model of Worsley and Friston ([1995]: Neuroimage 2:173-181).

Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations.

Fractal dynamics were recently detected in the apparently "noisy" variations in the stride interval of human walking. Dynamical analysis of these step-to-step fluctuations revealed a self-similar pattern: fluctuations at one time scale are statistically similar to those at multiple other time scales, at least over hundreds of steps, while healthy subjects walk at their normal rate. To study the stability of this fractal property, we analyzed data obtained from healthy subjects who walked for 1 h at their usual, slow, and fast paces. The stride interval fluctuations exhibited long-range correlations with power-law decay for up to 1,000 strides at all 3 walking rates. In contrast, during metronomically paced walking, these long-range correlations disappeared; variations in the stride interval were random (uncorrelated) and nonfractal. The long-range correlations observed during spontaneous walking were not affected by removal of drifts in the time series. Thus the fractal dynamics of spontaneous stride interval are normally quite robust and intrinsic to the locomotor system. Furthermore, this fractal property of neural output may be related to the higher nervous centers responsible for the control of walking rhythm.