A continuum model for the dynamics of the phase transition from slow-wave sleep to REM sleep

Previous studies have shown that activated cortical states (awake and rapid eye-movement (REM) sleep), are associated with increased cholinergic input into the cerebral cortex. However, the mechanisms that underlie the detailed dynamics of the cortical transition from slow-wave to REM sleep have not been quantitatively modeled. How does the sequence of abrupt changes in the cortical dynamics (as detected in the electrocorticogram) result from the more gradual change in subcortical cholinergic input? We compare the output from a continuum model of cortical neuronal dynamics with experimentally-derived rat electrocorticogram data. The output from the computer model was consistent with experimental observations. In slow-wave sleep, 0.5–2-Hz oscillations arise from the cortex jumping between “up” and “down” states on the stationary-state manifold. As cholinergic input increases, the upper state undergoes a bifurcation to an 8-Hz oscillation. The coexistence of both oscillations is similar to that found in the intermediate stage of sleep of the rat. Further cholinergic input moves the trajectory to a point where the lower part of the manifold in not available, and thus the slow oscillation abruptly ceases (REM sleep). The model provides a natural basis to explain neuromodulator-induced changes in cortical activity, and indicates that a cortical phase change, rather than a brainstem “flip-flop”, may describe the transition from slow-wave sleep to REM

Coupled Correlates of Attention and Consciousness

Introduction: Brain Computer Interfaces (BCIs) have been shown to restore lost motor function that occurs in stroke using electrophysiological signals. However, little evidence exists for the use of BCIs to restore non-motor stroke deficits, such as the attention deficits seen in hemineglect. Attention is a cognitive function that selects objects or ideas for further neural processing, presumably to facilitate optimal behavior. Developing BCIs for attention is different from developing motor BCIs because attention networks in the brain are more distributed and associative than motor networks. For example, hemineglect patients have reduced levels of arousal, which exacerbates their attentional deficits. More generally, attention is a state of high arousal and salient conscious experience. Current models of consciousness suggest that both slow wave sleep and Propofol-induced unconsciousness lie at one end of the consciousness spectrum, while attentive states lie at the other end. Accordingly, investigating the electrophysiology underlying attention and the extremes of consciousness will further the development of attentional BCIs. Phase amplitude coupling (PAC) of neural oscillations has been suggested as a mechanism for organizing local and global brain activity across regions. While evidence suggests that delta-high-gamma PAC, which includes very low frequencies (i.e. delta, 1-3 Hz) coupled with very high frequencies (i.e. gamma 70-150 Hz), is implicated in attention, less evidence exists for the involvement of coupled mid-range frequencies (i.e. theta, 4-7Hz, alpha: 8-15 Hz, beta: 15-30 Hz and low-gamma: 30-50 Hz, aka TABL PAC). We found that TABL PAC correlates with reaction time in an attention task. These mid-range frequencies are important because they can be used in non-invasive electroencephalography (EEG) BCI’s. Therefore, we investigated the origins of these mid-frequency interactions in both attention and consciousness. In this work, we evaluate the relationship between PAC to attention and arousal, with emphasis on developing control signals for an attentional BCI. Objective: To understand how PAC facilitates attention and arousal for building BCI’s that restore lost attentional function. More generally, our objective was to discover and understand potential control features for BCIs that enhance attention and conscious experience. Methods: We used four electrophysiological datasets in human subjects. The first dataset included six subjects with invasive ECoG recordings while subjects engaged in a Posner cued spatial attention task. The second dataset included five subjects with ECoG recordings during sleep and awake states. The third dataset included 6 subjects with invasively monitored ECoG during induction and emergence from Propofol anesthesia. We validated findings from the second dataset with an EEG dataset that included 39 subjects with EEG and sleep scoring. We developed custom, wavelet-based, signal processing algorithms designed to optimally calculate differences in mid-frequency-range (i.e. TABL) PAC and compare them to DH PAC across different attentional and conscious states. We developed non-parametric cluster-based permutation tests to infer statistical significance while minimizing the false-positive rate. In the attention experiment, we used the location of cued spatial stimuli and reaction time (RT) as markers of attention. We defined stimulus-related and behaviorally-related cortical sites and compared their relative PAC magnitudes. In the sleep dataset, we compared PAC across sleep states (e.g. Wake vs Slow Wave Sleep). In the anesthesia dataset, we compared the beginning and ending of induction and emergence (e.g. Wake vs Propofol Induced Loss of Consciousness) Results: We found different patterns of activity represented by TABL PAC and DH PAC in both attention and sleep datasets. First, during a spatial attention task TABL PAC robustly predicted whether a subject would respond quickly or slowly. TABL PAC maintained a consistent phase-preference across all cortical sites and was strongest in behaviorally-relevant cortical sites. In contrast, DH PAC represented the location of attention in spatially-relevant cortical sites. Furthermore, we discovered that sharp waves caused TABL PAC. These sharp waves appeared to be transient beta (50ms) waves that occurred at ~140 ms intervals, corresponding to a theta oscillation. In the arousal dataset DH PAC increased in both slow wave sleep (SWS) and Propofol-induced loss of consciousness (PILOC) states. However, TABL PAC increased only during PILOC and decreased during SWS, when compared to waking states. We provide evidence that TABL PAC represents “gating by inhibition” in the human brain. Conclusions: Our goal was to develop electrophysiological signals representing attention and to understand how these features explain the relationship between attention and low-arousal states. We found a novel biomarker, TABL PAC, that predicted non-spatial aspects of attention and discriminated between two states of unconsciousness. The evidence suggested that TABL PAC represents inhibitory activity that filters out irrelevant information in attention tasks. This inhibitory mechanism of was confirmed by significant increases in TABL PAC during Propofol anesthesia, when compared to SWS or waking brain activity. We conclude that TABL PAC informs the development of electrophysiological control signals for attention and the discrimination of unconscious states

Pain detection with bioimpedance methodology from 3-dimensional exploration of nociception in a postoperative observational trial

Although the measurement of dielectric properties of the skin is a long-known tool for assessing the changes caused by nociception, the frequency modulated response has not been considered yet. However, for a rigorous characterization of the biological tissue during noxious stimulation, the bioimpedance needs to be analyzed over time as well as over frequency. The 3-dimensional analysis of nociception, including bioimpedance, time, and frequency changes, is provided by ANSPEC-PRO device. The objective of this observational trial is the validation of the new pain monitor, named as ANSPEC-PRO. After ethics committee approval and informed consent, 26 patients were monitored during the postoperative recovery period: 13 patients with the in-house developed prototype ANSPEC-PRO and 13 with the commercial device MEDSTORM. At every 7 min, the pain intensity was measured using the index of Anspec-pro or Medstorm and the 0-10 numeric rating scale (NRS), pre-surgery for 14 min and post-anesthesia for 140 min. Non-significant differences were reported for specificity-sensitivity analysis between ANSPEC-PRO (AUC = 0.49) and MEDSTORM (AUC = 0.52) measured indexes. A statistically significant positive linear relationship was observed between Anspec-pro index and NRS (r(2) = 0.15, p < 0.01). Hence, we have obtained a validation of the prototype Anspec-pro which performs equally well as the commercial device under similar conditions

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

Synchronized activity in the main and accessory olfactory bulbs and vomeronasal amygdala elicited by chemical signals in freely behaving mice

Chemosensory processing in mammals involves the olfactory and vomeronasal systems, but how the activity of both circuits is integrated is unknown. In our study, we recorded the electrophysiological activity in the olfactory bulbs and the vomeronasal amygdala in freely behaving mice exploring a battery of neutral and conspecific stimuli. The exploration of stimuli, including a neutral stimulus, induced synchronic activity in the olfactory bulbs characterized by a dominant theta rhythmicity, with specific theta-gamma coupling, distinguishing between vomeronasal and olfactory structures. The correlated activation of the bulbs suggests a coupling between the stimuli internalization in the nasal cavity and the vomeronasal pumping. In the amygdala, male stimuli are preferentially processed in the medial nucleus, whereas female cues induced a differential response in the posteromedial cortical amygdala. Thus, particular theta-gamma patterns in the olfactory network modulates the integration of chemosensory information in the amygdala, allowing the selection of an appropriate behaviour

Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by mainly redundant or synergistic information transfer persisting across multiple time scales or even by the alternating prevalence of redundant and synergistic source interaction depending on the time scale. Then, we apply our method to an important topic in neuroscience, i.e., the detection of causal interactions in human epilepsy networks, for which we show the relevance of partial information decomposition to the detection of multiscale information transfer spreading from the seizure onset zone