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During propofol-induced general anesthesia, alpha rhythms measured using electroencephalography undergo a striking shift from posterior to anterior, termed anteriorization, where the ubiquitous waking alpha is lost and a frontal alpha emerges. The functional significance of alpha anteriorization and the precise brain regions contributing to the phenomenon are a mystery. While posterior alpha is thought to be generated by thalamocortical circuits connecting nuclei of the sensory thalamus with their cortical partners, the thalamic origins of the propofol-induced alpha remain poorly understood. Here, we used human intracranial recordings to identify regions in sensory cortices where propofol attenuates a coherent alpha network, distinct from those in the frontal cortex where it amplifies coherent alpha and beta activities. We then performed diffusion tractography between these identified regions and individual thalamic nuclei to show that the opposing dynamics of anteriorization occur within two distinct thalamocortical networks. We found that propofol disrupted a posterior alpha network structurally connected with nuclei in the sensory and sensory associational regions of the thalamus. At the same time, propofol induced a coherent alpha oscillation within prefrontal cortical areas that were connected with thalamic nuclei involved in cognition, such as the mediodorsal nucleus. The cortical and thalamic anatomy involved, as well as their known functional roles, suggests multiple means by which propofol dismantles sensory and cognitive processes to achieve loss of consciousness.

During propofol-induced general anesthesia, alpha rhythms undergo a striking shift from posterior to anterior, termed anteriorization. We combined human intracranial recordings with diffusion imaging to show that anteriorization occurs with opposing dynamics in two distinct thalamocortical subnetworks. The cortical and thalamic anatomy involved, as well as their known functional roles, suggest multiple means by which propofol dismantles sensory and cognitive processes to achieve loss of consciousness.

Phase amplitude coupling (PAC) is thought to play a fundamental role in the dynamic coordination of brain circuits and systems. There are however growing concerns that existing methods for PAC analysis are prone to error and misinterpretation. Improper frequency band selection can render true PAC undetectable, while non-linearities or abrupt changes in the signal can produce spurious PAC. Current methods require large amounts of data and lack formal statistical inference tools. We describe here a novel approach for PAC analysis that substantially addresses these problems. We use a state space model to estimate the component oscillations, avoiding problems with frequency band selection, nonlinearities, and sharp signal transitions. We represent cross-frequency coupling in parametric and time-varying forms to further improve statistical efficiency and estimate the posterior distribution of the coupling parameters to derive their credible intervals. We demonstrate the method using simulated data, rat local field potentials (LFP) data, and human EEG data.

Direct neural recordings from human auditory cortex have demonstrated encoding for acoustic-phonetic features of consonants and vowels. Neural responses also encode distinct acoustic amplitude cues related to timing, such as those that occur at the onset of a sentence after a silent period or the onset of the vowel in each syllable. Here, we used a group reduced rank regression model to show that distributed cortical responses support a low-dimensional latent state representation of temporal context in speech. The timing cues each capture more unique variance than all other phonetic features and exhibit rotational or cyclical dynamics in latent space from activity that is widespread over the superior temporal gyrus. We propose that these spatially distributed timing signals could serve to provide temporal context for, and possibly bind across time, the concurrent processing of individual phonetic features, to compose higher-order phonological (e.g. word-level) representations.

A controversy 5 has developed in recent years over the role that frontal and posterior cortices play in mediating consciousness and unconsciousness. One hypothesis proposes that posterior sensory and association cortices are the principal mediators of consciousness, citing evidence that strong slow-wave activity over posterior cortex during sleep disrupts the contents of dreaming. A competing hypothesis proposes that frontal-posterior interactions are critical to ignite a conscious percept, since activation of frontal cortex appears necessary for perception and can reverse unconsciousness under anesthesia. In both cases, EEG slow-waves (< 1 Hz) are considered evidence that up- and down-states are disrupting cortical activity necessary for consciousness. Here, we used anesthesia to study the interaction between the slow-wave and higher frequency activity in humans. If slow-waves are derived from underlying up and down-states, then they should modulate activity across a broad range of frequencies. We found that this broadband slow-wave modulation does occur: broadband slow-wave modulation occurs over posterior cortex when subjects initially become unconscious, but later encompasses both frontal and posterior cortex when subjects are more deeply anesthetized and likely unarousable. Based on these results, we argue that unconsciousness under anesthesia comprises several shifts in brain state that disrupt the sensory contents of consciousness distinct from arousal and awareness of those contents.Significance StatementThe roles of frontal and posterior cortices in mediating consciousness and unconsciousness are controversial. Disruption of posterior cortex during sleep appears to suppress the contents of dreaming, yet activation of frontal cortex appears necessary for perception and can reverse unconsciousness under anesthesia. We studied the time course of regional cortical disruption, as mediated by slow-wave modulation of broadband activity, during anesthesia-induced unconsciousness in humans. We found that broadband slow-wave modulation covered posterior cortex when subjects initially became unconscious, but later encompassed both frontal and posterior cortex when subjects were deeply anesthetized and likely unarousable. This suggests that unconsciousness under anesthesia comprises several shifts in brain state that disrupt the contents of consciousness distinct from arousal and awareness of those contents.

Understanding how different brain areas interact to generate complex behavior is a primary goal of neuroscience research. One approach, functional connectivity analysis, aims to characterize the connectivity patterns within brain networks. In this paper, we address the problem of discriminative connectivity, i.e. determining the differences in network structure under different experimental conditions. We introduce a novel model called Sparse Multi-task Inverse Covariance Estimation (SMICE) which is capable of estimating a common connectivity network as well as discriminative networks across different tasks. We apply the method to EEG signals after solving the inverse problem of source localization, yielding networks defined on the cortical surface. We propose an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve SMICE. We apply our newly developed framework to find common and discriminative connectivity patterns for α-oscillations during the Sleep Onset Process (SOP) and during Rapid Eye Movement (REM) sleep. Even though both stages exhibit a similar α-oscillations, we show that the underlying networks are distinct.