Dynamic attention states are necessary to navigate the ever changing task demands of daily life. Previous investigations commonly utilize a block paradigm to study sustained and transient changes in attention networks. fMRI investigations have shown that sustained attention in visual block design attention tasks corresponds to decreased signal in the default mode and visual processing networks. While task negative networks are anticipated to decrease during active task engagement, it is unexpected that visual networks would also be suppressed during a visual task where event-related fMRI studies have found transient increases to visual stimuli. To resolve these competing results, the current investigations utilized intracranial EEG to directly interrogate visual and default mode network dynamics during a visual continuous performance task. We used the electrophysiological data to model expected fMRI signals and to maximize interpretation of current results with previous investigations. Results show broadband gamma power decreases in the default mode network, corresponding to previous EEG and fMRI findings. Meanwhile, visual processing regions including the primary visual cortex and fusiform gyrus demonstrate both sustained decreases during task engagement and stimuli-driven transient increases in gamma power. Modeled fMRI based on gamma power reproduces signal decreases reported in the fMRI literature, and emphasizes the insensitivity of fMRI to transient, regularly spaced signal changes embedded within sustained network dynamics. The signal processing functions of the dynamic visual and default mode network changes explored in this study are unknown but may be elucidated through further investigation.

In human-computer interaction (HCI), there has been a push towards open science, but to date, this has not happened consistently for HCI research utilizing brain signals due to unclear guidelines to support reuse and reproduction. To understand existing practices in the field, this paper examines 110 publications, exploring domains, applications, modalities, mental states and processes, and more. This analysis reveals variance in how authors report experiments, which creates challenges to understand, reproduce, and build on that research. It then describes an overarching experiment model that provides a formal structure for reporting HCI research with brain signals, including definitions, terminology, categories, and examples for each aspect. Multiple distinct reporting styles were identified through factor analysis and tied to different types of research. The paper concludes with recommendations and discusses future challenges. This creates actionable items from the abstract model and empirical observations to make HCI research with brain signals more reproducible and reusable.

Recent work suggests an important role for cortical–subcortical networks in seizure-related loss of consciousness. Temporal lobe seizures disrupt subcortical arousal systems, which may lead to depressed cortical function and loss of consciousness. Extracellular recordings show ictal neocortical slow waves at about 1 Hz, but it is not known whether these simply represent seizure propagation or alternatively deep sleep-like activity, which should include cortical neuronal Up and Down states. In this study, using in vivo whole-cell recordings in a rat model of focal limbic seizures, we directly examine the electrophysiological properties of cortical neurons during seizures and deep anesthesia. We found that during seizures, the membrane potential of frontal cortical secondary motor cortex layer 5 neurons fluctuates between Up and Down states, with decreased input resistance and increased firing rate in Up states when compared to Down states. Importantly, Up and Down states in seizures are not significantly different from those in deep anesthesia, in terms of membrane potential, oscillation frequency, firing rate, and input resistance. By demonstrating these fundamental similarities in cortical electrophysiology between deep anesthesia and seizures, our results support the idea that a state of decreased cortical arousal may contribute to mechanisms of loss of consciousness during seizures.

Although recent work has made significant headway in understanding the temporal and spatial dynamics of the neural mechanisms of conscious perception, much of that work has focused on visual paradigms. To determine whether there are shared mechanisms for perceptual consciousness across sensory modalities, here we developed a task to test within the auditory domain. Participants (n=31) completed an auditory perceptual threshold task while undergoing intracranial electroencephalography (icEEG) for intractable epilepsy. Intracranial recordings from over 2,800 grey matter electrodes representing widespread cortical coverage were analyzed for power in the high gamma range (40–115 Hz)—a frequency range that reflects local neural activity. For trials that were perceived, we find activity in early auditory regions which is accompanied by activity in the right caudal middle frontal gyrus, and shortly thereafter by activity in non-auditory thalamus. This is followed by a wave of activity that sweeps through the higher auditory association regions and into parietal and frontal cortices, similar to the wave observed in our visual conscious perception paradigm. However, for not perceived trials, we find that significant activity is restricted to early auditory regions (and areas immediately adjacent to the Sylvian fissure). These findings show that the broad anatomical regions of cortical and subcortical networks involved in auditory perception are similar to the networks observed with vision, suggesting shared general mechanisms for conscious perception.