Kyle E. Mathewson

To See or Not to See: Prestimulus α Phase Predicts Visual Awareness

We often fail to see something that at other times is readily detectable. Because the visual stimulus itself is unchanged, this variability in conscious awareness is likely related to changes in the brain. Here we show that the phase of EEG α rhythm measured over posterior brain regions can reliably predict both subsequent visual detection and stimulus-elicited cortical activation levels in a metacontrast masking paradigm. When a visual target presentation coincides with the trough of an α wave, cortical activation is suppressed as early as 100 ms after stimulus onset, and observers are less likely to detect the target. Thus, during one α cycle lasting 100 ms, the human brain goes through a rapid oscillation in excitability, which directly influences the probability that an environmental stimulus will reach conscious awareness. Moreover, ERPs to the appearance of a fixation cross before the target predict its detection, further suggesting that cortical excitability level may mediate target detection. A novel theory of cortical inhibition is proposed in which increased α power represents a “pulsed inhibition” of cortical activity that affects visual awareness.

Soft Microfluidic Assemblies of Sensors, Circuits, and Radios for the Skin

Wearable Monitors Advances in microelectronics have yielded high-quality devices that allow for intensive signal collection or transmission. S. Xu et al. (p. 70) show how to make a soft wearable system that is constructed like a stretchable circuit board, where the electronic components are bridged electrically by thin, meandering conducting traces that float in a highly visco-elastic polymer. A complete soft circuit capable of multisignal physiological sensing on skin was created, with potential for use in health monitoring or neonatal care. Flexible skin-integrated electronic sensors enable continuous, wireless health monitoring. When mounted on the skin, modern sensors, circuits, radios, and power supply systems have the potential to provide clinical-quality health monitoring capabilities for continuous use, beyond the confines of traditional hospital or laboratory facilities. The most well-developed component technologies are, however, broadly available only in hard, planar formats. As a result, existing options in system design are unable to effectively accommodate integration with the soft, textured, curvilinear, and time-dynamic surfaces of the skin. Here, we describe experimental and theoretical approaches for using ideas in soft microfluidics, structured adhesive surfaces, and controlled mechanical buckling to achieve ultralow modulus, highly stretchable systems that incorporate assemblies of high-modulus, rigid, state-of-the-art functional elements. The outcome is a thin, conformable device technology that can softly laminate onto the surface of the skin to enable advanced, multifunctional operation for physiological monitoring in a wireless mode.

Pulsed out of awareness: EEG alpha oscillations represent a pulsed-inhibition of ongoing cortical processing.

Alpha oscillations are ubiquitous in the brain, but their role in cortical processing remains a matter of debate. Recently, evidence has begun to accumulate in support of a role for alpha oscillations in attention selection and control. Here we first review evidence that 8–12 Hz oscillations in the brain have a general inhibitory role in cognitive processing, with an emphasis on their role in visual processing. Then, we summarize the evidence in support of our recent proposal that alpha represents a pulsed-inhibition of ongoing neural activity. The phase of the ongoing electroencephalography can influence evoked activity and subsequent processing, and we propose that alpha exerts its inhibitory role through alternating microstates of inhibition and excitation. Finally, we discuss evidence that this pulsed-inhibition can be entrained to rhythmic stimuli in the environment, such that preferential processing occurs for stimuli at predictable moments. The entrainment of preferential phase may provide a mechanism for temporal attention in the brain. This pulsed inhibitory account of alpha has important implications for many common cognitive phenomena, such as the attentional blink, and seems to indicate that our visual experience may at least some times be coming through in waves.

Rugged and breathable forms of stretchable electronics with adherent composite substrates for transcutaneous monitoring

Research in stretchable electronics involves fundamental scientific topics relevant to applications with importance in human healthcare. Despite significant progress in active components, routes to mechanically robust construction are lacking. Here, we introduce materials and composite designs for thin, breathable, soft electronics that can adhere strongly to the skin, with the ability to be applied and removed hundreds of times without damaging the devices or the skin, even in regions with substantial topography and coverage of hair. The approach combines thin, ultralow modulus, cellular silicone materials with elastic, strain-limiting fabrics, to yield a compliant but rugged platform for stretchable electronics. Theoretical and experimental studies highlight the mechanics of adhesion and elastic deformation. Demonstrations include cutaneous optical, electrical and radio frequency sensors for measuring hydration state, electrophysiological activity, pulse and cerebral oximetry. Multipoint monitoring of a subject in an advanced driving simulator provides a practical example.

Making waves in the stream of consciousness: Entraining oscillations in eeg alpha and fluctuations in visual awareness with rhythmic visual stimulation

Rhythmic events are common in our sensory world. Temporal regularities could be used to predict the timing of upcoming events, thus facilitating their processing. Indeed, cognitive theories have long posited the existence of internal oscillators whose timing can be entrained to ongoing periodic stimuli in the environment as a mechanism of temporal attention. Recently, recordings from primate brains have shown electrophysiological evidence for these hypothesized internal oscillations. We hypothesized that rhythmic visual stimuli can entrain ongoing neural oscillations in humans, locking the timing of the excitability cycles they represent and thus enhancing processing of subsequently predictable stimuli. Here we report evidence for entrainment of neural oscillations by predictable periodic stimuli in the alpha frequency band and show for the first time that the phase of existing brain oscillations cannot only be modified in response to rhythmic visual stimulation but that the resulting phase-locked fluctuations in excitability lead to concomitant fluctuations in visual awareness in humans. This entrainment effect was dependent on both the amount of spontaneous alpha power before the experiment and the level of 12-Hz oscillation before each trial and could not be explained by evoked activity. Rhythmic fluctuations in awareness elicited by entrainment of ongoing neural excitability cycles support a proposed role for alpha oscillations as a pulsed inhibition of cortical activity. Furthermore, these data provide evidence for the quantized nature of our conscious experience and reveal a powerful mechanism by which temporal attention as well as perceptual snapshots can be manipulated and controlled.

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents’ beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

Learning to multitask: Effects of video game practice on electrophysiological indices of attention and resource allocation

Changes in attention allocation with complex task learning reflect processing automatization and more efficient control. We studied these changes using ERP and EEG spectral analyses in subjects playing Space Fortress, a complex video game comprising standard cognitive task components. We hypothesized that training would free up attentional resources for a secondary auditory oddball task. Both P3 and delta EEG showed a processing trade-off between game and oddball tasks, but only some game events showed reduced attention requirements with practice. Training magnified a transient increase in alpha power following both primary and secondary task events. This contrasted with alpha suppression observed when the oddball task was performed alone, suggesting that alpha may be related to attention switching. Hence, P3 and EEG spectral data are differentially sensitive to changes in attentional processing occurring with complex task training.

Different slopes for different folks: Alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks

We hypothesized that control processes, as measured using electrophysiological (EEG) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event-related spectral perturbations (ERSPs), and event-related brain potentials during early training of the Space Fortress task, and correlated these measures with subsequent learning rate and performance in transfer tasks. Once initial score was partialled out, the best predictors were frontal alpha power and alpha and delta ERSPs, but not P300. By combining these predictors, we could explain about 50% of the learning rate variance and 10%-20% of the variance in transfer to other tasks using only pretraining EEG measures. Thus, control processes, as indexed by alpha and delta EEG oscillations, can predict learning and skill improvements. The results are of potential use to optimize training regimes.

Dynamics of alpha control: Preparatory suppression of posterior alpha oscillations by frontal modulators revealed with combined eeg and event-related optical signal

We investigated the dynamics of brain processes facilitating conscious experience of external stimuli. Previously, we proposed that alpha (8–12 Hz) oscillations, which fluctuate with both sustained and directed attention, represent a pulsed inhibition of ongoing sensory brain activity. Here we tested the prediction that inhibitory alpha oscillations in visual cortex are modulated by top–down signals from frontoparietal attention networks. We measured modulations in phase-coherent alpha oscillations from superficial frontal, parietal, and occipital cortices using the event-related optical signal (EROS), a measure of neuronal activity affording high spatiotemporal resolution, along with concurrently recorded EEG, while participants performed a visual target detection task. The pretarget alpha oscillations measured with EEG and EROS from posterior areas were larger for subsequently undetected targets, supporting alphas inhibitory role. Using EROS, we localized brain correlates of these awareness-related alpha oscillations measured at the scalp to the cuneus and precuneus. Crucially, EROS alpha suppression correlated with posterior EEG alpha power across participants. Sorting the EROS data based on EEG alpha power quartiles to investigate alpha modulators revealed that suppression of posterior alpha was preceded by increased activity in regions of the dorsal attention network and decreased activity in regions of the cingulo-opercular network. Cross-correlations revealed the temporal dynamics of activity within these preparatory networks before posterior alpha modulation. The novel combination of EEG and EROS afforded localization of the sources and correlates of alpha oscillations and their temporal relationships, supporting our proposal that top–down control from attention networks modulates both posterior alpha and awareness of visual stimuli.

Combining energy and Laplacian regularization to accurately retrieve the depth of brain activity of diffuse optical tomographic data.

Abstract. Diffuse optical tomography (DOT) provides data about brain function using surface recordings. Despite recent advancements, an unbiased method for estimating the depth of absorption changes and for providing an accurate three-dimensional (3-D) reconstruction remains elusive. DOT involves solving an ill-posed inverse problem, requiring additional criteria for finding unique solutions. The most commonly used criterion is energy minimization (energy constraint). However, as measurements are taken from only one side of the medium (the scalp) and sensitivity is greater at shallow depths, the energy constraint leads to solutions that tend to be small and superficial. To correct for this bias, we combine the energy constraint with another criterion, minimization of spatial derivatives (Laplacian constraint, also used in low resolution electromagnetic tomography, LORETA). Used in isolation, the Laplacian constraint leads to solutions that tend to be large and deep. Using simulated, phantom, and actual brain activation data, we show that combining these two criteria results in accurate (error <2  mm) absorption depth estimates, while maintaining a two-point spatial resolution of <24  mm up to a depth of 30 mm. This indicates that accurate 3-D reconstruction of brain activity up to 30 mm from the scalp can be obtained with DOT.