Drones are controlled remotely through the on-board live camera often using a headset to immersively (without external distraction) situate the drone operator with a first-person view. The highly dynamic imagery of drone piloting can elicit significant VR sickness. In this poster, we demonstrate the application of mixing the reverse optical flow pattern into the drone piloting imagery for mitigating VR sickness, using only the features of the static landmarks in the scene. We compare it to the cases of applying no mitigation technique (baseline) and mixing in the optical flow pattern from “all” object features. The results show that the suggested method was significantly more effective in reducing sickness than when considering all object features, and had a higher preference for improved visibility and controllability.

Absence seizures are brief episodes of impaired consciousness, behavioral arrest, and unresponsiveness, with yet-unknown neuronal mechanisms. Here we report that an awake female rat model recapitulates the behavioral, electroencephalographic, and cortical functional magnetic resonance imaging characteristics of human absence seizures. Neuronally, seizures feature overall decreased but rhythmic firing of neurons in cortex and thalamus. Individual cortical and thalamic neurons express one of four distinct patterns of seizure-associated activity, one of which causes a transient initial peak in overall firing at seizure onset, and another which drives sustained decreases in overall firing. 40–60 s before seizure onset there begins a decline in low frequency electroencephalographic activity, neuronal firing, and behavior, but an increase in higher frequency electroencephalography and rhythmicity of neuronal firing. Our findings demonstrate that prolonged brain state changes precede consciousness-impairing seizures, and that during seizures distinct functional groups of cortical and thalamic neurons produce an overall transient firing increase followed by a sustained firing decrease, and increased rhythmicity.

Behavior during 3-4 Hz spike-wave discharges (SWDs) in absence epilepsy can vary from obvious behavioral arrest to no detectible deficits. Knowing if behavior is impaired is crucial for clinical care but may be difficult to determine without specialized behavioral testing, often inaccessible in practice. We aimed to develop a pure electroencephalography (EEG)-based machine-learning method to predict SWD-related behavioral impairment. Our classification goals were 100% predictive value, with no behaviorally impaired SWDs misclassified as spared; and maximal sensitivity. First, using labeled data with known behavior (130 SWDs in 34 patients), we extracted EEG time, frequency domain, and common spatial pattern features and applied support vector machines and linear discriminant analysis to classify SWDs as spared or impaired. We evaluated 32 classification models, optimized with 10-fold cross-validation. We then generalized these models to unlabeled data (220 SWDs in 41 patients), where behavior during individual SWDs was not known, but observers reported the presence of clinical seizures. For labeled data, the best classifier achieved 100% spared predictive value and 93% sensitivity. The best classifier on the unlabeled data achieved 100% spared predictive value, but with a lower sensitivity of 35%, corresponding to a conservative classification of 8 patients out of 23 as free of clinical seizures. Our findings demonstrate the feasibility of machine learning to predict impaired behavior during SWDs based on EEG features. With additional validation and optimization in a larger data sample, applications may include EEG-based prediction of driving safety, treatment adjustment, and insight into mechanisms of impaired consciousness in absence seizures.

The full neural circuits of conscious perception remain unknown. Using a visual perception task, we directly recorded a subcortical thalamic awareness potential (TAP). We also developed a unique paradigm to classify perceived versus not perceived stimuli using eye measurements to remove confounding signals related to reporting on conscious experiences. Using fMRI, we discovered three major brain networks driving conscious visual perception independent of report: first, increases in signal detection regions in visual, fusiform cortex, and frontal eye fields; and in arousal/salience networks involving midbrain, thalamus, nucleus accumbens, anterior cingulate, and anterior insula; second, increases in frontoparietal attention and executive control networks and in the cerebellum; finally, decreases in the default mode network. These results were largely maintained after excluding eye movement-based fMRI changes. Our findings provide evidence that the neurophysiology of consciousness is complex even without overt report, involving multiple cortical and subcortical networks overlapping in space and time.

Understanding consciousness is one of the most important and challenging questions in modern science. Existing theories have pursued single unifying mechanisms but do not succeed in explaining consciousness. Importantly, the neural circuits that distinguish messages that arrive from the outside world and attain consciousness have remained unknown. Here we identify signals throughout the entire brain at high spatiotemporal resolution specifically related to consciousness. To accomplish this, we combined a large sample size of electrical and neuroimaging data with a novel experimental approach to remove confounding signal unrelated to consciousness 1-3 . We discovered three major brain networks driving conscious visual perception. First, we found increases in signal detection regions in visual, fusiform cortex, and frontal eye fields; and in arousal/salience networks involving midbrain, thalamus, nucleus accumbens, anterior cingulate, and anterior insula. Second, we found increases in frontoparietal attention and executive control networks and in the cerebellum. Finally, we found decreases in the default mode network. Our results identify subcortical and cortical networks designed for signal detection, attentional amplification, and perceptual processing that together can explain visual consciousness. These findings provide evidence that understanding consciousness can be reframed as requiring multiple overlapping brain networks to produce consciousness of visual events 4 .

Consciousness is not explained by a single mechanism, rather it involves multiple specialized neural systems overlapping in space and time. We hypothesize that synergistic, large-scale subcortical and cortical attention and signal processing networks encode conscious experiences. To identify brain activity in conscious perception without overt report, we classified visual stimuli as perceived or not using eye measurements. Report-independent event-related potentials and functional magnetic resonance imaging (fMRI) signals both occurred at early times after stimuli. Direct recordings revealed a novel thalamic awareness potential linked to conscious visual perception based on report. fMRI showed thalamic and cortical detection, arousal, attentional salience, task-positive, and default mode networks were involved independent of overt report. These findings identify a specific sequence of neural mechanisms in human conscious visual perception. One-Sentence Summary Human conscious visual perception engages large-scale subcortical and cortical networks even without overt report.

Absence seizures are characterized by a brief behavioural impairment including apparent loss of consciousness. Neuronal mechanisms determining the behavioural impairment of absence seizures remain unknown, and their elucidation might highlight therapeutic options for reducing seizure severity. However, recent studies have questioned the similarity of animal spike-wave-discharges (SWD) to human absence seizures both behaviourally and neuronally. Here, we report that Genetic Absence Epilepsy Rats from Strasbourg recapitulate the decreased neuroimaging signals and loss of consciousness characteristic of human absence seizures. Overall neuronal firing is decreased but rhythmic in the somatosensory cortex and thalamus during these seizures. Interestingly, individual neurons in both regions tend to consistently express one of four distinct patterns of seizure-associated activity. These patterns differ in firing rate dynamics and in rhythmicity during seizure. One group of neurons showed a transient initial peak in firing at SWD onset, accounting for the brief initial increase in overall neuronal firing seen in cortex and thalamus. The largest group of neurons in both cortex and thalamus showed sustained decreases in firing during SWD. Other neurons showed either sustained increases or no change in firing. These findings suggest that certain classes of cortical and thalamic neurons may be particularly responsible for the paroxysmal oscillations and consequent loss of consciousness in absence epilepsy.

Studies of attention emphasize cortical circuits for salience monitoring and top-down control. However, subcortical arousal systems have a major influence on dynamic cortical state. We hypothesize that task-related increases in attention begin with a "pulse" in subcortical arousal and cortical attention networks, which are reflected indirectly through transient fMRI signals. We conducted general linear model and model-free analyses of fMRI data from two cohorts and tasks with mixed block and event-related design. 46 adolescent subjects at our center and 362 normal adults from the Human Connectome Project participated. We identified a core shared network of transient fMRI increases in subcortical arousal and cortical salience/attention networks across cohorts and tasks. Specifically, we observed a transient pulse of fMRI increases both at task block onset and with individual task events in subcortical arousal areas including midbrain tegmentum, thalamus, nucleus basalis and striatum; cortical-subcortical salience network regions including the anterior insula/claustrum and anterior cingulate cortex/supplementary motor area; in dorsal attention network regions including dorsolateral frontal cortex and inferior parietal lobule; as well as in motor regions including cerebellum, and left hemisphere hand primary motor cortex. The transient pulse of fMRI increases in subcortical and cortical arousal and attention networks was consistent across tasks and study populations, whereas sustained activity in these same networks was more variable. The function of the transient pulse in these networks is unknown. However, given its anatomical distribution, it could participate in a neuromodulatory surge of activity in multiple parallel neurotransmitter systems facilitating dynamic changes in conscious attention.

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.

Generalized spike-wave discharges (SWDs) are the hallmark of generalized epilepsy on the electroencephalogram (EEG). In clinically obvious cases, generalized SWDs produce myoclonic, atonic/tonic, or absence seizures with brief episodes of staring and behavioral unresponsiveness. However, some generalized SWDs have no obvious behavioral effects. A serious challenge arises when patients with no clinical seizures request driving privileges and licensure, yet their EEG shows generalized SWD. Specialized behavioral testing has demonstrated prolonged reaction times or missed responses during SWD, which may present a driving hazard even when patients or family members do not notice any deficits. On the other hand, some SWDs are truly asymptomatic in which case driving privileges should not be restricted. Clinicians often decide on driving privileges based on SWD duration or other EEG features. However, there are currently no empirically-validated guidelines for distinguishing generalized SWDs that are "safe" versus "unsafe" for driving. Here, we review the clinical presentation of generalized SWD and recent work investigating mechanisms of behavioral impairment during SWD with implications for driving safety. As a future approach, computational analysis of large sets of EEG data during simulated driving utilizing machine learning could lead to powerful methods to classify generalized SWD as safe vs. unsafe. This may ultimately provide more objective EEG criteria to guide decisions on driving safety in people with epilepsy.

Objective Generalized epileptiform discharges (GEDs) can occur during seizures or without obvious clinical accompaniment. Motor vehicle driving risk during apparently subclinical GEDs is uncertain. Our goals were to develop a feasible, realistic test to evaluate driving safety during GEDs, and to begin evaluating electroencephalographic (EEG) features in relation to driving safety. Methods Subjects were aged >= 15 years with generalized epilepsy, GEDs on EEG, and no clinical seizures. Using a high-fidelity driving simulator (miniSim) with simultaneous EEG, a red oval visual stimulus was presented every 5 minutes for baseline testing, and with each GED. Participants were instructed to pull over as quickly and safely as possible with each stimulus. We analyzed driving and EEG signals during GEDs. Results Nine subjects were tested, and five experienced 88 GEDs total with mean duration 2.31 +/- 1.89 (SD) seconds. Of these five subjects, three responded appropriately to all stimuli, one failed to respond to 75% of stimuli, and one stopped driving immediately during GEDs. GEDs with no response to stimuli were significantly longer than those with appropriate responses (8.47 +/- 3.10 vs 1.85 +/- 0.69 seconds, P < .001). Reaction times to stimuli during GEDs were significantly correlated with GED duration (r = 0.30, P = .04). In addition, EEG amplitude was greater for GEDs with no response to stimuli than GEDs with responses, both for overall root mean square voltage amplitude (66.14 mu V vs 52.99 mu V, P = .02) and for fractional power changes in the frequency range of waves (P < .05) and spikes (P < .001). Significance High-fidelity driving simulation is feasible for investigating driving behavior during GEDs. GEDs with longer duration and greater EEG amplitude showed more driving impairment. Future work with a large sample size may ultimately enable classification of GED EEG features to predict individual driving risk.

Focal limbic seizures often impair consciousness/awareness with major negative impact on quality of life. Recent work has shown that limbic seizures depress brainstem arousal systems, including reduced action potential firing in a key node: cholinergic neurons of the pedunculopontine tegmental nucleus (PPT). In vivo whole-cell recordings have not previously been achieved in PPT, but are used here with the goal of elucidating the mechanisms of reduced PPT cholinergic neuronal activity. An established model of focal limbic seizures was used in rats following brief hippocampal stimulation under light anesthesia. Whole-cell in vivo recordings were obtained from PPT neurons using custom -fabricated 9-10 mm tapered patch pipettes, and cholinergic neurons were identified histologically. Average membrane potential, input resistance, membrane potential fluctuations and variance were analyzed during seizures. A subset of PPT neurons exhibited reduced firing and hyperpolarization during seizures and stained positive for choline acetyltransferase. These PPT neurons showed a mean membrane potential hyperpolarization of -3.82 mV (+/- 0.81 SEM, P < .05) during seizures, and also showed significantly increased input resistance, fewer excitatory post-synaptic potential (EPSP)-like events (P<.05), and reduced membrane potential variance (P < .01). The combination of increased input resistance, decreased EPSP-like events and decreased variance weigh against active ictal inhibition and support withdrawal of excitatory input as the dominant mechanism of decreased activity of cholinergic neurons in the PPT. Further identifying synaptic mechanisms of depressed arousal during seizures may lead to new treatments to improve ictal and postictal cognition.

We define M-sequence non-squashing partitions, which specialize to m-ary partitions (studied by Andrews, Churchhouse, Erdös, Hirschhorn, Knuth, Mahler, Rødseth, Sellers, and Sloane, among others), factorial partitions, and numerous other general partition families of interest. We establish an exact formula, various combinatorial interpretations, as well as the asymptotic growth of M-sequence non-squashing partition functions, functions whose associated generating functions are non-modular. In particular, we obtain an exact formula for the m-ary partition function, and by new methods, we recover Mahler's and Erdös' asymptotic for the m-ary partition function. We also establish new results on factorial partitions, colored m-ary partitions, and many other general families which have not been well understood or systematically studied. Finally, we conjecture Ramanujan-like congruences for the M-sequence non-squashing partition functions.

BACKGROUND:The neural underpinnings of impaired consciousness and of the variable severity of behavioural deficits from one absence seizure to the next are not well understood. We aimed to measure functional MRI (fMRI) and electroencephalography (EEG) changes in absence seizures with impaired task performance compared with seizures in which performance was spared. METHODS:In this cross-sectional study done at the Yale School of Medicine, CT, USA, we recruited patients from 59 paediatric neurology practices in the USA. We did simultaneous EEG, fMRI, and behavioural testing in patients aged 6-19 years with childhood or juvenile absence epilepsy, and with an EEG with typical 3-4 Hz bilateral spike-wave discharges and normal background. The main outcomes were fMRI and EEG amplitudes in seizures with impaired versus spared behavioural responses analysed by t test. We also examined the timing of fMRI and EEG changes in seizures with impaired behavioural responses compared with seizures with spared responses. FINDINGS:93 patients were enrolled between Jan 1, 2005, and Sept 1, 2013; we recorded 1032 seizures in 39 patients. fMRI changes during seizures occurred sequentially in three functional brain networks. In the default mode network, fMRI amplitude was 0·57% (SD 0·26) for seizures with impaired and 0·40% (0·16) for seizures with spared behavioural responses (mean difference 0·17%, 95% CI 0·11-0·23; p<0·0001). In the task-positive network, fMRI amplitude was 0·53% (SD 0·29) for seizures with impaired and 0·39% (0·15) for seizures with spared behavioral responses (mean difference 0·14%, 95% CI 0·08-0·21; p<0·0001). In the sensorimotor-thalamic network, fMRI amplitude was 0·41% (0·25) for seizures with impaired and 0·34% (0·14) for seizures with spared behavioural responses (mean difference 0·07%, 95% CI 0·01-0·13; p=0·02). Mean fractional EEG power in the frontal leads was 50·4 (SD 15·2) for seizures with impaired and 24·8 (6·5) for seizures with spared behavioural responses (mean difference 25·6, 95% CI 21·0-30·3); middle leads 35·4 (6·5) for seizures with impaired, 13·3 (3·4) for seizures with spared behavioural responses (mean difference 22·1, 95% CI 20·0-24·1); posterior leads 41·6 (5·3) for seizures with impaired, 24·6 (8·6) for seizures with spared behavioural responses (mean difference 17·0, 95% CI 14·4-19·7); p<0·0001 for all comparisons. Mean seizure duration was longer for seizures with impaired behaviour at 7·9 s (SD 6·6), compared with 3·8 s (3·0) for seizures with spared behaviour (mean difference 4·1 s, 95% CI 3·0-5·3; p<0·0001). However, larger amplitude fMRI and EEG signals occurred at the outset or even preceding seizures with behavioural impairment. INTERPRETATION:Impaired consciousness in absence seizures is related to the intensity of physiological changes in established networks affecting widespread regions of the brain. Increased EEG and fMRI amplitude occurs at the onset of seizures associated with behavioural impairment. These finding suggest that a vulnerable state might exist at the initiation of some absence seizures leading them to have more severe physiological changes and altered consciousness than other absence seizures. FUNDING:National Institutes of Health, National Institute of Neurological Disorders and Stroke, National Center for Advancing Translational Science, the Loughridge Williams Foundation, and the Betsy and Jonathan Blattmachr Family.