On Feature Binding in Space and Time

When presented with a yellow Volkswagen and a red Ferrari, how does the brain ?gure out which color goes with which car? The binding problem refers to how the visual system pre-consciously combines visual features of objects in the physical world to create coherent mental equivalents in our consciousness. I discuss why feature binding is a problem for our brains despite its seemingly e?ortless resolution in every-day life. Drawing from experimental cognitive psychology, I demonstrate how it manifests in space and time

Analysis of microseismic events associated with hydraulic fracture propagation

Previous practice to determine the source mechanism of microseismic events associated with hydraulic fracture typically includes only far-field terms in moment tensor inversion. The intermediate-field terms and near-field term are normally ignored because of increased complexity in the calculation. Source-receiver distances in hydraulic fracturing are usually 1000 ft and the effects of near and intermediate-field terms are still unknown. We perform a study to improve the precision of the source mechanism by including the intermediate-field term in moment tensor inversion. We find that the intermediate-field term contributes 1/3 of the signal amplitude when the source-receiver distance is 1000 ft. The intermediate-field term contributes 1/20 of the signal amplitude when the source-receiver distance is 6700 ft. Note that 1/20 is at the noise level. Thus, when source-receiver distance is less than 6700ft, we need to consider the intermediate-field term. Especially, when the distance is 1000ft, the intermediate-field term becomes significant. Similarly, near-field terms contribute less than 1/20 of the signal amplitude when distances are larger than 300 ft. In our case, we confirm that the near-field term can be ignored in microseismic analysis. Our results indicate that the intermediate-field terms can improve moment tensor inversion by 2% to 40% at source-receiver ranges less than 1000 ft. When distances are larger than 6700, the improvement is limited to 1%. In the presence of noise, the intermediate-field terms help to improve the moment tensor inversion (15% improvement with noise present vs 3% improvement without noise). Our study provides a foundation for using intermediate-field terms in moment tensor inversion in the studies of hydraulic fractures

The cost of space independence in P300-BCI spellers.

Background: Though non-invasive EEG-based Brain Computer Interfaces (BCI) have been researched extensively over the last two decades, most designs require control of spatial attention and/or gaze on the part of the user. Methods: In healthy adults, we compared the offline performance of a space-independent P300-based BCI for spelling words using Rapid Serial Visual Presentation (RSVP), to the well-known space-dependent Matrix P300 speller. Results: EEG classifiability with the RSVP speller was as good as with the Matrix speller. While the Matrix speller’s performance was significantly reliant on early, gaze-dependent Visual Evoked Potentials (VEPs), the RSVP speller depended only on the space-independent P300b. However, there was a cost to true spatial independence: the RSVP speller was less efficient in terms of spelling speed. Conclusions: The advantage of space independence in the RSVP speller was concomitant with a marked reduction in spelling efficiency. Nevertheless, with key improvements to the RSVP design, truly space-independent BCIs could approach efficiencies on par with the Matrix speller. With sufficiently high letter spelling rates fused with predictive language modelling, they would be viable for potential applications with patients unable to direct overt visual gaze or covert attentional focus

A Trillion Coral Reef Colors: Deeply Annotated Underwater Hyperspectral Images for Automated Classification and Habitat Mapping

This paper describes a large dataset of underwater hyperspectral imagery that can be used by researchers in the domains of computer vision, machine learning, remote sensing, and coral reef ecology. We present the details of underwater data acquisition, processing and curation to create this large dataset of coral reef imagery annotated for habitat mapping. A diver-operated hyperspectral imaging system (HyperDiver) was used to survey 147 transects at 8 coral reef sites around the Caribbean island of Curacao. The underwater proximal sensing approach produced fine-scale images of the seafloor, with more than 2.2 billion points of detailed optical spectra. Of these, more than 10 million data points have been annotated for habitat descriptors or taxonomic identity with a total of 47 class labels up to genus- and species-levels. In addition to HyperDiver survey data, we also include images and annotations from traditional (color photo) quadrat surveys conducted along 23 of the 147 transects, which enables comparative reef description between two types of reef survey methods. This dataset promises benefits for efforts in classification algorithms, hyperspectral image segmentation and automated habitat mapping. Dataset: https://doi.org/10.1594/PANGAEA.911300 Dataset License: CC-BY-N

Which predictive uncertainty to resolve? Value of information sensitivity analysis for environmental decision models

Unidad de excelencia María de Maeztu CEX2019-000940-MUncertainties in environmental decisions are large, but resolving them is costly. We provide a framework for value of information (VoI) analysis to identify key predictive uncertainties in a decision model. The approach addresses characteristics that complicate this analysis in environmental management: dependencies in the probability distributions of predictions, trade-offs between multiple objectives, and divergent stakeholder perspectives. For a coral reef fisheries case, we predict ecosystem and fisheries trajectories given different management alternatives with an agent-based model. We evaluate the uncertain predictions with preference models based on utility theory to find optimal alternatives for stakeholders. Using the expected value of partially perfect information (EVPPI), we measure how relevant resolving uncertainty for various decision attributes is. The VoI depends on the stakeholder preferences, but not directly on the width of an attribute's probability distribution. Our approach helps reduce costs in structured decision-making processes by prioritizing data collection efforts

Mapping the microscale variability of microphytobenthos: Development of a hyperspectral imaging method

Microphytobenthos (MPB) is a grouping of microbial benthic phototrophs that inhabit the sediments of coastal regions. There are clear indications that MPB distributions exhibit profuse spatio-temporal variability on the scale of milli- to centi-meters, which corresponds to the spatial span of their proximal habitats. This microscale variability in MPB distributions escapes detection by most traditional measurement techniques. Analysis of the spatio-temporal aspects of MPB ecology is limited due to the inability of current methods to capture the MPB distributions with high spatial and temporal resolution. This doctoral study identifies a methodological gap in our ability to measure in situ MPB distributions at the microscale and attempts to rectify it through the development of a field instrument and measurement protocol that utilize hyperspectral imaging technology. The ecological applications of the novel ability to measure and visualize full-field patterns of MPB distribution with a high temporal resolution are explored in subsequent studies

Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness.

As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of alpha networks and the emergence of theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth

High-Resolution Dynamics of Hydrogen Peroxide on the Surface of Scleractinian Corals in Relation to Photosynthesis and Feeding

We developed and used a microsensor to measure fast (<1 s) dynamics of hydrogen peroxide (H2O2) on the polyp tissue of two scleractinian coral species (Stylophora pistillata and Pocillopora damicornis) under manipulations of illumination, photosynthesis, and feeding activity. Our real-time tracking of H2O2 concentrations on the coral tissue revealed rapid changes with peaks of up to 60 mu M. We observed bursts of H2O2 release, lasting seconds to minutes, with rapid increase and decrease of surficial H2O2 levels at rates up to 15 mu M s(-1). We found that the H2O2 levels on the polyp surface are enhanced by oxygenic photosynthesis and feeding, whereas H2O2 bursts occurred randomly, independently from photosynthesis. Feeding resulted in a threefold increase of baseline H2O2 levels and was accompanied by H2O2 bursts, suggesting that the coral host is the source of the bursts. Our study reveals that H2O2 levels at the surface of coral polyps are much higher and more dynamic than previously reported, and that bursts are a regular feature of the H2O2 dynamics in the coral holobiont

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported in these patients. These metrics could also identify patients in whom further assessment is warranted using neuroimaging or conventional clinical evaluation. Finally, by providing objective characterization of states of consciousness, repeated assessments of network metrics could help track individual patients longitudinally, and also assess their neural responses to therapeutic and pharmacological interventions.The authors received funding from the UK Engineering and Physical Sciences Research Council (EP/P033199/1), Evelyn Trust (Cambridge, UK), the UK National Institute for Health Research (NIHR) as part of the Acute Brain Injury and Repair Theme of the Cambridge Biomedical Research Centre, the NIHR Brain Injury Healthcare Technology Cooperative, the NIHR Senior Investigator award, the James S. McDonnell Foundation, the Belgian National Fund for Scientific Research (FNRS), the European Commission, the Human Brain Project (EUH2020-fetflagship-hbp-sga1-ga720270), the Luminous project (EU-H2020-fetopen-ga686764), the French Speaking Community Concerted Research Action, the Belgian American Educational Foundation, the WallonieBruxelles Federation, the European Space Agency, the University and University Hospital of Liege (Belgium)