Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs.

Brain-Computer Interfaces (BCI) aim to bypass the peripheral nervous system to link the brain to external devices via successful modeling of decoding mechanisms. BCI based on electrocorticogram or ECoG represent a viable compromise between clinical practicality, spatial resolution, and signal quality when it comes to extracellular electrical potentials from local neuronal assemblies. Classic analysis of ECoG traces usually falls under the umbrella of Time-Frequency decompositions with adaptations from Fourier analysis and wavelets as its most prominent variants. However, analyzing such high-dimensional, multivariate time series demands for specialized signal processing and neurophysiological principles. We propose a generative model for single-channel ECoGs that is able to fully characterize reoccurring rhythm-specific neuromodulations as weighted activations of prototypical templates over time. The set of timings, weights and indexes comprise a temporal marked point process (TMPP) that accesses a set of bases from vector spaces of different dimensions-a dictionary. The shallow nature of the model admits the equivalence between latent variables and representations. In this way, learning the model parameters is a case of unsupervised representation learning. We exploit principles of Minimum Description Length (MDL) encoding to effectively yield a data-driven framework where prototypical neuromodulations (not restricted to a particular duration) can be estimated alongside the timings and features of the TMPP. We validate the proposed methodology on discrimination of movement-related tasks utilizing 32-electrode grids implanted in the frontal cortex of six epileptic subjects. We show that the learned representations from the high-gamma band (85-145 Hz) are not only interpretable, but also discriminant in a lower dimensional space. The results also underscore the practicality of our algorithm, i.e., 2 main hyperparameters that can be readily set via neurophysiology, and emphasize the need of principled and interpretable representation learning in order to model encoding mechanisms in the brain.

The spatial and temporal scales of local dengue virus transmission in natural settings: a retrospective analysis.

BACKGROUND: Dengue is a vector-borne disease caused by the dengue virus (DENV). Despite the crucial role of Aedes mosquitoes in DENV transmission, pure vector indices poorly correlate with human infections. Therefore there is great need for a better understanding of the spatial and temporal scales of DENV transmission between mosquitoes and humans. Here, we have systematically monitored the circulation of DENV in individual Aedes spp. mosquitoes and human patients from Caratinga, a dengue endemic city in the state of Minas Gerais, in Southeast Brazil. From these data, we have developed a novel stochastic point process pattern algorithm to identify the spatial and temporal association between DENV infected mosquitoes and human patients. METHODS: The algorithm comprises of: (i) parameterization of the variogram for the incidence of each DENV serotype in mosquitoes; (ii) identification of the spatial and temporal ranges and variances of DENV incidence in mosquitoes in the proximity of humans infected with dengue; and (iii) analysis of the association between a set of environmental variables and DENV incidence in mosquitoes in the proximity of humans infected with dengue using a spatio-temporal additive, geostatistical linear model. RESULTS: DENV serotypes 1 and 3 were the most common virus serotypes detected in both mosquitoes and humans. Using the data on each virus serotype separately, our spatio-temporal analyses indicated that infected humans were located in areas with the highest DENV incidence in mosquitoes, when incidence is calculated within 2.5-3 km and 50 days (credible interval 30-70 days) before onset of symptoms in humans. These measurements are in agreement with expected distances covered by mosquitoes and humans and the time for virus incubation. Finally, DENV incidence in mosquitoes found in the vicinity of infected humans correlated well with the low wind speed, higher air temperature and northerly winds that were more likely to favor vector survival and dispersal in Caratinga. CONCLUSIONS: We have proposed a new way of modeling bivariate point pattern on the transmission of arthropod-borne pathogens between vector and host when the location of infection in the latter is known. This strategy avoids some of the strong and unrealistic assumptions made by other point-process models. Regarding virus transmission in Caratinga, our model showed a strong and significant association between high DENV incidence in mosquitoes and the onset of symptoms in humans at specific spatial and temporal windows. Together, our results indicate that vector surveillance must be a priority for dengue control. Nevertheless, localized vector control at distances lower than 2.5 km around premises with infected vectors in densely populated areas are not likely to be effective.

A Marked Point Process Framework for Extracellular Electrical Potentials.

Neuromodulations are an important component of extracellular electrical potentials (EEP), such as the Electroencephalogram (EEG), Electrocorticogram (ECoG) and Local Field Potentials (LFP). This spatially temporal organized multi-frequency transient (phasic) activity reflects the multiscale spatiotemporal synchronization of neuronal populations in response to external stimuli or internal physiological processes. We propose a novel generative statistical model of a single EEP channel, where the collected signal is regarded as the noisy addition of reoccurring, multi-frequency phasic events over time. One of the main advantages of the proposed framework is the exceptional temporal resolution in the time location of the EEP phasic events, e.g., up to the sampling period utilized in the data collection. Therefore, this allows for the first time a description of neuromodulation in EEPs as a Marked Point Process (MPP), represented by their amplitude, center frequency, duration, and time of occurrence. The generative model for the multi-frequency phasic events exploits sparseness and involves a shift-invariant implementation of the clustering technique known as k-means. The cost function incorporates a robust estimation component based on correntropy to mitigate the outliers caused by the inherent noise in the EEP. Lastly, the background EEP activity is explicitly modeled as the non-sparse component of the collected signal to further improve the delineation of the multi-frequency phasic events in time. The framework is validated using two publicly available datasets: the DREAMS sleep spindles database and one of the Brain-Computer Interface (BCI) competition datasets. The results achieve benchmark performance and provide novel quantitative descriptions based on power, event rates and timing in order to assess behavioral correlates beyond the classical power spectrum-based analysis. This opens the possibility for a unifying point process framework of multiscale brain activity where simultaneous recordings of EEP and the underlying single neuron spike activity can be integrated and regarded as marked and simple point processes, respectively.

Disentangling endogenous versus exogenous pattern formation in spatial ecology: a case study of the ant Azteca sericeasur in southern Mexico.

Spatial patterns in ecology can be described as reflective of environmental heterogeneity (exogenous), or emergent from dynamic relationships between interacting species (endogenous), but few empirical studies focus on the combination. The spatial distribution of the nests of Azteca sericeasur, a keystone tropical arboreal ant, is thought to form endogenous spatial patterns among the shade trees of a coffee plantation through self-regulating interactions with controlling agents (i.e. natural enemies). Using inhomogeneous point process models, we found evidence for both types of processes in the spatial distribution of A. sericeasur. Each year's nest distribution was determined mainly by a density-dependent relationship with the previous year's lagged nest density; but using a novel application of a Thomas cluster process to account for the effects of nest clustering, we found that nest distribution also correlated significantly with tree density in the later years of the study. This coincided with the initiation of agricultural intensification and tree felling on the coffee farm. The emergence of this significant exogenous effect, along with the changing character of the density-dependent effect of lagged nest density, provides clues to the mechanism behind a unique phenomenon observed in the plot, that of an increase in nest population despite resource limitation in nest sites. Our results have implications in coffee agroecological management, as this system provides important biocontrol ecosystem services. Further research is needed, however, to understand the effective scales at which these relationships occur.

The slow pathway in the electrosensory lobe of Gymnotus omarorum: field potentials and unitary activity.

This is a first communication on the self-activation pattern of the electrosensory lobe in the pulse weakly electric fish Gymnotus omarorum. Field potentials in response to the fish's own electric organ discharge (EOD) were recorded along vertical tracks (50µm step) and on a transversal lattice array across the electrosensory lobe (resolution 50µm×100µm). The unitary activity of 82 neurons was recorded in the same experiments. Field potential analysis indicates that the slow electrosensory path shows a characteristic post-EOD pattern of activity marked by three main events: (i) a small and early component at about 7ms, (ii) an intermediate peak about 13ms and (iii) a late broad component peaking after 20ms. Unit firing rate showed a wide range of latencies between 3 and 30ms and a variable number of spikes (median 0.28units/EOD). Conditional probability analysis showed monomodal and multimodal post-EOD histograms, with the peaks of unit activity histograms often matching the timing of the main components of the field potentials. Monomodal responses were sub-classified as phase locked monomodal (variance smaller than 1ms), early monomodal (intermediate variance, often firing in doublets, peaking range 10-17ms) and late monomodal (large variance, often firing two spikes separated about 10ms, peaking beyond 17ms). The responses of multimodal units showed that their firing probability was either enhanced, or depressed just after the EOD. In this last (depressed) subtype of unit the probability stepped down just after the EOD. Early inhibition and the presence of early phase locked units suggest that the observed pattern may be influenced by a fast feed forward inhibition. We conclude that the ELL in pulse gymnotiformes is activated in a complex sequence of events that reflects the ELL network connectivity.