Measurable fields-to-spike causality and its dependence on cortical layer and area.

Distinct dynamics in different cortical layers are apparent in neuronal and local field potential (LFP) patterns, yet their associations in the context of laminar processing have been sparingly analyzed. Here, we study the laminar organization of spike-field causal flow within and across visual (V4) and frontal areas (PFC) of monkeys performing a visual task. Using an event-based quantification of LFPs and a directed information estimator, we found area and frequency specificity in the laminar organization of spike-field causal connectivity. Gamma bursts (40-80 Hz) in the superficial layers of V4 largely drove intralaminar spiking. These gamma influences also fed forward up the cortical hierarchy to modulate laminar spiking in PFC. In PFC, the direction of intralaminar information flow was from spikes → fields where these influences dually controlled top-down and bottom-up processing. Our results, enabled by innovative methodologies, emphasize the complexities of spike-field causal interactions amongst multiple brain areas and behavior.

Marked point process representation of oscillatory dynamics underlying working memory.

Objective.Computational models of neural activity at the meso-scale suggest the involvement of discrete oscillatory bursts as constructs of cognitive processing during behavioral tasks. Classical signal processing techniques that attempt to infer neural correlates of behavior from meso-scale activity employ spectral representations of the signal, exploiting power spectral density techniques and time-frequency (T-F) energy distributions to capture band power features. However, such analyses demand more specialized methods that incorporate explicitly the concepts of neurophysiological signal generation and time resolution in the tens of milliseconds. This paper focuses on working memory (WM), a complex cognitive process involved in encoding, storing and retrieving sensory information, which has been shown to be characterized by oscillatory bursts in the beta and gamma band. Employing a generative model for oscillatory dynamics, we present a marked point process (MPP) representation of bursts during memory creation and readout. We show that the markers of the point process quantify specific neural correlates of WM.Approach.We demonstrate our results on field potentials recorded from the prelimbic and secondary motor cortices of three rats while performing a WM task. The generative model for single channel, band-passed traces of field potentials characterizes with high-resolution, the timings and amplitudes of transient neuromodulations in the high gamma (80-150 Hz,γ) and beta (10-30 Hz,ß) bands as an MPP. We use standard hypothesis testing methods on the MPP features to check for significance in encoding of task variables, sensory stimulus and executive control while comparing encoding capabilities of our model with other T-F methods.Main Results.Firstly, the advantages of an MPP approach in deciphering encoding mechanisms at the meso-scale is demonstrated. Secondly, the nature of state encoding by neuromodulatory events is determined. Third, we demonstrate the necessity of a higher time resolution alternative to conventionally employed T-F methods. Finally, our results underscore the novelty in interpreting oscillatory dynamics encompassed by the marked features of the point process.Significance.An MPP representation of meso-scale activity not just enables a rich, high-resolution parameter space for analysis but also presents a novel tool for diverse neural applications.

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.

Correntropy based Robust Decomposition of Neuromodulations.

Neuromodulations as observed in the extracellular electrical potential recordings obtained from Electroencephalograms (EEG) manifest as organized, transient patterns that differ statistically from their featureless noisy background. Leveraging on this statistical dissimilarity, we propose a non-iterative robust classification algorithm to isolate, in time, these neuromodulations from the temporally disorganized but structured background activity while simultaneously incorporating temporal sparsity of the events. Specifically, we exploit the ability of correntropy to asses higher - order moments as well as imply the degree of similarity between two random variables in the joint space regulated by the kernel bandwidth. We test our algorithm on DREAMS Sleep Spindle Database and further elaborate on the hyperparameters introduced. Finally, we compare the performance of the algorithm with two algorithms designed on similar ideas; one of which is a quick, simple norm based technique while the other parallels the state-of-the-art Robust Principal Component Analysis (RPCA) to achieve classification. The algorithm is able to match the performance of the state-of-the-art techniques while saving tremendously on computation time and complexity.

Quantitative Analysis of a Marked Point Process based Sleep Spindle Detector (MPP-SSD).

Sleep spindles result from interactions between the thalamic and cortical neurons during the NREM2 stage. Studies show that these waxing and waning episodes of field potentials may have an implied role in memory consolidation, cellular plasticity and neuronal development besides serving as important markers in several neuronal pathologies. For these reasons, accurate spindle scoring of polysomnographic signals is important and has garnered interest in automating the tedious process of scoring via visual inspection. In this paper, we employ a transient model for automatic sleep spindle detection designed as a Marked Point Process (MPP). Further, in order to simplify the model development, the determination of the atoms was done independently for each of the EEG bands. However, this brings the problem of quantifying the effect of the required bandpass filtering, which was not done in previous work. Here we change the Q- factor of the filters and evaluate the effect on the detections provided by the model, when compared with two sleep experts. Several statistics are utilized, and we conclude that the design of the bandpass filters affects the performance. Low Q filters were thought necessary, but the results show that the optimal Q - factor is around 2.