Representing and decomposing neural potential signals

Shift-invariant waveform learning on epileptic ECoG

Seizure detection algorithms must discriminate abnormal neuronal activity associated with a seizure from normal neural activity in a variety of conditions. Our approach is to seek spatiotemporal waveforms with distinct morphology in electrocorticographic (ECoG) recordings of epileptic patients that are indicative of a subsequent seizure (preictal) versus non-seizure segments (interictal). To find these waveforms we apply a shift-invariant k-means algorithm to segments of spatially filtered signals to learn codebooks of prototypical waveforms. The frequency of the cluster labels from the codebooks is then used to train a binary classifier that predicts the class (preictal or interictal) of a test ECoG segment. We use the Matthews correlation coefficient to evaluate the performance of the classifier and the quality of the codebooks. We found that our method finds recurrent non-sinusoidal waveforms that could be used to build interpretable features for seizure prediction and that are also physiologically meaningful.

Beyond spikes: Multiscale computational analysis of in vivo long-term recordings in the cockroach circadian clock

The circadian clock of the nocturnal Madeira cockroach is located in the accessory medulla, a small nonretinotopic neuropil in the brain's visual system. The clock comprises about 240 neurons that control rhythms in physiology and behavior such as sleep-wake cycles. The clock neurons contain an abundant number of partly colocalized neuropeptides, among them pigment-dispersing factor (PDF), the insects' most important circadian coupling signal that controls sleep-wake rhythms. We performed long-term loose-patch clamp recordings under 12:12-hr light-dark cycles in the cockroach clock in vivo. A wide range of timescales, from milliseconds to seconds, were found in spike and field potential patterns. We developed a framework of wavelet transform-based methods to detect these multiscale electrical events. We analyzed frequencies and patterns of events with interesting dynamic features, such as mixed-mode oscillations reminiscent of sharp-wave ripples. Oscillations in the beta/gamma frequency range (20-40 Hz) were observed to rise at dawn, when PDF is released, peaking just before the onset of locomotor activity of the nocturnal cockroach. We expect that in vivo electrophysiological recordings combined with neuropeptide/antagonist applications and behavioral analysis will determine whether specific patterns of electrical activity recorded in the network of the cockroach circadian clock are causally related to neuropeptide-dependent control of behavior.

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.

Estimation and modeling of EEG amplitude-temporal characteristics using a marked point process approach

We propose a novel interpretation of single channel Electroencephalogram (EEG) traces based on the transient nature of encoded processes in the brain. In particular, the proposed framework models EEG as the output of the noisy addition of temporal, reoccurring, transient patterns known as phasic events. This is not only neurophysiologically sound, but it also provides additional information that classical EEG analysis often disregards. Furthermore, by utilizing sparse decomposition techniques, it is possible to obtain amplitude and timing that is further modeled using estimation and fitting techniques. We model Brain-Computer Interfaces (BCI) competition data features as Gaussian Mixture Model (GMM) samples in order to show the potential of working in the joint space of the parameters. The results not only preserve the topographic discriminant behavior but also expand the realm of possible EEG analysis.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

The brain dynamics of linguistic computation

Neural oscillations at distinct frequencies are increasingly being related to a number of basic and higher cognitive faculties. Oscillations enable the construction of coherently organized neuronal assemblies through establishing transitory temporal correlations. By exploring the elementary operations of the language faculty-labeling, concatenation, cyclic transfer-alongside neural dynamics, a new model of linguistic computation is proposed. It is argued that the universality of language, and the true biological source of Universal Grammar, is not to be found purely in the genome as has long been suggested, but more specifically within the extraordinarily preserved nature of mammalian brain rhythms employed in the computation of linguistic structures. Computational-representational theories are used as a guide in investigating the neurobiological foundations of the human "cognome"-the set of computations performed by the nervous system-and new directions are suggested for how the dynamics of the brain (the "dynome") operate and execute linguistic operations. The extent to which brain rhythms are the suitable neuronal processes which can capture the computational properties of the human language faculty is considered against a backdrop of existing cartographic research into the localization of linguistic interpretation. Particular focus is placed on labeling, the operation elsewhere argued to be species-specific. A Basic Label model of the human cognome-dynome is proposed, leading to clear, causally-addressable empirical predictions, to be investigated by a suggested research program, Dynamic Cognomics. In addition, a distinction between minimal and maximal degrees of explanation is introduced to differentiate between the depth of analysis provided by cartographic, rhythmic, neurochemical, and other approaches to computation.