A common goodness-of-fit framework for neural population models using marked point process time-rescaling

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

In vivo flow cytometry reveals a circadian rhythm of circulating tumor cells

Circulating tumor cells (CTCs) is an established biomarker of cancer metastasis. The circulation dynamics of CTCs are important for understanding the mechanisms underlying tumor cell dissemination. Although studies have revealed that the circadian rhythm may disrupt the growth of tumors, it is generally unclear whether the circadian rhythm controls the release of CTCs. In clinical examinations, the current in vitro methods for detecting CTCs in blood samples are based on a fundamental assumption that CTC counts in the peripheral blood do not change significantly over time, which is being challenged by recent studies. Since it is not practical to draw blood from patients repeatedly, a feasible strategy to investigate the circadian rhythm of CTCs is to monitor them by in vivo detection methods. Fluorescence in vivo flow cytometry (IVFC) is a powerful optical technique that is able to detect fluorescent circulating cells directly in living animals in a noninvasive manner over a long period of time. In this study, we applied fluorescence IVFC to monitor CTCs noninvasively in an orthotopic mouse model of human prostate cancer. We observed that CTCs exhibited stochastic bursts over cancer progression. The probability of the bursting activity was higher at early stages than at late stages. We longitudinally monitored CTCs over a 24-h period, and our results revealed striking daily oscillations in CTC counts that peaked at the onset of the night (active phase for rodents), suggesting that the release of CTCs might be regulated by the circadian rhythm.

Assessing Goodness-of-Fit in Marked Point Process Models of Neural Population Coding via Time and Rate Rescaling

Marked point process models have recently been used to capture the coding properties of neural populations from multiunit electrophysiological recordings without spike sorting. These clusterless models have been shown in some instances to better describe the firing properties of neural populations than collections of receptive field models for sorted neurons and to lead to better decoding results. To assess their quality, we previously proposed a goodness-of-fit technique for marked point process models based on time rescaling, which for a correct model produces a set of uniform samples over a random region of space. However, assessing uniformity over such a region can be challenging, especially in high dimensions. Here, we propose a set of new transformations in both time and the space of spike waveform features, which generate events that are uniformly distributed in the new mark and time spaces. These transformations are scalable to multidimensional mark spaces and provide uniformly distributed samples in hypercubes, which are well suited for uniformity tests. We discuss the properties of these transformations and demonstrate aspects of model fit captured by each transformation. We also compare multiple uniformity tests to determine their power to identify lack-of-fit in the rescaled data. We demonstrate an application of these transformations and uniformity tests in a simulation study. Proofs for each transformation are provided in the appendix.

Continuous Prediction of Cognitive State Using A Marked-Point Process Modeling Framework

Behavioral outcomes in many cognitive tasks are often recorded in a trial structure at discrete times. To adapt to this structure, neural encoder and decoder models have been built to take into account the trial organization to characterize the connection between brain dynamics and behavior, e.g. through latent dynamical models. The challenge of these models is that they are limited to discrete trial times while neural data is continuous. Here, we propose a marked-point process framework to characterize multivariate behavioral outcomes recorded during a trial-structured cognitive task, to build an estimation of cognitive state at a fine time resolution. We propose a state-space marked-point process modeling framework to characterize the relationship between observed behavior and underlying dynamical cognitive processes. We define the framework for a class of behavioral readouts by a response time and a discrete mark signifying an observed binary decision, and develop the state estimation and system identification steps. We define the filter and smoother for the marked-point process observation and develop an EM algorithm to estimate the model's free parameters. We demonstrate this modeling approach in a behavioral readout captured while participants perform an emotional conflict resolution task (ECR) and show that we can estimate underlying cognitive processes at a fine temporal resolution beyond the trial by trial approach.