The role of Wnt signaling in the development of the epiblast and axial progenitors.

Understanding how the body plan is established during embryogenesis remains a fundamental biological question. The Wnt/ß-catenin signaling pathway plays a crucial and highly conserved role in body plan formation, functioning to polarize the primary anterior-posterior (AP) or head-to-tail body axis in most metazoans. In this chapter, we focus on the roles that the mammalian Wnt/ß-catenin pathway plays to prepare the pluripotent epiblast for gastrulation, and to elicit the emergence of multipotent axial progenitors from the caudal epiblast. Interactions between Wnt and retinoic acid (RA), another powerful family of developmental signaling molecules, in axial progenitors will also be discussed. Gastrulation movements and somitogenesis result in the anterior displacement of the RA source (the rostral somites and lateral plate mesoderm (LPM)), from the posterior Wnt source (the primitive streak (PS)), leading to the establishment of antiparallel gradients of RA and Wnt that control the self-renewal and successive differentiation of neck, trunk and tail progenitors.

Soft tissue mobilization techniques in treating chronic abdominal scar tissue: A quasi-experimental single subject design.

INTRODUCTION: Roughly 17 million abdominal surgeries are performed annually in the U.S. Up to 17% of those may be readmitted for adhesion related problems. This study evaluated the effectiveness of soft tissue mobilization (STM) techniques at improving chronic pain, mobility restrictions and functional deficits following complex abdominal surgery. METHODS: Subjects Two females aged 51 and 65. DESIGN: Single subject quasi-experimental A-B-A. INTERVENTION: Four 30-min treatment sessions of abdominal tissue mobilizations. Outcome measures Pain pressure threshold (PPT) and average scar mobility (ASM), Numeric Pain Rating Scale (NPRS), and the Oswestry Disability Index (ODI). RESULTS: Subject 1 ASM and PPT of the abdomen improved significantly and exceeded the established standard error of measurement (SEM). PPT of the scar decreased during the second baseline. This decrease exceeded the SEM for PPT but was not statistically significant. The changes in NPRS did not reach the minimal clinically important difference (MCID). Subject 2 abdominal PPT and ASM showed statistically significant improvements that exceeded their SEMs. Scar PPT showed improvement during the repeat baseline, however, this reached neither statistical significance nor the SEM. CONCLUSIONS: Scar mobility and abdominal PPT improved both statistically and clinically in both subjects after only 4 sessions of STM. Scar pain measured by NPRS and PPT did not show significant improvement. This study demonstrated that STM can be an effective way to treat chronic abdominal scars by increasing scar mobility and reducing abdominal sensitivity to pressure. It is non-invasive, and is a less costly alternative to laparoscopic adhesiolysis.

False discovery rate regression: an application to neural synchrony detection in primary visual cortex.

Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis. But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment. To address this issue, we introduce an approach called false-discovery-rate regression that directly uses this auxiliary information to inform the outcome of each test. The method can be motivated by a two-groups model in which covariates are allowed to influence the local false discovery rate, or equivalently, the posterior probability that a given observation is a signal. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the paper.

A framework for evaluating pairwise and multiway synchrony among stimulus-driven neurons.

Several authors have previously discussed the use of log-linear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual log-linear modeling techniques, however, do not allow time-varying firing rates that typically appear in stimulus-driven (or action-driven) neurons, nor do they incorporate non-Poisson history effects or covariate effects. We generalize the usual approach, combining point-process regression models of individual neuron activity with log-linear models of multiway synchronous interaction. The methods are illustrated with results found in spike trains recorded simultaneously from primary visual cortex. We then assess the amount of data needed to reliably detect multiway spiking.

ASSESSMENT OF SYNCHRONY IN MULTIPLE NEURAL SPIKE TRAINS USING LOGLINEAR POINT PROCESS MODELS.

Neural spike trains, which are sequences of very brief jumps in voltage across the cell membrane, were one of the motivating applications for the development of point process methodology. Early work required the assumption of stationarity, but contemporary experiments often use time-varying stimuli and produce time-varying neural responses. More recently, many statistical methods have been developed for nonstationary neural point process data. There has also been much interest in identifying synchrony, meaning events across two or more neurons that are nearly simultaneous at the time scale of the recordings. A natural statistical approach is to discretize time, using short time bins, and to introduce loglinear models for dependency among neurons, but previous use of loglinear modeling technology has assumed stationarity. We introduce a succinct yet powerful class of time-varying loglinear models by (a) allowing individual-neuron effects (main effects) to involve time-varying intensities; (b) also allowing the individual-neuron effects to involve autocovariation effects (history effects) due to past spiking, (c) assuming excess synchrony effects (interaction effects) do not depend on history, and (d) assuming all effects vary smoothly across time. Using data from primary visual cortex of an anesthetized monkey we give two examples in which the rate of synchronous spiking can not be explained by stimulus-related changes in individual-neuron effects. In one example, the excess synchrony disappears when slow-wave "up" states are taken into account as history effects, while in the second example it does not. Standard point process theory explicitly rules out synchronous events. To justify our use of continuous-time methodology we introduce a framework that incorporates synchronous events and provides continuous-time loglinear point process approximations to discrete-time loglinear models.

Local field potentials indicate network state and account for neuronal response variability.

Multineuronal recordings have revealed that neurons in primary visual cortex (V1) exhibit coordinated fluctuations of spiking activity in the absence and in the presence of visual stimulation. From the perspective of understanding a single cell's spiking activity relative to a behavior or stimulus, these network fluctuations are typically considered to be noise. We show that these events are highly correlated with another commonly recorded signal, the local field potential (LFP), and are also likely related to global network state phenomena which have been observed in a number of neural systems. Moreover, we show that attributing a component of cell firing to these network fluctuations via explicit modeling of the LFP improves the recovery of cell properties. This suggests that the impact of network fluctuations may be estimated using the LFP, and that a portion of this network activity is unrelated to the stimulus and instead reflects ongoing cortical activity. Thus, the LFP acts as an easily accessible bridge between the network state and the spiking activity.

Accounting for network effects in neuronal responses using L1 regularized point process models.

Activity of a neuron, even in the early sensory areas, is not simply a function of its local receptive field or tuning properties, but depends on global context of the stimulus, as well as the neural context. This suggests the activity of the surrounding neurons and global brain states can exert considerable influence on the activity of a neuron. In this paper we implemented an L1 regularized point process model to assess the contribution of multiple factors to the firing rate of many individual units recorded simultaneously from V1 with a 96-electrode "Utah" array. We found that the spikes of surrounding neurons indeed provide strong predictions of a neuron's response, in addition to the neuron's receptive field transfer function. We also found that the same spikes could be accounted for with the local field potentials, a surrogate measure of global network states. This work shows that accounting for network fluctuations can improve estimates of single trial firing rate and stimulus-response transfer functions.

Detection of bursts in extracellular spike trains using hidden semi-Markov point process models.

Neurons in vitro and in vivo have epochs of bursting or "up state" activity during which firing rates are dramatically elevated. Various methods of detecting bursts in extracellular spike trains have appeared in the literature, the most widely used apparently being Poisson Surprise (PS). A natural description of the phenomenon assumes (1) there are two hidden states, which we label "burst" and "non-burst," (2) the neuron evolves stochastically, switching at random between these two states, and (3) within each state the spike train follows a time-homogeneous point process. If in (2) the transitions from non-burst to burst and burst to non-burst states are memoryless, this becomes a hidden Markov model (HMM). For HMMs, the state transitions follow exponential distributions, and are highly irregular. Because observed bursting may in some cases be fairly regular-exhibiting inter-burst intervals with small variation-we relaxed this assumption. When more general probability distributions are used to describe the state transitions the two-state point process model becomes a hidden semi-Markov model (HSMM). We developed an efficient Bayesian computational scheme to fit HSMMs to spike train data. Numerical simulations indicate the method can perform well, sometimes yielding very different results than those based on PS.

Dynamics of response to perceptual pop-out stimuli in macaque V1.

Contextual modulation due to feature contrast between the receptive field and surrounding region has been reported for numerous stimuli in primary visual cortex. One type of this modulation, iso-orientation surround suppression, has been studied extensively. The degree to which surround suppression is related to other forms of contextual modulation remains unknown. We used shape-from-shading stimuli in a field of distractors to test the latency and magnitude of contextual modulation to a stimulus that cannot be distinguished with an orientation-selective mechanism. This stimulus configuration readily elicits perceptual pop-out in human observers and induces a long-latency contextual modulation response in neurons in macaque early visual cortex. We found that animals trained to detect the location of a pop-out stimulus were better at finding a sphere that appeared to be lit from below in the presence of distractors that were lit from above. Furthermore, neuronal responses were stronger and had shorter latency in the condition where behavioral performance was best. This asymmetry is compatible with earlier psychophysical findings in human observers. In the population of V1 neurons, the latency of the contextual modulation response is 145 ms on average (ranging from 70 to 230 ms). This is much longer than the latency for iso-orientation surround suppression, indicating that the underlying circuitry is distinct. Our results support the idea that a feature-specific feedback signal generates the pop-out responses we observe and suggest that V1 neurons actively participate in the computation of perceptual salience.