Correlation of in vitro growth properties and tumorigenicity of Syrian hamster cell lines

Several in vitro phenotypic characteristics frequently associated with neoplastic cells were examined in a series of spontaneous and benzo(a)pyrene-induced Syrian hamster clonal cell lines which differed in their degree of tumorigenicity. Nonparametric statistical analysis demonstrated cloning efficiency in semisolid agar, enhanced fibrinolytic activity, decreased serum requirement for growth, decreased organization of intracellular actin, and increased cloning efficiency in liquid medium to be correlated with tumorigenicity. These correlations were not only qualitative but also quantitative. This suggests that the factors determining the degree of tumorigenicity of a cell can be cellular growth properties

Pairwise maximum entropy models for studying large biological systems: when they can and when they can't work

One of the most critical problems we face in the study of biological systems is building accurate statistical descriptions of them. This problem has been particularly challenging because biological systems typically contain large numbers of interacting elements, which precludes the use of standard brute force approaches. Recently, though, several groups have reported that there may be an alternate strategy. The reports show that reliable statistical models can be built without knowledge of all the interactions in a system; instead, pairwise interactions can suffice. These findings, however, are based on the analysis of small subsystems. Here we ask whether the observations will generalize to systems of realistic size, that is, whether pairwise models will provide reliable descriptions of true biological systems. Our results show that, in most cases, they will not. The reason is that there is a crossover in the predictive power of pairwise models: If the size of the subsystem is below the crossover point, then the results have no predictive power for large systems. If the size is above the crossover point, the results do have predictive power. This work thus provides a general framework for determining the extent to which pairwise models can be used to predict the behavior of whole biological systems. Applied to neural data, the size of most systems studied so far is below the crossover point

Dendritic Slow Dynamics Enables Localized Cortical Activity to Switch between Mobile and Immobile Modes with Noisy Background Input

Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity – called a bump – can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability

Mild Sensory Stimulation Completely Protects the Adult Rodent Cortex from Ischemic Stroke

Despite progress in reducing ischemic stroke damage, complete protection remains elusive. Here we demonstrate that, after permanent occlusion of a major cortical artery (middle cerebral artery; MCA), single whisker stimulation can induce complete protection of the adult rat cortex, but only if administered within a critical time window. Animals that receive early treatment are histologically and behaviorally equivalent to healthy controls and have normal neuronal function. Protection of the cortex clearly requires reperfusion to the ischemic area despite permanent occlusion. Using blood flow imaging and other techniques we found evidence of reversed blood flow into MCA branches from an alternate arterial source via collateral vessels (inter-arterial connections), a potential mechanism for reperfusion. These findings suggest that the cortex is capable of extensive blood flow reorganization and more importantly that mild sensory stimulation can provide complete protection from impending stroke given early intervention. Such non-invasive, non-pharmacological intervention has clear translational potential

Reducing Crowding by Weakening Inhibitory Lateral Interactions in the Periphery with Perceptual Learning

We investigated whether lateral masking in the near-periphery, due to inhibitory lateral interactions at an early level of central visual processing, could be weakened by perceptual learning and whether learning transferred to an untrained, higher-level lateral masking known as crowding. The trained task was contrast detection of a Gabor target presented in the near periphery (4°) in the presence of co-oriented and co-aligned high contrast Gabor flankers, which featured different target-to-flankers separations along the vertical axis that varied from 2λ to 8λ. We found both suppressive and facilitatory lateral interactions at target-to-flankers distances (2λ - 4λ and 8λ, respectively) that were larger than those found in the fovea. Training reduces suppression but does not increase facilitation. Most importantly, we found that learning reduces crowding and improves contrast sensitivity, but has no effect on visual acuity (VA). These results suggest a different pattern of connectivity in the periphery with respect to the fovea as well as a different modulation of this connectivity via perceptual learning that not only reduces low-level lateral masking but also reduces crowding. These results have important implications for the rehabilitation of low-vision patients who must use peripheral vision to perform tasks, such as reading and refined figure-ground segmentation, which normal sighted subjects perform in the fovea

Imaging of Functional Connectivity in the Mouse Brain

Functional neuroimaging (e.g., with fMRI) has been difficult to perform in mice, making it challenging to translate between human fMRI studies and molecular and genetic mechanisms. A method to easily perform large-scale functional neuroimaging in mice would enable the discovery of functional correlates of genetic manipulations and bridge with mouse models of disease. To satisfy this need, we combined resting-state functional connectivity mapping with optical intrinsic signal imaging (fcOIS). We demonstrate functional connectivity in mice through highly detailed fcOIS mapping of resting-state networks across most of the cerebral cortex. Synthesis of multiple network connectivity patterns through iterative parcellation and clustering provides a comprehensive map of the functional neuroarchitecture and demonstrates identification of the major functional regions of the mouse cerebral cortex. The method relies on simple and relatively inexpensive camera-based equipment, does not require exogenous contrast agents and involves only reflection of the scalp (the skull remains intact) making it minimally invasive. In principle, fcOIS allows new paradigms linking human neuroscience with the power of molecular/genetic manipulations in mouse models

Spectrotemporal Processing in Spectral Tuning Modules of Cat Primary Auditory Cortex

Spectral integration properties show topographical order in cat primary auditory cortex (AI). Along the iso-frequency domain, regions with predominantly narrowly tuned (NT) neurons are segregated from regions with more broadly tuned (BT) neurons, forming distinct processing modules. Despite their prominent spatial segregation, spectrotemporal processing has not been compared for these regions. We identified these NT and BT regions with broad-band ripple stimuli and characterized processing differences between them using both spectrotemporal receptive fields (STRFs) and nonlinear stimulus/firing rate transformations. The durations of STRF excitatory and inhibitory subfields were shorter and the best temporal modulation frequencies were higher for BT neurons than for NT neurons. For NT neurons, the bandwidth of excitatory and inhibitory subfields was matched, whereas for BT neurons it was not. Phase locking and feature selectivity were higher for NT neurons. Properties of the nonlinearities showed only slight differences across the bandwidth modules. These results indicate fundamental differences in spectrotemporal preferences - and thus distinct physiological functions - for neurons in BT and NT spectral integration modules. However, some global processing aspects, such as spectrotemporal interactions and nonlinear input/output behavior, appear to be similar for both neuronal subgroups. The findings suggest that spectral integration modules in AI differ in what specific stimulus aspects are processed, but they are similar in the manner in which stimulus information is processed

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience