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250 Publications
Showing 121-130 of 250 resultsThe mammalian hippocampus is crucial for episodic memory formation and transiently retains information for about 3-4 weeks in adult mice and longer in humans. Although neuroscientists widely believe that neural synapses are elemental sites of information storage, there has been no direct evidence that hippocampal synapses persist for time intervals commensurate with the duration of hippocampal-dependent memory. Here we tested the prediction that the lifetimes of hippocampal synapses match the longevity of hippocampal memory. By using time-lapse two-photon microendoscopy in the CA1 hippocampal area of live mice, we monitored the turnover dynamics of the pyramidal neurons' basal dendritic spines, postsynaptic structures whose turnover dynamics are thought to reflect those of excitatory synaptic connections. Strikingly, CA1 spine turnover dynamics differed sharply from those seen previously in the neocortex. Mathematical modelling revealed that the data best matched kinetic models with a single population of spines with a mean lifetime of approximately 1-2 weeks. This implies ∼100% turnover in ∼2-3 times this interval, a near full erasure of the synaptic connectivity pattern. Although N-methyl-d-aspartate (NMDA) receptor blockade stabilizes spines in the neocortex, in CA1 it transiently increased the rate of spine loss and thus lowered spine density. These results reveal that adult neocortical and hippocampal pyramidal neurons have divergent patterns of spine regulation and quantitatively support the idea that the transience of hippocampal-dependent memory directly reflects the turnover dynamics of hippocampal synapses.
Unicellular organisms exhibit elaborate collective behaviors in response to environmental cues. These behaviors are controlled by complex biochemical networks within individual cells and coordinated through cell-to-cell communication. Describing these behaviors requires new mathematical models that can bridge scales-from biochemical networks within individual cells to spatially structured cellular populations. Here we present a family of "multiscale" models for the emergence of spiral waves in the social amoeba Dictyostelium discoideum. Our models exploit new experimental advances that allow for the direct measurement and manipulation of the small signaling molecule cyclic adenosine monophosphate (cAMP) used by Dictyostelium cells to coordinate behavior in cellular populations. Inspired by recent experiments, we model the Dictyostelium signaling network as an excitable system coupled to various preprocessing modules. We use this family of models to study spatially unstructured populations of "fixed" cells by constructing phase diagrams that relate the properties of population-level oscillations to parameters in the underlying biochemical network. We then briefly discuss an extension of our model that includes spatial structure and show how this naturally gives rise to spiral waves. Our models exhibit a wide range of novel phenomena. including a density-dependent frequency change, bistability, and dynamic death due to slow cAMP dynamics. Our modeling approach provides a powerful tool for bridging scales in modeling of Dictyostelium populations.
Focal adhesions (FAs) link the extracellular matrix to the actin cytoskeleton to mediate cell adhesion, migration, mechanosensing and signalling. FAs have conserved nanoscale protein organization, suggesting that the position of proteins within FAs regulates their activity and function. Vinculin binds different FA proteins to mediate distinct cellular functions, but how vinculin's interactions are spatiotemporally organized within FAs is unknown. Using interferometric photoactivation localization super-resolution microscopy to assay vinculin nanoscale localization and a FRET biosensor to assay vinculin conformation, we found that upward repositioning within the FA during FA maturation facilitates vinculin activation and mechanical reinforcement of FAs. Inactive vinculin localizes to the lower integrin signalling layer in FAs by binding to phospho-paxillin. Talin binding activates vinculin and targets active vinculin higher in FAs where vinculin can engage retrograde actin flow. Thus, specific protein interactions are spatially segregated within FAs at the nanoscale to regulate vinculin activation and function.
Modern supervised learning algorithms can learn very accurate and complex discriminating functions. But when these classifiers fail, this complexity can also be a drawback because there is no easy, intuitive way to diagnose why they are failing and remedy the problem. This important question has received little attention. To address this problem, we propose a novel method to analyze and understand a classifier's errors. Our method centers around a measure of how much influence a training example has on the classifier's prediction for a test example. To understand why a classifier is mispredicting the label of a given test example, the user can find and review the most influential training examples that caused this misprediction, allowing them to focus their attention on relevant areas of the data space. This will aid the user in determining if and how the training data is inconsistently labeled or lacking in diversity, or if the feature representation is insufficient. As computing the influence of each training example is computationally impractical, we propose a novel distance metric to approximate influence for boosting classifiers that is fast enough to be used interactively. We also show several novel use paradigms of our distance metric. Through experiments, we show that it can be used to find incorrectly or inconsistently labeled training examples, to find specific areas of the data space that need more training data, and to gain insight into which features are missing from the current representation. Code is available at https://github.com/kristinbranson/InfluentialNeighbors.
Mapping mammalian synaptic connectivity has long been an important goal of neuroscience because knowing how neurons and brain areas are connected underpins an understanding of brain function. Meeting this goal requires advanced techniques with single synapse resolution and large-scale capacity, especially at multiple scales tethering the meso- and micro-scale connectome. Among several advanced LM-based connectome technologies, Array Tomography (AT) and mammalian GFP-Reconstitution Across Synaptic Partners (mGRASP) can provide relatively high-throughput mapping synaptic connectivity at multiple scales. AT- and mGRASP-assisted circuit mapping (ATing and mGRASPing), combined with techniques such as retrograde virus, brain clearing techniques, and activity indicators will help unlock the secrets of complex neural circuits. Here, we discuss these useful new tools to enable mapping of brain circuits at multiple scales, some functional implications of spatial synaptic distribution, and future challenges and directions of these endeavors.
Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.
Intracellular Ca(2+) is a widely used neuronal activity indicator. Here we describe a transcriptional reporter of intracellular Ca(2+) (TRIC) in Drosophila that uses a binary expression system to report Ca(2+)-dependent interactions between calmodulin and its target peptide. We found that in vitro assays predicted in vivo properties of TRIC and that TRIC signals in sensory systems depend on neuronal activity. TRIC was able to quantitatively monitor neuronal responses that changed slowly, such as those of neuropeptide F-expressing neurons to sexual deprivation and neuroendocrine pars intercerebralis cells to food and arousal. Furthermore, TRIC-induced expression of a neuronal silencer in nutrient-activated cells enhanced stress resistance, providing a proof of principle that TRIC can be used for circuit manipulation. Thus, TRIC facilitates the monitoring and manipulation of neuronal activity, especially those reflecting slow changes in physiological states that are poorly captured by existing methods. TRIC's modular design should enable optimization and adaptation to other organisms.
Neural damage is a devastating outcome of physical trauma. The glia are one of the main effectors of neuronal repair in the nervous system, but the dynamic interactions between peripheral neurons and Schwann cells during injury and regeneration remain incompletely characterized. Here, we combine laser microsurgery, genetic analysis, high-resolution intravital imaging and lattice light-sheet microscopy to study the interaction between Schwann cells and sensory neurons in a zebrafish model of neurotrauma. We found that chronic denervation by neuronal ablation leads to Schwann-cell death, whereas acute denervation by axonal severing does not affect the overall complexity and architecture of the glia. Neuronal-circuit regeneration begins when Schwann cells extend bridging processes to close the injury gap. Regenerating axons grow faster and directionally after the physiological clearing of distal debris by the Schwann cells. This might facilitate circuit repair by ensuring that axons are guided through unoccupied spaces within bands of Büngner towards their original peripheral target. Accordingly, in the absence of Schwann cells, regenerating axons are misrouted, impairing the re-innervation of sensory organs. Our results indicate that regenerating axons use haptotaxis as a directional cue during the reconstitution of a neural circuit. These findings have implications for therapies aimed at neurorepair, which will benefit from preserving the architecture of the peripheral glia during periods of denervation.
The dense connectivity in the brain means that one neuron's activity can influence many others. To observe this interconnected system comprehensively, an aspiration within neuroscience is to record from as many neurons as possible at the same time. There are two useful routes toward this goal: one is to expand the spatial extent of functional imaging techniques, and the second is to use animals with small brains. Here we review recent progress toward imaging many neurons and complete populations of identified neurons in small vertebrates and invertebrates.
New technologies for monitoring and manipulating the nervous system promise exciting biology but pose challenges for analysis and computation. Solutions can be found in the form of modern approaches to distributed computing, machine learning, and interactive visualization. But embracing these new technologies will require a cultural shift: away from independent efforts and proprietary methods and toward an open source and collaborative neuroscience.