Nanoscale Resolution 3D Snapshot Particle Tracking by Multifocal Microscopy.

Accurate, precise, and rapid particle tracking in three dimensions remains a challenge; yet, its achievement will significantly enhance our understanding of living systems. We developed a multifocal microscopy (MFM) that allows snapshot acquisition of the imaging data, and an associated image processing approach, that together allow simultaneous 3D tracking of many fluorescent particles with nanoscale resolution. The 3D tracking was validated by measuring a known trajectory of a fluorescent bead with an axial accuracy of 19 nm through an image depth (axial range) of 3 µm and 4 nm precision of axial localization through an image depth of 4 µm. A second test obtained a uniform axial probability distribution and Brownian dynamics of beads diffusing in solution. We also validated the MFM approach by imaging fluorescent beads immobilized in gels and comparing the 3D localizations to their "ground truth" positions obtained from a confocal microscopy z-stack of finely spaced images. Finally, we applied our MFM and image processing approach to obtain 3D trajectories of insulin granules in pseudoislets of MIN6 cells to demonstrate its compatibility with complex biological systems. Our study demonstrates that multifocal microscopy allows rapid (video rate) and simultaneous 3D tracking of many "particles" with nanoscale accuracy and precision in a wide range of systems, including over spatial scales relevant to whole live cells.

Design and simulation of a snapshot multi-focal interferometric microscope.

Realizing both high temporal and spatial resolution across a large volume is a key challenge for 3D fluorescent imaging. Towards achieving this objective, we introduce an interferometric multifocus microscopy (iMFM) system, a combination of multifocus microscopy (MFM) with two opposing objective lenses. We show that the proposed iMFM is capable of simultaneously producing multiple focal plane interferometry that provides axial super-resolution and hence isotropic 3D resolution with a single exposure. We design and simulate the iMFM microscope by employing two special diffractive optical elements. The point spread function of this new iMFM microscope is simulated and the image formation model is given. For reconstruction, we use the Richardson-Lucy deconvolution algorithm with total variation regularization for 3D extended object recovery, and a maximum likelihood estimator (MLE) for single molecule tracking. A method for determining an initial axial position of the molecule is also proposed to improve the convergence of the MLE. We demonstrate both theoretically and numerically that isotropic 3D nanoscopic localization accuracy is achievable with an axial imaging range of 2um when tracking a fluorescent molecule in three dimensions and that the diffraction limited axial resolution can be improved by 3-4 times in the single shot wide-field 3D extended object recovery. We believe that iMFM will be a useful tool in 3D dynamic event imaging that requires both high temporal and spatial resolution.

Particle tracking by repetitive phase-shift interferometric super resolution microscopy.

Accurate and rapid particle tracking is essential for addressing many research problems in single molecule and cellular biophysics and colloidal soft condensed matter physics. We developed a novel three-dimensional interferometric fluorescent particle tracking approach that does not require any sample scanning. By periodically shifting the interferometer phase, the information stored in the interference pattern of the emitted light allows localizing particles positions with nanometer resolution. This tracking protocol was demonstrated by measuring a known trajectory of a fluorescent bead with sub-5 nm axial localization error at 5 Hz. The interferometric microscopy was used to track the RecA protein in Bacillus subtilis bacteria to demonstrate its compatibility with biological systems.

Computational multifocal microscopy.

Despite recent advances, high performance single-shot 3D microscopy remains an elusive task. By introducing designed diffractive optical elements (DOEs), one is capable of converting a microscope into a 3D "kaleidoscope," in which case the snapshot image consists of an array of tiles and each tile focuses on different depths. However, the acquired multifocal microscopic (MFM) image suffers from multiple sources of degradation, which prevents MFM from further applications. We propose a unifying computational framework which simplifies the imaging system and achieves 3D reconstruction via computation. Our optical configuration omits optical elements for correcting chromatic aberrations and redesigns the multifocal grating to enlarge the tracking area. Our proposed setup features only one single grating in addition to a regular microscope. The aberration correction, along with Poisson and background denoising, are incorporated in our deconvolution-based fully-automated algorithm, which requires no empirical parameter-tuning. In experiments, we achieve spatial resolutions of 0.35um (lateral) and 0.5um (axial), which are comparable to the resolution that can be achieved with confocal deconvolution microscopy. We demonstrate a 3D video of moving bacteria recorded at 25 frames per second using our proposed computational multifocal microscopy technique.

Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms.

Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

Visualizing large, heterogeneous data in hybrid-reality environments.

Constructing integrative visualizations that simultaneously cater to a variety of data types is challenging. Hybrid-reality environments blur the line between virtual environments and tiled display walls. They incorporate high-resolution, stereoscopic displays, which can be used to juxtapose large, heterogeneous datasets while providing a range of naturalistic interaction schemes. They thus empower designers to construct integrative visualizations that more effectively mash up 2D, 3D, temporal, and multivariate datasets.

Systematic comparison of the behaviors produced by computational models of epileptic neocortex.

Two existing models of brain dynamics in epilepsy, one detailed (i.e., realistic) and one abstract (i.e., simplified) are compared in terms of behavioral range and match to in vitro mouse recordings. A new method is introduced for comparing across computational models that may have very different forms. First, high-level metrics were extracted from model and in vitro output time series. A principal components analysis was then performed over these metrics to obtain a reduced set of derived features. These features define a low-dimensional behavior space in which quantitative measures of behavioral range and degree of match to real data can be obtained. The detailed and abstract models and the mouse recordings overlapped considerably in behavior space. Both the range of behaviors and similarity to mouse data were similar between the detailed and abstract models. When no high-level metrics were used and principal components analysis was computed over raw time series, the models overlapped minimally with the mouse recordings. The method introduced here is suitable for comparing across different kinds of model data and across real brain recordings. It appears that, despite differences in form and computational expense, detailed and abstract models do not necessarily differ in their behaviors.

propagation of seizure-like activity in a model of neocortex.

SUMMARY: Seizures in pediatric epilepsy are often associated with spreading, repetitive bursting activity in neocortex. The authors examined onset and propagation of seizure-like activity using a computational model of cortical circuitry. The model includes two pyramidal cell types and four types of inhibitory interneurons. Each neuron is represented by a multicompartmental model with biophysically realistic ion channels. The authors determined the role of bursting neurons and found that their capability of driving network oscillations is most prominent in networks with either weak or relatively strong excitatory synaptic coupling. Synaptic coupling strength was varied in a separate set of simulations to examine its role in network bursting. Oscillations both between cortical layers (vertical oscillations) and between cortical areas (horizontal oscillations) emerge at moderate excitatory coupling strengths. For horizontal propagation, existence of a fast-conducting fiber system and its properties are critical. Seizure-like oscillatory activity may originate from single neurons or small networks, and that activity may propagate in two principal fashions: one that can be represented by a unidirectional (pacemaker)-type process and the other as multi- or bidirectional propagating waves. The frequency of the bursting patterns relates to underlying propagating activity that can either sustain or disrupt the ongoing oscillation.

Framework for interactive million-neuron simulation.

SUMMARY: Large simulations have become increasingly complex in many fields, tending to incorporate scale-dependent modeling and algorithms and wide-ranging physical influences. This scale of simulation sophistication has not yet been matched in neuroscience. The authors describe a framework aimed at enabling natural interaction with complex simulations: their configuration, initial conditions, monitoring, and analysis. The architecture is built on three cornerstone components: active probes, adaptive data capture, and visual interface. The resulting synthesis will enable interactive exploration of live simulations running on supercomputing platforms.

Role of persistent sodium current in bursting activity of mouse neocortical networks in vitro.

Most types of electrographic epileptiform activity can be characterized by isolated or repetitive bursts in brain electrical activity. This observation is our motivation to determine mechanisms that underlie bursting behavior of neuronal networks. Here we show that the persistent sodium (Na(P)) current in mouse neocortical slices is associated with cellular bursting and our data suggest that these cells are capable of driving networks into a bursting state. This conclusion is supported by the following observations. 1) Both low concentrations of tetrodotoxin (TTX) and riluzole reduce and eventually stop network bursting while they simultaneously abolish intrinsic bursting properties and sensitivity levels to electrical stimulation in individual intrinsically bursting cells. 2) The sensitivity levels of regular spiking neurons are not significantly affected by riluzole or TTX at the termination of network bursting. 3) Propagation of cellular bursting in a neuronal network depended on excitatory connectivity and disappeared on bath application of CNQX (20 microM) + CPP (10 microM). 4) Voltage-clamp measurements show that riluzole (20 microM) and very low concentrations of TTX (50 nM) attenuate Na(P) currents in the neural membrane within a 1-min interval after bath application of the drug. 5) Recordings of synaptic activity demonstrate that riluzole at this concentration does not affect synaptic properties. 6) Simulations with a neocortical network model including different types of pyramidal cells, inhibitory interneurons, neurons with and without Na(P) currents, and recurrent excitation confirm the essence of our experimental observations that Na(P) conductance can be a critical factor sustaining slow population bursting.

Emergent epileptiform activity in neural networks with weak excitatory synapses.

Brain electrical activity recorded during an epileptic seizure is frequently associated with rhythmic discharges in cortical networks. Current opinion in clinical neurophysiology is that strongly coupled networks and cellular bursting are prerequisites for the generation of epileptiform activity. Contrary to expectations, we found that weakly coupled cortical networks can create synchronized cellular activity and seizure-like bursting. Evaluation of a range of synaptic parameters in a detailed computational model revealed that seizure-like activity occurs when the excitatory synapses are weakened. Guided by this observation, we confirmed experimentally that, in mouse neocortical slices, a pharmacological reduction of excitatory synaptic transmission elicited sudden onset of repetitive network bursting. Our finding provides powerful evidence that onset of seizures can be associated with a reduction in synaptic transmission. These results open a new avenue to explore network synchrony and may ultimately lead to a rational approach to treatment of network pathology in epilepsy.