Voltage-sensitive Dye Imaging Data Research Articles

A dynamic causal modeling of voltage sensitive dye imaging data from rodent hippocampus

A dynamic causal modeling of voltage sensitive dye imaging data from rodent hippocampus

Critical dynamics, anesthesia and information integration: Lessons from multi-scale criticality analysis of voltage imaging data

Critical dynamics are thought to play an important role in neuronal information-processing: near critical networks exhibit neuronal avalanches, cascades of spatiotemporal activity that are scale-free, and are considered to enhance information capacity and transfer. However, the exact relationship between criticality, awareness, and information integration remains unclear. To characterize this relationship, we applied multi-scale avalanche analysis to voltage-sensitive dye imaging data collected from animals of various species under different anesthetics. We found that anesthesia systematically varied the scaling behavior of neural dynamics, a change that was mirrored in reduced neural complexity. These findings were corroborated by applying the same analyses to a biophysically realistic cortical network model, in which multi-scale criticality measures were associated with network properties and the capacity for information integration. Our results imply that multi-scale criticality measures are potential biomarkers for assessing the level of consciousness.

Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data

Various patterns of neural activity are observed in dynamic cortical imaging data. Such patterns may reflect how neurons communicate using the underlying circuitry to perform appropriate functions; thus it is crucial to investigate the spatiotemporal characteristics of the observed neural activity patterns. In general, however, neural activities are highly nonlinear and complex, so it is a demanding job to analyze them quantitatively or to classify the patterns of observed activities in various types of imaging data. Here, we present our implementation of a novel method that successfully addresses the above issues for precise comparison and classification of neural activity patterns. Based on two-dimensional representations of the geometric structure and temporal evolution of activity patterns, our method successfully classified a number of computer-generated sample patterns created from combinations of various spatial and temporal patterns. In addition, we validated our method with voltage-sensitive dye imaging data of Alzheimer’s disease (AD) model mice. Our analysis algorithm successfully distinguished the activity data of AD mice from that of wild type with significantly higher performance than previously suggested methods. Our result provides a pragmatic solution for precise analysis of spatiotemporal patterns of neural imaging data.

A toolbox for spatiotemporal analysis of voltage-sensitive dye imaging data in brain slices.

Voltage-sensitive dye imaging (VSDI) can simultaneously monitor the spatiotemporal electrical dynamics of thousands of neurons and is often used to identify functional differences in models of neurological disease. While the chief advantage of VSDI is the ability to record spatiotemporal activity, there are no tools available to visualize and statistically compare activity across the full spatiotemporal range of the VSDI dataset. Investigators commonly analyze only a subset of the data, and a majority of the dataset is routinely excluded from analysis. We have developed a software toolbox that simplifies visual inspection of VSDI data, and permits unaided statistical comparison across spatial and temporal dimensions. First, the three-dimensional VSDI dataset (x,y,time) is geometrically transformed into a two-dimensional spatiotemporal map of activity. Second, statistical comparison between groups is performed using a non-parametric permutation test. The result is a 2D map of all significant differences in both space and time. Here, we used the toolbox to identify functional differences in activity in VSDI data from acute hippocampal slices obtained from epileptic Arx conditional knock-out and control mice. Maps of spatiotemporal activity were produced and analyzed to identify differences in the activity evoked by stimulation of each of two axonal inputs to the hippocampus: the perforant pathway and the temporoammonic pathway. In mutant hippocampal slices, the toolbox identified a widespread decrease in spatiotemporal activity evoked by the temporoammonic pathway. No significant differences were observed in the activity evoked by the perforant pathway. The VSDI toolbox permitted us to visualize and statistically compare activity across the spatiotemporal scope of the VSDI dataset. Sampling error was minimized because the representation of the data is standardized by the toolbox. Statistical comparisons were conducted quickly, across the spatiotemporal scope of the data, without a priori knowledge of the character of the responses or the likely differences between them.

Measurement of propagating waves from local field potentials and unit activity in the cortex of human and monkey

The existence of propagating waves, either spontaneous or stimulus-evoked, in neocortex during the awake state has been a subject of recent interest [1,2]. Here, following work done previously in voltage-sensitive dye imaging of the primary visual cortex in the awake monkey [3], we apply an analysis method for non-parametric, automated detection of propagating waves in noisy multichannel data to multielectrode array (MEA) recordings taken from the neocortex of the human (middle temporal gyrus, as studied in [4]) and monkey (primary visual cortex). In the spontaneous awake state, we detect transient, intermittent propagations occurring in the local field potential (LFP) across a wide frequency range. After selecting the propagation epochs within the highest confidence interval, we observe that the speed distribution falls naturally into the range of the horizontal fibers (0.1 - 0.5 m/s), suggesting a similar propagation substrate as that observed previously in voltage-sensitive dye imaging data [3]. With this in mind, we go on to compare the spatiotemporal dynamics on the array across various states of arousal. One question raised by our earlier VSD analysis is the extent to which the wave evoked by a small visual stimulus is supra- or sub-threshold. Specifically, because neurons during awake, activated cortical states sit a few millivolts below threshold [5,6], operating in a fluctuation-driven regime [7], the transient depolarization detected in the VSD signal as the wave passes may change the background spiking probability in the local circuit. To address this, we analyzed the relationship between single- and multi-unit activity (SUA/MUA) and LFP. We study the spatiotemporal dynamics of this relationship to determine the coupling of spiking activity with transient propagating waves, and compare the results with computational models of spiking neurons. In conclusion, we show here that a combination of multi-electrode recordings, time-series analysis, and computational modeling provides a powerful set of tools for quantifying spatiotemporal dynamics in awake states.

The stimulus-evoked population response in visual cortex of awake monkey is a propagating wave.

Propagating waves occur in many excitable media and were recently found in neural systems from retina to neocortex. While propagating waves are clearly present under anaesthesia, whether they also appear during awake and conscious states remains unclear. One possibility is that these waves are systematically missed in trial-averaged data, due to variability. Here we present a method for detecting propagating waves in noisy multichannel recordings. Applying this method to single-trial voltage-sensitive dye imaging data, we show that the stimulus-evoked population response in primary visual cortex of the awake monkey propagates as a travelling wave, with consistent dynamics across trials. A network model suggests that this reliability is the hallmark of the horizontal fibre network of superficial cortical layers. Propagating waves with similar properties occur independently in secondary visual cortex, but maintain precise phase relations with the waves in primary visual cortex. These results show that, in response to a visual stimulus, propagating waves are systematically evoked in several visual areas, generating a consistent spatiotemporal frame for further neuronal interactions.

Local active information storage as a tool to understand distributed neural information processing

Every act of information processing can in principle be decomposed into the component operations of information storage, transfer, and modification. Yet, while this is easily done for today's digital computers, the application of these concepts to neural information processing was hampered by the lack of proper mathematical definitions of these operations on information. Recently, definitions were given for the dynamics of these information processing operations on a local scale in space and time in a distributed system, and the specific concept of local active information storage was successfully applied to the analysis and optimization of artificial neural systems. However, no attempt to measure the space-time dynamics of local active information storage in neural data has been made to date. Here we measure local active information storage on a local scale in time and space in voltage sensitive dye imaging data from area 18 of the cat. We show that storage reflects neural properties such as stimulus preferences and surprise upon unexpected stimulus change, and in area 18 reflects the abstract concept of an ongoing stimulus despite the locally random nature of this stimulus. We suggest that LAIS will be a useful quantity to test theories of cortical function, such as predictive coding.

Generating Waves in Corticothalamocortical Networks

In this issue of Neuron, Stroh etal. (2013) investigate mechanisms of population calcium wave initiation and propagation across cortex and thalamus. They use a novel fiber optic-based method to simultaneously image and excite specific populations of neurons in multiple regions.

A model of propagating waves in cerebral cortex across network states

Propagating waves of activity have been measured in cerebral cortex in various experimental preparations, and show different propagation speed or patterns. Using network models, we investigated whether the “state” of the network can explain these differences. We used a modified version of a previous model, in which neuronal adaptation can facilitate the transition from activated, asynchronous irregular (AI) states to quiescence in random networks. [1] With a proper re-ignition mechanism, these networks can transition between UP/DOWN and AI states with different levels of adaptation. Here, we have studied the occurrence of propagating waves during this transition from UP/DOWN to self-sustained activated states in topographic spiking neural network models and compared these results to voltage-sensitive dye imaging data from the visual cortex, as well as to other known experimental results. The addition of local connections with realistic synaptic delays in these topographic spiking neural network models allows for the possibility of propagating slow waves in some network states. Further, while it is generally thought that the large, low-frequency propagation evoked by sensory stimulation during heavily anesthetized states gives way to a bump attractor during waking, activated states, we demonstrate the possibility that propagating activity exists throughout the whole spectrum of network activation and shifts from low frequency (predominantly controlled by adaptation) to high frequency (predominantly controlled by E/I interactions) as the level of network activation increases. With these results from network modeling, we aim to account both for observed effects of anesthesia on the spread of cortical activity in voltage-sensitive dye and electrophysiological experiments [2-4] and for the observation of traveling high-frequency oscillations in vitro [5] and in awake monkeys [6,7].

The membrane chamber: A new type of in vitro recording chamber

In vitro brain slice electrophysiology is a powerful and highly successful technique where a thin slice is cut from the brain and kept alive artificially in a recording chamber. The design of this recording chamber is pivotal to the success and the quality of such experiments. Most often one of two types of chambers is used today, the interface chamber or the submerged chamber. These chambers, however, have the disadvantage that they are limited in either their experimental or their physiological properties respectively. Here we present a new working principle for an in vitro chamber design which aims at combining the advantages of the classical designs whilst overcoming their disadvantages. This is achieved by using a semipermeable membrane on which the slice is placed. The membrane allows for a fast flow of artificial cerebrospinal fluid of up to at least 17ml/min. Due to a Bernoulli effect, this high speed flow also causes a 64% increase in flow of solution across the membrane on which the slice rests. The fact that the membrane is transparent introduces the possibility of wide field inverted optical imaging to brain slice electrophysiology. The utility of this setup was demonstrated in the recording of local field potential, single cell and voltage sensitive dye imaging data simultaneously from an area smaller then 1/8mm2. The combination of all these features in the membrane chamber make it a versatile and promising device for many current and future in vitro applications, especially in the regard to optical imaging.