Imaging epilepsy in larval zebrafish

Abstract

Our understanding of the genetic aetiology of paediatric epilepsies has grown substantially over the last decade. However, in order to translate improved diagnostics to personalised treatments, there is an urgent need to link molecular pathophysiology in epilepsy to whole-brain dynamics in seizures. Zebrafish have emerged as a promising new animal model for epileptic seizure disorders, with particular relevance for genetic and developmental epilepsies. As a novel model organism for epilepsy research they combine key advantages: the small size of larval zebrafish allows high throughput in vivo experiments; the availability of advanced genetic tools allows targeted modification to model specific human genetic disorders (including genetic epilepsies) in a vertebrate system; and optical access to the entire central nervous system has provided the basis for advanced microscopy technologies to image structure and function in the intact larval zebrafish brain. There is a growing body of literature describing and characterising features of epileptic seizures and epilepsy in larval zebrafish. Recently genetically encoded calcium indicators have been used to investigate the neurobiological basis of these seizures with light microscopy. This approach offers a unique window into the multiscale dynamics of epileptic seizures, capturing both whole-brain dynamics and single-cell behaviour concurrently. At the same time, linking observations made using calcium imaging in the larval zebrafish brain back to an understanding of epileptic seizures largely derived from cortical electrophysiological recordings in human patients and mammalian animal models is non-trivial. In this review we briefly illustrate the state of the art of epilepsy research in zebrafish with particular focus on calcium imaging of epileptic seizures in the larval zebrafish. We illustrate the utility of a dynamic systems perspective on the epileptic brain for providing a principled approach to linking observations across species and identifying those features of brain dynamics that are most relevant to epilepsy. In the following section we survey the literature for imaging features associated with epilepsy and epileptic seizures and link these to observations made from humans and other more traditional animal models. We conclude by identifying the key challenges still facing epilepsy research in the larval zebrafish and indicate strategies for future research to address these and integrate more directly with the themes and questions that emerge from investigating epilepsy in other model systems and human patients.

Keywords: Calcium imaging; Dynamical systems; Epilepsy; Genetic epilepsies; Zebrafish.

Fig. 1

Calcium imaging of larval zebrafish. (A) A single axial plane from live two-photon imaging of a larval zebrafish brain is shown at 7 days post fertilisation on the left. On the right, sagittal and coronal views of a reference image [http://www.zbbrowser.com] [90] are shown to illustrate the 3D location of different brain areas. A zebrafish larva (not to scale) is shown at the bottom of the panel. (B) Example time series data are shown for regional averages of gross anatomical brain regions, as well as example cells identified in each area.

Fig. 2

Example of bifurcation behaviour of a set of coupled oscillators. (A) Multiple Wilson-Cowan type neural mass models of excitatory and inhibitory populations were heterogeneously coupled and simulated at different values of excitatory population input P [112]. (B) This graph shows the membrane potential for five coupled microcolumns at steady state for different values of excitatory population input P, with dots of different shades representing a single microcolumn. Stepwise changes in P cause transitions in dynamics from fixed point steady states (shown as single dot per value P) to oscillatory states (shown as peak and trough of the oscillation for each value of P for P > 1.3) and back to fixed point (for P > 7.4). Simulations were run in small increasing (blue), and decreasing (green) steps, revealing bistability in the offset of the oscillation (note that outside of this bistability blue and green are largely overlapping). Even in this simplistic model many different state transition phenomena can be modelled, as sudden switches between high amplitude oscillations and fixed points at the bistable offset bifurcation, where two possible steady states co-exist. (C) Time series examples are shown for increasing values of the parameter P for illustration of different dynamic regimes associated with changes in just the single parameter P. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

Whole-brain state transition after PTZ exposure. (A) Example normalised fluorescence traces are shown for individual neurons for 1 h after addition of PTZ to the bath at time point 0 showing an increase of amplitude and frequency of neuronal firing events. (B) Firing of all >7000 active cells captured in this recording. (C) First three principal components over time varying fluorescence matrix shown in (B). These indicate both a persistent drift in components 2, and 3, as well as drastic changes in the loading of all components towards the end of the recording. (D) The same data is shown as a state space plot. Whilst most of the data points exist in a restricted region/state (indicated by blue circle), the late seizure is characterised by very different activity distribution readily apparent in this low-dimensional projection as points outside of the earlier range. (Time scale for all figures shown as colour bar at the bottom of the figure). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4

Increases in neural whole-brain neuronal synchrony during PTZ-induced seizures. (A) Example normalised fluorescence traces are shown as whole brain mean traces and examples of individual neurons at baseline (black) and PTZ-induced seizure (red) conditions (note the difference in amplitude scale). (B) Map of individual cell firing probabilities across the two conditions demonstrates an increase in firing probability in the PTZ-induced seizure condition, with regional heterogeneity across the larval fish brain. (C) Between-region functional connectivity shown as correlation matrix. Segmented cells were registered to a zebrafish brain atlas and then averaged, to measure correlation between major brain regions (>70) across baseline and PTZ conditions. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Microcircuit model of tectal microcircuits during PTZ-induced epileptic seizures. (A) Coupling parameters (time constants T and coupling strengths H) of a neuronal oscillator model were fitted to calcium imaging traces during induced epileptic seizures in larval zebrafish. (B–C) parameter values at different time points are plotted on two-dimensional projection. At each point of this two-dimensional space, model parameters can be used to simulate expected features of oscillatory (shown here in for delta-band oscillatory power as background grey scale map, and gamma-band oscillatory power as isoclines). This mapping between model parameters and predicted neuronal activity identifies key changes in parameters associated with PTZ exposure and seizure activity. Similar approaches may highlight regions in parameter space that are close to state transition points and render the brain more likely to display seizure activity. Figure reproduced with permission from Ref. [93].