NeuroActivityToolkit-Toolbox for Quantitative Analysis of Miniature Fluorescent Microscopy Data

Abstract

The visualization of neuronal activity in vivo is an urgent task in modern neuroscience. It allows neurobiologists to obtain a large amount of information about neuronal network architecture and connections between neurons. The miniscope technique might help to determine changes that occurred in the network due to external stimuli and various conditions: processes of learning, stress, epileptic seizures and neurodegenerative diseases. Furthermore, using the miniscope method, functional changes in the early stages of such disorders could be detected. The miniscope has become a modern approach for recording hundreds to thousands of neurons simultaneously in a certain brain area of a freely behaving animal. Nevertheless, the analysis and interpretation of the large recorded data is still a nontrivial task. There are a few well-working algorithms for miniscope data preprocessing and calcium trace extraction. However, software for further high-level quantitative analysis of neuronal calcium signals is not publicly available. NeuroActivityToolkit is a toolbox that provides diverse statistical metrics calculation, reflecting the neuronal network properties such as the number of neuronal activations per minute, amount of simultaneously co-active neurons, etc. In addition, the module for analyzing neuronal pairwise correlations is implemented. Moreover, one can visualize and characterize neuronal network states and detect changes in 2D coordinates using PCA analysis. This toolbox, which is deposited in a public software repository, is accompanied by a detailed tutorial and is highly valuable for the statistical interpretation of miniscope data in a wide range of experimental tasks.

Keywords: Minian; metrics; miniature fluorescence microscopy; miniscope; open-source toolbox; software; statistical analysis.

Figure 1

Pipeline of data processing using NeuroActivityToolkit. (A) Schematic illustration of the mouse with mounted version 3 miniscope. (B) Fluorescence of neurons in CA1 hippocampal area expressing GCaMP6s recorded via miniscope. (C) Calcium traces obtained from calcium recording processed with the Minian (version 1.2.1). (D) Active state determination as a first step of NeuroActivityToolkit pipeline. (E) Quantification of the miniscope recorded data in NeuroActivityToolkit toolbox. (F) Fluorescence intensity trace of the calcium indicator for the single neuron in the recording. Single-neuron active state determined using spike method (G) and full method (H). Active state is shown in red, inactive in blue.

Figure 2

Activation properties for the example recording. (A) Distribution of number of activations per minute for neurons from an example recording. (B) Number of activations per minute for the independent recording from the same mouse, acquired on the 3 different days (1–3). Data on the B graph are presented as a violin plot with median (continuous line) and quartiles (dotted line). ****: p < 0.0001 (Kruskal–Wallis test with multiple comparisons using Dunn’s test).

Figure 3

Neuronal network properties for the example recording. Distribution of network spike rate (A) and network spike peak (B) in the selected time interval of 3 s. Distribution for a single recording. (C) Distribution of network spike duration as a time when the amount of simultaneously active neurons was above the preset threshold value.

Figure 4

Neuronal-activity correlation analysis using Pearson’s coefficient. (A) Distribution of Pearson’s correlation coefficient. (B) Correlation map of co-active neuronal pairs. Correlated neurons are linked with the line between them in the space of the neuronal network. Only correlations above a 0.3 threshold value are shown. Axis values are indicated in pixels. (C) Correlation heatmap for connected pairs of neurons, from the highly negatively correlated in blue color to highly positively correlated in red color. Neurons are labeled by unit_id number. (D) Correlation heatmap in binary representation, where correlation above 0.3 threshold value is shown in black, and lower in white. For (C,D), clusters of closely related pairs of neurons are highlighted by squares. (E) Dependence of Pearson’s coefficient of correlation on the threshold level for 3 recordings for the same mouse (signal method).

Figure 5

Spatial position of neuronal co-active pairs and Pearson’s correlation coefficient. (A) The distance to neurons from the center of their mass in polar coordinates (Rho) for 3 independent recordings. (B) Dependence between the detected signal mean fluorescence and distance in polar coordinates for each neuron in the recordings for a single miniscope recording. (C) Dependence between the active-state ratio (active state of neuron duration/total recording duration) and distance in polar coordinates for each recorded neuron for a single miniscope recording. Distance between all correlated neuronal pairs, as Euclidean (D) and radial (E) distance correspondingly, for 3 independent recordings. All the data are presented as the median values, borders of the box plots are 1 and 3 quartiles, and all the errors are interquartile ranges. ns—there were no significant differences, **: p < 0.01, ****: p < 0.0001 (Kruskal–Wallis test with multiple comparisons using Dunn’s test).

Figure 6

Distance between correlated neuronal pairs. Dependence between Euclidean distance (A) or radial distance (B) and Pearson’s coefficient for co-active neuronal pairs calculated using active (spike) method for 3 independent recordings.

Figure 7

Shuffling module for variance estimation in the neuronal-activity data. (A) Presentation of the neuronal network in binarized form for original data (top) and shuffled with 1.0 ratio (bottom). (B) Pearson’s coefficient value for original and shuffled data. (C) Maximal amount of active neurons in 1 s (Network spike peak) for original and shuffled data. Data are presented as mean ± SEM; *: p < 0.05, Student’s t-test.

Figure 8

PCA dimensionality reduction method applied to obtained statistics. (A) Visualization of the results after applying the principal component method to reduce the dimension of the computed statistics for 3 recordings under the same experimental conditions (in green) and one recording in the state X. (B) Statistical metrics that make the greatest and least contributions to the PCA method.