Stephen Alkins

Stephen D. Alkins

August 26, 2012

Lab Rotation Report

Principal Investigator: Dr. Stephen Van Hooser

Title: Analyzing in vivo Calcium Imaging Data

A previous study indicated that high-speed in vivo calcium imaging demonstrates single-spike activity within neural networks, thus yielding improved temporal resolution. Utilizing a novel random-access scanning method pioneered within this study’s methodology, a collaborating lab at Duke University has been able to gather more accurate fluorescence data (ΔF/F) values following pulse trains at various stimulus intensities. The MATLAB analysis of the data still poses three major challenges: 1) Developing code to determine the number of action potentials (APs) from the amplitude information. 2) Define the time of peaks in the cross-correlation product and then search for peaks within specific time frames and characterize true peaks throughout the data set. 3) Develop code to provide information regarding the spatial relationships of the cells’ firing and determine whether particular cell types are more likely to fire together or inhibit each other. Here, we attempt to address these points.

Introduction: In vivo two-photon imaging has emerged as an innovative technique to image and analyze neural networks and architecture. It operates on the principle of utilizing focused lasers to excite fluorophores within biological samples that emit a fluorescent signal that can be subject to multiple quantitative analyses. Combining this technology with calcium-sensitive indicators has yielded improved capability of measurements, spanning single-cell (Kerr et al., 2005) and dendritic spine (Majewska et al., 2003) resolution to entire networks (Kerr et al., 2005). It also demonstrates wide-ranging temporal scales allowing for accurate measurements of calcium waves (Malik et al., 2011). While the technique has proven advantageous to research, inquiries regarding temporal resolution and methods of signal denoising and analysis still loom. Because two-photon imaging can only sample at 10-15Hz maximum rate no accurate spike time reconstructions can be made (Grewe et al., 2010). Grewe et al. addressed this problem via a novel, random-access acousto-optic deflectors (AODs) scanning model and demonstrated, in vivo, high-speed optical recording of spiking activity in mouse neocortex with single spike and “near-millsecond” precision.

Here, a collaborating lab at Duke University utilized the aforementioned method to scan their imaging data, but still requested assistance from the Van Hooser lab to address some of the signal analysis challenges the raw data presented. Each data set consists of mixtures of baseline (approximately 3.0 to 3.4 seconds of signal) and post-stimulus (3.0 to 3.4 seconds) fluorescent signals obtained from imaging data following stimuli at various frequencies and varying intensities. Our objective is to properly characterize this data and report on three points: 1) Developing code to determine the number of action potentials (APs) from the amplitude information. 2) Define the time of peaks in the cross-correlation product and then search for peaks within specific time frames and characterize true peaks throughout the data set. 3) Develop code to provide information regarding the spatial relationships of the cells’ firing and determine whether particular cell types are more likely to fire together or inhibit each other.

Methodology: MATLAB (Mathworks, Natick, MA)

Results -MATLA B Code: The following represents one of the data sets given to the Van Hooser Lab

d=load('120315s5f1fluoxKOnolhx_s5fov1_5pulse_40Hz_IO_raw.mat')

plot(d.t{1},d.data{1}); % Visualize the signal data…it can also be viewed in an “Excel” spreadsheet format

b=diff([-Inf;d.t{1}]); % This shows where the gaps in the signals are (time difference greater than 5 will be next)

c=b>5 % Groups together the time/signal gaps..shows value on spreadsheet

e=find(c); % Because the data can be viewed in spreadsheets this value gives the corresponding cell values of each time gap on the spreadsheet

gaps=1+e; Gives the spreadsheet “cell” value for where the gaps in total signal is acquired

baseline=d.data{1}(e:e+36);% Gives baseline signal up until the point signal is given

baseline; % View baseline data set

z=find(d.t{1}>3+d.t{1}(e));% Errors may arise here…see next steps

z=find(7>d.t{1}>3+(d.t{1}(e(1))),'first');% Finding signal data which is within a time window of about 3 seconds

3+d.t{1}(e(1))

z=find(d.t{1}>3+d.t{1}(e));

n=3+d.t{1}(e(1)); % Tried this variation because an error kept arising

z=find (d.t{1}>n);

signal=d.data{1}(z);

signal=d.data{1}(z:z+36);% Should give signal corresponding between 3.8 seconds and the end of the signal data

baseline=d.data{1}(e:e+36); % Should give signal corresponding to baseline time period

This example code corresponds to the first group of data points, but should serve as the basis for developing code to analyze entire data sets. Future steps should include the use of “I” and “for” loops.

Conclusion: Most of my lab rotation consisted of learning the principles of MATLAB as a basic programming language, but the aforementioned code represents a step in the proper direction for separating the stimulus from the signal data. To further define which data points are APs, one must set a threshold for an AP value and search for all data values above that threshold occurring within the signal period. These would be significant future steps to address the aforementioned challenges.

References & Background Reading

Grewe BJ, Lange D, Kasper H, Kampa BM, Helmchen F (2010) High-speed in vivo calcium imaging reveals neuronal network activity with near-millisecond precision. Nature Methods: Vol. 7 (5) 399-407.

Kerr JND, Greenberg D, Helmchen F (2005) Imaging input and output of neocortical networks in vivo. Proc. Natl. Acad. Sci. USA 102, 14063–14068.

Kerr JND, Denk W (2008) Imaging in vivo: watching the brain in action. Nature Neuroscience 9: 195–205.

Malik WQ, Schummers J, Sur M, Brown EN (2011) Denoising Two-Photon Calcium Imaging Data. PLoS ONE 6(6): e20490. doi:10.1371/journal.pone.0020490.

Ozden I, Lee HM, Sullivan MR, Wang SS-H (2008) Identification and clustering of event patterns from in vivo multiphoton optical recordings of neuronal ensembles. J Neurophysiol 100: 495–503.

Sullivan MR, Nimmerjahn A, Sarkisov DV, Helmchen F, Wang SS-H (2005) In vivo calcium imaging of circuit activity in cerebellar cortex. J Neurophysiol 94:1636–1644.