טכניון מכון טכנולוגי לישראל
הטכניון מכון טכנולוגי לישראל - בית הספר ללימודי מוסמכים  
M.Sc Thesis
M.Sc StudentRubin Yoav
SubjectTwo Photon Imaging with High Temporal Resolution of
Correlated Neural Activity
DepartmentDepartment of Medicine
Supervisor Professor Jackie Schiller
Full Thesis textFull thesis text - English Version


Abstract

Understanding information processing done within a neuronal network is complicated. It's believed that to perform even the simplest task, numerous neurons are involved. One of the biggest challenges, which lie in the heart of this complication, is recording the spatiotemporal pattern of the neuronal activity from numerous neurons simultaneously.

Still, the currently existing methods that address this issue are still lacking in many aspects. The most broadly used method is the extracellular single unit recording which is done by blindfold placing of electrodes in the area of interest. Although the temporal resolution of this method is very good, the spatial resolution is poor. 

In recent years, a new method to record neural activity in the living brain became available. This method consists of two photon laser scanning microscopy combined with the usage of calcium sensitive dyes. With this imaging technique the spatial resolution is very high and single cells can be identified within a volume of stained tissue. Nevertheless, the commercially available two photon laser scanning microscopes could not provide a sufficient temporal resolution when imaging the activity of many neurons.

The main goal of this work was to develop a method that will enable us to perform high speed two photon calcium imaging, while preserving good spatial resolution. Using the existing scanning hardware, we devised a high frequency imaging technique that provides temporal resolution of up to 500 Hz, thus allowing imaging the activity within a neural network with a high update rate, while keeping single cell spatial resolution.

In addition to the high speed scanning method we developed image analysis software that can handle the large body of collected data and extract the neuronal activity from an image composed of a large set of cells.

Based on the extracted neuronal activity, novel algorithms for mining correlated activity chains were created, optimized and implemented. We have used these algorithms on data gathered from imaging of spontaneous activity in rat’s barrel cortex and from electrophysiological single unit recordings done in rat’s hippocampus. We have shown on the hippocampal data that chains of correlated neurons can be detected and significant correlations between more then two cells can be found, thus pointing to the possibility of repetitive neuronal processes that are composed of several cells.