Automated analysis of neural calcium imaging datasets based on robust statistics
Abstract/Contents
- Abstract
- Calcium imaging is a prominent technique in systems neuroscience research that allows simultaneous recordings of large numbers of neurons in awake, behaving animals. Automated extraction of neurons and their temporal activity from imaging datasets is a key step in producing neuroscience results, but the diversity of the imaging setups and their associated noise statistics hinder the development of general purpose, high-fidelity tools for cell extraction. Here, we address the problem of inferring the neural calcium activity and its use in cell extraction through the lens of robust statistics. We first present a data generation model that aims to capture the generic nature of contamination in calcium imaging datasets, which includes high amplitude noise sources that are impractical to model with fixed probability distributions. We propose a robust M-estimator for our data model that achieves the minimum worst-case risk. Additionally, we propose a fast solver for robust estimation problem that enjoys fast convergence with small computational complexity. Next, we implement an automated cell extraction method that relies on our robust estimator for inferring both the spatial and the temporal components of identified cellular regions. We show that our overall method, termed EXTRACT, is suited to datasets having a broad range of signal and noise characteristics. The efficient implementation of our method, together with acceleration from graphical processing units (GPUs), allow processing of imaging datasets an order of magnitude faster than recording durations. We validate the quality of the output of EXTRACT on simulated datasets as well as real large-scale datasets. Furthermore, in real experimental setups, we show that the neural calcium activity reconstructed with EXTRACT leads to more accurate biological findings in subsequent analyses compared to alternative cell extraction methods.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Inan, Hakan | |
|---|---|---|
| Degree supervisor | Schnitzer, Mark Jacob, 1970- | |
| Thesis advisor | Schnitzer, Mark Jacob, 1970- | |
| Thesis advisor | El Gamal, Abbas A | |
| Thesis advisor | Ganguli, Surya, 1977- | |
| Degree committee member | El Gamal, Abbas A | |
| Degree committee member | Ganguli, Surya, 1977- | |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Hakan Inan. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Hakan Inan
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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