Deep learning for automated analysis of dendritic spines in volumetric microscopic images
Abstract/Contents
- Abstract
- Dendritic spines are postsynaptic structural correlates of excitatory synapses on pyramidal cells of mammalian cortex. They are considered a major readout of the cortical synaptic function and neural connectivity. A mature dendritic spine consists of a spine head connected to the dendritic branch by a thin neck. With novel experience, thin new dendritic spines appear, characterized by small spine heads. At the same time, pre-existing spines can undergo further plastic changes such as enlargement of spine heads. These positive changes in dendritic spine density and structure are thought to constitute a memory trace of novel learning. Dendritic spines that undergo these changes cluster discretely on a neuronal dendritic tree where stimulated inputs are making synaptic contacts. On the other hand, neighboring un-stimulated dendritic spines can exhibit shrinkage over time, and eventually disappear, a process that is exacerbated in neurodegenerative diseases such as Alzheimer's or Parkinson's disease. In-depth description of how dendritic spine numbers and structure change with normal experience is critical for understanding the scope of excitatory synapse plasticity in healthy brains, and how it is impacted in pathological situations. With some exceptions, most of the analysis of dendritic spines is performed by manual counting, sometimes supplemented with sorting spines into predetermined categories based on their overall shape. This limited approach is further exacerbated by the fact that the overall numbers of dendritic spines on any given pyramidal neuron can run in the tens of thousands. The more comprehensive analysis of dendritic spines under any condition is not attainable with current tools. In addition to the number of spines, there are other features of spines that humans cannot easily extract, including the spine shapes, spine head volumes, spine neck lengths, and spine clusters, all of which are informative about neural activities and hence motivated the need for automation. Here, we show an automated deep learning based approach that detects, segments, and characterize dendritic spines in microscopic volumes in an unbiased manner that is needed to fully capture how spines change with normal experience, and during dysfunctional disease states. Our analysis covered both two-dimensional and three-dimensional data. The two-dimensional analysis was an initial study on the application of deep learning in maximum intensity projected microscopic images. It paved the path for the three-dimensional analysis that is capable of capturing the full shapes of dendritic spines in the volumetric data. The detection models trained on confocal microscopic images can be generalized to two-photon microscopic images. Our detection results out-performed widely used software. The computed densities of dendritic spines through automation reproduced published results from manual analyses with a high level of accuracy. Furthermore, our segmentation algorithm enabled the extraction of 3D spine shapes. This allowed us to extract numerous spine features for exploring new biology, including density, geometric properties, and spatial distribution of spines.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Xiao, Xuerong |
|---|---|
| Degree supervisor | Rubin, Daniel (Daniel L.) |
| Thesis advisor | Rubin, Daniel (Daniel L.) |
| Thesis advisor | Nishimura, Dwight George |
| Thesis advisor | Shatz, Carla J |
| Thesis advisor | Yeung, Serena |
| Degree committee member | Nishimura, Dwight George |
| Degree committee member | Shatz, Carla J |
| Degree committee member | Yeung, Serena |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Xuerong Xiao. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/nt088bh4194 |
Access conditions
- Copyright
- © 2021 by Xuerong Xiao
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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