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Fully Affine Invariant Methods for Cross-Session Registration of Calcium Imaging Data
Chunyue Li1, Xiaofeng Yang1, Ya Ke2
1School of Biomedical Sciences and Gerald Choa Neuroscience Centre, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong 999077.
Eneuro
|July 26, 2020
Summary
Fully affine invariant methods accurately align neuronal activity data across multiple imaging sessions, even with image blurring or uneven brightness. This advance is crucial for studying long-term brain plasticity in neuroscience research.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Image Analysis
Background:
- Accurate field-of-view (FOV) alignment is essential for tracking neuronal populations across multiple imaging sessions in neuroscience.
- Existing FOV alignment methods struggle with challenges like image blurring, limited common neurons, and uneven background brightness.
Purpose of the Study:
- To explore the feasibility of using fully affine invariant methods for cross-session FOV alignment in calcium imaging.
- To evaluate the performance of fully affine invariant approaches against established methods in challenging alignment scenarios.
Main Methods:
- Examined the performance of five fully affine invariant methods for FOV alignment.
- Compared these methods with feature-based and classical approaches, including those with adaptive contrast enhancement.
- Utilized cellular resolution calcium imaging data from the mouse motor cortex recorded over weeks.
Main Results:
- Fully affine invariant methods consistently provided more accurate FOV alignment than other tested methods.
- This superior performance was observed generally and specifically in cases with few common neurons, uneven background brightness, or image blurring.
- Demonstrated the reliability of fully affine invariant methods for cross-session FOV alignment.
Conclusions:
- Fully affine invariant methods are feasible and reliable for aligning calcium imaging fields-of-view across multiple sessions.
- These methods offer a significant improvement for neuroscience research, particularly for studying experience-dependent plasticity over extended periods.
