Brain segmentation, spatial censoring, and averaging techniques for optical functional connectivity imaging in mice.
Brian R WhiteJonah A Padawer-CurryAkiva S CohenDaniel J LichtArjun G YodhPublished in: Biomedical optics express (2019)
Resting-state functional connectivity analysis using optical neuroimaging holds the potential to be a powerful bridge between mouse models of disease and clinical neurologic monitoring. However, analysis techniques specific to optical methods are rudimentary, and algorithms from magnetic resonance imaging are not always applicable to optics. We have developed visual processing tools to increase data quality, improve brain segmentation, and average across sessions with better field-of-view. We demonstrate improved performance using resting-state optical intrinsic signal from normal mice. The proposed methods increase the amount of usable data from neuroimaging studies, improve image fidelity, and should be translatable to human optical neuroimaging systems.
Keyphrases
- resting state
- functional connectivity
- high resolution
- deep learning
- high speed
- magnetic resonance imaging
- endothelial cells
- electronic health record
- convolutional neural network
- mouse model
- machine learning
- computed tomography
- mass spectrometry
- metabolic syndrome
- climate change
- quality improvement
- multiple sclerosis
- white matter
- contrast enhanced
- data analysis
- subarachnoid hemorrhage
- wild type
- diffusion weighted imaging