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Label-free optical imaging of neural activity
Iyer, Rishyashring Raman
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https://hdl.handle.net/2142/124487
Description
- Title
- Label-free optical imaging of neural activity
- Author(s)
- Iyer, Rishyashring Raman
- Issue Date
- 2024-02-16
- Director of Research (if dissertation) or Advisor (if thesis)
- Boppart, Stephen A
- Doctoral Committee Chair(s)
- Boppart, Stephen A
- Committee Member(s)
- Gillette, Martha U
- Vlasov, Yurii
- Chen, Yun-Sheng
- Department of Study
- Electrical and Computer Engineering
- Discipline
- Electrical and Computer Engineering
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- Ph.D.
- Degree Level
- Dissertation
- Keyword(s)
- Optical Imaging
- Biophotonics
- Neuroimaging
- Microscopy
- Neurophotonics
- Multiphoton Microscopy
- Optical Coherence Tomography
- Biomedical Imaging
- Language
- eng
- Abstract
- The progress in laser physics has propelled optical microscopy to the forefront of biological and biomedical imaging, spanning from fundamental biology to clinical diagnosis. Traditional microscopes necessitate external contrast agents, impacting cell environments. However, advanced label-free techniques, such as optical interferometry, autofluorescence lifetime imaging, and vibrational spectroscopy, cleverly exploit light-matter interactions, enabling live sample imaging without external labels. There is a current need to develop these techniques to target specific physiological processes rather than a generalized observation of dynamics. Particularly, the technologies to examine the neuronal microenvironment label-free remain critically underexplored. There is a gap in our knowledge of underlying metabolic, biochemical, and electrophysiological mechanisms behind several neurological processes at a cellular level. This gap can be traced back to the lack of versatile and high-throughput tools to investigate neural networks functionally. This thesis centers on developing label-free optical tools for real-time observation of native-state neuronal activity. In this thesis, four contrasts were explored as mechanisms to study neuronal activity, namely optical path length and scattering, birefringence, autofluorescence from metabolic cofactors and molecules, and local biochemistry. Overcoming challenges of observing neuronal activity spanning three orders of magnitude in space and time, new microscopes had to be developed to capture these contrasts quickly, with high resolution, over a large field of view, and multimodally. Towards this, two microscopes were developed, called Superfast Polarization-sensitive Off-axis Full-field (SPoOF) Optical Coherence Microscopy (OCM) and Versatile Autofluorescence lifetime, Multiharmonic generation, Polarization-sensitive Interferometry, and Raman imaging in Epi-detection (VAMPIRE) microscopy. The former could capture the optical phase and birefringence at 4000 frames per second over a large field of view with nanometer-scale displacement sensitivity. The latter combines autofluorescence lifetime imaging, coherent Raman scattering, and phase and polarization-sensitive OCM into a single-source microscope. The doctoral thesis details the development and application of these setups, showcasing their effectiveness in studying the neuronal microenvironment in vitro and ex vivo. SPoOF OCM captures network activity at a millisecond scale with cellular resolution, while VAMPIRE microscopy simultaneously observes multiple aspects of neuronal dynamics in 3D and real-time. These tools mark a transition from outdated methods to advanced, high-throughput optophysiology, providing valuable insights into complex neuronal processes.
- Graduation Semester
- 2024-05
- Type of Resource
- Text
- Handle URL
- https://hdl.handle.net/2142/124487
- Copyright and License Information
- Copyright 2024 Rishyashring Iyer
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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisDissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer EngineeringManage Files
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