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Geometric transformation adaptive optics (GTAO) for volumetric deep brain imaging through gradient-index lenses

Author

Listed:
  • Yuting Li

    (Purdue University
    Purdue University)

  • Zongyue Cheng

    (Purdue University
    Purdue University)

  • Chenmao Wang

    (Purdue University
    Purdue University)

  • Jianian Lin

    (Purdue University
    Purdue University)

  • Hehai Jiang

    (Purdue University
    Purdue University)

  • Meng Cui

    (Purdue University
    Purdue University
    Purdue University)

Abstract

The advance of genetic function indicators has enabled the observation of neuronal activities at single-cell resolutions. A major challenge for the applications on mammalian brains is the limited optical access depth. Currently, the method of choice to access deep brain structures is to insert miniature optical components. Among these validated miniature optics, the gradient-index (GRIN) lens has been widely employed for its compactness and simplicity. However, due to strong fourth-order astigmatism, GRIN lenses suffer from a small imaging field of view, which severely limits the measurement throughput and success rate. To overcome these challenges, we developed geometric transformation adaptive optics (GTAO), which enables adaptable achromatic large-volume correction through GRIN lenses. We demonstrate its major advances through in vivo structural and functional imaging of mouse brains. The results suggest that GTAO can serve as a versatile solution to enable large-volume recording of deep brain structures and activities through GRIN lenses.

Suggested Citation

  • Yuting Li & Zongyue Cheng & Chenmao Wang & Jianian Lin & Hehai Jiang & Meng Cui, 2024. "Geometric transformation adaptive optics (GTAO) for volumetric deep brain imaging through gradient-index lenses," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45434-5
    DOI: 10.1038/s41467-024-45434-5
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    References listed on IDEAS

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