rat-2p-area-characterizations

Code for characterizing neural responses in rat cortex to a battery of visual stimuli. Neural data acquired with 2-photon imaging and epifluorescent wide-field imaging.

Written by Juliana Rhee (Cox Lab, Harvard University).

Data sources

Data Acquisition

• Wide-field data acquired and analyzed with custom code available at julianarhee/retinotopy-mapper.
• Visual stimuli for 2p experiments were presented with MWorks and custom Python code. Custom protocols available at coxlab/protocols.
• 2p neural data was acquired using ScanImage (5).
• Additional custom code for data acquisition and syncing between stimulus presentation, neural data, and pupil/face-tracking at julianarhee/acquisition-tools.

Data Processing

• Custom code for the basic data processing pipeline can be found at julianarhee/2p-pipeline.
• Automated cell detection and trace extraction tested with CaImAn and suite2p.
• Face data (pupil and whisker tracking) was extracted with DeepLabCut (6) or FaceMap.

Visual Stimuli

Visual stimuli include a cycling bar (retinotopy), tiled gratings (receptive field mapping), drifting gratings (direction tuning), and objects (shape selectivity, transformation tolerance).

Wide-field retinotopy

• Retinotopic preferences are estimated using a phase-encoding protocol (adapted from 1-4). TODO: some examples

2-photon retinotopy

• Sub-portions of the cortex mapped with wide-field methods are targeted for 2-photon (2p) imaging, and retinotopic preferences of a given field-of-view (FOV) are estimated with the same phase-encoding protocol. This method allows for fine-scale characterizations of retinotopic organization and validation of visual area assignment of a given FOV. TODO: some examples

Receptive field mapping

• Receptive field characteristics of single neurons are measured using a tiling protocol that presents a dynamic stimulus one small square or tile at a time across the whole screen. TODO: some examples

Drifting gratings

• Drifting gratings that vary in direction of motion, size, spatial frequency, and speed measure single neuron preferences for low-level visual features (e.g., direction-tuning). TODO: some examples

Objects

• Object stimuli are complex shapes that vary along more than one dimension (unlike gratings, for example). Two axes of transformation are tested: identity-changing transformations and identity-preserving transformations. Stimuli are adapted from Zoccolan et al., 2009 (8). TODO: examples

Getting Started

Create the environment (conda).

$ conda env create -f rat2p.yml
$ source activate rat2p

References

1. Kalatsky VA, Stryker MP (2003) New paradigm for optical imaging: temporally encoded maps of intrinsic signal. Neuron 38:529-545.

2. Garrett ME, Nauhaus I, Marshel JH, Callaway EM (2014) Topography and areal organization of mouse visual cortex. J Neurosci 34:12587-12600.

3. Juavinett AL, Nauhaus I, Garrett ME, Zhuang J, Callaway EM (2017). Automated identification of mouse visual areas with intrinsic signal imaging. Nature Protocols. 12: 32-43.

4. Zhuang J, Ng L, Williams D, Valley M, Li Y, Garrett M, Waters J (2017) An extended retinotopic map of mouse cortex. eLife 6: e18372.

5. Pologruto TA, Sabatini BL, Svoboda K. ScanImage: flexible software for operating laser scanning microscopes. Biomed Eng Online. 2003 May 17;2:13.

6. Nath, T., Mathis, A., Chen, A.C. et al. Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nat Protoc 14, 2152–2176 (2019).

7. Zoccolan D, Oertelt N, DiCarlo JJ, Cox DD. A rodent model for the study of invariant visual object recognition. Proc Natl Acad Sci U S A. 2009 May 26;106(21):8748-53.