All Optical Brain Computer Interface #60
Description
Title
All Optical Brain Computer Interface
Leaders
Hailey Edelman @haileyedelman
Maxwell Sharp
Collaborators
Grace Adams, Camila Romero, Daniel Woods
Principal Investigator: Daniel Gonzales, PhD
Project description
This project aims to establish an all-optical brain-computer interface (BCI) to investigate synaptic plasticity during sensorimotor learning in healthy mouse models. Using real-time imaging data from individual cortical neurons, animals will learn to modulate neural activity patterns and move a virtual cursor toward a target. In addition to the functional readout, the imaging also allows us to scan synaptic connections and observe how they change over the course of BCI training. This project will provide unprecedented insights into how subcellular synaptic processes drive skill acquisition. Once validated in healthy animals, this approach can be applied to disease models such as autism spectrum disorder (ASD) to uncover the aberrant synaptic mechanisms underlying learning deficits. This innovative combination of imaging, closed-loop neural control, and real-time adaptability makes the project uniquely positioned to inform therapeutic strategies and improve understanding of learning disorders.
Image
Link to project repository/sources
https://github.com/haileyedelman/all-optical-brain-computer-interface-.git
Concerete goals with specific tasks for Brainhack Vanderbilt 2025
Primary Objective: Develop a rapid, real-time neural decoding algorithm to control a 2D cursor based on calcium transient dynamics recorded using two-photon imaging in healthy animals, with plans to extend this approach to ASD models.
Basic Goals:
• Motion Correction: Process two-photon microscopy imaging frames to correct for motion artifacts, ensuring stable spatial alignment of neurons across frames.
• Image Segmentation: Develop tools to identify and segment neuronal regions of interest (ROIs) based on calcium signal activity.
• Calcium Trace Extraction: Extract fluorescence traces from identified ROIs and identify calcium transients, which represent neuronal firing events.
• Complete all of these steps within a <100 ms time period per imaging frame
Intermediate Goals:
• Neural Decoding Algorithm: Develop an algorithm that translates calcium transient events into directional commands, enabling the real-time control of a 2D virtual cursor.
• Event-Based Analysis: Quantify the frequency, amplitude, and timing of calcium transients to evaluate their relationship with task performance and cursor movements.
Advanced Goals:
• Dynamic Neural Manifold Representation: Enable real-time adaptation of the decoder by switching to different subpopulations of neurons or adjusting decoding parameters based on calcium transient patterns. Test how animals adapt to these changes.
• Calcium Transient Clustering: Investigate spatial and temporal clustering of calcium transients among neurons during skill acquisition and how this relates to learning rates.
Good first issues
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issue one: Download MATLAB software
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issue two: Look at tutorial MATLAB file (located in google drive folder)
Skills
Data Processing:
• Experience with motion correction for imaging data.
• Familiarity with signal processing techniques for calcium transient detection and analysis.
Programming Languages:
• Proficiency in MATLAB for fluorescence trace extraction and transient event analysis.
• Python for building real-time neural decoding algorithms and visualization tools.
Data Interpretation:
• Ability to interpret calcium transient data and link event features to decoder performance and task outcomes.
What will participants learn?
Participants will gain interdisciplinary expertise in neuroscience, computational biology, and engineering:
Technical Skills:
• Mastering motion correction, image segmentation, and fluorescence trace extraction from two-photon imaging data.
• Identifying and analyzing calcium transients and their relationship to neuronal activity and task performance.
• Developing real-time neural decoding algorithms and understanding their applications in BCIs.
Conceptual Knowledge:
• Exploring synaptic plasticity and its implications for learning and memory in healthy and diseased models.
• Investigating neural adaptation mechanisms using dynamic decoding frameworks.
Research Applications:
• Translating calcium transient features into actionable insights for studying synaptic plasticity and skill deficits in disease models.
• Exploring neuroprosthetic technologies as tools for understanding and addressing disrupted synaptic processes in ASD and other disorders.
Public data to use
(https://drive.google.com/drive/folders/1rSHFr5iAfE-CrYZfGHz9_dFOTBRGZBaF?usp=share_link)
Number of collaborators
2-3
Credit to collaborators
Project contributors are listed on ReadMe, and if the project continues, they will have authorship on future publications.
Project Summary
This project develops neural decoding algorithms for an all-optical BCI to track synaptic changes during sensorimotor learning.
Type
visualization
Development status
0_concept_no_content
Topic
neural_decoding
Tools
other
Programming language
Matlab
Modalities
other
Git skills
4_not_applicable
Anything else?
Teo-photon imaging data will be provided.
Things to do after the project is submitted and ready to review.
- Add a comment below the main post of your issue saying:
Hi @brainhack-vandy/project-monitors my project is ready!