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Does the brain speed up when we move?

Lead Research Organisation: University of Sussex
Department Name: Sch of Life Sciences

Abstract

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Technical Summary

The aim of this project is to determine how self-movement influences the signals provided by sensory pathways through the brain. While it is now clear that, across modalities and species, sensory brain activity changes during self-movement, the purpose of these changes in activity is not known.

Our overall hypothesis is that the speed of visual processing increases during self-movement, allowing improved analysis of fast sensory signals.

The specific hypotheses we will test are:
1. Self-movement speeds up the processing of incoming sensory signals
2. Self-movement speeds up feedback signals in visual pathways
3. Self-movement improves the ability to respond to rapid sensory events

We will test these hypotheses by making measurements from the mouse visual system. We will record neural activity while mice are passively viewing visual stimuli and while they are performing a visual speed discrimination task we have developed for this purpose.

We will measure perceptual performance and record neural activity when the mouse is standing still and when it is running. To record from 100s of neurons in multiple brain areas simultaneously, we will use high-density electrophysiological probes (Neuropixels 2.0) targeted to the visual thalamus, mid-brain superior colliculus, primary visual cortex, and anterio-medial and posterio-medial higher visual areas. We will use two-photon imaging of retinal ganglion cell axon terminals to capture the influence of self-movement on the earliest stages of visual processing. We will assess the effects of movement on the temporal response dynamics of individual neurons in each area, on populations of neurons within and across different areas, and on perceptual performance.

Publications

10 25 50
 
Title Improved signal extraction from multi-plane two-photon imaging data 
Description The data analysis software improves the extraction of neural activity from multi-plane two-photon imaging data. Specifically, it assesses and corrects from movement of the brain in and out of the imaging plane. 
Type Of Material Data analysis technique 
Year Produced 2023 
Provided To Others? Yes  
Impact The software removes artefacts in neural recordings caused by brain movements. 
URL https://github.com/Schroeder-Lab/Data/tree/main/TwoP
 
Description Improving spike sorting algorithm 
Organisation RWTH Aachen University
Country Germany 
Sector Academic/University 
PI Contribution We have tested the spike sorting algorithm developed by our collaborator and have provided feedback for improvement. The algorithm improved our data analysis, i.e., the extraction of response times of single neurons from voltage traces recorded by us in the mouse brain.
Collaborator Contribution The team of our collaborator developed an algorithm to improve spike sorting in data of high-density voltage recordings in the brain (using Neuropixels probe).
Impact We are using the algorithm for our data analysis. No outcome (publications) has resulted from this yet.
Start Year 2024
 
Description Test data for improved signal detection in two-photon data of single axon terminals 
Organisation University College London
Department Institute of Ophthalmology UCL
Country United Kingdom 
Sector Academic/University 
PI Contribution We have provided data to our collaborator so they can test and benchmark their algorithm for improved signal extraction of two-photon imaging data of single neurons and neural compartments (like axon terminals).
Collaborator Contribution Our collaborator developed a new algorithm to detect single neurons (or compartments like axon terminals) in two-photon imaging data and to extract signal (changes in calcium concentration) from the data. The innovation of the algorithm is to reconstruct the volume imaging data in 3D rather than working on the imaged planes independently from each other. The algorithm increases the number of detected units and the signal-to-noise ratio.
Impact No outputs, yet. A manuscript describing and testing the new algorithm has been prepared. Submission is expected later this year.
Start Year 2023