Projects per year
Abstract
In vivo electrophysiology is the gold standard technique used to investigate sub-second neural dynamics in freely behaving animals. However, monitoring cell-type-specific population activity is not a trivial task. Over the last decade, fiber photometry based on genetically encoded calcium indicators (GECIs) has been widely adopted as a versatile tool to monitor cell-type-specific population activity in vivo. However, this approach suffers from low temporal resolution. Here, we combine these two approaches to monitor both sub-second field potentials and cell-type-specific population activity in freely behaving mice. By developing an economical custom-made system and constructing a hybrid implant of an electrode and a fiber optic cannula, we simultaneously monitor artifact-free mesopontine field potentials and calcium transients in cholinergic neurons across the sleep-wake cycle. We find that mesopontine cholinergic activity co-occurs with sub-second pontine waves, called P-waves, during rapid eye movement sleep. Given the simplicity of our approach, simultaneous electrophysiological recording and cell-type-specific imaging provides a novel and valuable tool for interrogating state-dependent neural circuit dynamics in vivo.
Original language | English |
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Article number | 148 |
Number of pages | 9 |
Journal | Frontiers in Neuroscience |
Volume | 14 |
DOIs | |
Publication status | Published - 21 Feb 2020 |
Keywords
- brain state
- REM sleep
- GCaMP
- acetylcholine
- pontine waves
- brainstem
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Dive into the research topics of 'Simultaneous electrophysiology and fiber photometry in freely behaving mice'. Together they form a unique fingerprint.Projects
- 4 Finished
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Photometry-based in vivo calcium imaging for circuit-based interrogation of Alzheimers pathology
Alzheimer's Research UK (ARUK)
1/04/19 → 1/05/20
Project: Research
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Datasets
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Data for: “Simultaneous electrophysiology and fiber photometry in freely behaving mice”
Sakata, S. (Creator), University of Strathclyde, 17 Feb 2020
DOI: 10.15129/c7bb43e9-ffa5-490b-9fb2-41250c2ce449
Dataset