CoSyNe2018_pachitariu.pdf (2.89 MB)
Drift correction for electrophysiology and two-photon calcium imaging
poster
posted on 2018-03-04, 22:03 authored by Marius PachitariuMarius Pachitariu, Carsen Stringer, Nicholas Steinmetz, Matteo Carandini, Kenneth HarrisVertical drift (Z-drift) is a major confound for neural recordings during behavior. The brain floats in liquid, and
movements of tens of microns can easily occur, even in head-fixed animals. In the mouse, for instance, postural
changes such as locomotion can cause vertical brain movements of up to 20 microns. This displacement creates
an apparent change in the activity of neurons recorded with either electrode arrays or two-photon calcium
imaging. Here, we present methods to estimate and correct the drift in both optical and electrical recordings. We
demonstrate three methods to recover Z-drift in 2-photon calcium imaging. (1) Alignment to a densely-scanned
reference volume (z-stack). (2) Estimation from a non-functional channel- such as tdTomato expressed in a neuronal
subclass. (3) Estimation from changes in the shape of identified cells in functional recordings. We validate
methods 2 and 3 by comparing to method 1, which provides ground truth. We then develop correction methods
that remove the effects of Z-drift, and show that correlations of neuronal activity with running are significantly
decreased. Finally, we develop a convenient online module for drift correction that eliminates Z-drift at sub-micron
resolution. Z-drift also affects electrophysiological recordings. The amplitude and shape of extracellular action potentials
changes when the electrode moves relative to the brain, and neurons may even disappear altogether from
the set of recorded channels. Fortunately, new electrodes such as Neuropixels have dense arrays of channels,
with inter-site spacings as low as 20um. We found that we could estimate the drift in extracellular recordings with
linear electrodes by tracking neuronal waveform shifts, and corrected for it by spatially interpolating the raw data
prior to spike sorting. In summary, the algorithms presented here provide effective methods to remove Z-drift, a
major confound for neural recordings during behavioral experiments. We provide the code as part of the Suite2p
and Kilosort pipelines.