In a living animal, the brain is moving up and down in the skull. This brain motion can be due to breathing, heartbeat, tongue movements, but also due to changes of posture or running. For each brain region and posture, specific brain movements are more likely to occur. For imaging of neuronal activity using two-photon microscopy through a window, the coverslip glass is usually gently pressed onto the brain to reduce motion. As a consequence, brain motion is typically smaller close to the glass surface and becomes more prominent for imaging deeper below the surface.

Why axial movement artifacts are bad

From the point of view of data analysis, there are two fundamentally different types of brain movements: Lateral movements (within the xy-imaging plane) and axial movement (in the axial z-direction). The first type can be corrected (except in the boundary region of the FOV) by shifting the image back to the “right place” within the plane. Axial movement, on the other hand, shifts the imaging focus to a different depth and cannot be corrected. From my experience, these axial motion artifacts are often underestimated and often ignored. But even if they are small (and barely visible by eye after non-rigid xy-motion correction), they might have an effect when an analysis averages across a lot of repetitions or “trials”. Especially for low imaging resolution and low signal-to-noise, it is often difficult to judge by eye whether something was due to z-motion or due to neuronal activity related to movement (or due to both). Imaging of small structures like axons or laterally-spreading dendrites is especially prone to movement artifacts, and there are many additional edge cases where brain movement might influence the analysis. Ideally, one would be able to correct for z-motion online during the recording. For example, when the brain moves up by 5 µm, the focus of the microscope would move up by the same amount, therefore imaging the same tissue despite the motion. Typically, z-motion of the brain is relatively slow, often below 20 Hz in mice. An online motion correction algorithm would therefore be required to work at a temporal responsiveness of, let’s say 40-50 Hz.

ECG-synchronized image acquisition

There have been several suggested methods to solve this problem. Paukert and Bergles (2012) developed a method to correct for only lateral (and not axial) movement, but the methods is still worth mentioning. They recorded the electro-cardiogram (ECG) and used the peak of the R-wave to reliable trigger image acquisition. Therefore, they did not really correct for heartbeat-induced motion but rather recorded such that brain motion was the same for all acquired imaging frames. Better imaging preparations (more pressure exerted onto the brain by the coverslip window) have made this approach less relevant for cortical imaging. However, for deeper imaging, e.g., with 3P microscopy, the stabilization through the coverslip is reduced. Therefore, Streich et al. (2021) used ECG-synchronized 3P scanning to improve image quality. They went a few steps further than Paukert and Bergles (2012) using the past heartbeats to predict – online with an FPGA – the next R-wave and to stop image acquisition around the R-wave window.

Correction of stereotypic axial motion with an ETL

A conceptually similar method has been developed by Chen et al. (2013). They also wanted to avoid motion artifacts induced by clearly defined motion-related events – not heartbeats in this case, but licking events. Depending on the brain region and the effort the mouse has to make, licking can cause huge motion artifacts. Chen et al. (2013) did not want to stop imaging during licking but instead wanted to correct the axial focus using a electrically tunable lens (ETL). They observed that licks consistently changed the focus of the imaging plane. They therefore used a lick sensor and fed its output to a corrective signal that moved the focus of the ETL in a calibrated manner. The idea is therefore based on the assumption that lick-induced movement is stereotyped. Therefore, one might have to recalibrate the correction algorithm for each mouse or for motion due to other parts of the body. However, it is, to my knowledge, the first attempt to really correct for axial brain motion in vivo.
It would be interesting to combine the recording of more diverse behaviors using a video camera with fast processing and with a predictive component as by Streich et al. (2021) to develop a more general brain movement predictor and corrector. For example, an algorithm could map behaviors to expected brain movements for 10 min and then perform recordings with perfectly predicted brain motion that can be anticipated and corrected by an ETL or another remote scanning device. Wouldn’t this be cool?

Using the sample surface as a reference

Another set of methods tries to image the brain and at the same time perform a scan of a reference object in the brain (a “guide star”), or the brain surface or the brain itself, in order to extract the desired corrective factor for scanning in real-time.
Laffray et al. (2011) used a reflective imaging modality to detect the position of the spinal cord (which is well known to move a lot!). In their approach, they only corrected for lateral brain motion, most likely because their microscope was not equipped with a z-scanning device. But it seems possible to generalize this method to axial movement correction as well. The limitation of this approach is that the brain surface is typically pressured by a coverslip and does not move so much – it is rather the deeper parts of the brain that move much more.

Scanning a reference object with an AOD scope

A technically more advanced method for real-time 3D movement correction was developed by Griffiths et al. (2020). In this study, the authors used a random-access AOD-based microscope, recording only small environments around single neurons. Therefore, post-hoc movement correction, also in the lateral direction, was not really an option. To address this problem, the authors developed a closed-loop system based on an FPGA to perform online motion correction. AODs (acousto-optic deflectors) are very fast scanners, so the scan speed is not an issue. The authors defined a 3D reference object that was scanned in parallel to the imaged neurons, and corrected the focus of the microscope according to movements of the reference object. This method seems to be very elegant. The only limitation that I can see is that it requires an AOD microscope – very few z-scanning techniques would be able to perform an ultra-fast z-scan as a reference (maybe TAG-lenses would be an option?). And AOD scopes have been long known to be very challenging to operate and align – although some companies seem to have managed to make the systems a bit more user-friendly during the last decade.

Scanning the brain with OCT-based imaging

Recently, I came across another paper about online movement correction, by Tucker et al., (2023). The authors used optical coherence tomography to measure brain motion and to online correct for it. In this paper, the authors present a method to correct for brain motion in x and y, not addressing the more relevant problem of z-motion (although this is discussed in depth as well). On a side-note, I noticed that corrections were applied by adding signals to the regular voltage signals used to drive the scan mirrors, reminding me of my simple solution a few years ago to add two analog signals with a summing amplifier. Anyway, a z-correction would be more difficult to implement in a standard microscope because it would require an existing z-scanning device (such as a remote z-scanner, a tunable lens, or a piezo attached to the objective). However, I found the idea compelling, and on paper the principle of OCT seems simple. An OCT is more or less an interferometer that uses infrared backscattered light to form an image. Imaging speed is high for the modern variants of OCT imaging, and the possible resolution seems to be around 1 μm. If you now have become interested in OCT, I can recommend this introduction, especially section 2.3 on “Signal formation in OCT”.

Open questions about OCT for online motion correction

Unfortunately, I have never seen or used an OCT microscope before. Therefore, I have no intuition how difficult it would be to achieve high-resolution OCT imaging. How does an OCT volumetric image of a mouse brain look like? Which structures in the brain generate the contrast? Is there any contrast? How long does it take to acquire such an image with sufficient contrast at high resolution? What is the price tag of a spectral-domain OCT system? Is it more or less difficult to build than e.g. a two-photon microscope?
I have the impression that the study by Tucker et al. (2023) only scratches the surface of what is possible, and that OCT imaging could be combined with 2P imaging without compromising excitation or detection pathways. If you are an expert in this technique and happen to meet me at the next conference, I’d be interested in knowing more!

P.S. Did I miss an interesting paper on this topic? Let me know!

[Update 2023-09-10: Also check out the comment below by Lior Golgher, who brings up additional interesting papers, including approaches to perform volumetric imaging in 3D, which enables post-hoc motion correction in the axial direction or the selction of a more motion-robust selection of an extended 3D-ROI.]

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References

Chen, J.L., Pfäffli, O.A., Voigt, F.F., Margolis, D.J., Helmchen, F., 2013. Online correction of licking-induced brain motion during two-photon imaging with a tunable lens. J. Physiol. 591, 4689–4698. https://doi.org/10.1113/jphysiol.2013.259804

Griffiths, V.A., Valera, A.M., Lau, J.Y., Roš, H., Younts, T.J., Marin, B., Baragli, C., Coyle, D., Evans, G.J., Konstantinou, G., Koimtzis, T., Nadella, K.M.N.S., Punde, S.A., Kirkby, P.A., Bianco, I.H., Silver, R.A., 2020. Real-time 3D movement correction for two-photon imaging in behaving animals. Nat. Methods 1–8. https://doi.org/10.1038/s41592-020-0851-7

Laffray, S., Pagès, S., Dufour, H., Koninck, P.D., Koninck, Y.D., Côté, D., 2011. Adaptive Movement Compensation for In Vivo Imaging of Fast Cellular Dynamics within a Moving Tissue. PLOS ONE 6, e19928. https://doi.org/10.1371/journal.pone.0019928

Paukert, M., Bergles, D.E., 2012. Reduction of motion artifacts during in vivo two-photon imaging of brain through heartbeat triggered scanning. J. Physiol. 590, 2955–2963. https://doi.org/10.1113/jphysiol.2012.228114

Streich, L., Boffi, J.C., Wang, L., Alhalaseh, K., Barbieri, M., Rehm, R., Deivasigamani, S., Gross, C.T., Agarwal, A., Prevedel, R., 2021. High-resolution structural and functional deep brain imaging using adaptive optics three-photon microscopy. Nat. Methods 18, 1253–1258. https://doi.org/10.1038/s41592-021-01257-6

Tucker, S.S., Giblin, J.T., Kiliç, K., Chen, A., Tang, J., Boas, D.A., 2023. Optical coherence tomography-based design for a real-time motion corrected scanning microscope. Opt. Lett. 48, 3805–3808. https://doi.org/10.1364/OL.490087