Abstract
Fiber photometry enables recording of population neuronal calcium dynamics in awake mice. While the popularity of fiber photometry has grown in recent years, it remains unclear whether photometry reflects changes in action potential firing (that is, ‘spiking’) or other changes in neuronal calcium. In microscope-based calcium imaging, optical and analytical approaches can help differentiate somatic from neuropil calcium. However, these approaches cannot be readily applied to fiber photometry. As such, it remains unclear whether the fiber photometry signal reflects changes in somatic calcium, changes in nonsomatic calcium or a combination of the two. Here, using simultaneous in vivo extracellular electrophysiology and fiber photometry, along with in vivo endoscopic one-photon and two-photon calcium imaging, we determined that the striatal fiber photometry does not reflect spiking-related changes in calcium and instead primarily reflects nonsomatic changes in calcium.
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Data availability
The datasets generated during and/or analyzed during the current study are available in the Open Science Framework repository, at https://osf.io/8j7g2/.Source data are provided with this paper.
Code availability
All custom code generated to analyze the datasets used in the current study are available in the Open Science Framework repository, at https://osf.io/8j7g2/.
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Acknowledgements
We thank the HHMI GENIE project for GCaMP reagents and M. Creed for critical reading of the manuscript. Research was supported by the American Heart Association Pre-Doctoral Fellowship (A.A.L), NIDDK DK126355 (B.A.M.-A.), Howard Hughes Medical Institute Hanna H. Gray Fellowship (B.A.), NINDS R35 Diversity Research Supplement Funding, 3R35NS097306-04S1 (B.A.), NINDS R01NS122840 (J.G.P.), Washington University Diabetes Research Center (DK020579, A.V.K.), Nutrition Obesity Research Center (DK056341, A.V.K.), McDonnell Centers for Systems and Cellular Neuroscience (A.V.K.).
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A.A.L. contributed to conceptualization, methodology, software, formal analysis, investigation, data curation, writing of the original draft, reviewing and editing the article and visualization. B.A.M.-A. contributed to the investigation and reviewing and editing the article. B.Y. contributed to data curation and formal analysis. B.A. contributed to software, methodology and reviewing and editing the article. J.A.L. contributed to conceptualization and the methodology. J.G.P. contributed to the methodology, data curation, writing of the original draft, reviewing and editing the article, resources and supervision. A.V.K. contributed to conceptualization, investigation, writing of the original draft, reviewing and editing the article, resources and supervision.
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Extended data
Extended Data Fig. 1 Photometry, and spiking activity, and locomotor activity reflect distinct responses around foot shocks.
(a) Motor response around 0.7 mA foot shocks of different length. (Left) Average response from −20 to 40 seconds. (Right) Maximum response from 0 to 5 seconds, time-locked to foot shock (F-Value = 0.64, p-value = 0.558). (b) Close-up of (a), showing motor response in a short time-interval around foot shock. (Left) Average response from −2 to 2 seconds. (Right) Maximum response from 0 to 1 second, excluding the stimulus time (F-value = 0.61, p-value = 0.572). (c) Same as (a) but for the photometry response. (Right) F-value = 0.93, p-value = 0.445. (d) Same as (b) but for photometry response. (Right) F-value = 1.89, p-value = 0.231. (e) Same as (a,c) for spiking activity. (Right) F-value = 8.57, p-value = 0.017 (f) Same as (b,d) but for spiking activity and showing the minimum response instead of maximum. (Right) F-value = 1.38, p-value = 0.321. For quantification, we ran repeated measures ANOVAs with post-hoc two-tailed paired t-tests with bonferroni corrections (n = 4 mice). * denotes p < 0.05 after correction. Shaded regions represent 95% confidence intervals. Box plots central value denotes the median, box bounds denote upper and lower quartiles and whiskers denote ±1.5 interquartile range.
Extended Data Fig. 2 The time derivative of photometry (derivative) and spiking activity show distinct responses to behavioral events.
(a) Derivative and population spiking response around lever press (n = 6 mice). (Left) Average response. (Right) Average response in baseline, stimulus and post-stimulus intervals (Signal~Interval F-Value = 0.33, p-value = 0.724). (b) Cross-correlations between the response of the population spiking and photometry (Left) and derivative (Right). (c) (Left) Maximum correlation between photometry and spiking (yellow), and derivative and spiking (pink); p-value = 0.053. (Right) Latency to maximum correlation (n = 6 mice); p-value = 0.027. (d–f) Same as (a-c) for air puff stimulus (n = 4 mice). (d, Right) Signal~Interval F-Value = 4.1, p-value = 0.075. (g-i) Same as (a-c, d-f) for foot shock stimulus (n = 5 mice); i-right: p-value = 0.013. (g, right) Signal~Interval F-Value = 22.22, p-value = 0.002. For quantification of (a,d,f), we ran a repeated measures ANOVA, with post-hoc two-tailed paired t-test with bonferroni corrections. For quantification of (c,f,h), we ran 2-tailed paired t-tests. * denotes p < 0.05. Line plots show mean±95% confidence interval. Error bars in (a,d,g right) denote standard deviation. Box plots central value denotes the median, box bounds denote upper and lower quartiles and whiskers denote ±1.5 interquartile range.
Extended Data Fig. 3 The time derivative and deconvolution of fiber photometry spiking activity.
(a) Example photometry trace (top) and its derivative (bottom). Vertical lines represent 2 standard deviations. (b) Derivative and spiking response around photometry transients overlapping with a spiking burst (T + B). (Left) Average derivative response. (Middle) Average population spiking response. (Right) Maximum response (n = 7 mice, F-stat = 60.80, p-value = 1 × 10−5). (c) Correlations between maximum derivative (Der) and population spiking (Spk) response around T + B. (d) Example photometry trace (top) and its respective deconvolution (bottom). Vertical lines represent 2 standard deviations. (e) Same as (b) but for deconvolution instead of derivative (n = 7 mice, F-stat = 37.74, p-value = 1 × 10−5). (f) Correlations between maximum deconvolution (Dec) and population spiking (Spk) response around T + B. Shaded regions represent 95% confidence intervals. Box plots central value denotes the median, box bounds denote upper and lower quartiles and whiskers denote ±1.5 interquartile range. For quantification of (b,e right), we used a repeated measures ANOVA with post-hoc two-tailed paired t-tests with Bonferroni corrections. * denotes p < 0.05; *** denotes p < 0.001 after corrections.
Extended Data Fig. 4 GCaMP6s fiber photometry reflects only a small proportion of spontaneous changes in spiking activity.
(a) Frequency of identified events in photometry or spiking (n = 8 mice, p-value = 1.75 × 10−5). (b) Similarity of photometry and spiking events (n = 8 mice). (Left) Proportion of overlap between photometry and spiking events (p-value = 2.36 × 10−4). (Right) Jaccard similarity. (c) Time course of maximum spiking activity around transients that overlapped with bursts (T + B). (d) Average correlations between photometry and spiking responses atound T + B. (e) Photometry and spiking response around T + B or shuffled timestamps. (Left) Average photometry response T + B (yellow) or shuffled timestamps (gray). (Middle) Average spiking response around T + B (blue) or shuffled timestamps (gray). (Right) Average maximum photometry/spiking response (n = 8 mice, F-value = 20.68, p-value = 1 × 10−5). (f) Same as (e) but for transients that did not overlap with bursts (T + nB) (Right) (n = 8 mice, F-value = 41.63, p-value < 0.0001). For quantification of (a,b), we ran two-tailed paired t-tests. For quantification of (e,f), we ran a repeated measures ANOVA, with post-hoc two-tailed paired t-tests with bonferroni corrections. * denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001 after corrections. Shaded regions in (f) represent 95% confidence intervals. Box plots central value denotes the median, box bounds denote upper and lower quartiles and whiskers denote ±1.5 interquartile range.
Extended Data Fig. 5 pPhotom correlates with whole-field changes in fluorescence signal.
(a) Experimental setup: D1-Cre mice were injected with Cre-dependent GCaMP6s in the DMS and imaged with a headmounted miniscope. (b), Three signals were extracted from raw miniscope movies: 1) average of the entire field (pPhotom), 2) somatic signals (via CNMFe cell extraction), and 3) soma-sized regions (6 × 6 pixels) throughout the field. (c) Representative heatmap showing correlations among extracted somatic signals (bottom), and among each soma-sized pixel (top). (d) (Bottom) Distribution of all correlations among extracted cells or soma-sized pixels (n = 6 mice, 9 subfields/movies per mouse, 80 ± 12 extracted cells or soma-sized pixels per subfield). (Top) Boxplot showing distribution of correlations among extracted cells or soma-sized pixels per mouse (n = 6 mice). (e) (Bottom) Distribution of all correlations between extracted cells or soma-sized pixels with pPhotom (n = 6 mice, 9 subfields/movies per mouse, 80 ± 12 extracted cells or soma-sized pixels per subfield). (Top) Boxplot showing distribution of correlations between extracted cells or soma-sized pixels with pPhotom per mouse (n = 6 mice). (f) Correlation between extracted cells or soma-sized pixels with pPhotom as more cells or pixels were averaged. Shaded regions represent 95% confidence intervals. Box plots central value denotes the median, box bounds denote upper and lower quartiles and whiskers denote ±1.5 interquartile range.
Extended Data Fig. 6 Out-of-focus cells do not contribute substantially to the pPhotom signal.
(a) Experimental set-up: we expressed GCaMP6s in the DMS and performed volumetric two-photon imaging of three consecutive optical planes. (b) The raw movie from optical plane 1 (OP1) was masked with somatic ROIs from either optical plane 1 only (OP1), or optical plane 1 and optical plane 2 (OP1 + 2), or from the three optical planes (OP1 + 2 + 3). (c) Correlations between the average signal of the raw movie (pPhotom) and the masked movies (n = 4 mice). (d) 2D-FFTs were used to test the contribution of different spatial frequencies. Top row shows an example of the transformation between the time and space domain without applying any bandpass filter. Bottom row shows the same process but applying a bandpass filter that includes only the signal that is between 0 and 2 cycles per frame (full-frame). (e) Correlations between the pPhotom signal and signal from different spatial frequencies (bin-width = 2 cycles/frame). Line plots show mean±95% confidence interval. Box plots central value denotes the median, box bounds denote upper and lower quartiles and whiskers denote ±1.5 interquartile range.
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Legaria, A.A., Matikainen-Ankney, B.A., Yang, B. et al. Fiber photometry in striatum reflects primarily nonsomatic changes in calcium. Nat Neurosci 25, 1124–1128 (2022). https://doi.org/10.1038/s41593-022-01152-z
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DOI: https://doi.org/10.1038/s41593-022-01152-z
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