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PinkyCaMP: an mScarlet-based calcium sensor with enhanced brightness, photostability and multiplexing capabilities.

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  1. [1] § Results › In vivo multiplexed and optogenetic fiber photometry ↔ Fiber-Photometry-Analysis-PinkyCaMP.ipynb, lines 201–220 · score 0.61 · closed arm, open arm, aversive airpuff, fiber photometry, behaviors

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The authors' code

Jupyter notebook · 832 lines · 25 KB · MIT · 1 match

  1. # %% [markdown]
  2. # The purpose of this Jupyter notebook is to analyze Fiber Photometry Data recorded by a TDT system
  3. #
  4. # The notebook is adapted from Thoam Akam & Lauren Burgeno by referring to Simpson et al. 2023
  5. #
  6. # The preprocessing consists of the following steps:
  7. #
  8. # 1. Lowpass filtering to reduce noise (10 Hz filter).
  9. #
  10. # 2. Correction for photobleaching, over time. A double exponential fit is utilized for this purpose.
  11. #
  12. # 3. Movement correction by subtracting a linear fit of the movement control channel.
  13. #
  14. # 4. z-scoring the data
  15. #
  17. # %% [markdown]
  18. # Import all necessary packages
  20. # %%
  21. import pandas as pd
  22. import numpy as np
  23. import matplotlib
  24. import tdt
  25. import scipy.stats as stats
  26. import matplotlib.pyplot as plt
  27. from scipy.signal import butter, filtfilt
  28. import matplotlib.pyplot as plt
  29. from scipy.signal import medfilt, butter,filtfilt
  30. from scipy.stats import linregress
  31. from scipy.optimize import curve_fit
  32. import matplotlib.pyplot as plt
  34. # %% [markdown]
  35. # Jupyter has a bug that requires import of matplotl
  36. # matplotlib inline magic to properly apply rcParams
  37. # Set some other parameter
  39. # %%
  40. matplotlib.rcParams["font.size"]
  41. REF_EPOC = "Note" # event store
  42. ISOS = "_405A" # 405nm channel.
  43. GCaMP = "_465A" # 465nm channel.
  44. Pinky = "_560B" # 560nm channel.
  46. # %% [markdown]
  47. # First define the path to your data
  49. # %%
  50. BLOCKPATH = "example-data-0934-241017-143128" # insert your file path here
  52. # %% [markdown]
  53. # Read the data
  55. # %%
  56. data = tdt.read_block(BLOCKPATH)
  58. # %% [markdown]
  59. # Make time vectors and plot the unprocessed data
  61. # %%
  62. # Make time vector for plotting
  64. # Make time vector for plotting
  65. time_405A = (
  66. np.linspace(
  67. 0,
  68. len(getattr(data.streams, ISOS).data) / getattr(data.streams, ISOS).fs,
  69. len(getattr(data.streams, ISOS).data)
  70. )
  71. )
  73. # Sampling rates
  74. sampling_rate_405 = getattr(data.streams, ISOS).fs
  75. sampling_rate_465 = getattr(data.streams, GCaMP).fs
  76. sampling_rate_560 = getattr(data.streams, Pinky).fs
  78. # Make time vectors for plotting
  79. time_465A = (
  80. np.linspace(
  81. 0,
  82. len(getattr(data.streams, GCaMP).data) / getattr(data.streams, GCaMP).fs,
  83. len(getattr(data.streams, GCaMP).data)
  84. )
  85. )
  87. time_560B = (
  88. np.linspace(
  89. 0,
  90. len(getattr(data.streams, Pinky).data) / getattr(data.streams, Pinky).fs,
  91. len(getattr(data.streams, Pinky).data)
  92. )
  93. )
  94. # Plot raw traces
  95. import matplotlib.pyplot as plt
  97. # Create subplots
  98. fig, axs = plt.subplots(3, 1)
  99. fig.suptitle("Raw Data")
  101. # Plotting on the first subplot
  102. axs[0].plot(time_405A, getattr(data.streams, ISOS).data, color="limegreen")
  103. axs[0].set_title('ISOS Data')
  104. axs[0].set_xlabel('Time (s)')
  105. axs[0].set_ylabel('Signal')
  107. # Plotting on the second subplot
  108. axs[1].plot(time_465A, getattr(data.streams, GCaMP).data, color="cyan")
  109. axs[1].set_title('GCaMP Data')
  110. axs[1].set_xlabel('Time (s)')
  111. axs[1].set_ylabel('Signal')
  113. # Plotting on the third subplot
  114. axs[2].plot(time_560B, getattr(data.streams, Pinky).data, color="magenta")
  115. axs[2].set_title('Pinky Data')
  116. axs[2].set_xlabel('Time (s)')
  117. axs[2].set_ylabel('Signal')
  119. # Adjust layout to prevent overlap
  120. plt.tight_layout(rect=[0, 0, 1, 0.96]) # Adjust `rect` to fit suptitle
  122. plt.show()
  124. # %% [markdown]
  125. # Typically you get an artifact from the LEDs in the beginning of the recording.Here we remove the first seconds to get rid of it
  127. # %%
  128. # Trim data to start of LED onset via values /PC0 and video recording onset
  129. length_of_array = len(time_405A)
  130. offset = length_of_array / data.streams._405A.fs
  131. onset_LED = 10 # choose where to start the trial
  132. offset_LED = offset
  133. #offset_LED = 305 #alternatialy also choose the end of the recording
  134. isobestic_raw = []
  135. GCaMP_raw = []
  136. Pinky_raw = []
  137. duration = offset_LED - onset_LED
  138. delay = 0
  140. # trim isobestic data
  141. for t, x in zip(time_405A, getattr(data.streams, ISOS).data):
  142. # Check if the time falls within the LEDon period
  143. if onset_LED + delay <= t <= offset_LED:
  144. isobestic_raw.append(x)
  146. for t, x in zip(time_465A, getattr(data.streams, GCaMP).data):
  147. # Check if the time falls within the LEDon period
  148. if onset_LED + delay <= t <= offset_LED:
  149. GCaMP_raw.append(x)
  151. for t, x in zip(time_560B, getattr(data.streams, Pinky).data):
  152. # Check if the time falls within the LEDon period
  153. if onset_LED + delay <= t <= offset_LED:
  154. Pinky_raw.append(x)
  156. time_LED_465A = np.linspace(0, len(GCaMP_raw), len(GCaMP_raw)) / data.streams._465A.fs
  157. time_LED_405A = (
  158. np.linspace(0, len(isobestic_raw), len(isobestic_raw)) / data.streams._405A.fs
  159. )
  160. time_LED_560B = np.linspace(0, len(Pinky_raw), len(Pinky_raw)) / data.streams._560B.fs
  162. time_seconds = time_LED_405A
  164. # %% [markdown]
  165. # Now implement a 10 Hz cut off filter
  167. # %%
  168. # Define the filter
  169. def butter_lowpass(cutoff, fs, order=5):
  170. nyquist = 0.5 * fs
  171. normal_cutoff = cutoff / nyquist
  172. b, a = butter(order, normal_cutoff, btype="low", analog=False)
  173. return b, a
  176. def butter_lowpass_filter(data, cutoff, fs, order=5):
  177. b, a = butter_lowpass(cutoff, fs, order=order)
  178. y = filtfilt(b, a, data)
  179. return y
  181. # Filter requirements 465A
  182. order = 2
  183. fs = data.streams._465A.fs # sample rate, Hz
  184. cutoff = 10 # desired cutoff frequency of the filter, Hz
  186. # Filter requirements 405A
  187. order = 2
  188. fs = data.streams._405A.fs # sample rate, Hz
  189. cutoff = 10 # desired cutoff frequency of the filter, Hz
  191. # Filter requirements 560B
  192. order = 2
  193. fs = data.streams._560B.fs # sample rate, Hz
  194. cutoff = 10 # desired cutoff frequency of the filter, Hz
  196. # Apply filter to all of your data
  197. filtered_data_405A = butter_lowpass_filter(isobestic_raw, cutoff, fs, order)
  198. filtered_data_465A = butter_lowpass_filter(GCaMP_raw, cutoff, fs, order)
  199. filtered_data_560B = butter_lowpass_filter(Pinky_raw, cutoff, fs, order)
  201. # %% [markdown]
  202. # Find events, such as an aversive Airpuff, open arm entry (...) that have been marked during your recording to later plot and align these to the FibPho data
  204. # %%
  205. # find the events, such as open arm to closed arm, airpuff (...)that are stored as a Note in the TDT data tank or insert the timestamps for these events manually in the varibale (onset_behavior)
  207. events = data.epocs.Note.notes
  208. onset = data.epocs.Note.onset
  209. offset = data.epocs.Note.offset
  210. EVENT = "Airpuff"
  211. ##EVENT = "closed arm to open"
  212. #EVENT = "open arm to closed arm"
  213. indices = np.where(events == EVENT)[0]
  214. event_values_onset = onset[indices]
  215. print(event_values_onset)
  217. # !!!! Subtract onset from each element in airpuff_values!!!! you trimmed the data before
  218. adjusted_event_values = event_values_onset - onset_LED
  219. onset_behavior = adjusted_event_values
  220. print(onset_behavior)
  223. # %% [markdown]
  224. # Plot filtered traces
  226. # %%
  227. # plotting
  229. # Adjust the figure size if necessary
  230. fig, axs = plt.subplots(6, 1, figsize=(6, 10))
  231. fig.suptitle("10Hz Lowpass Filter", fontsize=12)
  233. # Plot data in each subplot
  234. axs[0].plot(time_LED_405A, isobestic_raw, color="lime")
  235. axs[0].set_title("Unfiltered Isobestic Signal", fontsize=8)
  237. axs[1].plot(time_LED_405A, filtered_data_405A, color="limegreen")
  238. axs[1].set_title("Filtered Isos Signal", fontsize=8)
  240. axs[2].plot(time_LED_465A, GCaMP_raw, color="darkorchid")
  241. axs[2].set_title("Unfiltered GCaMP Signal", fontsize=8)
  243. axs[3].plot(time_LED_560B, filtered_data_465A, color="cyan")
  244. axs[3].set_title("Filtered GCaMP Signal", fontsize=8)
  246. axs[4].plot(time_LED_560B, Pinky_raw, color="blueviolet")
  247. axs[4].set_title("Unfiltered Pinky Signal", fontsize=8)
  249. axs[5].plot(time_LED_560B, filtered_data_560B, color="magenta")
  250. axs[5].set_title("Filtered Pinky Signal", fontsize=8)
  252. # Plotting behavioral events as vertical lines
  253. axs[5].vlines(
  254. onset_behavior, # X-coordinates for the lines
  255. # Starting point of the vertical line (y-min)
  256. ymin=min(filtered_data_560B),
  257. ymax=max(filtered_data_560B), # Ending point of the vertical line (y-max)
  258. color="black", # Line color
  259. linestyle="--", # Line style (dashed)
  260. label="Behavioral Event", # Legend label
  261. )
  263. # Set the font size for all axis tick labels
  264. for ax in axs:
  265. ax.tick_params(axis="both", labelsize=10) # Adjust the font size as needed
  267. # Adjust layout to prevent overlap and ensure everything fits
  268. plt.tight_layout(rect=[0, 0, 1, 0.96]) # Leaves space for the suptitle
  270. # Display the plot
  271. plt.show()
  274. # %% [markdown]
  275. # Correction for photobleaching via a double exponential fit
  277. # %%
  278. def double_exponential(t, const, amp_fast, amp_slow, tau_slow, tau_multiplier):
  279. """Compute a double exponential function with constant offset.
  280. Parameters:
  281. t : Time vector in seconds.
  282. const : Amplitude of the constant offset.
  283. amp_fast: Amplitude of the fast component.
  284. amp_slow: Amplitude of the slow component.
  285. tau_slow: Time constant of slow component in seconds.
  286. tau_multiplier: Time constant of fast component relative to slow.
  287. """
  288. tau_fast = tau_slow * tau_multiplier
  289. return const + amp_slow * np.exp(-t / tau_slow) + amp_fast * np.exp(-t / tau_fast)
  291. # Fit curve to GCaMP.
  292. max_sig = np.max(filtered_data_465A)
  293. inital_params = [max_sig / 2, max_sig / 4, max_sig / 4, 3600, 0.1]
  294. bounds = ([0, 0, 0, 600, 0], [max_sig, max_sig, max_sig, 36000, 1])
  295. GCaMP_parms, parm_cov = curve_fit(
  296. double_exponential,
  297. time_LED_465A,
  298. filtered_data_465A,
  299. p0=inital_params,
  300. bounds=bounds,
  301. maxfev=1000,
  302. )
  303. GCaMP_expfit = double_exponential(time_LED_465A, *GCaMP_parms)
  305. # fit cure to isobestic
  306. max_sig = np.max(filtered_data_405A)
  307. inital_params = [max_sig / 2, max_sig / 4, max_sig / 4, 3600, 0.1]
  308. bounds = ([0, 0, 0, 600, 0], [max_sig, max_sig, max_sig, 36000, 1])
  309. isos_parms, parm_cov = curve_fit(
  310. double_exponential,
  311. time_LED_405A,
  312. filtered_data_405A,
  313. p0=inital_params,
  314. bounds=bounds,
  315. maxfev=1000,
  316. )
  317. isos_expfit = double_exponential(time_LED_405A, *isos_parms)
  319. # fit curve to Pinky
  320. max_sig = np.max(filtered_data_560B)
  321. inital_params = [max_sig / 2, max_sig / 4, max_sig / 4, 3600, 0.1]
  322. bounds = ([0, 0, 0, 600, 0], [max_sig, max_sig, max_sig, 36000, 1])
  323. Pinky_parms, parm_cov = curve_fit(
  324. double_exponential,
  325. time_LED_560B,
  326. filtered_data_560B,
  327. p0=inital_params,
  328. bounds=bounds,
  329. maxfev=1000,
  330. )
  331. Pinky_expfit = double_exponential(time_LED_560B, *Pinky_parms)
  333. # Fit the double exponential curve to the data 405A by substraction of
  334. # the fit this is used if the bleaching is dominated by autofluorescence, then it will affect the baseline
  335. # but not the amplitude of pyhsiological variation recommendation by Simpson et al. 2023
  336. photobleaching_corr_465 = filtered_data_465A - GCaMP_expfit
  337. photobleaching_corr_405 = filtered_data_405A - isos_expfit
  338. photobleaching_corr_560 = filtered_data_560B - Pinky_expfit
  341. # Alternatively fit the double exponential curve to the data by division. Note: the signal is converted to deltaF/F by the division
  342. # photobleaching_corr_465 = filtered_data_465A - GCaMP_expfit / GCaMP_expfit
  343. # photobleaching_corr_405 = filtered_data_405A - isos_expfit / isos_expfit
  344. # photobleaching_corr_560 = filtered_data_560B - Pinky_expfit / Pinky_expfit #
  346. # %% [markdown]
  347. # Plot the original data and the fitted curve
  349. # %%
  350. fig, axs = plt.subplots(6, 1, figsize=(6, 10)) # Adjust figure size if needed
  352. # Set the main title font size
  353. fig.suptitle("Double Exponential Fit", fontsize=10)
  355. # Plot data in each subplot and set font sizes
  356. axs[0].plot(time_LED_405A, filtered_data_405A, label="Original data", color="lime")
  357. axs[0].plot(time_LED_405A, isos_expfit, color="black", label="Fitted curve")
  358. axs[0].set_title("Isos Signal + Fit", fontsize=10)
  359. axs[0].legend(fontsize=10) # Set legend font size
  361. axs[1].plot(
  362. time_LED_465A, photobleaching_corr_405, color="limegreen", label="Fitted curve"
  363. )
  364. axs[1].set_title("Fitted Isos Signal", fontsize=10)
  365. axs[1].legend(fontsize=10) # Set legend font size
  367. axs[2].plot(time_LED_465A, filtered_data_465A, label="Original data")
  368. axs[2].plot(time_LED_465A, GCaMP_expfit, color="black", label="Fitted curve")
  369. axs[2].set_title("GCaMP Signal + Fit", fontsize=10)
  370. axs[2].legend(fontsize=10) # Set legend font size
  372. axs[3].plot(time_LED_465A, photobleaching_corr_465, color="cyan", label="Fitted curve")
  373. axs[3].set_title("Fitted GCaMP Signal", fontsize=10)
  374. axs[3].legend(fontsize=10) # Set legend font size
  376. axs[4].plot(time_LED_560B, filtered_data_560B, label="Original data")
  377. axs[4].plot(time_LED_560B, Pinky_expfit, color="black", label="Fitted curve")
  378. axs[4].set_title("Pinky Signal + Fit", fontsize=10)
  379. axs[4].legend(fontsize=10) # Set legend font size
  381. axs[5].plot(
  382. time_LED_560B, photobleaching_corr_560, color="magenta", label="Fitted curve"
  383. )
  384. axs[5].set_title("Fitted Pinky Signal", fontsize=10)
  385. axs[5].legend(fontsize=10) # Set legend font size
  387. # Plotting behavioral events as vertical lines
  388. axs[5].vlines(
  389. onset_behavior, # X-coordinates for the lines
  390. ymin=min(photobleaching_corr_560), # Starting point of the vertical line (y-min)
  391. ymax=max(photobleaching_corr_560), # Ending point of the vertical line (y-max)
  392. color="black", # Line color
  393. linestyle="--", # Line style (dashed)
  394. label="Behavioral Event", # Legend label
  395. )
  397. # Set the font size for all axis tick labels
  398. for ax in axs:
  399. ax.tick_params(axis="both", labelsize=10) # Set tick label font size
  401. # Adjust layout to prevent overlap
  402. plt.tight_layout(
  403. rect=[0, 0, 1, 0.96]
  404. ) # Adjust the rect parameter as needed to fit the suptitle
  406. plt.show()
  408. # %% [markdown]
  409. # Substitute the data in data stream with the photoblaahcing corrected
  411. # %%
  412. # Ersetzen der Daten in data Struc mit neuen gefitteten
  413. data.streams._405A.data = photobleaching_corr_405
  414. data.streams._465A.data = photobleaching_corr_465
  415. data.streams._560B.data = photobleaching_corr_560
  417. # %% [markdown]
  418. # Motion correction by finding the best linear fit of the
  419. # isobestic signal to the signal and subtracting this estimated motion component from it.
  420. # We will use the data that was bleaching corrected using the double exponential fit as this is less likely to remove meaningful
  421. # slow variation in the signals.
  423. # %%
  424. isobestic_detrended = photobleaching_corr_405
  425. GCaMP_detrended = photobleaching_corr_465
  426. Pinky_detrended = photobleaching_corr_560
  427. time_seconds = time_LED_405A
  429. slope, intercept, r_value, p_value, std_err = linregress(
  430. x=isobestic_detrended, y=GCaMP_detrended
  431. )
  433. plt.scatter(isobestic_detrended[::5], GCaMP_detrended[::5], alpha=0.1, marker=".")
  434. x = np.array(plt.xlim())
  435. plt.plot(x, intercept + slope * x)
  436. plt.xlabel("isobestic")
  437. plt.ylabel("GCamP")
  438. plt.title("GCaMP - isobestic correlation.")
  440. print("Slope : {:.3f}".format(slope))
  441. print("R-squared: {:.3f}".format(r_value**2))
  443. # We now calculate the estimated motion component of the GCaMP signal and subtract to get the motion corrected
  444. GCaMP_est_motion = intercept + slope * isobestic_detrended
  445. GCaMP_corrected = GCaMP_detrended - GCaMP_est_motion
  446. fig, ax1 = plt.subplots()
  447. plot1 = ax1.plot(
  448. time_seconds, GCaMP_detrended, "b", label="GCaMP - pre motion correction", alpha=0.5
  449. )
  450. plot3 = ax1.plot(
  451. time_seconds, GCaMP_corrected, "g", label="GCaMP - motion corrected", alpha=0.5
  452. )
  453. plot4 = ax1.plot(time_seconds, GCaMP_est_motion - 0.05, "y", label="estimated motion")
  454. ax1.set_xlabel("Time (seconds)")
  455. ax1.set_ylabel("GCaMP Signal (V)", color="g")
  456. ax1.set_title("Motion Correction")
  457. plt.tight_layout()
  458. plt.show()
  460. slope, intercept, r_value, p_value, std_err = linregress(
  461. x=isobestic_detrended, y=Pinky_detrended
  462. )
  464. plt.scatter(isobestic_detrended[::5], Pinky_detrended[::5], alpha=0.1, marker=".")
  465. x = np.array(plt.xlim())
  466. plt.plot(x, intercept + slope * x)
  467. plt.xlabel("isobestic")
  468. plt.ylabel("Pinky")
  469. plt.title("Pinky - isobestic correlation.")
  470. print("Slope : {:.3f}".format(slope))
  471. print("R-squared: {:.3f}".format(r_value**2))
  473. # We now calculate the estimated motion component of the Pinky signal and subtract to get the motion corrected
  474. Pinky_est_motion = intercept + slope * isobestic_detrended
  475. Pinky_corrected = Pinky_detrended - Pinky_est_motion
  477. fig, ax1 = plt.subplots(figsize=(10, 6))
  479. # Plot Pinky - pre motion correction (detrended)
  480. plot1 = ax1.plot(
  481. time_seconds,
  482. Pinky_detrended,
  483. color="violet",
  484. label="Pinky - Pre Motion Correction",
  485. alpha=0.7,
  486. linewidth=2,
  487. )
  489. # Plot Pinky - motion corrected
  490. plot3 = ax1.plot(
  491. time_seconds,
  492. Pinky_corrected,
  493. color="magenta",
  494. label="Pinky - Motion Corrected",
  495. alpha=0.7,
  496. linewidth=2,
  497. )
  499. # Plot estimated motion, offset for better visibility
  500. plot4 = ax1.plot(
  501. time_seconds,
  502. Pinky_est_motion - 0.05,
  503. color="lightgray",
  504. label="Estimated Motion",
  505. linestyle="--",
  506. linewidth=2,
  507. )
  509. # Add grid, titles, and labels with enhanced font size
  510. ax1.set_xlabel("Time (seconds)", fontsize=12)
  511. ax1.set_ylabel("Pinky Signal (V)", fontsize=12)
  512. ax1.set_title("Motion Correction - Pinky Signal", fontsize=14, fontweight="bold")
  514. # Improve the appearance of the legend and place it outside the plot
  515. lines = plot1 + plot3 + plot4
  516. labels = [l.get_label() for l in lines]
  517. legend = ax1.legend(
  518. lines, labels, loc="upper left", bbox_to_anchor=(1, 1), fontsize=10, frameon=True
  519. )
  521. # Add gridlines
  522. ax1.grid(True, which="both", linestyle="--", linewidth=0.5)
  524. # Add shaded region for better visual emphasis on important signal parts (optional)
  525. ax1.axhspan(-0.1, 0.1, color="lightyellow", alpha=0.3, label="Shaded Area")
  527. # Make the y-axis labels a little more distinguishable
  528. ax1.tick_params(axis="y", colors="green", labelsize=10)
  529. ax1.tick_params(axis="x", labelsize=10)
  531. # Adjust layout to fit the legend
  532. plt.tight_layout()
  534. plt.show()
  536. # %% [markdown]
  537. # Z-scoring of the data
  539. # %%
  540. # Z-scoring all data _corrected is the motion corrected signal
  542. # Z-score the GCaMP data
  544. GCaMP_zscored = (GCaMP_corrected - np.mean(GCaMP_corrected)) / np.std(GCaMP_corrected)
  546. # Create the figure and axis
  547. fig, ax1 = plt.subplots(figsize=(10, 6))
  549. # Plot the GCaMP z-scored signal
  550. plot1 = ax1.plot(
  551. time_seconds,
  552. GCaMP_zscored,
  553. color="cyan",
  554. label="GCaMP z-score",
  555. linewidth=2,
  556. alpha=0.8,
  557. )
  559. # Plot reward cue markers
  560. reward_ticks = ax1.plot(
  561. onset_behavior,
  562. np.full(np.size(onset_behavior), 6), # Plot at a constant value of 6
  563. label="Reward Cue",
  564. color="k", # Black marker
  565. marker="v", # Downward triangle marker
  566. linestyle="None",
  567. mfc="white", # Marker face color white
  568. mec="black", # Marker edge color black
  569. ms=10, # Marker size
  570. )
  572. # Set labels and title
  573. ax1.set_xlabel("Time (seconds)", fontsize=12)
  574. ax1.set_ylabel("GCaMP z-score", fontsize=12)
  575. ax1.set_title("GCaMP z-scored Signal with Reward Cue", fontsize=14, fontweight="bold")
  577. # Combine plot lines for the legend
  578. lines = plot1 + reward_ticks
  579. labels = [l.get_label() for l in lines]
  581. # Create legend
  582. legend = ax1.legend(
  583. lines,
  584. labels,
  585. loc="upper right",
  586. bbox_to_anchor=(0.95, 0.98),
  587. fontsize=10,
  588. frameon=True,
  589. )
  591. # Add grid for better visibility of values
  592. ax1.grid(True, which="both", linestyle="--", linewidth=0.5)
  593. #ax1.set_xlim(0,300)
  594. # Adjust layout to make room for legend and avoid overlap
  595. plt.tight_layout()
  597. # Show the plot
  598. plt.show()
  601. # Z-score the Pinky data
  602. Pinky_zscored = (Pinky_corrected - np.mean(Pinky_corrected)) / np.std(Pinky_corrected)
  604. # Create figure and axis
  605. fig, ax1 = plt.subplots(figsize=(10, 6))
  607. # Plot the Pinky z-scored signal
  608. plot1 = ax1.plot(
  609. time_seconds,
  610. Pinky_zscored,
  611. color="magenta",
  612. label="Pinky z-score",
  613. linewidth=2,
  614. alpha=0.8,
  615. )
  617. # Plot reward cue markers
  618. reward_ticks = ax1.plot(
  619. onset_behavior,
  620. np.full(np.size(onset_behavior), 6), # Position markers at y=6
  621. label="Reward Cue",
  622. color="black", # Black outline and marker
  623. marker="v", # Downward triangle marker
  624. linestyle="None",
  625. mfc="white", # White face color for markers
  626. mec="black", # Black edge color for markers
  627. ms=10, # Size of the markers
  628. )
  630. # Set x and y axis labels
  631. ax1.set_xlabel("Time (seconds)", fontsize=12)
  632. ax1.set_ylabel("Pinky z-score", fontsize=12)
  633. #ax1.set_xlim(30,300)
  634. # Set title
  635. ax1.set_title("Pinky z-scored Signal with Reward Cue", fontsize=14, fontweight="bold")
  637. # Add legend
  638. lines = plot1 + reward_ticks
  639. labels = [l.get_label() for l in lines]
  640. legend = ax1.legend(
  641. lines,
  642. labels,
  643. loc="upper right",
  644. bbox_to_anchor=(0.95, 0.98),
  645. fontsize=10,
  646. frameon=True,
  647. )
  649. # Add gridlines for better readability
  650. ax1.grid(True, which="both", linestyle="--", linewidth=0.5)
  652. # Adjust the layout for better spacing
  653. plt.tight_layout()
  655. # Display the plot
  656. plt.show()
  658. # %% [markdown]
  659. # Plot z-scored data according to the choosen event, e.g. airpuff
  661. # %%
  662. # Sort data in time windws around the EVENT
  663. timing_vector = time_seconds
  665. # Define the window size (5 seconds before and after each onset)
  666. time_window = 5 # in seconds
  668. # Initialize lists to store extracted windows
  669. windows_time = []
  670. windows_gcamp = []
  671. windows_isos = []
  672. windows_pinky = []
  674. # Extract windows around each timepoint of interest
  675. for timepoint_of_interest in onset_behavior:
  676. # Find the index in the time vector closest to the timepoint of interest
  677. index = np.abs(timing_vector - timepoint_of_interest).argmin()
  679. # Calculate the start and end indices for the window
  680. start_index = max(
  681. 0,
  682. index
  683. - int(
  684. time_window * len(timing_vector) / (timing_vector[-1] - timing_vector[0])
  685. ),
  686. )
  687. end_index = min(
  688. len(timing_vector) - 1,
  689. index
  690. + int(
  691. time_window * len(timing_vector) / (timing_vector[-1] - timing_vector[0])
  692. ),
  693. )
  695. # Extract the window of time vector and corresponding data
  696. window_time = timing_vector[start_index : end_index + 1]
  697. window_gcamp = GCaMP_detrended[start_index : end_index + 1]
  698. window_pinky = Pinky_detrended[start_index : end_index + 1]
  699. window_isos = isobestic_detrended[start_index : end_index + 1]
  701. # Store the extracted window
  702. windows_time.append(window_time)
  703. windows_gcamp.append(window_gcamp)
  704. windows_isos.append(window_isos)
  705. windows_pinky.append(window_pinky)
  708. # calculate mean over all trials for each channel
  709. mean_GCaMP_event = np.mean(windows_gcamp, axis=0)
  710. time_vector = np.linspace(0, time_window + time_window, len(mean_GCaMP_event))
  711. sem_gcamp = np.std(windows_gcamp, axis=0) / np.sqrt(len(windows_gcamp))
  713. # calculate for isos
  714. mean_isos_event = np.mean(windows_isos, axis=0)
  715. # time_vector = np.linspace(0, 1, len(mean_GCamP_event))
  716. sem_isos = np.std(windows_isos, axis=0) / np.sqrt(len(windows_isos))
  718. mean_Pinky_event = np.mean(windows_pinky, axis=0)
  719. time_vector = np.linspace(0, time_window + time_window, len(mean_Pinky_event))
  720. sem_Pinky = np.std(windows_pinky, axis=0) / np.sqrt(len(windows_pinky))
  722. # Plot the mean GCaMP data
  723. plt.figure(figsize=(10, 6))
  724. plt.plot(time_vector, mean_GCaMP_event, "cyan", label="Mean GCaMP Data")
  725. # plt.plot(time_vector, mean_Pinky_event, "magenta", label="Mean Pinky Data")
  727. # Plot the error band representing SEM
  728. plt.fill_between(
  729. time_vector,
  730. mean_GCaMP_event - sem_gcamp,
  731. mean_GCaMP_event + sem_gcamp,
  732. alpha=0.2,
  733. label="SEM",
  734. )
  735. plt.xlabel("Time")
  736. plt.ylabel("z-Score sDarken")
  737. plt.title("Mean sDarken Data with Error Band (SEM)")
  738. plt.legend()
  739. # plt.grid(True)
  740. plt.show()
  742. plt.figure(figsize=(10, 6))
  743. plt.plot(time_vector, mean_Pinky_event, "magenta", label="Mean Pinky Data", zorder=3)
  744. plt.fill_between(
  745. time_vector,
  746. mean_Pinky_event - sem_Pinky,
  747. mean_Pinky_event + sem_Pinky,
  748. alpha=0.2,
  749. label="SEM",
  750. zorder=2,
  751. )
  753. plt.xlabel("Time")
  754. plt.ylabel("z-score PinkyCaMP", fontsize=10)
  755. plt.title("Mean PinkyCaMP Data with Error Band (SEM)")
  756. plt.show()
  758. # %% [markdown]
  759. # Heatmaps
  761. # %%
  762. # Heatmap GCaMP
  763. plt.figure(figsize=(10, 6))
  764. plt.imshow(windows_gcamp, aspect="auto", cmap="viridis", interpolation="nearest")
  765. # cbar = fig.colorbar(cs, pad=0.01, fraction=0.02)
  766. plt.colorbar(label="Value")
  767. plt.xlabel("Time")
  768. plt.ylabel("Trial")
  769. plt.title("Heatmap of Multiple Trials")
  771. # Define and set x-ticks and labels
  772. plt.xticks(
  773. ticks=np.linspace(-5, 5, 11), # Create 11 ticks from -5 to 5
  774. labels=np.linspace(-5, 5, 11).astype(int), # Labels for ticks
  775. )
  776. plt.show()
  779. # Heatmap PinkyCaMP
  780. plt.figure(figsize=(10, 6))
  781. plt.imshow(windows_pinky, aspect="auto", cmap="inferno", interpolation="nearest")
  782. # cbar = fig.colorbar(cs, pad=0.01, fraction=0.02)
  783. plt.colorbar(label="z-score")
  784. # Define and set x-ticks and labels
  785. # Here we map data range to label range -5 to 5
  786. x_ticks = np.linspace(0, 10000, num=6) # Create ticks from 0 to 10000
  787. x_labels = np.linspace(-5, 5, num=6) # Corresponding labels
  789. plt.xticks(ticks=x_ticks, labels=x_labels) # Set tick positions # Set tick labels
  790. plt.xlabel("Time")
  791. plt.ylabel("Trial")
  792. plt.title("Heatmap of Multiple Trials")
  794. # %% [markdown]
  795. # Export of z-score data as csv.
  797. # %%
  798. import pandas as pd
  799. import numpy as np
  800. import os
  802. # convert lists in NumPy-Arrays
  803. windows_gcamp_array = np.array(windows_gcamp)
  804. windows_pinky_array = np.array(windows_pinky)
  806. # make data frames for each data array
  807. df_gcamp = pd.DataFrame(
  808. windows_gcamp_array,
  809. columns=[f"GCaMP_{i}" for i in range(windows_gcamp_array.shape[1])],
  810. )
  811. df_pinky = pd.DataFrame(
  812. windows_pinky_array,
  813. columns=[f"Pinky_{i}" for i in range(windows_pinky_array.shape[1])],
  814. )
  816. # Check if path exsists and make directory
  817. directory = os.path.dirname(BLOCKPATH)
  818. if not os.path.exists(directory):
  819. os.makedirs(directory)
  821. # choose name for the CSV- data file
  822. gcamp_path = BLOCKPATH + "_GCaMP_data.csv"
  823. pinky_path = BLOCKPATH + "_Pinky_data.csv"
  824. time_path = BLOCKPATH + "_time_data.csv"
  826. # save GCamP/green channel
  827. df_gcamp.to_csv(gcamp_path, index=False)
  828. print(f"GCaMP Daten wurden erfolgreich in '{gcamp_path}' gespeichert.")
  830. # save PinkyCaMP/red channel
  831. df_pinky.to_csv(pinky_path, index=False)
  832. # print(f"Pinky Daten wurden erfolgreich in '{pinky_path}' gespeichert.")

Fiber-Photometry-Analysis-PinkyCaMP.ipynb at commit 18a0179, under MIT · at the source

Overview

Authors: Ryan Fink1,2, Shosei Imai3, Nala Gockel4, German Lauer5, Kim Renken2, Jonas Wietek6,7, Paul J. Lamothe-Molina8, Falko Fuhrmann4, Manuel Mittag4, Tim Ziebarth5, Annika Canziani8, Martin Kubitschke2, Vivien Kistmacher9, Anny Kretschmer10, Eva Sebastian11, Jana Ottens2, Dietmar Schmitz6,7,10,12,13,14, Takuya Terai3, Jan Gründemann11,15, Sami I. Hassan9, Tommaso Patriarchi8,16, Andreas Reiner5, Martin Fuhrmann4, Robert E. Campbell3,17, Olivia Andrea Masseck1,2
ORCID iDs: Ryan Fink, Shosei Imai, Nala Gockel, Jonas Wietek, Paul J. Lamothe-Molina, Tim Ziebarth, Annika Canziani, Eva Sebastian, Jana Ottens, Dietmar Schmitz, Takuya Terai, Jan Gründemann, Tommaso Patriarchi, Andreas Reiner, Martin Fuhrmann, Robert E. Campbell, Olivia Andrea Masseck
17 affiliations
  1. Neuromodulatory Circuits, Institute of Zoology, University of Cologne,Cologne, Germany
  2. Synthetic Biology, University of Bremen,Bremen, Germany
  3. Department of Chemistry, Graduate School of Science, The University of Tokyo,Tokyo, Japan
  4. Neuroimmunology and Imaging Group, German Center for Neurodegenerative Diseases (DZNE),Bonn, Germany
  5. Cellular Neurobiology, Department of Biology and Biotechnology, Ruhr University Bochum,Bochum, Germany
  6. Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Neuroscience Research Center,Berlin, Germany
  7. Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Cell and Neurobiology,Berlin, Germany
  8. Institute of Pharmacology and Toxicology, University of Zürich,Zurich, Switzerland
  9. System Neurobiology, University of Bremen,Bremen, Germany
  10. Network Dysfunction, German Center for Neurodegenerative Diseases (DZNE),Bonn, Germany
  11. Neural Circuit Computation, German Center for Neurodegenerative Diseases (DZNE),Bonn, Germany
  12. Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, Einstein Center for Neuroscience,Berlin, Germany
  13. Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Berlin, NeuroCure Cluster of Excellence,Berlin, Germany
  14. Humboldt-Universität zu Berlin, Bernstein Center for Computational Neuroscience,Berlin, Germany
  15. University of Bonn, Medical Faculty,Bonn, Germany
  16. Neuroscience Center Zürich, University and ETH Zürich,Zürich, Switzerland
  17. CERVO Brain Research Center and Department of Biochemistry, Microbiology, and Bioinformatics, Université Laval,Quebec, Quebec Canada
Institutions: University of Cologne (Germany); University of Bremen (Germany); The University of Tokyo (Japan); German Center for Neurodegenerative Diseases (Germany); Ruhr University Bochum (Germany); Charité - Universitätsmedizin Berlin (Germany); University of Zurich (Switzerland); Bernstein Center for Computational Neuroscience Berlin (Germany); University of Bonn (Germany); ETH Zurich (Switzerland); Université Laval (Canada)
Journal: Nature methods, volume 23, issue 5, pages 998-1010
Dates: received 16 December 2024; accepted 13 March 2026; published online 24 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41592-026-03065-2 · PMID 42032066 · PMCID PMC13167472 · OpenAlex W7155507564
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Evoked potentials, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Neuroscience, Protein design
MeSH: Calcium*, Luminescent Proteins*, Animals, Fluorescent Chemosensor Compounds, Humans, Mice, Neurons, Optogenetics, Red Fluorescent Protein, Signal-To-Noise Ratio (* major topic)
Topic: Photochromic and Fluorescence Chemistry (Materials Chemistry, Materials Science), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (MA4692/6-3, MA4692/12-1, 184695641, SFB1315, FOR3004, SFB1089, SPP2411, 394431587, FOR 3004, SPP2395); Japan Society for the Promotion of Science (KAKENHI 21H00273, 24H02267, 24H00489, 19H05633); EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) (891959, MicroSynCom 865618)
Citations: cited by 4 papers (Europe PMC); 63 references in the paper

Abstract

Genetically encoded calcium (Ca2+) indicators (GECIs) are essential tools for monitoring neuronal activity, but the performance of red fluorescent GECIs has remained limited. In particular, many red indicators are relatively dim, produce low signal-to-noise ratios and can undergo unwanted photoswitching when exposed to blue light, restricting their use in all-optical experiments that combine imaging with optogenetics or multicolor imaging. Here we show the development of PinkyCaMP, a Ca2+ sensor based on the bright red fluorescent protein mScarlet. PinkyCaMP exhibits markedly improved brightness, photostability and signal-to-noise ratio compared to existing red GECIs, while remaining fully compatible with blue-light-based optogenetic and dual-color imaging approaches. PinkyCaMP is well-tolerated by neurons, showing no detectable toxicity or aggregation, both in vitro and in vivo. PinkyCaMP enables a broad spectrum of imaging modalities, including single-photon methods, such as fiber photometry, widefield imaging and miniature microscopy imaging, as well as two-photon imaging in awake mice.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

masseck/FibPho-PinkyCaMP

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 18a0179077a897492b128144f6442240ff92fee6, 15 October 2025
Languages: Jupyter (2)
Size: 16 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), pandas (2 files), SciPy (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
4 files
At the source: github.com/masseck/FibPho-PinkyCaMP.git

Code availability

The Fiber Photometry analysis script is publicly available on GitHub (https://github.com/masseck/FibPho-PinkyCaMP.git), MIT License.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

DNA sequences are available in Supplementary Information and at GenBank under accession number: BankIt3060018 syntheticPZ111304. DNA plasmids used for viral production have been deposited both on the UZH Viral Vector Facility (https://vvf.ethz.ch/, v1177, v1197) and on Addgene (plasmids #232857–232861). Viral vectors can be obtained either from the UZH Viral Vector Facility or from the Masseck laboratory. Due to the large size of the raw imaging datasets, public deposition is currently not feasible. Raw data can be obtained by emailing the corresponding author. Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 25 authors, 2 keywords, 10 MeSH terms, 3 funders, 59 references.

Cite

This paper

Fink, R., Imai, S., Gockel, N., Lauer, G., Renken, K., Wietek, J., Lamothe-Molina, P. J., Fuhrmann, F., Mittag, M., Ziebarth, T., Canziani, A., Kubitschke, M., Kistmacher, V., Kretschmer, A., Sebastian, E., Ottens, J., Schmitz, D., Terai, T., Gründemann, J., . . . Masseck, O. A. (2026). PinkyCaMP: an mScarlet-based calcium sensor with enhanced brightness, photostability and multiplexing capabilities. Nature methods, 23(5), 998-1010. https://doi.org/10.1038/s41592-026-03065-2

BibTeX

@article{fink2026pinkycamp,
author = {Fink, Ryan and Imai, Shosei and Gockel, Nala and Lauer, German and Renken, Kim and Wietek, Jonas and Lamothe-Molina, Paul J. and Fuhrmann, Falko and Mittag, Manuel and Ziebarth, Tim and Canziani, Annika and Kubitschke, Martin and Kistmacher, Vivien and Kretschmer, Anny and Sebastian, Eva and Ottens, Jana and Schmitz, Dietmar and Terai, Takuya and Gründemann, Jan and Hassan, Sami I. and Patriarchi, Tommaso and Reiner, Andreas and Fuhrmann, Martin and Campbell, Robert E. and Masseck, Olivia Andrea},
title = {{PinkyCaMP: an mScarlet-based calcium sensor with enhanced brightness, photostability and multiplexing capabilities}},
journal = {Nature methods},
year = {2026},
month = apr,
volume = {23},
number = {5},
pages = {998--1010},
publisher = {Nature Portfolio},
issn = {1548-7091},
doi = {10.1038/s41592-026-03065-2},
url = {https://doi.org/10.1038/s41592-026-03065-2},
pmid = {42032066},
pmcid = {PMC13167472}
}

RIS

TY - JOUR
AU - Fink, Ryan
AU - Imai, Shosei
AU - Gockel, Nala
AU - Lauer, German
AU - Renken, Kim
AU - Wietek, Jonas
AU - Lamothe-Molina, Paul J.
AU - Fuhrmann, Falko
AU - Mittag, Manuel
AU - Ziebarth, Tim
AU - Canziani, Annika
AU - Kubitschke, Martin
AU - Kistmacher, Vivien
AU - Kretschmer, Anny
AU - Sebastian, Eva
AU - Ottens, Jana
AU - Schmitz, Dietmar
AU - Terai, Takuya
AU - Gründemann, Jan
AU - Hassan, Sami I.
AU - Patriarchi, Tommaso
AU - Reiner, Andreas
AU - Fuhrmann, Martin
AU - Campbell, Robert E.
AU - Masseck, Olivia Andrea
TI - PinkyCaMP: an mScarlet-based calcium sensor with enhanced brightness, photostability and multiplexing capabilities
T2 - Nature methods
J2 - Nat Methods
PY - 2026
DA - 2026/04/24
VL - 23
IS - 5
SP - 998
EP - 1010
SN - 1548-7091
PB - Nature Portfolio
DO - 10.1038/s41592-026-03065-2
UR - https://doi.org/10.1038/s41592-026-03065-2
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41592-026-03065-2",
"type": "article-journal",
"title": "PinkyCaMP: an mScarlet-based calcium sensor with enhanced brightness, photostability and multiplexing capabilities",
"container-title": "Nature methods",
"author": [
{
"family": "Fink",
"given": "Ryan"
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"family": "Imai",
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"given": "Kim"
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{
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"given": "Jonas"
},
{
"family": "Lamothe-Molina",
"given": "Paul J."
},
{
"family": "Fuhrmann",
"given": "Falko"
},
{
"family": "Mittag",
"given": "Manuel"
},
{
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{
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{
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"given": "Anny"
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{
"family": "Sebastian",
"given": "Eva"
},
{
"family": "Ottens",
"given": "Jana"
},
{
"family": "Schmitz",
"given": "Dietmar"
},
{
"family": "Terai",
"given": "Takuya"
},
{
"family": "Gründemann",
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},
{
"family": "Hassan",
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{
"family": "Patriarchi",
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{
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}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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An Open-Source Pipeline for Calcium Imaging and All-Optical Physiology in Human Stem Cell-Derived Neurons.
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
In common: pandas, SciPy, Matplotlib, 1 other tool, optical imaging (calcium, voltage, 2-photon), 5 references
[3] doi:10.1111/ejn.70480 [code]
Astrocyte Proximity Protects Synapses From Human Amyloid-Beta Induced Degeneration in a Mouse Ex Vivo Model of Early Alzheimer's Disease.
Journal: The European journal of neuroscience
In common: pandas, SciPy, Matplotlib, 1 other tool, mouse, cellular / molecular, 6 references
[4] doi:10.1111/ejn.70582 [code]
Multifiber Array-Based Photometry System for Multiregional Functional Mapping in the Mouse Brain.
Journal: The European journal of neuroscience
In common: pandas, SciPy, Matplotlib, 1 other tool, optical imaging (calcium, voltage, 2-photon), mouse, 4 references
[5] doi:10.1364/boe.600665 [code]
NeuroSeg-MF: robust neuron segmentation in two-photon Ca&lt;sup&gt;2+&lt;/sup&gt; imaging using multi-feature fusion and detection-guided SAM.
Journal: Biomedical optics express
In common: pandas, SciPy, Matplotlib, 1 other tool, optical imaging (calcium, voltage, 2-photon), 4 references
[6] doi:10.1038/s41598-026-52253-9 [code]
Sequential visual stimuli increase high frequency power in the visual cortex.
Journal: Scientific reports
In common: pandas, SciPy, Matplotlib, 1 other tool, mouse, author Dietmar Schmitz
[7] doi:10.1016/j.isci.2026.116206 [code]
Gut distension evokes rapid neural dynamics in vagal and hindbrain populations of larval zebrafish.
Journal: iScience
In common: pandas, SciPy, Matplotlib, 1 other tool, optical imaging (calcium, voltage, 2-photon), 4 references
[8] doi:10.1038/s41593-026-02379-w
Norepinephrine and dopamine sensor crosstalk depends on local innervation density.
Journal: Nature neuroscience
In common: mouse, cellular / molecular, 1 reference, author Tommaso Patriarchi
[9] doi:10.1038/s41592-026-03125-7
Simultaneous two- and three-photon multiplane imaging across cortical layers in freely moving mice.
Journal: Nature methods
In common: optical imaging (calcium, voltage, 2-photon), mouse, 5 references
[10] doi:10.1038/s41598-026-50423-3 [code]
Theoretical analysis of low power synergistic sono-optogenetic control of calcium-dependent synaptic plasticity.
Journal: Scientific reports
In common: cellular / molecular, 5 references

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