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Prefrontal parvalbumin neurons mediate working memory in a task demand-dependent manner.

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  1. [1] § Methods › Fiber photometry analysis ↔ photobatch/Processing/Signal/fitting.py, lines 53–99 · score 0.55 · linear regression, deviation, fit, isobestic, median, filtered
  2. [2] § Methods › Fiber photometry ↔ photobatch/Processing/IO/Photometry/doric.py, lines 145–282 · score 0.55 · Doric Studio software, TTL, channel, photometry, signals

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Python · 387 lines · 13 KB · GPL-3.0 · 1 match

  1. """Signal/fitting.py
  2. Baseline fitting and delta-F/F computation.
  4. Entry point: `signal_fit` (previously `doric_fit` on the PhotometryData class).
  6. Supported fitting strategies
  7. -----------------------------
  8. linear / lin
  9. Ordinary least-squares (polyfit) or robust HuberRegressor.
  10. irls
  11. Robust linear model using statsmodels RLM with Huber's T loss.
  12. expodecay / exp_decay / exp
  13. Single-component exponential decay fitted over time.
  14. biexponential / biexp
  15. Two-component exponential decay fitted over time.
  16. arpls / ar_pls / ar-pls
  17. Asymmetrically Reweighted Penalised Least Squares (Baek et al. 2015).
  18. """
  20. import logging
  22. import numpy as np
  23. import pandas as pd
  24. import scipy.sparse as sparse
  25. import statsmodels.api as sm
  26. from scipy.optimize import curve_fit
  27. from scipy.sparse.linalg import spsolve
  28. from sklearn.linear_model import HuberRegressor
  30. logger = logging.getLogger(__name__)
  33. def _ols_fit(filtered_f0, filtered_f):
  34. """Fit the active channel to the control channel with ordinary least squares.
  36. Parameters
  37. ----------
  38. filtered_f0 : numpy.ndarray
  39. Filtered isobestic/control signal.
  40. filtered_f : numpy.ndarray
  41. Filtered active signal.
  43. Returns
  44. -------
  45. numpy.ndarray
  46. Predicted active-channel baseline computed from a first-order
  47. polynomial fit of ``filtered_f0`` onto ``filtered_f``.
  48. """
  49. poly = np.polyfit(filtered_f0, filtered_f, 1)
  50. return np.multiply(poly[0], filtered_f0) + poly[1]
  53. def _linear_fit(filtered_f0, filtered_f, robust_fit=True, huber_epsilon='auto'):
  54. """Fit the isobestic channel to the active channel using linear regression.
  56. Parameters
  57. ----------
  58. filtered_f0 : numpy.ndarray
  59. Filtered isobestic/control signal.
  60. filtered_f : numpy.ndarray
  61. Filtered active signal to be modeled.
  62. robust_fit : bool, optional
  63. If ``True``, fit with ``HuberRegressor``. If ``False``, use an
  64. ordinary least-squares line fit.
  65. huber_epsilon : str or float, optional
  66. Huber loss threshold. Values ``"auto"`` and ``"mad"`` derive the
  67. threshold from the residual median absolute deviation. Numeric values
  68. are used directly and clipped to be greater than ``1.0``.
  70. Returns
  71. -------
  72. numpy.ndarray
  73. Predicted active-channel baseline for each sample in
  74. ``filtered_f0``.
  75. """
  76. if robust_fit:
  77. f0_reshaped = filtered_f0.reshape(-1, 1)
  79. epsilon_str = str(huber_epsilon).strip().lower()
  80. if epsilon_str in ('auto', 'mad'):
  81. f_naive_pred = _ols_fit(filtered_f0, filtered_f)
  82. residuals = filtered_f - f_naive_pred
  83. mad_val = np.median(np.abs(residuals - np.median(residuals)))
  84. epsilon_val = max(1.01, 1.4826 * mad_val)
  85. logger.debug("Huber epsilon (auto/MAD): %.4f", epsilon_val)
  86. else:
  87. try:
  88. epsilon_val = max(1.01, float(huber_epsilon))
  89. except (ValueError, TypeError):
  90. epsilon_val = 1.35
  91. logger.debug("Huber epsilon (user-specified): %.4f", epsilon_val)
  93. huber = HuberRegressor(epsilon=epsilon_val)
  94. huber.fit(f0_reshaped, filtered_f)
  95. fitted = huber.predict(f0_reshaped)
  96. else:
  97. fitted = _ols_fit(filtered_f0, filtered_f)
  99. return fitted
  102. def _irls_fit(filtered_f0, filtered_f):
  103. """Fit the control channel to the active channel with robust IRLS.
  105. Parameters
  106. ----------
  107. filtered_f0 : numpy.ndarray
  108. Filtered isobestic/control signal.
  109. filtered_f : numpy.ndarray
  110. Filtered active signal to be modeled.
  112. Returns
  113. -------
  114. numpy.ndarray
  115. Predicted active-channel baseline from a robust linear model fit with
  116. Huber's T loss.
  117. """
  118. design_matrix = sm.add_constant(np.asarray(filtered_f0, dtype=float), has_constant='add')
  119. response = np.asarray(filtered_f, dtype=float)
  120. rlm_result = sm.RLM(response, design_matrix, M=sm.robust.norms.HuberT()).fit()
  121. return rlm_result.predict(design_matrix)
  124. def _exp_decay_fit(filtered_f0, filtered_f, time_data):
  125. """Fit the active channel using a single-exponential decay over time.
  127. Parameters
  128. ----------
  129. filtered_f0 : numpy.ndarray
  130. Filtered isobestic/control signal. This is only used for fallback
  131. linear fitting if the exponential optimization fails.
  132. filtered_f : numpy.ndarray
  133. Filtered active signal to be modeled.
  134. time_data : numpy.ndarray
  135. Time vector associated with the filtered signals.
  137. Returns
  138. -------
  139. numpy.ndarray
  140. Predicted baseline from a single-exponential decay model. Falls back
  141. to an OLS control-to-active fit if nonlinear optimization fails.
  142. """
  143. yf = np.asarray(filtered_f, dtype=float)
  144. t = np.asarray(time_data, dtype=float)
  145. try:
  146. p_95 = np.percentile(yf, 95)
  147. p_05 = np.percentile(yf, 5)
  148. a0 = p_95 - p_05
  149. k0 = 1.0 / max((t[-1] - t[0]), 1.0)
  150. c0 = np.min(yf)
  151. popt, _ = curve_fit(
  152. lambda tt, amp, decay, offset: amp * np.exp(-decay * tt) + offset,
  153. t,
  154. yf,
  155. p0=[a0, k0, c0],
  156. maxfev=10000,
  157. )
  158. fitted = popt[0] * np.exp(-popt[1] * t) + popt[2]
  159. except (RuntimeError, TypeError, ValueError):
  160. fitted = _ols_fit(filtered_f0, filtered_f)
  161. return fitted
  164. def _biexp_decay_fit(filtered_f0, filtered_f, time_data):
  165. """Fit the active channel with a biexponential decay over time.
  167. Parameters
  168. ----------
  169. filtered_f0 : numpy.ndarray
  170. Filtered isobestic/control signal. Used for fallback linear fitting
  171. if the biexponential optimization fails.
  172. filtered_f : numpy.ndarray
  173. Filtered active signal to be modeled.
  174. time_data : numpy.ndarray
  175. Time vector associated with the filtered signals.
  177. Returns
  178. -------
  179. numpy.ndarray
  180. Predicted baseline from a two-component exponential decay model.
  181. Falls back to an OLS control-to-active fit if nonlinear optimization
  182. fails or the inputs are too short for stable fitting.
  183. """
  184. yf = np.asarray(filtered_f, dtype=float)
  185. control = np.asarray(filtered_f0, dtype=float)
  186. t = np.asarray(time_data, dtype=float)
  187. if yf.size < 5 or t.size != yf.size:
  188. return _ols_fit(control, yf)
  190. t_shifted = t - t[0]
  191. duration = max(float(t_shifted[-1]), 1.0)
  192. p10, p50, p90 = np.percentile(yf, [10, 50, 90])
  193. signal_span = max(float(p90 - p10), np.finfo(float).eps)
  194. baseline_floor = float(np.min(yf))
  196. amp_total = max(float(p90 - baseline_floor), np.finfo(float).eps)
  197. slow_amp = max(float(p90 - p50), 0.25 * amp_total)
  198. fast_amp = max(float(p50 - p10), 0.15 * amp_total)
  199. k_slow_0 = 0.5 / duration
  200. k_fast_0 = 5.0 / duration
  201. c0 = baseline_floor
  203. lower_bounds = [0.0, 0.0, 1e-8, 1e-8, baseline_floor - signal_span]
  204. upper_bounds = [
  205. 4.0 * amp_total,
  206. 4.0 * amp_total,
  207. 10.0 / duration,
  208. 100.0 / duration,
  209. float(np.max(yf)),
  210. ]
  212. def biexponential(tt, amp_slow, amp_fast, k_slow, k_fast, offset):
  213. return amp_slow * np.exp(-k_slow * tt) + amp_fast * np.exp(-k_fast * tt) + offset
  215. try:
  216. popt, _ = curve_fit(
  217. biexponential,
  218. t_shifted,
  219. yf,
  220. p0=[slow_amp, fast_amp, k_slow_0, k_fast_0, c0],
  221. bounds=(lower_bounds, upper_bounds),
  222. maxfev=20000,
  223. )
  224. fitted = biexponential(t_shifted, *popt)
  225. except (RuntimeError, TypeError, ValueError):
  226. fitted = _ols_fit(control, yf)
  228. return fitted
  231. def _arpls_drift_fit(
  232. dff_initial,
  233. arpls_lambda=1e5,
  234. arpls_max_iter=50,
  235. arpls_tol=1e-6,
  236. arpls_eps=1e-8,
  237. arpls_weight_scale=2.0,
  238. ):
  239. """Estimate baseline drift using Asymmetrically Reweighted Penalised Least Squares.
  241. Parameters
  242. ----------
  243. dff_initial : numpy.ndarray
  244. Initial delta-F/F trace from which slow baseline drift should be
  245. estimated.
  246. arpls_lambda : float, optional
  247. Smoothness penalty applied to the second-derivative term.
  248. arpls_max_iter : int, optional
  249. Maximum number of reweighting iterations.
  250. arpls_tol : float, optional
  251. Relative convergence tolerance for weight updates.
  252. arpls_eps : float, optional
  253. Lower bound applied to weights for numerical stability.
  254. arpls_weight_scale : float, optional
  255. Scaling factor controlling the sharpness of the logistic reweighting
  256. transition.
  258. Returns
  259. -------
  260. numpy.ndarray
  261. Estimated slow drift component with the same shape as
  262. ``dff_initial``.
  263. """
  264. y = dff_initial.astype(float)
  265. n = y.size
  266. lam = float(arpls_lambda)
  267. ratio = float(arpls_tol)
  268. max_iter = int(arpls_max_iter)
  269. eps = float(arpls_eps)
  270. weight_scale = float(arpls_weight_scale)
  272. e = np.ones(n)
  273. d_matrix = sparse.diags([e, -2 * e, e], [0, 1, 2], shape=(n - 2, n))
  274. penalty = lam * (d_matrix.transpose().dot(d_matrix))
  276. weights = np.ones(n)
  277. baseline = np.zeros(n)
  279. for _ in range(max_iter):
  280. weight_matrix = sparse.diags(weights, 0)
  281. system = (weight_matrix + penalty).tocsc()
  282. baseline = spsolve(system, weights * y)
  284. residuals = y - baseline
  285. negative_residuals = residuals[residuals < 0]
  286. if negative_residuals.size == 0:
  287. break
  289. mean_neg = negative_residuals.mean()
  290. std_neg = negative_residuals.std()
  291. if std_neg <= 0:
  292. break
  294. next_weights = 1.0 / (1.0 + np.exp(weight_scale * (residuals - (2.0 * std_neg - mean_neg)) / std_neg))
  295. next_weights = np.clip(next_weights, eps, 1.0)
  297. if np.linalg.norm(weights - next_weights) / np.linalg.norm(weights) < ratio:
  298. weights = next_weights
  299. break
  300. weights = next_weights
  302. return baseline
  305. def signal_fit(
  306. fit_type,
  307. filtered_f0,
  308. filtered_f,
  309. time_data,
  310. robust_fit=True,
  311. baseline_detrend=None,
  312. arpls_lambda=1e5,
  313. arpls_max_iter=50,
  314. arpls_tol=1e-6,
  315. arpls_eps=1e-8,
  316. arpls_weight_scale=2.0,
  317. huber_epsilon='auto',
  318. ):
  319. """Fit a baseline to the photometry signals and compute delta-F/F.
  321. Parameters
  322. ----------
  323. fit_type : str
  324. Baseline fitting strategy. Supported values include ``"linear"``,
  325. ``"lin"``, ``"irls"``, ``"expodecay"``, ``"exp_decay"``,
  326. ``"exp"``, ``"biexponential"``, and ``"biexp"``.
  327. filtered_f0 : numpy.ndarray
  328. Filtered isobestic/control signal.
  329. filtered_f : numpy.ndarray
  330. Filtered active signal.
  331. time_data : numpy.ndarray
  332. Time vector corresponding to the filtered signals.
  333. robust_fit : bool, optional
  334. If ``True``, the linear fit strategy uses ``HuberRegressor``.
  335. baseline_detrend : str or None, optional
  336. Optional post-fit detrending method. Currently ``"arpls"`` applies
  337. arPLS drift removal; any other value leaves delta-F/F unchanged.
  338. arpls_lambda : float, optional
  339. Smoothness penalty used when ``baseline_detrend`` is ``"arpls"``.
  340. arpls_max_iter : int, optional
  341. Maximum number of arPLS iterations.
  342. arpls_tol : float, optional
  343. Convergence tolerance for arPLS weight updates.
  344. arpls_eps : float, optional
  345. Lower clipping bound for arPLS weights.
  346. arpls_weight_scale : float, optional
  347. Logistic weight transition scale for arPLS.
  348. huber_epsilon : str or float, optional
  349. Huber threshold control for the linear robust fit path.
  351. Returns
  352. -------
  353. pandas.DataFrame
  354. DataFrame with columns ``Time`` and ``DeltaF`` containing the input
  355. time vector and the computed delta-F/F trace.
  356. """
  357. fit_type_lower = str(fit_type).lower() if fit_type is not None else 'linear'
  359. if fit_type_lower in ('linear', 'lin'):
  360. fitted = _linear_fit(filtered_f0, filtered_f, robust_fit=robust_fit, huber_epsilon=huber_epsilon)
  361. elif fit_type_lower == 'irls':
  362. fitted = _irls_fit(filtered_f0, filtered_f)
  363. elif fit_type_lower in ('expodecay', 'exp_decay', 'exp'):
  364. fitted = _exp_decay_fit(filtered_f0, filtered_f, time_data)
  365. elif fit_type_lower in ('biexponential', 'biexp'):
  366. fitted = _biexp_decay_fit(filtered_f0, filtered_f, time_data)
  367. else:
  368. fitted = _ols_fit(filtered_f0, filtered_f)
  370. with np.errstate(divide='ignore', invalid='ignore'):
  371. delta_f = (filtered_f - fitted) / fitted
  372. delta_f = np.nan_to_num(delta_f)
  374. if baseline_detrend == 'arpls':
  375. drift_fit = _arpls_drift_fit(
  376. delta_f,
  377. arpls_lambda=arpls_lambda,
  378. arpls_max_iter=arpls_max_iter,
  379. arpls_tol=arpls_tol,
  380. arpls_eps=arpls_eps,
  381. arpls_weight_scale=arpls_weight_scale,
  382. )
  383. delta_f -= drift_fit
  385. result_pd = pd.DataFrame({'Time': time_data, 'DeltaF': delta_f})
  387. return result_pd

fitting.py at commit 25f8e4a, under GPL-3.0 · at the source

Overview

Authors: Tyler D. Dexter1,2, Meira M. F. Machado2, Shahnaza Hamidullah1,2, Daniel Palmer2, Ahmed. M. Hashad2,3, Marcus Doyle1,2, Megan Attard1,2, Brian L. Allman4, Wataru Inoue2, Lisa M. Saksida2, Timothy J. Bussey2,5
ORCID iDs: Tyler D. Dexter, Meira M. F. Machado, Daniel Palmer, Wataru Inoue, Lisa M. Saksida, Timothy J. Bussey
  1. Neuroscience Program, Schulich School of Medicine & Dentistry, Western University,London, ON Canada
  2. Department of Physiology and Pharmacology, Schulich School of Medicine & Dentistry, Western University,London, ON Canada
  3. Department of Pharmacology and Toxicology, Faculty of Pharmacy, Alexandria University,Alexandria, Egypt
  4. Department of Anatomy and Cell Biology, Schulich School of Medicine & Dentistry, Western University,London, ON Canada
  5. Department of Psychiatry, Schulich School of Medicine & Dentistry, Western University,London, ON Canada
Institutions: Western University (Canada); Alexandria University (Egypt)
Journal: Nature communications, volume 17, issue 1, article 7349
Dates: received 29 November 2024; accepted 18 May 2026; published online 10 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73818-2 · PMID 42265093 · PMCID PMC13402680 · OpenAlex W7164002838
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), schizophrenia / psychosis (population), cognitive (subfield)
Methods: Statistics, Evoked potentials, Single-unit activity, calcium imaging
Keywords: Working memory, Neural circuits, Schizophrenia
MeSH: Memory, Short-Term*, Neurons*, Parvalbumins*, Prefrontal Cortex*, Animals, Cognition, Gamma Rhythm, Male, Mice, Mice, Inbred C57BL, Optogenetics (* major topic)
Topic: Stress Responses and Cortisol (Behavioral Neuroscience, Neuroscience), according to OpenAlex
Funding: Canadian Institutes of Health Research (426966, PJT 426966); Ontario Research Foundation (ORF); Natural Sciences and Engineering Research Council of Canada (2019-06102 and 2019-06087, RGPIN-2019-06087); Canada First Research Excellence Fund Canada Foundation for Innovation; Western University BrainsCAN Postdoctoral Fellowship; Canada First Research Excellence Fund (Fonds d'excellence en recherche Apogée Canada); Canada Foundation for Innovation
Citations: cited by 1 paper (Europe PMC); 96 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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dpalmer9/photobatch

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Languages: Python (28)
Size: 45 files, 28 scripts
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Found in: “Code availability”
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At the source: github.com/dpalmer9/PhotoBatch/tree/v1.0.0

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Cite

This paper

Dexter, T. D., Machado, M. M. F., Hamidullah, S., Palmer, D., Hashad, A. M., Doyle, M., Attard, M., Allman, B. L., Inoue, W., Saksida, L. M., & Bussey, T. J. (2026). Prefrontal parvalbumin neurons mediate working memory in a task demand-dependent manner. Nature communications, 17(1), 7349. https://doi.org/10.1038/s41467-026-73818-2

BibTeX

@article{dexter2026prefrontal,
author = {Dexter, Tyler D. and Machado, Meira M. F. and Hamidullah, Shahnaza and Palmer, Daniel and Hashad, Ahmed. M. and Doyle, Marcus and Attard, Megan and Allman, Brian L. and Inoue, Wataru and Saksida, Lisa M. and Bussey, Timothy J.},
title = {{Prefrontal parvalbumin neurons mediate working memory in a task demand-dependent manner}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7349},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73818-2},
url = {https://doi.org/10.1038/s41467-026-73818-2},
pmid = {42265093},
pmcid = {PMC13402680}
}

RIS

TY - JOUR
AU - Dexter, Tyler D.
AU - Machado, Meira M. F.
AU - Hamidullah, Shahnaza
AU - Palmer, Daniel
AU - Hashad, Ahmed. M.
AU - Doyle, Marcus
AU - Attard, Megan
AU - Allman, Brian L.
AU - Inoue, Wataru
AU - Saksida, Lisa M.
AU - Bussey, Timothy J.
TI - Prefrontal parvalbumin neurons mediate working memory in a task demand-dependent manner
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/10
VL - 17
IS - 1
SP - 7349
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73818-2
UR - https://doi.org/10.1038/s41467-026-73818-2
LA - en
ER -

CSL-JSON

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"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73818-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
10
]
]
}
}

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