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Functional specialization of mPFC-BLA and mPFC-NAc pathways in affective state representation.

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The authors' code

Python · 279 lines · 9.5 KB · MIT

  1. """Batch CaImAn processing for one-photon calcium imaging TIFF files.
  3. This script performs motion correction, CNMF source extraction, seeded CNMF
  4. refinement, component filtering, and export of fluorescence traces and spatial
  5. metadata. Paths, animal identifiers, and day ranges are provided on the command
  6. line so the pipeline can be reused without editing the source code.
  7. """
  9. from __future__ import annotations
  11. import argparse
  12. import glob
  13. import os
  14. import time
  15. from pathlib import Path
  17. import cv2
  18. import numpy as np
  19. import pandas as pd
  20. from scipy.ndimage import center_of_mass
  22. import caiman as cm
  23. from caiman.motion_correction import MotionCorrect
  24. from caiman.source_extraction.cnmf import cnmf, params
  25. from caiman.source_extraction.cnmf.utilities import detrend_df_f
  27. try:
  28. cv2.setNumThreads(0)
  29. except Exception:
  30. pass
  33. def parse_args() -> argparse.Namespace:
  34. parser = argparse.ArgumentParser(
  35. description="Run the CaImAn extraction pipeline on a batch of TIFF files."
  36. )
  37. parser.add_argument(
  38. "--tif-root",
  39. required=True,
  40. type=Path,
  41. help="Directory containing input TIFFs organized as <mouse>/<day>/<file>.tif.",
  42. )
  43. parser.add_argument(
  44. "--output-dir",
  45. required=True,
  46. type=Path,
  47. help="Directory where CaImAn outputs will be written.",
  48. )
  49. parser.add_argument(
  50. "--mice",
  51. required=True,
  52. help="Comma-separated animal/sample identifiers, e.g. 'Mouse01,Mouse02'.",
  53. )
  54. parser.add_argument(
  55. "--days",
  56. default="1-14",
  57. help="Day range or list. Examples: '1-14' or '1,2,5'. Default: 1-14.",
  58. )
  59. parser.add_argument(
  60. "--suffixes",
  61. default="motor training-Ch1-Z1",
  62. help="Comma-separated TIFF filename stems to process, without '.tif'.",
  63. )
  64. parser.add_argument("--frame-rate", default=13.088, type=float)
  65. parser.add_argument("--decay-time", default=1.162, type=float)
  66. parser.add_argument("--frames-window", default=300, type=int)
  67. return parser.parse_args()
  70. def parse_int_selection(selection: str) -> list[int]:
  71. if "-" in selection:
  72. start, end = selection.split("-", 1)
  73. return list(range(int(start), int(end) + 1))
  74. return [int(item.strip()) for item in selection.split(",") if item.strip()]
  77. def caiman_params(fnames: list[str], frame_rate: float, decay_time: float) -> params.CNMFParams:
  78. opts_dict = {
  79. "fnames": fnames,
  80. "fr": frame_rate,
  81. "decay_time": decay_time,
  82. # Motion correction.
  83. "strides": (48, 48),
  84. "overlaps": (24, 24),
  85. "max_shifts": (6, 6),
  86. "max_deviation_rigid": 3,
  87. "pw_rigid": True,
  88. # Source extraction and deconvolution.
  89. "p": 1,
  90. "nb": 2,
  91. "rf": 15,
  92. "K": 4,
  93. "gSig": [8, 8],
  94. "stride": 6,
  95. "method_init": "greedy_roi",
  96. "rolling_sum": True,
  97. "only_init": True,
  98. "ssub": 1,
  99. "tsub": 1,
  100. "merge_thr": 0.85,
  101. # Component evaluation.
  102. "min_SNR": 3.0,
  103. "rval_thr": 0.90,
  104. "use_cnn": False,
  105. "min_cnn_thr": 0.99,
  106. "cnn_lowest": 0.1,
  107. }
  108. return params.CNMFParams(params_dict=opts_dict)
  111. def start_cluster():
  112. return cm.cluster.setup_cluster(backend="local", n_processes=None, single_thread=False)
  115. def remove_caiman_logs() -> None:
  116. for log_file in glob.glob("*_LOG_*"):
  117. os.remove(log_file)
  120. def process_tiff(
  121. file_path: Path,
  122. save_path: Path,
  123. base_name: str,
  124. frame_rate: float,
  125. decay_time: float,
  126. frames_window: int,
  127. ) -> None:
  128. start_time = time.time()
  129. step_start = start_time
  130. fnames = [str(file_path)]
  131. opts = caiman_params(fnames, frame_rate, decay_time)
  133. save_path.mkdir(parents=True, exist_ok=True)
  134. print("-------------------------------------")
  135. print("Working on", file_path)
  136. print(" - Done setting up parameters")
  138. cluster, dview, n_processes = start_cluster()
  139. print(" - Done creating cluster", n_processes)
  141. try:
  142. mc = MotionCorrect(fnames, **opts.get_group("motion"))
  143. mc.motion_correct(save_movie=True)
  144. border_to_0 = 0 if mc.border_nan == "copy" else mc.border_to_0
  145. print(" - Done motion correction, cost", (time.time() - step_start) / 60, "minutes")
  146. step_start = time.time()
  148. fname_new = cm.save_memmap(
  149. mc.mmap_file,
  150. base_name="memmap_",
  151. order="C",
  152. border_to_0=border_to_0,
  153. )
  154. yr, dims, frames = cm.load_memmap(fname_new)
  155. images = np.reshape(yr.T, [frames] + list(dims), order="F")
  156. print(" - Done memory mapping, image shape=", images.shape, "cost", (time.time() - step_start) / 60, "minutes")
  157. step_start = time.time()
  159. cm.stop_server(dview=dview)
  160. cluster, dview, n_processes = start_cluster()
  162. cnm_model = cnmf.CNMF(n_processes, params=opts, dview=dview)
  163. cnm_model.fit(images)
  164. print(" - Done CNMF fitting, cost", (time.time() - step_start) / 60, "minutes")
  165. step_start = time.time()
  167. corr_img = cm.local_correlations(images.transpose(1, 2, 0))
  168. corr_img[np.isnan(corr_img)] = 0
  169. np.save(save_path / f"{base_name}-localCorrImg.npy", corr_img)
  170. print(" - Done getting correlation image, cost", (time.time() - step_start) / 60, "minutes")
  171. step_start = time.time()
  173. cnm_refit = cnm_model.refit(images, dview=dview)
  174. cnm_refit.estimates.evaluate_components(images, cnm_refit.params, dview=dview)
  175. print(" - Done seeded CNMF and component evaluation, cost", (time.time() - step_start) / 60, "minutes")
  177. save_outputs(cnm_refit, dims, save_path, base_name, frames_window)
  178. finally:
  179. cm.stop_server(dview=dview)
  180. remove_caiman_logs()
  182. print(" - The whole process took", (time.time() - start_time) / 60, "minutes")
  183. print(" - All clusters cleaned\n")
  186. def save_outputs(cnm_model, dims, save_path: Path, base_name: str, frames_window: int) -> None:
  187. accepted = cnm_model.estimates.idx_components
  188. metrics = pd.DataFrame(
  189. {
  190. "accepted_neuron": accepted,
  191. "SNR": cnm_model.estimates.SNR_comp[accepted],
  192. "spatial_correlation": cnm_model.estimates.r_values[accepted],
  193. }
  194. )
  195. selected = metrics[(metrics["SNR"] > 3) & (metrics["spatial_correlation"] >= 0.9)]
  196. candidate_neurons = selected["accepted_neuron"].values.tolist()
  198. spatial_components = cnm_model.estimates.A.toarray()
  199. x_coords: list[float] = []
  200. y_coords: list[float] = []
  201. final_neurons: list[int] = []
  202. for neuron_idx in candidate_neurons:
  203. image = spatial_components[:, neuron_idx].reshape(dims[1], dims[0])
  204. x_coord, y_coord = center_of_mass(image)
  205. if 20 < x_coord < dims[0] - 20 and 20 < y_coord < dims[1] - 20:
  206. x_coords.append(x_coord)
  207. y_coords.append(y_coord)
  208. final_neurons.append(neuron_idx)
  210. if not final_neurons:
  211. print(" - No neurons passed filtering.")
  212. return
  214. pd.DataFrame({"neuronIndex": final_neurons, "x": x_coords, "y": y_coords}).to_csv(
  215. save_path / f"{base_name}-C.csv"
  216. )
  217. selected[selected["accepted_neuron"].isin(final_neurons)].to_excel(
  218. save_path / f"{base_name}-allNeuronEvaluation.xlsx"
  219. )
  221. fluorescence = cnm_model.estimates.C[final_neurons, :]
  222. spikes = cnm_model.estimates.S[final_neurons, :]
  223. pd.DataFrame(fluorescence.T).to_csv(save_path / f"{base_name}-F.csv")
  224. pd.DataFrame(spikes.T).to_csv(save_path / f"{base_name}-S.csv")
  226. cnm_model.estimates.detrend_df_f(quantileMin=8, frames_window=frames_window)
  227. dff = cnm_model.estimates.F_dff[final_neurons, :]
  228. pd.DataFrame(dff.T).to_csv(save_path / f"{base_name}-DFF.csv")
  230. spatial = cnm_model.estimates.A[:, final_neurons].toarray()
  231. denoised = cnm_model.estimates.C[final_neurons, :]
  232. background_spatial = cnm_model.estimates.b
  233. background_temporal = cnm_model.estimates.f
  234. denoised_dff = detrend_df_f(
  235. spatial,
  236. background_spatial,
  237. denoised,
  238. background_temporal,
  239. frames_window=frames_window,
  240. )
  241. pd.DataFrame(denoised_dff).T.to_csv(save_path / f"{base_name}-DDFF.csv")
  242. cnm_model.save(save_path / f"{base_name}.hdf5")
  244. print(" - Done with DFF, fluorescence, and spikes", dff.T.shape, fluorescence.T.shape, spikes.T.shape)
  245. print(" - Done saving seeded CNMF results in HDF5")
  248. def main() -> None:
  249. args = parse_args()
  250. mice = [item.strip() for item in args.mice.split(",") if item.strip()]
  251. days = parse_int_selection(args.days)
  252. suffixes = [item.strip() for item in args.suffixes.split(",") if item.strip()]
  254. for mouse_name in mice:
  255. mouse_folder = mouse_name.replace(" ", "")
  256. for day_idx in days:
  257. day = f"day {day_idx}"
  258. for suffix in suffixes:
  259. file_path = args.tif_root / mouse_name / day / f"{suffix}.tif"
  260. base_name = f"{mouse_folder}-{day.replace(' ', '-')}-{suffix.replace(' ', '-')}"
  261. save_path = args.output_dir / mouse_folder
  262. if file_path.exists():
  263. process_tiff(
  264. file_path=file_path,
  265. save_path=save_path,
  266. base_name=base_name,
  267. frame_rate=args.frame_rate,
  268. decay_time=args.decay_time,
  269. frames_window=args.frames_window,
  270. )
  271. else:
  272. print("-------------------------------------")
  273. print(file_path, "does not exist.\n")
  275. print("Done.")
  278. if __name__ == "__main__":
  279. main()

CaImAn_Pipeline.py at commit 1360d78, under MIT · at the source

Overview

Authors: Chien-Hsien Lai1, Gyeongah Park2, Pan Xu1, Xiaoqian Sun3, Qian Ge2, Zhen Jin2, Sarah Betts1, Xiaojie Liu4, Qing-Song Liu4, Rahul Simha3, Chen Zeng5, Hui Lu1, Jianyang Du2,6
ORCID iDs: Chien-Hsien Lai, Gyeongah Park, Pan Xu, Qing-Song Liu, Hui Lu, Jianyang Du
  1. Department of Pharmacology and Physiology, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
  2. Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, United States
  3. Department of Computer Science, School of Engineering and Applied Science, The George Washington University, Washington, DC, United States
  4. Department of Pharmacology and Toxicology, Medical College of Wisconsin, Milwaukee, United States
  5. Department of Physics, Columbia College of Arts and Sciences, The George Washington University, Washington, DC, United States
  6. Neuroscience Institute, University of Tennessee Health Science Center, Memphis, United States
Institutions: George Washington University (United States); University of Tennessee Health Science Center (United States); Shandong First Medical University (China); Medical College of Wisconsin (United States)
Journal: eLife, volume 14, article RP105528
Dates: published online 7 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.105528 · PMID 42565461 · PMCID PMC13451018 · OpenAlex W4408620778
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Mouse
MeSH: Basolateral Nuclear Complex*, Neural Pathways*, Nucleus Accumbens*, Prefrontal Cortex*, Animals, Behavior, Animal, Emotions, Male, Mice, Mice, Inbred C57BL, Neurons (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS118197, R01NS118197, R01NS143931, R01 NS143931); NIBIB NIH HHS (R01 EB033364); NIMH NIH HHS (R01MH135862, R01 MH135862); Cystic Fibrosis Foundation (002544I221)
Citations: cited by 1 paper (Europe PMC); 44 references in the paper

Abstract

Effective emotional processing, crucial for adaptive behavior, is mediated by the medial prefrontal cortex (mPFC) via connections to the basolateral amygdala (BLA), and nucleus accumbens (NAc), traditionally considered functionally similar in modulating reward and aversion responses. However, the functional specialization of the mPFC→BLA and mPFC→NAc pathways in representing affective states remains unclear. We found that while overall firing patterns appeared consistent across emotional states, deeper analysis revealed distinct variabilities. Specifically, mPFC→BLA neurons, especially ‘center-ON’ neurons, exhibited heightened activity during behaviors classically associated with anxiety-like states, suggesting their involvement in aversive behavioral regulation. Conversely, mPFC→NAc neurons were more active during exploratory and approach-related behaviors, implicating them in the processing of positively valenced behavioral states. Notably, mPFC→NAc neurons showed significant pattern decorrelation during social interactions, suggesting a pivotal role in processing social preference. Additionally, repeated win/loss outcomes in the tube test produced distinct hierarchy-dependent behavioral changes and elevated corticosterone levels in loser mice, supporting the biological relevance of these behaviorally defined states. Together, these findings reveal pathway-specific representations of affect-related behavioral states in mPFC circuits and provide a framework for understanding how prefrontal outputs organize adaptive behavior across environmental contexts.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

xiaoqiansun0104/caiman-based-ca2-processing

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1360d78dcc7d00ab5ece8af13f1a7660142bc410, 17 June 2026
Languages: Python (2)
Size: 5 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Ca 2+ image processing”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (2 files), OpenCV (2 files), pandas (2 files), SciPy (2 files), CaImAn (1 file), h5py (1 file), Matplotlib (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/xiaoqiansun0104/caiman-based-ca2-processing

doi:10.5061/dryad.02v6wwqkj

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5061/dryad.02v6wwqkj

The paper's code and data availability statement is in the Data section.

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:

  • 2 repositories 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;
  • no match between paragraphs and code yet;
  • 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

All data supporting the findings of this study, along with the code used for data analysis, are available upon reasonable request and have been deposited in Dryad (https://doi.org/10.5061/dryad.02v6wwqkj) and Github (https://github.com/XiaoqianSun0104/CaImAn-based-Ca2-Processing copy archived at Sun, 2026).

The following dataset was generated:

Lai CH, Park G, Xu P, Sun X, Ge Q, Jin Z, Betts S, Liu X, Liu Q, Simha R, Zeng C, Lu H, Du J. 2026. Functional specialization of mPFC-BLA and mPFC-NAc pathways in affective state representation. Dryad Digital Repository.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 13 authors, 1 keyword, 11 MeSH terms, 4 funders, 43 references.

Cite

This paper

Lai, C.-H., Park, G., Xu, P., Sun, X., Ge, Q., Jin, Z., Betts, S., Liu, X., Liu, Q.-S., Simha, R., Zeng, C., Lu, H., & Du, J. (2026). Functional specialization of mPFC-BLA and mPFC-NAc pathways in affective state representation. eLife, 14, RP105528. https://doi.org/10.7554/elife.105528

BibTeX

@article{lai2026functional,
author = {Lai, Chien-Hsien and Park, Gyeongah and Xu, Pan and Sun, Xiaoqian and Ge, Qian and Jin, Zhen and Betts, Sarah and Liu, Xiaojie and Liu, Qing-Song and Simha, Rahul and Zeng, Chen and Lu, Hui and Du, Jianyang},
title = {{Functional specialization of mPFC-BLA and mPFC-NAc pathways in affective state representation}},
journal = {eLife},
year = {2026},
month = aug,
volume = {14},
pages = {RP105528},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.105528},
url = {https://doi.org/10.7554/elife.105528},
pmid = {42565461},
pmcid = {PMC13451018}
}

RIS

TY - JOUR
AU - Lai, Chien-Hsien
AU - Park, Gyeongah
AU - Xu, Pan
AU - Sun, Xiaoqian
AU - Ge, Qian
AU - Jin, Zhen
AU - Betts, Sarah
AU - Liu, Xiaojie
AU - Liu, Qing-Song
AU - Simha, Rahul
AU - Zeng, Chen
AU - Lu, Hui
AU - Du, Jianyang
TI - Functional specialization of mPFC-BLA and mPFC-NAc pathways in affective state representation
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/08/07
VL - 14
SP - RP105528
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.105528
UR - https://doi.org/10.7554/elife.105528
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.105528",
"type": "article-journal",
"title": "Functional specialization of mPFC-BLA and mPFC-NAc pathways in affective state representation",
"container-title": "eLife",
"author": [
{
"family": "Lai",
"given": "Chien-Hsien"
},
{
"family": "Park",
"given": "Gyeongah"
},
{
"family": "Xu",
"given": "Pan"
},
{
"family": "Sun",
"given": "Xiaoqian"
},
{
"family": "Ge",
"given": "Qian"
},
{
"family": "Jin",
"given": "Zhen"
},
{
"family": "Betts",
"given": "Sarah"
},
{
"family": "Liu",
"given": "Xiaojie"
},
{
"family": "Liu",
"given": "Qing-Song"
},
{
"family": "Simha",
"given": "Rahul"
},
{
"family": "Zeng",
"given": "Chen"
},
{
"family": "Lu",
"given": "Hui"
},
{
"family": "Du",
"given": "Jianyang"
}
],
"container-title-short": "eLife",
"volume": "14",
"page": "RP105528",
"DOI": "10.7554/elife.105528",
"PMID": "42565461",
"PMCID": "PMC13451018",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.105528",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
7
]
]
}
}

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