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  1. [1] § Material and methods › In vivo microendoscopic calcium imaging › Principal component analysis ↔ Functions_PCA.py, lines 549–585 · score 0.61 · Explained variance ratioes, PCs, component, PCA, behaviours

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Python · 662 lines · 20 KB · no license · 1 match

  1. # Functions.py
  2. #
  3. # Author: Xiaoqian Sun, Apr 2023
  4. #
  5. # Functions for projection project
  7. from IPython.core.debugger import set_trace
  10. # Import Packages
  11. #========================================================================================
  12. import os
  13. import cv2
  14. import math
  15. import itertools
  16. import scipy as sp
  17. import numpy as np
  18. import pandas as pd
  19. from PIL import Image
  20. import seaborn as sns
  21. import tifffile as tiff
  22. import matplotlib as mpl
  23. from itertools import groupby
  24. from operator import itemgetter
  25. import matplotlib.pyplot as plt
  26. from scipy.stats import pearsonr
  27. import matplotlib.colors as mcolors
  28. from sklearn.decomposition import PCA
  29. # from oasis.functions import deconvolve
  30. from sklearn.preprocessing import MinMaxScaler
  31. from scipy.ndimage.filters import gaussian_filter
  32. from sklearn.ensemble import RandomForestClassifier
  33. from sklearn.model_selection import train_test_split
  34. from scipy.signal import chirp, find_peaks, peak_widths
  37. import warnings
  38. warnings.filterwarnings('ignore')
  44. # Functions
  45. #========================================================================================
  46. def process_frame(df):
  48. '''
  49. Aim:
  50. frame 0 at trace file may not sart from time 0
  51. called in function `add_event_label()`
  52. '''
  54. # get frame list
  55. frame_list = df.index.values.tolist()
  57. # get starting value
  58. a = frame_list[0]
  59. new_frame_list = [(i-a) for i in frame_list]
  61. return(new_frame_list)
  63. def process_event_interval(df):
  65. '''
  66. Aim:
  67. time in event file doesn't always start from 0s, fix that
  68. called in function `add_event_label()`
  69. '''
  71. new_col = []
  72. for col in df.columns.tolist():
  73. new_col.append(col.strip())
  74. df.columns = new_col
  76. event_list = df[['From Second','To Second']].values.tolist()
  77. startTime = df['From Second'].values.tolist()[0]
  78. new_event_list = []
  79. for event in event_list:
  80. new_event = [x -startTime for x in event]
  81. new_event_list.append(new_event)
  82. return(new_event_list)
  84. # +
  85. def get_event_label(event_df):
  87. '''
  88. Aim:
  89. for OFT
  90. translate 'event' in eventFile to integer label
  91. each row (corresponding to each time interval) of event is assigned a label
  92. called in function `add_event_label()`
  93. '''
  95. # get event status list
  96. Event_status = event_df['Event'].values.tolist()
  97. # compact event label list for three sessions
  98. event_label = []
  100. # ['In_all',
  101. # 'In_right',
  102. # 'Investigating_RIGHT_SNIFF',
  103. # 'In_left',
  104. # 'Investigating_LEFT_SNIFF']
  106. # if which_status_to_check_frame == 'Investigating_RIGHT_SNIFF':
  107. # which_status_to_check_frame = str(11)
  108. # elif which_status_to_check_frame == 'Investigating_LEFT_SNIFF':
  109. # which_status_to_check_frame = str(12)
  110. # elif which_status_to_check_frame == 'In_right':
  111. # which_status_to_check_frame = str(21)
  112. # elif which_status_to_check_frame == 'In_all':
  113. # which_status_to_check_frame = str(22)
  114. # elif which_status_to_check_frame == 'In_left':
  115. # which_status_to_check_frame = str(23)
  116. # else:
  118. for i in Event_status:
  119. if i.strip() == 'In_right':
  120. event_label.append(21)
  121. elif i.strip() == 'Investigating_RIGHT_SNIFF' :
  122. event_label.append(11)
  123. elif i.strip() == 'In_left':
  124. event_label.append(23)
  125. elif i.strip() == 'Investigating_LEFT_SNIFF':
  126. event_label.append(12)
  127. else:
  128. event_label.append(22)
  130. # for i in Event_status:
  131. # if i.strip() == 'Area:Mouse 1 Center In floor':
  132. # event_label.append(10)
  133. # elif i.strip() == 'Area:Mouse 1 Center In All ' :
  134. # event_label.append(10)
  135. # elif i.strip() == 'Area:Mouse 1 Center In central area':
  136. # event_label.append(11)
  137. # elif i.strip() == 'Area:Mouse 1 Center In corner':
  138. # event_label.append(12)
  141. # elif i.strip() == 'Mouse 1 sniffing On wall':
  142. # event_label.append(30)
  143. # elif i.strip() == 'Mouse 1 Grooming in Area All':
  144. # event_label.append(31)
  145. # elif i.strip() == 'Mouse 1 Grooming in Area floor':
  146. # event_label.append(31)
  147. # elif i.strip() == 'Mouse 1 Locomotion in Area All':
  148. # event_label.append(32)
  150. # else:
  151. # event_label.append(90)
  153. return(event_label)
  156. # -
  158. def add_event_label(trace_df, event_path):
  160. '''
  161. Aim:
  162. add event_label to each frame
  163. one frame can have several label (since mouse can in a location and do something)
  165. Retrun:
  166. trace dataframe with one label column
  167. '''
  169. frame_list = process_frame(trace_df)
  170. #get event dataframe/eventIntervals/eventLabel
  171. # event_df = pd.read_csv(event_path)
  172. event_df = pd.read_excel(event_path)
  173. interval_list = process_event_interval(event_df)
  174. event_label = get_event_label(event_df)
  177. # add label to each frame
  178. frame_label_list = []
  179. for frame in frame_list:
  180. frame_label = str()
  182. for i in range(len(interval_list)): # one frame can be in several interval
  183. if min(interval_list[i]) <= frame <= max(interval_list[i]) :
  184. frame_label = frame_label+ str(event_label[i])
  186. # convert str to int
  187. if len(frame_label) == 0:
  188. frame_label = np.nan
  189. else:
  190. frame_label = int(frame_label)
  192. # append frame label to list
  193. frame_label_list.append(frame_label)
  196. # add label list to trace df
  197. new_df = trace_df.copy()
  198. new_df['Frame_Label'] = frame_label_list
  199. new_df = new_df.dropna()
  202. return(new_df)
  207. def find_compositeIntervals(df,
  208. which_status_to_check,
  209. intervalLength=20,
  210. if_CombineAdjacentChunks=True,
  211. gapMax=10):
  213. '''
  214. Aim:
  215. a mouse might get into a location/behavior several time during a whole session. this function is to
  216. find all intervals that the mouse gets into certain state.
  218. Arguments:
  219. - df: neuron activity df with status label for each frame (1-in state)
  220. - which_status_to_check: behavior/location (choose from center/corner/sniffing/grooming)
  221. - intervalLength: minimum size of state-interval, that is the interval must longer than #_frames, otherwise, this interval will be dumped
  222. - if_CombineAdjacentChunks: if merge 2 neighbours
  223. - gapMax: if gap between two chunks less than gapMax, merge the two
  225. Return:
  226. - kept chunks, sublists in list
  227. '''
  230. df_status = check_frame_interval(df, which_status_to_check, ifKeep=True)
  232. # get status list
  233. status_list = df_status['status'].values.tolist()
  234. # get 1 index
  235. index_1_list = [i for i, x in enumerate(status_list) if x == 1]
  237. # pick all consecutive chunks
  238. chunks = []
  239. try:
  240. for k, g in groupby(enumerate(index_1_list), lambda ix:ix[0]-ix[1]):
  241. chunk = list((map(itemgetter(1), g)))
  242. chunks.append([chunk[0], chunk[-1]])
  244. # combine small chunks
  245. if if_CombineAdjacentChunks:
  246. combinedchunks = []
  247. combinedchunks.append(chunks[0]) # initialize with first chunk
  248. #print('starting point', combinedchunks)
  249. combinedIndex = 0
  250. for chunkIndex in range(1, len(chunks)):
  251. chunkPre = combinedchunks[combinedIndex]
  252. chunkLat = chunks[chunkIndex]
  253. #print('1st round, pre:',chunkPre, ' lat:',chunkLat, ', diff=', chunkLat[0]-chunkPre[1] )
  255. if chunkLat[0]-chunkPre[1] <= gapMax:
  256. del combinedchunks[combinedIndex]
  257. #print('after del 0,', combinedchunks)
  258. combinedchunk = chunkPre+chunkLat
  259. #print('combined chunk is', combinedchunk)
  260. combinedchunks.append([combinedchunk[0], combinedchunk[-1]])
  261. else:
  262. combinedchunks.append(chunkLat)
  263. combinedIndex = combinedIndex + 1
  264. else:
  265. combinedchunks = chunks
  267. # remove small chunks
  268. keep_combinedchunks = []
  269. for chunk in combinedchunks:
  270. if chunk[1]-chunk[0] >= intervalLength:
  271. keep_combinedchunks.append(chunk)
  273. except Exception as e:
  274. keep_combinedchunks = chunks
  275. print(' ', e)
  277. return (keep_combinedchunks)
  280. def normBA(df,df_B_list,df_A_list,beforeB=False,baseSecond=2):
  282. '''
  283. Aim:
  284. normalize trace based on some frames before/start of the trace
  286. Arguments:
  287. - df: trace df
  288. - df_B_list: dataframe list BEFORE behavior/location onset
  289. - df_A_list: dataframe list AFTER behavior/location onset
  290. - beforeB: if use a period before df_B to calculate baseline.
  291. If True, than use baseSecond before de_B to calculate baselinse, otherwise use the entire df_B as baseline
  292. - baseSecond: how long before df_B are used to calculate baseline
  294. Return:
  295. - normalized df_B_list and df_A_list
  297. '''
  300. df_BN_list = []; df_AN_list = []
  301. if len(df_B_list)!=0 and len(df_A_list) !=0:
  303. for dfIndex in range(len(df_B_list)):
  304. dfB = df_B_list[dfIndex]
  305. dfA = df_A_list[dfIndex]
  307. # find baseline
  308. if beforeB:
  309. dfBM = df.loc[(dfB.index[0]-baseSecond):dfB.index[0]]
  310. else:
  311. dfBM = dfB.copy()
  312. dfBM.loc['mean'] = dfBM.mean(axis=0)
  314. # norm first/second half based on baseline
  315. dfBN = dfB-dfBM.loc['mean']
  316. dfAN = dfA-dfBM.loc['mean']
  318. df_BN_list.append(dfBN)
  319. df_AN_list.append(dfAN)
  322. return (df_BN_list, df_AN_list)
  325. def get_BonsetA(df,
  326. which_status_to_check,
  327. fps,
  328. secForward=10,
  329. secBackward=10,
  330. beforeB=False,baseSecond=2,intervalLength=20,if_CombineAdjacentChunks=True,gapMax=10,ifNorm=True):
  332. '''
  333. Aim:
  334. Through function find_compositeIntervals() we locate interval that mouse was in certain state.
  335. By calling this function, we enlarge each interval forward #secForward seconds and make it last #secBackward seconds
  336. For example (1 interval C), C=[second_10, second_25] or C=[second_10, second_15].
  337. By calling this function, we make C=[second_8, second_20]
  339. Arguments:
  340. df: dff
  341. which_status_to_check: the behavior of onset
  342. secForward: seconds to enlarge forward from onset
  343. secBackward: seconds to include backward from onset
  344. other arguments are either from function find_compositeIntervals() or normBA(), check there for more details
  346. '''
  349. df_status = check_frame_interval(df, which_status_to_check, ifKeep=True)
  350. combinedchunks = find_compositeIntervals(df,which_status_to_check,intervalLength=intervalLength,
  351. if_CombineAdjacentChunks=if_CombineAdjacentChunks,gapMax=gapMax)
  354. df_Bonset_list = []
  355. df_onsetA_list = []
  356. if len(combinedchunks) > 0:
  358. # enlargy each interval 100 frames (50 forward, 50 afterward)
  359. for interval in combinedchunks:
  360. if interval[0] >= secForward*fps:
  361. Bonset_start = interval[0] - secForward*fps
  362. Bonset_end = interval[0]
  363. onsetA_start = interval[0]
  364. if interval[0] + secBackward*fps < len(df):
  365. onsetA_end = interval[0] + secBackward*fps
  368. #----------------locate----------------#
  369. Bonset_df = df_status.iloc[Bonset_start:Bonset_end]
  370. Bonset_df = Bonset_df.drop('status', axis = 1)
  371. df_Bonset_list.append(Bonset_df)
  373. onsetA_df = df_status.iloc[onsetA_start:onsetA_end]
  374. onsetA_df = onsetA_df.drop('status', axis = 1)
  375. df_onsetA_list.append(onsetA_df)
  377. # norm
  378. if ifNorm:
  379. df_Bonset_listN, df_onsetA_listN = normBA(df_status,df_Bonset_list,df_onsetA_list,beforeB=beforeB,baseSecond=baseSecond)
  380. else:
  381. df_Bonset_listN, df_onsetA_listN = df_Bonset_list,df_onsetA_list
  384. return (df_Bonset_listN, df_onsetA_listN)
  387. def check_frame_interval(df, which_status_to_check_frame, ifKeep=True):
  388. # set_trace()
  389. # set up which_status_to_check_frame
  391. if which_status_to_check_frame == 'Investigating_RIGHT_SNIFF':
  392. which_status_to_check_frame = str(11)
  393. elif which_status_to_check_frame == 'Investigating_LEFT_SNIFF':
  394. which_status_to_check_frame = str(12)
  395. elif which_status_to_check_frame == 'In_right':
  396. which_status_to_check_frame = str(21)
  397. elif which_status_to_check_frame == 'In_all':
  398. which_status_to_check_frame = str(22)
  399. elif which_status_to_check_frame == 'In_left':
  400. which_status_to_check_frame = str(23)
  401. else:
  402. raise ValueError('Not approved status. choose from: Investigating_RIGHT_SNIFF, Investigating_LEFT_SNIFF, In_right, In_all, In_left')
  404. #df = add_event_label(df, event_1_path, event_2_path, event_3_path, mice_path)[which_session]
  405. frame_label_list = df['Frame_Label'].values.tolist()
  407. # to separate status label, eg:20214111
  408. n = 2
  410. check_frame_label_list = []
  412. for line in frame_label_list:
  413. line = str(line)
  414. line_list= [line[i:i+n] for i in range(0, len(line), n)]
  415. if which_status_to_check_frame in line_list:
  416. check_frame_label_list.append(1)
  417. else:
  418. check_frame_label_list.append(0)
  422. df['status'] = check_frame_label_list
  423. df_keep = df[ (df['status'] == 1)]
  424. # df_keep = df_keep.drop('status', axis = 1)
  425. # df_keep = df_keep.drop('Frame_Label', axis = 1)
  427. print('the shape of the dataframe in the status is:', df_keep.shape)
  428. return(df_keep)
  436. # +
  437. def average_nActEntries(df_list,neuron_list,fps,seconds):
  439. '''
  440. Aim:
  441. get average activity around onset for each neuron across entries
  443. Return:
  444. a dataframe with all mean activity of each neuron [num_frames, num_neurons]
  445. '''
  447. frames = seconds*fps
  448. avgNeuron_actEntries = pd.DataFrame()
  449. if len(df_list) > 0:
  450. for nCol in neuron_list:
  452. # get mean activity of this neuron across entries]
  453. nAct_entries = []
  454. for i in range(len(df_list)):
  455. if df_list[i].shape[0] == frames:
  456. # nAct_entries.append(df_list[i][nCol])
  457. nAct_entries.append(df_list[i][nCol].astype(float))
  459. # set_trace()
  460. nAct_mean = np.mean(np.array(nAct_entries), axis=0)
  461. avgNeuron_actEntries[nCol] = nAct_mean
  464. return(avgNeuron_actEntries)
  465. # -
  472. def filterOut_nonceONce(df, fps, secF):
  474. '''
  475. remove neurons that not showing ceON neurons around center
  476. specificly designed for 3-AverageTrace_4ONs_heatmap
  478. '''
  480. df_keep = df.copy()
  481. f, n = df.shape
  483. for i in range(n):
  484. act = df.iloc[:, i]
  485. Bavg = np.mean(act[:fps*secF])
  486. avgA = np.mean(act[fps*secF:fps*secF*3])
  488. if avgA<Bavg:
  489. df_keep = df_keep.drop(df.columns[i], axis=1)
  491. return(df_keep)
  495. def filterOut_nonceONco(df, fps, secF):
  497. '''
  498. remove neurons that not showing ceON neurons around corner (drop onset)
  499. specificly designed for 3-AverageTrace_4ONs_heatmap
  501. '''
  503. df_keep = df.copy()
  504. f, n = df.shape
  506. for i in range(n):
  507. act = df.iloc[:, i]
  508. Bavg = np.mean(act[:fps*secF])
  509. avgA = np.mean(act[fps*secF:fps*secF*3])
  511. if avgA>Bavg:
  512. df_keep = df_keep.drop(df.columns[i], axis=1)
  514. return(df_keep)
  518. def get_overlap(lists):
  519. ls=[set(l) for l in lists]
  520. fst=ls[0]
  521. for l in ls[1:]:
  522. fst.intersection_update(l)
  523. return (len(fst), fst)
  527. def fit_apply_PCA(input_df_Std, ifPrint=True):
  528. '''
  529. Aim:
  530. apply PCA
  532. '''
  533. pcaModel = PCA(n_components=4)
  534. input_Reduced_Std = pcaModel.fit_transform(input_df_Std)
  536. if ifPrint:
  537. print('first 3PCs explain', round(sum((pcaModel.explained_variance_ratio_[0:3])),3), \
  538. 'PC1='+str(round(pcaModel.explained_variance_ratio_[0],3))+\
  539. ' PC2='+str(round(pcaModel.explained_variance_ratio_[1],3))+\
  540. ' PC3='+str(round(pcaModel.explained_variance_ratio_[2],3)))
  541. #print('explained_variance_ratio_', pcaModel.explained_variance_ratio_)
  543. # reduced df
  544. input_Reduced_df = pd.DataFrame(input_Reduced_Std)
  545. input_Reduced_df.columns = ['PC1', 'PC2', 'PC3', 'PC4']
  547. return(input_Reduced_df, pcaModel)
  549. def generate_PCA_summaryDF(reduced_df, pcaModel, behaviorList, mouseName):
  550. '''
  551. Aim:
  552. generate PCA summary, 4 PCs
  553. eigenvector=features, eigenvalues=coefficient, mean=X.mean
  554. also generate df for running log, just explained variance ratio
  556. '''
  557. # PC summary
  558. #----------------------------------------------------
  559. dic_eigen = {'Eigenvector 1':pcaModel.components_[0],
  560. 'Eigenvector 2':pcaModel.components_[1],
  561. 'Eigenvector 3':pcaModel.components_[2],
  562. 'Eigenvector 4':pcaModel.components_[3],
  563. 'Sample Mean':pcaModel.mean_,
  564. 'Eigenvalues':pcaModel.explained_variance_,
  565. 'Explained Ratio':pcaModel.explained_variance_ratio_,
  566. 'singular Values':pcaModel.singular_values_,
  567. 'behavior':behaviorList}
  568. dic_eigen = dict([(k,pd.Series(v)) for k,v in dic_eigen.items() ])
  569. df_eigen = pd.DataFrame(dic_eigen)
  570. # concat PCs
  571. PCA_summary = pd.concat([df_eigen, reduced_df], axis=1)
  573. # running Log
  574. #----------------------------------------------------
  575. runningLog = pcaModel.explained_variance_ratio_
  576. runningLog_dic = {'mouseName':mouseName,
  577. 'first_3PCs': sum(runningLog),
  578. 'PC1':runningLog[0],
  579. 'PC2': runningLog[1],
  580. 'PC3':runningLog[2]}
  581. runningLog_dic = dict([(k,pd.Series(v)) for k,v in runningLog_dic.items() ])
  582. runningLog_df = pd.DataFrame(runningLog_dic)
  585. return(PCA_summary, runningLog_df)
  593. # Plot
  594. # ========================================================================================
  597. def plot_3D_4Behaviors(PCA_df,behaviors=['center', 'corner', 'sniffing', 'grooming'],cs=['r','g','b','black'],
  598. alphas=[0.6, 0.1, 0.8, 1], ifSave=True,savePath=None,filename=None):
  599. '''
  600. Aim:
  601. plot single 3D line plot and save
  602. different behavior, differetn scatter plot
  603. '''
  605. fig = plt.figure(figsize=(10,10))
  606. ax = fig.add_subplot(111, projection='3d')
  608. for bIndex in range(len(behaviors)):
  609. b = behaviors[bIndex]
  610. x = PCA_df[PCA_df['behavior']==b]['PC1']
  611. y = PCA_df[PCA_df['behavior']==b]['PC2']
  612. z = PCA_df[PCA_df['behavior']==b]['PC3']
  613. ax.scatter(x, y, z, c=cs[bIndex], marker='o', alpha=alphas[bIndex], label=b)
  614. plt.legend()
  617. if ifSave:
  618. if not os.path.exists(savePath):
  619. os.makedirs(savePath)
  620. plt.savefig(os.path.join(savePath, filename), dpi=300)
  621. plt.close()
  622. else:
  623. plt.show()
  625. def plot_2D_4Behaviors(PCA_df,
  626. behaviors=['center', 'corner', 'sniffing', 'grooming'],
  627. cs=['r','g','b','black'],
  628. alphas=[0.6, 0.1, 0.8, 1],
  629. ifSave=True,savePath=None,filename=None):
  630. '''
  631. Aim:
  632. plot single 3D line plot and save
  633. different behavior, differetn scatter plot
  634. '''
  636. fig = plt.figure(figsize=(10, 10))
  638. for bIndex in range(len(behaviors)):
  639. b = behaviors[bIndex]
  640. x = PCA_df[PCA_df['behavior']==b]['PC1']
  641. y = PCA_df[PCA_df['behavior']==b]['PC2']
  642. plt.scatter(x, y, marker='o', color=cs[bIndex], alpha=alphas[bIndex], label=b)
  643. plt.legend()
  645. if ifSave:
  646. if not os.path.exists(savePath):
  647. os.makedirs(savePath)
  648. plt.savefig(os.path.join(savePath, filename), dpi=300)
  649. plt.close()
  650. else:
  651. plt.show()

Functions_PCA.py at commit 4e3dd13, no license · at the source

Overview

Authors: Yujing Zhang1,2, Zi-Qi Zhang1, Zi-Jie Zhai3, Xuchao Ren1, Siyi Zuo1, Zheng Ma1, Min Liu1,4, Dong Li2,5, Yue Wang1,4, Pan Xu1
ORCID iDs: Dong Li, Yue Wang, Pan Xu
  1. School of Clinical and Basic Medical Sciences, Shandong First Medical University,Jinan, China
  2. School of Public Health, Shandong First Medical University,Jinan, China
  3. College of Dentistry, Shandong First Medical University,Jinan, China
  4. Jinan Maternity and Child Care Hospital Affiliated to Shandong First Medical University,Jinan, China
  5. School of Public Health, Jining Medical University,Jining, China
Institutions: Shandong First Medical University (China); Jining Medical University (China)
Journal: Communications biology, volume 9, issue 1, article 870
Dates: received 14 August 2025; accepted 9 April 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10089-z · PMID 42026195 · PMCID PMC13316100 · OpenAlex W7155419020
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), systems (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Social behaviour, Neural circuits, Stress and resilience
MeSH: Amygdala*, Hypothalamus*, Neurons*, Oxytocin*, Prefrontal Cortex*, Social Behavior*, Stress, Psychological*, Animals, Male, Mice, Mice, Inbred C57BL, Paraventricular Hypothalamic Nucleus, Signal Transduction (* major topic)
Topic: Neuroendocrine regulation and behavior (Social Psychology, Psychology), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82301696, 82471676); Shandong Provincial Natural Science Foundation (2024HWYQ-056, ZR2023QC008)
Citations: cited by 1 paper (Europe PMC); 60 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.

Repository

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

Xulab-SDFMU/mPFC_circuits_Stress_Social

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 4e3dd1392abb925ec135fc911b8f023680e37e40, 1 August 2025
Languages: Jupyter (6), Python (1)
Size: 8 files, 7 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 6 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (6 files), Matplotlib (4 files), NumPy (4 files), SciPy (3 files), scikit-learn (2 files), seaborn (2 files), OpenCV (1 file), Pillow (1 file), tifffile (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
8 files
At the source: github.com/Xulab-SDFMU/mPFC_circuits_Stress_Social

Code availability statement

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Read it in the paper: doi.org/10.1038/s42003-026-10089-z.

Tracing map

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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;
  • 7 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

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s42003-026-10089-z.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 13 MeSH terms, 2 funders, 60 references.

Cite

This paper

Zhang, Y., Zhang, Z.-Q., Zhai, Z.-J., Ren, X., Zuo, S., Ma, Z., Liu, M., Li, D., Wang, Y., & Xu, P. (2026). Medial prefrontal cortex neurons integrate amygdala and hypothalamic oxytocin signals to mediate stress-induced social alterations. Communications biology, 9(1), 870. https://doi.org/10.1038/s42003-026-10089-z

BibTeX

@article{zhang2026medial,
author = {Zhang, Yujing and Zhang, Zi-Qi and Zhai, Zi-Jie and Ren, Xuchao and Zuo, Siyi and Ma, Zheng and Liu, Min and Li, Dong and Wang, Yue and Xu, Pan},
title = {{Medial prefrontal cortex neurons integrate amygdala and hypothalamic oxytocin signals to mediate stress-induced social alterations}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {870},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10089-z},
url = {https://doi.org/10.1038/s42003-026-10089-z},
pmid = {42026195},
pmcid = {PMC13316100}
}

RIS

TY - JOUR
AU - Zhang, Yujing
AU - Zhang, Zi-Qi
AU - Zhai, Zi-Jie
AU - Ren, Xuchao
AU - Zuo, Siyi
AU - Ma, Zheng
AU - Liu, Min
AU - Li, Dong
AU - Wang, Yue
AU - Xu, Pan
TI - Medial prefrontal cortex neurons integrate amygdala and hypothalamic oxytocin signals to mediate stress-induced social alterations
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/23
VL - 9
IS - 1
SP - 870
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10089-z
UR - https://doi.org/10.1038/s42003-026-10089-z
LA - en
ER -

CSL-JSON

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"author": [
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"PMCID": "PMC13316100",
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}

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