OSCR

Real-time closed-loop feedback system for mouse mesoscale cortical signal and movement control

Code ↔ Paper

11 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 11 matches
  1. [1] § Materials and methods › Seed-pixel correlation matrices ↔ helper.py, lines 649–730 · score 0.68 · seed pixels, Allen, ALM, OB, RS, BC
  2. [2] § Results ↔ analysis/plot_clmf.py, lines 271–294 · score 0.62 · snout front, snout top, snout bottom, CLMF, mice
  3. [3] § Materials and methods › CLNF, CLMF setup, and behavior experiments ↔ PiCameraStream.py, lines 8–102 · score 0.58 · Pi Camera, OV5647, exposure, RGB, sensor, frame
  4. [4] § Materials and methods › CLNF, CLMF setup, and behavior experiments ↔ brain/cla_reward_punish_1roi.py, lines 287–373 · score 0.56 · config.ini, mouse_id, configured, channels, duration, pi
  5. [5] § Results › Cortical responses became focal and more correlated as mice learned the rewarded behavior in CLMF ↔ analysis/plot_clmf.py, lines 665–730 · score 0.55 · Bonferroni corrected, RM ANOVA, variables, correlations, seed, days
  6. [6] § Results ↔ analysis/plot_clmf.py, lines 271–294 · score 0.54 · Kullback Leibler divergence, divergence scores, snout, CLMF, day, mouse
  7. [7] § Materials and methods › CLNF, CLMF setup, and behavior experiments ↔ behavior/cla_dlc_trials_speed.py, lines 247–317 · score 0.53 · config.ini, mouse_id, licking, configured, speed, video
  8. [8] § Materials and methods › CLNF, CLMF setup, and behavior experiments ↔ brain/cla_reward_punish_1roi.py, lines 287–373 · score 0.52 · behavior pi, behavior recording, TTL, configured, brain, LED
  9. [9] § Materials and methods › CLNF, CLMF setup, and behavior experiments ↔ brain/cla_reward_punish_2roi.py, lines 289–334 · score 0.52 · behavior pi, behavior recording, TTL, configured, brain, LED
  10. [10] § Results › Cortical responses became focal and more correlated as mice learned the rewarded behavior in CLMF ↔ analysis/plot_clmf.py, lines 527–608 · score 0.52 · Benjamini Hochberg, Mann Whitney, mm, CLMF, days, Reward
  11. [11] § Results › Mice can explore and learn an arbitrary task, rule, and target conditions ↔ analysis/plot_clnf.py, lines 353–370 · score 0.51 · ROI1 ROI2, ROI rule, fast, CLNF, day, reward

Paper

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The authors' code

Python · 730 lines · 41 KB · GPL-3.0 · 4 matches

  1. """
  2. Created on Thu Jul 25 12:09:03 2024
  3. use trials_df and plot data stats and performance
  4. Requires file clmf_df_speed_daily_all.pkl clmf_df_beh_log_daily_all.pkl, clmf_avg_dff_response_daily.pkl
  5. Please download from here-https://drive.google.com/drive/folders/1eq0muLtc36jcU4sUy8fVOSmN4dxVxdn8?usp=sharing
  6. @author: pankaj gupta
  7. """
  8. import os
  9. import platform
  10. import sys
  11. from os.path import dirname, realpath
  12. filepath = realpath(__file__)
  13. dir_of_file = dirname(filepath)
  14. parent_dir_of_file = dirname(dir_of_file)
  15. sys.path.append(parent_dir_of_file)
  16. import matplotlib.pyplot as plt
  17. import matplotlib
  18. matplotlib.use('agg')
  19. from matplotlib.backends.backend_pdf import PdfPages
  20. from matplotlib import colors
  21. import numpy as np
  22. from statannot import add_stat_annotation
  23. import pymannkendall as mk
  24. from statsmodels.stats.anova import AnovaRM
  25. import pingouin as pg
  26. from scipy.stats import f_oneway
  27. from statannotations.Annotator import Annotator
  28. import itertools
  29. import tifffile as tif
  30. import pandas as pd
  31. import seaborn as sns
  32. from pathlib import Path
  33. import glob
  34. from tqdm import tqdm
  35. from scipy import signal
  36. import scipy.io as sio
  37. from scipy.ndimage import gaussian_filter
  38. import imageio
  39. import argparse
  40. from configparser import ConfigParser
  41. import napari
  42. import pickle
  43. os.environ[ 'NUMBA_CACHE_DIR' ] = '/tmp/numba_cache/'
  44. import dlc2kinematics
  45. from statsmodels.stats.multitest import multipletests
  46. # from pygifsicle import optimize
  47. from get_clmf_data import \
  48. expt1_data_list, expt2_data_list, expt3_data_list
  49. from helper import *
  51. data_root = '/home/pankaj/teamshare/pkg/closedloop_rig5_data/'
  52. out_dir = '../processed_data/'
  54. data_list = expt1_data_list
  55. data_list.extend(expt2_data_list)
  56. data_list.extend(expt3_data_list)
  58. # %%
  59. # Initiate the parser
  60. parser = argparse.ArgumentParser()
  61. # Add long and short argument
  62. parser.add_argument("--process_mouse", "-n", required=False, help="Folder name to process")
  63. parser.add_argument("--process_dir", "-d", type=int, required=False, help="Set recording dir to process")
  64. # Read arguments from the command line
  65. args = parser.parse_args()
  66. if args.process_mouse: # Check for --name
  67. print("Process mouse id %s" % args.process_mouse)
  68. if args.process_dir: # Check for --day
  69. print("Process dir %s" % args.process_dir)
  71. ppmm_brain = 14.56
  72. ppmm_beh = 3.4
  74. mice_list = [e[0] for e in data_list]
  75. groups = [e[2] for e in data_list]
  76. cal_days = list(itertools.chain.from_iterable([e[1] for e in data_list]))
  78. pp = PdfPages(out_dir + os.path.basename(__file__).split('.')[0] + '_summary_stats.pdf')
  79. #%%
  80. file_sessions_df_csv = out_dir + os.sep + 'clmf_sessions_df' + '.csv'
  81. if os.path.isfile(file_sessions_df_csv):
  82. sessions_df = pd.read_csv(file_sessions_df_csv, dtype = {'mouse_id':str, 'cal_day':str, 'day':int, 'control_joint':str,
  83. 'trials':int, 'rewards':int, 'fll_based_rewards':int,
  84. 'flr_based_rewards':int, 'fll_totaldist': float, 'flr_totaldist': float,
  85. 'pca_dims_brain': float, 'pca_dims_behavior': float, 'group':str})
  87. sessions_df = sessions_df[sessions_df['mouse_id'].isin( mice_list)]
  88. sessions_df = sessions_df[sessions_df['group'].isin( groups)]
  89. sessions_df = sessions_df[sessions_df['cal_day'].isin(cal_days)]
  90. #%% Figure not included in paper: Plot left and right paw movements over days
  91. for grp in np.unique(groups):
  92. print(grp)
  93. a = sessions_df[sessions_df['group'].isin([grp])]
  94. sessions_df_melted = a.melt(id_vars=['cal_day','day'], value_vars=['fll_totaldist', 'flr_totaldist'])
  95. fig = plt.figure();
  96. # gfg = sns.boxplot(x="day", y="value", hue="variable", data=sessions_df_melted)
  97. gfg = sns.pointplot(x="day", y="value", hue="variable", data=sessions_df_melted, errorbar=('ci', 95), dodge=True)
  98. gfg.legend(fontsize=15)
  99. sns.despine(offset=10, trim=True)
  100. plt.xlabel('Days', fontweight='bold', fontsize=14)
  101. plt.xticks(sessions_df.day.unique(), sessions_df.day.unique() +1, fontsize=20)
  102. plt.ylabel('Paw movement (mm)', fontweight='bold', fontsize=14)
  103. plt.yticks(fontsize=20)
  104. plt.title(grp + ' paw movements', fontsize=14)
  105. plt.tight_layout();
  106. pp.savefig(fig); plt.close()
  108. #%% Figure not included in paper: Left and right paw movement based rewards over days
  109. for grp in np.unique(groups):
  110. print(grp)
  111. a = sessions_df[sessions_df['group'].isin([grp])]
  112. sessions_df_melted = a.melt(id_vars=['cal_day','day'], value_vars=['fll_based_rewards', 'flr_based_rewards'])
  113. sessions_df_melted['value'] = sessions_df_melted['value'].div(60).mul(100)
  114. fig = plt.figure();
  115. # gfg = sns.boxplot(x="day", y="value", hue="variable", data=sessions_df_melted)
  116. # gfg = sns.lineplot(x="day", y="value", hue="variable", data=sessions_df_melted, err_style="bars", alpha=0.5)
  117. gfg = sns.pointplot(x="day", y="value", hue="variable", data=sessions_df_melted, errorbar=('ci', 95), dodge=True)
  118. gfg.legend(fontsize=15)
  119. sns.despine(offset=10, trim=True)
  120. plt.xlabel('Days', fontweight='bold', fontsize=14)
  121. plt.xticks(sessions_df.day.unique(), sessions_df.day.unique() +1, fontsize=20)
  122. plt.ylabel('Success Rate (%)', fontweight='bold', fontsize=14)
  123. plt.yticks(fontsize=20)
  124. plt.title(grp + ' FLL and FLR based rewards', fontsize=14);
  125. plt.tight_layout();
  126. pp.savefig(fig); plt.close()
  128. #%% Figure 4B Plot success rate over days for each group
  129. sessions_df['performance'] = (sessions_df['rewards'] - sessions_df['rewards'].min()) / (sessions_df['rewards'].max() - sessions_df['rewards'].min())
  130. fig = plt.figure(figsize=(12,8));
  131. gfg = sns.pointplot(x="day", y="performance", hue="group", data=sessions_df, dodge=0.2)
  132. gfg.legend(fontsize=15)
  133. sns.despine(offset=10, trim=True)
  134. plt.xlabel('Days', fontweight='bold', fontsize=20)
  135. plt.xticks(sessions_df.day.unique(), sessions_df.day.unique() +1, fontsize=20)
  136. plt.ylabel('Normalized', fontweight='bold', fontsize=20)
  137. plt.yticks(fontsize=20)
  138. plt.title('Group success rate', fontsize=20)
  139. plt.tight_layout();
  140. pp.savefig(fig); plt.close()
  142. #%% Statistical tests for Figure 4B Write statistical results summary in the pdf
  143. fig = plt.figure(figsize=(12,10))
  144. fig.clf()
  146. a = sessions_df[(sessions_df['day']<=4) & (sessions_df['group']=='grp1')]
  147. fig.text(0.05,0.97, 'RM-ANOVA grp1 before rule change', weight='bold')
  148. fig.text(0.05,0.89, pg.rm_anova(dv='performance', within=['day'], subject='mouse_id', data=a, detailed=True).to_markdown())
  150. a = sessions_df[(sessions_df['day']>4) & (sessions_df['group']=='grp1')]
  151. fig.text(0.05,0.81, 'RM-ANOVA grp1 after rule change', weight='bold')
  152. fig.text(0.05,0.73, pg.rm_anova(dv='performance', within=['day'], subject='mouse_id', data=a, detailed=True).to_markdown())
  154. a = sessions_df[(sessions_df['group']=='grp1')]
  155. fig.text(0.05,0.65, 'RM-ANOVA grp1 on all days', weight='bold')
  156. fig.text(0.05,0.57, pg.rm_anova(dv='performance', within=['day'], subject='mouse_id', data=a, detailed=True).to_markdown())
  158. a = sessions_df[(sessions_df['group']=='grp2')]
  159. fig.text(0.05,0.49, 'RM-ANOVA grp2 on all days', weight='bold')
  160. fig.text(0.05,0.41, pg.rm_anova(dv='performance', within=['day'], subject='mouse_id', data=a, detailed=True).to_markdown())
  162. a = sessions_df[(sessions_df['group']=='grp3')]
  163. fig.text(0.05,0.33, 'RM-ANOVA grp3 on all days', weight='bold')
  164. fig.text(0.05,0.25, pg.rm_anova(dv='performance', within=['day'], subject='mouse_id', data=a, detailed=True).to_markdown())
  166. pp.savefig(fig)
  167. plt.close()
  169. #%%
  170. file_trials_df_csv = out_dir + os.sep + 'clmf_trials_df' + '.csv'
  171. if os.path.isfile(file_trials_df_csv):
  173. trials_df = pd.read_csv(file_trials_df_csv, dtype = {'mouse_id':str, 'cal_day':str, 'day':int, 'control_joint':str, 'group':str,
  174. 'trial':int, 'start_ix':int, 'end_ix':int, 'tr_duration':float,
  175. 'reward': int, 'manual_label': str,
  176. 'fll_dist_tr': float, 'fll_dist_prestart': float, 'fll_dist_poststart': float,
  177. 'fll_dist_prereward': float, 'fll_dist_postreward': float, 'fll_maxspeed_prereward': float,
  178. 'flr_dist_tr': float, 'flr_dist_prestart': float, 'flr_dist_poststart': float,
  179. 'flr_dist_prereward': float, 'flr_dist_postreward': float, 'flr_maxspeed_prereward': float,
  180. 'fll_tortuosity_prereward': float, 'flr_tortuosity_prereward': float})
  182. # we can select part of the dataframe based on current analysis
  183. trials_df = trials_df[trials_df['mouse_id'].isin( mice_list)]
  184. trials_df = trials_df[trials_df['group'].isin( groups)]
  185. trials_df = trials_df[trials_df['cal_day'].isin(cal_days)]
  187. #%% re-assign 'day' column
  188. for expt in data_list:
  189. mouse_id, list_rec_dir, grp = expt
  190. for day, rec_dir in enumerate(list_rec_dir):
  191. if ((trials_df['mouse_id'] == mouse_id) & (trials_df['cal_day'] == rec_dir)).any():
  192. #row already exists, update all values except the manual_label
  193. trials_df.loc[(trials_df['mouse_id'] == mouse_id) & (trials_df['cal_day'] == rec_dir), ['day']] = day
  195. #%% Figure 4D maximum speeds
  196. for grp in np.unique(groups):
  197. print(grp)
  198. a = trials_df[trials_df['group'].isin([grp])]
  199. trials_df_melted = a.melt(id_vars=['cal_day','day'], value_vars=['fll_maxspeed_prereward', 'flr_maxspeed_prereward'])
  200. fig = plt.figure(figsize=(8,5));
  201. # gfg = sns.boxplot(x="day", y="value", hue="variable", data=trials_df_melted)
  202. gfg = sns.pointplot(x="day", y="value", hue="variable", data=trials_df_melted, errorbar=('ci', 95), dodge=True) #husl, colorblind, Set1, bright
  203. gfg.legend(fontsize=15)
  204. sns.despine(offset=10, trim=True)
  205. # plt.ylim([0,120])
  206. plt.xlabel('Days', fontweight='bold', fontsize=18)
  207. plt.xticks(np.arange(len(trials_df.day.unique())), trials_df.day.unique(), fontsize=20)
  208. plt.ylabel('Speed (mm/sec.)', fontweight='bold', fontsize=18)
  209. plt.yticks(fontsize=20)
  210. plt.title(grp + ': Maximum L/R paw speed during trials', fontsize=14)
  211. plt.tight_layout();
  212. pp.savefig(fig); plt.close()
  214. #%% Figure 4B success rate by group (another way to plot Figure 4B)
  215. trialcounts_by_mouseid_all = trials_df.groupby(['mouse_id', 'day', 'group'])['reward'].count()
  216. trialcounts_by_mouseid_rewarded = trials_df[trials_df.reward==1].groupby(['mouse_id', 'day', 'group'])['reward'].count()
  217. successrate_by_mouseid = (trialcounts_by_mouseid_rewarded / trialcounts_by_mouseid_all).reset_index()
  218. hue_plot_params = {
  219. 'data': successrate_by_mouseid,
  220. 'x': 'day',
  221. 'y': 'reward',
  222. "hue": "group",
  223. "palette": 'husl'
  224. }
  225. fig = plt.figure();
  226. ax = sns.pointplot(**hue_plot_params, dodge=0.2)
  227. sns.despine(offset=10, trim=True)
  228. plt.xlabel('Days', fontweight='bold', fontsize=14)
  229. plt.xticks(trials_df.day.unique(), trials_df.day.unique() +1, fontsize=20)
  230. plt.ylabel('Success Rate (%)', fontweight='bold', fontsize=14)
  231. plt.yticks(fontsize=20)
  232. plt.title('Group success rate')
  233. plt.tight_layout(pad=2)
  234. pairs = [
  235. [(1, 'grp1'), (3, 'grp1')],
  236. [(1, 'grp1'), (4, 'grp1')],
  237. [(1, 'grp1'), (5, 'grp1')],
  238. [(4, 'grp1'), (5, 'grp1')],
  239. [(5, 'grp1'), (6, 'grp1')],
  240. [(1, 'grp3'), (4, 'grp3')],
  241. ]
  242. # annotator = Annotator(ax, pairs, data=successrate_by_mouseid, x='day', y='reward')
  243. annotator = Annotator(ax, pairs, **hue_plot_params)
  244. # # test: Brunner-Munzel, Levene, Mann-Whitney, Mann-Whitney-gt, Mann-Whitney-ls, t-test_ind, t-test_welch, t-test_paired, Wilcoxon, Kruskal
  245. # # comparisons_correction: Bonferroni, Holm-Bonferroni, Benjamini-Hochberg, Benjamini-Yekutieli
  246. annotator.configure(test='Mann-Whitney', show_test_name=True, comparisons_correction='Benjamini-Hochberg', text_format='star') #bejamini hochberg, Bonferroni
  247. annotator.apply_and_annotate()
  248. pp.savefig(fig); plt.close()
  250. #%% Figure 4C task latency
  251. fig = plt.figure(figsize=(8,5))
  252. # ax = sns.violinplot(x='day', y='tr_duration', hue='group', data=trials_df,inner="box", palette="husl", linewidth=0.5, saturation=0.5)
  253. # ax = sns.lineplot(x='day', y='tr_duration', hue='group', data=trials_df, errorbar=('ci', 95), err_style="bars", markers = True, linewidth=2, alpha=0.5)
  254. ax = sns.pointplot(x='day', y='tr_duration', hue='group', data=trials_df, errorbar=('ci', 95))
  255. sns.despine(offset=10, trim=True)
  256. plt.xlabel('Day', fontweight='bold', fontsize=14)
  257. plt.xticks(trials_df.day.unique(), trials_df.day.unique() +1, fontsize=20)
  258. # plt.axvline(x=5, c='k')
  259. plt.ylabel('Task delay (s)', fontweight='bold', fontsize=14)
  260. plt.yticks(fontsize=20)
  261. plt.title('Group task delay', fontsize=14)
  262. plt.tight_layout();
  263. # pairs=[(0,3), (0,4), (3,4), (4,5),(4,9),(8,9)]
  264. # annotator = Annotator(ax, pairs, data=trials_df, x='day', y='tr_duration')
  265. # annotator.configure(test='Mann-Whitney', show_test_name=True, comparisons_correction='Benjamini-Hochberg', text_format='star')
  266. # annotator.apply_and_annotate()
  267. pp.savefig(fig)
  268. plt.close()
  269. print('Done')
  271. #%% Supplementary figure 4B Requires kld_df.csv located under data directory
  272. bp_select = ['snout_top', 'fl_l', 'fl_r', 'hl_l', 'hl_r', 'tailbase_bottom']
  273. file_kld_df_csv = out_dir + os.sep + 'clmf_kld_df' + '.csv'
  274. if os.path.isfile(file_kld_df_csv):
  275. kld_df = pd.read_csv(file_kld_df_csv, dtype = {'snout_front': float, 'fl_l_front': float, 'fl_r_front': float,
  276. 'snout_top': float, 'snout_bottom': float, 'fl_l': float, 'fl_r': float,
  277. 'hl_l': float, 'hl_r': float, 'tailbase_bottom': float,
  278. 'tailbase_top': float, 'day':str, 'mouse_id':str, 'group':str})
  280. kld_df = kld_df[kld_df['mouse_id'].isin( mice_list)]
  281. kld_df = kld_df[kld_df['group'].isin( groups)]
  282. days = kld_df.day.unique()
  283. for grp in np.unique(groups):
  284. print(grp)
  285. a = kld_df[kld_df['group'].isin([grp])].replace([np.nan, np.inf], 0)
  286. kld_days = np.stack([a[a.day==day][bp_select].mean() for day in days])
  287. fig, axs = plt.subplots(1,1, sharey=True, figsize=(5,3))
  288. im = sns.heatmap(kld_days.T, cmap='Greys', ax=axs)
  289. im.invert_yaxis()
  290. axs.set_xticklabels(days)
  291. axs.set_yticklabels(bp_select, rotation=0)
  292. plt.title(grp + ' Kullback-Leibler Divergence scores', fontsize=14)
  293. plt.tight_layout();
  294. pp.savefig(fig); plt.close()
  297. #%% correlation matrix of paw speeds on each trial
  299. file_df_speed_daily_all_pkl = out_dir + os.sep + 'clmf_df_speed_daily_all' + '.pkl'
  300. file_df_beh_log_daily_all_pkl = out_dir + os.sep + 'clmf_df_beh_log_daily_all' + '.pkl'
  301. df_speed_daily_all_file = open(file_df_speed_daily_all_pkl, 'rb')
  302. df_speed_daily_all = pickle.load(df_speed_daily_all_file)
  303. df_speed_daily_all_file.close()
  304. df_beh_log_daily_all_file = open(file_df_beh_log_daily_all_pkl, 'rb')
  305. df_beh_log_daily_all = pickle.load(df_beh_log_daily_all_file)
  306. df_beh_log_daily_all_file.close()
  308. # to select data for plotting
  309. data_list = [('FW2_ai94', 'grp1'),
  310. ('FW3_ai94', 'grp1'),
  311. ('FW22_ai94', 'grp1'),
  312. ('GT3_tta', 'grp1'),
  313. ('GT33_tta', 'grp1'), ('HA2+_tta', 'grp1'), ('GER2_ai94', 'grp1'), ('HYL3_tta', 'grp1'),
  314. ('GER2_ai94', 'grp2'), ('HYL3_tta', 'grp2'),
  315. ('BR1_vgat-ai203', 'grp2'), ('BR2_vgat-ai203', 'grp2'),
  316. ('GIL3_ai94', 'grp3'),
  317. ('GIR2_ai94', 'grp3')]
  318. groups = np.unique([d[1] for d in data_list])
  319. days = [1,2,3,4,5,6,7,8,9,10]
  320. beh_fps=30
  321. bp_select = ['fl_l', 'fl_r', 'hl_l', 'hl_r', 'tailbase_bottom', 'tailbase_top']
  323. #%% Figure 7D total bodypart movement during trial over days
  324. for g in groups:
  325. bodypart_distance_grp_stack = []
  326. for expt in data_list:
  327. mouse_id, grp = expt
  328. if grp != g:
  329. continue
  330. bodyparts_distance_stack = []
  331. for day in days:
  332. df = df_beh_log_daily_all[(df_beh_log_daily_all['mouse_id']==mouse_id) &
  333. (df_beh_log_daily_all['group']==grp) &
  334. (df_beh_log_daily_all['day']==day)]
  335. starts_ix = np.where(np.diff(df.trial.values) > 0)[0]
  336. ends_ix = np.where(np.diff(df.trial.values) < 0)[0]
  337. reward_ix = df[df.reward == 1].index
  338. fail_ix = df[df.reward == -1].index
  340. bodyparts_distance_stack.append(np.vstack([np.sum(df_speed_daily_all[bp_select].iloc[tr - 60:tr])/beh_fps for tr in reward_ix]).mean(axis=0))
  341. bodypart_distance_grp_stack.append(bodyparts_distance_stack)
  342. fig, axs = plt.subplots(1,1, sharey=True, figsize=(5,3))
  343. im = sns.heatmap(np.stack(bodypart_distance_grp_stack).mean(axis=0).T, cmap='Greys', ax=axs, vmin=10, vmax=40)
  344. im.invert_yaxis()
  345. axs.set_xticklabels(days)
  346. axs.set_yticklabels(bp_select, rotation=0)
  347. plt.title(g + ' bodyparts distances during trial')
  348. pp.savefig(fig); plt.close()
  350. #%% Figure 6C speed correlation matrix for each group (average) during rewards
  351. for g in groups:
  352. speed_corrmat_stack = []
  353. for expt in data_list:
  354. mouse_id, grp = expt
  355. if grp != g:
  356. continue
  357. fll_speed_stack = []
  358. flr_speed_stack = []
  359. for day in days:
  360. df = df_beh_log_daily_all[(df_beh_log_daily_all['mouse_id']==mouse_id) &
  361. (df_beh_log_daily_all['group']==grp) &
  362. (df_beh_log_daily_all['day']==day)]
  363. starts_ix = np.where(np.diff(df.trial.values) > 0)[0]
  364. ends_ix = np.where(np.diff(df.trial.values) < 0)[0]
  365. reward_ix = df[df.reward == 1].index
  366. fail_ix = df[df.reward == -1].index
  367. fll_speed_stack.append(np.stack([df_speed_daily_all['fl_l'].iloc[tr - 60:tr] for tr in reward_ix]).mean(axis=0))
  368. flr_speed_stack.append(np.stack([df_speed_daily_all['fl_r'].iloc[tr - 60:tr] for tr in reward_ix]).mean(axis=0))
  369. speed_corrmat_stack.append(np.corrcoef(np.vstack((fll_speed_stack, flr_speed_stack))))
  371. fig, axs = plt.subplots(1,1, sharey=True)
  372. im = sns.heatmap(np.stack(speed_corrmat_stack).mean(axis=0), cmap='Greys', ax=axs, vmin=0.5, vmax=1)
  373. im.invert_yaxis()
  374. axs.set_xticklabels(np.append(['FLL_'+str(day) for day in days], ['FLR_'+str(day) for day in days]), rotation=90)
  375. axs.set_yticklabels(np.append(['FLL_'+str(day) for day in days], ['FLR_'+str(day) for day in days]), rotation=0)
  376. plt.title(g + ' speed profile correlations')
  377. pp.savefig(fig); plt.close()
  378. ## speed correlation matrix for each group (average) during rest
  379. for g in groups:
  380. speed_corrmat_stack = []
  381. for expt in data_list:
  382. mouse_id, grp = expt
  383. if grp != g:
  384. continue
  385. fll_speed_stack = []
  386. flr_speed_stack = []
  387. for day in days:
  388. df = df_beh_log_daily_all[(df_beh_log_daily_all['mouse_id']==mouse_id) &
  389. (df_beh_log_daily_all['group']==grp) &
  390. (df_beh_log_daily_all['day']==day)]
  391. starts_ix = np.where(np.diff(df.trial.values) > 0)[0]
  392. ends_ix = np.where(np.diff(df.trial.values) < 0)[0]
  393. reward_ix = df[df.reward == 1].index
  394. fail_ix = df[df.reward == -1].index
  395. fll_speed_stack.append(np.stack([df_speed_daily_all['fl_l'].iloc[tr - 60:tr] for tr in starts_ix]).mean(axis=0))
  396. flr_speed_stack.append(np.stack([df_speed_daily_all['fl_r'].iloc[tr - 60:tr] for tr in starts_ix]).mean(axis=0))
  397. speed_corrmat_stack.append(np.corrcoef(np.vstack((fll_speed_stack, flr_speed_stack))))
  399. fig, axs = plt.subplots(1,1, sharey=True)
  400. im = sns.heatmap(np.stack(speed_corrmat_stack).mean(axis=0), cmap='Greys', ax=axs, vmin=0, vmax=1, square=True)
  401. im.invert_yaxis()
  402. axs.set_xticklabels(np.append(['FLL_'+str(day) for day in days], ['FLR_'+str(day) for day in days]), rotation=90)
  403. axs.set_yticklabels(np.append(['FLL_'+str(day) for day in days], ['FLR_'+str(day) for day in days]), rotation=0)
  404. plt.title(g + ' speed profile correlations')
  405. plt.tight_layout()
  407. #%%
  408. data_list = [('FW2_ai94', 'grp1'),
  409. ('FW22_ai94', 'grp1'),
  410. ('GT33_tta', 'grp1'), ('HA2+_tta', 'grp1'), ('GER2_ai94', 'grp1'), ('HYL3_tta', 'grp1'),
  411. ('GER2_ai94', 'grp2'), ('HYL3_tta', 'grp2'),
  412. ('GIL3_ai94', 'grp3'),
  413. ('GIR2_ai94', 'grp3')]
  414. days = [1,4,5,7,10]
  415. beh_fps = 30
  416. epochSize = 5*beh_fps # should be same as used in generating the pkl file
  417. t = np.arange(-epochSize, epochSize) / beh_fps
  419. file_dff_response_all_pkl = out_dir + os.sep + 'clmf_avg_dff_response_daily' + '.pkl'
  420. if not os.path.isfile(file_dff_response_all_pkl):
  421. sys.exit('DFF responses file doesnt exist')
  423. dff_response_file = open(file_dff_response_all_pkl, 'rb')
  424. dff_response_daily_all = pickle.load(dff_response_file)
  425. dff_response_file.close()
  427. #%% Figure 7A 7B Plot average DFF activity for each seed location, per day for all mice
  428. for expt in data_list:
  429. mouse_id, grp = expt
  430. for se in clmf_seeds_mm:
  431. print(mouse_id + ' ' + grp + ' ' + se)
  433. df = dff_response_daily_all[(dff_response_daily_all.mouse_id == mouse_id) &
  434. (dff_response_daily_all.group == grp) &
  435. (dff_response_daily_all.seedname == se)]
  436. if df.empty:
  437. print('No data')
  438. continue
  440. ## dff profiles at the seed location over the days
  441. fig, axs = plt.subplots(1, 2, sharey=True, figsize=(12,5))
  442. axs[0].set_title(mouse_id + ' ' + grp + ' ' + se + ' L', fontsize=14)
  443. axs[1].set_title(mouse_id + ' ' + grp + ' ' + se + ' R', fontsize=14)
  444. g1=sns.lineplot(data=np.stack([df[df.day==day]['reward_stack_l'].values[0].mean(axis=0) for day in days]).T, ax=axs[0], legend=False, palette='Greens');
  445. # g1=sns.lineplot(data=np.stack([df[df.day==day]['rest_stack_l'].values[0].mean(axis=0) for day in days]).T, ax=axs[0], legend=False, palette='Reds');
  446. g2=sns.lineplot(data=np.stack([df[df.day==day]['reward_stack_r'].values[0].mean(axis=0) for day in days]).T, ax=axs[1], palette='Greens');
  447. # g2=sns.lineplot(data=np.stack([df[df.day==day]['rest_stack_r'].values[0].mean(axis=0) for day in days]).T, ax=axs[1], palette='Reds');
  448. plt.legend(days)
  449. plt.xlabel('Time (sec.)', fontweight='bold')
  450. # plt.xticks(np.arange(0, len(t),45), t[::45])
  451. g1.set_xticks(np.arange(0, len(t),60)) # <--- set the ticks first
  452. g1.set_xticklabels(t[::60])
  453. axs[0].axvline(epochSize, c='b')
  454. axs[0].axvline(epochSize-beh_fps, c='g')
  455. g2.set_xticks(np.arange(0, len(t),60)) # <--- set the ticks first
  456. g2.set_xticklabels(t[::60])
  457. axs[1].axvline(epochSize, c='b')
  458. axs[1].axvline(epochSize-beh_fps, c='g')
  459. plt.ylabel('DFF', fontweight='bold')
  460. sns.despine(offset=10, trim=True)
  461. # plt.yticks(fontsize=20)
  462. plt.tight_layout();
  463. pp.savefig(fig); plt.close()
  465. ## correlation matrix of dff responses at seedpixel over days
  466. fig, axs = plt.subplots(1,2, sharey=True)
  467. # im = axs[0].imshow(np.corrcoef(np.vstack([stk for stk in df['reward_stack_l']])), cmap='Greys');
  468. # im = axs[1].imshow(np.corrcoef(np.vstack([stk for stk in df['reward_stack_r']])), cmap='Greys');
  469. im = axs[0].imshow(np.corrcoef(np.vstack([stk.mean(axis=0) for stk in df['reward_stack_l']])), cmap='Greys');
  470. im = axs[1].imshow(np.corrcoef(np.vstack([stk.mean(axis=0) for stk in df['reward_stack_r']])), cmap='Greys');
  471. axs[0].set_title(mouse_id + ' ' + grp + ' ' + se + ' L', fontsize=14)
  472. axs[0].set_xticks(np.arange(len(df['day'].unique()))); axs[0].set_xticklabels(df['day'].unique())
  473. axs[0].set_yticks(np.arange(len(df['day'].unique()))); axs[0].set_yticklabels(df['day'].unique())
  474. axs[1].set_title(mouse_id + ' ' + grp + ' ' + se + ' R', fontsize=14)
  475. axs[1].set_xticks(np.arange(len(df['day'].unique()))); axs[1].set_xticklabels(df['day'].unique())
  476. fig.colorbar(im, ax=axs.ravel().tolist(), shrink=0.5)
  477. pp.savefig(fig); plt.close()
  479. ## heatmap of dff responses at seedpixel over days
  480. minimum = np.nanmin([np.nanquantile(np.vstack([stk.mean(axis=0) for stk in df['reward_stack_l']]), 0.2), -0.001]); maximum = np.nanquantile(np.vstack([stk.mean(axis=0) for stk in df['reward_stack_l']]), 0.90)
  481. divnorm = colors.TwoSlopeNorm(vmin=minimum, vcenter=0, vmax=maximum)
  482. fig, axs = plt.subplots(1,2, sharey=True, figsize=(12,4))
  483. im = sns.heatmap(np.vstack([stk.mean(axis=0) for stk in df['reward_stack_l']]), cmap='coolwarm', vmin=minimum, vmax=maximum, center=0, ax=axs[0]);
  484. im = sns.heatmap(np.vstack([stk.mean(axis=0) for stk in df['reward_stack_r']]), cmap='coolwarm', vmin=minimum, vmax=maximum, center=0, ax=axs[1]);
  485. axs[0].set_title(mouse_id + ' ' + grp + ' ' + se + ' L', fontsize=14)
  486. axs[0].set_xlabel('Time (s)'); axs[0].set_ylabel('Day')
  487. axs[0].set_xticks(np.arange(0, len(t),30)); axs[0].set_xticklabels(t[::30])
  488. axs[0].set_yticklabels(df['day'].unique())
  489. axs[0].axvline(epochSize, c='k'); axs[0].axvline(epochSize-beh_fps, c='g')
  490. axs[1].set_title(mouse_id + ' ' + grp + ' ' + se + ' R', fontsize=14)
  491. axs[1].set_xlabel('Time (s)');
  492. axs[1].axvline(0, color='gray'); #axs[1].set_yticklabels(clmf_seeds_mm.keys())
  493. axs[1].set_xticks(np.arange(0, len(t),30)); axs[1].set_xticklabels(t[::30])
  494. axs[1].axvline(epochSize, c='k'); axs[1].axvline(epochSize-beh_fps, c='g')
  495. pp.savefig(fig); plt.close()
  497. ## seedpix corr during rewards on days
  498. for day in days:
  499. df = dff_response_daily_all[(dff_response_daily_all.mouse_id == mouse_id) &
  500. (dff_response_daily_all.group == grp) &
  501. (dff_response_daily_all.day == day)]
  503. fig, axs = plt.subplots(1,1, sharey=True)
  504. im = sns.heatmap(np.corrcoef(np.vstack([[stk.mean(axis=0) for stk in df['reward_stack_l']], [stk.mean(axis=0) for stk in df['reward_stack_r']]])), cmap='Greys', vmin=0, vmax=1);
  505. axs.set_title(mouse_id + ' ' + grp + ' ' + str(day) + ' seedpix corr rewards', fontsize=14)
  506. axs.set_xticklabels(np.concatenate([df.seedname.values + '_L', df.seedname.values + '_R']), rotation=90)
  507. axs.set_yticklabels(np.concatenate([df.seedname.values + '_L', df.seedname.values + '_R']), rotation=0)
  508. pp.savefig(fig); plt.close()
  510. ## heatmap of dff activity around reward on days
  511. df = dff_response_daily_all[(dff_response_daily_all.mouse_id == mouse_id) & (dff_response_daily_all.group == grp)]
  512. minimum = np.nanmin([np.nanquantile(np.vstack([stk.mean(axis=0) for stk in df['reward_stack_l']]), 0.2), -0.001]); maximum = np.nanquantile(np.vstack([stk.mean(axis=0) for stk in df['reward_stack_l']]), 0.90)
  513. divnorm = colors.TwoSlopeNorm(vmin=minimum, vcenter=0, vmax=maximum)
  514. for day in days:
  515. df = dff_response_daily_all[(dff_response_daily_all.mouse_id == mouse_id) &
  516. (dff_response_daily_all.group == grp) &
  517. (dff_response_daily_all.day == day)]
  519. fig, axs = plt.subplots(1,1, sharey=True)
  520. im = sns.heatmap(np.vstack([[stk.mean(axis=0) for stk in df['reward_stack_l']], [stk.mean(axis=0) for stk in df['reward_stack_r']]]), cmap='Greys', vmin=minimum, vmax=maximum);
  521. axs.set_title(mouse_id + ' ' + grp + ' ' + str(day) + ' dff response', fontsize=14)
  522. axs.set_xticks(np.arange(0, len(t),30)); axs.set_xticklabels(t[::30])
  523. axs.set_yticklabels(np.concatenate([df.seedname.values + '_L', df.seedname.values + '_R']), rotation=0)
  524. axs.axvline(epochSize, c='c'); axs.axvline(epochSize-beh_fps, c='g')
  525. pp.savefig(fig); plt.close()
  527. #%% Figure 7C Plot max dff values before reward and before trial start for all mice combined
  528. data_list = [('FW2_ai94', 'grp1'),
  529. ('FW22_ai94', 'grp1'),
  530. ('GT33_tta', 'grp1'), ('HA2+_tta', 'grp1'), ('GER2_ai94', 'grp1'), ('HYL3_tta', 'grp1'),
  531. ('GER2_ai94', 'grp2'), ('HYL3_tta', 'grp2'),
  532. ('GIR2_ai94', 'grp3')]
  533. days = [1,2,3,4,5,6,7,8,9,10]
  534. # First build a dataframe with max dff values for all mice in all groups we can plot by groups later
  535. for expt in data_list:
  536. mouse_id, grp = expt
  537. for se in clmf_seeds_mm:
  538. for day in days:
  539. df = dff_response_daily_all.loc[(dff_response_daily_all.mouse_id == mouse_id) &
  540. (dff_response_daily_all.group == grp) &
  541. (dff_response_daily_all.day == day) &
  542. (dff_response_daily_all.seedname == se)]
  543. if df.empty:
  544. print('No data')
  545. continue
  548. dff_response_daily_all.loc[(dff_response_daily_all.mouse_id == mouse_id) &
  549. (dff_response_daily_all.group == grp) &
  550. (dff_response_daily_all.day == day) &
  551. (dff_response_daily_all.seedname == se), ['max_prereward_avg_l']] = np.max(df['reward_stack_l'].values[0].mean(axis=0)[int(epochSize/2):epochSize])
  552. dff_response_daily_all.loc[(dff_response_daily_all.mouse_id == mouse_id) &
  553. (dff_response_daily_all.group == grp) &
  554. (dff_response_daily_all.day == day) &
  555. (dff_response_daily_all.seedname == se), ['max_prereward_avg_r']] = np.max(df['reward_stack_r'].values[0].mean(axis=0)[int(epochSize/2):epochSize])
  556. dff_response_daily_all.loc[(dff_response_daily_all.mouse_id == mouse_id) &
  557. (dff_response_daily_all.group == grp) &
  558. (dff_response_daily_all.day == day) &
  559. (dff_response_daily_all.seedname == se), ['max_postreward_avg_l']] = np.max(df['reward_stack_l'].values[0].mean(axis=0)[epochSize:epochSize+int(epochSize/2)])
  560. dff_response_daily_all.loc[(dff_response_daily_all.mouse_id == mouse_id) &
  561. (dff_response_daily_all.group == grp) &
  562. (dff_response_daily_all.day == day) &
  563. (dff_response_daily_all.seedname == se), ['max_postreward_avg_r']] = np.max(df['reward_stack_r'].values[0].mean(axis=0)[epochSize:epochSize+int(epochSize/2)])
  564. dff_response_daily_all.loc[(dff_response_daily_all.mouse_id == mouse_id) &
  565. (dff_response_daily_all.group == grp) &
  566. (dff_response_daily_all.day == day) &
  567. (dff_response_daily_all.seedname == se), ['max_rest_avg_l']] = np.max(df['rest_stack_l'].values[0].mean(axis=0)[int(epochSize/2):epochSize])
  568. dff_response_daily_all.loc[(dff_response_daily_all.mouse_id == mouse_id) &
  569. (dff_response_daily_all.group == grp) &
  570. (dff_response_daily_all.day == day) &
  571. (dff_response_daily_all.seedname == se), ['max_rest_avg_r']] = np.max(df['rest_stack_r'].values[0].mean(axis=0)[int(epochSize/2):epochSize])
  573. mouse_list = [d[0] for d in data_list]
  574. grp = 'grp1'
  575. for se in clmf_seeds_mm:
  576. df = dff_response_daily_all[(dff_response_daily_all.mouse_id.isin(mouse_list)) &
  577. (dff_response_daily_all.day.isin(days)) &
  578. (dff_response_daily_all.group == grp) &
  579. (dff_response_daily_all.seedname == se)]
  580. dfl = df.melt(id_vars=['day'], value_vars=['max_prereward_avg_l', 'max_rest_avg_l'], var_name='condition', value_name='DFF')
  581. dfr = df.melt(id_vars=['day'], value_vars=['max_prereward_avg_r', 'max_rest_avg_r'], var_name='condition', value_name='DFF')
  583. hue_plot_params = {
  584. 'data': dfl,
  585. 'x': 'day',
  586. 'y': 'DFF',
  587. "hue": "condition",
  588. "palette": 'husl'
  589. }
  590. pairs = [
  591. [(1, 'max_prereward_avg_l'), (4, 'max_prereward_avg_l')],
  592. [(4, 'max_prereward_avg_l'), (5, 'max_prereward_avg_l')]
  593. ]
  594. fig, axs = plt.subplots(1, 2, sharey=True, figsize=(12,6))
  595. ax1 = sns.pointplot(**hue_plot_params, dodge=0.2, errorbar=('ci', 95), ax=axs[0])
  596. ax2 = sns.pointplot(x='day', y='DFF', data=dfr, hue='condition', palette='husl', dodge=0.2, errorbar=('ci', 95), ax=axs[1])
  597. plt.title(grp + ' ' + se)
  598. sns.despine(offset=10, trim=True)
  599. # plt.yticks(fontsize=20)
  600. plt.tight_layout();
  601. # Annotate the figure with stats
  602. annotator = Annotator(ax1, pairs, **hue_plot_params)
  603. # # test: Brunner-Munzel, Levene, Mann-Whitney, Mann-Whitney-gt, Mann-Whitney-ls, t-test_ind, t-test_welch, t-test_paired, Wilcoxon, Kruskal
  604. # # comparisons_correction: Bonferroni, Holm-Bonferroni, Benjamini-Hochberg, Benjamini-Yekutieli
  605. annotator.configure(test='Mann-Whitney', show_test_name=True, comparisons_correction='Benjamini-Hochberg', text_format='star') #bejamini hochberg, Bonferroni
  606. _, test_results = annotator.apply_and_annotate()
  607. for res in test_results: print(res.data)
  608. pp.savefig(fig); plt.close()
  611. #%%
  612. file_corrmat_daily_all_pkl = out_dir + os.sep + 'clmf_corrmat_daily_all' + '.pkl'
  613. if not os.path.isfile(file_corrmat_daily_all_pkl):
  614. sys.exit('Correlation matrices file doesnt exist')
  615. corrmat_file = open(file_corrmat_daily_all_pkl, 'rb')
  616. df_corrmat_daily_all = pickle.load(corrmat_file)
  617. corrmat_file.close()
  619. bregma_loc = {'x': 64, 'y': 64}
  620. bregma = Position(bregma_loc['y'], bregma_loc['x'])
  621. seeds = generate_seeds(bregma, clmf_seeds_mm, ppmm_brain, 'u')
  623. #%% p-value matrix of statistical test
  624. data_list = [
  625. ('FW2_ai94', 'grp1'), ('FW3_ai94', 'grp1'), ('FW22_ai94', 'grp1'),
  626. ('GT33_tta', 'grp1'), ('HA2+_tta', 'grp1'), ('GER2_ai94', 'grp1'),
  627. ('GIL3_ai94', 'grp3'), ('GIR2_ai94', 'grp3')
  628. ]
  629. days = [1,2,3,4,5,6,7,8,9,10]
  630. mouse_list = [d[0] for d in data_list]
  631. groups = np.unique([d[1] for d in data_list])
  633. # Figure 8A average corrmat during trial on all days, each group
  634. for grp in groups:
  636. aa1 = np.mean(df_corrmat_daily_all[(df_corrmat_daily_all.mouse_id.isin(mouse_list)) &
  637. (df_corrmat_daily_all.group == grp)]['reward_corrmat'].values)
  638. aa2 = np.mean(df_corrmat_daily_all[(df_corrmat_daily_all.mouse_id.isin(mouse_list)) &
  639. (df_corrmat_daily_all.group == grp)]['rest_corrmat'].values)
  641. aa3 = aa1-aa2
  642. vmin = np.nanquantile(aa1, 0.2); vmax = np.quantile(aa3, 0.95)
  643. ## plot each of the difference matrix in the stack we created above
  644. fig = plt.figure(figsize=(6, 6))
  645. plt.subplot(1, 1, 1); ax=sns.heatmap(aa1, center=0, vmin=vmin, vmax=vmax, linewidths=.5,
  646. cmap='coolwarm', square=True, xticklabels=seeds.keys(),
  647. yticklabels=seeds.keys(), cbar_kws={"shrink":.5, "orientation":'horizontal'})
  648. plt.title(grp + ' mean seedpixel correlation during trial, all days')
  649. plt.tight_layout(); pp.savefig(fig); plt.close()
  651. fig = plt.figure(figsize=(6, 6))
  652. plt.subplot(1, 1, 1); ax=sns.heatmap(aa2, center=0, vmin=vmin, vmax=vmax, linewidths=.5,
  653. cmap='coolwarm', square=True, xticklabels=seeds.keys(),
  654. yticklabels=seeds.keys(), cbar_kws={"shrink":.5, "orientation":'horizontal'})
  655. plt.title(grp + ' mean seedpixel correlation during rest, all days')
  656. plt.tight_layout(); pp.savefig(fig); plt.close()
  658. fig = plt.figure(figsize=(6, 6))
  659. plt.subplot(1, 1, 1); ax=sns.heatmap(aa3, center=0, vmin=vmin, vmax=vmax, linewidths=.5,
  660. cmap='coolwarm', square=True, xticklabels=seeds.keys(),
  661. yticklabels=seeds.keys(), cbar_kws={"shrink":.5, "orientation":'horizontal'})
  662. plt.title(grp + ' difference of correlations between trial and rest, all days')
  663. plt.tight_layout(); pp.savefig(fig); plt.close()
  665. #%% Figure 8B 8C
  666. df_seeds_corrmat = pd.DataFrame()
  667. for expt in data_list:
  668. mouse_id, grp = expt
  670. for day in days:
  671. df = df_corrmat_daily_all[(df_corrmat_daily_all.mouse_id == mouse_id) &
  672. (df_corrmat_daily_all.group == grp) &
  673. (df_corrmat_daily_all.day == day)]
  674. if df.empty:
  675. print(mouse_id + ' ' + grp + ' No data')
  676. continue
  678. reward_corrmat = np.arctanh(df['reward_corrmat'].values[0]) # np.arctanh for fisher z transform the correlation values before applying statistical test
  679. rest_corrmat = np.arctanh(df['rest_corrmat'].values[0]) # np.arctanh for fisher z transform the correlation values before applying statistical test
  681. for i, se1 in enumerate(seeds):
  682. for j, se2 in enumerate(seeds):
  683. df_seeds_corrmat = pd.concat([df_seeds_corrmat, pd.DataFrame({'mouse_id': mouse_id, 'group': grp,
  684. 'day': day, 'condition': 'trial',
  685. 'seed1': se1, 'seed2': se2,
  686. 'correlation': reward_corrmat[i,j]}, index=[0])],
  687. ignore_index=True)
  688. df_seeds_corrmat = pd.concat([df_seeds_corrmat, pd.DataFrame({'mouse_id': mouse_id, 'group': grp,
  689. 'day': day, 'condition': 'rest',
  690. 'seed1': se1, 'seed2': se2,
  691. 'correlation': rest_corrmat[i,j]}, index=[0])],
  692. ignore_index=True)
  694. for grp in groups:
  695. # initialize dict to hold pvalues of statistical test anova
  696. pvalue_day_corrmat = np.ones((len(seeds), len(seeds)))
  697. pvalue_condition_corrmat = np.ones((len(seeds), len(seeds)))
  698. for i, se1 in enumerate(seeds):
  699. for j, se2 in enumerate(seeds):
  700. df = df_seeds_corrmat[(df_seeds_corrmat['seed1']==se1)
  701. & (df_seeds_corrmat['seed2']==se2)
  702. & (df_seeds_corrmat['group']==grp)
  703. # & (df_seeds_corrmat['condition']=='rest')
  704. ].reset_index()
  705. # repeated measure ANOVA because we have samples over days. this is two way anova
  706. # becasue we are adding condition as a variable too
  707. res = pg.rm_anova(dv='correlation', within=['day', 'condition'], subject='mouse_id', data=df, detailed=True)
  708. if 'p-unc' in res:
  709. pvalue_day_corrmat[i,j] = res['p-unc'][0]
  710. pvalue_condition_corrmat[i,j] = res['p-unc'][1]
  712. #### bonferroni correction
  713. pvalue_day_corrmat_corrected = multipletests(pvalue_day_corrmat.flatten(), method='bonferroni')
  714. fig = plt.figure(); ax=sns.heatmap(pvalue_day_corrmat_corrected[1].reshape(pvalue_day_corrmat.shape), linewidths=.5, cmap='Greens_r', square=True,
  715. vmin= 0.0001, vmax = 0.06, xticklabels=seeds.keys(), yticklabels=seeds.keys(),
  716. cbar_kws={"shrink":.5, "orientation":'horizontal'})
  717. plt.title(grp + ' RM-ANOVA pvalues on correlation values over days')
  718. pp.savefig(fig); plt.close()
  720. #### bonferroni correction
  721. pvalue_condition_corrmat_corrected = multipletests(pvalue_condition_corrmat.flatten(), method='bonferroni')
  722. fig = plt.figure(); ax=sns.heatmap(pvalue_condition_corrmat_corrected[1].reshape(pvalue_condition_corrmat.shape), linewidths=.5, cmap='Greens_r', square=True,
  723. vmin= 0.0001, vmax = 0.06, xticklabels=seeds.keys(), yticklabels=seeds.keys(),
  724. cbar_kws={"shrink":.5, "orientation":'horizontal'})
  725. plt.title(grp + ' RM-ANOVA pvalues on correlation values between trial and rest, over days')
  726. pp.savefig(fig); plt.close()
  729. print('done')
  730. pp.close()

plot_clmf.py at commit 6dfe0df, under GPL-3.0 · at the source

Overview

Authors: Pankaj Kumar Gupta1,2, Timothy H Murphy1,2
ORCID iDs: Pankaj Kumar Gupta, Timothy H Murphy
  1. Department of Psychiatry, University of British Columbia, Detwiller Pavilion Vancouver Canada
  2. Djavad Mowafaghian Centre for Brain Health (DMCBH), University of British Columbia Vancouver Canada
Institutions: University of British Columbia (Canada)
Journal: eLife, volume 14, article RP105070
Dates: published online 24 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI · PMCID PMC13609482
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism)
Methods: Spectral & time-frequency, Statistics, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions, Physiology & signal measures, Connectivity
Keywords: real-time, closed-loop, widefield, calcium imaging, GCaMP6s, clopy, Mouse
Journal subjects: Computational and Systems Biology, Neuroscience
Funding: Canadian Institutes of Health Research (PJT-1800631, FDN-143209); Fondation Brain Canada; Heart and Stroke Foundation of Canada; Natural Sciences and Engineering Research Council of Canada (GPIN-2022-03723)
Citations: not cited yet (Europe PMC); 58 references in the paper
Research resources: RRID:SCR_019086

Abstract

Increasingly, experiments designed to provide practical perturbations to circuits or behavior are required for hypothesis testing in various disciplines ranging from motor learning to recovery after injury. We present the implementation and efficacy of an open-source closed-loop neurofeedback (CLNF) and closed-loop movement feedback (CLMF) system. In CLNF, we measure mm-scale cortical mesoscale activity with GCaMP6s and provide graded auditory feedback (within ~63 ms) based on changes in dorsal-cortical activation within regions of interest (ROIs) and with a specified rule. Single or dual ROIs (ROI1, ROI2) on the dorsal cortical map were selected as targets. Both motor and sensory regions supported closed-loop training in male and female mice. Mice modulated activity in rule-specific target cortical ROIs to get increasing rewards over days (repeated-measures ANOVA [RM-ANOVA], p=2.83e-5) and adapted to changes in ROI rules (RM-ANOVA, p=8.3e-10, Table 4 for different rule changes). In CLMF, feedback (within ~67 ms) was based on tracking a specified body movement, and rewards were generated when the behavior reached a threshold. For movement training, the group that received graded auditory feedback performed significantly better (RM-ANOVA, p=9.6e-7) than a control group (RM-ANOVA, p=0.49) within 4 training days. Additionally, mice can learn a change in task rule from left forelimb to right forelimb within a day, after a brief performance drop on day 5. Offline analysis of neural data and behavioral tracking revealed changes in the overall distribution of Ca2+ fluorescence values in CLNF and body-part speed values in CLMF experiments. Increased CLMF performance was accompanied by a decrease in task latency and cortical ΔF/F0 amplitude during the task, indicating lower cortical activation as the task gets more familiar.

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 11 matches between paragraphs and lines of code.

pankajkgupta/clopy

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 6dfe0df1dc47e403111314ca90b301e8ac251dba, 15 September 2026
Languages: Python (13)
Size: 52 files, 13 scripts
Software Heritage: archived
Found in: “Data availability”
Holds: README, license file, environment (docs/requirements.txt), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: OpenCV (10 files), NumPy (8 files), Matplotlib (3 files), pandas (3 files), SciPy (3 files), imageio (2 files), Pingouin (2 files), seaborn (2 files), statannotations (2 files), statsmodels (2 files), h5py (1 file), Keras (1 file), napari (1 file), Numba (1 file), Pillow (1 file), scikit-image (1 file), scikit-learn (1 file), TensorFlow (1 file), tifffile (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
15 files
At the source: github.com/pankajkgupta/clopy

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 13 scripts, each with its path and the digest of its content;
  • 11 matches 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

The source data used in this paper is available here- https://doi.org/10.20383/103.01152. Code to replicate the system, recreate the figures, and associated pre-processed data are publicly available and hosted on GitHub - https://github.com/pankajkgupta/clopy (copy archived at Gupta, 2026). Detailed documentation can be found here https://clopy-docs.readthedocs.io.

The following dataset was generated:

GuptaKP MurphyT 2024Real-Time Closed-Loop Feedback System For Mouse Mesoscale Cortical Signal And Movement Control: CLoPyFRDR10.20383/103.0115242781854

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 7 keywords, 4 funders, 56 references, 1 RRID.

Cite

This paper

Gupta, P. K., & Murphy, T. H. (2026). Real-time closed-loop feedback system for mouse mesoscale cortical signal and movement control. eLife, 14, RP105070.

BibTeX

@article{gupta2026real,
author = {Gupta, Pankaj Kumar and Murphy, Timothy H},
title = {{Real-time closed-loop feedback system for mouse mesoscale cortical signal and movement control}},
journal = {eLife},
year = {2026},
month = sep,
volume = {14},
pages = {RP105070},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
pmcid = {PMC13609482}
}

RIS

TY - JOUR
AU - Gupta, Pankaj Kumar
AU - Murphy, Timothy H
TI - Real-time closed-loop feedback system for mouse mesoscale cortical signal and movement control
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/09/25
VL - 14
SP - RP105070
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
LA - en
ER -

CSL-JSON

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"id": "pmcid:PMC13609482",
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"title": "Real-time closed-loop feedback system for mouse mesoscale cortical signal and movement control",
"container-title": "eLife",
"author": [
{
"family": "Gupta",
"given": "Pankaj Kumar"
},
{
"family": "Murphy",
"given": "Timothy H"
}
],
"container-title-short": "Elife",
"volume": "14",
"page": "RP105070",
"PMCID": "PMC13609482",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
25
]
]
}
}

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